bluuebunny/crossref_metadata_2025_split huggingface dataset:
Midday napping in children: associations between nap frequency and duration across cognitive, positive psychological well-being, behavioral, and metabolic health outcomes
Authors: Liu Jianghong , Feng Rui , Ji Xiaopeng , Cui Naixue , Raine Adrian , Mednick Sara C 
Published: May 2019

AbstractStudy ObjectivesPoor sleep and daytime sleepiness in children and adolescents have short- and long-term consequences on various aspects of health. Midday napping may be a useful strategy to reduce such negative impacts. The effect of habitual napping on a wide spectrum of cognitive, behavioral, psychological, and metabolic outcomes has not been systematically investigated.MethodsThis study characterized midday napping habits in 3819 elementary school children from the China Jintan Cohort Study. In 2011, weekly nap frequency and average duration were collected once from students at grades 4–6. Prior to their completion of elementary school at grade 6 (in 2011–2013 respective to each grade), the following outcomes were collected once: behavioral and academic achievement evaluated by teachers, and self-reported positive psychology measures including grit, self-control, and happiness. IQ tests were conducted on a subgroup. Metabolic indices, including body mass index and fasting glucose concentration, were measured through physical exams. For the whole sample, we assessed associations between napping and each outcome, adjusted for sex, grade, school location, parental education, and time in bed at night. We also conducted stratified analyses on grade 6 (cross-sectional), grade 4 (2-year gap), and grade 5 (1-year gap) data.ResultsOverall, napping was significantly associated with higher happiness, grit, and self-control, reduced internalizing behavior problem, higher verbal IQs, and better academic achievement, although specific patterns varied across frequency and duration for different outcomes. More limited significant associations were found for decreased externalizing behavior problems, compared to non-nappers, while no significant associations were found for performance IQ and metabolic outcomes.ConclusionsResults indicate benefits of regular napping across a wide range of adolescent outcomes, including better cognition, better psychological wellness, and reduced emotional/behavioral problems. The current study underscores the need for further large-scale intervention studies to establish causal effects.
The interplay between sleeplessness and high-sensitivity C-reactive protein on risk of chronic musculoskeletal pain: longitudinal data from the Tromsø Study
Authors: Skarpsno Eivind Schjelderup , Mork Paul Jarle , Nilsen Tom Ivar Lund , Steingrímsdóttir Ólöf Anna , Zwart John Anker , Nilsen Kristian Bernhard 
Published: May 2019

AbstractStudy ObjectivesTo examine independent associations of sleeplessness and high-sensitivity C-reactive protein (hsCRP) with risk of chronic musculoskeletal pain, and to explore the joint effect of sleeplessness and hsCRP on risk of chronic musculoskeletal pain.MethodsA population-based prospective study of 3214 women and 3142 men (mean age: 55.4, range: 32–87) without severe chronic musculoskeletal pain and with hsCRP ≤ 10 mg/L at baseline in 2007–2008. Modified Poisson regression was used to calculate adjusted risk ratios (RRs) with 95% confidence intervals (CIs) for any chronic musculoskeletal pain and chronic widespread pain (CWP) at follow-up in 2015–2016 associated with self-reported sleeplessness and hsCRP at baseline.ResultsCompared with persons without sleeplessness, women and men reporting often/or always sleeplessness had RRs of CWP of 2.53 (95% CI: 1.94–3.29) and 2.48 (95% CI: 1.63–3.77), respectively. There was no clear association between hsCRP and risk of any chronic musculoskeletal pain or CWP. Joint effect analyses using persons without sleeplessness and with a hsCRP < 1.00 mg/L as the reference gave RRs for chronic musculoskeletal pain of 1.73 (95% CI: 1.26–2.37) for those with often/always sleeplessness and hsCRP < 1.00 mg/L; 1.01 (95% CI: 0.78–1.32) for those without sleeplessness and hsCRP ≥3.00 mg/L; and 2.47 (95% CI: 1.79–3.40) if they had both often/always sleeplessness and hsCRP ≥ 3.00 mg/L. The corresponding RRs for CWP were 1.89 (95% CI: 1.27–2.83), 0.96 (95% CI: 0.68–1.37), and 2.83 (95% CI: 1.91–4.20), respectively.ConclusionsThese results suggest that there is an interplay between sleeplessness and hsCRP on risk of any chronic musculoskeletal pain and CWP.
Factors Driving Unique Urination Phenotypes of Male and Female 9-week-old C57BL/6J Mice
Authors: Ruetten Hannah , Wegner Kyle A. , Zhang Helen L. , Wang Peiqing , Sandhu Jaskiran , Sandhu Simran , Mueller Brett , Wang Zunyi , Macoska Jill , Peterson Richard E. , Bjorling Dale E. , Ricke William A. , Marker Paul C. , Vezina Chad M. 
Published: May 2019

ABSTRACTLaboratory mice are used to identify causes of urinary dysfunction including prostate-related mechanisms of Lower Urinary Tract Symptoms (LUTS). Effective use of mice for this purpose requires a clear understanding of molecular, cellular, anatomical, and endocrine contributions to voiding function. Whether the prostate influences baseline voiding function has not been specifically evaluated, in part because most methods that alter prostate mass also change circulating testosterone concentrations. We performed void spot assay and cystometry to establish a multi-parameter “baseline” of voiding function in intact male and female 9-week-old (adult) C57BL/6J mice. We then compared voiding function in intact male mice to that of castrate males, males (and females) treated with the steroid five alpha reductase inhibitor finasteride, or males harboring alleles (Pbsn4cre/+;R26RDta/+) that significantly reduce prostate lobe mass by depleting prostatic luminal epithelial cells. We evaluated aging-related changes in male urinary voiding. We also treated intact male, castrate male, and female mice with exogenous testosterone to determine the influence of androgen on voiding function. The three methods used to reduce prostate mass (castration, finasteride,Pbsn4cre/+;R26RDta/+) changed voiding function from baseline but in a nonuniform manner. Castration feminized some aspects of male urinary physiology (making them more like intact female) while exogenous testosterone masculinized some aspects of female urinary physiology (making them more like intact male). Our results provide evidence that circulating testosterone is responsible in part for baseline sex differences in C57BL/6J mouse voiding function while prostate lobe mass in young, healthy adult mice has a lesser influence.
Impact of rare and common genetic variants on diabetes diagnosis by hemoglobin A1c in multi-ancestry cohorts: The Trans-Omics for Precision Medicine Program
Authors: Sarnowski Chloé , Leong Aaron , Raffield Laura M , Wu Peitao , de Vries Paul S , DiCorpo Daniel , Guo Xiuqing , Xu Huichun , Liu Yongmei , Zheng Xiuwen , Hu Yao , Brody Jennifer A , Goodarzi Mark O , Hidalgo Bertha A , Highland Heather M , Jain Deepti , Liu Ching-Ti , Naik Rakhi P , Perry James A , Porneala Bianca C , Selvin Elizabeth , Wessel Jennifer , Psaty Bruce M , Curran Joanne E , Peralta Juan M , Blangero John , Kooperberg Charles , Mathias Rasika , Johnson Andrew D , Reiner Alexander P , Mitchell Braxton D , Cupples L Adrienne , Vasan Ramachandran S , Correa Adolfo , Morrison Alanna C , Boerwinkle Eric , Rotter Jerome I , Rich Stephen S , Manning Alisa K , Dupuis Josée , Meigs James B ,   on behalf of the Trans-Omics for Precision Medicine (TOPMed) Diabetes and TOPMed Hematology and Hemostasis working groups and the NHLBI TOPMed Consortium
Published: May 2019

AbstractHemoglobin A1c (HbA1c) is widely used to diagnose diabetes and assess glycemic control in patients with diabetes. However, nonglycemic determinants, including genetic variation, may influence how accurately HbA1c reflects underlying glycemia. Analyzing the NHLBI Trans-Omics for Precision Medicine (TOPMed) sequence data in 10,338 individuals from five studies and four ancestries (6,158 Europeans, 3,123 African-Americans, 650 Hispanics and 407 East Asians), we confirmed five regions associated with HbA1c (GCK in Europeans and African-Americans, HK1 in Europeans and Hispanics, FN3K/FN3KRP in Europeans and G6PD in African-Americans and Hispanics) and discovered a new African-ancestry specific low-frequency variant (rs1039215 in HBG2/HBE1, minor allele frequency (MAF)=0.03). The most associated G6PD variant (p.Val98Met, rs1050828-T, MAF=12% in African-Americans, MAF=2% in Hispanics) lowered HbA1c (−0.88% in hemizygous males, −0.34% in heterozygous females) and explained 23% of HbA1c variance in African-Americans and 4% in Hispanics. Additionally, we identified a rare distinct G6PD coding variant (rs76723693 - p.Leu353Pro, MAF=0.5%; −0.98% in hemizygous males, −0.46% in heterozygous females) and detected significant association with HbA1c when aggregating rare missense variants in G6PD. We observed similar magnitude and direction of effects for rs1039215 (HBG2) and rs76723693 (G6PD) in the two largest TOPMed African-American cohorts and replicated the rs76723693 association in the UK Biobank African-ancestry participants. These variants in G6PD and HBG2 were monomorphic in the European and Asian samples. African or Hispanic ancestry individuals carrying G6PD variants may be underdiagnosed for diabetes when screened with HbA1c. Thus, assessment of these variants should be considered for incorporation into precision medicine approaches for diabetes diagnosis.
A Bayesian account of generalist and specialist formation under the Active Inference framework
Authors: Chen Anthony Guanxun , Benrimoh David , Parr Thomas , Friston Karl J. 
Published: May 2019

AbstractThis paper offers a formal account of policy learning, or habitual behavioural optimisation, under the framework of Active Inference. In this setting, habit formation becomes an autodidactic, experience-dependent process, based upon what the agent sees itself doing. We focus on the effect of environmental volatility on habit formation by simulating artificial agents operating in a partially observable Markov decision process. Specifically, we used a ‘two-step’ maze paradigm, in which the agent has to decide whether to go left or right to secure a reward. We observe that in volatile environments with numerous reward locations, the agents learn to adopt a generalist strategy, never forming a strong habitual behaviour for any preferred maze direction. Conversely, in conservative or static environments, agents adopt a specialist strategy; forming strong preferences for policies that result in approach to a small number of previously-observed reward locations. The pros and cons of the two strategies are tested and discussed. In general, specialization offers greater benefits, but only when contingencies are conserved over time. We consider the implications of this formal (Active Inference) account of policy learning for understanding the relationship between specialisation and habit formation.Author SummaryActive inference is a theoretical framework that formalizes the behaviour of any organism in terms of a single imperative – to minimize surprise. Starting from this principle, we can construct simulations of simple “agents” (artificial organisms) that show the ability to infer causal relationships and learn. Here, we expand upon currently-existing implementations of Active Inference by enabling synthetic agents to optimise the space of behavioural policies that they can pursue. Our results show that by adapting the probabilities of certain action sequences (which may correspond biologically to the phenomenon of synaptic plasticity), and by rejecting improbable sequences (synaptic pruning), the agents can begin to form habits. Furthermore, we have shown our agent’s habit formation to be environment-dependent. Some agents become specialised to a constant environment, while other adopt a more general strategy, each with sensible pros and cons. This work has potential applications in computational psychiatry, including in behavioural phenotyping to better understand disorders.
A single light-responsive sizer can control multiple-fission cycles inChlamydomonas
Authors: Heldt Frank S. , Tyson John J. , Cross Frederick R. , Novák Béla 
Published: May 2019

AbstractProliferating cells need to coordinate cell division and growth to maintain size homeostasis. Any systematic deviation from a balance between growth and division results in progressive changes of cell size over subsequent generations. While most eukaryotic cells execute binary division after a mass doubling, the photosynthetic green algaChlamydomonascan grow more than eight-fold during daytime before undergoing rapid cycles of DNA replication, mitosis and cell division at night, which produce up to 16 daughter cells. Here, we propose a mechanistic model for multiple fission and size control inChlamydomonas. The model comprises a light-sensitive and size-dependent biochemical toggle switch that acts as a sizer and guards transitions into and exit from a phase of cell-division cycle oscillations. We show that this simple ‘sizer-oscillator’ arrangement reproduces the experimentally observed features of multiple-fission cycles and the response ofChlamydomonascells to different light-dark regimes. Our model also makes testable predictions about the dynamical properties of the biochemical network that controls these features and about the network’s makeup. Collectively, these results provide a new perspective on the concept of a ‘commitment point’ during the growth ofChlamydomonascells and hint at an intriguing continuity of cell-size control in different eukaryotic lineages.Graphical abstractG1-sizer and S/M-oscillator can give rise to multiple-fission cycles inChlamydomonasLight-responsive bistable switch may guard transition between G1 and S/M-cyclesIllumination increases S/M-entry threshold, causing multiple-fission cyclesDark shift lowers S/M-entry threshold, allowing small cells to commit to fewer divisions
Prediction performance of a cardiovascular risk assessment tool using Stanford EHR data repository
Authors: Rezaee Mehrdad , Takeh Arsia , Putrenko Igor , Ganna Andrea , Ingelsson Erik 
Published: May 2019

AbstractBackgroundStratification of individuals for their risk to develop cardiovascular diseases can be used for effective prevention and intervention. A significant amount of information for risk assessment can be obtained through repurposing electronic health records (EHR). The objective of this study is to derive and assess the performance of prediction models for cardiovascular outcomes by using EHR-derived data.MethodsWe used the Stanford Medicine Research Data Repository (STARR) data from 2000-2017, containing over 2.1 million patients. A subset of 762,372 individuals with complete International Classification of Diseases (ICD) data was used to fit Cox proportional hazard models for prediction of six cardiovascular-related diseases and type 2 diabetes.ResultsThe derived prediction models indicated consistent high discrimination performance (C-index) for all diseases examined: coronary artery disease (0.85), hypertension (0.82), type 2 diabetes (0.77), stroke (0.76), atrial fibrillation (0.82) and abdominal aortic aneurysm (0.77). Lower prediction abilities were observed for deep vein thrombosis (0.67). These results were consistent across age groups and maintained good prediction abilities among individuals with pre-existing diabetes or hypertension. Assessment of model calibration is ongoing.ConclusionsWe proposed new prediction models for the seven diseases using ICD codes derived from EHR data. EHR data can be used for health risk assessment, but challenges related to data quality and model generalizability and calibration remain to be solved.
Application of Pharmacogenomics and Bioinformatics to Exemplify the Utility of Human ex vivo Organoculture Models in the Field of Precision Medicine
Authors: Cowan K , Macluskie G , Finch M , Palmer C.N.A , Hair J , Bylesjo M , Lynagh S , Brankin P , McNeil M , Low C , Mallinson D , Gourlay EM , Child H , Cheyne L , Bunton DC 
Published: May 2019

AbstractHere we describe a collaboration between industry, the National Health Service (NHS) and academia that sought to demonstrate how early understanding of both pharmacology and genomics can improve strategies for the development of precision medicines. Diseased tissue ethically acquired from patients suffering from chronic obstructive pulmonary disease (COPD), was used to investigate inter-patient variability in drug efficacy using ex vivo organocultures of fresh lung tissue as the test system. The reduction in inflammatory cytokines in the presence of various test drugs was used as the measure of drug efficacy and the individual patient responses were then matched against genotype and microRNA profiles in an attempt to identify unique predictors of drug responsiveness. Our findings suggest that genetic variation in CYP2E1 and SMAD3 genes may partly explain the observed variation in drug response.
Predictive analysis of patient recovery from cardiac-respiratory arrest
Authors: Floyrac A. , Doumergue A. , Kubis N. , Holcman D. 
Published: May 2019

AbstractThe severity of neuronal damages in comatose patients following anoxic brain injury can be probed by evoked auditory responses. However, it remains challenging to predict the return to full consciousness of post-anoxic coma of hospitalized patients. We presented here a method to predict the return to consciousness based on the analysis of periodic responses to auditory stimulations, recorded from surface cranial electrodes. The input data are event-related potentials (ERPs), recorded non-invasively with electro-encephalography (EEG). We extracted several novel features from the time series responses in a window of few hundreds of milliseconds from deviant and non-deviant auditory stimulations. We use these features to construct two-dimensional statistical maps, that show two separated clusters for recovered (conscience) and deceased patients, leading to a high classification success as tested by a cross-validation procedure. Finally, using Gaussian, K-neighborhood and SVM classifiers, we construct probabilistic maps to predict the outcome of post-anoxic coma. To conclude, statistics of deviant and non-deviant responses considered separately provide complementary and confirmatory predictions for the outcome of anoxic coma.
The neurocognitive gains of diagnostic reasoning training using simulated interactive veterinary cases
Authors: Nassar Maaly 
Published: May 2019

ABSTRACTThe present longitudinal study ascertained training-associated transformations in the neural underpinnings of diagnostic reasoning, using a simulation game named “Equine Virtual Farm” (EVF). Twenty participants underwent structural, EVF/task-based and resting-state MRI and diffusion tensor imaging (DTI) before and after completing their training on diagnosing simulated veterinary cases. Comparing playing veterinarian versus seeing a colorful image across training sessions revealed the transition of brain activity from scientific creativity regions pre-training (left middle frontal and temporal gyrus) to insight problem-solving regions post-training (right cerebellum, middle cingulate and medial superior gyrus and left postcentral gyrus). Further, applying linear mixed-effects modelling on graph centrality metrics revealed the central roles of the creative semantic (inferior frontal, middle frontal and angular gyrus and parahippocampus) and reward systems (orbital gyrus, nucleus accumbens and putamen) in driving pre-training diagnostic reasoning; whereas, regions implicated in inductive reasoning (superior temporal and medial postcentral gyrus and parahippocampus) were the main post-training hubs. Lastly, resting-state and DTI analysis revealed post-training effects within the occipitotemporal semantic processing region. Altogether, these results suggest that simulation-based training transforms diagnostic reasoning in novices from regions implicated in creative semantic processing to regions implicated in improvised rule-based problem-solving.
Population Health Genetic Screening for Tier 1 Inherited Diseases in Northern Nevada: 90% of At-Risk Carriers are Missed
Authors: Grzymski JJ , Elhanan G , Smith E , Rowan C , Slotnick N , Dabe S , Schlauch K , Read R , Metcalf WJ , Lipp B , Reed H , Levin E , Kao J , Rashkin M , Bowes J , Bolze A , Dunaway K , Washington N , Slonim A , Lu JT 
Published: May 2019

AbstractIn an unselected population of 23,713 participants who underwent clinical exome sequencing as a part of the Healthy Nevada Project (HNP) in Northern Nevada (Renown Health, Reno, Nevada) from March 15, 2018, to Sept 30, 2018 (Table S1) we find a 1.26% carrier rate for expected pathogenic and likely pathogenic genetic variants in (FH: LDLR, PCSK9, APOB), Hereditary Breast and Ovarian Cancer (HBOC: BRCA1, BRCA2) and Lynch Syndrome (LS: MLH1, MSH2, MSH6, PSM2) with over 90% of carriers undetected under current medical practice. 26% of carriers were found to have advanced disease with 70% first diagnosed before the age of 65. Less than 20% of all carriers had any documented suspicion for inherited genetic disease in the medical record and upon direct follow-up survey under 40% of carriers had family history of relevant disease. A population preventative genetic screening approach for patients under 45 may improve outcomes.
Higher Meta-cognitive Ability Predicts Less Reliance on Over Confident Habitual Learning System
Authors: Ershadmanesh Sara , Miandari Mostafa , Vahabie Abdol-hossein , Ahmadabadi Majid Nili 
Published: May 2019

AbstractMany studies on human and animals have provided evidence for the contribution of goal-directed and habitual valuation systems in learning and decision-making. These two systems can be modeled using model-based (MB) and model-free (MF) algorithms in Reinforcement Learning (RL) framework. Here, we study the link between the contribution of these two learning systems to behavior and meta-cognitive capabilities. Using computational modeling we showed that in a highly variable environment, where both learning strategies have chance level performances, model-free learning predicts higher confidence in decisions compared to model-based strategy. Our experimental results showed that the subjects’ meta-cognitive ability is negatively correlated with the contribution of model-free system to their behavior while having no correlation with the contribution of model-based system. Over-confidence of the model-free system justifies this counter-intuitive result. This is a new explanation for individual difference in learning style.