Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of ...synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.
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•ABC algorithm discovered the best set of genes to classify correctly cancer samples.•With less than 1% of information is possible to classify with an accuracy of 93.2%.•Multilayer ...perceptron performs better than radial basis function network.
DNA microarray is an efficient new technology that allows to analyze, at the same time, the expression level of millions of genes. The gene expression level indicates the synthesis of different messenger ribonucleic acid (mRNA) molecule in a cell. Using this gene expression level, it is possible to diagnose diseases, identify tumors, select the best treatment to resist illness, detect mutations among other processes. In order to achieve that purpose, several computational techniques such as pattern classification approaches can be applied. The classification problem consists in identifying different classes or groups associated with a particular disease (e.g., various types of cancer, in terms of the gene expression level). However, the enormous quantity of genes and the few samples available, make difficult the processes of learning and recognition of any classification technique. Artificial neural networks (ANN) are computational models in artificial intelligence used for classifying, predicting and approximating functions. Among the most popular ones, we could mention the multilayer perceptron (MLP), the radial basis function neural network (RBF) and support vector machine (SVM). The aim of this research is to propose a methodology for classifying DNA microarray. The proposed method performs a feature selection process based on a swarm intelligence algorithm to find a subset of genes that best describe a disease. After that, different ANN are trained using the subset of genes. Finally, four different datasets were used to validate the accuracy of the proposal and test the relevance of genes to correctly classify the samples of the disease.
•We developed a technique for extracting phenolic compounds for quantification.•The technique was applied to goat, cow, sheep and human milk.•The technique was validated, and it can be used in ...routine analyses of milk.•The variability of TPCs among species and among members of the same species is high.
Milk protects the health of newborns because it contains essential compounds that perform metabolic activities. Despite these benefits, the study of phenolic compounds in milk has been poorly explored. The objective of this study was to develop and validate a technique for extracting total phenolic compounds (TPCs) from a milk matrix and then analyzing them using the Folin–Ciocalteu method. The extraction technique was applied to goat milk and involved the addition of methanol, acetonitrile, and Carrez I and II reagents, after which protein was separated from fat through centrifugation. Subsequently, the technique was applied to goat (69.03±6.23mg GAE/L), cow (49.00±10.77mg GAE/L), sheep (167.6±58.77mg GAE/L) and human milk (82.45±12.3mg GAE/L). The technique showed an acceptable linearity (R2=0.9998), limit of detection (6.03mg GAE/L) and quantification (16.2mg GAE/L), repeatability (RSD=4%), reproducibility (RSD=6.8%) and recovery (>85.41%); it is thus effective and can be used in the routine analysis of milk. TPCs obtained from each type of milk indicate a high variability among species and among members of the same species.
Recently, it has been proven that spiking neurons can be used for some pattern recognition problems. Nonetheless, the spiking neurons models have many parameters that have to be manually adjusted in ...order to achieve the desired behavior. This paper has the purpose of showing an optimization method for one such model, the Integrate & Fire spiking model (I&F). A genetic algorithm (GA) is proposed to automatically adjust the parameters, removing the need of manual tuning and increasing efficiency. Initial experimentation is done by tuning the I&F model parameters by hand, to confirm the importance and relevance of determining the best parameter values. The GA is then used to automatically tune different parameter combinations of the pattern recognition model, which uses the I&F neuron as core, to determine which parameters are worth including in the GA. The proposed method was tested with five different datasets, where no change was required to apply the model to each. Very good results were achieved in all test cases, but experiments where parameters of the neuron model were included converged faster.
ELIC is a prokaryotic homopentameric ligand-gated ion channel that is homologous to vertebrate nicotinic acetylcholine receptors. Acetylcholine binds to ELIC but fails to activate it, despite ...bringing about conformational changes indicative of activation. Instead, acetylcholine competitively inhibits agonist-activated ELIC currents. What makes acetylcholine an agonist in an acetylcholine receptor context, and an antagonist in an ELIC context, is not known. Here we use available structures and statistical coupling analysis to identify residues in the ELIC agonist-binding site that contribute to agonism. Substitution of these ELIC residues for their acetylcholine receptor counterparts does not convert acetylcholine into an ELIC agonist, but in some cases reduces the sensitivity of ELIC to acetylcholine antagonism. Acetylcholine antagonism can be abolished by combining two substitutions that together appear to knock out acetylcholine binding. Thus, making the ELIC agonist-binding site more acetylcholine receptor-like, paradoxically reduces the apparent affinity for acetylcholine, demonstrating that residues important for agonist binding in one context can be deleterious in another. These findings reinforce the notion that although agonism originates from local interactions within the agonist-binding site, it is a global property with cryptic contributions from distant residues. Finally, our results highlight an underappreciated mechanism of antagonism, where agonists with appreciable affinity, but negligible efficacy, present as competitive antagonists.
•The composition of goat milk derivatives varies depending on feeding system and pasteurization.•Grazing systems may increase TPC and AC in goat milk and derivatives.•Dry season may increase TPC and ...AC in goat milk and derivatives.•Pasteurization causes a decrease in TPC concentrations and AC.
Phenolic compounds are present in goat milk and cheese. The composition of goat milk and its products may vary depending on factors such as season, feeding system and heat treatment. The aim of this work is to quantify total phenolic compounds (TPC) and antioxidant capacity in pasteurized and unpasteurized samples of milk, milk whey, and cheese from goats fed in two different systems (free-range grazing and permanent confinement), during dry and rainy seasons. TPC concentrations were highest in unpasteurized samples from dry season compared to pasteurized and rainy season: 132.4 ± 27.3, 76.5 ± 5.77 mg of gallic acid equivalent (GAE)/L for unpasteurized milk and milk whey, respectively, and 363.21 ± 52.97 mg GAE/Kg for cheese. Antioxidant capacity for dry season produce was significantly higher (P < 0.05) than rainy season produce. Free-range grazing was found to be a good option for producing a higher concentration of phenolic compounds and a higher antioxidant capacity.
Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern ...recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy.
Purpose:
The aim of this study was to evaluate the demographic characteristics, clinical and pathological factors, and the outcome of cancer and COVID-19 patients in Mexico.
Patients and methods:
A ...prospective, multicentric study was performed through a digital platform to have a national registry of patients with cancer and positive SARS-CoV-2 test results through reverse transcription quantitative polymerase chain reaction (RT-qPCR). We performed the analysis through a multivariate logistic regression model and Cox proportional hazard model.
Results:
From May to December 2020, 599 patients were registered with an average age of 56 years with 59.3% female; 27.2% had hypertension. The most frequent diagnoses were breast cancer (30.4%), lymphoma (14.7%), and colorectal cancer (14.0%); 72.1% of patients had active cancer and 23.5% of patients (141/599) were deceased, the majority of which were men (51.7%). This study found that the prognostic factors that reduced the odds of death were gender (OR = 0.42, p = 0.031) and oxygen saturation (OR = 0.90, p = 0.0001); meanwhile, poor ECOG (OR = 5.4, p = 0.0001), active disease (OR = 3.9, p = 0.041), dyspnea (OR = 2.5, p = 0.027), and nausea (OR = 4.0, p = 0.028) increased the odds of death. In the meantime, the factors that reduce survival time were age (HR = 1.36, p = 0.035), COPD (HR = 8.30, p = 0.004), having palliative treatment (HR = 10.70, p = 0.002), and active cancer without treatment (HR = 8.68, p = 0.008).
Conclusion:
Mortality in cancer patients with COVID-19 is determined by prognostic factors whose identification is necessary. In our cancer population, we have observed that being female, younger, non-COPD, with non-active cancer, good performance status, and high oxygen levels reduce the probability of death.
Purpose of Review
The objective of this review was to summarize the current scientific evidence of mobile health technology in the primary prevention of type 2 diabetes in patients with prediabetes ...derived from randomized clinical trials.
Recent Findings
Few randomized clinical trials are available using mobile health technologies in the prevention of type 2 diabetes. There is heterogeneity in regard to the main study outcomes, duration of interventions, and study findings.
Summary
Inconsistent findings have been reported whether mobile health technologies are effective in reducing HbA1C levels or the incidence of type 2 diabetes in patients with prediabetes. However, results are promising that mobile health interventions may decrease body weight. Future study may consistently measure changes in glycemic indicators as well as develop elements that better address behavior changes.
In the development of a brain-computer interface (BCI), some issues should be regarded in order to improve its reliability and performance. Perhaps, one of the most challenging issues is related to ...the high variability of the brain signals, which directly impacts the accuracy of the classification. In this sense, novel feature extraction techniques should be explored in order to select those able to face this variability. Furthermore, to improve the performance of the selected feature extraction technique, the parameters of the filter applied in the preprocessing stage need to be properly selected. Then, this work presents an analysis of the robustness of the fractal dimension as feature extraction technique under high variability of the EEG signals, particularly when the training data are recorded one day and the testing data are obtained on a different day. The results are compared with those obtained by an autoregressive model, which is a technique commonly used in BCI applications. Also, the effect of properly selecting the cutoff frequencies of the filter in the preprocessing stage is evaluated. This research is supported by several experiments carried out using a public data set from the BCI international competition, specifically data set 2a from BCIIC IV, related to motor tasks. By a statistical test, it is demonstrated that the performance achieved using the fractal dimension is significantly better than that reached by the AR model. Also, it is demonstrated that the selection of the appropriate cutoff frequencies improves significantly the performance in the classification. The increase rate is approximately of 17%.
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