Estimation of l0 Norm Penalized Models: A Statistical Treatment.

Fitting penalized models for the purpose of merging the estimation and model selection problem has become commonplace in statistical practice. Of the various regularization strategies that can be leveraged to this end, the use of the l0 norm to penalize parameter estimation poses the most daunting model fitting task. In fact, this particular strategy requires an end user to solve a non-convex NP-hard optimization problem irregardless of the underlying data model. For this reason, the use of the l0 norm as a regularization strategy has been woefully under utilized. To obviate this difficulty, a strategy to solve such problems that is generally accessible by the statistical community is developed. The approach can be adopted to solve l0 norm penalized problems across a very broad class of models, can be implemented using existing software, and is computationally efficient. The performance of the method is demonstrated through in-depth numerical experiments and through using it to analyze several prototypical data sets.

Association between vector-borne pathogen seroprevalence in shelter-housed and owned dog populations in the contiguous United States of America.

Domestic dogs are susceptible to numerous vector-borne pathogens that are of significant importance for their health. In addition to being of veterinary importance, many of these pathogens are zoonotic and thus may pose a risk to human health. In the USA, owned dogs are commonly screened for exposure to or infection with several canine vector-borne pathogens. Although the screening data are widely available to show areas where infections are being diagnosed, testing of owned dogs is expected to underestimate the actual prevalence in dogs that have no access to veterinary care. The goal of this study was to measure the association between the widely available data from a perceived low-risk population with temporally and spatially collected data from shelter-housed dog populations. These data were then used to extrapolate the prevalence in dogs that generally lack veterinary care. The focus pathogens included Dirofilaria immitis, Ehrlichia spp., Anaplasma spp., and Borrelia burgdorferi. There was a linear association between the prevalence of selected vector-borne pathogens in shelter-housed and owned dog populations and, generally, the data suggested that prevalence of heartworm (D. immitis) infection and seroprevalence of Ehrlichia spp. and B. burgdorferi are higher in shelter-housed dogs, regardless of their location, compared with the owned population. The seroprevalence of Anaplasma spp. was predicted to be higher in areas that have very low to low seroprevalence, but unexpectedly, in areas of higher seroprevalence within the owned population, the seroprevalence was expected to be lower in the shelter-housed dog population. If shelters and veterinarians make decisions to not screen dogs based on the known seroprevalence of the owned group, they are likely underestimating the risk of exposure. This is especially true for heartworm. With this new estimate of the seroprevalence in shelter-housed dogs throughout the USA, shelters and veterinarians can make evidence-based informed decisions on whether testing and screening for these pathogens is appropriate for their local dog population. This work represents an important step in understanding the relationships in the seroprevalences of vector-borne pathogens between shelter-housed and owned dogs, and provides valuable data on the risk of vector-borne diseases in dogs.

SIGHR: Side information guided high-dimensional regression.

In this work, we develop a novel Bayesian regression framework that can be used to complete variable selection in high dimensional settings. Unlike existing techniques, the proposed approach can leverage side information to inform about the sparsity structure of the regression coefficients. This is accomplished by replacing the usual inclusion probability in the spike and slab prior with a binary regression model which assimilates this extra source of information. To facilitate model fitting, a computationally efficient and easy to implement Markov chain Monte Carlo posterior sampling algorithm is developed via carefully chosen priors and data augmentation steps. The finite sample performance of our methodology is assessed through numerical simulations, and we further illustrate our approach by using it to identify genetic markers associated with the nicotine metabolite ratio; a key biological marker associated with nicotine dependence and smoking cessation treatment.

Estimating the prevalence of two or more diseases using outcomes from multiplex group testing.

When screening a population for infectious diseases, pooling individual specimens (e.g., blood, swabs, urine, etc.) can provide enormous cost savings when compared to testing specimens individually. In the biostatistics literature, testing pools of specimens is commonly known as group testing or pooled testing. Although estimating a population-level prevalence with group testing data has received a large amount of attention, most of this work has focused on applications involving a single disease, such as human immunodeficiency virus. Modern methods of screening now involve testing pools and individuals for multiple diseases simultaneously through the use of multiplex assays. Hou et al. (2017, Biometrics, 73, 656-665) and Hou et al. (2020, Biostatistics, 21, 417-431) recently proposed group testing protocols for multiplex assays and derived relevant case identification characteristics, including the expected number of tests and those which quantify classification accuracy. In this article, we describe Bayesian methods to estimate population-level disease probabilities from implementing these protocols or any other multiplex group testing protocol which might be carried out in practice. Our estimation methods can be used with multiplex assays for two or more diseases while incorporating the possibility of test misclassification for each disease. We use chlamydia and gonorrhea testing data collected at the State Hygienic Laboratory at the University of Iowa to illustrate our work. We also provide an online R resource practitioners can use to implement the methods in this article.

Covid-19 vaccine effectiveness against general SARS-CoV-2 infection from the omicron variant: A retrospective cohort study.

We aim to estimate the effectiveness of 2-dose and 3-dose mRNA vaccination (BNT162b2 and mRNA-1273) against general Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection (asymptomatic or symptomatic) caused by the omicron BA.1 variant. This propensity-score matched retrospective cohort study takes place in a large public university undergoing weekly Coronavirus Disease 2019 (Covid-19) testing in South Carolina, USA. The population consists of 24,145 university students and employees undergoing weekly Covid-19 testing between January 3rd and January 31st, 2022. The analytic sample was constructed via propensity score matching on vaccination status: unvaccinated, completion of 2-dose mRNA series (BNT162b2 or mRNA-1273) within the previous 5 months, and receipt of mRNA booster dose (BNT162b2 or mRNA-1273) within the previous 5 months. The resulting analytic sample consists of 1,944 university students (mean [SD] age, 19.64 [1.42] years, 66.4% female, 81.3% non-Hispanic White) and 658 university employees (mean [SD] age, 43.05 [12.22] years, 64.7% female, 83.3% non-Hispanic White). Booster protection against any SARS-CoV-2 infection was 66.4% among employees (95% CI: 46.1-79.0%; P<.001) and 45.4% among students (95% CI: 30.0-57.4%; P<.001). Compared to the 2-dose mRNA series, estimated increase in protection from the booster dose was 40.8% among employees (P=.024) and 37.7% among students (P=.001). We did not have enough evidence to conclude a statistically significant protective effect of the 2-dose mRNA vaccination series, nor did we have enough evidence to conclude that protection waned in the 5-month period after receipt of the 2nd or 3rd mRNA dose. Furthermore, we did not find evidence that protection varied by manufacturer. We conclude that in adults 18-65 years of age, Covid-19 mRNA booster doses offer moderate protection against general SARS-CoV-2 infection caused by the omicron variant and provide a substantial increase in protection relative to the 2-dose mRNA vaccination series.

A flexible parametric approach for analyzing arbitrarily censored data that are potentially subject to left truncation under the proportional hazards model.

The proportional hazards (PH) model is, arguably, the most popular model for the analysis of lifetime data arising from epidemiological studies, among many others. In such applications, analysts may be faced with censored outcomes and/or studies which institute enrollment criterion leading to left truncation. Censored outcomes arise when the event of interest is not observed but rather is known relevant to an observation time(s). Left truncated data occur in studies that exclude participants who have experienced the event prior to being enrolled in the study. If not accounted for, both of these features can lead to inaccurate inferences about the population under study. Thus, to overcome this challenge, herein we propose a novel unified PH model that can be used to accommodate both of these features. In particular, our approach can seamlessly analyze exactly observed failure times along with interval-censored observations, while aptly accounting for left truncation. To facilitate model fitting, an expectation-maximization algorithm is developed through the introduction of carefully structured latent random variables. To provide modeling flexibility, a monotone spline representation is used to approximate the cumulative baseline hazard function. The performance of our methodology is evaluated through a simulation study and is further illustrated through the analysis of two motivating data sets; one that involves child mortality in Nigeria and the other prostate cancer.

Patterns of drinking behavior around a treatment episode for alcohol use disorder: Predictions from pre-treatment measures.

BACKGROUND: Alcohol use disorder (AUD) has been described as a chronic disease given the high rates that affected individuals have in returning to drinking after a change attempt. Many studies have characterized predictors of aggregated alcohol use (e.g., percent heavy drinking days) following treatment for AUD. However, to inform future research on predicting drinking as an AUD outcome measure, a better understanding is needed of the patterns of drinking that surround a treatment episode and which clinical measures predict patterns of drinking. METHODS: We analyzed data from the Project MATCH and COMBINE studies (MATCH: n = 1726; 24.3% female, 20.0% non-White; COMBINE: n = 1383; 30.9% female, 23.2% non-White). Daily drinking was measured in the 90 days prior to treatment, 90 days (MATCH) and 120 days (COMBINE) during treatment, and 365 days following treatment. Gradient boosting machine learning methods were used to explore baseline predictors of drinking patterns. RESULTS: Drinking patterns during a prior time period were the most consistent predictors of future drinking patterns. Social network drinking, AUD severity, mental health symptoms, and constructs based on the addiction cycle (incentive salience, negative emotionality, and executive function) were associated with patterns of drinking prior to treatment. Addiction cycle constructs, AUD severity, purpose in life, social network, legal history, craving, and motivation were associated with drinking during the treatment period and following treatment. CONCLUSIONS: There is heterogeneity in drinking patterns around an AUD treatment episode. This study provides novel information about variables that may be important to measure to improve the prediction of drinking patterns during and following treatment. Future research should consider which patterns of drinking they aim to predict and which period of drinking is most important to predict. The current findings could guide the selection of predictor variables and generate hypotheses for those predictors.

binGroup2: Statistical Tools for Infection Identification via Group Testing.

Group testing is the process of testing items as an amalgamation, rather than separately, to determine the binary status for each item. Its use was especially important during the COVID-19 pandemic through testing specimens for SARS-CoV-2. The adoption of group testing for this and many other applications is because members of a negative testing group can be declared negative with potentially only one test. This subsequently leads to significant increases in laboratory testing capacity. Whenever a group testing algorithm is put into practice, it is critical for laboratories to understand the algorithm's operating characteristics, such as the expected number of tests. Our paper presents the binGroup2 package that provides the statistical tools for this purpose. This R package is the first to address the identification aspect of group testing for a wide variety of algorithms. We illustrate its use through COVID-19 and chlamydia/gonorrhea applications of group testing.

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging.

Alzheimer's disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. It is of critical scientific importance to gain a better understanding of early disease mechanisms in the brain to facilitate effective, targeted therapies. The volume of the hippocampus is often used in diagnosis and monitoring of the disease. Measuring this volume via neuroimaging is difficult since each hippocampus must either be manually identified or automatically delineated, a task referred to as segmentation. Automatic hippocampal segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each hippocampus is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms employ voting procedures with voting weights assigned directly or estimated via optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. Our results suggest that incorporating tissue classification (e.g, gray matter) into the label fusion procedure can greatly improve segmentation when relatively homogeneous, healthy brains are used as atlases for diseased brains. The fully Bayesian approach also produces meaningful uncertainty measures about hippocampal volumes, information which can be leveraged to detect significant, scientifically meaningful differences between healthy and diseased populations, improving the potential for early detection and tracking of the disease.

Predicting nicotine metabolism across ancestries using genotypes.

BACKGROUND: There is a need to match characteristics of tobacco users with cessation treatments and risks of tobacco attributable diseases such as lung cancer. The rate in which the body metabolizes nicotine has proven an important predictor of these outcomes. Nicotine metabolism is primarily catalyzed by the enzyme cytochrone P450 (CYP2A6) and CYP2A6 activity can be measured as the ratio of two nicotine metabolites: trans-3'-hydroxycotinine to cotinine (NMR). Measurements of these metabolites are only possible in current tobacco users and vary by biofluid source, timing of collection, and protocols; unfortunately, this has limited their use in clinical practice. The NMR depends highly on genetic variation near CYP2A6 on chromosome 19 as well as ancestry, environmental, and other genetic factors. Thus, we aimed to develop prediction models of nicotine metabolism using genotypes and basic individual characteristics (age, gender, height, and weight). RESULTS: We identified four multiethnic studies with nicotine metabolites and DNA samples. We constructed a 263 marker panel from filtering genome-wide association scans of the NMR in each study. We then applied seven machine learning techniques to train models of nicotine metabolism on the largest and most ancestrally diverse dataset (N=2239). The models were then validated using the other three studies (total N=1415). Using cross-validation, we found the correlations between the observed and predicted NMR ranged from 0.69 to 0.97 depending on the model. When predictions were averaged in an ensemble model, the correlation was 0.81. The ensemble model generalizes well in the validation studies across ancestries, despite differences in the measurements of NMR between studies, with correlations of: 0.52 for African ancestry, 0.61 for Asian ancestry, and 0.46 for European ancestry. The most influential predictors of NMR identified in more than two models were rs56113850, rs11878604, and 21 other genetic variants near CYP2A6 as well as age and ancestry. CONCLUSIONS: We have developed an ensemble of seven models for predicting the NMR across ancestries from genotypes and age, gender and BMI. These models were validated using three datasets and associate with nicotine dosages. The knowledge of how an individual metabolizes nicotine could be used to help select the optimal path to reducing or quitting tobacco use, as well as, evaluating risks of tobacco use.

Effectiveness and protection duration of Covid-19 vaccines and previous infection against any SARS-CoV-2 infection in young adults.

Data on effectiveness and protection duration of Covid-19 vaccines and previous infection against general SARS-CoV-2 infection in general populations are limited. Here we evaluate protection from Covid-19 vaccination (primary series) and previous infection in 21,261 university students undergoing repeated surveillance testing between 8/8/2021-12/04/2021, during which B.1.617 (delta) was the dominant SARS-CoV-2 variant. Estimated mRNA-1273, BNT162b2, and AD26.COV2.S effectiveness against any SARS-CoV-2 infection is 75.4% (95% CI: 70.5-79.5), 65.7% (95% CI: 61.1-69.8), and 42.8% (95% CI: 26.1-55.8), respectively. Among previously infected individuals, protection is 72.9% when unvaccinated (95% CI: 66.1-78.4) and increased by 22.1% with full vaccination (95% CI: 15.8-28.7). Statistically significant decline in protection is observed for mRNA-1273 (P < .001), BNT162b2 (P < .001), but not Ad26.CoV2.S (P = 0.40) or previous infection (P = 0.12). mRNA vaccine protection dropped 29.7% (95% CI: 17.9-41.6) six months post- vaccination, from 83.2% to 53.5%. We conclude that the 2-dose mRNA vaccine series initially offers strong protection against general SARS-CoV-2 infection caused by the delta variant in young adults, but protection substantially decreases over time. These findings indicate that vaccinated individuals may still contribute to community spread. While previous SARS-CoV-2 infection consistently provides moderately strong protection against repeat infection from delta, vaccination yields a substantial increase in protection.

Assessing Opioid Use Disorder Treatments in Trials Subject to Non-Adherence via a Functional Generalized Linear Mixed-Effects Model.

The opioid crisis in the United States poses a major threat to public health due to psychiatric and infectious disease comorbidities and death due to opioid use disorder (OUD). OUD is characterized by patterns of opioid misuse leading to persistent heavy use and overdose. The standard of care for treatment of OUD is medication-assisted treatment, in combination with behavioral therapy. Medications for opioid use disorder have been shown to improve OUD outcomes, including reduction and prevention of overdose. However, understanding the effectiveness of such medications has been limited due to non-adherence to assigned dose levels by study patients. To overcome this challenge, herein we develop a model that views dose history as a time-varying covariate. Proceeding in this fashion allows the model to estimate dose effect while accounting for lapses in adherence. The proposed model is used to conduct a secondary analysis of data collected from six efficacy and safety trials of buprenorphine maintenance treatment. This analysis provides further insight into the time-dependent treatment effects of buprenorphine and how different dose adherence patterns relate to risk of opioid use.

Cross-sectional geometry predicts failure location in maize stalks.

BACKGROUND: Stalk lodging (breaking of agricultural plant stalks prior to harvest) is a multi-billion dollar a year problem. Stalk lodging occurs when high winds induce bending moments in the stalk which exceed the bending strength of the plant. Previous biomechanical models of plant stalks have investigated the effect of cross-sectional morphology on stalk lodging resistance (e.g., diameter and rind thickness). However, it is unclear if the location of stalk failure along the length of stem is determined by morphological or compositional factors. It is also unclear if the crops are structurally optimized, i.e., if the plants allocate structural biomass to create uniform and minimal bending stresses in the plant tissues. The purpose of this paper is twofold: (1) to investigate the relationship between bending stress and failure location of maize stalks, and (2) to investigate the potential of phenotyping for internode-level bending stresses to assess lodging resistance. RESULTS: 868 maize specimens representing 16 maize hybrids were successfully tested in bending to failure. Internode morphology was measured, and bending stresses were calculated. It was found that bending stress is highly and positively associated with failure location. A user-friendly computational tool is presented to help plant breeders in phenotyping for internode-level bending stress. Phenotyping for internode-level bending stresses could potentially be used to breed for more biomechanically optimal stalks that are resistant to stalk lodging. CONCLUSIONS: Internode-level bending stress plays a potentially critical role in the structural integrity of plant stems. Equations and tools provided herein enable researchers to account for this phenotype, which has the potential to increase the bending strength of plants without increasing overall structural biomass.

Effects of Buprenorphine Dose and Therapeutic Engagement on Illicit Opiate Use in Opioid Use Disorder Treatment Trials.

The impact of agonist dose and of physician, staff and patient engagement on treatment have not been evaluated together in an analysis of treatment for opioid use disorder. Our hypotheses were that greater agonist dose and therapeutic engagement would be associated with reduced illicit opiate use in a time-dependent manner. Publicly-available treatment data from six buprenorphine efficacy and safety trials from the Federally-supported Clinical Trials Network were used to derive treatment variables. Three novel predictors were constructed to capture the time weighted effects of buprenorphine dosage (mg buprenorphine per day), dosing protocol (whether physician could adjust dose), and clinic visits (whether patient attended clinic). We used time-in-trial as a predictor to account for the therapeutic benefits of treatment persistence. The outcome was illicit opiate use defined by self-report or urinalysis. Trial participants (N = 3022 patients with opioid dependence, mean age 36 years, 33% female, 14% Black, 16% Hispanic) were analyzed using a generalized linear mixed model. Treatment variables dose, Odds Ratio (OR) = 0.63 (95% Confidence Interval (95%CI) 0.59−0.67), dosing protocol, OR = 0.70 (95%CI 0.65−0.76), time-in-trial, OR = 0.75 (95%CI 0.71−0.80) and clinic visits, OR = 0.81 (95%CI 0.76−0.87) were significant (p-values < 0.001) protective factors. Treatment implications support higher doses of buprenorphine and greater engagement of patients with providers and clinic staff.

A Bayesian mixed effects support vector machine for learning and predicting daily substance use disorder patterns.

Background: Substance use disorder (SUD) is a heterogeneous disorder. Adapting machine learning algorithms to allow for the parsing of intrapersonal and interpersonal heterogeneity in meaningful ways may accelerate the discovery and implementation of clinically actionable interventions in SUD research.Objectives: Inspired by a study of heavy drinkers that collected daily drinking and substance use (ABQ DrinQ), we develop tools to estimate subject-specific risk trajectories of heavy drinking; estimate and perform inference on patient characteristics and time-varying covariates; and present results in easy-to-use Jupyter notebooks. Methods: We recast support vector machines (SVMs) into a Bayesian model extended to handle mixed effects. We then apply these methods to ABQ DrinQ to model alcohol use patterns. ABQ DrinQ consists of 190 heavy drinkers (44% female) with 109,580 daily observations. Results: We identified male gender (point estimate; 95% credible interval: -0.25;-0.29,-0.21), older age (-0.03;-0.03,-0.03), and time varying usage of nicotine (1.68;1.62,1.73), cannabis (0.05;0.03,0.07), and other drugs (1.16;1.01,1.35) as statistically significant factors of heavy drinking behavior. By adopting random effects to capture the subject-specific longitudinal trajectories, the algorithm outperforms traditional SVM (classifies 84% of heavy drinking days correctly versus 73%). Conclusions: We developed a mixed effects variant of SVM and compare it to the traditional formulation, with an eye toward elucidating the importance of incorporating random effects to account for underlying heterogeneity in SUD data. These tools and examples are packaged into a repository for researchers to explore. Understanding patterns and risk of substance use could be used for developing individualized interventions.

Predicting COVID-19 Infected Individuals in a Defined Population from Wastewater RNA Data.

Wastewater surveillance of SARS-CoV-2 RNA has become an important tool for tracking the presence of the virus and serving as an early indicator for the onset of rapid transmission. Nevertheless, wastewater data are still not commonly used to predict the number of infected individuals in a sewershed. The main objective of this study was to calibrate a susceptible-exposed-infectious-recovered (SEIR) model using RNA copy rates in sewage (i.e., gene copies per liter times flow rate) and the number of SARS-CoV-2 saliva-test-positive infected individuals in a university student population that was subject to repeated weekly testing during the Spring 2021 semester. A strong correlation was observed between the RNA copy rates and the number of infected individuals. The parameter in the SEIR model that had the largest impact on calibration was the maximum shedding rate, resulting in a mean value of 7.72 log10 genome copies per gram of feces. Regressing the saliva-test-positive infected individuals on predictions from the SEIR model based on the RNA copy rates yielded a slope of 0.87 (SE=0.11), which is statistically consistent with a 1:1 relationship between the two. These findings demonstrate that wastewater surveillance of SARS-CoV-2 can be used to estimate the number of infected individuals in a sewershed.

COVID-19 wastewater epidemiology: a model to estimate infected populations.

BACKGROUND: Wastewater-based epidemiology provides an opportunity for near real-time, cost-effective monitoring of community-level transmission of SARS-CoV-2. Detection of SARS-CoV-2 RNA in wastewater can identify the presence of COVID-19 in the community, but methods for estimating the numbers of infected individuals on the basis of wastewater RNA concentrations are inadequate. METHODS: This is a wastewater-based epidemiology study using wastewater samples that were collected weekly or twice a week from three sewersheds in South Carolina, USA, between either May 27 or June 16, 2020, and Aug 25, 2020, and tested for SARS-CoV-2 RNA. We developed a susceptible-exposed-infectious-recovered (SEIR) model based on the mass rate of SARS-CoV-2 RNA in the wastewater to predict the number of infected individuals, and have also provided a simplified equation to predict this. Model predictions were compared with the number of confirmed cases identified by the Department of Health and Environmental Control, South Carolina, USA, for the same time period and geographical area. FINDINGS: We plotted the model predictions for the relationship between mass rate of virus release and numbers of infected individuals, and we validated this prediction on the basis of estimated prevalence from individual testing. A simplified equation to estimate the number of infected individuals fell within the 95% confidence limits of the model. The rate of unreported COVID-19 cases, as estimated by the model, was approximately 11 times that of confirmed cases (ie, ratio of estimated infections for every confirmed case of 10·9, 95% CI 4·2-17·5). This rate aligned well with an independent estimate of 15 infections for every confirmed case in the US state of South Carolina. INTERPRETATION: The SEIR model provides a robust method to estimate the total number of infected individuals in a sewershed on the basis of the mass rate of RNA copies released per day. This approach overcomes some of the limitations associated with individual testing campaigns and thereby provides an additional tool that can be used to inform policy decisions. FUNDING: Clemson University, USA.

Global estimated Disability-Adjusted Life-Years (DALYs) of diarrheal diseases: A systematic analysis of data from 28 years of the global burden of disease study.

BACKGROUND: Diarrheal disease (DD)-associated mortality has declined since 1990; however, the incidence of DD has experienced a less-pronounced decrease. Thus, it is important to track progress in managing DD by following loss of healthy years. A disability-adjusted life-year (DALY), which combines data on years-of-life lost (YLL) and years-lived with-disability (YLD), is a metric that can track such a burden. METHODS AND FINDINGS: Using all 28 years of data in the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, we compared DD DALYs among different demographic subsets including sex, age, country, and World Bank (WB) income level. We also evaluated DD DALYs as a function of the socio-demographic index (SDI), a measure of a region's socio-demographic development. On a global level, DD DALYs have decreased by approximately 85.43% from 1990 to 2017. Incidence and prevalence have decreased by 1.53% and 4.45%, respectively. A dramatic decrease in DD DALYs were observed for WB low-income countries, but not for WB high-income constituents. The temporal decrease in DD DALY rates in WB low-income countries was likely driven by a decrease in YLL. Alternatively, temporal increases in both YLL and YLD may have contributed to the apparent lack of progress in WB high-income countries. Regardless of WB income classification, children under the age of five and the elderly were the most vulnerable to DD. In nearly every year from 1990 to 2017, DD DALYs for females were higher than those for males in WB high-income regions, but lower than those for males in WB low-income constituents. The reason for these differences is not known. We also observed that the rate of DD DALYs was highly correlated to SDI regardless of WB income classification. CONCLUSIONS: To the best of our knowledge, this is the only temporal study of DD DALYs that encompasses all 28 years of data available from the GBD. Overall, our analyses show that temporal reductions in DD DALYs are not equivalent across regions, sexes and age groups. Therefore, careful attention to local and demography-specific risk factors will be necessary to tailor solutions in region- and demography-specific manners.

The Overlooked Biomechanical Role of the Clasping Leaf Sheath in Wheat Stalk Lodging.

The biomechanical role of the clasping leaf sheath in stalk lodging events has been historically understudied. Results from this study indicate that in some instances the leaf sheath plays an even larger role in reinforcing wheat against stalk lodging than the stem itself. Interestingly, it appears the leaf sheath does not resist bending loads by merely adding more material to the stalk (i.e., increasing the effective diameter). The radial preload of the leaf sheath on the stem, the friction between the sheath and the stem and several other complex biomechanical factors may contribute to increasing the stalk bending strength and stalk flexural rigidity of wheat. Results demonstrated that removal of the leaf sheath induces alternate failure patterns in wheat stalks. In summary the biomechanical role of the leaf sheath is complex and has yet to be fully elucidated. Many future studies are needed to develop high throughput phenotyping methodologies and to determine the genetic underpinnings of the clasping leaf sheath and its relation to stalk lodging resistance. Research in this area is expected to improve the lodging resistance of wheat.

Multiethnic Prediction of Nicotine Biomarkers and Association With Nicotine Dependence.

INTRODUCTION: The nicotine metabolite ratio and nicotine equivalents are measures of metabolism rate and intake. Genome-wide prediction of these nicotine biomarkers in multiethnic samples will enable tobacco-related biomarker, behavioral, and exposure research in studies without measured biomarkers. AIMS AND METHODS: We screened genetic variants genome-wide using marginal scans and applied statistical learning algorithms on top-ranked genetic variants, age, ethnicity and sex, and, in additional modeling, cigarettes per day (CPD), (in additional modeling) to build prediction models for the urinary nicotine metabolite ratio (uNMR) and creatinine-standardized total nicotine equivalents (TNE) in 2239 current cigarette smokers in five ethnic groups. We predicted these nicotine biomarkers using model ensembles and evaluated external validity using dependence measures in 1864 treatment-seeking smokers in two ethnic groups. RESULTS: The genomic regions with the most selected and included variants for measured biomarkers were chr19q13.2 (uNMR, without and with CPD) and chr15q25.1 and chr10q25.3 (TNE, without and with CPD). We observed ensemble correlations between measured and predicted biomarker values for the uNMR and TNE without (with CPD) of 0.67 (0.68) and 0.65 (0.72) in the training sample. We observed inconsistency in penalized regression models of TNE (with CPD) with fewer variants at chr15q25.1 selected and included. In treatment-seeking smokers, predicted uNMR (without CPD) was significantly associated with CPD and predicted TNE (without CPD) with CPD, time-to-first-cigarette, and Fagerström total score. CONCLUSIONS: Nicotine metabolites, genome-wide data, and statistical learning approaches developed novel robust predictive models for urinary nicotine biomarkers in multiple ethnic groups. Predicted biomarker associations helped define genetically influenced components of nicotine dependence. IMPLICATIONS: We demonstrate development of robust models and multiethnic prediction of the uNMR and TNE using statistical and machine learning approaches. Variants included in trained models for nicotine biomarkers include top-ranked variants in multiethnic genome-wide studies of smoking behavior, nicotine metabolites, and related disease. Association of the two predicted nicotine biomarkers with Fagerström Test for Nicotine Dependence items supports models of nicotine biomarkers as predictors of physical dependence and nicotine exposure. Predicted nicotine biomarkers may facilitate tobacco-related disease and treatment research in samples with genomic data and limited nicotine metabolite or tobacco exposure data.