Advancing Remote Monitoring for Patients With Alzheimer Disease and Related Dementias: Systematic Review.

Background: Using remote monitoring technology in the context of Alzheimer disease (AD) care presents exciting new opportunities to lessen caregiver stress and improve patient care quality. The application of wearables, environmental sensors, and smart home systems designed specifically for patients with AD represents a promising interdisciplinary approach that integrates advanced technology with health care to enhance patient safety, monitor health parameters in real time, and provide comprehensive support to caregivers.

Objective: The objectives of this study included evaluating the effectiveness of various remote sensing technologies in enhancing patient outcomes and identifying strategies to alleviate the burden on health care professionals and caregivers.

Computational models for prediction of protein-protein interaction in rice and .

Introduction: Plant-microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice () due to () fungus. Protein-protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease.

Internet of Things and Robotics in Transforming Current-Day Healthcare Services.

Technology has become an integral part of everyday lives. Recent years have witnessed advancement in technology with a wide range of applications in healthcare. However, the use of the Internet of Things (IoT) and robotics are yet to see substantial growth in terms of its acceptability in healthcare applications.

Dataset for EEG signals used to detect the effect of coffee consumption on the activation of SSVEP signal.

A set of electroencephalogram (EEG) data was obtained in the National Institute of Technology, Rourkela, India, from six individuals in the presence of seven photic stimuli of different frequencies (range: 3 Hz-30 Hz). The EEG data were recorded prior to, and post-consumption of caffeinated coffee for detecting the influence of coffee consumption on the initiation of steady-state visual evoked potential (SSVEP) signals in different regions of the brain. The data supports the article: "Data mining-based approach to study the effect of consumption of caffeinated coffee on the generation of steady-state visual evoked potential signals" [1].

Data mining based approach to study the effect of consumption of caffeinated coffee on the generation of the steady-state visual evoked potential signals.

The steady-state visual evoked potentials (SSVEP), are elicited at the parieto-occipital region of the cortex when a light source (3.5-75 Hz), flickering at a constant frequency, stimulates the retinal cells. In the last few decades, researchers have reported that caffeine enhances the vigilance and the executive control of visual attention.

Floristic composition and utilization of ethnomedicinal plant species in home gardens of the Eastern Himalaya.

Background: Home gardens are popular micro land-use system and are socioeconomically linked with people for their livelihood. In the foothill region of Eastern Himalaya, very less documentations are available on species richness of the home gardens, particularly on the ethnomedicinal plants. We assumed that the home garden owners of the study site are domesticating ethnomedicinal plants which are not easily accessible to them in the wild due to distant forest.

Indigenous uses of ethnomedicinal plants among forest-dependent communities of Northern Bengal, India.

Background: Traditional knowledge on ethnomedicinal plant is slowly eroding. The exploration, identification and documentation on utilization of ethnobotanic resources are essential for restoration and preservation of ethnomedicinal knowledge about the plants and conservation of these species for greater interest of human society.

Methods: The study was conducted at fringe areas of Chilapatta Reserve Forest in the foothills of the eastern sub-Himalayan mountain belts of West Bengal, India, from December 2014 to May 2016.

Classification of images based on small local features: a case applied to microaneurysms in fundus retina images.

Convolutional neural networks (CNNs), the state of the art in image classification, have proven to be as effective as an ophthalmologist, when detecting referable diabetic retinopathy. Having a size of [Formula: see text] of the total image, microaneurysms are early lesions in diabetic retinopathy that are difficult to classify. A model that includes two CNNs with different input image sizes, [Formula: see text] and [Formula: see text], was developed.