bluuebunny/crossref_metadata_2025_split huggingface dataset:
Defining, conceptualizing and evaluating pragmatic qualities of quantitative instruments measuring implementation determinants and outcomes: a scoping and critical review of the literature and recommendations for future research
Authors: Hull Louise , Boulton Richard , Jones Fiona , Boaz Annette , Sevdalis Nick 
Published: November 2022

AbstractThe pragmatic (i.e., practical) quality of quantitative implementation measures has received increased attention in the implementation science literature in recent years. Implementation measures that are judged to be pragmatic by implementation stakeholders are thought to be more likely to be applied in research and practice. Despite the need for pragmatic implementation measures, ambiguity and uncertainty regarding what constitutes a pragmatic measure remains. This study sought to identify and critically appraise the published literature to understand (i) how pragmatism is defined as a measurement construct/quality of implementation determinants and outcome instruments; (ii) how pragmatic qualities of instruments are evaluated; (iii) identify key gaps and limitations of the current evidence-base and (iv) identify recommendations for future research. We conducted a scoping review of the literature also employing methods of critical review. PubMed and PsycINFO databases, using the OVID interface, were searched for relevant articles published between January 2010 and September 2020. Articles that contained a definition and/or described characteristics of “pragmatism” as a measurement construct of quantitative implementation outcomes (as defined by Proctor’s Implementation Outcomes taxonomy) and/or implementation determinants were eligible for inclusion. Nine articles met inclusion criteria. A degree of overlap in definitions and terms used to describe the pragmatic qualities of quantitative implementation determinant and outcome instruments were found. The most frequently cited descriptors of pragmatism were “not burdensome”, “brief”, “reliable”, “valid” and “sensitive to change”. 3 of the 9 included articles involved international implementation stakeholders in defining and conceptualizing pragmatism and employed specific methods to do so, including a systematic literature review, stakeholder interviews, concept mapping, and a Delphi process. All other articles defined pragmatism, with or without citing relevant literature. One article objectively assessed the pragmatic qualities, above and beyond the psychometric qualities, of implementation measures, using the Psychometric and Pragmatic Evidence Rating Scale (PAPERS). The evidence base within the implementation instrumentation literature on what pragmatism is and how it might be assessed is limited. Some of the research identified in the review provides a strong foundation to build upon, by testing its applicability in other settings (including healthcare areas and countries) and among a more diverse group of stakeholders. We discuss directions for further development of the concept of pragmatism relating to the measurement of implementation determinants and outcomes.
Piloting a Mobility Tech Role at an Academic Medical Center
Authors: James David , Harrell Shari , Labbe Valerie , Ray Midge 
Published: November 2022

ABSTRACT

Introduction:
There is often a breakdown in communication between physical therapy and nursing regarding the responsibility of patient mobility. This communication problem contributes to mobility being the most frequently missed clinical intervention. To address this gap, a Mobility Tech (MT) role was implemented at a large academic medical center. The purpose of this article is to describe the implementation of this role.

Methods:
Using the Institute for health care improvement (IHI) model for improvement, iterative small tests of change were developed to integrate the MT role on four acute care units. Using process flow mapping, the team developed a MT workflow that was adapted to create MT protocols.

Results:
Program outcomes included fall rates and the highest level of mobility documented by staff. During the MT pilot from February 2020 through June 2020, MTs provided an average of over 500 interventions each month. There was a clinically and statistically significant increase in mobility noted in two of the four pilot units.

Conclusion:
We have demonstrated that MTs can be safely incorporated into acute care nursing workflow, and comments from staff and patients validate the role is “value added.”
Functional and molecular characterization of suicidality factors using phenotypic and genome-wide data
Authors: Quintero Reis Andrea , Newton Brendan A , Kessler Ronald , Polimanti Renato , Wendt Frank R 
Published: November 2022

ABSTRACTGenome-wide association studies (GWAS) of suicidal thoughts and behaviors support the existence of genetic contributions. Continuous measures of psychiatric disorder symptom severity can sometimes model polygenic risk better than binarized definitions. We compared two severity measures of suicidal thoughts and behaviors at the molecular and functional levels using genome-wide data. We used summary association data from GWAS of four traits analyzed in 122,935 individuals of European ancestry:thought life was not worth living(TLNWL),thoughts of self-harm, actual self-harm, andattempted suicide. The fifth trait, suicidality, was constructed with phenotypically as an aggregate of these four traits and genetically using Genomic Structural Equation modeling. Suicidality and S-factor were compared at the level of SNP-heritability (h2), genetic correlation, partitionedh2, effect size distribution, transcriptomic effects in the brain, and cross-population polygenic scoring (PGS). The S-factor had good model fit (χ2=0.21, AIC=16.21, CFI=1.00, SRMR=0.024). Suicidality (h2=7.6%) had higherh2than the S-factor (h2=2.54, Pdiff=4.78×10-13). Although the S-factor had a larger number of non-null susceptibility loci (πc=0.010), these loci had small effect sizes compared to those influencing suicidality (πc=0.005, Pdiff=0.045). Theh2of both traits was enrichment for conserved biological pathways. Therg andρGEsupport highly overlapping genetic and transcriptomic features between suicidality and the S-factor. PGS using European-ancestry SNP effect sizes strongly associated with TLNWL in Admixed Americans: Nagelkerke’sR2=8.56%, P=0.009 (PGSsuicidality) and Nagelkerke’sR2=7.48%, P=0.045 (PGSS-factor). An aggregate suicidality phenotype was statistically more heritable than the S-factor across all analyses and may be more informative for future study genetic designs than individual suicidality indicator traits.
Rare coding variants inCHRNB2reduce the likelihood of smoking
Authors: Rajagopal Veera M. , Watanabe Kyoko , Mbatchou Joelle , Ayer Ariane , Quon Peter , Sharma Deepika , Kessler Michael D. , Praveen Kavita , Gelfman Sahar , Parikshak Neelroop , Otto Jacqueline M. , Bao Suyin , Chim Shek Man , Pavlopoulos Elias , Avbersek Andreja , Kapoor Manav , Chen Esteban , Jones Marcus B. , Leblanc Michelle , Emberson Jonathan , Collins Rory , Torres Jason , Morales Pablo Kuri , Tapia-Conyer Roberto , Alegre Jesus , Berumen Jaime ,   GHS-REGN DiscovEHR collaboration,   Regeneron Genetics Center, Shuldiner Alan R. , Balasubramanian Suganthi , Abecasis Goncalo R. , Kang Hyun M. , Marchini Jonathan , Stahl Eli A. , Jorgenson Eric , Sanchez Robert , Liedtke Wolfgang , Anderson Matthew , Cantor Michael , Lederer David , Baras Aris , Coppola Giovanni 
Published: November 2022

AbstractHuman genetic studies of smoking behavior have been so far largely limited to common variations. Studying rare coding variants has potential to identify new drug targets and refine our understanding of the mechanisms of known targets. We performed an exome-wide association study (ExWAS) of smoking phenotypes in up to 749,459 individuals across multiple ancestries and discovered a protective association signal inCHRNB2that encodes the β2 subunit of α4β2 nicotine acetylcholine receptor (nAChR). Rare predicted loss-of-function (pLOF) and likely deleterious missense variants inCHRNB2in aggregate were associated with a 35% decreased odds for smoking more than 10 cigarettes per day (OR=0.65, CI=0.56-0.76, P=1.9e-8). An independent common variant association in the protective direction (rs2072659; OR=0.96; CI=0.94-0.98; P=5.3e-6) was also evident, suggesting an allelic series. The protective effects of both rare and common variants were detectable to some extent on phenotypes downstream of smoking including lung function, emphysema, chronic obstructive pulmonary disease (COPD) and lung cancer. α4β2 is the predominant nAChR in human brain and is one of the targets of varenicline, a partial nAChR agonist/antagonist used to aid smoking cessation. Our findings in humans align with decades-old experimental observations in mice that β2 loss abolishes nicotine mediated neuronal responses and attenuates nicotine self-administration. Our genetic discovery will inspire future drug designs targetingCHRNB2in the brain for the treatment of nicotine addiction.
The Impact of COVID-19 on Life Expectancy among Asian American Subgroups
Authors: Park Sung S. , Goldman Noreen , Andrasfay Theresa 
Published: November 2022

AbstractDespite recognition of the diversity of the Asian American population, the mortality impact of the COVID-19 pandemic has been limited to estimates for non-Hispanic Asian Americans in aggregate. This study provides estimates of life expectancy at birth before (2019) and during the pandemic, along with a broad set of demographic, health-related, and socioeconomic risk factors for COVID-19, for the six largest Asian American subgroups: Asian Indians, Chinese, Filipino, Japanese, Korean, and Vietnamese. Our study places these estimates in the context of the broader U.S. population by including the corresponding estimates for non-Hispanic Whites, non-Hispanic Blacks, Hispanics, and non-Hispanic Native Americans. We use data on age-specific all-cause mortality from CDC WONDER and population estimates from the 2015-2019 American Community Survey to construct life tables for each Asian subgroup by year. While losses in life expectancy during the second year of the pandemic diminished significantly among all racial/ethnic and Asian subgroups, these improvements do not compensate for the large increases in death rates in 2020. All major Asian subgroups except Japanese experienced greater losses of life in 2019-2020 as well as cumulatively (2019-2021) than Whites, with Vietnamese, Filipinos, and other South/Southeast Asians having suffered the largest declines in life expectancy among non-Hispanic Asians. Vietnamese and other Southeast Asians experienced the greatest cumulative losses across all racial/ethnic groups except Native Americans. Our findings underscore the heterogeneity of loss in life expectancy within the Asian American population while identifying some of the risk factors that likely underlie this large variation.
Best Practices for Interpretable Machine Learning in Computational Biology
Authors: Chen Valerie , Yang Muyu , Cui Wenbo , Kim Joon Sik , Talwalkar Ameet , Ma Jian 
Published: November 2022

AbstractAdvances in machine learning (ML) have enabled the development of next-generation prediction models for complex computational biology problems. These developments have spurred the use of interpretable machine learning (IML) to unveil fundamental biological insights through data-driven knowledge discovery. However, in general, standards and guidelines for IML usage in computational biology have not been well-characterized, representing a major gap toward fully realizing the potential of IML. Here, we introduce a workflow on the best practices for using IML methods to perform knowledge discovery which covers verification strategies that bridge data, prediction model, and explanation. We outline a workflow incorporating these verification strategies to increase an IML method’s accountability, reliability, and generalizability. We contextualize our proposed workflow in a series of widely applicable computational biology problems. Together, we provide an extensive workflow with important principles for the appropriate use of IML in computational biology, paving the way for a better mechanistic understanding of ML models and advancing the ability to discover novel biological phenomena.
Long-term Association between NO2and Human Mobility: A Two-year Spatiotemporal Study during the COVID-19 Pandemic in Southeast Asia
Authors: Liu Zhaoyin , Li Yangyang , Law Andrea , Tan Jia Yu Karen , Chua Wee Han , Zhu Yihan , Feng Chen-Chieh , Luo Wei 
Published: November 2022

AbstractSince the COVID-19 pandemic, governments have implemented lockdowns and movement restrictions to contain the disease outbreak. Previous studies have reported a significant positive correlation between NO2and mobility level during the lockdowns in early 2020. Though NO2level and mobility exhibited similar spatial distribution, our initial exploration indicated that the decreased mobility level did not always result in concurrent decreasing NO2level during a two-year time period in Southeast Asia with human movement data at a very high spatial resolution (i.e., Facebook origin-destination data). It indicated that factors other than mobility level contributed to NO2level decline. Our subsequent analysis used a trained Multi-Layer Perceptron model to assess mobility and other contributing factors (e.g., travel modes, temperature, wind speed) and predicted future NO2levels in Southeast Asia. The model results suggest that, while as expected mobility has a strong impact on NO2level, a more accurate prediction requires considering different travel modes (i.e., driving and walking). Mobility shows two-sided impacts on NO2level: mobility above the average level has a high impact on NO2, whereas mobility at a relatively low level shows negligible impact. The results also suggest that spatio-temporal heterogeneity and temperature also have impacts on NO2and they should be incorporated to facilitate a more comprehensive understanding of the association between NO2and mobility in the future study.
Investigation of intra-subtype recombination in Zimbabwean HIV 1 subtype C using bioinformatics. (December 2019)
Authors: Kambarami Milton S , Justen Manasa 
Published: November 2022

AbstractIntra-subtyperecombination is a common phenomenon among HIV strains which involves transfer of genetic information between two viral strains belonging to the same HIV subtype. However, more research has been done oninter-subtyperecombination which is the transfer of genetic information between two strains belonging to different HIV subtypes. In this study HIV 1 subtype Cenvsequences from Zimbabwean nucleotide sequences (n=2 915) downloaded from Los Alamos HIV databases were used to investigate intra-subtype recombination. Using the GENECUTTER tool, ∼44.4% of whole genomes were selected to have informativeenvregions (n = 1 295). Clustal was used for Multiple Sequence Alignment and RDP 4 was used for analysis and detection of Intra-subtype recombination. Apparently, GENECONV included in RDP 4 detected 47 recombination events. One of the most informative recombination events was further analysed using UPGMA phylogenetic tree to understand the extent of the intra-subtype recombination.
How density dependence, genetic erosion, and the extinction vortex impact evolutionary rescue
Authors: Nordstrom Scott W. , Hufbauer Ruth A. , Olazcuaga Laure , Durkee Lily F. , Melbourne Brett A. 
Published: November 2022

AbstractFollowing severe environmental change that reduces mean population fitness below replacement, populations must adapt to avoid eventual extinction, a process called evolutionary rescue. Models of evolutionary rescue demonstrate that initial size, genetic variation, and degree of maladaptation influence population fates. However, many models feature populations that grow without negative density dependence or with constant genetic diversity despite precipitous population decline, assumptions likely to be violated in conservation settings. We examined the simultaneous influences of density-dependent growth and erosion of genetic diversity on populations adapting to novel environmental change using stochastic, individual-based simulations. Density dependence decreased the probability of rescue and increased the probability of extinction, especially in large and initially well-adapted populations that previously have been predicted to be at low risk. Increased extinction occurred shortly following environmental change, as populations under density dependence experienced more rapid decline and reached smaller sizes. Populations that experienced evolutionary rescue lost genetic diversity through drift and adaptation, particularly under density dependence.Populations that declined to extinction entered an extinction vortex, where small size increased drift, loss of genetic diversity, and the fixation of maladaptive alleles, hindered adaptation, and kept populations at small densities where they were vulnerable to extinctionviademographic stochasticity.
Different genetic architectures of complex traits and their relevance to polygenic score performance in diverse populations
Authors: Carvalho Nuno R. G. , Harris Adrian M. , Lachance Joseph 
Published: November 2022

AbstractBackgroundDespite the many insights gleaned from GWAS, polygenic predictions of complex traits have had limited success, particularly when these predictions are applied to individuals of non-European descent. A deeper understanding of the genetic architecture of complex traits may inform why some traits are easier to predict than others.MethodsExamining 163 complex traits from the UK Biobank, we compared and contrasted three aspects of genetic architecture (SNP heritability, LD variability, and genomic inequality) with three aspects of polygenic score performance (prediction accuracy in the source population, portability across populations, and trait divergence across populations). Here, genomic inequality refers to how unequally the genetic variance of each trait is distributed across the top trait-associated SNPs, as quantified via a novel application of Gini coefficients.ResultsConsistent with reduced statistical power, polygenic predictions of binary traits performed worse than predictions of quantitative traits. Traits with low Gini coefficients (i.e., highly polygenic architectures) include hip circumference as well as systolic and diastolic blood pressure. Traits with large population-level differences in polygenic scores include skin pigmentation and hair color. Focusing on 96 quantitative traits, we found that highly heritable traits were easier to predict and had predictions that were more portable to other ancestries. Traits with highly divergent polygenic score distributions across populations were less likely to have portable predictions. Intriguingly, LD variability was largely uninformative regarding the portability of polygenic predictions. This suggests that factors other than the differential tagging of causal SNPs drive the reduction in polygenic score accuracy across populations. Subsequent analyses identified suites of traits with similar genetic architecture and polygenic score performance profiles. Importantly, lifestyle and psychological traits tended to have low heritability, as well as poor predictability and portability.ConclusionsNovel metrics capture different aspects of trait-specific genetic architectures and polygenic score performance. Our findings also caution against the application of polygenic scores to traits like general happiness, alcohol frequency, and average income, especially when polygenic scores are applied to individuals who have an ancestry that differs from the original source population.
Genetic variants of human platelet antigens in the Indian population from 1029 whole genomes
Authors: Rophina Mercy , Bhoyar Rahul C , Imran Mohamed , Senthivel Vigneshwar , Divakar Mohit Kumar , Mishra Anushree , Jolly Bani , Sivasubbu Sridhar , Scaria Vinod 
Published: November 2022

AbstractBackgroundGenetic variants in human platelet antigens (HPAs) considered as allo- or auto antigens are associated with various disorders including neonatal alloimmune thrombocytopenia, platelet transfusion refractoriness and post-transfusion purpura. While global differences in genotype frequencies were observed, the distribution of HPA variants in the Indian population are largely unknown. This study aims to explore the landscape of HPA variants in India to provide a basis for risk assessment and management of related complications.Materials and methodsPopulation specific frequencies of genetic variants associated with the 35 classes of HPAs (HPA-1 to HPA-35) were estimated by systematically analyzing genomic variations of 1029 healthy Indian individuals as well as from global population genome datasets..ResultsAllele frequencies of the most clinically relevant HPA systems in the Indian population were found as follows, HPA-1a – 0.89, HPA-1b – 0.15, HPA-2a – 0.94, HPA-2b – 0.05, HPA-3a – 0.66, HPA-3b – 0.36, HPA-4a – 1.00, HPA-4b – 0, HPA-5a – 0.92, HPA-5b – 0.08, HPA-6a – 1.00, HPA-6b – 0, HPA-15a – 0.58 and HPA-15b – 0.42. In addition, HPA-4b allele frequencies were found to be significantly higher in India in comparison to global populations.ConclusionThis study provides the first comprehensive analysis of HPA allele and genotype frequencies using large scale representative whole genome sequencing data of the Indian population.
Photonic-Plasmonic Coupling Enhanced Fluorescence Enabling Digital-Resolution Ultrasensitive Protein Detection
Authors: Barya Priyash , Xiong Yanyu , Shepherd Skye , Gupta Rohit , Akin Lucas D. , Tibbs Joseph , Lee Han Keun , Singamaneni Srikanth , Cunningham Brian T. 
Published: November 2022

ABSTRACTAssays utilizing molecular fluorophores are common throughout life science research and diagnostic testing, although detection limits are generally limited by weak emission intensity, thus requiring many labeled target molecules to combine their output to achieve signal-to-noise greater than the background. Here, we describe how the synergistic coupling of plasmonic and photonic resonance modes can significantly boost the emission from fluorescent dye molecules without increasing the illumination intensity while utilizing a microscopy approach with a broad field of view. By optimally matching the resonant modes of a plasmonic fluor (PF) nanoparticle and a photonic crystal (PC) surface with the absorption and emission spectrum of the PF’s fluorescent dye, we observe a 52-fold improvement in signal intensity, enabling individual PFs to be observed and digitally counted, using an approach in which one PF tag represents detection of one target molecule. The photonic amplification from the PF can be attributed to the strong near-field enhancement due to the cavity-induced activation of the PF, PC band structure-mediated improvement in collection efficiency of emitted photons, and increased rate of spontaneous emission. We demonstrate the applicability of the method by dose-response characterization of a sandwich immunoassay for human interleukin-6, a biomarker commonly used to assist diagnosis of cancer, inflammation, sepsis, and autoimmune disease. We achieve a limit of detection of 10 fg/ml, representing a capability three orders of magnitude lower than standard immunoassays.