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Aims: Psychotic symptoms during exposure to high altitude (HA) have been linked to accidents or near accidents on the mountains. It is thus of great importance to directly identify psychotic symptoms in individuals who are exposed to HA quickly and reliably on the mountain, even in the absence of experienced medical personnel. Psychotic features at HA frequently include positive psychotic symptoms such as hallucinations, delusions, or disorganized thinking/speech. The aim of the current study was to develop the first self-administered questionnaire (High Altitude Psychosis [HAPSY] Questionnaire), which individuals may use in the future to self-assess altitude-related psychotic symptoms on the mountain. Methods: We utilized two existing self-rating questionnaires for psychotic symptoms (the Psychotomimetic States Inventory, PSI and the Cardiff Anomalous Perception Scale, CAPS) as the basis for a two-round Delphi process. As part of this process, additional statements were suggested by the 40 Delphi participants. Results: Eleven self-administered statements-all of them related to positive psychotic symptoms-were identified to be most useful for the self-detection of HA-related psychotic symptoms on the mountain. Conclusion: This is the first self-administered questionnaire that allows the identification of HA-related psychotic symptoms on the mountain. A subsequent validation study is needed to address the psychometric properties of this questionnaire. Clinical validation will have to be performed in a field study.

The ambiguity of words is a crucial question when dealing with an automatic analysis of documentary data bases. In Text Mining, Word Sense Disambiguation is the task of giving a particular sense to a term with different meanings both in the case of coincidental and polysemous homographs. In literature, the proposed solutions are mainly based on two elements: some knowledge related to the term and the context in which the term appears. Limiting the related knowledge to grammatical tagging, and to an analysis of collocations, here we focus our attention on identifying the context, in a data driven approach. Our framework is based on Textual Data Analysis and we assume that language and knowledge can be modeled as networks of words and the relations between them. The aim of this paper is to propose an extension of the strategy for building lexical sources in Balbi et al. (2012), in order to deal with ambiguous words. The methodological basis is given by the joint use of lexical Correspondence Analysis and Network Analysis. Our idea is investigating the neighborhood of ambiguous terms, with respect to the different latent semantic components, emerging thanks to Correspondence Analysis of a training set of documents, in order to build some rules, useful in solving WSD problems in the entire corpus.

Assuming that language can be modelled as a network of words, it is difficult to mine knowledge in textual data bases, due to their high dimensionality and the ambiguity which characterises words and their use. From a methodological viewpoint, here we propose a strategy for stressing the differences in the manifest relations emerging by Network Analysis (NA) and the latent relations obtained by lexical Correspondence Analysis (CA). Aim of this paper is to deal with the word-sense disambiguation problem, not in the usual pre-processing step, but during the analysis. The results applied to the analysis of a management commentary are presented in order to propose some statistical lexical sources, useful in the peculiar domain of business information.

This paper has been developed in the frame of the European project BLUE-ETS (Economic and Trade Statistics), in the work-package devoted to propose new tools for collecting and analysing data. In order to obtain business information by documentary repositories, here we refer to documents produced with non statistical aims. The use of secondary sources, typical of data and text mining, is an opportunity not sufficiently explored by National Statistical Institutes. NSIs aim at collecting and representing information in a usable and easy-readable way. The use of textual data has been still viewed as too problematic, because of the complexity and the expensiveness of the pre-processing procedures and often for the lack of suitable analytical tools. Our aim is to identify statistical linguistic sources by a deep analysis of one management commentary. From a methodological viewpoint, here we propose a tool for exploring relations between words at a micro-data level, derived from network data analysis, namely ego networks, applied together with lexical correspondence analysis.

This paper investigates the relationship between spatial and institutional distances in research collaboration networks using data on co-authorship in scientific publications in the Italian “red” biotech sector. Specific tools of social network analysis complemented by multivariate analysis are used to show that there is a trade-off between institutional and geographic distances whatever the nature of the knowledge exchanged, namely the degree to which knowledge is basic or applied. However, the levels of geographic and institutional distances substantially differ across basic and applied research networks. Measures of the intensity of relationships are also considered in the analysis.

In this paper we analyse, through the instrument of the social network analysis, the network of co-authorships in the publications of the firms belonging to the Italian life-science biotech sector. We identify the kind of institutions the authors of the publications are affiliated to (we divide the institutions in universities, research centres, hospitals and firms), then we observe the role of the different institutions inside such network. The analysis shows the central role of the universities but also the importance of hospitals, frequent partners in publications, and of the research centres for their “bridging” role between different institutions.