Ovarian function during hormonal contraception assessed by endocrine and sonographic markers: a systematic review

This systematic review focuses on the literature evidence for residual ovarian function during treatment with hormonal contraceptives. We reviewed all papers which assessed residual ovarian activity during hormonal contraceptive use, using endocrine markers such as serum anti-Müllerian hormone (AMH) concentrations, FSH, LH, oestradiol, progesterone and sonographic markers such as antral follicle count (AFC), ovarian volume and vascular indices. We considered every type (oestroprogestin or only progestin) and dosage of hormonal contraceptive and every mode of administration (oral, vaginal ring, implant, transdermal patch). We performed an electronic database search for papers published from 1 January 1990 until 30 November 2015 using PubMed and MEDLINE. We pre-selected 113 studies and judged 48 studies suitable for the review. Most studies showed that follicular development continues during treatment with hormonal contraceptives, and that during treatment there is a reduction in serum concentrations of FSH, LH and oestradiol, and also a reduction in endometrial thickness, ovarian volume and the number and size of antral follicles. The ovarian reserve parameters, namely AFC and ovarian volume, are lower among users than among non-users of hormonal contraception; regarding the effect of hormonal contraception on AMH, there are still controversies in the literature

Healing dolphins? Cognitive and perceptual criticisms in Dolphin-Assisted Therapy

Since the '70s new therapeutic practices have been developed, involving the interaction between humans and dolphins - Tursiops truncatus in particular. Such practices are known as Dolphin-Assisted Therapies (DAT), a specific case of a more heterogeneous set of experiences with cetaceans called Dolphin-Assisted Activities (DAA). These include programs of dolphin watching and swimming in high seas, and shows in dolphinariums and marine parks. Although the promoters of this type of practices highlight the physiological, psychological and cognitive benefits on human participants, such putative positive effects have not been experimentally validated yet [1]. Studies supporting DAT seriously suffer from theoretical and methodological flaws, such as the small sample size, the lack of control on effects of exercising in aquatic environment and of control groups, the absence of a randomization of participants [2,3]. Human-dolphin interactions are characterized by two sets of perceptual and cognitive misinterpretations. On one side, humans are neglecting the animal\u2019s psycho-physiological dimension [4]. DAT causes suffering on several levels: physical (respiratory, peptic and vision diseases, stress-related disorders), behavioral (aberrant, hyper-sexual and stereotyped behaviors, unresponsiveness, self-inflicted trauma, excessive aggressiveness) and social (alteration of hierarchies, limitations of sexual partners) [4-5]. Even in the open water, cetaceans followed by the boats and approached by swimmers are disturbed by noises and human inappropriate behaviors [6]. On the other side, humans have a mislead interpretation of the dolphins\u2019 nature [8]. Several behaviours exhibited by dolphins are naively associated with playful and sociable attitudes. However, ethological observations have shown that surfing, breaching, leaping are behaviours linked to specific physiological (sometimes social) functions that have nothing to do with playful patterns. The \u201csmile\u201d on their faces is not a joyful sign, rather an anthropomorphic projection of it [7]. These types of perceptual and cognitive misinterpretations in the human-animal interaction expose non-human species, here represented by vulnerable dolphins, to activities that highly impact on animal welfare [4,9]

A Case Study on the Application and Implementation of Positive Behavioral Interventions and Supports for Students with Emotional Disabilities in Alternative Education

Alternative educational settings are serving students with emotional disabilities (ED) at an increasing rate; however, there a paucity of research examining the effectiveness of these programs. A review of the existing literature targeting students with problematic behaviors, supports the use of a positive, preventative, proactive, and systematic approaches such as the three-tiered, Positive Behavioral Interventions and Support (PBIS) framework. Although findings suggest that the PBIS framework is associated with favorable outcomes for students with ED in both traditional and nontraditional settings, few studies have explicitly examined whether PBIS can be effectively modified to fit the needs of students with ED in alternative education settings. As such, the current qualitative study aimed to address these gaps in the related literature by examining how and why PBIS was effective for students with ED in alternative education. A single case-study design was used to collect evidence of PBIS implementation for students in this population from the direct interviews of five staff members, archival school records, and PBIS-related documentation. Braun and Clarke’s (2006) six-step thematic analysis was used to interpret data and develop themes, resulting in the emergence of three specific themes: 1) strategies and practices; 2) data tracking: measuring progress and outcomes; and 3) systems and structures. In addition, the case study team identified nine corresponding subthemes to support these themes. Findings illustrated the specific adaptations and modifications made to the PBIS framework, contributing to the effective implementation of the strategy and meeting the needs of students in the ED population. Findings of this study provided several implications for constituents and potential future areas of research

A New Evolutionary Approach to Optimal Sensor Placement in Water Distribution Networks

The sensor placement problem is modeled as a multi-objective optimization problem with Boolean decision variables. A new multi objective evolutionary algorithm (MOEA) is proposed for approximating and analyzing the set of Pareto optimal solutions. The evaluation of the objective functions requires the execution of a hydraulic simulation model of the network. To organize the simulation results a data structure is proposed which enables the dynamic representation of a sensor placement and its fitness as a heatmap. This allows the definition of information spaces, in which the fitness of a placement can be represented as a matrix or, in probabilistic terms as a histogram. The key element in the new algorithm is this probabilistic representation which is embedded in a space endowed with a metric based on a specific notion of distance. Among several distances between probability distributions the Wasserstein (WST) distance has been selected: WST has enabled to derive new genetic operators, indicators of the quality of the Pareto set and criteria to choose among the Pareto solutions. The new algorithm has been tested on a benchmark water distribution network with two objective functions showing an improvement over NSGA-II, in particular for low generation counts, making it a good candidate for expensive black-box multi-objective optimizatio

A Hyper-Solution Framework for SVM Classification: Application for Predicting Destabilizations in Chronic Heart Failure Patients

Support Vector Machines (SVMs) represent a powerful learning paradigm able to provide accurate and reliable decision functions in several application fields. In particular, they are really attractive for application in medical domain, where often a lack of knowledge exists. Kernel trick, on which SVMs are based, allows to map non-linearly separable data into potentially linearly separable one, according to the kernel function and its internal parameters value. During recent years non-parametric approaches have also been proposed for learning the most appropriate kernel, such as linear combination of basic kernels. Thus, SVMs classifiers may have several parameters to be tuned and their optimal values are usually difficult to be identified a-priori. Furthermore, combining different classifiers may reduce risk to perform errors on new unseen data. For such reasons, we present an hyper-solution framework for SVM classification, based on meta-heuristics, that searches for the most reliable hyper-classifier (SVM with a basic kernel, SVM with a combination of kernel, and ensemble of SVMs), and for its optimal configuration. We have applied the proposed framework on a critical and quite complex issue for the management of Chronic Heart Failure patient: the early detection of decompensation conditions. In fact, predicting new destabilizations in advance may reduce the burden of heart failure on the healthcare systems while improving quality of life of affected patients. Promising reliability has been obtained on 10-fold cross validation, proving our approach to be efficient and effective for an high-level analysis of clinical data