Super-resolution of magnetic resonance images using Generative Adversarial Networks.

Magnetic Resonance Imaging (MRI) typically comes at the cost of small spatial coverage, high expenses and long scan times. Accelerating MRI acquisition by taking less measurements yields the potential to relax these inherent forfeits. Recent breakthroughs in the field of Machine Learning have shown high-resolution (HR) images could be recovered from low-resolution (LR) signals via super-resolution (SR). In particular, a novel class of neural networks named Generative Adversarial Networks (GAN) has manifested an alternative way of conceiving models capable of generating data. GANs can learn to infer details based on some prior information, subsequently recovering missing data. Accordingly, they manifest huge potential in MRI reconstruction and acceleration tasks. This paper conducts a review on GAN-based SR methods, exhibiting the immersive ability of GANs on upscaling MRIs by a scale factor of ×4 while at the same time maintaining trustworthy and high-frequency details. Despite quantitative results suggesting SRResCycGAN outperforms other popular deep learning methods in recovering ×4 downgraded images, qualitative results show Beby-GAN holds the best perceptual quality and proves GAN-based methods hold the capacity to reduce medical costs, distress patients and even enable new MRI applications where it is currently too slow or expensive.

The Return-to-Work experience of lower extremity amputees provided with prostheses at UERMMMCI CTC-PO / The Health Sciences Journal

INTRODUCTION@#Since there are limited studies about the return-to-work experiences of Filipino amputees, this study will be able to contribute to studies that delve deeper into the lower extremity amputees’ experiences and put into light the factors that may be present in relation to their return to work.@*METHODS@#This study utilized a qualitative phenomenological design. Participants who were willing to join the study were all gathered for a focus group discussion conducted by a hired interviewer. The researchers adapted Colaizzi’s descriptive phenomenological method for analyzing the data.@*RESULTS@#Factors that allowed amputees to have a successful return to work experience were motivation to continue with life, positive impact of lower extremity prosthesis, and rehabilitation. Factors that hindered the successful return to work of amputees were social barriers, work environment, negative self-image, discrimination from the community, and ft of prosthesis.@*CONCLUSION@#Employment was possible after amputation among amputees who were provided with prosthesis at UERMMMCI, since most of the respondents of this study were employed. Positive and negative factors that infuenced their return to work were also identifed. Non-compliance to rehabilitation limited the usage of prosthesis resulting in not being able to return to work.

Intervención integral en personas con obesidad: carencia de herramientas psicológicas / Comprehensive intervention in obese people: lack of psychological tools

La presente misiva busca contribuir a la discusión relacionada con la intervención médica en personas con obesidad, problema de salud pública que afecta a un gran número de personas en el mundo; circunscribiéndose a Latinoamérica, la realidad resulta alarmante, pues como lo refieren distintos informes recientes de la Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO), América Latina y el Caribe encabezan la lista de regiones geográficas que exhiben en los últimos años un aumento significativo de personas con obesidad.1 El Perú no es ajeno a la situación expuesta, el país experimenta un crecimiento gradual que está generando preocupación en las autoridades, ante lo cual, en los últimos años se emprendieron campañas que buscan promover hábitos saludables en la población, con especial atención en niños y adolescentes, grupos en relación con los cuales se registran evidencias que los catalogan como poblaciones de mayor vulnerabilidad a situaciones de riesgo, como la exposición a publicidad de alimentos no saludables y la ausencia de programas adecuados de actividad física en las instituciones educativas.2 Asimismo, diferentes organizaciones señalan que la obesidad está acarreando costos económicos y humanos,3 por lo que a las campañas preventivas, se suman actividades que orientan a los profesionales inmersos en el ámbito de la salud a reflexionar sobre el quehacer en la intervención en personas con obesidad. Esta intervención permitirá visualizar que la obesidad es una enfermedad que trae consigo consecuencias físicas y psicológicas, por lo que resulta necesaria una atención integral, sin embargo, en el contexto peruano y latinoamericano, la intervención a pacientes obesos se orienta al empleo de recursos de carácter médico y se descuida en muchos casos el uso de herramientas psicológicas.4 En las últimas décadas diversos estudios realizados en el escenario internacional publican que las personas con obesidad experimentan niveles elevados de ansiedad y depresión, lo cual conllevó al desarrollo de modelos psicológicos que explican la interacción clínica y sociocultural de variables como la personalidad, el comportamiento, las emociones y cogniciones, que permiten comprender los procesos psicológicos subyacentes al desarrollo de la imagen corporal, la conducta alimentaria y la actividad física en personas con obesidad.5 En función a lo mencionado, la intervención médica en obesidad, por la naturaleza del problema, amerita acompañarse de una intervención psicológica, sin embargo, en la práctica se aprecia lo contrario. Por lo expuesto, se presentan dos necesidades cruciales para trabajar un abordaje integral en personas que padecen obesidad, por una parte, la inclusión de variables psicológicas en el estudio de la obesidad que permita reconocer diferentes aspectos y generar conocimientos acerca del tema, y por otra, a partir de los conocimientos generados elaborar tecnologías (herramientas) para intervenciones eficaces. Cabe señalar que la tarea de trabajar con variables psicológicas no es menester exclusivo de los psicólogos, en problemáticas de salud pública constituye una necesidad impostergable romper fronteras y emprender estudios que involucren a equipos multidisciplinarios(AU)

Optimization and benchmarking of a perturbative Metropolis Monte Carlo quantum mechanics/molecular mechanics program.

In this work, we present an optimized perturbative quantum mechanics/molecular mechanics (QM/MM) method for use in Metropolis Monte Carlo simulations. The model adopted is particularly tailored for the simulation of molecular systems in solution but can be readily extended to other applications, such as catalysis in enzymatic environments. The electrostatic coupling between the QM and MM systems is simplified by applying perturbation theory to estimate the energy changes caused by a movement in the MM system. This approximation, together with the effective use of GPU acceleration, leads to a negligible added computational cost for the sampling of the environment. Benchmark calculations are carried out to evaluate the impact of the approximations applied and the overall computational performance.

BowMapCL: Burrows-Wheeler Mapping on Multiple Heterogeneous Accelerators.

The computational demand of exact-search procedures has pressed the exploitation of parallel processing accelerators to reduce the execution time of many applications. However, this often imposes strict restrictions in terms of the problem size and implementation efforts, mainly due to their possibly distinct architectures. To circumvent this limitation, a new exact-search alignment tool (BowMapCL) based on the Burrows-Wheeler Transform and FM-Index is presented. Contrasting to other alternatives, BowMapCL is based on a unified implementation using OpenCL, allowing the exploitation of multiple and possibly different devices (e.g., NVIDIA, AMD/ATI, and Intel GPUs/APUs). Furthermore, to efficiently exploit such heterogeneous architectures, BowMapCL incorporates several techniques to promote its performance and scalability, including multiple buffering, work-queue task-distribution, and dynamic load-balancing, together with index partitioning, bit-encoding, and sampling. When compared with state-of-the-art tools, the attained results showed that BowMapCL (using a single GPU) is 2 × to 7.5 × faster than mainstream multi-threaded CPU BWT-based aligners, like Bowtie, BWA, and SOAP2; and up to 4 × faster than the best performing state-of-the-art GPU implementations (namely, SOAP3 and HPG-BWT). When multiple and completely distinct devices are considered, BowMapCL efficiently scales the offered throughput, ensuring a convenient load-balance of the involved processing in the several distinct devices.

Prognostic models based on patient snapshots and time windows: Predicting disease progression to assisted ventilation in Amyotrophic Lateral Sclerosis.

Amyotrophic Lateral Sclerosis (ALS) is a devastating disease and the most common neurodegenerative disorder of young adults. ALS patients present a rapidly progressive motor weakness. This usually leads to death in a few years by respiratory failure. The correct prediction of respiratory insufficiency is thus key for patient management. In this context, we propose an innovative approach for prognostic prediction based on patient snapshots and time windows. We first cluster temporally-related tests to obtain snapshots of the patient's condition at a given time (patient snapshots). Then we use the snapshots to predict the probability of an ALS patient to require assisted ventilation after k days from the time of clinical evaluation (time window). This probability is based on the patient's current condition, evaluated using clinical features, including functional impairment assessments and a complete set of respiratory tests. The prognostic models include three temporal windows allowing to perform short, medium and long term prognosis regarding progression to assisted ventilation. Experimental results show an area under the receiver operating characteristics curve (AUC) in the test set of approximately 79% for time windows of 90, 180 and 365 days. Creating patient snapshots using hierarchical clustering with constraints outperforms the state of the art, and the proposed prognostic model becomes the first non population-based approach for prognostic prediction in ALS. The results are promising and should enhance the current clinical practice, largely supported by non-standardized tests and clinicians' experience.