Tolerance spaces and behavior

Tolerance spaces used in human behavior model

Identification of control strategies to manage microbiological risks in typical pork products

Starting from 2009 a pilot project has been implemented by a local veterinary service of the Veneto region of Italy (AZ-ULSS 8) in collaboration with IZSVe (Istituto Zooprofilattico Sperimentale delle Venezie) with the aim of identifying control measures based on own-checks and official controls in order to manage microbiological risks related to traditional pork fermented sausages (Italian salami end soppresse) consumption. According to the data obtained a control strategy based on microbiological tests performed by the Competent Authority (CA) and the monitoring of the weight decrease in sausages by the food business operator (FBO) has been implemented for 2010-2011 production season

Functional Brain Imaging Predicts Public Health Campaign Success

Mass media can powerfully affect health decision-making. Pre-testing through focus groups or surveys is a standard, though inconsistent, predictor of effectiveness. Converging evidence demonstrates that activity within brain systems associated with self-related processing can predict individual behavior in response to health messages. Preliminary evidence also suggests that neural activity in small groups can forecast population-level campaign outcomes. Less is known about the psychological processes that link neural activity and population-level outcomes, or how these predictions are affected by message content. We exposed 50 smokers to antismoking messages and used their aggregated neural activity within a ‘self-localizer’ defined region of medial prefrontal cortex to predict the success of the same campaign messages at the population level (n = 400 000 emails). Results demonstrate that: (i) independently localized neural activity during health message exposure complements existing self-report data in predicting population-level campaign responses (model combined R2 up to 0.65) and (ii) this relationship depends on message content—self-related neural processing predicts outcomes in response to strong negative arguments against smoking and not in response to compositionally similar neutral images. These data advance understanding of the psychological link between brain and large-scale behavior and may aid the construction of more effective media health campaigns

Surface-modified polysulfone membranes: aqueous phase oxidation via persulfate radical

Peer reviewed: YesNRC publication: Ye

Constraint Based Diagnosis Algorithms For Multiprocessors

Constraint-based diagnosis algorithms for multiprocessors A. Petri, P. Urban, J. Altmann, M. Dal Cin, E. Selenyi, K. Tilly, A. Pataricza In the latest years, new ideas appeared in system level diagnosis of multiprocessor systems. In contrary to the traditional diagnosis models (like PMC, BGM, etc.) which use strictly graph-oriented methods to determine the faulty components in a system, these new theories prefer AI-based algorithms, especially CSP methods. Syndrome decoding, the basic problem of self-diagnosis, can be easily transformed into constraints between the state of the tester and the tested components. Therefore, the diagnosis algorithm can be derived from a special constraint solving algorithm. The "benign" nature of the constraints (all their variables, representing the fault states of the components, have a very limited domain; the constraints are simple and similar to each other) reduces the algorithm's complexity so it can be converted to a powerful distributed diagnosis method with a minimal overhead. Experimental algorithms (using both centralized and distributed approach) were implemented for a Parsytec GC massively parallel multiprocessor system

Immunohistochemical Detection of MYC-driven Diffuse Large B-Cell Lymphomas

Diffuse large B cell lymphoma (DLBCL) is a clinically and genetically heterogeneous disease. A small subset of DLBCLs has translocations involving the MYC locus and an additional group has a molecular signature resembling Burkitt lymphoma (mBL). Presently, identification of such cases by morphology is unreliable and relies on cytogenetic or complex molecular methods such as gene transcriptional profiling. Herein, we describe an immunohistochemical (IHC) method for identifying DLBCLs with increased MYC protein expression. We tested 77 cases of DLBCL and identified 15 cases with high MYC protein expression (nuclear staining in >50% of tumor cells). All MYC translocation positive cases had increased MYC protein expression by this IHC assay. In addition, gene set enrichment analysis (GSEA) of the DLBCL transcriptional profiles revealed that tumors with increased MYC protein expression (regardless of underlying MYC translocation status) had coordinate upregulation of MYC target genes, providing molecular confirmation of the IHC results. We then generated a molecular classifier derived from the MYC IHC results in our cases and employed it to successfully classify mBLs from two previously reported independent case series, providing additional confirmation that the MYC IHC results identify clinically important subsets of DLBCLs. Lastly, we found that DLBCLs with high MYC protein expression had inferior overall survival when treated with R-CHOP. In conclusion, the IHC method described herein can be used to readily identify the biologically and clinically distinct cases of MYC-driven DLBCL, which represent a clinically significant subset of DLBCL cases due to their inferior overall survival