bluuebunny/crossref_metadata_2025_split huggingface dataset:
Use of a Multidisciplinary Mechanical Ventilation Weaning Protocol to Improve Patient Outcomes and Empower Staff in a Medical Intensive Care Unit
Authors: Gunther Ingrid , Pradhan Deepak , Lubinsky Anthony , Urquhart Annie , Thompson Julie A. , Reynolds Staci 
Published: January 2021

Background
Prolonged duration of mechanical ventilation is associated with higher mortality and increased patient complications; conventional physician-directed weaning methods are highly variable and permit significant time that weaning is inefficient and ineffective.

Objectives
The primary objective of this quality improvement project was to implement a registered nurse (RN)– and respiratory therapist (RT)-driven mechanical ventilation weaning protocol in a medical intensive care unit (ICU) at a tertiary care academic medical center.

Methods
This quality improvement project used a quasi-experimental design with a retrospective usual care group who underwent physician-directed (conventional) weaning (n = 51) and a prospective intervention group who underwent protocol-directed weaning (n = 54). Outcomes included duration of mechanical ventilation, ICU length of stay, reintubation rates, and RN and RT satisfaction with the weaning protocol.

Results
Patients in the RN- and RT-driven mechanical ventilation weaning protocol group had significantly lower duration of mechanical ventilation (74 vs 152 hours; P = .002) and ICU length of stay (6.7 vs 10.2 days; P = .031). There was no significant difference in reintubation rates between groups. Staff surveys indicate that both RN and RTs were satisfied with the process change.

Discussion
Implementation of a multidisciplinary mechanical ventilation weaning protocol is a safe and effective way to improve patient outcomes and empower ICU staff.
Characteristics of Pulmonary Rehabilitation Programs Following an Exacerbation of Chronic Obstructive Pulmonary Disease
Authors: Wageck Bruna , Cox Narelle S. , Lee Joanna Y. T , Romero Lorena , Holland Anne E. 
Published: January 2021

Purpose:
Early pulmonary rehabilitation (PR) has beneficial impacts on people with chronic obstructive pulmonary disease (COPD) when delivered after an exacerbation; however, program characteristics are diverse. This systematic review aimed to determine the impact of PR program characteristics (mode, length, commencement, frequency, location, and supervision) on clinical outcomes following an exacerbation of COPD.

Methods:
Studies were screened from Medline, Medline in progress, Embase, CINAHL, SCOPUS, CENTRAL, and PEDro. Included studies were randomized controlled trials of early PR after an exacerbation of COPD (commenced during hospital stay or ≤4 wk of hospital discharge). The primary outcomes were hospital readmissions and mortality.

Results:
Thirty studies were included. Exercise training alone was delivered in 43% of studies. Program duration varied from length of inpatient stay to 12 wk. The interventions commenced as early as ≤24 hr of hospitalization for acute exacerbation, and up to 2 wk after discharge. Early PR was compared to usual care, and no studies made a direct comparison of the program characteristics of interest. Program characteristics associated with reduced risk of hospital admission were commencement after hospital discharge, duration longer than 3 wk, and programs that included exercise training and education (relative risk of readmission range 0.6-0.79); however, it was not possible to determine which of these characteristics made the most important contribution. Mortality risk did not vary according to PR program characteristics.

Conclusion:
Programs >3 wk, started after hospital discharge or including an educational component in addition to exercise, were most effective at reducing hospital readmissions.
Predictors of Cardiac Rehabilitation Participation
Authors: Khadanga Sherrie , Savage Patrick D. , Gaalema Diann E. , Ades Philip A. 
Published: January 2021

Purpose:
Participation in cardiac rehabilitation (CR) is low despite proven benefits. The aim of this study was to assess medical, psychosocial, and behavioral predictors of participation in a phase 2 CR.

Methods:
This was a prospective observational study. Participants hospitalized for an acute cardiac event and eligible for CR completed in-hospital assessments, and the primary outcome was CR participation over a 4-mo follow-up. Measures included age, sex, educational attainment, smoking status, medical diagnosis, ejection fraction, and electronic referral to CR. Data included General Anxiety Disorder, Patient Health Questionnaire, Medical Outcomes Study Short Form-36, Behavioral Rating Inventory of Executive Function, and Duke Social Support Index. Logistic regression and Classification and Regression Tree analysis were performed.

Results:
Of 378 hospitalized patients approached, 294 (31% females) enrolled in the study and 175 participated in CR. The presence of electronic referral, surgical diagnosis, non/former smoker, and strength of physician recommendation (all Ps < .02) were independent predictors for CR participation. No differences were seen in participation by measures of anxiety, depression, or executive function. Males with a profile of electronic referral to CR, high school or higher education, ejection fraction >50%, and strong physician recommendation were the most likely cohort to participate in CR (89%). Patients not referred to CR were the least likely to attend (20%).

Conclusions:
Lack of CR referral, lower educational attainment, nonsurgical diagnosis, current smoking, and reduced ejection fraction can predict patients at a highest risk of CR nonparticipation. Specific interventions such as electronic referral and a strong in-person recommendation from a medical provider may enhance CR participation rates.
AI-driven Deep Visual Proteomics defines cell identity and heterogeneity
Authors: Mund Andreas , Coscia Fabian , Hollandi Réka , Kovács Ferenc , Kriston András , Brunner Andreas-David , Bzorek Michael , Naimy Soraya , Rahbek Gjerdrum Lise Mette , Dyring-Andersen Beatrice , Bulkescher Jutta , Lukas Claudia , Gnann Christian , Lundberg Emma , Horvath Peter , Mann Matthias 
Published: January 2021

ABSTRACTThe systems-wide analysis of biomolecules in time and space is key to our understanding of cellular function and heterogeneity in health and disease1. Remarkable technological progress in microscopy and multi-omics technologies enable increasingly data-rich descriptions of tissue heterogeneity2,3,4,5. Single cell sequencing, in particular, now routinely allows the mapping of cell types and states uncovering tremendous complexity6. Yet, an unaddressed challenge is the development of a method that would directly connect the visual dimension with the molecular phenotype and in particular with the unbiased characterization of proteomes, a close proxy for cellular function. Here we introduce Deep Visual Proteomics (DVP), which combines advances in artificial intelligence (AI)-driven image analysis of cellular phenotypes with automated single cell laser microdissection and ultra-high sensitivity mass spectrometry7. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. Individually excising nuclei from cell culture, we classified distinct cell states with proteomic profiles defined by known and novel proteins. AI also discovered rare cells with distinct morphology, whose potential function was revealed by proteomics. Applied to archival tissue of salivary gland carcinoma, our generic workflow characterized proteomic differences between normal-appearing and adjacent cancer cells, without admixture of background from unrelated cells or extracellular matrix. In melanoma, DVP revealed immune system and DNA replication related prognostic markers that appeared only in specific tumor regions. Thus, DVP provides unprecedented molecular insights into cell and disease biology while retaining spatial information.
A joint analysis of longevity and age-related disease variants for gene expression association
Authors: Zeng Lu , Peng Shouneng , Kim Seungsoo , Zhu Jun , Zhang Bin , Suh Yousin , Tu Zhidong 
Published: January 2021

AbstractA large number of genetic variants associated with human longevity have been reported but how they play their functions remains elusive. We performed an integrative analysis on 113 genome-wide significant longevity and 14,529 age-related disease variants in the context of putative gene expression regulation. We found that most of the longevity allele types were different from the genotype of disease alleles when they were localized at the same chromosomal positions. Longevity variants were about eight times more likely to be associated with gene expression than randomly selected variants. The directions of the gene expression association were more likely to be opposite between longevity and disease variants when the association occurred to the same gene. Many longevity variants likely function through down-regulating inflammatory response and up-regulating healthy lipid metabolisms. In conclusion, this work helps to elucidate the potential mechanisms of longevity variants for follow-up studies to discover methods to extend human healthspan.
Transposable elements contribute to genome dynamics and gene expression variation in the fungal plant pathogenVerticillium dahliae
Authors: Torres David E , Thomma Bart PHJ , Seidl Michael F 
Published: January 2021

AbstractTransposable elements (TEs) are a major source of genetic and regulatory variation in their host genome and are consequently thought to play important roles in evolution. Many fungal and oomycete plant pathogens have evolved dynamic and TE-rich genomic regions containing genes that are implicated in host colonization. TEs embedded in these regions have typically been thought to accelerate the evolution of these genomic compartments, but little is known about their dynamics in strains that harbor them. Here, we used whole-genome sequencing data of 42 strains of the fungal plant pathogenVerticillium dahliaeto systematically identify polymorphic TEs that may be implicated in genomic as well as in gene expression variation. We identified 2,523 TE polymorphisms and characterize a subset of 8% of the TEs as dynamic elements that are evolutionary younger, less methylated, and more highly expressed when compared with the remaining 92% of the TE complement. As expected, the dynamic TEs are enriched in the dynamic genomic regions. Besides, we observed an association of dynamic TEs with pathogenicity-related genes that localize nearby and that display high expression levels. Collectively, our analyses demonstrate that TE dynamics inV. dahliaecontributes to genomic variation, correlates with expression of pathogenicity-related genes, and potentially impacts the evolution of dynamic genomic regions.Significance statementTransposable elements (TEs) are ubiquitous components of genomes and are major sources of genetic and regulatory variation. Many plant pathogens have evolved TE-rich genomic regions containing genes with roles in host colonization, and TEs are thought to contribute to accelerated evolution of these dynamic regions. We analyzed the fungal plant pathogenVerticillium dahliaeto identify TE variation between strains and to demonstrate that polymorphic TEs have specific characteristic that separates them from the majority of TEs. Polymorphic TEs are enriched in dynamic genomic regions and are associated with structural variants and highly expressed pathogenicity-related genes. Collectively, our results provide evidence for the hypothesis that dynamic TEs contribute to increased genomic diversity, functional variation, and the evolution of dynamic genomic regions.
The Genetic Diversity of Two Subpopulations of Shenxian Pigs as Revealed by Genome Resequencing
Authors: Diao Liu , Hongzhan Cao , Chunlian Lu , Shang Li , Mengyu Jia , Sai Li , Liqin Ren 
Published: January 2021

ABSTRACTShenxian pigs can be divided into two main strains from their shape and appearance: Huangguazui and Wuhuatou. There are significant differences in the phenotypic characters between the two subpopulations. The Shenxian pig, as the only local pig breed listed in China Pig Breeds in Hebei Province, has the excellent traits of Chinese local pigs. In order to explore the genetic diversity and genetic distance of the different subpopulations of Shenxian pig, as well as understand their evolutionary process, whole genome resequencing and genetic structure analyses were performed for the two sub-population types of Shenxian pigs.The results showed that a total of 14,509,223 SNP sites were detected in the Huangguazui type and 13,660,201 SNP sites were detected in the Wuhuatou type. This study’s principal component analysis results showed that the genetic differentiation of the Shenxian pig population was serious, and there was obvious stratification in the population. The phylogenetic tree analysis results indicated that there was a certain genetic distance between the Huangguazui and Wuhuatou types.
Optimal use of simplified social information in sequential decision-making
Authors: Mann Richard P. 
Published: January 2021

AbstractSocial animals can improve their decisions by attending to the choices made by others. The rewards gained by attending to this social information must be balanced against the costs of obtaining and processing it. Previous work has investigated the behaviour of rational agents that respond optimally to a full sequence of prior decisions. However, such full sequences are potentially difficult to perceive and costly to process. As such, real animals are likely to rely on simpler forms of information when making decisions, which in turn will affect the social behaviour they exhibit. In this paper I derive the optimal policy for rational agents responding to specific simplified forms of social information. I show how the behaviour of agents attending to the total aggregate number of previous choices differs from those attending to more dynamic information provided by the most recent prior decision, and I propose a hybrid strategy that incorporates both information sources to give a highly accurate approximation to the optimal policy with the full sequence. Finally I analyse the evolutionary stability of each strategy depending on the cost of cognition and perception, showing that a hybrid strategy dominates when this cost is low but non-zero, while attending to the most recent decision is dominant when costs are high. These results show that agents can employ highly effective social decision-making rules without requiring unrealistic cognitive capacities, and point to likely ecological variation in the social information different animals attend to.
Revisiting the role of synaptic plasticity and network dynamics for fast learning in spiking neural networks
Authors: Subramoney Anand , Bellec Guillaume , Scherr Franz , Legenstein Robert , Maass Wolfgang 
Published: January 2021

AbstractSpike-based neural network models have so far not been able to reproduce the capability of the brain to learn from very few, often even from just a single example. We show that this deficiency of models disappears if one allows synaptic weights to store priors and other information that optimize the learning process, while using the network state to quickly absorb information from new examples. For that, it suffices to include biologically realistic neurons with spike frequency adaptation in the neural network model, and to optimize the learning process through meta-learning. We demonstrate this on a variety of tasks, including fast learning and deletion of attractors, adaptation of motor control to changes in the body, and solving the Morris water maze task – a paradigm for fast learning of navigation to a new goal.Significance StatementIt has often been conjectured that STDP or other rules for synaptic plasticity can only explain some of the learning capabilities of brains. In particular, learning a new task from few trials is likely to engage additional mechanisms. Results from machine learning show that artificial neural networks can learn from few trials by storing information about them in their network state, rather than encoding them in synaptic weights. But these machine learning methods require neural networks with biologically unrealistic LSTM (Long Short Term Memory) units. We show that biologically quite realistic models for neural networks of the brain can exhibit similar capabilities. In particular, these networks are able to store priors that enable learning from very few examples.
Analyzing cancer gene expression data through the lens of normal tissue-specificity
Authors: Frost H. Robert 
Published: January 2021

AbstractThe genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.
Training Data: How can we best prepare instructors to teach data science in undergraduate biology and environmental science courses?
Authors: Emery Nathan , Crispo Erika , Supp Sarah R. , Kerkhoff Andrew J. , Farrell Kaitlin J. , Bledsoe Ellen K. , O’Donnell Kelly L. , McCall Andrew C. , Aiello-Lammens Matthew 
Published: January 2021

AbstractThere is a clear and concrete need for greater quantitative literacy in the biological and environmental sciences. Data science training for students in higher education necessitates well-equipped and confident instructors across curricula. However, not all instructors are versed in data science skills or research-based teaching practices. Our study sought to survey the state of data science education across institutions of higher learning, identify instructor needs, and illuminate barriers to teaching data science in the classroom. We distributed a survey to instructors around the world, focused on the United States, and received 106 complete responses. Our results indicate that instructors across institutions use, teach, and view data management, analysis, and visualization as important for students to learn. Code, modeling, and reproducibility were less valued by instructors, although there were differences by institution type (doctoral, masters, or baccalaureate), and career stage (time since terminal degree). While there were a variety of barriers highlighted by respondents, instructor background, student background, and space in the curriculum were the greatest barriers of note. Interestingly, instructors were most interested in receiving training for how to teach code and data analysis in the undergraduate classroom. Our study provides an important window into how data science is taught in higher education as well as suggestions for how we can best move forward with empowering instructors across disciplines.
Prior dengue immunity enhances Zika virus infection of the maternal-fetal interface in rhesus macaques
Authors: Crooks C. M. , Weiler A. M. , Rybarczyk S. L. , Bliss M. I. , Jaeger A. S. , Murphy M. E. , Simmons H. A. , Mejia A. , Fritsch M. K. , Hayes J. M. , Eickhoff J. C. , Mitzey A. M. , Razo E. , Braun K. M. , Brown E. A. , Yamamoto K. , Shepherd P. M. , Possell A. , Weaver K. , Antony K. M. , Morgan T. K. , Newman C. M. , Dudley D. M. , Schultz-Darken N. , Peterson E. , Katzelnick L. C. , Balmaseda A. , Harris E. , O’Connor D. H. , Mohr E. L. , Golos T. G. , Friedrich T. C. , Aliota M. T. 
Published: January 2021

ABSTRACTConcerns have arisen that pre-existing immunity to dengue virus (DENV) could enhance Zika virus (ZIKV) disease, due to the homology between ZIKV and DENV and the observation of antibody-dependent enhancement (ADE) among DENV serotypes. To date, no study has examined the impact of pre-existing DENV immunity on ZIKV pathogenesis during pregnancy in a translational non-human primate model. Here we show that prior DENV-2 exposure enhanced ZIKV infection of maternal-fetal interface tissues in macaques. However, pre-existing DENV immunity had no detectable impact on ZIKV replication kinetics in maternal plasma, and all pregnancies progressed to term without adverse outcomes or gross fetal abnormalities detectable at delivery. Understanding the risks of ADE to pregnant women worldwide is critical as vaccines against DENV and ZIKV are developed and licensed and as DENV and ZIKV continue to circulate.