Spatial extent and historical context of North Sea oxygen depletion in August 2010

Prompted by recent observations of seasonal low dissolved oxygen from two moorings in the North Sea, a hydrographic survey in August 2010 mapped the spatial extent of summer oxygen depletion. Typical near-bed dissolved oxygen saturations in the stratified regions of the North Sea were 75–80 % while the well-mixed regions of the southern North Sea reached 90 %. Two regions of strong thermal stratification, the area between the Dooley and Central North Sea Currents and the area known as the Oyster Grounds, had oxygen saturations as low as 65 and 70 % (200 and 180 µmol dm-3) respectively. Low dissolved oxygen was apparent in regions characterised by low advection, high stratification, elevated organic matter production from the spring bloom and a deep chlorophyll maximum. Historical data over the last century from the International Council for the Exploration of the Sea oceanographic database highlight an increase in seasonal oxygen depletion and a warming over the past 20 years. The 2010 survey is consistent with, and reinforces, the signal of recent depleted oxygen at key locations seen in the (albeit sparse) historical data

Ice sheets as a missing source of silica to the polar oceans

Ice sheets play a more important role in the global silicon cycle than previously appreciated. Input of dissolved and amorphous particulate silica into natural waters stimulates the growth of diatoms. Here we measure dissolved and amorphous silica in Greenland Ice Sheet meltwaters and icebergs, demonstrating the potential for high ice sheet export. Our dissolved and amorphous silica flux is 0.20 (0.06-0.79) Tmol year(-1), ∼50% of the input from Arctic rivers. Amorphous silica comprises >95% of this flux and is highly soluble in sea water, as indicated by a significant increase in dissolved silica across a fjord salinity gradient. Retreating palaeo ice sheets were therefore likely responsible for high dissolved and amorphous silica fluxes into the ocean during the last deglaciation, reaching values of ∼5.5 Tmol year(-1), similar to the estimated export from palaeo rivers. These elevated silica fluxes may explain high diatom productivity observed during the last glacial-interglacial period

Age differences in physiological responses to self-paced and incremental V˙O2max\dot V O_{2max} testing

Purpose: A self-paced maximal exercise protocol has demonstrated higher $\dot V O_{2max}$ values when compared against traditional tests. The aim was to compare physiological responses to this self-paced $\dot V O_{2max}$ protocol (SPV) in comparison to a traditional ramp $\dot V O_{2max}$ (RAMP) protocol in young (18–30 years) and old (50–75 years) participants. Methods: Forty-four participants (22 young; 22 old) completed both protocols in a randomised, counter-balanced, crossover design. The SPV included 5 × 2 min stages, participants were able to self-regulate their power output (PO) by using incremental ‘clamps’ in ratings of perceived exertion. The RAMP consisted of either 15 or 20 W min$^{−1}$. Results: Expired gases, cardiac output (Q), stroke volume (SV), muscular deoxyhaemoglobin (deoxyHb) and electromyography (EMG) at the vastus lateralis were recorded throughout. Results demonstrated significantly higher $\dot V O_{2max}$ in the SPV (49.68 ± 10.26 ml kg$^{−1}$ min$^{−1}$) vs. the RAMP (47.70 ± 9.98 ml kg$^{−1}$ min$^{−1}$) in the young, but not in the old group (>0.05). Q and SV were significantly higher in the SPV vs. the RAMP in the young (0.05). No differences seen in deoxyHb and EMG for either age groups (>0.05). Peak PO was significantly higher in the SPV vs. the RAMP in both age groups (<0.05). Conclusion: Findings demonstrate that the SPV produces higher $\dot V O_{2max}$, peak Q and SV values in the young group. However, older participants achieved similar $\dot V O_{2max}$ values in both protocols, mostly likely due to age-related differences in cardiovascular responses to incremental exercise, despite them achieving a higher physiological workload in the SPV

Reproducible, Ultra High-Throughput Formation of Multicellular Organization from Single Cell Suspension-Derived Human Embryonic Stem Cell Aggregates

Background: Human embryonic stem cells (hESC) should enable novel insights into early human development and provide a renewable source of cells for regenerative medicine. However, because the three-dimensional hESC aggregates [embryoid bodies (hEB)] typically employed to reveal hESC developmental potential are heterogeneous and exhibit disorganized differentiation, progress in hESC technology development has been hindered. Methodology/Principal Findings: Using a centrifugal forced-aggregation strategy in combination with a novel centrifugalextraction approach as a foundation, we demonstrated that hESC input composition and inductive environment could be manipulated to form large numbers of well-defined aggregates exhibiting multi-lineage differentiation and substantially improved self-organization from single-cell suspensions. These aggregates exhibited coordinated bi-domain structures including contiguous regions of extraembryonic endoderm- and epiblast-like tissue. A silicon wafer-based microfabrication technology was used to generate surfaces that permit the production of hundreds to thousands of hEB per cm 2. Conclusions/Significance: The mechanisms of early human embryogenesis are poorly understood. We report an ultra high throughput (UHTP) approach for generating spatially and temporally synchronised hEB. Aggregates generated in this manner exhibited aspects of peri-implantation tissue-level morphogenesis. These results should advance fundamental studies into early human developmental processes, enable high-throughput screening strategies to identify conditions tha

Profiles of Human Serum Antibody Responses Elicited by Three Leading HIV Vaccines Focusing on the Induction of Env-Specific Antibodies

In the current report, we compared the specificities of antibody responses in sera from volunteers enrolled in three US NIH-supported HIV vaccine trials using different immunization regimens. HIV-1 Env-specific binding antibody, neutralizing antibody, antibody-dependent cell-mediated cytotoxicity (ADCC), and profiles of antibody specificity were analyzed for human immune sera collected from vaccinees enrolled in the NIH HIV Vaccine Trial Network (HVTN) Study #041 (recombinant protein alone), HVTN Study #203 (poxviral vector prime-protein boost), and the DP6-001 study (DNA prime-protein boost). Vaccinees from HVTN Study #041 had the highest neutralizing antibody activities against the sensitive virus along with the highest binding antibody responses, particularly those directed toward the V3 loop. DP6-001 sera showed a higher frequency of positive neutralizing antibody activities against more resistant viral isolate with a significantly higher CD4 binding site (CD4bs) antibody response compared to both HVTN studies #041 and #203. No differences were found in CD4-induced (CD4i) antibody responses, ADCC activity, or complement activation by Env-specific antibody among these sera. Given recent renewed interest in realizing the importance of antibody responses for next generation HIV vaccine development, different antibody profiles shown in the current report, based on the analysis of a wide range of antibody parameters, provide critical biomarker information for the selection of HIV vaccines for more advanced human studies and, in particular, those that can elicit antibodies targeting conformational-sensitive and functionally conserved epitopes

Assembly of Inflammation-Related Genes for Pathway-Focused Genetic Analysis

Recent identifications of associations between novel variants in inflammation-related genes and several common diseases emphasize the need for systematic evaluations of these genes in disease susceptibility. Considering that many genes are involved in the complex inflammation responses and many genetic variants in these genes have the potential to alter the functions and expression of these genes, we assembled a list of key inflammation-related genes to facilitate the identification of genetic associations of diseases with an inflammation-related etiology. We first reviewed various phases of inflammation responses, including the development of immune cells, sensing of danger, influx of cells to sites of insult, activation and functional responses of immune and non-immune cells, and resolution of the immune response. Assisted by the Ingenuity Pathway Analysis, we then identified 17 functional sub-pathways that are involved in one or multiple phases. This organization would greatly increase the chance of detecting gene-gene interactions by hierarchical clustering of genes with their functional closeness in a pathway. Finally, as an example application, we have developed tagging single nucleotide polymorphism (tSNP) arrays for populations of European and African descent to capture all the common variants of these key inflammation-related genes. Assays of these tSNPs have been designed and assembled into two Affymetrix ParAllele customized chips, one each for European (12,011 SNPs) and African (21,542 SNPs) populations. These tSNPs have greater coverage for these inflammation-related genes compared to the existing genome-wide arrays, particularly in the African population. These tSNP arrays can facilitate systematic evaluation of inflammation pathways in disease susceptibility. For additional applications, other genotyping platforms could also be employed. For existing genome-wide association data, this list of key inflammation-related genes and associated subpathways can facilitate comprehensive inflammation pathway- focused association analyses

Machine Learning Approach for Prescriptive Plant Breeding

We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding