bluuebunny/crossref_metadata_2025_split huggingface dataset:
Maternal Wnt11b regulates cortical rotation duringXenopusaxis formation: analysis of maternal-effectwnt11bmutants
Authors: Houston Douglas W. , Elliott Karen L. , Coppenrath Kelsey , Wlizla Marcin , Horb Marko E. 
Published: February 2022

AbstractAsymmetric signalling centres in the early embryo are essential for axis formation in vertebrates. These regions, namely the dorsal morula, yolk syncytial layer, and distal hypoblast/anterior visceral endoderm (in amphibians, teleosts and mammals, respectively), require the localised stabilisation of nuclear Beta-catenin (Ctnnb1), implying that localised Wnt/Beta-catenin signalling activity is critical in their establishment. However, it is becoming increasingly apparent that the stabilisation of Beta-catenin in this context may be initiated independently of secreted Wnt growth factor activity. InXenopus, dorsal Beta-catenin stabilisation is initiated by a requisite microtubule-mediated symmetry-breaking event in the fertilised egg: “cortical rotation”. Vegetally-localisedwnt11bmRNA has been implicated upstream of Beta-catenin in this context, as has the dorsal enrichment of Wnt ligand-independent activators of Beta-catenin, but the extent that each of these processes contribute to axis formation in this paradigm remains unclear. Here we describe a maternal effect mutation inXenopus laevis wnt11b.L, generated by CRISPR mutagenesis. We demonstrate a maternal requirement for timely and complete gastrulation morphogenesis and a zygotic requirement for proper left-right asymmetry. We also show that a subset of maternalwnt11bmutants have axis and dorsal gene expression defects, but that Wnt11b likely does not act through the Wnt coreceptor Lrp6 or through Dishevelled, which we additionally show (using exogenous constructs) do not exhibit patterns of activity consistent with roles in early Beta-catenin stabilisation. Instead, we find that microtubule assembly and cortical rotation are reduced inwnt11bmutant eggs, leading to less organised and directed vegetal microtubule arrays. In conclusion, we propose that Wnt11b signals to the cytoskeleton in the egg or early zygote to enable robust cortical rotation, and thus acts in the distribution of putative dorsal determinants rather than as a component or effector of the determinants themselves.
Superstitious learning of abstract order from random reinforcement
Authors: Jin Yuhao , Jensen Greg , Gottlieb Jacqueline , Ferrera Vincent P. 
Published: February 2022

ABSTRACTSurvival depends on identifying learnable features of the environment that predict reward, and avoiding others that are random and unlearnable. However, humans and other animals often infer spurious associations among unrelated events, raising the question of how well they can distinguish learnable patterns from unlearnable events. Here, we tasked monkeys with discovering the serial order of two pictorial sets: a “learnable” set in which the stimuli were implicitly ordered and monkeys were rewarded for choosing the higher-rank stimulus and an “unlearnable” set in which stimuli were unordered and feedback was random regardless of the choice. We replicated prior results that monkeys reliably learned the implicit order of the learnable set. Surprisingly, the monkeys behaved as though some ordering also existed in the unlearnable set, showing consistent choice preference that transferred to novel untrained pairs in this set, even under a preference-discouraging reward schedule that gave rewards more frequently to the stimulus that was selected less often. In simulations, a model-free RL algorithm (Q-learning) displayed a degree of consistent ordering among the unlearnable set but, unlike the monkeys, failed to do so under the preference, discouraging reward schedule. Our results suggest that monkeys infer abstract structures from objectively random events using heuristics that extend beyond stimulus-outcome conditional learning to more cognitive model-based learning mechanisms.
Population Estimates of People with Spina Bifida in the United States in 2020
Authors: Briggs Virginia G. 
Published: February 2022

ABSTRACTIntroductionSpina bifida is a birth defect that results in deficits of neurological function. Individuals diagnosed with spina bifida often require a lifetime of medical care to manage this condition. Currently, the number of people living with spina bifida in the United States is unclear. The purpose of this study is to provide estimates of the magnitude of this condition, and its distribution by gender and age.MethodsTotal births affected by spina bifida were calculated using rates from the Birth Defects Monitoring Program (BDMP) and state-based birth defects tracking systems supported by the Centers for Disease Control (CDC), over an 80-year period (1940-2020). Spina bifida mortality rates were determined using death certificate data available through the Centers for Disease Control and Prevention, National Center for Health Statistics. Life tables were created for each year of birth between 1940 and 2020 to estimate the total number of people with spina bifida alive in 2020 in the United States.ResultsIn 2020, the estimated number of people in the U.S. living with spina bifida (0-80 years of age) was 124,150 (67,662 female; 56,488 male). The majority were adults aged 30 to 80 years (66.6%), compared to children and young adults (33.4%).ConclusionUnderstanding the approximate size and distribution by age and gender may assist health care providers in planning services for this changing population.
Biologic correlates of beneficial convalescent plasma therapy in a COVID-19 patient reveal disease resolution mechanisms
Authors: Bruiners Natalie , Guerrini Valentina , Ukey Rahul , Dikdan Ryan , Yang Jason , Mishra Pankaj Kumar , Onyuka Alberta , Handler Deborah , Vieth Joshua , Carayannopulos Mary , Guo Shuang , Pollen Maressa , Pinter Abraham , Tyagi Sanjay , Feingold Daniel , Philipp Claire , Libutti Steven , Gennaro Maria Laura 
Published: February 2022

AbstractBackgroundWhile the biomarkers of COVID-19 severity have been thoroughly investigated, the key biological dynamics associated with COVID-19 resolution are still insufficiently understood.Main bodyWe report a case of full resolution of severe COVID-19 due to convalescent plasma transfusion in a patient with underlying multiple autoimmune syndrome. Following transfusion, the patient showed fever remission, improved respiratory status, and rapidly decreased viral burden in respiratory fluids and SARS-CoV-2 RNAemia. Longitudinal unbiased proteomic analysis of plasma and single-cell transcriptomics of peripheral blood cells conducted prior to and at multiple times after convalescent plasma transfusion identified the key biological processes associated with the transition from severe disease to disease-free state. These included (i) temporally ordered upward and downward changes in plasma proteins reestablishing homeostasis and (ii) post-transfusion disappearance of a particular subset of dysfunctional monocytes characterized by hyperactivated Interferon responses and decreased TNF-α signaling.ConclusionsMonitoring specific subsets of innate immune cells in peripheral blood may provide prognostic keys in severe COVID-19. Moreover, understanding disease resolution at the molecular and cellular level should contribute to identify targets of therapeutic interventions against severe COVID-19.
Predicting personalised risk of disability worsening in multiple sclerosis with machine learning
Authors: Eshaghi Arman , Wijeratne Peter A , Oxtoby Neil P , Arnold Douglas L , Collins Louis , Narayanan Sridar , Guttmann Charles R. G. , Thompson Alan J , Alexander Daniel C , Barkhof Frederik , Chard Declan , Ciccarelli Olga 
Published: February 2022

AbstractMultiple sclerosis is a heterogeneous disease with an unpredictable course. We applied machine learning to generate individualised risk scores of disability worsening and stratify patients into subgroups with different prognosis.Clinical data and MRI scans from published randomised clinical trials in patients with relapsing-remitting and progressive MS were divided into training (n=5,483) and external validation data sets (n=2,668). We processed brain MRI scans to obtain 18 measures for lobar grey matter, deep grey matter and lesion volumes, and T1-/T2-weighted ratio of the normal-appearing white matter regions. We developed a machine learning model, called subpopulation risk stratification (SunRiSe), that combines multi-parametric clinical and MRI data to estimate individualised risk scores and stratify patients into subgroups on the basis of this risk; in particular, we entered MRI measures, the Expanded Disability Status Scale, age and gender to generate risk scores of disability worsening (i.e., the time to confirmed disability worsening). Based on SunRiSe risk scores, high-, medium-, and low-risk subpopulations were defined at study entry. We assessed whether selecting patients at high risk of disability worsening reduces sample size compared to when all risk groups were sampled together.In both the training and external validation data sets, SunRiSe-stratified patients in three groups associated with different levels of risk of disability worsening. In the external validation data set, patients at high risk were mainly progressive MS and had more disability events compared to those at medium-risk (hazard ratio [HR]=1.34, p<0.0001) and low-risk (HR=1.51, p<0.0001). At study entry, male gender, older age, higher lesion load, higher disability, lower lobar cortical grey matter, lower normal-appearing white matter T1/T2 ratio and lower deep grey matter volumes, were the most important variables in defining the SunRiSe risk score.The inclusion of patients predicted to be at high risk, reduced (i) duration of an event-driven trial by an average of 4.5 months (±2.1 months); (ii) the number of participants in a randomised trial by approximately 200, with 80% statistical power to detect a 30% treatment effect.Machine learning provides a personalised risk score that can identify patients who have the greatest risk of disability worsening and therefore should be treated with the most effective medications and monitored more closely. Risk stratification allows the enrichment of clinical trials with patients more likely to worsen, and thereby reduces trial duration and sample size.
Concordance of B and T cell responses to SARS-CoV-2 infection, irrespective of symptoms suggestive of COVID-19
Authors: Österdahl Marc F. , Christakou Eleni , Hart Deborah , Harris Ffion , Shahrabi Yasaman , Pollock Emily , Wadud Muntaha , Spector Tim D. , Brown Matthew A. , Seow Jeffrey , Malim Michael H. , Steves Claire J. , Doores Katie J. , Duncan Emma L , Tree Timothy 
Published: February 2022

AbstractObjectivesTo assess T cell responses in individuals with and without a positive antibody response to SARS-CoV-2, in symptomatic and asymptomatic individuals during the COVID-19 pandemic.MethodsParticipants were drawn from the TwinsUK cohort, selected according to a) presence or absence of COVID-associated symptoms (S+, S-), logged prospectively through the COVID Symptom Study app, and b) Anti-IgG Spike and anti-IgG Nucleocapsid antibodies measured by ELISA (Ab+, Ab-), during the first wave of the UK pandemic. T cell helper and regulatory responses after stimulation with SARS-CoV-2 peptides were assessed.Results32 participants were included in final analysis. 14 of 15 with IgG Spike antibodies had a T cell response to SARS-CoV-2-specific peptides; none of 17 participants without IgG Spike antibodies had a T cell response (Chi-squared 28.2, p<0.001). Quantitative T cell responses correlated strongly with fold-change in IgG Spike antibody titre (rho=0.79, p<0.0001) but not to symptom score (rho=0.17, p=0.35).ConclusionsHumoral and cellular immune responses to SARS-CoV-2 are highly correlated, with no evidence that cellular immunity differs from antibody status four months after acute illness.
COVID-19 Vaccination is Associated with Decreasing Cases, Hospitalizations, and Deaths Across Age Groups and Variants over 9 months in Switzerland
Authors: Jucker Jean-Luc 
Published: February 2022

AbstractRecent studies assessing COVID-19 vaccine efficacy at the population level found counterintuitive results, such as positive associations between vaccination and infections or deaths. These ecological studies have limitations, including too short observation periods, focusing on infections, and not controlling for age groups and dominant variants. The current study addresses these limitations by investigating the relations between vaccination and COVID-19 cases, hospitalizations, and deaths over a longer period (9½ months) while also considering age groups (from 10 to 80+ years old) and variants (Alpha and Delta), utilizing data from Switzerland. Results suggest that vaccination is negatively related to cases overall and in all cantons of Switzerland, and that vaccination is negatively related to hospitalizations and deaths from 50 years old. Furthermore, vaccination is a significant predictor of cases, hospitalizations, and deaths while holding the effects of age and dominant variant constant.
MACHINE LEARNING IMPACT ASSESSMENT OF CLIMATE FACTORS ON DAILY COVID-19 CASES
Authors: Afrifa Stephen , Ato Essien Felix , Appiahene Peter , Wiafe Isaac , Mensah Gyening Rose-Mary Owusuaa , Opoku Michael 
Published: February 2022

AbstractCoronavirus disease 2019 (also known as COVID-19) is a vastly infectious virus instigated by the coronavirus-2, which causes severe acute respiratory illness (SARS-Cov-2). Scientists and researchers are conducting a number of studies to better understand the COVID-19 pandemic’s behavioral nature and spread, and machine learning provides useful tools. We used machine learning techniques to study the effect of climate conditions on daily instances of COVID-19 in this study. The study has three main objectives: first, to investigate the most climatic features that could affect the spread of novel COVID-19 cases; second, to assess the influence of government strategies on COVID-19 using our dataset; third, to do a comparative analysis of two different machine learning models, and develop a model to predict accurate response to the most features on COVID-19 spread. The goal of this research is to assist health-care facilities and governments with planning and decision-making. The study compared random forest and artificial neural network models for analysis. In addition, feature importance among the independent variables (climate variables) were identified with the random forest. The study used publicly available datasets of COVID-19 cases from the World Health Organization and climate variables from National Aeronautics and Space Administration websites respectively. Our results showed that relative humidity and solar had significant impact as a feature of weather variables on COVID-19 recorded cases; and that random forest predicted accurate response to the most climatic features on COVID-19 spread. Based on this, we propose the random forest model to predict COVID-19 cases using weather variables.
Structural features of mTORC2 that control substrate-specific activities
Authors: Yu Zanlin , Chen Junliang , Takagi Enzo , Wang Feng , Saha Bidisha , Liu Xi , Joubert Lydia-Marie , Gleason Catherine E. , Jin Mingliang , Li Chengmin , Nowotny Carlos , Agard David , Cheng Yifan , Pearce David 
Published: February 2022

AbstractmTORC2 is a multi-subunit kinase complex that is central to multiple essential signaling pathways. Two core subunits, Rictor and mSin1 distinguish it from its relative, mTORC1 and support context-dependent phosphorylation of its substrates. mTORC2 structures have been determined previously, however, important questions remain, particularly regarding structural determinants of substrate specificity and context dependent activities. We used cryo-EM to obtain high resolution structures of the human mTORC2 apo-complex, as well as structures in the presence of substrates, Akt and SGK1. Specific predictions suggested by substrate-induced structural changes were tested in functional assays. First, side chain interactions between Rictor and mTOR that prevent recruitment of mTORC1 substrates and confer resistance to the mTORC1 inhibitor rapamycin were visualized for the first time in the apo-state, demonstrating the steric occlusion that prevents mTORC2 interaction with mTORC1 substrates and rapamycin. Also in the apo-state, mSin1 was seen to form extensive contacts with Rictor, including a pair of short α-helices nestled between two Rictor helical repeat clusters, followed by an extended strand, which makes multiple weak contacts with Rictor helical cluster 1. In co-complex structures, SGK1, but not Akt, markedly altered the conformation of the mSin1 N-terminal extended strand, disrupting multiple weak interactions while inducing a large rotation of mSin1/Arg-83, which comes to interact with a negative patch within Rictor. Mutation of Arg-83 to Ala selectively disrupted mTORC2 dependent phosphorylation of SGK1 but not of Akt, supporting context-dependent substrate selection. These findings provide new structural and functional insights into mTORC2 specificity and context-dependent activities.
Comprehensive analysis of next-generation sequencing data in COVID-19 and its secondary complications
Authors: Giriyappagoudar Muttanagouda , Vastrad Basavaraj , Horakeri Rajeshwari , Vastrad Chanabasayya 
Published: February 2022

AbstractThe ongoing pandemic of coronavirus disease 2019 (COVID-19) has made a serious public health threat globally. To discover key molecular changes in COVID-19 and its secondary complications, we analyzed next-generation sequencing (NGS) data of COVID-19. NGS data (GSE163151) was screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were identified in the present study, using DESeq2 package in R programming software. Gene ontology (GO) and pathway enrichment analysis were performed, and the protein-protein interaction (PPI) network, module analysis, miRNA-hub gene regulatory network and TF-hub gene regulatory network were established. Subsequently, receiver operating characteristic curve (ROC) analysis was used to validate the diagonostics valuesof the hub genes. Firstly, 954 DEGs (477 up regulated and 477 down regulated) were identified from the four NGS dataset. GO enrichment analysis revealed enrichment of DEGs in genes related to the immune system process and multicellular organismal process, and REACTOME pathway enrichment analysis showed enrichment of DEGs in the immune system and formation of the cornified envelope. Hub genes were identified from the PPI network, module analysis, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Furthermore, the ROC analysis indicate that COVID-19 and its secondary complications with following hub genes, namely, RPL10, FYN, FLNA, EEF1A1, UBA52, BMI1, ACTN2, CRMP1, TRIM42 and PTCH1, had good diagnostics values. This study identified several genes associated with COVID-19 and its secondary complications, which improves our knowledge of the disease mechanism.
2’-O-methylation of the second transcribed nucleotide within mRNA 5’ cap impacts protein production level in a cell specific manner and contributes to RNA immune evasion
Authors: Drazkowska Karolina , Baran Natalia , Warminski Marcin , Tomecki Rafal , Depaix Anaïs , Cysewski Dominik , Kasprzyk Renata , Kowalska Joanna , Jemielity Jacek , Sikorski Pawel J. 
Published: February 2022

AbstractIn higher eukaryotes, m7G-adjacent nucleotides undergo extensive modifications. Ribose of the first or first and second transcribed nucleotides can be subjected to 2’-O-methylation to form cap1 or cap2, respectively. Additionally, when the first transcribed nucleotide is adenosine, it can not only undergo 2’-O-methylation but can also be methylated at position N6 forming N6,2’-O-dimethyladenosine (m6Am). Recent studies have shed some light on the functions of cap1, showing that cap1 in mammalian cells plays a crucial role in distinguishing between ‘self’ and ‘non-self’ RNA during viral infection. Here, we attempted to understand the impact of other cap methylations on RNA-related processes. Therefore, we synthesized tetranucleotide cap analogs and used them for efficient co-transcriptional RNA capping during in vitro transcription. Using this tool, we found that 2’-O-methylation of the second transcribed nucleotide within the mRNA 5’ cap influences protein production levels in a cell-specific manner. The presence of this modification can strongly hamper protein biosynthesis or do not influence protein production levels. Interestingly, 2’-O-methylation of the second transcribed nucleotide as well as the presence of N6,2’-O-dimethyladenosine as the first transcribed nucleotide serve as determinants that define transcripts as ‘self’ and contribute to transcript escape from the host innate immune response. Additionally, cap methylation status does not influence transcript affinity towards translation initiation factor 4E or in vitro susceptibility to decapping by DCP2; however what we observe is resistance of RNA capped with cap2 to DXO-mediated decapping and degradation.Significance StatementMethylation of mRNA cap structure regulates protein biosynthesis in a cell-dependent manner. Among the three known m7G cap modifications, the 2’-O-methylation is dominant. 2’-O-methylation of the first transcribed nucleotide can boost protein production, whereas the same modification of the second transcribed nucleotide can strongly decrease translation. Interestingly, we show that in the JAWS II cell line, 2’-O-methylation of mRNA cap had a prominent impact on the composition of the protein interactome associated with the RNA bearing mentioned modifications. Further analysis revealed that 2’-O-methylation of the second transcribed nucleotide and N6-methylation of adenosine as the first transcribed nucleotide serve as determinants defining transcripts as ‘self’ and contribute to transcript escape from the host innate immune response.
Gal1 repression memory in budding yeast
Authors: Schuh Lea , Kukhtevich Igor , Bheda Poonam , Schulz Melanie , Bordukova Maria , Schneider Robert , Marr Carsten 
Published: February 2022

ABSTRACTCells must continuously adapt to changing environments and, thus, have evolved mechanisms allowing them to respond to repeated stimuli. For example, faster gene induction upon a repeated stimulus aids adaptation - a process known as reinduction memory. However, whether such a memory exists for gene repression is unclear. Here, we studied gene repression across repeated carbon source shifts in over 2,500 single Saccharomyces cerevisiae cells. By monitoring the expression of a carbon source-responsive gene, galactokinase 1 (Gal1), and mathematical modeling, we discovered repression memory at the population and single-cell level. Using a repressor model to estimate single-cell repression parameters, we show that repression memory is due to a shortened repression delay, the estimated time gap between carbon source shift and Gal1 expression termination, upon the repeated carbon source shift. Additionally, we show that cells lacking Elp6 display a gain-of-repression-memory phenotype characterized by a stronger decrease in repression delay between two consecutive carbon source shifts. Collectively, our study provides the first quantitative description of repression memory in single cells.