Historically, the war on cannabis drew a line between “productive” members of society and “dangerous” elements , thereby serving normality judgments. Today, cannabis is in a transition state: although the law made it legal in California, large segments of the population and local governments still do not accept the idea of recreational cannabis and refuse its incorporation in the communities. I explore cannabis regulation as an example of moral laws, i.e., policies that deal with problems of moral order and deviance . According to Max Weber, certain laws, especially those relating to moral issues, are not accepted on the basis of their legality, but because they express legitimate moral values . The legalization of cannabis in California presents an ideal context to unpack the mechanisms that explain why some jurisdictions move toward more permissive moral policies, and others do not. The implementation of morality policies at the city level is a neglected area of research. Many writings focus on the nature of morality policies and whether or not the state should regulate certain individual practices. Still, little efforts have been directed toward understanding how these legislations are created and carried out. This study provides an empirical test of the morality of law and explains what drives the adoption of permissive cannabis regulation at the bottom level of US politics—cities. There is a commonly held misconception that since most citizens supported the legalization of cannabis in California, its use and sale is allowed throughout the state. However, a closer look at local political projects shows that public support cannot fully explain the adoption of pro-cannabis legislation. For example, in Santa Monica, 75 % of voters supported cannabis legalization in 2016, but the city government forbade all economic activities related to recreational cannabis. Meanwhile, most of Firebaugh’s citizens voted against the legalization of recreational cannabis, but the city government permitted cannabis grow equipment businesses. By focusing on cannabis legalization in the cities of California, this project brings to the fore the importance of local actors and decisions.

Despite the significance of local policies, we still lack a comprehensive body of theoretically driven research explaining variation in policy outcomes at the local level . Studying local policies can be beneficial for many reasons. First, the success or failure of state or federal regulations depends on how it is operationalized and put into effect by local jurisdictions . Local practices may change an idea laid by legislators. Second, the decisions produced by local governments have a direct impact on the well being of citizens and communities. Lastly, a large number of national initiatives have grown out of local activities . For example, San Francisco was the first American city that ignored state and federal laws and decided not to prosecute “underground cannabis pharmacies”, which eventually paved the way for legalizing cannabis across the state . This research delves into the complex nature of change. I purposely do not say “legal change”, for the term oversimplifies the dynamics inherent to the transition from illegal to legal cannabis. Legalization is not a mere outcome and does not happen overnight. It is, first of all, a gradual process of social, cultural, legal, and institutional transformation, which started in California well before Proposition 64 in 2016 and may extend long into the future. Therefore, I adopt a process-oriented approach that overcomes the limitations of the outcome-based perspective, widely used in sociolegal studies, and allows us to speak about legalizing cannabis in California as a project under construction. Socio-legal scholars examine the drug problem predominantly through the lens of control, prohibition, and punishment. Drugs are viewed as a card played within the bigger political project that is the creation of the penal state. Various studies have demonstrated how drugs became both a target and a source of the war on crime, and how by associating drug use to violent crimes, the federal government made the war on drugs an integral part of American life . These analyses present a causal explanation of penal change, which contains the following key elements: the intensification of state control as a response to the public’s fears incited by the mass media. Although such theorizing is essential for understanding the drug problem, it is not sufficient. The preponderance of the “crime and punishment” framework stifles other possible research angles and reduces the perimeter of the drug problem.

Drugs are not only a criminal justice issue but also a societal problem, a medical problem, a moral problem, a market problem, and so on. Another limitation of the existing socio-legal literature on the drug problem is its outcome based and top-down orientation. The research heavily relies on causal explanations and focuses on macro-level trends, elite political actors, class conflicts, federal policies, and national mass media. At the same time, it neglects other layers of interpretation, such as local-level processes, institutional practices, and social relations. As Andrew Abbott notes, the vast majority of sociological studies aim to evaluate the causes of “what happens” but lack a reflective concept of how to temporarily conceptualize “what happens” . Instead of focusing on where we ended up, Abbott says, we should look at the walk itself. The outcome-based theorizing may be problematic since it reinforces a “normal” and “objective” view of life . By explicating the causal relationship between crime and drugs, socio-legal scholars conceptualize the drug problem in the same inadequate way as policymakers and therefore run the risk of exacerbating intolerance and prejudices . Currently, socio-legal scholarship lacks an alternative language to speak about the drug problem and continues investing in the criminal justice approach . Cannabis legalization raises interest among various social science disciplines—jurisprudence, sociology, criminology, political science—which focus on a narrow range of topics, such as the discrepancy between federal and state regulation, or the effect of legalization on crime rates, drug use, and driving under the influence. Much of this scholarship defines law in instrumental terms: legal change simply ensues from broader policy and regulatory shifts, such as the passage of the Controlled Substance Act in 1970 or Proposition 64 in 2016. However, the law is not a direct reflection of collective consciousness; neither is it the immediate result of lawyers’ work. The alternative way to study law is to look at it as a communicative practice that directs attention to the law’s power in constructing meanings, legal discourses, symbols, interpretations, and knowledge. In short, to understand the nature of the legalization process, it is necessary to employ a bottom-up perspective and move from the enactment of federal and state legislation to local political projects, from the patent outcomes to latent everyday practices, and from the direct impacts to negotiated agreements.Many intricacies of the legalization process become apparent only when we apply a processual view and focus on local political projects. What do we mean when we say that cannabis became legal in California?

From a strictly juridical point of view, it means that possession, cultivation, distribution, and sale of cannabis do not represent a criminal act anymore. Individuals who consume, grow, or sell cannabis cannot be arrested or prosecuted. From a market point of view, it means that the state oversees the operation of the legal cannabis market. Namely, it creates a legal infrastructure for market development, enforces contracts, safeguards competition, protects property rights, and provides standards. Finally, from a cultural perspective, vertical grow rack legalization means a cultural shift reflecting the broader public acceptance of cannabis. Some scholars call this process “normalization” , arguing that today, cannabis use is more tolerated, and cannabis users are less stigmatized. But how do all these processes evolve in practice? First, even though de jure cannabis is legal throughout California, de facto its status is controversial. Under the Controlled Substances Act , cannabis is classified as a Schedule I drug, along with such potent substances as heroin and LSD. Thereby, the federal government has the authority to prohibit and prosecute any use of cannabis. As separate sovereigns, the states may decide whether to cooperate with the federal government or not, but they cannot prevent the federal agencies from enforcing the law. Therefore, cannabis is legal in a state that has voted to allow it, but only to the extent the federal government chooses not to enforce the CSA . Despite several attempts to resolve the conflict between federal and state laws, cannabis is still in a legal limbo. In 2013, the Department of Justice issued a memo notifying that prosecuting local cannabis cases is not a priority. However, under the Trump administration, US Attorney General Jeff Sessions rescinded all Obama-era lenient policies towards cannabis, including the memo limiting federal prosecution of local cannabis cases.4 Currently, Congress is debating over the Marijuana Opportunity and Reinvestment and Expungement Act , which decriminalizes cannabis and completely removes it from the list of controlled substances.5 On December 4, 2020, the House of Representatives approved the ACT, but many experts are pessimistic about its passing in the Senate. Second, the continued status of marijuana as a federally prohibited substance significantly hampers the states’ capacity to implement new regulatory policies effectively and creates legal jeopardy for those in the legal cannabis market . As Sam Kamin notes, “federal prohibition acts as a brake on an industry that otherwise might grow with unhealthy pace” . Cannabis dispensaries cannot obtain banking services since financial institutions are not ready to support companies selling a product that the federal government treats as an illegal drug. They have to rely mostly on cash and thus become an easy target for robberies . Additional obstacles for legal cannabis companies are associated with high taxes, difficulties with obtaining legal aid, and unavailability of property rights protection and other business necessities . In 2018, California legal dispensaries sold fewer products than a year before, when only medical cannabis was allowed6 , a picture which many specialists associated with the persistence of illegal or semi-legal economic activities.7 Third, in the last decades, cannabis use has undergone a transition from a largely marginal activity to a more prevalent and tolerated one . California issued more than 20,000 cannabis licenses within the first two years of the legalization of recreational cannabis. The number of professional associations, networks, business-related newspapers, websites, and the variety of cannabis products is continually growing. There is a broader acceptance of cannabis in the mainstream media and among the public. According to Gallup, the support of cannabis legalization grew from 12% in 1969 to 66% in 2018.8 After the possession of less than an ounce of cannabis was reclassified as a misdemeanor9 in 2010, the number of arrests for cannabis possession has dropped considerably—from 56,000 in 2010 to 10,000 in 2011 . The evidence indicates that cultural attitudes to cannabis have changed. However, there is still a certain stigma associated with cannabis. Despite its normalization, cannabis use continues to be perceived as risky, marginal, and deviant and is often kept private to escape conflicts with family, landlords, employers, or police . The war on drugs generated various misconceptions about cannabis, which detrimentally affect the image of current cannabis users and cannabis dispensaries. For example, under Proposition 64, public and private companies have a right to ask job candidates and current employees to pass a test on cannabis to “maintain a drug and alcohol-free workplace.” If a drug test shows traces of cannabis, a person can lose a job. 10 The legal status and the meaning of specific products vary over time and space . The case of cannabis is very telling since it went through different stages of neutrality, hostility, and affirmation in the last hundred years. In the mid-19th century, cannabis was a legitimate medical substance , included in The Pharmacopeia of the United States and attributed to helping with rheumaThism, tetanus, epidemic cholera, hysteria, depression, and other illnesses. In the course of the 1930’s anti-cannabis campaign, the plant was framed as an evil drug that leads to criminality and violence. The mass media and state officials popularized the term “marijuana,” a Spanish word used by farm workers, to transform the public perception of cannabis and tie it with “dangerous” Mexican migrants. In 1937, cannabis was prohibited at the federal level and, five years later, removed from The Pharmacopeia of the United States.

The most efficient way to minimize marijuana use would be to use funds from taxes on marijuana sales to implement a marijuana prevention and control program, modeled on the successful California Tobacco Control Program , under the administration of the California Department of Public Health. The key to the success of the CTCP has been that it is a broad-based campaign focused on reinforcing the nonsmoking norm aimed at the population as a whole – not just smokers or youth, for each element of the program, including the statewide hard hitting, evidence-based media campaign. Indeed, by focusing on adults through its comprehensive tobacco control program, California has achieved one of the lowest youth smoking rates in the country. Similar to the CTCP, an effective marijuana prevention and control program would implement social norm change strategies including: 1) Countering pro-marijuana influences in the community; 2) Reducing exposure to secondhand marijuana smoke and aerosol, marijuana smoke and aerosol residue, marijuana waste, and other marijuana products ; 3) Reducing availability of marijuana and marijuana products; and 4) Promoting and supporting services that help marijuana users quit. There would be a state-level administrative office housed in the Department of Public Health with separate funding, dedicated to the primary purpose of preventing and reducing marijuana use and protecting the public from secondhand smoke exposure. Funding would be earmarked in the initiative and be protected from diversion by the Legislature or Governor. Chapter 10 If modeled on the CTCP, a marijuana prevention and control program would allocate funding to local health departments and, cannabis grow racks on a competitive basis, to community-based organizations to create marijuana prevention and control coalitions, and to coordinate efforts with schools.

The marijuana prevention and control program would mount an ongoing statewide media campaign, and would provide continuous training and technical assistance to local marijuana prevention and control programs, in large part through the competitive statewide grantees. The marijuana tax would provide an ongoing annual revenue stream to support implementation of a statewide media campaign that would consist of paid radio, television, billboard, internet and social media, and print adverThising. The media campaign would include public relations campaigns for general market and population-specific communities, including various ethnic populations, young adult, and Lesbian Gay Bisexual Transgender communities. The media campaign would frame the messages and inform the public on the harms of marijuana use and secondhand exposure, expose and publicize predatory marketing by the marijuana industry, and encourage quitting through a cessation helpline. The CTCP statewide campaign aimed at the general population has reduced smoking and provided billions of dollars in healthcare savings for Californians, both as individuals and as taxpayers. It is reasonable to hypothesize that a marijuana prevention and control program based on the CTCP would have similar effectiveness and financial benefits. The CTCP was most effective in its early years when it was larger, Chapter 10 and before inflation eroded its purchasing power. Based on the experience of the CTCP, an annual budget of $340 million would be adequate and would allow for mounting an effective campaign to counter the adverse public health impact of the new marijuana industry.The AUMA initiative imposes a cultivation and retail sales tax on marijuana and marijuana products. The cultivation tax will be for marijuana flower: $9.25 per dry weight ounce and for marijuana leaves: $2.75 per dry weight ounce. The retail sales tax will be an ad valorem tax of 15% of the total sale. The Board of Equalization will have the authority to adjust the tax rate for marijuana leaves annually to reflect changes in the price of marijuana flowers and will have the authority to adjust the cultivation and retail sales tax for inflation .

After money is dispersed to the regulatory agencies to cover administrative costs, the marijuana tax will be used to support youth substance abuse and prevention programs, economic development, medical marijuana research, and to research the implementation and effect of AUMA. Marijuana tax will also be dedicated to the California Highway Patrol for enforcement and to develop standards and programs, including field sobriety testing protocols, and to environmental restoration and protection . The AUMA initiative states that retail marijuana sales will “generate hundreds of millions of dollars annually”7 to cover the costs of administrating the new law and will provide funds for programs designed to educate and treat substance use disorders in youth. Most of the first money, however, is allocated to programs that prioritize marijuana businesses rather than to programs that would prevent marijuana use and reduce consumption, likely increasing the external costs associated with marijuana legalization, such as increased healthcare spending . The program that receives the most funding is the Governor’s Office of Business and Economic Development that will reach $50 million in 2023 to invest in economic development and job placement for communities affected by “past federal and state drug policies.” The initiative provides $2 million annually to the University of California, San Diego Center for Medicinal Cannabis Research to conduct research on the benefits and adverse effects of marijuana as a pharmacological agent. Other research priorities that will be conducted by universities in California are discussed below and do not prioritize funding marijuana-related disease research as a basis for future policy. The AUMA initiative provides $3 million for five years to the California Highway Patrol and law enforcement to develop standards and programs, including field sobriety testing protocols. Given the lack of accurate and reliable chemical tests to determine marijuana impairment, five years might not be sufficient to develop methods to determine marijuana impairment while driving. In Colorado, in which retail sale of marijuana became legal in 2014, regulators are still trying to determine the best way to detect marijuana impairment while driving.

While this is an important provision that should be addressed, a more effective approach would be to earmark marijuana tax revenue to a comprehensive marijuana prevention and control program aimed at the general population. Such program would have the effect of preventing marijuana initiation and heavy consumption that would be associated with increased marijuana-related traffic accidents and fatalities. Earmarked funds to support comprehensive prevention and control programs over time, which are not included in the initiative, will be critical to reduce marijuana prevalence, marijuana-related diseases, and costs arising from marijuana use. A public health framework for legalized marijuana would require that the marijuana tax be at least budget neutral, so as to cover the costs of legalizing marijuana, including marijuana prevention and education, marijuana related disease research and education, as well as the costs of managing the business aspects of the new marijuana market. The marijuana tax would also need to be high enough to cover health related costs as a result of increased marijuana use . Additional tax increases would be permitted if doing so was determined to be appropriate as a way of reducing marijuana initiation and promoting cessation, as a general revenue source, or both. While an ad valorem tax is simple to implement, cannabis drying racks there is no guarantee that it would cover the costs associated with legalizing marijuana in California. In particular, as market prices fall, the revenues generated from the marijuana tax will also fall. As evidenced by the Colorado experience, greater supply likely will drive down the price of marijuana and marijuana products in California. There is also a danger of price manipulation by the marijuana industry. In Colorado, price manipulation has been an issue, in which retailers are increasingly lowering prices to compete with other marijuana retail outlets. The existence of an illicit untaxed market complicates tax policy making due to concerns that if the marijuana tax is too high it would drive consumers to the illicit market. A comprehensive demand reduction program would probably reduce this problem. If marijuana-related costs follow the same trajectory as tobacco, then it is likely that the tax will not generate enough revenue to cover administrative costs and healthcare costs to California taxpayers. While the costs of treating marijuana-induced illness is unknown, in 2009, the healthcare costs of smoking in California was $18.1 billion, 156 an amount that would have been much higher without California’s comprehensive tobacco control program. For an ad valorem tax to be large enough to cover total costs, it would need to be increased as costs go up, which, if marijuana use increases, are likely to grow faster than inflation. The fiscal impact estimate reports of the California Legislative Analyst’s Office of the two initiatives do not include the economic impact of a retail marijuana market on increasing health care costs for California government or the state as a whole. The AUMA initiative is strong in that it will prohibit marijuana use wherever smoking or e-cigarette use is prohibited by state or local laws, and grants authority to local governments to adopt smoke free laws stronger than the state. Because the 1995 California statewide smoke free law contained exemptions, marijuana use will be permitted in 65% of hotel/motel guestrooms, banquet facilities and meeting rooms, small businesses , designated break rooms, private smoker lounges, warehouses, taxis, long-term health care facilities, and in multi-unit housing, unless these venues are covered under local laws stronger than the state law.

The AUMA initiative also creates a problematic loophole that will allow local governments to permit marijuana use in licensed facilities that admit only adults 21 years and older, are not visible to the public, and do not sell tobacco or alcohol. It will also permit smoking in private residences in the 1,000-foot buffer zone “only if such smoking is not detectable by others,” which is an unenforceable measure. They also have the beneficial side effect of decreasing the normalization of tobacco use, and supporting smoking cessation. To accomplish these goals for legalized marijuana, a public health framework would prohibit consumption anywhere combustible tobacco product consumption is prohibited under local and state smoke free laws. Local governments would not be preempted from adopting stronger regulations than the state, and local and state smoke free laws would not contain exemptions for indoor use in hospitality venues, marijuana retail stores, and marijuana clubs. Both initiatives grant local governments authority to permit marijuana smoking inside marijuana retail stores or marijuana clubs. This provision ignores an important lesson from tobacco control that smoke free bars are particularly effective at protecting workers from secondhand smoke exposure and at denormalizing smoking. For example, employees working in bars and nightclubs with higher ambient nicotine concentrations have higher levels of nicotine exposure regardless of own smoking status. If local governments implement laws that would permit marijuana smoking inside marijuana stores or clubs, it will likely negatively impact the working class poor, immigrants, and individuals from communities of color, and will contribute to health disparities. Lower socioeconomic status individuals are more likely to work in establishments that do not have 100% smoke free coverage or circumvent the law through exemptions . In California, for example, exemptions in the statewide smoke free law disproportionately expose low income workers, Latinos, and young adults to secondhand tobacco smoke in the workplace, thereby contributing to health disparities. In addition, women may be disproportionately impacted by permitting marijuana smoking in hospitality venues because women are over represented in the hospitality industry. This potential loophole also ignores the fact that exemptions in smoke free laws are difficult to amend once the law has been passed. For example, over the last twenty years, the State of California has failed to close important loopholes in the state smoke free law despite attempts from legislators. This situation highlights the importance of enacting effective measures initially to prevent unnecessary secondhand exposure to the public. In addition, these exemptions will likely promote consumption and other risky behavior, such as driving while under the influence of marijuana. While allowing for such on-site consumption at marijuana businesses or clubs is based on the view that it will allow for adults to use marijuana in a responsible way, such social use away from home not only creates a risk of increasing overall use but also facilitates marijuana consumption prior to driving home while still under its influence. The reality is that similar to bars failing to promote socially responsible alcohol consumption, marijuana retailers with the financial incentive to promote over consumption will replicate this behavior with marijuana.

We recently demonstrated that vapor inhalation of THC using an e-cigarette based approach produces anti-nociceptive effects and reduces the body temperature and spontaneous activity of male and female rats . These are canonical effects that are observed after injection or oral delivery of THC in laboratory vertebrates including in rats , mice , monkeys and dogs , although the individual behavioral or physiological outcomes may be observed after different doses. Behavioral and physiological effects, and plasma THC levels, of THC delivered by vapor inhalation depend on the exposure duration as well as the dose administered. We have shown that dose can be controlled during inhalation exposure by varying either the concentration of THC in the e-liquid vehicle or the duration of exposure at a fixed concentration . This validated platform is therefore ideal to test the hypothesis that aerosol THC exposure of lobsters has physiological effect. Development of different animal model species, including invertebrates, for the evaluation of drug effects can offer both unique and converging advantages, as recently reviewed by Smith . The lobster is an established model for evaluating neuronal morphology, central pattern generation and synaptic mechanisms in the stomato-gastric ganglion . More practically, the lobster can be studied within institutions that are not equipped to oversee vertebrate animal research, or can be studied at reduced expense in institutions where vertebrate research is supported. A recent review indicates there are no clear data on lobster nociception and decapod crustacean investigations of nociception do not typically involve thermal stimuli ; only one available report shows that crayfish are sensitive to a thermal stimulus delivered by soldering iron .Thus it serves the additional goal of determining if thermal nociception exists in this crustacean species which would add to the evidence for thermoception in decapod crustaceans more generally. There is very limited evidence on whether the lobster would be behaviorally sensitive to THC exposure, however, pipp mobile systems the neuromuscular junction of lobsters appears to be regulated, in part, by cannabinoid mechanisms.

Turkanis and Karler showed that THC had doserelated effects on excitatory neuromuscular junction potential amplitudes, increasing them at moderate concentrations and decreasing amplitude at higher concentrations . This enhances confidence that some cannabinoid-sensitive mechanisms are present in the lobster and that THC might affect locomotor behavior, despite the fact there may not be a homolog or ortholog of the vertebrate endocannabinoid receptor expressed in the lobster . The invertebrate C. elegans expresses a cannabinoid-like ortholog receptor which mediates effects of the endogenous cannabinoid agonists 2-arachidonoylglycerol and anandamide , suggesting that there may be as yet un-identified ortholog receptors in the lobster. It is also possible that THC acts in the lobster via transient receptor potential channels, since THC appears to function as a ligand at TRPV2, TRPV3, TRPV4, TRPA1, and TRPV8, as reviewed . The Caribbean spiny lobster expresses 17 TRP including TRPA and TRPV homologs . Hypolocomotion is a canonical sign of cannabinoid action in rats and mice , and occurs after vapor inhalation of THC . Thus, locomotor activity was selected to assay for evidence of in vivo behavioral effect. Less directly, recent studies in the crayfish, a related aquatic crustacean, have shown locomotor effects of cocaine, morphine and methamphetamine and intravenous self-administration of amphetamine . This further enhances confidence that behavioral pharmacological effects of recreational drugs can be effectively assessed in the lobster. Traditional cooking of lobster is by immersion of live animals in either boiling water or steam, leading to concerns by some that the animal might experience pain. Indeed, live cooking has been banned in Switzerland . There is no available evidence demonstrating clearly that lobsters are sensitive to temperature, however one paper has shown that crayfish respond to a hot metal rod stimulus applied to the claw .

Since TRPA1 homolog receptors in invertebrates are activated by high temperature, e.g., at about 48 °C in one study and over 43 °C in other work , these mechanisms may be the primary thermoreceptor. It is thus of interest to develop assays to determine if lobsters exhibit thermal nociceptive behavioral responses and then to determine if those responses can be altered by THC exposure. The hot-water tail withdrawal assay in rats involves a reflexive tail movement when it is inserted in hot water and has been shown to be altered in rats after vapor inhalation of THC . Thus, one goal was to determine if warm water immersion produces a behavioral response in the lobster and if so, if THC exposure decreased thermal nociception as it does in rodents . As part of a model development, it was important to determine if different responses could be obtained from tail, claw or antenna immersion and if the response depended on the temperature of the water bath, as in . Wild caught female and male Maine lobster were obtained from a local supermarket. When housed longer than several hours in the laboratory, the animals were maintained in chilled , aerated aquariums and fed variously with frozen krill, fish flakes and anacharis. The tissue distribution studies were conducted under protocols approved by the Institutional Animal Care and Use Committee of The Scripps Research Institute due to a decision that a protocol was required for this invertebrate species. The remaining studies were conducted at the University of California, San Diego where the institution does not require protocol supervision/approval for this invertebrate species. Sealed exposure chambers were modified from the 259 mm × 234 mm × 209 mm Allentown, Inc. rat cage to regulate airflow and the delivery of vaporized drug to the chamber using e-cigarette cartridges as has been previously described . The controllers were triggered to deliver the scheduled series of puffs by a computerized controller designed by the equipment manufacturer . The chamber air was vacuum controlled by a chamber exhaust valve to flow room ambient air through an intake valve at ~1 L per minute. This also functioned to ensure that vapor entered the chamber on each device triggering event. The vapor stream was integrated with the ambient air stream once triggered. The chambers were empty of any water or bedding material for these exposures .For these studies, animals were obtained, dosed and euthanized for tissue collection within 4–6 h.

Lobsters were exposed to THC vapor for 30 or 60 minutes, then removed from the chamber and rinsed with tap water. Thereafter, they were rapidly euthanized by transection of the thoracic nerve cord using heavy kitchen shears and then transection of the thorax behind the brain by a heavy chef’s knife. Samples included the gills , claw muscle obtained from proximal and distal aspects, anterior and posterior segments of tail muscle, a red membrane surrounding the claw muscle , brain, heart, liver and hemolymph. Hemolymph was allowed to coagulate to facilitate analysis as ng of THC per mg of tissue, as with the other tissues. For N = 2 per exposure-duration group, one claw was cooked immediately after euthanasia by boiling it in water for 10 min, prior to collection of muscle tissue and the red membrane that surrounds it. Tissues were frozen for storage until analysis was conducted. Tissue THC content was quantified using liquid chromatography/mass spectrometry adapted from methods describe previously . THC was extracted from brain tissue by homogenization in chloroform/ACN containing 100ng/mL of THC-d3 as internal standard followed by centrifugation, decanting of the lower supernatant phase, evaporation and reconstitution in acetonitrile for analysis. Specifically, ~200–300 mg of tissue was homogenized in 1.5 mL of chloroform, 0.500 mL of acetonitrile and 0.100 mL of deuterated internal standard . Samples were centrifuged at 3000 RPM for 10 min, followed by decanting of the lower supernatant phase, evaporation using a SpeedVac, and reconstitution in 200 μL of an acetonitrile/methanol/water mixture. Separation was performed on an Agilent LC1100 using a gradient elution of water and methanol at 300 μL/min on an Eclipse XDB-C18 column . THC was quantified using an Agilent MSD6180 single quadrupole with electrospray ionization and selected ion monitoring [THC and THC-d3 ]. Calibration curves were generated each day at a concentration range of 0–200 ng/mL with observed correlation coefficients of 0.9990. For these studies, industrial drying rack animals were maintained in the laboratory for 4–21 days in chilled aquariums. Salt-water baths for the nociception assay were maintained at the target temperature and confirmed by thermometer immediately prior to each test. The investigator held the animal gently by the thorax and the tail or the tip of the antenna was inserted approximately 3 cm; the claws were inserted to a depth of approximately 5 cm. The latency to respond was recorded by stopwatch and a maximum 15 s interval was used as a cutoff for the assay. Homarus genus lobsters exhibit asymmetry of their claws with one larger and one smaller claw that can be on either the left or the right; feral experience appears to be necessary for proper development of the claw asymmetry as it is less pronounced in cultivated lobsters . This asymmetry produces a crusher muscle that is constituted of 100% slow fibers, whereas the cutter muscle exhibits only 90% fast fibers as assessed by ATPase staining and fast and slow motoneuron innervation . Thus, for this study the cutter and crusher claws were assessed independently. The order of assessment was always tail, claw, claw then antenna. The body part was inserted in the ambient water bath for 5–10 s after each warm water assessment.

The temperatures for assessment were “ambient”, and then three warm temperatures ; the order of testing of the warmer temperatures was in a counterbalanced order with at least two hours between assessments and no more than two tests per day. Lobsters were next assessed for the reaction of tail, crusher and cutter claws, and the antenna to insertion in 48 °C water after vapor exposure to PG or THC for 30 or 60 min, with the testing order counterbalanced. Distinct responses of tail, antenna and claw were observed following insertion in warm water but not in the ambient temperature water bath . Sometimes, a reflexive and powerful contraction of the tail muscle was observed first . This appeared to be the caridoid escape response best described in crayfish, but likely present in the lobster , which is a complex behavior mediated by lateral giant and medial giant interneurons . In other cases, the lobster initiated distinct movements of legs and claws, this often preceded the powerful tail contraction. Thus, the tail assay was scored with two latency values, the very first reaction of any type and the tail contraction if it occurred. For analysis, the time of first overt response was used. The post-hoc test failed to confirm any significant difference associated with Vapor Condition for any individual body part. The primary finding of this study was that vapor exposure of Maine lobsters to Δ9-tetrahydrocannabinol , using an e-cigarette based system, produces tissue levels of THC in a dose dependent manner. THC was confirmed in the hemolymph , claw and tail muscle, brain, heart and liver . This wide distribution across body tissues is consistent with respiratory uptake, i.e., via the gills with distribution by the hemolymph circulation of the lobster. This conclusion is further supported by the much higher amount of THC that was associated with the gill tissue, consistent with a limited uptake by the respiration system of the lobster. The THC exposure also had behavioral consequences, since locomotor activity was significantly reduced after exposure to THC vapor compared with exposure to the vehicle vapor . Hypolocomotion is a canonical feature of THC exposure in rats and mice, at least at higher doses, thereby confirming a similarity of effect across the vertebrate and invertebrate organisms in which this has been evaluated. Thus, the assertions of the restaurateur that cannabinoids could be introduced into the lobster by atmospheric exposure , and that this would be in sufficient amount to induce behavioral effect, is supported. The impact of THC on thermal nociception was, however, minimal. The locomotor effects may be specifically related to a report of THC altering the amplitude of excitatory potentials at the lobster neuromuscular junction in a concentration dependent manner . Overall, however, it confirms that the levels of THC achieved by as little as 30 min of vapor exposure were behaviorally significant.

Unemployment rates from 2005- 2014 will be used in order to compare it to our MMIC data. Referring again to Table 4.1, we observe a mean unemployment rate of 9.8. All data sets contain 560 total observations from the 56 counties used within the 10-year period. The mortality rates are divided into three categories: Alcohol-Induced Causes, Drug-Induced Causes, and All Other Causes. These rates are given per 100,000, as shown in 7.1.3 of the Appendix. Estimated population sizes per year are also included in the data set. Like the MMIC data, the crude rates are reported per county, per year from 2005-2014. Within this time period, these crude rates have ranged from 4.9 to 1328.6 per 100,000. Referring above to Table 4.1, the mean alcohol-induced, drug-induced, and other crude rates are 13.5, 15.8, and 759.4, respectively. In addition to the mortality data, arrest rates will be examined to determine if medical marijuana is a substitute for other drugs and alcohol. The arrest data comes from the State of California Department of Justice’s Criminal Justice Statistics Center and includes 76 arrest variables. Of these 76 variables, I will be using 7 of them in my data analysis. These variables include marijuana, drunk, felony drug offenses, narcotics, dangerous drugs, other drugs, and total arrests. Other drugs represent all misdemeanor drug arrests excluding marijuana. However, the marijuana variable used in our data is the sum of both misdemeanor and felony marijuana arrests. As stated by the CJSC, “A felony offense is defined as a crime which is punishable by death or by imprisonment in a state prison. A misdemeanor offense is a crime punishable by imprisonment in a county jail for up to one year.” 13 Full variable definitions are given in Table 7.2.1 in the Appendix. All variables in the data set were given as number of arrests per county, per year again from 2005-2014. As presented in part 7.1.2 of the Appendix, I converted these numbers into arrests per 100,000 so the analysis of all variables could be more easily interpreted. The CJSC has also provided crime data from 2005-2014 to be used in the regressions. Not to be confused with arrest data, pipp grow rack the crime data set contains all individuals convicted of a crime, whereas arrests occur when a person is simply taken into custody for a crime.

The crime data presented by the CJSC offers 66 variables, from which I selected the 10 main types of crime, including, violent crime, burglary, larceny/theft, property crime, aggravated assault, motor vehicle theft, robbery, forcible rape, homicide, and total crime. Property crime is the sum of burglaries, larceny/thefts, and motor-vehicle thefts and violent crime is the sum of forcible rapes, homicides, and robberies. For full definitions of crime variables, refer to Table 7.2.2 in the Appendix. The crime data set originally included city and county distinction, but I collapsed the data into strictly per county observations. Computed the same as the MMIC, crude, and arrest rates, the third calculation shown in 7.1.3 of the Appendix was used to convert the numbers into crimes per 100,000 people. Table 4.2 below offers summarized statistics of all data collected from the CJSC.To begin analyzing the effect of medical marijuana in California, all nine individual crime rates and total crime rates were regressed on MMIC rates and unemployment rates with county and year fixed effects. It is necessary to include county fixed effects in the model because there are unobservable factors that could affect crime rates. For example, high-income counties in California may have lower crime rates by being able to afford tighter security. It is also obligatory to include year fixed effects in the crime rates model. This type of fixed effect absorbs any event or time trend that could potentially adjust crime rates. Because the data ranges from 2005- 2014, the housing market crash could have affected crime rates. Referring to Graph 5.1, it is indicated that crime rates don’t necessarily have a linear time trend. Thus, the individual year dummy variables will be the best fit to combat the unobservable events that occur across time. Here we see that for every additional medical marijuana card issued, total crime decreases by one and a half crimes.

This appears to be a significantly large effect. However, looking at the average MMIC rate of 53 and the average total crime rate of 6,210, it is unlikely that medical marijuana could completely eradicate crime. The estimated results imply that if the mean of MMICs goes up to 54, crime rates will fall to an average of 6,208.5. This is only a decrease of 0.024% of total crime, which is a small, yet reasonable estimate. While this is a small effect on total crime, the 95% confidence level suggests the true estimate is between -2.46 and -0.55. Because these values are negative, it is acceptable to assume medical marijuana will not negatively impact society by increasing crime rates. After observing that medical marijuana has a negative effect on total crime, it can also be seen that medical marijuana also has negative effects on larceny-theft and property crime, with estimates shown in tables 5.4 and 5.5. Table 5.4 indicates that for every additional MMIC issued, larceny/theft declines by about half of a crime, while Table 5.5 suggests that for every additional MMIC issued, property crime decreases by ¾ a crime. Because property crime is defined as the sum of larceny/thefts, burglaries, and motor-vehicle thefts, the effect on larceny/theft is contained within the effect on overall property crime. Many individuals who argue against the legalization of marijuana claim that marijuana usage would increase crime, thereby negatively impacting society. By building a 95% confidence interval it is shown that the true estimates are negative and that 95% of the time, the estimate will fall between -1.18 and -0.33. Thus, medical marijuana will not increase overall property crimes, specifically larceny/thefts. This answers the common argument that marijuana use increases crime rates.The other seven crime variables regressed on MMIC, using Equation 5.2, showed no significant effects of medical marijuana on crime. However, vehicle theft showed a statistically significant negative effect at the 90% confidence level. This can be explained by the above regression results on property crime, given that vehicle theft is included in the overall property crime rates by definition. All other crimes displayed zero effect from medical marijuana. While we can comfortably say that medical marijuana does not increase crime rates, there needs to be an explanation for why it has a significantly negative effect on both total crime and property crime. One explanation is that allowing consumers to purchase legally decreases the amount of associated crime that comes with the illegal marijuana market. It is often true that individuals who enact in criminal activity participate in more than one crime. This means when individuals are purchasing marijuana illegally, they are more likely to commit other crimes. Thus, when additional MMICs are issued, individuals are purchasing marijuana legally and are less likely to be crime participants. This effect can be seen in the above regression results where additional MMICs lead to a slight fall in committed crimes. A second explanation could be that there are substitution effects for marijuana and other drugs and alcohol. With evidence of marijuana reducing violent behavior, as explained further below, individuals are less likely to commit crimes. Because many crimes are committed while drunk or intoxicated, an increase in marijuana use with significant substitution effects on other drugs or alcohol could lead to a slight decrease in crime.This brings us to the next two models, created to observe whether or not marijuana is a substitution drug for alcohol and/or other drugs. Equation 5.6 regresses every individual arrest rate on MMICs and unemployment rates, while Equation 5.7 regresses drug-induced, alcohol-induced, and all other mortality rates on MMICs and unemployment rates.

These two equations will allow us to examine any substitution effects going on between marijuana and other drugs and alcohol. Both equations are again controlled for county and time fixed effects.This means that 99.9% of the time medical marijuana has a negative effect on drunken arrests. While this indicates that there may be a substitution effect for alcohol, pipp horticulture racks cost it is a small effect with a 1:4 substitution ratio. For this effect to decrease drunken arrest rates by 1%, MMICs would have to increase by about 20 per 100,00. This could be a possible scenario, given that the standard deviation of MMICs is 95.34. In the likelihood of this event, medical marijuana could be a significant substitute for alcohol. As briefly mentioned earlier in this analysis, a substitution effect between marijuana and alcohol can justify why we see a decrease in crime. It has been observed by many studies that a large proportion of crimes are committed when an individual is intoxicated. According to the Huffington Post, the National Institute on Alcohol Abuse and Alcoholism “found that 25-30% of violent crimes are linked to alcohol use,” and the journal of Addictive Behaviors performed a study that suggested “cannabis reduces likelihood of violence during intoxication,” thus explaining why an increase in marijuana use can decrease crime rates.14 By finding a slight substitution effect between marijuana and alcohol, we are able to explain some of the negative effect that marijuana has on crime. After regressing all other arrest rates, drunken arrests remains the only significant category affected by MMICs. So with the given data, there is no evidence that marijuana is a substitute for dangerous drugs, other drugs, felony drugs, nor narcotics. It is particularly surprising that we see no effect on narcotics, considering most medical marijuana patients specifically use cannabis as a substitute for narcotics. An explanation for this can be that some medical marijuana users do not use for medical reasons many of the MMIC holders in this particular data base may only use for recreational purposes. To observe any further substitution effects, I used Equation 5.7 to regress alcohol induced crude rates, drug-induced crude rates, and all other crude rates on MMICs and unemployment still controlling for county and year fixed effects. Unlike the arrest rate data, no substitution effects were found. Referring to the regression output in Table 5.9 for alcohol-induced deaths, MMICs actually had a statistically significant positive effect on alcohol related deaths. The interpretation is that for every new medical marijuana user, the alcohol crude rate increases by 0.0068 deaths per 100,000. However, observing that zero is in the confidence interval and that the t-statistic is borderline significant, it is likely that there is no effect at all. While this is still a positive number, its suggested effect is so small, it becomes negligible. This can be determined by looking at the average crude rate for alcohol related deaths, which is 15.8. There would have to be an additional 147 MMICs per 100,000 to increase this crude rate by 1 death per 100,000. This is a highly unlikely scenario, and could therefore be dismissed. By applying this same model to drug-related deaths, we again get a statistically significant positive effect on the crude rate, shown in Table 5.10. While this would typically suggest that marijuana is a complement drug to other drugs, the effect is again, miniscule. With the average drug-induced crude rate of 13.4 deaths per 100,000, the number of medical marijuana cardholders would have to increase by 142 to cause 1 drug-related death. Similar to the effect on alcohol-induced mortality rates, this is a very unlikely event, and can be disregarded. While the drug and alcohol related deaths were affected slightly by medical marijuana, all other crude rates did not. There was no statistically significant effect when applying Equation 5.7 to all other crude rates. After implementing all regressions, there is evidence to suggest that medical marijuana has a negative effect on crime rates, decreasing total crime by 1.5 per 100,000 for every additional MMIC issued. It is also found that medical marijuana has a significantly negative effect on both property crime and larceny/theft rates. While medical marijuana could not completely alleviate crime all together because of the significantly higher crime rate average, there is no evidence that marijuana use would increase crime; thereby disproving the common argument that implementing marijuana legislation will increase crime rates.

This results in fewer farms, producing more and more product, until the next technology comes along to perpetuate the cycle, hence the idea of the ‘treadmill.’The following is a summary of trends and themes documented in the literature about dairy production in the United States and Global North. Past and current geography research on dairy depicts an industry in flux, under the influence of capitalism, policy, and the environmental contexts of the region at hand. Here , I define and describe the six themes of transformation occurring in the dairy industry, and a seventh theme, the alternatives that have arisen in reaction to these dominant transformations.Intensification is a major buzzword in the world of agriculture, but it has varied definitions and applications. Fundamentally, intensification implies a change or transformation of the mode of production. In the original sense of the term, intensification is an increasing ratio between inputs and outputs; the variation in meaning exists in the consideration of different typesof inputs. While agricultural economists look at increasing production relative to inputs of feed, fertilizer or water, geographers consider land and capital to be inputs capable of intensification and use the term intensification to describe all manner of increased productivity or large-scale agricultural systems. Clay et al. define intensification in dairy as the increased milk output relative to inputs of feed, labor, land, or herd size, while Bojovic and McGregor describe the intensification of capital, land, and animals within the industry. The term is used to convey negative or increased impacts on the environment, because of the increased use of resources or pollution. The concept of “sustainable intensification” is commonly found in recent literature on agriculture, flood and drain hydroponics which is defined as increasing food production while minimizing the effects of production on the environment and not expanding the area of land used for agriculture .

The use of technological innovations in agriculture, as described in the background section, has been a key transformation to enable intensification and allow for capital penetration. Many of these technological innovations come in the form of machines, or practices that require the use of machines, like concentrated animal feeding operations , automated milking machines, or anaerobic digesters. The technological fix is a concept in agriculture and technology studies that describes the act of inventing a new technology to solve every new problem, which is often criticized for creating new problems of their own, and perpetuating systems that should be abandoned . Dairy technologies that may be subject to this line of criticism are anaerobic digesters or the recent research to use CRISPR to genetically modify the methane producing microbes in the cows’ stomachs . Mechanization contributes to enlargement and specialization in dairies by reducing the space or labor needed to feed and milk cows, as is the case with CAFOs or automated milking machines, and by encouraging investment and specialization in that specific stage of production.One of the hallmarks of change in industrial dairy production is the enlargement of herd sizes. The average number of cows per farm is increasing in the U.S., and dairy production increasingly comes from large farms, measured by farm income . Willis documents the dramatic enlargement of diaries that occurred in New Zealand between 1971 and 2001, where the number of cows increased 51%, the average herd size increased 128%. Enlargement is type of intensification – in which the input is the number of farms and the output is the herd size or amount of milk produced per farm. Enlargement both requires and allows for specialization and mechanization to occur by making the investment in technology and machines more affordable, thus encouraging more expansion thereafter.Specialization is the focus on fewer commodities or stages of production within each farm. In the case of dairy, operations specialize to only produce milk, purchasing their cows and feed from other sources, and selling milk to a processor. This involves enlarging herd sizes and investing in equipment that increases efficiency of milk production .

Specialization is related to horizontal integration, in which operations expand their production of milk by increasing their herd size or acreage. The opposite model is vertical integration, in which a single farm may breed, raise, milk, and slaughter their own cows, grow their own feed,manage their own pasture, or process their own milk, cheese, or butter. The process of specialization is stretching these stages of production across multiple operations, meaning that the raising, feed production, milk production and dairy processing each happens in a different location. As milk production, mechanization, herd sizes, and specialization all increase, the industry is consolidating. The number of dairy farms has been rapidly declining everywhere in the United States. Consolidation is happening across all agriculture in the US but especially so in dairy. As MacDonald et al. report, “the pace of farm consolidation appears to have slowed after 2007. In livestock, only dairy shows continued rapid consolidation” . Consolidation has not occurred evenly across the livestock sector; dairy, chickens, turkeys, and hogs are highly consolidated, while beef and cattle operations are not, suggesting that consolidation has more to do with the confinement style of raising livestock or the frequent milkings required on a dairy farm, than the species of animal itself. This consolidation may be due to smaller farms going out of business, merging with larger farms, or moving to other regions. Gould documents consolidation in dairy farms, co-operatives and processing facilities and argues that this high level of consolidation differentiates dairy from other agricultural sub-sectors. Cross characterizes the restructuring of dairies as a shift toward megadairies in California, away from the traditional dairy belt in the Midwest and Northeast. He also points out that Amish farmers are the ones who continue to run dairy farms at a small scale, speaking to the technology-driven industrialization of large-scale farms. The final trend observed in the literature about dairy is the discussion of regional shifts in where dairy is produced. The specialization of dairy production led to the creation of identifiable dairy regions in the United States, which have historically been in the Northeast and Midwest, also known as the ‘dairy belt’ . Scholars observe the trend of regional shifts in production both within the United States and on a global scale. Cross describes the shift from the traditional dairy belt in the Midwest and Northeast states out west to California and Idaho.

Harrington et al. describe the movement of large dairies into the plains of Southwestern Kansas. Gould names the expansion of dairies in Texas, Idaho, and New Mexico as concurrent with the reduction of dairies in traditional dairy states, such as Vermont, where organic dairy farmers resist pressures to expand or sell . Dairy production on a global scale is also expanding into the Global South as Western diets and higher rates of milk consumption are adopted in East Asian and African countries . All these observations of the regional shifts of milk production are described alongside processes of consolidation, specialization, enlargement, indoor vertical farming mechanization, and intensification. Finally, although the topic is beyond the scope of my research, the many alternatives to the structural transformations that are observed in dairy literatures in the United States and beyond must be addressed. Alternative production trends include sustainable intensification, multi-functionality or vertical integration, and agroecology , as well as regenerative and organic dairy production and increasing disruption from the rise of plant-based non-dairy milks . These all come in reaction to the negative environmental, human, and animal impacts of intensive dairy production and fall under the “better milk” or “less milk” narratives for the future trajectories of milk . In California, Marin and Sonoma Counties are the region with the strongest collective effort to produce milk alternatively to the industrial model of the rest of the state . In summary, dairy industries in the United States and Global North have undergone, and continue to experience, significant transformation. Six dominant themes of transformation discussed in the literature are intensification, mechanization or technological innovation, enlargement, specialization, consolidation, and regional shifts. Evidence of these transformations are seen in the trends of milk production and number of operations derived from the following methodology and analysis. To understand the changing geography of the dairy industry in the California, I used data from the USDA Census of Agriculture and California Crop Reports to map changes over time in milk production and number of dairy operations across California’s 58 counties. Previous work on dairy in California has typically focused on policy, natural resources, environmental footprints, and other factors related to milk production for the state as a whole , though some work has been attentive to regional differences in production and the typology of production systems found within the state . In 1896, Wickson wrote a report for the USDA titled “Dairying in California” which included a hand-drawn map of the dairying areas of California . So far, there has been no study of regional changes in dairy production over time in California. By mapping data at the county level, this research reveals spatial patterns and regional variations in California’s dairy industry obscured by typical state-level summaries of milk production.The United States Department of Agriculture National Agricultural Statistics Service publishes annual crop reports from the California County Agricultural Commissioners. These reports compile the total production, acres, yields, prices per unit and value of many agricultural commodities from each county in California.

The data are available for every year from 1980 to 2020 for download from the USDA NASS website under the California County Ag Commissioners’ Data Listing . The most recent year’s report was partially incomplete at the time of this study. Some counties were absent from the data and had to be interpolated using the average of the years before and after each missing year. Data collection and reporting is the responsibility of each individual county, and not standardized in either definitions, data collection, or reporting, so the way values are measured or aggregated may have differences across different counties and years. It is not possible to readily know the differences in method or definition. The crop reports also do not disclose any information about farm sizes or the number of farms, so production values are the sum of all operations’ production at the county level, and prices are the average price. This obscures any nuance between different operations, but for a state-wide analysis like this one, these crop reports offer the best available data on agricultural production by county on an annual basis. The Census of Agriculture is the only source of uniform and comprehensive data about agricultural producers, acreage, activities, and sales in the United States. In recent decades conducted by the USDA, the Census of Agriculture is a national survey of all agricultural activities, operations, and producers, conducted every five years that attempts to collect information from every relevant farm operation. The most recent agricultural census, for 2022, was still being conducted at the time of this study, therefore 2017 was the most recent year available. The Census Bureau began collecting data on household agricultural activity in 1820 as part of the national decennial census. From 1840 to 1950, a separate census of agriculture was collected the same year as the national census, until it was switched to a five-year interval in years ending in 4 and 9, and then again in 1982 to years ending in ‘2 and ‘7. In 1997, funding responsibility shifted to the USDA, but questionnaires and mailing are still carried out by the Census Bureau. The census captures dairy in a few ways, including Milk – Operations with Sales, Milk – Sales in US Dollars, and Milk Cows Inventory, but it does not have a definitive count of active dairies. Operations with milk sales may differ from operations with milk cows in a given census year if the operation is going out of business and still has milk to sell but no longer has cows on site. Farms may also have a family milk cow, or a cow as part of a child’s 4-H project. In these cases, the farm may have other activities but not be engaged in milk sales and therefore not be considered an active commercial dairy farm. For the purposes of this study, I chose to use operations with milk sales to capture most of the active dairies from each year.

Trial interventionists were 2 veterans of the armed forces with some prior exposure to counseling, hereafter referred to as “veteran peers.” Of note, in this study, veteran peers were different from VA-employed Veteran Peer Specialists, who are also considered peers, not only because of their prior military service, but also because they are in MH recovery themselves. Veteran peers for this study were trained by psychologists at their respective study sites to use MI techniques, such as open-ended questions, affirmations, reflections, and summary statements, as well as key MI principles, such as expressing empathy and rolling with resistance with the goal of having veteran peers conduct MI-informed coaching rather than formal MI. Veteran peers were encouraged to relate to study participants as veteran peers and potentially disclose more personal information than is typical when using manualized MI. Psychologist supervisors also trained the veteran peers in how to address potential suicidal and homicidal ideation and race/ethnic and sexual orientation and identity issues. During the trial, with participant consent, motivational coaching sessions were audio recorded for fidelity monitoring . Psychologist supervisors reviewed the audio-recordings and provided feedback to veteran peers at weekly group supervision meetings. For participants assigned to the intervention arm, veteran peers started by providing participants feedback on each of their baseline MH screens and used MI-informed, open-ended questions to elicit participants’ reactions to their MH screen results. For example, a veteran peer coach would inform a veteran participant of their PHQ-9 score and explain the meaning of a positive score for depression as either mild, moderate, or severe, flood drain tray asking veterans to share their thoughts or feelings on hearing this information, using psychoeducation and normalizing data as appropriate.

Veteran peers then explored participants’ readiness for MH treatment, reminded veterans that they themselves were not licensed practitioners and, based on their location and preferences, asked permission to provide a customized list of MH treatment and self-care options. Subsequently, participants received up to 3 additional 20- to 30-min motivational coaching calls at 2, 4, and 8 weeks to encourage MH treatment initiation using MI principles as described above. For example, a veteran peer coach attempting to elicit change talk and motivation for MH treatment might use the Readiness Ruler to ask a participant how ready they were to receive MH treatment and would reflect back to them, “You gave yourself a 4 out of 10, why not a lower number? What would need to happen to move you up one or two numbers?” Because veteran peers were trained in coaching in addition to MI, they might add additional coaching language, such as: “If you decided to start receiving outside help, what kind of help do you think would work the best for you?” and, “If we take a step back and think about the big picture, what really matters in your life?” During the 8-week motivational coaching intervention, if a participant scheduled or engaged in clinician-directed MH treatment , the peer-delivered coaching intervention shifted to treatment retention. Treatment retention calls consisted of 20- to 30-min calls at 2 and 6 weeks after MH treatment initiation. During retention calls, veteran peers focused on eliciting the benefits of sustained MH treatment engagement, that is, “Now that you are receiving outside help, how do you see your life getting better?”VA and non-VA community MH services in Northern California and Louisiana were identified and vetted to create a comprehensive annotated list of MH treatment referrals for veterans. As described above, MH treatment referrals were provided to veterans with positive MH screens in both study arms following the baseline assessment and were grouped as follows: clinician-directed MH treatment either within VA or in the community reimbursed by VA, or through a non-VA community MH facility; non-clinician-directed MH care either through VA or in the community ; and self care .

For participants in both arms, veteran peers provided contact information for referrals by phone and letter but did not schedule referrals for veterans.All primary and secondary study outcomes were assessed by blinded research staff at 8 and 16 weeks using the same battery of items administered at baseline and only MH treatment engagement was assessed at 32 weeks. The primary outcome was initiation of clinician-directed MH treatment, and among participants who initiated treatment, retention in MH treatment for ≥ 2 visit, as determined by self-report, VA administrative data, or both . Any new VA or non-VA MH appointment during follow-up between participants’ baseline assessment to 60 days after the final 32-week time point was counted as MH treatment engagement. An MH treatment experiences self-report questionnaire was used to identify categories of clinician-directed VA or non-VA MH treatment. Only MH treatment encounters at VA or in non-VA community settings reimbursed by VA are included in VA administrative data. Secondary outcomes included: non-clinician-directed MH care at VA or in the community and engagement in self-care activities , defined as activities that reduce stress and promote well-being, which can be particularly important for rural veterans and influenced by location. Other secondary outcomes included MH symptoms and quality of life domains .At baseline , following randomization, the 135 controls and 137 telephone motivational coaching participants did not differ in terms of sociodemographics. Overall, the majority was male and middle-aged . Although most participants were White, racial minorities were over represented compared to the US population. The majority earned < $50,000/year; 72% had a military service-connected disability ; 14%- 26% enrolled in VA health care had used private insurance or Medicaid/Medicare within the past 6 months; and the majority received care at rural VA health care facilities. As shown in Table 2, the 2 groups also did not differ at baseline in terms of quality of life measures, MH symptoms , as well as most substance use scores .

Overall, most participants screened positive for MH symptoms, including depression and anxiety , followed by PTSD , and roughly one-quarter screened positive for high-risk drinking. Controls demonstrated significantly higher baseline opioid and amphetamine use than intervention participants , although use of both was extremely low. The 2 groups did not differ in terms of barriers to MH care, but at baseline, controls were significantly more ready than intervention participants to obtain MH treatment. At baseline, the 2 arms did not differ regarding past 5-year MH treatment or self-care activities . In the intervention arm, of 4 possible motivational coaching sessions for MH treatment initiation, participants competed a mean of 2.6 sessions, and of 2 possible MH treatment retention sessions , a mean of 1.72 sessions were completed. As shown in Table 4, a similar number of controls and intervention participants initiated clinician-directed MH treatment during follow-up . Of those initiating MH treatment, a similar proportion reported ≥ 2 MH visits: 41% of controls reported a mean of 6.6 visits, SD = 9.6, and 37% of intervention participants reported a mean of 4.4 visits, SD = 4.6. While there were no between-group differences in type of clinician-directedMH treatment, in this largely rural veteran sample, most MH care was within primary care, followed by MH clinics and receiving “psychiatric medication.” Adjusted Cox proportional hazards regression confirmed no independent differences between the 2 arms with regard to MH treatment initiation , after adjusting for site, MH treatment history, baseline MH symptom severity, baseline opioid and amphetamine scores, and readiness for MH treatment. Of note, the only positive independent association with MH treatment initiation was greater MH symptom severity. Table 6 shows that there were also no significant between group differences regarding engagement in nonclinician-directed MH treatment. Figure 3 summarizes the proportion of participants in each arm initiating self-care activities during follow-up. Compared with controls, more participants in the intervention arm engaged in MH-related Internet or mobile self-help applications and MH-focused community classes . Self-reported MH screen scores and quality of life domain scores were captured at 8 and 16 weeks . Varying numbers of participants did not complete assessments at these 2 time points, resulting in missing values. Nevertheless, as shown in Table 7, compared with controls, intervention participants had significantly lower depression scores and cannabis scores . The COACH trial tested a veteran peer-delivered telephone motivational coaching intervention to improve MH treatment initiation among veterans who primarily used rural VA health care facilities and screened positive for MH symptoms but were not in MH care; which, to our knowledge, is the only study of its kind. No significant differences were found between groups in clinician-directed MH treatment initiation , nor in MH treatment retention. Notably, however, veterans randomized to the intervention were significantly more likely than controls to demonstrate modest improvements in several secondary outcomes, flood and drain tray including MH symptoms, quality of life indicators, and self-care. Qualitative findings may explain how achieving these secondary MH and quality of life outcomes in the intervention arm may have paradoxically obviated veterans’ need to engage in more formal MH treatment. Both participant- and intervention-related factors may explain the lack of difference observed between the 2 groups regarding MH treatment engagement. First, rural veterans prefer to address MH concerns on their own terms , largely influenced by geography and culture, as opposed to engaging in traditional MH treatment. 

In addition, stoicism, self-reliance, and preference for community, family, and peers may have presented barriers to MH treatment engagement among rural veterans not observed in prior similar studies of urband welling veterans. Regarding the intervention, while other studies have employed MH professionals to deliver MI, this study trained peer veterans to conduct motivational coaching. MITI scores demonstrated fair fidelity to MI, raising the question of whether the primary outcome may have been enhanced by stronger adherence to MI principles. Additionally, intervention participants received a mean of 2.6 of 4 motivational coaching sessions, suggesting that dose may have been attenuated, although other studies with fewer doses of MI have achieved treatment engagement. Nevertheless, this study achieved overall enhanced MH treatment engagement in all participants, likely through components common across both study arms, for example, multiple MH assessments, feedback of MH results, and personalized treatment referrals by veteran peers. These findings align with studies which have demonstrated that assessment of substance use alone is associated with significant reductions in use, known as “assessment reactivity.” Similar to our study, Walker et al found that repeated assessment for alcohol abuse followed by a single session of telephone-delivered MI versus psychoeducation were both associated with increased treatment seeking in soldiers with untreated alcohol abuse, pinpointing repeated assessment as a key ingredient. The between-group descriptive analyses for the secondary outcomes demonstrated that veteran peer motivational coaching resulted in improved MH symptoms, reduced cannabis use, improved quality of life scores, and encouraged self-care activities compared to controls as observed in another study. This finding may be explained by the fact that “partnership” was the highest of the peers’ MITI global ratings. Self-disclosure about their experiences may have explained the higher partnership scores, although self disclosure is not measured by the MITI.Non-clinician peers were specifically selected as study interventionists for this trial because rural veterans are known to prefer and trust insiders over “outside experts.” Qualitative exit interviews did suggest that the veteran peers achieved a therapeutic effect themselves, possibly through partnership and relatability, in their delivery of the motivational coaching intervention. For example, one study participant explained, “When she opened up that she was a veteran, I think it made me -. I let my guard down a lot more. It gave me more freedom to express myself and actually talk.” Another consideration is that greater MH symptom severity , and hence perceived need for MH treatment, is a major driver of MH treatment engagement. Thus, as veteran peers achieved secondary outcomes of improved MH symptoms, quality of life, and increased self-care through motivational coaching, they may have paradoxically reduced veterans’ need for formal MH treatment engagement, perhaps explaining our findings in this trial. For example, one participant described the veteran peer coach as helping them, “catch it quickly, without it getting so out of hand that I have to call somebody for mental health. That was—to me—the highlight of all this.” This trial had several limitations that should be considered in interpreting results. First, as evidenced by the CONSORT diagram , veterans enrolling in the trial were likely a biased sample as roughly half who were assessed for eligibility declined to participate. However, this attrition is not wholly unexpected because administrative data were used for recruitment. Second, the sample was largely White, male, and VA service-connected , so findings may not generalize to veterans of color and non-veteran populations. Third, there was large loss to follow-up among rural veterans , but reasons for drop-out are not known. Fourth, fidelity to the MI component of the intervention, intended to enhance MH treatment engagement, may have been supplanted by veteran peers’ “peerness” or relatability, which may have favored the study’s secondary outcomes.

The control plane runs on the controller server, with the purpose of sending instructions to the network to update routes. Our orchestrator server set up applications in the computing nodes and collects statistics to measure performance. The northbound and southbound interfaces enable the communication from the SDN controller to the control plane and the orchestrator. We configured SNMP and SCPI as well, for coarse-grain statistics and to send instructions to the optical switch. To differentiate between host and guest machines for both servers and switches, we use the terms host or physical when not referring to the virtualized elements. The purpose of the control network is to create out-of-band links that facilitate experiment orchestration and collecting data from the testbed, without sending instructions through UC Davis network. In such manner, we have full control over the infrastructure, the network design and we keep dedicated links without worrying for bandwidth availability nor external outages. The individual clouds in Figure 2.3 illustrate different networks, with their own IP addressing. Orchestrator, EPS chassis, controller, node1, and node2 are connected with physical 1 Gbps ethernet links through an electronic switch. The Opticalswitch is also connected with a 1 Gbps ethernet cable to the orchestrator. Our computing nodes vm1, vm2, vm3 and vm4 are hosted in node1 and node2, interconnected with virtual links. As we mentioned at the beginning of the chapter, our data plane is a reconfigurable optical network driven by an optical switch and four electronic packet switches. Br1, br2, br3 and br4 are hosted in an EPS chassis configured for running in OVS mode, which means that they are optimized for OpenFlow applications and receive instructions from a centralized SDN controller. Each individual link can forward data at rates up to 10 Gbps. Figure 2.4 represent the general topology of our data plane. Nevertheless, grow racks the physical links can be attached or removed with software to or from different bridges as a consequence of the flexibility that OVS yields. We use the terms bridge, EPS and ToR as synonyms.

SDN-enabled devices communicate with a controller through a channel, using OpenFlow protocol. In our testbed, the SDN controller is the built-in application OFCTL OpenFlow switches use flow tables to route packets by matching one or more fields. to achieve dynamic routing in a software defined network. Pica8 PICOS is the Network Operating System that runs in our physical EPS. It allows the chassis to run in two modes: Traditional L2/L3 mode and OVS. We use the latter, as it fits in our SDN design. In our testbed, we handle the traffic as needed without traditional routing protocols like OSPF. If there is any overlap in the flow tables, the forwarding action will be random and this behavior is not desired. To avoid that, the priority field is varied in our experiments. We use a single flow table for each bridge, and the addressing is done with IPv4. The relevant fields for our testbed are described in Table 2.1. An alternative to run tests with SDN is to use the L2/L3 mode in PICOS with cross flow enabled. This hybrid approach allows a combination of both networking paradigms. Network operators interact with the switch with Linux commands or a CLI similar to Juniper Junos interface. The flexibility of having two consoles is useful for researchers with different backgrounds. Some may be experienced with Linux servers, others with enterprise or service provider networking. However, throughout our experiments we found issues while performing optical reconfiguration in hybrid operation. We observed that the ports where we connect the transceivers remained in down state after we execute the optical reconfiguration. We had to reboot the Picos service and reinstall the flows to get the ports up, adding at least one minute to the reconfiguration. Hence we decided to run OVS mode. So far, we have discussed the pertinence of flows in our testbed and how we use the SDN controller to update routes from the orchestrator using a REST API. Now we will introduce the concept of flow in computer networks. RFCs are technical documents published by the IETF after being written and reviewed by interested parties.

They cover foundations for computer networks, namely transport, addressing and routing. From RFCs 2722 and 3697, a flow is defined as the packets sent from a source to a destination that may be unicast, multicast or anycast, with specific attributes . An alternate definition for flow refers to the packets in a single physical media or stream of data. In the context of our testbed, a flow is an entry in a table that is attached to a specific bridge, with different fields that will be matched to follow the desired route towards their destination.Two EXXACT chassis , node1 and node2, host the virtual machines, vm1, vm2, vm3 and vm4. Each physical server has an Intel X710 dual NIC which supports up to 10 Gbps per port. The main issue with this card was the transceiver compatibility, because it does not read generic devices but only products listed in the Intel compatibility tool. Every virtual server has its own 10 Gbps dedicated port that connects to the data plane, as illustrated in Figure 2.4. Additionally, we connected the virtual machines to the orchestrator with dedicated 1 Gbps NICs apart from the 10 Gbps cards. Figure 3.2 represents the wired and virtual links between the orchestrator, host servers and virtual machines. Virtual bridges inside node1 and node2 map the traffic from the guest servers to the exterior. These interconnections enable the orchestration of applications in our experiments from a principal server. In the rest of the thesis, we use virtual machines, virtual server, computing nodes, computing servers as synonyms.Technologies for optical switches were introduced in chapter 1. In our testbed, optical reconfigurations were performed with a Polatis optical switch tray , a single-mode MEMS device that takes up to 25 ms to steer the light beam to the new port. Instructions for switching were sent from the orchestrator with SCPI commands through a TCP socket. The ethernet interface of the switch speeds up the deployment and integration with our testbed control network. The lack of an openflow agent in the OST does not allow to seamlessly integrate the optical switch with an SDN controller. Nevertheless, most recent chassis such as the Polatis 6000 and 7000 series come with open flow agents to enable centralized management with SDN.

These products offer flexibility for theindustry. However, they are built on top of the same MEMS-based technology and the switching latency is still in the order of tens of millisecond.Small Form-factor Pluggable are defined by Cisco as compact optical transceivers. They create an interface between optical and electrical communications, with a transmitter and a receiver in both sides of the link, and support several communication standards like Ethernet, SONET and PON . They are manufactured for different purposes: single mode, multimode, for short and long distances, fiber and copper. It is important to choose the appropriate SFP for the application, otherwise the deployments could not work properly or the devices could get damaged. We installed two types of 10 Gbps transceivers. The first works with multi mode fiber in the 850 nm band, grow table and allow us to connect the computing nodes with the electronic packet switches, as observed in Figure 2.4. The second kind is 10 Gbps DWDM SFP, operating in the C band. By using Single Mode Fiber patch cords connecting our electronic packet switches and optical switch, we created a reconfigurable optical network which supports multiple wavelengths. Various tools are available to generate packets between hosts. Some are packeth, ostinato, D-ITG, MGEN iperf and TRex. The latest is developed and maintained by Cisco, and it offers scalability up to 200 Gbps per server. However, this tool is made for a single server, that is, the transmitter and receiver are hosted in the same physical machine, which must have at least two NICs from the supported models. The Device Under Test can be an external element or virtualized. Codilime, a company that specializes in software and computer networks, customized TRex to allow testing a network from different start and end points. We tested this traffic generator, but the installation, configuration and integration with our testbed was not straightforward. In contrast, iPerf installation and execution is quick, it does not show issues with drivers and the integration with our experiments was successful. This tool has been widely used in research and generates synthetic packets to emulate traffic between servers. Several studies have shown different approaches to gather statistics to analyze network performance. Platforms like Netseer, Jetstream, Planck have demonstrated improvements in the detection of network performance anomalies, including packet drops, decrease in throughput and increased latency, at different scales like data center and cloud. Zhang et al. [99] compare two ways of gathering data from computer networks, fine and coarse sampling. To analyze testbed latencies and network performance metrics,we used tcpdump as we need end-to-end per-packet resolution. On the other hand, SNMP counters work well to confirm that the data stream is going through the desired route when we design the reconfiguration paths. Zabbix is a popular tool in enterprises for monitoring systems, including SNMP statistics, and there are docker versions for agile implementation. With this software we observed how the traffic flows through the EPSs interfaces. To monitor traffic per flow, we deployed an sflow collector and sflow agents in the virtual bridges. Recalling the first chapter, the goals for this research comprehend building a networking and computing testbed with SDN capabilities to demonstrate the benefit of make before break approach combining optical and electronic packet switches to achieve hitless reconfiguration. In Figure 2.1 we show the architecture of the testbed and the meaning of each element in the network diagram. The data plane encompasses an optical switch and four electronic packet switches , connected to an SDN control plane. The topology can be modified as needed with Open vSwitch commands. Additionally, an orchestrator sends the route updates to the controller and optical switch. In the first subsection we analyze the hardware and software switching latency introduced by our control plane. Next, we compare the throughput, round-trip time and packet loss of a single data stream between a pair of servers . We show the benefit of our make before break approach for updating the route, compared to a plain optical reconfiguration. Finally, similar to the single data stream experiments, we show the metrics of doing a route update with two data streams to demonstrate the benefit of make before break. We refer to the last subsection as bandwidth steering experiments. Five main switching latencies were found in our testbed. A summary is shown in Table 4.1. We discuss each one in the following subsections. Overall, the dominant latency of 605ms is introduced by the transceivers and the operating system of the host electronic packet switch. In later sections, we show how to avoid interrupting the data stream due to a link unavailability caused by a path reconfiguration, by using our MBB approach. We define the optical switching latency as the time it takes from the beginning of the reconfiguration performed by the optical switch, which leads the ports to go down, until all the transceivers report operativity in the ToR chassis. The topology in Figure 4.1 shows a single data stream between servers vm2 and vm3. At the beginning, the data transfer goes through EPS br2, br1, br4, and br3 . Then we perform the path reconfiguration with the optical switch ost1, and the new route between servers passes through switches br2 and br3 . A summary of steps performed in our experiment is shown in Table 4.2. Server 2 and server 3 run iperf in server and client mode, respectively. The data rate on the sender side is set at values from 5 Gbps to 10 Gbps, with a duration of 20s. After 485 experiments, we obtained the logs from the physical electronic packet switch, which hosts the virtual switches br1, br2, br3, br4. Then we identify all the interface flapping events of the ports in the topology shown in Figure 4.1. Finally, we calculate the time difference between these events, port down and port up, to obtain the summary of statistics in Table 4.3 and Figure 4.2 as well.

Though border concerns have existed since the conception of the United States, post September 11, 2001, these concerns grew markedly. Fences, walls and guard posts along the U.S. Mexico border were established or fortified. More regular patrols of the borderline by Homeland Security agents were initiated. These infrastructure and personnel investments are expensive to initiate and maintain and are still ineffective at complete surveillance of the 3145-km border; approximately 250,000 people try to cross the border illegally each year who have stepped into action. Aside from the human rights violations that such forms of surveillance pose to groups seeking to cross the border, these homeland security activities are also threats to the environment. Environmentalists worry that the new roads, fences and facilities created to accommodate these new forms of surveillance are degrading fragile desert landscapes, ripping up vegetation, compacting soil and threatening wildlife movements . Finally, these operations seriously threaten precious and irreplaceable archeological sites located along the nations’ borders. Although the use of remote sensing cannot address the human rights violations, geopolitical tensions or cultural and ethical problems posed by current forms of border surveillance, it does offer ways to make homeland security efforts more efficient at recognizing extra-legal migrations, while also potentially lessening the impact of current efforts on the environment and cultural heritage sites. Using aerial and satellite images, remote sensing can allow Homeland Security officers to target their surveillance and enforcement efforts by revealing where smuggling or migrations are taking place by displaying habitual paths through the desert. They may also allow law enforcement agents to stay out of harms’ way during reconnaissance missions. Further, with increasingly advanced technology, 4×4 flood tray remote sensors affixed to light aircraft provide almost real-time detection of human movement across the landscape. Targeted efforts and fewer law enforcement vehicles and patrols for surveillance may lessen the impact of border security on local ecologies.

Remotely detecting smuggling or extra-legal migrations is more difficult than detecting the growth of drug crops, because these illicit activities are not static; people and goods can move across a landscape in a matter of hours. Thus, the adoption of these techniques is still in progress. To try to detect where drug, human and arms smugglers were traveling through the landscape, Kaiser and others used ADAR 5500 mounted on a helicopter to try to detect trails crossing the desert border along the southern limit of the United States. Cao and others conducted a similar study in 2007 [87]. Coulter and others used a Canon EOS 5D Mark II camera system fixed on a light aircraft to detect the active movement of people through the landscape in near-real time. In all of the terrestrial cases we reviewed , it is assumed that the movement of any people through this landscape is linked with smuggling or extra-legal migrations, since remote sensors are unable to detect the intentions or identities of these people. This, in and of itself, is a problematic assumption given that these areas have traditionally been used by indigenous groups, homesteaders and cattlemen for generations. In situ interactions with people in these areas would facilitate the detection of various people’s identities and intentions. Problematic at a different level, remote sensing would be unable to detect smuggling operations that occur via trucks or through tunnels under the U.S.-Mexico border, which may be equipped with electricity and rail systems . The U.S.-Mexico border is not the only place where remotely sensed surveillance for crimes and criminals is taking place, however. As Zhao shows, many countries in Europe, Asia and North America are working to develop ship surveillance systems to detect ships that may be used for extra-legal migration, illegal fishing, piracy and smuggling along maritime borders . These surveillance systems integrate Synthetic Aperture Radar satellite data with automatic identification systems that are ship, land and space-based. Although we did not find any studies that chronicled the active detection of crime , there exists a plethora of studies that present theoretical or retrospective case studies of how this might take place.

These studies tested the use of TerraSAR-X, TanDEM-X, RapidEye, RADARSAT, Envisat-ASAR, Cosmo-Skymed, MODIS and ALOS images to detect the presence of ships in the Mediterranean, the North Sea, the Gulf of Aden, the Campos Basin, the English Channel, the Port of Halifax, the Bosporus, the Ionian Sea, the Southern Ocean and the Strait of Italy. There also exists a fairly extensive literature that deals with the active detection of oil spills , as well as illicit drift-net fishing . Both of these topics fall outside the realm of our analysis, however. While these studies differ from terrestrial studies of human and drug trafficking in that they acknowledge that ships may have many uses that are not nefarious, these studies do seek to survey some of the most vast and unmanned areas on the planet. Differentiating between legitimate ship users and potential pirates or smugglers presents a challenge. Some scholars have proposed methods of differentiating “abnormal behavior” from standard shipping procedures to identify piracy in action. These studies consistently must deal with false alarms in their detection algorithms caused by oceanographic or meteorological phenomena and bathymetry—underwater banks and azimuth ambiguity. Any credible remote sensing project should assess the accuracy of its results, and particularly those used in the active detection of crime. In these projects, accuracy or validity can be thought of as the “correctness” of the resulting map or classification product . The means by which accuracy assessments have been carried out have changed over time, starting as an afterthought and progressing to a well-defined and necessary component of remote sensing analyses . These “first order” accuracy assessment protocols ideally include well-distributed independent samples from the ground or a data source of higher accuracy , development of error matrix reporting of the overall error, errors of omission and commission per land cover class and the kappa statistic. Although remote sensing analysts attempting to detect crimes acknowledge that ground-referenced data is the gold standard for accuracy assessment, publicly available gray literature and peer reviewed papers agree that this method is not always feasible, due to security concerns, rugged and remote terrain and funding limitations. Eleven of the 58 studies on drug production that we reviewed reported that their accuracy assessments were limited due to insecurity issues on the ground. Of the same group, eight reported that no accuracy assessments were possible because of insecurity . The security concerns addressed in these reports are very serious. For example, a member of a ground survey crew in Afghanistan was killed while collecting data on cannabis production in 2009. Ground validation of extra-legal migrations in U.S. borderlands was also limited by security concerns and dense vegetation. In order to avoid the issues presented by potentially dangerous and/or expensive field missions for ground reference data collection, analysts seeking to assess the accuracy of their illicit drug identification have come up with alternative methods . For example, Chuinsiri et al. used large-scale aerial photographs collected at the same time as the satellite data for accuracy assessment instead of gathering ground reference data. Unfortunately, these aerial surveys may not be as effective as ground surveys. For example, in a similar study, aerial surveys were often unable to detect shade-grown coca. In other cases, bad weather delayed the collection of data from aircraft, putting the utility of the data collected for accuracy assessment into question. Further, one UNODC report notes that even over-flights were too dangerous in certain regions, thus limiting the accuracy assessment within those areas. In cases where ground or aerial validations proved unfeasible, analysts sought other means of performing accuracy assessments of their detection of illicit crops. In some cases , “surrogate” ground-reference data were produced using the visual interpretation of two satellite images using poppy reflectance, disappearance of the vegetation in the second image , apparent fields surrounded by natural vegetation, distance to populated spaces and accessibility. UNODC used a quality control mechanism that involved each analyst’s work being checked by two other experts and then cross-validating first and second dated photographs rather than using ground validation data.

Wang used UNODC and the Islamic Republic of Afghanistan Ministry of Counter Narcotics’ surveys from the same time period as satellite data were collected to calculate the accuracy of his classification of opium crops. Where no survey data were available, Wang used coarsely constructed opium maps. Surprisingly, hydroponic tray over thirty-six percent of the drug production studies reviewed did not mention accuracy assessments in any way . Those that did discuss validation often did so in limited ways. In one study, analysts did not update the previous years’ accuracy assessment, assuming that a similar level of accuracy could be considered for the year at hand. Similarly, in all three of the studies of illicit human movements in the landscape that we reviewed, accuracy assessments either were not performed, or the methods for assessment were not mentioned or clearly discussed. For example, despite the fact that Coulter and others have a table assessing the accuracy of their detections of trails, they do not describe how they calculated these percentages. The lack of discussion of accuracy assessments in drug and human-movement studies is surprising given the potentially serious impacts these reports may have on local communities and ecologies. Because this is a review paper, we were unable to independently research the potentially harmful ecological and social impacts that a lack of validation may have had, but we believe it is important to raise the point that studies with such important real-world implications should be validated; and many are not. Most of the retrospective and theoretical marine case studies relied on ship-specific automatic identification systems data to validate the remote sensing of ships. Posada and others point out three problems with AIS to validate remote sensing. First, AIS equipment is often misused by its operators, resulting in the wrong ship ID numbers being attached to a given vessel, potentially misrepresenting the type of ship that is on the water. Second, AIS messages “regularly contain errors ”, leading to confusion in ship tracking. Third, AIS do not report ship position rapidly, thus, if there is a significant time gap between when SAR data were collected and when AIS data were reported, the ship may have moved a significant distance, making validation very difficult. Beyond these three limitations, Lehner and others point out that smaller vessels may not have AIS and may also be more difficult to differentiate from false-alarms, like breaking waves. We posit that few ships intent on criminal activity would have AIS either. Finally, Paes and others note that the Earth’s curvature and meteorological influences on data transmission leads to instances where vessels far from the coast are not present in the AIS databases. To get around some of these issues, some scholars used maritime patrol aircraft to survey blank areas, had analysts do manual inspection of images or did on-the-ground validations of ships. All of these techniques are difficult, time consuming and expensive to enact, thus making it likely that validation of actively identified marine crimes will follow similar trends as terrestrial drug production or smuggling. Aside from the more refined remote sensing techniques we mention above, law enforcement and government officials have leveraged the power of freely available remotely sensed products, like Google Earth, to detect crime. Although, to date, there is a limited discussion of the use of Google Earth to detect crime in the academic literature, it is widely discussed in the popular press . These discussions note that Google Earth is being deployed by law enforcement officers, government employees, scientists and even private citizens to actively detect crimes in progress around the world. For example, a Swiss police department “stumbled across a large marijuana plantation while using Google Earth”. Aside from international agencies and law enforcement departments, researchers, like Anthony Silvaggio, an environmental sociologist at Humboldt State University, have sought to point out where large-scale, unregulated industrial marijuana grow sites are occurring in Humboldt county, California, including in national forests. Amateur searchers have also started seeking out and identifying marijuana growing using Google Earth . Google Earth’s use for crime investigation does not stop at drug production, however. In Greece, Italy, Argentina, India and the United States, Google Earth has been used by government officials to identify homes that have violated building codes, built swimming pools without permits and to compare declared home values with actual existing structures.

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. The measures of economic deprivation that are controlled for in this study are poverty and unemployment. 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, 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, indoor weed growing accessories 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 . This theoretical link is supported by empirical evidence. In their reassessment of Sampson and Groves’s original analysis, Veysey and Messner report that family disruption has significant relationship with crime even independently of other social disorganization processes. Indeed, percent of single person households was significantly related with crime in the Sacramento dispensary studies conducted by Williams and colleagues .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 . Conclusion 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 as well as 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.Empirical testing is needed in order to assess the strength of these claims. I develop the model that will be used to test these claims in the first section of this chapter. In the latter sections I describe the various measures that comprise this model, which are tested in the following chapter. In comparing crime rates across city neighborhoods, it is important to control for neighborhood characteristics that are related to crime independently of MCDs. In the present study I consider three vectors of social and demographic variables, drawn from social disorganization theory, that will be used as control variables in my model: economic deprivation, family disruption, and residential instability . “Social disorganization” is a concept used to describe communities in which there is a lack of sufficient social cohesion among members—characterized by low socioeconomic status, fewer stable families, and high residential turnover—that would otherwise serve as a deterrent against crime. Higher levels of these social disorganization indicators are associated with higher crime rates at the citywide and neighborhood level . Controlling for these variables across city neighborhoods will help determine whether, and to what extent, MCDs have an independent effect on crime. Current debates about the relationship between MCDs and crime tend to revolve around politicized rhetoric and anecdotal claims .

The only scholarly discussion of this question known by this author at the time of this writing consists of a pair of studies out of UCLA that examined crime data from 95 census tracts in Sacramento for the year 2009 . Nancy J. Williams and colleagues found that crime rates were not significantly associated with neighborhood density of medical marijuana dispensaries. The researchers identified several other variables with higher explanatory power with respect to crime, including unemployment, percent of the population that is young, and percent of one-person households. These findings correspond with the assertion made by social disorganization researchers that low levels of SES, high residential instability, and high rates of family disruption correspond with relatively higher rates of crime. In the following sections of this chapter, I will discuss these and other measures that comprise the empirical model developed by this study. First, I will briefly review the two criminological theories that I draw upon: routine activities theory and social disorganization theory.Stemming from human ecology, routine activities theory attempts to explain crime on the basis of three conjoining factors: suitable targets, likely offenders, and absence of capable guardians . This approach suggests that MCDs could potentially lead to crime by increasing the concentration of likely offenders in the local neighborhood, or, more directly, by themselves presenting suitable targets for crime. These are precisely the sorts of claims that are made by critics of MCDs . Conversely, it is conceivable that MCDs might decrease crime in a neighborhood by introducing increased guardianship, rolling benches which could serve as a deterrent against crime. Proponents of MCDs make arguments of this type when asserting that dispensaries—especially those in compliance with local regulatory schemes requiring stringent security protocols—actually reduce crime in their neighborhoods . Routine activities theory is an appropriate and compelling lens through which to analyze these competing claims. It is appropriate because it presents direct causal mechanisms through which MCDs might affect crime, and it is compelling because those mechanisms plausibly run in either direction . Another useful approach found in the criminological literature is social disorganization theory, first developed by Shaw and McKay, which links urban crime to the concept of “social disorganization” . Studies have identified several measures of social disorganization that are positively associated with crime . In the present study I examine three “exogenous sources of social disorganization”: socioeconomic deprivation, residential instability, and family disruption. If these neighborhood characteristics predict crime, as social disorganization theory suggests, then they ought to be incorporated into more specific models of urban crime—including the present attempt to explain the relationship between MCDs and crime. I do not argue that there is a direct relationship between MCDs and social disorganization. Rather, I look to social disorganization theory to identify neighborhood characteristics, independent of cannabis, which might confound the real relationship between MCDs and crime. Variables from each of the three categories of social disorganization variables used in the present model are summarized in Table 3.1 below. For a more thorough review of the underlying theories and concepts, see the previous chapter.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 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 present the research methodology employed by this study and discuss their implications. 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 seven 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. Findings indicate a consistently weak but statistically significant relationship between MCDs and crime. MCD-containing tracts have slightly higher rates of both property crime and violent crime than tracts that do not contain MCDs. The link is stronger for violent crime than property crime . I am careful not to infer too much from this finding, due to the limited number of cases under review—26 dispensaries across 16 census tracts, compared to 173 non-MCD-containing tracts. Crime is more strongly predicted by the social disorganization variables examined by this study: socioeconomic disadvantage, family disruption, and residential instability. Consistent with social disorganization theory, socioeconomic disadvantage and family disruption are found to be strong predictors of high crime rates. 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, this link is stronger than the link between MCD density and crime. The third category of variables drawn from Sampson and Groves’ “exogenous sources of social disorganization” , residential instability, was not as strongly predictive of crime as socioeconomic disadvantage or family disruption. In the following sections I explore these findings more deeply and discuss their implications for research as well as policy making. 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 a cross-sectional analysis 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.

This phenomenon may explain why isolated compounds derived from cannabis appear only capable of exerting a limited effect as seen in drugs such as with the synthetic ∆-9 THC drug, dronabinol. To what extent each of these components confers anti-inflammatory benefits is a current focus of research. To what extent each of these components confers anti-inflammatory benefits is a current focus of research.An extensive literature beyond the scope of this review demonstrates persistently elevated levels of inflammatory and immune activation biomarkers even in PWH on antiretroviral therapy who have achieved plasma HIV RNA levels less than 50 copies/mL, designated “virally suppressed”. These include interleukin-6 , C-reactive protein and tumor necrosis factor alpha, CD4+ T cell depletion in gut lymphoid tissue persist, activated T cells are increased, and many immune cells are senescent. PWH also have a dysfunctional gut epithelial barrier , an increase in the permeability of the intestinal barrier accompanied by gut dysbiosis and immune dysregulation from loss of T cells in the GALT, resulting in activation of pro-inflammatory soluble CD14 , a response to circulating bacterial products, or microbial translocation . The central nervous system is an important site of inflammation in HIV, as demonstrated by measuring inflammatory biomarkers in cerebrospinal fluid, by brain magnetic resonance spectroscopy, and by positron emission tomography using microglial activation markers. Several mechanisms contribute to the persistence of inflammation in virally suppressed PWH. These include coinfections and gut dysbiosis. Cytomegalovirus co-infection is highly prevalent in PWH; CMV replication is frequently reactivated, triggering an inflammatory response. As mentioned above, gut microbial dysbiosis, which promotes dysfunction of the gut epithelial barrier, drying room resulting in a positive feedback loop sustained by increased microbial translocation of pro-inflammatory antigens such as lipopolysaccharide and subsequent immune activation and chronic inflammation.

Whereas normal commensal flora contribute to tolerance and balance between T helper subsets, loss or replacement of these beneficial flora leads to loss of T-helper type 1 function, amplifying GALT dysfunction in HIV infection. Depletion of Th17 cells in the GALT leads to reduced IL-22 production, diminishing epithelium repair processes and maintenance of tight gap junctions. Such barrier defects create a pathway for microbial products to escape the gut lumen and enter the systemic circulation. The entry of microbial products into the blood, known as microbial antigen translocation triggers the innate immune response and release of pro-inflammatory cytokines, such as interleukin -1β, TNF-α and others. Abundant data show activation of the NLRP3 inflammasome in virally suppressed PWH. Hepatitis C virus co-infection is common in PWH and may be another source of microbial translocation that drives inflammation. Plasma levels indicative of microbial translocation in HIV-HCV co-infection were higher than in monoinfected PWH virally suppressed on ART. PWH co-infected with HCV had a marked increase in markers of microbial translocation than uninfected healthy controls, whereas the plasma 16S rDNA was relatively similar, suggesting that it is the immune activation that persists as opposed to the circulating bacterial products. Tudesq and colleagues demonstrated for the first time that the plasma 16S rDNA levels increased with the duration of HIV infection in HIV-HCV co-infection, independent of HCV progression.Increased inflammation in virally suppressed PWH as described above is associated with adverse health outcomes such as myocardial infarction and even death. Persistent inflammation also affects the central nervous system , where microglia and astrocytes are chronically activated , ddimer, IL-6, CRP, monocyte chemoattractant protein 1 , soluble CD14 and sCD40L. Adjusting for age, comorbidity status, sex, ethnicity, AIDS status, current and nadir CD4, and virologic suppression on ART, factor analyses reduced the dimensionality of the biomarkers, yielding three factors, one of which was loaded on d-dimer, IL-6 and CRP and was correlated with worse depressed mood. We also reported that poorer social support was associated with higher levels of plasma MCP-1, IL-8 and VEGF, as well as CSF MCP-1 and IL-6 , suggesting that that enhancing social support might be an intervention to reduce inflammation and its associated adverse outcomes among PWH.The ECS comprises a network of receptors, endogenous ligands and enzymes expressed in diverse cell types.

Among the many functions of the ECS is regulation of energy use and substrate metabolism to maintain homeostasis. Components of exogenously administered cannabis bind to EC receptors, thereby modulating the function of the ECS. ECS signaling pathways have been pursued as a target for future pharmacotherapy to reduce inflammation and provide therapy in pathological conditions. The cannabinoid receptors type-1 and -2 are expressed in most tissues. CB2Rs are densely expressed in immune tissue and organs in diverse cell types including macrophages, splenocytes, microglia, monocytes, and T-cells resident in the thymus, spleen, and bone marrow and tonsils, providing a mechanism by which cannabinoids can exert anti-inflammatory effects. CB1Rs are most abundant in the brain, where they serve to modulate neurotransmitter activities, thereby mediating effects of phytocannabinoids on neurobehavior. They are particularly highly expressed in nociceptive areas of the CNS, as well as in the cerebellum, hippocampus, limbic system, and basal ganglia. They are not found in the medullary respiratory centers and thus, unlike opioids, do not cause respiratory depression. CB1Rs are also expressed on immune, cardiac, and testicular cells. In the GI tract, CB1Rs are involved in feeding, gastrointestinal motility, satiety signaling and energy balance. CB1R peripheral activity includes lipogenesis and inhibition of adiponectin, found at elevated levels in obese and diabetic individuals . CB1R signaling has been linked to increased levels of free fatty acids, low HDL, high triglycerides and insulin resistance. The two main endocannabinoids are arachidonoyl ethanolamide, or anadamide , and 2-arachidonoylglycerol , both derived from lipid precursors and synthesized on demand. Endocannabinoids in the postsynaptic neuron are released into the synaptic cleft, and travel retrograde to the presynaptic neuron, where they inhibit neurotransmitter release. The principal enzymes for degradation of ECs are fatty acid amide hydrolase and monoacylglycerol lipase . Additional EC-degrading enzymes include cyclooxygenase-2 , lipoxygenase , serine esterases and cytochrome P450.The anti-inflammatory effects of exogenous cannabinoids are mediated by the ECS , likely through CB2Rs in the periphery that have immunomodulatory functions.

Both preclinical and clinical evidence support theseanti-inflammatory effects of exogenous cannabinoids, particularly THC and CBD. This may be particularly important in the context of HIV, which is characterized by persistent inflammation as described above. For example, PWH heavy cannabis users had decreased frequencies of T-cells bearing the activation marker HLA-DR+ CD38+ CD4+ compared to non-cannabis-using individuals. Heavy cannabis users also showed reduced frequencies of antigen-presenting cells that produced pro-inflammatory interleukin-23 and tumor necrosis factor-α. In another study, HIV-infected cannabis users had lower IFN-γ-inducible protein 10 levels in plasma. In an experimental study examining the interaction between the ECS and cytokine networks in humans, CB1 and CB2 expression were significantly induced by TNF-α, IL-β, and IL-6. CBD may be a particularly potent anti-inflammatory component of cannabis. CBD reduces pro-inflammatory cytokines, inhibits T cell proliferation and reduces migration and adhesion of immune cells. These effects translate to improved outcomes in disease models. Thus, CBD protected against the deleterious effects of inflammation in a viral model of multiple sclerosis. Cannabidivarin , structurally similar to CBD, is a non-psychoactive cannabinoid found in cannabis. Very little work has been conducted on CBDV in PWH. In one study, CBDV was safe but failed to reduce neuropathic pain in patients with HIV. In the laboratory, it has been shown that CBDV decreases fat formation and inflammation in human skin cells. Many anti-inflammatory actions of cannabinoids may be mediated through the gut, particularly through stabilization of the gut barrier. The gut barrier is composed of epithelial cells, tight junctions, and a mucus layer. It controls beneficial nutrient absorption and protects against the deleterious invasion of pathogenic bacteria and toxins from the gut lumen into the blood. The ECS, how to trim cannabis together with the gut microbiota, regulates epithelial barrier permeability. In an animal model of HIV, macaques infected with simian immunodeficiency virus showed increased markers of inflammation and immune activation in epithelial crypt cells; these markers were reduced after chronic THC administration. Exposure to phytocannabinoids may reduce neural injury by decreasing excitotoxicity and neuroinflammation. In a large cohort of PWH, we recently reported that neurocognitive impairment was less frequent in cannabis users than non-users, regardless of viral suppression. In comparison, cannabis exposure was not related to NCI among PWoH. Unlike many prior reports, this analysis carefully controlled for any non-cannabis substance use disorders, positive urine toxicology for other illicit drugs and any past methamphetamine use disorder, positive breathalyzer test for alcohol, major depressive disorder and HIV disease characteristics. A possible mechanism of the specificity of the benefits of cannabis only for PWH is the anti-inflammatory effect of cannabis, which may be particularly important for PWH who have persistent inflammation despite good antiretroviral treatment. In contrast to cannabis’s beneficial actions in PWH, research on PWoH typically reports adverse effects on brain development and neurocognition. Examples include attentional and memory deficits, behavioral problems and structural and functional brain changes. The data are particularly concerning for adolescents. It is possible that PWH are less likely to suffer these adverse consequences than PWoH because of the counterbalancing effects of cannabis in reducing neuroinflammation, as we discussed when considering stroke. In an animal study, euroinflammation, measured as levels of TNFα, IL-1β, IL-6 and MCP-1, was reduced in the striatum of SIV-infected animals treated with THC.

Enteroendocrine signaling and the vagus nerve may provide a mechanism through which the gut microbiota may influence the central nervous system. Additionally, signaling through CB1Rs is influenced by Akkermansia muciniphila and administration of this organism to obese and type 2 diabetic mice increased intestinal levels of ECs that control gut inflammation and the gut barrier. These relationships between the gutmicrobiota and the ECS may be therapeutically useful. Thus, in zebrafish treated with a probiotic formulation for 30 days, gene expression of FAAH and MAGL, the enzymes responsible for degradation of the endocannabinoids AEA and 2-AG, decreased. Thus, probiotic treatment enhanced endocannabinoid signaling and improved gut integrity. Gut bacteria control the differentiation and function of immune cells in the intestine, periphery, and brain. There is increasing evidence that gut microbiota and the immune system are critical factors in the pathogenesis of neurodevelopmental, psychiatric and neurodegenerative disease as microbiota immunomodulation orchestrates communication between the gut and brain. Some cognitive domains are subject to immune-mediated CNS injury from HIV-induced microglial activation and contribute to HIV-related cognitive dysfunction. Furthermore, microglia are exquisitely responsive to the gut microbiome and commensal bacteria support the maintenance of microglia in normal homeostasis conditions. When microbiota is absent, microglia lose the ability to mature, becoming defected in differentiation, and function. In a study with germ-free mice, severely defected microglia led to impaired innate immune responses. LPS activates microglial cells leading to neuroinflammation and when chronic, is a likely contributor to CNS pathologies, via a leaky gut–brain barrier. Microbial antigen translocation refers to the entry of bacterial, fungal and viral components, such as LPS, and metabolites, such as short-chain fatty acids , cross from the gut lumen into the bloodstream. The endogenous cannabinoid, AEA, contributes to the process by which the gut immune system actively tolerates such microbial antigens. In HIV, MAT is associated with monocyte activation and inflammation. Thus, β-D-glucan is a microbially derived antigen that serves as one index of MAT. The anti-inflammatory effects of cannabinoids may be beneficial with respect to HIV reservoirs, which are the principal barrier to HIV cure. We analyzed HIV DNA in blood as a marker of reservoir size in men who had sex with men and initiated ART within a median of 4 months of estimated date of HIV infection. All achieved suppressed HIV RNA within a median of 5 months. Exclusive use of cannabis, as compared to no substance use or use of other drugs, was associated with a faster decay of HIV DNA during suppressive ART. These results are in line with prior reports of reduced HIV replication and cellular infection rate in the presence of cannabinoids in vitro. Thus, the potential anti-inflammatory effects of cannabis could translate to a beneficial impact in reducing HIV persistence. However, there is no consistent evidence that cannabis use affects levels of plasma HIV RNA. In addition to their expression in the peripheral immune system, CB2Rs are also expressed in the CNS . In humans, the bulk of CB2R expression is by microglia and astrocytes, consistent with a role in neuroinflammation.