bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genetic maternal effects contributes to the risk of Tourette’s disorder
Authors: Mahjani Behrang , Klei Lambertus , Hultman Christina M. , Larsson Henrik , Sandin Sven , Devlin Bernie , Buxbaum Joseph D. , Grice Dorothy E. 
Published: December 2020

AbstractBackgroundRisk for Tourette’s and related tic disorders (CTD) derives from a combination of genetic and environmental factors. While multiple studies have demonstrated the importance of direct additive genetic variation for CTD, little is known about the role of cross-generational transmission of genetic risks, such as maternal effects. Here, we partition sources of variation on CTD risk into direct additive genetic effect and maternal effects.MethodsThe study population consists of 2,522,677 individuals from the Swedish Medical Birth Register, born in Sweden between January 1, 1982, to December 31, 1990, and followed for a diagnosis of CTD through December 31, 2013.ResultsWe identified 6,227 (0.25%) individuals in the birth cohort diagnosed with CTD. Using generalized linear mixed models, we estimated 4.7% (95% CrI, 4.4%-4.8%) genetic maternal effects, 0.5% (95% CrI, 0.2%-7%) environmental maternal effects, and 61% (95% CrI, 59%-63%) direct additive genetic effects. Around 1% of genetic maternal effects were due to maternal effects from the individual with comorbid obsessive-compulsive disorder.ConclusionsOur results demonstrate genetic maternal effects contributing to the risk of CTD in offspring and also highlight new sources of overlapping risk between CTD and obsessive-compulsive disorder.
Missed childhood immunizations during the COVID-19 pandemic in Brazil: analyses of routine statistics and of a national household survey
Authors: Silveira Mariangela F , Tonial Cristian T , Maranhão Ana Goretti K , Teixeira Antonia MS , Hallal Pedro C , Menezes Ana Maria B , Horta Bernardo L , Hartwig Fernando P , Barros Aluisio JD , Victora Cesar G 
Published: December 2020

AbstractIntroductionThere is widespread concern that disruption to health services during the COVID-19 pandemic has led to declines in immunization coverage among young children, but there is limited information on the magnitude of such impact.MethodsWe used data from two nationwide sources covering the whole of Brazil. Data from the Information System of the National Immunization Program (SIPNI) on the monthly number of vaccine doses administered to young children were analyzed. The second source was a survey in 133 large cities in the 27 states in the country, carried out from August 24-27. Respondents answered a question on whether children under the age of three years had missed any scheduled vaccinations during the pandemic, and available vaccination cards were photographed for later examination.ResultsSIPNI data showed that, relative to January and February 2020, there was a decline of about 20% in vaccines administered to children aged two months or older during March and April, when social distancing was at the highest level in the country. After May, vaccination levels returned to pre-pandemic values. Survey data, based on the interviews and on examination of the vaccine cards, showed that 19.0% (95% CI 17.0;21.1%) and 20.6% (95% CI 19.0;23.1%) of children, respectively, had missed immunizations. Missed doses were most common in the North (Amazon) region and least common in the South and Southeast, and also more common among children from poor than from wealthy families.InterpretationOur results show that the pandemic was associated with a reduction of about 20% in child vaccinations, but this was reverted in recent months. Children from poor families and from the least developed regions of the country were most affected. There is an urgent need to booster immunization activities in the country to compensate for missed doses, and to reduce geographic and socioeconomic inequalities.
Smoking increases the risk of COVID-19 positivity, while Never-smoking reduces the risk
Authors: Barasa Samson , Kiage-Mokaya Josephine , Cruz-Madrid Katya , Friedlander Michael 
Published: December 2020

IntroductionDoes smoking decrease the risk of testing positive for COVID-19 because the never-smokers (84%) prevalence is high and the current-smokers prevalence is low among COVID-19 positive patients?1,2,3,4,5,6We sought to determine whether never smoking increases the risk of COVID-19 positivity among the 50 to 69-year old patients because they are more likely to test positive for COVID-19.7MethodWe conducted a retrospective chart review of COVID-19 Polymerase chain reaction, in-hospital tested ≥18-year-old patients. A Poisson regression analysis stratified into never-smokers and history of smoking (current + former smokers) was conducted.Results277 COVID-19 negative and 117 COVID-19 positive patients’ charts with a never-smokers prevalence of 42.32% and 54% respectively were analyzed. The never-smokers prevalence was 54%, 20-39-years; 61 %, 40 -49-years; 41%, 50 – 69-years; and 43%, 70 – 100-years.The 40-49-year-old current and former smokers were more likely to test positive for COVID-19 [1.309 (1.047 - 1.635)], unlike the 40-49-year-old never-smokers [0.976 (0.890-1.071)] who had a lower risk.Regardless of their smoking status, males [1.084(1.021 - 1.151)] and the 50-69-year-old patients [1.082 (1.014 -1.154)] were more likely to test positive for COVID-19, while end stage renal disease [0.908(0.843-0.978)] and non-COVID-19 respiratory viral illness [0.907 (0.863 - 0.953)] patients had a lower risk of COVID-19 positivity.Heart failure [0.907 (0.830 - 0.991)], chronic obstructive pulmonary disease (COPD) [0.842 (0.745 - 0.952)] and Parkinson’s disease [0.823 (0.708 - 0.957)] never-smokers were less likely to test positive for COVID-19.ConclusionThis is the first study to show that smoking increases the risk of COVID-19 positivity among the 40-49-year-old patients, while not smoking reduces the risk of COVID-19 positivity among the heart failure, COPD and Parkinson’s disease patients. This study emphasizes that COVID-19 positivity risk is not reduced by smoking and not increased by not smoking.
Ste20-like kinase is critical for inhibitory synapse maintenance and its deficiency confers a developmental dendritopathy
Authors: Schoch Susanne , Quatraccioni Anne , Robens Barbara K. , Maresch Robert , van Loo Karen M.J. , Kelly Tony , Opitz Thoralf , Borger Valeri , Dietrich Dirk , Pitsch Julika , Beck Heinz , Becker Albert J. 
Published: December 2020

SummaryThe size and structure of the dendritic arbor play important roles in determining how synaptic inputs of neurons are converted to action potential output. The regulatory mechanisms governing the development of dendrites, however, are insufficiently understood. The evolutionary conserved Ste20/Hippo kinase pathway has been proposed to play an important role in regulating the formation and maintenance of dendritic architecture. A key element of this pathway, Ste20-like kinase (SLK), regulates cytoskeletal dynamics in non-neuronal cells and is strongly expressed throughout neuronal development. However, its function in neurons is unknown. We show that during development of mouse cortical neurons, SLK has a surprisingly specific role for proper elaboration of higher, ≥ 3rd, order dendrites. Moreover, we demonstrate that SLK is required to maintain excitation-inhibition balance. Specifically, SLK knockdown caused a selective loss of inhibitory synapses and functional inhibition after postnatal day 15, while excitatory neurotransmission was unaffected. Finally, we show that this mechanism may be relevant for human disease, as dysmorphic neurons within human cortical malformations revealed significant loss of SLK expression. Overall, the present data identify SLK as a key regulator of both dendritic complexity during development and of inhibitory synapse maintenance.
NGScloud2: optimized bioinformatic analysis using Amazon Web Services
Authors: Mora-Márquez Fernando , Vázquez-Poletti José Luis , de Heredia Unai López 
Published: December 2020

AbstractNGScloud was a bioinformatic system developed to performde novoRNAseq analysis of non-model species by exploiting the cloud computing capabilities of Amazon Web Services. The rapid changes undergone in the way this cloud computing service operates, along with the continuous release of novel bioinformatic applications to analyze next generation sequencing data, have made the software obsolete. NGScloud2 is an enhanced and expanded version of NGScloud that permits the access toad hoccloud computing infrastructure, scaled according to the complexity of each experiment. NGScloud2 presents major technical improvements, such as the possibility of running spot instances and the most updated AWS instances types, that can lead to significant cost savings. As compared to its initial implementation, this improved version updates and includes common applications forde novoRNAseq analysis, and incorporates tools to operate workflows of bioinformatic analysis of reference-based RNAseq, RADseq and functional annotation. NGScloud2 optimizes the access to Amazon’s large computing infrastructures to easily run popular bioinformatic software applications, otherwise inaccessible to non-specialized users lacking suitable hardware infrastructures. The correct performance of the pipelines forde novoRNAseq, reference-based RNAseq, RADseq and functional annotation was tested with real experimental data. NGScloud2 code, instructions for software installation and use are available athttps://github.com/GGFHF/NGScloud2. NGScloud2 includes a companion package, NGShelper that contains python utilities to post-process the output of the pipelines for downstream analysis athttps://github.com/GGFHF/NGShelper.
The generation of cortical novelty responses through inhibitory plasticity
Authors: Schulz Auguste , Miehl Christoph , Berry Michael J. , Gjorgjieva Julijana 
Published: December 2020

AbstractAnimals depend on fast and reliable detection of novel stimuli in their environment. Indeed, neurons in multiple sensory areas respond more strongly to novel in comparison to familiar stimuli. Yet, it remains unclear which circuit, cellular and synaptic mechanisms underlie those responses. Here, we show that inhibitory synaptic plasticity readily generates novelty responses in a recurrent spiking network model. Inhibitory plasticity increases the inhibition onto excitatory neurons tuned to familiar stimuli, while inhibition for novel stimuli remains low, leading to a network novelty response. Generated novelty responses do not depend on the exact temporal structure but rather on the distribution of presented stimuli. By including tuning of inhibitory neurons, the network further captures stimulus-specific adaptation. Finally, we suggest that disinhibition can control the amplification of novelty responses. Therefore, inhibitory plasticity provides a flexible, biologically-plausible mechanism to detect the novelty of bottom-up stimuli, enabling us to make numerous experimentally testable predictions.
Reproductive proteins evolve faster than non-reproductive proteins amongSolanumspecies
Authors: Moyle Leonie C. , Wu Meng , Gibson Matthew J. S. 
Published: December 2020

AbstractElevated rates of evolution in reproductive proteins are commonly observed in animal species, and are thought to be driven by the action of sexual selection and sexual conflict acting specifically on reproductive traits. Whether similar patterns are broadly observed in other biological groups is equivocal. Here we examine patterns of protein divergence among wild tomato species (SolanumsectionLycopersicon), to understand forces shaping the evolution of reproductive genes in this diverse, rapidly evolving plant clade. By comparing rates of molecular evolution among loci expressed in reproductive and non-reproductive tissues, our aims were to test if: a) reproductive-specific loci evolve more rapidly, on average, than non-reproductive loci; ‘male’-specific loci evolve at different rates than ‘female’-specific loci; c) genes expressed exclusively in gametophytic (haploid) tissue evolve differently from genes expressed in sporophytic (diploid) tissue or in both tissue types; and d) mating system variation (a potential proxy for the expected strength of sexual selection and/or sexual conflict) affects patterns of protein evolution. We observed elevated evolutionary rates in reproductive proteins. However this pattern was most evident for female- rather than male-specific loci, both broadly and for individual loci inferred to be positively selected. These elevated rates might be facilitated by greater tissue-specificity of reproductive proteins, as faster rates were also associated with more narrow expression domains. In contrast we found little evidence that evolutionary rates are consistently different in loci experiencing haploid selection (gametophytic-exclusive loci), or in lineages with quantitatively different mating systems. Overall while reproductive protein evolution is generally elevated in this diverse plant group, some specific patterns of evolution are more complex than those reported in other (largely animal) systems, and include a more prominent role for female-specific loci among adaptively evolving genes.
Multigenerational exposure to heat stress induces phenotypic resilience, and genetic and epigenetic variations inArabidopsis thalianaoffspring
Authors: Yadav Narendra Singh , Titov Viktor , Ayemere Ivie , Byeon Boseon , Ilnytskyy Yaroslav , Kovalchuk Igor 
Published: December 2020

AbstractPlants are sedentary organisms that constantly sense changes in their environment and react to various environmental cues. On a short-time scale, plants respond through alterations in their physiology, and on a long-time scale, plants alter their development and pass on the memory of stress to the progeny. The latter is controlled genetically and epigenetically and allows the progeny to be primed for future stress encounters, thus increasing the likelihood of survival. The current study intended to explore the effects of multigenerational heat stress inArabidopsis thaliana.25 generations ofArabidopsis thalianawere propagated in the presence of heat stress. The multigenerational stressed lineage F25H exhibited a higher tolerance to heat stress and elevated frequency of homologous recombination, as compared to the parallel control progeny F25C. A comparison of genomic sequences revealed that the F25H lineage had a three-fold higher number of mutations (SNPs and INDELs) as compared control lineages, suggesting that heat stress induced genetic variations in the heat-stressed progeny. The F25H stressed progeny showed a 7-fold higher number of non-synonymous mutations than the F25C line. Methylome analysis revealed that the F25H stressed progeny showed a lower global methylation level in the CHH context than the control progeny. The F25H and F25C lineages were different from the parental control lineage F2C by 66,491 and 80,464 differentially methylated positions (DMPs), respectively. F25H stressed progeny displayed higher frequency of methylation changes in the gene body and lower in the body of transposable elements (TEs). Gene Ontology analysis revealed that CG-DMRs were enriched in processes such as response to abiotic and biotic stimulus, cell organizations and biogenesis, and DNA or RNA metabolism. Hierarchical clustering of these epimutations separated the heat stressed and control parental progenies into distinct groups which revealed the non-random nature of epimutations. We observed an overall higher number of epigenetic variations than genetic variations in all comparison groups, indicating that epigenetic variations are more prevalent than genetic variations. The largest difference in epigenetic and genetic variations was observed between control plants comparison (F25C vs F2C), which clearly indicated that the spontaneous nature of epigenetic variations and heat-inducible nature of genetic variations. Overall, our study showed that progenies derived from multigenerational heat stress displayed a notable adaption in context of phenotypic, genotypic and epigenotypic resilience.
Automatic Reconstruction of Cerebellar Cortex from Standard MRI Using Diffeomorphic Registration of a High-Resolution Template (ARCUS)
Authors: Samuelsson John G. , Rosen Bruce , Hämäläinen Matti S. 
Published: December 2020

AbstractAs cumulating evidence points to a wider range of functional tasks and neurological conditions that involve the cerebellum than previously known, the interest for examining the cerebellum with non-invasive neuroimaging techniques is growing. However, the standard methods of computational neuroanatomy for segmenting and reconstructing the cerebral cortex work poorly for the cerebellar cortex at the resolutions attainable with contemporary MRI technology because of its extremely intricate folding, making detailed and topologically correct reconstructions of the geometry of the cerebellar cortical surface unfeasible. Recently, a detailed surface reconstruction of the human cerebellar cortex was achieved from an ex-vivo specimen. These novel anatomical data enable a new reconstruction technique where this detailed surface reconstruction is morphed to subject space based on standard in-vivo MRI data. The result is an approximate reconstruction of the cerebellar cortex that requires only standard-resolution MRI data and can be used e.g., in functional neuroimaging, for integrating topographic population data or for visualizing topographic data on flattened surface patches.
KDClassifier: Urinary Proteomic Spectra Analysis Based on Machine Learning for Classification of Kidney Diseases
Authors: Zhao Wanjun , Zhang Yong , Li Xinming , Mao Yonghong , Wu Changwei , Zhao Lijun , Liu Fang , Zhu Jingqiang , Cheng Jingqiu , Yang Hao , Li Guisen 
Published: December 2020

AbstractBackgroundBy extracting the spectrum features from urinary proteomics based on an advanced mass spectrometer and machine learning algorithms, more accurate reporting results can be achieved for disease classification. We attempted to establish a novel diagnosis model of kidney diseases by combining machine learning with an extreme gradient boosting (XGBoost) algorithm with complete mass spectrum information from the urinary proteomics.MethodsWe enrolled 134 patients (including those with IgA nephropathy, membranous nephropathy, and diabetic kidney disease) and 68 healthy participants as a control, and for training and validation of the diagnostic model, applied a total of 610,102 mass spectra from their urinary proteomics produced using high-resolution mass spectrometry. We divided the mass spectrum data into a training dataset (80%) and a validation dataset (20%). The training dataset was directly used to create a diagnosis model using XGBoost, random forest (RF), a support vector machine (SVM), and artificial neural networks (ANNs). The diagnostic accuracy was evaluated using a confusion matrix. We also constructed the receiver operating-characteristic, Lorenz, and gain curves to evaluate the diagnosis model.ResultsCompared with RF, the SVM, and ANNs, the modified XGBoost model, called a Kidney Disease Classifier (KDClassifier), showed the best performance. The accuracy of the diagnostic XGBoost model was 96.03% (CI = 95.17%-96.77%; Kapa = 0.943; McNemar’s Test, P value = 0.00027). The area under the curve of the XGBoost model was 0.952 (CI = 0.9307-0.9733). The Kolmogorov-Smirnov (KS) value of the Lorenz curve was 0.8514. The Lorenz and gain curves showed the strong robustness of the developed model.ConclusionsThis study presents the first XGBoost diagnosis model, i.e., the KDClassifier, combined with complete mass spectrum information from the urinary proteomics for distinguishing different kidney diseases. KDClassifier achieves a high accuracy and robustness, providing a potential tool for the classification of all types of kidney diseases.
Parallel Hippocampal-Parietal Circuits for Self- and Goal-oriented Processing
Authors: Zheng Annie , Montez David F. , Marek Scott , Gilmore Adrian W. , Newbold Dillan J. , Laumann Timothy O. , Kay Benjamin P. , Seider Nicole A. , Van Andrew N. , Hampton Jacqueline M. , Alexopolous Dimitrios , Schlaggar Bradley L. , Sylvester Chad M. , Greene Deanna J. , Shimony Joshua S. , Nelson Steven M. , Wig Gagan S. , Gratton Caterina , McDermott Kathleen B. , Raichle Marcus E. , Gordon Evan M. , Dosenbach Nico U.F. 
Published: December 2020

SUMMARYThe hippocampus is critically important for a diverse range of cognitive processes, such as episodic memory, prospective memory, affective processing, and spatial navigation. The human hippocampus has been thought of as being solely functionally connected to a set of neocortical regions known as the default mode network (DMN), which supports self-referential cognition. Using individual-specific precision functional mapping of resting state fMRI data, we found the anterior hippocampus (head and body) to be preferentially connected to the DMN as expected. The hippocampal tail, however, was strongly preferentially connected to the parietal memory network (PMN), which supports goal-oriented cognition and stimulus recognition. This resting state functional connectivity (RSFC) anterior-posterior dichotomy was well-matched by differences in task deactivations and anatomical segmentations of the hippocampus. Task deactivations were localized to the head and body of the hippocampus (DMN), relatively sparing the tail (PMN). Anterior and posterior hippocampal connectivity was network-specific even though the DMN and PMN are interdigitated in medial parietal cortex. The functional dichotomization of the hippocampus into anterior DMN-connected and posterior PMN-connected parcels suggests parallel, but distinct circuits between the hippocampus and medial parietal cortex for self vs. goal-oriented processing.
Multiple variants of the blast fungus effector AVR-Pik bind the HMA domain of the rice protein OsHIPP19 with high affinity
Authors: Maidment Josephine H. R. , Franceschetti Marina , Maqbool Abbas , Saitoh Hiromasa , Jantasuriyarat Chatchawan , Kamoun Sophien , Terauchi Ryohei , Banfield Mark J. 
Published: December 2020

AbstractMicrobial plant pathogens secrete effector proteins which manipulate the host to promote infection. Effectors can be recognised by plant intracellular nucleotide-binding leucine-rich repeat (NLR) receptors, initiating an immune response. The AVR-Pik effector from the rice blast fungusMagnaporthe oryzaeis recognised by a pair of rice NLR receptors, Pik-1 and Pik-2. Pik-1 contains a non-canonical integrated heavy metal-associated (HMA) domain, which directly binds AVR-Pik to activate plant defences. Non-canonical integrated domains are widespread in plant NLRs and are thought to resemble the host target of the recognised effector. AVR-Pik interacts with specific rice HMA domain-containing proteins, namely heavy metal-associated isoprenylated plant proteins (HIPPs) and heavy metal-associated plant proteins (HPPs). Here, we define the biochemical and structural basis of the interaction between AVR-Pik and OsHIPP19, and compare the interaction with the HMA domain of Pik-1. Using analytical gel filtration and surface plasmon resonance, we show that multiple AVR-Pik variants, including the stealthy variants AVR-PikC and AVR-PikF which do not interact with any characterised Pik-1 alleles, bind to OsHIPP19 with nanomolar affinity. The crystal structure of OsHIPP19 in complex with AVR-PikF reveals differences at the interface that underpin high-affinity binding of OsHIPP19-HMA to a wider set of AVR-Pik variants than achieved by the integrated HMA domain of Pik-1. Our results provide a foundation for engineering the HMA domain of Pik-1 to extend binding to currently unrecognised AVR-Pik variants and expand disease resistance in rice to divergent pathogen strains.