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 4.0 has promoted the generation of teaching challenges. Our education strategy proposes the synergy between laboratory guides and the classroom with the following actions: the content of the topic is presented, followed by the presentation of an issue focused into a realistic context, with practical exercises integrating software and hardware for the deployment of the solution to be reported as a final project. Moreover, undergraduate students in the biomedical engineering area acquired new knowledge about IoT, but at the same time, they may develop skills in the field of programming and structuring different architectures to solve real-world problems. Finally, traditional models of education require new teaching initiatives in the field of biomedical engineering concerning the current challenges and needs of the labor market.

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. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual's performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual's characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%.

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. Different methods have been proposed for fatigue estimation, such as: monitoring the subject's physiological parameters and subjective scales. However, there is still a need for practical procedures that provide an objective estimation, especially for HIEs. In this work, considering that the sit-to-stand (STS) exercise is one of the most implemented in physical rehabilitation, a computational model for estimating fatigue during this exercise is proposed. A study with 60 healthy volunteers was carried out to obtain a data set to develop and evaluate the proposed model. According to the literature, this model estimates three fatigue conditions (low, moderate, and high) by monitoring 32 STS kinematic features and the heart rate from a set of ambulatory sensors (Kinect and Zephyr sensors). Results show that a random forest model composed of 60 sub-classifiers presented an accuracy of 82.5% in the classification task. Moreover, results suggest that the movement of the upper body part is the most relevant feature for fatigue estimation. Movements of the lower body and the heart rate also contribute to essential information for identifying the fatigue condition. This work presents a promising tool for physical rehabilitation.

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

The Dwarf Palm, Butia lallemantii 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. We conducted field observations on pollination ecology, identified floral visitors, and designed six breeding system experiments to test cross-compatibility, self-compatibility, and apomixis. Additionally, we conducted a literature review to propose conservation strategies. Butia lallemantii is pollinator-dependent and self-compatible. The flowers are mostly melittophilous and offer pollen and nectar for floral visitors. The main pollinators are native Meliponinae and Halictinae bees and the introduced Apis mellifera. This study represents the first comprehensive and complete examination of the breeding system and pollination process on Butia palms. This palm can provide materials for industries, but urgent actions are needed to preserve the remaining populations through effective policies and strategies. Furthermore, this palm should be integrated into diversified agroecosystems to evaluate its adaptability to cultivation.

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. The documented species diversity in the region could dramatically increase in the next few years with additional collecting efforts in the eastern slopes of the Andes nested in Caquetá. About 9% (418/4600) of all Orchidaceae species recorded for Colombia are reported for this area, showing the important contribution to orchid diversity of Andean-Amazonian foothills of Caquetá.

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. Model training requires extensive annotations to achieve successful generalization, which can be highly problematic given the costly expert annotations. This work aims at designing and training a novel multi-task deep learning model that leverages the similarities of related eye-fundus tasks and measurements used in glaucoma diagnosis. The model simultaneously learns different segmentation and classification tasks, thus benefiting from their similarity. The evaluation of the method in a retinal fundus glaucoma challenge dataset, including 1200 retinal fundus images from different cameras and medical centers, obtained a [Formula: see text] AUC performance compared to an [Formula: see text] obtained by the same backbone network trained to detect glaucoma. Our approach outperforms other multi-task learning models, and its performance pairs with trained experts using [Formula: see text] times fewer parameters than training each task separately. The data and the code for reproducing our results are publicly available.

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. Then the scans were labeled as control or ocular disease among diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), and retinal vein occlusion (RVO) by two expert ophthalmologists. Our method was developed by following four consecutive phases: segmentation of BMs, the combination of BMs, feature extraction with convolutional neural networks to achieve binary classification for each disease, and, finally, multiclass classification of diseases and control images. Results: The accuracy of our model for nAMD was 97%, and for DME, RVO, and control were 94%, 93%, and 93%, respectively. Area under curve values were 0.99, 0.98, 0.96, and 0.97, respectively. The mean Cohen's kappa coefficient for the multiclass classification task was 0.84. Conclusions: The proposed DL model may identify OCT scans as normal and ME. In addition, it may classify its cause among three major exudative retinal diseases with high accuracy and reliability. Translational Relevance: Our DL approach can optimize the efficiency and timeliness of appropriate etiological diagnosis of ME, thus improving patient access and clinical decision making. It could be useful in places with a shortage of specialists and for readers that evaluate OCT scans remotely.

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. This paper describes a deep learning model able to detect medically interpretable information in relevant images from a volume to classify diabetes-related retinal diseases. METHODS: This article presents a new deep learning model, OCT-NET, which is a customized convolutional neural network for processing scans extracted from optical coherence tomography volumes. OCT-NET is applied to the classification of three conditions seen in SD-OCT volumes. Additionally, the proposed model includes a feedback stage that highlights the areas of the scans to support the interpretation of the results. This information is potentially useful for a medical specialist while assessing the prediction produced by the model. RESULTS: The proposed model was tested on the public SERI-CUHK and A2A SD-OCT data sets containing healthy, diabetic retinopathy, diabetic macular edema and age-related macular degeneration. The experimental evaluation shows that the proposed method outperforms conventional convolutional deep learning models from the state of the art reported on the SERI+CUHK and A2A SD-OCT data sets with a precision of 93% and an area under the ROC curve (AUC) of 0.99 respectively. CONCLUSIONS: The proposed method is able to classify the three studied retinal diseases with high accuracy. One advantage of the method is its ability to produce interpretable clinical information in the form of highlighting the regions of the image that most contribute to the classifier decision.

Primeros registros documentados y categorización del estado de conservación de Dressleria dodsoniana y Galeandra minax (Orchidaceae; Catasetinae) de Colombia

Registramos la presencia de Dressleria dodsoniana y Galeandra minax en Caquetá, Colombia, basados en dos poblaciones encontradas en la vertiente oriental de la Cordillera Oriental de los Andes y en el piedemonte Andino-Amazónico, respectivamente. Estas especies han sido previamente reportadas para el país en documentos impresos y en bases de datos, pero, sin la mención de ejemplares de herbario, siendo los registros fotográficos la única evidencia para su registro. Nuestros reportes resaltan la necesidad de confirmar la identidad y ocurrencia de las especies con la inclusión de colecciones botánicas en herbarios. Categorizamos ambas especies para Colombia como Críticamente Amenazadas (CR), debido principalmente, al deterioro de su hábitat y por el conocimiento de una única población registrada.

We document the presence of Dressleria dodsoniana and Galeandra minax in Caquetá, Colombia, based on two populations found in the eastern slope of the Eastern Cordillera of the Andes and in the Andean-Amazonian Piedmont, respectively. These species were previously reported in the country through printed documents and databases, but without herbarium specimens mentioned, with photographic records being the only evidence for their record. Our reports emphasize the need to confirm the identity and occurrence of the species by including botanical collections in herbaria. Both species are categorized as Critically Endangered (CR) in Colombia, primarily due to habitat deterioration and the knowledge of a single recorded population.

Nuevos registros de Epidendrum (Orchidaceae) para Colombia: Epidendrum porphyreonocturnum Hágsater & R. Jiménez y Epidendrum whitteniiHágsater & Dodson / New records of Epidendrum (Orchidaceae) for Colombia: Epidendrum porphyreonocturnum Hágsater & R. Jiménez and Epidendrum whittenii Hágsater & Dodson

Resumen Reportamos por primera vez para Colombia Epidendrum porphyreonocturnum Hágsater & R. Jiménez y Epidendrum whittenii Hágsater & Dodson, dos especies de orquídeas encontradas en el Piedemonte Andino-Amazónico del departamento de Caquetá. Estas especies eran previamente conocidas solo de Ecuador y Perú. Las dos especies fueron categorizadas como Vulnerables de acuerdo con los criterios de la IUCN debido a las amenazas sobre su hábitat y el bajo número de poblaciones conocidas. Estos reportes enriquecen el conocimiento de la orquideoflora colombiana y resaltan la necesidad de realizar mayores esfuerzos en pro del conocimiento y la conservación del piedemonte.

Abstract We report for the first time for Colombia Epidendrum porphyreonocturnum Hágsater & R. Jiménez and Epidendrum whittenii Hágsater & Dodson, two species of orchids found in the Andean-Amazonian Foothills from the department of Caquetá. These species were previously known in Ecuador and Peru. Both species were categorized as Vulnerable according to IUCN criteria due to the menaces over their habitat and the low number of known populations. These records enrich the knowledge of the Colombian orchid flower and highlight the need to make greater efforts for the knowledge and conservation of the foothill.

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. This study aims: to model and detect useful patterns in the evaluation of T wave morphology, which explains the different changes in ventricular repolarization during inhalation of Salbutamol.