A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial Sensors.

Abnormal locomotor patterns may occur in case of either motor damages or neurological conditions, thus potentially jeopardizing an individual's safety. Pathological gait recognition (PGR) is a research field that aims to discriminate among different walking patterns. A PGR-oriented system may benefit from the simulation of gait disorders by healthy subjects, since the acquisition of actual pathological gaits would require either a higher experimental time or a larger sample size.

A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma.

Background And Objective: In Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging to answer unmet clinical needs to derive novel quantitative prognostic factors. We realized a pipeline that relies on survival machine-learning (SML) classifiers and explainability based on patients' follow-up (FU) to stratify prognosis from the public-available multi-omic datasets of the CPTAC-PDA project.

Materials And Methods: Analyzed datasets included tumor-annotated radiologic images, clinical, and mutational data.

Isochronous singing in 3 crested gibbon species ( spp.).

The search for common characteristics between the musical abilities of humans and other animal species is still taking its first steps. One of the most promising aspects from a comparative point of view is the analysis of rhythmic components, which are crucial features of human communicative performance but also well-identifiable patterns in the vocal displays of other species. Therefore, the study of rhythm is becoming essential to understand the mechanisms of singing behavior and the evolution of human communication.

Exploring Aesthetic Perception in Impaired Aging: A Multimodal Brain-Computer Interface Study.

In the field of neuroscience, brain-computer interfaces (BCIs) are used to connect the human brain with external devices, providing insights into the neural mechanisms underlying cognitive processes, including aesthetic perception. Non-invasive BCIs, such as EEG and fNIRS, are critical for studying central nervous system activity and understanding how individuals with cognitive deficits process and respond to aesthetic stimuli. This study assessed twenty participants who were divided into control and impaired aging (AI) groups based on MMSE scores.