bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cryptic Native American ancestry recapitulates population-specific migration and settlement of the continental United States
Authors: Jordan I. King , Rishishwar Lavanya , Conley Andrew B. 
Published: May 2018

AbstractEuropean and African descendants settled the continental US during the 17th-19thcenturies, coming into contact with established Native American populations. The resulting admixture among these groups yielded a significant reservoir of cryptic Native American ancestry in the modern US population. We analyzed the patterns of Native American admixture seen for the three largest genetic ancestry groups in the US population: African American, European American, and Hispanic/Latino. The three groups show distinct Native American ancestry profiles, which are indicative of their historical patterns of migration and settlement across the country. Native American ancestry in the modern African American population does not coincide with local geography, instead forming a monophyletic group with origins in the southeastern US, consistent with the Great Migration of the early 20thcentury. European Americans show Native American ancestry that tracks their geographic origins across the US, indicative of ongoing contact during westward expansion, and Native American ancestry can resolve Hispanic/Latino individuals into distinct local groups formed by more recent migration from Mexico and Puerto Rico. We found an anomalous pattern of Native American ancestry from the US southwest, which most likely corresponds to theNuevomexicanodescendants of early Spanish settlers to the region. We addressed a number of controversies surrounding this population, including the extent of Sephardic Jewish ancestry.Nuevomexicanosare less admixed than nearby Mexican-American individuals, with more European and less Native American and African ancestry, and while they do show demonstrable Sephardic Jewish ancestry, the fraction is no greater than seen for other Hispanic/Latino populations.
On an algorithmic definition for the components of the minimal cell
Authors: Martínez Octavio , Reyes-Valdés M. Humberto 
Published: May 2018

AbstractLiving cells are highly complex systems comprising a multitude of elements that are engaged in the many convoluted processes observed during the cell cycle. However, not all elements and processes are essential for cell survival and reproduction under steady-state environmental conditions. To distinguish between essential from expendable cell components and thus define the ‘minimal cell’ and the corresponding ‘minimal genome’, we postulate that the synthesis of all cell elements can be represented as a finite set of binary operators, and within this framework we show that cell elements that depend on their previous existence to be synthesized are those that are essential for cell survival. An algorithm to distinguish essential cell elements is presented and demonstrated within an interactome. Data and functions implementing the algorithm are given as supporting information. We expect that this algorithmic approach will lead to the determination of the complete interactome of the minimal cell, which could then be experimentally validated. The assumptions behind this hypothesis as well as its consequences for experimental and theoretical biology are discussed.
Cost-efficient high throughput capture of museum arthropod specimen DNA using PCR-generated baits
Authors: Knyshov Alexander , Gordon Eric R.L. , Weirauch Christiane 
Published: May 2018

AbstractGathering genetic data for rare species is one of the biggest remaining obstacles in modern phylogenetics, particularly for megadiverse groups such as arthropods. Next generation sequencing techniques allow for sequencing of short DNA fragments contained in preserved specimens >20 years old, but approaches such as whole genome sequencing are often too expensive for projects including many taxa. Several methods of reduced representation sequencing have been proposed that lower the cost of sequencing per specimen, but many remain costly because they involve synthesizing nucleotide probes and target hundreds of loci. These datasets are also frequently unique for each project and thus generally incompatible with other similar datasets.Here, we explore utilization of in-house generated DNA baits to capture commonly utilized mitochondrial and ribosomal DNA loci from insect museum specimens of various age and preservation types without the a priori need to know the sequence of the target loci. Both within species and cross-species capture are explored, on preserved specimens ranging in age from one to 54 years old.We found most samples produced sufficient amounts of data to assemble the nuclear ribosomal rRNA genes and near complete mitochondrial genomes and produce well-resolved phylogenies in line with expected results. The dataset obtained can be straightforwardly combined with the large cache of existing Sanger-sequencing-generated data built up over the past 30 years and targeted loci can be easily modified to those commonly used in different taxa. Furthermore, the protocol we describe allows for inexpensive data generation (as low as ∼$35/sample), of at least 20 kilobases per specimen, for specimens at least as old as ∼1965, and can be easily conducted in most laboratories.If widely applied, this technique will accelerate the accurate resolution of the Tree of Life especially on non-model organisms with limited existing genomic resources.
Mapping Antarctic suspension feeder abundances and seafloor food-availability, and modelling their change after a major glacier calving
Authors: Jansen Jan , Hill Nicole A. , Dunstan Piers K. , Cougnon Eva A. , Galton-Fenzi Benjamin K. , Johnson Craig R. 
Published: May 2018

AbstractSeafloor communities are a critical part of the unique and diverse Antarctic marine life. Processes at the ocean-surface can strongly influence the diversity and abundance of these communities, even when they live at hundreds of meters water depth. However, even though we understand the importance of this link, there are so far no quantitative spatial predictions on how seafloor communities will respond to changing conditions at the ocean surface.Here, we map patterns in abundance of important habitat-forming suspension feeders on the seafloor in East Antarctica, and predict how these patterns change after a major disturbance in the icescape, caused by the calving of the Mertz Glacier Tongue. We use a purpose-built ocean model for the time-period before and after the calving of the Mertz-Glacier Tongue in 2010, data from satellites and a validated food-availability model to estimate changes in horizontal flux of food since the glacier calving. We then predict the post-calving distribution of suspension feeder abundances using the established relationships with the environmental variables, and changes in horizontal flux of food.Our results indicate strong increases in suspension feeder abundances close to the glacier calving site, fueled by increased food supply, while the remainder of the region maintains similar suspension feeder abundances despite a slight decrease in total food supply. The oceanographic setting of the entire region changes, with a shorter ice-free season, altered seafloor currents and changes in food-availability.Our study provides important insight into the flow-on effects of a changing icescape on seafloor habitat and fauna in polar environments. Understanding these connections is important in the context of current and future effects of climate change, and the mapped predictions of the seafloor fauna as presented for the study region can be used as a decision-tool for planning potential marine protected areas, and for focusing future sampling and monitoring initiatives.
Synergy and convergence of pathways controlling functional regeneration in the spinal cord
Authors: Carlstrom Lucas P. , Cheever Thomas R. , Schoenfuss Heiko L. , McGee Meghan R. , Ekker Stephen C. , Henley John R. 
Published: May 2018

ABSTRACTBarriers to regeneration in the mammalian central nervous system (CNS) include the presence of inhibitory factors like myelin-associated glycoprotein (MAG) that block re-growth of injured axons. Inhibition by MAG antagonizes the induction of integrin-based substrate adhesions in axonal growth cones by brain-derived neurotrophic factor (BDNF). Here, using a novel approach to overcome inhibitory actions of MAG by activating integrins, we provide cellular and molecular evidence that integrin activity modulates the actions of chemotropic cues on substrate adhesions and supports axon regeneration in vertebrates. Potentiating integrin activity in cultured spinal neurons blocked negative integrin remodeling and inhibition of axon outgrowth induced by MAG, but also restored BDNF-dependent integrin clustering and stimulated outgrowth. In a zebrafish complete spinal cord transection model, combined integrin activation and BDNF treatment synergistically triggered functional regeneration of long projection axons that lack regenerative capacity from the hindbrain. The combined treatment also promoted functional repair even in the presence of exogenous mammalian inhibitory factors, including MAG, which alone impaired recovery of swimming movements. Thus, integrin activation state plays complementary roles in modulating the output activity of opposing cues on integrin-based adhesions and supports functional nerve regenerationin vivo. Our findings reveal effective reversal of downstream actions of inhibitory cues, thereby overcoming a major barrier to regeneration in the mammalian CNS, while simultaneously supporting neurotrophin-stimulated outgrowth. Discovery of therapeutic strategies targeting integrin activation state therefore holds promise for promoting axon regeneration after traumatic injury, which is a critical step in restoring connectivity and functional recovery.
Anywhere but here: local conditions alone drive dispersal inDaphnia
Authors: Erm Philip , Hall Matthew D. , Phillips Ben L. 
Published: May 2018

AbstractDispersal is fundamental to population dynamics and it is increasingly apparent that, despite most models treating dispersal as a constant, many organisms make dispersal decisions based upon information gathered from the environment. Ideally, organisms would make fully informed decisions, with knowledge of both intra-patch conditions (conditions in their current location) and extra-patch conditions (conditions in alternative locations). Acquiring information is energetically costly however, and extra-patch information will typically be costlier to obtain than intra-patch information. As a consequence, theory suggests that organisms will often make partially informed dispersal decisions, utilising intra-patch information only. We test this proposition in an experimental two-patch system using populations of the aquatic crustacean,Daphnia carinata. We manipulated conditions (food availability) in the population’s home patch, and in its alternative patch. We found thatD. carinatamade use of intra-patch information (resource limitation in the home patch induced a ten-fold increase in dispersal probability) but made no use of extra-patch information (resource limitation in the alternative patch did not affect dispersal probability). Our work highlights the very large influence that information can have on dispersal probability, but also that dispersal decisions will often be made in only a partially informed manner. The magnitude of the response we observed also adds to the growing chorus that condition-dependence may be a significant driver of variation in dispersal.
Genotypic diversity, circulation patterns, and co-detections among rhinoviruses in Queensland, 2001
Authors: Arden Katherine E. , Greer Ristan M. , Wang Claire Y.T. , Mackay Ian M. 
Published: May 2018

AbstractRhinoviruses (RVs) occur more frequently than other viruses and more often in people displaying symptoms than in those without. RVs exacerbate chronic airway disease and confound the clinical diagnosis of influenza-like illness. We sought to estimate the spectrum of RV diversity, RV species seasonality and to breakdown RV involvement in respiratory virus co-detections by comprehensive molecular testing of a convenience collection of airway sample extracts from patients with suspected respiratory infections, collected during 2001.RVs were the most common virus detected. We were able to genotype ∼90% of RV detections, identifying 70 distinct RVs, spanning all three species. RV-Bs were under-represented. We found RV species co-circulated at times, although one species usually dominated. Each species displayed a bimodal distribution.Notably, RVs and influenza A viruses (IFAV) seldom co-occurred, supporting their roles as primary pathogens of the airway among acutely ill infants. Whether RV circulation has a moderating or controlling effect on the IFAV season or is controlled by it cannot be determined from these data.Despite the frequent perception that RVs commonly co-occur with another virus, our findings indicated this was not the case. Nearly 80% of RV detections occurred alone. Understanding more about population-level interference between viruses may allow us to harness aspects of it to generate a non-specific antiviral intervention that mimics a putative protective effect.For routine respiratory virus screening to best serve the patient, RV testing should be a principal component of any acute respiratory illness testing algorithm throughout the year.
BDNF controls cognitive processes related to neuropsychiatric manifestations via autophagic regulation of p62 and GABAA receptor trafficking
Authors: Tomoda Toshifumi , Sumitomo Akiko , Shukla Rammohan , Hirota-Tsuyada Yuki , Miyachi Hitoshi , Oh Hyunjung , French Leon , Sibille Etienne 
Published: May 2018

SummaryReduced BDNF and GABAergic inhibition co-occur in neuropsychiatric diseases, including major depression. Genetic rodent studies show a causal link, suggesting the presence of biological pathways that mediate this co-occurrence. Here we show that mice with reduced Bdnf (Bdnf+/-) have upregulated expression of sequestosome-1/p62, an autophagy-associated stress response protein, and reduced surface presentation of α5 subunit-containing GABAA receptor (α5-GABAAR) in prefrontal cortex (PFC) pyramidal neurons. Reducing p62 gene dosage restored α5-GABAAR surface expression and rescued the PFC-relevant behavioral deficits of Bdnf+/- mice, including cognitive inflexibility and sensorimotor gating deficits. Increasing p62 levels was sufficient to recreate the molecular and behavioral profiles of Bdnf+/- mice. Finally, human postmortem corticolimbic transcriptome analysis suggested reduced autophagic activity in depression. Collectively, the data reveal that autophagy regulation through control of p62 dosage may serve as a mechanism linking reduced BDNF signaling, GABAergic deficits, and psychopathology associated with PFC functional deficits across psychiatric disorders.HIGHLIGHTSBDNF constitutively promotes autophagy in cortical pyramidal neuronsReduced BDNF causes elevated autophagy-regulator p62 expression, leading to lower surface α5-GABAAR presentationIncreasing p62 levels mimics cognition-related behavioral deficits in Bdnf+/- miceAltered postmortem corticolimbic gene expression suggests reduced autophagic activity in depression
Tracing a protein’s folding pathway over evolutionary time using ancestral sequence reconstruction and hydrogen exchange
Authors: Lim Shion A. , Bolin Eric R. , Marqusee Susan 
Published: May 2018

AbstractThe conformations populated during protein folding have been studied for decades; yet, their evolutionary importance remains largely unexplored. Ancestral sequence reconstruction allows access to proteins across evolutionary time, and new methods such as pulsed-labeling hydrogen exchange coupled with mass spectrometry allow determination of folding intermediate structures at near amino-acid resolution. Here, we combine these techniques to monitor the folding of the ribonuclease H family along the evolutionary lineages ofT. thermophilusandE. coliRNase H. All homologs and ancestral proteins studied populate a similar folding intermediate despite being separated by billions of years of evolution. Even though this conformation is conserved, the pathway leading to it has diverged over evolutionary time, and rational mutations can alter this trajectory. Our results demonstrate that evolutionary processes can affect the energy landscape to preserve or alter specific features of a protein’s folding pathway.
Nucleotide-pair encoding of 16S rRNA sequences for host phenotype and biomarker detection
Authors: Asgari Ehsaneddin , Münch Philipp C. , Lesker Till R. , McHardy Alice C. , Mofrad Mohammad R.K. 
Published: May 2018

ABSTRACTIdentifying combinations of taxa distinctive for microbiome-associated diseases is considered key to the establishment of diagnosis and therapy options in precision medicine and imposes high demands on accuracy of microbiome analysis techniques. We propose subsequence based 16S rRNA data analysis, as a new paradigm for microbiome phenotype classification and biomarker detection. This method and software called DiTaxa substitutes standard OTU-clustering or sequence-level analysis by segmenting 16S rRNA reads into the most frequent variable-length subsequences. These subsequences are then used as data representation for downstream phenotype prediction, biomarker detection and taxonomic analysis. Our proposed sequence segmentation called nucleotide-pair encoding (NPE) is an unsupervised data-driven segmentation inspired by Byte-pair encoding, a data compression algorithm. The identified subsequences represent commonly occurring sequence portions, which we found to be distinctive for taxa at varying evolutionary distances and highly informative for predicting host phenotypes. We compared the performance of DiTaxa to the state-of-the-art methods in disease phenotype prediction and biomarker detection, using human-associated 16S rRNA samples for periodontal disease, rheumatoid arthritis and inflammatory bowel diseases, as well as a synthetic benchmark dataset. DiTaxa identified 17 out of 29 taxa with confirmed links to periodontitis (recall= 0.59), relative to 3 out of 29 taxa (recall= 0.10) by the state-of-the-art method. On synthetic benchmark data, DiTaxa obtained full precision and recall in biomarker detection, compared to 0.91 and 0.90, respectively. In addition, machine-learning classifiers trained to predict host disease phenotypes based on the NPE representation performed competitively to the state-of-the art using OTUs or k-mers. For the rheumatoid arthritis dataset, DiTaxa substantially outperformed OTU features with a macro-F1 score of 0.76 compared to 0.65. Due to the alignment- and reference free nature, DiTaxa can efficiently run on large datasets. The full analysis of a large 16S rRNA dataset of 1359 samples required ≈1.5 hours on 20 cores, while the standard pipeline needed ≈6.5 hours in the same setting.AvailabilityAn implementation of our method called DiTaxa is available under the Apache 2 licence at http://llp.berkeley.edu/ditaxa.
Semi-supervised Encoding for Outlier Detection in Clinical Observation Data
Authors: Estiri Hossein , Murphy Shawn N 
Published: May 2018

AbstractBackground and ObjectiveTo evaluate the utility of encoding for outlier detection in clinical observation data from Electronic Health Records (EHR).MethodsThis article presents a semi-supervise encoding approach (super-encoding) for constructing a non-linear exemplar data distribution from EHR data and detecting non-conforming observations as outliers. Two hypotheses are tested using experimental design and non-parametric hypothesis testing procedures to evaluate the outlier detection performance of the semi-supervised encoding approach and increasing demographic precision in encoding.ResultsThe experiments involved applying 492 encoders to 30 laboratory tests extracted from the Research Patient Data Registry (RPDR) from Partners HealthCare. We report results obtained from 14,760 encoders. The semi-supervised encoders (super-encoders) outperformed conventional autoencoders in outlier detection. Adding age at observation to the baseline encoder (that only included observation value as the feature) slightly improved outlier detection. Top-nine performing encoders are introduced. The best outlier detection performance was from a semi-supervised encoder, with observation value as the single feature and a single hidden layer, built on one percent of the data and one percent reconstruction error. At least one encoder had a Youden’s J index higher than 0.9999 for all 30 observations.ConclusionGiven the multiplicity of distributions for a single observation in EHR data (i.e., same observation represented with different names or units), as well as non-linearity of human observations, encoding offers huge promises for outlier detection in large-scale data repositories.
Disinhibition as a canonical neural mechanism for flexible behavior
Authors: Standage Dominic , Paré Martin , Blohm Gunnar 
Published: May 2018

AbstractFlexibility is a hallmark of human and animal behavior, but the context-dependent neural computations that generate flexible behavior are poorly understood. Here, we use a biophysically-based cortical network model to explore the hypothesis that vasoactive intestinal polypeptide (VIP) expressing inhibitory interneurons control local circuit dynamics by targeting other classes of inhibitory interneuron, supporting context-dependent computations. Depending on the strength of this disinhibition (simulating VIP activity), network dynamics support multiple-item working memory (WM, strong disinhibition) or decision making (DM, weak disinhibition). Within these regimes, disinhibition controls WM capacity and speedaccuracy-trade-off in choice behavior. Our findings suggest that long-range trans-cortical VIP-mediated disinhibition is a canonical neural mechanism for the top-down control of flexible behavior.
Using machine learning models to predict oxygen saturation following ventilator support adjustment in critically ill children: a single center pilot study
Authors: Ghazal Sam , Sauthier Michael , Brossier David , Bouachir Wassim , Jouvet Philippe , Noumeir Rita 
Published: May 2018

AbstractClinicians’ experts in mechanical ventilation are not continuously at each patient’s bedside in an intensive care unit to adjust mechanical ventilation settings and to analyze the impact of ventilator settings adjustments on gas exchange. The development of clinical decision support systems analyzing patients’ data in real time offers an opportunity to fill this gap. The objective of this study was to determine whether a machine learning predictive model could be trained on a set of clinical data and used to predict hemoglobin oxygen saturation 5 min after a ventilator setting change. Data of mechanically ventilated children admitted between May 2015 and April 2017 were included and extracted from a high-resolution research database. More than 7.105 rows of data were obtained from 610 patients, discretized into 3 class labels. Due to data imbalance, four different data balancing process were applied and two machine learning models (artificial neural network and Bootstrap aggregation of complex decision trees) were trained and tested on these four different balanced datasets. The best model predicted SpO2 with accuracies of 76%, 62% and 96% for the SpO2 class “< 84%”, “85 to 91%” and “> 92%”, respectively. This pilot study using machine learning predictive model resulted in an algorithm with good accuracy. To obtain a robust algorithm, more data are needed, suggesting the need of multicenter pediatric intensive care high resolution databases.