bluuebunny/crossref_metadata_2025_split huggingface dataset:
National and Institutional Trends in Adverse Events Over Time: A Systematic Review and Meta-analysis of Longitudinal Retrospective Patient Record Review Studies
Authors: Connolly Warren , Li Brian , Conroy Ronan , Hickey Anne , Williams David J. , Rafter Natasha 
Published: December 2020

Objective
This study aimed to determine if the implementation of large-scale patient safety initiatives have been successful in reducing overall and preventable adverse event rates in hospital inpatients.

Design
The design used in this study was systematic review and meta-analysis.

Data Resources
We followed our published protocol (PROSPERO [CRD42019140058]) and searched the following databases: PubMed, CINAHL, PsycINFO, Cochrane Library, and Embase from inception to February 2020. The reference lists of eligible studies were also searched.

Eligibility
All longitudinal retrospective record review studies that examined adverse event rates before and after the introduction of patient safety initiatives in hospital inpatients were included.

Data Extraction
Data extraction, quality, and risk of bias assessment were carried out by 2 independent reviewers. Information on study design, setting, demographics, interventions, and safety outcome measures was extracted.

Results
A total of 3894 articles were screened, and 7 articles met the eligibility criteria for our systematic review with 5 of these providing sufficient information for inclusion in the meta-analysis. The degree of heterogeneity was high among studies. The meta-analysis demonstrated a minimal risk reduction in overall adverse event rates of 0.017 (95% confidence interval, 0.002–0.032) when the lower-quality studies were excluded, with one adverse event being prevented for every 59 hospital admissions.

Conclusions
These findings are significant when the large numbers of admissions to a hospital every year are considered. Given the low numbers of large-scale implementation studies, there is a need for more research on the effectiveness of patient safety initiatives to further assess the impact of such initiatives on adverse events.
Optimizing Discharge Summaries: A Multispecialty, Multicenter Survey of Primary Care Clinicians
Authors: Silver Aaron M. , Goodman Leigh Anne , Chadha Romil , Higdon Jason , Burton Michael , Palabindala Venkataraman , Jonnalagadda Nageshwar , Thomas Abey , O’Donnell Christopher 
Published: December 2020

Background
Patient care in the United States has become increasingly more fragmented, and the discharge summary serves as a critical tool for transmitting information on a patient’s hospital admission to the primary care clinician. Some guidelines regarding how to write discharge summaries exist, but few are focused on prioritizing content that is most important to optimize a patient’s transition of care.

Methods
We conducted a national survey across various medical primary care specialties, including trainees and advanced practice providers, to understand the priorities of primary care clinicians. We distributed the survey to 2184 clinicians affiliated with 8 large academic institutions. Our response rate was 21%.

Results
Hospital course, discharge diagnoses, medication reconciliation, and follow-up sections were ranked as the most important categories with a 95.5% concordance rate among surveyed institutions. The least important sections were contact numbers for inpatient clinicians, ancillary services, weight-bearing status, and wound care. Similar themes were also identified via consensus review of the free-texted comments, adding that discharge summary style was also important. Other identified barriers to high-quality transition of care are both the limited time primary care clinicians can spend reviewing discharge summaries and lack of adequate communication between hospitalists and the outpatient clinician.

Conclusions
High-yield content should be presented at the beginning of the discharge summary and conveyed in a brief, succinct manner to ensure maximal utility of the document as a transition of care tool.
In-depth evaluation of root infection systems with the vascular fungusVerticillium longisporumas soil-borne model pathogen
Authors: Fröschel Christian 
Published: December 2020

ABSTRACTPREMISEWhile leaves are far more accessible for analysing plant defences, roots are hidden in the soil leading to difficulties in studying soil-borne interactions. Literature describes inoculation strategies to infect model plants with model root pathogens, but it remains demanding to obtain a methodological overview. To address this challenge, this study uses the model root pathogenVerticillium longisporumonArabidopsis thalianaand provides recommendations based on evident examples for the selection and management of suitable infection systems to investigate root-microbe interactions.METHODS AND RESULTSA novel root infection system is introduced, while two existing ones are precisely described and optimized. Advantages and disadvantages of each are assessed, step-by-step protocols are presented and accompanied by pathogenicity tests, transcriptional analyses of indole-glucosinolate markers and independent confirmations using reporter constructs. The results validate the importance of indole-glucosinolates as secondary metabolites limitingV. longisporumpropagation in hosts.DISCUSSIONWe provide detailed guidelines for studying host responses and defence strategies againstV. longisporum. Furthermore, other soil-borne microorganisms or other model plants, such as economically important oilseed rape, can be used in the infection systems described. Hence, these proven manuals help to find a root infection system for your specific research questions to decipher root-microbe interactions.
A long reads-based de-novo assembly of the genome of the Arlee homozygous line reveals structural genome variation in rainbow trout
Authors: Gao Guangtu , Magadan Susana , Waldbieser Geoffrey C. , Youngblood Ramey C. , Wheeler Paul A. , Scheffler Brian E. , Thorgaard Gary H. , Palti Yniv 
Published: December 2020

AbstractCurrently, there is still a need to improve the contiguity of the rainbow trout reference genome and to use multiple genetic backgrounds that will represent the genetic diversity of this species. The Arlee doubled haploid line was originated from a domesticated hatchery strain that was originally collected from the northern California coast. The Canu pipeline was used to generate the Arlee line genome de-novo assembly from high coverage PacBio long-reads sequence data. The assembly was further improved with Bionano optical maps and Hi-C proximity ligation sequence data to generate 32 major scaffolds corresponding to the karyotype of the Arlee line (2N=64). It is composed of 938 scaffolds with N50 of 39.16 Mb and a total length of 2.33 Gb, of which ∼95% was in 32 chromosome sequences with only 438 gaps between contigs and scaffolds. In rainbow trout the haploid chromosome number can vary from 29 to 32. In the Arlee karyotype the haploid chromosome number is 32 because chromosomes Omy04, 14 and 25 are divided into six acrocentric chromosomes. Additional structural variations that were identified in the Arlee genome included the major inversions on chromosomes Omy05 and Omy20 and additional 15 smaller inversions that will require further validation. This is also the first rainbow trout genome assembly that includes a scaffold with the sex-determination gene (sdY) in the chromosome Y sequence. The utility of this genome assembly is demonstrated through the improved annotation of the duplicated genome loci that harbor the IGH genes on chromosomes Omy12 and Omy13.Article SummaryA de-novo genome assembly was generated for the Arlee homozygous line of rainbow trout to enable identification and characterization of genome variants towards developing a rainbow trout pan-genome reference. The new assembly was generated using the PacBio sequencing technology and scaffolding with Hi-C contact maps and Bionano optical mapping. A contiguous genome assembly was obtained, with the contig and scaffold N50 over 15.6 Mb and 39 Mb, respectively, and 95% of the assembly in chromosome sequences. The utility of this genome assembly is demonstrated through the improved annotation of the duplicated genome loci that harbor the IGH genes.
Fast end-to-end learning on protein surfaces
Authors: Sverrisson Freyr , Feydy Jean , Correia Bruno E. , Bronstein Michael M. 
Published: December 2020

AbstractProteins’ biological functions are defined by the geometric and chemical structure of their 3D molecular surfaces. Recent works have shown that geometric deep learning can be used on mesh-based representations of proteins to identify potential functional sites, such as binding targets for potential drugs. Unfortunately though, the use of meshes as the underlying representation for protein structure has multiple drawbacks including the need to pre-compute the input features and mesh connectivities. This becomes a bottleneck for many important tasks in protein science.In this paper, we present a new framework for deep learning on protein structures that addresses these limitations. Among the key advantages of our method are the computation and sampling of the molecular surface on-the-fly from the underlying atomic point cloud and a novel efficient geometric convolutional layer. As a result, we are able to process large collections of proteins in an end-to-end fashion, taking as the sole input the raw 3D coordinates and chemical types of their atoms, eliminating the need for any hand-crafted pre-computed features.To showcase the performance of our approach, we test it on two tasks in the field of protein structural bioinformatics: the identification of interaction sites and the prediction of protein-protein interactions. On both tasks, we achieve state-of-the-art performance with much faster run times and fewer parameters than previous models. These results will considerably ease the deployment of deep learning methods in protein science and open the door for end-to-end differentiable approaches in protein modeling tasks such as function prediction and design.
Statistics or biology: the zero-inflation controversy about scRNA-seq data
Authors: Jiang Ruochen , Sun Tianyi , Song Dongyuan , Li Jingyi Jessica 
Published: December 2020

AbstractResearchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as missing data to be corrected. To help address the controversy, here we discuss the sources of biological and non-biological zeros; introduce five mechanisms of adding non-biological zeros in computational benchmarking; evaluate the impacts of non-biological zeros on data analysis; benchmark three input data types: observed counts, imputed counts, and binarized counts; discuss the open questions regarding non-biological zeros; and advocate the importance of transparent analysis.
Epitope-Based Peptide Vaccine against Bombali Ebolavirus Viral Protein 40: An Immunoinformatics Combined with Molecular Docking Studies
Authors: Mustafa Mujahed I. , Shantier Shaza W. , Abdelmageed Miyssa I. , Makhawi Abdelrafie M. 
Published: December 2020

AbstractBackgroundBombali Ebolavirus is RNA viruses belong to the Filoviridae family. They are causing lethal hemorrhagic fever with high mortality rate. Despite having available molecular knowledge of this virus, no approved vaccine or antiviral drugs have been developed yet for the eradication of Bombali Ebolavirus infections in humans.Objectivethe present study described a multi epitope-based peptide vaccine against Bombali Ebolavirus matrix protein VP40, using several immunoinformatics tools.Materials and MethodsThe six strains of Ebolavirus were retrieved from NCBI and Uniprot databases and submitted to VaxiJen to identify the most antigenic protein among all. Then PSIPRED, SOPMA, QMEAN, and PROCHECK tools were used to check the protein quality. T-cell prediction, population coverage, and molecular docking analysis were achieved to select peptides containing multiple Bombali VP40 epitopes showing interaction with multiple HLA molecules for expected immune response across the world.ResultBombali Ebola (YP_009513276.1) was found to be the most antigenic protein among all. Which it has been used in all required analysis. For T cell three epitopes showed high affinity to MHC class I (YSFDSTTAA, VQLPQYFTF, and MVNVISGPK) and high population coverage against Africa and the world. Furthermore in MHC class II, six promising epitopes that associated with most common MHC class II alleles.ConclusionThe above result conclude that, these peptides capable of provoking T-cell response and being interacted with a wide range of HLA molecules have a strong potential to be a vaccine against Bombali Ebolavirus.
Coupled feedback loops involving PAGE4, EMT and Notch signaling can give rise to non-genetic heterogeneity in prostate cancer cells
Authors: Singh Divyoj , Bocci Federico , Kulkarni Prakash , Jolly Mohit Kumar 
Published: December 2020

AbstractNon-genetic heterogeneity is emerging to be a crucial factor underlying therapy resistance in multiple cancers. However, the design principles of regulatory networks underlying non-genetic heterogeneity in cancer remain poorly understood. Here, we investigate the coupled dynamics of feedback loops involving a) oscillations in androgen receptor (AR) signaling mediated through an intrinsically disordered protein PAGE4, b) multistability in epithelial-mesenchymal transition (EMT), and c) Notch-Delta-Jagged signaling mediated cell-cell communication, each of which can generate non-genetic heterogeneity through multistability and/or oscillations. Our results show how different coupling strengths between AR and EMT signaling can lead to possible bistability in the levels of AR. These results reveal the emergent dynamics of coupled oscillatory and multi-stable systems and unravel mechanisms by which non-genetic heterogeneity in AR levels can be generated, which can act as a barrier to most existing therapies for prostate cancer patients.
Estimation of Kinematics from Inertial Measurement Units Using a Combined Deep Learning and Optimization Framework
Authors: Rapp Eric , Shin Soyong , Thomsen Wolf , Ferber Reed , Halilaj Eni 
Published: December 2020

AbstractThe difficulty of estimating joint kinematics remains a critical barrier toward widespread use of inertial measurement units in biomechanics. Traditional sensor-fusion filters are largely reliant on magnetometer readings, which may be disturbed in uncontrolled environments. Careful sensor-to-segment alignment and calibration strategies are also necessary, which may burden users and lead to further error in uncontrolled settings. We introduce a new framework that combines deep learning and top-down optimization to accurately predict lower extremity joint angles directly from inertial data, without relying on magnetometer readings. We trained deep neural networks on a large set of synthetic inertial data derived from a clinical marker-based motion-tracking database of hundreds of subjects. We used data augmentation techniques and an automated calibration approach to reduce error due to variability in sensor placement and limb alignment. On left-out subjects, lower extremity kinematics could be predicted with a mean (±STD) root mean squared error of less than 1.27 ° (±0.38°) in flexion/extension, less than 2.52°(±0.98 °) in ad/abduction, and less than 3.34°(±1.02°) internal/external rotation, across walking and running trials. Errors decreased exponentially with the amount of training data, confirming the need for large datasets when training deep neural networks. While this framework remains to be validated with true inertial measurement unit (IMU) data, the results presented here are a promising advance toward convenient estimation of gait kinematics in natural environments. Progress in this direction could enable large-scale studies and offer an unprecedented view into disease progression, patient recovery, and sports biomechanics.
Defective NET clearance contributes to sustained FXII activation in COVID-19-associated pulmonary thrombo-inflammation
Authors: Englert Hanna , Rangaswamy Chandini , Deppermann Carsten , Sperhake Jan-Peter , Krisp Christoph , Schreier Danny , Gordon Emma , Konrath Sandra , Haddad Munif , Pula Giordano , Mailer Reiner K. , Schlüter Hartmut , Kluge Stefan , Langer Florian , Püschel Klaus , Panousis Kosta , Stavrou Evi X. , Maas Coen , Renné Thomas , Frye Maike 
Published: December 2020

AbstractBackgroundCoagulopathy and inflammation are hallmarks of Coronavirus disease 2019 (COVID-19) and are associated with increased mortality. Clinical and experimental data have revealed a role for neutrophil extracellular traps (NETs) in COVID-19 disease. The mechanisms that drive thrombo-inflammation in COVID-19 are poorly understood.MethodsWe performed proteomic analysis and immunostaining of postmortem lung tissues from COVID-19 patients and patients with other lung pathologies. We further compared coagulation factor XII (FXII) and DNase activities in plasma samples from COVID-19 patients and healthy control donors and determined NET-induced Factor XIII (FXII) activation using a chromogenic substrate assay.FindingsFXII expression and activity were increased in the lung parenchyma, within the pulmonary vasculature and in fibrin-rich alveolar spaces of postmortem lung tissues from COVID-19 patients. In agreement with this, plasma FXII activation (FXIIa) was increased in samples from COVID-19 patients. Furthermore, FXIIa colocalized with NETs in COVID-19 lung tissue indicating that NETs accumulation leads to FXII contact activation in COVID-19. We further showed that an accumulation of NETs is partially due to impaired NET clearance by extracellular DNases as DNase substitution improved NET dissolution and reduced FXII activationin vitro.InterpretationCollectively, our study supports that the NETs/FXII axis contributes to the pathogenic chain of procoagulant and proinflammatory responses in COVID-19. Targeting both, NETs and FXIIa, could provide a strategy to mitigate COVID-19-induced thrombo-inflammation.FundingThis study was supported by the European Union (840189), the Werner Otto Medical Foundation Hamburg (8/95) and the German Research Foundation (FR4239/1-1, A11/SFB877, B08/SFB841 and P06/KFO306).
Indian Red Jungle fowl depicts close genetic relationship with Indian native chicken breeds as evidenced through whole mitochondrial genome intersection
Authors: Kanakachari M. , Chatterjee R.N. , Rajkumar U. , Haunshi S. , Reddy M. R. , Bhattacharya T.K. 
Published: December 2020

AbstractNative chickens are dispersed in a wide range of geometry and they have influenced hereditary assets that are kept by farmers for various purposes. Mitochondrial DNA (mtDNA) is a widely utilized marker in molecular study because of its quick advancement, matrilineal legacy, and simple molecular structure. In a genomics study, it is important for understanding the origins, history, and adjustment of domestication. In this report, for the first time, we utilized Next-generation sequencing (NGS) to investigate the mitochondrial genomes and to evaluate the hereditary connections, diversity, and measure of gene stream estimation in seven Indian native chicken breeds along with twenty-two Asian native breeds. The absolute length of each mtDNA was 16775bp harboring 4 tRNA genes, 2 rRNA genes, 12 protein-coding genes, and 1 D-loop region. The chicken breeds were genotyped by using the D-loop region and 23 haplotypes were identified. In addition, when compared to only Indian native breeds more haplotypes were identified in the NADH dehydrogenase subunit (ND4 and ND5), Cytochrome c oxidase subunit (COXI and COXII), Cytochrome b, mitochondrial encoded ATP synthase membrane subunit 6, and Ribosomal RNA genes. The phylogenetic examination utilizing N-J computational algorithms indicated that the analyzed all native chicken breeds were divided into six significant clades: A, B, C, D, E, and F. All Indian native breeds are coming under the F clade and it says all Indian breeds are domesticated in India. Besides, the sequencing results effectively distinguished SNPs, INDELs, mutations, and variants in seven Indian native breeds. Additionally, our work affirmed that Indian Red Jungle Fowl is the origin of reference Red Jungle Fowl as well as all Indian breeds, which is reflected in the dendrogram as well as network analysis based on whole mtDNA and D-loop region. Albeit, Indian Red Jungle Fowl is distributed as an outgroup, proposing that this ancestry was reciprocally monophyletic. The seven Indian native chickens of entire mtDNA sequencing and disclosure of variations gave novel insights about adaptation mechanisms and the significance of important mtDNA variations in understanding the maternal lineages of native chickens.
STORM-Net: Simple and Timely Optode Registration Method for Functional Near-Infrared Spectroscopy (fNIRS)
Authors: Erel Yotam , Jaffe-Dax Sagi , Yeshurun Yaara , Bermano Amit H. 
Published: December 2020

AbstractWe introduce a robust video-based method for estimating the positions of fNIRS optodes on the scalp. The method is fast, requires no special hardware, and is intuitive to use with developmental populations. Co-registration is a crucial step for reliable analysis of FNIRS data, yet it still remains an open problem when considering these populations. Existing methods pose motion constraints, require expert annotation, or are only applicable in laboratory conditions. Using novel computer-vision technologies, we implement a fully-automatic appearance-based method that estimates the registration parameters of a mounted cap to the scalp from a raw video of the subject. We validate our method on 10 adult subjects and demonstrate its usability with infants. We compare our method to the standard 3D digitizer, and to other photogrammetry based approaches. We show our method achieves comparable accuracy to current appearance-based methods, while being orders of magnitude faster. Our fast registration facilitates more spatially precise fNIRS analysis with developmental populations even in unconventional environments. The method is implemented as a open-source toolbox athttps://github.com/yoterel/STORM-Net.
Allelic variants of the NLR protein Rpi-chc1 differentially recognise members of thePhytophthora infestansPexRD12/31 effector superfamily through the leucine-rich repeat domain
Authors: Monino-Lopez Daniel , Nijenhuis Maarten , Kodde Linda , Kamoun Sophien , Salehian Hamed , Schentsnyi Kyrylo , Stam Remco , Lokossou Anoma , Abd-El-Haliem Ahmed , Visser Richard GF , Vossen Jack H 
Published: December 2020

SummaryPhytophthora infestansis a pathogenic oomycete that causes the infamous potato late blight disease. Resistance (R) genes from diverseSolanumspecies encode intracellular receptors that recognizeP. infestansRXLR effector proteins and provide effective defence responses. To deploy theseRgenes in a durable fashion in agriculture, we need to understand the mechanism of effector recognition and the way the pathogen evades recognition.We cloned sixteen allelic variants of theRpi-chc1gene fromSolanum chacoenseand otherSolanumspecies, and identified the cognateP. infestansRXLR effectors. These tools were used to study receptor-ligand interactions and co-evolution.Functional and non-functional alleles ofRpi-chc1encode Coiled-Coil-Nucleotide Binding-Leucine-Rich-Repeat (CNL) proteins.Rpi-chc1.1recognised multiple PexRD12 (AVRchc1.1) proteins whileRpi-chc1.2recognised multiple PexRD31 (AVRchc1.2) proteins, both from the PexRD12/31 superfamily. Domain swaps between Rpi-chc1.1 and Rpi-chc1.2 revealed that overlapping subdomains in the LRR were responsible for the difference in effector recognition.This study showed thatRpi-chc1.1andRpi-chc1.2, evolved to recognize distinct members of the same PexRD12/31 effector family via the LRR domain. The biased distribution of polymorphisms suggests that exchange of LRRs during host-pathogen co-evolution can lead to novel recognition specificities. These insights will help future strategies to breed for durable resistant varieties.