Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions

In any economic analysis, regions or municipalities should not be regarded as isolated spatial units, but rather as highly interrelated small open economies. These spatial interrelations must be considered also when the aim is to forecast economic variables. For example, policy makers need accurate forecasts of the unemployment evolution in order to design short- or long-run local welfare policies. These predictions should then consider the spatial interrelations and dynamics of regional unemployment. In addition, a number of papers have demonstrated the improvement in the reliability of long-run forecasts when spatial dependence is accounted for. We estimate a heterogeneouscoefficients dynamic panel model employing a spatial filter in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment, as well as a spatial vector-autoregressive (SVAR) model. We compare the short-run forecasting performance of these methods, and in particular, we carry out a sensitivity analysis in order to investigate if different number and size of the administrative regions influence their relative forecasting performance. We compute short-run unemployment forecasts in two countries with different administrative territorial divisions and data frequency: Switzerland (26 regions, monthly data for 34 years) and Spain (47 regions, quarterly data for 32 years)

The spectrum of BRCA1 and BRCA2 pathogenic sequence variants in Middle Eastern, North African, and South European countries.

BRCA1 BRCA2 mutational spectrum in the Middle East, North Africa, and Southern Europe is not well characterized. The unique history and cultural practices characterizing these regions, often involving consanguinity and inbreeding, plausibly led to the accumulation of population-specific founder pathogenic sequence variants (PSVs). To determine recurring BRCA PSVs in these locales, a search in PUBMED, EMBASE, BIC, and CIMBA was carried out combined with outreach to researchers from the relevant countries for unpublished data. We identified 232 PSVs in BRCA1 and 239 in BRCA2 in 25 of 33 countries surveyed. Common PSVs that were detected in four or more countries were c.5266dup (p.Gln1756Profs), c.181T>G (p.Cys61Gly), c.68_69del (p.Glu23Valfs), c.5030_5033del (p.Thr1677Ilefs), c.4327C>T (p.Arg1443Ter), c.5251C>T (p.Arg1751Ter), c.1016dup (p.Val340Glyfs), c.3700_3704del (p.Val1234Glnfs), c.4065_4068del (p.Asn1355Lysfs), c.1504_1508del (p.Leu502Alafs), c.843_846del (p.Ser282Tyrfs), c.798_799del (p.Ser267Lysfs), and c.3607C>T (p.Arg1203Ter) in BRCA1 and c.2808_2811del (p.Ala938Profs), c.5722_5723del (p.Leu1908Argfs), c.9097dup (p.Thr3033Asnfs), c.1310_1313del (p. p.Lys437Ilefs), and c.5946del (p.Ser1982Argfs) for BRCA2. Notably, some mutations (e.g., p.Asn257Lysfs (c.771_775del)) were observed in unrelated populations. Thus, seemingly genotyping recurring BRCA PSVs in specific populations may provide first pass BRCA genotyping platform.[CIMBA: The CIMBA data management and data analysis were supported by Cancer Research – UK grants C12292/A20861, C12292/A11174. iCOGS: the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer (CRN-87521), and the Ministry of Economic Development, Innovation and Export Trade (PSR-SIIRI-701), Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The work of Barbara Pasini has been supported by the program "Dipartimenti di Eccellenza 2018 – 2022". Project n°D15D18000410001. This work was partially funded by the Associazione Italiana Ricerca Cancro (AIRC)"; IG2015 no.16732) to P. Peterlongo. Funds from Italian citizens who allocated the 5x1000 share of their tax payment in support of the Fondazione IRCCS Istituto Nazionale Tumori, according to Italian laws (INT-Institutional strategic projects ‘5x1000’) to S. Manoukian. DEMOKRITOS: European Union (European Social Fund – ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program of the General Secretariat for Research & Technology: SYN11_10_19 NBCA. kConFab: The National Breast Cancer Foundation, and previously by the National Health and Medical Research Council (NHMRC), the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia. MAYO: NIH grants CA116167, CA192393 and CA176785, an NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201) and a grant from the Breast Cancer Research Foundation. UCHICAGO: NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA125183), R01 CA142996, 1U01CA161032 and by the Ralph and Marion Falk Medical Research Trust, the Entertainment Industry Fund National Women's Cancer Research Alliance and the Breast Cancer research Foundation. OIO is an ACS Clinical Research Professor. UCLA: Jonsson Comprehensive Cancer Center Foundation; Breast Cancer Research Foundation

SCAview: an Intuitive Visual Approach to the Integrative Analysis of Clinical Data in Spinocerebellar Ataxias

With SCAview, we present a prompt and comprehensive tool that enables scientists to browse large datasets of the most common spinocerebellar ataxias intuitively and without technical effort. Basic concept is a visualization of data, with a graphical handling and filtering to select and define subgroups and their comparison. Several plot types to visualize all data points resulting from the selected attributes are provided. The underlying synthetic cohort is based on clinical data from five different European and US longitudinal multicenter cohorts in spinocerebellar ataxia type 1, 2, 3, and 6 (SCA1, 2, 3, and 6) comprising > 1400 patients with overall > 5500 visits. First, we developed a common data model to integrate the clinical, demographic, and characterizing data of each source cohort. Second, the available datasets from each cohort were mapped onto the data model. Third, we created a synthetic cohort based on the cleaned dataset. With SCAview, we demonstrate the feasibility of mapping cohort data from different sources onto a common data model. The resulting browser-based visualization tool with a thoroughly graphical handling of the data offers researchers the unique possibility to visualize relationships and distributions of clinical data, to define subgroups and to further investigate them without any technical effort. Access to SCAview can be requested via the Ataxia Global Initiative and is free of charge