Prediction of impending type 1 diabetes through automated dual-label measurement of proinsulin:C-peptide ratio

Background : The hyperglycemic clamp test, the gold standard of beta cell function, predicts impending type 1 diabetes in islet autoantibody-positive individuals, but the latter may benefit from less invasive function tests such as the proinsulin: C-peptide ratio (PI:C). The present study aims to optimize precision of PI:C measurements by automating a dual-label trefoil-type time-resolved fluorescence immunoassay (TT-TRFIA), and to compare its diagnostic performance for predicting type 1 diabetes with that of clamp-derived C-peptide release. Methods : Between-day imprecision (n = 20) and split-sample analysis (n = 95) were used to compare TT-TRFIA (Auto Delfia, Perkin-Elmer) with separate methods for proinsulin (in-house TRFIA) and C-peptide (Elecsys, Roche). High-risk multiple autoantibody-positive firstdegree relatives (n = 49; age 5-39) were tested for fasting PI:C, HOMA2-IR and hyperglycemic clamp and followed for 20-57 months (interquartile range). Results : TT-TRFIA values for proinsulin, C-peptide and PI:C correlated significantly (r(2) = 0.96-0.99; P<0.001) with results obtained with separate methods. TT-TRFIA achieved better between-day % CV for PI:C at three different levels (4.5-7.1 vs 6.7-9.5 for separate methods). In high-risk relatives fasting PI:C was significantly and inversely correlated ( r(s) = -0.596; P<0.001) with first-phase C-peptide release during clamp ( also with second phase release, only available for age 12-39 years; n = 31), but only after normalization for HOMA2-IR. In ROC- and Cox regression analysis, HOMA2-IR-corrected PI:C predicted 2-year progression to diabetes equally well as clamp-derived C-peptide release. Conclusions : The reproducibility of PI:C benefits from the automated simultaneous determination of both hormones. HOMA2-IR-corrected PI:C may serve as a minimally invasive alternative to the more tedious hyperglycemic clamp test

Reviewer Integration and Performance Measurement for Malware Detection

We present and evaluate a large-scale malware detection system integrating machine learning with expert reviewers, treating reviewers as a limited labeling resource. We demonstrate that even in small numbers, reviewers can vastly improve the system's ability to keep pace with evolving threats. We conduct our evaluation on a sample of VirusTotal submissions spanning 2.5 years and containing 1.1 million binaries with 778GB of raw feature data. Without reviewer assistance, we achieve 72% detection at a 0.5% false positive rate, performing comparable to the best vendors on VirusTotal. Given a budget of 80 accurate reviews daily, we improve detection to 89% and are able to detect 42% of malicious binaries undetected upon initial submission to VirusTotal. Additionally, we identify a previously unnoticed temporal inconsistency in the labeling of training datasets. We compare the impact of training labels obtained at the same time training data is first seen with training labels obtained months later. We find that using training labels obtained well after samples appear, and thus unavailable in practice for current training data, inflates measured detection by almost 20 percentage points. We release our cluster-based implementation, as well as a list of all hashes in our evaluation and 3% of our entire dataset.Comment: 20 papers, 11 figures, accepted at the 13th Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA 2016

Road traffic pollution and childhood leukemia: a nationwide case-control study in Italy

Background The association of childhood leukemia with traffic pollution was considered in a number of studies from 1989 onwards, with results not entirely consistent and little information regarding subtypes. Aim of the study We used the data of the Italian SETIL case-control on childhood leukemia to explore the risk by leukemia subtypes associated to exposure to vehicular traffic. Methods We included in the analyses 648 cases of childhood leukemia (565 Acute lymphoblastic–ALL and 80 Acute non lymphoblastic-AnLL) and 980 controls. Information on traffic exposure was collected from questionnaire interviews and from the geocoding of house addresses, for all periods of life of the children. Results We observed an increase in risk for AnLL, and at a lower extent for ALL, with indicators of exposure to traffic pollutants. In particular, the risk was associated to the report of closeness of the house to traffic lights and to the passage of trucks (OR: 1.76; 95% CI 1.03–3.01 for ALL and 6.35; 95% CI 2.59–15.6 for AnLL). The association was shown also in the analyses limited to AML and in the stratified analyses and in respect to the house in different period of life. Conclusions Results from the SETIL study provide some support to the association of traffic related exposure and risk for AnLL, but at a lesser extent for ALL. Our conclusion highlights the need for leukemia type specific analyses in future studies. Results support the need of controlling exposure from traffic pollution, even if knowledge is not complete