bluuebunny/crossref_metadata_2025 huggingface dataset:
Human pathogen co-occurrence in Ixodes ricinus ticks: effects of landscape topography, climatic factors and microbiota interactions
Authors: Aivelo Tuomas , Norberg Anna , Tschirren Barbara
Published: February 2019

The factors shaping microbial communities within organisms are still poorly understood. Besides ecological factors and host characteristics, direct interactions among microbes may shape the occurrence of microbes and the structure of communities. In the past it has been difficult to disentangle if patterns of microbial co-occurrence are due to facilitation or competition effects, or shaped by shared ecological preferences (i.e., environmental filtering). Here we use a joint species distribution model to characterize the bacterial microbiota composition of an important human disease vector, the sheep tick Ixodes ricinus, along ecological gradients in the Swiss Alps, and to test for facilitation or competition effects among human pathogens and tick endosymbionts. We identify a number of ecological variables that significantly predicted the diversity of tick microbial community and the occurrence of specific tick endosymbionts and human pathogens. However, ecological associations were generally microbe-specific rather than universal. We also found evidence for significant microbe interactions, in particular widespread facilitation among pathogens, which promotes pathogen co-infection within ticks, as well as competition between the tick endosymbiont Spiroplasma and a number of human pathogens. These findings highlight that direct interactions among microbes can affect the vector competence of ticks and thereby tick-borne disease dynamics.
Hierarchies & Lower Bounds in Theoretical Connectomics
Authors: Ramaswamy Venkatakrishnan
Published: February 2019

Connectomics is a sub-field of Neuroscience aimed at determining connectomes – exact structures of neurons and their synaptic connections in nervous systems. A number of ongoing initiatives at the present time are working towards the goal of ascertaining the connectomes or parts thereof of various organisms. Determining the detailed physiological response properties of all the neurons in these connectomes is out of reach of current experimental technology. It is therefore unclear, to what extent knowledge of the connectome alone will advance a mechanistic understanding of computation occurring in these neuronal circuits, especially when the high-level function(s) of the said circuit is unknown.

We are pursuing a research program to build theory in order to investigate these issues. In previously published work [1], towards this end, we have developed a theory of connectomic constraints for feedforward networks of neurons. Specifically, for feedforward networks equipped with neurons that obey a deterministic spiking neuron model, we asked if just by knowing the structure of a network, we could rule out spike-timed computations that it could be doing, no matter what response properties each of its neurons may have. Our neurons were abstract mathematical objects that satisfied a small number of axioms that correspond to certain broadly-obeyed properties of neurons.

Here, we develop additional theoretical tools and notions to address these questions. The idea is to study the space of all possible spike-train to spike-train transformations. We are interested in asking how the subset of transformations spanned by networks of specific architectures can be related to hierarchical subsets of the space that are characterized by particular mathematical properties of transformations. In particular, given such a hierarchy of subsets, what is the “smallest” subset that contains the set of transformations spanned by networks of a specific class of architectures? Even if one cannot establish such a subset exactly, proving bounds on it (according to the hierarchy) might offer insight. After setting up the mathematical framework to make these notions precise, we construct explicit classes of hierarchies and prove a number of such lower bounds.
Computational redesign of theEscherichia coliribose-binding protein ligand binding pocket for 1,3-cyclohexanediol and cyclohexanol
Authors: Tavares Diogo , Reimer Artur , Roy Shantanu , Joublin Aurélie , Sentchilo Vladimir , van der Meer Jan Roelof
Published: February 2019

Bacterial periplasmic-binding proteins have been acclaimed as general biosensing platform, but their range of natural ligands is too limited for optimal development of chemical compound detection. Computational redesign of the ligand-binding pocket of periplasmic-binding proteins may yield variants with new properties, but, despite earlier claims, genuine changes of specificity to non-natural ligands have so far not been achieved. In order to better understand the reasons of such limited success, we revisited here theEscherichia coliRbsB ribose-binding protein, aiming to achieve perceptible transition from ribose to structurally related chemical ligands 1,3-cyclohexanediol and cyclohexanol. Combinations of mutations were computationally predicted for nine residues in the RbsB binding pocket, then synthesized and tested in anE. colireporter chassis. Two million variants were screened in a microcolony-in-bead fluorescence-assisted sorting procedure, which yielded six mutants no longer responsive to ribose but with 1.2-1.5 times induction in presence of 1 mM 1,3-cyclohexanediol, one of which responded to cyclohexanol as well. Isothermal microcalorimetry confirmed 1,3-cyclohexanediol binding, although only two mutant proteins were sufficiently stable upon purification. Circular dichroism spectroscopy indicated discernable structural differences between these two mutant proteins and wild-type RbsB. This and further quantification of periplasmic-space abundance suggested most mutants to be prone to misfolding and/or with defects in translocation compared to wild-type. Our results thus affirm that computational design and library screening can yield RbsB mutants with recognition of non-natural but structurally similar ligands. The inherent arisal of protein instability or misfolding concomitant with designed altered ligand-binding pockets should be overcome by new experimental strategies or by improved future protein design algorithms.
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

Various 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

Histopathological 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

Mechanism-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

Motivation

The 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.

Results

We 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.

Availability

Our software is available at https://github.com/vgteam/vg, https://github.com/jltsiren/gbwt, and https://github.com/jltsiren/gcsa2.

Contact

jouni.siren@iki.fi

Supplementary information

Supplementary 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

Understanding 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

Terrestrial 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.

Significance

In 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

Spinal 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

Motivation

The 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.

Results

We 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 Implementation

https://github.com/refresh-bio/fqsqueezer

Contact

sebastian.deorowicz@polsl.pl

Supplementary information

Supplementary 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

Objective

Diagnostic 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.

Results

In 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

Measurements 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

Hybrid 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

The 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

Infamously, 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.
Mutation Effects on Remote Epitopes of An Autoantigen Decoded with Simulated B-Factors
Authors: Pang Yuan-Ping , Moura Marta Casal , Thompson Gwen E. , Hummel Amber M. , Nelson Darlene R. , Jenne Dieter E. , Specks Ulrich
Published: February 2019

Proteinase 3 (PR3) is a neutrophil serine protease targeted by anti-neutrophil cytoplasmic antibodies (ANCAs) in the autoimmune disease granulomatosis with polyangiitis (GPA)1–5. PR3 mutants were developed to investigate how PR3 interacts with the ANCAs and whether the interactions can be intervened by therapeutics. One mutant with a Ser195Ala mutation (iPR3-Val103) recognized as many ANCAs as wild-type PR3 (PR3-Val103)6–9, indicating that PR3-Val103 and iPR3-Val103 have equivalent ANCA-binding capabilities. A triple mutant of the latter (iHm5-Val103) bound a monoclonal antibody (moANCA518) from a patient with GPA on an epitope remote from the mutation sites. Unexpectedly, the corresponding epitope of iPR3-Val103 was inaccessible to moANCA518 under the same experimental conditions10,11. These observations demonstrate that a latent epitope of PR3 can be activated surprisingly by remote mutations in PR311. Here we report a comparative analysis of simulated B-factors (i.e., measurements of local mobility) of PR3-Val103, iPR3-Val103, and iHm5-Val103, demonstrating that the binding of moANCA518 to iHm5-Val103 is enabled by an increase in main-chain flexibility in the latent epitope caused by the remote muta-tions in iHm5-Val103. This epitope activation—achieved in vitro by remote mutations as we demonstrated or in vivo conceivably by remote protein»protein interactions12 or remote po-lymorphisms—may be a fundamental feature of antibody-mediated autoimmune diseases. Rigidifying B-cell epitopes on an autoantigen with therapeutics designed using the B-factor analysis disclosed here may lead to effective treatments for these autoimmune diseases by making the autoantigen inaccessible to existing autoantibodies. This analysis may also be used to predict and characterize epitopes and remote mutation effects.
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

Bacteriophage 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

Members 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.
Estimation of Brain Age Delta from Brain Imaging
Authors: Smith Stephen M. , Vidaurre Diego , Alfaro-Almagro Fidel , Nichols Thomas E. , Miller Karla L.
Published: February 2019

It is of increasing interest to study “brain age” - the apparent age of a subject, as inferred from brain imaging data. The difference between brain age and actual age (the “delta”) is typically computed, reflecting deviation from the population norm. This therefore may reflect accelerated aging (positive delta) or resilience (negative delta) and has been found to be a useful correlate with factors such as disease and cognitive decline. However, although there has been a range of methods proposed for estimating brain age, there has been little study of the optimal ways of computing the delta. In this technical note we describe problems with the most common current approach, and present potential improvements. We evaluate different estimation methods on simulated and real data. We also find the strongest correlations of corrected brain age delta with 5,792 non-imaging variables (non-brain physical measures, life-factor measures, cognitive test scores, etc.), and also with 2,641 multimodal brain imaging-derived phenotypes, with data from 19,000 participants in UK Biobank.
DrugComb - an integrative cancer drug combination data portal
Authors: Zagidullin Bulat , Aldahdooh Jehad , Zheng Shuyu , Wang Wenyu , Wang Yinyin , Saad Joseph , Malyutina Alina , Pessia Alberto , Tang Jing
Published: February 2019

Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided the web server to analyze and visualize users’ own drug combination screening data. The users have an option to upload their data to DrugComb, as part of a crowdsourcing data curation effort. To initiate the data repository, we collected 437,932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity and synergy of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.
Emergence ofβandγnetworks following multisensory training
Authors: Rocca Daria La , Ciuciu Philippe , Engemann Denis Alexander , Wassenhove Virginie van
Published: February 2019

Our perceptual reality relies on inferences about the causal structure of the world given by multiple sensory inputs. In ecological settings, multisensory events that cohere in time and space benefit inferential processes: hearing and seeing a speaker enhances speech comprehension, and the acoustic changes of flapping wings naturally pace the motion of a flock of birds. Here, we asked how a few minutes of (multi)sensory training could shape cortical interactions in a subsequent perceptual task, and investigated oscillatory activity and functional connectivity as a function of sensory history in training. Human participants performed a visual motion coherence discrimination task while being recorded with magnetoencephalography (MEG). Three groups of participants performed the same task with visual stimuli only, while listening to acoustic textures temporally comodulated with the strength of visual motion coherence, or with auditory noise uncorrelated with visual motion. The functional connectivity patterns before and after training were contrasted to resting-state networks to assess the variability of common task-relevant networks, and the emergence of new functional inter-actions following training. One main finding is the emergence of a large-scale synchronization in the highγ(gamma: 60−120Hz) andβ(beta:15−30Hz) bands for individuals who underwent comodulated multisensory training. The post-training network involved prefrontal, parietal, and visual cortices. Our results suggest that the integration of evidence and decision-making strategies become more efficient following congruent multisensory training through plasticity in network routing and oscillatory regimes.
R-DECO: An open-source Matlab based graphical user interface for the detection and correction of R-peaks
Authors: Moeyersons Jonathan , Amoni Matthew , Huffel Sabine Van , Willems Rik , Varon Carolina
Published: February 2019

Many of the existing ECG toolboxes focus on the derivation of heart rate variability features from RR-intervals. By doing so, they assume correct detection of the QRS-complexes. However, it is highly likely that not all detections are correct. Therefore, it is recommended to visualize the actual R-peak positions in the ECG signal and allow manual adaptations.

In this paper we present R-DECO, an easy-to-use graphical user interface for the detection and correction of R-peaks. Within R-DECO, the R-peaks are detected by an adaptation of the Pan-Tompkins algorithm. Instead of using all the pre-processing steps of the latter algorithm, the proposed algorithm uses only an envelope-based procedure to flatten the ECG and enhance the QRS-complexes. The algorithm obtained an overall sensitivity of 99.60% and PPV of 99.69% on the MIT/BIH arrhythmia database.

Additionally, R-DECO includes support for several input data formats for ECG signals, three basic filters, the possibility to load other R-peak locations and intuitive methods to correct ectopic, wrong, or missed heartbeats. All functionalities can be accessed via the graphical user interface and the analysis results can be exported as Matlab or Excel files. The software is publicly available.

Through its easy-to-use-graphical user interface, R-DECO allows both clinicians and researchers to use all functionalities, without previous knowledge.
Development and validation of microsatellite markers fromde novotranscriptome assembly of eggplant (Solanum melongenaL.) and its putative progenitorS. incanumL. cultivars
Authors: Tiwari Shailesh K. , Mishra Pallavi , Singh Sakshi , Singh Vinay K , Kashyap Sarvesh P , Singh Major , Tiwari Kavindra N , Naik Prakash S , Singh Bijendra
Published: February 2019

An elite cultivar of eggplant, Ramnagar Giant (Solanum melongenaL.) and W-4 (S. incanumL.) with contrasting horticultural traits were used as parental lines to develop a mapping population of RILs. To accelerate breeding programs and to develop large scale SSR markers to be used in QTL mapping, RNASeqlibraries from different tissues of both the parental plants were deep sequenced and assembled into representation of a high qualityde novotranscriptome using Illumina-based Next Generation Sequencing technology. 99.99% of high quality bases were obtained from all the tissues and deposited in TSA database at the NCBI link. Total 3, 156 and 3, 196 SNVs were detected inS. melongenaandS. incanum, respectively. InS. melongena, 11, 262 SSR while inS. incanum11, 829 SSR containing regions were identified. Based on functional annotation, 21, 914 unique genes could be identified forS. melongena, 21,706 unique genes forS. incanumand overall, 60 different transcription factors were identified in both the lines. Further, a total of 536 SSR markers were designed and screened for polymorphism of which, 157 markers produced polymorphism between the parental lines. The polymorphic SSRs shall be used for genotyping of RILs to map QTLs for various horticultural traits in eggplant and identification of candidate genes in response to biotic and abiotic stress.
The effects of combining focus of attention and autonomy support on shot accuracy in the penalty kick
Authors: Makaruk Hubert , Porter Jared M. , Sadowski Jerzy , Bodasińska Anna , Zieliński Janusz , Niźnikowski Tomasz , Mastalerz Andrzej
Published: February 2019

The penalty kick is of great importance in the sport of soccer. Therefore, the aim of this study was to test predictions of the OPTIMAL theory and identify key attentional and motivational factors that impact the accuracy of the penalty kick. The following six groups of skilled participants performed penalty kicks following instructions that directed their focus of attention or impacted their autonomy support: external focus with autonomy support (EF/AS), external focus alone (EF), internal focus with autonomy support (IF/AS), internal focus alone (IF), autonomy support alone (AS) and control (C) groups. The analysis showed that the EF/AS group demonstrated better kicking accuracy relatively to the IF/AS, IF and C groups, but there were no significant differences between the EF/AS and EF or AS groups. Interestingly, the EF/AS group showed higher self-efficacy compared to the EF, IF/AS, IF and C groups. The finding suggest that a combination of attentional and motivational factors may produce benefits in motor performance.
Resolution of human ribosomal DNA occurs in anaphase, dependent on tankyrase 1, condensin II, and topoisomerase IIα
Authors: Daniloski Zharko , Bisht Kamlesh K. , McStay Brian , Smith Susan
Published: February 2019

Formation of individualized sister chromatids is essential for their accurate segregation. In budding yeast, while most of the genome segregates at the metaphase to anaphase transition, resolution of the ribosomal DNA (rDNA) repeats is delayed. The timing and mechanism in human cells is unknown. Here we show that resolution of human rDNA occurs in anaphase after the bulk of the genome, dependent on tankyrase 1, condensin II, and topoisomerase IIα. Defective resolution leads to rDNA bridges, rDNA damage, and aneuploidy of an rDNA-containing acrocentric chromosome. Thus, temporal regulation of rDNA segregation is conserved between yeast and man and is essential for genome integrity.
Cooperative game-based profit allocation for joint distribution alliance under online shopping environment
Authors: Li Longxiao , Wang Xu , Lin Yun , Zhou Fuli , Chen Shan
Published: February 2019

Purpose

In the context of sharing economy and online shopping, establishing a stable urban joint distribution alliance (JDA) is extremely necessary for the entire logistics service market. The purpose of this paper is to rationally allocate the profits and determine the most stable allocation scheme for the urban JDA as well as provide a direction for cooperation between express enterprises and lead managers to pay more attention to the comprehensive performance.

Design/methodology/approach

Cooperative game-based methodologies including the proportion method, the core theory, nucleolus and Shapley value have been employed. Four criteria consisting of enterprise operation, customer satisfaction, environmental sustainability and information technology have been incorporated into Shapley value for improvement.

Findings

This paper reveals that express enterprises in logistics service market can achieve more benefit from JDA than those who operate separately. Among proposed profit allocation schemes, improved Shapley value scheme shows more rationality by considering partners’ asymmetric contribution. Besides, a stable alliance can be always ensured with partners’ lower propensity to disrupt and relatively balanced negotiation power under improved Shapley value scheme.

Originality/value

This paper makes a few attempts to contribute to the literature on the improvement of Shapley value and applies the concept of “propensity to disrupt” into the field of logistics. Besides, this paper provides various profit allocation schemes and incorporates influencing factors into Shapley value for an improvement thus helping policy-makers make better-informed decisions on urban distribution. Additionally, a case study based on urban express enterprises in Southwest China has been conducted to verify the proposed profit allocation schemes.
Heterogeneity in a dual personal values–dual purchase consequences–green consumption commitment framework
Authors: Le Angelina Nhat Hanh , Tran Mai Dong , Nguyen Dong Phong , Cheng Julian Ming Sung
Published: February 2019

Purpose

The purpose of this paper is to study the roles of dual personal values (individualistic and collectivistic) and consumer attitudes toward dual purchase consequences (individual and environmental) as the precursors of a commitment to green consumption. Furthermore, the variance within the studied relationships is explored across the segments of the selected consumer sample.

Design/methodology/approach

Partial least squares and finite mixture–partial least squares path modeling approaches are employed to examine the studied relationships and check for heterogeneity, respectively, among the sample of 406 Vietnamese consumers.

Findings

The results indicate that individualistic values positively and negatively affect attitudes toward purchase consequences at the individual and environmental levels, respectively, while collectivistic values have only a positive impact on attitudes at the environment level. Compared to the individual level, attitudes toward environmental purchase consequences propagate a fuller commitment to green consumption. Collectivistic, but not individualistic, consumers are a suitable target segment for green business. Consumers within the selected sample exhibit different green behavioral patterns.

Originality/value

This research provides valuable insights into the under-researched aspect of green consumption commitment based on an extended value–attitude–behavior model. Previously unobserved heterogeneity is revealed and green consumption tendency segments are identified.