Media Use and Its Associations With Paranoia in Schizophrenia and Bipolar Disorder: Ecological Momentary Assessment.

Background: Paranoia is a spectrum of fear-related experiences that spans diagnostic categories and is influenced by social and cognitive factors. The extent to which social media and other types of media use are associated with paranoia remains unclear. Objective: We aimed to examine associations between media use and paranoia at the within- and between-person levels. Methods: Participants were 409 individuals diagnosed with schizophrenia spectrum or bipolar disorder. Measures included sociodemographic and clinical characteristics at baseline, followed by ecological momentary assessments (EMAs) collected 3 times daily over 30 days. EMA evaluated paranoia and 5 types of media use: social media, television, music, reading or writing, and other internet or computer use. Generalized linear mixed models were used to examine paranoia as a function of each type of media use and vice versa at the within- and between-person levels. Results: Of the 409 participants, the following subgroups reported at least 1 instance of media use: 261 (63.8%) for using social media, 385 (94.1%) for watching TV, 292 (71.4%) for listening to music, 191 (46.7%) for reading or writing, and 280 (68.5%) for other internet or computer use. Gender, ethnoracial groups, educational attainment, and diagnosis of schizophrenia versus bipolar disorder were differentially associated with the likelihood of media use. There was a within-person association between social media use and paranoia: using social media was associated with a subsequent decrease of 5.5% (fold-change 0.945, 95% CI 0.904-0.987) in paranoia. The reverse association, from paranoia to subsequent changes in social media use, was not statistically significant. Other types of media use were not significantly associated with paranoia. Conclusions: This study shows that social media use was associated with a modest decrease in paranoia, perhaps reflecting the clinical benefits of social connection. However, structural disadvantage and individual factors may hamper the accessibility of media activities, and the mental health correlates of media use may further vary as a function of contents and contexts of use.

Potency Assay Variability Estimation in Practice.

During the drug development process, testing potency plays an important role in the quality assessment required for the manufacturing and marketing of biologics. Due to multiple operational and biological factors, higher variability is usually observed in bioassays compared with physicochemical methods. In this paper, we discuss different sources of bioassay variability and how this variability can be statistically estimated. In addition, we propose an algorithm to estimate the variability of reportable results associated with different numbers of runs and their corresponding OOS rates under a given specification. Numerical experiments are conducted on multiple assay formats to elucidate the empirical distribution of bioassay variability.

Predicting Longitudinal Cognitive Decline and Alzheimer's Conversion in Mild Cognitive Impairment Patients Based on Plasma Biomarkers.

The increasing burden of Alzheimer's disease (AD) emphasizes the need for effective diagnostic and therapeutic strategies. Despite available treatments targeting amyloid beta (Aß) plaques, disease-modifying therapies remain elusive. Early detection of mild cognitive impairment (MCI) patients at risk for AD conversion is crucial, especially with anti-Aß therapy. While plasma biomarkers hold promise in differentiating AD from MCI, evidence on predicting cognitive decline is lacking. This study's objectives were to evaluate whether plasma protein biomarkers could predict both cognitive decline in non-demented individuals and the conversion to AD in patients with MCI. This study was conducted as part of the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD), a prospective, community-based cohort. Participants were based on plasma biomarker availability and clinical diagnosis at baseline. The study included MCI (n = 50), MCI-to-AD (n = 21), and cognitively unimpaired (CU, n = 40) participants. Baseline plasma concentrations of six proteins-total tau (tTau), phosphorylated tau at residue 181 (pTau181), amyloid beta 42 (Aß42), amyloid beta 40 (Aß40), neurofilament light chain (NFL), and glial fibrillary acidic protein (GFAP)-along with three derivative ratios (pTau181/tTau, Aß42/Aß40, pTau181/Aß42) were analyzed to predict cognitive decline over a six-year follow-up period. Baseline protein biomarkers were stratified into tertiles (low, intermediate, and high) and analyzed using a linear mixed model (LMM) to predict longitudinal cognitive changes. In addition, Kaplan-Meier analysis was performed to discern whether protein biomarkers could predict AD conversion in the MCI subgroup. This prospective cohort study revealed that plasma NFL may predict longitudinal declines in Mini-Mental State Examination (MMSE) scores. In participants categorized as amyloid positive, the NFL biomarker demonstrated predictive performance for both MMSE and total scores of the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet (CERAD-TS) longitudinally. Additionally, as a baseline predictor, GFAP exhibited a significant association with cross-sectional cognitive impairment in the CERAD-TS measure, particularly in amyloid positive participants. Kaplan-Meier curve analysis indicated predictive performance of NFL, GFAP, tTau, and Aß42/Aß40 on MCI-to-AD conversion. This study suggests that plasma GFAP in non-demented participants may reflect baseline cross-sectional CERAD-TS scores, a measure of global cognitive function. Conversely, plasma NFL may predict longitudinal decline in MMSE and CERAD-TS scores in participants categorized as amyloid positive. Kaplan-Meier curve analysis suggests that NFL, GFAP, tTau, and Aß42/Aß40 are potentially robust predictors of future AD conversion.

Genome-wide association analysis of fleece traits in Northwest Xizang white cashmere goat.

Northwest Xizang White Cashmere Goat (NXWCG) is the first new breed of cashmere goat in the Xizang Autonomous Region. It has significant characteristics of extremely high fineness, gloss, and softness. Genome-wide association analysis is an effective biological method used to measure the consistency and correlation of genotype changes between two molecular markers in the genome. In addition, it can screen out the key genes affecting the complex traits of biological individuals. The aim of this study was to analyze the genetic mechanism of cashmere trait variation in NXWCG and to discover SNP locus and key genes closely related to traits such as superfine cashmere. Additionally, the key genes near the obtained significant SNPs were analyzed by gene function annotation and biological function mining. In this study, the phenotype data of the four traits (cashmere length, fiber length, cashmere diameter, and cashmere production) were collected. GGP_Goat_70K SNP chip was used for genotyping the ear tissue DNA of the experimental group. Subsequently, the association of phenotype data and genotype data was performed using Gemma-0.98.1 software. A linear mixed model was used for the association study. The results showed that four fleece traits were associated with 18 significant SNPs at the genome level and 232 SNPs at the chromosome level, through gene annotated from Capra hircus genome using assembly ARS1. A total of 107 candidate genes related to fleece traits were obtained. Combined with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, we can find that CLNS1A, CCSER1, RPS6KC1, PRLR, KCNRG, KCNK9, and CLYBL can be used as important candidate genes for fleece traits of NXWCG. We used Sanger sequencing and suitability chi-square test to further verify the significant loci and candidate genes screened by GWAS, and the results show that the base mutations loci on the five candidate genes, CCSER1 (snp12579, 34,449,796, A → G), RPS6KC1 (snp41503, 69,173,527, A → G), KCNRG (snp41082, 67,134,820, G → A), KCNK9 (14:78472665, 78,472,665, G → A), and CLYBL (12: 9705753, 9,705,753, C → T), significantly affect the fleece traits of NXWCG. The results provide a valuable basis for future research and contribute to a better understanding of the genetic structure variation of the goat.

Exploring predictors of substance use disorder treatment engagement with machine learning: The impact of social determinants of health in the therapeutic landscape.

BACKGROUND: Improved knowledge of factors that influence treatment engagement could help treatment providers and systems better engage patients. The present study used machine learning to explore associations between individual- and neighborhood-level factors, and SUD treatment engagement. METHODS: This was a secondary analysis of the Global Appraisal of Individual Needs (GAIN) dataset and United States Census Bureau data utilizing random forest machine learning and generalized linear mixed modelling. Our sample (N = 15,873) included all people entering SUD treatment at GAIN sites from 2006 to 2012. Predictors included an array of demographic, psychosocial, treatment-specific, and clinical measures, as well as environment-level measures for the neighborhood in which patients received treatment. RESULTS: Greater odds of treatment engagement were predicted by adolescent age and psychiatric comorbidity, and at the neighborhood-level, by low unemployment and high population density. Lower odds of treatment engagement were predicted by Black/African American race, and at the neighborhood-level by high rate of public assistance and high income inequality. Regardless of the degree of treatment engagement, individuals receiving treatment in areas with high unemployment, alcohol sale outlet concentration, and poverty had greater substance use and related problems at baseline. Although these differences reduced with treatment and over time, disparities remained. CONCLUSIONS: Neighborhood-level factors appear to play an important role in SUD treatment engagement. Regardless of whether individuals engage with treatment, greater loading on social determinants of health such as unemployment, alcohol sale outlet density, and poverty in the therapeutic landscape are associated with worse SUD treatment outcomes.

Serum-Derived Bovine Immunoglobulin Promotes Barrier Integrity and Lowers Inflammation for 24 Human Adults Ex Vivo.

Serum-derived bovine immunoglobulin (SBI) prevents translocation and inflammation via direct binding of microbial components. Recently, SBI also displayed potential benefits through gut microbiome modulation. To confirm and expand upon these preliminary findings, SBI digestion and colonic fermentation were investigated using the clinically predictive ex vivo SIFR® technology (for 24 human adults) that was, for the first time, combined with host cells (epithelial/immune (Caco-2/THP-1) cells). SBI (human equivalent dose (HED) = 2 and 5 g/day) and the reference prebiotic inulin (IN; HED = 2 g/day) significantly promoted gut barrier integrity and did so more profoundly than a dietary protein (DP), especially upon LPS-induced inflammation. SBI also specifically lowered inflammatory markers (TNF-α and CXCL10). SBI and IN both enhanced SCFA (acetate/propionate/butyrate) via specific gut microbes, while SBI specifically stimulated valerate/bCFA and indole-3-propionic acid (health-promoting tryptophan metabolite). Finally, owing to the high-powered cohort (n = 24), treatment effects could be stratified based on initial microbiota composition: IN exclusively stimulated (acetate/non-gas producing) Bifidobacteriaceae for subjects classifying as Bacteroides/Firmicutes-enterotype donors, coinciding with high acetate/low gas production and thus likely better tolerability of IN. Altogether, this study strongly suggests gut microbiome modulation as a mechanism by which SBI promotes health. Moreover, the SIFR® technology was shown to be a powerful tool to stratify treatment responses and support future personalized nutrition approaches.

A Statistical Analysis of the Impact of Gun Ownership on Mass Shootings in the USA Between 2013 and 2022.

Mass shootings (incidents with four or more people shot in a single event, not including the shooter) are becoming more frequent in the United States, posing a significant threat to public health and safety in the country. In the current study, we intended to analyze the impact of state-level prevalence of gun ownership on mass shootings-both the frequency and severity of these events. We applied the negative binomial generalized linear mixed model to investigate the association between gun ownership rate, as measured by a proxy (i.e., the proportion of suicides committed with firearms to total suicides), and population-adjusted rates of mass shooting incidents and fatalities at the state level from 2013 to 2022. Gun ownership was found to be significantly associated with the rate of mass shooting fatalities. Specifically, our model indicated that for every 1-SD increase-that is, for every 12.5% increase-in gun ownership, the rate of mass shooting fatalities increased by 34% (p value < 0.001). However, no significant association was found between gun ownership and rate of mass shooting incidents. These findings suggest that restricting gun ownership (and therefore reducing availability to guns) may not decrease the number of mass shooting events, but it may save lives when these events occur.

The impact of incident stroke on cognitive trajectories in later life.

BACKGROUND: Cognitive impairment is common after stroke, and a large proportion of stroke patients will develop dementia. However, there have been few large prospective studies which have assessed cognition both prior to and after stroke. This study aims to determine the extent to which incident stroke impacts different domains of cognitive function in a longitudinal cohort of older community-dwelling individuals. METHODS: 19,114 older individuals without cardiovascular disease or major cognitive impairment were recruited and followed over a maximum 11 years. Stroke included ischaemic and haemorrhagic stroke and was adjudicated by experts. Cognitive function was assessed regularly using Modified Mini-Mental State Examination (3MS), Hopkins Verbal Learning Test-Revised (HVLT-R), Symbol Digit Modalities Test (SDMT), and Controlled Oral Word Association Test (COWAT). Linear mixed models were used to investigate the change in cognition at the time of stroke and decline in cognitive trajectories following incident stroke. RESULTS: During a median follow-up period of 8.4 [IQR: 7.2, 9.6] years, 815 (4.3%) participants experienced a stroke. Over this time, there was a general decline observed in 3MS, HVLT-R delayed recall, and SDMT scores across participants. However, for individuals who experienced a stroke, there was a significantly greater decline across all cognitive domains immediately after the event immediately after the event (3MS: -1.03 [95%CI: -1.45, -0.60]; HVLT-R: -0.47 [-0.70, -0.24]; SDMT: -2.82 [-3.57, -2.08]; COWAT: -0.67 [-1.04, -0.29]) and a steeper long-term decline for three of these domains (3MS -0.62 [-0.88, -0.35]; COWAT: -0.30 [-0.46, -0.14]); HVLT-R: -0.12 [95%CI, -0.70, -0.24]). However individuals with stroke experienced no longer-term decline in SDMT compared to the rest of the participants. CONCLUSIONS: These findings highlight the need for comprehensive neuropsychology assessments for ongoing monitoring of cognition following incident stroke; and potential early intervention.

Assessment of biometrics and stress indicators of the adult Village Weaver birds (Ploceus cucullatus) during breeding and post-breeding seasons in Jos, Nigeria.

In this era of climate change, some biological conservationists' concerns are based on seasonal studies that highlight how wild birds' physiological fitness are interconnected with the immediate environment to avoid population decline. We investigated how seasonal biometrics correlated to stress parameters of the adult Village Weavers (Ploceus cucullatus) during breeding and post-breeding seasons of the Weaver birds in Amurum Forest Reserve. Specifically, we explored the following objectives: (i) the seasonal number of birds captured; (ii) whether seasonal baseline corticosterone (CORT), packed cell volume (PCV), and heterophil to lymphocytes ratio (H:L) were sex-dependent; (iii) whether H:L ratio varied with baseline (CORT); (iv) whether phenotypic condition (post-breeding moult) and brood patch varied with baseline (CORT) and H:L ratio; and (v) how body biometrics co-varied birds' seasonal baseline (CORT), (PCV) and (H:L) ratio. Trapping of birds (May-November) coincided with breeding and post-breeding seasons. The birds (n = 53 males, 39 females) were ringed, morphologically assessed (body mass, wing length, moult, brood patch) and blood collected from their brachial vein was used to assess CORT, PCV and H:L ratio. Although our results indicated more male birds trapped during breeding, the multiple analyses of variance (MANOVA) indicated that the seasonal temperature of the trapping sites correlated (P < 0.05) significantly to baseline (CORT). The general linear mixed model analyses (GLMMs) indicated that the baseline (CORT) also correlated significantly to H:L ratio of the male and female birds. However, PCV correlated significantly to body size of the birds (wing length) and not body mass. Haematological parameters such as the baseline CORT and the H:L ratio as indicators of stress in wild birds. Hence, there is the possibility that the Village Weaver birds suffered from seasonally induced stress under the constrained effect of environmental temperature. Hence, future studies should investigate whether the effect observed is also attributable to other passerine species.

Ecological and anthropogenic drivers of waterfowl productivity are synchronous across species, space, and time.

Knowledge of interspecific and spatiotemporal variation in demography-environment relationships is key for understanding the population dynamics of sympatric species and developing multispecies conservation strategies. We used hierarchical random-effects models to examine interspecific and spatial variation in annual productivity in six migratory ducks (i.e., American wigeon [Mareca americana], blue-winged teal [Spatula discors], gadwall [Mareca strepera], green-winged teal [Anas crecca], mallard [Anas platyrhynchos] and northern pintail [Anas acuta]) across six distinct ecostrata in the Prairie Pothole Region of North America. We tested whether breeding habitat conditions (seasonal pond counts, agricultural intensification, and grassland acreage) or cross-seasonal effects (indexed by flooded rice acreage in primary wintering areas) better explained variation in the proportion of juveniles captured during late summer banding. The proportion of juveniles (i.e., productivity) was highly variable within species and ecostrata throughout 1961-2019 and generally declined through time in blue-winged teal, gadwall, mallard, pintail, and wigeon, but there was no support for a trend in green-winged teal. Productivity in Canadian ecostrata declined with increasing agricultural intensification and increased with increasing pond counts. We also found a strong cross-seasonal effect, whereby more flooded rice hectares during winter resulted in higher subsequent productivity. Our results suggest highly consistent environmental and anthropogenic effects on waterfowl productivity across species and space. Our study advances our understanding of current year and cross-seasonal effects on duck productivity across a suite of species and at finer spatial scales, which could help managers better target working-lands conservation programs on both breeding and wintering areas. We encourage other researchers to evaluate environmental drivers of population dynamics among species in a single modeling framework for a deeper understanding of whether conservation plans should be generalized or customized given limited financial resources.

RETROSPECTIVE VARYING COEFFICIENT ASSOCIATION ANALYSIS OF LONGITUDINAL BINARY TRAITS: APPLICATION TO THE IDENTIFICATION OF GENETIC LOCI ASSOCIATED WITH HYPERTENSION.

Many genetic studies contain rich information on longitudinal phenotypes that require powerful analytical tools for optimal analysis. Genetic analysis of longitudinal data that incorporates temporal variation is important for understanding the genetic architecture and biological variation of complex diseases. Most of the existing methods assume that the contribution of genetic variants is constant over time and fail to capture the dynamic pattern of disease progression. However, the relative influence of genetic variants on complex traits fluctuates over time. In this study, we propose a retrospective varying coefficient mixed model association test, RVMMAT, to detect time-varying genetic effect on longitudinal binary traits. We model dynamic genetic effect using smoothing splines, estimate model parameters by maximizing a double penalized quasi-likelihood function, design a joint test using a Cauchy combination method, and evaluate statistical significance via a retrospective approach to achieve robustness to model misspecification. Through simulations we illustrated that the retrospective varying-coefficient test was robust to model misspecification under different ascertainment schemes and gained power over the association methods assuming constant genetic effect. We applied RVMMAT to a genome-wide association analysis of longitudinal measure of hypertension in the Multi-Ethnic Study of Atherosclerosis. Pathway analysis identified two important pathways related to G-protein signaling and DNA damage. Our results demonstrated that RVMMAT could detect biologically relevant loci and pathways in a genome scan and provided insight into the genetic architecture of hypertension.

The course of acute low back pain: a community-based inception cohort study.

Introduction: Acute low back pain (LBP) is increasingly recognized for its potential recurrent nature and long-term implications. Objectives: This community-based inception cohort study aimed to delineate trajectories of acute LBP over one year and investigate associated biopsychosocial variables. Methods: One hundred seventy-six participants with acute LBP were monitored at 5 follow-up time points over 52 weeks. Pain trajectories were identified using a latent class linear mixed model, and their associations with baseline biopsychosocial factors were evaluated through multinomial logistic regression. Results: Four distinct LBP trajectories were discerned: "mild/moderate fluctuating pain" (54.0%), "delayed recovery by week 52" (6.2%), "persistent moderate pain" (33.0%), and "moderate/severe fluctuating pain" (6.8%). Increased baseline pain intensity and history of LBP episodes were significantly linked with less favorable trajectories. Contrary to expectations, psychological variables like stress, anxiety, and depression did not significantly associate with unfavorable trajectories. Discussion: This study underscores the heterogeneity of acute LBP's course over a year, challenging the conventionally benign perception of the condition. Recognizing these distinct trajectories might enable more tailored, effective clinical interventions for LBP patients. The small sample size of certain trajectories may influence the generalizability of the results. Conclusion: Acute LBP can manifest in different trajectories, with nearly half of the participants experiencing less favorable trajectories. Baseline pain intensity and previous episodes of LBP emerged as key factors, whereas psychological variables had no discernible influence. Recognition of these trajectories may be necessary for improved patient management and targeted interventions.

Association of the medication protocols and longitudinal change of COVID-19 symptoms: a hospital-based mixed-statistical methods study.

The objective of this study was to identify the relationship between hospitalization treatment strategies leading to change in symptoms during 12-week follow-up among hospitalized patients during the COVID-19 outbreak. In this article, data from a prospective cohort study on COVID-19 patients admitted to Khorshid Hospital, Isfahan, Iran, from February 2020 to February 2021, were analyzed and reported. Patient characteristics, including socio-demographics, comorbidities, signs and symptoms, and treatments during hospitalization, were investigated. Also, to investigate the treatment effects adjusted by other confounding factors that lead to symptom change during follow-up, the binary classification trees, generalized linear mixed model, machine learning, and joint generalized estimating equation methods were applied. This research scrutinized the effects of various medications on COVID-19 patients in a prospective hospital-based cohort study, and found that heparin, methylprednisolone, ceftriaxone, and hydroxychloroquine were the most frequently prescribed medications. The results indicate that of patients under 65 years of age, 76% had a cough at the time of admission, while of patients with Cr levels of 1.1 or more, 80% had not lost weight at the time of admission. The results of fitted models showed that, during the follow-up, women are more likely to have shortness of breath (OR = 1.25; P-value: 0.039), fatigue (OR = 1.31; P-value: 0.013) and cough (OR = 1.29; P-value: 0.019) compared to men. Additionally, patients with symptoms of chest pain, fatigue and decreased appetite during admission are at a higher risk of experiencing fatigue during follow-up. Each day increase in the duration of ceftriaxone multiplies the odds of shortness of breath by 1.15 (P-value: 0.012). With each passing week, the odds of losing weight increase by 1.41 (P-value: 0.038), while the odds of shortness of breath and cough decrease by 0.84 (P-value: 0.005) and 0.56 (P-value: 0.000), respectively. In addition, each day increase in the duration of meropenem or methylprednisolone decreased the odds of weight loss at follow-up by 0.88 (P-value: 0.026) and 0.91 (P-value: 0.023), respectively (among those who took these medications). Identified prognostic factors can help clinicians and policymakers adapt management strategies for patients in any pandemic like COVID-19, which ultimately leads to better hospital decision-making and improved patient quality of life outcomes.

Prediction of Junior High School Students' Problematic Internet Use: The Comparison of Neural Network Models and Linear Mixed Models in Longitudinal Study.

Purpose: With the rise of big data, deep learning neural networks have garnered attention from psychology researchers due to their ability to process vast amounts of data and achieve superior model fitting. We aim to explore the predictive accuracy of neural network models and linear mixed models in tracking data when subjective variables are predominant in the field of psychology. We separately analyzed the predictive accuracy of both models and conduct a comparative study to further investigate. Simultaneously, we utilized the neural network model to examine the influencing factors of problematic internet usage and its temporal changes, attempting to provide insights for early interventions in problematic internet use. Patients and Methods: This study compared longitudinal data of junior high school students using both a linear mixed model and a neural network model to ascertain the efficacy of these two methods in processing psychological longitudinal data. Results: The neural network model exhibited significantly smaller errors compared to the linear mixed model. Furthermore, the outcomes from the neural network model revealed that, when analyzing data from a single time point, the influences of seventh grade better predicted Problematic Internet Use in ninth grade. And when analyzing data from multiple time points, the influences of sixth, seventh, and eighth grades more accurately predicted Problematic Internet Use in ninth grade. Conclusion: Neural network models surpass linear mixed models in precision when predicting and analyzing longitudinal data. Furthermore, the influencing factors in lower grades provide more accurate predictions of Problematic Internet Use in higher grades. The highest prediction accuracy is attained through the utilization of data from multiple time points.

Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework.

BACKGROUND: The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies. METHODS: We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies. RESULTS: We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue. CONCLUSION: Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.

A Modern Approach to Stability Studies via Bayesian Linear Mixed Models Incorporating Auxiliary Effects.

In preparation to the launch of a pharmaceutical product, an estimate of its shelf life via stability testing is required by regulatory agencies. The ICH-Q1E guidance has been the worldwide reference to reach this objective, but in recent years several authors have criticized many of its aspects. To that end we discuss a complete Bayesian transcript of the ICH-Q1E, treating all the apparent shortcomings, while also addressing the presence of multiple batches using a linear mixed model (LMM) for proper shelf life prediction by explicitly modelling the batch-to-batch variability. This comprises a redefinition of the linear models proposed in the ICH-Q1E by suitable LMM counterparts, and a Bayesian analogue for model selection, which is more intuitive and remedies detrimental features of the ICH approach. In that context, a proper mathematical foundation of shelf life is provided that we use to investigate and mathematically compare the two available approaches to shelf life determination via shelf life distribution and batch distribution. The discussed method is then tested and evaluated using real data in comparison with the ICH-Q1E approach demonstrating their approximate equivalency for 6 batches. As a major objective, we extended the LMM with auxiliary fixed effects, here the concentration, which interconnect data sets allowing a prediction of shelf lives for concentrations lacking a sufficient number of batches. This establishes a novel approach to accelerate the speed to submission while retaining the patients' safety. Both case studies underline the inherent superiority of LMMs within a Bayesian framework regarding predictability and interpretability, and we hope that the relevant authorities will accept this approach in the future.

Gene-based association tests in family samples using GWAS summary statistics.

Genome-wide association studies (GWAS) have led to rapid growth in detecting genetic variants associated with various phenotypes. Owing to a great number of publicly accessible GWAS summary statistics, and the difficulty in obtaining individual-level genotype data, many existing gene-based association tests have been adapted to require only GWAS summary statistics rather than individual-level data. However, these association tests are restricted to unrelated individuals and thus do not apply to family samples directly. Moreover, due to its flexibility and effectiveness, the linear mixed model has been increasingly utilized in GWAS to handle correlated data, such as family samples. However, it remains unknown how to perform gene-based association tests in family samples using the GWAS summary statistics estimated from the linear mixed model. In this study, we show that, when family size is negligible compared to the total sample size, the diagonal block structure of the kinship matrix makes it possible to approximate the correlation matrix of marginal Z scores by linkage disequilibrium matrix. Based on this result, current methods utilizing summary statistics for unrelated individuals can be directly applied to family data without any modifications. Our simulation results demonstrate that this proposed strategy controls the type 1 error rate well in various situations. Finally, we exemplify the usefulness of the proposed approach with a dental caries GWAS data set.

An expression-directed linear mixed model discovering low-effect genetic variants.

Detecting genetic variants with low-effect sizes using a moderate sample size is difficult, hindering downstream efforts to learn pathology and estimating heritability. In this work, by utilizing informative weights learned from training genetically predicted gene expression models, we formed an alternative approach to estimate the polygenic term in a linear mixed model. Our linear mixed model estimates the genetic background by incorporating their relevance to gene expression. Our protocol, expression-directed linear mixed model, enables the discovery of subtle signals of low-effect variants using moderate sample size. By applying expression-directed linear mixed model to cohorts of around 5,000 individuals with either binary (WTCCC) or quantitative (NFBC1966) traits, we demonstrated its power gain at the low-effect end of the genetic etiology spectrum. In aggregate, the additional low-effect variants detected by expression-directed linear mixed model substantially improved estimation of missing heritability. Expression-directed linear mixed model moves precision medicine forward by accurately detecting the contribution of low-effect genetic variants to human diseases.

Anticoagulant Rodenticide Toxicity in Terrestrial Raptors: Tools to Estimate the Impact on Populations in North America and Globally.

Anticoagulant rodenticides (ARs) have caused widespread contamination and poisoning of predators and scavengers. The diagnosis of toxicity proceeds from evidence of hemorrhage, and subsequent detection of residues in liver. Many factors confound the assessment of AR poisoning, particularly exposure dose, timing and frequency of exposure, and individual and taxon-specific variables. There is a need, therefore, for better AR toxicity criteria. To respond, we compiled a database of second-generation anticoagulant rodenticide (SGAR) residues in liver and postmortem evaluations of 951 terrestrial raptor carcasses from Canada and the United States, 1989 to 2021. We developed mixed-effects logistic regression models to produce specific probability curves of the toxicity of ∑SGARs at the taxonomic level of the family, and separately for three SGARs registered in North America, brodifacoum, bromadiolone, and difethialone. The ∑SGAR threshold concentrations for diagnosis of coagulopathy at 0.20 probability of risk were highest for strigid owls (15 ng g-1) lower and relatively similar for accipitrid hawks and eagles (8.2 ng g-1) and falcons (7.9 ng g-1), and much lower for tytonid barn owls (0.32 ng g-1). These values are lower than those we found previously, due to compilation and use of a larger database with a mix of species and source locations, and also to refinements in the statistical methods. Our presentation of results on the family taxonomic level should aid in the global applicability of the numbers. We also collated a subset of 440 single-compound exposure events and determined the probability of SGAR-poisoning symptoms as a function of SGAR concentration, which we then used to estimate relative SGAR toxicity and toxic equivalence factors: difethialone, 1, brodifacoum, 0.8, and bromadiolone, 0.5. Environ Toxicol Chem 2024;43:988-998. © 2024 His Majesty the King in Right of Canada and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC Reproduced with the permission of the Minister of Environment and Climate Change Canada.