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SARS-CoV-2 is an endemic positive-sense RNA virus naturally transmissible between numerous species with notable infectivity and associated mortality. It is characterized by a poly-adenylated structure capping the genomic terminus. This poly(A) tail is crucial to a cascade of viral replicative activity occurring both extra- and intra-cellular during infection. As a route to proposing potential chemotherapy, this study suggests simple biplanar adenine quadruplexes (A4s) which may fold in specific sequences of the viral genome. To the best of our knowledge, uniquely biplanar A4s have not been previously described in any context. Using molecular modeling techniques and molecular dynamics simulations, some of these non-canonical structures show reasonable stability in a biological context. Notably, mRNA configured as a biplanar A4, shows less dynamic activity than DNA equivalents. This observation may be especially relevant in a physiological context. Furthermore, in contrast to well-characterized guanine quadruplexes, co-ordination with cations appears not to impact on stability. Our molecular dynamics simulations and analyses demonstrate that some A4s are stable in biologically relevant terms. These conclusions may apply to SARS-CoV-2, its variants and other pathogenic RNA viruses. © 2021 World Scientific Publishing Company.

There is a scarcity of long-term groundwater hydrographs from sub-Saharan Africa to investigate groundwater sustainability, processes and controls. This paper presents an analysis of 21 hydrographs from semi-arid South Africa. Hydrographs from 1980 to 2000 were converted to standardised groundwater level indices and rationalised into four types (C1–C4) using hierarchical cluster analysis. Mean hydrographs for each type were cross-correlated with standardised precipitation and streamflow indices. Relationships with the El Niño–Southern Oscillation (ENSO) were also investigated. The four hydrograph types show a transition of autocorrelation over increasing timescales and increasingly subdued responses to rainfall. Type C1 strongly relates to rainfall, responding in most years, whereas C4 notably responds to only a single extreme event in 2000 and has limited relationship with rainfall. Types C2, C3 and C4 have stronger statistical relationships with standardised streamflow than standardised rainfall. C3 and C4 changes are significantly (p

Dynamic treatment regimes (DTRs) include a sequence of treatment decision rules, in which treatment is adapted over time in response to the changes in an individual’s disease progression and health care history. In medical practice, nested test-and-treat strategies are common to improve cost-effectiveness. For example, for patients at risk of prostate cancer, only patients who have high prostate-specific antigen (PSA) need a biopsy, which is costly and invasive, to confirm the diagnosis and help determine the treatment if needed. A decision about treatment happens after the biopsy, and is thus nested within the decision of whether to do the test. However, current existing statistical methods are not able to accommodate such a naturally embedded property of the treatment decision within the test decision. Therefore, we developed a new statistical learning method, Step-adjusted Tree-based Reinforcement Learning, to evaluate DTRs within such a nested multi-stage dynamic decision framework using observational data. At each step within each stage, we combined the robust semi-parametric estimation via Augmented Inverse Probability Weighting with a tree-based reinforcement learning method to deal with the counterfactual optimization. The simulation studies demonstrated robust performance of the proposed methods under different scenarios. We further applied our method to evaluate the necessity of prostate biopsy and identify the optimal test-and-treat regimes for prostate cancer patients using data from the Johns Hopkins University prostate cancer active surveillance dataset.

As organizations across the United States resume activities even as the novel coronavirus endures, millions of employees could come into contact with sick coworkers and become exposed to the 2019 novel coronavirus (COVID-19). Unfortunately, little is known about how sick individuals might be treated at work. Because working with a sick coworker may simultaneously evoke concerns about oneself and one's ailing colleague, we propose dual mechanisms of self-concern and coworker-orientation to explain the relationship between coworker presenteeism (i.e., a coworker attends work while ill) and interpersonal mistreatment. Across two studies with employees working face-to-face during the pandemic, our findings showed that coworker presenteeism decreases subsequent workplace mistreatment through coworker-orientation. Moreover, we found that coworker presenteeism increases mistreatment through self-concern when employees experience higher workloads. Implications and future research directions are discussed.

Despite wide-ranging negative consequences of interpersonal mistreatment, research offers few practical solutions to reduce such behavior in organizations. Given that interpersonal relationships are strengthened and desired employee behaviors are more frequent when individuals purposefully cultivate feelings of gratitude, the present study tests the effectiveness of a 10-day gratitude journaling intervention in reducing workplace incivility, gossip, and ostracism. Because research has not examined the mechanisms by which gratitude interventions influence outcomes, we draw on theory and research from the gratitude literature to propose and test a multiple mediator model. Specifically, we examine the moral affect theory of gratitude, find-remind-and-bind theory, self-regulation theory, and social exchange theory as possible explanations for the effects of the intervention. Two field experiments involving 147 (Study 1) and 204 (Study 2) employees demonstrated that the intervention decreased mistreatment (as reported by coworkers) by enhancing self-control resources. We also found that the effects of the intervention were stronger for individuals who perceive higher norms for gratitude in their workplace. The findings support the resource-building nature of gratitude interventions and demonstrate that a gratitude intervention is one effective way to decrease interpersonal mistreatment in organizations. Implications for theory and practice are discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Across the United States, 20% of women ages 18 and over are victims of penetrative sexual assault, with the highest rates occurring in early adulthood (i.e., ages 18-24). Despite the high prevalence and severe mental and physical health problems resulting from sexual assault, with few exceptions, little is known about how victims of sexual assault attribute subsequent academic and career outcomes. Approximately 901 adult participants were recruited from a probability-based nationally representative sample to examine the prevalence of and outcomes attributed to sexual assault victimization experienced during early adulthood. Of the 36% of female participants who reported sexual assault perpetrated against them between the ages of 18 and 24, 69.5% attributed at least one negative academic or career outcome to the sexual assault. Participants who identified as White and who were over the age of 30 at the time of the survey were more likely to attribute negative academic and career outcomes as a result of a sexual assault perpetrated against them during early adulthood than women of color and younger women (i.e., ages 25-29). Our findings expand the knowledge on negative outcomes attributed to sexual assault and yields more questions about the larger societal impacts. More research is needed to understand the intersection between an assault during young adulthood and the demographic characteristics of survivors.

Summary It is becoming increasingly common for researchers to consider incorporating external information from large studies to improve the accuracy of statistical inference instead of relying on a modestly sized data set collected internally. With some new predictors only available internally, we aim to build improved regression models based on individual-level data from an “internal” study while incorporating summary-level information from “external” models. We propose a meta-analysis framework along with two weighted estimators as the composite of empirical Bayes estimators, which combines the estimates from different external models. The proposed framework is flexible and robust in the ways that (i) it is capable of incorporating external models that use a slightly different set of covariates; (ii) it is able to identify the most relevant external information and diminish the influence of information that is less compatible with the internal data; and (iii) it nicely balances the bias-variance trade-off while preserving the most efficiency gain. The proposed estimators are more efficient than the naïve analysis of the internal data and other naïve combinations of external estimators.

Objectives A growing proportion of the U.S. labor force juggles paid work with family caregiving of older adults. However, no research has examined caregivers’ work environments. The purpose of this brief report is to develop typologies of the work environments of family caregivers. Methods This study used data drawn from the 2008–2012 waves of the Health and Retirement Study. Our sample includes employed individuals who also provided regular help with daily activities to a parent or spouse (n = 976). We used latent class analysis to develop caregiver work environment typologies. Results Our analyses revealed 4 typologies among caregivers: (a) high-quality work environments (n = 340; 35%); (b) average work environments with high job lock (n = 293; 30%); (c) low-quality work environments (n = 203; 21%); and (d) high personal interference in supportive work environments (n = 140; 14%). Although only 21% of working caregivers were in a low-quality work environment (Type C), descriptive results suggest that these workers were most likely to be minorities who needed to work for financial reasons, reporting the highest number of health problems, and the most work hours. Discussion Our findings provide insights into the types of environments that caregivers work in, and the characteristics of individuals in those environments. We discuss implications of our findings for future research and work-based policy development.

The Mediterranean Basin is a biodiversity hotspot. Wetlands make a key contribution to this status, but many of them remain outside the Ramsar network fifty years after the establishment of the Ramsar Convention. Here we evaluate the extent to which the Mediterranean Ramsar network covers wetlands of international importance for wintering waterbirds using the Ramsar Convention criteria 2 (species of conservation concern), 5 (> 20,000 waterbirds) and 6 (1% of a population). These criteria were applied to 4186 sites in 24 Mediterranean countries using counts of 145 wintering waterbird species from 1991 to 2017. We identified 161 sites of international importance for waterbirds that have not yet been declared as Ramsar sites, which could be added to the 180 current Mediterranean Ramsar sites established based on waterbird criteria (criteria 5 and/or 6). Among these sites, a subset of 32 very important sites reached double the required level for at least one criterion and 95 were not protected by any site conservation status. Coastal wetlands represented half of the Ramsar gap for waterbirds. We identified that an additional 1218 monitored sites could be provisionally considered as internationally important and thus require more survey efforts to assess their status. This study highlights a lack of participation of the Mediterranean countries to build the Ramsar network for wetland protection. Our results should help policymakers and managers to prioritize future Ramsar site designation, notably in the Middle East and Western European region where important gaps were identified.

It is challenging to communicate uncertainty for high-impact weather events to the public and decision-makers. As a result, there is an increased emphasis and training within the National Weather Service (NWS) for “impact-based decision support.” A Collaborative Science, Technology, And Research (CSTAR) project led by Stony Brook University (SBU) in collaboration with the Alan Alda Center for Communicating Science, several NWS forecast offices, and NWS operational centers held two workshops at SBU on effective forecast communication of probabilistic information for high-impact weather. Trainers in two 1.5-day workshops helped 15–20 forecasters learn to distill their messages, engage audiences, and more effectively communicate risk and uncertainty to decision-makers, media, and the general public. The novel aspect of the first workshop focused on using improvisational techniques to connect with audiences along with exercises to improve communication skills using short, clear, conversational statements. The same forecasters participated in the second workshop, which focused on matching messages to intended audiences and stakeholder interaction. Using a recent high-impact weather event, representatives in emergency management, TV media, departments of transportation, and emergency services provided feedback on the forecaster oral presentations (2–3 min) and a visual slide. This article describes our innovative workshop approach, illustrates some of the techniques used, and highlights participant feedback.

We investigated the immunomodulatory effect of varying levels of dietary ω6/ω3 fatty acids (FA) on Atlantic salmon (Salmo salar) antibacterial response. Two groups were fed either high-18:3ω3 or high-18:2ω6 FA diets for 8 weeks, and a third group was fed for 4 weeks on the high-18:2ω6 diet followed by 4 weeks on the high-18:3ω3 diet and termed “switched-diet”. Following the second 4 weeks of feeding (i.e., at 8 weeks), head kidney tissues from all groups were sampled for FA analysis. Fish were then intraperitoneally injected with either a formalin-killedRenibacterium salmoninarumbacterin (5 × 107cells mL−1) or phosphate-buffered saline (PBS control), and head kidney tissues for gene expression analysis were sampled at 24 h post-injection. FA analysis showed that the head kidney profile reflected the dietary FA, especially for C18FAs. The qPCR analyses of twenty-three genes showed that both the high-ω6 and high-ω3 groups had significant bacterin-dependent induction of some transcripts involved in lipid metabolism (ch25haandlipe), pathogen recognition (clec12bandtlr5), and immune effectors (znrf1andcish).In contrast, these transcripts did not significantly respond to the bacterin in the “switched-diet” group. Concurrently, biomarkers encoding proteins with putative roles in biotic inflammatory response (tnfrsf6b) and dendritic cell maturation (ccl13) were upregulated, and a chemokine receptor (cxcr1) was downregulated with the bacterin injection regardless of the experimental diets. On the other hand, an inflammatory regulator biomarker,bcl3, was only significantly upregulated in the high-ω3 fed group, and a C-type lectin family member (clec3a) was only significantly downregulated in the switched-diet group with the bacterin injection (compared with diet-matched PBS-injected controls). Transcript fold-change (FC: bacterin/PBS) showed thattlr5was significantly over 2-fold higher in the high-18:2ω6 diet group compared with other diet groups. FC and FA associations highlighted the role of DGLA (20:3ω6; anti-inflammatory) and/or EPA (20:5ω3; anti-inflammatory) vs. ARA (20:4ω6; pro-inflammatory) as representative of the anti-inflammatory/pro-inflammatory balance between eicosanoid precursors. Also, the correlations revealed associations of FA proportions (% total FA) and FA ratios with several eicosanoid and immune receptor biomarkers (e.g., DGLA/ARA significant positive correlation withpgds,5loxa,5loxb,tlr5, andcxcr1). In summary, dietary FA profiles and/or regimens modulated the expression of some immune-relevant genes in Atlantic salmon injected withR. salmoninarumbacterin. The modulation of Atlantic salmon responses to bacterial pathogens and their associated antigens using high-ω6/high-ω3 diets warrants further investigation.

Global food security depends on the sustainability of irrigated agriculture. Rising groundwater withdrawals from seasonally humid, alluvial plains across tropical Asia have enabled dry-season rice cultivation. This groundwater pumpage increases available subsurface storage that under favorable conditions amplifies groundwater replenishment during the subsequent monsoon. We empirically quantified this nature-based solution to seasonal freshwater storage capture described as the “Bengal Water Machine,” revealing its potential and limitations. On the basis of a million piezometric observations from 465 monitoring wells, we show that the collective operation of ~16 million smallholder farmers in the Bengal Basin of Bangladesh from 1988 to 2018 has induced cumulative freshwater capture that volumetrically (75 to 90 cubic kilometers) is equivalent to twice the reservoir capacity of the Three Gorges Dam.

To create an algorithm able to accurately detect IVC filters on radiographs without human assistance, capable of being used to screen radiographs to identify patients needing IVC filter retrieval.A primary dataset of 5225 images, 30% of which included IVC filters, was assembled and annotated. 85% of the data was used to train a Cascade R-CNN (Region Based Convolutional Neural Network) object detection network incorporating a pre-trained ResNet-50 backbone. The remaining 15% of the data, independently annotated by three radiologists, was used as a test set to assess performance. The algorithm was also assessed on an independently constructed 1424-image dataset, drawn from a different institution than the primary dataset.On the primary test set, the algorithm achieved a sensitivity of 96.2% (95% CI 92.7-98.1%) and a specificity of 98.9% (95% CI 97.4-99.5%). Results were similar on the external test set: sensitivity 97.9% (95% CI 96.2-98.9%), specificity 99.6 (95% CI 98.9-99.9%).Fully automated detection of IVC filters on radiographs with high sensitivity and excellent specificity required for an automated screening system can be achieved using object detection neural networks. Further work will develop a system for identifying patients for IVC filter retrieval based on this algorithm.

Monocytes play a large role in chronic inflammatory conditions such as obesity, atherosclerosis and infection. Marine-derived omega-3 fatty acids such as docosahexaenoic acid (DHA) beneficially alter immune function and attenuate chronic inflammation in part by modifying gene expression. Comparisons with plant-derived omega-3 α-linolenic acid (ALA) on immune cell gene expression and function are limited.Transcriptome analysis was performed on THP-1 human monocytes treated with ALA, DHA or vehicle for 48 hr using fold change analysis, principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), variable importance analysis (VIP), and ingenuity pathway analysis (IPA). Candidate genes were validated by qPCR. Functional assays evaluated the transcriptomic predictions. Expression of candidate transcripts identified in THP-1 cells were examined in PBMC from clinical trial (OXBIO; NCT03583281) participants consuming ALA- or DHA-rich oil supplements.ALA and DHA-treated monocytes presented distinct transcriptomic profiles as per VIP and PLS-DA. Both fatty acids were predicted to reduce cellular cholesterol content, while ALA would uniquely increase response to infection and chemotactic signals. Functional assays revealed ALA and DHA decreased cholesterol content. DHA significantly decreased the response to infection and chemotaxis, but ALA had no effect. Candidate transcripts responded similarly in PBMC from n-3 PUFA supplemented women with obesity.ALA and DHA differentially alter the transcription profiles and functions associated with the response to infection, chemotaxis, and cholesterol metabolism in mononuclear immune cells. Thus, they may uniquely affect related disease processes contributing to obesity, atherosclerosis, and the response to infection.

In rapidly growing cities in the tropics, unregulated urban development presents a major risk to groundwater quality. Here, we assess the vulnerability of an unconfined aquifer of Quaternary sands in the Thiaroye area of Dakar (Senegal) to contamination using four GIS-based indices (DRASTIC, DRASTIC_N, SINTACS, SI). Our correlation of assessed vulnerability to observed impact is semi-quantitative, relating observed groundwater quality, based on nitrate concentrations and tryptophan-like fluorescence to vulnerability degrees (i.e. coincidence rates). We show that considerably more of the Thiaroye area has a “very high vulnerability” according to SI (36%) relative to DRASTIC (5%) and SINTACS (9%); “high vulnerability” is estimated using DRASTIC_N (100%), DRASTIC (66%) and SINTACS (69%). Single-parameter sensitivity tests show that groundwater depth, soil, topography, land use and redox parameters strongly influence assessments of groundwater vulnerability. Correlation with observed nitrate concentrations reveals aquifer vulnerability is better represented by SI (coincidence rates of 56%) relative to DRASTIC_N (43%), SINTACS (38%) and DRASTIC (34%). The underestimation of groundwater vulnerability in Dakar using DRASTIC, DRASTIC_N and SINTACS is attributed to their reliance on an assumed capacity of the unsaturated zone to attenuate surface or near-surface contaminant loading, which in the low-income (Thiaroye) area of Dakar is thin and affords limited protection. The inclusion of a land-use parameter in SI improves the characterization of groundwater vulnerability in this low-income, rapidly urbanizing area of Dakar.

Valid surrogate endpoints S can be used as a substitute for a true outcome of interest T to measure treatment efficacy in a clinical trial. We propose a causal inference approach to validate a surrogate by incorporating longitudinal measurements of the true outcomes using a mixed modeling approach, and we define models and quantities for validation that may vary across the study period using principal surrogacy criteria. We consider a surrogate-dependent treatment efficacy curve that allows us to validate the surrogate at different time points. We extend these methods to accommodate a delayed-start treatment design where all patients eventually receive the treatment. Not all parameters are identified in the general setting. We apply a Bayesian approach for estimation and inference, utilizing more informative prior distributions for selected parameters. We consider the sensitivity of these prior assumptions as well as assumptions of independence among certain counterfactual quantities conditional on pretreatment covariates to improve identifiability. We examine the frequentist properties (bias of point and variance estimates, credible interval coverage) of a Bayesian imputation method. Our work is motivated by a clinical trial of a gene therapy where the functional outcomes are measured repeatedly throughout the trial.

Dynamic prediction methods incorporate longitudinal biomarker information to produce updated, more accurate predictions of conditional survival probability. There are two approaches for obtaining dynamic predictions: (1) a joint model of the longitudinal marker and survival process, and (2) an approximate approach that specifies a model for a specific component of the joint distribution. In the case of a binary marker, an illness-death model is an example of a joint modeling approach that is unified and produces consistent predictions. However, previous literature has shown that approximate approaches, such as landmarking, with additional flexibility can have good predictive performance. One such approach proposes using a Gaussian copula to model the joint distribution of conditional continuous marker and survival distributions. It has the advantage of specifying established, flexible models for the marginals for which goodness-of-fit can be assessed, and has easy estimation that can be implemented in standard software. In this article, we provide a Gaussian copula approach for dynamic prediction to accommodate a binary marker using a continuous latent variable formulation. We compare the predictive performance of this approach to joint modeling and landmarking using simulations and demonstrate its use for obtaining dynamic predictions in an application to a prostate cancer study.

We examine groundwater recharge processes and their relationship to rainfall intensity in the semi-arid, southwestern Lake Chad Basin of Nigeria using a newly compiled database of stable isotope da...

Emotional regulation and intolerance of uncertainty have been independently implicated in the overuse of experiential avoidance in stress-related situations. However, little research has examined how this relationship manifests altogether in a non-clinical sample. The current study aimed to determine the predictive nature of intolerance of uncertainty and the ability to regulate emotions on experiential avoidance. A secondary aim was to establish the role of worry in these relationships. Participants (N = 360) completed questionnaires that measured intolerance of uncertainty, emotion dysregulation, worry and experiential avoidance. In stage one of the analysis, a bivariate linear regression was conducted to see if both intolerance of uncertainty and emotion regulations would significant predict avoidance. These were then entered into a multiple linear regression model with forced entry method to explain the variance in experiential avoidance. Subsequently, a mediation analysis was conducted using worry, between intolerance of uncertainty and experiential avoidance and between emotion regulation and experiential avoidance. As expected, individuals’ tendency to intolerance of uncertainty and poor emotion regulation individually predicted experiential avoidance. Furthermore, worry mediated these relationships. These findings have implications for self-awareness responses in healthy individuals who show avoidant behaviours. Inflexible use of emotion regulation and low tolerance of uncertainty make a significant contribution to experiential avoidance. Therefore, therapeutic interventions specifically targeting both intolerance of uncertainty and appropriate use of emotion regulation may help enhance individuals’ self-awareness in relation to avoidant responses to prevent the potential creation of the cycle that maintains anxiety disorders.

With medical tests becoming increasingly available, concerns about over-testing, over-treatment and health care cost dramatically increase. Hence, it is important to understand the influence of testing on treatment selection in general practice. Most statistical methods focus on average effects of testing on treatment decisions. However, this may be ill-advised, particularly for patient subgroups that tend not to benefit from such tests. Furthermore, missing data are common, representing large and often unaddressed threats to the validity of most statistical methods. Finally, it is often desirable to conduct analyses that can be interpreted causally. Using the Rubin Causal Model framework, we propose to classify patients into four potential outcomes subgroups, defined by whether or not a patient’s treatment selection is changed by the test result and by the direction of how the test result changes treatment selection. This subgroup classification naturally captures the differential influence of medical testing on treatment selections for different patients, which can suggest targets to improve the utilization of medical tests. We can then examine patient characteristics associated with patient potential outcomes subgroup memberships. We used multiple imputation methods to simultaneously impute the missing potential outcomes as well as regular missing values. This approach can also provide estimates of many traditional causal quantities of interest. We find that explicitly incorporating causal inference assumptions into the multiple imputation process can improve the precision for some causal estimates of interest. We also find that bias can occur when the potential outcomes conditional independence assumption is violated; sensitivity analyses are proposed to assess the impact of this violation. We applied the proposed methods to examine the influence of 21-gene assay, the most commonly used genomic test in the United States, on chemotherapy selection among breast cancer patients.

We present a revised Spectral Adaptive Light Curve Template (SALT2) surface (SALT2-2021) for fitting the light curves of Type Ia supernovae (SNe Ia), which incorporates new measurements of zero-point calibration offsets and Milky Way extinction. The most notable change in the new surface occurs in the UV region. This new surface alters the distance measurements of SNe Ia, which can be used to investigate the nature of dark energy by probing the expansion history of the Universe. Using the revised SALT2 surface on public data from the first 3 yr of the Dark Energy Survey Supernova Program (combined with an external low-z SNe Ia sample) and combining with cosmic microwave background constraints, we find a change in the dark energy equation-of-state parameter, Δw = 0.015 ± 0.004. This result highlights the continued importance of controlling and reducing systematic uncertainties, particularly with the next generation of supernova analyses aiming to improve constraints on dark energy properties.