Pollination Ecology, Breeding System, and Conservation of Deble & Marchiori (Arecaceae): A Useful Dwarf Palm Tree from the Pampa.

The Dwarf Palm, Deble & Marchiori, is an endangered species endemic to the Pampa biome and typically grows in sandy and rocky soils. Given its economic, ecological, and cultural relevance, it is crucial to understand the ecology and biology of this species to encourage its preservation and highlight its significance for the Pampa. This study aims to investigate whether this palm relies on animal vectors for pollination, analyze its breeding system, and propose strategies for its conservation and sustainable use.

Checklist of Orchidaceae from Caquetá, Colombia.

A checklist of Orchidaceae from Caquetá, Colombia is presented here. We recorded 98 genera and 418 species, exceeding a previous inventory by 276 species. The checklist is conservative in the number of genera and species by including only taxa that were fully and reliably identified and that are either linked to a corresponding herbarium voucher, a living collection specimen or a photo taken in the field and published in iNaturalist by one of the authors or a collaborator.

Proposal of Design and Innovation in the Creation of the Internet of Medical Things Based on the CDIO Model through the Methodology of Problem-Based Learning.

The educational framework-Conceive, Design, Implement, and Operate-is part of an international proposal to improve education in the field of engineering, emphasizing how to teach engineering comprehensively, which allows the standardization of skills in professionals as a model for teaching engineering. Moreover, problem-based learning allows students to experiment with challenging situations through cases that simulate natural contexts with their profession. The integration of these two education strategies applied to the Internet of Things (IoT) Education for Industry 4.

Etiology of Macular Edema Defined by Deep Learning in Optical Coherence Tomography Scans.

Purpose: To develop an automated method based on deep learning (DL) to classify macular edema (ME) from the evaluation of optical coherence tomography (OCT) scans.

Methods: A total of 4230 images were obtained from data repositories of patients attended in an ophthalmology clinic in Colombia and two free open-access databases. They were annotated with four biomarkers (BMs) as intraretinal fluid, subretinal fluid, hyperreflective foci/tissue, and drusen.

Multi-task deep learning for glaucoma detection from color fundus images.

Glaucoma is an eye condition that leads to loss of vision and blindness if not diagnosed in time. Diagnosis requires human experts to estimate in a limited time subtle changes in the shape of the optic disc from retinal fundus images. Deep learning methods have been satisfactory in classifying and segmenting diseases in retinal fundus images, assisting in analyzing the increasing amount of images.

A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States.

Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients' intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications.

Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise.

Physical exercise (PE) has become an essential tool for different rehabilitation programs. High-intensity exercises (HIEs) have been demonstrated to provide better results in general health conditions, compared with low and moderate-intensity exercises. In this context, monitoring of a patients' condition is essential to avoid extreme fatigue conditions, which may cause physical and physiological complications.

Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography.

Background And Objectives: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance has steadily increased through the use of deep learning models that automatically learn relevant features for specific tasks, instead of designing visual features manually. Nevertheless, providing insights and interpretation of the predictions made by the model is still a challenge.

Morphological analysis of T-wave in vectorcardiographic leads system by a bi-Gaussian approach in patients under effect of salbutamol.

There are several models of decomposition of the electrocardiogram (ECG). Some of these models are intended to describe the ECG signal, and others are more specific to extract the relevant information relating to individual waveform which contributes to explain the P-QRS complex. The latter approach may be particularly suitable for a portion where a morphological analysis of the ECG is of particular interest, as the cardiac repolarization segment or T-wave.