Functional anatomy of stereoscopic visual process assessed using functional magnetic resonance imaging and structural equation modelling.

The purpose of this thesis is to study the functional anatomy of stereoscopic vision. Although many studies have investigated the physiological mechanisms by which the brain transforms the retinal disparities into three-dimensional representations, the invasive nature of the techniques available have restricted them to studies in non-human primates, whilst the research on humans has been limited to psychophysical studies. Modem non-invasive neuroimaging techniques now allow the investigation of the functional organisation of the human brain. Although PET and fMRI studies have been widely used, few researchers have explored the functional anatomy of stereoscopic vision. Most of these studies appear to be pilot work, showing inconsistency, not only in the areas sensitive to stereo disparities, but also in the specific role that each of these possesses in the perception of depth. In order to investigate the cortical regions involved in stereoscopic vision, four fMRI studies were performed using anaglyph random dot stereo grams. Our results suggest that the stereo disparity processing is widespread over a network of cortical regions which include VI, V3A, V3B and B7. Functionally, the V3A region seems to be the main processing centre of pure stereo disparities and the V3B region to be engaged in motion defined purely by spatio-temporal changes of local horizontal disparities (stereoscopic -cyclopean- motion). Interregional connectivity was investigated with two approaches. Structural Equation Modelling (SEM), as the classical technique for the analysis of effective connectivity, was used to assess one connectivity model proposed to· explain the cortical interaction observed in the first experiment. The implementation and application of this technique permitted us to identify some of its weaknesses in representing fMRI data. An extension of the SEM technique was introduced as a Non-linear Auto-Regressive Moving Average with eXogenous variables (NARMAX) approach. This can be thought of as an attempt to bring SEM towards a non-linear dynamic system modelling technique which permits a more appropriate representation of effective connectivity models using fMRI time series

Berry Supplementation and Their Beneficial Effects on Some Central Nervous System Disorders

This chapter is based in the compilation and analysis of different in vitro, preclinical, and clinical studies, which explored the potential beneficial bioactivity of supplementation with berries on some alterations in the central nervous system (CNS). The last section of the chapter describes the possible mechanisms of action of polyphenols, anthocyanins, and other compounds present in berries as well as their relationship with anxiety, depression, and Alzheimer’s (AD) and Parkinson’s diseases (PD) and their implication in the prevention of cognitive decline and senescence motor functions. Electronic databases as Springer, PubMed, Scopus, and Elsevier were used. Papers were selected by topic specially those related with berries, year of publication, and authors. The present chapter evidenced the potential health effect as neuroprotector of different berries and their bioactive compounds mainly flavonoids, polyphenols, and anthocyanins, on diseases such as anxiety, depression, and Alzheimer’s and Parkinson’s diseases. In conclusion, for human nutrition berry fruit supplementation might be an excellent source of antioxidant and alternative for prevention and reduction of symptoms in diseases such as anxiety, depression, Alzheimer’s, and Parkinson’s

Vehicle Make and Model Recognition as an Open-Set Recognition Problem and New Class Discovery

One of the main limitations of traditional neural-network-based classifiers is the assumption that all query data are well represented within their training set. Unfortunately, in real-life scenarios, this is often not the case, and unknown class data may appear during testing, which drastically weakens the robustness of the algorithms. For this type of problem, open-set recognition (OSR) proposes a new approach where it is assumed that the world knowledge of algorithms is incomplete, so they must be prepared to detect and reject objects of unknown classes. However, the goal of this approach does not include the detection of new classes hidden within the rejected instances, which would be beneficial to increase the model’s knowledge and classification capability, even after training. This paper proposes an OSR strategy with an extension for new class discovery aimed at vehicle make and model recognition. We use a neuroevolution technique and the contrastive loss function to design a domain-specific CNN that generates a consistent distribution of feature vectors belonging to the same class within the embedded space in terms of cosine similarity, maintaining this behavior in unknown classes, which serves as the main guide for a probabilistic model and a clustering algorithm to simultaneously detect objects of new classes and discover their classes. The results show that the presented strategy works effectively to address the VMMR problem as an OSR problem and furthermore is able to simultaneously recognize the new classes hidden within the rejected objects. OSR is focused on demonstrating its effectiveness with benchmark databases that are not domain-specific. VMMR is focused on improving its classification accuracy; however, since it is a real-world recognition problem, it should have strategies to deal with unknown data, which has not been extensively addressed and, to the best of our knowledge, has never been considered from an OSR perspective, so this work also contributes as a benchmark for future domain-specific OSR

Anthocyanins estimation in homogeneous bean landrace (<em>Phaseolus vulgaris</em> L.) using probabilistic representation and convolutional neural networks

Studying chemical components in food of natural origin allows us to understand their nutritional contents. However, nowadays, this analysis is performed using invasive methods that destroy the sample under study. These methods are also expensive and time-consuming. Computer vision is a non-invasive alternative to determine the nutritional contents through digital image processing to obtain the colour properties. This work employed a probability mass function (PMF) in colour spaces HSI (hue, saturation, intensity) and CIE L*a*b* (International Commission on Illumination) as inputs for a convolutional neural network (CNN) to estimate the anthocyanin contents in landraces of homogeneous colour. This proposal is called AnthEstNet (Anthocyanins Estimation Net). Before applying the CNN, a methodology was used to take digital images of the bean samples and extract their colourimetric properties represented by PMF. AnthEstNet was compared against regression methods and artificial neural networks (ANN) with different characterisation in the same colour spaces. The performance was measured using precision metrics. Results suggest that AnthEstNet presented a behaviour statistically equivalent to the invasive method results (pH differential method). For probabilistic representation in channels H and S, AnthEstNet obtained a precision value of 87.68% with a standard deviation of 10.95 in the test set of samples. As to root mean square error (RMSE) and R2, this configuration was 0.49 and 0.94, respectively. On the other hand, AnthEstNet, with probabilistic representations on channels a* and b* of the CIE L*a*b* colour model, reached a precision value of 87.49% with a standard deviation of 11.84, an RMSE value of 0.51, and an R2 value of 0.93

An Image Registration Method for Colposcopic Images

A nonrigid body image registration method for spatiotemporal alignment of image sequences obtained from colposcopy examinations to detect precancerous lesions of the cervix is proposed in this paper. The approach is based on time series calculation for those pixels in the first image of the sequence and a division of such image into small windows. A search process is then carried out to find the window with the highest affinity in each image of the sequence and replace it with the window in the reference image. The affinity value is based on polynomial approximation of the time series computed and the search is bounded by a search radius which defines the neighborhood of each window. The proposed approach is tested in ten 310-frame real cases in two experiments: the first one to determine the best values for the window size and the search radius and the second one to compare the best obtained results with respect to four registration methods found in the specialized literature. The obtained results show a robust and competitive performance of the proposed approach with a significant lower time with respect to the compared methods

Neuroevolution of Convolutional Neural Networks for Breast Cancer Diagnosis Using Western Blot Strips

Breast cancer has become a global health problem, ranking first in incidences and fifth in mortality in women around the world. In Mexico, the first cause of death in women is breast cancer. This work uses deep learning techniques to discriminate between healthy and breast cancer patients, based on the banding patterns obtained from the Western Blot strip images of the autoantibody response to antigens of the T47D tumor line. The reaction of antibodies to tumor antigens occurs early in the process of tumorigenesis, years before clinical symptoms. One of the main challenges in deep learning is the design of the architecture of the convolutional neural network. Neuroevolution has been used to support this and has produced highly competitive results. It is proposed that neuroevolve convolutional neural networks (CNN) find an optimal architecture to achieve competitive ranking, taking Western Blot images as input. The CNN obtained reached 90.67% accuracy, 90.71% recall, 95.34% specificity, and 90.69% precision in classifying three different classes (healthy, benign breast pathology, and breast cancer)

Risk factors associated with gastric cancer in Mexico: education, breakfast and chili

ABSTRACT Background and aim: the aim of the study was to use a validated questionnaire to identify factors associated with the development of gastric cancer (GC) in the Mexican population. Methods: the study included cases and controls that were paired by sex and ± 10 years of age at diagnosis. In relation to cases, 46 patients with a confirmed histopathological diagnosis of adenocarcinoma-type GC, as reported in the hospital records, were selected, and 46 blood bank donors from the same hospital were included as controls. The previously validated Questionnaire to Find Factors Associated with Gastric Cancer (QUFA-GC(c)) was used to collect data. Odds ratio (OR) and 95% confidence interval (IC) were estimated via univariate analysis (paired OR). Multivariate analysis was performed by logistic regression. A decision tree was constructed using the J48 algorithm. Results: an association was found by univariate analysis between GC risk and a lack of formal education, having smoked for ≥ 10 years, eating rapidly, consuming very hot food and drinks, a non-suitable breakfast within two hours of waking, pickled food and capsaicin. In contrast, a protective association against GC was found with taking recreational exercise and consuming fresh fruit and vegetables. No association was found between the development of GC and having an income that reflected poverty, using a refrigerator, perception of the omission of breakfast and time period of alcoholism. In the final multivariate analysis model, having no formal education (OR = 17.47, 95% CI = 5.17-76.69), consuming a non-suitable breakfast within two hours of waking (OR = 8.99, 95% CI = 2.85-35.50) and the consumption of capsaicin ˃ 29.9 mg capsaicin per day (OR = 3.77, 95% CI = 1.21-13.11) were factors associated with GC. Conclusions: an association was found by multivariate analysis between the presence of GC and education, type of breakfast and the consumption of capsaicin. These variables are susceptible to intervention and can be identified via the QUFA-GC(c)

Evaluation of the Diagnostic Power of Thermography in Breast Cancer Using Bayesian Network Classifiers

Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored: thermography. In this paper, we will evaluate the diagnostic power of thermography in breast cancer using Bayesian network classifiers. We will show how the information provided by the thermal image can be used in order to characterize patients suspected of having cancer. Our main contribution is the proposal of a score, based on the aforementioned information, that could help distinguish sick patients from healthy ones. Our main results suggest the potential of this technique in such a goal but also show its main limitations that have to be overcome to consider it as an effective diagnosis complementary tool

How good is crude MDL for solving the bias-variance dilemma? An empirical investigation based on Bayesian networks.

The bias-variance dilemma is a well-known and important problem in Machine Learning. It basically relates the generalization capability (goodness of fit) of a learning method to its corresponding complexity. When we have enough data at hand, it is possible to use these data in such a way so as to minimize overfitting (the risk of selecting a complex model that generalizes poorly). Unfortunately, there are many situations where we simply do not have this required amount of data. Thus, we need to find methods capable of efficiently exploiting the available data while avoiding overfitting. Different metrics have been proposed to achieve this goal: the Minimum Description Length principle (MDL), Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC), among others. In this paper, we focus on crude MDL and empirically evaluate its performance in selecting models with a good balance between goodness of fit and complexity: the so-called bias-variance dilemma, decomposition or tradeoff. Although the graphical interaction between these dimensions (bias and variance) is ubiquitous in the Machine Learning literature, few works present experimental evidence to recover such interaction. In our experiments, we argue that the resulting graphs allow us to gain insights that are difficult to unveil otherwise: that crude MDL naturally selects balanced models in terms of bias-variance, which not necessarily need be the gold-standard ones. We carry out these experiments using a specific model: a Bayesian network. In spite of these motivating results, we also should not overlook three other components that may significantly affect the final model selection: the search procedure, the noise rate and the sample size