bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dimensionality Reduction of Diffusion MRI Measures for Improved Tractometry of the Human Brain
Authors: Chamberland Maxime , Raven Erika P. , Genc Sila , Duffy Kate , Descoteaux Maxime , Parker Greg D. , Tax Chantal M.W. , Jones Derek K. 
Published: February 2019

AbstractVarious diffusion MRI measures have been proposed for characterising tissue microstructure over the last 15 years. Despite the growing number of experiments using different diffusion measures in assessments of white matter, there has been limited work on: 1) examining their covariance along specific pathways; and on 2) combining these different measures to study tissue microstructure. In this work, we first demonstrate redundancies in the amount of information captured by 10 diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) measures. Using a data-reduction approach, we identified two biologically-interpretable components that capture 80% of the variance in commonly-used DTI and HARDI measures profiled along 22 brain pathways extracted from typically developing children aged 8 - 18 years (n = 36). The first derived component captures properties related to hindrance and restriction in tissue microstructure, while the second component reflects characteristics related to tissue complexity and orientational dispersion. We demonstrate that the components generated by this approach preserve the biological relevance of the original measurements by showing age-related effects across developmentally sensitive pathways. Our results also suggest that HARDI measures are more sensitive at detecting age-related changes in tissue microstructure than DTI measures.
A Deep learning approach for Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images
Authors: Tabibu Sairam , Vinod P.K. , Jawahar C.V. 
Published: February 2019

ABSTRACTHistopathological images contain morphological markers of disease progression that have diagnostic and predictive values. However, complex morphological information remains unutilized in unaided approach to histopathology. In this study, we demonstrate how deep learning framework can be used for an automatic classification of Renal Cell Carcinoma (RCC) subtypes, and for identification of features that predict survival outcome from digital histopathological images. Convolutional neural networks (CNN’s) trained on whole-slide images distinguish clear cell and chromophobe RCC from normal tissue with a classification accuracy of 93.39 % and 87.34 %, respectively. Further, a CNN trained to distinguish clear cell, chromophobe and papillary RCC achieves a classification accuracy of 92.61 %. Here, we introduced a novel support vector machine based method to deal with data imbalance in multi-class classification to improve the accuracy. Finally, we extracted the morphological features from high probability tumor regions identified by the CNN to predict patient survival outcome of most common clear cell RCC. The generated risk index based on both tumor shape and nuclei features are significantly associated with patient survival outcome. These results highlight that deep learning can play a role in both cancer diagnosis and prognosis.
Massive computational acceleration by using neural networks to emulate mechanism-based biological models
Authors: Wang Shangying , Fan Kai , Luo Nan , Cao Yangxiaolu , Wu Feilun , Zhang Carolyn , Heller Katherine A. , You Lingchong 
Published: February 2019

AbstractMechanism-based mathematical models are the foundation for diverse applications. It is often critical to explore the massive parametric space for each model. However, for many applications, such as agent-based models, partial differential equations, and stochastic differential equations, this practice can impose a prohibitive computational demand. To overcome this limitation, we present a fundamentally new framework to improve computational efficiency by orders of magnitude. The key concept is to train an artificial neural network using a limited number of simulations generated by a mechanistic model. This number is small enough such that the simulations can be completed in a short time frame but large enough to enable reliable training of the neural network. The trained neural network can then be used to explore the system dynamics of a much larger parametric space. We demonstrate this notion by training neural networks to predict self-organized pattern formation and stochastic gene expression. With this framework, we can predict not only the 1-D distribution in space (for partial differential equation models) and probability density function (for stochastic differential equation models) of variables of interest with high accuracy, but also novel system dynamics not present in the training sets. We further demonstrate that using an ensemble of neural networks enables the self-contained evaluation of the quality of each prediction. Our work can potentially be a platform for faster parametric space screening of biological models with user defined objectives.
Haplotype-aware graph indexes
Authors: Sirén Jouni , Garrison Erik , Novak Adam M. , Paten Benedict , Durbin Richard 
Published: February 2019

AbstractMotivationThe variation graph toolkit (VG) represents genetic variation as a graph. Although each path in the graph is a potential haplotype, most paths are nonbiological, unlikely recombinations of true haplotypes.ResultsWe augment the VG model with haplotype information to identify which paths are more likely to exist in nature. For this purpose, we develop a scalable implementation of the graph extension of the positional Burrows–Wheelertransform (GBWT). We demonstrate the scalability of the new implementation by building a whole-genome index of the 5,008 haplotypes of the 1000 Genomes Project, and an index of all 108,070 TOPMed Freeze 5 chromosome 17 haplotypes. We also develop an algorithm for simplifying variation graphs for k-mer indexing without losing any k-mers in the haplotypes.AvailabilityOur software is available at https://github.com/vgteam/vg, https://github.com/jltsiren/gbwt, and https://github.com/jltsiren/gcsa2.Contactjouni.siren@iki.fiSupplementary informationSupplementary data are available.
Demographic histories and genome-wide patterns of divergence in incipient species of shorebirds
Authors: Wang Xuejing , Maher Kathryn H. , Zhang Nan , Que Pingjia , Zheng Chenqing , Liu Simin , Wang Biao , Huang Qin , Chen De , Yang Xu , Zhang Zhengwang , Székely Tamás , Urrutia Araxi O. , Liu Yang 
Published: February 2019

AbstractUnderstanding how incipient species are maintained with gene flow is a fundamental question in evolutionary biology. Whole genome sequencing of multiple individuals holds great potential to illustrate patterns of genomic differentiation as well as the associated evolutionary histories. Kentish (Charadrius alexandrinus) and the white-faced (C. dealbatus) plovers, which differ in their phenotype, ecology and behaviour, are two incipient species and parapatrically distributed in East Asia. Previous studies show evidence of genetic diversification with gene flow between the two plovers. Under this scenario, it is of great importance to explore the patterns of divergence at the genomic level and to determine whether specific regions are involved in reproductive isolation and local adaptation. Here we present the first population genomic analysis of the two incipient species based on the de novo Kentish plover reference genome and resequenced populations. We show that the two plover lineages are distinct in both nuclear and mitochondrial genomes. Using model-based coalescence analysis, we found that population sizes of Kentish plover increased whereas white-faced plovers declined during the Last Glaciation Period. Moreover, the two plovers diverged allopatrically, with gene flow occurring after secondary contact. This has resulted in low levels of genome-wide differentiation, although we found evidence of a few highly differentiated genomic regions in both the autosomes and the Z-chromosome. This study illustrates that incipient shorebird species with gene flow after secondary contact can exhibit discrete divergence at specific genomic regions and provides basis to further exploration on the genetic basis of relevant phenotypic traits.
Extreme niche partitioning promotes a remarkably high diversity of soil microbiomes across eastern Antarctica
Authors: Zhang Eden , Thibaut Loïc M. , Terauds Aleks , Wong Sinyin , van Dorst Josie , Tanaka Mark M. , Ferrari Belinda C. 
Published: February 2019

AbstractTerrestrial Antarctica, a predominantly microbial realm, encompasses some of the most unique environments on Earth where resident soil microbiota play key roles in the sustainability and evolution of the ecosystem. Yet the fundamental ecological processes that govern the assemblage of these natural communities remain unclear. Here, we combined multivariate analyses, co-occurrence networks and fitted species abundance distributions of amplicon sequencing data to disentangle community assemblage patterns of polar soil microbiomes across two ice-free deserts (Windmill Islands and Vestfold Hills) situated along the coastline of eastern Antarctica. Our findings report that communities were predominantly structured by non-neutral processes, with niche partitioning being particularly strong for bacterial communities at the Windmill Islands. In contrast, both eukaryotic and archaeal communities exhibited multimodal distributions, indicating the potential emergence of neutrality. Between the three microbial domains, polar soil bacterial communities consistently demonstrated the greatest taxonomic diversity, estimated richness, network connectivity and linear response to contemporary environmental soil parameters. We propose that reduced niche overlap promotes greater phylogenetic diversity enabling more bacterial species to co-exist and essentially thrive under adversity. However, irrespective of overall relative abundance, consistent and robust associations between co-existing community members from all three domains of life highlights the key roles that diverse taxa play in ecosystem dynamics.SignificanceIn the face of a warming Antarctica, contemporary dynamics between polar soil microbial communities will inevitably change due to the climate-induced expansion of new ice-free areas. Increasing concern about disturbance and rapid biodiversity loss has intensified the need to better understand microbial community structure and function in high-latitude soils. We have taken an integrated approach to elucidate domain-level assemblage patterns across east Antarctic soil microbiomes. These assemblage patterns will be available to feed into policy management and conservation planning frameworks to potentially mitigate future biodiversity loss.
Distinct Spinal V2a and V0d Microcircuits Distribute Locomotor Control in Larval Zebrafish
Authors: Menelaou Evdokia , Kishore Sandeep , McLean David L. 
Published: February 2019

SUMMARYSpinal interneurons coordinate adjustments in the rhythm and pattern of locomotor movements. Two prevailing models predict that interneurons either share or hierarchically distribute control of these key parameters. Here, we have tested each model in the coordination of swimming in larval zebrafish by circumferential excitatory V2a and commissural inhibitory V0d interneurons. We define two types of V2a neuron based on morphology, electrophysiology and connectivity. Type I V2as primarily propagate and amplify rhythmic signals biased to interneurons, while type II V2as primarily segregate and expedite patterning signals biased to motor neurons. Distributed control arises by differences in the likelihood of connections within types and the relative weights of connections between them, but not by a strict anatomical hierarchy. Heterogeneity among V0d neurons supports a similar functional distinction. Our findings provide a hybrid conceptual framework to better understand the origins of rhythm and pattern control in the spinal cord.
FQSqueezer: k-mer-based compression of sequencing data
Authors: Deorowicz Sebastian 
Published: February 2019

AbstractMotivationThe amount of genomic data that needs to be stored is huge. Therefore it is not surprising that a lot of work has been done in the field of specialized data compression of FASTQ files. The existing algorithms are, however, still imperfect and the best tools produce quite large archives.ResultsWe present FQSqueezer, a novel compression algorithm for sequencing data able to process single- and paired-end reads of variable lengths. It is based on the ideas from the famous prediction by partial matching and dynamic Markov coder algorithms known from the general-purpose-compressors world. The compression ratios are often tens of percent better than offered by the state-of-the-art tools.Availability and Implementationhttps://github.com/refresh-bio/fqsqueezerContactsebastian.deorowicz@polsl.plSupplementary informationSupplementary data are available at publisher’s Web site.
Mycobacterium tuberculosis detection from oral swabs with Xpert MTB/RIF ULTRA: A pilot study
Authors: Mesman Annelies , Calderon Roger , Soto Martin , Coit Julia , Aliaga Juan , Mendoza Milagros , Franke Molly 
Published: February 2019

AbstractObjectiveDiagnostic testing for tuberculosis depends on microbiological detection of Mycobacterium tuberculosis (Mtb) in sputum. For patients unable to expectorate sputum, such as children and individuals living with HIV, this poses barriers to rapid diagnosis and treatment initiation. Therefore, this study aimed to use oral swabs have as alternative sample type for Mtb detection via molecular testing.ResultsIn a pilot study, we aimed to evaluate sensitivity of Mtb detection via oral swabs using Xpert MTB/RIF ULTRA. We enrolled 63 TB cases and controls from Lima, Peru, and detected Mtb from oral swabs with a sensitivity of 45% (95% confidence interval (CI): 29%-63%) and specificity of 100% (95% CI: 84%-100%) using liquid culture of sputum as reference test. Our current protocol will need optimization, but these results support future exploration of the use of oral swabs for Mtb detection.
Consistent and correctable bias in metagenomic sequencing experiments
Authors: McLaren Michael R. , Willis Amy D. , Callahan Benjamin J. 
Published: February 2019

AbstractMeasurements of biological communities by marker-gene and metagenomic sequencing are biased: The measured relative abundances of taxa or their genes are systematically distorted from their true values because each step in the experimental workflow preferentially detects some taxa over others. Bias can lead to qualitatively incorrect conclusions and makes measurements from different protocols quantitatively incomparable. A rigorous understanding of bias is therefore essential. Here we propose, test, and apply a simple mathematical model of how bias distorts marker-gene and metagenomics measurements: Bias multiplies the true relative abundances within each sample by taxon-and protocol-specific factors that describe the different efficiencies with which taxa are detected by the workflow. Critically, these factors are consistent across samples with different compositions, allowing bias to be estimated and corrected. We validate this model in 16S rRNA gene and shotgun metagenomics data from bacterial communities with defined compositions. We use it to reason about the effects of bias on downstream statistical analyses, finding that analyses based on taxon ratios are less sensitive to bias than analyses based on taxon proportions. Finally, we demonstrate how this model can be used to quantify bias from samples of defined composition, partition bias into steps such as DNA extraction and PCR amplification, and to correct biased measurements. Our model improves on previous models by providing a better fit to experimental data and by providing a composition-independent approach to analyzing, measuring, and correcting bias.
RPW8/HR Repeats Predict NLR-dependent Hybrid Performance
Authors: Barragan Cristina A. , Wu Rui , Kim Sang-Tae , Xi Wanyan , Habring Anette , Hagmann Jörg , Van de Weyer Anna-Lena , Zaidem Maricris , Ho Ho William Wing , Wang George , Bezrukov Ilja , Weigel Detlef , Chae Eunyoung 
Published: February 2019

SummaryHybrid offspring can look very different from their parents, including having greatly increased or decreased fitness. In many plant species, conflicts between divergent elements of the immune system can cause hybrids to express autoimmunity, a generally deleterious syndrome known as hybrid necrosis. We are investigating multiple hybrid necrosis cases in Arabidopsis thaliana that are caused by allele-specific interactions between different variants at two unlinked resistance (R) gene clusters. One is the RESISTANCE TO PERONOSPORA PARASITICA 7 (RPP7) cluster, which encodes an intracellular nucleotide binding site-leucine rich repeat (NLR) immune receptors that confer strain-specific resistance to oomycetes. The other is the RESISTANCE TO POWDERY MILDEW 8 (RPW8)/HOMOLOG OF RPW8 (HR) locus, which encodes atypical resistance proteins that can confer broad-spectrum resistance to filamentous pathogens. There is extensive structural variation in the RPW8/HR cluster, both at the level of gene copy number and at the level of C-terminal protein repeats of unknown function. We demonstrate that the number of RPW8/HR repeats correlate, albeit in a complex manner, with the severity of hybrid necrosis when these alleles are combined with specific RPP7 variants. This observation suggests that gross structural differences, rather than individual amino acid polymorphisms, guide the genetic interaction between RPW8/HR and RPP7 alleles. We discuss these findings in light of the similarity of RPW8/HR proteins with pore-forming toxins, MLKL and HET-S, from mammals and fungi.
Coding triplets in the tRNA acceptor-TΨC arm and their role in present and past tRNA recognition
Authors: Agmon Ilana , Fayerverker Itay , Mor Tal 
Published: February 2019

AbstractThe mechanism and evolution of the recognition scheme between key components of the translation system, i.e., tRNAs, synthetases and elongation factors, are fundamental issues in understanding the translation of genetic information into proteins. Statistical analysis of bacterial tRNA sequences reveals that for six amino acids, i.e. for Ala, Asp, Gly, His, Pro and Ser, a string of 10 nucleotides preceding the tRNA 3’end, carries cognate coding triplets to nearly full extent. The triplets conserved in positions 63-67 are implicated in the recognition by EF-Tu, and those conserved in positions 68-72, in the identification of cognate tRNAs and their derived minihelices, by class IIa synthetases. These coding triplets are suggested to have primordial origin, being engaged in aminoacylation of prebiotic tRNAs and in the establishment of the canonical codon set.
Strategic Inattention in the Sir Philip Sidney Game
Authors: Whitmeyer Mark 
Published: February 2019

AbstractInfamously, the presence of honest communication in a signaling environment may be difficult to reconcile with small (relative) signaling costs or a low degree of common interest between sender (beneficiary) and receiver (donor). This paper posits that one mechanism through which such communication can arise is through inattention on the part of the receiver, which allows for honest communication in settings where–should the receiver be fully attentive–honest communication would be impossible. We explore this idea through the Sir Philip Sidney game in detail and show that some degree of inattention is always weakly better for the receiver and may be strictly better. We compare limited attention to Lachmann and Bergstrom’s (1998) notion of a signaling medium and show that the receiver-optimal degree of inattention is equivalent to the receiver-optimal choice of medium.
Lytic bacteriophage have diverse indirect effects in a synthetic cross-feeding community
Authors: Fazzino Lisa , Anisman Jeremy , Chacón Jeremy M. , Heineman Richard H. , Harcombe William R. 
Published: February 2019

AbstractBacteriophage shape the composition and function of microbial communities. Yet, it remains difficult to predict the effect of phage on microbial interactions. Specifically, little is known about how phage influence mutualisms in networks of cross-feeding bacteria. We modeled the impacts of phage in a synthetic microbial community in whichEscherichia coliandSalmonella entericaexchange essential metabolites. In this model, phage attack of either species was sufficient to inhibit both members of the mutualism; however, the evolution of phage resistance ultimately allowed both species to attain yields similar to those observed in the absence of phage. In laboratory experiments, attack ofS. entericawith P22virphage followed these modeling expectations of delayed community growth with little change in the final yield of bacteria. In contrast, whenE. coliwas attacked with T7 phage,S. enterica, the non-host species, reached higher yields compared to no-phage controls. T7 increased non-host yield by releasing consumable cell debris and by driving evolution of phage resistantE. colithat secreted more carbon. Additionally,E. colievolved only partial resistance, increasing the total amount of lysed cells available forS. entericato consume. Our results demonstrate that phage can have extensive indirect effects in microbial communities, and that the nature of these indirect effects depends on metabolic and evolutionary mechanisms.
The genome and mRNA transcriptome of the cosmopolitan calanoid copepod Acartia tonsa Dana improve the understanding of copepod genome size evolution
Authors: Jørgensen Tue Sparholt , Petersen Bent , Petersen H. Cecilie B. , Browne Patrick Denis , Prost Stefan , Stillman Jonathon H. , Hansen Lars Hestbjerg , Hansen Benni Winding 
Published: February 2019

AbstractMembers of the crustacean subclass Copepoda are likely the most abundant metazoans worldwide. Pelagic marine species are critical in converting planktonic microalgae to animal biomass, supporting oceanic food webs. Despite their abundance and ecological importance, only five copepod genomes are publicly available, owing to a number of factors including large genome size, repetitiveness, GC-content, and small animal size. Here, we report the sixth representative copepod genome and the first genome and transcriptome from the calanoid copepod species Acartia tonsa Dana, which is among the most numerous mesozooplankton in boreal coastal and estuarine waters. The ecology, physiology and behavior of A. tonsa has been studied extensively. The genetic resources contributed in this work will allow researchers to link experimental results to molecular mechanisms. From PCRfree WGS and mRNA Illumina data, we assemble the largest copepod genome to date. We estimate A. tonsa has a total genome size of 2.5 Gb including repetitive elements we could not resolve. The non-repetitive fraction of the genome assembly is estimated to be 566Mb. Our DNA sequencing-based analyses suggest there is a 14-fold difference in genome size between the six members of Copepoda with available genomic information through NCBI. This finding complements nucleus staining genome size estimations, where 100-fold difference has been reported within 70 species. We briefly analyze the repeat structure in the existing copepod WGS datasets. The information presented here confirms the evolution of genome size in Copepoda and expands the scope for evolutionary inferences in Copepoda by providing several levels of genetic information from a key planktonic crustacean species.