Steroid hormones represent an amazing class of molecules that play pleiotropic roles in vertebrates. In mammals, during postnatal development, sex steroids significantly influence the organization of sexually dimorphic neural circuits underlying behaviors critical for survival, such as the reproductive one. During the last decades, multiple studies have shown that many cortical and subcortical brain regions undergo sex steroid-dependent structural organization around puberty, a critical stage of life characterized by high sensitivity to external stimuli and a profound structural and functional remodeling of the organism. Here, we first give an overview of current data on how sex steroids shape the peripubertal brain by regulating neuroplasticity mechanisms. Then, we focus on adult neurogenesis, a striking form of persistent structural plasticity involved in the control of social behaviors and regulated by a fine-tuned integration of external and internal cues. We discuss recent data supporting that the sex steroid-dependent peripubertal organization of neural circuits involves a sexually dimorphic set-up of adult neurogenesis that in turn could be relevant for sex-specific reproductive behaviors.

Next Generation Sequencing (NGS) has revolutionized genome biology, enabling the rapid sequencing of an entire human genome and facilitating the integration of Whole Genome Sequencing (WGS) into both research and clinical applications. The high-throughput nature of NGS and the complex data processing required has driven the need for advanced computational infrastructures to analyse these large datasets. The aim of this work is to introduce an innovative bioinformatic pipeline, named GeNePi, for the efficient and precise analysis of WGS short paired-end reads. Built on the Nextflow framework with a modular structure, GeNePi incorporates GPU-accelerated algorithms and supports multiple workflow configurations. The pipeline automates the extraction of biologically relevant insights from raw WGS data, including: disease-related variants such as single nucleotide variants (SNVs), small insertions or deletions (INDELs), copy number variants (CNVs), and structural variants (SVs). Optimized for high-performance computing (HPC) environments, it takes advantage of job-scheduler submissions, parallelised processing, and tailored resource allocation for each analysis step. Tested on synthetic and real datasets, GeNePi accurately identifies genomic variants, with performances comparable to that of state-of-art tools. These features make GeNePi a valuable instrument for large-scale analyses in both research and clinical contexts, representing a key step towards the establishment of National Centers for Computational and Technological Medicine.