According to current dogma, there is little or no ongoing neurogenesis in the fully developed adult enteric nervous system. This lack of neurogenesis leaves unanswered the question of how enteric neuronal populations are maintained in adult guts, given previous reports of ongoing neuronal death. Here, we confirm that despite ongoing neuronal cell loss because of apoptosis in the myenteric ganglia of the adult small intestine, total myenteric neuronal numbers remain constant. This observed neuronal homeostasis is maintained by new neurons formed in vivo from dividing precursor cells that are located within myenteric ganglia and express both Nestin and p75NTR, but not the pan-glial marker Sox10. Mutation of the phosphatase and tensin homolog gene in this pool of adult precursors leads to an increase in enteric neuronal number, resulting in ganglioneuromatosis, modeling the corresponding disorder in humans. Taken together, our results show significant turnover and neurogenesis of adult enteric neurons and provide a paradigm for understanding the enteric nervous system in health and disease.

The excitatory neurotransmitter glutamate is involved in the control of most, perhaps all, neuroendocrine systems, yet the sites of glutamatergic neurons and their processes are unknown. Here, we used in situ hybridization and immunohistochemistry for the neuron-specific vesicular glutamate transporter-2 (VGLUT2) to identify the neurons in female rats that synthesize the neurotransmitter glutamate as well as their projections throughout the septum-hypothalamus. The results show that glutamatergic neurons are present in the septum-diagonal band complex and throughout the hypothalamus. The preoptic area and ventromedial and dorsomedial nuclei are particularly rich in glutamatergic neurons, followed by the supraoptic, paraventricular, and arcuate nuclei, whereas the suprachiasmatic nucleus does not express detectable amounts of VGLUT2 mRNA. Immunoreactive neurites are seen in very high densities in all regions analyzed, particularly in the preoptic region, followed by the ventromedial, dorsomedial, and arcuate nuclei as well as the external layer of the median eminence, whereas the mammillary complex does not exhibit VGLUT2 immunoreactivity. Many VGLUT2 immunoreactive fibers also contained synaptophysin, suggesting that the transporter is indeed localized to presynaptic terminals. Together, the results identify glutamatergic cell bodies throughout the septum-hypothalamus in region-specific patterns and show that glutamatergic nerve terminals are present in very large numbers such that most neurons in these brain regions can receive glutamatergic input. We examined the GnRH system as an example of a typical neuroendocrine system and could show that the GnRH perikarya are closely apposed by many VGLUT2-immunoreactive boutons, some of which also contained synaptophysin. The presence of VGLUT2 mRNA-containing cells in specific nuclei of the hypothalamus indicates that many neuroendocrine neurons coexpress glutamate as neurotransmitter, in addition to neuropeptides. These systems include the oxytocin, vasopressin, or CRH neurons as well as many others in the periventricular and mediobasal hypothalamus. The presence of VGLUT2 mRNA in steroid-sensitive regions of the hypothalamus, such as the anteroventral periventricular, paraventricular, or ventromedial nuclei indicates that gonadal and adrenal steroid can directly alter the functions of these glutamatergic neurons.

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The fields of medicine science and health informatics have made great progress recently and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data. Meanwhile, we are entering a new period where novel technologies are starting to analyze and explore knowledge from tremendous amount of data, bringing limitless potential for information growth. One fact that cannot be ignored is that the techniques of machine learning and deep learning applications play a more significant role in the success of bioinformatics exploration from biological data point of view, and a linkage is emphasized and established to bridge these two data analytics techniques and bioinformatics in both industry and academia. This survey concentrates on the review of recent researches using data mining and deep learning approaches for analyzing the specific domain knowledge of bioinformatics. The authors give a brief but pithy summarization of numerous data mining algorithms used for preprocessing, classification and clustering as well as various optimized neural network architectures in deep learning methods, and their advantages and disadvantages in the practical applications are also discussed and compared in terms of their industrial usage. It is believed that in this review paper, valuable insights are provided for those who are dedicated to start using data analytics methods in bioinformatics.