bluuebunny/crossref_metadata_2025_split huggingface dataset:
The genetics of specific cognitive abilities
Authors: Procopio Francesca , Zhou Quan , Wang Ziye , Gidziela Agnieska , Rimfeld Kaili , Malanchini Margherita , Plomin Robert 
Published: February 2022

AbstractMost research on individual differences in performance on tests of cognitive ability focuses on general cognitive ability (g), the highest level in the three-level Cattell-Horn-Carroll (CHC) hierarchical model of intelligence. About 50% of the variance of g is due to inherited DNA differences (heritability) which increases across development. Much less is known about the genetics of the middle level of the CHC model, which includes 16 broad factors such as fluid reasoning, processing speed, and quantitative knowledge. We provide a meta-analytic review of 863,041 monozygotic-dizygotic twin comparisons from 80 publications for these middle-level factors, which we refer to as specific cognitive abilities (SCA). Twin comparisons were available for 11 of the 16 CHC domains. The average heritability across all SCA is 55%, similar to the heritability of g. However, there is substantial differential heritability and the SCA do not show the dramatic developmental increase in heritability seen for g. We also investigated SCA independent of g (g-corrected SCA, which we refer to as SCA.g). A surprising finding is that SCA.g remain substantially heritable (53% on average), even though 25% of the variance of SCA that covaries with g has been removed. Our review frames expectations for genomic research that will use polygenic scores to predict SCA and SCA.g. Genome-wide association studies of SCA.g are needed to create polygenic scores that can predict SCA profiles of cognitive abilities and disabilities independent of g. These could be used to foster children’s cognitive strengths and minimise their weaknesses.
TOWARDS A SINGLE PARAMETER FOR THE ASSESSMENT OF EEG OSCILLATIONS
Authors: Tozzi Arturo , Peters James F. 
Published: February 2022

ABSTRACTThe single macroscopic flow on the boundary of a closed curve equals the sum of the countless microscopic flows in the enclosed area. According to the dictates of the Green’s theorem, the counterclockwise movements on the border of a two-dimensional shape must equal all the counterclockwise movements taking place inside the shape. This mathematical approach might be useful to analyse neuroscientific data sets for its potential capability to describe the whole cortical activity in terms of electric flows occurring in peripheral brain areas. Once mapped raw EEG data to coloured ovals in which different colours stand for different amplitudes, the theorem suggests that the sum of the electric amplitudes measured inside every oval equals the amplitudes measured just on the oval’s edge. This means that the collection of the vector fields detected from the scalp can be described by a novel, single parameter summarizing the counterclockwise electric flow detected in the outer electrodes. To evaluate the predictive power of this parameter, in a pilot study we investigated EEG traces from ten young females performing Raven’s intelligence tests of various complexity, from easy tasks (n=5) to increasingly complex tasks (n=5). Despite the seemingly unpredictable behavior of EEG electric amplitudes, the novel parameter proved to be a valuable tool to to discriminate between the two groups and detect hidden, statistically significant differences. We conclude that the application of this promising parameter could be expanded to assess also data sets extracted from neurotechniques other than EEG.
Single-cell transcriptome landscape of zebrafish liver reveals hepatocytes and immune cell interactions in understanding nonalcoholic fatty liver disease
Authors: Huang Yingyi , Liu Xiang , Wang Hong-Yan , Chen Jian-Yang , Zhang Xianghui , Li Yubang , Lu Yifang , Dong Zhongdian , Liu Kaiqiang , Wang Zhongduo , Wang Qian , Fan Guangyi , Zou Jun , Liu Shanshan , Shao Changwei 
Published: February 2022

AbstractZebrafish have emerged as an attractive animal model for studying nonalcoholic fatty liver disease (NAFLD). However, little is known about the cell types and intercellular interactions in zebrafish liver. Here, we established a liver atlas that consists of 10 cell types using single-cell RNA sequencing. By examining the heterogeneity of hepatocytes and analyzing the expression of NAFLD-associated genes in the specific cluster, we provide a potential target cell model to study NAFLD. Additionally, our analysis identified two distinct resident macrophages with inflammatory and noninflammatory functions and characterized the successive stepwise development of T cell subtypes in the liver. Importantly, we uncovered possible molecular mechanisms and revealed the central regulation of macrophages on target cells of fatty liver by analyzing the cellular interaction between hepatocytes and immune cells. Our data provide valuable information for future research on NAFLD in zebrafish.
NAD homeostasis maintained by NMNAT2 supports vesicular glycolysis and fuels fast axonal transport in distal axons of cortical glutamatergic neurons in mice
Authors: Yang Sen , Niou Zhen-Xian , Enriquez Andrea , LaMar Jacob , Huang Jui-Yen , Ling Karen , Jafar-Nejad Paymaan , Gilley Jonathan , Coleman Michael P. , Tennessen Jason M. , Rangaraju Vidhya , Lu Hui-Chen 
Published: February 2022

AbstractBackgroundBioenergetic maladaptations and axonopathy are often found in the early stages of neurodegeneration. Nicotinamide adenine dinucleotide (NAD), an essential cofactor for energy metabolism, is mainly synthesized by Nicotinamide mononucleotide adenylyl transferase 2 (NMNAT2) in CNS neurons. NMNAT2 mRNA levels are reduced in the brains of Alzheimer’s, Parkinson’s and Huntington’s disease. Here we addressed whether NMNAT2 is required for axonal health of cortical glutamatergic neurons, whose far-projecting axons are vulnerable to neurodegenerative conditions. We also tested if NMNAT2 maintains axonal health by ensuring proper axonal ATP levels for axonal transport, a critical function of axons.MethodsWe generated mouse and cultured neuron models to determine the impact of NMNAT2 loss from cortical glutamatergic neurons on axonal transport, energetic metabolism, and morphological integrity. In addition, we determined if exogenous NAD supplementation or inhibiting NAD hydrolase sterile alpha and TIR motif-containing protein 1 (SARM1) prevented axonal deficits caused by NMNAT2 loss. Our study used a combination of genetic, molecular biology, immunohistochemistry, biochemistry, fluorescent time-lapse imaging, live imaging with optical sensors, and anti-sense oligos application.ResultsWe providein vivoevidence that NMNAT2 in cortical glutamatergic neurons is required for axonal survival. Usingin vivoandin vitrostudies we demonstrate that NMNAT2 protects axons by ensuring the proper NAD-redox potential in distal axons of cortical neurons to support glycolysis on vesicular cargos, thus ensuring “onboard” ATP production fueling axonal transport. Exogenous NAD+supplementation to NMNAT2 KO cortical neurons restores glycolysis and resumes fast axonal transport. Finally, we demonstrate bothin vitroandin vivothat reducing the activity of SARM1, an NAD degradation enzyme, can reduce axonal transport deficits and suppress axon degeneration in NMNAT2 KO neurons.ConclusionNMNAT2 ensures axonal health by maintaining NAD redox potential in distal axons to ensure efficient vesicular glycolysis required for fast axonal transport.
Automatic identification of intestinal parasites in reptiles using microscopic stool images and convolutional neural networks
Authors: Grijalva Felipe , Núñez Bryan , Núñez Alejandra , Parra Carla , Pérez Noel , Benítez Diego 
Published: February 2022

AbstractCaptive environments trigger the propagation and multiplication of parasites among different reptile species, thus weakening their immune response and causing infections and diseases. Technological advances of convolutional neural networks have opened a new field for detecting and classifying diseases which have shown great potential to overcome the shortcomings of manual detection performed by experts. Therefore, we propose an approach to identify six captive reptiles parasitic agents (Ophionyssus natricis, Blastocystis sp, Oxiurdo egg, Rhytidoides similis, Strongyloides, Taenia) from a microscope stool images dataset. Towards this end, we first use an image segmentation stage to detect the parasite within the image, which combines the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique, the OTSU binarization method, and morphological operations. Then, we carry out a classification stage through Mobilenet CNN under a transfer learning scheme. This method was validated on a stool image dataset containing 3616 images data samples and 26 videos from the six parasites mentioned above. The results obtained indicate that our transfer learning-based approach can learn a helpful representation from the dataset. We obtained an average accuracy of 98.66% across the six parasitic agent classes, which statistically outperformed, at a 95% confidence level, a custom CNN trained from scratch.
SARS-CoV-2 Seroprevalence in three Kenyan Health and Demographic Surveillance Sites, December 2020-May 2021
Authors: Etyang Anthony O. , Adetifa Ifedayo , Omore Richard , Misore Thomas , Ziraba Abdhalah K. , Ng’oda Maurine A. , Gitau Evelyn , Gitonga John , Mugo Daisy , Kutima Bernadette , Karanja Henry , Toroitich Monica , Nyagwange James , Tuju James , Wanjiku Perpetual , Aman Rashid , Amoth Patrick , Mwangangi Mercy , Kasera Kadondi , Ng’ang’a Wangari , Akech Donald , Sigilai Antipa , Karia Boniface , Karani Angela , Voller Shirine , Agoti Charles N. , Ochola-Oyier Lynette I. , Otiende Mark , Bottomley Christian , Nyaguara Amek , Uyoga Sophie , Gallagher Katherine , Kagucia Eunice W. , Onyango Dickens , Tsofa Benjamin , Mwangangi Joseph , Maitha Eric , Barasa Edwine , Bejon Philip , Warimwe George M. , Scott J Anthony G , Agweyu Ambrose 
Published: February 2022

ABSTRACTBackgroundMost of the studies that have informed the public health response to the COVID-19 pandemic in Kenya have relied on samples that are not representative of the general population. We conducted population-based serosurveys at three Health and Demographic Surveillance Systems (HDSSs) to determine the cumulative incidence of infection with SARS-CoV-2.MethodsWe selected random age-stratified population-based samples at HDSSs in Kisumu, Nairobi and Kilifi, in Kenya. Blood samples were collected from participants between 01 Dec 2020 and 27 May 2021. No participant had received a COVID-19 vaccine. We tested for IgG antibodies to SARS-CoV-2 spike protein using ELISA. Locally-validated assay sensitivity and specificity were 93% (95% CI 88-96%) and 99% (95% CI 98-99.5%), respectively. We adjusted prevalence estimates using classical methods and Bayesian modelling to account for the sampling scheme and assay performance.ResultsWe recruited 2,559 individuals from the three HDSS sites, median age (IQR) 27 (10-78) years and 52% were female. Seroprevalence at all three sites rose steadily during the study period. In Kisumu, Nairobi and Kilifi, seroprevalences (95% CI) at the beginning of the study were 36.0% (28.2-44.4%), 32.4% (23.1-42.4%), and 14.5% (9.1-21%), and respectively; at the end they were 42.0% (34.7-50.0%), 50.2% (39.7-61.1%), and 24.7% (17.5-32.6%), respectively. Seroprevalence was substantially lower among children (<16 years) than among adults at all three sites (p≤0.001).ConclusionBy May 2021 in three broadly representative populations of unvaccinated individuals in Kenya, seroprevalence of anti-SARS-CoV-2 IgG was 25-50%. There was wide variation in cumulative incidence by location and age.
Methylation risk scores are associated with a collection of phenotypes within electronic health record systems
Authors: Thompson Mike , Hill Brian L. , Rakocz Nadav , Chiang Jeffrey N. ,   IPH, Sankararaman Sriram , Hofer Ira , Cannesson Maxime , Zaitlen Noah , Halperin Eran 
Published: February 2022

AbstractInference of clinical phenotypes is a fundamental task in precision medicine, and has therefore been heavily investigated in recent years in the context of electronic health records (EHR) using a large arsenal of machine learning techniques, as well as in the context of genetics using polygenic risk scores (PRS). In this work, we considered the epigenetic analog of PRS, methylation risk scores (MRS), a linear combination of methylation states. Since methylation states are influenced by both environmental and genetic factors, we hypothesized that MRS would complement PRS and EHR-based machine-learning methods, improving overall prediction accuracy. To evaluate this hypothesis, we performed the largest assessment of methylation risk scores in clinical datasets to be conducted to date. We measured methylation across a large cohort (n=831) of diverse samples in the UCLA Health biobank, for which both genetic and complete EHR data are available. We constructed MRS for 607 phenotypes spanning diagnoses, clinical lab tests, and medication prescriptions. When added to a baseline set of predictive features, MRS significantly improved the imputation of 139 outcomes, whereas the PRS improved only 22 (median improvement for methylation 10.74%, 141.52%, and 15.46% in medications, labs and diagnosis codes, respectively, whereas genotypes only improved the labs at a median increase of 18.42%). We added significant MRS to state-of-the-art EHR imputation methods that leverage the entire set of medical records, and found that including MRS as a medical feature in the algorithm significantly improves EHR imputation in 37% of lab tests examined (median R2 increase 47.6%). Finally, we replicated several MRS in multiple external studies of methylation (minimum p-value of 2.72 × 10−7) and replicated 22 of 30 tested MRS internally in two separate cohorts of different ethnicity. In summary, our work provides a comprehensive evaluation of MRS in comparison to PRS and EHR imputation on the largest dataset consisting of methylation, genotype, and EHR data. Our publicly available results and weights show promise for methylation risk scores as clinical and scientific tools.
Bias amplification of unobserved confounding in pharmacoepidemiological studies using indication-based sampling: there is no free lunch in restricting the sample to those with a particular drug-indication
Authors: Ahlqvist Viktor H. , Madley-Dowd Paul , Ly Amanda , Rast Jessica , Lundberg Michael , Jónsson-Bachmann Egill , Berglind Daniel , Rai Dheeraj , Magnusson Cecilia , Lee Brian K. 
Published: February 2022

AbstractEstimating causal effects in observational pharmacoepidemiology is a challenging task, as it is often plagued by confounding by indication. Restricting the sample to those with an indication for drug use is a commonly performed procedure; indication-based sampling ensures that the exposed and unexposed are exchangeable on the indication - limiting the potential for confounding by indication. However, indication-based sampling has received little scrutiny, despite the hazards of exposure-related covariate control.Using causal diagrams, simulations, and empirical examples, we demonstrate that indication-based sampling in the presence of unobserved confounding can give rise to bias amplification, a self-inflicted phe-nomenon where one inflates pre-existing bias through inappropriate covariate control. Additionally, we show that indication-based sampling generally leads to a greater net bias than alternative approaches, such as regression adjustment. Finally, we expand on how bias amplification should be reasoned about when distinct clinically relevant effects on the outcome among those with an indication (effect-heterogeneity) exist.We conclude that studies using indication-based sampling should have robust justification - and that it should by no means be considered a “free lunch” to adopt such approaches. As such, we suggest that future observational studies stay wary of bias amplification when considering drug indications.
A FLOT1 host regulatory allele is associated with a recently expanded Mtb clade in patients with tuberculosis
Authors: Luo Yang , Huang Chuan-Chin , Liu Qingyun , Howard Nicole , Li Xinyi , Zhu Junhao , Amariuta Tiffany , Asgari Samira , Ishigaki Kazuyoshi , Calderon Roger , Moody D. Branch , Lecca Leonid , Fortune Sarah M. , Murray Megan B. , Raychaudhuri Soumya 
Published: February 2022

AbstractThe outcome of infectious diseases may depend on the interaction between human and pathogen genomic variations. We explore this relationship in tuberculosis (TB) by conducting a genome-to-genome (g2g) study of paired genomes from humans and the infectious agent Mycobacterium tuberculosis (Mtb) in 1,556 Peruvian TB patients. We identified a significant association between a human variant in the FLOT1 gene and a unique Mtb Lineage 2 (L2) subclade. The host allele affects FLOT1 expression in multiple tissue and cell types including lung, the primary site of TB disease. Phylogenetic analysis shows that the Mtb subclade has expanded rapidly in Peru since its emergence in the 1950s. Unbiased phenotypic profiling demonstrates that strains from the interacting Mtb subclade display different redox metabolism from other L2 strains. This study presents clear evidence that human and bacterial genetic variation interact together to produce different clinical outcomes.
The interaction effect between hemoglobin and hypoxemia on COVID-19 mortality in a sample from Bogotá, Colombia: An exploratory study
Authors: Patiño-Aldana Andrés Felipe , Ruíz-Sternberg Angela María , Pinzón-Rondón Angela María , Molano-González Nicolás , Rodríguez Lima David Rene 
Published: February 2022

AbstractPurposeWe aimed to assess the effect of hemoglobin (Hb) concentration and oxygenation index on COVID-19 patients’ mortality risk.Patients and methodsWe retrospectively reviewed sociodemographic and clinical characteristics, laboratory findings, and clinical outcomes from patients admitted to a tertiary care hospital in Bogotá, Colombia. We assessed exploratory associations between oxygenation index and Hb concentration at admission and clinical outcomes. We used a generalized additive model (GAM) to evaluate the nonlinear relations observed and the classification and regression trees (CART) algorithm to assess the interaction effects found.ResultsFrom March to July 2020, 643 patients were admitted, of which 52% were male. The median age was 60 years old, and the most frequent comorbidity was hypertension (35.76%). The median value of SpO2/FiO2 was 419, and the median Hb concentration was 14.8 g/dL. The mortality was 19.1% (123 patients). Age, sex, and history of hypertension were independently associated with mortality. We described a nonlinear relationship between SpO2/FiO2, Hb concentration and neutrophil-to-lymphocyte ratio with mortality and an interaction effect between SpO2/FiO2 and Hb concentration. Patients with a similar oxygenation index had different mortality likelihoods based upon their Hb at admission. CART showed that patients with SpO2/FiO2 < 324, who were older than 62 years, and had an Hb of ≥ 16 g/dl had the highest mortality risk (96%). Additionally, patients with SpO2/FiO2 > 324 but Hb of < 12 and neutrophil-to-lymphocyte ratio of > 4 had a higher mortality likelihood (57%). In contrast, patients with SpO2/FiO2 > 324 and Hb of > 12 g/dl had the lowest mortality risk (10%).ConclusionWe found that a decreased SpO2/FiO2 increased mortality risk. Extreme values of Hb, either low or high, showed an increase in likelihood of mortality. However, Hb concentration modified the SpO2/FiO2 effect on mortality; the likelihood of death in patients with low SpO2/FiO2 increased as Hb increased.
Risk Factors for Early PICC Removal: A Retrospective Study of Adult Inpatients at an Academic Medical Center
Authors: Shen Burton H. , Mahoney Lindsey , Molino Janine , Mermel Leonard 
Published: February 2022

AbstractBackgroundUse of PICCs has been rising since 2001. They are used when long-term intravenous access is needed and for blood draws in patients with difficult venous access.ObjectiveTo determine which risk factors contribute to inappropriate PICC line insertion defined as removal of a PICC within five days of insertion for reasons other than a PICC complication.DesignRetrospective, observational study.SettingTertiary-care, Level 1 trauma center.PatientsAdult patients with a PICC removed 1/1/2017 to 5/4/2020.MeasurementsFrequency of PICC removal within five days of insertion and associated risk factors for early removal.ResultsBetween 1/1/17 and 5/4/2020, 995 of 5348 PICCs inserted by the IV nursing team were removed within five days (19%). In 2017, 5 of 429 PICCs developed a central line-associated infection (1.2%) and 29 of 429 PICCs developed symptomatic venous thromboembolism (6.7%). Patients with PICCs whose primary service was a medical subspecialty were independently at higher risk of early removal (OR 1.44, 95% CI 1.14, 1.83); weekday insertion was independently associated with a lower likelihood of early removal compared to weekend insertion (OR 0.60; 95% CI 0.49, 0.75).LimitationPICC removal after discharge was not assessed and paper records were likely incomplete and biased.ConclusionNearly one in five PICCs were removed within five days. Patients whose primary team was a medical subspecialty were at independently higher risk of early removal.
Marker genes of incident type 1 diabetes in peripheral blood mononuclear cells of children: A machine learning strategy for large-p, small-n scenarios
Authors: De Silva Kushan , Demmer Ryan T. , Jönsson Daniel , Mousa Aya , Forbes Andrew , Enticott Joanne 
Published: February 2022

ABSTRACTBackground and objectiveType 1 diabetes (TID) is a complex, polygenic disorder, the etiology of which is not fully elucidated. Machine learning (ML) genomics could provide novel insights on disease dynamics while high-dimensionality remains a challenge. This study aimed to identify marker genes of incident T1D in peripheral blood mononuclear cells (PBMC) of children via a ML strategy attuned to high-dimensionality.MethodsUsing samples from 105 children (81 with incident T1D and 24 healthy controls), we analyzed microarray transcriptomics via a workflow consisting of three sequential steps: application of dimension reduction strategies on the processed transcriptome; ML on the reduced gene expression matrix; and downstream network analyses to demarcate seed nodes (statistically significant genes) and hub genes. Sixteen dimension-reduction algorithms belonging to three groups (3 tailored; 3 regularizations; 10 classic) were applied. Four ML algorithms (multivariate adaptive regression splines, adaptive boosting, random forests, XGB-DART) were trained on the reduced feature set and internally-validated using repeated, 10-fold cross-validation. Marker genes were determined via variable importance metrics. Seed nodes were identified by the ‘OmicsNet’ platform while nodes having above average betweenness, closeness, and degree in the network were demarcated as hub genes.ResultsThe processed gene expression matrix comprised 13515 genes which was reduced to contain 1003 genes collectively selected by dimension reduction algorithms. All four ML algorithms on this reduced feature set attained perfect and uniform predictive performance on internal validation. On removal of redundancies, variable importance metrics identified 30 marker genes of incident T1D in this cohort, while Early Growth Response 2 (EGR2) was uniformly selected by all four ML algorithms as the most important marker gene. Network analyses classified all 30 marker genes as seed nodes. Additionally, we identified 14 hub genes, 7 of which were found to be marker genes of incident T1D elucidated by ML.ConclusionsWe identified marker genes of incident T1D in PBMC of children via a ML analytic strategy attuned to the high dimensional structure of microarrays, with downstream analyses providing high biological plausibility. The demonstrated ML strategy would be useful in analyzing other high-dimensional biomedical data for biomarker discovery.