A Deep Learning Approach to Distance Map Generation Applied to Automatic Fiber Diameter Computation from Digital Micrographs.

Precise measurement of fiber diameter in animal and synthetic textiles is crucial for quality assessment and pricing; however, traditional methods often struggle with accuracy, particularly when fibers are densely packed or overlapping. Current computer vision techniques, while useful, have limitations in addressing these challenges. This paper introduces a novel deep-learning-based method to automatically generate distance maps of fiber micrographs, enabling more accurate fiber segmentation and diameter calculation. Our approach utilizes a modified U-Net architecture, trained on both real and simulated micrographs, to regress distance maps. This allows for the effective separation of individual fibers, even in complex scenarios. The model achieves a mean absolute error (MAE) of 0.1094 and a mean square error (MSE) of 0.0711, demonstrating its effectiveness in accurately measuring fiber diameters. This research highlights the potential of deep learning to revolutionize fiber analysis in the textile industry, offering a more precise and automated solution for quality control and pricing.

Graph embedding on mass spectrometry- and sequencing-based biomedical data.

Graph embedding techniques are using deep learning algorithms in data analysis to solve problems of such as node classification, link prediction, community detection, and visualization. Although typically used in the context of guessing friendships in social media, several applications for graph embedding techniques in biomedical data analysis have emerged. While these approaches remain computationally demanding, several developments over the last years facilitate their application to study biomedical data and thus may help advance biological discoveries. Therefore, in this review, we discuss the principles of graph embedding techniques and explore the usefulness for understanding biological network data derived from mass spectrometry and sequencing experiments, the current workhorses of systems biology studies. In particular, we focus on recent examples for characterizing protein-protein interaction networks and predicting novel drug functions.

Estimated Worldwide Prevalence of the PI*ZZ Alpha-1 Antitrypsin Genotype in Subjects With Chronic Obstructive Pulmonary Disease

Introduction: The prevalence of α1-antitrypsin PI*ZZ genotypes in patients with COPD is only partially known. We aimed to estimate this prevalence worldwide. Method: A systematic review of the literature was conducted for studies investigating the prevalence of COPD and the prevalence of severe alpha-1-antitrypsin deficiency (AATD) PI*ZZ genotype. Results are shown in tables and on a whole world interpolation map. Results: Studies from 48 countries with available data (21 from Europe, 9 from the Americas, 5 from Africa, 11 from Asia and 2 from Australasia) were selected. About 235,000 individuals with PI*ZZ genotypes were accounted: 50% in Europe, 37% in America, 9% in Asia, 3% in Australasia and 1% in Africa. The estimated crude prevalence of COPD in adults older than 40 years was 12.45% in Europe, 13.51% in America, 13.22% in Africa, 11.70% in Asia and 11.86% in Australasia. The highest PI*ZZ weighted average prevalence among COPD subjects (expressed as 1/x [95% confidence intervals]) were found in Northern Europe (395 [252–576]) followed by Western (797 [538–1165]), Southern (944 [600–1475]) and Central Europe (1096 [687–1738]). Outside Europe, high values were found in Australia–New Zealand (1007 [684–1509]), Saudi Arabia (1276 [563–2961]), United States (1298 [1094–1540]), Canada (1482 [1057–2083]) and Thailand (1807 [717–4692]). In the rest of the world, prevalence was significantly lower, especially in vast regions of Asia and Africa where the PI*Z gene is practically non-existent. Conclusions: Severe AATD is associated with a significant number of cases of COPD, especially in Europe, USA, Canada, New Zealand and Australia. (AU)

Estimated Worldwide Prevalence of the PI*ZZ Alpha-1 Antitrypsin Genotype in Subjects With Chronic Obstructive Pulmonary Disease.

INTRODUCTION: The prevalence of α1-antitrypsin PI*ZZ genotypes in patients with COPD is only partially known. We aimed to estimate this prevalence worldwide. METHOD: A systematic review of the literature was conducted for studies investigating the prevalence of COPD and the prevalence of severe alpha-1-antitrypsin deficiency (AATD) PI*ZZ genotype. Results are shown in tables and on a whole world interpolation map. RESULTS: Studies from 48 countries with available data (21 from Europe, 9 from the Americas, 5 from Africa, 11 from Asia and 2 from Australasia) were selected. About 235,000 individuals with PI*ZZ genotypes were accounted: 50% in Europe, 37% in America, 9% in Asia, 3% in Australasia and 1% in Africa. The estimated crude prevalence of COPD in adults older than 40 years was 12.45% in Europe, 13.51% in America, 13.22% in Africa, 11.70% in Asia and 11.86% in Australasia. The highest PI*ZZ weighted average prevalence among COPD subjects (expressed as 1/x [95% confidence intervals]) were found in Northern Europe (395 [252-576]) followed by Western (797 [538-1165]), Southern (944 [600-1475]) and Central Europe (1096 [687-1738]). Outside Europe, high values were found in Australia-New Zealand (1007 [684-1509]), Saudi Arabia (1276 [563-2961]), United States (1298 [1094-1540]), Canada (1482 [1057-2083]) and Thailand (1807 [717-4692]). In the rest of the world, prevalence was significantly lower, especially in vast regions of Asia and Africa where the PI*Z gene is practically non-existent. CONCLUSIONS: Severe AATD is associated with a significant number of cases of COPD, especially in Europe, USA, Canada, New Zealand and Australia.

An Approach to Growth Delimitation of Straight Line Segment Classifiers Based on a Minimum Bounding Box.

Several supervised machine learning algorithms focused on binary classification for solving daily problems can be found in the literature. The straight-line segment classifier stands out for its low complexity and competitiveness, compared to well-knownconventional classifiers. This binary classifier is based on distances between points and two labeled sets of straight-line segments. Its training phase consists of finding the placement of labeled straight-line segment extremities (and consequently, their lengths) which gives the minimum mean square error. However, during the training phase, the straight-line segment lengths can grow significantly, giving a negative impact on the classification rate. Therefore, this paper proposes an approach for adjusting the placements of labeled straight-line segment extremities to build reliable classifiers in a constrained search space (tuned by a scale factor parameter) in order to restrict their lengths. Ten artificial and eight datasets from the UCI Machine Learning Repository were used to prove that our approach shows promising results, compared to other classifiers. We conclude that this classifier can be used in industry for decision-making problems, due to the straightforward interpretation and classification rates.

Using lexical language models to detect borrowings in monolingual wordlists.

Lexical borrowing, the transfer of words from one language to another, is one of the most frequent processes in language evolution. In order to detect borrowings, linguists make use of various strategies, combining evidence from various sources. Despite the increasing popularity of computational approaches in comparative linguistics, automated approaches to lexical borrowing detection are still in their infancy, disregarding many aspects of the evidence that is routinely considered by human experts. One example for this kind of evidence are phonological and phonotactic clues that are especially useful for the detection of recent borrowings that have not yet been adapted to the structure of their recipient languages. In this study, we test how these clues can be exploited in automated frameworks for borrowing detection. By modeling phonology and phonotactics with the support of Support Vector Machines, Markov models, and recurrent neural networks, we propose a framework for the supervised detection of borrowings in mono-lingual wordlists. Based on a substantially revised dataset in which lexical borrowings have been thoroughly annotated for 41 different languages from different families, featuring a large typological diversity, we use these models to conduct a series of experiments to investigate their performance in mono-lingual borrowing detection. While the general results appear largely unsatisfying at a first glance, further tests show that the performance of our models improves with increasing amounts of attested borrowings and in those cases where most borrowings were introduced by one donor language alone. Our results show that phonological and phonotactic clues derived from monolingual language data alone are often not sufficient to detect borrowings when using them in isolation. Based on our detailed findings, however, we express hope that they could prove to be useful in integrated approaches that take multi-lingual information into account.

Epigallocatechin 3-Gallate Has a Neuroprotective Effect in Retinas of Rabbits with Ischemia/Reperfusion through the Activation of Nrf2/HO-1.

Retinal ischemia-reperfusion (rI/R) generates an oxidative condition causing the death of neuronal cells. Epigallocatechin 3-gallate (EGCG) has antioxidant and anti-inflammatory properties. Nonetheless, its correlation with the pathway of nuclear factor erythroid 2-related factor 2/heme oxygenase-1 (Nrf2/HO-1) for the protection of the retina is unknown. We aimed to evaluate the neuroprotective efficacy of single-doses of EGCG in rI/R and its association with Nrf2/Ho-1 expression. In albino rabbits, rI/R was induced and single-doses of EGCG in saline (0-30 mg/kg) were intravenously administered to select an optimal EGCG concentration that protects from retina damage. To reach this goal, retinal structural changes, gliosis by glial fibrillary acidic protein (GFAP) immunostaining, and lipid peroxidation level by TBARS (thiobarbituric acid reactive substance) assay were determined. EGCG in a dose of 15 mg/kg (E15) presented the lowest levels of histological damage, gliosis, and oxidative stress in the studied groups. To determine the neuroprotective efficacy of E15 in a timeline (6, 24, and 48 h after rI/R), and its association with the Nrf2/HO-1 pathway, the following assays were done by immunofluorescence: apoptosis (TUNEL assay), necrosis (high-mobility group box-1; HMGB1), Nrf2, and HO-1. In addition, the Ho-1 mRNA (qPCR) and lipid peroxidation levels were evaluated. E15 showed a protective effect during the first 6 h, compared to 24 and 48 h after rI/R, as revealed by a decrease in the levels of all damage markers. Nuclear translocation Nrf2 and HO-1 staining were increased, including Ho-1 mRNA levels. In conclusion, a single dose of E15 decreases the death of neuronal cells induced by oxidative stress during the first 6 h after rI/R. This protective effect is associated with the nuclear translocation of Nrf2 and with an elevation of Ho-1 expression.

Caracterización de imágenes médicas aplicando transformada de Wavelet para sistemas de recuperación de imágenes por contenido / Characterization of medical images applying Wavelet transform for recuperation systems of images by containing

En el presente artículo se propone una técnica de caracterización de imágenes médicas aplicando una metodología de multi-resolución, con la finalidad de facilitar su respectiva indexación dentro de una estructura multi-dimensional. Una de las características de las imágenes médicas, es la de presentar cambios de color en tonos de gris dentro de regiones localizadas de la imagen. Hasta ahora no se tiene una técnica adecuada que haga posible extraer estas regiones mediante un completo procesamiento automático de las imágenes. Una estrategia para abordar este problema consiste en la generación de vectores de características basados en las transformadas de wavelet, estas características extraídas generaran un vector de características que se va a constituir en la identificación de la imagen. En la presente propuesta, el sistema extrae las características más relevantes de la imagen, calcula la distancia entre una imagen de consulta y las que seencuentran en el banco de datos, y recupera las n imágenes más similares. El enfoque presentado estábasado en la aplicación de los filtros de las wavelets de Daubechies 4 sobre las características globales de la imagen para luego así facilitar la indexación dentro de una estructura multi-dimensional. El objetivo del presente trabajo es destacar la utilidad de la transformada de wavelet en la caracterización de imágenes médicas y su utilidad dentro de una apropiada técnica de indexación de las mismas.