bluuebunny/crossref_metadata_2025_split huggingface dataset:
The impact of increasing the minimum legal drinking age to 20 years in Lithuania on all-cause mortality – an interrupted time-series analysis
Authors: Tran Alexander , Jiang Huan , Lange Shannon , Livingston Michael , Manthey Jakob , Neufeld Maria , Room Robin , Štelemėkas Mindaugas , Telksnys Tadas , Petkevičienė Janina , Radišauskas Ričardas , Rehm Jürgen 
Published: April 2021

AbstractAimsTo determine the effect of an alcohol policy change, which increased the minimum legal drinking age (MLDA) from 18 years of age to 20 years of age on all-cause mortality rates in young adults in Lithuania.MethodsAn interrupted time series analysis was conducted on a dataset from 2001 to 2019 (n= 228 months). The model tested the effects of the MLDA on all-cause mortality rates (deaths per 100,000 individuals) in 3 age categories (15-17 years old, 18-19 years old, 20-22 years old). Additional models that included GDP as a covariate and taxation policy were tested as well.ResultsThere was a significant effect of the MLDA on all-cause mortality rates in those 18-19 years old, when modelled alone. Additional analyses controlling for the mortality rate of other age groups showed similar findings. Inclusion of confounding factors (policies on alcohol taxation, GDP) eliminated the effects of MLDA.ConclusionsAlthough there was a notable decline in all-cause mortality rates among young adults in Lithuania, a direct causal impact of MLDA on all-cause mortality rates in young adults was not definitively found.Short SummaryWe investigated the effect of an increase in minimum legal drinking age (MLDA) on all-cause mortality in young adults (aged 18-19). MLDA had a negative effect on all-cause mortality (even when controlling for mortality rates in other age groups), however when confounding factors were included, these effects were attenuated.
Direct and indirect effectiveness of mRNA vaccination against SARS-CoV-2 infection in long-term care facilities in Spain
Authors: Monge Susana , Olmedo Carmen , Alejos Belén , Lapeña María Fé , Sierra María José , Limia Aurora ,   COVID-19 registries study group
Published: April 2021

AbstractObjectivesTo estimate indirect and total (direct plus indirect) effects of COVID-19 vaccination in residents in long-term care facilities (LTCF).DesignRegistries-based cohort study including all residents in LTCF ≥65 years offered vaccination between 27 December 2020 and 10 March 2021. Risk of SARS-CoV-2 infection following vaccination was compared with the risk in the same individuals in a period before vaccination. Risk in non-vaccinated was also compared to a period before the vaccination programme to estimate indirect protection. Standardized cumulative risk was computed adjusted by previous documented infection (before the start of follow-up) and daily-varying SARS-CoV-2 incidence and reproductive number.Participants573,533 records of 299,209 individuals in the National vaccination registry were selected; 99.0% had ≥1 vaccine-dose, 99.8% was Pfizer/BioNTech (BNT162b2). Residents mean age was 85.9, 70.9% were females. A previous SARS-CoV-2 infection was found in around 25% and 13% of participants, respectively, at the time of vaccine offer and in the reference period.Main outcome measuresDocumented SARS-CoV-2 infection identified in the National COVID-19 laboratory registry.ResultsTotal VE was 57.2% (95% Confidence Interval: 56.1%-58.3%), and was highest ≥28 days after the first vaccine-dose (proxy of ≥7 days after the second dose) and for individuals naïve to SARS-CoV-2 [81.8% (81.0%-82.7%)] compared to those with previous infection [56.8% (47.1%-67.7%)]. Vaccination prevented up to 9.6 (9.3-9.9) cases per 10.000 vaccinated per day; 11.6 (11.3-11.9) if naïve vs. 0.8 (0.5-1.0) if previous infection. Indirect protection in the non-vaccinated could only be estimated for naïve individuals, at 81.4% (73.3%-90.3%) and up to 12.8 (9.4-16.2) infections prevented per 10.000 indirectly protected per day.ConclusionsOur results confirm the effectiveness of mRNA vaccination in institutionalized elderly population, endorse the policy of universal vaccination in this setting, including in people with previous infection, and suggest that even non-vaccinated individuals benefit from indirect protection.Key messagesCOIVD-19 vaccination reduced the risk of documented SARS-CoV-2 infection in institutionalized elderly by 57.2% (56.1% to 58.3%), which increased to 81.2% (80.2% to 82%) for the fully vaccinated.In individuals naïve to SARS-CoV-2 vaccination reduced the risk by up to 81.8% and averted up to 11.6 cases per 10,000 vaccinated persons per day.Those with previous infection also benefited from a risk reduction of 57%, which translated in less than 1 infection averted per 10,000 vaccinated persons per day.Non-vaccinated individuals living in facilities where the majority (residents and staff) had been vaccinated showed a risk reduction similar to those actually vaccinated.
A multi-scale transcriptional regulatory network knowledge base forEscherichia coli
Authors: Lamoureux Cameron R. , Decker Katherine T. , Sastry Anand V. , Rychel Kevin , Gao Ye , McConn John Luke , Zielinski Daniel C. , Palsson Bernhard O. 
Published: April 2021

SummaryTranscriptomic data is accumulating rapidly; thus, development of scalable methods for extracting knowledge from this data is critical. We assembled a top-down transcriptional regulatory network forEscherichia colifrom a 1035-sample, single-protocol, high-quality RNA-seq compendium. The compendium contains diverse growth conditions, including: 4 temperatures; 9 media; 39 supplements, including antibiotics; and 76 unique gene knockouts. Using unsupervised machine learning, we extracted 117 regulatory modules that account for 86% of known regulatory network interactions. We also identified two novel regulons. After expanding the compendium with 1675 publicly available samples, we extracted similar modules, highlighting the method’s scalability and stability. We provide workflows to enable analysis of new user data against this knowledge base, and demonstrate its utility for experimental design. This work provides a blueprint for top-down regulatory network elucidation across organisms using existing data, without any prior annotation and using existing data.Highlights- Single protocol, high quality RNA-seq dataset contains 1035 samples fromEscherichia colicovering a wide range of growth conditions- Machine learning identifies 117 regulatory modules that capture the majority of known regulatory interactions- Resulting knowledge base combines expression levels and module activities to enable regulon discovery and empower novel experimental design- Standard workflows provided to enable application of knowledge base to new user dataGraphical Abstract
Multicenter evaluation of a fully automated high-throughput SARS-CoV-2 antigen immunoassay
Authors: Nörz Dominik , Olearo Flaminia , Perisic Stojan , Bauer Matthias F. , Riester Elena , Schneider Tanja , Schönfeld Kathrin , Laengin Tina , Lütgehetmann Marc 
Published: April 2021

ABSTRACTIntroductionMolecular testing for SARS-CoV-2 continues to suffer from delays and shortages. Antigen tests have recently emerged as a viable alternative to detect patients with high viral loads, associated with elevated risk of transmission. While rapid lateral flow tests greatly improved accessibility of SARS-CoV-2 detection in critical areas, their manual nature limits scalability and suitability for large-scale testing schemes. The Elecsys® SARS-CoV-2 Antigen assay allows antigen immunoassays to be carried out on fully automated high-throughput serology platforms.MethodsA total of 3139 nasopharyngeal and oropharyngeal swabs were collected at 3 different testing sites in Germany. Swab samples were pre-characterized by RT-qPCR and consecutively subjected to the antigen immunoassay on either the cobas e 411 or cobas e 801 analyzers.ResultsOf the tested respiratory samples, 392 were PCR positive for SARS-CoV-2 RNA. Median concentration was 2.95×104 (interquartile range [IQR] 5.1×102–3.5×106) copies/mL. Overall sensitivity and specificity of the antigen immunoassay were 60.2% (95% confidence interval [CI] 55.2–65.1) and 99.9% (95% CI 99.6–100), respectively. A 93.7% (95% CI 89.7–96.5) sensitivity was achieved at a viral RNA concentration ≥104 copies/mL (∼cycle threshold (Ct) value<29.9).ConclusionThe Elecsys SARS-CoV-2 Antigen assay reliably detected patient samples with viral loads of 10,000 copies/mL and higher. It thus represents a viable high-throughput alternative for screening of patients, or in situations where PCR testing is not readily available.Key Summary PointsWhy carry out this study?The SARS-CoV-2 pandemic has led to a surge in demand for reliable, mass diagnostic tests worldwide.A thorough clinical evaluation of a fully automated high-throughput Elecsys® SARS-CoV-2 Antigen assay on a total of 3139 clinical samples pre-characterized by quantitative RT-PCR was carried out.What was learned from the study?The assay demonstrated excellent specificity (99.9%) and good relative sensitivity, with an overall sensitivity of 60.2% and a sensitivity of 93.7% for samples containing ≥104 viral RNA copies/mL.The Elecsys SARS-CoV-2 Antigen assay is a viable high-throughput, automated alternative to manual lateral-flow antigen tests.
Selective Laser Sintering of a Photosensitive Drug: Impact of Processing and Formulation Parameters on Degradation, Solid-State, and Quality of 3D Printed Dosage Forms
Authors: Thakkar Rishi , Davis Daniel A. , Williams Robert O. , Maniruzzaman Mohammed 
Published: April 2021

AbstractThis research study utilized a light-sensitive drug, nifedipine (NFD), to understand the impact of processing parameters, and formulation composition on drug degradation, crystallinity, and quality attributes (dimensions, hardness, disintegration time) of selective laser sintering (SLS) based 3D printed dosage forms. Selective laser sintering (SLS), in most cases, uses an ultraviolet laser source, and drugs tend to absorb radiation at varying intensities around this wavelength (455 nm). This phenomenon may lead to chemical degradation, and solid-state transformation, which was assessed for nifedipine in formulations with varying amounts of vinyl pyrrolidone-vinyl acetate copolymer (Kollidon® VA 64) and potassium aluminum silicate-based pearlescent pigment (Candurin®), processed under different SLS conditions in the presented work. After preliminary screening Candurin®, surface temperature (ST), and laser speed (LS) were identified as the significant independent variables. Further, using the identified independent variables a 17-run, randomized, Box-Behnken design was developed to understand the correlation trends and quantify the impact on degradation (%), crystallinity, quality attributes (dimensions, hardness, disintegration time) employing qualitative and quantitative analytical tools. The design of experiments (DoE) and statistical analysis observed that LS and Candurin® (%wt) had a strong negative correlation on drug degradation, hardness, and weight, whereas ST had a strong positive correlation with, drug degradation, amorphous conversion, and hardness of the 3D printed dosage form. From this study, it can be concluded that formulation and processing parameters have a critical impact on stability and performance; hence these parameters should be evaluated and optimized before exposing light-sensitive drugs to the SLS processes.
TRIMER: Transcription Regulation Integrated with MEtabolic Regulation
Authors: Niu Puhua , Soto Maria J. , Yoon Byung-Jun , Dougherty Edward R. , Alexander Francis J. , Blaby Ian , Qian Xiaoning 
Published: April 2021

ABSTRACTAdvances in bioengineering have enabled numerous bio-based commodities. Yet most traditional approaches do not extend beyond a single metabolic pathway and do not attempt to modify gene regulatory networks in order to buffer metabolic perturbations. This is despite access to near universal technologies allowing genome-scale engineering. To help overcome this limitation, we have developed a pipeline enabling analysis of Transcription Regulation Integrated with MEtabolic Regulation (TRIMER). TRIMER utilizes a Bayesian network (BN) inferred from transcriptomic data to model the transcription factor regulatory network. TRIMER then infers the probabilities of gene states that are of relevance to the metabolism of interest, and predicts metabolic fluxes resulting from deletion of transcription factors at the genome scale. Additionally, we have developed a simulation framework to mimic the TF-regulated metabolic network, capable of generating both gene expression states and metabolic fluxes, thereby providing a fair evaluation platform for benchmarking models and predictions. Here, we present this computational pipeline. We demonstrate TRIMER’s applicability to both simulated and experimental data and show that it outperforms current approaches on both data types.
Genetic regulation of homeostatic immune architecture in the lungs of Collaborative Cross mice
Authors: Hampton Brea K. , Jensen Kara L. , Whitmore Alan C. , Linnertz Colton L. , Maurizio Paul , Miller Darla R. , Morrison Clayton R. , Noll Kelsey E. , Plante Kenneth S. , Shaw Ginger D. , West Ande , Baric Ralph S. , de Villena Fernando Pardo-Manuel , Heise Mark T. , Ferris Martin T. 
Published: April 2021

SummaryVariation in immune homeostasis, immune system stability, in organ systems such as the lungs is likely to shape the host response to infection at these exposed tissues. We evaluated immune homeostasis in immune cell populations in the lungs of the Collaborative Cross (CC) mouse genetic reference population. We found vast heritable variation in leukocyte populations with the frequency of many of these cell types showing distinct patterns relative to classic inbred strains C57BL/6J and BALB/cJ. We identified 28 quantitative trait loci (QTL) associated with variation in baseline lung immune cell populations, including several loci that broadly regulate the abundance of immune populations from distinct developmental lineages, and found that many of these loci have predictive value for influenza disease outcomes, demonstrating that genetic determinants of homeostatic immunity in the lungs regulate susceptibility to virus-induced disease. All told, we highlight the need to assess diverse mouse strains in understanding immune homeostasis and resulting immune responses.
Flashfm: A Flexible and Shared Information Fine-mapping Approach for Multiple Quantitative Traits
Authors: Hernández N , Soenksen J , Newcombe P , Sandhu M , Barroso I , Wallace C , Asimit J 
Published: April 2021

AbstractJoint fine-mapping that leverages information between quantitative traits could improve accuracy and resolution over single-trait fine-mapping. Using summary statistics, flashfm (FLexible And SHared information Fine-Mapping) fine-maps signals for multiple traits, allowing for missing trait measurements and use of related individuals. In a Bayesian framework, prior model probabilities are formulated to favour model combinations that share causal variants to capitalise on information between traits. Simulation studies demonstrate that both approaches produce broadly equivalent results when traits have no shared causal variants. When traits share at least one causal variant, flashfm reduces the number of potential causal variants by 30% compared with single-trait fine-mapping. In a Ugandan cohort with 33 cardiometabolic traits, flashfm gave a 20% reduction in the total number of potential causal variants from single-trait fine-mapping. Flashfm is computationally efficient and can easily be deployed across publicly available summary statistics for signals in up to six traits.
Single-cell transcriptomics reveals a conserved metaplasia program in pancreatic injury
Authors: Ma Zhibo , Lytle Nikki K. , Chen Bob , Jyotsana Nidhi , Novak Sammy Weiser , Cho Charles J. , Caplan Leah , Ben-Levy Olivia , Neininger Abigail C. , Burnette Dylan T. , Trinh Vincent Q. , Tan Marcus C.B. , Patterson Emilee A. , Drigo Rafael Arrojo e , Giraddi Rajshekhar R. , Ramos Cynthia , Means Anna L. , Matsumoto Ichiro , Manor Uri , Mills Jason C. , Goldenring James R. , Lau Ken S. , Wahl Geoffrey M. , DelGiorno Kathleen E. 
Published: April 2021

ABSTRACTBACKGROUND & AIMSAcinar to ductal metaplasia (ADM) occurs in the pancreas in response to tissue injury and is a potential precursor for adenocarcinoma. The goal of these studies was to define the populations arising from ADM, the associated transcriptional changes, and markers of disease progression.METHODSAcinar cells were lineage-traced with enhanced yellow fluorescent protein (EYFP) to follow their fate upon injury. Transcripts of over 13,000 EYFP+ cells were determined using single-cell RNA sequencing (scRNA-seq). Developmental trajectories were generated. Data were compared to gastric metaplasia,KrasG12D-induced neoplasia, and human pancreatitis. Results were confirmed by immunostaining and electron microscopy.KrasG12Dwas expressed in injury-induced ADM using several inducible Cre drivers. Surgical specimens of chronic pancreatitis from 15 patients were evaluated by immunostaining.RESULTSscRNA-seq of ADM revealed emergence of a mucin/ductal population resembling gastric pyloric metaplasia. Lineage trajectories suggest that some pyloric metaplasia cells can generate tuft and enteroendocrine cells (EECs). Comparison toKrasG12D-induced ADM identifies populations associated with disease progression. Activation ofKrasG12Dexpression in HNF1B+ or POU2F3+ ADM populations leads to neoplastic transformation and formation of MUC5AC+ gastric-pit-like cells. Human pancreatitis samples also harbor pyloric metaplasia with a similar transcriptional phenotype.CONCLUSIONSUnder conditions of chronic injury, acinar cells undergo a pyloric-type metaplasia to mucinous progenitor-like populations, which seed disparate tuft cell and EEC lineages. ADM-derived EEC subtypes are diverse.KrasG12Dexpression is sufficient to drive neoplasia from injury-induced ADM and offers an alternative origin for tumorigenesis. This program is conserved in human pancreatitis, providing insight into early events in pancreas diseases.
Machine learning approaches to identify sleep genes
Authors: Lee Yin Yeng , Endale Mehari , Wu Gang , Ruben Marc D , Francey Lauren J , Morris Andrew R , Choo Natalie Y , Anafi Ron C , Smith David F , Liu Andrew Chuanyin , Hogenesch John B 
Published: April 2021

AbstractGenetics impacts sleep, yet, the molecular mechanisms underlying sleep regulation remain elusive. We built machine learning (ML) models to predict genes based on their similarity to known sleep genes. Using a manually curated list of 109 labeled sleep genes, we trained a prediction model on thousands of published datasets, representing circadian, immune, sleep deprivation, and many other processes. Our predictions fit with prior knowledge of sleep regulation and also identify several key genes/pathways to pursue in follow-up studies. We tested one of our findings, the NF-κB pathway, and showed that its genetic alteration affects sleep duration in mice. Our study highlights the power of ML to integrate prior knowledge and genome-wide data to study genetic regulation of sleep and other complex behaviors.
Online Closed-Loop Real-Time tES-fMRI for Brain Modulation: Feasibility, Noise/Safety and Pilot Study
Authors: Mulyana Beni , Tsuchiyagaito Aki , Smith Jared , Misaki Masaya , Kuplicki Rayus , Soleimani Ghazaleh , Rashedi Ashkan , Shereen Duke , Bergman Til Ole , Cheng Samuel , Paulus Martin , Bodurka Jerzy , Ekhtiari Hamed 
Published: April 2021

AbstractRecent studies suggest that transcranial electrical stimulation (tES) can be performed during functional magnetic resonance imaging (fMRI). The novel approach of using concurrent tES-fMRI to modulate and measure targeted brain activity/connectivity may provide unique insights into the causal interactions between the brain neural responses and psychiatric/neurologic signs and symptoms, and importantly, guide the development of new treatments. However, tES stimulation parameters to optimally influence the underlying brain activity in health and disorder may vary with respect to phase, frequency, intensity and electrode’s montage. Here, we delineate how a closed-loop tES-fMRI study of frontoparietal network modulation can be designed and performed. We also discuss the challenges of running a concurrent tES-fMRI, describing how we can distinguish clinically meaningful physiological changes caused by tES from tES-related artifacts. There is a large methodological parameter space including electrode types, electrolytes, electrode montages, concurrent tES-fMRI hardware, online fMRI processing pipelines and closed-loop optimization algorithms that should be carefully selected for closed-loop tES-fMRI brain modulation. We also provide technical details on how safety and quality of tES-fMRI settings can be tested, and how these settings can be monitored during the study to ensure they do not exceed safety standards. The initial results of feasibility and applicability of closed-loop tES-fMRI are reported and potential hypotheses for the outcomes are discussed.Highlight pointsMethodological details of a closed-loop tES-fMRI study protocol are provided.The protocol is performed successfully on a frontoparietal network without side-effects.The temperature of electrodes in concurrent tES-fMRI remains in the safe range.Properly setup concurrent tES does not introduce MRI artifacts and noise.Simplex optimizer could be used to find an optimal tES stimulation parameter.
Escherichia coliCryptic Prophages Sense Nutrients to Control Persister Cell Resuscitation
Authors: Song Sooyeon , Kim Jun-Seob , Yamasaki Ryota , Oh Sejong , Benedik Michael , Wood Thomas K. 
Published: April 2021

ABSTRACTWe determined previously that some cryptic prophages are not genomic junk but instead enable cells to combat myriad stresses as part of an active stress response. However, how these phage fossils affect the extreme stress response of dormancy; i.e., how cryptic prophages affect persister cell formation and resuscitation, has not been fully explored. Persister cells form as a result of stresses such as starvation, antibiotics, and oxidative conditions, and resuscitation of these persister cells likely causes recurring infections such as those associated with tuberculosis, cystic fibrosis, and Lyme disease. Unlike for the active stress response, here we find that deletion of each of the nineEscherichia colicryptic prophages has no effect on persister cell formation. Strikingly, elimination of each cryptic prophage results in an increase in persister cell resuscitation with a dramatic increase in resuscitation upon deleting all nine prophages. This increased resuscitation includes eliminating the need for a carbon source and is due to activation of the phosphate import system as a result of inactivating transcriptional regulator AlpA of the CP4-57 cryptic prophage, since we found ΔalpAincreases persister resuscitation, and AlpA represses phosphate regulator PhoR. Therefore, we report a novel cellular stress mechanism controlled by cryptic prophages: regulation of phosphate uptake which controls the exit of the cell from dormancy and prevents premature resuscitation in the absence of nutrients.