University of Siena

About: University of Siena is a education organization based out in Siena, Italy. It is known for research contribution in the topics: Population & Cancer. The organization has 12179 authors who have published 33334 publications receiving 1008287 citations. The organization is also known as: Università degli studi di Siena & Universita degli studi di Siena.

Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism

Abstract: We present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n=35,584 total samples, 11,986 with ASD). Using an enhanced Bayesian framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate ≤ 0.1. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained for severe neurodevelopmental delay, while 53 show higher frequencies in individuals ascertained for ASD; comparing ASD cases with mutations in these groups reveals phenotypic differences. Expressed early in brain development, most of the risk genes have roles in regulation of gene expression or neuronal communication (i.e., mutations effect neurodevelopmental and neurophysiological changes), and 13 fall within loci recurrently hit by copy number variants. In human cortex single-cell gene expression data, expression of risk genes is enriched in both excitatory and inhibitory neuronal lineages, consistent with multiple paths to an excitatory/inhibitory imbalance underlying ASD.

Hereditary diffuse gastric cancer: updated clinical guidelines with an emphasis on germline CDH1 mutation carriers.

Abstract: Germline CDH1 mutations confer a high lifetime risk of developing diffuse gastric (DGC) and lobular breast cancer (LBC). A multidisciplinary workshop was organised to discuss genetic testing, surgery, surveillance strategies, pathology reporting and the patient's perspective on multiple aspects, including diet post gastrectomy. The updated guidelines include revised CDH1 testing criteria (taking into account first-degree and second-degree relatives): (1) families with two or more patients with gastric cancer at any age, one confirmed DGC; (2) individuals with DGC before the age of 40 and (3) families with diagnoses of both DGC and LBC (one diagnosis before the age of 50). Additionally, CDH1 testing could be considered in patients with bilateral or familial LBC before the age of 50, patients with DGC and cleft lip/palate, and those with precursor lesions for signet ring cell carcinoma. Given the high mortality associated with invasive disease, prophylactic total gastrectomy at a centre of expertise is advised for individuals with pathogenic CDH1 mutations. Breast cancer surveillance with annual breast MRI starting at age 30 for women with a CDH1 mutation is recommended. Standardised endoscopic surveillance in experienced centres is recommended for those opting not to have gastrectomy at the current time, those with CDH1 variants of uncertain significance and those that fulfil hereditary DGC criteria without germline CDH1 mutations. Expert histopathological confirmation of (early) signet ring cell carcinoma is recommended. The impact of gastrectomy and mastectomy should not be underestimated; these can have severe consequences on a psychological, physiological and metabolic level. Nutritional problems should be carefully monitored.

On the complexity of neural network classifiers: a comparison between shallow and deep architectures.

Abstract: Recently, researchers in the artificial neural network field have focused their attention on connectionist models composed by several hidden layers. In fact, experimental results and heuristic considerations suggest that deep architectures are more suitable than shallow ones for modern applications, facing very complex problems, e.g., vision and human language understanding. However, the actual theoretical results supporting such a claim are still few and incomplete. In this paper, we propose a new approach to study how the depth of feedforward neural networks impacts on their ability in implementing high complexity functions. First, a new measure based on topological concepts is introduced, aimed at evaluating the complexity of the function implemented by a neural network, used for classification purposes. Then, deep and shallow neural architectures with common sigmoidal activation functions are compared, by deriving upper and lower bounds on their complexity, and studying how the complexity depends on the number of hidden units and the used activation function. The obtained results seem to support the idea that deep networks actually implements functions of higher complexity, so that they are able, with the same number of resources, to address more difficult problems.