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Background: Global estimates for cholera annually approximate 4 million cases worldwide with 95,000 deaths. Recent outbreaks, including Haiti and Yemen, are reminders that cholera is still a global health concern. Cholera outbreaks can rapidly induce high death tolls by overwhelming the capacity of health facilities, especially in remote areas or areas of civil unrest. Recent studies demonstrated that stool specimens preserved on filter paper facilitate molecular analysis of Vibrio cholerae in resource limited settings. Specimens preserved in a rapid, low-cost, safe and sustainable manner for sequencing provides previously unavailable data about circulating cholera strains. This may ultimately provide new information to shape public policy response on cholera control and elimination. Methodology/Principal findings: Whole genome sequencing (WGS) recovered close to a complete sequence of the V. cholerae O1 genome with satisfactory genome coverage from stool specimens enriched in alkaline peptone water (APW) and V. cholerae culture isolates, both spotted on filter paper. The minimum concentration of V. cholerae DNA sufficient to produce quality genomic information was 0.02 ng/μL. The genomic data confirmed the presence or absence of genes of epidemiological interest, including cholera toxin and pilus loci. WGS identified a variety of diarrheal pathogens from APW-enriched specimen spotted filter paper, highlighting the potential for this technique to explore the gut microbiome, potentially identifying co-infections, which may impact the severity of disease. WGS demonstrated that these specimens fit within the current global cholera phylogenetic tree, identifying the strains as the 7th pandemic El Tor. Conclusions: WGS results allowed for mapping of short reads from APW-enriched specimen and culture isolate spotted filter papers this provided valuable molecular epidemiological sequence information on V. cholerae strains from remote, low-resource settings. These results identified the presence of co-infecting pathogens while providing rare insight into the specific V. cholerae strains causing outbreaks in cholera-endemic areas. [ABSTRACT FROM AUTHOR]

Cooperation among kin is common in animal societies. Kin groups may form by individuals directly discriminating relatives based on kin recognition cues, or form passively through natal philopatry and limited dispersal. We describe the genetic landscape for a primitively eusocial wasp, Polistes dominula, and ask whether individuals choose cooperative partners that are nearby and/or that are genetic relatives. Firstly, we genotyped an entire sub-population of 1361 wasps and found genetic structuring on an extremely fine scale: the probability of finding genetic relatives decreases exponentially within just a few meters of an individual’s nest. At the same time, however, we found a lack of genetic structuring between natural nest aggregations within the population. Secondly, in a separate dataset where ~2000 wasps were genotyped, we show that wasps forced experimentally to make a new nest choice tended to choose new nests near to their original nests, and that these nests tended to contain some full sisters. However, a significant fraction of wasps chose nests that did not contain sisters, despite sisters being present in nearby nests. Although we cannot rule out a role for direct kin recognition or natal nest-mate recognition, our data suggest that kin groups may form via a philopatric rule-of-thumb, whereby wasps simply select groups and nesting sites that are nearby. The result is that most subordinate helpers obtain indirect fitness benefits by breeding cooperatively. [ABSTRACT FROM AUTHOR]

Paper mulberry, Broussonetia papyrifera (L.) L’Hér. ex Vent. (Moraceae), a dioecious species, was transported by humans from Taiwan to the islands of Remote Oceania. Its introduction and cultivation in Remote Oceania was intentional due to its cultural importance as a fiber source for barkcloth textiles. The aim of this study was to explore the genetic diversity and structure of paper mulberry populations within Remote Oceania in order to infer dispersal patterns that may reflect past human interaction among island groups. We present the integrated analysis of 380 samples (313 contemporary and 67 herbarium specimens) collected in Near and Remote Oceania. Genetic characterization was based on a set of ten microsatellites developed for B. papyrifera and complemented with the analysis of the ribosomal internal transcribed spacer ITS-1 sequence, a sex marker and the chloroplast ndhF–rpl32 intergenic spacer. Microsatellite data identify a total of 64 genotypes, despite this being a clonally propagated crop, and show three major dispersal hubs within Remote Oceania, centered on the islands of Fiji, Tonga, and Pitcairn. Of 64 genotypes identified, 55 correspond to genotypes associated to female-sexed plants that probably descend from plants introduced by the prehistoric Austronesian-speaking voyagers. The ratio of accessions to genotypes between herbarium and contemporary samples, suggests recent loss of genetic diversity. In addition to the chloroplast haplotypes described previously, we detected two new haplotypes within Remote Oceania both originating in Taiwan. This is the first study of a commensal species to show genetic structuring within Remote Oceania. In spite of the genetic bottleneck, the presence of only one sex, a timespan of less than 5000 years, and asexual propagation of this crop in Remote Oceania, we detect genetic diversity and regional structuring. These observations suggest specific migration routes between island groups within Remote Oceania. [ABSTRACT FROM AUTHOR]

Biosensors have emerged as a valuable tool with high specificity and sensitivity for fast and reliable detection of hazardous substances in drinking water. Numerous substances have been addressed using synthetic biology approaches. However, many proposed biosensors are based on living, genetically modified organisms and are therefore limited in shelf life, usability and biosafety. We addressed these issues by the construction of an extensible, cell-free biosensor. Storage is possible through freeze drying on paper. Following the addition of an aqueous sample, a highly efficient cell-free protein synthesis (CFPS) reaction is initiated. Specific allosteric transcription factors modulate the expression of ‘superfolder’ green fluorescent protein (sfGFP) depending on the presence of the substance of interest. The resulting fluorescence intensities are analyzed with a conventional smartphone accompanied by simple and cheap light filters. An ordinary differential equitation (ODE) model of the biosensors was developed, which enabled prediction and optimization of performance. With an optimized cell-free biosensor based on the Shigella flexneri MerR transcriptional activator, detection of 6 μg/L Hg(II) ions in water was achieved. Furthermore, a completely new biosensor for the detection of gamma-hydroxybutyrate (GHB), a substance used as date-rape drug, was established by employing the naturally occurring transcriptional repressor BlcR from Agrobacterium tumefaciens. [ABSTRACT FROM AUTHOR]

The paper mulberry is one of the multifunctional tree species in agroforestry systems and is also commonly utilized in traditional medicine in China and other Asian countries. However, little is known about its molecular genetics, which hinders research on and exploitation of this valuable resource. To discern the correlation between gene expression and the essential properties of the paper mulberry, we performed a transcriptomics analysis, assembling a total of 37,725 unigenes from 54,638,676 reads generated by RNA-seq. Among these, 22,692 unigenes showed greater than 60% similarity with genes from other species. The lengths of 13,566 annotated unigenes were longer than 1,000 bp. Functional clustering analysis with COG (Cluster of Orthologous Groups) revealed that 17,184 unigenes are primarily involved in transcription, translation, signal transduction, carbohydrate metabolism, secondary metabolism, and energy metabolism. GO (Gene Ontology) annotation suggests enrichment of genes encoding antioxidant activity, transporter activity, biosynthesis, metabolism and stress response, with a total of 30,659 unigenes falling in these categories. KEGG (Kyoto Encyclopedia of Genes and Genomes) metabolic pathway analysis showed that 7,199 unigenes are associated with 119 metabolic pathways. In addition to the basic metabolism, these genes are enriched for plant pathogen interaction, flavonoid metabolism and other secondary metabolic processes. Furthermore, differences in the transcriptomes of leaf, stem and root tissues were analyzed and 7,233 specifically expressed unigenes were identified. This global expression analysis provided novel insights about the molecular mechanisms of the biosynthesis of flavonoid, lignin and cellulose, as well as on the response to biotic and abiotic stresses including the remediation of contaminated soil by the paper mulberry. [ABSTRACT FROM AUTHOR]

Amphimeriasis, a fish-borne zoonotic disease caused by the liver fluke Amphimerus spp., has recently been reported as an emerging disease affecting an indigenous Ameridian group, the Chachi, living in Ecuador. The only method for diagnosing amphimeriasis was the microscopic detection of eggs from the parasite in patients' stool samples with very low sensitivity. Our group developed an ELISA technique for detection of anti-Amphimerus IgG in human sera and a molecular method based on LAMP technology (named LAMPhimerus) for specific and sensitive parasite DNA detection. The LAMPhimerus method showed to be much more sensitive than classical parasitological methods for amphimeriasis diagnosis using human stool samples for analysis. The objective of this work is to demonstrate the feasibility of using dried stool samples on filter paper as source of DNA in combination with the effectiveness of our previously designed LAMPhimerus assay for successfully Amphimerus sp. detection in clinical stool samples. A total of 102 untreated and undiluted stool samples collected from Chachi population were spread as thin layer onto common filter paper for easily transportation to our laboratory and stored at room temperature for one year until DNA extraction. When LAMPhimerus method was applied for Amphimerus sp. DNA detection, a higher number of positive results was detected (61/102; 59.80%) in comparison to parasitological methods (38/102; 37.25%), including 28/61 (45.90%) microscopy-confirmed Amphimerus sp. infections. The diagnostic parameters for the sensitivity and specificity werecalculated for our LAMPhimerus assay, which were 79.17% and 65.98%, respectively. We demonstrate, for the first time, that common filter paper is useful for easy collection and long-term storage of human stool samples for later DNA extraction and molecular analysis of human-parasitic trematode eggs. This simple, economic and easily handling method combined with the specific and sensible LAMPhimerus assay has the potential to beused as an effective molecular large-scale screening test for amphimeriasis-endemic areas. [ABSTRACT FROM AUTHOR]

Swarm-founding wasps are endemic and common representatives of neotropical fauna and compose an interesting social tribe of vespids, presenting both complex social characteristics and uncommon traits for a eusocial group, such as the absence of castes with distinct morphology. The paper wasp Protonectarina sylveirae (Saussure) presents a broad distribution from Brazil, Argentina and Paraguay, occurring widespread in the Atlantic rainforest and arboreal Caatinga, being absent in the Amazon region. Given the peculiar distribution among swarm-founding wasps, an integrative approach to reconstruct the evolutionary history of P. sylveirae in a spatial-temporal framework was performed to investigate: the presence of genetic structure and its relationship with the geography, the evolution of distinct morphologic lineages and the possible historical event(s) in Neotropical region, which could explain the observed phylogeographic pattern. Individuals of P. sylveirae were obtained from populations of 16 areas throughout its distribution for DNA extraction and amplification of mitochondrial genes 12S, 16S and COI. Analysis of genetic diversity, construction of haplotype net, analysis of population structure and dating analysis of divergence time were performed. A morphometric analysis was also performed using 8 measures of the body of the adult (workers) to test if there are morphological distinction among populations. Thirty-five haplotypes were identified, most of them exclusively of a group and a high population structure was found. The possibility of genetic divergence because of isolation by distance was rejected. Morphological analysis pointed to a great uniformity in phenotypes, with only a small degree of differentiation between populations of south and the remaining. Divergence time analysis showed a Middle/Late Miocene origin, a period where an extensive marine ingression occurred in South America. Divergence of haplogroups began from the Plio/Pleistocene boundary and the last glacial maximum most likely modeled the current distribution of species, even though it was not the cause of genetic breaks. [ABSTRACT FROM AUTHOR]

Endocrine disrupting compounds (EDCs) are chemicals that negatively impact endocrine system function, with effluent from paper mills one example of this class of chemicals. In Florida, female Eastern mosquitofish (Gambusia holbrooki) have been observed with male secondary sexual characteristics at three paper mill-impacted sites, indicative of EDC exposure, and are still found at one site on the Fenholloway River. The potential impacts that paper mill effluent exposure has on the G. holbrooki endocrine system and the stream ecosystem are unknown. The objective of this study was to use gene expression analysis to determine if exposure to an androgen receptor agonist was occurring and to couple this analysis with in vitro assays to evaluate the presence of androgen and progesterone receptor active chemicals in the Fenholloway River. Focused gene expression analyses of masculinized G. holbrooki from downstream of the Fenholloway River paper mill were indicative of androgen exposure, while genes related to reproduction indicated potential progesterone exposure. Hepatic microarray analysis revealed an increase in the expression of metabolic genes in Fenholloway River fish, with similarities in genes and biological processes compared to G. holbrooki exposed to androgens. Water samples collected downstream of the paper mill and at a reference site indicated that progesterone and androgen receptor active chemicals were present at both sites, which corroborates previous chemical analyses. Results indicate that G. holbrooki downstream of the Fenholloway River paper mill are impacted by a mixture of both androgens and progesterones. This research provides data on the mechanisms of how paper mill effluents in Florida are acting as endocrine disruptors. [ABSTRACT FROM AUTHOR]

White birch (Betula papyrifera) is a dominant tree species of the Boreal Forest. Recent studies have shown that it is fairly resistant to heavy metal contamination, specifically to nickel. Knowledge of regulation of genes associated with metal resistance in higher plants is very sketchy. Availability and annotation of the dwarf birch (B. nana) enables the use of high throughout sequencing approaches to understanding responses to environmental challenges in other Betula species such as B. papyrifera. The main objectives of this study are to 1) develop and characterize the B. papyrifera transcriptome, 2) assess gene expression dynamics of B. papyrifera in response to nickel stress, and 3) describe gene function based on ontology. Nickel resistant and susceptible genotypes were selected and used for transcriptome analysis. A total of 208,058 trinity genes were identified and were assembled to 275,545 total trinity transcripts. The transcripts were mapped to protein sequences and based on best match; we annotated the B. papyrifera genes and assigned gene ontology. In total, 215,700 transcripts were annotated and were compared to the published B. nana genome. Overall, a genomic match for 61% transcripts with the reference genome was found. Expression profiles were generated and 62,587 genes were found to be significantly differentially expressed among the nickel resistant, susceptible, and untreated libraries. The main nickel resistance mechanism in B. papyrifera is a downregulation of genes associated with translation (in ribosome), binding, and transporter activities. Five candidate genes associated to nickel resistance were identified. They include Glutathione S–transferase, thioredoxin family protein, putative transmembrane protein and two Nramp transporters. These genes could be useful for genetic engineering of birch trees. [ABSTRACT FROM AUTHOR]

In this months editorial, the PLOS Medicine Editors announce an upcoming Special Issue and call for papers, with Guest Editors Carol Brayne and Bruce Miller, on dementia across the lifespan and around the globe. [ABSTRACT FROM AUTHOR]

Written communication plays a crucial role in the history of modern civilizations as manuscripts do not only exist contemporarily, but are passed on to subsequent generations. Besides a document’s content, information is stored in the materials used for its production. Analyses of the composition allow, for example, identifying the biological origins of materials, dating, and help to understand degradation patterns. A combination of microscopic and DNA approaches was applied in order to analyze various plant derived writing sheets. Given their diversity and abundance in museum collections, plant based writing supports are yet an underexplored target for DNA studies. DNA retrieval of paper is low compared to raw paper plant material, which is likely due to the loss of organic components during paper production. Optimizing DNA extraction for each respective material drastically increased DNA recovery. Finally, we present a non-invasive DNA sampling method that utilizes nylon membranes, commonly used for bacterial DNA sampling and that is applicable to delicate material. Although bacterial infestation was visible on one sample, as indicated by scanning electron microscopy, endogenous DNA was retrieved. The results presented here are promising as they extend the scope of sources for DNA analyses by demonstrating that DNA molecules can be retrieved from a variety of plant derived writing supports. In future, such analyses can help to explore the biological diversity not only of plants and of additives utilized for producing writing supports, but also of the plenty products made from paper. [ABSTRACT FROM AUTHOR]

The interaction between phenotypic plasticity, e.g. learning, and evolution is an important topic both in Evolutionary Biology and Machine Learning. The evolution of learning is commonly studied in Evolutionary Biology, while the use of an evolutionary process to improve learning is of interest to the field of Machine Learning. This paper takes a different point of view by studying the effect of learning on the evolutionary process, the so-called Baldwin effect. A well-studied result in the literature about the Baldwin effect is that learning affects the speed of convergence of the evolutionary process towards some genetic configuration, which corresponds to the environment-induced plastic response. This paper demonstrates that learning can change the outcome of evolution, i.e., lead to a genetic configuration that does not correspond to the plastic response. Results are obtained both analytically and experimentally by means of an agent-based model of a foraging task, in an environment where the distribution of resources follows seasonal cycles and the foraging success on different resource types is conditioned by trade-offs that can be evolved and learned. This paper attempts to answer a question that has been overlooked: whether learning has an effect on what genotypic traits are evolved, i.e. the selection of a trait that enables a plastic response changes the selection pressure on a different trait, in what could be described as co-evolution between different traits in the same genome. [ABSTRACT FROM AUTHOR]

Open access publication rates have been steadily increasing over time. In spite of this growth, academics in low income settings struggle to gain access to the full canon of research literature. While the vast majority of open access repositories and funding organizations with open access policies are based in high income countries, the geographic patterns of open access publication itself are not well characterized. In this study, we developed a computational approach to better understand the topical and geographical landscape of open access publications in the biomedical research literature. Surprisingly, we found a strong negative correlation between country per capita income and the percentage of open access publication. Open access publication rates were particularly high in sub-Saharan Africa, but vastly lower in the Middle East and North Africa, South Asia, and East Asia and the Pacific. These effects persisted when considering papers only bearing authors from within each region and income group. However, papers resulting from international collaborations did have a higher percentage of OA than single-country papers, and inter-regional collaboration increased OA publication for all world regions. There was no clear relationship between the number of open access policies in a region and the percentage of open access publications in that region. To understand the distribution of open access across topics of biomedical research, we examined keywords that were most enriched and depleted in open access papers. Keywords related to genomics, computational biology, animal models, and infectious disease were enriched in open access publications, while keywords related to the environment, nursing, and surgery were depleted in open access publications. This work identifies geographic regions and fields of research that could be priority areas for open access advocacy. The finding that open access publication rates are highest in sub-Saharan Africa and low income countries suggests that factors other than open access policy strongly influence authors’ decisions to make their work openly accessible. The high proportion of OA resulting from international collaborations indicates yet another benefit of collaborative research. Certain applied fields of medical research, notably nursing, surgery, and environmental fields, appear to have a greater proportion of fee-for-access publications, which presumably creates barriers that prevent researchers and practitioners in low income settings from accessing the literature in those fields. [ABSTRACT FROM AUTHOR]

Developing new software tools for analysis of large-scale biological data is a key component of advancing modern biomedical research. Scientific reproduction of published findings requires running computational tools on data generated by such studies, yet little attention is presently allocated to the installability and archival stability of computational software tools. Scientific journals require data and code sharing, but none currently require authors to guarantee the continuing functionality of newly published tools. We have estimated the archival stability of computational biology software tools by performing an empirical analysis of the internet presence for 36,702 omics software resources published from 2005 to 2017. We found that almost 28% of all resources are currently not accessible through uniform resource locators (URLs) published in the paper they first appeared in. Among the 98 software tools selected for our installability test, 51% were deemed “easy to install,” and 28% of the tools failed to be installed at all because of problems in the implementation. Moreover, for papers introducing new software, we found that the number of citations significantly increased when authors provided an easy installation process. We propose for incorporation into journal policy several practical solutions for increasing the widespread installability and archival stability of published bioinformatics software. [ABSTRACT FROM AUTHOR]

Computational modeling of engineered gene circuits is an important while challenged task in systems biology. In order to describe and predict the response behaviors of genetic circuits using reliable model parameters, this paper applies an optimal experimental design(OED) method to obtain input signals. In order to obtain informative observations, this study focuses on maximizing Fisher information matrix(FIM)-based optimal criteria and to provide optimal inputs. Furthermore, this paper designs a two-stage optimization with the modified E-optimal criteria and applies harmony search(HS)-based OED algorithm to minimize estimation errors. The proposed optimal identification methodology involves estimation errors and the sample size to pursue a trade-off between estimation accuracy and measurement cost in modeling gene networks. The designed cost function takes two major factors into account, in which experimental costs are proportional to the number of time points. Experiments select two types of synthetic genetic networks to validate the effectiveness of the proposed HS-OED approach. Identification outcomes and analysis indicate the proposed HS-OED method outperforms two candidate OED approaches, with reduced computational effort. [ABSTRACT FROM AUTHOR]

To solve the problem of the deadline-constrained task scheduling in the cloud computing system, this paper proposes a deadline-constrained scheduling algorithm for cloud computing based on the driver of dynamic essential path (Deadline-DDEP). According to the changes of the dynamic essential path of each task node in the scheduling process, the dynamic sub-deadline strategy is proposed. The strategy assigns different sub-deadline values to every task node to meet the constraint relations among task nodes and the user’s defined deadline. The strategy fully considers the dynamic sub-deadline affected by the dynamic essential path of task node in the scheduling process. The paper proposed the quality assessment of optimization cost strategy to solve the problem of selecting server for each task node. Based on the sub-deadline urgency and the relative execution cost in the scheduling process, the strategy selects the server that not only meets the sub-deadline but also obtains much lower execution cost. In this way, the proposed algorithm will make the task graph complete within its deadline, and minimize its total execution cost. Finally, we demonstrate the proposed algorithm via the simulation experiments using Matlab tools. The experimental results show that, the proposed algorithm produces remarkable performance improvement rate on the total execution cost that ranges between 10.3% and 30.8% under meeting the deadline constraint. In view of the experimental results, the proposed algorithm provides better-quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment than IC-PCP, DCCP and CD-PCP. [ABSTRACT FROM AUTHOR]

Introduction: The system of clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (cas) is a new technology that allows easier manipulation of the genome. Its potential to edit genes opened a new door in treatment development for incurable neurological monogenic diseases (NMGDs). The aim of this systematic review was to summarise the findings on the current development of CRISPR-cas for therapeutic purposes in the most frequent NMGDs and provide critical assessment. Methods and data acquisition: We searched the MEDLINE and EMBASE databases, looking for original studies on the use of CRISPR-cas to edit pathogenic variants in models of the most frequent NMGDs, until end of 2017. We included all the studies that met the following criteria: 1. Peer-reviewed study report with explicitly described experimental designs; 2. In vitro, ex vivo, or in vivo study using human or other animal biological systems (including cells, tissues, organs, organisms); 3. focusing on CRISPR as the gene-editing method of choice; and 5. featured at least one NMGD. Results: We obtained 404 papers from MEDLINE and 513 from EMBASE. After removing the duplicates, we screened 490 papers by title and abstract and assessed them for eligibility. After reading 50 full-text papers, we finally selected 42 for the review. Discussion: Here we give a systematic summary on the preclinical development of CRISPR-cas for therapeutic purposes in NMGDs. Furthermore, we address the clinical interpretability of the findings, giving a comprehensive overview of the current state of the art. Duchenne’s muscular dystrophy (DMD) paves the way forward, with 26 out of 42 studies reporting different strategies on DMD gene editing in different models of the disease. Most of the strategies aimed for permanent exon skipping by deletion with CRISPR-cas. Successful silencing of the mHTT gene with CRISPR-cas led to successful reversal of the neurotoxic effects in the striatum of mouse models of Huntington’s disease. Many other strategies have been explored, including epigenetic regulation of gene expression, in cellular and animal models of: myotonic dystrophy, Fraxile X syndrome, ataxias, and other less frequent dystrophies. Still, before even considering the clinical application of CRISPR-cas, three major bottlenecks need to be addressed: efficacy, safety, and delivery of the systems. This requires a collaborative approach in the research community, while having ethical considerations in mind. [ABSTRACT FROM AUTHOR]

The varietal authentication of wines is fundamental for assessing wine quality, and it is part of its compositional profiling. The availability of historical, cultural and chemical composition information is extremely important for quality evaluation. DNA-based techniques are a powerful tool for proving the varietal composition of a wine. SSR-amplification of genomic residual Vitis vinifera DNA, namely Wine DNA Fingerprinting (WDF) is able to produce strong, analytical evidence concerning the monovarietal nature of a wine, and for blended wines by generating the probability of the presence/absence of a certain variety, all in association with a dedicated bioinformatics elaboration of genotypes associated with possible varietal candidates. Together with WDF we could exploit Bioinformatics techniques, due to the number of grape genomes grown. In this paper, the use of WDF and the development of a bioinformatics tool for allelic data validation, retrieved from the amplification of 7 to 10 SSRs markers in the Vitis vinifera genome, are reported. The wines were chosen based on increasing complexity; from monovarietal, experimental ones, to commercial monovarietals, to blended commercial wines. The results demonstrate that WDF, after calculation of different distance matrices and Neighbor-Joining input data, followed by Principal Component Analysis (PCA) can effectively describe the varietal nature of wines. In the unknown blended wines the WDF profiles were compared to possible varietal candidates (Merlot, Pinot Noir, Cabernet Sauvignon and Zinfandel), and the output graphs show the most probable varieties used in the blend as closeness to the tested wine. This pioneering work should be meant as to favor in perspective the multidisciplinary building-up of on-line databanks and bioinformatics toolkits on wine. The paper concludes with a discussion on an integrated decision support system based on bioinformatics, chemistry and cultural data to assess wine quality. [ABSTRACT FROM AUTHOR]

Understanding expression levels of proteins and their interactions is a key factor to diagnose and explain the Down syndrome which can be considered as the most prevalent reason of intellectual disability in human beings. In the previous studies, the expression levels of 77 proteins obtained from normal genotype control mice and from trisomic Ts65Dn mice have been analyzed after training in contextual fear conditioning with and without injection of the memantine drug using statistical methods and machine learning techniques. Recent studies have also pointed out that there may be a linkage between the Down syndrome and the immune system. Thus, the research presented in this paper aim at in silico identification of proteins which are significant to the learning process and the immune system and to derive the most accurate model for classification of mice. In this paper, the features are selected by implementing forward feature selection method after preprocessing step of the dataset. Later, deep neural network, gradient boosting tree, support vector machine and random forest classification methods are implemented to identify the accuracy. It is observed that the selected feature subsets not only yield higher accuracy classification results but also are composed of protein responses which are important for the learning and memory process and the immune system. [ABSTRACT FROM AUTHOR]

Genetic maps order genetic markers along chromosomes. They are, for instance, extensively used in marker-assisted selection to accelerate breeding programs. Even for the same species, people often have to deal with several alternative maps obtained using different ordering methods or different datasets, e.g. resulting from different segregating populations. Having efficient tools to identify the consistency and discrepancy of alternative maps is thus essential to facilitate genetic map comparisons. We propose to encode genetic maps by bucket order, a kind of order, which takes into account the blurred parts of the marker order while being an efficient data structure to achieve low complexity algorithms. The main result of this paper is an O(n log(n)) procedure to identify the largest agreements between two bucket orders of n elements, their Longest Common Subsequence (LCS), providing an efficient solution to highlight discrepancies between two genetic maps. The LCS of two maps, being the largest set of their collinear markers, is used as a building block to compute pairwise map congruence, to visually emphasize maker collinearity and in some scaffolding methods relying on genetic maps to improve genome assembly. As the LCS computation is a key subroutine of all these genetic map related tools, replacing the current LCS subroutine of those methods by ours –to do the exact same work but faster– could significantly speed up those methods without changing their accuracy. To ease such transition we provide all required algorithmic details in this self contained paper as well as an R package implementing them, named LCSLCIS, which is freely available at: . [ABSTRACT FROM AUTHOR]

Introduction: Stroke is a prominent cause of death, disability, and dementia in sub-Saharan Africa (SSA). The Stroke Investigative Research and Education Network works collaboratively with stroke survivors and individuals serving as community controls to comprehensively characterize the genomic, sociocultural, economic and behavioral risk factors for stroke in SSA. Purpose: In this paper, we aim to: i) explore the attitudes, beliefs, and practices related to stroke in Ghana and Nigeria using the process of qualitative description; and ii) propose actions for future research and community-based participation and education. Methods: Stroke survivors, their caregivers, health care professionals, and community representatives and faith-based leaders participated in one of twenty-six focus groups, which qualitatively explored community beliefs, attitudes and practices related to stroke in Ghana and Nigeria. Arthur Kleinman’s Explanatory Model of Illness and the Social Ecological Model guided the questions and/or thematic analysis of the qualitative data. We hereby describe our focus group methods and analyses of qualitative data, as well as the findings and suggestions for improving stroke outcomes. Results and discussion: The major findings illustrate the fears, causes, chief problems, treatment, and recommendations related to stroke through the views of the participants, as well as recommendations for working effectively with the SIREN communities. Findings are compared to SIREN quantitative data and other qualitative studies in Africa. As far as we are aware, this is the first paper to qualitatively explore and contrast community beliefs, attitudes, and practices among stroke survivors and their caregivers, community and faith-based leaders, and health professionals in multiple communities within Nigeria and Ghana. [ABSTRACT FROM AUTHOR]

Selfish genetic elements (historically also referred to as selfish genes, ultra-selfish genes, selfish DNA, parasitic DNA, genomic outlaws) are genetic segments that can enhance their own transmission at the expense of other genes in the genome, even if this has no or a negative effect on organismal fitness. [–] Genomes have traditionally been viewed as cohesive units, with genes acting together to improve the fitness of the organism. However, when genes have some control over their own transmission, the rules can change, and so just like all social groups, genomes are . Early observations of selfish genetic elements were made almost a century ago, but the topic did not get widespread attention until several decades later. Inspired by the popularized by [] and ,[] two papers were published back-to-back in Nature in 1980—by and [] and and Carmen Sapienza[] respectively—introducing the concept of selfish genetic elements (at the time called “selfish DNA”) to the wider scientific community. Both papers emphasized that genes can spread in a population regardless of their effect on organismal fitness as long as they have a transmission advantage. Selfish genetic elements have now been described in most groups of organisms, and they demonstrate a remarkable diversity in the ways by which they promote their own transmission.[] Though long dismissed as genetic curiosities, with little relevance for evolution, they are now recognized to affect a wide swath of biological processes, ranging from genome size and architecture to speciation.[] [ABSTRACT FROM AUTHOR]

In recent years, the explosion of genomic data and bioinformatic tools has been accompanied by a growing conversation around reproducibility of results and usability of software. However, the actual state of the body of bioinformatics software remains largely unknown. The purpose of this paper is to investigate the state of source code in the bioinformatics community, specifically looking at relationships between code properties, development activity, developer communities, and software impact. To investigate these issues, we curated a list of 1,720 bioinformatics repositories on GitHub through their mention in peer-reviewed bioinformatics articles. Additionally, we included 23 high-profile repositories identified by their popularity in an online bioinformatics forum. We analyzed repository metadata, source code, development activity, and team dynamics using data made available publicly through the GitHub API, as well as article metadata. We found key relationships within our dataset, including: certain scientific topics are associated with more active code development and higher community interest in the repository; most of the code in the main dataset is written in dynamically typed languages, while most of the code in the high-profile set is statically typed; developer team size is associated with community engagement and high-profile repositories have larger teams; the proportion of female contributors decreases for high-profile repositories and with seniority level in author lists; and, multiple measures of project impact are associated with the simple variable of whether the code was modified at all after paper publication. In addition to providing the first large-scale analysis of bioinformatics code to our knowledge, our work will enable future analysis through publicly available data, code, and methods. Code to generate the dataset and reproduce the analysis is provided under the MIT license at . Data are available at . [ABSTRACT FROM AUTHOR]

Mutation cluster analysis is critical for understanding certain mutational mechanisms relevant to genetic disease, diversity, and evolution. Yet, whole genome sequencing for detection of mutation clusters is prohibitive with high cost for most organisms and population surveys. Single nucleotide polymorphism (SNP) genotyping arrays, like the Mouse Diversity Genotyping Array, offer an alternative low-cost, screening for mutations at hundreds of thousands of loci across the genome using experimental designs that permit capture of de novo mutations in any tissue. Formal statistical tools for genome-wide detection of mutation clusters under a microarray probe sampling system are yet to be established. A challenge in the development of statistical methods is that microarray detection of mutation clusters is constrained to select SNP loci captured by probes on the array. This paper develops a Monte Carlo framework for cluster testing and assesses test statistics for capturing potential deviations from spatial randomness which are motivated by, and incorporate, the array design. While null distributions of the test statistics are established under spatial randomness via the homogeneous Poisson process, power performance of the test statistics is evaluated under postulated types of Neyman-Scott clustering processes through Monte Carlo simulation. A new statistic is developed and recommended as a screening tool for mutation cluster detection. The statistic is demonstrated to be excellent in terms of its robustness and power performance, and useful for cluster analysis in settings of missing data. The test statistic can also be generalized to any one dimensional system where every site is observed, such as DNA sequencing data. The paper illustrates how the informal graphical tools for detecting clusters may be misleading. The statistic is used for finding clusters of putative SNP differences in a mixture of different mouse genetic backgrounds and clusters of de novo SNP differences arising between tissues with development and carcinogenesis. [ABSTRACT FROM AUTHOR]

Background: Body size, nutrition and socioeconomic position (SEP) in early life have been associated with a wide range of long-term health effects. Epigenetics is one possible mechanism through which these early life exposures can impact later life health. We conducted a systematic review examining the observational evidence for the impact of body size, nutrition and SEP in early life on the epigenome in humans. Methods: This systematic review is registered with the PROSPERO database (registration number: CRD42016050193). Three datasets were simultaneously searched using Ovid and the resulting studies were evaluated by at least two independent reviewers. Studies measuring epigenetic markers either at the same time as, or after, the early life exposure and have a measure of body size, nutrition or SEP in early life (up to 12 years), written in English and from a community-dwelling participants were included. Results: We identified 90 eligible studies. Seventeen of these papers examined more than one early life exposure of interest. Fifty six papers examined body size, 37 nutrition and 17 SEP. All of the included papers examined DNA methylation (DNAm) as the epigenetic marker. Overall there was no strong evidence for a consistent association between these early life variables in DNAm which may be due to the heterogeneous study designs, data collection methods and statistical analyses. Conclusions: Despite these inconclusive results, the hypothesis that the early life environment can impact DNAm, potentially persisting into adult life, was supported by some studies and warrants further investigation. We provide recommendations for future studies. [ABSTRACT FROM AUTHOR]

In this month's editorial, the PLOS Medicine Editors announce an upcoming Special Issue, with Guest Editors Elaine Mardis and Marc Ladanyi, on actionable advances in research on the cancer genome. [ABSTRACT FROM AUTHOR]

Engineering faster cellulose deconstruction is difficult because it is a complex, cooperative, multi-enzyme process. Here we use experimental evolution to select for populations of Trichoderma citrinoviride that deconstruct up to five-fold more cellulose. Ten replicate populations of T. citrinoviride were selected for growth on filter paper by serial culture. After 125 periods of growth and transfer to fresh media, the filter paper deconstruction increased an average of 2.5 fold. Two populations were examined in more detail. The activity of the secreted cellulase mixtures increased more than two-fold relative to the ancestor and the largest increase was in the extracellular β-glucosidase activity. qPCR showed at least 16-fold more transcribed RNA for egl4 (endoglucanase IV gene), cbh1 (cellobiohydrolase I gene) and bgl1 (extracellular β-glucosidase I gene) in selected populations as compared to the ancestor, and earlier peak expressions of these genes. Deep sequencing shows that the regulatory strategies used to alter cellulase secretion differ in the two strains. The improvements in cellulose deconstruction come from earlier expression of all cellulases and increased relative amount of β-glucosidase, but with small increases in the total secreted protein and therefore little increase in metabolic cost. [ABSTRACT FROM AUTHOR]

In this paper we compare systematically the most promising neuroevolutionary methods and two new original methods on the double-pole balancing problem with respect to: the ability to discover solutions that are robust to variations of the environment, the speed with which such solutions are found, and the ability to scale-up to more complex versions of the problem. The results indicate that the two original methods introduced in this paper and the Exponential Natural Evolutionary Strategy method largely outperform the other methods with respect to all considered criteria. The results collected in different experimental conditions also reveal the importance of regulating the selective pressure and the importance of exposing evolving agents to variable environmental conditions. The data collected and the results of the comparisons are used to identify the most effective methods and the most promising research directions. [ABSTRACT FROM AUTHOR]

Established cell lines are widely used in research, however an appealing question is the comparability of the cells between various laboratories, their characteristics and stability in time. Problematic is also the cell line misidentification, genetic and phenotypic shift or Mycoplasma contamination which are often forgotten in research papers. The monocyte/macrophage-like cell line RAW 264.7 has been one of the most commonly used myeloid cell line for more than 40 years. Despite its phenotypic and functional stability is often discussed in literature or at various scientific discussion panels, their stability during the consecutive passages has not been confirmed in any solid study. So far, only a few functional features of these cells have been studied, for example their ability to differentiate into osteoclasts. Therefore, in the present paper we have investigated the phenotype and functional stability of the RAW 264.7 cell line from passage no. 5 till passage no. 50. We found out that the phenotype (expression of particular macrophage-characteristic genes and surface markers) and functional characteristics (phagocytosis and NO production) of RAW 264.7 cell line remains stable through passages: from passage no. 10 up to passage no. 30. Overall, our results indicated that the RAW 264.7 cell line should not be used after the passage no. 30 otherwise it may influence the data reliability. [ABSTRACT FROM AUTHOR]

The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm’s performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms. [ABSTRACT FROM AUTHOR]

Environmental stochasticity and climate affect outcomes in evolutionary games, which can thereby affect biological diversity. Our maximum likelihood (ML) estimates of replicator dynamics for morph frequency data from control (25 years) and three experimentally perturbed populations (14 years) of side-blotched lizards yield a 3 × 3 payoff matrix in the generalized Rock-Paper-Scissors family; it has intransitive best replies, and each strategy is its own worst reply. ML estimates indicate significant interactive effects of density and temperature on morph frequency. Implied dynamics feature a powerful interior attractor and recover (for the first time) observed 4-5 year oscillations. Our evolutionary experiment on morph frequency confirms that oscillations are driven by frequency dependent selection, but climate entrains the cycles across the perturbed and control populations within 10 generations. Applying the model across the species range, we find that climate also accounts for morph fixation and mating system diversity, suggesting climate may similarly impact ecosystem diversity. [ABSTRACT FROM AUTHOR]

The gut microbial community structure of adult Thrips tabaci collected from 10 different agro-climatically diverse locations of India was characterized by using the Illumina MiSeq platform to amplify the V3 region of the 16S rRNA gene of bacteria present in the sampled insects. Analyses were performed to study the bacterial communities associated with Thrips tabaci in India. The complete bacterial metagenome of T. tabaci was comprised of 1662 OTUs of which 62.25% belong to known and 37.7% of unidentified/unknown bacteria. These OTUs constituted 21 bacterial phyla of 276 identified genera. Phylum Proteobacteria was predominant, followed by Actinobacteria, Firmicutes, Bacteroidetes and Cyanobacteria. Additionally, the occurrence of the reproductive endosymbiont, Wolbachia was detected at two locations (0.56%) of the total known OTUs. There is high variation in diversity and species richness among the different locations. Alpha-diversity metrics indicated the higher gut bacterial diversity at Bangalore and lowest at Rahuri whereas higher bacterial species richness at T. tabaci samples from Imphal and lowest at Jhalawar. Beta diversity analyses comparing bacterial communities between the samples showed distinct differences in bacterial community composition of T. tabaci samples from different locations. This paper also constitutes the first record of detailed bacterial communities associated with T. tabaci. The location-wise variation in microbial metagenome profile of T. tabaci suggests that bacterial diversity might be governed by its population genetic structure, environment and habitat. [ABSTRACT FROM AUTHOR]

The Epstein-Barr virus (EBV) induces B-cell proliferation with high efficiency through expression of latent proteins and microRNAs. This process takes place in vivo soon after infection, presumably to expand the virus reservoir, but can also induce pathologies, e.g. an infectious mononucleosis (IM) syndrome after primary infection or a B-cell lymphoproliferation in immunosuppressed individuals. In this paper, we investigated the growth characteristics of EBV-infected B-cells isolated from transplant recipients or patients with IM. We found that these cells grew and withstood apoptosis at highly variable rates, suggesting that the expansion rate of the infected B-cells widely varies between individuals, thereby influencing the size of the B-cell reservoir and the ability to form tumors in infected individuals. All viruses investigated were type 1 and genetically close to western strains. EBV-infected B-cells expressed the transforming EBV latent genes and microRNAs (miRNAs) at variable levels. We found that the B-cell growth rates positively correlated with the BHRF1 miRNA levels. Comparative studies showed that infected B-cells derived from transplant recipients with iEBVL on average expressed higher levels of EBV miR-BHRF1 miRNAs and grew more rapidly than B-cells from IM patients, suggesting infection by more transforming viruses. Altogether, these findings suggest that EBV infection has a highly variable impact on the B-cell compartment that probably reflects the genetic diversity of both the virus and the host. It also demonstrates the unexpected finding that B-cells from different individuals can grow at different speed under the influence of the same virus infection. [ABSTRACT FROM AUTHOR]

Cancer driver genes, i.e., oncogenes and tumor suppressor genes, are involved in the acquisition of important functions in tumors, providing a selective growth advantage, allowing uncontrolled proliferation and avoiding apoptosis. It is therefore important to identify these driver genes, both for the fundamental understanding of cancer and to help finding new therapeutic targets or biomarkers. Although the most frequently mutated driver genes have been identified, it is believed that many more remain to be discovered, particularly for driver genes specific to some cancer types. In this paper, we propose a new computational method called LOTUS to predict new driver genes. LOTUS is a machine-learning based approach which allows to integrate various types of data in a versatile manner, including information about gene mutations and protein-protein interactions. In addition, LOTUS can predict cancer driver genes in a pan-cancer setting as well as for specific cancer types, using a multitask learning strategy to share information across cancer types. We empirically show that LOTUS outperforms five other state-of-the-art driver gene prediction methods, both in terms of intrinsic consistency and prediction accuracy, and provide predictions of new cancer genes across many cancer types. [ABSTRACT FROM AUTHOR]

The solution to the job shop scheduling problem (JSSP) is of great significance for improving resource utilization and production efficiency of enterprises. In this paper, in view of its non-deterministic polynomial properties, a multi-agent genetic algorithm based on tabu search (MAGATS) is proposed to solve JSSPs under makespan constraints. Firstly, a multi-agent genetic algorithm (MAGA) is proposed. During the process, a multi-agent grid environment is constructed based on characteristics of multi-agent systems and genetic algorithm (GA), and a corresponding neighbor interaction operator, a mutation operator based on neighborhood structure and a self-learning operator are designed. Then, combining tabu search algorithm with a MAGA, the algorithm MAGATS are presented. Finally, 43 benchmark instances are tested with the new algorithm. Compared with four other algorithms, the optimization performance of it is analyzed based on obtained test results. Effectiveness of the new algorithm is verified by analysis results. [ABSTRACT FROM AUTHOR]

Methods have been developed for Mendelian randomization that can obtain consistent causal estimates under weaker assumptions than the standard instrumental variable assumptions. The median-based estimator and MR-Egger are examples of such methods. However, these methods can be sensitive to genetic variants with heterogeneous causal estimates. Such heterogeneity may arise from over-dispersion in the causal estimates, or specific variants with outlying causal estimates. In this paper, we develop three extensions to robust methods for Mendelian randomization with summarized data: 1) robust regression (MM-estimation); 2) penalized weights; and 3) Lasso penalization. Methods using these approaches are considered in two applied examples: one where there is evidence of over-dispersion in the causal estimates (the causal effect of body mass index on schizophrenia risk), and the other containing outliers (the causal effect of low-density lipoprotein cholesterol on Alzheimer’s disease risk). Through an extensive simulation study, we demonstrate that robust regression applied to the inverse-variance weighted method with penalized weights is a worthwhile additional sensitivity analysis for Mendelian randomization to provide robustness to variants with outlying causal estimates. The results from the applied examples and simulation study highlight the importance of using methods that make different assumptions to assess the robustness of findings from Mendelian randomization investigations with multiple genetic variants. [ABSTRACT FROM AUTHOR]

Circadian rhythms are generated by interlocked transcriptional-translational negative feedback loops (TTFLs), the molecular process implemented within a cell. The contributions, weighting and balancing between the multiple feedback loops remain debated. Dissociated, free-running dynamics in the expression of distinct clock genes has been described in recent experimental studies that applied various perturbations such as slice preparations, light pulses, jet-lag, and culture medium exchange. In this paper, we provide evidence that this “presumably transient” dissociation of circadian gene expression oscillations may occur at the single-cell level. Conceptual and detailed mechanistic mathematical modeling suggests that such dissociation is due to a weak interaction between multiple feedback loops present within a single cell. The dissociable loops provide insights into underlying mechanisms and general design principles of the molecular circadian clock. [ABSTRACT FROM AUTHOR]

Introduction: Amyotrophic lateral sclerosis (ALS) is a rare, progressive neurodegenerative disease. There is heterogeneity of clinical phenotypes while a clinical characterization of ALS in Cyprus is still lacking. The aim of this 30-year retrospective study of ALS in Cyprus is to determine the demographic characteristics of patients, the clinical features of the disease, the uptake of supportive therapies and factors influencing survival. Methods: All ALS patients seen at the Cyprus Institute of Neurology and Genetics from January 1985 until July 2015 were included. Medical records of eligible patients were used for data extraction and compilation of an ALS database. Clinical features were compared between gender categories using univariate tests, while survival was assessed using Kaplan-Meier curves. Cox proportional hazards models were used to identify prognostic factors for survival. Results: One hundred and seventy-nine ALS patients were included in the study, of whom 7 had a positive family history. Most clinical characteristics of ALS did not differ from what is observed in other European countries. However, some clinical characteristics were unique to our population, such as an increased acceptability and utilisation of supportive treatments such as gastrostomy. Conclusions: Overall, clinical characteristics of patients with ALS in the Republic of Cyprus do not differ from other European counties. Our study demonstrates a high acceptance and utilisation of supportive interventions enhancing survival, in the context of a multidisciplinary approach offered in the single tertiary centre that services the whole Cypriot ALS population. The findings of this paper are of value to the health professionals treating ALS in Cyprus. [ABSTRACT FROM AUTHOR]

Sigma factors are key proteins that mediate the recruitment of RNA polymerase to the promoter regions of genes, for the initiation of bacterial transcription. Multiple sigma factors in a bacterium selectively recognize their cognate promoter sequences, thereby inducing the expression of their own regulons. In this paper, we report the crystal structure of the σ4 domain of Bacillus subtilis SigW bound to the -35 promoter element. Purine-specific hydrogen bonds of the -35 promoter element with the recognition helix α9 of the σ4 domain occurs at three nucleotides of the consensus sequence (G-35, A-34, and G’-31 in G-35A-34A-33A-32C-31C-30T-29). The hydrogen bonds of the backbone with the α7 and α8 of the σ4 domain occurs at G’-30. These results elucidate the structural basis of the selective recognition of the promoter by SigW. In addition, comparison of SigW structures complexed with the -35 promoter element or with anti-sigma RsiW reveals that DNA recognition and anti-sigma factor binding of SigW are mutually exclusive. [ABSTRACT FROM AUTHOR]

The analyses of large volumes of metagenomic data extracted from aggregate populations of microscopic organisms residing on and in the human body are advancing contemporary understandings of the integrated participation of microbes in human health and disease. Next generation sequencing technology facilitates said analyses in terms of diversity, community composition, and differential abundance by filtering and binning microbial 16S rRNA genes extracted from human tissues into operational taxonomic units. However, current statistical tools restrict study designs to investigations of limited numbers of host characteristics mediated by limited numbers of samples potentially yielding a loss of relevant information. This paper presents a Bayesian hierarchical negative binomial model as an efficient technique capable of compensating for multivariable sets including tens or hundreds of host characteristics as covariates further expanding analyses of human microbiome count data. Simulation studies reveal that the Bayesian hierarchical negative binomial model provides a desirable strategy by often outperforming three competing negative binomial model in terms of type I error while simultaneously maintaining consistent power. An application of the Bayesian hierarchical negative binomial model using subsets of the open data published by the American Gut Project demonstrates an ability to identify operational taxonomic units significantly differentiable among persons diagnosed by a medical professional with either inflammatory bowel disease or irritable bowel syndrome that are consistent with contemporary gastrointestinal literature. [ABSTRACT FROM AUTHOR]

Our understanding of how chromosomes structurally organize and dynamically interact has been revolutionized through the lens of long-chain polymer physics. Major protein contributors to chromosome structure and dynamics are condensin and cohesin that stochastically generate loops within and between chains, and entrap proximal strands of sister chromatids. In this paper, we explore the ability of transient, protein-mediated, gene-gene crosslinks to induce clusters of genes, thereby dynamic architecture, within the highly repeated ribosomal DNA that comprises the nucleolus of budding yeast. We implement three approaches: live cell microscopy; computational modeling of the full genome during G1 in budding yeast, exploring four decades of timescales for transient crosslinks between 5kbp domains (genes) in the nucleolus on Chromosome XII; and, temporal network models with automated community (cluster) detection algorithms applied to the full range of 4D modeling datasets. The data analysis tools detect and track gene clusters, their size, number, persistence time, and their plasticity (deformation). Of biological significance, our analysis reveals an optimal mean crosslink lifetime that promotes pairwise and cluster gene interactions through “flexible” clustering. In this state, large gene clusters self-assemble yet frequently interact (merge and separate), marked by gene exchanges between clusters, which in turn maximizes global gene interactions in the nucleolus. This regime stands between two limiting cases each with far less global gene interactions: with shorter crosslink lifetimes, “rigid” clustering emerges with clusters that interact infrequently; with longer crosslink lifetimes, there is a dissolution of clusters. These observations are compared with imaging experiments on a normal yeast strain and two condensin-modified mutant cell strains. We apply the same image analysis pipeline to the experimental and simulated datasets, providing support for the modeling predictions. [ABSTRACT FROM AUTHOR]

The pepper weevil, Anthonomus eugenii, is a major pest on Capsicum species. Apart from natural spread, there is a risk of spread via international pepper trade. In the Netherlands, a pepper weevil outbreak occurred in 2012 and affected six greenhouses producing different sweet pepper varieties. The following year, a pepper weevil outbreak occurred in Italy. To trace the origin of the Dutch outbreak and to establish if the Dutch and Italian outbreaks were linked, we determined the mitogenomes of A. eugenii specimens collected at outbreak locations, and compared these with specimens from the native area, and other areas where the pest was introduced either by natural dispersal or via trade. The circular 17,257 bp A. eugenii mitogenome comprises thirteen mitochondrial genes typically found in insect species. Intra-species variation of these mitochondrial genes revealed four main mitochondrial lineages encompassing 41 haplotypes. The highest diversity was observed for specimens from its presumed native area (i.e. Mexico). The Dutch outbreak specimens represented three highly similar haplotypes, suggesting a single introduction of the pest. The major Dutch haplotype was also found in two specimens from New Jersey. As the Netherlands does not have pepper trade with New Jersey, it is likely that the specimens sampled in New Jersey and those sampled in the Netherlands originate from a shared source that was not included in this study. In addition, our analysis shows that the Italian and Dutch outbreaks were not linked. The mitochondrial genome is a useful tool to trace outbreak populations and the methodology presented in this paper could prove valuable for other invasive pest species, such as the African fruit moth Thaumatotibia leucotreta and emerald ash borer Agrilus planipennis. [ABSTRACT FROM AUTHOR]

The predominant clonal evolution (PCE) model seeks to formulate a common population genetics framework for all micropathogens (namely, parasitic protozoa, fungi and yeasts, bacteria, and viruses). It relies on a definition of clonality that is only based on population structure features (namely, strongly restrained genetic recombination). Its clear-cut properties make it of strong interest for applied and basic research, since it permits the definition of stable, clearly delimited units of analysis below the species level: clonal genotypes and discrete genetic subdivisions (“near-clades”). These units of analysis can be used for clinical and epidemiological studies, vaccine and drug design, species description, and evolutionary studies on natural and experimental populations. In this review, the evolutionary and population genetics background of the model will be only briefly mentioned, while considerable emphasis will be given to its practical significance for the study and control of neglected tropical diseases. The goal of the paper is to make this practical usefulness accessible to a broad audience of readers, including scientists who are not evolution specialists, such as epidemiologists, field scientists, and clinicians. For extensive developments about the evolutionary background of the model, see our previous papers [–]. Citations of these former articles lead to the many references quoted in them, which cannot be listed again here. [ABSTRACT FROM AUTHOR]

Background: Paper currency by its very nature is frequently transferred from one person to another and represents an important medium for human contact with—and potential exchange of—microbes. In this pilot study, we swabbed circulating $1 bills obtained from a New York City bank in February (Winter) and June (Summer) 2013 and used shotgun metagenomic sequencing to profile the communities found on their surface. Using basic culture conditions, we also tested whether viable microbes could be recovered from bills. Results: Shotgun metagenomics identified eukaryotes as the most abundant sequences on money, followed by bacteria, viruses and archaea. Eukaryotic assemblages were dominated by human, other metazoan and fungal taxa. The currency investigated harbored a diverse microbial population that was dominated by human skin and oral commensals, including Propionibacterium acnes, Staphylococcus epidermidis and Micrococcus luteus. Other taxa detected not associated with humans included Lactococcus lactis and Streptococcus thermophilus, microbes typically associated with dairy production and fermentation. Culturing results indicated that viable microbes can be isolated from paper currency. Conclusions: We conducted the first metagenomic characterization of the surface of paper money in the United States, establishing a baseline for microbes found on $1 bills circulating in New York City. Our results suggest that money amalgamates DNA from sources inhabiting the human microbiome, food, and other environmental inputs, some of which can be recovered as viable organisms. These monetary communities may be maintained through contact with human skin, and DNA obtained from money may provide a record of human behavior and health. Understanding these microbial profiles is especially relevant to public health as money could potentially mediate interpersonal transfer of microbes. [ABSTRACT FROM AUTHOR]

Estrogen is a steroidal hormone involved in several physiological functions in the female body including regulation of serum lipid metabolism and breast cancer (BC). Estrogen actions on serum lipids mostly occur through its binding to intracellular Estrogen Receptor alpha (ERalpha) isoform, expressed in most of tissues. This gene (ESR1) exhibit many polymorphic sites (SNPs) located either on translated and non-translated regions that regulate ERalpha protein expression and function. This paper aimed to investigate the association of two intronic SNPs of ESR1 gene, namely c454-397T>C (PvuII) and c454-351A>G (XbaI) to alterations in serum levels of total cholesterol (T-chol), total lipid (TL), low density lipoprotein cholesterol (LDL), high density lipoprotein (HDL), and triglycerides (TG) in a cohort of post-menopausal women. In addition, we aimed to associate presence of these SNPs to development of BC along 5 years period. To do so, a group of healthy 499, highly miscigenated, post-menopausal Brazilian women, were carried using PCR-FRLP technique and further confirmed by automatic sequence analysis as well followed through 5 years for BC incidence. Measurements of serum lipid profile by standard commercial methods were carried individually whereas Dual Energy X-ray Absorciometry (DXA) measured Body Mass Indexes (BMI), Fat Mass (FM), Lean Body Mass (LBM), and Body Water Content (BWC). No effects of PvuII SNP on ESR1 gene were observed on patient´s serum T-chol, TL, LDL, HDL, and TG. However, c454-397T>C PvuII SNP is associated to lower body fat and higher levels of lean mass and body water and lower incidence of BC. On the other hand, statistically significant effect of XbaI c454-351A>G SNP on serum TG and TL levels. Patients homozygous for X allele were followed up from 2010–2015. They showed higher incidence of breast cancer (BC) when compared to either heterozygous and any P allele combination. Moreover, the increasing of TG and TL serum concentrations associated to SNP XbaI c454-351A>G on ESR1 gene is enhanced in both obese (higher BMI) and elder women. Taken together, these results suggested that XbaI c454-351A>G SNP is associated to changes in lipid profile, particularly in serum TG and TL, in this cohort of post-menopausal woman. Also, this paper shows another link between obesity and BC corroborating the current thesis that both diseases are interlinked. [ABSTRACT FROM AUTHOR]

Combinatorial test design is a plan of test that aims to reduce the amount of test cases systematically by choosing a subset of the test cases based on the combination of input variables. The subset covers all possible combinations of a given strength and hence tries to match the effectiveness of the exhaustive set. This mechanism of reduction has been used successfully in software testing research with t-way testing (where t indicates the interaction strength of combinations). Potentially, other systems may exhibit many similarities with this approach. Hence, it could form an emerging application in different areas of research due to its usefulness. To this end, more recently it has been applied in a few research areas successfully. In this paper, we explore the applicability of combinatorial test design technique for Fractional Order (FO), Proportional-Integral-Derivative (PID) parameter design controller, named as FOPID, for an automatic voltage regulator (AVR) system. Throughout the paper, we justify this new application theoretically and practically through simulations. In addition, we report on first experiments indicating its practical use in this field. We design different algorithms and adapted other strategies to cover all the combinations with an optimum and effective test set. Our findings indicate that combinatorial test design can find the combinations that lead to optimum design. Besides this, we also found that by increasing the strength of combination, we can approach to the optimum design in a way that with only 4-way combinatorial set, we can get the effectiveness of an exhaustive test set. This significantly reduced the number of tests needed and thus leads to an approach that optimizes design of parameters quickly. [ABSTRACT FROM AUTHOR]

The process of Nuclei detection in high-grade breast cancer images is quite challenging in the case of image processing techniques due to certain heterogeneous characteristics of cancer nuclei such as enlarged and irregularly shaped nuclei, highly coarse chromatin marginalized to the nuclei periphery and visible nucleoli. Recent reviews state that existing techniques show appreciable segmentation accuracy on breast histopathology images whose nuclei are dispersed and regular in texture and shape; however, typical cancer nuclei are often clustered and have irregular texture and shape properties. This paper proposes a novel segmentation algorithm for detecting individual nuclei from Hematoxylin and Eosin (H&E) stained breast histopathology images. This detection framework estimates a nuclei saliency map using tensor voting followed by boundary extraction of the nuclei on the saliency map using a Loopy Back Propagation (LBP) algorithm on a Markov Random Field (MRF). The method was tested on both whole-slide images and frames of breast cancer histopathology images. Experimental results demonstrate high segmentation performance with efficient precision, recall and dice-coefficient rates, upon testing high-grade breast cancer images containing several thousand nuclei. In addition to the optimal performance on the highly complex images presented in this paper, this method also gave appreciable results in comparison with two recently published methods—Wienert et al. (2012) and Veta et al. (2013), which were tested using their own datasets. [ABSTRACT FROM AUTHOR]

Despite their recognized limitations, bibliometric assessments of scientific productivity have been widely adopted. We describe here an improved method to quantify the influence of a research article by making novel use of its co-citation network to field-normalize the number of citations it has received. Article citation rates are divided by an expected citation rate that is derived from performance of articles in the same field and benchmarked to a peer comparison group. The resulting Relative Citation Ratio is article level and field independent and provides an alternative to the invalid practice of using journal impact factors to identify influential papers. To illustrate one application of our method, we analyzed 88,835 articles published between 2003 and 2010 and found that the National Institutes of Health awardees who authored those papers occupy relatively stable positions of influence across all disciplines. We demonstrate that the values generated by this method strongly correlate with the opinions of subject matter experts in biomedical research and suggest that the same approach should be generally applicable to articles published in all areas of science. A beta version of iCite, our web tool for calculating Relative Citation Ratios of articles listed in PubMed, is available at . [ABSTRACT FROM AUTHOR]

The development of high-throughput sequencing technologies for 16S rRNA gene profiling provides higher quality compositional data for microbe communities. Inferring the direct interaction network under a specific condition and understanding how the network structure changes between two different environmental or genetic conditions are two important topics in biological studies. However, the compositional nature and high dimensionality of the data are challenging in the context of network and differential network recovery. To address this problem in the present paper, we proposed two new loss functions to incorporate the data transformations developed for compositional data analysis into D-trace loss for network and differential network estimation, respectively. The sparse matrix estimators are defined as the minimizer of the corresponding lasso penalized loss. Our method is characterized by its straightforward application based on the ADMM algorithm for numerical solution. Simulations show that the proposed method outperforms other state-of-the-art methods in network and differential network inference under different scenarios. Finally, as an illustration, our method is applied to a mouse skin microbiome data. [ABSTRACT FROM AUTHOR]

In recent years, increasing associations between microRNAs (miRNAs) and human diseases have been identified. Based on accumulating biological data, many computational models for potential miRNA-disease associations inference have been developed, which saves time and expenditure on experimental studies, making great contributions to researching molecular mechanism of human diseases and developing new drugs for disease treatment. In this paper, we proposed a novel computational method named Ensemble of Decision Tree based MiRNA-Disease Association prediction (EDTMDA), which innovatively built a computational framework integrating ensemble learning and dimensionality reduction. For each miRNA-disease pair, the feature vector was extracted by calculating the statistical measures, graph theoretical measures, and matrix factorization results for the miRNA and disease, respectively. Then multiple base learnings were built to yield many decision trees (DTs) based on random selection of negative samples and miRNA/disease features. Particularly, Principal Components Analysis was applied to each base learning to reduce feature dimensionality and hence remove the noise or redundancy. Average strategy was adopted for these DTs to get final association scores between miRNAs and diseases. In model performance evaluation, EDTMDA showed AUC of 0.9309 in global leave-one-out cross validation (LOOCV) and AUC of 0.8524 in local LOOCV. Additionally, AUC of 0.9192+/-0.0009 in 5-fold cross validation proved the model’s reliability and stability. Furthermore, three types of case studies for four human diseases were implemented. As a result, 94% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 96% (Breast Neoplasms) and 88% (Carcinoma Hepatocellular) of top 50 predicted miRNAs were confirmed by experimental evidences in literature. [ABSTRACT FROM AUTHOR]