A comparative analysis of the precipitation extremes obtained from tropical rainfall-measuring mission satellite and rain gauges datasets over a semiarid region

The objectives of this research were to compare precipitation extremes obtained from Tropical Rainfall‐Measuring Mission (TRMM) satellite and those of rain gauges over a semiarid area in Iran. Extreme precipitation indices (EPIs) (i.e., the number of days with a precipitation value over 10 mm, the maximum duration of wet and dry days, the number of days with precipitation over the 95th percentile, total precipitation higher than the 95th percentile, and maximum daily precipitation) were calculated across Fars province, Iran, 2000–2014 on seasonal time scales. The gauges data were interpolated at a spatial resolution of 0.25 × 0.25° to match the 3B42 data using Inverse Distance Weighting (IDW). Then, EPIs from the two datasets were compared with each other. The findings showed that mean values computed from gauges and satellite data did not present any significant differences among all of the extreme indices. Furthermore, their variances presented a good level of congruence. Finally, the majority of indices presented a satisfactory correlation between the two dataset. To evaluate the prediction of extreme events in different temporal and tolerated distances, a fuzzy method was used. The results showed that the percentage of grid cells with useful predictions tripled with spatial tolerance extending by just one pixel. To evaluate methods of eliminating the uncertainty of probable missing rainfall data and the seasonal changes in rainfall averages, probabilistic methods based on Weibull distribution and truncated geometric distribution (TGD) were employed to eliminate uncertainties in estimation of extreme precipitation amounts and extreme wet periods (WPs). The results showed that as to extreme precipitation amounts, a satisfactory method could not be drawn for arid southern regions of Fars, Iran. Similarly, as to extreme WPs, the consistency between gauges and satellite data could not be improved significantly

Exceptionally large two- and three-photon absorption cross-sections by OPV organometalation

Oligo(p-phenylenevinylene)s (OPVs) containing up to 8 PV units and end-functionalized by ruthenium alkynyl groups have been prepared and their nonlinear absorption properties assessed using the Z-scan technique and employing low repetition rate femtosecond pulses. Exceptionally large two-photon absorption (ca. 12 500 GM at 725 nm) and three-photon absorption cross sections (ca. 1.6 × 10⁻⁷⁶ cm⁶s² at 1100 nm) are found for the 8PV-containing example, highlighting the potential of an “organometalation” approach to NLO-efficient organic materialsWe thank the Australian Research Council (ARC), the National Natural Science Foundation of China (51432006), the Chinese Government Ministry of Education, the Chinese Government State Administration of Foreign Experts Affairs (111 Project: B13025), and the National Science Centre of Poland (grant 2013/10/A/ST4/00114) for financial support. M. P. C. thanks the ARC for an Australian Research Fellowship and C. Q. thanks CONICYT (Chile) for a Chile PhD Scholarship Abroad

Disruption of Glucagon-Like Peptide 1 Signaling in Sim1 Neurons Reduces Physiological and Behavioral Reactivity to Acute and Chronic Stress

Organismal stress initiates a tightly orchestrated set of responses involving complex physiological and neurocognitive systems. Here, we present evidence for glucagon-like peptide 1 (GLP-1)-mediated paraventricular hypothalamic circuit coordinating the global stress response. The GLP-1 receptor (Glp1r) in mice was knocked down in neurons expressing single-minded 1, a transcription factor abundantly expressed in the paraventricular nucleus (PVN) of the hypothalamus. Mice with single-minded 1-mediated Glp1r knockdown had reduced hypothalamic-pituitary-adrenal axis responses to both acute and chronic stress and were protected against weight loss associated with chronic stress. In addition, regional Glp1r knockdown attenuated stress-induced cardiovascular responses accompanied by decreased sympathetic drive to the heart. Finally, Glp1r knockdown reduced anxiety-like behavior, implicating PVN GLP-1 signaling in behavioral stress reactivity. Collectively, these findings support a circuit whereby brainstem GLP-1 activates PVN signaling to mount an appropriate whole-organism response to stress. These results raise the possibility that dysfunction of this system may contribute to stress-related pathologies, and thereby provide a novel target for intervention

Deep neural network or dermatologist?

Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A down-side is that it is difficult to understand the rationale of the model predictions and to identify potential failure modes. This is a major barrier to adoption of deep learning in clinical practice. In this paper we ask if two existing local interpretability methods, Grad-CAM and Kernel SHAP, can shed light on convolutional neural networks trained in the context of melanoma detection. Our contributions are (i) we first explore the domain space via a reproducible, end-to-end learning framework that creates a suite of 30 models, all trained on a publicly available data set (HAM10000), (ii) we next explore the reliability of GradCAM and Kernel SHAP in this context via some basic sanity check experiments (iii) finally, we investigate a random selection of models from our suite using GradCAM and Kernel SHAP. We show that despite high accuracy, the models will occasionally assign importance to features that are not relevant to the diagnostic task. We also show that models of similar accuracy will produce different explanations as measured by these methods. This work represents first steps in bridging the gap between model accuracy and interpretability in the domain of skin cancer classification

Screening and Characterization of Novel Polyesterases from Environmental Metagenomes with High Hydrolytic Activity against Synthetic Polyesters

International audienc

Activity screening of environmental metagenomic libraries reveals novel carboxylesterase families

Metagenomics has made accessible an enormous reserve of global biochemical diversity. To tap into this vast resource of novel enzymes, we have screened over one million clones from metagenome DNA libraries derived from sixteen different environments for carboxylesterase activity and identified 714 positive hits. We have validated the esterase activity of 80 selected genes, which belong to 17 different protein families including unknown and cyclase-like proteins. Three metagenomic enzymes exhibited lipase activity, and seven proteins showed polyester depolymerization activity against polylactic acid and polycaprolactone. Detailed biochemical characterization of four new enzymes revealed their substrate preference, whereas their catalytic residues were identified using site-directed mutagenesis. The crystal structure of the metal-ion dependent esterase MGS0169 from the amidohydrolase superfamily revealed a novel active site with a bound unknown ligand. Thus, activity-centered metagenomics has revealed diverse enzymes and novel families of microbial carboxylesterases, whose activity could not have been predicted using bioinformatics tools

Mapping of QTLs conferring resistance in rice to brown planthopper, Nilaparvata lugens

Brown planthopper (BPH), Nilaparvata lugens Stål (Hemiptera: Delphacide), is a destructive insect pest of rice, Oryza sativa L. (Poaceae), in rice-producing areas worldwide. Host plant resistance is a major aspect of managing this pest. In this study, a mapping population consisting of 150 F3 lines, derived from a cross of MR276 and Rathu Heenati, was used to detect and analyse quantitative trait loci (QTLs) for the resistance to BPH. Composite Interval Mapping (CIM) was used for QTL detection. In total 10 QTLs controlling BPH resistance were mapped on chromosomes 1, 3, 6, 7, 9, 10, and 12. Four QTLs – qBph-1-1, qBph-3-1, qBph-6-1, and qBph-7-1 – were mapped on chromosomes 1, 3, 6, and 7 in the standard seedbox screening test, explaining 41% of the phenotypic variance. Two QTLs, qBph-6-1 and qBph-9-1, were detected on chromosomes 6 and 9 in the honeydew test, accounting for 32% of the total phenotypic variance. Moreover, four QTLs – qBph-3-1, qBph-6-1, qBph-10-1, and qBph-12-1 – were identified on chromosomes 3, 6, 10, and 12 expressing antixenosis to BPH and explaining 41% of the phenotypic variance. QTL qBph-3-1 was located in the chromosomal region between markers RM231 and RM3872 on chromosome 3, and QTL qBph-6-1 was located in the region between RM588 and RM204 on chromosome 6, indicating that these regions have a major effect in controlling the resistance to BPH in the population studied. The molecular markers linked to QTLs that are identified will be useful in the development of varieties resistant to BPH. Our study contributes to the development of genetic material for breeding programmes and marker-assisted selection (MAS) in rice to improve BPH resistance

Molecular insights into the regulation of rice kernel elongation

A large number of rice agronomic traits are complex, multi factorial and polygenic. As the mechanisms and genes determining grain size and yield are largely unknown, the identification of regulatory genes related to grain development remains a preeminent approach in rice genetic studies and breeding programs. Genes regulating cell proliferation and expansion in spikelet hulls and participating in endosperm development are the main controllers of rice kernel elongation and grain size. We review here and discuss recent findings on genes controlling rice grain size and the mechanisms, epialleles, epigenomic variation, and assessment of controlling genes using genome-editing tools relating to kernel elongation

Improvement of drought tolerance in rice (Oryza sativa L.): genetics, genomic tools, and the WRKY gene family

Drought tolerance is an important quantitative trait with multipart phenotypes that are often further complicated by plant phenology. Different types of environmental stresses, such as high irradiance, high temperatures, nutrient deficiencies, and toxicities, may challenge crops simultaneously; therefore, breeding for drought tolerance is very complicated. Interdisciplinary researchers have been attempting to dissect and comprehend the mechanisms of plant tolerance to drought stress using various methods; however, the limited success of molecular breeding and physiological approaches suggests that we rethink our strategies. Recent genetic techniques and genomics tools coupled with advances in breeding methodologies and precise phenotyping will likely reveal candidate genes and metabolic pathways underlying drought tolerance in crops. The WRKY transcription factors are involved in different biological processes in plant development. This zinc (Zn) finger protein family, particularly members that respond to and mediate stress responses, is exclusively found in plants. A total of 89 WRKY genes in japonica and 97 WRKY genes in O. nivara (OnWRKY) have been identified and mapped onto individual chromosomes. To increase the drought tolerance of rice ( Oryza sativa L.), research programs should address the problem using a multidisciplinary strategy, including the interaction of plant phenology and multiple stresses, and the combination of drought tolerance traits with different genetic and genomics approaches, such as microarrays, quantitative trait loci (QTLs), WRKY gene family members with roles in drought tolerance, and transgenic crops. This review discusses the newest advances in plant physiology for the exact phenotyping of plant responses to drought to update methods of analysing drought tolerance in rice. Finally, based on the physiological/morphological and molecular mechanisms found in resistant parent lines, a strategy is suggested to select a particular environment and adapt suitable germplasm to that environment

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores