Body shape: Implications in the study of obesity and related traits.

OBJECTIVES: The diagnosis and treatment of obesity are usually based on traditional anthropometric variables including weight, height, and several body perimeters. Here we present a three-dimensional (3D) image-based computational approach aimed to capture the distribution of abdominal adipose tissue as an aspect of shape rather than a relationship among classical anthropometric measures. METHODS: A morphometric approach based on landmarks and semilandmarks placed upon the 3D torso surface was performed in order to quantify abdominal adiposity shape variation and its relation to classical indices. Specifically, we analyzed sets of body cross-sectional circumferences, collectively defining each, along with anthropometric data taken on 112 volunteers. Principal Component Analysis (PCA) was performed on 250 circumferences located along the abdominal region of each volunteer. An analysis of covariance model was used to compare shape variables (PCs) against anthropometric data (weight, height, and waist and hip circumferences). RESULTS: The observed shape patterns were mainly related to nutritional status, followed by sexual dimorphism. PC1 (12.5%) and PC2 (7.5%) represented 20% of the total variation. In PCAs calculated independently by sex, linear regression analyses provide statistically significant associations between PC1 and the three classical indexes: body mass index, waist-to-height ratio, and waist-hip ratio. CONCLUSION: Shape indicators predict well the behavior of classical markers, but also evaluate 3D and geometric features with more accuracy as related to the body shape under study. This approach also facilitates diagnosis and follow-up of therapies by using accessible 3D technology.

Evaluation of sublethal effects of polymer-based essential oils nanoformulation on the german cockroach.

The German cockroach, Blattella germanica (L.), is a serious household and public health pest worldwide. The aim of the present study was to evaluate the sublethal activity of polymer-based essential oils (EOs) nanoparticles (NPs) on adults of B. germanica. The LC50 and LC25 for contact toxicity were determined. To evaluate the repellency of EOs and NPs at LC25, a software was specially created in order to track multiple insects on just-recorded videos, and generate statistics using the obtained information. The effects of EOs and NPs at LC25 and LC50 on the nutritional physiology were also evaluated. The results showed that NPs exerted sublethal effects on the German cockroach, since these products enhance the repellent effects of the EOs and negatively affected the nutritional indices and the feeding deterrence index.

Multifractal spectrum and lacunarity as measures of complexity of osseointegration.

OBJECTIVES: The goal of this study is to contribute to a better quantitative description of the early stages of osseointegration, by application of fractal, multifractal, and lacunarity analysis. MATERIALS AND METHODS: Fractal, multifractal, and lacunarity analysis are performed on scanning electron microscopy (SEM) images of titanium implants that were first subjected to different treatment combinations of i) sand blasting, ii) acid etching, and iii) exposition to calcium phosphate, and were then submersed in a simulated body fluid (SBF) for 30 days. All the three numerical techniques are applied to the implant SEM images before and after SBF immersion, in order to provide a comprehensive set of common quantitative descriptors. RESULTS: It is found that implants subjected to different physicochemical treatments before submersion in SBF exhibit a rather similar level of complexity, while the great variety of crystal forms after SBF submersion reveals rather different quantitative measures (reflecting complexity), for different treatments. In particular, it is found that acid treatment, in most combinations with the other considered treatments, leads to a higher fractal dimension (more uniform distribution of crystals), lower lacunarity (lesser variation in gap sizes), and narrowing of the multifractal spectrum (smaller fluctuations on different scales). CONCLUSION: The current quantitative description has shown the capacity to capture the main features of complex images of implant surfaces, for several different treatments. Such quantitative description should provide a fundamental tool for future large scale systematic studies, considering the large variety of possible implant treatments and their combinations. CLINICAL RELEVANCE: Quantitative description of early stages of osseointegration on titanium implants with different treatments should help develop a better understanding of this phenomenon, in general, and provide basis for further systematic experimental studies. Clinical practice should benefit from such studies in the long term, by more ready access to implants of higher quality.

Structure-from-motion approach for characterization of bioerosion patterns using UAV imagery.

The aim of this work is to evaluate the applicability of the 3D model obtained through Structure-from-Motion (SFM) from unmanned aerial vehicle (UAV) imagery, in order to characterize bioerosion patterns (i.e., cavities for roosting and nesting) caused by burrowing parrots on a cliff in Bahía Blanca, Argentina. The combined use of SFM-UAV technology was successfully applied for the 3D point cloud model reconstruction. The local point density, obtained by means of a sphere of radius equal to 0.5 m, reached a mean value of 9749, allowing to build a high-resolution model (0.013 m) for resolving fine spatial details in topography. To test the model, we compared it with another point cloud dataset which was created using a low cost do-it-yourself terrestrial laser scanner; the results showed that our georeferenced model had a good accuracy. In addition, an innovative method for the detection of the bioerosion features was implemented, through the processing of data provided by SFM like color and spatial coordinates (particularly the y coordinate). From the 3D model, we also derived topographic calculations such as slope angle and surface roughness, to get associations between the surface topography and bioerosion features.

Dataset of clinical cases, images, image labels and captions from open access case reports from PubMed Central (1990-2023).

This paper details the acquisition, structure and preprocessing of the MultiCaRe Dataset, a multimodal case report dataset which contains data from 75,382 open access PubMed Central articles spanning the period from 1990 to 2023. The dataset includes 96,428 clinical cases, 135,596 images, and their corresponding labels and captions. Data extraction was performed using different APIs and packages such as Biopython, requests, Beautifulsoup, BioC API for PMC and EuropePMC RESTful API. Image labels were created based on the contents of their corresponding captions, by using Spark NLP for Healthcare and manual annotations. Images were preprocessed with OpenCV in order to remove borders and split figures containing multiple images, data were analyzed and described, and a subset was randomly selected for quality assessment. The dataset's structure allows for seamless integration of different types of data, making it a valuable resource for training or fine-tuning medical language, computer vision or multi-modal models.

ErgoExplorer: Interactive Ergonomic Risk Assessment from Video Collections.

Ergonomic risk assessment is now, due to an increased awareness, carried out more often than in the past. The conventional risk assessment evaluation, based on expert-assisted observation of the workplaces and manually filling in score tables, is still predominant. Data analysis is usually done with a focus on critical moments, although without the support of contextual information and changes over time. In this paper we introduce ErgoExplorer, a system for the interactive visual analysis of risk assessment data. In contrast to the current practice, we focus on data that span across multiple actions and multiple workers while keeping all contextual information. Data is automatically extracted from video streams. Based on carefully investigated analysis tasks, we introduce new views and their corresponding interactions. These views also incorporate domain-specific score tables to guarantee an easy adoption by domain experts. All views are integrated into ErgoExplorer, which relies on coordinated multiple views to facilitate analysis through interaction. ErgoExplorer makes it possible for the first time to examine complex relationships between risk assessments of individual body parts over long sessions that span multiple operations. The newly introduced approach supports analysis and exploration at several levels of detail, ranging from a general overview, down to inspecting individual frames in the video stream, if necessary. We illustrate the usefulness of the newly proposed approach applying it to several datasets.

Assessment of Landsat-8 and Sentinel-2 Water Indices: A Case Study in the Southwest of the Buenos Aires Province (Argentina).

The accuracy assessment of three different Normalized Difference Water indices (NDWIs) was performed in La Salada, a typical lake in the Pampean region. Data were gathered during April 2019, a period in which floods occurred in a large area in the Southwest of the Buenos Aires Province (Argentina). The accuracy of the estimations using spaceborne medium-resolution multi-spectral imaging and the reliability of three NDWIs to highlight shallow water features in satellite images were evaluated using a high-resolution airbone imagery as ground truth. We show that these indices computed using Landsat-8 and Sentinel-2 imagery are only loosely correlated to the actual flooded area in shallow waters. Indeed, NDWI values vary significantly depending on the satellite mission used and the type of index computed.

Monitoring multiple parameters in complex water scenarios using a low-cost open-source data acquisition platform.

Water monitoring faces challenges that are driven by the infrastructure, protection, financial resources, science and innovation policies, among others. A modular, low-cost, fully open-source and small-sized Unmanned Surface Vessel (USV) called EMAC-USV (EMAC: Estación de Monitoreo Ambiental Costero), is proposed for monitoring bathymetry and water quality parameters (i.e. temperature, suspended solids concentration and hydrocarbon concentration) in complex water scenarios. A detailed description of each part of the platform as well as all electronic connections and functioning is presented.The field works were carried out in two small waste stabilization ponds and in a portion of the main tidal channel of the Bahía Blanca port. The EMAC-USV is the result of a cautious design, regarding the balancing performance, communications, payload capacity, among others.

Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory.

Several harmonization techniques have recently been proposed for connectomics/networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) acquired at multiple sites. These techniques have the objective of mitigating site-specific biases that complicate its subsequent analysis and, therefore, compromise the quality of the results when these images are analyzed together. Thus, harmonization is indispensable when large cohorts are required in which the data obtained must be independent of the particular condition of each resonator, its make and model, its calibration, and other features or artifacts that may affect the significance of the acquisition. To date, no assessment of the actual efficacy of these harmonization techniques has been proposed. In this work, we apply recently introduced Information Theory tools to analyze the effectiveness of these techniques, developing a methodology that allows us to compare different harmonization models. We demonstrate the usefulness of this methodology by applying it to some of the most widespread harmonization frameworks and datasets. As a result, we are able to show that some of these techniques are indeed ineffective since the acquisition site can still be determined from the fMRI data after the processing.

Reconstruction of Iberian ceramic potteries using generative adversarial networks.

Several aspects of past culture, including historical trends, are inferred from time-based patterns observed in archaeological artifacts belonging to different periods. The presence and variation of these objects provides important clues about the Neolithic revolution and given their relative abundance in most archaeological sites, ceramic potteries are significantly helpful in this purpose. Nonetheless, most available pottery is fragmented, leading to missing morphological information. Currently, the reassembly of fragmented objects from a collection of thousands of mixed fragments is a daunting and time-consuming task done almost exclusively by hand, which requires the physical manipulation of the fragments. To overcome the challenges of manual reconstruction and improve the quality of reconstructed samples, we present IberianGAN, a customized Generative Adversarial Network (GAN) tested on an extensive database with complete and fragmented references. We trained the model with 1072 samples corresponding to Iberian wheel-made pottery profiles belonging to archaeological sites located in the upper valley of the Guadalquivir River (Spain). Furthermore, we provide quantitative and qualitative assessments to measure the quality of the reconstructed samples, along with domain expert evaluation with archaeologists. The resulting framework is a possible way to facilitate pottery reconstruction from partial fragments of an original piece.

Bulsarapp: Interactive Visual Analysis for Surname Trend Exploration.

The study of surnames for a given population, together with their distribution and spatial patterns identification, has been a long-standing problem in the fields of human biology, public health, and social sciences. The ancestry inferred from surname information can be a useful means to understand the dynamics of human populations. This knowledge allows us to characterize geographically the ethnicity of populations, and to understand the complex relationships between identity, migration, and health issues in a demographic view. However, in most cases, a detailed geolocalization of this data can be a daunting task. We propose a visual analytic tool that summarizes the heterogeneous surname and geographic information collected from Argentinean electoral rolls. This tool allows a massive data analysis, and facilitates interdisciplinary studies about population dynamics related to ancestry, migration, and health. It also offers an easy-to-use interface that allows interactive exploration of isonymy and surname origins, their distribution, and spatial trends in a high population density context.

Essential oils loaded on polymeric nanoparticles: bioefficacy against economic and medical insect pests and risk evaluation on terrestrial and aquatic non-target organisms.

This paper introduces the lethal, sublethal, and ecotoxic effects of peppermint and palmarosa essential oils (EOs) and their polymeric nanoparticles (PNs). The physicochemical analyses indicated that peppermint PNs were polydisperse (PDI > 0.4) with sizes of 381 nm and loading efficiency (LE) of 70.3%, whereas palmarosa PNs were monodisperse (PDI < 0.25) with sizes of 191 nm and LE of 89.7%. EOs and their PNs were evaluated on the adults of rice weevil (Sitophilus oryzae L.) and cigarette beetle (Lasioderma serricorne F.) and the larvae of Culex pipiens pipiens Say. On S. oryzae and L. serricorne, PNs increased EOs' lethal activity, extended repellent effects for 84 h, and also modified behavioral variables during 24 h. Moreover, EOs and PNs generated toxic effects against C. pipiens pipiens. On the other hand, peppermint and palmarosa EOs and their PNs were not toxic to terrestrial non-target organisms, larvae of mealworm (Tenebrio molitor L.), and nymphs of orange-spotted cockroach (Blaptica dubia S.). In addition, PNs were slightly toxic to aquatic non-target organisms, such as brine shrimp (Artemia salina L.). Therefore, these results show that PNs are a novel and eco-friendly formulation to control insect pests.

GEMA-An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices.

Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments.

Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence.

The Developmental Origins of Health and Disease (DOHaD) framework aims to understand how early life exposures shape lifecycle health. To date, no comprehensive list of these exposures and their interactions has been developed, which limits our ability to predict trajectories of risk and resiliency in humans. To address this gap, we developed a model that uses text-mining, machine learning, and natural language processing approaches to automate search, data extraction, and content analysis from DOHaD-related research articles available in PubMed. Our first model captured 2469 articles, which were subsequently categorised into topics based on word frequencies within the titles and abstracts. A manual screening validated 848 of these as relevant, which were used to develop a revised model that finally captured 2098 articles that largely fell under the most prominently researched domains related to our specific DOHaD focus. The articles were clustered according to latent topic extraction, and 23 experts in the field independently labelled the perceived topics. Consensus analysis on this labelling yielded mostly from fair to substantial agreement, which demonstrates that automated models can be developed to successfully retrieve and classify research literature, as a first step to gather evidence related to DOHaD risk and resilience factors that influence later life human health.

Early life risk and resiliency factors and their influences on developmental outcomes and disease pathways: a rapid evidence review of systematic reviews and meta-analyses.

The Developmental Origins of Health and Disease (DOHaD) framework aims to understand how environmental exposures in early life shape lifecycle health. Our understanding and the ability to prevent poor health outcomes and enrich for resiliency remain limited, in part, because exposure-outcome relationships are complex and poorly defined. We, therefore, aimed to determine the major DOHaD risk and resilience factors. A systematic approach with a 3-level screening process was used to conduct our Rapid Evidence Review following the established guidelines. Scientific databases using DOHaD-related keywords were searched to capture articles between January 1, 2009 and April 19, 2019. A final total of 56 systematic reviews/meta-analyses were obtained. Studies were categorized into domains based on primary exposures and outcomes investigated. Primary summary statistics and extracted data from the studies are presented in Graphical Overview for Evidence Reviews diagrams. There was substantial heterogeneity within and between studies. While global trends showed an increase in DOHaD publications over the last decade, the majority of data reported were from high-income countries. Articles were categorized under six exposure domains: Early Life Nutrition, Maternal/Paternal Health, Maternal/Paternal Psychological Exposure, Toxicants/Environment, Social Determinants, and Others. Studies examining social determinants of health and paternal influences were underrepresented. Only 23% of the articles explored resiliency factors. We synthesized major evidence on relationships between early life exposures and developmental and health outcomes, identifying risk and resiliency factors that influence later life health. Our findings provide insight into important trends and gaps in knowledge within many exposures and outcome domains.

A Comparison of Radial and Linear Charts for Visualizing Daily Patterns.

Radial charts are generally considered less effective than linear charts. Perhaps the only exception is in visualizing periodical time-dependent data, which is believed to be naturally supported by the radial layout. It has been demonstrated that the drawbacks of radial charts outweigh the benefits of this natural mapping. Visualization of daily patterns, as a special case, has not been systematically evaluated using radial charts. In contrast to yearly or weekly recurrent trends, the analysis of daily patterns on a radial chart may benefit from our trained skill on reading radial clocks that are ubiquitous in our culture. In a crowd-sourced experiment with 92 non-expert users, we evaluated the accuracy, efficiency, and subjective ratings of radial and linear charts for visualizing daily traffic accident patterns. We systematically compared juxtaposed 12-hours variants and single 24-hours variants for both layouts in four low-level tasks and one high-level interpretation task. Our results show that over all tasks, the most elementary 24-hours linear bar chart is most accurate and efficient and is also preferred by the users. This provides strong evidence for the use of linear layouts - even for visualizing periodical daily patterns.

body2vec: 3D Point Cloud Reconstruction for Precise Anthropometry with Handheld Devices.

Current point cloud extraction methods based on photogrammetry generate large amounts of spurious detections that hamper useful 3D mesh reconstructions or, even worse, the possibility of adequate measurements. Moreover, noise removal methods for point clouds are complex, slow and incapable to cope with semantic noise. In this work, we present body2vec, a model-based body segmentation tool that uses a specifically trained Neural Network architecture. Body2vec is capable to perform human body point cloud reconstruction from videos taken on hand-held devices (smartphones or tablets), achieving high quality anthropometric measurements. The main contribution of the proposed workflow is to perform a background removal step, thus avoiding the spurious points generation that is usual in photogrammetric reconstruction. A group of 60 persons were taped with a smartphone, and the corresponding point clouds were obtained automatically with standard photogrammetric methods. We used as a 3D silver standard the clean meshes obtained at the same time with LiDAR sensors post-processed and noise-filtered by expert anthropological biologists. Finally, we used as gold standard anthropometric measurements of the waist and hip of the same people, taken by expert anthropometrists. Applying our method to the raw videos significantly enhanced the quality of the results of the point cloud as compared with the LiDAR-based mesh, and of the anthropometric measurements as compared with the actual hip and waist perimeter measured by the anthropometrists. In both contexts, the resulting quality of body2vec is equivalent to the LiDAR reconstruction.

Noise reduction using novel loss functions to compute tissue mineral density and trabecular bone volume fraction on low resolution QCT.

Micro-structural parameters of the thoracic or lumbar spine generally carry insufficient accuracy and precision for clinical in vivo studies when assessed on quantitative computed tomography (QCT). We propose a 3D convolutional neural network with specific loss functions for QCT noise reduction to compute micro-structural parameters such as tissue mineral density (TMD) and bone volume ratio (BV/TV) with significantly higher accuracy than using no or standard noise reduction filters. The vertebra-phantom study contained high resolution peripheral and clinical CT scans with simulated in vivo CT noise and nine repetitions of three different tube currents (100, 250 and 360 mAs). Five-fold cross validation was performed on 20466 purely spongy pairs of noisy and ground-truth patches. Comparison of training and test errors revealed high robustness against over-fitting. While not showing effects for the assessment of BMD and voxel-wise densities, the filter improved thoroughly the computation of TMD and BV/TV with respect to the unfiltered data. Root-mean-square and accuracy errors of low resolution TMD and BV/TV decreased to less than 17% of the initial values. Furthermore filtered low resolution scans revealed still more TMD- and BV/TV-relevant information than high resolution CT scans, either unfiltered or filtered with two state-of-the-art standard denoising methods. The proposed architecture is threshold and rotational invariant, applicable on a wide range of image resolutions at once, and likely serves for an accurate computation of further micro-structural parameters. Furthermore, it is less prone for over-fitting than neural networks that compute structural parameters directly. In conclusion, the method is potentially important for the diagnosis of osteoporosis and other bone diseases since it allows to assess relevant 3D micro-structural information from standard low exposure CT protocols such as 100 mAs and 120 kVp.

NIR-based Sudan I to IV and Para-Red food adulterants screening.

Spices are added in order to enhance the organoleptic characteristics of food and culinary dishes, making them more attractive for consumers. The use of illicit cheap colourants might be profitable along the food supply chain, posing undue risks to human health. This work evaluates the feasibility of NIR spectroscopy with chemometrics as a rapid, simple, non-destructive and affordable screening tool to determine the presence of Sudan I, II, III, IV and Para-red dyes in paprika. The dataset comprised unadulterated and adulterated samples with the five studied dyes at different concentration levels. Several multivariate classification models were built with Linear Discriminant Analysis (LDA) and different machine learning techniques. Preliminary results show that a classifier based on only six wavenumbers is able to determine the presence of some of these dyes in food samples in levels that may represent risk to human health. Sensitivities and specificities above 90% were obtained in almost all cases. These results show the feasibility of inexpensive and portable devices that can be useful for screening out adulterated stock along the food chain supply.

Local texture descriptors for the assessment of differences in diffusion magnetic resonance imaging of the brain.

PURPOSE: Descriptors extracted from magnetic resonance imaging (MRI) of the brain can be employed to locate and characterize a wide range of pathologies. Scalar measures are typically derived within a single-voxel unit, but neighborhood-based texture measures can also be applied. In this work, we propose a new set of descriptors to compute local texture characteristics from scalar measures of diffusion tensor imaging (DTI), such as mean and radial diffusivity, and fractional anisotropy. METHODS: We employ weighted rotational invariant local operators, namely standard deviation, inter-quartile range, coefficient of variation, quartile coefficient of variation and skewness. Sensitivity and specificity of those texture descriptors were analyzed with tract-based spatial statistics of the white matter on a diffusion MRI group study of elderly healthy controls, patients with mild cognitive impairment (MCI), and mild or moderate Alzheimer's disease (AD). In addition, robustness against noise has been assessed with a realistic diffusion-weighted imaging phantom and the contamination of the local neighborhood with gray matter has been measured. RESULTS: The new texture operators showed an increased ability for finding formerly undetected differences between groups compared to conventional DTI methods. In particular, the coefficient of variation, quartile coefficient of variation, standard deviation and inter-quartile range of the mean and radial diffusivity detected significant differences even between previously not significantly discernible groups, such as MCI versus moderate AD and mild versus moderate AD. The analysis provided evidence of low contamination of the local neighborhood with gray matter and high robustness against noise. CONCLUSIONS: The local operators applied here enhance the identification and localization of areas of the brain where cognitive impairment takes place and thus indicate them as promising extensions in diffusion MRI group studies.