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The homoscleromorph sponge Oscarella carmela, first described from central California, USA is shown to represent two superficially similar but both morphologically and phylogenetically distinct species that are co-distributed. We here describe a new species as Oscarella pearsei, sp. nov. and re-describe Oscarella carmela; the original description was based upon material from both species. Further, we correct the identification of published genomic/transcriptomic resources that were originally attributed to O. carmela, and present new Illumina-sequenced transcriptome assemblies for each of these species, and the mitochondrial genome sequence for O. pearsei sp. nov. Using SSU and LSU ribosomal DNA and the mitochondrial genome, we report the phylogenetic relationships of these species relative to other Oscarella species, and find strong support for the placement of O. pearsei sp. nov. in a distinct clade within genus Oscarella defined by the presence of spherulous cells that contain paracrystalline inclusions; O. carmela lacks this cell type. Oscarella pearsei sp. nov and O. carmela can be tentatively distinguished based upon gross morphological differences such as color, surface texture and extent of mucus production, but can be more reliably identified using mitochondrial and nuclear barcode sequencing, ultrastructural characteristics of cells in the mesohyl, and the morphology of the follicle epithelium which surrounds the developing embryo in reproductively active individuals. [ABSTRACT FROM AUTHOR]

Background: Climate change produces extremes in both temperature and precipitation causing increased drought severity and increased reliance on groundwater resources. Agricultural practices, which rely on groundwater, are sensitive to but also sources of contaminants, including nitrate. How agricultural contamination drives groundwater geochemistry through microbial metabolism is poorly understood. Methods: On an active cow dairy in the Central Valley of California, we sampled groundwater from three wells at depths of 4.3 m (two wells) and 100 m (one well) below ground surface (bgs) as well as an effluent surface water lagoon that fertilizes surrounding corn fields. We analyzed the samples for concentrations of solutes, heavy metals, and USDA pathogenic bacteria of the Escherichia coli and Enterococcus groups as part of a long term groundwater monitoring study. Whole metagenome shotgun sequencing and assembly revealed taxonomic composition and metabolic potential of the community. Results: Elevated nitrate and dissolved organic carbon occurred at 4.3m but not at 100m bgs. Metagenomics confirmed chemical observations and revealed several Planctomycete genomes, including a new Brocadiaceae lineage and a likely Planctomycetes OM190, as well novel diversity and high abundance of nano-prokaryotes from the Candidate Phyla Radiation (CPR), the Diapherotrites, Parvarchaeota, Aenigmarchaeota, Nanoarchaeota, Nanohaloarchaea (DPANN) and the Thaumarchaeota, Aigarchaeota, Crenarchaeota, Korarchaeota (TACK) superphyla. Pathway analysis suggests community interactions based on complimentary primary metabolic pathways and abundant secondary metabolite operons encoding antimicrobials and quorum sensing systems. Conclusions: The metagenomes show strong resemblance to activated sludge communities from a nitrogen removal reactor at a wastewater treatment plant, suggesting that natural bioremediation occurs through microbial metabolism. Elevated nitrate and rich secondary metabolite biosynthetic capacity suggest incomplete remediation and the potential for novel pharmacologically active compounds. [ABSTRACT FROM AUTHOR]

Katherine Possin and colleagues report on the implementation, development, and early findings of the Care Ecosystem, an adaptive, personalized, and scalable dementia care program. [ABSTRACT FROM AUTHOR]

Land use change in rangeland ecosystems is pervasive throughout the western United States with widespread ecological, social and economic implications. In California, rangeland habitats have high biodiversity value, provide significant habitat connectivity and form the foundation for a number of ecosystem services. To comprehensively assess the conservation status of these habitats, we analyzed the extent and drivers of habitat loss and the degree of protection against future loss across a 13.5 M ha study area in California. We analyzed rangeland conversion between 1984 and 2008 using time series GIS data and classified resulting land uses with aerial imagery. In total, over 195,000 hectares of rangeland habitats were converted during this period. The majority of conversions were to residential and associated commercial development (49% of the area converted), but agricultural intensification was surprisingly extensive and diverse (40% across six categories). Voluntary enrollment in an agricultural tax incentive program provided widespread protection from residential and commercial conversions across 37% of the remaining rangeland habitat extent (7.5 M ha), though this program did not protect rangeland from conversion to more intensive agricultural uses. Additionally, 24% of the remaining rangeland was protected by private conservation organizations or public agencies through land or easement ownership while 38% had no protection status at all. By developing a spatial method to analyze the drivers of loss and patterns of protection, this study demonstrates a novel approach to prioritize conservation strategies and implementation locations to avert habitat conversion. We propose that this approach can be used in other ecosystem types, and can serve as a regional conservation baseline assessment to focus strategies to effect widespread, cost-effective conservation solutions. [ABSTRACT FROM AUTHOR]

White sharks are highly migratory and segregate by sex, age and size. Unlike marine mammals, they neither surface to breathe nor frequent haul-out sites, hindering generation of abundance data required to estimate population size. A recent tag-recapture study used photographic identifications of white sharks at two aggregation sites to estimate abundance in “central California” at 219 mature and sub-adult individuals. They concluded this represented approximately one-half of the total abundance of mature and sub-adult sharks in the entire eastern North Pacific Ocean (ENP). This low estimate generated great concern within the conservation community, prompting petitions for governmental endangered species designations. We critically examine that study and find violations of model assumptions that, when considered in total, lead to population underestimates. We also use a Bayesian mixture model to demonstrate that the inclusion of transient sharks, characteristic of white shark aggregation sites, would substantially increase abundance estimates for the adults and sub-adults in the surveyed sub-population. Using a dataset obtained from the same sampling locations and widely accepted demographic methodology, our analysis indicates a minimum all-life stages population size of >2000 individuals in the California subpopulation is required to account for the number and size range of individual sharks observed at the two sampled sites. Even accounting for methodological and conceptual biases, an extrapolation of these data to estimate the white shark population size throughout the ENP is inappropriate. The true ENP white shark population size is likely several-fold greater as both our study and the original published estimate exclude non-aggregating sharks and those that independently aggregate at other important ENP sites. Accurately estimating the central California and ENP white shark population size requires methodologies that account for biases introduced by sampling a limited number of sites and that account for all life history stages across the species' range of habitats. [ABSTRACT FROM AUTHOR]

The article offers information on the topics discussed at the Eighth International Society for Computational Biology (ISCB) Student Council Symposium held in Long Beach, California on July 13-14, 2012. Topics discussed included RNA editing, computational biology, and single nucleotide polymorphisms (SNPs) in microRNA. The symposium was attended by researchers including Matthew Hibbs, Robin Dowell, and Jonathan Eisen.

We describe the proceedings and conclusions from the “Workshop on Applications of Protein Models in Biomedical Research” (the Workshop) that was held at the University of California, San Francisco on 11 and 12 July, 2008. At the Workshop, international scientists involved with structure modeling explored (i) how models are currently used in biomedical research, (ii) the requirements and challenges for different applications, and (iii) how the interaction between the computational and experimental research communities could be strengthened to advance the field. [Copyright &y& Elsevier]

Machine learning algorithms, including recent advances in deep learning, are promising for tools for detection and classification of broadband high frequency signals in passive acoustic recordings. However, these methods are generally data-hungry and progress has been limited by challenges related to the lack of labeled datasets adequate for training and testing. Large quantities of known and as yet unidentified broadband signal types mingle in marine recordings, with variability introduced by acoustic propagation, source depths and orientations, and interacting signals. Manual classification of these datasets is unmanageable without an in-depth knowledge of the acoustic context of each recording location. A signal classification pipeline is presented which combines unsupervised and supervised learning phases with opportunities for expert oversight to label signals of interest. The method is illustrated with a case study using unsupervised clustering to identify five toothed whale echolocation click types and two anthropogenic signal categories. These categories are used to train a deep network to classify detected signals in either averaged time bins or as individual detections, in two independent datasets. Bin-level classification achieved higher overall precision (>99%) than click-level classification. However, click-level classification had the advantage of providing a label for every signal, and achieved higher overall recall, with overall precision from 92 to 94%. The results suggest that unsupervised learning is a viable solution for efficiently generating the large, representative training sets needed for applications of deep learning in passive acoustics., Competing Interests: The author has declared that no competing interests exist.

The ocean is a dynamic environment inhabited by a diverse array of highly migratory species, many of which are under direct exploitation in targeted fisheries. The timescales of variability in the marine realm coupled with the extreme mobility of ocean-wandering species such as tuna and billfish complicates fisheries management. Developing eco-informatics solutions that allow for near real-time prediction of the distributions of highly mobile marine species is an important step towards the maturation of dynamic ocean management and ecological forecasting. Using 25 yr (1990-2014) of NOAA fisheries' observer data from the California drift gillnet fishery, we model relative probability of occurrence (presence-absence) and catchability (total catch per gillnet set) of broadbill swordfish Xiphias gladius in the California Current System. Using freely available environmental data sets and open source software, we explore the physical drivers of regional swordfish distribution. Comparing models built upon remotely sensed data sets with those built upon a data-assimilative configuration of the Regional Ocean Modelling System (ROMS), we explore trade-offs in model construction, and address how physical data can affect predictive performance and operational capacity. Swordfish catchability was found to be highest in deeper waters (>1,500 m) with surface temperatures in the 14-20°C range, isothermal layer depth (ILD) of 20-40 m, positive sea surface height (SSH) anomalies, and during the new moon (<20% lunar illumination). We observed a greater influence of mesoscale variability (SSH, wind speed, isothermal layer depth, eddy kinetic energy) in driving swordfish catchability (total catch) than was evident in predicting the relative probability of presence (presence-absence), confirming the utility of generating spatiotemporally dynamic predictions. Data-assimilative ROMS circumvent the limitations of satellite remote sensing in providing physical data fields for species distribution models (e.g., cloud cover, variable resolution, subsurface data), and facilitate broad-scale prediction of dynamic species distributions in near real time., (© 2017 by the Ecological Society of America.)

Purpose: The purpose of this study was to compare performance metrics of postanalytical interpretive tools of the Region 4 Stork collaborative project to the actual outcome based on cutoff values for amino acids and acylcarnitines selected by the California newborn screening program., Methods: This study was a retrospective review of the outcome of 176,186 subjects born in California between 1 January and 30 June 2012. Raw data were uploaded to the Region 4 Stork Web portal as .csv files to calculate tool scores for 48 conditions simultaneously using a previously unpublished functionality, the tool runner. Scores for individual target conditions were deemed informative when equal or greater to the value representing the first percentile rank of known true-positive cases (17,099 cases in total)., Results: In the study period, the actual false-positive rate and positive predictive value were 0.26 and 10%, respectively. Utilization of the Region 4 Stork tools, simple interpretation rules, and second-tier tests could have achieved a false-positive rate as low as 0.02% and a positive predictive value >50% by replacing the cutoff system with Region 4 Stork tools as the primary method for postanalytical interpretation., Conclusion: Region 4 Stork interpretive tools, second-tier tests, and other evidence-based interpretation rules could have reduced false-positive cases by up to 90% in California.

We believe that undergraduate biology students must acquire a foundational background in computing including how to formulate a computational problem; develop an algorithmic solution; implement their solution in software and then test, document and use their code to explore biological phenomena. Moreover, by learning these skills in the first year, students acquire a powerful tool set that they can use and build on throughout their studies. To address this need, we have developed a first-year undergraduate course that teaches students the foundations of computational thinking and programming in the context of problems in biology. This article describes the structure and content of the course and summarizes assessment data on both affective and learning outcomes.

Genes involved in host-pathogen interactions are expected to be evolving under complex coevolutionary dynamics, including positive directional and/or frequency-dependent selection. Empirical work has largely focused on the evolution of immune genes at the level of the protein sequence. We examine components of genetic variance for transcript abundance of defense genes in Drosophila melanogaster and D. simulans using a diallel and a round robin breeding design, respectively, and infer modes of evolution from patterns of segregating genetic variation. Defense genes in D. melanogaster are overrepresented relative to nondefense genes among genes with evidence of significant additive variance for expression. Directional selection is expected to deplete additive genetic variance, whereas frequency-dependent selection is expected to maintain additive variance. However, relaxed selection (reduced or no purifying selection) is an alternative interpretation of significant additive variation. Of the three classes of defense genes, the recognition and effector classes show an excess of genes with significant additive variance; whereas signaling genes, in contrast, are overrepresented for dominance variance. Analysis of protein-coding sequences revealed no evidence for an association between additive or dominance variation in expression and directional selection. Both balancing selection driven by host-pathogen coevolution and relaxed selection for expression of uninduced defense genes are viable interpretations of these data., (© 2010 The Author(s). Evolution© 2010 The Society for the Study of Evolution.)

The human oropharynx is a reservoir for many potential pathogens, including streptococcal species that cause endocarditis. Although oropharyngeal microbes have been well described, viral communities are essentially uncharacterized. We conducted a metagenomic study to determine the composition of oropharyngeal DNA viral communities (both phage and eukaryotic viruses) in healthy individuals and to evaluate oropharyngeal swabs as a rapid method for viral detection. Viral DNA was extracted from 19 pooled oropharyngeal swabs and sequenced. Viral communities consisted almost exclusively of phage, and complete genomes of several phage were recovered, including Escherichia coli phage T3, Propionibacterium acnes phage PA6, and Streptococcus mitis phage SM1. Phage relative abundances changed dramatically depending on whether samples were chloroform treated or filtered to remove microbial contamination. pblA and pblB genes of phage SM1 were detected in the metagenomes. pblA and pblB mediate the attachment of S. mitis to platelets and play a significant role in S. mitis virulence in the endocardium, but have never previously been detected in the oral cavity. These genes were also identified in salivary metagenomes from three individuals at three time points and in individual saliva samples by PCR. Additionally, we demonstrate that phage SM1 can be induced by commonly ingested substances. Our results indicate that the oral cavity is a reservoir for pblA and pblB genes and for phage SM1 itself. Further studies will determine the association between pblA and pblB genes in the oral cavity and the risk of endocarditis.

The University of Southern California (USC, CA, USA) has a long tradition of excellence in epigenetics. With the recent explosive growth and technological maturation of the field of epigenetics, it became clear that a dedicated high-throughput epigenomic data production facility would be needed to remain at the forefront of epigenetic research. To address this need, USC launched the USC Epigenome Center as the first large-scale center in academics dedicated to epigenomic research. The Center is providing high-throughput data production for large-scale genomic and epigenomic studies, and developing novel analysis tools for epigenomic research. This unique facility promises to be a valuable resource for multidisciplinary research, education and training in genomics, epigenomics, bioinformatics, and translational medicine.

A multicopy cloning approach was used to search for metagenomic DNA fragments that affect Escherichia coli mutational pathways. Soil metagenomic expression libraries were constructed with DNA samples prepared directly from soil samples collected from the UCLA Botanical Garden. Using frameshift mutator screening, we obtained a total of 26 unique metagenomic fragments that stimulate frameshift rates in an E. coli wild-type host. Mutational enhancer strains such as an ndk-deficient strain and a temperature sensitive mutS strain (mutS60) were used to further verify the mutator phenotype. We found that the presence of multiple copies of certain types of metagenomic DNA sequence repeats cause general genome instability in the wild-type E. coli host and the effect can be suppressed by overproducing a DNA mismatch component MutL. In addition, we identified nine metagenomic mutator genes (designated as smu genes) that encode proteins that have not been linked to mutator phenotypes prior to this study including a putative RNA methyltransferase Smu10A. The strain overproducing Smu10A displays one prominent base substitution hotspot in the rpoB gene, which coincides with the base substitution hotspot we have observed in cells that are partially deficient in the proofreading function carried out by the DNA polymerase III epsilon subunit. Based on the structural conservation of DNA replication/recombination/repair machineries among microorganisms, this approach would allow us to both identify new mutational pathways in E. coli and to find genes involved in DNA replication, recombination or DNA repair from vast unculturable microbes.

The FlyBase Drosophila genetics database and the public interfaces of the Berkeley Drosophila Genome Project (BDGP) and European Drosophila Genome Project (EDGP) are in the process of integrating. At present, the data of these projects are available from independent, but hyperlinked, WWW sites (FlyBase URL, http://flybase. bio.indiana.edu/; BDGP URL, http://fruitfly.berkeley.edu/; EDGP URL, http://edgp.ebi.ac.uk/ ). Because of the considerable overlap of data classes between the contributions of the Drosophila genome projects and the Drosophila community, the new and enlarged FlyBase consortium views the implementation of a single integrated Drosophila genomics/genetics server as essential to the scientific community. This integration will occur in a stepwise fashion over the next 1-2 years. In this report, the salient features of the current databases and how to interrogate and navigate the extensive data sets are discussed.