Behavior patterns in hormonal treatments using fuzzy logic models

Assisted reproductive technologies are a combination of medical strategies designed to treat infertility patients. Ideal stimulation treatment has to be individualized, but one of the main challenges which clinicians face in the everyday clinic is how to select the best medical protocol for a patient. This work aims to look for behavior patterns in this kind of treatments, using fuzzy logic models with the objective of helping gynecologists and embryologists to make decisions that could improve the process of in vitro fertilization. For this purpose, a real-world dataset composed of one hundred and twenty-three (123) patients and five hundred and fifty-nine (559) treatments applied in relation to such patients provided by an assisted reproduction clinic, has been used to obtain the fuzzy models. As conclusion, this work corroborates some known clinic experiences, provides some new ones and proposes a set of questions to be solved in future experiments.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-RMinisterio de Economía y Competitividad TIN2016-76956- C3-2-RMinisterio de Economía y Competitividad TIN2015-71938-RED

Artificial intelligence tools to better understand seed dormancy and germination

Despite a large number of publications available, the control mechanisms of seed dormancy and germination are far to be fully understood. Seed dormancy and germination are very complex biological processes and because they involve multiple factors (physiological, mechanical, and environmental) and their nonlinear interactions. This explains why extremely little variations on some of those factors and in the way they interact caused enormous variation in the obtained results. Multifactorial process like these can be modeled using computer-based tools to predict better results. In this chapter, some basic concepts relative to seed dormancy and germination and the main factors (physiological, involved in seed dormancy, particularly dormancy-inducers and dormancy-breakers, and seed germination) are reviewed. In the next two, we describe the use of artificial intelligence computer-based models to better understand the physiological mechanisms of seed dormancy (how dormancy is controlled and how can be released) and seed germination. Finally, some applications of artificial neural networks, fuzzy logic, and genetic algorithms to elucidate critical factors and predict optimal condition for seed dormancy-breaking and germination are given as examples of the utility of this powerful computer-based tools

Artificial Intelligence Tools to Better Understand Seed Dormancy and Germination

Despite a large number of publications available, the control mechanisms of seed dormancy and germination are far to be fully understood. Seed dormancy and germination are very complex biological processes and because they involve multiple factors (physiological, mechanical, and environmental) and their nonlinear interactions. This explains why extremely little variations on some of those factors and in the way they interact caused enormous variation in the obtained results. Multifactorial process like these can be modeled using computer-based tools to predict better results. In this chapter, some basic concepts relative to seed dormancy and germination and the main factors (physiological, involved in seed dormancy, particularly dormancy-inducers and dormancy-breakers, and seed germination) are reviewed. In the next two, we describe the use of artificial intelligence computer-based models to better understand the physiological mechanisms of seed dormancy (how dormancy is controlled and how can be released) and seed germination. Finally, some applications of artificial neural networks, fuzzy logic, and genetic algorithms to elucidate critical factors and predict optimal condition for seed dormancy-breaking and germination are given as examples of the utility of this powerful computer-based tools

ASSESSMENT OF THE TECHNICAL AND ECONOMIC POTENTIAL OF AUTOMATED ESTRUS DETECTION TECHNOLOGIES FOR DAIRY CATTLE

Poor estrus detection can limit the reproductive performance of a dairy herd. One objective of this research was to evaluate an alternative method to traditional estrus detection in the form of automated monitoring technologies. To accomplish this, the first study considered the ability of automatically monitored parameters (activity, number of steps, lying bouts, lying time, feeding time, rumination time, and temperature) to detect estrus. A second study compared automated activity monitoring to timed artificial insemination as reproductive management strategies on commercial herds. The other objective of this research was to evaluate the economic potential of automated estrus detection technologies. This was accomplished by creating and evaluating a farm specific decision support tool to determine the net present value of adopting an automated estrus detection technology

Epilepsy

Epilepsy is the most common neurological disorder globally, affecting approximately 50 million people of all ages. It is one of the oldest diseases described in literature from remote ancient civilizations 2000-3000 years ago. Despite its long history and wide spread, epilepsy is still surrounded by myth and prejudice, which can only be overcome with great difficulty. The term epilepsy is derived from the Greek verb epilambanein, which by itself means to be seized and to be overwhelmed by surprise or attack. Therefore, epilepsy is a condition of getting over, seized, or attacked. The twelve very interesting chapters of this book cover various aspects of epileptology from the history and milestones of epilepsy as a disease entity, to the most recent advances in understanding and diagnosing epilepsy

Recent translational research: computational studies of breast cancer

The combination of mathematics – queen of sciences – and the general utility of computers has been used to make important inroads into insight-providing breast cancer research and clinical aids. These developments are in two broad areas. First, they provide useful prognostic guidelines for individual patients based on historic evidence. Second, by suggesting numeric tumor growth laws that are correlated to clinical parameters, they permit development of biologically relevant theories and comparison with patient data to help us understand complex biologic processes. These latter studies have produced many new ideas that are testable in clinical trials. In this review we discuss these developments from a clinical perspective, and ask whether and how they translate into useful tools for patient treatment

Invisible designers: Brain evolution through the lens of parasite manipulation.

The ability of parasites to manipulate host behavior to their advantage has been studied extensively, but the impact of parasite manipulation on the evolution of neural and endocrine mechanisms has remained virtually unexplored. If selection for countermeasures has shaped the evolution of nervous systems, many aspects of neural functioning are likely to remain poorly understood until parasites—the brain’s invisible designers—are included in the picture. This article offers the first systematic discussion of brain evolution in light of parasite manipulation. After reviewing the strategies and mechanisms employed by parasites, the paper presents a taxonomy of host countermeasures with four main categories, namely: Restrict access to the brain; increase the costs of manipulation; increase the complexity of signals; and increase robustness. For each category, possible examples of countermeasures are explored, and the likely evolutionary responses by parasites are considered. The article then discusses the metabolic, computational, and ecological constraints that limit the evolution of countermeasures. The final sections offer suggestions for future research and consider some implications for basic neuroscience and psychopharmacology. The paper aims to present a novel perspective on brain evolution, chart a provisional way forward, and stimulate research across the relevant disciplines

Systems Modeling to Predict Mechano-Chemo Interactions In Cardiac Fibrosis

Cardiac fibrosis poses a central challenge in preventing heart failure for patients who have suffered a cardiac injury such as myocardial infarction or aortic valve stenosis. This chronic condition is characterized by a reduction in contractile function through combined hypertrophy and excessive scar formation, and although currently prescribed therapeutics targeting hypertrophy have shown improvements in patient outcomes, pathological fibrosis remains a leading cause of reduced cardiac function for patients long-term. Cardiac fibroblasts play a key role in regulating scar formation during heart failure progression, and interacting biochemical and biomechanical cues within the myocardium guide the activation of fibroblasts and expression of extracellular matrix proteins. While targeted experimental studies of fibroblast activation have elucidated the roles of individual pathways in fibroblast activation, intracellular crosstalk between mechanotransduction and chemotransduction pathways from multiple biochemical cues has largely confounded efforts to control overall cell behavior within the myocardial environment. Computational networks of intracellular signaling can account for complex interactions between signaling pathways and provide a promising approach for identifying influential mechanisms mediating cell behavior. The overarching goal of this dissertation is to improve our understanding of complex signaling in fibroblasts by investigating the role of mechano-chemo interactions in cardiac fibroblast-mediated fibrosis using a combination of experimental studies and systems-level computational models. Firstly, using an in vitro screen of cardiac fibroblast-secreted proteins in response to combinations of biochemical stimuli and mechanical tension, we found that tension modulated cell sensitivity towards biochemical stimuli, thereby altering cell behavior based on the mechanical context. Secondly, using a curated model of fibroblast intracellular signaling, we expanded model topology to include robust mechanotransduction pathways, improved accuracy of model predictions compared to independent experimental studies, and identified mechanically dependent mechanisms-of- ction and mechano-adaptive drug candidates in a post-infarction scenario. Lastly, using an inferred network of fibroblast transcriptional regulation and model fitting to patient-specific data, we showed the utility of model-based approaches in identifying influential pathways underlying fibrotic protein expression during aortic valve stenosis and predicting patient-specific responses to pharmacological intervention. Our work suggests that computational-based approaches can generate insight into influential mechanisms among complex systems, and such tools may be promising for further therapeutic development and precision medicine

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Predicting cervical cancer biopsy results using demographic and epidemiological parameters: a custom stacked ensemble machine learning approach

The human papillomavirus (HPV) is responsible for most cervical cancer cases worldwide. This gynecological carcinoma causes many deaths, even though it can be treated by removing malignant tissues at a preliminary stage. In many developing countries, patients do not undertake medical examinations due to the lack of awareness, hospital resources and high testing costs. Hence, it is vital to design a computer aided diagnostic method which can screen cervical cancer patients. In this research, we predict the probability risk of contracting this deadly disease using a custom stacked ensemble machine learning approach. The technique combines the results of several machine learning algorithms on multiple levels to produce reliable predictions. In the beginning, a deep exploratory analysis is conducted using univariate and multivariate statistics. Later, the one-way ANOVA, mutual information and Pearson’s correlation techniques are utilized for feature selection. Since the data was imbalanced, the Borderline-SMOTE technique was used to balance the data. The final stacked machine learning model obtained an accuracy, precision, recall, F1-score, area under curve (AUC) and average precision of 98%, 97%, 99%, 98%, 100% and 100%, respectively. To make the model explainable and interpretable to clinicians, explainable artificial intelligence algorithms such as Shapley additive values (SHAP), local interpretable model agnostic explanation (LIME), random forest and ELI5 have been effectively utilized. The optimistic results indicate the potential of automated frameworks to assist doctors and medical professionals in diagnosing and screening potential cervical cancer patients