bluuebunny/crossref_metadata_2025_split huggingface dataset:
Immunoinformatics Prediction of Epitope Based Peptide Vaccine Against Schistosoma Mansoni Fructose Bisphosphate Aldolase Protein
Authors: Elhag Mustafa , Alaagib Ruaa Mohamed , Haroun Esraa Musa , Ahmed Nagla Mohamed , Albagi Sahar Obi Abd , Hassan Mohammed A. 
Published: September 2019

AbstractSchistosoma Mansonirepresents an important tropical disease that can cause schistosomiasis mostly in Africa and Middle East with high mortality rates. Moreover, no vaccine against it exists. This study predicts an effective epitope-based vaccine against Fructose 1,6 Bisphosphate Aldolase (FBA) enzyme of Schistosoma Mansoni using immunoinformatics approaches. FBA is important for production of energy required for different schistosome activities and survival. The sequences were retrieved from NCBI and several prediction tests were conducted to analyze possible epitopes for B-cell, T-cell MHC class I and II. Tertiary structure of the most promising epitopes was obtained. Two epitopes showed high binding affinity for B-cells, while four epitopes showed high binding affinity for MHCI and MHCII. The results were promising to formulate a vaccine with more than 99.5% population coverage. We hope that these promising epitopes serves as a preventive measure for the disease in the future and recommend invivo and invitro studies.
Preference and familiarity mediate spatial responses of a large herbivore to experimental manipulation of resource availability
Authors: Ranc Nathan , Moorcroft Paul R. , Hansen K. Whitney , Ossi Federico , Sforna Tobia , Ferraro Enrico , Brugnoli Alessandro , Cagnacci Francesca 
Published: September 2019

AbstractThe link between spatio-temporal resource patterns and movement behaviour is a key ecological process, however, limited experimental support has been produced at the home range scale. In this study, we analysed the spatial responses of a resident large herbivore (roe deerCapreolus capreolus) during anin situmanipulation of a concentrated food resource. Specifically, we experimentally altered feeding site accessibility to roe deer and recorded (for 25 animal-years) individual responses by GPS tracking. We found that, following the loss of their preferred resource, roe deer actively tracked resource dynamics leading to more exploratory movements, and larger, spatially-shifted home ranges. Then, we showed, for the first time experimentally, the importance of site fidelity in the maintenance of large mammal home ranges by demonstrating the return of individuals to their familiar, preferred resource despite the presence of alternate, equally-valuable food resources. This behaviour was modulated at the individual level, where roe deer characterised by a high preference for feeding sites exhibited more pronounced behavioural adjustments during the manipulation. Together, our results establish the connections between movement, space-use, individual preference, and the spatio-temporal pattern of resources in home ranging behaviour.
Predictive masking is associated with a system-wide reconfiguration of neural populations in the human visual cortex
Authors: Carvalho Joana , Renken Remco J. , Cornelissen Frans W. 
Published: September 2019

AbstractThe human visual system masks the perceptual consequences of retinal or cortical lesion-induced scotomas by predicting what is missing from nearby regions of the visual field. To reveal the neural mechanisms underlying this remarkable capacity, known as predictive masking, we used fMRI and neural modeling to track changes in cortical population receptive fields (pRFs) and connectivity in response to the introduction of an artificial scotoma (AS). Consistent with predictive masking, we found that extrastriate areas increased their sampling of the V1 region outside the AS projection zone. Moreover, throughout the visual field and hierarchy, pRFs shifted their preferred position towards the AS border. A gain field model, centered at this border, accounted for these shifts, especially for extrastriate areas. This suggests that a system-wide reconfiguration of neural populations in response to a change in visual input is guided by extrastriate signals and underlies the predictive masking of scotomas.
ColocAI: artificial intelligence approach to quantify co-localization between mass spectrometry images
Authors: Ovchinnikova Katja , Rakhlin Alexander , Stuart Lachlan , Nikolenko Sergey , Alexandrov Theodore 
Published: September 2019

AbstractMotivationImaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referred to as co-localization. However, no comprehensive evaluation of co-localization measures has ever been performed; this leads to arbitrary choices and hinders method development.ResultsWe present ColocAI, an artificial intelligence approach addressing this gap. With the help of 42 imaging MS experts from 9 labs, we created a gold standard of 2210 pairs of ion images ranked by their co-localization. We evaluated existing co-localization measures and developed novel measures using tf-idf and deep neural networks. The semi-supervised deep learning Pi model and the cosine score applied after median thresholding performed the best (Spearman 0.797 and 0.794 with expert rankings respectively). We illustrate these measures by inferring co-localization properties of 10273 molecules from 3685 public METASPACE datasets.Availability and Implementationhttps://github.com/metaspace2020/colocContacttheodore.alexandrov@embl.de
Integrated Proteogenomic Deep Sequencing and Analytics Accurately Identify Non-Canonical Peptides in Tumor Immunopeptidomes
Authors: Chong Chloe , Müller Markus , Pak HuiSong , Harnett Dermot , Huber Florian , Grun Delphine , Leleu Marion , Auger Aymeric , Arnaud Marion , Stevenson Brian J. , Michaux Justine , Bilic Ilija , Hirsekorn Antje , Calviello Lorenzo , Simó-Riudalbas Laia , Planet Evarist , Lubiński Jan , Bryśkiewicz Marta , Wiznerowicz Maciej , Xenarios Ioannis , Zhang Lin , Trono Didier , Harari Alexandre , Ohler Uwe , Coukos George , Bassani-Sternberg Michal 
Published: September 2019

AbstractEfforts to precisely identify tumor human leukocyte antigen (HLA) bound peptides capable of mediating T cell-based tumor rejection still face important challenges. Recent studies suggest that non-canonical tumor-specific HLA peptides that derive from annotated non-coding regions could elicit anti-tumor immune responses. However, sensitive and accurate mass-spectrometry (MS)-based proteogenomics approaches are required to robustly identify these non-canonical peptides. We present an MS-based analytical approach that characterizes the non-canonical tumor HLA peptide repertoire, by incorporating whole exome sequencing, bulk and single cell transcriptomics, ribosome profiling, and a combination of two MS/MS search tools. This approach results in the accurate identification of hundreds of shared and tumor-specific non-canonical HLA peptides and of an immunogenic peptide from a downstream reading frame in the melanoma stem cell marker gene ABCB5. It holds great promise for the discovery of novel cancer antigens for cancer immunotherapy.
The sources and transmission routes of microbial populations throughout a meat processing facility
Authors: Zwirzitz Benjamin , Wetzels Stefanie U. , Dixon Emmanuel D. , Stessl Beatrix , Zaiser Andreas , Rabanser Isabel , Thalguter Sarah , Pinior Beate , Roch Franz-Ferdinand , Strachan Cameron , Zhangellini Jürgen , Dzieciol Monika , Wagner Martin , Mann Evelyne 
Published: September 2019

AbstractMicrobial food spoilage is responsible for a considerable amount of waste and can cause food-borne diseases in humans, particularly in immunocompromised individuals and children. Therefore, preventing microbial food spoilage is a major concern for health authorities, regulators, consumers, and the food industry. However, the contamination of food products is difficult to control because there are several potential sources during production, processing, storage, distribution, and consumption, where microorganisms come in contact with the product. Here, we conduct the first study that uses high-throughput full-length 16S rRNA gene sequencing to provide novel insights into bacterial community structure throughout a pork processing plant. Specifically, we investigated what proportion of bacteria on meat are not animal-associated and are therefore transferred during cutting via personnel, equipment, machines, or the slaughter environment. We then created a facility-specific transmission map of bacterial flow which revealed previously unknown sources of bacterial contamination. This allowed us to pinpoint specific taxa to particular environmental sources and provide the facility with essential information for targeted disinfection. For example,Moraxellaspp., a prominent meat spoilage organism which was one of the most abundant amplicon sequence variants (ASVs) detected on the meat, was most likely transferred from the gloves of employees, a railing at the classification step, and the polishing tunnel whips. Finally, we provide evidence that 1000 sequences per sample provides a reasonable sequencing depth for microbial source tracking in food processing, suggesting that this approach could be implemented in regular monitoring systems.
Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life
Authors: Zhao Zhengqiao , Cristian Alexandru , Rosen Gail 
Published: September 2019

AbstractIt is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI RefSeq bacterial genome database. When new reference sequences are added to training data, statically trained classifiers must be rerun on all data, resulting in a highly inefficient process. The rich literature of “incremental learning” addresses the need to update an existing classifier to accommodate new data without sacrificing much accuracy compared to retraining the classifier with all data. We demonstrate how classification improves over time by incrementally training a classifier on progressive RefSeq snapshots and testing it on: (a) all known current genomes (as a ground truth set) and (b) a real experimental metagenomic gut sample. We demonstrate that as a classifier model’s knowledge of genomes grows, classification accuracy increases. The proof-of-concept naïve Bayes implementation, when updated yearly, now runs in 1/4th of the non-incremental time with no accuracy loss. In conclusion, it is evident that classification improves by having the most current knowledge at its disposal. Therefore, it is of utmost importance to make classifiers computationally tractable to keep up with the data deluge.
Test-statistic correlation and data-row correlation
Authors: Zhuo Bin , Jiang Duo , Di Yanming 
Published: September 2019

AbstractWhen a statistical test is repeatedly applied to rows of a data matrix—such as in differential-expression analysis of gene expression data, correlations among data rows will give rise to correlations among corresponding test statistic values. Correlations among test statistic values create many inferential challenges in false-discovery-rate control procedures, gene-set enrichment analysis, or other procedures aiming to summarize the collection of test results. To tackle these challenges, researchers sometimes will—explicitly or implicitly—use the correlations (e.g., as measured by the Pearson correlation coefficients) among the data rows to approximate the correlations among the corresponding test statistic values. We show that, however, such approximations are only valid under limited settings. We investigate the relationship between the correlation coefficient between a pair of test statistics (test-statistic correlation) and the correlation coefficient between the two corresponding data rows (data-row correlation). We derive an analytical formula for the test-statistic correlation as a function of the data-row correlation for a general class of test statistics: in particular, two-sample t-test is a special case. The analytical formula implies that the test-statistic correlation is generally weaker than the corresponding data-row correlation, and in general, the latter will not well approximate the former when the involved null hypotheses are false. We verify our analytical results through simulations.
Motion Displaces Population Receptive Fields in the Direction Opposite to Motion
Authors: Schneider Marian , Marquardt Ingo , Sengupta Shubharthi , De Martino Federico , Goebel Rainer 
Published: September 2019

ABSTRACTMotion signals can bias the perceived position of visual stimuli. While the apparent position of a stimulus is biased in the direction of motion, electro-physiological studies have shown that the receptive field (RF) of neurons is shifted in the direction opposite to motion, at least in cats and macaque monkeys. In humans, it remains unclear how motion signals affect population RF (pRF) estimates. We addressed this question using psychophysical measurements and functional magnetic resonance imaging (fMRI) at 7 Tesla. We systematically varied two factors: the motion direction of the carrier pattern (inward, outward and flicker motion) and the contrast of the mapping stimulus (low and high stimulus contrast). We observed that while physical positions were identical across all conditions, presence of low-contrast motion, but not high-contrast motion, shifted perceived stimulus position in the direction of motion. Correspondingly, we found that pRF estimates in early visual cortex were shifted against the direction of motion for low-contrast stimuli but not for high stimulus contrast. We offer an explanation in form of a model for why apertures are perceptually shifted in the direction of motion even though pRFs shift in the opposite direction.
Stability of excitatory structural connectivity predicts the probability of CA1 pyramidal neurons to become engram neurons
Authors: Castello-Waldow Tim P. , Weston Ghabiba , Chenani Alireza , Loewenstein Yonatan , Chen Alon , Attardo Alessio 
Published: September 2019

SUMMARYNeurons undergoing activity-dependent plasticity represent experience and are functional for learning and recall thus they are considered cellular engrams of memory. Although increase in excitability and stability of structural synaptic connectivity have been implicated in the formation and persistance of engrams, the mechanisms bringing engrams into existence are still largely unknown. To investigate this issue, we tracked the dynamics of structural excitatory synaptic connectivity of hippocampal CA1 pyramidal neurons over two weeks using deep-brain two-photon imaging in live mice. We found that neurons that will prospectively become part of an engram display higher stability of connectivity than neurons that will not. A novel experience significantly stabilizes the connectivity of non-engram neurons. Finally, the density and survival of dendritic spines negatively correlates to freezing to the context but not to the tone in a trace fear conditioning learning paradigm.
A two-stage approach to identifying and validating modifiable factors for the prevention of depression
Authors: Choi Karmel W. , Stein Murray B. , Nishimi Kristen , Ge Tian , Coleman Jonathan R.I. , Chen Chia-Yen , Ratanatharathorn Andrew , Zheutlin Amanda B. , Dunn Erin C. ,   23andMe Research Team,   Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Breen Gerome , Koenen Karestan C. , Smoller Jordan W. 
Published: September 2019

AbstractBackgroundAlthough depression is recognized as the leading cause of disability worldwide, decades of research have identified few actionable preventive factors. Using phenotypic and genomic data from the UK Biobank, we took advantage of a unique opportunity to screen a wide range of potentially modifiable factors that could offset known risk factors for depression.MethodsWe curated baseline data on more than 100 lifestyle and environmental factors in participants’ lives, including behavioral (e.g., exercise, sleep, media use, diet), social (e.g., support, activities), and environmental (e.g., greenspace, pollution) variables. In a follow-up survey, participants reported on their traumatic life experiences and mental health, including depression. Polygenic risk scores for depression were generated based on large-scale genome-wide association results. Excluding those meeting criteria for depression at baseline, we identified at-risk individuals at high predicted probability (> 90th percentile) for clinically significant depression at follow-up based on their (i) polygenic risk, or (ii) reported traumatic life events. Using a factors-wide design corrected for multiple testing and adjusted for potential confounders, we identified modifiable factors associated with follow-up depression in the full sample and among at-risk individuals. Using a two-sample Mendelian randomization (MR) design, we then examined which significant factors showed potential causal influences on depression risk, or vice versa.ResultsA range of baseline modifiable factors were prospectively associated with follow-up depression, including factors related to social engagement, physical activity, media use, and diet. MR follow-up analyses provided further support for the effects of social support-seeking, TV use, and other factors on depression risk.ConclusionAs the field increasingly quantifies the role of genetic factors in complex conditions such as depression, knowledge of modifiable factors that could offset one’s genetic risk has become highly relevant. Here, we present an approach to screening for potentially modifiable factors that may offset the risk of depression in general and among at-risk individuals. In light of the burden of disease associated with depression and the urgent need for actionable preventive strategies, this approach could help prioritize candidates for follow-up studies including clinical trials for depression prevention.
N-acetyl-L-leucine treatment attenuates neuronal cell death and suppresses neuroinflammation after traumatic brain injury in mice
Authors: Sarkar Chinmoy , Hegdekar Nivedita , Lipinski Marta M. 
Published: September 2019

AbstractTraumatic brain injury (TBI) is a major cause of mortality and long-term disability around the world. Even mild to moderate TBI can lead to lifelong neurological impairment due to acute and progressive neurodegeneration and neuroinflammation induced by the injury. Thus, the discovery of novel treatments which can be used as early therapeutic interventions following TBI is essential to restrict neuronal cell death and neuroinflammation. In this study, we demonstrate that orally administered treatment with N-acetyl-L-leucine (NALL) led to attenuation of cell death and reduced the expression of neuroinflammatory markers after controlled cortical impact (CCI) induced experimental TBI in mice. Our data indicate that partial restoration of autophagy flux mediated by NALL may account for the positive effect of treatment in the injured mouse brain. Taken together, our study indicates treatment with NALL would be expected to be beneficial in restricting neuronal death and hence improving neurological function after injury, and is a promising novel, neuroprotective drug candidate for the treatment of TBI.
Genomewide association study reveals transient loci underlying the genetic architecture of biomass accumulation under cold stress in Sorghum
Authors: Shakoor Nadia , Agnew Erica , Ziegler Greg , Lee Scott , Lizárraga César , Fahlgren Noah , Baxter Ivan , Mockler Todd C. 
Published: September 2019

ABSTRACTSorghum bicolor is a promising cellulosic feedstock crop for bioenergy because of its potential for high biomass yields. However, in its early growth phases, sorghum is sensitive to cold stress, preventing early planting in temperate environments. Cold temperature adaptability is vital for the successful cultivation of both bioenergy and grain sorghum at higher latitudes and elevations, and for early season planting or to extend the growing season. Identification of genes and alleles that enhance biomass accumulation of sorghum grown under early cold stress would enable the development of improved bioenergy sorghum through breeding or genetic engineering. We conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment. The BAP is a collection of densely genotyped and racially, geographically, and phenotypically diverse accessions. The plants were weighed, watered, and imaged daily to measure growth dynamics and water use efficiency (WUE). Daily, non-destructive imaging allowed for a temporal analysis of growth-related traits in response to cold stress. We performed a genome-wide association study (GWAS) to identify candidate genomic intervals and genes controlling response to early cold stress. GWAS identified transient quantitative trait loci (QTL) strongly associated with each growth-related trait, permitting an investigation into the genetic basis of cold stress response at different stages of development. The analysis identified a priori and novel candidate genes associated with growth-related traits and the temporal response to cold stress.SIGNIFICANCE STATEMENTGenome-wide association study of bioenergy sorghum accessions phenotyped under early season cold stress revealed transient QTLs for highly heritable biomass and growth-related traits that appeared as the temperature increased and plants developed. Sorghum accessions clustered into multiple groups for each heritable trait with distinct growth profiles. GWAS identified candidate genes associated with growth traits and cold stress responses. The top-performing accessions with the highest growth-related trait values over time and temperature shifts will be useful for further genetic analysis and breeding or engineering efforts directed at biomass yield enhancements.
“Bolder” together – response to human social cues in free-ranging dogs
Authors: Bhattacharjee Debottam , Sau Shubhra , Bhadra Anindita 
Published: September 2019

AbstractInterspecific interactions within an ecosystem have different direct and indirect effects on the two interacting species. In the urban environment, humans are a part of an interaction network of several species. While indirect human influence on different urban species has been measured extensively, experimental studies concerning direct human influence are lacking. In this study, we tested interactions between groups of urban free-ranging dogs (Canis lupus familiaris) and solitary unfamiliar humans in ecologically relevant contexts. We provided different sets of dogs with four commonly used human social cues (neutral, friendly, low and high impact threatening) to understand their responses at the group-level and identify potential inter-individual differences. Finally, we compared data from a previous study to investigate the differences in behavioural outcomes between solitary and groups of dogs while interacting with humans. The study not only strengthens the idea of situation-relevant responsiveness in free-ranging dogs but also highlights the minute differences between solitary and group-level reactions in the form of higher approach and less anxious behaviour of groups towards the unfamiliar human. Additionally, we report inter-individual differences and the effect of sex while responding to the threatening cues. Our study suggests a direct benefit of group-living over a solitary lifestyle in free-ranging dogs while interacting with humans in the streets.Summary statementFree-ranging dogs can benefit by living in groups over a solitary lifestyle while interacting with unfamiliar humans in urban habitats irrespective of having significant inter-individual differences.