Finite Element Analysis to model ischemia experienced in the development of device related pressure ulcers.

Pressure ulcers are a common occurrence of damage to skin. Severity ranges from slightly discoloured skin to full thickness tissue damage which can be fatal in some cases. Engineering effort, typically developing computational models had made significant progress in the understanding and demonstration of the formation mechanism of pressure ulcers with the aetiology of excessive stress however relatively limited attempts had been made to develop relevant models for pressure ulcers caused by ischemia. The aim of this paper is to present evidence of a computational model developed to simulate ischemic pressure ulcer formation and demonstrate the established relationship between the computational data and the acquired clinically relevant experimental data by utilising laser Doppler velocimetry. The application of the presented computational model and the established relationship allows the evaluation of the effect of a mechanical loading to the cutaneous blood flow velocity which is a step closing to understand and evaluate a mechanical load to the formation of pressure ulcers caused by ischemia

A Platform-Independent Method for Detecting Errors in Metagenomic Sequencing Data: DRISEE

We provide a novel method, DRISEE (duplicate read inferred sequencing error estimation), to assess sequencing quality (alternatively referred to as “noise” or “error”) within and/or between sequencing samples. DRISEE provides positional error estimates that can be used to inform read trimming within a sample. It also provides global (whole sample) error estimates that can be used to identify samples with high or varying levels of sequencing error that may confound downstream analyses, particularly in the case of studies that utilize data from multiple sequencing samples. For shotgun metagenomic data, we believe that DRISEE provides estimates of sequencing error that are more accurate and less constrained by technical limitations than existing methods that rely on reference genomes or the use of scores (e.g. Phred). Here, DRISEE is applied to (non amplicon) data sets from both the 454 and Illumina platforms. The DRISEE error estimate is obtained by analyzing sets of artifactual duplicate reads (ADRs), a known by-product of both sequencing platforms. We present DRISEE as an open-source, platform-independent method to assess sequencing error in shotgun metagenomic data, and utilize it to discover previously uncharacterized error in de novo sequence data from the 454 and Illumina sequencing platforms

Predator-Induced Demographic Shifts in Coral Reef Fish Assemblages

In recent years, it has become apparent that human impacts have altered community structure in coastal and marine ecosystems worldwide. Of these, fishing is one of the most pervasive, and a growing body of work suggests that fishing can have strong effects on the ecology of target species, especially top predators. However, the effects of removing top predators on lower trophic groups of prey fishes are less clear, particularly in highly diverse and trophically complex coral reef ecosystems. We examined patterns of abundance, size structure, and age-based demography through surveys and collection-based studies of five fish species from a variety of trophic levels at Kiritimati and Palmyra, two nearby atolls in the Northern Line Islands. These islands have similar biogeography and oceanography, and yet Kiritimati has ∼10,000 people with extensive local fishing while Palmyra is a US National Wildlife Refuge with no permanent human population, no fishing, and an intact predator fauna. Surveys indicated that top predators were relatively larger and more abundant at unfished Palmyra, while prey functional groups were relatively smaller but showed no clear trends in abundance as would be expected from classic trophic cascades. Through detailed analyses of focal species, we found that size and longevity of a top predator were lower at fished Kiritimati than at unfished Palmyra. Demographic patterns also shifted dramatically for 4 of 5 fish species in lower trophic groups, opposite in direction to the top predator, including decreases in average size and longevity at Palmyra relative to Kiritimati. Overall, these results suggest that fishing may alter community structure in complex and non-intuitive ways, and that indirect demographic effects should be considered more broadly in ecosystem-based management

Integrated metatranscriptomic and metagenomic analyses of stratified microbial assemblages in the open ocean

As part of an ongoing survey of microbial community gene expression in the ocean, we sequenced and compared ~38 Mbp of community transcriptomes and ~157 Mbp of community genomes from four bacterioplankton samples, along a defined depth profile at Station ALOHA in North Pacific subtropical gyre (NPSG). Taxonomic analysis suggested that the samples were dominated by three taxa: Prochlorales, Consistiales and Cenarchaeales, which comprised 36–69% and 29–63% of the annotated sequences in the four DNA and four cDNA libraries, respectively. The relative abundance of these taxonomic groups was sometimes very different in the DNA and cDNA libraries, suggesting differential relative transcriptional activities per cell. For example, the 125 m sample genomic library was dominated by Pelagibacter (~36% of sequence reads), which contributed fewer sequences to the community transcriptome (~11%). Functional characterization of highly expressed genes suggested taxon-specific contributions to specific biogeochemical processes. Examples included Roseobacter relatives involved in aerobic anoxygenic phototrophy at 75 m, and an unexpected contribution of low abundance Crenarchaea to ammonia oxidation at 125 m. Read recruitment using reference microbial genomes indicated depth-specific partitioning of coexisting microbial populations, highlighted by a transcriptionally active high-light-like Prochlorococcus population in the bottom of the photic zone. Additionally, nutrient-uptake genes dominated Pelagibacter transcripts, with apparent enrichment for certain transporter types (for example, the C4-dicarboxylate transport system) over others (for example, phosphate transporters). In total, the data support the utility of coupled DNA and cDNA analyses for describing taxonomic and functional attributes of microbial communities in their natural habitats.Gordon and Betty Moore FoundationUnited States. Dept. of EnergyNational Science Foundation (U.S.) (Science and Technology Center Award EF0424599

Bacterial Community Profiling of Milk Samples as a Means to Understand Culture-Negative Bovine Clinical Mastitis

Inflammation and infection of bovine mammary glands, commonly known as mastitis, imposes significant losses each year in the dairy industry worldwide. While several different bacterial species have been identified as causative agents of mastitis, many clinical mastitis cases remain culture negative, even after enrichment for bacterial growth. To understand the basis for this increasingly common phenomenon, the composition of bacterial communities from milk samples was analyzed using culture independent pyrosequencing of amplicons of 16S ribosomal RNA genes (16S rDNA). Comparisons were made of the microbial community composition of culture negative milk samples from mastitic quarters with that of non-mastitic quarters from the same animals. Genomic DNA from culture-negative clinical and healthy quarter sample pairs was isolated, and amplicon libraries were prepared using indexed primers specific to the V1–V2 region of bacterial 16S rRNA genes and sequenced using the Roche 454 GS FLX with titanium chemistry. Evaluation of the taxonomic composition of these samples revealed significant differences in the microbiota in milk from mastitic and healthy quarters. Statistical analysis identified seven bacterial genera that may be mainly responsible for the observed microbial community differences between mastitic and healthy quarters. Collectively, these results provide evidence that cases of culture negative mastitis can be associated with bacterial species that may be present below culture detection thresholds used here. The application of culture-independent bacterial community profiling represents a powerful approach to understand long-standing questions in animal health and disease

Challenges for machine learning in clinical translation of big data imaging studies

Combining deep learning image analysis methods and large-scale imaging datasets offers many opportunities to neuroscience imaging and epidemiology. However, despite these opportunities and the success of deep learning when applied to a range of neuroimaging tasks and domains, significant barriers continue to limit the impact of large-scale datasets and analysis tools. Here, we examine the main challenges and the approaches that have been explored to overcome them. We focus on issues relating to data availability, interpretability, evaluation, and logistical challenges and discuss the problems that still need to be tackled to enable the success of “big data” deep learning approaches beyond research