TEMPROT: protein function annotation using transformers embeddings and homology search.

BACKGROUND: Although the development of sequencing technologies has provided a large number of protein sequences, the analysis of functions that each one plays is still difficult due to the efforts of laboratorial methods, making necessary the usage of computational methods to decrease this gap. As the main source of information available about proteins is their sequences, approaches that can use this information, such as classification based on the patterns of the amino acids and the inference based on sequence similarity using alignment tools, are able to predict a large collection of proteins. The methods available in the literature that use this type of feature can achieve good results, however, they present restrictions of protein length as input to their models. In this work, we present a new method, called TEMPROT, based on the fine-tuning and extraction of embeddings from an available architecture pre-trained on protein sequences. We also describe TEMPROT+, an ensemble between TEMPROT and BLASTp, a local alignment tool that analyzes sequence similarity, which improves the results of our former approach. RESULTS: The evaluation of our proposed classifiers with the literature approaches has been conducted on our dataset, which was derived from CAFA3 challenge database. Both TEMPROT and TEMPROT+ achieved competitive results on [Formula: see text], [Formula: see text], AuPRC and IAuPRC metrics on Biological Process (BP), Cellular Component (CC) and Molecular Function (MF) ontologies compared to state-of-the-art models, with the main results equal to 0.581, 0.692 and 0.662 of [Formula: see text] on BP, CC and MF, respectively. CONCLUSIONS: The comparison with the literature showed that our model presented competitive results compared the state-of-the-art approaches considering the amino acid sequence pattern recognition and homology analysis. Our model also presented improvements related to the input size that the model can use to train compared to the literature methods.

Multi-Coil MRI Reconstruction Challenge-Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations.

Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess the MRI reconstruction quality of high-resolution brain images, and evaluate how these proposed algorithms will behave in the presence of small, but expected data distribution shifts. The multi-coil MRI (MC-MRI) reconstruction challenge provides a benchmark that aims at addressing these issues, using a large dataset of high-resolution, three-dimensional, T1-weighted MRI scans. The challenge has two primary goals: (1) to compare different MRI reconstruction models on this dataset and (2) to assess the generalizability of these models to data acquired with a different number of receiver coils. In this paper, we describe the challenge experimental design and summarize the results of a set of baseline and state-of-the-art brain MRI reconstruction models. We provide relevant comparative information on the current MRI reconstruction state-of-the-art and highlight the challenges of obtaining generalizable models that are required prior to broader clinical adoption. The MC-MRI benchmark data, evaluation code, and current challenge leaderboard are publicly available. They provide an objective performance assessment for future developments in the field of brain MRI reconstruction.

Litter Detection with Deep Learning: A Comparative Study.

Pollution in the form of litter in the natural environment is one of the great challenges of our times. Automated litter detection can help assess waste occurrences in the environment. Different machine learning solutions have been explored to develop litter detection tools, thereby supporting research, citizen science, and volunteer clean-up initiatives. However, to the best of our knowledge, no work has investigated the performance of state-of-the-art deep learning object detection approaches in the context of litter detection. In particular, no studies have focused on the assessment of those methods aiming their use in devices with low processing capabilities, e.g., mobile phones, typically employed in citizen science activities. In this paper, we fill this literature gap. We performed a comparative study involving state-of-the-art CNN architectures (e.g., Faster RCNN, Mask-RCNN, EfficientDet, RetinaNet and YOLO-v5), two litter image datasets and a smartphone. We also introduce a new dataset for litter detection, named PlastOPol, composed of 2418 images and 5300 annotations. The experimental results demonstrate that object detectors based on the YOLO family are promising for the construction of litter detection solutions, with superior performance in terms of detection accuracy, processing time, and memory footprint.

Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction.

Protein secondary structures are important in many biological processes and applications. Due to advances in sequencing methods, there are many proteins sequenced, but fewer proteins with secondary structures defined by laboratory methods. With the development of computer technology, computational methods have (started to) become the most important methodologies for predicting secondary structures. We evaluated two different approaches to this problem-driven by the recent results obtained by computational methods in this task-(i) template-free classifiers, based on machine learning techniques; and (ii) template-based classifiers, based on searching tools. Both approaches are formed by different sub-classifiers-six for template-free and two for template-based, each with a specific view of the protein. Our results show that these ensembles improve the results of each approach individually.

Reconstruction of Panoramic Dental Images Through Bézier Function Optimization.

Computed tomography (CT) and X-ray images have been extensively used as a valuable diagnostic tool in dentistry for surgical planning and treatment. Nowadays, dental cone beam CT has been extensively used in dental clinics. Therefore, it is possible to employ three-dimensional (3D) data from the CT to reconstruct a two-dimensional (2D) panoramic dental image that provides a longitudinal view of the mandibular region of the patient, avoiding an additional exposure to X-ray. In this work, we developed a new automatic method for reconstructing 2D panoramic images of the dental arch based on 3D CT images, using Bézier curves and optimization techniques. The proposed method was applied to five patients, some of them with missing teeth, and smooth panoramic images with good contrast were obtained.

Spatial trends in congenital malformations and stream water chemistry in Southern Brazil.

The incidence of variable congenital malformation (CM) among 399 municipalities in the state of Paraná, southern Brazil, suggests the etiological role of environmental factors. This study examined a) environmental concentrations of chlorine anions (Cl-) associated with organochlorines (OCs) and b) associations between these chemicals and agricultural output with CMs using a geographical information system. In one of the three years during the sampling period (2008, 2009 or 2010) Cl-, dichlorodiphenyltrichloroethane (p,p'-DDT), dichlorodiphenyldichloroethylene (p,p'-DDE), dichlorodiphenyldichloroethane (p,p'-DDD), and endosulfan levels were measured in 465 (465/736, 63%) catchment basins. Agricultural outputs for crops during 2006-2010 were also evaluated (t/km2). Further, CM kernel density for the 399 municipalities in Paraná during 2007-2014 was investigated. Cl- levels increased significantly in one of the three years (2008, 2009 or 2010) in western catchment basins, compared to 1996 (p < 0.0001). The municipalities were divided according to the obtained Cl- levels, where sub-region C2 (central-southern) < 1.8 mg/L ≤ sub-regions C1 (northern-western) and C3 (eastern-southern). We identified 8756 cases of CMs among 1,221,287 newborns (NB) in all sub-regions. C1 had higher DDT-DDE-DDD (p,p'-DDT + p,p'-DDE + p,p'-DDD) concentrations, agricultural output, and CM kernel density. C2 and C3 had minor agricultural outputs (per square kilometer) and CM densities. A 2.96 mg/L increase in Cl- between sub-regions C1 and C2 was co-localized with a 45% increase in CM density (spatial relative risk = 1.45, CI 95%: 1.36-1.55). C1 had the highest log likelihood ratios (p = 0.001) identified via SaTScan clustering analyses. Organochlorines and other toxic chlorinated chemicals may contribute to CMs in humans, and these chemicals are ultimately transformed and release Cl- in rivers. Higher Cl- levels were correlated significantly with higher agricultural productivity, DDT-DDE-DDD levels, and CMs in some parts of the northern and western sub-regions (C1).

Shearlet and contourlet transforms for analysis of electrocardiogram signals.

BACKGROUND AND OBJECTIVE: Cardiac arrhythmia is an abnormal variation in the heart electrical activity that affects millions of people worldwide. Electrocardiogram (ECG) signals have been widely used to assess and diagnose cardiac abnormalities. METHODS: A novel methodology based on shearlet and contourlet transforms for automatically classify an input ECG signal into different heart beat types is proposed and evaluated in this work. Classifiers are trained through a set of features extracted from these time-frequency coefficients. RESULTS: Tests are conducted on MIT-BIH data set to demonstrate the effectiveness of the proposed classification method. The shearlet and contourlet transforms achieved high classification accuracy rates. CONCLUSIONS: The developed system can help cardiologists obtain structural and functional information of the heart by means of ECG patterns, improving their diagnostic tasks.

Down syndrome detection based on facial features using a geometric descriptor.

Down syndrome is one of the most common genetic disorders caused by chromosome abnormalities in humans. Among other physical characteristics, certain facial features are typically associated in people with Down syndrome. We investigate the problem of Down syndrome detection from a collection of face images. As the main contribution, a compact geometric descriptor is used to extract facial features from the images. Experiments are conducted on an available dataset to demonstrate the performance of the proposed methodology.

Inferring patterns in mitochondrial DNA sequences through hypercube independent spanning trees.

Given a graph G, a set of spanning trees rooted at a vertex r of G is said vertex/edge independent if, for each vertex v of G, v≠r, the paths of r to v in any pair of trees are vertex/edge disjoint. Independent spanning trees (ISTs) provide a number of advantages in data broadcasting due to their fault tolerant properties. For this reason, some studies have addressed the issue by providing mechanisms for constructing independent spanning trees efficiently. In this work, we investigate how to construct independent spanning trees on hypercubes, which are generated based upon spanning binomial trees, and how to use them to predict mitochondrial DNA sequence parts through paths on the hypercube. The prediction works both for inferring mitochondrial DNA sequences comprised of six bases as well as infer anomalies that probably should not belong to the mitochondrial DNA standard.

An Approach to Supporting Incremental Visual Data Classification.

Automatic data classification is a computationally intensive task that presents variable precision and is considerably sensitive to the classifier configuration and to data representation, particularly for evolving data sets. Some of these issues can best be handled by methods that support users' control over the classification steps. In this paper, we propose a visual data classification methodology that supports users in tasks related to categorization such as training set selection; model creation, application and verification; and classifier tuning. The approach is then well suited for incremental classification, present in many applications with evolving data sets. Data set visualization is accomplished by means of point placement strategies, and we exemplify the method through multidimensional projections and Neighbor Joining trees. The same methodology can be employed by a user who wishes to create his or her own ground truth (or perspective) from a previously unlabeled data set. We validate the methodology through its application to categorization scenarios of image and text data sets, involving the creation, application, verification, and adjustment of classification models.

Face Spoofing Detection Through Visual Codebooks of Spectral Temporal Cubes.

Despite important recent advances, the vulnerability of biometric systems to spoofing attacks is still an open problem. Spoof attacks occur when impostor users present synthetic biometric samples of a valid user to the biometric system seeking to deceive it. Considering the case of face biometrics, a spoofing attack consists in presenting a fake sample (e.g., photograph, digital video, or even a 3D mask) to the acquisition sensor with the facial information of a valid user. In this paper, we introduce a low cost and software-based method for detecting spoofing attempts in face recognition systems. Our hypothesis is that during acquisition, there will be inevitable artifacts left behind in the recaptured biometric samples allowing us to create a discriminative signature of the video generated by the biometric sensor. To characterize these artifacts, we extract time-spectral feature descriptors from the video, which can be understood as a low-level feature descriptor that gathers temporal and spectral information across the biometric sample and use the visual codebook concept to find mid-level feature descriptors computed from the low-level ones. Such descriptors are more robust for detecting several kinds of attacks than the low-level ones. The experimental results show the effectiveness of the proposed method for detecting different types of attacks in a variety of scenarios and data sets, including photos, videos, and 3D masks.

Improved similarity trees and their application to visual data classification.

An alternative form to multidimensional projections for the visual analysis of data represented in multidimensional spaces is the deployment of similarity trees, such as Neighbor Joining trees. They organize data objects on the visual plane emphasizing their levels of similarity with high capability of detecting and separating groups and subgroups of objects. Besides this similarity-based hierarchical data organization, some of their advantages include the ability to decrease point clutter; high precision; and a consistent view of the data set during focusing, offering a very intuitive way to view the general structure of the data set as well as to drill down to groups and subgroups of interest. Disadvantages of similarity trees based on neighbor joining strategies include their computational cost and the presence of virtual nodes that utilize too much of the visual space. This paper presents a highly improved version of the similarity tree technique. The improvements in the technique are given by two procedures. The first is a strategy that replaces virtual nodes by promoting real leaf nodes to their place, saving large portions of space in the display and maintaining the expressiveness and precision of the technique. The second improvement is an implementation that significantly accelerates the algorithm, impacting its use for larger data sets. We also illustrate the applicability of the technique in visual data mining, showing its advantages to support visual classification of data sets, with special attention to the case of image classification. We demonstrate the capabilities of the tree for analysis and iterative manipulation and employ those capabilities to support evolving to a satisfactory data organization and classification.

Avaliação da intensidade das rugas periorbitais por processamento digital de imagens: um estudo de validação / Periorbital wrinkle severity evaluation using digital image processing: a validation study

Introdução: Dermatologistas podem avaliar a qualidade da pele de um paciente através de suas rugas faciais. Existem diversos métodos descritos para quantifi car as microelevações e rugas da pele. Objetivo: O objetivo deste estudo é utilizar a tecnologia de processamento de imagem para avaliar a área das rugas e sua correlação com a avaliação do dermatologista. Material e Métodos: Cento e setenta e um voluntários selecionados aleatoriamente, com idades variando de 18 a 69 anos, de ambos os sexos, com pele tipos II-IV de Fitzpatrick e I-IV de Glogau, participaram deste estudo. Fotografi as digitais foram tiradas sob duas condições diferentes: com as rugas relaxadas (RR) ou contraídas (RC). Foram tiradas pelo menos seis fotografi as para determinar a reprodutibilidade de cada método. Seis dermatologistas avaliaram as fotografi as e marcaram as rugas dos pacientes com uma caneta digital. Determinou-se a intensidade das rugas por três diferentes métodos de processamento de imagens. A avaliação quantitativa da intensidade das pregas cutâneas e a consistência inter e intramétodo foram estabelecidas e comparadas à avaliação manual. Resultados: A variabilidade dos dermatologistas foi de 61,49%. A concordância intratestes variou de 4,4 a 31%. O método do gradiente obteve os melhores resultados e detectava as condições das rugas (p < 0,001), tendo apresentado também uma correlação positiva com a avaliação manual. Conclusões: As rugas periorbitais foram identifi cadas. Os dermatologistas demonstraram precisão interna, mas baixa exatidão. O processamento digital do operador de Sobel é um instrumento válido e confi ável para a avaliação quantitativa de rugas.

Introduction: Dermatologists get an idea of how old a patient is by looking face's wrinkles. Different methods exist to quantify skin microrelief and wrinkles. Objective: To use imaging process technology to assess the wrinkle area and its interrelation with dermatologist determination. Material and methods: One hundred seventy-one volunteers, randomly selected, aged 18­69 years, both gender, Fitzpatrick skin type II­IV, with Glocau I­IV were selected. Digital photography was obtained. Two different conditions, relaxed or contract wrinkle condition (CWC), and at least six repeated pictures for repeatability capacity of each method. Six participating dermatologists evaluated the pictures and ascertained patients' wrinkle using a digital pen. Wrinkle intensity was done by three different image processing methods. Quantitative assessment of facial skin folds intensity, and inter- plus intra-method consistency were determined and compared to manual evaluation. Results: Dermatologists variability was 61.49%. Intra-tests agreement varied from 4.4 to 31%. The gradient method had the best results and could detect the wrinkle conditions (p < 0.001). Also, this method had a positive correlation with the manual assessment. Conclusions: Periorbital wrinkles could be determined. Dermatologists had internal precision but low accuracy. Sobel Operator digital processing is a valid and reliable instrument for quantitative wrinkle assessment.

Integrando reconstrução 3D de imagens tomográficas e prototipagem rápida para aabricação de modelos médicos / Integrating 3D reconstruction of tomographic images and rapid prototyping for fabrication of medical models

Este trabalho descreve um sistema que integra reconstruçãotridimensional a partir de um conjunto de imagenstomográficas bidimensionais, técnicas de processamentode imagens e prototipagem rápida (RP), possibilitando acriação de modelos médicos através de dados tomográficos.Tais modelos podem ser manufaturados por meio de processosde RP e podem ser úteis em muitas aplicações médicas, tais como fabricação de próteses, diagnósticos, planejamento de tratamento ou em procedimentos cirúrgicos.Em imageamento médico, a Tomografia Computadorizada(CT) e a Ressonância Magnética (MRI) são duas técnicascomuns para capturar informação da anatomia dos pacientes.A Prototipagem Rápida é uma técnica relativamenterecente para produzir objetos com formas complexas diretamente de dados digitais tridimensionais. Estes modelos sólidos são construídos pela adição de camadas de material, camada por camada. Diversos processos de manufatura são atualmente disponíveis, tais como Modelagempor Deposição de Material Fundido (FDM), Estereolitografia(SLA) e Sinterização Seletiva a Laser (SLS). Um método de triangulação é usado para a reconstrução de imagens tridimensionais a partir de um conjunto de seçõestransversais. O sistema apresentado neste trabalho atuacomo interface entre as imagens tomográficas e a máquinade prototipagem...