Extraordinarily high biomass benthic community on Southern Ocean seamounts

We describe a previously unknown assemblage of seamount-associated megabenthos that has by far the highest peak biomass reported in the deep-sea outside of vent communities. The assemblage was found at depths of 2–2.5 km on rocky geomorphic features off the southeast coast of Australia, in an area near the Sub-Antarctic Zone characterised by high rates of surface productivity and carbon export to the deep-ocean. These conditions, and the taxa in the assemblage, are widely distributed around the Southern mid-latitudes, suggesting the high-biomass assemblage is also likely to be widespread. The role of this assemblage in regional ecosystem and carbon dynamics and its sensitivities to anthropogenic impacts are unknown. The discovery highlights the lack of information on deep-sea biota worldwide and the potential for unanticipated impacts of deep-sea exploitation

Effects of exposure to facial expression variation in face learning and recognition.

Facial expression is a major source of image variation in face images. Linking numerous expressions to the same face can be a huge challenge for face learning and recognition. It remains largely unknown what level of exposure to this image variation is critical for expression-invariant face recognition. We examined this issue in a recognition memory task, where the number of facial expressions of each face being exposed during a training session was manipulated. Faces were either trained with multiple expressions or a single expression, and they were later tested in either the same or different expressions. We found that recognition performance after learning three emotional expressions had no improvement over learning a single emotional expression (Experiments 1 and 2). However, learning three emotional expressions improved recognition compared to learning a single neutral expression (Experiment 3). These findings reveal both the limitation and the benefit of multiple exposures to variations of emotional expression in achieving expression-invariant face recognition. The transfer of expression training to a new type of expression is likely to depend on a relatively extensive level of training and a certain degree of variation across the types of expressions

Th17 Cytokines and the Gut Mucosal Barrier

Local immune responses serve to contain infections by pathogens to the gut while preventing pathogen dissemination to systemic sites. Several subsets of T cells in the gut (T-helper 17 cells, γδ T cells, natural killer (NK), and NK-T cells) contribute to the mucosal response to pathogens by secreting a subset of cytokines including interleukin (IL)-17A, IL-17F, IL-22, and IL-26. These cytokines induce the secretion of chemokines and antimicrobial proteins, thereby orchestrating the mucosal barrier against gastrointestinal pathogens. While the mucosal barrier prevents bacterial dissemination from the gut, it also promotes colonization by pathogens that are resistant to some of the inducible antimicrobial responses. In this review, we describe the contribution of Th17 cytokines to the gut mucosal barrier during bacterial infections

Science Priorities for Seamounts: Research Links to Conservation and Management

Seamounts shape the topography of all ocean basins and can be hotspots of biological activity in the deep sea. The Census of Marine Life on Seamounts (CenSeam) was a field program that examined seamounts as part of the global Census of Marine Life (CoML) initiative from 2005 to 2010. CenSeam progressed seamount science by collating historical data, collecting new data, undertaking regional and global analyses of seamount biodiversity, mapping species and habitat distributions, challenging established paradigms of seamount ecology, developing new hypotheses, and documenting the impacts of human activities on seamounts. However, because of the large number of seamounts globally, much about the structure, function and connectivity of seamount ecosystems remains unexplored and unknown. Continual, and potentially increasing, threats to seamount resources from fishing and seabed mining are creating a pressing demand for research to inform conservation and management strategies. To meet this need, intensive science effort in the following areas will be needed: 1) Improved physical and biological data; of particular importance is information on seamount location, physical characteristics (e.g. habitat heterogeneity and complexity), more complete and intensive biodiversity inventories, and increased understanding of seamount connectivity and faunal dispersal; 2) New human impact data; these shall encompass better studies on the effects of human activities on seamount ecosystems, as well as monitoring long-term changes in seamount assemblages following impacts (e.g. recovery); 3) Global data repositories; there is a pressing need for more comprehensive fisheries catch and effort data, especially on the high seas, and compilation or maintenance of geological and biodiversity databases that underpin regional and global analyses; 4) Application of support tools in a data-poor environment; conservation and management will have to increasingly rely on predictive modelling techniques, critical evaluation of environmental surrogates as faunal “proxies”, and ecological risk assessment

An NF-κB and Slug Regulatory Loop Active in Early Vertebrate Mesoderm

BACKGROUND: In both Drosophila and the mouse, the zinc finger transcription factor Snail is required for mesoderm formation; its vertebrate paralog Slug (Snai2) appears to be required for neural crest formation in the chick and the clawed frog Xenopus laevis. Both Slug and Snail act to induce epithelial to mesenchymal transition (EMT) and to suppress apoptosis. METHODOLOGY & PRINCIPLE FINDINGS: Morpholino-based loss of function studies indicate that Slug is required for the normal expression of both mesodermal and neural crest markers in X. laevis. Both phenotypes are rescued by injection of RNA encoding the anti-apoptotic protein Bcl-xL; Bcl-xL's effects are dependent upon IκB kinase-mediated activation of the bipartite transcription factor NF-κB. NF-κB, in turn, directly up-regulates levels of Slug and Snail RNAs. Slug indirectly up-regulates levels of RNAs encoding the NF-κB subunit proteins RelA, Rel2, and Rel3, and directly down-regulates levels of the pro-apopotic Caspase-9 RNA. CONCLUSIONS/SIGNIFICANCE: These studies reveal a Slug/Snail–NF-κB regulatory circuit, analogous to that present in the early Drosophila embryo, active during mesodermal formation in Xenopus. This is a regulatory interaction of significance both in development and in the course of inflammatory and metastatic disease

Predicting probable Alzheimer's disease using linguistic deficits and biomarkers

BackgroundThe manual diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.ResultsOur models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).ConclusionsExperimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD