Megafaunal Community Structure of Andaman Seamounts Including the Back-Arc Basin – A Quantitative Exploration from the Indian Ocean

Species rich benthic communities have been reported from some seamounts, predominantly from the Atlantic and Pacific Oceans, but the fauna and habitats on Indian Ocean seamounts are still poorly known. This study focuses on two seamounts, a submarine volcano (cratered seamount – CSM) and a non-volcano (SM2) in the Andaman Back–arc Basin (ABB), and the basin itself. The main purpose was to explore and generate regional biodiversity data from summit and flank (upper slope) of the Andaman seamounts for comparison with other seamounts worldwide. We also investigated how substratum types affect the megafaunal community structure along the ABB. Underwater video recordings from TeleVision guided Gripper (TVG) lowerings were used to describe the benthic community structure along the ABB and both seamounts. We found 13 varieties of substratum in the study area. The CSM has hard substratum, such as boulders and cobbles, whereas the SM2 was dominated by cobbles and fine sediment. The highest abundance of megabenthic communities was recorded on the flank of the CSM. Species richness and diversity were higher at the flank of the CSM than other are of ABB. Non-metric multi-dimensional scaling (nMDS) analysis of substratum types showed 50% similarity between the flanks of both seamounts, because both sites have a component of cobbles mixed with fine sediments in their substratum. Further, nMDS of faunal abundance revealed two groups, each restricted to one of the seamounts, suggesting faunal distinctness between them. The sessile fauna corals and poriferans showed a significant positive relation with cobbles and fine sediments substratum, while the mobile categories echinoderms and arthropods showed a significant positive relation with fine sediments only

Characterising and Predicting Benthic Biodiversity for Conservation Planning in Deepwater Environments

Understanding patterns of biodiversity in deep sea systems is increasingly important because human activities are extending further into these areas. However, obtaining data is difficult, limiting the ability of science to inform management decisions. We have used three different methods of quantifying biodiversity to describe patterns of biodiversity in an area that includes two marine reserves in deep water off southern Australia. We used biological data collected during a recent survey, combined with extensive physical data to model, predict and map three different attributes of biodiversity: distributions of common species, beta diversity and rank abundance distributions (RAD). The distribution of each of eight common species was unique, although all the species respond to a depth-correlated physical gradient. Changes in composition (beta diversity) were large, even between sites with very similar environmental conditions. Composition at any one site was highly uncertain, and the suite of species changed dramatically both across and down slope. In contrast, the distributions of the RAD components of biodiversity (community abundance, richness, and evenness) were relatively smooth across the study area, suggesting that assemblage structure (i.e. the distribution of abundances of species) is limited, irrespective of species composition. Seamounts had similar biodiversity based on metrics of species presence, beta diversity, total abundance, richness and evenness to the adjacent continental slope in the same depth ranges. These analyses suggest that conservation objectives need to clearly identify which aspects of biodiversity are valued, and employ an appropriate suite of methods to address these aspects, to ensure that conservation goals are met

Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model

Anthropogenic activities are causing widespread degradation of ecosystems worldwide, threatening the ecosystem services upon which all human life depends. Improved understanding of this degradation is urgently needed to improve avoidance and mitigation measures. One tool to assist these efforts is predictive models of ecosystem structure and function that are mechanistic: based on fundamental ecological principles. Here we present the first mechanistic General Ecosystem Model (GEM) of ecosystem structure and function that is both global and applies in all terrestrial and marine environments. Functional forms and parameter values were derived from the theoretical and empirical literature where possible. Simulations of the fate of all organisms with body masses between 10 µg and 150,000 kg (a range of 14 orders of magnitude) across the globe led to emergent properties at individual (e.g., growth rate), community (e.g., biomass turnover rates), ecosystem (e.g., trophic pyramids), and macroecological scales (e.g., global patterns of trophic structure) that are in general agreement with current data and theory. These properties emerged from our encoding of the biology of, and interactions among, individual organisms without any direct constraints on the properties themselves. Our results indicate that ecologists have gathered sufficient information to begin to build realistic, global, and mechanistic models of ecosystems, capable of predicting a diverse range of ecosystem properties and their response to human pressures

How much locomotive activity is needed for an active physical activity level: analysis of total step counts

<p>Abstract</p> <p>Background</p> <p>Although physical activity recommendations for public health have focused on locomotive activity such as walking and running, it is uncertain how much these activities contribute to overall physical activity level (PAL). The purpose of the present study was to determine the contribution of locomotive activity to PAL using total step counts measured in a calorimeter study.</p> <p>Methods</p> <p>PAL, calculated as total energy expenditure divided by basal metabolic rate, was evaluated in 11 adult men using three different conditions for 24-hour human calorimeter measurements: a low-activity day (L-day) targeted at a low active level of PAL (1.45), and a high-frequency moderate activity day (M-day) or a high-frequency vigorous activity day (V-day) targeted at an active level of PAL (1.75). These subjects were permitted only light activities except prescribed activities. In a separate group of 41 adults, free-living PAL was evaluated using doubly-labeled water (DLW). In both experiments, step counts per day were also measured using an accelerometer.</p> <p>Results</p> <p>In the human calorimeter study, PAL and step counts were 1.42 ± 0.10 and 8,973 ± 543 steps/d (L-day), 1.82 ± 0.14 and 29,588 ± 1,126 steps/d (M-day), and 1.74 ± 0.15 and 23,755 ± 1,038 steps/d (V-day), respectively. In the DLW study, PAL and step counts were 1.73 ± 0.15 and 10,022 ± 2,605 steps/d, and there was no significant relationship between PAL and daily step counts.</p> <p>Conclusions</p> <p>These results indicate that an enormous number of steps are needed for an active level of PAL if individuals extend physical activity-induced energy expenditure by only locomotive activity. Therefore, non-locomotive activity such as household activity should also play a significant role in increasing PAL under free-living conditions.</p

Biophysical Factors Affecting the Distribution of Demersal Fish around the Head of a Submarine Canyon Off the Bonney Coast, South Australia

We sampled the demersal fish community of the Bonney Canyon, South Australia at depths (100–1,500 m) and locations that are poorly known. Seventy-eight species of demersal fish were obtained from 12 depth-stratified trawls along, and to either side, of the central canyon axis. Distributional patterns in species richness and biomass were highly correlated. Three fish assemblage groupings, characterised by small suites of species with narrow depth distributions, were identified on the shelf, upper slope and mid slope. The assemblage groupings were largely explained by depth (ρw = 0.78). Compared to the depth gradient, canyon-related effects are weak or occur at spatial or temporal scales not sampled in this study. A conceptual physical model displayed features consistent with the depth zonational patterns in fish, and also indicated that canyon upwelling can occur. The depth zonation of the fish assemblage was associated with the depth distribution of water masses in the area. Notably, the mid-slope community (1,000 m) coincided with a layer of Antarctic Intermediate Water, the upper slope community (500 m) resided within the core of the Flinders Current, and the shelf community was located in a well-mixed layer of surface water (<450 m depth)

Risk Assessment of Gastric Cancer Caused by Helicobacter pylori Using CagA Sequence Markers

As a marker of Helicobacter pylori, Cytotoxin-associated gene A (cagA) has been revealed to be the major virulence factor causing gastroduodenal diseases. However, the molecular mechanisms that underlie the development of different gastroduodenal diseases caused by cagA-positive H. pylori infection remain unknown. Current studies are limited to the evaluation of the correlation between diseases and the number of Glu-Pro-Ile-Tyr-Ala (EPIYA) motifs in the CagA strain. To further understand the relationship between CagA sequence and its virulence to gastric cancer, we proposed a systematic entropy-based approach to identify the cancer-related residues in the intervening regions of CagA and employed a supervised machine learning method for cancer and non-cancer cases classification.An entropy-based calculation was used to detect key residues of CagA intervening sequences as the gastric cancer biomarker. For each residue, both combinatorial entropy and background entropy were calculated, and the entropy difference was used as the criterion for feature residue selection. The feature values were then fed into Support Vector Machines (SVM) with the Radial Basis Function (RBF) kernel, and two parameters were tuned to obtain the optimal F value by using grid search. Two other popular sequence classification methods, the BLAST and HMMER, were also applied to the same data for comparison.Our method achieved 76% and 71% classification accuracy for Western and East Asian subtypes, respectively, which performed significantly better than BLAST and HMMER. This research indicates that small variations of amino acids in those important residues might lead to the virulence variance of CagA strains resulting in different gastroduodenal diseases. This study provides not only a useful tool to predict the correlation between the novel CagA strain and diseases, but also a general new framework for detecting biological sequence biomarkers in population studies

Plant Trait Diversity Buffers Variability in Denitrification Potential over Changes in Season and Soil Conditions

BACKGROUND: Denitrification is an important ecosystem service that removes nitrogen (N) from N-polluted watersheds, buffering soil, stream, and river water quality from excess N by returning N to the atmosphere before it reaches lakes or oceans and leads to eutrophication. The denitrification enzyme activity (DEA) assay is widely used for measuring denitrification potential. Because DEA is a function of enzyme levels in soils, most ecologists studying denitrification have assumed that DEA is less sensitive to ambient levels of nitrate (NO(3)(-)) and soil carbon and thus, less variable over time than field measurements. In addition, plant diversity has been shown to have strong effects on microbial communities and belowground processes and could potentially alter the functional capacity of denitrifiers. Here, we examined three questions: (1) Does DEA vary through the growing season? (2) If so, can we predict DEA variability with environmental variables? (3) Does plant functional diversity affect DEA variability? METHODOLOGY/PRINCIPAL FINDINGS: The study site is a restored wetland in North Carolina, US with native wetland herbs planted in monocultures or mixes of four or eight species. We found that denitrification potentials for soils collected in July 2006 were significantly greater than for soils collected in May and late August 2006 (p<0.0001). Similarly, microbial biomass standardized DEA rates were significantly greater in July than May and August (p<0.0001). Of the soil variables measured--soil moisture, organic matter, total inorganic nitrogen, and microbial biomass--none consistently explained the pattern observed in DEA through time. There was no significant relationship between DEA and plant species richness or functional diversity. However, the seasonal variance in microbial biomass standardized DEA rates was significantly inversely related to plant species functional diversity (p<0.01). CONCLUSIONS/SIGNIFICANCE: These findings suggest that higher plant functional diversity may support a more constant level of DEA through time, buffering the ecosystem from changes in season and soil conditions

Differential Extinction and the Contrasting Structure of Polar Marine Faunas

Background: The low taxonomic diversity of polar marine faunas today reflects both the failure of clades to colonize or diversify in high latitudes and regional extinctions of once-present clades. However, simple models of polar evolution are made difficult by the strikingly different faunal compositions and community structures of the two poles. Methodology/Principal Findings: A comparison of early Cenozoic Arctic and Antarctic bivalve faunas with modern ones, within the framework of a molecular phylogeny, shows that while Arctic losses were randomly distributed across the tree, Antarctic losses were significantly concentrated in more derived families, resulting in communities dominated by basal lineages. Potential mechanisms for the phylogenetic structure to Antarctic extinctions include continental isolation, changes in primary productivity leading to turnover of both predators and prey, and the effect of glaciation on shelf habitats. Conclusions/Significance: These results show that phylogenetic consequences of past extinctions can vary substantially among regions and thus shape regional faunal structures, even when due to similar drivers, here global cooling, and provide the first phylogenetic support for the ‘‘retrograde’ ’ hypothesis of Antarctic faunal evolution

Engineering of Insulin Receptor Isoform-Selective Insulin Analogues

BACKGROUND: The insulin receptor (IR) exists in two isoforms, A and B, and the isoform expression pattern is tissue-specific. The C-terminus of the insulin B chain is important for receptor binding and has been shown to contact the IR just adjacent to the region where the A and B isoforms differ. The aim of this study was to investigate the importance of the C-terminus of the B chain in IR isoform binding in order to explore the possibility of engineering tissue-specific/liver-specific insulin analogues. METHODOLOGY/PRINCIPAL FINDINGS: Insulin analogue libraries were constructed by total amino acid scanning mutagenesis. The relative binding affinities for the A and B isoform of the IR were determined by competition assays using scintillation proximity assay technology. Structural information was obtained by X-ray crystallography. Introduction of B25A or B25N mutations resulted in analogues with a 2-fold preference for the B compared to the A isoform, whereas the opposite was observed with a B25Y substitution. An acidic amino acid residue at position B27 caused an additional 2-fold selective increase in affinity for the receptor B isoform for analogues bearing a B25N mutation. Furthermore, the combination of B25H with either B27D or B27E also resulted in B isoform-preferential analogues (2-fold preference) even though the corresponding single mutation analogues displayed no differences in relative isoform binding affinity. CONCLUSIONS/SIGNIFICANCE: We have discovered a new class of IR isoform-selective insulin analogues with 2-4-fold differences in relative binding affinities for either the A or the B isoform of the IR compared to human insulin. Our results demonstrate that a mutation at position B25 alone or in combination with a mutation at position B27 in the insulin molecule confers IR isoform selectivity. Isoform-preferential analogues may provide new opportunities for developing insulin analogues with improved clinical benefits