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Heterogeneous wireless sensor networks are a source of large amount of different information representing environmental aspects such as light, temperature, and humidity. A very important research problem related to the analysis of the sensor data is the detection of relevant anomalies. In this work, we focus on the detection of unexpected sensor data resulting either from the sensor system itself or from the environment under scrutiny. We propose a novel approach for automatic anomaly detection in heterogeneous sensor networks based on coupling edge data analysis with cloud data analysis. The former exploits a fully unsupervised artificial neural network algorithm, whereas cloud data analysis exploits the multi-parameterized edit distance algorithm. The experimental

OBJECTIVE— The minor allele of the nonsynonymous single nucleotide polymorphism (SNP) +1858C>T within the PTPN22 gene is positively associated with type 1 diabetes and other autoimmune diseases. Genetic and functional data underline its causal effect, but some studies suggest that this polymorphism does not entirely explain disease association of the PTPN22 region. The aim of this study was to evaluate type 1 diabetes association within this gene in the Sardinian population. RESEARCH DESIGN AND METHODS— We resequenced the exons and potentially relevant portions of PTPN22 and detected 24 polymorphisms (23 SNPs and 1 deletion insertion polymorphism [DIP]), 8 of which were novel. A representative set of 14 SNPs and the DIP were sequentially genotyped and assessed for disease association in 794 families, 490 sporadic patients, and 721 matched control subjects. RESULTS— The +1858C>T variant, albeit rare in the general Sardinian population (allele frequency 0.014), was positively associated with type 1 diabetes (Pone tail = 3.7 × 10−3). In contrast, the background haplotype in which this mutation occurred was common (haplotype frequency 0.117) and neutrally associated with disease. We did not confirm disease associations reported in other populations for non +1858C>T variants (rs2488457, rs1310182, and rs3811021), although they were present in appreciable frequencies in Sardinia. Additional weak disease associations with rare variants were detected in the Sardinian families but not confirmed in independent case-control sample sets and are most likely spurious. CONCLUSIONS— We provide further evidence that the +1858C>T polymorphism is primarily associated with type 1 diabetes and exclude major contributions from other purportedly relevant variants within this gene.