bluuebunny/crossref_metadata_2025_split huggingface dataset:
ntHits:de novorepeat identification of genomics data using a streaming approach
Authors: Mohamadi Hamid , Chu Justin , Coombe Lauren , Warren Rene , Birol Inanc 
Published: November 2020

AbstractMotivationRepeat elements such as satellites, transposons, high number of gene copies, and segmental duplications are abundant in eukaryotic genomes. They often induce many local alignments, complicating sequence assembly and comparisons between genomes and analysis of large-scale duplications and rearrangements. Hence, identification and classification of repeats is a fundamental step in many genomics applications and their downstream analysis tools.ResultsIn this work, we present an efficient streaming algorithm and software tool, ntHits, forde novorepeat identification based on the statistical analysis of thek-mer content profile of large-scale DNA sequencing data. In the proposed algorithm, we first obtain thek-mer coverage histograms of input datasets using the ntCard algorithm, an efficient streaming algorithm for estimating thek-mer coverage histograms. From the obtainedk-mer coverage histogram, the repetitivek-mers would present a long tail to the distribution ofk-mer coverage profile. Experimental results show that ntHits can efficiently and accurately identify the repeat content in large-scale DNA sequencing data. For example, ntHits accurately identifies the repeatk-mers in the white spruce sequencing data set with 96× sequencing coverage in about 12 hours and using less than 150GB of memory, while using the exact methods for reporting the repeatedk-mers takes several days and terabytes of memory and disk space.AvailabilityntHits is written in C++ and is released under the MIT License. It is freely available athttps://github.com/bcgsc/ntHits.Contacthmohamadi@bcgsc.ca
Estimating direct and indirect genetic effects on offspring phenotypes using genome-wide summary results data: a comparison of multivariate methods
Authors: Warrington Nicole M. , Hwang Liang-Dar , Nivard Michel , Evans David M. 
Published: November 2020

AbstractEstimation of the direct genetic effect of an individuals’ own genotype on their phenotype, independent of any contaminating indirect parental genetic effects is becoming increasingly important. These conditional estimates are of interest in their own right, but are also useful for downstream analyses such as intergenerational Mendelian randomization. We compare several available multivariate methods that utilize summary results statistics from genome-wide association studies to determine how well they estimate conditional direct and indirect genetic effects, while accounting for sample overlap. Using robustly associated birth weight variants and data from the UK Biobank, we contrasted the point estimates and their standard errors at each of the individual loci compared to those obtained using individual level genotype data, in addition to comparing the computational time, inflation of the test statistics and number of genome-wide significant SNPs identified by each of the methods. We show that Genomic SEM outperforms the other methods in accurately estimating conditional genetic effects and their standard errors. We subsequently applied Genomic SEM to fertility data in the UK Biobank and partitioned the genetic effect into female and male fertility in addition to a sibling specific effect. This analysis identified one novel locus for fertility and replicated seven previously identified loci. We also identified genetic correlations between fertility and educational attainment, risk taking behaviour, autism and subjective well-being. We therefore recommend Genomic SEM to be used to partition genetic effects across the genome into direct and indirect components.
Demarcation line determination for diagnosis of gastric cancer disease range using unsupervised machine learning in magnifying narrow-band imaging
Authors: Okumura Shunsuke , Goudo Misa , Hiwa Satoru , Yasuda Takeshi , Kitae Hiroaki , Yasuda Yuriko , Tomie Akira , Omatsu Tatsushi , Ichikawa Hiroshi , Yagi Nobuaki , Hiroyasu Tomoyuki 
Published: November 2020

ABSTRACTObjectivesIt is important to determine an accurate demarcation line (DL) between the cancerous lesions and background mucosa in magnifying narrow-band imaging (M-NBI)-based diagnosis. However, it is difficult for novice endoscopists. Our aim was to automatically determine the accurate DL using a machine learning method.MethodsWe used an unsupervised machine learning approach to determine the DLs because it can reduce the burden of training machine learning models and labeling large datasets. Our method consists of the following four steps: 1) An M-NBI image is segmented into superpixels (a group of neighboring pixels) using simple linear iterative clustering. 2) The image features are extracted for each superpixel. 3) The superpixels are grouped into several clusters using the k-means method. 4) The boundaries of the clusters are extracted as DL candidates. To validate the proposed method, 23 M-NBI images of 11 cases were used for performance evaluation. The evaluation investigated the similarity of the DLs identified by endoscopists and our method, and the Euclidean distance between the two DLs was calculated. For the single case of 11 cases, the histopathological examination was also conducted and was used to evaluate the proposed system.ResultsThe average Euclidean distances for the 11 cases were10.65, 11.97, 7.82, 8.46, 8.59, 9.72, 12.20, 9.06, 22.86, 8.45, and 25.36. The results indicated that the specific selection of the number of clusters enabled the proposed method to detect DLs that were similar to those of the endoscopists. The DLs identified by our method represented the complex shapes of the DLs, similarly to those identified by experienced doctors. Also, it was confirmed that the proposed system could generate the pathologically valid DLs by increasing the number of clusters.ConclusionsOur proposed system can support the training of inexperienced doctors, as well as enrich the knowledge of experienced doctors in endoscopy.
Retrospective survey from Vascular Access Team Lombardy Net in COVID-19 era
Authors: Antonio Gidaro , Davide Vailati , Marco Gemma , Francesca Lugli , Francesco Casella , Chiara Cogliati , Antonio Canelli , Cremonesi Nadia , Davide Monolo , Giuseppe Cordio , Chiara Frosi , Riccardo Destefanis , Anna Rossi , Chiara Alemanno Maria , Franco Valenza , Stefano Elli , Andrea Caldarini , Alberto Lucchini , Stefano Paglia , Monica Baroni , Davide Giustivi 
Published: November 2020

AbstractBackgroundVenous Access Devices (VADs) are the most used device in COVID-19 patients.ObjectiveIdentify VADs implanted, catheter related thrombosis (CRT), catheter-related bloodstream infection (CRBSI), and accidental remove of VADs in both COVID-19 positive and COVID-19 free patients. Successive analysis was conducted comparing COVID-19 positive patients with COVID-19 free with inverse probability propensity score weights using simple regression to account for these two confounders (peripheral tip as central/peripheral and hospitalization as no/yes).MethodsThis multicenter, retrospective cohort study was conducted using data from 7 hospitals in Lombardy during the pandemic period from February 21st to May 31st 2020.Results2206 VADs were evaluated, of which 1107 (50.2%) were inserted in COVID-19 patients. In COVID-19 cohort the first choice was Long Peripheral Cannula in 388 patients (35.1%) followed by Midline Catheter in 385 (34.8%). The number of “central tip” VADs inserted in COVID-free inpatients and COVID-19 positive were similar (307vs334). We recorded 42 (1.9%) CRT; 32 (79.2%) were observed in COVID-19 patients. 19 CRBSI were diagnosed; 15 (78.95%) were observed in COVID-19. Accidental removals were the more represented complication with 123 cases, 85 (69.1%) of them were in COVID-19. COVID-19 significantly predicted occurrence of CRT (OR = 2.00(1.85-5.03); P<0.001), CRSB (OR = 3.82(1.82-8.97); P<0.001), and Accidental Removal (OR = 2.39(1.80-3.20); P<0.001) in our propensity score weighted models.ConclusionsCRT, CRBSI, and accidental removal are significantly more frequent in COVID-19 patients. Accidental removals are the principal complication, for this reason use of subcutaneously anchored securement is recommended for shorter period than usual.
Connection “Stripes” in the Primate Insula
Authors: Krockenberger M. , Saleh T.O. , Logothetis N.K. , Evrard H.C. 
Published: November 2020

AbstractThe insula has been classically divided into vast granular, dysgranular and agranular sectors. Over the years, several distinct studies proposed subdivisions of these sectors, with however no consensus. We recently proposed a cyto- and myelo-architectonic partition in which each sector contained sharply delimited areas (Evrardet al.2014 J Comp Neurol 522: 64-97). Some of these areas were further divided into distinct subareas with obvious functional implications. Here, we examined the spatial relationship between architectonic boundaries and tract-tracing labeling in the insula in the macaque monkey. Injections of neuronal tracers in distinct areas of the prefrontal or anterior cingulate cortices produced heterogeneous and discontinuous patterns of anterograde and retrograde labeling in the insula. These patterns were made of sharply delimited patches forming anteroposterior stripes across consecutive coronal sections. While the overall pattern of labeling varied with the injection site, the patches systematically coincided with specific architectonic subareas, particularly in the dysgranular insula. This unequivocally validates our prior architectonic partition and strongly supports the idea of a refined modularBauplanof the primate insula. This modular organization may underlie a serial stream of integration of interoception with ‘self-agency’ and ‘social’ activities across distinct insulo-prefrontal processing units that need to be explored.
Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features
Authors: Juppet Quentin , De Martino Fabio , Weigert Martin , Burri Olivier , Unser Michaël , Brisken Cathrin , Sage Daniel 
Published: November 2020

AbstractPatient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cells contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link:https://github.com/Biomedical-Imaging-Group/SingleCellClassifier.Author summaryBreast cancer is the most commonly diagnosed tumor in women worldwide and its incidence in the population is increasing over time. Because our understanding of such disease has been hampered by the lack of adequate human preclinical model, efforts have been made in order to develop better approaches to model the human complexity. Recent advances in this regard were achieved with Patient-Derived Xenografts (PDXs), which entail the implantation of human-derived specimens to recipient immunosuppressed mice and are, thus far, the preclinical system best recapitulating the heterogeneity of both normal and malignant human tissues. However, histological analyses of the resulting tissues are usually confounded by the presence of cells of different species. To circumvent this hurdle and to facilitate the discrimination of human and murine cells in xenografted samples, we developed Single Cell Classifier (SCC), a deep learning-based open-source software, available as a plugin in ImageJ/Fiji, performing automated species classification of individual cells in H&E stained sections. We show that SCC can reach up to 96% classification accuracy to classify cells of different species mainly leveraging on their contextual features in both normal and tumor PDXs. SCC will improve and automate histological analyses of human-in-mouse xenografts and is open to new in-house built models for further classification tasks and applications in image analysis.
Proximate and Genetic Analysis of Blackfin Tuna (T. atlanticus)
Authors: Núñez-Mata Yuridia M. , Ríos Rodríguez Jesse R. , Perales-Torres Adriana L. , De la Rosa-Reyna Xochitl F. , Vázquez-Rodríguez Jesús A. , Fernández-Santos Nadia A. , Montoya Humberto Martínez 
Published: November 2020

ABSTRACTThe tuna meat is a nutritious food that possesses high content of protein, its low content of saturated fatty acids makes it a high demand food in the world. TheThunnusgenus is composed of eight species, albacore (T. alalunga), bigeye (T. obesus), long tail tuna (T. tonggol), yellowfin tuna (T. albacares), pacific bluefin tuna (T. orientalis), bluefin tuna (T. maccoyii), Atlantic bluefin tuna (T. thynnus) and blackfin tuna (T. atlanticus). The blackfin tuna (BFT) (Thunnus atlanticus) represent the smallest species within theThunnusgenus. This species inhabits the warm waters of the West Atlantic Ocean, from the shore of Massachusetts in the north, to Rio De Janeiro in Brazil. The first objective of this study was to evaluate the nutritional composition of BFT captured in the Gulf of Mexico, we determined ash, moisture, fat, protein and carbohydrates in BFT muscle and compared the obtained data with the nutritional reports from commercial tuna species including yellowfin tuna, Atlantic bluefin tuna and salmon (Salmo salar).Secondly, we report the genetic diversity and genetic differentiation of BFT within its geographical distribution range using the Cytochrome Oxidase I (COI) and control region sequenced data and from specimens collected in the Gulf of Mexico. We observed a nucleotide diversity π=0.001, 24 segregating sites and 10 parsimony informative. Within the CR we found nine different haplotypes π=0.044, 39 segregating sites, 16 parsimony informative sites. We concluded that according with the haplotype distribution there are differences among the BFT from the Gulf of Mexico and the North Atlantic compared to the South Atlantic. The Caribbean Sea is a migration point of the BFT, where all except the South Atlantic haplotypes were found.
Transcriptome atlas ofPhalaenopsis equestris
Authors: Klepikova Anna V. , Kasianov Artem S. , Ezhova Margarita A. , Penin Aleksey A. , Logacheva Maria D. 
Published: November 2020

AbstractThe vast diversity of Orchidaceae together with sophisticated adaptations to pollinators and other unique features make this family an attractive model for evolutionary and functional studies. The sequenced genome ofPhalaenopsis equestrisfacilitates Orchidaceae research. Here we present an RNA-seq based transcriptome map ofP. equestriswhich covers 19 organs of the plant including leaves, roots, floral organs and shoot apical meristem. We demonstrated the high quality of the data and showed the similarity ofP. equestristranscriptome map with gene expression atlases of other plants. The transcriptome map can be easily accessed through our database Transcriptome Variation Analysis (TraVA) visualizing gene expression profiles. As an example of the application we analyzed the expression ofPhalaenopsis“orphan” genes – the ones that do not have recognizable similarity with genes of other plants. We found that about a half of them are not expressed; the ones that are expressed have a predominant expression pattern in reproductive structures.
No leading-edge effect in North Atlantic harbor porpoises: Evolutionary and conservation implications
Authors: Chehida Yacine Ben , Loughnane Roisin , Thumloup Julie , Kaschner Kristin , Garilao Cristina , Rosel Patricia E. , Fontaine Michael C. 
Published: November 2020

AbstractUnderstanding a species response to past environmental changes can help forecast how they will cope with ongoing climate changes. Harbor porpoises are widely distributed in the North Atlantic and were deeply impacted by the Pleistocene changes with the split of three sub-species. Despite major impacts of fisheries on natural populations, little is known about population connectivity and dispersal, how they reacted to the Pleistocene changes and how they will evolve in the future. Here, we used phylogenetics, population genetics, and predictive habitat modelling to investigate population structure and phylogeographic history of the North Atlantic porpoises. A total of 925 porpoises were characterized at 10 microsatellite loci and one-quarter of the mitogenome (mtDNA). A highly divergent mtDNA lineage was uncovered in one porpoise off Western Greenland, suggesting that a cryptic group may occur and could belong to a recently discovered mesopelagic ecotype off Greenland. Aside from it and the southern sub-species, spatial genetic variation showed that porpoises from both sides of the North Atlantic form a continuous system belonging to the same subspecies (Phocoena phocoena phoceona). Yet, we identified important departures from random mating and restricted intergenerational dispersal forming a highly significant isolation-by-distance (IBD) at both mtDNA and nuclear markers. A ten times stronger IBD at mtDNA compared to nuclear loci supported previous evidence of female philopatry. Together with the lack of spatial trends in genetic diversity, this IBD suggests that migration-drift equilibrium has been reached, erasing any genetic signal of a leading-edge effect that accompanied the predicted recolonization of the northern habitats freed from Pleistocene ice. These results illuminate the processes shaping porpoise population structure and provide a framework for designing conservation strategies and forecasting future population evolution.
How simple cell to cell communication rules can generate and maintain scale invariant gradients of signalling activity across a multicellular population
Authors: Oldham Jack 
Published: November 2020

AbstractThis paper shows computationally and conceptually how gradients of signalling activity can be generated and dynamically maintained across a population of cells using very simple cell to cell communication rules. The rules work on the basis of cells regulating their production rate of a signalling molecule according to the production rates of their immediate neighbours. Highly stable, scale invariant signalling gradients can be formed across the population, with highest rates at the centre and lowest at the periphery.The cell to cell communication behaviour that causes gradient formation is first explained in a descriptive, thought experiment type manner. It is then defined more formally using a conceptual, mathematically discrete computational model, which provides a network or graph type framework in which it is easy to analyse and control discrete signals that are sent between neighbouring cells. This provides an intuitive method of explaining how the signalling gradient emerges as a result of local cell to cell communication. Finally, examples of gradient formation are shown using software implementations of the model.
Highly conserved andcis-acting lncRNAs produced from paralogous regions in the center of HOXA and HOXB clusters in the endoderm lineage
Authors: Degani Neta , Ainbinder Elena , Ulitsky Igor 
Published: November 2020

AbstractLong noncoding RNAs (lncRNAs) have been shown to play important roles in gene regulatory networks acting in early development. There has been rapid turnover of lncRNA loci during vertebrate evolution, with few human lncRNAs conserved beyond mammals. The sequences of these rare deeply conserved lncRNAs are typically not similar to each other. Here, we characterizeHOXA-AS3andHOXB-AS3, lncRNAs produced from the central regions of the HOXA and HOXB clusters. Sequence-similar homologs of both lncRNAs are found in multiple vertebrate species and there is evident sequence similarity between their promoters, suggesting that the production of these lncRNAs predates the duplication of the HOX clusters at the root of the vertebrate lineage. This conservation extends to similar expression patterns of the two lncRNAs, in particular in cells transiently arising during early development or in the adult colon, and their co-regulation by the CDX1/2 transcription factors. Functionally, the RNA products ofHOXA-AS3andHOXB-AS3regulate the expression of their overlapping HOX5–7 genes both in HT-29 cells and during differentiation of human embryonic stem cells. Beyond production of paralogous protein-coding and microRNA genes, the regulatory program in the HOX clusters therefore also relies on paralogous lncRNAs acting in restricted spatial and temporal windows of embryonic development and cell differentiation.
Environmental factors drive release ofPerkinsus marinusfrom infected oysters
Authors: Gignoux-Wolfsohn Sarah A. , Newcomb Matilda S. R. , Ruiz Gregory M. , Pagenkopp Lohan Katrina M. 
Published: November 2020

AbstractSince the discovery ofPerkinsus marinusas the cause of dermo disease inCrassostrea virginica, salinity and temperature have been identified as the main environmental drivers of parasite prevalence. However, little is known about how these variables affect the movement of parasites from host to water column. In order to elucidate how environmental factors can influence the abundance of this parasite in the water column, we conducted a series of experiments testing the effects of time of day, temperature, and salinity on release ofP. marinuscells from infected oysters. We found thatP. marinuscells were released on a diurnal cycle, with most cells released during the hottest and brightest period of the day (12:00-18:00). Temperature also had a strong and immediate effect on number of cells released, but salinity did not, only influencing the intensity of infection over the course of several months. Taken together, our results demonstrate that 1) the number of parasites in the water column fluctuates according to a diurnal cycle, 2) temperature and salinity act on different timescales to influence parasite abundance, and 3) live infected oysters may substantially contribute to the abundance of transmissive parasites in the water column under particular environmental conditions.