Notulae to the Italian native vascular flora: 8

In this contribution, new data concerning the distribution of native vascular flora in Italy are presented. It includes new records, confirmations, exclusions, and status changes to the Italian administrative regions for taxa in the genera Ajuga, Chamaemelum, Clematis, Convolvulus, Cytisus, Deschampsia, Eleocharis, Epipactis, Euphorbia, Groenlandia, Hedera, Hieracium, Hydrocharis, Jacobaea, Juncus, Klasea, Lagurus, Leersia, Linum, Nerium, Onopordum, Persicaria, Phlomis, Polypogon, Potamogeton, Securigera, Sedum, Soleirolia, Stachys, Umbilicus, Valerianella, and Vinca. Nomenclatural and distribution updates, published elsewhere, and corrigenda are provided as Suppl. material 1

Notulae to the Italian alien vascular flora: 12

In this contribution, new data concerning the distribution of vascular flora alien to Italy are presented. It includes new records, confirmations, exclusions, and status changes for Italy or for Italian administrative regions. Nomenclatural and distribution updates published elsewhere are provided as Suppl. material 1

Notulae to the Italian alien vascular flora: 11

In this contribution, new data concerning the distribution of vascular flora alien to Italy are presented. It includes new records, confirmations, exclusions, and status changes for Italy or for Italian administrative regions. Nomenclatural and distribution updates published elsewhere are provided as Suppl. material 1

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Using Dynamic Hypergraphs to Reveal the Evolution of the Business Network of a 17th Century French Woman Merchant

International audienceMany digital humanity use cases require the analysis of relationships between entities (e.g. people or countries). The analysis of those relationships is particularly difficult when these relations change over time. A common representations of such relations is through graphs, which connect pairs of entities. However, in the real world relationships are often more complex and can be better described using hypergraphs (where edges can connect more than two entities). In this paper, we present a digital humanity case study of the analysis of people mentioned in 16th and 17th Century legal documents, modeled as a dynamic hypergraph. We use a new representation called Parallel Aggregated Ordered Hypergraph. Our prototype implementation of Parallel Aggregated Ordered Hypergraph, and the benefits of the PAOH representation are discussed

Using Dynamic Hypergraphs to Reveal the Evolution of the Business Network of a 17th Century French Woman Merchant

International audienceMany digital humanity use cases require the analysis of relationships between entities (e.g. people or countries). The analysis of those relationships is particularly difficult when these relations change over time. A common representations of such relations is through graphs, which connect pairs of entities. However, in the real world relationships are often more complex and can be better described using hypergraphs (where edges can connect more than two entities). In this paper, we present a digital humanity case study of the analysis of people mentioned in 16th and 17th Century legal documents, modeled as a dynamic hypergraph. We use a new representation called Parallel Aggregated Ordered Hypergraph. Our prototype implementation of Parallel Aggregated Ordered Hypergraph, and the benefits of the PAOH representation are discussed

Analyzing Dynamic Hypergraphs with Parallel Aggregated Ordered Hypergraph Visualization

International audienceParallel Aggregated Ordered Hypergraph (PAOH) is a novel technique to visualize dynamic hypergraphs. Hypergraphs are a generalization of graphs where edges can connect several vertices. Hypergraphs can be used to model networks of business partners or co-authorship networks with multiple authors per article. A dynamic hypergraph evolves over discrete time slots. PAOH represents vertices as parallel horizontal bars and hyperedges as vertical lines, using dots to depict the connections to one or more vertices. We describe a prototype implementation of Parallel Aggregated Ordered Hypergraph, report on a usability study with 9 participants analyzing publication data, and summarize the improvements made. Two case studies and several examples are provided. We believe that PAOH is the first technique to provide a highly readable representation of dynamic hypergraphs. It is easy to learn and well suited for medium size dynamic hypergraphs (50-500 vertices) such as those commonly generated by digital humanities projects-our driving application domain