Progressive Rehabilitation Based on EMG Gesture Classification and an MPC-Driven Exoskeleton.

Stroke is a leading cause of disability and death worldwide, with a prevalence of 200 millions of cases worldwide. Motor disability is presented in 80% of patients. In this context, physical rehabilitation plays a fundamental role for gradually recovery of mobility. In this work, we designed a robotic hand exoskeleton to support rehabilitation of patients after a stroke episode. The system acquires electromyographic (EMG) signals in the forearm, and automatically estimates the movement intention for five gestures. Subsequently, we developed a predictive adaptive control of the exoskeleton to compensate for three different levels of muscle fatigue during the rehabilitation therapy exercises. The proposed system could be used to assist the rehabilitation therapy of the patients by providing a repetitive, intense, and adaptive assistance.

Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots.

The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for selecting the best varieties, improving yield predictions, and supporting ecosystem monitoring and conservation efforts. In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. A UAV acquires multispectral reflectance channels across a rice field of subplots containing different genotypes. Based on the ground-truth data, yields are characterized for the 59 plots and correlated with the Vegetation Indices (VIs) calculated from the photogrammetric mapping. The VIs are weighted by the segmentation of the plants from the soil and used as a feature matrix to estimate, via machine learning models, the biomass and nitrogen of the selected rice genotypes. The genotype IR 93346 presented the highest yield with a biomass gain of 10,252.78 kg/ha and an average daily biomass gain above 49.92 g/day. The VIs with the highest correlations with the ground-truth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. The machine learning model that performed best in estimating the variables of the 59 plots was the Gaussian Process Regression (GPR) model with a correlation factor of 0.98 for wet biomass, 0.99 for dry biomass, and 1 for nitrogen. The results presented demonstrate that it is possible to characterize the yields of rice plots containing different genotypes through ground-truth data and VIs.

The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization.

The OMICAS alliance is part of the Colombian government's Scientific Ecosystem, established between 2017-2018 to promote world-class research, technological advancement and improved competency of higher education across the nation. Since the program's kick-off, OMICAS has focused on consolidating and validating a multi-scale, multi-institutional, multi-disciplinary strategy and infrastructure to advance discoveries in plant science and the development of new technological solutions for improving agricultural productivity and sustainability. The strategy and methods described in this article, involve the characterization of different crop models, using high-throughput, real-time phenotyping technologies as well as experimental tissue characterization at different levels of the omics hierarchy and under contrasting conditions, to elucidate epigenome-, genome-, proteome- and metabolome-phenome relationships. The massive data sets are used to derive in-silico models, methods and tools to discover complex underlying structure-function associations, which are then carried over to the production of new germplasm with improved agricultural traits. Here, we describe OMICAS' R&D trans-disciplinary multi-project architecture, explain the overall strategy and methods for crop-breeding, recent progress and results, and the overarching challenges that lay ahead in the field.

Optimal Deployment of WSN Nodes for Crop Monitoring Based on Geostatistical Interpolations.

This paper proposes an integrated method for the estimation of soil moisture in potato crops that uses a low-cost wireless sensor network (WSN). Soil moisture estimation maps were created by applying the Kriging technique over a WSN composed of 11×11 nodes. Our goal is to estimate the soil moisture of the crop with a small-scale WSN. Using a perfect mesh approach on a potato crop, experimental results demonstrated that 25 WSN nodes were optimal and sufficient for soil moisture characterization, achieving estimations errors <2%. We provide a strategy to select the number of nodes to use in a WSN, to characterize the moisture behavior for spatio-temporal analysis of soil moisture in the crop. Finally, the implementation cost of this strategy is shown, considering the number of nodes and the corresponding margin of error.

Corrigendum: The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization.

[This corrects the article DOI: 10.3389/fpls.2022.992663.].

Assist-As-Needed Exoskeleton for Hand Joint Rehabilitation Based on Muscle Effort Detection.

Robotic-assisted systems have gained significant traction in post-stroke therapies to support rehabilitation, since these systems can provide high-intensity and high-frequency treatment while allowing accurate motion-control over the patient's progress. In this paper, we tackle how to provide active support through a robotic-assisted exoskeleton by developing a novel closed-loop architecture that continually measures electromyographic signals (EMG), in order to adjust the assistance given by the exoskeleton. We used EMG signals acquired from four patients with post-stroke hand impairments for training machine learning models used to characterize muscle effort by classifying three muscular condition levels based on contraction strength, co-activation, and muscular activation measurements. The proposed closed-loop system takes into account the EMG muscle effort to modulate the exoskeleton velocity during the rehabilitation therapy. Experimental results indicate the maximum variation on velocity was 0.7 mm/s, while the proposed control system effectively modulated the movements of the exoskeleton based on the EMG readings, keeping a reference tracking error <5%.

Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops.

Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery. The first method is called GFKuts, an approach that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo-based K-means, and a guided image filtering for the extraction of canopy vegetation indices associated with biomass yield. The second method is based on a Graph-Based Data Fusion (GBF) approach that does not depend on calculating vegetation-index image reflectances. Both methods are experimentally tested and compared through rice growth stages: vegetative, reproductive, and ripening. Biomass estimation correlations are calculated and compared against an assembled ground-truth biomass measurements taken by destructive sampling. The proposed GBF-Sm-Bs approach outperformed competing methods by obtaining biomass estimation correlation of 0.995 with R2=0.991 and RMSE=45.358 g. This result increases the precision in the biomass estimation by around 62.43% compared to previous works.

Organizing pneumonia associated with SARS-CoV-2 infection.

Organizing pneumonia is a nonspecific pulmonary response pattern associated with a variety of clinical contexts including viral infections. The classic radiological manifestations are peribronchovascular/peripheral ground glass opacities or consolidations and may be accompanied by nodules, masses, and interstitial opacities. We describe the case of a 62-year-old male patient with SARS-CoV-2 pneumonia and torpid clinical and radiological evolution in whom organizing pneumonia was documented through transbronchial biopsy and imaging findings, with a good response to corticosteroids. The importance of recognizing the development of organizing pneumonia lies in the better prognosis and outcome in those patients who receive treatment with corticosteroids, however, the clinical and radiological suspicion must be confirmed with biopsy because radiological findings associated with bacterial coinfection may overlap.

Consenso Colombiano para la prevención, diagnóstico y manejo de condiciones trombóticas en pacientes adultos con COVID-19: aplicando el Marco de la Evidencia a la Decisión (EtD) GRADE / Colombian consensus for the prevention, diagnosis and treatment of thrombotic conditions in adult patients with COVID-19: applying GRADE Evidence to Decision (EtD) Frameworks

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Abstract Recent studies have reported the occurrence of thrombotic phenomena or coagulopathy in patients with COVID-19. There are divergent positions regarding the prevention, diagnosis, and treatment of these phenomena, and current clinical practice is based solely on deductions by extension from retrospective studies, case series, observational studies, and international guidelines developed prior to the pandemic. In this context, the aim was to generate a group of recommendations on the prevention, diagnosis and management of thrombotic complications associated with COVID-19. Methods: A rapid guidance was carried out applying the GRADE Evidence to Decision (EtD) frameworks and an iterative participation system, with statistical and qualitative analysis. Results: 31 clinical recommendations were generated focused on: a) Coagulation tests in symptomatic adults with suspected infection or confirmed SARS CoV-2 infection; b) Thromboprophylaxis in adults diagnosed with COVID-19 (Risk scales, thromboprophylaxis for outpatient, in-hospital management, and duration of thromboprophylaxis after discharge from hospitalization), c) Diagnosis and treatment of thrombotic complications, and d) Management of people with previous indication of anticoagulant agents. Conclusions: Recommendations of this consensus guide clinical decision-making regarding the prevention, diagnosis, and treatment of thrombotic phenomena in patients with COVID-19, and represent an agreement that will help decrease the dispersion in clinical practices according to the challenge imposed by the pandemic.

Mediastinitis necrotizante descendente de origen odontogénico / Descending necrotizing mediastinitis of odontogenic origin

Introducción: las infecciones odontogénicas afectan al 80 %-90 % de la población y pueden dar lugar a complicaciones potencialmente mortales como la mediastinitis necrotizante descendente, una infección polimicrobiana aguda en el mediastino con una tasa de mortalidad entre el 11 % y 40 %. Su detección precoz y el tratamiento adecuado con manejo antibiótico y drenaje quirúrgico a cargo de un equipo multidisciplinario representan factores influyentes en el pronóstico de la enfermedad. Objetivo: en este artículo presentamos un caso de mediastinitis necrotizante descendente en un paciente masculino de 34 años, que se originó por una infección odontogénica que requirió un procedimiento dental. El paciente fue hospitalizado por el empeoramiento del estado clínico a pesar de la profilaxis antibiótica. La tomografía axial computarizada (TAC) confirmó la extensión de la infección al mediastino, y el paciente fue tratado con éxito con antibioterapia intravenosa y múltiples drenajes quirúrgicos intraorales, cervicotomía y toracoscopia. Materiales y métodos: se realizó una búsqueda en bases de datos globales y se seleccionaron los artículos por los siguientes términos MeSH y DeCS: "mediastinitis necrotizante descendente", "angina de Ludwig", "absceso", "toracoscopia" y sus respectivos en inglés, priorizando los referentes a la mediastinitis necrotizante descendente de origen odontogénico. Conclusiones: en la mediastinitis necrotizante descendente, la combinación de diagnóstico precoz, tratamiento antibiótico de amplio espectro, mantenimiento de la vía aérea y drenaje quirúrgico amplio y a cargo de un equipo multidisciplinario mejora de manera importante el pronóstico de la enfermedad.

Introduction: Odontogenic infections affect 80%-90% of the population and can lead to life-threatening complications such as descending necrotizing mediastinitis. Is an acute polymicrobial infection in the mediastinum with a mortality rate of 11%-40%. Its early detection and adequate treatment with antibiotic management and surgical drainage by a multidisciplinary team represent influential factors in the prognosis of the disease. Objective: In this article, we present a case of descending necrotizing mediastinitis in a 34-year-old male patient that originated from an odontogenic infection that required a dental procedure. The patient was hospitalized for worsening clinical status despite antibiotic prophylaxis. Computed axial tomography confirmed the extension of the infection to the mediastinum and the patient was successfully treated with intravenous antibiotherapy and multiple intraoral, cervicotomy, and thoracoscopic surgical drains. Materials and methods: A search was made in global databases, articles were selected by the following MeSH and DeCS terms: "descending necrotizing mediastinitis", "Ludwig's angina", "abscess", "thoracoscopy", and their respective English terms, prioritizing those referring to descending necrotizing mediastinitis of odontogenic origin. Conclusions: In descending necrotizing mediastinitis, the combination of early diagnosis, broad-spectrum antibiotic treatment, airway maintenance, and extensive surgical drainage by a multidisciplinary team significantly improves the prognosis of the disease.

A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops.

Traditional methods to measure spatio-temporal variations in biomass rely on a labor-intensive destructive sampling of the crop. In this paper, we present a high-throughput phenotyping approach for the estimation of Above-Ground Biomass Dynamics (AGBD) using an unmanned aerial system. Multispectral imagery was acquired and processed by using the proposed segmentation method called GFKuts, that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo based K-means, and a guided image filtering. Accurate plot segmentation results enabled the extraction of several canopy features associated with biomass yield. Machine learning algorithms were trained to estimate the AGBD according to the growth stages of the crop and the physiological response of two rice genotypes under lowland and upland production systems. Results report AGBD estimation correlations with an average of r = 0.95 and R2 = 0.91 according to the experimental data. We compared our segmentation method against a traditional technique based on clustering. A comprehensive improvement of 13% in the biomass correlation was obtained thanks to the segmentation method proposed herein.