Intelligent Video Analytics for Human Action Recognition: The State of Knowledge

The paper presents a comprehensive overview of intelligent video analytics and human action recognition methods. The article provides an overview of the current state of knowledge in the field of human activity recognition, including various techniques such as pose-based, tracking-based, spatio-temporal, and deep learning-based approaches, including visual transformers. We also discuss the challenges and limitations of these techniques and the potential of modern edge AI architectures to enable real-time human action recognition in resource-constrained environments

Challenges in introduction of artificial intelligence in medical practice – a review of clinical trials concerning adaptation of artificial intelligence in medicine

An interest in Artificial Intelligence [AI] as science is growing in the last years. It has become gradually more used in the medicine. Methodology of development and testing of AI algorithms is generally well established. Use of AI in medicine requires elaboration of standards of its validation in clinical settings. This paper is a review of literature concerning clinical trials on AI adaptation in medicin

Detection of linear features including bone and skin areas in ultrasound images of joints.

Identifying the separate parts in ultrasound images such as bone and skin plays a crucial role in the synovitis detection task. This paper presents a detector of bone and skin regions in the form of a classifier which is trained on a set of annotated images. Selected regions have labels: skin or bone or none. Feature vectors used by the classifier are assigned to image pixels as a result of passing the image through the bank of linear and nonlinear filters. The filters include Gaussian blurring filter, its first and second order derivatives, Laplacian as well as positive and negative threshold operations applied to the filtered images. We compared multiple supervised learning classifiers including Naive Bayes, k-Nearest Neighbour, Decision Trees, Random Forest, AdaBoost and Support Vector Machines (SVM) with various kernels, using four classification performance scores and computation time. The Random Forest classifier was selected for the final use, as it gives the best overall evaluation results

Detection of linear features including bone and skin areas in ultrasound images of joints.

Identifying the separate parts in ultrasound images such as bone and skin plays a crucial role in the synovitis detection task. This paper presents a detector of bone and skin regions in the form of a classifier which is trained on a set of annotated images. Selected regions have labels: skin or bone or none. Feature vectors used by the classifier are assigned to image pixels as a result of passing the image through the bank of linear and nonlinear filters. The filters include Gaussian blurring filter, its first and second order derivatives, Laplacian as well as positive and negative threshold operations applied to the filtered images. We compared multiple supervised learning classifiers including Naive Bayes, k-Nearest Neighbour, Decision Trees, Random Forest, AdaBoost and Support Vector Machines (SVM) with various kernels, using four classification performance scores and computation time. The Random Forest classifier was selected for the final use, as it gives the best overall evaluation results.publishedVersio

Detection of linear features including bone and skin areas in ultrasound images of joints.

Identifying the separate parts in ultrasound images such as bone and skin plays a crucial role in the synovitis detection task. This paper presents a detector of bone and skin regions in the form of a classifier which is trained on a set of annotated images. Selected regions have labels: skin or bone or none. Feature vectors used by the classifier are assigned to image pixels as a result of passing the image through the bank of linear and nonlinear filters. The filters include Gaussian blurring filter, its first and second order derivatives, Laplacian as well as positive and negative threshold operations applied to the filtered images. We compared multiple supervised learning classifiers including Naive Bayes, k-Nearest Neighbour, Decision Trees, Random Forest, AdaBoost and Support Vector Machines (SVM) with various kernels, using four classification performance scores and computation time. The Random Forest classifier was selected for the final use, as it gives the best overall evaluation results

Nanosecond PEF Induces Oxidative Stress and Apoptosis via Proteasomal Activity Inhibition in Gastric Adenocarcinoma Cells with Drug Resistance

Nanosecond (ns) pulsed electric field (PEF) is a technology in which the application of ultra-short electrical pulses can be used to disrupt the barrier function of cell plasma and internal membranes. Disruptions of the membrane integrity cause a substantial imbalance in cell homeostasis in which oxidative stress is a principal component. In the present study, nsPEF-induced oxidative stress was investigated in two gastric adenocarcinoma cell lines (EPG85-257P and EPG85-257RDB) which differ by their sensitivity to daunorubicin. Cells were exposed to 200 pulses of 10 ns duration, with the amplitude and pulse repetition frequency at 1 kHz, with electric field intensity varying from 12.5 to 50 kV/cm. The electroporation buffer contained either 1 mM or 2 mM calcium chloride. CellMask DeepRed visualized cell plasma permeabilization, Fluo-4 was used to visualize internal calcium ions content, and F-actin was labeled with AlexaFluor®488 for the cytoskeleton. The cellular viability was determined by MTT assay. An alkaline and neutral comet assay was employed to detect apoptotic and necrotic cell death. The luminescent method estimated the modifications in GSSG/GSH redox potential and the imbalance of proteasomal activity (chymotrypsin-, trypsin- and caspase-like). The reactive oxygen species (ROS) level was measured by flow cytometry using dihydroethidium (DHE) dye. Morphological visualization indicated cell shrinkage, affected cell membranes (characteristic bubbles and changed cell shape), and the reorganization of actin fibers with sites of its dense concentration; the effect was more intense with the increasing electric field strength. The most significant decrease in cell viability and GSSG/GSH redox potential was noted at the highest amplitude of 50 kV/cm, and calcium ions amplified this effect. nsPEF, particularly with calcium ions, inhibited proteasomal activities, resulting in increased protein degradation. nsPEF increased the percentage of apoptotic cells and ROS levels. The EPG85-257 RDB cell line, which is resistant to standard chemotherapy, was more sensitive to applied nsPEF protocols. The applied nsPEF method disrupted the metabolism of cancer cells and induced apoptotic cell death. The nsPEF ability to cause apoptosis, oxidative stress, and protein degradation make the nsPEF methodology a suitable alternative to current anticancer pharmacological methods