bluuebunny/crossref_metadata_2025_split huggingface dataset:
Associations between evening shift work, irregular sleep timing, and gestational diabetes in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b)
Authors: Wallace Danielle A , Reid Kathryn , Grobman William A , Facco Francesca L , Silver Robert M , Pien Grace W , Louis Judette , Zee Phyllis C , Redline Susan , Sofer Tamar 
Published: May 2022

ABSTRACTStudy ObjectivesShift work is a risk factor for cardiometabolic disease, possibly through effects on sleep-wake rhythms. We hypothesized that shift work during pregnancy is associated with increased odds of preeclampsia, preterm birth, and gestational diabetes mellitus (GDM), and that the association is mediated by irregular sleep timing.MethodsThe Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b) is a prospective cohort study (n=10,038) designed to investigate risk factors for adverse pregnancy outcomes. Medical outcomes were determined with medical record abstraction and/or questionnaires; sleep midpoint was measured in a subset of participants with ≥5-day wrist actigraphy (ActiWatch). We estimated the association of shift work during pregnancy with preeclampsia, preterm birth, and GDM using logistic regression, adjusted for adversity (a cumulative variable for poverty, education, health insurance, and partner status), smoking, self-reported race/ethnicity, and age. Finally, we performed an analysis to explore the extent to which to the association between shiftwork and GDM was mediated by variability in sleep timing.ResultsEvening shift work during pregnancy is associated with approximately 75% increased odds of developing GDM (adjusted OR=1.75, 95% CI:1.12-2.66); there was no association with preterm birth and no association with preeclampsia after adjustment. Pregnant evening shift workers were found to have approximately 45 minutes greater variability in sleep timing compared to day workers (p<0.005); mediation analysis estimated that 27% of the association between shift work and GDM was explained by sleep-timing variability.ConclusionsEvening shift work was associated with GDM, and this relationship may be mediated by variability in sleep timing.
Resolving heterogeneity in Diffuse Large B-cell Lymphoma using a comprehensive modular expression map
Authors: Care Matthew A. , Painter Daniel , Barrans Sharon , Sha Chulin , Johnson Peter , Davies Andy , Du Ming-Qing , Crouch Simon , Smith Alex , Roman Eve , Burton Cathy , Doody Gina , Westhead David , Klein Ulf , Hodson Daniel J. , Tooze Reuben 
Published: May 2022

AbstractDiffuse large B-cell lymphoma (DLBCL) is characterised by pronounced genetic and biological heterogeneity. Several partially overlapping classification systems exist – developed from mutation, rearrangement or gene expression data. We apply a customised network analysis to nearly five thousand DLBCL cases to identify and quantify modules indicative of tumour biology. We demonstrate that network-level patterns of gene co-expression can enhance the separation of DLBCL cases. This allows the resolution of communities of related cases which correlate with genetic mutation and rearrangement status, supporting and extending existing concepts of disease biology and delivering insight into relationships between differentiation state, genetic subtypes, rearrangement status and response to therapeutic intervention. We demonstrate how the resulting fine-grained resolution of expression states is critical to accurately identify potential responses to treatment.Significance statementWe demonstrate how exploiting data integration and network analysis of gene expression can enhance the segregation of diffuse large B-cell lymphoma, resolving pattens of disease biology and demonstrating how the resolution of heterogeneity can enhance the understanding of treatment response.
Diagnostic accuracy of the Panbio™ COVID-19 Antigen rapid test device for SARS-CoV-2 detection in Kenya, 2021: A field evaluation
Authors: Karuga Irungu , Munyua Peninah , Ochieng Caroline , Juma Bonventure , Amoth Patrick , Kuria Francis , Kiiru John , Makayotto Lyndah , Abade Ahmed , Bulterys Marc , Hunsperger Elizabeth , Emukule Gideon O. , Onyango Clayton , Samandari Taraz , Tippett Barr Beth A. , Akelo Victor , Weyenga Herman , Munywoki Patrick K , Bigogo Godfrey , Otieno Nancy A. , Kisivuli Jackton Azenga , Ochieng Edwin , Nyaga Rufus , Hull Noah , Herman-Roloff Amy , Aman Rashid 
Published: May 2022

AbstractBackgroundAccurate and timely diagnosis is essential in limiting the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Real-time reverse transcription-polymerase chain reaction (rRT-PCR), the reference standard, requires specialized laboratories, costly reagents, and a long turnaround time. Antigen rapid diagnostic tests (Ag RDTs) provide a feasible alternative to rRT-PCR since they are quick, relatively inexpensive, and do not require a laboratory. The WHO requires that Ag RDTs have a sensitivity ≥80% and specificity ≥97%.MethodsThis evaluation was conducted at 11 health facilities in Kenya between March and July 2021. We enrolled persons of any age with respiratory symptoms and asymptomatic contacts of confirmed COVID-19 cases. We collected demographic and clinical information and two nasopharyngeal specimens from each participant for Ag RDT testing and rRT-PCR. We calculated the diagnostic performance of the Panbio™ Ag RDT against the US Centers for Disease Control and Prevention’s (CDC) rRT-PCR test.ResultsWe evaluated the Ag RDT in 2,245 individuals where 551 (24.5%, 95% CI: 22.8-26.3%) tested positive by rRT-PCR. Overall sensitivity of the Ag RDT was 46.6% (95% CI: 42.4-50.9%), specificity 98.5% (95% CI: 97.8-99.0%), PPV 90.8% (95% CI: 86.8-93.9%) and NPV 85.0% (95% CI: 83.4-86.6%). Among symptomatic individuals, sensitivity was 60.6% (95% CI: 54.3-66.7%) and specificity was 98.1% (95% CI: 96.7-99.0%). Among asymptomatic individuals, sensitivity was 34.7% (95% CI 29.3-40.4%) and specificity was 98.7% (95% CI: 97.8-99.3%). In persons with onset of symptoms <5 days (594/876, 67.8%), sensitivity was 67.1% (95% CI: 59.2-74.3%), and 53.3% (95% CI: 40.0-66.3%) among those with onset of symptoms >7 days (157/876, 17.9%). The highest sensitivity was 87.0% (95% CI: 80.9-91.8%) in symptomatic individuals with cycle threshold (Ct) values ≤30.ConclusionThe overall sensitivity and NPV of the Panbio™ Ag RDT were much lower than expected. The specificity of the Ag RDT was high and satisfactory; therefore, a positive result may not require confirmation by rRT-PCR. The kit may be useful as a rapid screening tool for only symptomatic patients in high-risk settings with limited access to RT-PCR. A negative result should be interpreted based on clinical and epidemiological information and may require retesting by rRT-PCR.
Measuring Community Resilience During the COVID-19 based on Community Wellbeing and Resource Distribution
Authors: Valinejad Jaber , Guo Zhen , Cho Jin-Hee , Chen Ing-Ray 
Published: May 2022

AbstractThe COVID-19 pandemic has severely harmed every aspect of our daily lives, resulting in a slew of social problems. Therefore, it is critical to accurately assess the current state of community functionality and resilience under this pandemic for successful recovery. To this end, various types of social sensing tools, such as tweeting and publicly released news, have been employed to understand individuals’ and communities’ thoughts, behaviors, and attitudes during the COVID-19 pandemic. However, some portions of the released news are fake and can easily mislead the community to respond improperly to disasters like COVID-19. This paper aims to assess the correlation between various news and tweets collected during the COVID-19 pandemic on community functionality and resilience. We use fact-checking organizations to classify news as real, mixed, or fake, and machine learning algorithms to classify tweets as real or fake to measure and compare community resilience (CR). Based on the news articles and tweets collected, we quantify CR based on two key factors, community wellbeing and resource distribution, where resource distribution is assessed by the level of economic resilience, and community capital. Based on the estimates of these two factors, we quantify CR from both news articles and tweets and analyze the extent to which CR measured from the news articles can reflect the actual state of CR measured from tweets. To improve the operationalization and sociological significance of this work, we use dimension reduction techniques to integrate the dimensions.
Covid-19 is a leading cause of death in children and young people ages 0-19 years in the United States
Authors: Flaxman Seth , Whittaker Charles , Semenova Elizaveta , Rashid Theo , Parks Robbie M , Blenkinsop Alexandra , Unwin H Juliette T , Mishra Swapnil , Bhatt Samir , Gurdasani Deepti , Ratmann Oliver 
Published: May 2022

AbstractCovid-19 has caused more than 1 million deaths in the US, including at least 1,204 deaths among children and young people (CYP) aged 0-19 years, with 796 occurring in the one year period April 1, 2021 - March 31, 2022. Deaths among US CYP are rare in general, and so we argue here that the mortality burden of Covid-19 in CYP is best understood in the context of all other causes of CYP death. Using publicly available data from CDC WONDER on NCHS’s 113 Selected Causes of Death, and comparing to mortality in 2019, the immediate pre-pandemic period, we find that Covid-19 mortality is among the 10 leading causes of death in CYP aged 0-19 years in the US, ranking 8th among all causes of deaths, 5th in disease-related causes of deaths (excluding accidents, assault and suicide), and 1st in deaths caused by infectious or respiratory diseases. Covid-19 deaths constitute 2.3% of the 10 leading causes of death in this age group. Covid-19 caused substantially more deaths in CYP than major vaccine-preventable diseases did historically in the period before vaccines became available. Various factors including underreporting and Covid-19’s role as a contributing cause of death from other diseases mean that our estimates may understate the true mortality burden of Covid-19. Our findings underscore the public health relevance of Covid-19 to CYP. In the likely future context of sustained SARS-CoV-2 circulation, pharmaceutical and non-pharmaceutical interventions will continue to play an important role in limiting transmission of the virus in CYP and mitigating severe disease.
Humoral Immunity to SARS-CoV-2 and Inferred Protection from Infection in a French Longitudinal Community Cohort
Authors: Woudenberg Tom , Pinaud Laurie , Garcia Laura , Tondeur Laura , Pelleau Stéphane , De Thoisy Alix , Donnadieu Françoise , Backovic Marija , Attia Mikaël , Hozé Nathanael , Duru Cécile , Koffi Aymar Davy , Castelain Sandrine , Ungeheuer Marie-Noelle , Pellerin Sandrine Fernandes , Planas Delphine , Bruel Timothée , Cauchemez Simon , Schwartz Olivier , Fontanet Arnaud , White Michael 
Published: May 2022

AbstractPopulation-level immunity to SARS-CoV-2 is growing through vaccination as well as ongoing circulation. Given waning immunity and emergence of new variants, it is important to dynamically determine the risk of re-infection in the population. For estimating immune protection, neutralization titers are most informative, but these assays are difficult to conduct at a population level. Measurement of antibody levels can be implemented at high throughput, but has not been robustly validated as a correlate of protection. Here, we have developed a method that predicts neutralization and protection based on variant-specific antibody measurements to SARS-CoV-2 antigens. This approach allowed us to estimate population-immunity in a longitudinal cohort from France followed for up to 2 years. Participants with a single vaccination or immunity caused by infection only are especially vulnerable to COVID-19 or hospitalization due to SARS-CoV-2. While the median reduced risk to COVID-19 in participants with 3 vaccinations was 96%, the median reduced risk among participants with infection-acquired immunity only was 42%. The results presented here are consistent with data from vaccine-effectiveness studies indicating robustness of our approach. Our multiplex serological assay can be readily optimized and employed to study any new variant and provides a framework for development of an assay that would include protection estimates.
Sex differences in the nasal microbiome of healthy young adults
Authors: Ju Yanmei , Zhang Zhe , Liu Mingliang , Lin Shutian , Sun Qiang , Song Zewei , Liang Weiting , Tong Xin , Jie Zhuye , Lu Haorong , Cai Kaiye , Chen Peishan , Jin Xin , Xu Xun , Yang Huanming , Wang Jian , Hou Yong , Xiao Liang , Jia Huijue , Zhang Tao , Guo Ruijin 
Published: May 2022

SummaryRespiratory diseases impose an immense health burden worldwide. Epidemiological studies have revealed extensive disparities in the incidence and severity of respiratory tract infections (RTIs) between males and females. It is recently hypothesized that there might also be a nasal microbiome axis contributing to the observed sex disparities, but without evidence. In this work, we study the nasal microbiome of healthy young adults in, as of today, the largest cohort based on deep shot-gun metagenomic sequencing. We mainly focus on the bacteriome, but also integrate the mycobiome to get a more holistic perspective. De novo assembly is performed to catalog the nasal bacterial colonizers/residents, which also identify and therefore account for uncharacterized components of the community. The bacteriome is then profiled based on the non-redundant metagenome-assembled genomes (MAGs) catalog constructed therefrom. Unsupervised clustering reveals clearly separable structural patterns in the nasal microbiome between the two sexes. Following this link, we systematically evaluate sex differences for the first time and reveal extensive sex-specific features in the nasal microbiome composition. More importantly, through network analyses, we capture markedly higher ecological stability and antagonistic potentials in the nasal microbiome of females than that of males. The analysis of the keystone bacteria of the communities reveal that the sex-dependent evolutionary characteristics might have contributed to this difference.HighlightsThe non-redundant nasal bacterial MAGs catalog constructed from ultra-deeply sequenced metagenomic data provides a valuable resource.Integrating nasal bacteriome and mycobiome data provides a more holistic perspective for the understudied human nasal microbiome.Unsupervised clustering helps uncover extensive sex differences in the nasal microbiome compositions.Network analyses capture markedly higher ecological stability and antagonistic potentials in the nasal microbiome of females than that of males.Sex-dependent genetic evolutionary forces play a role in the shaping of keystones in the nasal microbial community.
High-throughput deep learning variant effect prediction with Sequence UNET
Authors: Dunham Alistair S. , Beltrao Pedro , AlQuraishi Mohammed 
Published: May 2022

AbstractUnderstanding the consequences of protein coding mutations is important for many applications in biology and medicine. The vast number of possible mutations across species makes comprehensive experimental characterisation impossible, even with recent high-throughput techniques, which means computationally predicting the consequences of variation is essential for many analyses. Previous variant effect prediction (VEP) tools, generally based on evolutionary conservation and protein structure, are often computationally intensive, making them difficult to scale and limiting potential applications. Recent developments in deep learning techniques, including protein language models, and biological data scale have led to a new generation of predictors. These models have improved prediction performance but are still often intensive to run because of slow training steps, hardware requirements and large model sizes. In this work we introduce a new highly scalable deep learning architecture, Sequence UNET, that classifies and predicts variant frequency directly from protein sequence. This model learns to build representations of protein sequence features at a range of scales using a fully convolutional U-shaped compression/expansion architecture. We show that it can generalise to pathogenicity prediction, achieving comparable performance on ClinVar to methods including EVE and ESM-1b at greatly reduced computational cost. We further demonstrate its scalability by analysing the consequences of 8.3 billion variants in 904,134 proteins detected in a large-scale proteomics analysis, showing a link between conservation and protein abundance. Sequence UNET can be run on modest hardware through an easy to use Python package.
Accurate angular integration with only a handful of neurons
Authors: Noorman Marcella , Hulse Brad K , Jayaraman Vivek , Romani Sandro , Hermundstad Ann M 
Published: May 2022

ABSTRACTTo flexibly navigate, many animals rely on internal spatial representations that persist when the animal is standing still in darkness, and update accurately by integrating the animal’s movements in the absence of localizing sensory cues. Theories of mammalian head direction cells have proposed that these dynamics can be realized in a special class of networks that maintain a localized bump of activity via structured recurrent connectivity, and that shift this bump of activity via angular velocity input. Although there are many different variants of these so-called ring attractor networks, they all rely on large numbers of neurons to generate representations that persist in the absence of input and accurately integrate angular velocity input. Surprisingly, in the fly, Drosophila melanogaster, a head direction representation is maintained by a much smaller number of neurons whose dynamics and connectivity resemble those of a ring attractor network. These findings challenge our understanding of ring attractors and their putative implementation in neural circuits. Here, we analyzed failures of angular velocity integration that emerge in small attractor networks with only a few computational units. Motivated by the peak performance of the fly head direction system in darkness, we mathematically derived conditions under which small networks, even with as few as 4 neurons, achieve the performance of much larger networks. The resulting description reveals that by appropriately tuning the network connectivity, the network can maintain persistent representations over the continuum of head directions, and it can accurately integrate angular velocity inputs. We then analytically determined how performance degrades as the connectivity deviates from this optimally-tuned setting, and we find a trade-off between network size and the tuning precision needed to achieve persistence and accurate integration. This work shows how even small networks can accurately track an animal’s movements to guide navigation, and it informs our understanding of the functional capabilities of discrete systems more broadly.
Development of an In Vitro Test for the Optimization of Drug Diffusion in Pediatric Solid Tumors
Authors: Ivy Rachel , Hendricks-Wenger Alissa , Kennedy Lyndon , Jones Anna , Riley Deanna , Handy Ashley , Barker Elizabeth D. 
Published: May 2022

AbstractThere is significant long-term morbidity and mortality associated with the treatment of childhood cancer, and the risk of these effects continues to increase years after completion of therapy. Among childhood cancer survivors the cumulative incidence of a chronic health condition is 99% within 50 years of the original cancer diagnosis. There is a high risk for severe, disabling, or life-threatening chronic condition caused by the chemotherapy used to treat the initial malignancy. Current standards for determining chemotherapy dosage to treat solid tumor malignancies of pediatric patients is based on several factors, including the patient’s surface area, age, weight, and height. To reduce the long-term effects of chemotherapy in pediatric patients our group is focused on developing novel local drug delivery systems to treat solid tumors and minimize systemic effects. The aim of the current study is to develop anin vitromethod to quantify drug diffusion through tumor tissue that will allow us to optimize the dose required to treat solid tumor malignanciesin vivo. We hope by modeling the significant parameters that influence drug penetration of chemotherapy drugs, we can facilitate the development of innovative drug delivery methods and more effective administration of anticancer agents to better treat pediatric malignancies and improve both short-term and long-term outcomes for childhood cancer.
A partially averaged system to model neuron responses to interferential current stimulation
Authors: Cerpa Eduardo , Courdurier Matías , Hernández Esteban , Medina Leonel E. , Paduro Esteban 
Published: May 2022

AbstractThe interferential current (IFC) therapy is a noninvasive electrical neurostimulation technique intended to activate deep neurons using surface electrodes. In IFC, two independent kilohertz-frequency currents purportedly intersect where an interference field is generated. However, the effects of IFC on neurons within and outside the interference field are not completely understood, and it is unclear whether this technique can reliable activate deep target neurons without side effects. In recent years, realistic computational models of IFC have been introduced to quantify the effects of IFC on brain cells, but they are often complex and computationally costly. Here, we introduce a simplified model of IFC based on the FitzHugh-Nagumo (FHN) model of a neuron. By considering a modified averaging method, we obtain a non-autonomous approximated system, with explicit representation of relevant IFC parameters. For this approximated system we determine conditions under which it reliably approximates the complete FHN system under IFC stimulation, and we mathematically prove its ability to predict nonspiking states. In addition, we perform numerical simulations that show that the interference effect is observed only for a narrow set of IFC parameters and, in particular, for a beat frequency no higher than about 100 [Hz]. Our novel model tailored to the IFC technique contributes to the understanding of neurostimulation modalities using this type of signals, and can have implications in the design of noninvasive electrical stimulation therapies.
An Engineered Amber-Emitting Nano Luciferase and Its Use for Immunobioluminescence Imaging in Vivo
Authors: Xiong Ying , Zhang Yiyu , Li Zefan , Reza Md Shamim , Li Xinyu , Tian Xiaodong , Ai Huiwang 
Published: May 2022

ABSTRACTThe NanoLuc luciferase (NLuc) and its furimazine (FRZ) substrate have revolutionized bioluminescence (BL) assays and imaging. However, the use of the NLuc-FRZ luciferase-luciferin pair for mammalian tissue imaging is hindered by the low tissue penetration of the emitting blue photons. Here, we present the development of an NLuc mutant, QLuc, which catalyzes the oxidation of a synthetic QTZ luciferin for bright and red-shifted emission peaking at ∼ 585 nm. This amber-light-emitting luciferase-luciferin pair exhibited improved performance for imaging deep-tissue targets in live mice. Leveraging this novel bioluminescent reporter, we further pursued in vivo immunobioluminescence imaging (immunoBLI), which used a fusion protein of a single-chain variable antibody fragment (scFv) and QLuc for molecular imaging of tumor-associated antigens in a xenograft mouse model. As one of the most red-shifted NLuc variants, we expect QLuc to find broad applications in noninvasive imaging in mammals. Moreover, the immunoBLI method complements immunofluorescence imaging and immuno-positron emission tomography (immunoPET), serving as a convenient and nonradioactive molecular imaging tool for animal models in basic and preclinical research.