Is the meiofauna a good indicator for climate change and anthropogenic impacts?

Our planet is changing, and one of the most pressing challenges facing the scientific community revolves around understanding how ecological communities respond to global changes. From coastal to deep-sea ecosystems, ecologists are exploring new areas of research to find model organisms that help predict the future of life on our planet. Among the different categories of organisms, meiofauna offer several advantages for the study of marine benthic ecosystems. This paper reviews the advances in the study of meiofauna with regard to climate change and anthropogenic impacts. Four taxonomic groups are valuable for predicting global changes: foraminifers (especially calcareous forms), nematodes, copepods and ostracods. Environmental variables are fundamental in the interpretation of meiofaunal patterns and multistressor experiments are more informative than single stressor ones, revealing complex ecological and biological interactions. Global change has a general negative effect on meiofauna, with important consequences on benthic food webs. However, some meiofaunal species can be favoured by the extreme conditions induced by global change, as they can exhibit remarkable physiological adaptations. This review highlights the need to incorporate studies on taxonomy, genetics and function of meiofaunal taxa into global change impact research

Identification of the risk for liver fibrosis on CHB patients using an artificial neural network based on routine and serum markers

<p>Abstract</p> <p>Background</p> <p>Liver fibrosis progression is commonly found in patients with CHB. Liver biopsy is a gold standard for identifying the extent of liver fibrosis, but has many draw-backs. It is essential to construct a noninvasive model to predict the levels of risk for liver fibrosis. It would provide very useful information to help reduce the number of liver biopsies of CHB patients.</p> <p>Methods</p> <p>339 chronic hepatitis B patients with HBsAg-positive were investigated retrospectively, and divided at random into 2 subsets with twice as many patients in the training set as in the validation set; 116 additional patients were consequently enrolled in the study as the testing set. A three-layer artificial neural network was developed using a Bayesian learning algorithm. Sensitivity and ROC analysis were performed to explain the importance of input variables and the performance of the neural network.</p> <p>Results</p> <p>There were 329 patients without significant fibrosis and 126 with significant fibrosis in the study. All markers except gender, HB, ALP and TP were found to be statistically significant factors associated with significant fibrosis. The sensitivity analysis showed that the most important factors in the predictive model were age, AST, platelet, and GGT, and the influence on the output variable among coal miners were 22.3-24.6%. The AUROC in 3 sets was 0.883, 0.884, and 0.920. In the testing set, for a decision threshold of 0.33, sensitivity and negative predictive values were 100% and all CHB patients with significant fibrosis would be identified.</p> <p>Conclusions</p> <p>The artificial neural network model based on routine and serum markers would predict the risk for liver fibrosis with a high accuracy. 47.4% of CHB patients at a decision threshold of 0.33 would be free of liver biopsy and wouldn't be missed.</p

The interstitium in cardiac repair: role of the immune-stromal cell interplay

Cardiac regeneration, that is, restoration of the original structure and function in a damaged heart, differs from tissue repair, in which collagen deposition and scar formation often lead to functional impairment. In both scenarios, the early-onset inflammatory response is essential to clear damaged cardiac cells and initiate organ repair, but the quality and extent of the immune response vary. Immune cells embedded in the damaged heart tissue sense and modulate inflammation through a dynamic interplay with stromal cells in the cardiac interstitium, which either leads to recapitulation of cardiac morphology by rebuilding functional scaffolds to support muscle regrowth in regenerative organisms or fails to resolve the inflammatory response and produces fibrotic scar tissue in adult mammals. Current investigation into the mechanistic basis of homeostasis and restoration of cardiac function has increasingly shifted focus away from stem cell-mediated cardiac repair towards a dynamic interplay of cells composing the less-studied interstitial compartment of the heart, offering unexpected insights into the immunoregulatory functions of cardiac interstitial components and the complex network of cell interactions that must be considered for clinical intervention in heart diseases

A new deep learning approach based on bilateral semantic segmentation models for sustainable estuarine wetland ecosystem management.

Nowadays, estuarial areas have been strongly affected by the construction of electrical power dams from upstream, downstream urbanization and many types of hazards along the coastal regions. It has resulted in significant changes in estuarine wetland ecosystems between rainy and dry seasons. To avoid estuary vulnerability, monitoring and evaluation of the estuarine ecosystems are very critical tasks. The main goal of this research is to propose and implement a novel deep learning method in monitoring various ecosystems in estuarine regions. The processing speed and accuracy of common neural networks is improved more than ten times through spatial and context paths integrated into a novel Bilateral Segmentation Network (BiSeNet). The multi-sensor and multi-temporal satellite images (including Sentinel-2, ALOS-DEM, and NOAA-DEM images) served as input data. As a result, four BiSeNet models out of 20 trained models achieved a greater than 90% accuracy, especially for interpreting estuarine waters, intertidal forested wetlands, and aquacultural lands in subtidal regions. These models outperformed Random Forest and Support Vector Machine approaches. The best one was used to map estuarine ecosystems from 12 satellite images over a five-year period in the largest estuary in northern Vietnam. The ecosystem changes between dry and rainy seasons were analyzed in detail to assess the ecological succession in estuaries. Furthermore, this model can potentially update new estuarine ecosystem types in other estuarine areas across the world, making possible real-time monitoring and assessing estuarine ecological conditions for sustainable management of wetland ecosystem

Vietnamese students' transition to international non-government organisations

In Vietnam the large-scale historical change under the influence of globalisation are discernible in the economic reform (Đổi Mới) in the transition from a centrally planned economy to a market oriented one, and in the disjunction between traditional modes of theoretical focus education and the diversity of knowledge and skills required in a globalised labour market. While the traditional view of university education as training for life-long work within one profession is no longer appropriate, the traditional, profession-focused structure at universities in Vietnam has persisted, challenging the skills and knowledge that young graduates typically developed. International non-government organisations (INGOs), by the nature of their work, need employees to mediate between different structures, resources and models, and to develop new models for specific projects. The challenges of working in an INGO may be intensified, especially in the early stages of employment or career, and this raises questions about how universities and employers can assist new graduates to make the transition from formal education to such a workplace. This inductive, qualitative research explores how young Vietnamese university graduates have strategically acquired and developed the attributes required for this intercultural work environment. This paper presents findings from the interviews with young Vietnamese graduates working at INGOs, conducted in two phases, between December 2010–November 2012. The key findings show that the strategies to apply ‘far transfer’ graduate attributes were essential for meeting work requirements. An approach that draws on three bodies of literature - graduate attributes, intercultural competency, and agency, structure and reflexivity - promises to shed light on the graduates’ experiences