Intracellular free calcium ([Ca2+]i) has multiple functional roles in renal epithelia, including mediating ligand- and volume-activated K+ and Cl- channels, modulating the permeability of apical membrane to Na+, and regulating tubuloglomerular feedback. We investigated glucose effects on intracellular pH (pHi) and [Ca2+]i in Madin-Darby canine kidney (MDCK) cells using fluorescent probes, SNARF-1 and fura 2, respectively. The addition of glucose decreased both pHi and [Ca2+]i in a dose-dependent fashion. Thapsigargin (TG) and cyclopiazonic acid (CPA), well-known endoplasmic reticulum (ER) Ca(2+)-adenosinetriphosphatase (Ca(2+)-ATPase) inhibitors, abolished the glucose-induced [Ca2+]i decrease. Without glucose, 1 microM TG induced a sustained elevation in [Ca2+]i, which increased further with glucose addition, whereas 15 microM CPA induced a transient increase in [Ca2+]i that was not affected by further addition of glucose. The sustained elevation in [Ca2+]i induced by TG was dependent on extracellular Ca2+. TG-induced [Ca2+]i increase was modulated by glucose, i.e., at higher glucose concentrations, TG induced a larger and more rapid rise in [Ca2+]i. We conclude that glucose has dual effects on [Ca2+]i regulation. Glucose alone reduces [Ca2+]i by activating ER-type Ca(2+)-ATPase, since this phenomenon is TG and CPA sensitive. In the presence of TG, glucose increases [Ca2+]i probably by increasing Ca2+ entry. Our data suggest a model in which TG activates capacitative Ca2+ entry by depletion of the ER Ca2+ pool. Glucose increases TG-induced [Ca2+]i elevation by further enhancing capacitative Ca2+ entry.

Osteoarthritis (OA) is the most common form of arthritis. According to the evidence presented on both sides of the knee bones, radiologists assess the severity of OA based on the Kellgren–Lawrence (KL) grading system. Recently, computer-aided methods are proposed to improve the efficiency of OA diagnosis. However, the human interventions required by previous semiautomatic segmentation methods limit the application on large-scale datasets. Moreover, well-known CNN architectures applied to the OA severity assessment do not explore the relations between different local regions. In this work, by integrating the object detection model, YOLO, with the visual transformer into the diagnosis procedure, we reduce human intervention and provide an end-to-end approach to automatic osteoarthritis diagnosis. Our approach correctly segments 95.57% of data at the expense of training on 200 annotated images on a large dataset that contains more than 4500 samples. Furthermore, our classification result improves the accuracy by 2.5% compared to the traditional CNN architectures.

One of the most undesirable consequences induced by blasting in open-pit mines and civil activities is flyrock. Furthermore, the production of oversize boulders creates many problems for the continuation of the work and usually imposes additional costs on the project. In this way, the breakage of oversize boulders is associated with throwing small fragments particles at high speed, which can lead to serious risks to human resources and infrastructures. Hence, the accurate prediction of flyrock induced by boulder blasting is crucial to avoid possible consequences and its’ environmental side effects. This study attempts to develop an optimized artificial neural network (ANN) by particle swarm optimization (PSO) and jellyfish search algorithm (JSA) to construct the hybrid models for anticipating flyrock distance resulting in boulder blasting in a quarry mine. The PSO and JSA algorithms were used to determine the optimum values of neurons’ weight and biases connected to neurons. In this regard, a database involving 65 monitored boulders blasting for recording flyrock distance was collected that comprises six influential parameters on flyrock distance, i.e., hole depth, burden, hole angle, charge weight, stemming, and powder factor and one target parameter, i.e., flyrock distance. The ten various models of ANN, PSO–ANN, and JSA–ANN were established for estimating flyrock distance, and their results were investigated by applying three evaluation indices of coefficient of determination (R2), root mean square error (RMSE) and value accounted for (VAF). The results of the calculation of evaluation indicators revealed that R2, values of (0.957, 0.972 and 0.995) and (0.945, 0.954 and 0.989) were determined to train and test of proposed predictive models, respectively. The yielded results denoted that although ANN model is capable of anticipating flyrock distance, the hybrid PSO–ANN and JSA–ANN models can anticipate flyrock distance with more accuracy. Furthermore, the performance and accuracy level of the JSA–ANN predictive model can estimate better compared to ANN and PSO–ANN models. Therefore, the JSA–ANN model is identified as the superior predictive model in estimating flyrock distance induced from boulder blasting. In the final, a sensitivity analysis was conducted to determine the most influential parameters in flyrock distance, and the results showed that charge weight, powder factor, and hole angle have a high impact on flyrock changes.

Wearable computing devices and body sensor networks (BSNs) are becoming more prevalent. Collecting the data necessary to develop new concepts for these systems can be difficult. We present the MotionSynthesis Toolset (MoST) to alleviate some of the difficulties in data collection and algorithm development. This toolset allows researchers to generate a sequence of movements (i.e. a diary), synthesize a data stream using real sensor data, visualize, and validate the sequence of movements and data with video and waveforms.

No abstract