bluuebunny/crossref_metadata_2025_split huggingface dataset:
AGEAS: Automated Machine Learning based Genetic Regulatory Element Extraction System
Authors: Nakamoto Masayoshi , Tao Jiawang , Yu Jack 
Published: February 2022

ABSTRACTAs rapid progress in sequencing technology since last decade, numerous mechanisms underlying cell functions and developmental processes have been revealed as complex regulations of gene expressions. Since single-cell RNA sequencing (scRNA-seq) made high-resolution transcriptomic view increasingly accessible, precise identification of gene regulatory network (GRN) describing cell types and cell states became achievable. However, extracting key regulatory elements, including gene regulatory pathways (GRPs), transcription factors (TFs), and targetomes, that accurately and completely reflects functionality changes in biological phenomena remains challenging. Herein, we describe AGEAS, an semi-supervised automated machine learning (AutoML) based genetic regulatory element extraction system that assesses importances of GRPs in resulting biological phenomena, such as cell type differentiation, physiological and pathological development, and reconstructs GRNs with extracted important GRPs for comprehensive inference. With several case studies in divergent research areas, we show that AGEAS can indeed extract informative regulatory elements and reconstruct networks to indicate regulatory changes in biological phenomena of interest.Availability and implementationThe AGEAS code is available athttps://github.com/JackSSK/Ageas.
Diagnostic Ion Data Analysis Reduction (DIDAR) allows rapid quality control analysis and filtering of multiplexed single cell proteomics data
Authors: Jenkins Conor , Orsburn Benjamin C. 
Published: February 2022

AbstractRecent advances in the sensitivity and speed of mass spectrometers utilized for proteomics and metabolomics workflows has led to a dramatic increase in data file size and density. For a field already challenged by data complexity due to a dependence on desktop PC architecture and the Windows operating systems, further compromises appear inevitable as data density scales. As one method to reduce data complexity, we present herein a light-weight python script that can rapidly filter and provide analysis metrics from tandem mass spectra based on the presence and number of diagnostic fragment ions determined by the end user. Diagnostic Ion Data Analysis Reduction (DIDAR) can be applied to any mass spectrometry dataset to create smaller output files containing only spectra likely to contain post-translational modifications or chemical labels of interest. In this study we describe the application DIDAR within the context of multiplexed single cell proteomics workflows. When applied in this manner using reporter fragment ions as diagnostic signatures, DIDAR can provide quality control metrics based on the presence of reporter ions derived from single human cells and simplified output files for search engine analysis. The simple output metric text files can be used to rapidly flag entire LCMS runs with technical issues and remove them from downstream analysis based on end user minimum requirements. Acquisition files that pass these criteria are further improved through the automatic removal of spectra where insufficient signal from single cells is observed. We describe the application of DIDAR to two recently described multiplexed single cell proteomics datasets.Abstract Graphic
Single-Case Disconnectome Lesion-symptom Mapping: Identifying Two Subtypes of Limb Apraxia
Authors: Metzgar Rachel , Stoll Harrison , Grafton Scott T. , Buxbaum Laurel J. , Garcea Frank E. 
Published: February 2022

AbstractInfluential theories of skilled action posit that distinct cognitive mechanisms and neuroanatomic substrates support meaningless gesture imitation and tool use pantomiming, and poor performance on these tasks are hallmarks of limb apraxia. Yet prior research has primarily investigated brain-behavior relations at the group level; thus, it is unclear whether we can identify individuals with isolated impairments in meaningless gesture imitation or tool use pantomiming whose performance is associated with a distinct neuroanatomic lesion profile. The goal of this study was to test the hypothesis that individuals with disproportionately worse performance in meaningless gesture imitation would exhibit cortical damage and white matter disconnection in left fronto-parietal brain regions, whereas individuals with disproportionately worse performance in tool use pantomiming would exhibit cortical damage and white matter disconnection in left temporo-parietal brain regions. Fifty-eight participants who experienced a left cerebrovascular accident took part in a meaningless gesture imitation task, a tool use pantomiming task, and a T1 structural MRI. Two participants were identified who had relatively small lesions and disproportionate impairments on one task relative to the other, as well as below-control-level performance on one task and not the other. Using these criteria, one participant was disproportionately impaired at meaningless gesture imitation, and the other participant was disproportionately impaired at pantomiming tool use. Graph theoretic analysis of each participant’s structural disconnectome demonstrated that disproportionately worse meaningless gesture imitation performance was associated with disconnection among the left inferior parietal lobule, the left superior parietal lobule, and the left middle and superior frontal gyri, whereas disproportionately worse tool use pantomiming performance was associated with disconnection between left temporal and parietal regions. Our results demonstrate that relatively focal lesions to specific portions of the Tool Use Network can be associated with distinct limb apraxia subtypes.HighlightsDistinct subtypes of apraxia are supported by distinct neuroanatomic substrates.Fronto-parietal lesions are associated with meaningless imitation deficits.Temporo-parietal lesions are associated with tool use pantomiming deficits.Structural connectivity among cortical areas supports a distributed praxis system.
The gene signatures of human alpha cells in types 1 and 2 diabetes indicate disease-specific pathways of alpha cell dysfunction
Authors: Bosi Emanuele , Marchetti Piero , Rutter Guy A. , Eizirik Decio L. 
Published: February 2022

AbstractGlucagon secretion is perturbed in both type 1 and type 2 diabetes (T1D, T2D) the pathophysiological changes at the level of individual pancreatic alpha cells are still largely obscure. Using recently-curated single-cell RNA data from human donors with either T1D or T2D and appropriate controls, we leveraged alpha cell transcriptomic alterations consistent with both common and discrete pathways. Firstly, altered expression of genes associated with alpha cell identity (ARX, MAFB) was common to both diseases. In contrast, increased expression of cytokine-regulated genes and genes involved in glucagon biosynthesis and processing were apparent in T1D, whereas mitochondrial genes associated with reactive oxygen species generation (COX7B, NQO2) were dysregulated in alpha cells from T2D patients. Conversely, T1D alpha cells displayed alterations in genes associated with autoimmune-induced ER stress (ERLEC1, HSP90) whilst those from T2D patients showed changes in glycolytic and citrate cycle genes (LDH, PDHB, PDK4) which were unaffected in T1D. These findings suggest that despite some similarities related to loss-of-function, the alterations of alpha cells present important disease-specific signatures, suggesting that they are secondary to the main pathogenic events characteristic to each disease, namely immune-mediated-or metabolic-mediated-stress in respectively T1D and T2D.
The phase of sensorimotor mu and beta oscillations has the opposite effect on corticospinal excitability
Authors: Wischnewski Miles , Haigh Zachary J. , Shirinpour Sina , Alekseichuk Ivan , Opitz Alexander 
Published: February 2022

AbstractNeural oscillations in the primary motor cortex (M1) shape corticospinal excitability. Power and phase of ongoing mu (8-13 Hz) and beta (14-30 Hz) activity may mediate motor cortical output. However, the functional dynamics of both mu and beta phase and power relationships and their interaction, are largely unknown. Here, we employ recently developed real-time targeting of the mu and beta rhythm, to apply phase-specific brain stimulation and probe motor corticospinal excitability non-invasively. For this, we used instantaneous read-out and analysis of ongoing oscillations, targeting four different phases (0°, 90°, 180°, and 270°) of mu and beta rhythms with suprathreshold single-pulse transcranial magnetic stimulation (TMS) to M1. Ensuing motor evoked potentials (MEPs) in the right first dorsal interossei muscle were recorded. Twenty healthy adults took part in this double-blind randomized crossover study. Mixed model regression analyses showed significant phase-dependent modulation of corticospinal output by both mu and beta rhythm. Strikingly, these modulations exhibit a double dissociation. MEPs are larger at the mu trough and rising phase and smaller at the peak and falling phase. For the beta rhythm we found the opposite behavior. Also, mu power, but not beta power, was positively correlated with corticospinal output. Power and phase effects did not interact for either rhythm, suggesting independence between these aspects of oscillations. Our results provide insights into real-time motor cortical oscillation dynamics, which offers the opportunity to improve the effectiveness of TMS by specifically targeting different frequency bands.
Ribosome profiling reveals the translational landscape and allele-specific translation efficiency in rice
Authors: Zhu Xi-Tong , Zhou Run , Che Jian , Zheng Yu-Yu , Qamar Muhammad Tahir ul , Feng Jia-Wu , Zhang Jianwei , Gao Junxiang , Chen Ling-Ling 
Published: February 2022

AbstractTranslational regulation is a critical step in the process of gene expression and governs the synthesis of proteins from mRNAs. Many studies have revealed the translational regulation in plants in response to various environmental stimuli. However, there has been no comprehensive landscape of translational regulation and allele-specific translation efficiency in multiple tissues of plants, especially in rice, a main staple crop feeding nearly half of the world population. Here, we used RNA-seq and Ribo-seq data to analyze the transcriptome and translatome of an elite hybrid rice SY63 and its parental varieties ZS97 and MH63. The results revealed that gene expression patterns varied more significantly between tissues than between varieties at both transcriptional and translational levels. Besides, we identified 3,392 upstream open reading frames (uORFs), and most of the uORF-containing genes were enriched for transcription factors. Only 668 long non-coding RNAs could be translated into peptides. Finally, we discovered numerous genes with allele-specific translation efficiency in SY63, and further demonstrated that somecis-regulatory elements (secondary structures of mRNAs and the binding of miRNAs) may contribute to allelic divergence in translation efficiency. Overall, our findings may improve the understanding of translational regulation in rice and provide information for the molecular basis of breading research.
Neprosin belongs to a new family of glutamic peptidase based on in silico evidence
Authors: Ting Tiew-Yik , Baharin Anis , Ramzi Ahmad Bazli , Ng Chyan-Leong , Goh Hoe-Han 
Published: February 2022

AbstractNeprosin was first discovered in the insectivorous tropical pitcher plants of Nepenthes species as a novel protease with prolyl endopeptidase (PEP) activity. Neprosin has two uncharacterized domains of neprosin activation peptide and neprosin. A previous study has shown neprosin activity in hydrolyzing proline-rich gliadin, a gluten component that triggers celiac disease. In this study, we performed in silico structure-function analysis to investigate the catalytic mechanism of neprosin. Neprosin sequences lack the catalytic triad and motifs of PEP family S9. Protein structures of neprosins from Nepenthes × ventrata (NvNpr) and N. rafflesiana (NrNpr1) were generated by ab initio methods and comparatively assessed to obtain high-quality models. Structural alignment of models to experimental structures in the Protein Data Bank (PDB) found a high structural similarity to glutamic peptidases. Further investigations reveal other resemblances to the glutamic peptidases with low optimum pH that activates the enzyme via autoproteolysis for maturation. Two highly conserved glutamic acid residues, which are stable according to the molecular dynamics simulation, can be found at the active site of the substrate cleft. Protein docking demonstrated that mature neprosins bind well with potent antigen αI-gliadin at the putative active site. Taken together, neprosins represent a new glutamic peptidase family, with a putative catalytic dyad of two glutamic acids. This study illustrates a hypothetical enzymatic mechanism of the neprosin family and demonstrates the useful application of an accurate ab initio protein structure prediction in the structure-function study of a novel protein family.
Negative Impact of Daily Screen Use on Inhibitory Control Network in Preadolescence: A Two-Year Follow-Up Study
Authors: Chen Ya-Yun , Yim Hyungwook , Lee Tae-Ho 
Published: February 2022

AbstractThe COVID-19 pandemic has made an unprecedented shift in children’s daily lives. Children are increasingly spending time with screens to learn and connect with others. As the online environment rapidly substitutes in-person experience, understanding children’s neuropsychological trajectories associated with screen experiences is important. Previous findings suggest that excessive screen use can lead children to prefer more immediate rewards over delayed outcomes. We hypothesized that increased screen time delays a child’s development of inhibitory control. By analyzing neuropsychological data from 8,324 children (9-11ys) from the ABCD Study, we found that children who had more screen time showed a higher reward orientation and a weaker inhibitory control system (i.e., fronto-striatal circuitry) in the brain. Importantly, we found that the interaction between screen exposure and reward sensitivity negatively influenced the development of the inhibitory control system in the brain over a two year period. These results indicate possible negative long-term impacts of increased daily screen time on children’s neuropsychological development. The results further demonstrated that screen time influences dorsal striatum connectivity, which suggests that the effect of daily screen use is a habitual seeking behavior. The study provides neural and behavioral evidence on the negative impact of daily screen use on developing children.
In vivodetection of substantia nigra and locus coeruleus volume loss in Parkinson’s disease using neuromelanin-sensitive MRI: Replication in two cohorts
Authors: Hwang Kristy S. , Langley Jason , Tripathi Richa , Hu Xiaoping P. , Huddleston Daniel E. 
Published: February 2022

AbstractPatients with Parkinson’s disease undergo a loss of melanized neurons in substantia nigra pars compacta and locus coeruleus. Very few studies have assessed substantia nigra pars compacta and locus coeruleus pathology in Parkinson’s disease simultaneously with magnetic resonance imaging (MRI). Neuromelanin-sensitive MRI measures of substantia nigra pars compacta and locus coeruleus volume based on explicit magnetization transfer contrast have been shown to have high scan-rescan reproducibility in controls, but no study has replicated detection of Parkinson’s disease-associated volume loss in substantia nigra pars compacta and locus coeruleus in multiple cohorts with the same methodology.Two separate cohorts of Parkinson’s disease patients and controls were recruited from the Emory Movement Disorders Clinic and scanned on two different MRI scanners. In cohort 1, imaging data from 19 controls and 22 Parkinson’s disease patients were acquired with a Siemens Trio 3 Tesla scanner using a 2D gradient echo sequence with magnetization transfer preparation pulse. Cohort 2 consisted of 33 controls and 39 Parkinson’s disease patients who were scanned on a Siemens Prisma 3 Tesla scanner with a similar imaging protocol. Locus coeruleus and substantia nigra pars compacta volumes were segmented in both cohorts.Substantia nigra pars compacta volume (Cohort 1:p=0.0148; Cohort 2:p=0.0011) and locus coeruleus volume (Cohort 1:p=0.0412; Cohort 2:p=0.0056) were significantly reduced in the Parkinson’s disease group as compared to controls in both cohorts. This imaging approach robustly detects Parkinson’s disease effects on these structures, indicating that it is a promising marker for neurodegenerative neuromelanin loss.
When mistrust in the government and scientists reinforce social inequalities in vaccination against Covid-19
Authors: Bajos Nathalie , Spire Alexis , Silberzan Léna , Sireyjol Antoine , Jusot Florence , Meyer Laurence , Franck Jeanna-Eve , Warszawski Josiane ,   the EpiCov study group
Published: February 2022

AbstractObjectiveTo assess whether mistrust in the government and scientists reinforces social and racial inequalities in vaccination practisesDesignA follow-up of a random population-based cohort survey.SettingIn July 2021, in France.Participants80,971 participants aged 18 years and more.Main outcome measuresAdjusted odds ratios of Covid-19 vaccination status (received at least one dose/ intends to get vaccinated/ does not know whether to get vaccinated/refuses vaccination) were assessed using multinomial regressions to test associations with social and mistrust factors and to study how these two factors interacted with each other.ResultsIn all, 72.2% were vaccinated at the time of the survey. The population of unvaccinated people was younger, less educated, had lower incomes, and more often belonged to racialised minorities, as compared to vaccinated people. Mistrust of government and scientists to curb the spread of the epidemic were the factors most associated with refusing to be vaccinated: OR=8.86 (7.13 to 11.00) for the government and OR=9.07 (7.71 to 10.07) for scientists, compared to vaccinated people. Mistrust was more prevalent among the poorer which consequently reinforced social inequalities in vaccination. The 10% poorest who did not trust the government reached an OR of 16.2 (11.9 to 22) for refusing to be vaccinated compared to the 10% richest who did.ConclusionThere is a need to develop depoliticised outreach programmes targeted at the most socially disadvantaged groups, and to design vaccination strategies conceived with people from different social and racial backgrounds to enable them to make fully informed choices.
Trajectories of Perceived Negative Impact in Parents of Individuals with Autism Spectrum Disorders from 9 to 25 years of age
Authors: Christopher Kourtney , Elias Rebecca , Lord Catherine 
Published: February 2022

AbstractCaregivers raising a child with autism experience increased parental burden, though many have positive experiences as well. Perceived negative impact, a form of parental burden, is the degree to which a caregiver reports negative financial, social, and emotional experiences associated with having a child with a disability. This longitudinal study defined parental perceived negative impact trajectory classes across time, determined predictors, and explored the relationship between functional adult outcomes and impact class. Participants (n = 209) were comprised of caregivers whose child received an ASD diagnosis or had developmental delay. Latent class growth modeling defined three trajectory classes (e.g., low [n = 68, 32.54%], medium [n = 98, 46.89%], and high [n = 43, 20.57%]). Regressions revealed the low impact trajectory class had more caregivers who were racial minorities, less educated, and more socially supported. Membership in the high negative impact class was associated with increased childhood hyperactivity, irritability, autism symptomology, and poor adaptive skills. The low negative impact trajectory class was associated with young adults achieving more functional outcomes. Overall negative impact declined over time all for all classes, though never completely subsided. Possible implications for clinical practices are discussed
Demographic and socioeconomic factors predict maternal postpartum rehospitalization: a retrospective nuMoM2b dataset study
Authors: Wakefield Colin , Frasch Martin G 
Published: February 2022

AbstractThe advent of artificial intelligence/machine learning (AI/ML) in medicine has opened opportunities for harnessing the power of ML to predict patient outcomes based on diverse data contained in the electronic medical records (EMR). Taken by themselves singular features represented by such diverse data are often not clearly predictive but combined in an ML modeling framework such so-called weak learners have the power to yield highly predictive models of health outcomes. We hypothesized that sociodemographic and basic, easily obtainable health characteristics of pregnant mothers strongly predict the risk of rehospitalization. Using the nuMoM2b dataset from NICHD and reproducible, open access AI/ML techniques we tested and validated this hypothesis. Among a large (n=10,038) and geographically diverse cohort of nulliparous women with singleton gestations, the present findings show that while some of these characteristics highlight the known disparities in health outcomes (race/ethnicity), most of them are modifiable through social policies and health counseling. These include access to and support during education, comprehensive health insurance, alleviating coming out of poverty, smoking, and unhealthy lifestyle habits. Across the four ML models deployed, the results are mutually reinforcing yielding a clear cohesive picture. This solution can be used to develop a prospective risk prediction for this important health outcome in pregnant mothers. The strengths of the presented solution are its focus on preventable maternal morbidities and risk factors, demonstration of the impact on adversely affected populations, and reproducible step-by-step executable, documented open-access code with relatively low computational requirements. This enables easy adoption and further development on this and other similar datasets to study the contributions and predictive power of various factors to maternal morbidities.