Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model.

Every one of us has a unique manner of communicating to explore the world, and such communication helps to interpret life. Sign language is the popular language of communication for hearing and speech-disabled people. When a sign language user interacts with a non-sign language user, it becomes difficult for a signer to express themselves to another person.

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data.

Unlabelled: Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020).

MultiResUNet3+: A Full-Scale Connected Multi-Residual UNet Model to Denoise Electrooculogram and Electromyogram Artifacts from Corrupted Electroencephalogram Signals.

Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG's usability. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN), i.e.

Increase in conduction velocity in myelinated nerves due to stretch - An experimental verification.

Background: Based on published experimental evidence, a recent publication revealed an anomalous phenomenon in nerve conduction: for myelinated nerves the nerve conduction velocity (NCV) increases with stretch, which should have been the opposite according to existing concepts and theories since the diameter decreases on stretching. To resolve the anomaly, a new conduction mechanism for myelinated nerves was proposed based on physiological changes in the nodal region, introducing a new electrical resistance at the node. The earlier experimental measurements of NCV were performed on the ulnar nerve at different angles of flexion, focusing at the elbow region, but left some uncertainty for not reporting the lengths of nerve segments involved so that the magnitudes of stretch could not be estimated.

Design and Implementation of a Smart Insole System to Measure Plantar Pressure and Temperature.

An intelligent insole system may monitor the individual's foot pressure and temperature in real-time from the comfort of their home, which can help capture foot problems in their earliest stages. Constant monitoring for foot complications is essential to avoid potentially devastating outcomes from common diseases such as diabetes mellitus. Inspired by those goals, the authors of this work propose a full design for a wearable insole that can detect both plantar pressure and temperature using off-the-shelf sensors.

Recent developments in the kinetics of ruptures of giant vesicles under constant tension.

External tension in membranes plays a vital role in numerous physiological and physicochemical phenomena. In this review, recent developments in the constant electric- and mechanical-tension-induced rupture of giant unilamellar vesicles (GUVs) are considered. We summarize the results relating to the kinetics of GUV rupture as a function of membrane surface charge, ions in the bathing solution, lipid composition, cholesterol content in the membrane, and osmotic pressure.

Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques.

Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches applied to such infrared images may have utility in the early diagnosis of diabetic foot complications.

Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model.

A real-time Bangla Sign Language interpreter can enable more than 200 k hearing and speech-impaired people to the mainstream workforce in Bangladesh. Bangla Sign Language (BdSL) recognition and detection is a challenging topic in computer vision and deep learning research because sign language recognition accuracy may vary on the skin tone, hand orientation, and background. This research has used deep machine learning models for accurate and reliable BdSL Alphabets and Numerals using two well-suited and robust datasets.

A new purification technique to obtain specific size distribution of giant lipid vesicles using dual filtration.

A new purification technique is developed for obtaining distribution of giant unilamellar vesicles (GUVs) within a specific range of sizes using dual filtration. The GUVs were prepared using well known natural swelling method. For filtration, different combinations of polycarbonate membranes were implemented in filter holders.

Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques.

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user.

Location of Peptide-Induced Submicron Discontinuities in the Membranes of Vesicles Using ImageJ.

Cell penetrating peptide transportan 10 and antimicrobial peptide melittin formed submicron pores in the lipid membranes of vesicles which are explained by the leakage of water-soluble fluorescent probes from the inside of vesicles to the outside. It is hypothesized that these submicron pores induce submicron discontinuities in the membranes. Considering this hypothesis, a technique has developed to locate the submicron discontinuities in the membranes of giant unilamellar vesicles (GUVs) using ImageJ.

Deformation and poration of giant unilamellar vesicles induced by anionic nanoparticles.

The interaction of anionic magnetite nanoparticles (MNPs) of size 18 nm with negatively charged giant unilamellar vesicles (GUVs) formed from a mixture of neutral dioleoylphosphatidylcholine (DOPC) and negatively charged dioleoylphosphatidylglycerol (DOPG) lipids has been investigated. It has been obtained that NPs induces the deformation of spherical GUVs. The reaction of other GUVs on NPs consists in the appearance of pores in their membranes.

Study of molecular transport through a single nanopore in the membrane of a giant unilamellar vesicle using COMSOL simulation.

The antimicrobial peptide (AMP) magainin 2 induces nanopores in the lipid membranes of giant unilamellar vesicles (GUVs), as observed by the leakage of water-soluble fluorescent probes from the inside to the outside of GUVs through the pores. However, molecular transport through a single nanopore has not been investigated in detail yet and is studied in the present work by simulation. A single pore was designed in the membrane of a GUV using computer-aided design software.

Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: An alternative to conventional spiral MR Fingerprinting.

Purpose: To develop an accelerated Cartesian MRF implementation using a multi-shot EPI sequence for rapid simultaneous quantification of T1 and T2 parameters.

Methods: The proposed Cartesian MRF method involved the acquisition of highly subsampled MR images using a 16-shot EPI readout. A linearly varying flip angle train was used for rapid, simultaneous T1 and T2 quantification.

Presence of time-dependent diffusion in the brachial plexus.

Purpose: This work describes the development of a method to measure the variation of apparent diffusion coefficient (ADC) with diffusion time (Δ) in the brachial plexus, as a potential method of probing microstructure.

Methods: Diffusion-weighted MRI with body signal suppression was used to highlight the nerves from surrounding tissues, and sequence parameters were optimized for sensitivity to change with diffusion time. A porous media-restricted diffusion model based on the Latour-Mitra equation was fitted to the diffusion time-dependent ADC data from the brachial plexus nerves and cord.

Frequency domain analysis to identify neurological disorders from evoked EMG responses.

Evoked EMG M-responses obtained from the thenar muscle in the palm by electrical stimulation of the median nerve demonstrate a well-established smooth bipolar shape for normal healthy subjects. Kinks in this curve are observed in certain neurological disorders and preliminary work suggests their relationship to cervical spondylosis. The present work was taken up to develop an objective method for the identification of such neurological disorders for automated diagnosis by analysing the M-responses.