States where medical marijuana was illegal had higher proportions of non-Hispanic Whites and Blacks/ African-Americans and a slightly higher proportion of college graduates. In this analysis, 8.7% of the sample reported current marijuana use and 23.3% reported current cigarette use. As expected, there was a higher prevalence of current marijuana use in states that have legalized medical marijuana compared to those where medical marijuana was illegal , and this association was stable and significant across age categories, even after adjusting for covariates and applying a Bonferroni’s correction to account for multiple comparisons . Findings indicate an association between statewide legalization of medical marijuana and cigarette and marijuana co-use despite lower cigarette prevalence in states where medical marijuana was legal. Co-use was particularly robust among 18–34 year olds. Overall, cousers were more likely to be nicotine dependent compared to those who did not use marijuana, and 12–17 year old adolescent and 50–64 year old adult co-users were 3-times more likely to have nicotine dependence . These data suggest that medical marijuana legalization could inadvertently affect prevalence of co-use, which is linked to greater nicotine dependence, and the potential to create more barriers to smoking cessation . As more states pass marijuana laws, and the legal marijuana industry is poised to cultivate a landscape of greater access and exposure to marijuana , it is recommended that stakeholders in tobacco control prepare for any unintended effects on tobacco use including the possibility of tobacco initiation/ reinitiation among former smokers and greater nicotine dependence in current smokers . Longitudinal research is needed to evaluate the effect of state marijuana policy on tobacco use and marijuana and tobacco co-use.

Co-use was higher and cigarette prevalence was lower in states where medical marijuana was legal. Given the nationwide increase in co-use ,pipp racks there may be uptake of marijuana use among cigarette users as states, change their marijuana policies and cigarettes smokers gain greater exposure and access to legal marijuana. It is possible that medical marijuana may be providing cigarette smokers with an alternative to tobacco especially as the stigma associated with tobacco continues to rise and the perceived harmfulness of marijuana decreases with legalization . Further, it might be perceived that the effects of marijuana can curb nicotine cravings and withdrawal symptoms to aid in smoking cessation . Finally, alternative tobacco products such as electronic nicotine delivery systems, which are commonly promoted as cessation aids and “safe” alternatives to smoking cigarettes , might also promote use of marijuana and THC oil with vaporizers . Co-use should therefore be monitored over time and examined in response to changes in marijuana policies that will further propel industry promotion of co-use and vaping. As expected, the prevalence of cigarette and marijuana co-use differed according to age. The positive association between medical marijuana legalization and co-use was greatest among 18–34 year olds. Previous studies with adolescents have reported greater prevalence but no increase in marijuana use or changes in permissive attitudes in states where medical marijuana was legal , suggesting that greater marijuana use, and therefore greater co-use, preceded medical marijuana legalization. However, most published studies have focused only on adolescents under the age of 18 years and do not reflect the adult population to which medical marijuana policies apply . Therefore, long-term longitudinal studies are needed to monitor the effects of marijuana legalization, marijuana initiation/ re-initiation, cigarette initiation/ reinitiation, and patterns of co-use across all age categories.

Additionally, it is recommended that such studies take into account statewide variables including number of years since the policy went into effect to adequately capture any measurable changes. These data are needed to explore the growing evidence and public health concerns about the potential “gateway” effect of marijuana on cigarette initiation and nicotine dependence in adolescents and young adults in addition to the potential for re-initiation of cigarettes among former tobacco users. As more states pass marijuana policies, potential increases in co-use could have important treatment implications. Cigarette smokers who also reported current marijuana use were more likely to have nicotine dependence, which is a known predictor of smoking and quitting behavior . The positive link between co-use and nicotine dependence was observed across age categories but these associations differed across measures of dependence . We analyzed both NDSS and TTFC. NDSS scores might have been a better measure of nicotine dependence in our comparison across age groups since the scale addresses five aspects of dependence . In comparison, the TTFC single-item scores might not have captured dependency, particularly in adolescent and young adult populations, who have yet to become regular and established smokers. Other studies have shown problems in using TTFC as a measure of dependence in young adults . Since our analysis included both adolescents and adults, we report both NDSS and TTFC measures of nicotine dependence. In addition, in the present study, cigarette smokers who reported ever but not current marijuana use were at greater risk of having nicotine dependence compared to never marijuana users. This finding supports that the effect of THC exposure on nicotine receptors may be irreversible . Studies are needed to further examine both short term and possibly even the long-term effects of THC and nicotine exposure on nicotine dependence and tobacco cessation. In this analysis, 12–17 year old adolescent and 50–64 year old cigarette and marijuana cousers had the highest odds of having nicotine dependence.

These findings support previous studies linking co-use and nicotine dependence in adolescents and young adults and add to preliminary data that this association was also stable in adults and, surprisingly, particularly robust in 50–64 year old adults. These findings reflect evidence of a U-shaped effect between age and nicotine dependence which peaks at age 50 years due to changes in nicotinic receptors and nicotine-associated metabolism with age , and suggest that this relationship was stable among co-users. Studies are needed to determine the extent to which THC exposure and/or current marijuana use add to this effect . Additionally, 50–64 year olds may represent a unique birth cohort who spent their formative years during the 1960’s and 1970’s with minimal tobacco regulations coupled with a counterculture that promoted marijuana use among a large population . More studies on the Baby Boomer generation, specifically, their perceptions about marijuana, current marijuana use including purpose of use , modality, cigarette co-use, and health outcomes could provide a glimpse into the future as continued legalization will likely influence social norms across the general population . As more states adopt liberal marijuana policies, more studies are needed to understand co-use including the relationship between THC and nicotine in addition to other individual-level factors such as genetics and personality traits that might influence dependence and cessation . We found higher percentages of non-Hispanic Whites and Blacks/ African-Americans in states where medical marijuana was illegal. In this study, these results may be attenuated since our analysis comparing nicotine dependence depended on exclusion of blunt use. The American Civil Liberties Union report data from the NSDUH and Uniform Crime Reporting Data showing that Black males were no more likely to report marijuana use, but 4-times more likely to be incarcerated for marijuana possession compared to their non-Hispanic White male counterparts . Epidemiologic data have shown a linear increase in cigarette and marijuana co-use in Whites, Blacks/ African-Americans, and Hispanics with the fastest rate of increase among Blacks/ African-Americans . Among Blacks/ African-Americans,pipp rack it is possible that statewide legalization of medical marijuana could help to reduce marijuana-related incarcerations, and at the same time, influence the rate of couse. We are cognizant of the many layers that add to the complexities around the issue of marijuana legalization that are well beyond the scope of our study. We recommend future research will assess potential and actual benefits/ costs of marijuana legalization to society at large, and in states where marijuana is legal, identify issues that can be addressed with specific regulatory measures . Study limitations include the cross sectional nature of these analyses which limits our ability to infer causality. Interpretation of our findings is limited to cigarette smokers which is distinct from those who reported other tobacco products . We were unable to examine statewide legalization of medical marijuana by the number of years the policy went into effect using the NSDUH to account for time lags from adoption to full implementation. The NSDUH public dataset only provides a binary categorization of states that were legal vs. illegal that lumps states that just passed the law with long-term legalization states limits our ability to detect long-term effects and may have attenuated our findings. Further study is needed to examine the effect of combusted vs. non-combusted marijuana use on nicotine given increasing prevalence of edible and aerosolized delivery of marijuana with vaporizers . At present, the NSDUH does not ask respondents to indicate whether use was combusted and/ or non-combusted and we recommend that future surveys collect information on marijuana modality to elucidate the relationship between various forms of marijuana intake and nicotine and/ or THC dependence. Data on combusted vs. non-combusted THC intake can also help to identify if there might be differences in health effects across marijuana use modality. In addition, the present study did not examine population density which might be a potential covariate for marijuana use.

Strengths of the study were use of a large national dataset representative of the U.S. population and internal validity of nicotine dependence comparisons across age categories using the same dataset, which eliminates methodological variations from one study to another. Medical marijuana legalization was positively associated with cigarette and marijuana couse and co-users were at greater risk for nicotine dependence. Long-term longitudinal data across age groups are needed to elucidate these results. In the meantime, it is recommended that stakeholders in tobacco control participate in policy discussions involving marijuana legalization including regulatory measures to prevent further co-use and develop novel cessation treatments to help co-users who may have a harder time with quitting.Cannabis is an adaptive and highly successful annual with the ability to grow in most climates across the globe. Cannabis belongs to the Cannabaceae family, “has a life cycle of only three to five months and germinates within six days.”Cannabis can occur in a wild, reproducing state throughout the California floristic provinces, and is cultivated even outside of areas where it may naturally reproduce. Cannabis planting, growing, and harvesting seasons are similar throughout California and typically take place April through October. “Exposed river banks, meadows, and agricultural lands are ideal habitats for Cannabis” since these ecosystems provide “an open sunny environment, light well-drained composted soil, and ample irrigation.” The Cannabis plant has been utilized to produce a diverse set of products with various applications. Today, Cannabis is most commonly produced for its psychoactive properties, though historically it has been used for agricultural production, nutritional value, and industrial purposes. The two species of Cannabis cultivated for psychoactive and physiological effects are Cannabis sativa and Cannabis indica. Marijuana is the most common name referring to varieties of Cannabis produced for mind altering affects. Marijuana contains high levels of chemical cannabinoid compounds including delta-9 tetrahydracannabinol, or THC, the primary psychoactive component of Cannabis. Cannabis has a long history of use in the United States. During the 17th century, the government encouraged hemp production, a fibrous form of Cannabis, for use as rope, clothing, and sails. In the early 20th century after the Mexican Revolution, the recreational use of marijuana was introduced by Mexican immigrants. In 1937, the Marijuana Tax Act was enacted to effectively criminalize marijuana consumption as a result of an anti-marijuana propaganda campaign led by the commissioner of the Federal Bureau of Narcotics, Harry J. Anslinger. During World War II the U.S. Department of Agriculture provided incentives, including draft deferment, for farmers to grow hemp to meet wartime fiber needs. In the 1950s, a series of federal laws were enacted to create mandatory sentencing for people convicted of using drugs classified as illegal, including marijuana. Despite stricter regulation, marijuana was embraced by popular counter-culture movements in the 1960s. This act classified marijuana as a Schedule I controlled substance, the most restrictive schedule of illegal drugs “found by the government to have a high abuse potential, a lack of accepted safety under medical supervision, and no currently accepted medical use.” In fact, the whole Cannabis plant was classified as Schedule I, which means that possession of any portion of the Cannabis plant became illegal under federal law.

Our study area was situated within the Oregon portion of the Klamath-Siskiyou Ecoregion and consisted of farms spread across three sub-watersheds in Josephine County, southwestern Oregon . We set cameras at 1,240 m to 1,910 m above sea level. The study area included a mix of vegetation types, including open pasture, serpentine meadows, oak woodland, and mixed conifer forest. Rainfall in this region varies seasonally and by elevation, with an average of 82.7 cm annually . Mean temperatures ranged between 3.9-20.6°C in 2018–2019 . The Klamath-Siskiyou Ecoregion is one of the most biodiverse temperate forest regions on Earth, in an area that straddles the Oregon-California border and contains several regions identified as critical climate change refugia . Several species of concern are present in the county, including native salmonids, threatened Humboldt martens , Pacific fishers , and spotted owls , all of which are hypothesized to be directly or indirectly affected by cannabis agriculture . Southern Oregon, and Josephine County in particular, have a long history of illicit and medical cannabis cultivation, as well as an active presence in the growing legal industry in Oregon . Southern Oregon has become known as a prime destination for outdoor cannabis production, and Josephine County has the highest number of licensed producers relative to population size in the state . Production in the county accelerated after recreational legalization in 2014 , and takes a similar form to cultivation occurring across the border in northern California, with clusters of small farms surrounded by undeveloped or less developed rural land .Cannabis farms for this study included one licensed recreational production site, one medically licensed production site, and six unlicensed sites. All farms were producing cannabis for sale, though in different markets depending on their access to licensed markets. We selected these eight farms because they were representative of the size and style of cultivation predominant in Josephine County in the years immediately following recreational legalization in 2015 , were all established after recreational legalization except for the medical farm, did not replace other plant-based agriculture, and granted us permission to set up cameras on site.

Our sampled farms were small ,indoor grow rack had conducted some form of clearing for production space, and three had constructed some form of fence or barrier around their crop. Nonetheless, specific land use practices and production philosophies differed between farms . We cannot disclose farm locations, as per our research agreement for access.Monitored farms were clustered within each watershed: one farm in Slate Creek, five in Lower Deer Creek, and two in Lower East Fork Illinois River. We placed un-baited motion sensitive cameras on and surrounding cannabis farm clusters as well as in random locations up to 1.5 km from the farms. To guide the placement of cameras, we overlaid the area surrounding each cannabis farm cluster with a 50 x 50 m grid and then selected a random sample of at least one quarter of grid cells , stratified by vegetation openness and distance to cannabis farm. We rotated 15-20 cameras through the sampled grid cells, ensuring each camera was deployed for a minimum of two weeks. As a result of sampling across two years, we likely violated the model’s assumption of geographic and demographic closure , but given our interest was in space use associations and not estimates of occupancy, we believe this is a minimal issue. For this analysis, we restricted our data to a subset of cameras on cannabis farms and cameras in 500 m proximity to farms active during the same camera rotation . Because of rotations and field constraints, all cannabis sites were not monitored at the same time or for the same length of time . Each cannabis site had at least one, and up to three comparison cameras within 500 m during each of its active rounds. Because of farm clustering, some comparison cameras were within 500 m of more than one farm. Half the cameras on farms were monitored for more than one round, but the comparison camera were not always the same for all rounds due to rotations. We summarized species observations at cannabis farms and created detection histories using the package CamtrapR in program R . We used a 24-hr time interval because our focus was on estimating space use associations instead of occupancy, and a short interval reduced the likelihood of the same individual animal being detected on both the farm and comparison camera . We used the detection matrix to summarize detection rates per 100 operation nights for species found on cannabis sites and comparison sites.

We then modeled the occupancy probabilities of the three most commonly detected wild species, which included black-tailed deer, lagomorphs , and common gray foxes , using the UNMARKED package in Program R . We used single-species occupancy models to assess factors influencing the likelihood that a species used the area around each camera station and the probability that the species would be detected given they were present . In this case, detection can also be influenced by fine scale activity and/or habitat use patterns We hypothesized that cannabis cultivation, elevation, water access, and vegetation type would influence species’ spatial relationships, and therefore included them as predictors of occupancy in the model. We predicted that cannabis cultivation would have a negative influence on a species’ probability of using an area. We included a binary, categorical variable in the models to characterize whether detection occurred on a cannabis site or a nearby comparison site . This variable reflected and distilled the on-site practices that are common across farms, including increased human activity and fencing. We expected regional elevation to influence species’ vegetation use, and therefore used the average elevation within a 1 km buffer of each camera location, from the 30 m National Elevation Dataset . Water access is frequently an important predictor for wildlife occupancy , especially during dry periods such as during our study years, so we included distance to streams as a predictor of occupancy . To represent vegetation, we used the percent evergreen forest, as determined via the National Land Cover Database within a 1 km buffer of each camera site as a vegetation predictor variable. Finally, to distinguish general biogeographic variation between regions, we used watershed as a categorical predictor for occupancy . For modeling detection, we hypothesized that cannabis production sites would negatively influence the probability that a species was photographed given they were available in the general area, due to both physical barriers to wildlife accessing these sites, and to behavioral shifts, such as animals moving less or moving more cautiously around areas of higher human activity .

We used distance to road as a proxy for human activity separate from cannabis production that might also negatively influence detection probability. Although cannabis cultivation can be associated with the creation of new roads , the roads used in these analyses were not those created or used exclusively by cultivators. Finally, we included year as a categorical variable to account for potential inter-annual variation in detection ability. We standardized covariates to have a mean of zero and a standard deviation of one. We used Akaike’s Information Criterion  to compare model fits. We modeled all of the detection covariates first, and then kept our top ranked model for detection constant before modeling our occupancy covariates. We used our top ranked model to assess covariate relationships and determine which variables influenced species use and probabilities of being photographed.We analyzed over 5,000 animal detections over 957 operation nights . We found that the communities of wildlife present on cannabis farms were qualitatively different from the surrounding, uncultivated areas. Wildlife on cannabis farms were often smaller-bodied species, and co-occurred with higher human and domestic dog activity. There were 18 different species recorded on cannabis farms, and 24 on comparison cameras. Wild predators were predominantly detected on comparison cameras rather than cannabis farms. For example, gray foxes had 18.5 detections per 100 operation nights on cannabis sites compared to a detection rate of 31.6 on comparison sites,ebb and flow system while black bears had a detection rate of 2.5 on cannabis sites compared to 4.9 on comparison sites, and coyotes had a rate of 1.9 on cannabis sites and 6.1 on comparison sites. By contrast, domestic predators such as cats and dogs, had a detection rate twice as high on cannabis production sites than comparison sites . It is also worth noting detections of two rarer carnivores: we detected mountain lions seven times on a cannabis farm and once on a comparison site, and bobcats two times on each.For the single species occupancy models, detection variables varied by species. The top models for deer and gray foxes included a negative association with cannabis production for detection, while the top model for lagomorphs did not have similar associations . Distance to roads was retained in all models for detection, and was positively associated with detection for all species, such that detection increased with increasing distance from roads. For occupancy, here defined as use, cannabis production had a weak negative association with gray fox occupancy, and was not a top occupancy variable for any of the other species .

Because watershed and forest cover were correlated , we only used the variable with the highest univariate effect size for each species. For instance, watershed had a higher univariate effect size than forest cover for deer and gray fox occupancy, so we used watershed for candidate selection in those models, and forest cover for lagomorphs. No single variable was consistently selected as a predictor of occupancy across all species.This study represents a first step to quantify patterns of wildlife avoidance and coexistence on and surrounding active small-scale cannabis farms on private land. Our observational monitoring data suggest that wildlife species may be affected by these locations and may be altering their use of these environments. Specifically, our results suggest that 1) wildlife are consistently present on and around cannabis farms, 2) private land cannabis production may influence the local space use of some species more than others, and 3) cannabis farms may deter larger-bodied wildlife species in particular. Although limited by a small dataset, these results offer valuable insights into the ecological outcomes of the emerging cannabis industry. The assessment of wildlife detection rates suggest that many wildlife species are consistently present at cannabis production sites . Whereas some species detected on cannabis farms are ones that have been recorded in the western United States as more tolerant to agriculture or disturbance , others are species that tend to avoid human activity . While we did detect some relatively rare species , we did not detect others such as fishers or ring tails , and cannot assess whether this is due to true absence or simply short study duration. We infer detection of wildlife on cannabis farms implies a potential for these species to move through these areas. In addition, some photos revealed foraging or resting behavior , which may indicate that cannabis agriculture could maintain biodiversity as other small scale agricultural crops have in other systems . However, understanding long term impacts of cannabis production would require information on farm-level land use practices. For example, if animals on private land cannabis farms suffer fitness consequences similar to the toxicant exposure occurring on public land production, then coexistence on these sites may be detrimental in the long term . Modeled use and detection probability results indicate that despite a general wildlife presence at cannabis farms, some animals may be more affected by these areas than others. For detection, both deer and gray fox were influenced by cannabis farms . Distance to roads was positively associated with all species detection, suggesting that animals are consistently avoiding roads, but no other variable was consistent across all species for either detection or use. For occupancy , cannabis farms were not selected for deer or lagomorph models , but we suspect this could have been due to our close proximity of cannabis and comparison locations. It is possible that these species would move >500m within a 24-hour period, making it difficult to distinguish space use. Additionally, because we pooled lagomorph species, it is possible that either brush rabbits or black tailed jackrabbits individually might have responded differently to cannabis production. Nonetheless, cannabis farms influencing detection probabilities for deer and gray foxes may imply an influence on repeated visits over our time period, and potentially a behavioral adjustment near cannabis farms.

Countries adopting medical cannabis laws utilize the latitude allowed by the UN drug conventions regarding the definition of the term “medical and scientific purposes.” Importantly, they challenge the powerful view that marijuana has no demonstrated medical use. In this regard, the medical cannabis movement has demonstrated the effectiveness of bottom-up legal mobilization strategies operating at the subnational level to contest authoritative interpretations of transnational prohibition norms produced by powerful global actors. Building on the successes of the medical marijuana reform movement, advocacy networks in various countries have campaigned for the enactment of more radical models of decriminalizing and even legalizing the recreational use of cannabis. The seeds of this development were sown in the 1990s when European countries increased the thresholds of the amounts of cannabis possession exempted from criminal responsibility. Portugal, for example, adopted threshold parameters based on “the quantity required for an average individual consumption during a period of ten days.”Whereas Portugal adopted this policy as part of a comprehensive redesign of its drug laws on the basis of harm reduction principles,in other countries, these steps toward legalizing cannabis use were stimulated by court rulings reviewing the constitutionality of cannabis prohibitions. For example, in Argentina, a 2009 ruling by the Supreme Court struck down Article 14 of the country’s drug control legislation, which punished the possession of small amounts of cannabis with prison sentences ranging from one month to two years.The Court stated that the possession of cannabis is protected by Article 19 of Argentina’s Constitution, which states that “private actions that in no way offend public order or morality,grow tables 4×8 nor are detrimental to a third party, are reserved for God and are beyond the authority of legislators.”

Recent developments in Canada and nine US states signify the growing momentum of the trend toward the legalization of recreational uses of cannabis and the development of more complex regulatory models to govern legal cannabis markets.In different ways, these jurisdictions grant licenses to professional farmers and pharmacies to produce and to sell cannabis commercially and exempt individuals from criminal responsibility for noncommercial uses. The trend toward liberalizing cannabis prohibitions illustrates the recursive nature of transnational processes of legal change. The networks of actors participating in these processes—comprised of grassroots activists, legislatures, bureaucratic elites, criminal justice actors, scientists, journalists, and public health officials—created new regulatory models that gradually transformed the application of cannabis prohibition norms in various jurisdictions. These actors invoked the indeterminacy of treaty provisions, contested the framing of cannabis use as indicative of a moral malaise, and highlighted the diverse ways in which the enforcement of cannabis prohibitions produces social harms that are severer than those generated by cannabis use. They also utilized the space for norm-making provided by the mismatch between the institutions and actors that formulate globalnorms and those assigned with the actual implementation of these norms in national and sub-national settings. The success of these campaigns warrants a reflection on the conditions under which local and national acts of contesting TLOs can reshape the agenda of global actors invested in preserving the current normative settlements. The following section focuses on this question. The rapid and widespread transnational diffusion of new models of decriminalizing, depenalizing or legalizing the use of marijuana serves as a product and a catalyst of the declining capacity of the cannabis prohibition TLO to shape the policy choices of criminal lawmakers and the routine practices of enforcement officials. However, to what extent do these reforms change the agendas of the global actors that play key roles in shaping and maintaining the normative and institutional structures of this TLO? Faced with the global spread of cannabis liberalization reforms, the INCB has positioned itself as the most steadfast defender of the normative expectancies of the cannabis prohibition TLO.In its annual reports, the Board contested the legitimacy of the legal interpretations underpinning states’ engagement with decriminalization, depenalization, and legalization initiatives.

The Board repeatedly expressed its concern that the introduction of civil sanctions for possession offenses was sending the wrong signal, downplaying the health risks of marijuana use. It criticized medical cannabis reforms and questioned the scientific basis on which they are premised. Most recently, the Board condemned Uruguay and Canada for adopting legalization schemes, stating that such reforms constituted clear breaches of the international conventions. The literature examining the roles of naming and shaming mechanisms in international politics observes that most countries are inclined to bring their laws into formal compliance with international standards to avoid being stigmatized as “deviant states.”The efforts of the INCB to achieve such influence by condemning countries deviating from the prohibitionist expectancies of the international drug conventions failed to generate such adaptive responses.Some countries have practically ignored the Board’s proposed interpretation of the international obligations set by the conventions. Others have argued that the Board’s interpretive approach was too narrow and relied on selective use of the available evidence-base concerning the medical uses of cannabis. Still others contended that the Board was exceeding its mandate when it adopted a hostile stance toward legitimate policy choices of sovereign states.The limited impact of the Board’s attempts to delegitimize the adoption of non-punitive models of cannabis regulation provides important insights into the conditions under which naming and shaming strategies can succeed.One reason for this limited impact is that some of the central countries pioneering the experimentation with decriminalization and legalization schemes are not particularly vulnerable to economic and reputational pressures.Supporters of cannabis liberalization reforms across Europe and North America justify these policies on the grounds that they are needed to reconcile drug policies with fundamental human rights values as well as with human development concerns.In this polemical context, it is unsurprising that the INCB, which has long failed to restrain the human rights abuses inflicted in the name of the war on drugs, has not succeeded in harnessing transnational civil society actors to support its line of attack on the perceived departures from the settled interpretations of the international drug conventions.

Whereas the INCB has remained unambiguously committed to the task of defending the normative settlements of the cannabis prohibition TLO, the approach taken by the US has been marked by ambivalence.President Barack Obama’s administration adopted the ambiguous position of respecting the decisions of US states legalizing the medical and recreational use of marijuana while continuing to condemn steps toward legalization in Latin American and Caribbean countries. Responding to shifts in national public opinion, the administration set out lenient guidelines for the federal prosecution of marijuana users in states that had legalized its medical and recreational uses.It thereby allowed legalized drug markets to take roots in Colorado and Washington, and subsequently in other states. Like other national governments, the US federal government invoked its domestic constitutional principles to argue that its policies are in compliance with the international standards. However, during the same period, the US continued to apply its strict punitive approach to evaluating the compliance of other countries with the UN drug conventions. The annual certification process continues to include assessments of the extent to which the seventeen countries currently identified as “drug majors” are willing to eradicate the cultivation of cannabis and to penalize its growers and sellers. With a majority of Americans supporting the legalization of marijuana and a majority of US states already implementing decriminalization schemes for medical marijuana,ebb flow tray lawmakers in the House and Senate are facing increasing pressure to end the federal ban on cannabis. Despite efforts by Attorney General Jeff Sessions to revive the zero-tolerance approach of the federal government, President Donald J. Trump has recently expressed his intention to support such reforms. It is too early to predict whether and when such a change will take place or how it will impact the federal government’s foreign policy stance on the issue of cannabis legalization. However, as long as the US adheres to this “do as I say, not as I do” message, its ambivalent posture enables further steps toward the unsettling of cannabis prohibition norms. Nevertheless, it is important to note that despite its declining regulatory effectiveness, the cannabis prohibition TLO continues to exert considerable influence on the development of drug policies at the international, regional, national, and local levels. In this context, it is notable that countries that have liberalized their cannabis laws emphasize their commitment to remain bound by the confines of the current treaty regimes of the international drug control system. Remarkably, the extensive recognition of the severe failures and counterproductive effects of the cannabis prohibition TLO has not generated viable political efforts to amend the international treaties underpinning its operation. To a considerable extent, the reluctance to renegotiate the treaty norms governing cannabis policies stems from the notion that the cannabis prohibition TLO is embedded within the mega-TLO of the international narcotic control system.This serves as a powerful mechanism of issue linkage, leading countries that support cannabis liberalization reforms to avoid initiating formal treaty amendments out of concern that such actions might destabilize the settled norms prevailing in other issue-areas of narcotic control . The fact that the UN drug conventions regulate the global trade of both the illicit and licit uses of drugs, including substances on the World Health Organization’s list of essential medicines, further escalates the stakes in renegotiating the terms of these treaties. In addition, the reputational costs of defecting from UN crime suppression treaties might be higher than those suffered by persistent objectors in other areas of public international law.

The branding of countries as pariah states, or “narco-states,” as it were, carries a stigma that resonates with the censuring functions performed by criminal labels in domestic contexts.These factors help explain why current efforts to restructure the regulatory frameworks governing cannabis markets are contained within the narrow space of policy experimentalism created by the textual ambiguity of the current treaties. Under these circumstances, many of the inherent weaknesses of the prohibitionist approach resurface in the new regulatory landscapes created by the decriminalization and depenalization of possession offenses. The involvement of criminal organizations in illicit drug markets remains significant given the illegality of supply-related activities. The growing formalizationof intermediate sanctions has a net-widening effect, which expands the use of control measures against low-risk drug offenders.Most fundamentally, the insistence on promoting drug liberalization reforms within the confines of the current system constrains the capacity of individual states and of the international community to imagine more effective and humane alternatives, such as those offered by harm-reduction and development-centered approaches. The enabling function of TLOs rests not only on the institutionalization of measures of negotiating, codifying and implementing legal norms with a global reach, but also on their tendency to generate dynamics of resistance and contestation which are conducive to the production of new norms and institutional forms.This chapter analyzed the ways in which such acts of norm-making unfolded in the issue-area of cannabis prohibition, driven by recursive mechanisms such as legal indeterminacy, diagnostic struggles, actor mismatch, and ideological contradictions. The discussion has also demonstrated that even when they undergo processes of fragmentation and polarization, TLOs can constrain the capacity of these acts of contestation to generate new normative settlements. Mindful of Niels Bohr’s advice that “prediction is very hard, particularly about the future,” we conclude this chapter by hoping that a better understanding of how transnational legal orders facilitate and hinder recursive legal change can illuminate some of the possible trajectories for the future development of cannabis regulations. The second set of male and female mice were treated as above, but they were subdivided into the following experimental groups: Control , LdWIN , and NIC/LdWIN . All above groups were tested in multiple smaller cohorts to enhance rigor and reproducibility of the findings. The current studies were designed to systematically assess changes following adolescent exposure under the varying conditions by maintaining precise dosing conditions via peripheral injections.Mice were mildly food restricted to 85%–90% of their free-feeding body weight and trained to press a lever in an operant chamber for food pellets under a fixed-ratio 5, time out 20 seconds schedule of reinforcement. We have previously shown that these adolescent exposure groups do not differ in operant food learning. Once stable responding was achieved , subjects were surgically catheterized as previously described. Briefly, mice were anesthetized with an isoflurane /oxygen vapor mixture and prepared with intravenous catheters.

We performed mRNA extraction on the Aspergillus nidulans strain transformed with Ti_OvaA, Ti_OvaB, Ti_OvaC, vrtD, and NphB and obtained the cDNA and performed a PCR of the cDNA and observed bands for both vrtD and NphB, the two genes we were investigating. We then purified the bands and sent them out for sequencing in the case that the genes may be mutated but the sequenced results displayed no mutations. With this information in mind, we decided to perform the NphB reaction with GPP and olivetolic acid using A. nidulans lysates expressing NphB. Zirpel et al. had demonstrated that whole cell bioconversion in Saccharomyces cerevisiae expressing wildtype NphB, olivetolic acid, and GPP did not produce CBGA; however, employing lysates of that same Saccharomyces cerevisiae strain in assays with supplemented GPP and olivetolic acid did produce CBGA as well the O-geranylated analog.Therefore we used A. nidulans lysates expressing NphB and supplemented olivetolic acid and GPP and we did observed CBGA production, much greater than what was observed in vivo. . We concluded therefore, that from the transcription and lysate data, that NphB was in fact correctly expressed in A. nidulans and that the issue could be low availability of GPP or that NphB is localized away from olivetolic acid and/or GPP. To further explore the issue of localization, we tagged the NphB enzyme C-terminal with green fluorescent protein using a flexible 5 amino acid linker . Microscopic images of the tagged NphB enzyme in A. nidulans displayed that the NphB was not localized in any punctuate organelles but rather was localized all throughout the fungal body indicating that the enzyme is located in the cytoplasm. 

As previously described,hydroponic flood table most of olivetolic acid and its analogs produced from our platform are found in the media indicating that the compounds are being secreted from the fungal body and therefore, olivetolic acid and its analogs go through the secretory channel in Aspergillus nidulans into the media. Therefore, we sought to localize the NphB to where GPP and the compounds were. We had to then understand where GPP was localized.Further genome mining in our lab for prenyltransferases harboring activity to produce CBGA from olivetolic acid and GPP, revealed a prenyltransferase similar to the ascA prenyltransferase from Acremonium egyptiacum, found in Colletotrichum higginsianum. Thisenzyme was discovered by Colin Johnson, a graduate student in the Tang Lab. The ascA prenyltransferase, a prenyltransferase belonging to the UbiA family, from Acremonium egyptiacum has been characterized to prenylate orsellinic acid with a farnesyl group.However, we demonstrated that the prenyltransferase from Colletotrichum higginsianum, labeled colA, was able to prenylated orsellinic acid with a geranyl group instead of a farnesyl group which is ideal since CBGA contains a geranyl group as opposed to a farnesyl group. Colin had searched through databases of isolated fungal products for C3 geranylated β-resorcylic moieties. He was able to find a couple compounds known as Colletorin B and Colletotrichum B that fit the description. Through genome mining, he was able to identify the cluster producing these compounds which contained a NRPKS, an NRPKS-like enzyme, a halogenase, and UbiA-like prenyltransferase which he labeled colA. Therefore, the colA gene was seen as a good candidate to test its ability to prenylate olivetolic acid and its analogs to CBGA and its analogs. Heterologous expression of colA with Ma_OvaA, Ma_OvaB, and Ma_OvaC as well as heterologous expression of colA with Ti_OvaA, Ti_OvaB, and Ti_OvaC unfortunately showed no production of prenylated olivetolic acid, unsaturated olivetolic acid, sphaerophorolcarboxylic acid, or prenylated unsaturated sphaerophorolcarboxylic acid; however, production of geranylated orsellinic acid was observed.

Further probing through the literature indicated that Aspergillus nidulans harbors an endogenous bio-synthetic pathway responsible for the production of orsellinic acid explaining the geranylated orsellinic acid result. Furthermore, the production of geranylated orsellinic acid did indicate that the GPP pool in Aspergillus nidulans is sufficient answering our concerns and therefore further indicating that localization is the key reason why NphB has not been shown effective in Aspergillus nidulans.ColA, a UbiA-prenyltransferase predicted to have seven transmembrane domains, could therefore not be purified and was subjected to feeding studies in both Saccharomyces cerevisiae and Aspergillus nidulans. Saccharomyces cerevisiae and Aspergillus nidulans strains expressing colA were supplemented individually with 200 µM orsellinic acid, 200 µM divarinic acid, 200 µM olivetolic acid and 200 µM sphaerophorolcarboxylic acid. The Saccharomyces cerevisiae feeding results demonstrated that colA was very efficiently able to prenylate orsellinic acid and to a lesser extent divarinic acid but although able to prenylate olivetolic acid and sphaerophorolcarboxylic acid, at very low efficiencies. A. nidulans feed results demonstrated that similar to S. cerevisae, colA was able to efficiently prenylate orsellinic acid: however, less so divarinic acid and prenylation of olivetolic acid and sphaerophorolcarboxylic acid was not observed. The differences between the feeding results of S. cerevisiae and A. nidulans were attributed to the fact that the S. cerevisiae strain was much more heavily engineered with regards to optimization of pathways than the A. nidulans strain and therefore was more optimal for secondary metabolite production. Prediction software indicated that the colA gene had its transmembrane domains localized in the endoplasmic reticulum . Additionally, regarding the mevalonate pathway responsible for the production of the intermediate GPP, one key enzyme in the pathway, hydroxymethylglutaryl-coenzyme A reductase , a rate determining enzyme responsible for the conversion of HMG-CoA to mevalonate163 was also predicted by TMHMM 2.0 to be located in the ER, which we hypothesize explained why colA was able to geranylate orsellinic acid and divarinic acid and why NphB, located in the cytoplasm, was unable to geranylate any B-resorcylic acid in vivo.

Faced with the difficulty that the engineered NphB which is able to efficiently geranylate olivetolic acid to CBGA is not able to do so in vivo based on localization issues and the issue that colA which is able to utilize GPP to prenylate orsellinic and divarinic acid in vivo but not longer alkyl chain variants, we decided therefore that fusion of the NphB enzyme C-terminal to colA would solve the localization problem, allowing NphB to utilize GPP to efficiently prenylate olivetolic acid and sphaerophorolcarboxylic acid. Once again, employing a flexible linker , we fused NphB C-terminal to colA and heterologously expressed the fusion product with both the Ma_OvaA, Ma_OvaB, Ma_OvaC and Ti_OvaA, Ti_OvaB, and Ti_OvaC set of genes in Aspergillus nidulans . LCMS traces of heterologous expression results did show that CBGA was in fact produced further indicating that localization was the key issue, but the CBGA production was at low levels which was perplexing. We purposed then to utilize this fusion approach with a wide variety of endogeneous A. nidulans enzymes localized in various membranes in the cell with the thought that there was possibly another region where the compounds and GPP were localized. We fused the NphB Cterminal to three other endoplasmic reticulum localized proteins: HMG-CoA reductase previously described, sec12p,ebb and flood table the guanine nucleotide exchange factor , specific for the SAR1 gene which acts as a regulator of COPII vesicle budding from ER exit sites , sec63p, encoding a protein essential for secretory protein translocation into the ER.In addition to localizing NphB to the ER, we also localized the enzyme to the peroxisome employing the peroxisome targeting signal 1 as well as to the nucleus using a nuclear localization signal . Not only did we localize NphB to the ER, peroxisome, and nucleus, we also fused the protein C-terminal to the mitochondria protein acetyl-CoA acyltransferase, responsible for converting 2 units of acetyl-CoA to CoA and acetoacetyl-CoA molecules.Lastly, we fused NphB to C-terminal to the plasma membrane protein tmpA, an oxidoreductase involved in the A. nidulans conidiation pathway.Similar to the colA-NphB construct, heterologous expression of all these tagged and fusion constructs were expressed in combination with Ti_OvaA, Ti_OvaB, and Ti_OvaC, the set of enzymes responsible for predominately producing olivetolic acid. LC-MS trace results showed that similar to the colA-NphB results, that in most of the fusion and localization tagged constructs, CBGA production was observed but at low levels. This could be due to the fact that NphB may not be folding as well in the fusion construct. Therefore, there is continued need to mine for other prenyltransferases.Blasting the colA enzyme across NCBI based genomes yielded a hit in the genome of Talaromyces islandicus having 55% identity to colA. Heterologous expression of this UbiA-type prenyltransferase from Talaromyces islandicus, labeled TislaUbiA, with the Ti_OvA, Ti_OvaB, and Ti_OvaC enzymes in Aspergillus nidulans showed the expected CBGA methyl variant result as well as prenylated olivetolic acid, albeit small, a result not observed with colA. Therefore, with four fungal genes, we were able to access those two cannabinoids, a result not observed before without utilizing genes from the Cannabis sativa plant. To increase the CBGA production utilizing the TislaUbiA enzyme, we would need to perform mutations in the active site of the enzyme responsible for binding to the aromatic prenyl acceptor. To do this, we had to identify the active site. TislaUbiA, colA, and CsPT4 are all UbiA prenyltransferases, membrane embedded prenyltransferases harboring two aspartate rich motifs associated for the divalent, cation-dependent prenylation.A crystal structure of archaeal UbiA in both its substrate bound and apo form was elucidated by Cheng et al. Cheng et al were able to obtain a 3.3 crystal structure of the archaeal organism Aeropyrum pernix UbiA The group observed that the structure of the enzyme contained nine transmembrane helices arrange counterclockwise with a large central cavity.

They also obtained a 360 crystal structure of ApUbiA in a substrate bound state, with the substrates p-hydroxybenzoic acid and geranyl thiolpyrophosphate activated with magnesium ions. In the crystal structure, GSPP was bound in the central cavity and a small basic pocket near the GSPP binding site was determined to be binding pocket for PHB binding. With this in mind, Cheng et al performed mutations to determine which amino acids were critical for binding. For the PHB binding pocket site, they determined that Arg43 and Asn50 were both critical to PHB binding.We therefore used this information to generate mutations for TislaUbiA with the purpose of opening the small basic pocket to accept large ß-resorcylic acid moieties. Using Alphafold, we generated a structural model for our TislaUbiA enzyme and comparted it to ApUbiA. The next steps, then would be to select for mutations that we postulate would open the binding pocket. Going back to Saccharomyces cerevisiae, we tested to see if we were able to achieve functional expression of CsPT4, the prenyltransferase from Cannabis sativa that Luo et al. characterized. We had decided to continue production of the cannabinoid bio-synthetic pathway in our model engineered A. nidulans host due to the high titers of olivetolic acid and its analogues that we were producing. We were unsure if changing the platform to S. cerevisiae would replicate the high titer production. Similar to Luo et al, we removed the N-terminal chloroplast targeting sequence of CsPT4. We heterologously expressed the enzyme in our S. cerevisiae super strain and subjected the transformed strain to feeding assays. We fed 200 µM of orsellinic acid and 200 µM of sphaerophorolcarboxylic acid and observed geranylation of both. We were able to produce the heptyl version of CBGA at moderate to high titer quantities, an exciting result since this is the direct precursor to THCP. We also saw that although we did take a hit in titer when we moved our platform to S. cerevisiae, we were still able to produce about 500 mg/L of SA. We then sought to achieve functional expression of THCAS. Production of the elaborated cannabinoids from CBGA involves the use of just one cyclase enzyme, with the final elaborated cannabinoid structure dependent on the cyclase enzyme employed. There are three elucidated dedicated cyclase enzymes from the Cannabis plant capable of cyclizing CBGA to the final cannabinoid: tetrahydrocannabinolic acid synthase , which forms tetrahydrocannabinolic acid from CBGA, cannabidiolic acid synthase , which forms cannabidiolic acid from CBGA, and cannabichromenic acid synthase which forms cannabichromenic acid from CBGA. All three of these oxidocyclase enzymes are part of the berberine-bridge enzyme -like family of enzymes, harboring a flavin adenine dinucleotide -binding domain, a substrate-bindingdomain, an N-terminal signal peptide, and a BBE-like C-terminus part of the FAD-binding module.

Phylogeny-based genome mining is based on the understanding of the mostly modular structure of bio-synthetic gene clusters.It is theorized that this mostly modular structure comes from a quickly evolving defense system where new molecules are produced by randomly swapping and shuffling domains and modules. As an example, the program Natural Product Domain Seeker , constructs a phylogenic tree based on ketosynthase domains and condensation domains of PKS and NRPS genes, respectively. KS and C domains are two of the enzyme families used to construct phylogenic trees in order to predict compound structures. This phylogenic tree can be utilized to give information about the function of the PKS and NRPS gene searched for, its evolutionary history, and the novelty of products produced in the secondary metabolite cluster containing the gene. Lastly, resistance gene directed genome mining and target directed genome mining involve identifying bio-synthetic gene clusters that contain self-resistance genes. For organisms that produce antibiotics or anti-fungals, there needs to be a development of a self-resistance method to avoid suicide. One self-resistance mechanism is the use of efflux pumps to transport the compounds to extracellular space. Another self-resistance mechanism involves the inclusion of self-resistance enzymes in bio-synthetic gene clusters. These SREs are mutated copies of the housekeeping target that retain activity and are not inhibited by the natural product produced, thereby keeping the organism alive. These SREs are typically found in secondary metabolite clusters. Therefore, an approach searching for these SREs can be developed to find bio-synthetic gene clusters.

Utilizing this knowledge, Moore et al. were one of the first groups to utilize a targeted genome mining approach. They screened for housekeeping copies of genes in 86 similar strains of Salinospora and screened for location near bio-synthetic gene clusters. They identified the second copy of a bacterial fatty acid synthase colocalized within a cluster that contained a PKS-NRPS hybrid gene. They annotated the cluster, heterologously expressed the genes,flood table and after chemical characterization, elucidated that the cluster produced thiolactomycin, which is a fatty acid synthase inhibitor. To demonstrate its capabilities, target directed genome mining has been used to locate bio-synthetic gene clusters with known bio-molecular targets, for discovering natural products with desired bio-molecular targets, and for discovering the bio-molecular targets of known natural products. Therefore, searching for resistance enzymes in a secondary metabolite cluster has become an increasingly appealing genome mining approach for finding new clusters and subsequently, novel natural products. In recent years, tools and programs have been developed to search for new bio-synthetic clusters more quickly. These programs have the ability to predict the entire secondary metabolite gene cluster.Anti-SMASH identifies polyketide synthase and non-ribosomal peptide synthetase core genes in potential clusters and then outputs the cluster information in a user-friendly interface that can be readily searched through. Secondary Metabolite Unknown Regions Finder is another one of these programs. SMURF evaluates secondary metabolite gene clusters by scoring the nearness of core genes with the different tailoring genes near the core gene. Additionally, there is another program that is more specified in its search called Antibiotic Resistance Target Seeker . ARTS specifically queries for antibiotic resistance genes in bacteria that can lead to bio-synthetic gene clusters for possible novel drug targets.

We utilized anti-SMASH to elucidate the non-plant olivetolic bio-synthetic pathway.Since its inception, the Tang lab has utilized various methods of genome mining to identify many natural products and novel enzymes, as well as elucidate the bio-synthetic pathways of natural products in addition to the production of novel natural products through the engineering of bio-synthetic genes. One such example is the further characterization ofthe bio-synthetic pathway of zearalenone, a member of the resorcylic acid lactone family of products produced from the fungal species Gibberella zeae, and production of novel resorcylic acid lactones achieved through the reconstitution of the polyketide synthase involved in the biosynthesis of zearalenone. RALs are polyketides, exclusively produced by fungi, consisting of a macrolactone ring with a 2,4-dihydroxybenzoic acid moiety embedded. The first discovered RAL, radicicol was characterized from the fungal species Monocillium nordinii in 1953, with 200 more RALs having been identified from a variety of fungal species since then. RALs are potent molecules that exhibit of variety of biological activities including having antimalarial, anti-cancer, anti-microbial, mitogen activating protein -kinase inhibitor, TAK1 inhibitor, heat shock protein inhibitor, and estrogen receptor against properties. Many RALs consist of 14 membered lactone rings, although there also exists RALs consisting of 10, 12, and 16 membered lactone rings. The RAL bio-synthetic gene cluster typically consists of two polyketide synthases: a highly reducing polyketide synthase and a non-reducing polyketide synthase . Regarding the bio-synthetic pathway of RALs, the HRPKS generates the terminal hydroxyl group that becomes the macrocyclizing nucleophile. The chain is then transferred to the NRPKS where it is further elongated and then goes through aldol cyclization to form the enzyme bound resorcylic thioester. A fused thioesterase domain in the NRPKS then performs macrocyclization to release the final RAL product. Furthermore, considerable structural diversity at the C6 position of the RAL can be generated by utilizing different HRPKSs that are able to synthesize a variety of reduced products.

Type I polyketide synthases contain multiple functional and catalytic domains, generating most of the polyketides that have been characterized. Furthermore, type I polyketide synthases are divided into two separate categories: iterative type I and modular type I. Modular type I polyketide synthases are more commonly found in bacteria. They are large multimodular enzymes having assembly line like characteristics, condensing acyl substrates module by module, where the order of the module defines the order of the functional groups of the final elaborated compound. Iterative type I polyketide synthases, more commonly found in fungi, contain a single multidomain, and iteratively use the domain, similarly to fatty acid synthases operate, to generate the programmed polyketide product. Type III polyketide synthases are found in plants , although a few have been elucidated from microbes, and are much smaller than type I and type II PKSs. They are homodimers of ketosynthases; therefore, they extend chain length through iterative decarboxylative Claisen condensation and are responsible for producing compounds such as stilbene, flavonoids, and alkylresorcinols from plants. Type III PKSs release their products to either the active site cysteine of the enzyme or the carrier molecule, coenzyme A. There have also been reports of Type III PKSs utilizing an acyl carrier protein bound substrate as the starter substrate, similar to type I and type II PKSs.Since our platform utilizes two type I iterative polyketide synthases, it is appropriate to go into more detail concerning these megasynthase enzymes. Fungal PKSs resemble bacterial type II PKSs in that the catalytic domains of both classes of enzymes are iteratively utilized during polyketide synthesis and resemble bacterially type I modular PKSs in that the catalytic domains of both fungal PKSs and bacterial type I modular PKSs are linearly arranged. However, fungal PKSs differ from bacterial type I modular PKSs in rules dedicated to chain elongation, regioselective cyclization, and starter-unit selection.There are three types of fungal polyketide synthases: highly reducing polyketide synthases , partial reducing polyketide synthases , and non-reducing polyketide synthases .HRPKSs generate highly reduced compounds that can be furthered modified to produce compounds such as lovastatin. Fungal HRPKS domains contain, minimally, a ketosynthase domain, a malonyl-CoA: acyl carrier protein transacylase domain, and an acyl carrier protein domain. These HRPKSs also contain tailoring domains such as an enoyl reductase domain, a dehydratase domain, a methyltransferase domain, and a ketoreductase domain. These domains are interactively utilized to produce the reduced polyketide product, with the HRPKS employing the tailoring domains in different arrangements for each extension cycle. PRPKSs typically synthesize phenolic aromatic compounds such as 2,4-dihydroxybenzene and 6-methylsalicylic acid . As their name implies, these enzymes utilize their iterative domains to generate partially reduced polyketide compounds. The ketoreductase domain is the key domain controlling the reductive programming in PRPKSs, through judicious reduction of the polyketide compounds.

6-MSA is a perfect example of this, with the PRPKS responsible for producing 6-MSA undergoing just one round of reduction by the KR domain and one round of dehydration by the DH domain. NRPKSs, similar to HRPKSs and PRPKSs, minimally contain KS, AT, and ACP domains. Separate from the other two polyketide synthase types, however, NRPKSs also harbor a starter unit: acyl carrier protein transacylase domain that takes up the starter unit,4×8 flood tray and a product template domain which acts as an aldol cyclase. They also may contain a methyltransferase domain and usually contain a domain for product release such as a thioesterase domain. The SAT domain’s role is to take up the starter unit, and to transfer the starter unit onto the ACP domain where it is moved to the KS domain, undergoing decarboxylative Claisen condensation with an extender unit transferred from the AT domain. An example of a starter unit would be a malonyl-CoA unit or if in conjunction with a HRPKS, the product produced from the HRPKS. Iterative use of these domains of the NRPKS extend the chain and the PT domain cyclizes the product and then the product is programmed for release by the releasing domain. All the RALs elucidated contain the 2,4-dihydroxybenzoic acid moiety otherwise known as the β-resorcylic acid moiety, the same moiety comprising the core of tetrahydrocannabinol, cannabidiol, cannabigerol, and the rest of the cannabinoids from the Cannabis sativa plant. Furthermore, the first key intermediate in the cannabinoid bio-synthetic pathway is olivetolic acid, a β-resorcylic acid with a pentyl alkyl chain at the C6 position. Olivetolic acid is found in small quantities in Cannabis sativa extracts; therefore, this key intermediate is expensive. Additionally, although not fully studied for its biological activity, it is proposed to have antimicrobial, photoprotective, and cytotoxic activities. Due to the similarities between olivetolic acids and RALs which the Tang lab is quite familiar with, we hypothesized that fungal bio-synthetic pathways containing a tandem PKS pair may be able to produce olivetolic acid or related molecules that vary in the C6 position chain length and saturation. Therefore, we hypothesized that, by using genome mining to look for tandem fungal polyketide synthases, we could find a bio-synthetic gene cluster in fungi that produces olivetolic acid. The terminal TE domains in the NRPKSs that produce RALs are responsible for the macrocyclization reaction. In order to produce resorcylic acid instead of RALs, the releasing enzyme must catalyze a hydrolysis reaction instead of esterification. In fungal PKSs, TEs that catalyze hydrolytic release have been characterized and are typically free-standing enzymes. With this in mind, we performed genome mining of sequenced fungal genomes for bio-synthetic gene clusters that encode a HRPKS, a NRPKS, and a standalone TE. Among theclusters identified by antiSMASH,one set of homologous clusters satisfied this particular criterion . The ova cluster from Metarhizium anisopliae encodes a typical HRPKS and a NRPKS that is not fused to a terminal TE domain. Instead, a didomain enzyme Ma_OvaC containing an N-terminal ACP and a C-terminal TE is present in the cluster. Further sequence analysis of the ACP domain showed the well-conserved DSL triad in all functional ACPs, in which the serine is post-translationally phosphopantetheinylated, is mutated to NQI.This suggests the ACP domain is unlikely to carry out the canonical function of acyl chain shuttling, thus the enzyme is designated as a ψACP-TE. Previously, a ψACPmethyltransferase fusion enzyme was found in a fungal PKS pathway, in which the ψACP facilitates protein-protein interactions between the NRPKS and the ψACP- MT to enable methylation of the growing polyketide intermediate.Hence, we hypothesize the ψACP domain in Ma_OvaC may have a similar role in facilitating the catalytic function of the TE domain on a PKS-bound intermediate. The M. anisopliae cluster contains additional genes encoding a transcriptional factor and a flavin-dependent monooxygenase. Alignment of homologous clusters from various fungal species showed that HRPKS, NRPKS, and ψACP-TE are conserved , including the inactivated ACP triad . None of these clusters have been characterized and no product has been reported in the literature. Based on these analyses, we predict that the trio of HRPKS, NRPKS, and ψACP-TE will make resorcylic acids that are structurally related to OA.To examine the product profile of the ψACP-TE containing pathways, we heterologously expressed Ma_OvaA, B, and C in the model fungus Aspergillus nidulans A1145 ΔSTΔEM strain.This strain has been used in reconstitution of fungal bio-synthetic pathways, and contains genetic deletions that inactivated biosynthesis of endogenous metabolites sterigmatocystin and emericellamide B.

During the dehumidification process, the liquid desiccant is in contact with air through a permeable membrane that allows water vapor interaction but prevents the flow of LiCl into the air. LiCl absorbs the water vapor in the air until it reaches water vapor pressure equilibrium with the air. This process is exothermic. The desiccant is kept cool by the evaporation of water to the exhaust heat stream. Figure 34 shows the dehumidifier unit schematic. Equations for each stream are presented below. Mass transfer is driven by a vapor pressure differential between the air and desiccant solution as shown in Equation 66. The supply air side heat transfer for the dehumidifier is given by Equation 68. The heat transferred to the solution includes the sensible heat due to the temperature difference between the desiccant and air, and desiccant and water, plus the latent heat of absorption and enthalpy of dilution, given by Equation 72.Absorption process weakens the desiccant solution and reduce its ability to absorb water vapor. To desorb water vapor from LiCl, the desiccant is heated to have equilibrium water vapor pressure that is higher than that of the air. This regeneration process is the reverse of dehumidification and can use low grade heat sources. In this study, the SOFC system exhaust heat is used in this regeneration process to increase the concentration of LiCl in solution. Then, the concentrated liquid desiccant solution is stored. When moisture must be removed, the high concentration solution is used to dehumidify the outside air. Figure 36 shows the regenerator schematic.The dehumidifier’s model inlet parameters are the weather conditions, the return air condition,cannabis drying rack desiccant temperature and concentration, and cold water temperature and flow. The model output is supply air temperature, desiccant outlet temperature and exhaust air temperature.

In order to keep the humidity of the air cooling the servers below the allowable limits, the air humidity in the dehumidifier is controlled by manipulating the percentage of return air. In this model, the desiccant outlet concentration is also controlled by the desiccant flow rate. In the regenerator system the inputs of the model are weather condition, desiccant inlet temperature and concentration, and hot water temperature and flow. The outlets are air temperature, desiccant temperature, and hot water temperature. To use the desiccant for dehumidification purposes, it is required to regenerate it to a certain concentration. In this model the manipulating parameter to control the desiccant concentration is the desiccant flow rate. The validity of the model can be assessed by comparing its predicted supply conditions to the measured supply conditions. These comparisons are done for each stage independently: the first-stage dehumidifier and the second-stage Indirect Evaporative Cooler . Experimental data from DEVap prototype testing is used to verify the dehumidifier and indirect evaporative cooler in the next two section, respectively.For indirect evaporative cooler, 5 different cases from have been used to verify the model. Table 7 shows the input conditions as well as experimental outcome and model output for supply air temperature and relative humidity. Figure 38 compares the model predictions and the experiments of the relative temperature of the supply side air. For the Indirect evaporative cooler, the measured supply-side temperature change predicted by the model matches the experiments within 10% except for test number 3. In case 3 the temperature difference is higher and has low mass flow which shows the weakness of bulk model to predict the result and the need for a discretized model. Modern data centers try to use adiabatic cooling whenever the weather condition allows. However, adiabatic cooling is not possible in all locations at all times. Different types of common data center cooling systems were presented in chapter 2.

This chapter presents the method for calculating the cooling demand of data center at various locations. Also, the cooling demand for seven different data center locations that are used as case studies of this research are analyzed. In order to calculate the amount of cooling required for data center a MATLAB data center model has been developed which calculates the amount of cooling required by a data center. The inputs of the model are weather data associated with data center locations including temperature, pressure, and relative humidity. The weather data are obtained on an hourly basis from Typical Meteorological Year data from 2006 to 2016. The model takes this data and contains multiple functions that have been developed for calculating thermodynamic parameters such as saturated temperature, wet bulb, and dew point temperatures based upon the knowns weather data. In order to calculate the load, the acceptable operating conditions for servers within a data center are required. ASHRAE is the association that updates and releases an industry standard for data center operations every couple of years, based upon industry technology improvements. Table 8 shows the boundaries that define the ASHRAE recommended and allowable environmental envelope from the 2016 standard.In order to calculate the number of hours that the data centers in each location need mechanical cooling, TMY data for seven locations in the United States that are home to Microsoft data centers have been used as the input for the code. The number of hours of each cooling type that is required in each location based on both allowable and recommended envelope is shown in Figure 39. As expected, by expanding the range of temperature and humidity, the number of hours that mechanical cooling is required decreases. For data centers located in California, Seattle, and Wyoming a mechanical cooling system is barely required, while economizer and evaporative cooling will be sufficient throughout the year to keep the servers in acceptable range. However, Illinois, Iowa, Virginia and Texas require between 1000hr to 4500hr of mechanical cooling based on the location and ASHRAE requirements. Server load profiles tend to be confidential information that are rarely published.

However, the profile is roughly constant and utilization changes between 60% to 80%. For the current work and data center simulation results, either NREL published server load profiles as shown in Figure 40 are used by scaling it to the size of the targeted data center, or it is assumed that the utilization is constant at 70% throughout the entire operating period. The following data center simulation results for each location are based upon the assumption of a 50MW designed data center that follows the load demand of. The designed temperature difference of air entering and leaving the servers is 15℃. The number of cooling hours and cooling device correspondent to that is based on ASHRAE recommended envelope. The mechanical cooling system in these results is assumed air cooled chiller. Figure 41 to Figure 44 show the results for California and Texas which are the two ends of the spectrum with California being the location with the lowest overall energy use and Texas the highest. Figure 41 shows the TMY dry bulb and wet bult temperature for California and Texas which are the parameters that determine what type of cooling is required for the data center. California and Texas average dry bulb temperature are 14℃ and 20.2℃ and wet bulb temperature are 11.6℃ and 15℃, respectively. California has the least variation in temperature throughout the year while Texas temperature changes more than 40℃ during the year which has a significant impact on change in the cooling required for Texas.PUE is the ratio of total energy used by facility to energy used by the serves. This parameter shows how effectively a data center uses energy. As the number gets closer to 1 it means that the facility becomes very efficient, with most of the energy being directly converted in the servers for the computational demands of the data center. The following two graphs shows the PUE for the entire year. The spikes that bring PUE up to the 1.4- 1.5 range are because of energy being consumed by mechanical coolers. The average PUE for California and Texas for the whole year as simulated with the current model are 1.16 and 1.32, respectively. The results for the TMY data, Power usage breakdown,vertical grow system percentage of energy usage, and PUE for the other 5 locations are presented in APPENDIX A. Figure 45 shows energy use for each location for a 50MW designed data center following Figure 40 load demand. The air temperature difference is 15℃ and ASHRAE 2016 standards is followed for temperature and humidity limits. Table 12 shows the average PUE for all the locations with California having the lowest PUE at 1.167 and Texas the highest at 1.315.The type of cooling system, designed temperature difference, and changing allowable range has a significant effect on the amount of energy that data center consumes. For example, as the technology is rising IT manufacturers are pushing the boundaries on safe temperature that IT equipment’s can tolerate. Figure 46 and Table 13 show the energy used and percentage consumed by different part of data center for various combinations. Water cooled system use less energy than air cooled system. Increasing the temperature difference for the air entering and leaving the server room means less flow of air is required, which leads to less energy required for cooling the air. As the IT technology progresses, the IT equipment can tolerate higher temperature which leads to higher range of acceptable temperature and humidity. This means wider range of outside temperature is acceptable for cooling the server, leading to lower energy usage. In this chapter, a data center cooling model has been developed to calculate the amount of cooling required by a data center.

The model takes the weather data for each location and acceptable range of temperature and humidity for data center to calculate the load. In addition, the cooling demand for California, Seattle, Wyoming, Illinois, Iowa, Virginia, and Texas have been calculated and analyzes. Texas had the highest cooling demand with a PUE of 1.315 and California had the lowest with a PUE of 1.167. Energy usage of data center based on different types of cooling device and at different designed temperature difference has been compared. Results showed that higher temperature difference and water-cooled system lead to less energy consumed by the cooling system. In this section, the possibility of using a highly efficient, zero emission SOFC system to produce electricity and cooling in various amounts to meet electricity and cooling demands of a data center is investigated. In this configuration each fuel cell powers one server rack and heat from each individual SOFC system in used in a small-scale LDD to produce cooling for one server. Figure 47 shows the integrated system configuration for rack level power and cooling. For this analysis, each server rack power is considered nominally 12kW. We assume that a fuel cell equivalent to eight 1.5kW BlueGEN SOFC systems is used to meet the server electrical demand . The exhaust of the SOFC is used to regenerate LiCl liquid desiccant to provide 1400CFM cold and dehumidified air for each server rack. Figure 48 shows the integrated SOFC-LDD system. The SOFC exhaust gas produces hot water that will supply the heat demand for regenerating the liquid desiccant. The regeneration process occurs within a heat and mass exchanger where the vapor desorbs from the desiccant and is carried away by an air stream due to desiccant solution that has higher water vapor pressure than the air. The high concentration LiCl is stored in a tank. When air conditioning is required, the high concentration LiCl is used to dehumidify the air. As mentioned before the ASHRAE recommended suitable range of temperature and humidity for all environmental classes inside the data centers is 18˚C to 28˚C dry bulb temperature and 9˚C to15˚C dew point and 60% RH . Figure 49 shows the model results of the first test on a psychrometric chart. The green line labeled ‘LiCl – 35%’ shows the humidity ratio of the air in equilibrium with the liquid desiccant at a mass fraction of 0.35 LiCl and at each of the temperatures considered. Line black shows the first stage dehumidification process for supply air and then the cooling air process. The dehumidification process is internally cooled by evaporation of water to exhaust air to keep the desiccant cooler and increase its dehumidification potential. Then, the supply air is cooled by indirect evaporative cooling at constant humidity ratio. Horizontal black line shows the second stage process for supply air, which goes through indirect evaporative cooling at constant humidity ratio.

Furthermore, female rats that were permitted to self-administer nicotine beginning in later adolescence exhibited higher levels of nicotine intake compared to those that initiated self-administration in adulthood. Thus, the stage of development when nicotine and cannabinoid exposure occur as well as the duration of the exposure are important factors that impact later drug-taking. Cannabinoid and nicotine co-exposure in adulthood also appear to alter later drug related behaviors. Of further interest, while WIN exposure decreased nicotine self administration in adult male rats at a moderate nicotine dose, this effect was reversed when the level of effort required to obtain drug infusions was increased under a progressive ratio schedule of reinforcement. Similarly, under operant conditions requiring high levels of behavioral effort, a brief history of THC administration in adulthood increased subsequent nicotine self-administration in male rats. Thus, in high effort situations, cannabinoid exposure can drive an increase in effort to obtain nicotine. Finally, cannabinoid signaling may also be involved in cue-associated nicotine seeking. Male rats administered WIN prior to a cue-induced reinstatement session exhibited increased nicotine-seeking behavior. This suggests that acute cannabinoid receptor activation heightens the responsivity to cues in triggering reward-seeking behaviors. Taken together, these studies highlight the importance of prior drug history at varying developmental stages and level of effort required on the effectiveness of cannabinoids in modulating nicotine reinforcement.Nicotine and/or cannabinoid use may also alter cognitive and emotion-associated behaviors,vertical growing systems which are often correlated with substance use disorders.

Acute cannabinoid or nicotine exposure has been shown to induce either anxiolytic or anxiogenic effects dependent on dose, age, or sex. For example, nicotine decreased anxiety associated behaviors in adolescent male rats, but paradoxically increased anxiety-associated behaviors in females. Further, male and female adolescent rats exposed to cannabinoids exhibited a decrease in short-term and spatial working memory but an increase in depressive-like behaviors. In a study assessing chronic co-exposure of nicotine and the synthetic cannabinoid CP 55,940, both male and female adolescent rats developed increased anxiety-like behavior that was further reflected physiologically by elevated corticosterone, a stress-associated hormone. In contrast, in adult mice, chronic co-exposure to both nicotine and THC decreased anxiety-like behaviors. Similarly, nicotine treatment can reduce some of the anxiogenic effects of acute THC exposure, and THC treatment can attenuate the anxiogenic effects of acute nicotine exposure. Finally, nicotine and/or cannabinoids can induce a significant developmental impact on cognitive outcomes when consumed during pregnancy. Chronic in utero exposure to nicotine, THC, or co-exposure to both drugs has been associated with long-term effects into adolescence. Specifically, adolescent male and female rats exposed prenatally to THC exhibited deficits in short-term memory. Interestingly, the adolescent male rats with a prenatal history of nicotine and THC co-exposure exhibited similar deficits in short-term memory, as well as a deficit in pre-pulse inhibition, a behavioral outcome associated with schizophrenia symptomology. It is worthwhile to note that the nicotine and THC prenatal co-exposure condition only induced memory-related effects in the males but not females, suggesting that nicotine may have buffered the effects of THC on the developing female brain.

Together, these findings indicate that nicotine and cannabinoids induce complex interactions on the brain across various stages of development.Less than 10% of those who want to quit smoking cigarettes are successful in the long-term. Most people attempt to quit ‘cold-turkey’, without the help of any nicotine replacement therapies , other pharmacotherapies, or behavioral support programs. Unfortunately, this cold-turkey approach induces significant nicotine withdrawal symptoms, such as cravings, irritability, difficulty concentrating, headaches, and insomnia, which can promote relapse as the user attempts to alleviate symptoms with drug re-exposure. By using NRTs, such as nicotine patches, lozenges, or gum, the success of quitting increases to 50-60% at the six-month time point. This is likely due to smokers being able to obtain nicotine from a source other than cigarettes, thereby reducing withdrawal symptoms and easing the transition to abstinence. ENDS were also developed as a type of NRT for adult smokers. It was proposed that this method of administration may be more successful given that the same physical and sensory cues are present as with smoking cigarettes, such as raising the hand to the mouth and inhaling/exhaling smoke. In 2014, 4% of adults in the US reported using ENDS for cigarette cessation, but by 2018, the percentage decreased to 3.2%. Furthermore, about half of adults who vape nicotine also smoke tobacco cigarettes, a behavior known as ‘dual use’. Surprisingly, a recent study found that people who quit smoking for more than a year have an increased risk of relapse if they vape nicotine during that time. Additionally, there is increasing evidence of cannabis use in vaping devices among teens and adults. People who use THC vapes report a high incidence of tobacco product use as well. As such, the absolute effectiveness of ENDS for tobacco cessation remains to be determined. Regardless of the smoking cessation tools implemented, high rates of nicotine relapse remain prevalent.

Modulation of the cannabinoid receptor has also been employed as a novel approach for smoking cessation. In rat models, CB1R antagonists were shown to decrease nicotine self administration and reduce nicotine-induced dopamine release in the NAc, which then led to the progression along the drug development pipeline. Two different CB1R antagonists, rimonabant and taranabant, underwent clinical trials for smoking cessation and were found to be marginally effective. However, both drugs have now been withdrawn from the market due to adverse psychological side effects in humans, including increased anxiety and depression. Cannabidiol, a CB1R and GPR55 antagonist and CB2R reverse agonist, has also been assessed as a modulator for nicotine withdrawal symptoms in a pre-clinical study. Coexposure to cannabidiol during chronic nicotine exposure reduced the somatic signs of nicotine withdrawal, including paw tremors, head shakes, jumps, and abdominal contractions, in rats, suggesting that cannabidiol may be a potential therapeutic in future clinical studies. Individuals with cannabis use disorder exhibit similar withdrawal symptoms as nicotine, including increased irritability, aggression and depression, sleep difficulty, and physical symptoms. Indeed, daily cannabis users who attempted to quit report similar withdrawal symptom severity as daily cigarette smokers attempting to quit. A preliminary study has shown that synthetic cannabinoids, such as nabilone, may be used to attenuate these withdrawal symptoms, which was demonstrated in a small sample of cannabis users in a clinical setting. Targeting nAChRs may also be effective as a treatment for cannabis use disorder. A pre-clinical study in rats showed that blocking a7 nAChRs with a selective antagonist, methyllacotinine, reduced self administration of the synthetic cannabinoid WIN and prevented THC from increasing dopamine in the NAc shell. This is quite promising because this putative therapeutic did not result in any depressant or toxic effects. More recently, nicotine patches have been examined for alleviation of cannabis withdrawal symptoms. A low-dose nicotine patch was shown to reduce negative affective withdrawal symptoms in subjects that were not heavy tobacco users, but a side effect of nausea was also observed. Importantly, in consideration of the co-use condition, adult tobacco smokers who also smoke cannabis are twice as likely as non-cannabis users to continue smoking tobacco even years later. This could be due to the cannabinoids enhancing the effects of nicotine-associated cues in reinstating the drug-seeking behavior after a quit attempt. However, one study found that people attempting to quit or reduce cannabis intake also report using less tobacco on abstinent days. Thus, research on effective cessation methods for co-users is heavily understudied and needs to be conducted to aid in the smoking cessation of people suffering from co-occurring cannabis use disorder and nicotine use disorder. People battling with nicotine, cannabis, or co-occurring substance use disorders may try to quit taking the drugs, but the risk of relapse is quite high as most people begin smoking again within the first week [90]. Relapse can occur due to trying to alleviate the negative withdrawal symptoms or it can be triggered by things like stress, acute exposure to the drug, or certain cues that were previously associated with drug-taking. Cues can include the physical environment, people with whom the drug taking typically occurs, as well as any associated auditory, visual, olfactory, or tactile signals. In animal models, this phenomenon is known as incubation of drug craving in which cue-induced drug-seeking behavior increases over time during abstinence after drug self-administration. In other words,pruning cannabis a rodent is allowed to intravenously self-administer a drug of abuse for a while, such as cocaine or nicotine, then the drug is taken away. During this abstinence period, when the rodent is put back in the same environment with the same sensory cues as when receiving the drug, they will actively seek it more.

This active drug seeking is usually measured in lever pressing or nosepokes . The cue-induced portion of this paradigm is highly pertinent as it is the visual, auditory, and olfactory cues that trigger this drug-seeking behavior. This phenomenon was first seen in humans experiencing progressively higher rates of cue-induced cocaine craving and cigarette craving during the first few weeks of abstinence. The incubation of drug craving effect has now been replicated in rodent models using a wide variety of drugs of abuse, including heroin, alcohol, and nicotine. Understanding how prior drug history might impact this cue-induced drug-seeking behavior can help create more effective relapse interventions for those in the early stages of abstinence.Beyond the research itself, it is important to have insight into the scientists conducting the research, the populations being studied, the dissemination of this new scientific knowledge, as well as the people being impacted by the findings. The final chapter of this dissertation explores a broader perspective of these crucial issues and the necessity of more support for people from historically marginalized backgrounds in the field of neuroscience at every level. Publications within this chapter include discussions on Black, Indigenous, and Hispanic early career scientists being cited less often, receiving less grants, having fewer authorships, and receiving lower salaries while still doing the majority of diversity, equity, and inclusion work to make academia more accessible for subsequent generations. This section also discusses the high attrition of trainees from disadvantaged backgrounds as well as the need for stronger community, professional resources, and culturally competent mentors to advocate on their behalf. Finally, it delves into the necessity of representation and accountability in the scientific community as well as the systemic issues that inhibit this progress. Valuing diversity, equity, and inclusion principles, not only makes academia more welcoming while bringing in a wealth of knowledge and unique perspectives, but it also strengthens the research being done. In fact, studies have shown how productivity, innovation and the success of research is enhanced when these ideals are embraced. Furthermore, it allows those researchers to then disseminate information effectively about critical research progresses to their own communities while conducting meaningful outreach and mentoring the next generation of researchers.Nicotine, the main psychoactive component in tobacco, is considered to be responsible for the development and maintenance of dependence in humans. Nicotine’s effects on adolescent development have become of increasing concern given the emergence of ecigarettes, which deliver vaporized nicotine. According to a nationwide CDC survey, ~30– 45% of high school students self-reported prior use of cigarettes, vaporized nicotine products, and/or cannabis. Given that legalization of recreational cannabis across states since the time of this survey, the number of adolescents exposed to this drug will likely continue to increase through both primary and second-hand exposure. Importantly, studies in humans examining co-use of these drugs have found that individuals who reported smoking both cannabis and tobacco cigarettes consumed more cigarettes than those using tobacco alone. Furthermore, the practice of mulling has been reported as frequently occurring in adolescent users, with high incidence among daily cigarette smokers in some populations. Interestingly, chronic male cannabis users show decreased activation of the caudate nucleus in relation to reward anticipation as compared to nicotine users and non-smokers [6], suggesting altered function of reward-related circuitries dependent on prior drug exposure. Chronic use of cannabis during adolescence has also been linked to an elevated risk of psychosis, anxiety disorders, and depression. For instance, Crane and colleagues found that symptoms of depression were positively correlated with both cannabis use and tobacco smoking frequency in male, but not female, subjects. In contrast, Wright and colleagues report that cannabis use predicted increased depressive symptoms in both males and females, but increased anxiety symptoms and behavioral disinhibition were only found in females.

In the original test of social disorganization theory by Sampson and Groves , low SES was found to be significantly associated with crime. The link between economic deprivation and crime may be more complex than the simple explanation that poor people have higher incentives to commit crime and suffer lower opportunity costs for doing so. For example, Sampson reports in an earlier work that low SES neighborhoods have higher levels of police supervision, independent of actual law violative behavior . Sampson concludes that “the influence of SES on police contacts is contextual in nature, and stems from an ecological bias with regard to police control” . Whether economic deprivation has a direct relationship with crime through individuals’ behavioral mechanisms , or an indirect effect on crime rates through the operation of police bias, there is a strong theoretical basis for the finding that poor neighborhoods report higher crime rates than rich ones. Mollie Orshansky developed the original poverty thresholds in 1963-64 when she was an economist working for the Social Security Administration, shortly before the declaration of a “War On Poverty” by President Johnson . An individual or household is in poverty when its total cash income falls below the applicable threshold, determined by family size and composition . The 2010 poverty thresholds range from $11,139 for a single individual living alone to $42,156 for a family of eight or more people living in the same household.Indicators of socioeconomic disadvantage, including poverty and unemployment, have been associated with higher crime rates in Miami, Florida and Columbus, Ohio . Other studies show that rates of crime and violence are extremely high in neighborhoods containing public housing developments,cannabis dryer which are areas of extremely concentrated socioeconomic disadvantage .

In their study of medical marijuana dispensaries in Sacramento, Williams and colleagues found that violent crimes were significantly associated with “concentrated disadvantage”, a variable constructed from 2008 poverty guidelines. Property and violent crimes were not associated with density of marijuana dispensaries, but both categories were significantly related to unemployment rate . Further evidence of the link between crime and unemployment is found in crime data from across the United States in the 1990’s, a decade of incredible crime reduction. Raphael and Winter-Ebmer found that a substantial amount of the reduction in property crimes could be explained by the corresponding decline in unemployment rate. A weaker relationship existed between unemployment and violent crime, according to the researchers. Freeman presents similar findings and concludes that as much as one-third of the drop in crime in the 1990’s can be explained by the expanding job market. Theorizing from earlier empirical work by Sampson , which found that macrolevel indicators of family disruption were related to rates of juvenile crime, Sampson and Groves include family disruption among their “exogenous sources of social disorganization” . The theoretical basis for this lies in the notion that “traditional” families provide their communities with greater parental supervisory resources, compared to single-parent families. Additional supervision results in greater social control and more effective prevention against crime . According to social disorganization theory, communities with higher levels of residential turnover suffer from correspondingly lower levels of social control and are therefore likely to report higher rates of crime . The present study conceptualizes “residential instability” as an index of the percent of housing units in a given census tract that are vacant and the percent of individuals living within the tract who are between the ages of 18 and 29. These variables are also used by Martínez and colleagues in their study of crime and drug use in Miami neighborhoods. Williams and colleagues found that property crimes—but not violent crimes— were significantly associated with percent of owner-occupied households, which is a measure of residential stability. Hipp and colleagues examined residential turnover in an ethnic context and found it to be significantly associated with crime.

They conceptualized “residential stability” as the average length of residence of households in the relevant census tract .Although it is not included in the forthcoming analysis, there is one final measure of social disorganization that appears in the theoretical literature which is relevant to the present discussion of crime in city neighborhoods: population heterogeneity . The argument here is that segregated communities suffer from lower rates of communication and interaction, which prevent them from organizing collectively to reduce crime and delinquency—even when the different population groups have a shared interest in law and order. A number of studies have found that population heterogeneity is associated with higher crime rates .In this chapter I have reviewed the literature on cannabis, MCDs, and crime that is relevant to the present study. Particular attention has been given to routine activities theory and social disorganization theory . In the next chapter I extrapolate from these theories in developing a conceptual model of crime to test for the criminogenic effect of MCDs.This study examines the spatial relationship between medical cannabis dispensaries and crime across 189 census tracts in San Francisco in the year 2010. I test two competing hypotheses drawn from the theoretical literature on routine activities theory , controlling for neighborhood characteristics drawn from social disorganization theory . The first is that MCDs increase crime by attracting likely offenders and presenting them with suitable targets; the second is that MCDs actually decrease crime by protecting their surrounding community with adequate security measures and thereby providing capable guardianship. The theoretical bases for these claims are discussed in greater length in the previous two chapters. In this chapter I discuss the research methodology employed by this study and present results. Data are collected from the San Francisco Police Department, Planning Department, and Department of Public Health; the California Department of Finance; the American Community Survey ; and the United States Census Bureau. Linear regression models are tested using four dependent variables at the census tract level: total property crimes, property crimes per 1,000 residents, total violent crimes, and violent crimes per 1,000 residents.

Findings are largely but not perfectly consistent across these different models with respect to the spatial relationships between crime, MCD density, and the eight other neighborhood characteristics analyzed: poverty, unemployment, family stability, vacancy rate, percent of the population ages 18-29, percent of the population that is male, total population size, and percent of land commercially zoned.As a matter of simple spatial correlation, MCD-containing tracts have higher rates of both property crime and violent crime than tracts that do not contain MCDs. But this relationship may be obscured by the limited number of cases under review—26 dispensaries across 16 census tracts, compared to 173 non-MCD-containing tracts—and the fact that MCDs are clustered in busy downtown areas . This highlights the need to consider other variables related to crime. A more nuanced approach reveals that crime is more strongly predicted by certain “exogenous sources of social disorganization” than by MCD density. Poverty is a strong predictor of high crime rates across all four of the regression analyses conducted by this study—much stronger than MCD density. By “stronger” I mean that it has a larger correlation coefficient and a higher degree of statistical significance . “Family stability” is negatively associated with crime across all four models. Again,vertical farming systems this link is stronger than the link found between MCD density and crime. Residential instability is not as strongly predictive of crime in the present model as socioeconomic disadvantage or family disruption. In the following sections I discuss the current research design in greater detail and present empirical findings. In a recent working paper for the California Center for Population Research at UCLA, Nancy Williams and colleagues present a routine activities approach for examining the link between MCDs and crime. They examine 95 census tracts in Sacramento using data for the year 2009. Their findings indicate that tracts containing dispensaries are not significantly associated with higher rates of crime when controlling for neighborhood characteristics associated with crime . This study borrows from their work in conducting an observational study of the spatial relationship between MCDs and crime in 189 San Francisco census tracts for the year 2010. All measures are aggregated to the level of census tracts. Census tracts are convenient units of analysis because they have similar population sizes, their boundaries align with the physical environment, and they are intended to be homogenous with respect to population characteristics and living conditions . Thus they roughly approximate city neighborhoods. From a routine activities perspective, I argue that it is reasonable to assume in the case of densely populated cities like San Francisco that likely offenders, in choosing whether, where, and when to commit a crime—that is, in weighing the target suitability and guardianship of potential victims—are going to consider targets within an area roughly the size of a census tract. Maps presented in the forthcoming analysis should illustrate the geographic implications of this assumption. Another advantage to using census tracts as the spatial unit of analysis is that there is an abundance of demographic information available at the census tract level via the U.S. Census Bureau and ACS. This provides for an excellent range of control variables.Greenbaum’s second line of contention. The present model does not account for criminal activity in other tracts and therefore misses the “spillover effects” that a land use such as MCDs may have on crime in neighboring tracts.

This presents a significant limitation for the present model—although one that could theoretically be corrected for, to some extent, through more sophisticated spatial analyses. Considering the lack of empirical evidence currently available with respect to this issue—and its significant implications for policy making and future research—I argue that, as a preliminary analysis, this study has tremendous value despite this and other limitations. It may not account for inter-tract crime, but it does provide new knowledge about the nature of intra-tract crime. City residents probably are concerned about businesses in adjacent neighborhoods; but when it comes to crime they are concerned, first and foremost, with the people next door.In this study I examine the relationship between MCDs and crime rates across San Francisco neighborhoods in the year 2010. This provides an excellent case study for analyzing the criminological impact of MCDs because it offers a high level of certainty and relevance. It can be said with a high level of certainty that there were 26 dispensaries operating in San Francisco in 2010 and that they were open for most or all of that year; similar statements are difficult to make with respect to other jurisdictions. In some other municipalities, governments and MCD operators have undergone heated legal battles with one another. This has resulted in a “regulatory vacuum” with respect to MCDs in many jurisdictions . In such a vacuum, it is difficult if not impossible to determine exactly how many MCDs are open at a given time . Perhaps the most notable example of this is Los Angeles, where MCDs have been in legal limbo for years. Studies of MCD density and local crime rates in Los Angeles, while certainly compelling, would require a substantial amount of field research. San Francisco provides a case in which important information can be determined with high certainty and low cost. Other cities may provide even higher certainty, but these generally suffer from limited relevance because of the small number of dispensaries that they contain. San Francisco is not the only municipality to escape the “regulatory vacuum”. Other California cities have enacted similar MCD ordinances, including two prominent examples that can be found directly across the water from San Francisco in the cities of Berkeley and Oakland. But as a case study San Francisco has several advantages over these and other alternatives. First and foremost, it is a major city with a large sample of MCDs in the year for which data are collected. By comparison, Berkeley and Oakland have smaller populations and “hard caps” on the number of dispensaries allowed.So although they present interesting pieces of the legal, social, and political puzzles presented by California’s medical cannabis law, their small sample size limits the extent to which they are useful cases for empirical study. Unlike the “regulatory vacuum” experienced by MCDs in some other jurisdictions, MCDs in San Francisco face a number of local regulations. Thus San Francisco is not only a convenient case to study, it is also relevant and important—it touches directly on whether, and to what extent, locally regulated MCDs are related to crime.

All pollen samples were stored at −20°C after collection. Our collection protocols were as follows, during hand collection, we placed a 50-mL centrifuge vial at the base of inflorescences with dehiscent anthers and used tweezers to tap or brush the dehiscent flowers, allowing pollen to fall into the collection vial. During the water collection method, we flipped plants upside down and dipped them into a 100-mL graduated cylinder filled with 50 mL of distilled water to wash all the pollen off the plant. The water sample containing pollen was then transferred into a 50-mL centrifuge vial for storage. During bag collection, we placed brown paper bags on the plants and loosely tied the base of the bag using twine. We then flipped the plants upside down and lightly shook them for 10 s to encourage dehiscence of pollen, after which we untied the twine from the base of the bag to remove the plant, and quickly sealed the bag to prevent pollen loss. After the first trial was completed and it appeared that bag and water collection methods would likely be less successful and/or efficient than hand collection, we focused on comparing the efficiency of vacuum to hand collection in the second trial. We retrofitted small paper cups to act as filters inside a hand-held vacuum by reducing the height of the cup to 2 cm and poking four holes along the cup’s sides to allow a small amount of airflow through the filter. Retrofitted cup filters were then placed inside the body of the vacuum, between the nozzle and the motor, intercepting and storing all of the vacuumed particles. Pollen was then collected by vacuuming the leaves and inflorescences of male plants, after which the filter was carefully removed and transferred to plastic containers for storage.Using a stopwatch, we recorded the time spent collecting pollen for each method. For the treatments that did not involve water collection,ebb and flow rolling benches we mixed each pollen sample into 50 mL of distilled water, and then vortexed the samples for 30 s to create a liquid suspension with a consistent distribution of pollen grains.

To quantify the relative grain density in each sample, we used visible light spectroscopy, employing the absorption reading as a response variable. We pipetted 2 mL of the vortexed suspension into a 3-mL cuvette and then used the light spectrometer to quantify the proportion of light that was reflected by the sample. We ultimately chose 425 nm as the reflectance wavelength for the absorption reading by testing multiple wavelengths on the first sample and then identifying the wavelength region that corresponded to the peak in the absorption curve. To verify that our light spectroscopy readings were truly indicative of the amount of pollen in each sample, we pipetted 5 μL of the suspension in each cuvette onto a glass slide and used a light microscope at 10× magnification to count the number of grains contained in the sample. Counting was done using a hand-held tally counter , and counting extended across the entire length and width of the slide cover . To determine if any of the pollen grains had burst and, if so, what proportion of the total sample they represented, we counted the number of burst grains on each slide using a second hand-held tally counter. All data used in this paper are provided Appendix S1.All analyses were run in R version 3.6.0 . To test if spectroscopy readings were correlated with microscopy-derived pollen count data, we used a linear regression model. We used the adjusted R2 value and correlation of the linear model to evaluate how well reflectance predicts pollen counts. A strong positive relationship was confirmed , and so we used this method to compare yield and efficiency for the four different collection protocols. We compared the effectiveness of the collection methods using repeated measures analysis of variance , where method was a fixed effect, collection event was the repeated measure, and plant ID treatment was used as an error term. We log-transformed the response variables, i.e., the spectroscopy reading and efficiency , to satisfy the assumption of normally distributed residuals.

We then compared transformed estimates of pollen yield and collection efficiency with a repeated-measures multivariate ANOVA  followed by individual repeated-measures ANOVAs when factors were found to be statistically significant, using the manova and aov functions. Any experimental factors that were determined to be statistically significant underwent subsequent post-hoc analysis using Tukey’s honest significant difference test function in the R stats package, version 3.6.0.Visible light spectroscopy readings strongly predicted microscopy-derived pollen counts in liquid samples , indicating that this method accurately quantifies pollen abundance. Correlation between the two variables was high , implying that there was a strong linear relationship between pollen counts and light spectroscopy readings. On average, only 0.88% of pollen grains had burst. Both trial 1 and trial 2 showed significant differences between collection protocols in the initial repeated measures MANOVAs . Trial 1, which compared hand, bag, and water collection, showed significant differences between methods for relative yield , with post-hoc analysis revealing that hand collection yielded significantly more pollen than the other two methods . Water collection resulted in a somewhat higher yield than bag collection . Collection yield did not differ across time points, nor was there a time point by-method interaction . Collection efficiency was not affected by collection method, time, or their interaction . Trial 2, which compared hand collection to vacuum collection, showed no significant influence of collection method, time, or their interaction for pollen yield or collection efficiency , implying that increases in collection time directly resulted in increases in relative yield .Artificial selection for preferential traits in wind-pollinated species like cannabis critically depends upon effective and efficient methods for pollen collection and storage so as to prevent unintended genetic contamination of selected lines . Similarly, methods for pollen handling are also essential in cannabis production, where growers have conflicting needs: to maximize yield of the current crop, pollen must be excluded from production plants, but to generate future crops, pollen is essential. A gap in the literature comparing the relative success and efficiency of pollen collection methods highlighted the need to explore the often laborious process of mass collection of pollen for controlled cross-fertilization. A key step is to determine the best method for the controlled capture of pollen. Here we compared the yield and efficiency of multiple collection methods , and also compared two approaches for quantifying the relative pollen yield of different methods.

We found that light spectroscopy was an effective method for quickly and easily quantifying the abundance of pollen when suspended in distilled water. Light spectroscopy is a much faster method for quantifying pollen abundance than microscopy and is successful in predicting the pollen abundance in a collection sample. We anticipated this result, as light spectroscopy has often been used for measuring the abundance of particles in a suspension , and variations have previously been used on pollen . Hand collection resulted in a higher pollen yield than water or bag collection in our first trial, but the efficiency with which they collected pollen did not differ. In the second trial, hand collection and vacuum collection did not differ in their yield or efficiency, implying that they are equally suitable for pollen capture. Bag and water collection did require significantly less time for pollen capture; however, the substantially lower yield inhibits their application as an effective method of controlled capture. We further note that while our vacuum device did not outperform hand collection, improvement of the design to engineer a better filtration system and tailor suction power to individual growers’ needs could improve yield and efficiency. Ultimately, the results of these experiments serve as an important early step in the establishment of a practical framework for breeding cannabis,rolling grow benches as well as other economically valuable wind-pollinated crops.The coronavirus disease 2019 is caused by severe acute respiratory syndrome coronavirus 2 . COVID-19 was declared as a global pandemic by the World Health Organization . The main cause of the disease was a previously unknown coronavirus which was first identified and reported in Wuhan, China in late December 2019 . The possible zoonotic nature of the disease and viral spillover have made it more serious due to the transmission patterns from animals to humans and with the progression of the COVID-19 pandemic, transmisson from humans to animals and spill back events to other animal species were reproted . Calssification of coronaviruses within the family Coronaviridae is shown in Fig. 1. The possible transmission patterns of SARS-CoV-2 from human-to-animal and animal-to-human are shown in Fig. 2. Based on the available data at the time of this review, COVID-19 is believed to have emerged in a seafood market in Wuhan, China . Bats could be the proximal origin of SARS-CoV-2 and pangolins could be a potential intermediate host because of high genome sequence similarities of isolated SARS-related viruses with the SARS-CoV-2 genome . However, the origins of SARS-CoV-2 were recently reviewed elsewhere , yet the host range and intermediate hosts of SARS-CoV-2 remain unknown . SARS-CoV-2 is not the first coronavirus to cross species and infect humans leading to the first pandemic in history to be caused by a coronavirus. Previously, two highly pathogenic coronaviruses, severe acute respiratory syndrome coronavirus 1 and Middle East respiratory syndrome coronavirus infected humans and caused severe diseases . As a result of the uncontrollable spread of COVID-19, countries imposed lock downs, postponed or banned international travel, and limited exports and imports to control the transmission of SARS-CoV-2 , 2020. There were significant concerns on whether food and animal products may contribute to the transmission of SARS-CoV-2 and whether the disruption of the agricultural production chain including livestock production systems would significantly harm the global economy. The effect of the COVID-19 pandemic on agricultural production, including crop and animal products, depends on the product, the location, and the economic status of the impacted location . The current pandemic has had serious impacts on animal production, animal health and welfare, global food safety, and the global economy. Impacts include the disruption of food supply chain, shortage of labor, reduced access to markets and veterinary health services, in addition to movement restrictions and limitations on international trade.

The COVID-19 pandemic has also resulted in ominous impacts on food security, leading to hunger and increased poverty in resource-limited countries . Milk and meat industries, animal and animal-product processing industries such as slaughterhouses, and poultry sectors were negatively impacted during the course of the COVID-19 pandemic. Smashing of eggs, dumping of milk, inhumane culling of animals, and disruptions of animal feed supply chain have resulted in crisis in the global economy . In the current review, we highlight different routes of transmission of SARS-CoV-2 in animals and humans, possible ways COVID-19 can disrupt the animal production chain, and effects of COVID-19 pandemic on animal health and welfare, diagnosis and treatment of diseases, and the global economy. We also provide recommendations for the prevention and control of the COVID-19 pandemic and for boosting up animal production and ultimately global economy. Since SARS-CoV-2 is primarily a respiratory, not a food borne, pathogen, the risk of food borne transmission of COVID-19 is negligible . The possibility of SARS-CoV-2 being transmitted via feces is much lower than enteric viruses which are transmitted via the fecal-oral route which may be explained by the lower relative amounts of infectious viruses in feces . In addition to the droplet transmission, the National Health Commission of the People’s Republic of China confirmed that aerosol transmission of SARS-CoV-2 is possible in special circumstances including long exposure to high concentration in a closed environment . Additionally, it was recently reported that aerosole transmission plays a role in the spread of COVID-19 . Food industry premises can be considered a closed workplace setting where infected workers can transmit the virus to their co-workers through air due to the close proximity. Viral particles of SARS-CoV-2 can also end up on surfaces of food preparation areas, meat, dairy, or other animal products. In addition, SARS-CoV-2 viral particles can also be present on swab samples from isolation wards, other hospital wards, sewage treatment units, and nursing homes . The deposition on surfaces can lead to subsequent hand-to-mouth, hand-to-nose, or hand-to-eye transmission .

Delaware and Rhode Island permitted courts to waive requirements to pay existing legal obligations. Colorado vacated underlying convictions as part of its expungement process, and voided remaining fees, fines, and restitution associated with vacated cases.Among the 39 states and Washington DC that decriminalized or legalized cannabis use, we found that 36 permitted some type of expungement of conviction records related to cannabis and four states prohibited the expungement of existing convictions. Within the 36 states that allowed expungement, 34 offered general expungement, 21 offered cannabis-specific expungement, and 11 offered general drug offense expungement . Among the 34 states with general expungement programs, 33 allowed expungements by petition, 9 expunged records automatically , and 7 allowed pardons as part of records expungement. Among the 21 states with cannabis specific expungement programs, 17 allowed expungements by petition, 9 expunged records automatically, 5 allowed pardons, and 7 provided multiple expungement programs. States with petition systems require applicants to navigate complex procedures that consume time and resources . Administrative burdens are broadly defined as the compliance, learning, and psychological costs endured by persons interacting with civic institutions that are correlated with reductions in uptake of government services . Petition systems increase administrative burdens, and as a result navigating them may require record holders to secure legal assistance,cannabis grow systems further increasing costs and limiting relief for people who are socioeconomically disadvantaged due to their convictions . Cannabis laws have imposed disproportionate criminal burdens on minorities,making accessible expungement important to improving racial equity outcomes as cannabis legalization expands .

Limited knowledge of expungement, the education needed to navigate petition systems, stress incurred during the process, the requirement to wait a specific period before seeking relief, and the payment of fees likely contributes to limited use of petition-based expungement . State policymakers may create, or retain, systems based on petitions that make expungement more difficult in the belief that this will encourage lawful behavior, and that it will prevent those who are likely to reoffend from having their records expunged . However, research suggests that expungement reduces recidivism , improves earnings, and increases employability by removing criminal records that limit the ability to obtain work, housing, or secure education funding . Expungements are also low-cost relative to job training programs and improve economic outcomes, as persons with criminal records earn less over time . Our findings suggest that potential beneficiaries may have been denied this economic and social relief, since the majority of states require record holders to petition for expungement. The majority of expungement programs also imposed administrative burdens in the form of waiting periods and fees. Administrative burdens can compound racial disparities associated with stigma, and even small administrative burdens can limit access to government aid and services . For example, waiting periods established by some states, in the expectation that these will ensure only “truly reformed” persons obtain record relief, can perpetuate inequalities as racial minorities are disproportionately criminalized by cannabis offenses . Among the states offering expungement, all but two had waiting periods, which varied in duration by offense levels and types. Waiting periods are administrative burdens because they create a psychological hurdle and sustain stress related to criminal record stigma and penalties . Shorter waiting periods improve record holders’ long-term earnings , while longer waiting periods can increase recidivism by hindering reentry into employment. Some states have reduced waiting periods to improve access to expungement . Waiting periods could arguably be eliminated for former cannabis offenders in states where use is legal and or decriminalized, given that criminalization has already been overturned .

We also found that most states required applicants to pay administrative fees and resolve outstanding judgments and legal financial obligations, pay restitution, or all of the above. Fees constitute another burden, either directly by increasing financial costs or indirectly by requiring applicants to expend time to learn how to navigate indigency waiver processes. We were unable to find prior research assessing the impacts of administrative fees on cannabis-related record holders or applicants for expungement. However, legal financial penalties disproportionately burden minorities and persons of lower socioeconomic status and are associated with increased poverty . Although multiple states permit courts to grant indigency waivers, judges do not always allow this assistance . Many record holders are unaware of indigency waivers, creating an invisible barrier to expungement . The inability to pay financial penalties may also inflict collateral consequences, such as driver’s license revocation or having debt reported to credit agencies . States could reduce these harms by eliminating administrative fees for expungement and postponing or eliminating payment of outstanding legal financial obligations. Previous research has suggested that states explore automated expungement systems to reduce administrative burdens and increase rates of expungement . Reducing administrative burdens has historically increased records expungement . Nonetheless, further research is needed on automatic expungement programs. For example, California’s historic petition-based expungement program for cannabis offenses resulted in 5% to 7% of eligible candidates applying . The state implemented automated expungement for cannabis offenses in 2018 to provide greater relief , but progress on automatically clearing the 220,000 cannabis conviction records estimated to qualify for clearance has been uneven . As of July 2021, 34,000 records remained unprocessed, spurring the introduction of new legislation to provide further automatic relief by January 1, 2023 .Our study has limitations. It focused on expungement for cannabis offenses in states that decriminalized or legalized cannabis use and sales by September 2022 and cannot be generalized to other offenses. People with cannabis offenses are more likely to secure records expungement than people with non-cannabis offenses, given the growing social permissiveness around cannabis and the expansion of legalization.

States also have expungement practices that this study did not capture, such as filing and dissemination requirements for expungement petitions and orders, mandatory hearings, imposing a burden of proof on applicants, or program qualifiers limiting expungement to either first-time offenders or persons that are diverted to and complete probation or drug treatment. In addition, we did not explore regulations that prevent legal aid organizations from filing petitions or other factors that could increase or reduce expungement access, such as prohibiting relief for people with unrelated offenses, imposing caps on total offenses, or on certain types of offenses. These requirements are administrative burdens that further complicate the process of records expungement. Although we cataloged expungement provisions pertaining to violations, misdemeanors, and felonies, we did not survey the levels and types of cannabis offenses in each state or how those may have changed over time . Instead, our paper surveyed all potentially applicable statutes and restrictions that could apply to a cannabis offense. Finally, cannabis is a dynamic policy area and legalization laws as well as expungement statutes have changed rapidly since 2012. Nonetheless,ebb and flow tables this research suggests how current expungement programs may affect the ability of cannabis-related record holders to secure relief in states that have attempted to reduce cannabis criminalization, and provides the groundwork for further exploration.Warehouse storage occupancies are currently reaching heights on the order of 24 to 30 m high stacks of storage commodity, which have not been considered by existing fire codes and engineering correlations. Over the last 50 years, fire protection engineers have relied on large-scale tests to classify commodities into one of seven classes that are representative of their fire performance under specific geometric configurations and ignition conditions. This classification process, which relies on expensive full scale testing, results in increased safety gaps as the industry creates new and untested materials that are stored in large quantities. Only a limited amount of fundamental science has been performed in this area, which is largely due to the range of complexities that occur in large-scale fire phenomena. Some correlations for large-scale flame heights of some commodities and fuels are present in the literature, but they are limited to specific fuels/configurations and some of the correlations require heat release rate values from full-scale tests. These correlations will be discussed further in the following sections of this paper. Currently, no tests that are known to the authors provide a complete set of fundamental, non-dimensional parameters that can be used in engineering calculations towards the safer design of large storage facilities. Efforts that result in the development of such test methods and classification methodology with a sound scientific basis may fulfill an urgent need to improve upon the current warehouse design methods. In most of these incidents, which were reviewed in Part I of this paper, the facilities were protected by automatic sprinkler systems that were installed in accordance with their respective current codes and standards. The negative impacts of these devastating fire incidents were felt by the occupants, firefighters, insurance interests, and local environments. From a business aspect, millions of dollars of materials or products are lost, and operations may be halted. Furthermore, insurance premiums are increased as a result of the fire, and the lost time can never be recovered. From a life-safety aspect, the lives of workers and responding firefighters are endangered, which can result in injuries or death.

The water runoff from firefighting operations and the resulting smoke plumes can also adversely affect the surrounding environment. The development of an approach to protect these facilities based upon the combustible materials that are stored, the layout of these materials, and the complex interaction with potential suppression systems is a critical step towards reducing the amount of devastating warehouse losses. As a first step towards improving the methods for commodity classification, two existing non-dimensional parameters are used to represent the physical phenomena present at the large-scale and model one part of large-scale flame spread. In Part I of this paper, a method was developed to experimentally quantify the burning rate of a material based upon the non-dimensional comparison of a materials chemical energy released during the combustion process with the energy required to vaporize the fuel, which was measured as a B-number. Commodities are classified to design sprinkler protection systems for most warehouse scenarios, and because such a sprinkler systems goal is to suppress or control a fire, the ranking of materials based upon the burning and spread rates of a potential fire is appropriate. Experiments were performed on a standard warehouse commodity, a Group A plastic, which is typically used to represent the worst-case commodity in large-scale tests. The commodity consisted of a single corrugated cardboard box that measured 53 x 53 x 51 cm and contained 125 crystallized polystyrene cups that were segregated by corrugated cardboard dividers. All of the faces except for the front face of the commodity were uniformly insulated, and the front face of the commodity was ignited at its base. The experimental observations of the Group A plastic commodity resulted in a qualitative description of the burning process over three distinct stages of burning. The first stage was characterized by upward flame spread over the front face of the corrugated cardboard, followed by a decreased burning rate as the cardboard smoldered and the polystyrene heated, and finally a sharp increase in the burning rate after ignition of the polystyrene. Despite the complex configuration, each stage resulted in distinct material involvement, which indicates the potential to model distinct material involvement from each stage using parameters derived from bench-scale testing. Fluctuations between the repeated tests also indicated the difficulty in obtaining repeatable measurements during these larger tests; therefore, small-scale test methods that can be repeated at a level of statistical accuracy may greatly improve the applicability of the results. Part II of this study continues the development of a non-dimensional approach to characterizing the burning behavior of materials. The bench scale tests that were performed in this study involved a small, flat sample of corrugated cardboard or polystyrene oriented vertically in which the burning was isolated to the front surface of the sample. The flow was considered to be laminar due to the observed behavior of the flow in the experiments. At the bench scale, the tests captured the effects of the commodity material properties on the flame spread process while separating the large-scale effects such as turbulence and radiation. Non-dimensional B-numbers were experimentally determined for the samples with greater accuracy than previous experiments. A flame spread model was then utilized to demonstrate the application of the experimentally measured B-numbers to predict in-rack flame heights in large-scale configurations.