Capillary drops, capillary pooled, and venous blood samples for determining hemoglobin concentration using HemoCue: A measurement system analysis.

Several external and internal factors can affect the performance and variability of Hemoglobin concentration [Hb] measurements using HemoCue, and documentation on the contribution of different sources of [Hb] variation is limited. We used an experimental repeated measurements design with nine randomly selected participants, three HemoCue devices, and three trained field workers. HemoCue measurements for all samples were repeated three times.

Monitoring social-distance in wide areas during pandemics: a density map and segmentation approach.

With the relaxation of the containment measurements around the globe, monitoring the social distancing in crowded public spaces is of great importance to prevent a new massive wave of COVID-19 infections. Recent works in that matter have limited themselves by assessing social distancing in corridors up to small crowds by detecting each person individually, considering the full body in the image. In this work, we propose a new framework for monitoring the social-distance using end-to-end Deep Learning, to detect crowds violating social-distancing in wide areas, where important occlusions may be present.

Improving convolutional neural network learning based on a hierarchical bezier generative model for stenosis detection in X-ray images.

Background And Objective: Automatic detection of stenosis on X-ray Coronary Angiography (XCA) images may help diagnose early coronary artery disease. Stenosis is manifested by a buildup of plaque in the arteries, decreasing the blood flow to the heart, increasing the risk of a heart attack. Convolutional Neural Networks (CNNs) have been successfully applied to identify pathological, regular, and featured tissues on rich and diverse medical image datasets.

VSD-MOEA: A Dominance-Based Multiobjective Evolutionary Algorithm with Explicit Variable Space Diversity Management.

Most state-of-the-art Multiobjective Evolutionary Algorithms (moeas) promote the preservation of diversity of objective function space but neglect the diversity of decision variable space. The aim of this article is to show that explicitly managing the amount of diversity maintained in the decision variable space is useful to increase the quality of moeas when taking into account metrics of the objective space. Our novel Variable Space Diversity-based MOEA (vsd-moea) explicitly considers the diversity of both decision variable and objective function space.

Food insecurity and maternal-child nutritional status in Mexico: cross-sectional analysis of the National Health and Nutrition Survey 2012.

Objective: To examine the association between household food insecurity (HFI) and risk of childhood stunting and to determine whether this association is modified by maternal-child overweight/obesity.

Design: Observational cross-sectional study.

Setting: Data come from the Mexican National Health and Nutrition Survey ( by its initials in Spanish), representative of rural and urban areas.

Dietary quality and household food insecurity among Mexican children and adolescents.

Seventy percent of Mexican households experience some level of food insecurity (FI). Studies have shown positive associations between FI and poor dietary quality. As far as it is known, this is the first time the Healthy Eating Index (HEI-2010) has been used to assess dietary quality of children and adolescents in Mexico, and to examine if FI is related to it.

Vascular Structure Identification in Intraoperative 3D Contrast-Enhanced Ultrasound Data.

In this paper, a method of vascular structure identification in intraoperative 3D Contrast-Enhanced Ultrasound (CEUS) data is presented. Ultrasound imaging is commonly used in brain tumor surgery to investigate in real time the current status of cerebral structures. The use of an ultrasound contrast agent enables to highlight tumor tissue, but also surrounding blood vessels.

A maximum linear separation criterion for the analysis of neurophysiological data.

In this paper we propose an approach for the extraction of features that differentiate two populations or two experimental conditions in a neurophysiological experiment. These features consist of summarizing variables defined as total activity (e.g.

Morphology-based hypothesis testing in discrete random fields: a non-parametric method to address the multiple-comparison problem in neuroimaging.

A new method for detecting activations in random fields, which may be useful for addressing the issue of multiple comparisons in neuroimaging, is presented. This method is based on some constructs of mathematical morphology--specifically, morphological erosions and dilations--that enable the detection of active regions in random fields possessing moderate activation levels and relatively large spatial extension, which may not be detected by the standard methods that control the family-wise error rate. The method presented here permits an appropriate control of the false positive errors, without having to adjust any threshold values, other than the significance level.

Morphology-based hypothesis testing in discrete random fields: a non-parametric method to address the multiple-comparison problem in neuroimaging.

A new method for detecting activations in random fields, which may be useful for addressing the issue of multiple comparisons in neuroimaging, is presented. This method is based on some constructs of mathematical morphology - specifically, morphological erosions and dilations - that enable the detection of active regions in random fields possessing moderate activation levels and relatively large spatial extension, which may not be detected by the standard methods that control the family-wise error rate. The method presented here permits an appropriate control of the false positive errors, without having to adjust any threshold values, other than the significance level.

High-performance multimedia encryption system based on chaos.

Current chaotic encryption systems in the literature do not fulfill security and performance demands for real-time multimedia communications. To satisfy these demands, we propose a generalized symmetric cryptosystem based on N independently iterated chaotic maps (N-map array) periodically perturbed with a three-level perturbation scheme and a double feedback (global and local) to increase the system's robustness to attacks. The first- and second-level perturbations make cryptosystem extremely sensitive to changes in the plaintext data since the system's output itself (ciphertext global feedback) is used in the perturbation process.