Te menstrual cycle is the result of an orchestra of hormones. It involves the interaction of many endocrine glands as well as a responsive uterus. Te menstrual cycle remains a complex process where many aspects are still not well understood. In this chapter we will examine the control of the menstrual cycle through the interaction of the central nervous system, namely, the hypothalamus and pituitary, and the ovaries, resulting in the cyclic and ordered sloughing of the uterine endometrial lining. Te frst section of this chapter, Te Menstrual Cycle, will review the phases of the menstrual cycle. In the second section, Anatomy of the Menstrual Cycle, the hypothalamic, pituitary, ovarian, and uterine activities will be reviewed. Te key hormones that play a role in the control of the menstrual cycle include gonadotropin-releasing hormone (GnRH), follicle- stimulating hormone (FSH), luteinizing hormone (LH), estradiol, and progesterone (. Table 1.1). In addition to these key hormones, there are other peptide and non-peptide hormones that play a role in the menstrual cycle that will also be discussed. Tese hormones will be discussed in the third section, Endocrinology of the Menstrual Cycle.

Over millions of years of human evolution, dietary fber (fber) has played an important role in maintaining healthy colonic microbiota and laxation, energy and cardiometabolic processes, and immune and inflammatory signaling required for human health and chronic disease prevention. • It is estimated that approximately 95% of populations consuming Western diets eat an inadequate daily fber level. This fber gap can adversely affect human health via increased risk of weight gain and dysfunctional colonic microbiota, contributing to global obesity and chronic disease pandemics. • Fiber comes from whole or minimally processed plant foods including whole grains, fruits, vegetables, legumes, nuts, and seeds eaten as part of a healthy diet; isolated from natural sources (e.g., β-glucan, psyllium); or from synthetic forms (e.g., polydextrose, methylcellulose) added to processed foods or consumed as dietary supplements. Fiber food functionality or health effects depend on the specifc fber's composition, physical properties, and degree and rate of fermentation in the colon. • The effects of increased fber intake and its fermentation metabolites can result in reduced risk of obesity, cardiometabolic chronic diseases and related premature mortality, and improved odds for healthy aging. • No tolerable upper limit has been set for fber intake in healthy individuals, but excessive intake of some highly fermentable fbers may increase risk of flatulence and gastrointestinal distress in sensitive individuals. When increasing fber intake, it is recommended to do so gradually along with increasing fluid intake to help allow the gastrointestinal tract to adapt and to take medication at least 1 h before or 2 h after fber-rich foods or supplements are consumed to avoid possible fberdrug interactions.

This paper proposes a novel supervised learning method for single-layer feedforward neural networks. This approach uses an alternative objective function to that based on the MSE, which measures the errors before the neuron’s nonlinear activation functions instead of after them. In this case, the solution can be easily obtained solving systems of linear equations, i.e., requiring much less computational power than the one associated with the regular methods. A theoretical study is included to proof the approximated equivalence between the global optimum of the objective function based on the regular MSE criterion and the one of the proposed alternative MSE function. Furthermore, it is shown that the presented method has the capability of allowing incremental and distributed learning. An exhaustive experimental study is also presented to verify the soundness and efficiency of the method. This study contains 10 classification and 16 regression problems. In addition, a comparison with other high performance learning algorithms shows that the proposed method exhibits, in average, the highest performance and low-demanding computational requirements.