Comparative Effectiveness of Switching to Bictegravir From Dolutegravir-, Efavirenz-, or Raltegravir-Based Antiretroviral Therapy Among Individuals With HIV Who are Virologically Suppressed.

Background: We aimed to determine the effectiveness of switching to bictegravir in maintaining an undetectable viral load (<50 copies/mL) among people with HIV (PWH) as compared with continuing dolutegravir-, efavirenz-, or raltegravir-based antiretroviral therapy using nationwide observational data from Mexico. Methods: We emulated 3 target trials comparing switching to bictegravir vs continuing with dolutegravir, efavirenz, or raltegravir. Eligibility criteria were PWH aged ≥16 years with a viral load <50 copies/mL and at least 3 months of current antiretroviral therapy (dolutegravir, efavirenz, or raltegravir) between July 2019 and September 2021. Weekly target trials were emulated during the study period, and individuals were included in every emulation if they continued to be eligible. The main outcome was the probability of an undetectable viral load at 3 months, which was estimated via an adjusted logistic regression model. Estimated probabilities were compared via differences, and 95% CIs were calculated via bootstrap. Outcomes were also ascertained at 12 months, and sensitivity analyses were performed to test our analytic choices. Results: We analyzed data from 3 028 619 PWH (63 581 unique individuals). The probability of an undetectable viral load at 3 months was 2.9% (95% CI, 1.9%-3.8%), 1.3% (95% CI, .9%-1.6%), and 1.2% (95% CI, .8%-1.7%) higher when switching to bictegravir vs continuing with dolutegravir, efavirenz, and raltegravir, respectively. Similar results were observed at 12 months and in other sensitivity analyses. Conclusions: Our findings suggest that switching to bictegravir could be more effective in maintaining viral suppression than continuing with dolutegravir, efavirenz, or raltegravir.

Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks.

Mobile cognitive radio networks (MCRNs) have arisen as an alternative mobile communication because of the spectrum scarcity in actual mobile technologies such as 4G and 5G networks. MCRN uses the spectral holes of a primary user (PU) to transmit its signals. It is essential to detect the use of a radio spectrum frequency, which is where the spectrum sensing is used to detect the PU presence and avoid interferences. In this part of cognitive radio, a third user can affect the network by making an attack called primary user emulation (PUE), which can mimic the PU signal and obtain access to the frequency. In this paper, we applied machine learning techniques to the classification process. A support vector machine (SVM), random forest, and K-nearest neighbors (KNN) were used to detect the PUE in simulation and emulation experiments implemented on a software-defined radio (SDR) testbed, showing that the SVM technique detected the PUE and increased the probability of detection by 8% above the energy detector in low values of signal-to-noise ratio (SNR), being 5% above the KNN and random forest techniques in the experiments.

A digital cardiac disease biomarker from a generative progressive cardiac cine-MRI representation.

Cardiac cine-MRI is one of the most important diagnostic tools used to assess the morphology and physiology of the heart during the cardiac cycle. Nonetheless, the analysis on cardiac cine-MRI is poorly exploited and remains highly dependent on the observer's expertise. This work introduces an imaging cardiac disease representation, coded as an embedding vector, that fully exploits hidden mapping between the latent space and a generated cine-MRI data distribution. The resultant representation is progressively learned and conditioned by a set of cardiac conditions. A generative cardiac descriptor is achieved from a progressive generative-adversarial network trained to produce MRI synthetic images, conditioned to several heart conditions. The generator model is then used to recover a digital biomarker, coded as an embedding vector, following a backpropagation scheme. Then, an UMAP strategy is applied to build a topological low dimensional embedding space that discriminates among cardiac pathologies. Evaluation of the approach is carried out by using an embedded representation as a potential disease descriptor in 2296 pathological cine-MRI slices. The proposed strategy yields an average accuracy of 0.8 to discriminate among heart conditions. Furthermore, the low dimensional space shows a remarkable grouping of cardiac classes that may suggest its potential use as a tool to support diagnosis. The learned progressive and generative representation, from cine-MRI slices, allows retrieves and coded complex descriptors that results useful to discriminate among heart conditions. The cardiac disease representation expressed as a hidden embedding vector could potentially be used to support cardiac analysis on cine-MRI sequences.

Fuzzy Controller Applied to a Remote Energy Harvesting Emulation Platform.

In the last decades, a lot of effort has been made in order to improve the use of environmentally friendly and renewable energy sources. In a context of small energy usage, energy harvesting takes place and thermal energy sources are one of its main energy sources because there are several unused heat sources available in the environment that may be used as renewable energy sources. To rapidly evaluate the energy potential of such thermal sources is a hard task, therefore, a way to perform this is welcome. In this work, a thermal pattern emulation system to evaluate potential thermal source in a easy way is proposed. The main characteristics of the proposed system is that it is online and remote, that is, while the thermal-source-under-test is being measured, the system is emulating it and evaluating the generated energy remotely. The main contribution of this work was to replace the conventional Proportional Integral Derivative (PID) controller to a Fuzzy-Proportional Integral (PI) controller. In order to compare both controllers, three tests were carried out, namely: (a) step response, (b) perturbation test, (c) thermal emulation of the thermal pattern obtained from a potential thermal source: tree trucks. Experimental results show that the Fuzzy-PI controller was faster than the PID, achieving a setting time 41.26% faster, and also was more efficient with a maximum error 53% smaller than the PID.

Diseño de Exoesqueleto con base en Cuatro Casos de Estudio de Rehabilitación de Miembro Superior / Design of an Exoskeleton based on Four Cases of Upper Limb Rehabilitation Study

Resumen: En este artículo se presenta un enfoque para rehabilitación pasiva de miembro superior mediante la formulación de cuatro casos de estudio haciendo un análisis de las patologías y los ejercicios que se aplican. Para llevar a cabo la experimentación en los casos propuestos se registraron los datos de las trayectorias de las articulaciones del brazo de un paciente realizando los ejercicios de rehabilitación con un terapeuta. Se diseñó el exoesqueleto ERMIS de siete grados de libertad para emular los movimientos anatómicos en el brazo durante la rehabilitación a partir de los requerimientos de los casos de estudio. Para validar el funcionamiento del exoesqueleto en los casos se simuló el modelo dinámico del ERMIS y se compararon los datos con los datos muestreados de los ejercicios. Al final se presentan los resultados obtenidos de los ejercicios realizados con el exoesqueleto, obteniendo en la precisión un desempeño promedio del 95% en los movimientos de hombro, codo y muñeca al emular la terapia con timón.

Abstract: This paper presents an approach for passive upper limb rehabilitation based on four case studies by analyzing the pathologies and exercises that are applied. To carry out the experimentation in the proposed cases, the data from the trajectories of the patient's arm articulations were registered, performing the rehabilitation exercises with a therapist. The ERMIS exoskeleton´s seven degrees of freedom was designed to emulate the anatomical movements in the arm during rehabilitation from the requirements of the case studies. To validate the exoskeleton performance in the study cases, the ERMIS's dynamic model was simulated and the data were compared with the sampled data of the exercises. At the end, the results obtained from exoskeleton exercises emulating rudder therapy, where shoulder, elbow and wrist movements were showed with an accuracy of 95%.