Absence of Evidence for Sustained Effects of Daily Cannabidiol Administration on Anandamide Plasma Concentration in Individuals with Cocaine Use Disorder: Exploratory Findings from a Randomized Controlled Trial.

Background: Cannabidiol (CBD) has been proposed to have a therapeutic potential over a wide range of neuropsychiatric disorders, including substance use disorders. Pre-clinical evidence suggests that CBD can increase anandamide (AEA) plasma concentration, possibly mediating some of its therapeutic properties. Whether CBD exerts such an effect on AEA in individuals with cocaine use disorder (CUD) remains unknown. Aims: To explore the sustained effects of daily CBD administration on AEA plasma concentrations compared with placebo in CUD. Methods: We used data from a randomized, double-blind, placebo-controlled trial evaluating CBD's efficacy in CUD. Seventy-eight individuals were randomized to receive a daily oral dose of 800 mg CBD (n = 40) or a placebo (n = 38). Participants stayed in an inpatient detoxification setting for 10 days, after which they were followed in an outpatient setting for 12 weeks. AEA plasma concentration was measured at baseline and at 23-h post CBD ingestion on day 8 and week 4. A generalized estimating equation model was used to assess CBD's effects on AEA, and sensitivity analyses were computed using Bayesian linear regressions. Results: Sixty-four participants were included in the analysis. Similar mean AEA plasma concentrations in both treatment groups (p = 0.357) were observed. At day 8, mean AEA plasma concentrations (± standard deviation) were 0.26 (± 0.07) ng/mL in the CBD group and 0.29 (± 0.08) ng/mL in the placebo group (p = 0.832; Bayes factor [BF] = 0.190). At week 4, they were 0.27 (± 0.09) ng/mL in the CBD group and 0.30 (± 0.09) ng/mL in the placebo group (p = 0.181; BF = 0.194). Conclusion: While not excluding any potential acute and short-term effect, daily CBD administration did not exert a sustained impact on AEA plasma concentrations in individuals with CUD compared with placebo. Registration: clinicaltrials.gov (NCT02559167).

Impact of benzodiazepine use on the risk of occupational accidents.

Benzodiazepines (BZDs) are drugs commonly used for treating insomnia and anxiety. Although they are known to induce cognitive and psychomotor impairments, their effect on the risk of causing accidents at work remains understudied. The objective of this study is to estimate this risk by differentiating between the recommended use and overuse of these drugs (i.e., uninterrupted use for four months). The data come from the French National Health Data System, which provide a population composed of French people who had at least one work accident (WA) from 2017 to 2019 (approximately 2.5 million people). A linear probability model with two-way fixed effects is used to deal with time-constant heterogeneity and the time effect independent of individuals. The results show a reduction in the risk of WA after a short period of BZD use (one month) compared with no use at all, but the risk of WA increases when treatment exceeds the recommended duration. The intensity of use results in a greater risk of WAs: a 1% increase in BZD use (expressed as the amount reimbursed) leads to a 4.4% (p<0.001) increase in the monthly risk of WAs. Moreover, we see an increase in risk in the month following the treatment discontinuation (+3.6%, p<0.001), which could be due to rebounding and catch-up effects. Health professionals and BZD users should be made aware of the WA risk induced by the use of BZDs, particularly after prolonged use and after discontinuation of treatment. This study provides more evidence for the need to limit the duration of BZD treatment.

R-tPA Resistance Is Specific for Platelet-Rich Stroke Thrombi and Can Be Overcome by Targeting Nonfibrin Components.

BACKGROUND: Resistance to r-tPA (recombinant tissue-type plasminogen activator) is a well-known but poorly understood phenomenon that hampers successful recanalization in patients with acute ischemic stroke. Using clinically relevant thrombi from patients with acute ischemic stroke, we investigated if and how thrombus composition impacts r-tPA-mediated lysis. In addition, we explored strategies to overcome r-tPA resistance. METHODS: Thrombi were split into 2 parts, 1 of which was used for thrombolysis and the other for detailed histological analysis. Thrombolysis was performed in normal human plasma using r-tPA alone, using r-tPA in combination with DNase-1 or using r-tPA in combination with N,N'-diacetyl-l-cystine. Thrombus lysis was calculated as the percentage of residual thrombus weight compared with its initial weight and the degree of lysis was linked to thrombus composition determined via histology. RESULTS: Interestingly, we found that the efficacy of r-tPA-mediated thrombolysis was strongly correlated with the composition of the thrombi. Thrombi containing high amounts of red blood cells and low amounts of DNA and von Willebrand Factor were efficiently degraded by r-tPA, whereas thrombi containing low amounts of red blood cells and higher amounts of DNA and von Willebrand Factor were resistant to r-tPA. Importantly, combination of r-tPA with DNase-1 or N,N'-diacetyl-l-cystine significantly and specifically improved the lysis of these r-tPA-resistant thrombi. CONCLUSIONS: Using patient thrombus material, our results for the first time show that the composition of stroke thrombi largely determines their susceptibility to r-tPA-mediated thrombolysis. Red blood cell-poor thrombi have a specific resistance to r-tPA, which can be overcome by targeting nonfibrin components using DNase-1 or N,N'-diacetyl-l-cystine.

Work accident effect on the use of psychotropic drugs: the case of benzodiazepines.

BACKGROUND: A work accident constitutes a shock to health, likely to alter mental states and affect the use of psychotropic drugs. We focus on the use of benzodiazepines, which are a class of drugs commonly used to treat anxiety and insomnia. Prolonged use can lead to dependence. Our objective is to determine the extent to which work accidents lead to benzodiazepine use and overuse (i.e. exceedance of medical guidelines). METHOD: We use a two-step selection model (the Heckman method) based on data from the French National Health Data System (Système National des Données de Santé, SNDS). Our study sample includes all general plan members who experienced a single work accident in 2016 (and not since 2007). This sample includes 350,000 individuals in the work accident group and more than 1.1 million people randomly drawn from the population without work accidents from 2007 to 2017 (the non-work accident group). RESULTS: The occurrence of a work accident leads to an increase in benzodiazepine use and overuse the following year. The selection model shows a clear influence of the accident on the use probability (+ 39%), but a very slight impact on the risk of overuse among users (+ 1.7%), once considered the selection effect. The effect on overuse risk is higher for more severe accidents and among women. CONCLUSION: The increase in the risk of benzodiazepine overuse is due to an increase in the likelihood of using benzodiazepines after a work accident that leads to overuse, rather than an increase in likelihood of overuse among people who use benzodiazepines. Results call for targeting the first-time prescription to limit the risk of overuse after a work accident.

In Vitro Resistance of Natural Molars vs. Additive-Manufactured Simulators Treated with Pulpotomy and Endocrown.

Endocrowns are designed to restore endodontically treated teeth with root canal treatment (Rct). Recently, endocrowns were proposed for teeth treated with full pulpotomy (FP). No data exist on in vitro evaluations for this combination. This study aimed to evaluate the mechanical behavior of pulpotomy-treated teeth with endocrowns according to different protocols for preparation design and materials and to assess whether 3D-printed resin simulators could be a reliable alternative for human teeth during in vitro strength tests. One hundred and ten extracted natural molars were randomized into 11 groups according to the type of endodontic treatment, the material used, and the design of peripheric preparation. One hundred and ten resin simulators were separated similarly. The samples were embedded in epoxy resin blocks before being subjected to oblique compressive load until failure. For natural teeth, the variance analysis separated two homogeneous groups, one regrouping the endodontically treated or pulpotomy-treated teeth without coronal restoration and the other one regrouping all the other samples, i.e., the untreated teeth (positive controls) and the treated and restored teeth. The strength resistance was lower for the resin simulators than for natural teeth in all groups. Within the limit of this study, strength resistance is not the most important criterion for choosing the type of material, preparation, or endodontic treatment for endocrowns. Resin simulators are not efficient for in vitro strength studies.

A Quantitative Theory for Genomic Offset Statistics.

Genomic offset statistics predict the maladaptation of populations to rapid habitat alteration based on association of genotypes with environmental variation. Despite substantial evidence for empirical validity, genomic offset statistics have well-identified limitations, and lack a theory that would facilitate interpretations of predicted values. Here, we clarified the theoretical relationships between genomic offset statistics and unobserved fitness traits controlled by environmentally selected loci and proposed a geometric measure to predict fitness after rapid change in local environment. The predictions of our theory were verified in computer simulations and in empirical data on African pearl millet (Cenchrus americanus) obtained from a common garden experiment. Our results proposed a unified perspective on genomic offset statistics and provided a theoretical foundation necessary when considering their potential application in conservation management in the face of environmental change.

Identifying ex vivo acute ischemic stroke thrombus composition using electrochemical impedance spectroscopy.

BACKGROUND: Intra-procedural characterization of stroke thromboemboli might guide mechanical thrombectomy (MT) device choice to improve recanalization rates. Electrochemical impedance spectroscopy (EIS) has been used to characterize various biological tissues in real time but has not been used in thrombus. OBJECTIVE: To perform a feasibility study of EIS analysis of thrombi retrieved by MT to evaluate: (1) the ability of EIS and machine learning to predict red blood cell (RBC) percentage content of thrombi and (2) to classify the thrombi as "RBC-rich" or "RBC-poor" based on a range of cutoff values of RBC. METHODS: ClotbasePilot was a multicentric, international, prospective feasibility study. Retrieved thrombi underwent histological analysis to identify proportions of RBC and other components. EIS results were analyzed with machine learning. Linear regression was used to evaluate the correlation between the histology and EIS. Sensitivity and specificity of the model to classify the thrombus as RBC-rich or RBC-poor were also evaluated. RESULTS: Among 514 MT,179 thrombi were included for EIS and histological analysis. The mean composition in RBC of the thrombi was 36% ± 24. Good correlation between the impedance-based prediction and histology was achieved (slope of 0.9, R2 = 0.53, Pearson coefficient = 0.72). Depending on the chosen cutoff, ranging from 20 to 60% of RBC, the calculated sensitivity for classification of thrombi ranged from 77 to 85% and the specificity from 72 to 88%. CONCLUSION: Combination of EIS and machine learning can reliably predict the RBC composition of retrieved ex vivo AIS thrombi and then classify them into groups according to their RBC composition with good sensitivity and specificity.

High-Dimensional Mediation Analysis: A New Method Applied to Maternal Smoking, Placental DNA Methylation, and Birth Outcomes.

BACKGROUND: High-dimensional mediation analysis is an extension of unidimensional mediation analysis that includes multiple mediators, and increasingly it is being used to evaluate the indirect omics-layer effects of environmental exposures on health outcomes. Analyses involving high-dimensional mediators raise several statistical issues. Although many methods have recently been developed, no consensus has been reached about the optimal combination of approaches to high-dimensional mediation analyses. OBJECTIVES: We developed and validated a method for high-dimensional mediation analysis (HDMAX2) and applied it to evaluate the causal role of placental DNA methylation in the pathway between exposure to maternal smoking (MS) during pregnancy and gestational age (GA) and birth weight of the baby at birth. METHODS: HDMAX2 combines latent factor regression models for epigenome-wide association studies with max2 tests for mediation and considers CpGs and aggregated mediator regions (AMRs). HDMAX2 was carefully evaluated using simulated data and compared to state-of-the-art multidimensional epigenetic mediation methods. Then, HDMAX2 was applied to data from 470 women of the Etude des Déterminants pré et postnatals du développement de la santé de l'Enfant (EDEN) cohort. RESULTS: HDMAX2 demonstrated increased power in comparison with state-of-the-art multidimensional mediation methods and identified several AMRs not identified in previous mediation analyses of exposure to MS on birth weight and GA. The results provided evidence for a polygenic architecture of the mediation pathway with a posterior estimate of the overall indirect effect of CpGs and AMRs equal to 44.5g lower birth weight representing 32.1% of the total effect [standard deviation (SD)=60.7g]. HDMAX2 also identified AMRs having simultaneous effects both on GA and on birth weight. Among the top hits of both GA and birth weight analyses, regions located in COASY, BLCAP, and ESRP2 also mediated the relationship between GA and birth weight, suggesting reverse causality in the relationship between GA and the methylome. DISCUSSION: HDMAX2 outperformed existing approaches and revealed an unsuspected complexity of the potential causal relationships between exposure to MS and birth weight at the epigenome-wide level. HDMAX2 is applicable to a wide range of tissues and omic layers. https://doi.org/10.1289/EHP11559.

Differential effect of cannabis use on opioid agonist treatment outcomes: Exploratory analyses from the OPTIMA study.

INTRODUCTION: Conflictual evidence exists regarding the effects of cannabis use on the outcomes of opioid agonist therapy (OAT). In this exploratory analysis, we examined the effect of recent cannabis use on opioid use, craving, and withdrawal symptoms, in individuals participating in a trial comparing flexible buprenorphine/naloxone (BUP/NX) take-home dosing model to witnessed ingestion of methadone. METHODS: We analyzed data from a multi-centric, pragmatic, 24-week, open label, randomized controlled trial in individuals with prescription-type opioid use disorder (n = 272), randomly assigned to BUP/NX (n = 138) or methadone (n = 134). The study measured last week cannabis and opioid use via timeline-follow back, recorded at baseline and every two weeks during the study. Craving symptoms were measured using the Brief Substance Craving Scale at baseline, and weeks 2, 6, 10, 14, 18 and 22. The study measured opioid withdrawal symptoms via Clinical Opiate Withdrawal Scale at treatment initiation and weeks 2, 4, and 6. RESULTS: The mean maximum dose taken during the study was 17.3 mg/day (range = 0.5-32 mg/day) for BUP/NX group and 67.7 mg/day (range = 10-170 mg/day) in the methadone group. Repeated measures generalized linear mixed models demonstrated that cannabis use in the last week (mean of 2.3 days) was not significantly associated with last week opioid use (aß ± standard error (SE) = -0.06 ± 0.04; p = 0.15), craving (aß ± SE = -0.05 ± 0.08, p = 0.49), or withdrawal symptoms (aß ± SE = 0.09 ± 0.1, p = 0.36). Bayes factor (BF) for each of the tested models supported the null hypothesis (BF < 0.3). CONCLUSIONS: The current study did not demonstrate a statistically significant effect of cannabis use on outcomes of interest in the context of a pragmatic randomized-controlled trial. These findings replicated previous results reporting no effect of cannabis use on opioid-related outcomes.

Theoretical analysis of principal components in an umbrella model of intraspecific evolution.

Principal component analysis (PCA) is one of the most frequently-used approach to describe population structure from multilocus genotype data. Regarding geographic range expansions of modern humans, interpretations of PCA have, however, been questioned, as there is uncertainty about the wave-like patterns that have been observed in principal components. It has indeed been argued that wave-like patterns are mathematical artifacts that arise generally when PCA is applied to data in which genetic differentiation increases with geographic distance. Here, we present an alternative theory for the observation of wave-like patterns in PCA. We study a coalescent model - the umbrella model - for the diffusion of genetic variants. The model is based on genetic drift without any particular geographical structure. In the umbrella model, splits from an ancestral population occur almost continuously in time, giving birth to small daughter populations at a regular pace. Our results provide detailed mathematical descriptions of eigenvalues and eigenvectors for the PCA of sampled genomic sequences under the model. When variants uniquely represented in the sample are removed, the PCA eigenvectors are defined as cosine functions of increasing periodicity, reproducing wave-like patterns observed in equilibrium isolation-by-distance models. Including singleton variants in the analysis, the eigenvectors corresponding to the largest eigenvalues exhibit complex wave shapes. The accuracy of our predictions is further investigated with coalescent simulations. Our analysis supports the hypothesis that highly structured wave-like patterns could arise from genetic drift only, and may not always be artificial outcomes of spatially structured data. Genomic data related to the peopling of the Americas are reanalyzed in the light of our new theory.

Museum Genomics of an Agricultural Super-Pest, the Colorado Potato Beetle, Leptinotarsa decemlineata (Chrysomelidae), Provides Evidence of Adaptation from Standing Variation.

Despite extensive research on agricultural pests, our knowledge about their evolutionary history is often limited. A mechanistic understanding of the demographic changes and modes of adaptation remains an important goal, as it improves our understanding of organismal responses to environmental change and our ability to sustainably manage pest populations. Emerging genomic datasets now allow for characterization of demographic and adaptive processes, but face limits when they are drawn from contemporary samples, especially in the context of strong demographic change, repeated selection, or adaptation involving modest shifts in allele frequency at many loci. Temporal sampling, however, can improve our ability to reconstruct evolutionary events. Here, we leverage museum samples to examine whether population genomic diversity and structure has changed over time, and to identify genomic regions that appear to be under selection. We focus on the Colorado potato beetle (CPB), Leptinotarsa decemlineata (Say 1824; Coleoptera: Chrysomelidae), which is widely regarded as a super-pest due to its rapid, and repeated, evolution to insecticides. By combining whole genome resequencing data from 78 museum samples with modern sampling, we demonstrate that CPB expanded rapidly in the 19th century, leading to a reduction in diversity and limited genetic structure from the Midwest to Northeast United States. Temporal genome scans provide extensive evidence for selection acting in resistant field populations in Wisconsin and New York, including numerous known insecticide resistance genes. We also validate these results by showing that known selective sweeps in modern populations are identified by our genome scan. Perhaps most importantly, temporal analysis indicates selection on standing genetic variation, as we find evidence for parallel evolution in the two geographical regions. Parallel evolution involves a range of phenotypic traits not previously identified as under selection in CPB, such as reproductive and morphological functional pathways that might be important for adaptation to agricultural habitats.

Sparse latent factor regression models for genome-wide and epigenome-wide association studies.

Association of phenotypes or exposures with genomic and epigenomic data faces important statistical challenges. One of these challenges is to account for variation due to unobserved confounding factors, such as individual ancestry or cell-type composition in tissues. This issue can be addressed with penalized latent factor regression models, where penalties are introduced to cope with high dimension in the data. If a relatively small proportion of genomic or epigenomic markers correlate with the variable of interest, sparsity penalties may help to capture the relevant associations, but the improvement over non-sparse approaches has not been fully evaluated yet. Here, we present least-squares algorithms that jointly estimate effect sizes and confounding factors in sparse latent factor regression models. In simulated data, sparse latent factor regression models generally achieved higher statistical performance than other sparse methods, including the least absolute shrinkage and selection operator and a Bayesian sparse linear mixed model. In generative model simulations, statistical performance was slightly lower (while being comparable) to non-sparse methods, but in simulations based on empirical data, sparse latent factor regression models were more robust to departure from the model than the non-sparse approaches. We applied sparse latent factor regression models to a genome-wide association study of a flowering trait for the plant Arabidopsis thaliana and to an epigenome-wide association study of smoking status in pregnant women. For both applications, sparse latent factor regression models facilitated the estimation of non-null effect sizes while overcoming multiple testing issues. The results were not only consistent with previous discoveries, but they also pinpointed new genes with functional annotations relevant to each application.

Adaptive potential of Coffea canephora from Uganda in response to climate change.

Understanding vulnerabilities of plant populations to climate change could help preserve their biodiversity and reveal new elite parents for future breeding programmes. To this end, landscape genomics is a useful approach for assessing putative adaptations to future climatic conditions, especially in long-lived species such as trees. We conducted a population genomics study of 207 Coffea canephora trees from seven forests along different climate gradients in Uganda. For this, we sequenced 323 candidate genes involved in key metabolic and defence pathways in coffee. Seventy-one single nucleotide polymorphisms (SNPs) were found to be significantly associated with bioclimatic variables, and were thereby considered as putatively adaptive loci. These SNPs were linked to key candidate genes, including transcription factors, like DREB-like and MYB family genes controlling plant responses to abiotic stresses, as well as other genes of organoleptic interest, such as the DXMT gene involved in caffeine biosynthesis and a putative pest repellent. These climate-associated genetic markers were used to compute genetic offsets, predicting population responses to future climatic conditions based on local climate change forecasts. Using these measures of maladaptation to future conditions, substantial levels of genetic differentiation between present and future diversity were estimated for all populations and scenarios considered. The populations from the forests Zoka and Budongo, in the northernmost zone of Uganda, appeared to have the lowest genetic offsets under all predicted climate change patterns, while populations from Kalangala and Mabira, in the Lake Victoria region, exhibited the highest genetic offsets. The potential of these findings in terms of ex situ conservation strategies are discussed.

The TRACTOR Project: TRACking and MoniToring Occupational Risks in Agriculture Using French Insurance Health Data (MSA).

OBJECTIVES: A vast data mining project called 'TRACking and moniToring Occupational Risks in agriculture' (TRACTOR) was initiated in 2017 to investigate work-related health events among the entire French agricultural workforce. The goal of this work is to present the TRACTOR project, the challenges faced during its implementation, to discuss its strengths and limitations and to address its potential impact for health surveillance. METHODS: Three routinely collected administrative health databases from the National Health Insurance Fund for Agricultural Workers and Farmers (MSA) were made available for the TRACTOR project. Data management was required to properly clean and prepare the data before linking together all available databases. RESULTS: After removing few missing and aberrant data (4.6% values), all available databases were fully linked together. The TRACTOR project is an exhaustive database of agricultural workforce (active and retired) from 2002 to 2016, with around 10.5 million individuals including seasonal workers and farm managers. From 2012 to 2016, a total of 6 906 290 individuals were recorded. Half of these individuals were active and 46% had at least one health event (e.g. declared chronic disease, reimbursed drug prescription) during this 5-year period. CONCLUSIONS: The assembled MSA databases available in the TRACTOR project are regularly updated and represent a promising and unprecedent dataset for data mining analysis dedicated to the early identification of current and emerging work-related illnesses and hypothesis generation. As a result, this project could help building a prospective integrated health surveillance system for the benefit of agricultural workers.

Potential Benefit of Add-on Δ9-Tetrahydrocannabinol in Pediatric Drug-Resistant Epilepsy: A Case Series.

We present five cases of pediatric drug-resistant epilepsy (DRE) that failed management using high cannabidiol (CBD) doses, but had significant reduction in seizure frequency with reintroduction or increasing doses of tetrahydrocannabinol (THC). There is growing evidence supporting the use of whole-plant CBD-rich extracts (containing THC and other cannabinoids) in the treatment of pediatric DRE. Based on our experiences and reports in the literature, we propose that, in patients who fail management with an initial trial of high-dose CBD-focused therapy, there may be a role for add-on THC-focused formulations.

Genome-Scale Data Reveal Deep Lineage Divergence and a Complex Demographic History in the Texas Horned Lizard (Phrynosoma cornutum) throughout the Southwestern and Central United States.

The southwestern and central United States serve as an ideal region to test alternative hypotheses regarding biotic diversification. Genomic data can now be combined with sophisticated computational models to quantify the impacts of paleoclimate change, geographic features, and habitat heterogeneity on spatial patterns of genetic diversity. In this study, we combine thousands of genotyping-by-sequencing (GBS) loci with mtDNA sequences (ND1) from the Texas horned lizard (Phrynosoma cornutum) to quantify relative support for different catalysts of diversification. Phylogenetic and clustering analyses of the GBS data indicate support for at least three primary populations. The spatial distribution of populations appears concordant with habitat type, with desert populations in AZ and NM showing the largest genetic divergence from the remaining populations. The mtDNA data also support a divergent desert population, but other relationships differ and suggest mtDNA introgression. Genotype-environment association with bioclimatic variables supports divergence along precipitation gradients more than along temperature gradients. Demographic analyses support a complex history, with introgression and gene flow playing an important role during diversification. Bayesian multispecies coalescent analyses with introgression (MSci) analyses also suggest that gene flow occurred between populations. Paleo-species distribution models support two southern refugia that geographically correspond to contemporary lineages. We find that divergence times are underestimated and population sizes are overestimated when introgression occurred and is ignored in coalescent analyses, and furthermore, inference of ancient introgression events and demographic history is sensitive to inclusion of a single recently admixed sample. Our analyses cannot refute the riverine barrier or glacial refugia hypotheses. Results also suggest that populations are continuing to diverge along habitat gradients. Finally, the strong evidence of admixture, gene flow, and mtDNA introgression among populations suggests that P. cornutum should be considered a single widespread species under the General Lineage Species Concept.

Studying Stroke Thrombus Composition After Thrombectomy: What Can We Learn?

The composition of ischemic stroke thrombi has gained an increasing amount of interest in recent years. The implementation of endovascular procedures in standard stroke care has granted researchers the unique opportunity to examine patient thrombus material. Increasing evidence indicates that stroke thrombi are complex and heterogenous, consisting of various biochemical (eg, fibrin, von Willebrand Factor, and neutrophil extracellular traps) and cellular (eg, red blood cells, platelets, leukocytes, and bacteria) components. This complex composition may explain therapeutic limitations and also offer novel insights in several aspects of stroke management. Better understanding of thrombus characteristics could, therefore, potentially lead to improvements in the management of patients with stroke. In this review, we provide a comprehensive overview of the lessons learned by examining stroke thrombus composition after endovascular thrombectomy and its potential relevance for thrombectomy success rates, thrombolysis, clinical outcomes, stroke etiology, and radiological imaging.

Genome-Wide Characterization of the MLO Gene Family in Cannabis sativa Reveals Two Genes as Strong Candidates for Powdery Mildew Susceptibility.

Cannabis sativa is increasingly being grown around the world for medicinal, industrial, and recreational purposes. As in all cultivated plants, cannabis is exposed to a wide range of pathogens, including powdery mildew (PM). This fungal disease stresses cannabis plants and reduces flower bud quality, resulting in significant economic losses for licensed producers. The Mildew Locus O (MLO) gene family encodes plant-specific proteins distributed among conserved clades, of which clades IV and V are known to be involved in susceptibility to PM in monocots and dicots, respectively. In several studies, the inactivation of those genes resulted in durable resistance to the disease. In this study, we identified and characterized the MLO gene family members in five different cannabis genomes. Fifteen Cannabis sativa MLO (CsMLO) genes were manually curated in cannabis, with numbers varying between 14, 17, 19, 18, and 18 for CBDRx, Jamaican Lion female, Jamaican Lion male, Purple Kush, and Finola, respectively (when considering paralogs and incomplete genes). Further analysis of the CsMLO genes and their deduced protein sequences revealed that many characteristics of the gene family, such as the presence of seven transmembrane domains, the MLO functional domain, and particular amino acid positions, were present and well conserved. Phylogenetic analysis of the MLO protein sequences from all five cannabis genomes and other plant species indicated seven distinct clades (I through VII), as reported in other crops. Expression analysis revealed that the CsMLOs from clade V, CsMLO1 and CsMLO4, were significantly upregulated following Golovinomyces ambrosiae infection, providing preliminary evidence that they could be involved in PM susceptibility. Finally, the examination of variation within CsMLO1 and CsMLO4 in 32 cannabis cultivars revealed several amino acid changes, which could affect their function. Altogether, cannabis MLO genes were identified and characterized, among which candidates potentially involved in PM susceptibility were noted. The results of this study will lay the foundation for further investigations, such as the functional characterization of clade V MLOs as well as the potential impact of the amino acid changes reported. Those will be useful for breeding purposes in order to develop resistant cultivars.

A spectral theory for Wright's inbreeding coefficients and related quantities.

Wright's inbreeding coefficient, FST, is a fundamental measure in population genetics. Assuming a predefined population subdivision, this statistic is classically used to evaluate population structure at a given genomic locus. With large numbers of loci, unsupervised approaches such as principal component analysis (PCA) have, however, become prominent in recent analyses of population structure. In this study, we describe the relationships between Wright's inbreeding coefficients and PCA for a model of K discrete populations. Our theory provides an equivalent definition of FST based on the decomposition of the genotype matrix into between and within-population matrices. The average value of Wright's FST over all loci included in the genotype matrix can be obtained from the PCA of the between-population matrix. Assuming that a separation condition is fulfilled and for reasonably large data sets, this value of FST approximates the proportion of genetic variation explained by the first (K - 1) principal components accurately. The new definition of FST is useful for computing inbreeding coefficients from surrogate genotypes, for example, obtained after correction of experimental artifacts or after removing adaptive genetic variation associated with environmental variables. The relationships between inbreeding coefficients and the spectrum of the genotype matrix not only allow interpretations of PCA results in terms of population genetic concepts but extend those concepts to population genetic analyses accounting for temporal, geographical and environmental contexts.