Supercapsular percutaneouslyassisted overall fashionable arthroplasty radiographic outcomes and also operative approach

The research on alternative computation paradigms has been initiated mainly because of the apparent limits induced by the nature of the materials and the methods used in current computing technologies. Due to the above observation, various bio-inspired computing methods have already been proposed and studied, both in practice and theory. In this paper, a review of such models is outlined with emphasis on biomolecular forms of computing. In addition, a novel biomolecular model of computation based on P systems is proposed inspired by the structure of mitochondria, namely, the mitochondria P systems and automata.Neurofeedback video games respond to electrical brain signals instead to a mouse, joystick, or game controller input. These games embody the concept of improving physiological functioning by rewarding specific healthy body signals with success at playing a video game. In this paper, a threefold framework in reference to attention deficit disorder (ADD) and attention deficit hyperactivity disorder (ADHD) treatment blending with neurofeedback techniques and video game implementation is presented. In particular, the specifications of a neurofeedback-based video game for children dealing with ADHD, in order to enhance attention and concentration skills, are analyzed. Potential boundaries of this cognitive enhancement approach and authors future directions are also discussed.Antibodies are proteins that are the first line of defense in the adaptive immune response of vertebrates. Thereby, they are involved in a multitude of biochemical mechanisms and clinical manifestations with significant medical interest, such as autoimmunity, the regulation of infection, and cancer. An emerging field in antibody science that is of huge medicinal interest is the development of novel antibody-interacting drugs. Such entities are the antibody-drug conjugates (ADCs), which are a new type of targeted therapy, which consist of an antibody linked to a payload drug. Overall, the underlying principle of ADCs is the discerning delivery of a drug to a target, hoping to increase the potency of the original drug. Drugena suite is a pioneering platform that employs state-of-the-art computational biology methods in the fight against neurodegenerative diseases using ADCs. Drugena encompasses an up-to-date structural database of specialized antibodies for neurological disorders and the NCI database with over 96 million entities for the in silico development of ADCs. The pipeline of the Drugena suite has been divided into several steps and modules that are closely related with a synergistic fashion under a user-friendly graphical user interface.Dementia describes a group of symptoms linked with cognitive decline. Alzheimer's disease (AD) is the most common form of dementia. Identifying accurate diagnostic biomarkers is a key goal. Technological advancements result in the generation of an ever-increasing volume of data. An interdisciplinary field of bioinformatics, known as machine learning (ML), allows scientists to explore and analyse said data. ML is broadly categorized into two groups (i) unsupervised learning and (ii) supervised learning. This paper focuses on supervised learning methodologies. These approaches are not only helpful for biomarker discovery but for neuroimaging studies as well since they are able to analyse many variables simultaneously and to identify patterns in neuroimaging data. Furthermore, this paper lists several other computational approaches used for dementia care.The exponential growth of the number and variety of IoT devices and applications for personal use, as well as the improvement of their quality and performance, facilitates the realization of intelligent eHealth concepts. Nowadays, it is easier than ever for individuals to monitor themselves, quantify, and log their everyday activities in order to gain insights about their body's performance and receive recommendations and incentives to improve it. Of course, in order for such systems to live up to the promise, given the treasure trove of data that is collected, machine learning techniques need to be integrated in the processing and analysis of the data. This systematic and automated quantification, logging, and analysis of personal data, using IoT and AI technologies, have given birth to the phenomenon of Quantified-Self. This work proposes a prototype decentralized Quantified-Self application, built on top of a dedicated IoT gateway that aggregates and analyzes data from multiple sources, such as biosignal sensors and wearables, and performs analytics on it.This article is about "kinesia paradoxa," a phenomenon presented in Parkinson's disease patients who generally suffer from bradykinesia and freezing of gait (FOG) but under certain circumstances exhibit a sudden, brief period of mobility. The objective of this paper was to identify the mechanisms causing this phenomenon, record possible brain circuits involved, and try to locate interconnections between these circuits. Moreover, we are proposing various modeling schemes in order to form the appropriate conditions for experimental design.Research investigating treatments and interventions for cognitive decline and Alzheimer's disease (AD) suffer due to difficulties in accurately identifying individuals at risk of AD in the pre-symptomatic stages of the disease. There is an urgent need for better identification of such individuals in order to enable earlier treatment and to properly stage and stratify participants for clinical trials and intervention studies. Although some biological measures (biomarkers) can identify Alzheimer's-related changes before significant changes in cognitive function occur, such biomarkers are not ideal as they are only able to place individuals in rudimentary stages of the disease/cognitive decline (Tarnanas et al., Alzheimers Dement (Amst) 1(4)521-532, 2015) and sometimes mistakenly diagnose individuals (Edmonds et al. 2015). Two tests, based on real-world functioning, which have been used to screen for pre-symptomatic AD are (i) dual-task walking tests (Belghali et al. this website 2017) and (ii) day-out tasks (Tarnanas et al.