A novel hybrid swarm optimized multilayer neural network for spatial prediction of flash floods in tropical areas using sentinel-1 SAR imagery and geospatial data

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Enhancing Electron Transfer and Stability of Screen-Printed Carbon Electrodes Modified with AgNP-Reduced Graphene Oxide Nanocomposite

This paper presents a reliable solution to enhance the electron transfer and stability of screen-printed carbon electrodes (SPCEs) for the direct detection of pathogenic bacteria. A nanocomposite of silver nanoparticles (AgNPs) and reduced graphene oxide (rGO) was used to modify the SPCEs. Herein, the nanocomposite was synthesized via a hydrothermal method and then characterized by physicochemical methods. The electron transfer rate and electrochemical properties of the AgNP-rGO nanocomposite-modified SPCEs were investigated using cyclic voltammetry (CV) and electrochemical impedance spectroscopy. Measurements were performed for the detection of Salmonella bacteria without any labels. Results showed that the nanocomposite firmly adhered to the surfaces of the SPCEs, led to an increase of approximately 160% in the peak current, and decreased the charge transfer resistance to 0.45 kΩ. Electrochemical stability was found in 30 CV cycles. The modified SPCEs could detect Salmonella bacteria directly at concentrations of 10–105 CFU/mL, with a limit of detection (LoD) of as low as 22 CFU/mL. A possible mechanism was proposed to explain the enhanced electron transfer on the surface and the stability of the AgNP-rGO nanocomposite-modified SPCEs. The biosensor showed high stability, cost-effectiveness, and simplicity for the direct detection of pathogenic bacteria. Graphical Abstract: [Figure not available: see fulltext.