bluuebunny/crossref_metadata_2025_split huggingface dataset:
Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes
Authors: Johnson Lisa K. , Alexander Harriet , Brown C. Titus 
Published: May 2018

AbstractBackgroundDe novo transcriptome assemblies are required prior to analyzing RNAseq data from a species without an existing reference genome or transcriptome. Despite the prevalence of transcriptomic studies, the effects of using different workflows, or “pipelines”, on the resulting assemblies are poorly understood. Here, a pipeline was programmatically automated and used to assemble and annotate raw transcriptomic short read data collected by the Marine Microbial Eukaryotic Transcriptome Sequencing Project (MMETSP). The resulting transcriptome assemblies were evaluated and compared against assemblies that were previously generated with a different pipeline developed by the National Center for Genome Research (NCGR).ResultsNew transcriptome assemblies contained the majority of previous contigs as well as new content. On average, 7.8% of the annotated contigs in the new assemblies were novel gene names not found in the previous assemblies. Taxonomic trends were observed in the assembly metrics, with assemblies from the Dinoflagellata and Ciliophora phyla showing a higher percentage of open reading frames and number of contigs than transcriptomes from other phyla.ConclusionsGiven current bioinformatics approaches, there is no single ‘best’ reference transcriptome for a particular set of raw data. As the optimum transcriptome is a moving target, improving (or not) with new tools and approaches, automated and programmable pipelines are invaluable for managing the computationally-intensive tasks required for re-processing large sets of samples with revised pipelines and ensuring a common evaluation workflow is applied to all samples. Thus, re-assembling existing data with new tools using automated and programmable pipelines may yield more accurate identification of taxon-specific trends across samples in addition to novel and useful products for the community.Key PointsRe-assembly with new tools can yield new resultsAutomated and programmable pipelines can be used to process arbitrarily many samples.Analyzing many samples using a common pipeline identifies taxon-specific trends.
Convolutional Neural Network-Based Instance Segmentation Algorithm to Acquire Quantitative Criteria of Early Mouse Development
Authors: Tokuoka Yuta , Yamada Takahiro G , Hiroi Noriko F , Kobayashi Tetsuya J , Yamagata Kazuo , Funahashi Akira 
Published: May 2018

AbstractIn embryology, image processing methods such as segmentation are applied to acquiring quantitative criteria from time-series three-dimensional microscopic images. When used to segment cells or intracellular organelles, several current deep learning techniques outperform traditional image processing algorithms. However, segmentation algorithms still have unsolved problems, especially in bioimage processing. The most critical issue is that the existing deep learning-based algorithms for bioimages can perform only semantic segmentation, which distinguishes whether a pixel is within an object (for example, nucleus) or not. In this study, we implemented a novel segmentation algorithm, based on deep learning, which segments each nucleus and adds different labels to the detected objects. This segmentation algorithm is called instance segmentation. Our instance segmentation algorithm, implemented as a neural network, which we named QCA Net, substantially outperformed 3D U-Net, which is the best semantic segmentation algorithm that uses deep learning. Using QCA Net, we quantified the nuclear number, volume, surface area, and center of gravity coordinates during the development of mouse embryos. In particular, QCA Net distinguished nuclei of embryonic cells from those of polar bodies formed in meiosis. We consider that QCA Net can greatly contribute to bioimage segmentation in embryology by generating quantitative criteria from segmented images.
Microscopy quantification of microbial birth and death dynamics
Authors: Hart Samuel F. M. , Skelding David , Waite Adam J. , Burton Justin , Xie Li , Shou Wenying 
Published: May 2018

AbstractMicrobes live in dynamic environments where nutrient concentrations fluctuate. Quantifying fitness (birth and death) in a wide range of environments is critical for understanding microbial evolution as well as ecological interactions where one species alters the fitness of another. Here, using high-throughput time-lapse microscopy, we have quantified howSaccharomyces cerevisiaemutants incapable of synthesizing an essential metabolite grow or die in various concentrations of the required metabolite. We establish that cells normally expressing fluorescent proteins lose fluorescence upon death and that the total fluorescence in an imaging frame is proportional to the number of live cells even when cells form multiple layers. We validate our microscopy approach of measuring birth and death rates using flow cytometry, cell counting, and chemostat culturing. For lysine-requiring cells, very low concentrations of lysine are not detectably consumed and do not support cell birth, but delay the onset of death phase and reduce the death rate. In contrast, in low hypoxanthine, hypoxanthine-requiring cells can produce new cells, yet also die faster than in the absence of hypoxanthine. For both strains, birth rates under various metabolite concentrations are better described by the sigmoidal-shaped Moser model than the well-known Monod model, while death rates depend on the metabolite concentration and can vary with time. Our work reveals how time-lapse microscopy can be used to discover non-intuitive microbial dynamics and to quantify growth rates in many environments.
Cellular Crowding Influences Extrusion and Proliferation to Facilitate Epithelial Tissue Repair
Authors: Franco Jovany , Atieh Youmna , Bryan Chase D. , Kwan Kristen M. , Eisenhoffer George T. 
Published: May 2018

AbstractEpithelial wound healing requires a complex orchestration of cellular rearrangements and movements to restore tissue architecture and function after injury. While it is well-known that mechanical forces can affect tissue morphogenesis and patterning, how the biophysical cues generated after injury influence cellular behaviors during tissue repair is not well understood. Using time-lapsed confocal imaging of epithelial tissues in living zebrafish larvae, we provide evidence that localized increases in cellular crowding during wound closure promote the extrusion of non-apoptotic cells via mechanically regulated stretch-activated ion channels (SACs). Directed cell migration toward the injury site promoted the rapid changes in cell number and generated shifts in tension at cellular interfaces over long spatial distances. Perturbation of SAC activity resulted in failed extrusion and increased proliferation in crowded areas of the tissue. Together, we conclude that localized cell number plays a key role in dictating cellular behaviors that facilitate wound closure and tissue repair.
Archaeal, bacterial, and eukaryal microbial community structure of sediment and seawater in a coastal region near Puerto Nuevo, Baja California
Authors: Ul-Hasan Sabah , Bowers Robert M. , Figueroa-Montiel Andrea , Licea-Navarro Alexei F. , Beman J. Michael , Woyke Tanja , Nobile Clarissa J. 
Published: May 2018

AbstractMicrobial communities control numerous biogeochemical processes critical for ecosystem function and health, particularly in coastal ecosystems. However, comparatively little is known about microbial community structure in coastal regions, such that basic patterns of microbial biodiversity, such as species richness and community composition, are generally understudied. To better understand the global patterns of microbial biodiversity in coastal ecosystems, we characterized sediment and seawater microbial communities for three sites near Puerto Nuevo (Baja California, Mexico) using 16S and 18S rRNA gene amplicon sequencing methods. We found that sediment bacteria, archaea, and eukaryote microbial communities contained approximately 5 × 10^2 fold greater operational taxonomic units (OTUs) than their seawater-based counterparts (p < 0.001). Further, distinct bacterial, archaeal and eukaryal phyla were found in sediment and seawater samples. The phyla Acidobacteria, Chlorobi, and Chloroflexi were found to be abundant and unique to the sediment and Cyanobacteria, Spirochaetae, and Woesearchaeota to the seawater environment. Apicomplexa and Arthropoda were abundant eukaryal phyla found uniquely in the sediment whereas the Cryptomonadales and Protalveolata were detected only in the seawater. Furthermore, bacterial and archaeal communities were statistically different by site (p < 0.05) in both seawater and sediment samples for the Major Outlet site, the site closest to a residential area. In contrast, eukaryal microbial communities were only different among sites in the seawater samples. Overall, these results suggest that our understanding of coastal microbial biodiversity patterns require spatially robust sampling. This study contributes to a growing body of foundational microbial biodiversity and ecology knowledge, providing context to the global change that is induced by urban development.
Consistent and reproducible long-termin vitrogrowth of health and disease-associated oral subgingival biofilms
Authors: Velsko Irina M. , Shaddox Luciana M. 
Published: May 2018

AbstractBackground: Severalin vitrooral biofilm growth systems can reliably construct oral microbiome communities in culture, yet their stability and reproducibility through time has not been well characterized. Long-termin vitrogrowth of natural biofilms would enable use of these biofilms in bothin vitroandin vivostudies that require complex microbial communities with minimal variation over a period of time. Understanding biofilm community dynamics in continuous culture, and whether they maintain distinct signatures of health and disease, is necessary to determine the reliability and applicability of such models to broader studies. To this end, we performed next-generation sequencing on biofilms grown from healthy and disease-site subgingival plaque for 80 days to assess stability and reliability of continuous oral biofilm growth.Results: Biofilms were grown from subgingival plaque collected from periodontitis-affected sites and healthy individuals for ten eight-day long generations, using hydroxyapatite disks. The bacterial community in each generation was determined using Human Oral Microbe Identification by Next-Generation Sequencing (HOMINGS) technology, and analyzed in QIIME. Profiles were steady through the ten generations, as determined by species abundance and prevalence, Spearman’s correlation coefficient, and Faith’s phylogenetic distance, with slight variation predominantly in low abundance species. Community profiles were distinct between healthy and disease site-derived biofilms as demonstrated by weighted UniFrac distance throughout the ten generations. Differentially abundant species between healthy and disease site-derived biofilms were consistent throughout the generations.Conclusions: Healthy and disease site-derived biofilms can reliably maintain consistent communities through ten generations ofin vitrogrowth. These communities maintain signatures of health and disease and of individual donors despite culture in identical environments. This subgingival oral biofilm growth and perpetuation model may prove useful to studies involving oral infection or cell stimulation, or those measuring microbial interactions, which require the same biofilms over a period of time.
Full Bayesian Comparative Phylogeography from Genomic Data
Authors: Oaks Jamie R. 
Published: May 2018

AbstractA challenge to understanding biological diversification is accounting for community-scale processes that cause multiple, co-distributed lineages to co-speciate. Such processes predict non-independent, temporally clustered divergences across taxa. Approximate-likelihood Bayesian computation (ABC) approaches to inferring such patterns from comparative genetic data are very sensitive to prior assumptions and often biased toward estimating shared divergences. We introduce a full-likelihood Bayesian approach,ecoevolity, which takes full advantage of information in genomic data. By analytically integrating over gene trees, we are able to directly calculate the likelihood of the population history from genomic data, and efficiently sample the model-averaged posterior via Markov chain Monte Carlo algorithms. Using simulations, we find that the new method is much more accurate and precise at estimating the number and timing of divergence events across pairs of populations than existing approximate-likelihood methods. Our full Bayesian approach also requires several orders of magnitude less computational time than existing ABC approaches. We find that despite assuming unlinked characters (e.g., unlinked single-nucleotide polymorphisms), the new method performs better if this assumption is violated in order to retain the constant characters of whole linked loci. In fact, retaining constant characters allows the new method to robustly estimate the correct number of divergence events with high posterior probability in the face of character-acquisition biases, which commonly plague loci assembled from reduced-representation genomic libraries. We apply our method to genomic data from four pairs of insular populations ofGekkolizards from the Philippines that arenotexpected to have co-diverged. Despite all four pairs diverging very recently, our method strongly supports that they diverged independently, and these results are robust to very disparate prior assumptions.
Common Neural Code for Reward and Information Value
Authors: Kobayashi Kenji , Hsu Ming 
Published: May 2018

AbstractAdaptive acquisition of information is critical for goal-directed behavior. Popular theories posit that information acquisition is driven by intrinsic motives (curiosity or exploration bonus) and mediated by valuation system. However, they are insufficient when agents need to evaluate instrumental benefit of new information in a forward-looking manner. We tested whether human brain computes value of information (VOI) on a scale common with more basic rewards to acquire information. In an fMRI task, subjects purchased information for choices on monetary lotteries. Behaviorally, subjective VOI was largely driven by instrumental benefit, as normatively predicted, but additionally affected by non-instrumental motive, particularly the utility of anticipation. Neurally, VOI was represented in striatum, ventromedial prefrontal cortex, and dorsolateral prefrontal cortex. Cross-categorical decoding revealed that these regions use a common scale for VOI and another type of value, expected utility of the lotteries. These provide new insight on neurocognitive mechanism of forward-looking, value-based information acquisition.
Exceptional Solvent Tolerance inYarrowia lipolyticaIs Enhanced by Sterols
Authors: Walker Caleb , Ryu Seunghyun , Trinh Cong T. 
Published: May 2018

AbstractMicrobial biocatalysis in organic solvents such as ionic liquids (ILs) is attractive for making fuels and chemicals from complex substrates including lignocellulosic biomass. However, low IL concentrations of 0.5-1.0 % (v/v) can drastically inhibit microbial activity. In this study, we engineered an exceptionally robust oleaginous yeastYarrowia lipolytica, YlCW001, by adaptive laboratory evolution (ALE). The mutant YlWC001 shows robust growth in up to 18% (v/v) 1-ethyl-3-methylimidazolium acetate ([EMIM][OAc]), which makes it the most IL-tolerant microorganism published to our knowledge. Remarkably, YlCW001 exhibits broad tolerance in most commonly used hydrophilic ILs beyond [EMIM][OAc]. Scanning electron microscopy revealed that ILs significantly damage cell wall and/or membrane of wildtypeY. lipolyticawith observed cavities, dents, and wrinkles while YlCW001 maintains healthy morphology even in high concentrations of ILs up to 18% (v/v). By performing comprehensive metabolomics, lipidomics, and transcriptomics to elucidate this unique phenotype, we discovered that both wildtypeY. lipolyticaand YlCW001 reconfigured membrane composition (e.g., glycerophospholipids and sterols) and cell wall structure (e.g., chitin) under IL-stressful environments. By probing the steroid pathway at transcriptomic, enzymatic, and metabolic levels, we validated that sterols (i.e., ergosterol) are a key component of the cell membrane that enablesY. lipolyticato resist IL-responsive membrane damage and hence tolerate high IL concentrations. This study provides a better understanding of exceptional robustness ofY. lipolyticathat can be potentially harnessed as a microbial manufacturing platform for production of fuels and chemicals in organic solvents.
Multi-scale coordination of planar cell polarity in planarians
Authors: Vu Hanh Thi-Kim , Mansour Sarah , Kücken Michael , Blasse Corinna , Basquin Cyril , Azimzadeh Juliette , Myers Eugene Wimberly , Brusch Lutz , Rink Jochen Christian 
Published: May 2018

SummaryPolarity is a universal design principle of biological systems that manifests at all organizational scales. Although well understood at the cellular level, the mechanisms that coordinate polarity at the tissue or organismal scale remain poorly understood. Here, we make use of the extreme body plan plasticity of planarian flatworms to probe the multi-scale coordination of polarity. Quantitative analysis of ciliary rootlet orientation in the epidermis reveals a global polarization field with head and tail as independent mediators of anteroposterior (A/P) polarization and the body margin influencing mediolateral (M/L) polarization. Mathematical modeling demonstrates that superposition of separate A/P- and M/L-fields can explain the global polarity field and we identify the core planar cell polarity (PCP) and Ft/Ds pathways as their specific mediators. Overall, our study establishes a mechanistic framework for the multi-scale coordination of planar polarity in planarians and establishes the core PCP and Ft/Ds pathways as evolutionarily conserved 2D-polarization module.
Reliable and automatic epilepsy classification with affordable, consumer-grade electroencephalography in rural sub-Saharan Africa
Authors: van Hees Vincent T. , van Diessen Eric , Sinke Michel R.T. , Buitenhuis Jan W. , van der Maas Frank , Ridder Lars , Otte Willem M. 
Published: May 2018

AbstractEpilepsy is largely under-diagnosed in low-income and middle-income countries, due to lack of medical specialists and expensive electroencephalography (EEG) hardware. In this study we investigate if low-cost consumer-grade EEG in combination with machine learning techniques can offer a reliable screening tool to improve diagnosis rates.We acquired brain signals in people with epilepsy (N=163) and healthy controls (N=138) in two difficult-to-reach areas in rural Guinea-Bissau and Nigeria. Five minutes of fourteen channel resting-state EEG data were acquired with a portable, low-cost consumer-grade EEG recording headset. EEG channel time-series were divided in four-second artifact-free epochs and transformed into delta, theta, alpha, beta and gamma wavelet frequencies. Summary measures such as the mean, standard deviation, minimal value and maximal value of the epoch signal fluctuations were used to train a random forest classifier. Epilepsy diagnosis based on at least three months seizure calendar data was used as the gold standard diagnosis. To prevent too optimistic classification the trained model was evaluated with EEG data from subjects not used in the training. In addition, we tested a classification model trained on Nigeria data against data from people in Guinea-Bissau and vice versa. The most contributing data features in the EEG were found in the beta and theta frequencies in Guinea-Bissau and Nigeria, respectively. Within-country model performance was good with area under the receiver-operating curves of 0.85 and 0.78 (± 0.02 standard errors) in unseen data in Guinea-Bissau and Nigeria, respectively. Across-country performance was moderate (0.62 and 0.64 ± 0.02).Our data suggests that a combination of low cost electroencephalography and machine learning techniques may facilitate diagnostic screening for epilepsy in the most remote areas of the world.
Microbial life under ice: metagenome diversity and in situ activity of Verrucomicrobia in seasonally ice-covered lakes
Authors: Tran Patricia , Ramachandran Arthi , Khawasik Ola , Beisner Beatrix E. , Rautio Milla , Huot Yannick , Walsh David A. 
Published: May 2018

SummaryNorthern lakes are ice-covered for a large part of the year, yet our understanding of microbial diversity and activity during winter lags behind that of the ice-free period. In this study, we investigated under-ice diversity and metabolism of Verrucomicrobia in seasonally ice-covered lakes in temperate and boreal regions of Quebec, Canada using 16S rRNA sequencing, metagenomics and metatranscriptomics. Verrucomicrobia, particularly the V1, V3 and V4 subdivisions, were abundant during ice-covered periods. A diversity of Verrucomicrobia genomes were reconstructed from Quebec lake metagenomes. Several genomes were associated with the ice-covered period and were represented in winter metatranscriptomes, supporting the notion that Verrucomicrobia are metabolically active under ice. Verrucomicrobia transcriptome analysis revealed a range of metabolisms potentially occurring under ice, including carbohydrate degradation, glycolate utilization, scavenging of chlorophyll degradation products, and urea use. Genes for aerobic sulfur and hydrogen oxidation were expressed, suggesting chemolithotrophy may be an adaptation to conditions where labile carbon may be limited. The expression of genes for flagella biosynthesis and chemotaxis was detected, suggesting Verrucomicrobia may be actively sensing and responding to winter nutrient pulses, such as phytoplankton blooms. These results increase our understanding on the diversity and metabolic processes occurring under ice in northern lakes ecosystems.
Genomic evidence for the degradation of terrestrial organic matter by pelagic Arctic Ocean Chloroflexi bacteria
Authors: Colatriano David , Tran Patricia , Guéguen Celine , William Williams J. , Lovejoy Connie , Walsh David A. 
Published: May 2018

AbstractThe Arctic Ocean currently receives a large supply of global river discharge and terrestrial dissolved organic matter. Moreover, an increase in freshwater runoff and riverine transport of organic matter to the Arctic Ocean is a predicted consequence of thawing permafrost and increased precipitation. The fate of the terrestrial humic-rich organic material and its impact on the marine carbon cycle are largely unknown. Here, the first metagenomic survey of the Canada Basin in the Western Arctic Ocean showed that pelagic Chloroflexi from the Arctic Ocean are replete with aromatic compound degradation genes, acquired in part by lateral transfer from terrestrial bacteria. Our results imply marine Chloroflexi have the capacity to use terrestrial organic matter and that their role in the carbon cycle may increase with the changing hydrological cycle.