Multi-Decadal Changes in Mangrove Extent, Age and Species in the Red River Estuaries of Viet Nam

This research investigated the performance of four different machine learning supervised image classifiers: artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machine (SVM) using SPOT-7 and Sentinel-1 images to classify mangrove age and species in 2019 in a Red River estuary, typical of others found in northern Viet Nam. The four classifiers were chosen because they are considered to have high accuracy, however, their use in mangrove age and species classifications has thus far been limited. A time-series of Landsat images from 1975 to 2019 was used to map mangrove extent changes using the unsupervised classification method of iterative self-organizing data analysis technique (ISODATA) and a comparison with accuracy of K-means classification, which found that mangrove extent has increased, despite a fall in the 1980s, indicating the success of mangrove plantation and forest protection efforts by local people in the study area. To evaluate the supervised image classifiers, 183 in situ training plots were assessed, 70% of them were used to train the supervised algorithms, with 30% of them employed to validate the results. In order to improve mangrove species separations, Gram–Schmidt and principal component analysis image fusion techniques were applied to generate better quality images. All supervised and unsupervised (2019) results of mangrove age, species, and extent were mapped and accuracy was evaluated. Confusion matrices were calculated showing that the classified layers agreed with the ground-truth data where most producer and user accuracies were greater than 80%. The overall accuracy and Kappa coefficients (around 0.9) indicated that the image classifications were very good. The test showed that SVM was the most accurate, followed by DT, ANN, and RF in this case study. The changes in mangrove extent identified in this study and the methods tested for using remotely sensed data will be valuable to monitoring and evaluation assessments of mangrove plantation projects

Small RNAs with 5′-Polyphosphate Termini Associate with a Piwi-Related Protein and Regulate Gene Expression in the Single-Celled Eukaryote Entamoeba histolytica

Small interfering RNAs regulate gene expression in diverse biological processes, including heterochromatin formation and DNA elimination, developmental regulation, and cell differentiation. In the single-celled eukaryote Entamoeba histolytica, we have identified a population of small RNAs of 27 nt size that (i) have 5′-polyphosphate termini, (ii) map antisense to genes, and (iii) associate with an E. histolytica Piwi-related protein. Whole genome microarray expression analysis revealed that essentially all genes to which antisense small RNAs map were not expressed under trophozoite conditions, the parasite stage from which the small RNAs were cloned. However, a number of these genes were expressed in other E. histolytica strains with an inverse correlation between small RNA and gene expression level, suggesting that these small RNAs mediate silencing of the cognate gene. Overall, our results demonstrate that E. histolytica has an abundant 27 nt small RNA population, with features similar to secondary siRNAs from C. elegans, and which appear to regulate gene expression. These data indicate that a silencing pathway mediated by 5′-polyphosphate siRNAs extends to single-celled eukaryotic organisms

Learning from natural sediments to tackle microplastics challenges: A multidisciplinary perspective

Although the study of microplastics in the aquatic environment incorporates a diversity of research fields, it is still in its infancy in many aspects while comparable topics have been studied in other disciplines for decades. In particular, extensive research in sedimentology can provide valuable insights to guide future microplastics research. To advance our understanding of the comparability of natural sediments with microplastics, we take an interdisciplinary look at the existing literature describing particle properties, transport processes, sampling techniques and ecotoxicology. Based on our analysis, we define seven research goals that are essential to improve our understanding of microplastics and can be tackled by learning from natural sediment research, and identify relevant tasks to achieve each goal. These goals address (1) the description of microplastic particles, (2) the interaction of microplastics with environmental substances, (3) the vertical distribution of microplastics, (4) the erosion and deposition behaviour of microplastics, (5) the impact of biota on microplastic transport, (6) the sampling methods and (7) the microplastic toxicity. When describing microplastic particles, we should specifically draw from the knowledge of natural sediments, for example by using shape factors or applying methods for determining the principal dimensions of non-spherical particles. Sediment transport offers many fundamentals that are transferable to microplastic transport, and could be usefully applied. However, major knowledge gaps still exist in understanding the role of transport modes, the influence of biota on microplastic transport, and the importance and implementation of the dynamic behaviour of microplastics as a result of time-dependent changes in particle properties in numerical models. We give an overview of available sampling methods from sedimentology and discuss their suitability for microplastic sampling, which can be used for creating standardised guidelines for future application with microplastics. In order to comprehensively assess the ecotoxicology of microplastics, a distinction must be made between the effects of the polymers themselves, their physical form, the plastic-associated chemicals and the attached pollutants. This review highlights areas where we can rely on understanding and techniques from sediment research - and areas where we need new, microplastic-specific knowledge - and synthesize recommendations to guide future, interdisciplinary microplastic research

The role of the myosin ATPase activity in adaptive thermogenesis by skeletal muscle

Resting skeletal muscle is a major contributor to adaptive thermogenesis, i.e., the thermogenesis that changes in response to exposure to cold or to overfeeding. The identification of the “furnace” that is responsible for increased heat generation in resting muscle has been the subject of a number of investigations. A new state of myosin, the super relaxed state (SRX), with a very slow ATP turnover rate has recently been observed in skeletal muscle (Stewart et al. in Proc Natl Acad Sci USA 107:430–435, 2010). Inhibition of the myosin ATPase activity in the SRX was suggested to be caused by binding of the myosin head to the core of the thick filament in a structural motif identified earlier by electron microscopy. To be compatible with the basal metabolic rate observed in vivo for resting muscle, most myosin heads would have to be in the SRX. Modulation of the population of this state, relative to the normal relaxed state, was proposed to be a major contributor to adaptive thermogenesis in resting muscle. Transfer of only 20% of myosin heads from the SRX into the normal relaxed state would cause muscle thermogenesis to double. Phosphorylation of the myosin regulatory light chain was shown to transfer myosin heads from the SRX into the relaxed state, which would increase thermogenesis. In particular, thermogenesis by myosin has been proposed to play a role in the dissipation of calories during overfeeding. Up-regulation of muscle thermogenesis by pharmaceuticals that target the SRX would provide new approaches to the treatment of obesity or high blood sugar levels

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified