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Flooding is one of the leading threats of natural disasters to human life and property, especially in densely populated urban areas. Rapid and precise extraction of the flooded areas is key to supporting emergency-response planning and providing damage assessment in both spatial and temporal measurements. Unmanned Aerial Vehicles (UAV) technology has recently been recognized as an efficient photogrammetry data acquisition platform to quickly deliver high-resolution imagery because of its cost-effectiveness, ability to fly at lower altitudes, and ability to enter a hazardous area. Different image classification methods including SVM (Support Vector Machine) have been used for flood extent mapping. In recent years, there has been a significant improvement in remote sensing image classification using Convolutional Neural Networks (CNNs). CNNs have demonstrated excellent performance on various tasks including image classification, feature extraction, and segmentation. CNNs can learn features automatically from large datasets through the organization of multi-layers of neurons and have the ability to implement nonlinear decision functions. This study investigates the potential of CNN approaches to extract flooded areas from UAV imagery. A VGG-based fully convolutional network (FCN-16s) was used in this research. The model was fine-tuned and a k-fold cross-validation was applied to estimate the performance of the model on the new UAV imagery dataset. This approach allowed FCN-16s to be trained on the datasets that contained only one hundred training samples, and resulted in a highly accurate classification. Confusion matrix was calculated to estimate the accuracy of the proposed method. The image segmentation results obtained from FCN-16s were compared from the results obtained from FCN-8s, FCN-32s and SVMs. Experimental results showed that the FCNs could extract flooded areas precisely from UAV images compared to the traditional classifiers such as SVMs. The classification accuracy achieved by FCN-16s, FCN-8s, FCN-32s, and SVM for the water class was 97.52%, 97.8%, 94.20% and 89%, respectively.

Among the different types of natural disasters, floods are the most devastating, widespread, and frequent. Floods account for approximately 30% of the total loss caused by natural disasters. Accurate flood-risk mapping is critical in reducing such damages by correctly predicting the extent of a flood when coupled with rain and stage gage data, supporting emergency-response planning, developing land use plans and regulations with regard to the construction of structures and infrastructures, and providing damage assessment in both spatial and temporal measurements. The reliability and accuracy of such flood assessment maps is dependent on the quality of the digital elevation model (DEM) in flood conditions. This study investigates the quality of an Unmanned Aerial Vehicle (UAV)-based DEM for spatial flood assessment mapping and evaluating the extent of a flood event in Princeville, North Carolina during Hurricane Matthew. The challenges and problems of on-demand DEM production during a flooding event were discussed. An accuracy analysis was performed by comparing the water surface extracted from the UAV-derived DEM with the water surface/stage obtained using the nearby US Geologic Survey (USGS) stream gauge station and LiDAR data.

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Nanosecond pulsed electric fields (nsPEFs) perturb membranes of cultured mammalian cells in a dose-dependent manner with different types of cells exhibiting characteristic survivability. Adherent cells appear more robust than non-adherent cells during whole-cell exposure. We hypothesize that cellular elasticity based upon the actin cytoskeleton is a contributing parameter, and the alteration of a cell's actin cortex will significantly affect viability upon nsPEF exposure. Chinese hamster ovary (CHO) cells that are (a) untreated, (b) treated with latrunculin A to inhibit actin polymerization, or (c) exposed to nsPEFs have been probed using atomic force microscopy (AFM) force-indentations. Exposure to 50 or 100 pulses of 10 ns duration and 150 kV/cm in a single dosage approximately lowers average CHO cell elastic modulus by half, whereas latrunculin lowers it more than 75%. Latrunculin pre-treatment disrupts the actin cortex enough that it negates cumulative damage by equally fractionated (i.e., two rounds of 50 pulses each, separated by 10 min) dosages of nsPEFs as seen in untreated and dimethyl sulfoxide (DMSO)-treated cells with propidium uptake, phosphatidylserine externalization, and 24 h viability according to MTT and CellTiter Glo assays. These results suggest a correlation among cell stiffness, cytoskeletal integrity, and susceptibility to recurrent exposures to nsPEFs, which emphasizes a mechanobiological underpinning of nsPEF bioeffects.

Functional recognition imaging in Scanning Probe Microscopy (SPM) using artificial neural network identification is demonstrated. This approach utilizes statistical analysis of complex SPM responses to identify the target behavior, reminiscent of associative thinking in the human brain and obviating the need for analytical models. As an example of recognition imaging, we demonstrate rapid identification of cellular organisms using difference in electromechanical activity in a broad frequency range. Single-pixel identification of model Micrococcus lysodeikticus and Pseudomonas fluorescens bacteria is achieved, demonstrating the viability of the method.

The cellular response to subtle membrane damage following exposure to nanosecond pulsed electric fields (nsPEF) is not well understood. Recent work has shown that when cells are exposed to nsPEF, ion permeable nanopores (<2  nm) are created in the plasma membrane in contrast to larger diameter pores (>2  nm) created by longer micro- and millisecond duration pulses. Nanoporation of the plasma membrane by nsPEF has been shown to cause a transient increase in intracellular calcium concentration within milliseconds after exposure. Our research objective is to determine the impact of nsPEF on calcium-dependent structural and repair systems in mammalian cells. Chinese hamster ovary (CHO-K1) cells were exposed in the presence and absence of calcium ions in the outside buffer to either 1 or 20, 600-ns duration electrical pulses at 16.2  kV/cm, and pore size was determined using propidium iodide and calcium green. Membrane organization was observed with morphological changes and increases in FM1-43 fluorescence. Migration of lysosomes, implicated in membrane repair, was followed using confocal microscopy of red fluorescent protein-tagged LAMP1. Microtubule structure was imaged using mEmerald-tubulin. We found that at high 600-ns PEF dosage, calcium-induced membrane restructuring and microtubule depolymerization coincide with interruption of membrane repair via lysosomal exocytosis.

Electromechanical coupling is ubiquitous in biological systems, with examples ranging from simple piezoelectricity in calcified and connective tissues to voltage-gated ion channels, energy storage in mitochondria, and electromechanical activity in cardiac myocytes and outer hair cell stereocilia. Piezoresponse force microscopy (PFM) originally emerged as a technique to study electromechanical phenomena in ferroelectric materials, and in recent years has been employed to study a broad range of non-ferroelectric polar materials, including piezoelectric biomaterials. At the same time, the technique has been extended from ambient to liquid imaging on model ferroelectric systems. Here, we present results on local electromechanical probing of several model cellular and biomolecular systems, including insulin and lysozyme amyloid fibrils, breast adenocarcinoma cells, and bacteriorhodopsin in a liquid environment. The specific features of PFM operation in liquid are delineated and bottlenecks on the route towards nanometre-resolution electromechanical imaging of biological systems are identified.