bluuebunny/crossref_metadata_2025_split huggingface dataset:
Factors Associated with the Outcomes of ST-Elevation Myocardial Infarction Patients in the ED
Authors: Hum Bill , Shibly Yusef , Taneja Kamil , Patel Karan , Diaz Michael J. , Baccouche Basil M. , Taneja Tanisha , Batchu Sai , Mohamed Aleem , Zhang Alex , Hsiung Hailey , Patel Urvish 
Published: November 2022

AbstractBackgroundST-elevation myocardial infarction (STEMI) places a significant burden on the US healthcare system. However, there are gaps in our understanding of how patient demographics influence a STEMI’s risk to be admitted and the length of stay (LOS).MethodsWe conducted a retrospective analysis of the 2019 Nationwide Emergency Department Sample of patients with a primary diagnosis of STEMI. Multivariate regressions were used to determine factors associated with being admitted and longer length of stay (LOS).ResultsIn 2019, 175,689 STEMI patients presented to the ED and 136,738 (77.8%) patients were admitted. Factors associated with higher risk of being admitted were coronary artery disease (OR:14.34, 95% confidence interval (CI): 12.43-16.54, p<0.001), modified Charlson Comorbidity Index (mCCI) of at least 3 (OR: 9.45, 95% CI: 7.33-12.17, p<0.001), and hyperlipidemia (OR:4.65, 95% CI:4.01-5.39, p<0.001). Black STEMI patients were less likely to be admitted than White STEMI patients (OR: 0.57, 95%CI: 0.43-0.75, p<0.001). Factors associated with a longer LOS include a mCCI of at least 3 (p<0.001), heart failure (p<0.001), and being an elderly patient (p<0.001). Black patients had a longer LOS than White patients (p<0.001). Medicaid beneficiaries were associated with a longer LOS than non-Medicaid beneficiaries (p<0.001).ConclusionRace and insurance status substantially affect a STEMI patient’s outcome in the ED.
Emphysema Quantification and Severity Classification with 3-Dimensional Averaging Kernel and Airways Removal
Authors: Zhang Jianxiang , Chaudhari Gunvant R. , Bonderenko Masha , Sohn Jae Ho 
Published: November 2022

AbstractBackgroundEmphysema is a common pulmonary pathology known to be associated with increased risk of lung cancer and lung biopsy complications. Prevailing quantitation method of calculating voxel-wise percentage of low attenuation area (LAA) of lung tissue from CT scans is prone to noise and error due overcounting of single voxel LAA and incomplete segmentation of airways.PurposeWe aim to develop an accurate algorithm to quantitatively measure emphysema and classify its severity..Methods and MaterialsTwo chest CT datasets were obtained from two tertiary hospitals as training and external validation datasets. Exclusion criteria included any patients whose emphysema extent was not specified by the accompanying report. The training dataset included 722 patients, and the validation dataset included 1006 patients. Following lung segmentation and airways removal, we applied convolution of the segmented lung with averaging kernels of different sizes in 2D and 3D. Cutoffs between “none,” “mild to moderate,” and “severe” emphysema were determined via weighted logistic regression on the training dataset, and the categorical emphysema extent was obtained for each patient. The main measure for evaluating model performance was area under the curve (AUC) of the receiver operating characteristic (ROC) on the training dataset and accuracy of classification on both the training and the validation dataset. The 1×1×1 kernel, which is equivalent to the traditional LAA score, was used for comparison to other kernels for performance evaluation.ResultsThe best model used a 3D 3×3×3 kernel for average filtering with airways post processing and achieved a mean AUC of 0.782 and 0.985 for “none”-versus-rest and “severe”-versus-rest classifications respectively. It achieved a 0.676 and 0.757 multiclass classification accuracy on the training and validation dataset respectively.Conclusions and RelevanceWe present an automated pipeline that can achieve accurate emphysema quantification and severity classification. We showed that convolving the segmented lung with a 3D 3×3×3 kernel and post-processing to remove airways can reliably quantify emphysema.
Meta-analysis of gestational duration and spontaneous preterm birth identifies new maternal risk loci
Authors: Pasanen A. , Karjalainen M. K. , FinnGen  , Zhang G. , Tiensuu H. , Haapalainen A. M. , Ojaniemi M. , Feenstra B. , Jacobsson B. , Palotie A. , Laivuori H. , Muglia L. J. , Rämet M. , Hallman M. 
Published: November 2022

AbstractBackgroundPreterm birth (<37 weeks of gestation) is a major cause of neonatal death and morbidity. Up to 40% of the variation in timing of birth results from genetic factors, mostly due to the maternal genome.MethodsWe conducted a genome-wide meta-analysis of gestational duration and spontaneous preterm birth in 68,732 and 98,371 European mothers, respectively.ResultsWe detected 19 associated loci of which seven were novel. The loci mapped to several biologically plausible genes, includingHAND2whose expression was previously shown to decrease during gestation, associated with gestational duration, andGCencoding Vitamin D-binding protein, associated with preterm birth. Downstreamin silico-analysis suggested regulatory roles as underlying mechanisms for the associated loci. LD score regression found birth weight measures as the most strongly correlated traits, highlighting the unique nature of spontaneous preterm birth phenotype. Tissue expression and colocalization analysis revealed reproductive tissues and immune cell types as the most relevant sites of action.ConclusionWe report novel genetic risk loci that associate with preterm birth or gestational duration, and reproduce findings from previous genome-wide association studies. Altogether, our findings provide new insight into the genetic background of preterm birth. Better characterization of the causal genetic mechanisms will be important to public health as it could suggest new strategies to treat and prevent preterm birth.
Informed consent and trial prioritization for human subject research during the COVID-19 pandemic. Stakeholder experiences and viewpoints
Authors: Weigold Stefanie , Schorr Susanne Gabriele , Faust Alice , Woydack Lena , Strech Daniel 
Published: November 2022

AbstractBackgroundVery little is known about the practice-oriented challenges and mitigation strategies for effective and efficient translation of informed consent and study prioritization in times of a pandemic. This stakeholder interview study aimed to identify the full spectrum of challenges and mitigation strategies for informed consent and study prioritization in a pandemic setting.MethodsWe performed semi-structured interviews with German stakeholders involved in human subject research during the COVID-19 pandemic. We continued sampling and thematic text analysis of interview transcripts until thematic saturation of challenges and mitigation strategies was reached.ResultsWe conducted 21 interviews with investigators, oversight bodies, funders and research support units. For the first topic informed consent we identified three main categories: consent challenges, impact of consent challenges on clinical research, and potential response strategies for consent challenges. For the second topic prioritization of trials, we identified two main categories: need for prioritization of clinical studies and potential response strategies for prioritization of clinical studies. All main categories are further specified with subcategories. A supplementary table provides original quotes from the interviews for all subcategories.DiscussionMitigation strategies for challenges with informed consent and study prioritization partly share common ground. High quality procedures for study prioritization, for example, seem to be a core mitigation strategy in dealing with informed consent challenges. Especially in a research environment with particularly high uncertainty regarding potential treatment effects and further limitations for valid informed consent should the selection of clinical trials be very well justified from a scientific, medical, and ethics viewpoint.
Digital tools to improve parenting behavior in low-income settings: A mixed-methods feasibility study
Authors: Jäggi Lena , Melgar Leonel Aguilar , Llatance Milagros Alvarado , Castellanos Andreana , Fink Günther , Hinckley Kristen , Bustamante Maria-Luisa Huaylinos , McCoy Dana C. , Verastegui Hector , Mäusezahl Daniel , Pena Stella M. Hartinger 
Published: November 2022

ABSTRACTIntroductionDigital parenting interventions could be potentially cost-effective means for providing early child development services in low-income settings. This 5-month mixed-methods pilot study evaluated the feasibility of usingAfinidata, a comprehensiveFBMessenger-based digital parenting intervention in a remote rural setting in Latin America and explored necessary adaptations to local context.MethodsThe study was conducted in three provinces in the Cajamarca region, Peru, from February to July 2021. 180 mothers with children aged between 2-24 months and regular access to a smartphone were enrolled. Mothers were interviewed three times in-person. Selected mothers also participated in focus groups or in-depth qualitative interviews.ResultsDespite the rural and remote study site, 88% of local families with children between 0-24 months had access to internet and smartphones. Two months after baseline, 84% of mothers reported using the platform at least once, and of those, 87% rated it as useful to very useful. After 5 months, 42% of mothers were still active on the platform, with little variation between urban and rural settings. Modifications to the intervention focused on assisting mothers in navigating the platform independently and included adding a laminated booklet with general information on child development, sample activities and detailed instructions on how to self-enroll in case of lost phones.ConclusionsWe found high access to smartphones and the intervention was well received and used in very remote areas of Peru, suggesting that digital parenting interventions could be a promising path forward for supporting low-income families in remote parts of Latin America.What is already known on this topic–Digital parenting interventions are potentially cost-effective means for providing early child development services in low-income settings but have not been well-studied to date.What this study adds–This mixed-methods pilot study in a very remote setting in Latin America showed that 88% of local families with children between 0-24 months had access to smartphones with internet and 42% were still engaging with a digital parenting intervention after 5 months.How this study might affect research, practice or policy–High access to smartphones suggest that digital parenting interventions could be a promising path forward for supporting low-income families in remote parts of Latin America, but mothers might benefit from additional instructions to navigate all features of digital interventions on their own.
A bistable autoregulatory module in the developing embryo commits cells to binary fates
Authors: Zhao Jiaxi , Perkins Mindy Liu , Norstad Matthew , Garcia Hernan G. 
Published: November 2022

AbstractPositive autoregulation has been repeatedly proposed as a mechanism for cells to adopt binary fates during embryonic development through bistability. However, without quantitatively determining their parameters, it is unclear whether the plethora of positive autoregulatory modules found within developmental gene regulatory networks are actually bistable. Here, we combinein vivolive imaging with mathematical modeling to dissect the binary cell fate dynamics of the fruit fly pair-rule genefushi tarazu(ftz), which is regulated by two known enhancers: the early (non-autoregulating) element and the autoregulatory element. Live imaging of transcription and protein concentration in the blastoderm revealed that binary Ftz cell states are achieved asftzexpression rapidly transitions from being dictated by the early element to the autoregulatory element. Moreover, we discovered that Ftz concentration alone is insufficient to activate the autoregulatory element, and that this element only becomes responsive to Ftz at a prescribed developmental time. Based on these observations, we developed a dynamical systems model, and quantitated its kinetic parameters directly from experimental measurements. Our model demonstrated that theftzautoregulatory module is indeed bistable and that the early element transiently establishes the content of the binary cell fate decision to which the autoregulatory module then commits. Further analysisin silicorevealed that the autoregulatory element locks the Ftz expression fate quickly, within 35 min of exposure to the transient signal of the early element. Overall, our work confirms the widely held hypothesis that autoregulation can establish developmental fates through bistability and, most importantly, provides a framework for the quantitative dissection of cellular decision-making based on systems dynamics models and real-time measurements of transcriptional and protein dynamics.
Reconstructing Computational Dynamics from Neural Measurements with Recurrent Neural Networks
Authors: Durstewitz Daniel , Koppe Georgia , Thurm Max Ingo 
Published: November 2022

AbstractMechanistic and computational models in neuroscience usually take the form of systems of differential or time-recursive equations. The spatio-temporal behavior of such systems is the subject of dynamical systems theory (DST). DST provides a powerful mathematical toolbox for describing and analyzing neurobiological processes at any level, from molecules to behavior, and has been a mainstay of computational neuroscience for decades. Recently, recurrent neural networks (RNNs) became a popular machine learning tool for studying the nonlinear dynamics underlying neural or behavioral observations. By training RNNs on the same behavioral tasks as employed for animal subjects and dissecting their inner workings, insights and hypotheses about the neuro-computational underpinnings of behavior could be generated. Alternatively, RNNs may be traineddirectlyon the physiological and behavioral time series at hand. Ideally, the once trained RNN would then be able to generate data with thesame temporal and geometrical propertiesas those observed. This is calleddynamical systems reconstruction, a burgeoning field in machine learning and nonlinear dynamics. Through this more powerful approach the trained RNN becomes asurrogatefor the experimentally probed system, as far as its dynamical and computational properties are concerned. The trained system can then be systematically analyzed, probed and simulated. Here we will review this highly exciting and rapidly expanding field, including recent trends in machine learning that may as yet be less well known in neuroscience. We will also discuss important validation tests, caveats, and requirements of RNN-based dynamical systems reconstruction. Concepts and applications will be illustrated with various examples from neuroscience.
Cooltools: enabling high-resolution Hi-C analysis in Python
Authors:   Open2C, Abdennur Nezar , Abraham Sameer , Fudenberg Geoffrey , Flyamer Ilya M. , Galitsyna Aleksandra A. , Goloborodko Anton , Imakaev Maxim , Oksuz Betul A. , Venev Sergey V. 
Published: November 2022

AbstractChromosome conformation capture (3C) technologies reveal the incredible complexity of genome organization. Maps of increasing size, depth, and resolution are now used to probe genome architecture across cell states, types, and organisms. Larger datasets add challenges at each step of computational analysis, from storage and memory constraints to researchers’ time; however, analysis tools that meet these increased resource demands have not kept pace. Furthermore, existing tools offer limited support for customizing analysis for specific use cases or new biology. Here we introducecooltools(https://github.com/open2c/cooltools), a suite of computational tools that enables flexible, scalable, and reproducible analysis of high-resolution contact frequency data.Cooltoolsleverages the widely-adopted cooler format which handles storage and access for high-resolution datasets.Cooltoolsprovides a paired command line interface (CLI) and Python application programming interface (API), which respectively facilitate workflows on high-performance computing clusters and in interactive analysis environments. In short,cooltoolsenables the effective use of the latest and largest genome folding datasets.
Low neutralization of SARS-CoV-2 Omicron BA.2.75.2, BQ.1.1, and XBB.1 by 4 doses of parental mRNA vaccine or a BA.5-bivalent booster
Authors: Kurhade Chaitanya , Zou Jing , Xia Hongjie , Liu Mingru , Chang Hope C. , Ren Ping , Xie Xuping , Shi Pei-Yong 
Published: November 2022

AbstractThe newly emerged SARS-CoV-2 Omicron BQ.1.1, XBB.1, and other sublineages have accumulated additional spike mutations that may affect vaccine effectiveness. Here we report neutralizing activities of three human serum panels collected from individuals 1-3 months after dose 4 of parental mRNA vaccine (post-dose-4), 1 month after a BA.5-bivalent-booster (BA.5-bivalent-booster), or 1 month after a BA.5-bivalent-booster with previous SARS-CoV-2 infection (BA.5-bivalent-booster-infection). Post-dose-4 sera neutralized USA-WA1/2020, BA.5, BF.7, BA.4.6, BA.2.75.2, BQ.1.1, and XBB.1 SARS-CoV-2 with geometric mean titers (GMTs) of 1533, 95, 69, 62, 26, 22, and 15, respectively; BA.5-bivalent-booster sera improved the GMTs to 3620, 298, 305, 183, 98, 73, and 35; BA.5-bivalent-booster-infection sera further increased the GMTs to 5776, 1558,1223, 744, 367, 267, and 103. Thus, although BA.5-bivalent-booster elicits better neutralization than parental vaccine, it does not produce robust neutralization against the newly emerged Omicron BA.2.75.2, BQ.1.1, and XBB.1. Previous infection enhances the magnitude and breadth of BA.5-bivalent-booster-elicited neutralization.
Structure of the human UBR5 E3 ubiquitin ligase
Authors: Wang Feng , He Qing , Zhan Wenhu , Yu Ziqi , Finkin-Groner Efrat , Ma Xiaojing , Lin Gang , Li Huilin 
Published: November 2022

ABSTRACTThe human UBR5 (also known as EDD) is a single polypeptide chain HECT-type E3 ubiquitin ligase essential for embryonic development in mammals. Although widely expressed,UBR5is markedly amplified and overexpressed in breast, ovarian, prostate, gastric and pancreatic cancers. Dysregulated UBR5 functions like an oncoprotein to promote cancer growth and metastasis, making UBR5 a potential target for therapeutics. Unexpectedly, we found that human UBR5 assembles a dimer and a tetramer in solution. We determined the dimer structure at 2.8 Å and the tetramer structure at 3.5 Å average resolution. UBR5 is a crescent shaped molecule with a seven-bladed β-propeller and two small β-barrel domains (SBB1/2) at the N-terminal region, a catalytic HECT domain at the C-terminus, and an extended helical scaffold and an N-degron-recognizing UBR box in the middle. The dimer is assembled as a stable head-to-tail dimer via extensive interactions in the middle helical scaffold region. The tetramer is assembled via SBB2-SBB2 interaction from two face-to-face dimers, forming a large cage with all four catalytic HECT domains facing the central cavity. Importantly, the N-terminal region of one subunit and the HECT of the other form an “intermolecular jaw” in the dimer. Using enzymatic and cellular assays, we showed that the jaw-lining residues are important for function, suggesting that the intermolar jaw functions to recruit ubiquitin loaded E2 to UBR5 for the transthiolation reaction. Further work is needed to understand how oligomerization regulates the UBR5 ligase activity. This work provides a framework for structure-based anticancer drug development against the distinctive HECT E3 ligase and contributes to a growing appreciation of E3 ligase diversity.
Protein design using structure-based residue preferences
Authors: Ding David , Shaw Ada , Sinai Sam , Rollins Nathan , Prywes Noam , Savage David F. , Laub Michael T. , Marks Debora S. 
Published: November 2022

AbstractRecent developments in protein design have adapted large neural networks with up to 100s of millions of parameters to learn complex sequence-function mappings. However, it is unclear which dependencies between residues are critical for determining protein function, and a better empirical understanding could enable high quality models that are also more data- and resource-efficient. Here, we observe that the per residue amino acid preferences - without considering interactions between mutations are sufficient to explain much, and sometimes virtually all of the combinatorial mutation effects across 7 datasets (R2∼ 78-98%), including one generated here. These preference parameters (20*N, where N is the number of mutated residues) can be learned from as few as ∼5*20*N observations to predict a much larger number (potentially up to 20N) of combinatorial variant effects with high accuracy (Pearson r > 0.8). We hypothesized that the local structural dependencies surrounding a residue could be sufficient to learn these required mutation preferences, and developed an unsupervised design approach, which we term CoVES for ‘CombinatorialVariantEffects fromStructure’. We show that CoVES outperforms not just model free sampling approaches but also complicated, high-capacity autoregressive neural networks in generating functional and diverse sequence variants for two example proteins. This simple, biologically-rooted model can be an effective alternative to high-capacity, out of domain models for the design of functional proteins.
Yeast population dynamics in Brazilian bioethanol production
Authors: Rego-Costa Artur , Huang I-Ting , Desai Michael M. , Gombert Andreas K. 
Published: November 2022

AbstractThe large scale and non-aseptic fermentation of sugarcane feedstocks into fuel ethanol in biorefineries represents a unique ecological niche, in which the yeastSaccharomyces cerevisiaeis the predominant organism. Several factors, such as sugarcane variety, process design, and operating and weather conditions, make each of the ∼400 industrial units currently operating in Brazil a unique ecosystem. Here, we track yeast population dynamics in two different biorefineries through two production seasons (April to November of 2018 and 2019), using a novel statistical framework on a combination of metagenomic and clonal sequencing data. We find that variation from season to season in one biorefinery is small compared to the differences between the two units. In one biorefinery, all lineages present during the entire production period derive from one of the starter strains, while in the other, invading lineages took over the population and displaced the starter strain. However, despite the presence of invading lineages and the non-aseptic nature of the process, all yeast clones we isolated are phylogenetically related to other previously sequenced bioethanol yeast strains, indicating a common origin from this industrial niche. Despite the substantial changes observed in yeast populations through time in each biorefinery, key process indicators remained quite stable through both production seasons, suggesting that the process is robust to the details of these population dynamics.