This approach is also particularly relevant when studying the provisions of a single umbrella policy. For example, for provisions of recreational cannabis legalization, exposure categories based on the overall approach to legalization in 1 state versus another may be of greater interest than the effects of individual provisions. Similarly, Erickson et al. categorized states into 4 groups on the basis of stringency of the overall alcohol policy environment and found that these categories were associated with levels of past-month alcohol consumption. Several options are available to define clusters, including manual selection, hierarchical cluster analysis, latent class analysis, and principal components analysis . Heatmaps like those presented here can help inform the selection of appropriate clusters by offering an intuitive visual reference for the likelihood that sets of policies were adopted together. Evaluating situations when each clustering approach might be preferable is a future research direction.With the rapid growth of planetary scale web services, the past few years have seen the consolidation of data center facilities at a scale never seen before. Companies like Google, Amazon and Microsoft are building huge data centers comprising several thousands of servers. Economies of scale and advances in virtualization have favored the consolidation of data center facilities resulting in the emergence of mega data centers containing hundreds of thousands of servers with billions of dollars of investment. The emergence of mega data centers came with an accompanying trend in the basic building block of data centers. The cookie cutter approach to building data centers in the early half of this decade involved using racks with 20 to 40 servers and a top of rack switch as the basic building block. But the consolidation of data center facilities and increase in size of data centers has also led to a shift in the basic building block from a rack to a modular shipping container with anywhere from 250 to 1000 servers. These self-contained shipping containers also known as pods not only include servers, but are also geared with networking, power and cooling equipment.

At the scale of a pod,hydroponic trays it is possible to build non-blocking switch fabrics to interconnect all these servers. Interconnecting multiple modular data centers or pods to construct a mega data center requires careful design of the core interconnection network. Providing the required bisection bandwidth between pods is often expensive. The traditional technique of interconnecting pods involves using a few core packet switches such as the Cisco Catalyst 6509 [Cis] and connecting all the pod switches to the core switches. As the data centers grow in size, the problem of providing sufficient inter pod bandwidth has become more and more challenging. A key driving factor that has led to consolidation of resources is the economies of scale afforded by this consolidation and the increased flexibility of placing computation and services across a large cluster. But the flexibility in placing services or virtual machines has to also be supported by sufficient bandwidth between nodes that require it. For example a large scale service such as a search engine might run on thousands of servers spread across multiple pods requiring significant inter-pod bandwidth. But this set of nodes might change over time, or the number of nodes used to serve the users might change due to increased popularity of the service. In general, it is expected that the communication patterns and bandwidth demands between different sets of nodes changes over time. With the traditional data center network architectures, the only way to provision a network with sufficient bandwidth between any set of nodes that changes over time is to build a complete non-blocking network for the entire data center with electrical packet switches. One might claim that provisioning a fully non-blocking network between all the nodes is an overkill since only some nodes would actually require significant bandwidth resources. However, this fully provisioned topology is required to support full bisection bandwidth even between a pair of pods unless the set of pods that require high bisection bandwidth never changes over time.

For example, consider a traditional network with pods containing 1000 servers each connected by 10 Gb/s links and the pods are all connected through a core layer with an over subscription ratio of 2. Now even if only 2 pods had a full bisection bandwidth requirement and the other pods require only a small amount of bandwidth, this network cannot support the full bisection bandwidth required. This makes it essential to construct fully provisioned networks even to support localized bursts that require high bandwidth as long as these sets of nodes that require high bandwidth is not fixed. Two promising technologies to provision bandwidth more flexibly in the data center include optical circuit switching and wavelength division multiplexing . Optical circuit switches are oblivious to bandwidth and a single optical port can carry several parallel channels of 10 Gb/s using WDM. If a lot of bandwidth is required from a particular source to a particular destination, optical circuits offer a cost effective way of provisioning this bandwidth. A key limitation is switching time – it can take tens of milliseconds to switch from one destination to another. So if the bandwidth demand fluctuates very rapidly, then optical switches are not very useful. Optical circuit switching has been used in the telecom industry for a long time for provisioning long haul links where the capacity is typically provisioned or changed once in a few hours if not days. In the data center, using optical circuit switching at the level of individual hosts is infeasible since hosts speak to several other hosts at short timescales of a few milliseconds or seconds. But modular data centers provide a good opportunity to leverage optical switching since the bandwidth demand when aggregated at the level of pods is relatively more stable. It is still likely that there will be some bursty communication from each pod which would be best served by electrical packets switches which can switch bursty traffic even at a nanosecond scale. Optics already form a relatively large fraction of the data center network cost.

The inter-pod network typically uses 10 GigE or faster links that span long distances of few tens of metres and require the use of optic fibres since 10 GigE over copper is only feasible over short distances of around 10 m. The use of optical fibers requires expensive SFP+ tranceivers each of which costs $200 or more. We propose Helios [FPR+10], a hybrid electrical/optical data center network architecture that unifies the benefits of electrical and optical switching to provide nearly the same performance of a traditional electrically switched network but at a much lower cost, power consumption and complexity . Helios uses packet switches and circuit switches to interconnect pods and dynamically forwards traffic over them based on the nature of traffic and provisions circuits between pods that currently require bandwidth. We have built a fully functional prototype of Helios using commercially available networking equipment and by implementing software to perform various tasks required for Helios. The main contribution of this research is the proposed design for combining optical circuit switching and electrical packet switching in a data center for a more efficient network design. We also present a technique for estimating the natural interpod bandwidth demand by ignoring any bottlenecks caused by current network conditions. We identify key challenges in building a large scale deployment like Helios and describe several research opportunities that can advance our ability to build more efficient large scale data center network designs. Our prototype illustrates the feasibility of the design and indicates the opportunity to get large benefits in cost and power by using a hybrid network architecture for interconnecting pods. Helios uses a combination of electrical packet switches and optical circuit switches at the core layer to interconnect pods. Figure 2.1 illustrates a Helios network. If there are N1 electrical core packet switches, then N1 up link ports from each pod switch connect to these N1 core packet switches. The remaining N2 up links ports from each pod switch are connected to core circuit switches. The relative fraction of packet switches and circuit switches in the core layer depends on the extent of stability in communication patterns. If the traffic pattern is very stable, most of the core switches in the network can be optical circuit switches. Intuitively,seedling starter trays this allows the circuit switching overhead to be amortized over a long period of high utilization of the circuit that is just setup. The servers in each pod are connected to the pod switch by copper links which are feasible over short distances. Due to the relatively large distances involved, the links between the pod switches and the core layer are optical links. Each up link port in the pod switches contains an optical transceiver. The links from the pod switches to the core packet switches require an optical transceiver at the core packet switch end as well. The up links which connect to the optical core circuit switches do not require transceivers in the core layer and terminate at the switch directly as it operates entirely in the optical domain. The up links that are connected to optical circuit switches can also make use of wave division multiplexing .

Suppose we use a WDM factor of w, then w up links would be combined through a passive multiplexer/demultip lexer module into a single super link that is connected to a single core circuit switch port. The different up link ports that use a single super link use transceivers of different wavelengths to allow the multiplexer/demultiplexer module to work correctly. Eventually when a circuit is setup through this core circuit switch, this particular WDM super link would be connected to another pod thereby establishing w links from the source pod to the destination pod. Essentially higher values of w allow more data to pass through a single fiber or optical circuit switch thereby reducing the number of optical circuit switches required in the topology.The software for Helios consists of 3 primary components – Pod Switch Manager, Circuit Switch Manager and the Topology Manager. All these components act in a coordinated fashion to provision bandwidth resources where they are required, when they are required. The interactions between these components is illustrated in Figure 2.2. Besides these 3 components, the core packet switches are just traditional switches that can act in a plug-and-play fashion with simple software similar to learning switches. They do not require any dynamic or specialized configuration for Helios. They can be preconfigured with the MAC addresses or IP prefixes for different pods, but this is not required.TLC was performed using Merck 60 F254 aluminum-backed plates. Flash column chromatography was performed using Silicycle silica gel . Melting points were determined using an automated Buchi B-545 melting point apparatus, which provides a specific melting point, not a range, and are corrected. 1H NMR spectra were obtained on a Bruker Avance spectrometer. 13C NMR spectra were obtained on Bruker Avance NEO and Bruker Avance spectrometers. Chemical shifts are referenced to the residual solvent signal . Infrared spectra were recorded on a Bruker Alpha spectrometer. High-resolution mass spectra were obtained using an Agilent 6545 LC/SFC Hybrid Q-TOF spectrometer. Optical rotations were taken on a Rudolph AutoPol IV polarimeter. Circular dichroism experiments were performed on a Jasco J-815 CD spectrometer. Using the procedure of Zhang,60% NaH was portionwise added to a stirred solution of 3-formylindole in tetrahydrofuran cooled in an ice bath and then the reaction was slowly warmed to room temperature. After stirring at room temperature for 30 min, phenylsulfonyl chloride was added drop wise. The reaction was stirred for 24 h at room temperature. The resulting heterogenous mixture was concentrated under reduced pressure into a crude solid. The solid was dissolved in a mixture of water and methylene chloride.The combined organic layer was dried with sodium sulfate and concentrated in vacuo. The resulting solid was dissolved in minimum amount of hot methylene chloride/hexanes mixture and allowed to cool slowly to room temperature to afford off-white crystals . The spectroscopic data of the product agreed with the reported literature.Using the procedure of Zhang,60% NaH was portion wise added to a stirred solution of 3-acetylindole in tetrahydrofuran cooled in an ice bath and then the reaction was slowly warmed to room temperature. After stirring at room temperature for 30 min, phenylsulfonyl chloride was added drop wise. The reaction was stirred for 72 h at room temperature.

Output of this phase interpolator is driven to the next repeater as depicted in Fig. 4.1. The interpolating between the two signals results in the summation of the FIR shown in Fig. 4.1. The phase interpolator weighting adjustment represents the FIR filter coefficient, and the transfer function is given by H = α + z −1 . The added phase interpolator has very little power and area cost and allows programmable α. With a programmable interpolator, the filter function can be adjusted. An α of zero passes the MDLL divided clock to the output, and an α of one forwards the reference clock like a simple buffer. Tuning the phase interpolator setting, α, changes the -3dB bandwidth. Fig. 3.15 shows the cascade of two clock repeaters with FIR filtering for the output clock. The phase noise side-band spectrum of two cascaded clock repeaters with 40% FIR interpolation coefficient is shown in Fig. 4.2. Jitter is reduced with each stage of the filtering. Since each stage can have the filtering coefficient independently adjusted, Fig. 4.3 illustrates the impact of varying α for each of 4 cascaded clock repeaters. The best combination for least jitter is found to be an α of 40% for the first 3 repeaters and an α of 0.6 in the fourth repeater. Proper choice can reduces jitter by up to 50% in a repeater stage. In our system where more repeaters are used, additional stages of FIR filtering do not reduce jitter significantly due the sharp roll-off of filtering beyond a fourth-order filter. MDLL input mux is implemented as a configurable phase interpolator, shown in Fig. 4.4, to change the relative injection strengths of the reference edge and the VCO feedback edge. It can be configured from 0 to 100% injection strength with 20% steps. Where 0%setting denotes the mux is configured to operate as a normal delay cell in the VCO, thus the CMU is configured as a PLL. A 100% setting permits full injection of reference edge, thus CMU is configured as an MDLL. Intermediate settings interpolate between reference and VCO edges,how to dry cannabis and the CMU operates as a semi-PLL/MDLL. At the output of the CMU we are adding a second mux/phase interpolator structure that takes the incoming reference clockand a divided version of the CMU as inputs.

Output of this phase interpolator goes directly to the clock driver, as depicted in Fig. 4.1. With such configurability, jitter accumulation across the different repeaters can be kept low. depending on its location on the cable. Early on in the link, when forward clock is still clean and didn’t suffer jitter accumulation, the CMU is configured in a MDLL mode to reset VCO jitter accumulation. We also set the PI to forward the incoming clock to the next stage, as depicted in Fig. 4.5. Similarly, later on the link, when clock has undergone significant jitter accumulation, we tune the CMU in a PLL mode to filter incoming clock jitter. We also set the PI to forward a divided version of the filtered PLL clock, Fig. 4.5. Intermediate settings can be used in the middle of the cable link. Multiplying DLLs have gained much interest in recent publications because of their inherent ability to reset jitter accumulation inside the VCOs compared to MPLLs. This is attributed to the fact that reference clock edges are injected into the VCO each reference cycle and thus remove the jitter accumulation memory of the VCO. This can be interpreted as designing an MPLL that has a bandwidth equivalent to the reference frequency bandwidth, compared to traditional MPLLs where bandwidth can’t be greater than one tenth of the reference frequency. One challenging aspect in the design of MDLLs is the alignment of the injected reference edge to the VCO feedback signal. As shown in Fig. 4.6, the select logic is responsible for generating an aperture that allows the reference edge inside the loop and blocks the VCO feedback signal. As shown in Fig. 4.6, any delay mismatch between the reference edge and the VCO feedback edge, or a mismatch in the charge pump would cause the pulse following the injected edge different than the remaining VCO pulses, an effect that would manifest itself as period jitter or reference spur in the frequency domain, which limits the minimum jitter attained by the MDLL. Solutions are provided to this problem in literature. In a slave oscillator is injected with the MDLL master oscillator.

The slave oscillator acts as a LPF for the period jitter. In an auxiliary calibration loop is used to measure the duty cycle error of the output. This error is then used to unbalance the charge pump current to absorb this mismatch. In digital duty cycle measurements are correlated between consecutive samples. The error is then used to steer the control voltage of the VCO. This alleviates the need of PFD and charge pump altogether. While in a phase detector that is based on chopping and correlated double sampling is used to minimize mismatches.In all of the published techniques, a select logic block generates the SEL pulse that opens the aperture for reference injection. These prior works assumed that the SEL pulse could be generated quickly enough to select the next reference edge. Moreover the position of the SEL pulse with respect to the Ref edge was overlooked as a possible factor to affect the pattern jitter. As can be seen from Fig. 4.6 the SEL pulse is asserted when the DIV signal goes high and then OUT1 signal, one of the VCO phases, goes high. The delay for generating the SEL signal from the time the VCO signal OUT1 goes high is thus equal to the delay of the CMOS level restoration buffer after OUT1, not shown in Fig. 4.6, plus 3 or 4 gate delays inside the select logic. This has to be shorter than half a clock cycle of the VCO frequency. This delay is also function of process, voltage and temperature variations. A simulation that shows the impact of the SEL pulse phase shift is illustrated in Fig. 4.7. When the SEL pulses arrives early with respect to the VCO and reference edges, a hold violation inside the select multiplexer causes significant period jitter in the VCO output. Similarly, a SEL pulse arriving late would cause a VCO setup violation inside the multiplexer and also a period jitter increase. This constraint made the design of MDLLs for multi-gigahertz applications quite a challenging task. In fact, none of the published MDLLs, as far as we know, exceeded 2GHz operation. Figure 4.8 shows the schematic of the MDLL with the proposed modification. A 360o phase rotator uses the quadrature phases of the VCO to vary delay of the SEL pulse by 1 complete clock cycle. This can compensate for any amount of latency in the select logic generation and precisely positions the SEL pulse with respect to the ref edge to minimize the period jitter. The VCO comprises 4 delay stages.

The MUX stage is configured as a delay cell and used as the third stage of the VCO to maintain the quadrature nature between the two halves of the VCO. By doing this we can have constant phase steps for the SEL pulse tuning. The output of the phase rotators is then connected to the divider and the select logic to generate the required SEL signal. A calibration loop based on a search algorithm reads the duty cycle error of the VCO output and uses this reading as a measure of period jitter. This represents the optimal aperture position. Design of the phase rotator is shown in the inset of Fig. 4.8. The interpolator comprises a bank of capacitors, connected from one terminal together to form the interpolation node,and from the other terminal each capacitor is connected to a pair of digitally controlled pass gates. Each pass gate is connected to one of the clock inputs CLKI and CLKQ. Interpolation takes place at the common terminal of the capacitors bank as a weighted sum of input clock voltages. Because summation takes place with passive components,cannabis drying rack this implementation provides better linearity performance than the conventional current-source based phase interpolator. Conventional phase interpolators suffer from nonlinearity due to finite output impedance of current sources and clock feedthrough from input to output. A 4fF/unit capacitor is used to achieve the 4-bit matching requirement for the phase interpolator. Charge pump is a critical component of this design. Any mismatches in the charge pump will be interpreted as phase mismatches between reference and divided edge. Unlike PLLs, phase mismatches in MDLL are manifested as period jitter. The charge pump used in this design minimizes static and dynamic mismatches to less than 0.5%, which was satisfactory for our application. Fig. 4.9 shows the charge pump design. Static mismatch is corrected by Transistors M1-M4, which form a singled ended replica of the charge pump. Amplifier 1 forces the output node of the replica to be equal to the control voltage by changing the down current. This guarantees that the DC up and down currents of the charge pump are equal . It also guarantees that the down current is tracking the up current with different control voltages. This means that the charge pump can be used with wider control voltage ranges and therefore lower VCO gain. Using a voltage follower amplifier connected between VCTRL and VCTRL1 is used traditionally to minimize dynamic mismatch. Keeping VCTRL1 equals to VCTRL reduces charge sharing caused by switching currents between these 2 nodes. In our design, dynamic mismatch is corrected by another replica charge pump M5-M10. This replica guarantees that current is always flowing through the charge pump branch formed by M13-M14. This branch now is identical to the single ended replica and node VCTRL1 remains equal to VCTRL. Current consumed by this replica is tens of microamperes. To minimize area, the MDLL loop filter capacitor is implemented using core thin-oxide devices rather than thick oxide devices. This reduces loop filter area by 63%.

Due to the high gate leakage of these devices, an additional compensation technique similar to is used. Amplifier 2, M17 and a replica of the loop filter capacitor are connected in a negative feedback configuration as shown in Fig. 4.9. The loop sets M17 current to be equal to the replica capacitor leakage. Consequently, the leakage current in the loop filter is provided by M18 instead of leaking the charge on the loop filter capacitor.The noise analysis in chapter 3 and section 4.1 ensures the clock propagates across the entire cable length with sufficiently low noise. For data transmission, the distance between data repeaters is essentially a point-to-point link. The primary constraint is to minimize the power/meter for the targeted data rate. Longer repeat distances reduce power/meter by amortizing the repeater circuit’s power. However, with more channel attenuation, more power is needed for equalization. For this study, CAT7 cable is used which has approximately 2.2-dB loss per meter at the data Nyquist frequency of 6GHz. This power trade-off with distance is analyzed and illustrated in Fig. 4.10. Total energy per bit for repeated data transmission across 100 meters is shown versus section repeat distance. Short distances require no equalization but pays the power penalty of terminating the cable for a given minimum receiver sensitivity. Modest equalization power is possible if data repeat distance is kept below 20dB channel loss, which is equivalent to 9m of CAT7 cable. Channel loss beyond this requires added filtering which increases the power per repeater. This analysis uses realistic circuit simulations and includes varying the driver power for higher signal swing to compensate for cable loss. A shallow optimum exists between 4 and 8 meters. An 8-m data repeating distance is chosen to minimize the cost of inserting a large number of repeaters. Figure 4.11 shows the transceiver block diagram of each repeater. The transmitter uses a half-rate architecture and comprises a 16-1 data serializer. The delay for the pre-emphasis FIR is performed in the low frequency digital section to save power. The CML driver has 1 precursor and 2 post-cursor pre-emphasis taps to achieve 9 dB of equalization. The pattern generator and the serializer are used to generate data at each repeater for signal quality and BER characterization.

We will conduct malaria vector population surveillance on a monthly basis continuously till at least 8 months after the last round of larvicide application . We will monitor both indoor- and outdoor-biting mosquito abundance using CO2-baited Centers for Disease Control light traps equipped with collection bottle rotators . The collection bottle rotator, which has eight separate plastic collection bottles, will be programmed to collect active mosquitoes at 2-h intervals between 16:00–08:00. We will place two traps within each sampling compound: one inside the living room, the other outside the house 5 m away. We will conduct a total of 64 trap-nights of vector sampling per cluster per month. This will provide an estimation precision of 0.2 mosquitoes using the previously determined standard deviation. Species of collected mosquitoes will be identified and blood-feeding status will be recorded. We will test for P. falciparum sporozoite infection and blood meal source using an enzyme-linked immunosorbent assay on all specimens. For each house where the vector population was sampled, we will record the number of sleeping persons at each house on the same day as the vector survey. We will calculate sporozoite rate and EIR for each cluster. EIRs will be calculated as, and standardized to a monthly basis. The trapping method will allow for comparison of indoor- and outdoor-biting mosquito abundance and determination of nightly biting activity patterns. We will calculate indoor and outdoor transmission intensities separately assuming that all mosquitoes collected from a compound had their blood meal from the same household. We will calculate EIR for the four study periods as describe above: preintervention period: baseline vector surveillance started at least 6 months prior to the application of long-lasting microbial larvicides till intervention, intervention period, the 8-month washout period,grow trays and post intervention period: vector surveillance continued till 8 months after the last round of larvicide application.

To determine whether new malaria vector species are present in the study sites, we will sequence the ribosomal second internal transcribed spacer and mitochondrial CO1 gene in anopheline specimens that are not amplified by the recombinant deoxyribonucleic acid polymerase chain reaction method, and we will conduct phylogenetic analysis to determine whether the new species found by Stevenson et al. are also present in the study sites.We will conduct the intervention using a two-step approach. First, we will conduct a small-scale four-cluster trial to optimize the time, duration, and quantity of LLML application. Second, we will conduct a cluster randomized trial to test the effectiveness and cost effectiveness of LLML. The design has two parallel arms, i.e., control and intervention, and allows for baseline survey without intervention and crossover .We will select four clusters, two in each county, for an entomological evaluation of the optimal larvicide application scheme . We will randomly select two clusters, one in each county, treated with larvicides and the other two sites will serve as controls . We will treat temporary habitats with FourStar controlled release granule formulation, which maintains effectiveness through wet and dry periods for up to 1 month. We will treat semipermanent habitats with FourStar 90-day briquettes and permanent habitats with FourStar 180-day briquettes. Application dosage will follow the recommendation of the manufacturer, Central Life Sciences: 10 lbs per acre of water surface for the granule formulation, and one briquette per 100 ft2 of water surface for the briquette formulations, regardless of water depth. We will re-treat the habitats every 4 to 5 months. On a weekly basis in the treatment and control sites, we will use aerial samplers to determine habitat pupal productivity, and use standard dippers to determine larval abundance. This will allow for determination of habitat productivity with a tolerable error of 0.5 mosquitoes, based on the standard deviation identified in previous studies. We will monitor indoor and outdoor vector abundance using 64 trap nights per cluster per month. This sample size will allow detection of a difference in average vector abundance of 0.12 mosquitoes with 80 % statistical power and 0.05 type-I error.

We will use ELISA methods to determine Anopheles mosquitoes’ sporozoite infection and blood feeding host preference. We will analyze the data immediately after the small scale trial using analysis of variance with repeated measures and appropriate transformation to determine the effects of habitat larviciding on mosquito abundance and transmission intensity. The percentage reduction in malaria transmission intensity will be calculated.We will assign fourteen clusters each in the two counties to intervention or no intervention by a block randomization method on the basis of clinical malaria incidence, vector density, and human population size per site. Year 1 will focus on preparing the study sites and working with clinics and hospitals to help them improve their routine malaria surveillance . In year 2, we will conduct preliminary surveys on all 28 sites to determine clinical malaria incidence, vector density, geographic information system coordinates of larval habitats, and human population size. Human population size for each cluster, stratified into three age groups will be ascertained from our existing data. We will obtain age-group level aggregated morbidity data from local hospitals and clinics where the sampled residents seek treatment. We will determine vector abundance using CO2-baited CDC light traps for 16 trap-nights per cluster per month in each of the indoor and outdoor environments. Using these data, each cluster will be allocated to either treatment or control through randomization using the following procedures. First, each of the four parameters listed above will be standardized with the highest cluster as 1. Second, we will assign the highest weight for clinical malaria cases , the lowest weight for human population size , and intermediate weights for expected vector density and larval habitats , following the method of Corbel et al.. For each cluster a rank score will be computed as the sum of weighted clinical malaria incidence, vector density, habitat abundance, and human population size. Finally, the 14 clusters within each county will be sequentially numbered according to their rank scores and sorted into seven blocks of two clusters having successive rank scores.

We expect the two clusters within each block to have similar risk characteristics for clinical malaria, vector abundance, larval habitats, and human population size. In each block, the ranks of the two clusters are put into two sealed envelopes, one cluster will be randomly allocated to treatment and another to control, using computer-generated random numbers .After the larvicide application optimization and study cluster randomization, we will treat each treatment cluster with LLML at the time interval of 4 or 5 months . The first treatment will be conducted in February-March about 1 month before the beginning of the long rainy season which usually starts in April. After three treatments, we will perform no treatments for the next 8 months. This will provide useful data on the dynamics of action of the LLML and the waning efficacy of LLML over time. These data will be important in analyzing cost-effectiveness to help optimize the timing of re-treatments. After 8 months, a total washout of the LLMLs will be assumed to have taken place. Next, we will perform a crossover and switch of the control and treatment clusters. Former control clusters will receive three rounds of LLML treatment at appropriate time intervals, and the former treatment clusters will receive no LLMLs. This strategy will minimize ascertainment biases that might be attributed to care-seeking behaviors of the population or to malaria detection and reporting by malaria treatment clinics. We will test LLMLs manufactured by Central Life Sciences. The larvicide application regime is as follows: temporary, semipermanent, and permanent habitats will be treated with FourStar controlled release granule formulation, 90-day briquettes, and 180-day briquettes, respectively. Application dosage will follow the recommendation of the manufacturer: 10 lbs per acre of water surface for the granule formulation,dry racks for weed and 100 ft2 water surface per briquette. We will conduct monthly vector surveys throughout the study period to determine indoor- and outdoor-biting vector abundance, using the same sample size of 64 trap-nights per cluster per month, and sporozoite infection and mosquito blood meal analysis will be conducted on all collected specimens. To confirm larviciding efficacy, we will examine larval abundance, age structure, and pupal productivity on a monthly basis in 100 randomly selected larval habitats each from treatment and control sites using our GIS maps and data on sites where LLML was applied.Sample size was calculated based on 2010 and 2011 active case surveillance results from Iguhu and Emutete areas. Then the number of clusters required and the number of individuals required for each cluster were calculated following the methods developed by Hayes and Bennett based on cluster-randomized trials assuming equal population for each cluster. The observed malaria incidence rate was 52.7 cases per 1000 people year in 2011. We calculated the numbers of clusters and individuals required for epidemiological assessment of the long-lasting larvicide treatments to detect a 50 % protective efficacy conferred by the treatment compared with the reference group , with a power of 80 %, significance level of 5 % and the coefficient of variation of true proportions between clusters within each treatment was assumed to be 0.15.

The estimated number of clusters for the intervention will be five and the required number of individuals for each matched-pair will be 1196; assuming a design effect of 0.25 and 20 % of subjects lost to follow-up. The estimated number of clusters for the intervention will be seven and the required number of individuals for each of the matched-pairs will be fewer than 2000. The 28 clusters proposed in the randomized cluster study will detect 50 % malaria incidence reduction with 99.9 % power and 30 % incidence reduction with 85.3 % power. This is based on the current malaria incidence rate in the study sites and a two-tailed alpha with a human population size of 2000 per cluster . If the malaria incidence is 50 % lower than the current value, the design will still detect 50 % incidence reduction with 99.7 % power and 40 % reduction with 95.2 % power .We will monitor primary and secondary endpoint outcomes throughout the 5-year study period ; data analysis will be conducted in year 5. The difference in clinical malaria incidence between treatment and control groups will be compared using Poisson multivariate regression models with intervention, age, and calendar time as covariates, using a generalized estimating equations approach. GEE is necessary since incidence will be modeled monthly as a temporally-correlated repeated measure using grouped data. Intervention will be a time-varying covariate since the treatment crosses over after three intervention rounds. Since there is no intervention in the 8 months during the washout period, interval censoring will be performed to exclude the second 4 months of data during this period. The odds ratio and the 95 % confidence interval for clinical malaria rates between treatment and control groups will be calculated. Difference in vector density and EIR will be analyzed using a negative binomial regression model and the GEE approach. In all these analyses, clusters will be indicated as intervention and control, calendar time will be categorized into: pre intervention, intervention, post intervention , washout , crossover intervention, post intervention , and nonintervention, and months since intervention will also be included as an independent variable. These variables will allow for comparison between intervention and control clusters based on baseline observations, e.g., relative reduction in vector density, and allow for evaluation of cumulative effect,e.g., the second round of treatment may produce added effect following first-round treatment. Finally, for the economic evaluation, we will calculate incremental cost-effectiveness ratios based on the primary endpoint and on long-term health outcomes including malaria deaths averted. Using the “ingredients approach”, costs will be classified according to: initial setup investment , running costs , and costs of program management and quality control . Cost data will be estimated from health facility and Ministry of Health records, LLML manufacturers and financial accounts of the research project. One-way and multi-way sensitivity analysis will be undertaken to examine the implications of potential changes in variables such as larvicide price and larviciding application frequency. ICERs will be reported from both provider and societal perspectives for different transmission intensity scenarios.Larval control and environmental management have played very important roles in malaria elimination in the United States and Europe, where today larval control using biological larvicides is the primary vector control method.

These two models were able to isolate the impacts of recreational marijuana legalization from the impacts of medical marijuana legalization on opioid prescriptions. Comparing eight states and DC to all the remaining 42 states violated the difference-in-difference assumption, because states without medical marijuana legalization had different trends in opioid prescriptions compared to states with medical marijuana legalization . To test the assumption of parallel trends in treatment and comparison states in the absence of policy change, we used repeated ANOVA to compare time trends in opioid outcomes between treatment and comparison states in years 2010, 2011, and 2012 when none of these states adopted recreational marijuana legalization. Linear regressions were used, controlling for time-varying state covariates, state indicators, year and quarter indicators, and state-specific linear time trends. State indicators accounted for time-invariant state-level unobserved heterogeneities such as social norms about opioid use. Year and quarter indicators accounted for time-specific heterogeneities common to all the states at the same time, such as CDC Guideline for Prescribing Opioids for Chronic Pain published in 2016 . State-specific linear time trends accounted for statelevel time-variant trends in outcomes. Standard errors in the regression were clustered at the state level. To test the robustness of results, we conducted a series of sensitivity tests. 1) Because California, Maine, Massachusetts, and Nevada had limited number of post-legalization observations, the policy impacts in these states may not be statistically discernable. In addition, the most recent quarters of Medicaid State Drug Utilization data may contain errors and are often subject to future revision. We excluded the observations after 3rd quarter of 2016 when these states implemented recreational marijuana legalization to focus the analyses on states legalizing recreational marijuana before 2016. 2) We moved Hydrocodone-combination drugs to Schedule III opioids to test results sensitivity to recent drug reclassifications. 3) It is suggested that adding state-specific time trends may attenuate estimates of policy impact if the policy impact acts upon the trend itself . We removed state-specific time trends in regressions and expected that the associations would be more discernable. 4) Following previous research ,ebb and flow tray we also performed falsification tests on 4 drug classes, including blood-thinning agents, phosphorous-stimulating agents, antivirals, and antibiotics, as there is no scientific evidence suggesting the associations between marijuana and the underlying conditions that these drugs treat.

These drugs were assumed to be not associated with recreational marijuana legalization but associated with common unmeasured confounding factors such as those affecting general prescribing, healthcare utilization, and healthcare resources at the state level. Table 2 reports the descriptive statistics of pooled data by recreational marijuana legalization status. Compared to six states with medical marijuana legalization but without recreational marijuana legalization, eight states and DC that legalized recreational marijuana in the study period had slightly and insignificantly higher rates of Schedule II and III opioid prescriptions. Supplemental Table S22 reports ANOVA tests for time trend comparisons, suggesting that the time trends prior to legalization did not significantly differ between the states legalizing recreational marijuana in 2012 and the six comparison states, between the states legalizing recreational marijuana in 2015 and the six comparison states, or between the states legalizing recreational marijuana in 2016/7 and the six comparison states. Figure 1 shows the unadjusted time trends in number of Schedule III opioid prescriptions by legalization status. States that legalized recreational marijuana in 2015 and 2016/7 saw a reduction in number of Schedule III opioid prescriptions after the legalization took effect, whereas states that legalized recreational marijuana in 2012 saw a slight increase. Trends in number of Schedule II opioid prescriptions did not appear to differ by legalization status . Based on difference-in-difference regressions, Figure 2 reports predicted percentage changes in number of opioid prescriptions associated with recreational marijuana legalization . In Model A that compared among eight states and DC with recreational marijuana legalization, recreational marijuana legalization was not associated with number of prescriptions, total doses, or spending of Schedule II opioids.In Model B that compared eight states and DC to six states with medical marijuana legalization, recreational marijuana legalization was not associated with any Schedule II or Schedule III opioid outcome. Using eight-year quarterly data on prescription opioids received by Medicaid enrollees in the US, the study added to the still limited literature about the impacts of recreational marijuana legalization on opioid use. It enhanced internal validity by adding comparison states and controlling for multiple confounders that were absent in previous research , such as presence of prescription drug monitoring program, Medicaid expansion.

It also enhanced generalizability by investigating all states legalizing recreational marijuana in the US. We found no evidence to support the concern that recreational marijuana legalization increased opioid prescriptions received by Medicaid enrollees. Instead, there was some evidence in some model specifications that the legalization might be associated with reduction in Schedule III opioids in states that implemented legalization in 2015 . It appeared that, if the hypotheses about marijuana’s substitution effect and gateway effect on opioid use are both valid, the gateway effect of marijuana did not outweigh its substitution effect. Another possibility is that the hypothesis about marijuana’s gateway effect lacks support. Unfortunately, we were not able to directly assess these mechanisms in this study. It is not clear why two comparisons yielded slightly different results. Both models have advantages and limitations. The treatment and comparison states in the first model comparing among eight states and DC were more comparable, as they all had adopted recreational marijuana legalization at some time points. On the other hand, the second model comparing eight states and DC to six states with medical marijuana legalization had a larger sample size to detect statistical significance. We therefore chose to report findings in both comparisons. Irrespective of their slight differences, the core findings from the two comparisons were consistent that recreational marijuana legalization did not increase prescription opioids received and most coefficients for the outcome variables were non-significant. In accordance with our previous study on medical marijuana legalization and prescription opioids received by Medicaid enrollees , the association between recreational marijuana legalization and reduction in prescription opioids seemed to be only evident in some models for Schedule III opioids but not for Schedule II opioids. Because this line of research only emerged recently, the explanation for the differential associations remains unknown. As discussed in our previous study , we hypothesized that such differences may be partly attributable to the differences in clinical practice and drug efficacy between the two drug classes. According to Controlled Schedule Schedules classified by US Drug Enforcement Administration, Schedule II opioids have greater potential for opioid misuse and overdose than Schedule III opioids . In clinical practice, Schedule II opioids must be refilled with monthly prescriptions whereas Schedule III opioids are fillable within six months without new prescriptions . Receiving regular monitoring and evaluations from physicians, patients prescribed with Schedule II may be less likely to switch to other drugs. Regarding drug efficacy, Schedule III opioids are often used to treat mild to moderate pain symptoms,rolling greenhouse benches for which marijuana is suggested to be also effective . But the evidence for marijuana’s efficacy to treat severe pain symptoms is still limited. Patients prescribed with Schedule II opioids might be less likely to receive recommendation from physicians to switch to marijuana. These hypotheses need future research on individual observations to provide empirical support. It is also worth noting that despite large effect size detected for Schedule III opioids in terms of percentage point reduction , the absolute level of opioid prescribing rates was low for this drug class . The impact of the legalization converted to absolute levels was modest. This study has limitations primarily related to data availability. First, we evaluated the implementation of legalization instead of commercialization . Because several states did not open retail markets during our study period, our results may be biased toward the null. Second, despite the size of state-level observations is larger in this study than previous research, our study sample is still small and some statistically non-significant associations may simply reflect the lack of statistical power. Particularly, observations in post legalization period were limited for states implementing legalization in 2016/7. Third, we were not able to explore why states implementing legalization at different time points may demonstrate differential changes in opioid prescriptions. Fourth, we grouped states based on their law implementation dates. However, the states implementing the legalization on the same dates may have opened their retail markets on different dates . We were not able to identify the degree of marijuana commercialization in each state or evaluate the independent impacts of commercialization because of limited sample size. Further, similar to other state-level investigations of aggregate data, we were not able to explore causal mechanisms of the findings at individual level.

Particularly, the hypotheses about substitution and gateway effects of marijuana cannot be directly tested. Additionally, the outcomes analyzed in this study represented opioid prescribing but not patients’ legitimate use or misuse of prescription opioids. Finally, the findings may not be generalizable to opioids dispensed in non-outpatient settings or to the general population. The findings represented a limited number of states in the US but may not be generalizable to other states in the US or to population in other countries. Alcohol and marijuana use are common in adolescence. In 2003, 31% of 12th graders reported getting drunk in the past month, 21% of 12th graders revealed using marijuana in the past month, and 6% of 12th graders disclosed daily marijuana use . Further, 40% of high school students who used marijuana in the past year met criteria for marijuana abuse or dependence . Moreover, 58% of adolescent drinkers also report marijuana use , and alcohol and marijuana use disorders are highly comorbid . Despite the prevalence of heavy alcohol and marijuana use in teenagers, it is unclear how such protracted use may affect brain functioning during youth, particularly as adolescent neuromaturation continues. Neuropsychological studies of teens with alcohol use disorders have reported decrements in language skills, problem solving, verbal and non-verbal retention, working memory, and visuospatial performance . In addition, we previously examined functional magnetic resonance imaging brain response during a spatial working memory task among teens with AUD and demographically similar non-abusing controls . Groups performed comparably on the task, but AUD teens demonstrated less brain response than controls in the midline precuneus/posterior cingulate, and more activation in bilateral posterior parietal cortex, suggesting subtle alcohol-related neural reorganization and compensation. These neuropsychological and imaging findings suggest that heavy alcohol use during youth adversely affects frontal and parietal circuitry, but the additional impact of marijuana use is less well understood. Neuropsychological assessments of substance use disordered teens have described marijuana use related deficits in learning and memory and attention . A longitudinal study of marijuana dependent adolescents demonstrated further short term memory decrements that persisted after 6 weeks of monitored abstinence . In addition, compared to individuals with adult-onset cannabis use disorder and non-abusing controls, adolescent-onset cannabis use disordered adults showed attenuated electrophysiological response during selective attention , as well as smaller frontal and parietal volumes and increased cerebral blood flow . These studies indicate that heavy marijuana use during youth may adversely affect cognition and brain functioning, particularly short-term memory and attention, and raise questions about the integrity of frontal and parietal brain regions in adolescents with marijuana use disorders. In order to understand the neural correlates of concomitant heavy marijuana and alcohol use during youth, we assessed blood oxygen level dependent fMRI response among short term abstinent teens with comorbid marijuana and alcohol use disorders compared to AUD-only and non-abusing control teens reported in a previous study . We measured BOLD response during an SWM task that typically activates bilateral prefrontal and posterior parietal networks among adults and youths . Based on our earlier findings among AUD and control adolescents, we predicted that MAUD teens would show greater fMRI response than controls in regions sub-serving SWM, including prefrontal and bilateral posterior parietal cortices. We hypothesized further that MAUD teens would show more prefrontal and parietal activation than AUD youths, since we predicted that concurrent heavy marijuana and alcohol use would influence functioning more than protracted alcohol use alone.Flyers were distributed at local high schools to recruit adolescents, as described previously .

Column gives results from a reduced form specification regressing market prices and controls on worker productivity directly, and column provides the results of my preferred two-stage least squares specification instrumenting for wages with market prices. When I instrument for wages, their effect on worker productivity remains statistically insignificant, but the relevant point estimate becomes barely positive. The temperature response function is quite stable across columns and lending support to the conclusion that I accurately recover a true relationship. While the richness of my data allows me to exploit intra-day variation in temperature, I can also collapse my data to the day-level and investigate how daily temperature affects daily worker productivity. Figure 1.15 reports the results of three different day-level temperature specifications. The first uses time-weighted average daily temperature experienced by each picker, the second uses daily maximum temperature, and the third uses daily minimum temperature. Overall, the results from these specifications support the qualitative results of my primary specification: extreme temperatures lower picker productivity, and cool temperatures are more damaging than very hot temperatures. One threat to the credibility of my findings in tables 1.2 and 1.3 is that temperature and wages may affect workers’ labor supply, both on the intensive and extensive margins. That is, workers may decide to work fewer hours on a particularly hot day, or choose not to come to work at all if the piece rate wage is particularly low.Such behavior would bias my estimates of how temperature and wages affect productivity by introducing unobserved systematic selection into or out of my sample. I investigate this possibility in table 1.5 by regressing temperature, wages, and controls on both hours worked and the probability of working.

In column ,cannabis grower supplies the dependent variable is the number of hours worked by a picker in a single day, and temperature is measured as a time-weighted average experienced by the picker during that day. Here, I control for a picker’s start-time rather than their picking “midpoint.” In column , the dependent variable is an indicator for whether a picker worked at all in a given day, and temperature is measured as a daily midpoint temperature: /2. I use daily midpoint temperature in column in order to provide a consistent comparison between employees who show up to work and employees who do not, since I do not know when or for how long these absent employees would have worked had they come to work. Figure 1.16 displays the relevant temperature results from columns and of table 1.5. Overall, table 1.5 reports that neither wages nor temperatures affect labor supply in a statistically significant way. Similar to Graff Zivin and Neidell , I find the labor supply of agricultural workers to be highly inelastic in the short run. This also matches the findings of Sudarshan et al. for weaving workers in India. This evidence gives me confidence in the validity of my baseline results.I now turn to how temperature affects berry pickers’ wage responsiveness. Table 1.6 reports the results of estimating a variant of equation separately across eight temperature bins.I find that wages have no meaningful effect on productivity at most temperatures, but have a statistically significant and positive effect on productivity at cool temperatures: those between 50 and 60 degrees. In particular, my estimate suggests an increase in the piece rate wage of one cent per pound at temperatures below 60 degrees increases average productivity by 0.28 pounds per hour. This reflects an elasticity of productivity with respect to the wage of roughly 1.6 at cool temperatures,and an elasticity statistically indistinguishable from zero at other temperatures. This “productivity elasticity” is considerably smaller than the 2.14 number estimated by Paarsch and Shearer . Table 1.7, which repeats the analysis from table 1.6 using ordinary least squares , highlights the importance of instrumenting for piece rate wages. This table highlights two important things. First, the effects of wages on productivity at low temperatures do not show up in a statistically significant way without correctly instrumenting for wages with market prices. Second, I am able to rule out any dramatically large effect of wages on productivity at most temperatures.

Another threat to my findings is that workers who do not out-earn the hourly minimum wage in a given day may shirk when they know that additional productivity will not increase their take-home pay. Figure 1.13 reports the frequency with which workers fall below this minimum wage threshold. I face an econometric problem if the effects of temperature reduce workers’ productivity, increase the probability that workers earn the minimum wage, and hence encourage shirking. To ensure my findings are not meaningfully altered by this phenomenon, I re-estimate my main results using only picker observations where the picker out-earns the minimum wage for the day. This procedure drops my number of picking period observations from 305,980 to 257,689: a decrease of 15.8%. Figure 1.17 and table 1.8 present the results of my main temperature and piece rate wage specifications using this subsample. My findings remain qualitatively stable and statistically significant.Finally, even if temperature and wages do not affect labor supply directly in a statistically significant manner, and even though worker-specific fixed effects capture individual workers’ average productivity levels, I still face a potential adverse selection problem. Specifically, if variation in temperature and wages affects which sorts of workers choose to show up for work, my results may capture workforce compositional effects rather than individual productivity effects. To address this concern, I re-estimate my results only using observations from those workers who work more than thirty days in the relevant season. The intention here is to focus on workers who are likely to have the least elastic extensive labor supply. The results of this robustness exercise are presented in figure 1.18 and table 1.9. Taken together with the other available evidence, these results largely support my baseline findings. My primary finding is that labor productivity, on average, is very inelastic with respect to piece rate wages: I can reject with 95% confidence even modest positive elasticities of up to 0.7. This upper bound is considerably lower than the estimates derived by Paarsch and Shearer and Haley . I show that, without controlling for seasonality, a regression of productivity on piece rate wages results in a negative and significant point estimate . However, even once I control for seasonality, a naïve OLS regression of productivity on piece rate wage may be biased toward zero of table 1.4.

By instrumenting for piece rate wages with the market price for blueberries, I can identify a precisely-estimated inelastic effect of table 1.2. However, my primary specification makes the restrictive assumption that wages affect productivity linearly and in the same manner at all temperatures. Table 1.6 confirms that piece rates’ effect on productivity is very much non-linear across different temperatures. Specifically, wages seem to spur productivity at cool temperatures . At other temperatures, wages do not affect productivity in a statistically significant way. This empirical finding directly challenges one of the core assumptions of the model presented in section 1.2.1: that productivity always rises with the wage . What is going on? One possible explanation for my findings is that, at moderate to hot temperatures, workers’ face some binding physiological constraint on effort that prevents them from responding to changes in their wage. Put bluntly, blueberry pickers in general may already be “giving all they’ve got” at the temperatures and wages I observe.Figure 1.19 summarizes this possibility using the theoretical framework developed in section 1.2.1. While the model in section 1.2.1 is straightforward and tractable, it is not the only way to conceptualize worker effort and productivity. In particular, rather than modeling effort as an unrestricted choice variable,dry racks for weed one could assume each worker has a finite daily budget of effort that must be allocated across different activities throughout a day and Becker. Such a model would allow Xr to be zero or even negative under certain conditions, implying a backwards-bending effort supply curve, somewhat analogous to the canonical backward-bending labor supply curve . The downside of such models is that they fail to provide comparative statics that can be tested with the data I observe in this setting. A growing literature has rigorously documented the non-linear impact of temperature on everything from corn yields to cognitive performance , but has not focused specifically on how temperature affects agricultural workers.Nevertheless, several recent papers in this literature seem particularly relevant to my findings. Adhvaryu et al. show that factory workers in India produce more output when heat-emitting conventional light bulbs are replaced LED lighting, especially on hot days. Sudarshan et al. find similar evidence that temperature reduces worker productivity in a variety of Indian manufacturing firms. Finally, Seppänen et al. show that temperature even has large effects on the productivity of office workers.Other researchers have asked broader questions about how temperature affects aggregate production or labor decisions at the county- or country-level. The growing consensus is that weather shocks – particularly exposures to extreme heat – reduce aggregate production in a wide variety of settings. For instance, Hsiang exploits natural variation in cyclones to find negative impacts of high temperatures in both agricultural and non-agricultural sectors at the country-level. Deryugina and Hsiang and Park find similar county level effects of daily temperature in the United States, despite widespread adoption of air conditioning. Heal and Park document relevant findings throughout the economics literature and provide a useful theoretical link between heat’s physiological effects and aggregate economic activity.Extreme heat may reduce aggregate production through several channels. The first possibility, discussed at length in the previous paragraph, is that employees are less productive while working at high temperatures.

Another possibility is that employees may choose to work fewer hours when temperatures are particularly high. In other words, there may be a labor supply response to temperature on the extensive margin. Graff Zivin and Neidell provide support for this hypothesis by analyzing data from the American Time Use Survey. They find that at high temperatures, individuals reduce the time they spend working and increase the time they spend on indoor leisure. Finally, temperature can affect even broader aspects of the labor market like aggregate demand for agricultural labor in India , or the composition of labor in urban vs. rural regions of Eastern Africa . While this paper examines how a particularly salient environmental condition, temperature, affects labor productivity, previous research has shown that other environmental factors matter as well. Chang et al. , for instance, find that outdoor air pollution negatively affects the indoor productivity of pear packers. The same authors conduct a similar exercise using data from Chinese call-centers and find comparable results. Adhvaryu et al. find a steep pollution-productivity gradient in the context of an Indian garment factory, and Graff Zivin and Neidell find large damages from ozone in an agricultural context somewhat similar to my own. In an older case study, Crocker and Horst, Jr. study seventeen citrus pickers in southern California and find negative effects of both high temperatures and air pollution. It is useful to think of temperature not as a single sufficient statistic to describe environmental quality, but rather as one condition among many that is relevant for understanding labor productivity. This paper makes several important contributions to the literature discussed above. First, because I observe berry-pickers’ productivity multiple times during a single day, the variation I observe in both productivity and temperature is much more temporally precise than in many previous studies. Additionally, since I use temperature observations that are taken hourly, and sometimes more frequently, I do not need to interpolate temperature over time. Second, I study a setting where both very hot and cool temperatures have negative effects on productivity, highlighting the particularities of different production processes when it comes to temperature impacts. Third, and most importantly, I look at how how environmental conditions and incentive schemes interact.Table 1.2 and figure 1.14 provide my estimates of the direct effects of temperature on labor productivity in the California blueberry industry. Whereas most previous studies have focused on the negative effects of extreme heat , I find that cool temperatures have just as large negative effects as very hot temperatures, if not larger.

Motives of celebration, coping, and social anxiety are significantly associated with symptoms of anxiety at p ≤ 0.05. Only coping remains significantly associated with symptoms of anxiety using the Bonferroni corrected p ≤ 0.003. Coping is positively and significantly associated with symptoms of anxiety whereas the more often marijuana use is motivated by coping, the higher the score for symptoms of anxiety. The magnitude of the association of motives of coping with symptoms of anxiety is of almost 1 indicating that for any one unit change in the strength of coping motive there is almost a one point change in scores of symptoms of anxiety. Post hoc power analyses indicate that the statistical power greater than 0.99. Results from the mediation analysis with past 90 days marijuana use as a mediator are presented in Tables 4.45a-d. There is no evidence of any indirect effects of motives of marijuana use on symptoms of anxiety through past ninety days marijuana use. All 95% bootstrap confidence interval for the indirect effect, based on 10,000 bootstraps, include zero. There is however evidence of a positive direct effect with symptoms of anxiety for motives of coping and social anxiety, independent of past 90 days use. Results from the mediation analysis with daily number of hits as a mediator are presented in Tables 4.46a-d. All 95% bootstrap confidence interval for the indirect effect, based on 10,000 bootstraps, include zero. There is, however, evidence of a negative direct effect with symptoms of anxiety for motive of celebration and a positive direct effect for motives of coping and social anxiety. After controlling for age, gender, user group, and race/ethnicity, there is a negative direct effect between motives of marijuana use and symptoms of anxiety for motives of celebration and sleep,vertical grow systems and a positive direct effect for motives of coping and social anxiety. Table 4.47 presents the regression estimates without and with control variables.

Motives of marijuana use account for approximately 24% of the variance of overall psychological distress. Motives of celebration, coping, conformity and social anxiety are significantly associated with overall psychological distress at p ≤ 0.05. Only coping remains significantly associated with overall psychological distress using the Bonferroni corrected p ≤ 0.003. Coping is positively, significantly associated with overall psychological distress whereas the more often marijuana use is motivated by coping the higher the score for psychological distress. The magnitude of the association of motives of coping with psychological distress is of approximately 3 indicating that for any one unit change in the strength of coping motive there is almost a three-point change in scores of symptoms of anxiety. Post hoc power analyses indicate that the statistical power greater than 0.99.Results from the mediation analysis with past 90 days marijuana use as a mediator are presented in Tables 4.48a-d. There is no evidence of any indirect effects of motives of marijuana use on overall psychological distress through past 90 days marijuana use. All 95% bootstrap confidence interval for the indirect effect, based on 10,000 bootstraps, include zero. There is however evidence of a positive direct effect with overall psychological distress for motives of coping and social anxiety, and evidence of a negative direct effect for motives of celebration and conformity. The negative direct effect with celebration is no longer significant after controlling for gender, age, user group, and race/ethnicity. Results from the mediation analysis with daily number of hits as a mediator are presented in Tables 4.49a-d. There is no evidence of any indirect effects of motives of marijuana use on overall psychological distress through daily number of hits. All 95% bootstrap confidence interval for the indirect effect, based on 10,000 bootstraps, include zero. There is however evidence of a negative direct effect with psychological distress for motives of celebration and conformity, and a positive direct effect for motives of coping and social anxiety.

When controlling for age, gender, user group, and race/ethnicity, the negative direct effect between motives of marijuana use and psychological distress for motives of celebration and conformity remains as well as the positive direct effect for motives of coping and social anxiety. Gender was found to moderate the association between social anxiety motives of use and symptoms of depression when tested with and without control variables. The addition of the interaction term between the motive of social anxiety and gender explained a significant increase in variance for symptoms of depression ∆R2 = 0.012, p < 0.05 for the model without control variables, and ∆R2 = 0.014, p < 0.05 for the model with control variables. The interaction was probed by testing the conditional effect of the social anxiety motive of use on symptoms of depression for both men and women. For women, but not men, the motive of social anxiety was significantly associated with more symptoms of depression . Furthermore, the slope of the interaction term indicates that women scored higher on symptoms of depression than men at the average level of the social anxiety motive. When analyzed with and without control variables, gender was found to moderate the associations for the motives of experimentation and availability with symptoms of anxiety. The addition of the interaction term between the motive of experimentation and gender explained a significant increase in variance for symptoms of anxiety: ∆R 2 = 0.012, p < 0.05. The addition of the interaction term between the motive of availability and gender explained a significant increase in variance for symptoms of anxiety: ∆R2 = 0.01, p < 0.05. Probing of the interactions, for both motives of experimentation and availability, however yielded no significant conditional effect for neither men or women. Conditional effects for motives of experimentation are as follows: This could therefore indicate a crossover interaction where there is no overall effect of either motives of use or gender on symptoms of anxiety. In both cases, the effect of gender on symptoms of anxiety is opposite, depending on the value of motives of use.

Although gender was initially found to moderate the association between motives of boredom and symptoms of anxiety, the interaction was no longer significant following the addition of control variables. When analyzed with and without control variables, gender was found to moderate the association for the motive of social anxiety with overall psychological distress. The addition of the interaction term explained a significant increase in variance for psychological distress ∆R2 = 0.010, p < 0.05. The interaction was probed by testing the conditional effect of social anxiety for both men and women. For women, but not men, the motive of social anxiety was significantly associated to overall psychological distress . Furthermore, the slope of the interaction term indicates that women score higher on psychological distress than men at the average level of social anxiety motive. Although gender was initially found to moderate the association between motives of boredom and psychological distress, and motives of availability with psychological distress, these interactions were no longer significant following the addition of control variables. The purpose of this dissertation was to determine the associations between motives of marijuana use and symptoms of depression, symptoms of anxiety, and overall psychological distress in a sample of young adults who use marijuana for medical and/or recreational reasons. Furthermore, I sought to establish whether these associations differ by gender. As marijuana use is common and on the rise amongst young adults , and as young adulthood is a period of increased mental health vulnerabilities , it is urgent to disentangle the potential effects of marijuana use on the mental health of young adults, particularly because mental health in young adulthood is the strongest predictor of mental health in adulthood .The work presented in this dissertation advances our understanding of motives of marijuana use as well as the associations between motives of marijuana use and symptoms of depression, symptoms of anxiety, vertical grow rack and overall psychological distress in young adults who use marijuana for medical and/or recreational reasons. The purpose of the first aim was to confirm the factor structure of the motives of marijuana use questionnaire used to study motives of marijuana use in young adults of Los Angeles who use marijuana for medical and/or recreational reasons. It was hypothesized that from the fifty-one-item questionnaire, seventeen motives of marijuana use would emerge. Twelve of these motives would replicate those found by Lee et al. in their study to develop and validate a comprehensive marijuana motive questionnaire. The other five motives to be confirmed would be the medical use motives drafted by the CHAYA team. Furthermore, it was hypothesized that there would be no gender differences in the factor structure of motives of marijuana use. The best fitting and most psychometrically sound factor structure for motives of marijuana use for this sample was the originally hypothesized seventeen factor structure composed of Lee et al.’s twelve motives and the five medical motives drafted by the CHAYA team.

The final twelve non-medical items are: boredom, availability, coping, conformity, experimentation, alcohol, celebration, altered perceptions, social anxiety, relative low risk, and sleep. The final five medical motives are: pain, nausea, substitution, natural remedy, and attention. Following and extending Cooper’s Motivational Model of Use , these motives can be conceptualized as motives promoting positive experiences, motives to avoid negative experiences, and medical use motives. Motives that promote positive experiences are motives of celebration, altered perceptions, experimentation, enjoyment, alcohol, relative low risk, and, availability. Motives for avoidance of negative experiences are motives of coping, conformity, sleep, boredom, and social anxiety. Medical motives are motives of attention, substitution, natural remedy, pain, and nausea. These seventeen motives proved to be consistently well fitting, stable over time, and gender invariant when tested using both wave 1 and 2 data. Although these findings need to be replicated using a random sample, the Amended Comprehensive Marijuana Motive Questionnaire, is the first to integrate both recreational and medical motives of use. Given the high rates of overlap between recreational and medical use , the validation of such an instrument, and its stability over time and across gender, will allow for a more accurate assessment of motives of marijuana use. To date, neither gender invariance for the motives from the Comprehensive Marijuana Motive Questionnaire nor endorsement of motives by gender had been examined. Interestingly, in this sample, except for the motives of experimentation and boredom, the reporting trend was higher for women compared to men. There were also significant differences in mean scores of reported motives of use between men and women for motives of attention, celebration, enjoyment, natural remedy, nausea, pain, sleep and social anxiety. This indicates that women endorse any given motive more strongly than men do. As discussed in Chapter 2, the gap in marijuana use prevalence between men and women is closing . Additionally, in line with gender socialization and changing gender norms, whereas marijuana use was considered acceptable for men but less so for women, it is now increasingly considered acceptable behavior for women . These changes in norms and behaviors may be starting to reflect in data collected. With that said, it is important to note that these preexisting differences between genders may be a confounding factor for causal inferences and reflect the unbalanced nature of our sample due to it being nonrandom rather than a true reflection of patterns within the population. The work presented in this dissertation also advances our understanding of the associations between motives of marijuana use and mental health outcomes in a sample of young adults who use marijuana heavily for medical and/or recreational reasons. It does so by: 1) replicating previous findings for the coping motive of use whereas the more an individual endorses coping motives of use, the poorer the associated outcomes are; 2) extending knowledge with regards to indirect effects of motives on mental health outcomes through frequency of use; and 3) establishing that some of the associations between motives of use and mental health outcomes vary by gender. The second and third aims of this dissertation were to investigate the associations between motives of marijuana use and symptoms of depression, symptoms of anxiety, and overall psychological distress, and to determine whether these associations varied by gender in a sample of young adults who use marijuana for medical and/or recreational reasons in a context of legalized medical marijuana.

As a central tenet of this model is the conceptualization that use behavior motivated by different needs constitutes phenomenologically distinct behaviors, and that these distinct use behaviors may be differently associated with mental health outcomes. Data will come from the Cannabis, Health and Young Adult Study , with a sample size of 366 comprised of young adults, in Los Angeles, who use marijuana for recreational and/or medical reasons. The first aim focuses on confirming and validating the instrument used to operationalize motives of marijuana use in young adults who use marijuana for recreational and/or medical reasons and to evaluate whether this factor structure varies by gender. The second aim investigates the associations between motives of marijuana use and symptoms of depression, symptoms of anxiety, and overall psychological distress for young adults in the CHAYA study. The third aim examines whether the associations between motives of marijuana use and symptoms of depression, symptoms of anxiety, and overall psychological distress differ by gender in this sample. The Literature Review is presented in chapter 2, followed by Methods in chapter 3. Chapters 4 and 5 cover the Results and Discussion, respectively. Finally, a Conclusion and Future Directions are presented in chapter 6. Young adulthood. Emerging or young adulthood, the period between 18 and 25 years of age, is a distinct developmental phase with unique tasks and expectations. It is characterized by pervasive changes in autonomy, residence, identity, social roles, and career pursuits . Successfully negotiating the transitions of young adulthood is associated with positive trajectories of mental health well being and allows for optimal development during adulthood . Emerging adulthood is a period that involves extensive and often concurrent contextual and social role changes, increased self-direction and opportunities for exploration flexibility . In young adulthood,planting drying rack symptoms of depression and symptoms of anxiety are the most common mental health concerns . Mental health. Poor mental health in early adulthood has been shown to be a strong individual predictor of persistent and recurrent mental health problems into adulthood . Mental health processes during these critical transitional years can however be positively influenced, given opportunities to do so .

Differently said, there are as many opportunities to disrupt and negatively influence mental health and the transition from young adulthood to adulthood as there are opportunities to positively impact mental health and promote a successful transition from young adulthood to adulthood. Depression1 . As one of the most common health disorders in the United States , depression is a leading cause of disability, diminished quality of life and heightened risk for physical health problems . Depression is a serious psychopathological disorder that can have a consequential economic drain on individuals, families, society, lead to long-term suffering, risk of suicide, occupational impairment, and interpersonal impairment in peer and family relationships . Depressive disorders are characterized “by pervasive mood disturbances that involve feelings of sadness and loss of interest or pleasure in most activities in conjunction with disturbances in sleep, appetite, concentration, libido and energy” . The chronicity of the disorder can remain burdensome for a significant period . Individuals between the ages of 15 and 24 experience the highest rates of depressive disorders in the United States . The incidence of depression increases in adolescence and peaks in young adulthood . Prevalence estimates place the rate for Major Depressive Disorders in young adults at 15.4% . Between 2013 and 2015, the 12-month prevalence of a Major Depressive Episode, a period characterized by low mood and depression symptoms, among young adults ages 18 to 25 rose from 8.7% to 10.3% . Furthermore, rates of Major Depressive Episodes are almost double for females compared to males ages 18 and over .Depressed mood, one of our outcomes of interest, is defined as a single symptom or group of symptoms that involve a dysphoric effect . Between 2013 and 2015, approximately 5% of the 18-24 age group reported experiencing two or more symptoms of depression in the past 30 days . Anxiety. Anxiety disorders are often comorbid with depression and substance use disorders, and are associated with fear, nervousness, apprehension, and panic, but may also involve the cardiovascular, respiratory, gastro or nervous system, individually or in combination . Anxiety disorders are subdivided into panic disorder, social phobia, posttraumatic stress disorders, obsessive compulsive disorders, and generalized anxiety disorders .

They tend to start early in life, and affect school and work performance as well as psychological functioning, and social relationships, and are persistent and chronic . Anxiety disorders are a leading cause of disability among all psychiatric disorders . Anxiety can be as disabling as chronic somatic disorders, and is associated with reduced productivity, absenteeism from school or work, suicide, increased likelihood of school dropout, marital instability, and poor career choices , all of which are crucial to successfully transition from young adulthood to adulthood. Young adulthood is a period of heightened risk for the onset of anxiety disorders . Past year rates of anxiety amongst 18 to 29-year-old were elevated at 30.2% in 2005 . Rates of anxiety amongst young adults are as worrisome with the lifetime prevalence of any anxiety disorder in the 18 to 29 age bracket being 30.2% in 2005 , compared to a lifetime prevalence of 28.8% in the total United States population . Furthermore, past year prevalence of any anxiety disorder was higher for females than for males . In addition to being a period marked by mental health vulnerabilities , young adulthood is also a period marked by increased drug use. Mental health vulnerabilities, such as those present in young adulthood, can be exacerbated by drug use, thus potentially hindering or delaying a successful transition to adulthood. Traditional risk factors associated with onset of marijuana use in adolescence and maintenance of use in young adulthood are being male, prior or concurrent alcohol and tobacco use, poor parental relationships, and peers who use marijuana . Marijuana use is associated with poor academic achievement, lower expectations for success, family problems, and other drug use . Marijuana use is also common among young adults and is on the rise. Rates of marijuana use by adults ages 18 to 29 have steadily risen from 10.5 percent to 21.1 percent since 2005 and 19.8 percent of 18 to 25-year-old report using marijuana in the past month . Furthermore, between 1990 and 2002, rates of marijuana disorders increased from 25% to 32% amongst 18 to 29 year olds . There are gender differences in rates of marijuana use by young adults with 23.4% of males ages 18-25 reporting past month use of marijuana, and 16.2% of females of the same age group reporting past month use. Past year use was 36.0% for males and 28.4% for females ages 18-25 in 2015 .

These prevalence rates suggest that marijuana use varies across gender and that there may be inherent differences in patterns of use and associated outcomes across groups. Thus far, research that has sought to disentangle the association between marijuana use and associated outcomes has largely been conducted in a context where marijuana use is illegal. As more states move forward with either the legalization of recreational or medical marijuana use, it is important to understand what the associations between motives of marijuana use and associated outcomes might be in such a context. Prior work has demonstrated key differences between states that have moved toward legalization compared to those who have not. For instance,hydroponic rack populations in states that have moved forward with legalization had higher rates of marijuana use to begin with and perceived marijuana use as not risky . Marijuana use has also been found to be higher in states that allow medical use . In these states, past month marijuana use as well as heavy marijuana use were higher than in states without legalized medical marijuana . Legalization of medical marijuana has also been associated with increases in reported marijuana use. Using Los Angeles County as an example, past year rates of marijuana use have increased for both men and women and across all racial and ethnic groups between 2005 and 2015 . Among those who reported marijuana use in Los Angeles County, adults between the ages of 18 and 29 are those that reported the highest rates of use compared to other age groups . Other work by Pacula et al. has demonstrated a significant overlap between medical and recreational use, even in states where recreational use was not legal. In a different study, with regards to reasons of use, 89.5% of adults who report marijuana use report doing so mainly for recreational purposes, 10.5% uniquely for medical purposes, and 36.1% reported a mixed use . In sum, it appears as though legalizing marijuana, whether only medical or both medical and recreational, has brought forth changes not only in the prevalence of use but also contributes to validating the perception of marijuana as a safe drug to use. Furthermore, for some individuals who use marijuana, there does not seem to be a clear divide between medical use and recreational use. There are three hypothesized ways in which marijuana and mental health are thought to be associated, and these may not be mutually exclusive. First, through a common risk factor such as family or individual characteristics .This suggests that the relationship between marijuana use and mental health is non-causal, and explained by overlapping psychosocial risk factors . Second, via early self-medication and subsequent association with a subculture that uses drugs . Here, early use to alleviate symptoms encourages later use which can have an impact on anticonventional behaviors, increase of delinquency, and personal difficulties . Third, marijuana use can bring about its own consequences by worsening mental health through direct effects on psychological and physiological functioning or related effects on interpersonal and role functioning .

This third point is reinforced by work that demonstrates clear and consistent associations and dose-response relations between the frequency of adolescent marijuana use and all adverse young adult outcomes, which included decreased odds of high school completion, and degree attainment, increased odds of marijuana use disorder or alcohol and other use disorders, and suicide attempts . Although there is increasing recognition that marijuana use could be associated to affect based psychological susceptibility , the evidence is inconclusive. Use of marijuana among young people has been inconsistently associated with co-morbid or concurrent mental health problems in cross sectional and longitudinal studies . Some studies have demonstrated that frequent marijuana use is associated with higher levels of anxiety . Other studies, have demonstrated that marijuana may not play a causal role in the development of anxiety , or that the associations between marijuana use and mental health outcomes disappear after adjusting for confounders . The directionality of the association between marijuana use and mental health outcomes also remains unclear. Although the anxiolytic effects of marijuana have been supported in cross sectional studies , longitudinal studies have demonstrated that frequent marijuana use preceded anxiety disorders , while in others anxiety disorders preceded use . Other longitudinal studies have also demonstrated no associations between marijuana and anxiety disorders . This illustrates the importance of choice and inclusion of confounders and intervening variables in the study of marijuana use and mental health. Depressive and anxious disorders are more common in women compare to men whereas substance use disorders are more common in men than women . Two possible explanations for these trends are gender socialization and the operationalization of mental health symptoms. Gender socialization is the process whereby both men and women learn of and conform to gender specific traits . Illustrative of that are previously demonstrated gender differences in responses to stressors whereas men are more likely to externalize distress and turn to substance use and women are more likely to internalize stress and exhibit more symptoms of depression and anxiety . Instruments used to operationalize mental health and symptoms of mental health rely heavily on women gendered symptoms. As a result, men may under report or misreport their mental health distress or status because the indicators or symptoms assessed are not reflective of their experiences. Work by Martin et al. has demonstrated that men who are depressed are more likely to endorse symptoms such as anger, self-destructive behavior, risk taking, and substance use over the more, traditionally women endorsed, symptoms of sadness, loss of interest, and hopelessness. In fact, in the same study by Martin et al. , there were no differences in prevalence rates between men and women when symptoms of depression were assessed using a scale that combined both men and women specific symptoms.

Regardless of the product, the supply of recombinant proteins is challenging during emergency situations due to the simultaneous requirements for rapid manufacturing and extremely high numbers of doses. The realities we must address include: the projected demand exceeds the entire manufacturing capacity of today’s pharmaceutical industry ; there is a shortage of delivery devices and the means to fill them; there is insufficient lyophilization capacity to produce dry powder for distribution; and distribution, including transportation and vaccination itself, will be problematic on such a large scale without radical changes in the public health systems of most countries. Vaccines developed by a given country will almost certainly be distributed within that country and to its allies/neighbors first and, thereafter, to countries willing to pay for priority. One solution to the product access challenge is to decentralize the production of countermeasures, and in fact one of the advantages of plant-based manufacturing is that it decouples developing countries from their reliance on the pharmaceutical infrastructure. Hence, local production facilities could be set up based on greenhouses linked to portable clean rooms housing disposable DSP equipment. In this scenario, the availability of multiple technology platforms, including plant-based production, can only be beneficial.Several approaches can be used to manage potential IP conflicts in public health emergencies that require the rapid production of urgently needed products. Licensing of key IP to ensure freedom to operate is preferred because such agreements are cooperative rather than competitive. Likewise, cooperative agreements to jointly develop products with mutually beneficial exit points offer another avenue for productive exploitation. These arrangements allow collaborating institutions to work toward a greater good. Licensing has been practiced in past emergencies when PMP products were developed and produced using technologies owned by multiple parties. In the authors’ experience,indoor growing trays the ZMapp cocktail was subject to IP ownership by multiple parties covering the compositions, the gene expression system, manufacturing process technology/knowhow, and product end-use.

Stakeholders included the Public Health Agency of Canada’s National Microbiology Laboratory, the United States Army Medical Research Institute of Infectious Diseases , Mapp Biopharmaceutical, Icon Genetics, and Kentucky Bio-processing, among others. Kentucky Bio-processing is also involved in a more recent collaboration to develop a SARS-CoV-2 vaccine candidate, aiming to produce 1–3 million doses of the antigen, with other stakeholders invited to take on the tasks of large scale antigen conjugation to the viral delivery vector, product fill, and clinical development.25 Collaboration and pooling of resources and know how among big pharma/biopharma companies raises concerns over antitrust violations, which could lead to price fixing and other unfair business practices. With assistance from the United States Department of Justice , this hurdle has been temporarily overcome by permitting several biopharma companies to share know how around manufacturing facilities and other information that could accelerate the manufacturing of COVID-19 mAb products.26 Genentech , Amgen, AstraZeneca, Eli Lilly, GlaxoSmithKline, and AbCellera Biologics will share information about manufacturing facilities, capacity, raw materials, and supplies in order to accelerate the production of mAbs even before the products gain regulatory approval. This is driven by the realization that none of these companies can satisfy more than a small fraction of projected demands by acting alone. Under the terms imposed by the DOJ, the companies are not allowed to exchange information about manufacturing cost of goods or sales prices of their drugs, and the duration of the collaboration is limited to the current pandemic. Yet another approach is a government-led strategy in which government bodies define a time-critical national security need that can only be addressed by sequestering critical technology controlled by the private sector. In the United States, for example, the Defense Production Act was first implemented in 1950 but has been reauthorized more than 50 times since then . Similar national security directives exist in Canada and the EU. In the United States, the Defense Production Act gives the executive branch substantial powers, allowing the president, largely through executive order, to direct private companies to prioritize orders from the federal government.

The president is also empowered to “allocate materials, services, and facilities” for national defense purposes. The Defense Production Act has been implemented during the COVID-19 crisis to accelerate manufacturing and the provision of medical devices and personal protective equipment, as well as drug intermediates. Therefore, a two-tiered mechanism exists to create FTO and secure critical supplies: the first and more preferable involving cooperative licensing/cross-licensing agreements and manufacturing alliances, and alternatively , a second mechanism involving legislative directives.Many companies have modified their production processes to manufacture urgently-required products in response to COVID- 19, including distillers and perfume makers switching to sanitizing gels, textiles companies making medical gowns and face masks, and electronics companies making respirators.27 Although this involves some challenges, such as production safety and quality requirements, it is far easier than the production of APIs, where the strict regulations discussed earlier in this article must be followed. The development of a mammalian cell line achieving titers in the 5 g L−1 range often takes 10–12 months or at least 5–6 months during a pandemic . These titers can often be achieved for mAbs due to the similar properties of different mAb products and the standardized DSP unit operations , but the titers of other biologics are often lower due to product toxicity or the need for bespoke purification strategies. Even if developmental obstacles are overcome, pharmaceutical companies may not be able to switch rapidly to new products because existing capacity is devoted to the manufacture of other important bio-pharmaceuticals. The capacity of mammalian cell culture facilities currently exceeds market demand by ~30% . Furthermore, contract manufacturing organizations , which can respond most quickly to a demand for new products due to their flexible business model, control only ~19% of that capacity. From our experience, this CMO capacity is often booked in advance for several months if not years, and little is available for short-term campaigns. Furthermore, even if capacity is available, the staff and consumables must be available too. Finally, there is a substantial imbalance in the global distribution of mammalian cell culture capacity, favoring North America and Europe. This concentration is risky from a global response perspective because these regions were the most severely affected during the early and middle stages of the COVID-19 pandemic, and it is, therefore, possible that this capacity would become unusable following the outbreak of a more destructive virus.

Patents covering several technologies related to transient expression in plants will end during or shortly after 2020, facilitating the broader commercial adoption of the technology. This could accelerate the development of new PMP products in a pandemic situation . However, PMP production capacity is currently limited. There are less than five large scale PMP facilities in operation, and we estimate that these facilities could manufacture ~2,200 kg of product per year, assuming a combined annual biomass output of ~1,100 tons as well as similar recombinant protein production and DSP losses as for mammalian cells. Therefore, plant-based production certainly does currently not meet the anticipated demand for pandemic countermeasures. We have estimated a global demand of 500–5,200 tons per year for mAbs, depending on the dose, but only ~259 tons per year can be produced by using the current global capacity provided by mammalian cell bioreactors and plant-based systems currently represent less than 1% of the global production capacity of mammalian cell bioreactors. Furthermore, the number of plant molecular farming companies decreased from 37 to 23 between 2005 and 2020, including many large industry players that would be most able to fund further technology development . Nevertheless, the current plant molecular farming landscape has three advantages in terms of a global first-line response compared to mammalian cells. First, almost two thirds of global production capacity is held by CMOs or hybrid companies ,mobile vertical grow racks which can make their facilities available for production campaigns on short notice, as shown by their rapid response to COVID-19 allowing most to produce initial product batches by March 2020. In contrast, only ~20% of fermentation facilities are operated by CMOs . Second, despite the small number of plant molecular farming facilities, they are distributed around the globe with sites in the United States, Canada, United Kingdom, Germany, Japan, Korea, and South Africa, with more planned or under construction in Brazil and China . Finally, transient expression in plants is much faster than any other eukaryotic system with a comparable production scale, moving from gene to product within 20 days and allowing the production of up to 7,000 kg biomass per batch with product accumulation of up to 2 g kg−1 . Even if the time required for protein production in mammalian cells can be reduced to 6 months as recently proposed , Medicago has shown that transient expression in plants can achieve the same goals in less than 3 months . Therefore, the production of vaccines, therapeutics, and diagnostics in plants has the potential to function as a first line of defense against pandemics. Given the limited number and size of plant molecular farming facilities, we believe that the substantial investments currently being allocated to the building of bio-pharmaceutical production capacity should be shared with PMP production sites, allowing this technology to be developed as another strategy to improve our response to future pandemics.In the past decade, the massive scale-up of insecticide treated bed nets and indoor residual spraying , together with the use of artemisinin-based combination treatments, have led to major changes in malaria epidemiology and vector biology. Overall malaria prevalence and incidence have been greatly reduced worldwide. But the reductions in malaria have not been achieved uniformly; some sites have experienced continued reductions in both clinical malaria and overall parasite prevalence, while other sites showed stability or resurgence in malaria despite high coverage of ITNs and IRS.

More importantly, extensive use of ITNs and IRS has created intensive selection pressures for malaria vector insecticide resistance as well as for potential outdoor transmission, which appears to be limiting the success of ITNs and IRS. For example, in Africa, where malaria is most prevalent and pyrethroid-impregnated ITNs have been used for more than a decade, there is ample evidence of the emergence and spread of pyrethroid resistance in Anopheles gambiae s.s., the major African malaria vector, as well as in An. arabiensis and An. funestus s.l.. Both the prevalence of An. gambiae s.s. resistance to pyrethroids and DDT and the frequency of knock-down resistance have reached alarming levels throughout Africa from 2010–2012. Unfortunately, pyrethroids are the only class of insecticides that the World Health Organization recommends for the treatment of ITNs . Furthermore, a number of recent studies have documented a shift in the biting behavior of An. gambiae s.s. and An. funestus, from biting exclusively indoors at night to biting both indoors and outdoors during early evening and morning hours when people are active but not protected by IRS or ITNs, or to biting indoors but resting outdoors. Apart from these intraspecific changes in biting behavior, shifts in vector species composition, i.e., from the previously predominant indoor-biting An. gambiae s.s. to the concurrently predominant species An. arabiensis, which prefers to bite and rest outdoors in some parts of Africa, can also increase outdoor transmission. Because IRS and ITNs have little impact on outdoor-resting and outdoor and early-biting vectors, outdoor transmission represents one of the most important challenges in malaria control. New interventions are urgently needed to augment current public health measures and reduce outdoor transmission. Larval control has historically been very successful and is widely used for mosquito control in many parts of the developed world, but is not commonly used in Africa. Field evaluation of anopheline mosquitoes in Africa found that larviciding was effective in killing anopheline larvae and reducing adult malaria vector abundance in various sites. Microbial larvicides are effective in controlling malaria vectors, and they can be used on a large scale in combination with ongoing ITN and IRS programs. However, conventional larvicide formulations are associated with high material and operational costs due to the need for frequent habitat re-treatment, i.e., weekly re-treatment, as well as logistical issues in the field. Recently, an improved slow-release larvicide formulation was field-tested for controlling Anopheles mosquitoes, yielding an effective duration of approximately 4 weeks.

Some tags may be approved in certain circumstances , but their immunogenicity may depend on the context of the fusion protein. The substantial toolkit available for rapid plant biomass processing and the adaptation of even large-scale plant-based production processes to new protein products ensure that plants can be used to respond to pandemic diseases with at least an equivalent development time and, in most cases, a much shorter one than conventional cell-based platforms. Although genetic vaccines for SARS-CoV-2 have been produced quickly , they have never been manufactured at the scale needed to address a pandemic and their stability during transport and deployment to developing world regions remains to be shown.Regulatory oversight is a major and time-consuming component of any drug development program, and regulatory agencies have needed to revise internal and external procedures in order to adapt normal schedules for the rapid decision-making necessary during emergency situations. Just as important as rapid methods to express, prototype, optimize, produce, and scale new products are the streamlining of regulatory procedures to maximize the technical advantages offered by the speed and flexibility of plants and other high-performance manufacturing systems. Guidelines issued by regulatory agencies for the development of new products, or the repurposing of existing products for new indications, include criteria for product manufacturing and characterization, containment and mitigation of environmental risks, stage-wise safety determination, clinical demonstration of safety and efficacy, and various mechanisms for product licensure or approval to deploy the products and achieve the desired public health benefit. Regardless of which manufacturing platform is employed, the complexity of product development requires that continuous scrutiny is applied from preclinical research to drug approval and post-market surveillance,cannabis vertical farming thus ensuring that the public does not incur an undue safety risk and that products ultimately reaching the market consistently conform to their label claims.

These goals are common to regulatory agencies worldwide, and higher convergence exists in regions that have adopted the harmonization of standards as defined by the International Council for Harmonization ,2 in key product areas including quality, safety, and efficacy.Both the United States and the EU have stringent pharmaceutical product quality and clinical development requirements, as well as regulatory mechanisms to ensure product quality and public safety. Differences and similarities between regional systems have been discussed elsewhere and are only summarized here. Stated simply, the United States, EU, and other jurisdictions follow generally a two-stage regulatory process, comprising clinical research authorization and monitoring and result’s review and marketing approval. The first stage involves the initiation of clinical research via submission of an Investigational New Drug application in the United States or its analogous Clinical Trial Application in Europe. At the preclinicalclinical translational interphase of product development, a sponsor must formally inform a regulatory agency of its intention to develop a new product and the methods and endpoints it will use to assess clinical safety and preliminary pharmacologic activity . Because the EU is a collective of independent Member States, the CTA can be submitted to a country-specific regulatory agency that will oversee development of the new product. The regulatory systems of the EU and the United States both allow pre-submission consultation on the proposed development programs via discussions with regulatory agencies or expert national bodies. These are known as pre-IND meetings in the United States and Investigational Medicinal Product Dossier 3 discussions in the EU. These meetings serve to guide the structure of the clinical programs and can substantially reduce the risk of regulatory delays as the programs begin. PIND meetings are common albeit not required, whereas IMPD discussions are often necessary prior to CTA submission.

At intermediate stages of clinical development , pauses for regulatory review must be added between clinical study phases. Such End of Phase review times may range from one to several months depending on the technology and disease indication. In advanced stages of product development after pivotal, placebo-controlled randomized Phase III studies are complete, drug approval requests that typically require extensive time for review and decision-making on the part of the regulatory agencies. In the United States, the Food and Drug Administration controls the centralized marketing approval/authorization/ licensing of a new product, a process that requires in-depth review and acceptance of a New Drug Application for chemical entities, or a Biologics License Application for biologics, the latter including PMP proteins. The Committee for Medicinal Products for Human Use , part of the European Medicines Agency , has responsibilities similar to those of the FDA and plays a key role in the provision of scientific advice, evaluation of medicines at the national level for conformance with harmonized positions across the EU, and the centralized approval of new products for market entry in all Member States.The statute-conformance review procedures practiced by the regulatory agencies require considerable time because the laws were established to focus on patient safety, product quality, verification of efficacy, and truth in labeling. The median times required by the FDA, EMA, and Health Canada for full review of NDA applications were reported to be 322, 366, and 352 days, respectively . Collectively, typical interactions with regulatory agencies will add more than 1 year to a drug development program. Although these regulatory timelines are the status quo during normal times, they are clearly incongruous with the needs for rapid review, approval, and deployment of new products in emergency use scenarios, such as emerging pandemics.

Plant-made intermediates, including reagents for diagnostics, antigens for vaccines, and bio-active proteins for prophylactic and therapeutic medical interventions, as well as the final products containing them, are subject to the same regulatory oversight and marketing approval pathways as other pharmaceutical products. However, the manufacturing environment as well as the peculiarities of the plant-made active pharmaceutical ingredient can affect the nature and extent of requirements for compliance with various statutes, which in turn will influence the speed of development and approval. In general, the more contained the manufacturing process and the higher the quality and safety of the API, the easier it has been to move products along the development pipeline. Guidance documents on quality requirements for plant-made biomedical products exist and have provided a framework for development and marketing approval . Upstream processes that use whole plants grown indoors under controlled conditions, including plant cell culture methods, followed by controlled and contained downstream purification, have fared best under regulatory scrutiny. This is especially true for processes that use non-food plants such as Nicotiana species as expression hosts. The backlash over the Prodigene incident of 2002 in the United States has refocused subsequent development efforts on contained environments . In the United States, field-based production is possible and even practiced, but such processes require additional permits and scrutiny by the United States Department of Agriculture . In May 2020, to encourage innovation and reduce the regulatory burden on the industry, the USDA’s Agricultural Plant Health Inspection Service revised legislation covering the interstate movement or release of genetically modified organisms into the environment in an effort to regulate such practices with higher precision [SECURE Rule revision of 7 Code of Federal Regulations 340].In contrast, the production of PMPs using GMOs or transient expression in the field comes under heavy regulatory scrutiny in the EU, and several statutes have been developed to minimize environmental, food, and public risk. Many of these regulations focus on the use of food species as hosts. The major perceived risks of open-field cultivation are the contamination of the food/feed chain,cannabis drying rack and gene transfer between GM and non-GM plants. This is true today even though containment and mitigation technologies have evolved substantially since those statutes were first conceived, with the advent and implementation of transient and selective expression methods; new plant breeding technologies; use of non-food species; and physical, spatial, and temporal confinement . The United States and the EU differ in their philosophy and practice for the regulation of PMP products. In the United States, regulatory scrutiny is at the product level, with less focus on how the product is manufactured. In the EU, much more focus is placed on assessing how well a manufacturing process conforms to existing statutes. Therefore, in the United States, PMP products and reagents are regulated under pre-existing sections of the United States CFR, principally under various parts of Title 21 , which also apply to conventionally sourced products. These include current good manufacturing practice covered by 21 CFR Parts 210 and 211, good laboratory practice toxicology , and a collection of good clinical practice requirements specified by the ICH and accepted by the FDA . In the United States, upstream plant cultivation in containment can be practiced using qualified methods to ensure consistency of vector, raw materials, and cultivation procedures and/or, depending on the product, under good agricultural and collection practices . For PMP products, cGMP requirements do not come into play until the biomass is disrupted in a fluid vehicle to create a process stream. All process operations from that point forward, from crude hydrolysate to bulk drug substance and final drug product, are guided by 21 CFR 210/211 .

In Europe, bio-pharmaceuticals regardless of manufacturing platform are regulated by the EMA, and the Medicines and Healthcare products Regulatory Agency in the United Kingdom. Pharmaceuticals from GM plants must adhere to the same regulations as all other biotechnology-derived drugs. These guidelines are largely specified by the European Commission in Directive 2001/83/EC and Regulation No 726/2004. However, upstream production in plants must also comply with additional statutes. Cultivation of GM plants in the field constitutes an environmental release and has been regulated by the EC under Directive 2001/18/EC and 1829/2003/EC if the crop can be used as food/feed . The production of PMPs using whole plants in greenhouses or cell cultures in bioreactors is regulated by the “Contained Use” Directive 2009/41/EC, which are far less stringent than an environmental release and do not necessitate a fully-fledged environmental risk assessment. Essentially, the manufacturing site is licensed for contained use and production proceeds in a similar manner as a conventional facility using microbial or mammalian cells as the production platform. With respect to GMP compliance, the major differentiator between the regulation of PMP products and the same or similar products manufactured using other platforms is the upstream production process. This is because many of the DSP techniques are product-dependent and, therefore, similar regardless of the platform, including most of the DSP equipment, with which regulatory agencies are already familiar. Of course, the APIs themselves must be fully characterized and shown to meet designated criteria in their specification, but this applies to all products regardless of source.During a health emergency, such as the COVID-19 pandemic, regulatory agencies worldwide have re-assessed guidelines and restructured their requirements to enable the accelerated review of clinical study proposals, to facilitate clinical studies of safety and efficacy, and to expedite the manufacturing and deployment of re-purposed approved drugs as well as novel products . These revised regulatory procedures could be implemented again in future emergency situations. It is also possible that some of the streamlined procedures that can expedite product development and regulatory review and approval will remain in place even in the absence of a health emergency, permanently eliminating certain redundancies and bureaucratic requirements. Changes in the United States and European regulatory processes are highlighted, with a cautionary note that these modified procedures are subject to constant review and revision to reflect an evolving public health situation.In the spring of 2020, the FDA established a special emergency program for candidate diagnostics, vaccines, and therapies for SARS-CoV-2 and COVID-19. The Coronavirus Treatment Acceleration Program 5 aims to utilize every available method to move new treatments to patients in need as quickly as possible, while simultaneously assessing the safety and efficacy of new modes of intervention. As of September 2020, CTAP was overseeing more than 300 active clinical trials for new treatments and was reviewing nearly 600 preclinical-stage programs for new medical interventions. Responding to pressure for procedural streamlining and rapid response, the FDA refocused staff priorities, modified its guidelines to fit emergency situations, and achieved a remarkable set of benchmarks . In comparison to the review and response timelines described in the previous section, the FDA’s emergency response structure within CTAP is exemplary and, as noted, these changes have successfully enabled the rapid evaluation of hundreds of new diagnostics and candidate vaccine and therapeutic products.