bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving imputation quality in BEAGLE for crop and livestock data
Authors: Pook T. , Mayer M. , Geibel J. , Weigend S. , Cavero D. , Schoen C.C. , Simianer H. 
Published: March 2019

ABSTRACTImputation is one of the key steps in the preprocessing and quality control protocol of any genetic study. Most imputation algorithms were originally developed for the use in human genetics and thus are optimized for a high level of genetic diversity. Different versions of BEAGLE were evaluated on genetic datasets of doubled haploids of two European maize landraces, a commercial breeding line and a diversity panel in chicken, respectively, with different levels of genetic diversity and structure which can be taken into account in BEAGLE by parameter tuning. Especially for phasing BEAGLE 5.0 outperformed the newest version (5.1) which in turn also lead to improved imputation. Earlier versions were far more dependent on the adaption of parameters in all our tests. For all versions, the parameter ne (effective population size) had a major effect on the error rate for imputation of ungenotyped markers, reducing error rates by up to 98.5%. Further improvement was obtained by tuning of the parameters affecting the structure of the haplotype cluster that is used to initialize the underlying Hidden Markov Model of BEAGLE. The number of markers with extremely high error rates for the maize datasets were more than halved by the use of a flint reference genome (F7, PE0075 etc.) instead of the commonly used B73. On average, error rates for imputation of ungenotyped markers were reduced by 8.5% by excluding genetically distant individuals from the reference panel for the chicken diversity panel. To optimize imputation accuracy one has to find a balance between representing as much of the genetic diversity as possible while avoiding the introduction of noise by including genetically distant individuals.
Reduced proteasome activity in the aging brain results in ribosome stoichiometry loss and aggregation
Authors: Sacramento Erika Kelmer , Kirkpatrick Joanna M. , Mazzetto Mariateresa , Baumgart Mario , Bartolome Aleksandar , Di Sanzo Simone , Caterino Cinzia , Sanguanini Michele , Papaevgeniou Nikoletta , Lefaki Maria , Childs Dorothee , Bagnoli Sara , Tozzini Eva Terzibasi , Di Fraia Domenico , Romanov Natalie , Sudmant Peter , Huber Wolfgang , Chondrogianni Niki , Vendruscolo Michele , Cellerino Alessandro , Ori Alessandro 
Published: March 2019

SummaryA progressive loss of protein homeostasis is characteristic of aging and a driver of neurodegeneration. To investigate this process quantitatively, we characterized proteome dynamics during brain aging in the short-lived vertebrate Nothobranchius furzeri combining transcriptomics and proteomics. We detected a progressive reduction in the correlation between protein and mRNA, mainly due to post-transcriptional mechanisms that account for over 40% of the age-regulated proteins. These changes cause a progressive loss of stoichiometry in several protein complexes, including ribosomes, which show impaired assembly / dis-assembly and are enriched in protein aggregates in old brains. Mechanistically, we show that reduction of proteasome activity is an early event during brain aging and is sufficient to induce proteomic signatures of aging and loss of stoichiometry in vivo. Using longitudinal transcriptomic data, we show that the magnitude of early life decline in proteasome levels is the major risk factor for mortality. Our work defines causative events in the aging process that can be targeted to prevent loss of protein homeostasis and delay the onset of age-related neurodegeneration.HighlightsProgressive loss of stoichiometry affects multiple protein complexesRibosomes aggregate in old brainsPartial reduction of proteasome activity is sufficient to induce loss of stoichiometryReduced proteasome levels are a major risk factor for early death in killifish
Bayesian population receptive field modeling in human somatosensory cortex
Authors: Puckett Alexander M. , Bollmann Saskia , Junday Keerat , Barth Markus , Cunnington Ross 
Published: March 2019

AbstractSomatosensation is fundamental to our ability to sense our body and interact with the world. Our body is continuously sampling the environment using a variety of receptors tuned to different features, and this information is routed up to primary somatosensory cortex. Strikingly, the spatial organization of the peripheral receptors in the body are well maintained, with the resulting representation of the body in the brain being referred to as the somatosensory homunculus. Recent years have seen considerable advancements in the field of high-resolution fMRI, which have enabled an increasingly detailed examination of the organization and properties of this homunculus. Here we combined advanced imaging techniques at ultra-high field (7T) with a recently developed Bayesian population receptive field (pRF) modeling framework to examine pRF properties in primary somatosensory cortex. In each subject, vibrotactile stimulation of the fingertips (i.e., the peripheral mechanoreceptors) modulated the fMRI response along the post-central gyrus and these signals were used to estimate pRFs. We found the pRF center location estimates to be in accord with previous work as well as evidence of other properties in line with the underlying neurobiology. Specifically, as expected from the known properties of cortical magnification, we find a larger representation of the index finger compared to the other stimulated digits (middle, index, little). We also show evidence that the little finger is marked by the largest pRF sizes. The ability to estimate somatosensory pRFs in humans provides an unprecedented opportunity to examine the neural mechanisms underlying somatosensation and is critical for studying how the brain, body, and environment interact to inform perception and action.
Neurophysiological predictors of memory impairment in schizophrenia: decoupled slow-wave dynamics during non-REM sleep
Authors: Bartsch Ullrich , Simpkin Andrew , Demanuele Charmaine , Wamsley Erin , Marston Hugh , Jones Matthew W 
Published: March 2019

AbstractThe slow-waves (SW) of non-rapid eye movement sleep (NREM) reflect neocortical components of network activity during sleep-dependent information processing; their disruption may therefore contribute to impaired memory consolidation. Here, we quantify SW dynamics relative to motor sequence memory in patients suffering schizophrenia and healthy volunteers.Patients showed normal intrinsic SW properties but impaired SW coherence, which failed to exhibit the learning-dependent increases evident in healthy volunteers. SW-spindle phase amplitude coupling across distributed EEG electrodes was also dissociated from experience in patients, with long-range fronto-parietal and -occipital networks most severely affected. Partial least squares regression modelling confirmed distributed SW coherence and SW-spindle coordination as predictors of overnight memory consolidation in healthy controls, but not in patients.Quantifying the full repertoire of NREM EEG oscillations and their long-range covariance therefore presents learning-dependent changes in distributed SW and spindle coordination as fingerprints of impaired cognition in schizophrenia.
The homeostatic dynamics of feeding behaviour identify novel mechanisms of anorectic agents
Authors: McGrath Thomas M , Spreckley Eleanor , Rodriguez Aina Fernandez , Viscomi Carlo , Alamshah Amin , Akalestou Elina , Murphy Kevin G , Jones Nick S 
Published: March 2019

AbstractBetter understanding of feeding behaviour will be vital in reducing obesity and metabolic syndrome, but we lack a standard model that captures the complexity of feeding behaviour. We construct an accurate stochastic model of rodent feeding at the bout level in order to perform quantitative behavioural analysis. Analysing the different effects on feeding behaviour of PYY3-36, lithium chloride, GLP-1 and leptin shows the precise behavioural changes caused by each anorectic agent. Our analysis demonstrates that the changes in feeding behaviour evoked by the anorectic agents investigated not mimic satiety. In thead libitumfed state during the light period, meal initiation is governed by complete stomach emptying, whereas in all other conditions there is a graduated response. We show how robust homeostatic control of feeding thwarts attempts to reduce food intake, and how this might be overcome.In silicoexperiments suggest that introducing a minimum intermeal interval or modulating gastric emptying can be as effective as anorectic drug administration.
Circuits that encode and predict alcohol associated preference
Authors: Scaplen Kristin M. , Talay Mustafa , Salamon Sarah , Nuñez Kavin M. , Waterman Amanda G. , Gang Sydney , Song Sophia L. , Barnea Gilad , Kaun Karla R. 
Published: March 2019

AbstractSubstance use disorders are chronic relapsing disorders often impelled by enduring memories and persistent cravings. Alcohol, as well as other addictive substances, remolds neural circuits important for memory to establish obstinate preference despite aversive consequences. How pertinent circuits are selected and shaped to result in these unchanging, inflexible memories is unclear. Using neurogenetic tools available inDrosophila melanogasterwe define how circuits required for alcohol associated preference shift from population level dopaminergic activation to select dopamine neurons that predict behavioral choice. During memory expression, these dopamine neurons directly, and indirectly via the mushroom body (MB), modulate the activity of interconnected glutamatergic and cholinergic output neurons. Transsynaptic tracing of these output neurons revealed at least two regions of convergence: 1) a center of memory consolidation within the MB implicated in arousal, and 2) a structure outside the MB implicated in integration of naïve and learned responses. These findings provide a circuit framework through which dopamine neuron activation shifts from reward delivery to cue onset, and provides insight into the inflexible, maladaptive nature of alcohol associated memories.
5-methoxy-N,N-dimethyltryptamine: An ego-dissolving endogenous neurochemical catalyst of creativity
Authors: Germann Christopher B. 
Published: March 2019

Abstract5-Methoxy-N,N-dimethyltryptamine (acronymized as 5-MeO-DMT) is sui generis among the numerous naturally-occurring psychoactive substances due to its unparalleled ego-dissolving effects which can culminate in a state of nondual consciousness (which is phenomenologically similar to transformative peak experiences described in various ancient contemplative traditions, e.g., Advaita Vedānta, Mahāyāna Buddhism). The enigmatic molecule is endogenous to the human brain and has profound psychological effects which are hitherto only very poorly understood due to the absence of scientifically controlled human experimental trials. Its exact neuronal receptor binding profile is a matter of ongoing scientific research, however, its remarkable psychoactivity is presumably mediated via agonism of the 5-HT2A (serotonin) receptor subtype. Anthropological/ethnopharmacological evidence indicates that various cultures utilized 5-MeO-DMT containing plants for medicinal, psychological, and spiritual purposes for millennia. In this paper we argue that this naturally occurring serotonergic compound could be fruitfully utilized as a neurochemical research tool which has the potential to significantly advance our understanding of the cognitive and neuronal processes which underpin cognition and creativity (downregulation of the default-mode network, increased neuronal functional connectivity, etc.). An eclectic interdisciplinary perspective is adopted, and we present converging evidence from a plurality of sources in support of this conjecture. Specifically, we suggest that 5-MeO-DMT has great potential in this respect due to its incommensurable capacity to completely disintegrate self-referential cognitive/neuronal processes (viz., “ego death”). The importance of unbiased systematic scientific research on naturally occurring endogenous psychoactive compounds is discussed from a Jamesian radical empiricism perspective and potential scenarios of abuse are discussed (particularly in the context of military torture).
Adaptation to seasonal reproduction and temperature-associated factors drive temporal and spatial differentiation in northwest Atlantic herring despite gene flow
Authors: Fuentes-Pardo Angela P. , Stanley Ryan , Bourne Christina , Singh Rabindra , Emond Kim , Pinkham Lisa , McDermid Jenni L. , Andersson Leif , Ruzzante Daniel E. 
Published: March 2019

AbstractNatural selection can maintain local adaptation despite the presence of gene flow. However, the genomic basis and environmental factors involved in adaptation at fine-spatial scales are not well understood. Here, we address these questions using Atlantic herring (Clupea harengus), an abundant, migratory, and widely distributed marine species with substantial genomic resources including a chromosome-level genome assembly and genomic data from the eastern Atlantic and Baltic populations. We analysed whole-genome sequence and oceanographic data to examine the genetic variation of 15 spawning aggregations across the northwest Atlantic Ocean (∼1,600 km of coastline) and the association of this variation with environmental variables. We found that population structure lies in a small fraction of the genome involving adaptive genetic variants of functional importance. We discovered 10 highly differentiated genomic regions distributed across four chromosomes. Two of these loci appear to be private to the northwest, four loci share a large number of adaptive variants between northwest and northeast Atlantic, and four shared loci exhibit an outstanding diversity in haplotype composition, including a novel putative inversion on chromosome 8. Another inversion on chromosome 12 underlies a latitudinal genetic pattern discriminating populations north and south of a biogeographic transition zone on the Scotian Shelf. Our genome-environment association analysis indicates that sea water temperature during winter is the environmental factor that best correlates with the latitudinal pattern of this inversion. We conclude that the timing and geographic location of spawning and early development are under diverse selective pressures related to environmental gradients. Natural selection appears to act on early-life performance traits with differential fitness across environments. Our study highlights the role of genomic architecture, ancestral haplotypes, and selection in maintaining adaptive divergence in species with large population sizes and presumably high gene flow.
pysradb: A Python package to query next-generation sequencing metadata and data from NCBI Sequence Read Archive
Authors: Choudhary Saket 
Published: March 2019

AbstractNCBIs Sequence Read Archive (SRA) is the primary archive of next-generation sequencing datasets. SRA makes metadata and raw sequencing data available to the research community to encourage reproducibility, and to provide avenues for testing novel hypotheses on publicly available data. However, existing methods to programmatically access these data are limited. We introduce a Python packagepysradbthat provides a collection of command line methods to query and download metadata and data from SRA utilizing the curated metadata database available through the SRAdb project. We demonstrate the utility ofpysradbon multiple use cases for searching and downloading SRA datasets. It is available freely athttps://github.com/saketkc/pysradb.
Exploring the limitations of event-related potential measures in moving subjects. Case studies of four different technical modifications in ergometer rowing
Authors: Hill Holger 
Published: March 2019

AbstractMeasuring brain activity outside the laboratory is of great importance for investigating human behavior under naturalistic conditions (e.g., in cognition and movement research, application of brain-computer interfaces). To measure neuronal activity in moving subjects, only modified NIRS and EEG systems are applicable. Because conventional EEG systems are too sensitive to movement artifacts, artifact sources should be eliminated beforehand to improve signal quality. Four different approaches for EEG/ERP measures with moving subjects were tested in case studies: (i) a purpose-built head-mounted preamplifier, (ii) a laboratory system with active electrodes, (iii)+(iv) a wireless headset combined with (iii) passive or (iv) active electrodes. A standard visual oddball task was applied during rest (without movement) and during ergometer rowing. All 14 measures revealed very similiar (within subjects) visual evoked potentials for rowing and rest. The small intraindividual differences between rowing and rest, in comparison to the typically larger interindividual differences in the ERP waveforms revealed that ERPs can be measured reliably even in an athletic movement like rowing. The expected modulation of the motor-related activity by force output, on the other hand, was largely affected by movement artifacts. Therefore, for a successful application of ERP measures in movement research, further developments to differentiate between movement-related neuronal activity and movement-related artifacts are required. However, it cannot be excluded that activities with small magnitudes related to motor learning and motor control cannot be detected because they are superimposed by the very large motor potential which increases with force output.
Constrained sampling from deep generative image models reveals mechanisms of human target detection
Authors: Fruend Ingo 
Published: March 2019

The first steps of visual processing are often described as a bank of oriented filters followed by divisive normalization. This approach has been tremendously successful at predicting contrast thresholds in simple visual displays. However, it is unclear to what extent this kind of architecture also supports processing in more complex visual tasks performed in naturally looking images.We used a deep generative image model to embed arc segments with different curvatures in naturalistic images. These images contain the target as part of the image scene, resulting in considerable appearance variation of target as well as background. Three observers localized arc targets in these images, achieving an accuracy of 74.7% correct responses on average. Data were fit by several biologically inspired models, 4 standard deep convolutional neural networks (CNN) from the computer vision literature, and by a 5-layer CNN specifically trained for this task. Four models were particularly good at predicting observer responses, (i) a bank of oriented filters, similar to complex cells in primate area V1, (ii) a bank of oriented filters followed by tuned gain control, incorporating knowledge about cortical surround interactions, (iii) a bank of oriented filters followed by local normalization, (iv) the 5-layer specifically trained CNN. A control experiment with optimized stimuli based on these four models showed that the observers’ data were best explained by model (ii) with tuned gain control.These data suggest that standard models of early vision provide good descriptions of performance in much more complex tasks than what they were designed for, while general purpose non-linear models such as convolutional neural networks do not.
Flexible representation of higher-dimensional cognitive variables with grid cells
Authors: Klukas Mirko , Lewis Marcus , Fiete Ila 
Published: March 2019

AbstractWe shed light on the theoretical capabilities of entorhinal grid cells to encode variables of dimension greater than two. Our model constructs representations of high-dimensional inputs through a combination of low-dimensional random projections and “classical” low-dimensional hexagonal grid cell responses. Without reconfiguration of the recurrent circuit, the same system can flexibly encode multiple variables of different dimensions while maximizing the coding range (per dimension) by automatically trading-off dimension with an exponentially large coding range. In contrast to previously proposed schemes, the model does not require the formation of higher-dimensional grid responses, a cell-inefficient and rigid mechanism. The firing fields observed in flying bats or climbing rats can be generated by neurons that combine activity from multiple grid modules, each representing higher-dimensional spaces according to our model. The idea expands our understanding of grid cells, suggesting that they could implement a general circuit that generates on-demand coding and memory states for variables in high-dimensional vector spaces.
Pervasive convergent evolution and extreme phenotypes define chaperone requirements of protein homeostasis
Authors: Draceni Yasmine , Pechmann Sebastian 
Published: March 2019

AbstractMaintaining protein homeostasis is an essential requirement for cell and organismal viability. An elaborate regulatory system within cells, the protein homeostasis network, safeguards that proteins are correctly folded and functional. At the heart of this regulatory system lies a class of specialized protein quality control enzymes called chaperones that are tasked with assisting proteins in their folding, avoiding aggregation, and degradation. Failure and decline of protein homeostasis are directly associated with conditions of aging and aging-related neurodegenerative diseases such as Alzheimer’s and Parkinson’s. However, it is not clear what tips the balance of protein homeostasis and leads to onset of aging and diseases. Here, we present a comparative genomics analysis of protein homeostasis in eukaryotes and report general principles of maintaining protein homeostasis across the eukaryotic tree of life. Expanding a previous analysis of 16 eukaryotes to 216 eukaryotic genomes, we find a strong correlation between the size of eukaryotic chaperone networks and size of the genomes that is distinct for different species kingdoms. Importantly, organisms with pronounced phenotypes clearly buck this trend.Northobranchius furzeri, the shortest-lived vertebrate and widely used model for fragile protein homeostasis is found to be chaperone limited.Heterocephalus glaberas the longest-lived rodent thus especially robust organism is characterized by above average numbers of chaperones. Our work thus indicates that the balance in protein homeostasis may be a key variable in explaining organismal robustness. Finally, our work provides an elegant example of harnessing the power of evolution and comparative genomics to address fundamental open questions in biology with direct relevance to human diseases.
Genomic signatures of extreme body size divergence in baboons
Authors: Chiou Kenneth L. , Bergey Christina M. , Burrell Andrew S. , Disotell Todd R. , Rogers Jeffrey , Jolly Clifford J. , Phillips-Conroy Jane E. 
Published: March 2019

AbstractKinda and gray-footed chacma baboons occupy opposite extremes of the body size distribution in extant baboons (genusPapio). In order to detect signatures of natural selection in these two species, we genotyped 24,790 genome-wide autosomal SNPs from populations of Zambian baboons using double digest RADseq. We scanned the genome for evidence of selection by identifying regions with extreme differentiation between populations. We find evidence of selection on body size influencing multiple genes in one or both species, includingFGF1, ATXN2, andPRKCE. We also find an enriched signal of selection associated with biological processes involved in multicellular organism growth and development, cell proliferation and cell growth, nutrient metabolism, and chondrocyte differentiation. Finally, we find that selection has impacted components of the CCKR signaling pathway, which regulates food intake and metabolism, and the JAK/STAT signaling pathway, which mediates the effect of cytokine signals on processes including epiphyseal chondrocyte proliferation essential for longitudinal bone growth. Our findings highlight promising avenues for future studies disentangling the genetic architecture of body size in primates including humans.