Miracle-dz/UniFAR-FTUs huggingface dataset:
Suporte para Diagnóstico de COVID-19 por Meio de Classificação Automática de Imagens de Raio-x e Modelos Explicáveis

O combate contra a pandemia causada pela COVID-19 é atualmente um dos maiores desafios enfrentados pela humanidade. Um fator chave que tende a desacelerar a disseminação do vírus é o diagnóstico rápido e o isolamento dos pacientes infectados. Pesquisadores em todo mundo vêm buscando formas alternativas ao exame PCR-RT para o diagnóstico da doença, e o aprendizado profundo aplicado às radiografias de tórax de pacientes suspeitos tem mostrado resultados favoráveis. A falta de dados de qualidade e o elevado número de modelos enviesados tornam as soluções propostas um problema. Dessa forma, o presente trabalho apresenta um sistema de diagnóstico automático de COVID-19 por meio de imagens de raio-x, com o objetivo de auxiliar a tomada de decisão do médico. Foi feito um amplo estudo para a seleção de conjuntos de imagens de qualidade de forma que a solução não ficasse enviesada. Para a aplicação, primeiro os pulmões são segmentados pelo método U-Net para que, em seguida, a imagem seja classificada por um conjunto de modelos treinados para uma classificação binária. Por fim, os resultados são explicados de forma a auxiliar o médico em sua tomada de decisão. Os resultados obtidos com uma taxa de assertividade em 92% mostram que a solução proposta é promissora. Estes resultados indicam que a capacidade do modelo em avaliar os pacientes infectados por COVID-19 pode ser usada no gerenciamento de recursos referentes ao atendimento em hospitais e postos de saúde, bem como auxiliar gestores públicos na tomada de decisões relativas à restrição de atividades para promover o isolamento social.
A Comparative Analysis of Punicalagin Interaction with PDIA1 and PDIA3 by Biochemical and Computational Approaches

In a previous work, it was shown that punicalagin, an active ingredient of pomegranate, is able to bind to PDIA3 and inhibit its disulfide reductase activity. Here we provide evidence that punicalagin can also bind to PDIA1, the main expressed form of protein disulfide isomerase (PDI). In this comparative study, the affinity and the effect of punicalagin binding on each protein were evaluated, and a computational approach was used to identify putative binding sites. Punicalagin binds to either PDIA1 or PDIA3 with a similar affinity, but the inhibition efficacy on protein reductase activity is higher for PDIA3. Additionally, punicalagin differently affects the thermal denaturation profile of both proteins. Molecular docking and molecular dynamics simulations led to propose a punicalagin binding mode on PDIA1 and PDIA3, identifying the binding sites at the redox domains a’ in two different pockets, suggesting different effects of punicalagin on proteins’ structure. This study provides insights to develop punicalagin-based ligands, to set up a rational design for PDIA3 selective inhibitors, and to dissect the molecular determinant to modulate the protein activity.
A greenhouse modeling and control using deep neural networks

ABSTRACT Deep learning approaches have attracted a lot of interest and competition in a variety of fields. The major goal is to design an effective deep learning process in automatic modeling and control field. In this context, our aim is to ameliorate the modeling and control tasks of the greenhouse using deep neural network techniques. In order to emulate the direct dynamics of the system an Elman neural network has been trained and a deep multi-layer perceptron (MLP) neural network has been formed in order to reproduce its inverse dynamics and then used as a neural controller. This later was been associated in cascade with the deep Elman neural model to control the greenhouse internal climate. After performing experiments, simulation results show that the best performances were obtained when we have used a neural controller having two hidden layers and an Elman neural model with two hidden and context layers.
Genetic diversity in a population of creole maize (Zea mays L.) evaluated by microsatellite markers in Puerto Libertador, Córdoba

Maize a plant of Mesoamerican origin, has evolved in different microenvironments, generating the great diversity of maize that exists in the world. In order to determine the genetic diversity of a population of Creole maize, twelve microsatellite markers were evaluated in 30 accessions, in Puerto Libertador, Córdoba. The DNA of each accession was extracted using the PROMEGA kit, the markers were amplified by the PCR technique and the amplicons were run on polyacrylamide gels, the gels were digitalized and the molecular sizes were determined by an exponential model. Results showed a total of 66 alleles and an average of alleles of 5.5, the expected heterozygosity was 0.655, the values of the polymorphic information content (PIC) ranged from 0.352 to 0.838, with an average of 0.592 and the Hardy-Weinberg equilibrium showed imbalance (p <0.05). This work revealed that the studied accessions of Creole maize showed a high degree of polymorphism, high genetic variability and microsatellite markers were the appropriate for the evaluation of genetic diversity. This information shows to be useful for the conservation and protection of the genetic diversity of the studied Creole Maize.
Lithospheric structure beneath the Qinling Orogenic Belt and its surrounding regions: Implications for regional lithosphere deformation

We present a lithospheric shear‐wave velocity model for the Qinling Orogenic Belt (QOB) and its surrounding regions through joint inversion of surface wave dispersion and receiver functions. Our model reveals large‐scale extremely low‐velocity zones (Vs ≤ 3.4 km/s) beneath the Northeastern Tibetan Plateau without further eastward extension, implying the absence ductile crustal flow beneath the eastern part of QOB. Pronounced upper‐mantle low‐velocity anomalies are imaged beneath the North Qinling and Weihe basin, suggesting the existence of deep mantle upwellings that lead to the extensional tectonics in these regions. A high‐velocity zone running continuously from the Alxa block to the eastern part of Qilian orogen in the uppermost mantle is revealed. We identify the high‐velocity zone to be the Asian mantle lithosphere and infer the development of southward underthrusting towards Qilian orogen.
Radiotherapy-activated cancer-associated fibroblasts promote tumor progression through paracrine IGF-1 R activation Running title : Irradiated CAF promote tumor progression

Joke Tommelein , Elly De Vlieghere , Laurine Verset, Elodie Melsens , Justine Leenders, Benedicte Descamps, Annelies Debucquoy, Christian Vanhove, Patrick Pauwels, Christian P. Gespach, Anne Vral, Astrid De Boeck, Karin Haustermans, Pascal de Tullio, Wim Ceelen , Pieter Demetter, Tom Boterberg , Marc Bracke , Olivier De Wever * Laboratory of Experimental Cancer Research, Department of Radiation Oncology and Experimental Cancer Research, Ghent University, Ghent, Belgium Cancer Research Institute Ghent (CRIG), Ghent, Belgium Department of Pathology, Erasme University Hospital, Université Libre de Bruxelles, Brussels, Belgium Department of Surgery, Ghent University Hospital, Ghent, Belgium Center for Interdisciplinary Research on Medicines (CIRM), Université de Liège, Liège, Belgium Department of Electronics and Information System, iMinds-IBiTech-MEDISIP, Ghent University, Ghent, Belgium 7 KU Leuven, University of Leuven, Department of Oncology, Experimental Radiotherapy; University Hospitals Leuven, Radiation Oncology, Leuven, Belgium Center for Oncological Research (CORE), University of Antwerp, Antwerp, Belgium Institut National de la Santé et de la Recherche Médicale, INSERM U938, Molecular and Clinical Oncology, Université Paris VI Pierre et Marie Curie, Paris, France Department of Basic Medical Sciences, Physiology Group, Ghent University, Ghent, Belgium Arnie Charbonneau Cancer Centre, University of Calgary, Calgary, AB, Canada
and J . hierarchy process Defining desired genetic gains for rainbow trout breeding objective using analytic

P. Sae-Lim, H. Komen, A. Kause, J. A. M. van Arendonk, A. J. Barfoot, K. E. Martin and J. hierarchy process Defining desired genetic gains for rainbow trout breeding objective using analytic doi: 10.2527/jas.2011-4267 originally published online December 16, 2011 2012, 90:1766-1776. J ANIM SCI http://www.journalofanimalscience. org/content/90/6/1766 the World Wide Web at: The online version of this article, along with updated information and services, is located on
Generalized Image Reconstruction in Optical Coherence Tomography Using Redundant and Non-Uniformly-Spaced Samples

In this paper, we use Frame Theory to develop a generalized OCT image reconstruction method using redundant and non-uniformly spaced frequency domain samples that includes using non-redundant and uniformly spaced samples as special cases. We also correct an important theoretical error in the previously reported results related to OCT image reconstruction using the Non-uniform Discrete Fourier Transform (NDFT). Moreover, we describe an efficient method to compute our corrected reconstruction transform, i.e., a scaled NDFT, using the Fast Fourier Transform (FFT). Finally, we demonstrate different advantages of our generalized OCT image reconstruction method by achieving (1) theoretically corrected OCT image reconstruction directly from non-uniformly spaced frequency domain samples; (2) a novel OCT image reconstruction method with a higher signal-to-noise ratio (SNR) using redundant frequency domain samples. Our new image reconstruction method is an improvement of OCT technology, so it could benefit all OCT applications.
Local Constraint and Label Embedding Multi-layer Dictionary Learning for Sperm Head Classification

Morphological classification of human sperm heads is a key technology for diagnosing male infertility. Due to its sparse representation and learning capability, dictionary learning has shown remarkable performance in human sperm head classification. To promote the discriminability of the classification model, a novel local constraint and label embedding multi-layer dictionary learning model called LCLM-MDL is proposed in this study. Based on the multi-layer dictionary learning framework, two dictionaries are built on the basis of Laplacian regularized constraint and label embedding term in each layer, and the two dictionaries are approximated to each other as much as possible, so as to well exploit the nonlinear structure and discriminability features of the morphology of human sperm heads. In addition, to promote the robustness of the model, the asymmetric Huber loss is adopted in the last layer of LCLM-MDL, which approximates the misclassification error by using the absolute error function. Finally, the experimental results on HuSHeM dataset demonstrate the validity of the LCLM-MDL.
Prevention of Runtime Malware Injection Attack in Cloud Using Unsupervised Learning

Cloud computing utilizes various Internet-based technologies to enhance the Internet user experience. Cloud systems are on the rise, as this technology has completely revolutionized the digital industry. Currently, many users rely on cloud-based solutions to acquire business information and knowledge. As a result, cloud computing services such as SaaS and PaaS store a warehouse of sensitive and valuable information, which has turned the cloud systems into the obvious target for many malware creators and hackers. These malicious attackers attempt to gain illegal access to a myriad of valuable information such as user personal information, password, credit/debit card numbers, etc., from systems as the unsecured e-learning ones. As an important part of cloud services, security is needed to protect business customers and users from unauthorized threats. This paper aims to identify malware that attacks cloud-based software solutions using an unsupervised learning model with fixed-weight Hamming and Mexiannet. Different types of attack methodologies and various ways of malicious instructions targeting unknown files in cloud services are investigated. The result and analysis in this study provide an evolution of the unsupervised learning detection algorithm with an accuracy of 94.05%.
Investigation on Fabrication of SS316 Gear by Wire-EDM

Manufacturing of miniature gears with good accuracy and quality, and at low cost is a challenging task for traditional manufacturing processes. Wire-cut electric discharge machining (WEDM) has the capacity to machine any conductive material to fabricate typical shaped products like gears with high level of accuracy and quality. In present research, stainless steel (SS) alloy 316 is used to fabricate miniature gears by WEDM. The experimental array is designed according to Response Surface Methodology (RSM) based central composite face centered (CCFC). Four input parameters namely servo feed (SF), duty factor (DF), servo voltage (SV) and wire feed (WF) with three levels each are used for experimental array to investigate the process performance in terms of cutting rate (CR), and product i.e. gear quality in terms of mean roughness depth (Rz) and dimensional deviation (DD). After WEDM parameter optimization, confirmation experiments are performed at the parametric setting by considering equal importance to all response variables. A very close agreement between the experimental and predicted values have been observed.
The Importance of Being a ‘Lark’ in Post-Menopausal Women with Obesity: A Ploy to Prevent Type 2 Diabetes Mellitus?

Chronotype is defined as the behavioral manifestation of circadian rhythms related to the external light–dark cycle. Evening chronotype has been associated with an increased risk of developing cardiometabolic diseases in obesity. Menopause is a lifestage associated with an increased risk of developing cardiometabolic diseases and a change in circadian rhythmicity compared to pre-menopause. However, the prevalence of chronotype categories in menopause and their role in determining menopause-related cardiometabolic risk, mostly in obesity, have not been investigated. Thus, we aimed to investigate the prevalence of chronotype categories in post-menopausal women with obesity and their role in menopause-related cardiometabolic risk. In this cross-sectional study we enrolled 49 pre-menopausal and 74 post-menopausal women with obesity. Anthropometric parameters, lifestyle habits, adherence to the Mediterranean Diet (MD), sleep quality, chronotype and the presence of type 2 diabetes mellitus (T2DM) and cardiovascular diseases (CVD) were studied. No significance differences were detected in terms of lifestyle and adherence to the MD between pre- and post-menopausal women. Chronotype was classified as morning in 66 (53.6%), evening in 20 (16.3%) and intermediate in 37 (30.1%) women. In addition, pre-menopausal women with obesity showed a significantly higher chance to have an intermediate chronotype (OR = 2.21, 95% CI 1.28–3.83; p = 0.004), whereas post-menopausal women with obesity showed a trend to have a higher morning chronotype (OR = 1.42, 95% CI 0.98–2.06; p = 0.051), although this did not reach statistical significance. No significant differences were detected in terms of prevalence of evening chronotype between the two groups. However, the evening chronotype had a significantly higher risk to have T2DM compared to the morning (OR = 17.29, 95% CI 2.40–124.27; p = 0.005) and intermediate chronotypes (OR = 30.86, 95% CI 2.05–464.32; p = 0.013) in both pre- and post-menopausal women with obesity. In conclusion, the intermediate chronotype was significantly more prevalent in pre-menopausal women with obesity compared to post-menopausal women. Evening chronotype was associated to T2DM in both pre- and post-menopause. These results support the importance of including the assessment of chronotype in the management of women with obesity in post-menopause.
Omnidirectional Fingertip Pressure Sensor Using Hall Effect

When grasping objects with uneven or varying shapes, accurate pressure measurement on robot fingers is critical for precise robotic gripping operations. However, measuring the pressure from the sides of the fingertips remains challenging owing to the poor omnidirectionality of the pressure sensor. In this study, we propose an omnidirectional sensitive pressure sensor using a cone-shaped magnet slider and Hall sensor embedded in a flexible elastomer, which guarantees taking pressure measurements from any side of the fingertip. The experimental results indicate that the proposed pressure sensor has a high sensitivity (61.34 mV/kPa) in a wide sensing range (4–90 kPa) without blind spots on the fingertip, which shows promising application prospects in robotics.
The perceived risk of illicit drug use and views on drug policy in the general population

Abstract This article analyzes the riskiness attached to illicit drug use by means of a social representations framework. The implications of these social representations are further studied through their associations with views on drug policy: restrictive control and harm reduction measures. The data for the study is from a Finnish Drug Survey (N = 3229). Latent class analysis (LCA) and multinomial logistic regression analysis were used to analyze the data. Four representational profiles of perceived risk were identified: high-risk (70% of respondents), cannabis OK (15%), experimenting OK (10%), and low-risk (2%). All illicit drug use was considered as a moderate or high risk in the high-risk profile. Cannabis was considered less risky than other substances in the cannabis OK profile and experimenting was a less risky way of use in the experimenting OK profile. Most people in the low-risk profile considered the risks related to illicit drug use as nonexistent or minor. These representational profiles were also connected to opinions on drug policy: those who saw the most risks with use tended to support restrictive control measures, while those who considered illicit drug use to be less risky were more accepting toward harm reduction measures.
Government Debt: An Empirical Analysis with Structural Break for The Economy of Malaysia

Malaysia recorded a fiscal policy with a deficit budget since the economic crisis in 1997. During the tough economic crisis to maintain economic growth, the government committed to stimulus fiscal budget controlling. According to the Keynesian theory, a fiscal policy may induce economic growth at moderate levels of public debt. Although debt and economic growth are not recent issues faced by the Malaysia government, the impact government debt on economic growth has been debated, especially in terms of imbalances development. This work aims to investigate the relationship between economic growth and government debt in Malaysia, with the application of structural break analysis. We utilized econometric techniques such as unit root tests and Granger. Regarding the unit root test, we used structural break combinations with pure and partial structural change models. The analysis is based on data across the period of 1970 to 2018. We find more evidence against the unit root test with structural breaks in Malaysia, with causality between debt and economic growth.
Environmental flow assessment in the context of climate change: a case study in Southern Quebec (Canada)

Through a case study in Southern Quebec (Canada), the assessment of environmental flows in light of the effects of climate change is investigated. Currently, the 7Q2 flow metric (7-day average flow with a 2-year return period) is used for water abstraction management. Several flow metrics were calculated using flow time series simulated by a deterministic hydrological model (HYDROTEL) and climate change scenarios as inputs. Results were compared within homogeneous low flow regions defined using ascendant hierarchical clustering, for the 1990, 2020 and 2050 horizons and annual, summer and winter periods. The impact of each flow metric on the potential availability of physical habitat was analyzed using the wetted perimeter as a proxy. Results indicated that: (1) the increasing non-stationarity of simulated flow data sets over time will complicate the use of frequency analysis to calculate the 7Q2 flow metric; (2) summer low flow values are expected to be lower than winter low flows; and (3) flow-duration curve metrics like the LQ50 (median discharge value of the month with the lowest flow) may become relevant environmental flow metrics by 2050. Results question current water abstraction management tools and permit to anticipate future local and regional issues during low flow periods.
Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models

Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accuracy and low-power indoor positioning systems that can provide accuracy of less than 5 m and enable battery operation for mobility and long-term use. We propose and implement an intelligent, highly accurate and low-power indoor positioning system for smart homes leveraging Gaussian Process Regression (GPR) model using information-theoretic gain based on reduction in differential entropy. The system is based on Time Difference of Arrival (TDOA) and uses ultra-low-power radio transceivers working at 434 MHz. The system has been deployed and tested using indoor measurements for two-dimensional (2D) positioning. In addition, the proposed system provides dual functionality with the same wireless links used for receiving telemetry data, with configurable data rates of up to 600 Kbauds. The implemented system integrates the time difference pulses obtained from the differential circuitry to determine the radio frequency (RF) transmitter node positions. The implemented system provides a high positioning accuracy of 0.68 m and 1.08 m for outdoor and indoor localization, respectively, when using GPR machine learning models, and provides telemetry data reception of 250 Kbauds. The system enables low-power battery operation with consumption of <200 mW power with ultra-low-power CC1101 radio transceivers and additional circuits with a differential amplifier. The proposed system provides low-cost, low-power and high-accuracy indoor localization and is an essential element of public well-being in future smart homes.
A Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning

Recently, Electroencephalography (EEG) motor imagery (MI) signals have received increasing attention because it became possible to use these signals to encode a person’s intention to perform an action. Researchers have used MI signals to help people with partial or total paralysis, control devices such as exoskeletons, wheelchairs, prostheses, and even independent driving. Therefore, classifying the motor imagery tasks of these signals is important for a Brain-Computer Interface (BCI) system. Classifying the MI tasks from EEG signals is difficult to offer a good decoder due to the dynamic nature of the signal, its low signal-to-noise ratio, complexity, and dependence on the sensor positions. In this paper, we investigate five multilayer methods for classifying MI tasks: proposed methods based on Artificial Neural Network, Convolutional Neural Network 1 (CNN1), CNN2, CNN1 with CNN2 merged, and the modified CNN1 with CNN2 merged. These proposed methods use different spatial and temporal characteristics extracted from raw EEG data. We demonstrate that our proposed CNN1-based method outperforms state-of-the-art machine/deep learning techniques for EEG classification by an accuracy value of 68.77% and use spatial and frequency characteristics on the BCI Competition IV-2a dataset, which includes nine subjects performing four MI tasks (left/right hand, feet, and tongue). The experimental results demonstrate the feasibility of this proposed method for the classification of MI-EEG signals and can be applied successfully to BCI systems where the amount of data is large due to daily recording.