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Congenital hypogonadotropic hypogonadism (CHH) is caused by dysfunction of hypothalamic gonadotropic-releasing hormone (GnRH) axis. The condition is both clinically and genetically heterogeneous with more than 40 genes implicated in pathogenesis. The goal of the present study was to identify causative mutations in CHH individuals employing 2 step procedure with a targeted NGS panel as first-line diagnostics and subsequently whole exome sequencing in unsolved cases. Known or novel potentially deleterious variants were found in 28 out of 47 tested CHH patients. Molecular diagnosis was reached in 19/47 CHH cases. In 13 cases monogenic variants were identified in ANOS1, FGFR1, GNRHR, CHD7, SOX10, and PROKR2, while 6 patients showed digenic or trigenic inheritance patterns. The achieved diagnostic rate was comparable to other studies on genetics of CHH. By evaluating and reporting more patients with CHH we make progress in unravelling its genetic complexity and move a step closer to personalised medicine.Breast and ovarian cancers are the second and the fifth leading causes of cancer death among women. Predicting the overall survival of breast and ovarian cancer patients can facilitate the therapeutics evaluation and treatment decision making. Multi-scale multi-omics data such as gene expression, DNA methylation, miRNA expression, and copy number variations can provide insights on personalized survival. However, how to effectively integrate multi-omics data remains a challenging task. In this paper, we develop multi-omics integration methods to improve the prediction of overall survival for breast cancer and ovarian cancer patients. Because multi-omics data for the same patient jointly impact the survival of cancer patients, features from different -omics modality are related and can be modeled by either association or causal relationship (e.g., pathways). By extracting these relationships among modalities, we can get rid of the irrelevant information from high-throughput multi-omics data. However, it is infeasible to use the Brute Force method to capture all possible multi-omics interactions. Thus, we use deep neural networks with novel divergence-based consensus regularization to capture multi-omics interactions implicitly by extracting modality-invariant representations. In comparing the concatenation-based integration networks with our new divergence-based consensus networks, the breast cancer overall survival C-index is improved from 0.655±0.062 to 0.671±0.046 when combing DNA methylation and miRNA expression, and from 0.627±0.062 to 0.667±0.073 when combing miRNA expression and copy number variations. In summary, our novel deep consensus neural network has successfully improved the prediction of overall survival for breast cancer and ovarian cancer patients by implicitly learning the multi-omics interactions.Methods and technologies enabling the estimation at large scale of important traits for the dairy sector are of great interest. Those phenotypes are necessary to improve herd management, animal genetic evaluation, and milk quality control. In the recent years, the research was very active to predict new phenotypes from the mid-infrared (MIR) analysis of milk. Models were developed to predict phenotypes such as fine milk composition, milk technological properties or traits related to cow health, fertility and environmental impact. Most of models were developed within research contexts and often not designed for routine use. The implementation of models at a large scale to predict new traits of interest brings new challenges as the factors influencing the robustness of models are poorly documented. The first objective of this work is to highlight the impact on prediction accuracy of factors such as the variability of the spectral and reference data, the spectral regions used and the complexity of models. The seighlighted with an improvement up to 86% with the tested models, and the monitoring of individual spectrometer stability over time appears essential. [https://www.selleckchem.com/products/bay-876.html see more] This list inspired from our experience is of course not exhaustive. The displayed results are only examples and not general rules and other aspects play a role in the quality of final predictions. However, this work highlights good practices, methods and indicators to increase and evaluate quality of phenotypes predicted at a large scale. The results obtained argue for the development of guidelines at international levels, as well as international collaborations in order to constitute large and robust datasets and enable the use of models in routine conditions.Centrosome, composed of two centrioles arranged in an orthogonal configuration, is an indispensable cellular organelle for mitosis. 130 years after its discovery, the structural-functional relationship of centrosome is still obscure. Encouraged by the telltale signs of the "Mouse and Magnet experiment", Paul Schafer pioneered in the research on electromagnetism of centriole with electron microscopy(EM) in the late 1960s. Followed by the decades-long slow progression of the field with sporadic reports indicating the electromagnetisms of mitosis. Piecing together the evidences, we generated a mechanistic model for centrosome function during mitosis, in which centrosome functions as an electronic generator. In particular, the spinal rotations of centrioles transform the cellular chemical energy into cellular electromagnetic energy. The model is strongly supported by multiple experimental evidences. It offers an elegant explanation for the self-organized orthogonal configuration of the two centrioles in a centrosome, that is through the dynamic electromagnetic interactions of both centrioles of the centrosome.One of the main challenges of the social sciences is to explain metasystem transitions from biological to social systems in the process of evolution. These transitions correspond to the emergence of the structure of the subject in which the external world is internalized as a symbolic image. This structure has the potential of rationally reflecting the external world and encoding it in human language. The structure of the subject was defined by Freud and Lacan within the framework of psychoanalysis and modeled by Vladimir Lefebvre using the algebra of simple relations. In that context, the binary oppositions of the Western (W) and Eastern (E) types of cognitive reflection generate not only opposite types of societies but also form the spatiotemporal pattern within a society as a whole underlying its homeostasis and internal dynamics. This opposition between Western and Eastern types is not identical to, but mirrors the probably genetically determined opposition between individuals with primarily selfish or altruistic behavior.