Similarities between the lipid proile of Mexican patients with lupus and the general population

Premature cardiovascular events have been observed in systemic lupus erythematosus (SLE) patients, but the reason for this accelerated process is still debatable; although traditional risk factors are more prevalent in such patients than in the general population, the do not seem to fully explain that enhanced risk. One of the most important conditions is a proatherogenic lipid proile. There is not enough data about it in Mexican SLE patients. Objective: To establish the differences in the lipid proiles between Mexican patients with SLE and the general population. Material and methods: Observational, transversal, descriptive and comparative study, between SLE patients and age-sex-matched healthy volunteers. We performed a full lipid proile (by spectrophotometry) 14 hours of fast. The results obtained were analyzed by the statistical program SPSS® Statistics version 17. Results: We studied the full lipid proiles of 138 subjects, 69 with a diagnosis of SLE and 69 agesex- matched healthy volunteers; 95.7% were females and 4.3% males. Average age was 30 years; average body mass index (BMI) 25.96 ± 5.96 kg/m² in SLE patients and 26.72 ± 4.36 kg/m² in the control group (p = 0.396). Average of total cholesterol 156 mg/dl in the SLE patients and 169.4 mg/dl in the control group (p =0.028); average of low density lipoprotein (LDL) cholesterol 85.27 mg/dl in the SLE patients and 97.57 mg/dl in the control group (p = 0.023). Conclusions: We did not ind statistical differences in the lipid proiles among patients and healthy volunteers, which could explain increased cardiovascular morbidity and mortality observed in SLE patient

An Intelligent traffic network optimisation by use of Bayesian inference methods to combat air pollution

CCI Group has contributed to the researchTraffic flow related air pollution is one of the major problems in urban areas, and is often difficult to avoid it if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our introduced work here, an artificial intelligence technique such as Bayesian networks are used for a robust traffic data analysis and modelling. The most common challenge in traditional data analysis is a lack of capability of unveiling the hidden links between the distant data attributes (e.g. pollution sources, dynamic traffic parameters, geographic location characteristics, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long term basis

Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images

Cell instance segmentation in fluorescence microscopy images is becoming essential for cancer dynamics and prognosis. Data extracted from cancer dynamics allows to understand and accurately model different metabolic processes such as proliferation. This enables customized and more precise cancer treatments. However, accurate cell instance segmentation, necessary for further cell tracking and behavior analysis, is still challenging in scenarios with high cell concentration and overlapping edges. Within this framework, we propose a novel cell instance segmentation approach based on the well-known U-Net architecture. To enforce the learning of morphological information per pixel, a deep distance transformer (DDT) acts as a back-bone model. The DDT output is subsequently used to train a top-model. The following top-models are considered: a three-class (\emph{e.g.,} foreground, background and cell border) U-net, and a watershed transform. The obtained results suggest a performance boost over traditional U-Net architectures. This opens an interesting research line around the idea of injecting morphological information into a fully convolutional model.Comment: Accepted at the IWANN 2021 (International Work-Conference on Artificial and Natural Neural Networks

Application of data augmentation techniques towards metabolomics

Niemann–Pick Class 1 (NPC1) disease is a rare and debilitating neurodegenerative lysosomal storage disease (LSD). Metabolomics datasets of NPC1 patients available to perform this type of analysis are often limited in the number of samples and severely unbalanced. In order to improve the predictive capability and identify new biomarkers in an NPC1 disease urinary dataset, data augmentation (DA) techniques based on computational intelligence have been employed to create synthetic samples, i.e. the addition of noise, oversampling techniques and conditional generative adversarial networks. These techniques have been used to evaluate their predictive capacities on a set of urine samples donated by 13 untreated NPC1 disease and 47 heterozygous (parental) carrier control participants. Results on the prediction have also been obtained using different machine learning classification models and the partial least squares techniques. These results provide strong evidence for the ability of DA techniques to generate good quality synthetic data. Results acquired show increases in sensitivity of 20%–50%, an F1 score of 6%–30%, and a predictive capacity of 0.3 (out of 1). Additionally, more conventional forms of multivariate data analysis have been employed. These have allowed the detection of unusual urinary metabolite profiles, and the identification of biomarkers through the use of synthetically augmented datasets. Results indicate that urinary branched-chain amino acids such as valine, 3-aminoisobutyrate and quinolinate, may be employable as valuable biomarkers for the diagnosis and prognostic monitoring of NPC1 diseaseThe authors acknowledge the support from MINECO (Spain) through grants TIN2017-88728-C2-1-R and PID2020-116898RB-I00 (MICINN), from Universidad de Málaga y Junta de Andalucía through grant UMA20-FEDERJA-045, and from Instituto de Investigación Biomédica de Málaga – IBIMA (all including FEDER funds). Funding for open access charge: Universidad de Málaga / CBUA

Skin lesion classification by ensembles of deep convolutional networks and regularly spaced shifting

Skin lesions are caused due to multiple factors, like allergies, infections, exposition to the sun, etc. These skin diseases have become a challenge in medical diagnosis due to visual similarities, where image classification is an essential task to achieve an adequate diagnostic of different lesions. Melanoma is one of the best-known types of skin lesions due to the vast majority of skin cancer deaths. In this work, we propose an ensemble of improved convolutional neural networks combined with a test-time regularly spaced shifting technique for skin lesion classification. The shifting technique builds several versions of the test input image, which are shifted by displacement vectors that lie on a regular lattice in the plane of possible shifts. These shifted versions of the test image are subsequently passed on to each of the classifiers of an ensemble. Finally, all the outputs from the classifiers are combined to yield the final result. Experiment results show a significant improvement on the well-known HAM10000 dataset in terms of accuracy and Fscore. In particular, it is demonstrated that our combination of ensembles with test-time regularly spaced shifting yields better performance than any of the two methods when applied alone.This work is partially supported by the Ministry of Science, Innovation and Universities of Spain under grant RTI2018-094645-B-I00, project name Automated detection with low-cost hardware of unusual activities in video sequences. It is also partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name Detection of anomalous behavior agents by deep learning in low-cost video surveillance intelligent systems. All of them include funds from the European Regional Development Fund (ERDF). It is also partially supported by the University of Malaga (Spain) under grants B1-2019_02, project name Self-Organizing Neural Systems for Non-Stationary Environments, and B1-2019_01, project name Anomaly detection on roads by moving cameras. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs. The authors acknowledge the funding from the Universidad de Málaga. Funding for open access charge: Universidad de Málaga / CBUA

Foreground object detection enhancement by adaptive super resolution for video surveillance

Foreground object detection is a fundamental low level task in current video surveillance systems. It is usually accomplished by keeping a model of the background at each frame pixel. Many background learning algorithms have difficulties to attain real time operation when applied directly to the output of state of the art high resolution surveillance cameras, due to the large number of pixels. Here we propose a strategy to address this problem which consists in maintaining a low resolution model of the background which is upscaled by adaptive super resolution in order to produce a foreground detection mask of the same size as the original input frame. Extensive experimental results demonstrate the suitability of our proposal, in terms of reduction of the computational load and foreground detection accuracy.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Homography estimation with deep convolutional neural networks by random color transformations

Most classic approaches to homography estimation are based on the filtering of outliers by means of the RANSAC method. New proposals include deep convolutional neural networks. Here a new method for homography estimation is presented, which supplies a deep neural homography estimator with color perturbated versions of the original image pair. The obtained outputs are combined in order to obtain a more robust estimation of the underlying homography. Experimental results are shown, which demonstrate the adequate performance of our approach, both in quantitative and qualitative terms.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

mldr.resampling: Efficient Reference Implementations of Multilabel Resampling Algorithms

Resampling algorithms are a useful approach to deal with imbalanced learning in multilabel scenarios. These methods have to deal with singularities in the multilabel data, such as the occurrence of frequent and infrequent labels in the same instance. Implementations of these methods are sometimes limited to the pseudocode provided by their authors in a paper. This Original Software Publication presents mldr.resampling, a software package that provides reference implementations for eleven multilabel resampling methods, with an emphasis on efficiency since these algorithms are usually time-consuming

Use of Bayesian Inference Method to Model Vehicular Air Pollution in Local Urban Areas

The file attached to this record is the author's final peer reviewed version.Traffic Related Air Pollution (TRAP) studies are usually investigated using different categories such as air pollution exposure for health impacts, urban transportation network design to mitigate pollution, environmental impacts of pollution, etc. All of these subfields often rely on a robust air pollution model, which also necessitates an accurate prediction of future pollutants. As is widely accepted by the heath authorities, TRAP is considered to be the major health issue in urban areas, and it is difficult to keep pollution at harmless levels if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our work here, artificial intelligence techniques, such as Bayesian Networks with an optimized configuration, are used to deliver a probabilistic traffic data analysis and predictive modelling for air pollution (SO2, NO2 and CO) at very local scale of an urban region with up to 85% accuracy. The main challenge for traditional data analysis is a lack of capability to reveal the hidden links between distant data attributes (e.g. pollution sources, dynamic traffic parameters, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long-term basis. This study focuses on the optimisation of Bayesian Networks to unveil hidden links and to increase the prediction accuracy of TRAP considering its further association with a predictive GIS syste