bluuebunny/crossref_metadata_2025_split huggingface dataset:
New approach and new program for analyses of false negatives-contaminated data in medicine and biology
Authors: Flegr Jaroslav , Tureček Petr 
Published: June 2019

AbstractBackgroundNo serological assay has 100% sensitivity. Statistically, the concentration of specific antibodies against antigens of parasites decreases with the duration of infection. This can result in false negative outputs of diagnostic tests for the subjects with old infectiong, e.g., for individuals infected in childhood. When a property of seronegative and seropositive subjects is compared under these circumstances, the statistical tests can detect no significant difference between these two groups of subjects, despite the fact that infected and noninfected subjects differ. When the effect of the infection has a cumulative character and subjects with an older infection (potential false negatives) are affected to a greater degree, we can even get paradoxical result of the comparison – the seronegative subjects have on average lower value of certain traits, e.g. IQ, despite the infection having a negative effect on the trait. A permutation test for the contaminated data, implemented, e.g., in the program Treept or available as a comprehensibly commented R function in the supplement of this paper, can be used to reveal and to eliminate the effect of false negatives.MethodsWe used a Monte Carlo simulation in the program R to show that the permutation test implemented in the programs Treept and PTPT is a conservative test.ResultsWe showed that the test could provide false negative but not false positive results if the studied population contains no subpopulation of false negative subjects. We also introduced R version of the test expanded by skewness analysis, which helps to estimate the proportion of false negative subjects based on the assumption of equal data skewness in groups of healthy and infected individuals.ConclusionsBased on the results of simulations and our experience with empirical studies we recommend the usage of permutation test for contaminated data whenever seronegative and seropositive individuals are compared.
Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia
Authors: Zhu Xiaowei , Zhou Bo , Pattni Reenal , Gleason Kelly , Tan Chunfeng , Kalinowski Agnieszka , Sloan Steven , Fiston-Lavier Anna-Sophie , Mariani Jessica ,   Brain Somatic Mosaicism Network, Abyzov Alexej , Petrov Dimitri , Barres Ben A. , Vogel Hannes , Moran John V. , Vaccarino Flora M. , Tamminga Carol A. , Levinson Douglas F. , Urban Alexander E. 
Published: June 2019

AbstractActive retrotransposons in the human genome (L1,Aluand SVA elements) can create genomic mobile element insertions (MEIs) in both germline and somatic tissue1. Specific somatic MEIs have been detected at high levels in human cancers2, and at lower to medium levels in human brains3. Dysregulation of somatic retrotransposition in the human brain has been hypothesized to contribute to neuropsychiatric diseases4, 5. However, individual somatic MEIs are present in small proportions of cells at a given anatomical location, and thus standard whole-genome sequencing (WGS) presents a difficult signal-to-noise problem, while single-cell approaches suffer from limited scalability and experimental artifacts introduced by enzymatic whole-genome amplification6. Previous studies produced widely differing estimates for the somatic retrotransposition rates in human brain3, 6–8. Here, we present a highly precise machine learning method (RetroSom) to directly identify somatic L1 andAluinsertions in <1% cells from 200× deep WGS, which allows circumventing the restrictions of whole-genome amplification. Using RetroSom we confirmed a lower rate of retrotransposition for individual somatic L1 insertions in human neurons. We discovered that anatomical distribution of somatic L1 insertion is as widespread in glia as in neurons, and across both hemispheres of the brain, indicating retrotransposition occurs during early embryogenesis. We characterized two of the detected brain-specific L1 insertions in great detail in neurons and glia from a donor with schizophrenia. Both insertions are within introns of genes active in brain (CNNM2,FRMD4A) in regions with multiple genetic associations with neuropsychiatric disorders9–11. Gene expression was significantly reduced by both somatic insertions in a reporter assay. Our results provide novel insights into the potential for pathological effects of somatic retrotransposition in the human brain, now including the large glial fraction. RetroSom has broad applicability in all disease states where somatic retrotransposition is expected to play a role, such as autoimmune disorders and cancer.
Resting-state “Physiological Networks”
Authors: Chen Jingyuan E. , Lewis Laura D. , Chang Catie , Fultz Nina E. , Ohringer Ned A. , Rosen Bruce R. , Polimeni Jonathan R. 
Published: June 2019

AbstractSlow changes in systemic brain physiology can elicit large fluctuations in fMRI time series, which may manifest as structured spatial patterns of temporal correlations between distant brain regions. These correlations can appear similar to large-scale networks typically attributed to coupled neuronal activity. However, little effort has been devoted to a systematic investigation of such “physiological networks”—sets of segregated brain regions that exhibit similar physiological responses—and their potential influence on estimates of resting-state brain networks. Here, by analyzing a large group of subjects from the 3T Human Connectome Project database, we demonstrate brain-wide and noticeably heterogenous dynamics attributable to either respiratory variation or heart rate changes. We show that these physiologic dynamics can give rise to apparent “connectivity” patterns that resemble previously reported resting-state networks derived from fMRI data. Further, we show that this apparent “physiological connectivity” cannot be removed by the use of a single nuisance regressor for the entire brain (such as global signal regression) due to the clear regional heterogeneity of the physiological responses. Possible mechanisms causing these apparent “physiological networks”, and their broad implications for interpreting functional connectivity studies are discussed.
Deformed wing virus type A, a major honey bee pathogen, is vectored by the mite Varroa destructor in a non-propagative manner
Authors: Posada-Florez Francisco , Childers Anna K. , Heerman Matthew C. , Egekwu Noble I. , Cook Steven C. , Chen Yanping , Evans Jay D. , Ryabov Eugene V. 
Published: June 2019

AbstractHoney bees, the primary managed insect pollinator, suffer considerable losses due to Deformed wing virus (DWV), an RNA virus vectored by the mite Varroa destructor. Mite vectoring has resulted in the emergence of virulent DWV variants. The basis for such changes in DWV is poorly understood. Most importantly, it remains unclear whether replication of DWV occurs in the mite. In this study, we exposed Varroa mites to DWV type A via feeding on artificially infected honey bees. A significant, 357-fold increase in DWV load was observed in these mites after 2 days. However, after 8 additional days of passage on honey bee pupae with low viral loads, the DWV load dropped by 29-fold. This decrease significantly reduced the mites’ ability to transmit DWV to honey bees. Notably, negative-strand DWV RNA, which could indicate viral replication, was detected only in mites collected from pupae with high DWV levels but not in the passaged mites. We also found that Varroa mites contain honey bee mRNAs, consistent with the acquisition of honey bee cells which would additionally contain DWV replication complexes with negative-strand DWV RNA. We propose that transmission of DWV type A by Varroa mites occurs in a non-propagative manner.
proDA: Probabilistic Dropout Analysis for Identifying Differentially Abundant Proteins in Label-Free Mass Spectrometry
Authors: Ahlmann-Eltze Constantin , Anders Simon 
Published: June 2019

AbstractProtein mass spectrometry with label-free quantification (LFQ) is widely used for quantitative proteomics studies. Nevertheless, well-principled statistical inference procedures are still lacking, and most practitioners adopt methods from transcriptomics. These, however, cannot properly treat the principal complication of label-free proteomics, namely many non-randomly missing values.We present proDA, a method to perform statistical tests for differential abundance of proteins. It models missing values in an intensity-dependent probabilistic manner. proDA is based on linear models and thus suitable for complex experimental designs, and boosts statistical power for small sample sizes by using variance moderation. We show that the currently widely used methods based on ad hoc imputation schemes can report excessive false positives, and that proDA not only overcomes this serious issue but also offers high sensitivity. Thus, proDA fills a crucial gap in the toolbox of quantitative proteomics.
Spontaneous eye blink rate predicts individual differences in exploration and exploitation during reinforcement learning
Authors: Van Slooten Joanne C. , Jahfari Sara , Theeuwesu Jan 
Published: June 2019

AbstractSpontaneous eye blink rate (sEBR) has been linked to striatal dopamine function and to how individuals make value-based choices after a period of reinforcement learning (RL). While sEBR is thought to reflect how individuals learn from the negative outcomes of their choices, this idea has not been tested explicitly. This study assessed how individual differences in sEBR relate to learning by focusing on the cognitive processes that drive RL. Using Bayesian latent mixture modelling to quantify the mapping between RL behaviour and its underlying cognitive processes, we were able to differentiate low and high sEBR individuals at the level of these cognitive processes. Further inspection of these cognitive processes indicated that sEBR uniquely indexed explore-exploit tendencies during RL: lower sEBR predicted exploitative choices for high valued options, whereas higher sEBR predicted exploration of lower value options. This relationship was additionally supported by a network analysis where, notably, no link was observed between sEBR and how individuals learned from negative outcomes. Our findings challenge the notion that sEBR predicts learning from negative outcomes during RL, and suggest that sEBR predicts individual explore-exploit tendencies. These then influence value sensitivity during choices to support successful performance when facing uncertain reward.
A hidden gene in astroviruses encodes a cell-permeabilizing protein involved in virus release
Authors: Lulla Valeria , Firth Andrew E. 
Published: June 2019

ABSTRACTHuman astroviruses are small nonenveloped viruses with positive-sense single-stranded RNA genomes that contain three main open reading frames: ORF1a, ORF1b and ORF2. Astroviruses cause acute gastroenteritis in children worldwide and have been associated with encephalitis and meningitis in immunocompromised individuals. Through comparative genomic analysis of >400 astrovirus sequences, we identified a conserved “ORFX” overlapping the capsid-encoding ORF2 in genogroup I, III and IV astroviruses. ORFX appears to be subject to purifying selection, consistent with it encoding a functional protein product, termed XP. Using ribosome profiling of cells infected with human astrovirus 1, we confirm initiation at the ORFX AUG. XP-knockout astroviruses are strongly attenuated and after passaging can partly restore viral titer via pseudo-reversions, thus demonstrating that XP plays an important role in virus growth. To further investigate XP, we developed an astrovirus replicon system. We demonstrate that XP has only minor effects on RNA replication and structural protein production. Instead, XP associates with the plasma membrane with an extracellular N-terminus topology and promotes efficient virus release. Using two different assays, we show that expression of human or related astrovirus XPs leads to cell permeabilization, suggesting a viroporin-like activity. The discovery of XP advances our knowledge of these important human viruses and opens a new direction of research into astrovirus replication and pathogenesis.
Molecular characterization of the stress network in the human brain
Authors: Meijer Mandy , Keo Arlin , van Leeuwen Judith M.C. , Dzyubachyk Oleh , Meijer Onno C. , Vinkers Christiaan H. , Mahfouz Ahmed 
Published: June 2019

ABSTRACTThe biological mechanisms underlying inter-individual differences in human stress reactivity remain poorly understood. We aimed to identify the molecular underpinning of neural stress sensitivity. Linking mRNA expression data from the Allen Human Brain Atlas to task-based fMRI revealed 201 differentially expressed genes in cortex-specific brain regions differentially activated by stress in individuals with low or high stress sensitivity. These genes are associated with stress-related psychiatric disorders (e.g. schizophrenia and anxiety) and include markers for specific neuronal populations (e.g.ADCYAP1, GABRB1, SSTR1, andTNFRSF12A), neurotransmitter receptors (e.g.GRIN3A, SSTR1, GABRB1, andHTR1E), and signaling factors that interact with the corticosteroid receptor and hypothalamic-pituitary-adrenal axis (e.g. ADCYAP1,IGSF11, and PKIA). Overall, the identified genes potentially underlie altered stress reactivity in individuals at risk for psychiatric disorders and play a role in mounting an adaptive stress response, making them potentially druggable targets for stress-related diseases.
Fine-grained simulations of the microenvironment of vascularized tumours
Authors: Fredrich Thierry , Rieger Heiko , Chignola Roberto , Milotti Edoardo 
Published: June 2019

ABSTRACTOne of many important features of the tumour microenvironment is that it is a place of active Darwinian selection where different tumour clones become adapted to the variety of ecological niches that make up the microenvironment. These evolutionary processes turn the microenvironment into a powerful source of tumour heterogeneity and contribute to the development of drug resistance in cancer. Here, we describe a computational tool to study the ecology of the microenvironment and report results about the ecology of the tumour microenvironment and its evolutionary dynamics.
Automated Microfossil Identification and Segmentation Using a Deep Learning Approach
Authors: Carvalho L.E , Fauth G. , Baecker Fauth S. , Krahl G. , Moreira A. C. , Fernandes C.P. , von Wangenheim A 
Published: June 2019

AbstractThe applicability of computational analysis to paleontological images ranges from the study of the animals, plants and evolution of microorganisms to the simulation of the habitat of living beings of a given epoch. It also can be applied in several niches, such as oil exploration, where there are several factors to be analyzed in order to minimize the expenses related to the oil extraction process. One factor is the characterization of the environment to be explored. This analysis can occur in several ways: use of probes, extraction of samples for petrophysical components evaluation, the correlation with logs of other drilling wells and so on. In the samples extraction part the Computed Tomography (CT) is of importance because it preserves the sample and makes it available for several analyzes. Based on 3D images generated by CT, several analyzes and simulations can be performed and processes, currently performed manually and exhaustively, can be automated. In this work we propose and validate a method for fully automated microfossil identification and extraction. A pipeline is proposed that begins in the scanning process and ends in an identification process. For the identification a Deep Learning approach was developed, which resulted in a high rate of correct microfossil identification (98% of Intersection Over Union). The validation was performed both through an automated quantitative analysis based upon ground truths generated by specialists in the micropaleontology field and visual inspection by these specialists. We also present the first fully annotated MicroCT-acquired publicly available microfossils dataset.
Hippocampal subfields and limbic white matter jointly predict learning rate in older adults
Authors: Bender Andrew R. , Brandmaier Andreas M. , Düzel Sandra , Keresztes Attila , Pasternak Ofer , Lindenberger Ulman , Kühn Simone 
Published: June 2019

AbstractAge-related memory impairments have been linked to differences in structural brain parameters, including cerebral white matter (WM) microstructure and hippocampal (HC) volume, but their combined influences are rarely investigated. In a population-based sample of 337 older participants 61–82 years of age (Mage=69.66, SDage=3.92 years) we modeled the independent and joint effects of limbic WM microstructure and HC subfield volumes on verbal learning. Participants completed a verbal learning task over five learning trials and underwent magnetic resonance imaging (MRI), including structural and diffusion scans. We segmented three HC subregions on high-resolution MRI data and sampled mean fractional anisotropy (FA) from bilateral limbic WM tracts identified via deterministic fiber tractography. Using structural equation modeling, we evaluated the associations between learning rate and latent factors representing FA sampled from limbic WM tracts, and HC subfield volumes, as well as their latent interaction. Results showed limbic WM and the interaction of HC and WM – but not HC volume alone – predicted verbal learning rates. Model decomposition revealed HC volume is only positively associated with learning rate in individuals with higher levels of WM anisotropy. We conclude that structural characteristics of limbic WM regions and HC volume jointly contribute to verbal learning in older adults.
Convergent Hybrid Phase Ligation Strategy for Efficient Total Synthesis of Large Proteins Demonstrated for 212-residue Linker Histone H1.2
Authors: Hong Ziyong Z. , Yu Ruixuan R. , Zhang Xiaoyu , Webb Allison M. , Burge Nathaniel L. , Poirier Michael G. , Ottesen Jennifer J. 
Published: June 2019

AbstractSimple and efficient total chemical synthesis of large proteins remains a significant challenge. Here, we report development of a convergent hybrid phase native chemical ligation (CHP-NCL) strategy that should be generally applicable for facile preparation of large proteins. Key to the strategy is the use of sequential ligation on the solid phase for the directed assembly of ~100-residue segments from short, synthetically accessible peptide components. These segments can then be assembled via convergent solution phase ligation, exploiting o-aminoaniline as a chemically flexible cryptic thioester with multiple activation modalitiies on resin and in situ. We demonstrate the feasibility of our approach through the total synthesis of 212-residue linker histone H1.2 in unmodified, phosphorylated, and citrullinated forms, each from eight component peptide segments. We further demonstrate that fully synthetic H1.2 replicates the binding interactions of linker histones to intact mononucleosomes, as a proxy for the essential function of linker histones in the formation and regulation of higher order chromatin structure.
Multiple memories can be simultaneously reactivated during sleep as effectively as a single memory
Authors: Schechtman Eitan , Antony James W. , Lampe Anna , Wilson Brianna J. , Norman Kenneth A. , Paller Ken A. 
Published: June 2019

AbstractMemory consolidation involves the reactivation of memory traces during sleep. If many memories are reactivated each night, how much do they interfere with one another? To explore this question, we examined whether reactivating multiple memories incurs a cost to sleep-related benefits by contrasting reactivation of multiple memories versus single memories during sleep. First, participants learned the on-screen location of different images. Each image was part of a semantically interconnected group (e.g., images of different cats). Groups were comprised of either one, two, or six images. During sleep, group-related sounds (e.g., “meow”) were unobtrusively presented to reactivate memories for half of the groups. The benefit in location recall for cued versus non-cued items was independent of the number of items in the group, suggesting that reactivation occurs in a simultaneous, promiscuous manner. Intriguingly, sleep spindles and delta-theta power modulations were sensitive to group size and reflected the extent of previous learning. Our results demonstrate that multiple memories may be consolidated in parallel without compromising each memory’s sleep-related benefit, suggesting that the brain’s capacity for reactivation is not strictly limited by separate resources needed for individual memories. These findings highlight alternative models for parallel consolidation that should be considered in future studies.
Fine-mapping complex inversion breakpoints and investigating somatic pairing in theAnopheles gambiaespecies complex using proximity-ligation sequencing
Authors: Corbett-Detig Russell B. , Said Iskander , Calzetta Maria , Genetti Max , McBroome Jakob , Maurer Nicholas W. , Petrarca Vincenzo , Torre Alessandra della , Besansky Nora J. 
Published: June 2019

AbstractChromosomal inversions are fundamental drivers of genome evolution. In the main afro-tropical malaria vector species, belonging to theAnopheles gambiaespecies complex, inversions play an important role in local adaptation and have a rich history of cytological study. Despite the importance and ubiquity of some chromosomal inversions across the species complex, inversion breakpoints are often challenging to map molecularly due to the presence of large repetitive regions. Here, we develop an approach that uses Hi-C sequencing data to molecularly fine-map the breakpoints of inversions 2Rbc and 2Rd inA. coluzzii. We found that inversion breakpoints occur in large repetitive regions, and strikingly among three inversions analyzed, two breakpoints appear to be reused in two separate inversions. Additionally, we use heterozygous individuals to quantitatively investigate somatic pairing disruption in the regions immediately surrounding inversion breakpoints, and we find that pairing disruption is undetectable beyond approximately 250 Kb from the inversion breakpoints.
Biomarker discovery in inflammatory bowel diseases using network-based feature selection
Authors: Abbas Mostafa , Matta John , Le Thanh , Bensmail Halima , Obafemi-Ajayi Tayo , Honavar Vasant , EL-Manzalawy Yasser 
Published: June 2019

ABSTRACTReliable identification of inflammatory biomarkers from metagenomics data is a promising direction for developing non-invasive, cost-effective, and rapid clinical tests for early diagnosis of IBD. We present an integrative approach to Network-Based Biomarker Discovery (NBBD) which integrates network analyses methods for prioritizing potential biomarkers and machine learning techniques for assessing the discriminative power of the prioritized biomarkers. Using a large dataset of new-onset pediatric IBD metagenomics biopsy samples, we compare the performance of Random Forest (RF) classifiers trained on features selected using a representative set of traditional feature selection methods against NBBD framework, configured using five different tools for inferring networks from metagenomics data, and nine different methods for prioritizing biomarkers as well as a hybrid approach combining best traditional and NBBD based feature selection. We also examine how the performance of the predictive models for IBD diagnosis varies as a function of the size of the data used for biomarker identification. Our results show that (i) NBBD is competitive with some of the state-of-the-art feature selection methods including Random Forest Feature Importance (RFFI) scores; and (ii) NBBD is especially effective in reliably identifying IBD biomarkers when the number of data samples available for biomarker discovery is small.