Achieving Generalizable Robustness of Deep Neural Networks by Stability Training

We study the recently introduced stability training as a general-purpose method to increase the robustness of deep neural networks against input perturbations. In particular, we explore its use as an alternative to data augmentation and validate its performance against a number of distortion types and transformations including adversarial examples. In our image classification experiments using ImageNet data stability training performs on a par or even outperforms data augmentation for specific transformations, while consistently offering improved robustness against a broader range of distortion strengths and types unseen during training, a considerably smaller hyperparameter dependence and less potentially negative side effects compared to data augmentation.Comment: 18 pages, 25 figures; Camera-ready versio

The Effect of Class Noise on Continuous Test Case Selection: A Controlled Experiment on Industrial Data

Continuous integration and testing produce a large amount of data about defects in code revisions, which can be utilized for training a predictive learner to effectively select a subset of test suites. One challenge in using predictive learners lies in the noise that comes in the training data, which often leads to a decrease in classification performances. This study examines the impact of one type of noise, called class noise, on a learner’s ability for selecting test cases. Understanding the impact of class noise on the performance of a learner for test case selection would assist testers decide on the appropriateness of different noise handling strategies. For this purpose, we design and implement a controlled experiment using an industrial data-set to measure the impact of class noise at six different levels on the predictive performance of a learner. We measure the learning performance using the Precision, Recall, F-score, and Mathew Correlation Coefficient (MCC) metrics. The results show a statistically significant relationship between class noise and the learners performance for test case selection. Particularly, a significant difference between the three performance measures (Precision, F-score, and MCC)under all the six noise levels and at 0% level was found, whereas a similar relationship between recall and class noise was found at a level above30%. We conclude that higher class noise ratios lead to missing out more tests in the predicted subset of test suite and increases the rate of false alarms when the class noise ratio exceeds 30

A novel feature selection-based sequential ensemble learning method for class noise detection in high-dimensional data

© 2018, Springer Nature Switzerland AG. Most of the irrelevant or noise features in high-dimensional data present significant challenges to high-dimensional mislabeled instances detection methods based on feature selection. Traditional methods often perform the two dependent step: The first step, searching for the relevant subspace, and the second step, using the feature subspace which obtained in the previous step training model. However, Feature subspace that are not related to noise scores and influence detection performance. In this paper, we propose a novel sequential ensemble method SENF that aggregate the above two phases, our method learns the sequential ensembles to obtain refine feature subspace and improve detection accuracy by iterative sparse modeling with noise scores as the regression target attribute. Through extensive experiments on 8 real-world high-dimensional datasets from the UCI machine learning repository [3], we show that SENF performs significantly better or at least similar to the individual baselines as well as the existing state-of-the-art label noise detection method

ECTRIMS/EAN consensus on vaccination in people with multiple sclerosis: Improving immunization strategies in the era of highly active immunotherapeutic drugs

Background: With the new highly active drugs available for people with multiple sclerosis (pwMS), vaccination becomes an essential part of the risk management strategy. / Objective: To develop a European evidence-based consensus for the vaccination strategy of pwMS who are candidates for disease-modifying therapies (DMTs). / Methods: This work was conducted by a multidisciplinary working group using formal consensus methodology. Clinical questions (defined as population, interventions, and outcomes) considered all authorized DMTs and vaccines. A systematic literature search was conducted and quality of evidence was defined according to the Oxford Centre for Evidence-Based Medicine Levels of Evidence. The recommendations were formulated based on the quality of evidence and the risk–benefit balance. / Results: Seven questions, encompassing vaccine safety, vaccine effectiveness, global vaccination strategy and vaccination in sub-populations (pediatric, pregnant women, elderly and international travelers) were considered. A narrative description of the evidence considering published studies, guidelines, and position statements is presented. A total of 53 recommendations were agreed by the working group after three rounds of consensus. / Conclusion: This first European consensus on vaccination in pwMS proposes the best vaccination strategy according to current evidence and expert knowledge, with the goal of homogenizing the immunization practices in pwMS

Molecular identification of CTX-M and blaOXY/K1 β-lactamase genes in Enterobacteriaceae by sequencing of universal M13-sequence tagged PCR-amplicons

<p>Abstract</p> <p>Background</p> <p>Plasmid encoded ^<it>bla</it>CTX-M enzymes represent an important sub-group of class A β-lactamases causing the ESBL phenotype which is increasingly found in <it>Enterobacteriaceae </it>including <it>Klebsiella </it>spp. Molecular typing of clinical ESBL-isolates has become more and more important for prevention of the dissemination of ESBL-producers among nosocomial environment.</p> <p>Methods</p> <p>Multiple displacement amplified DNA derived from 20 <it>K. pneumoniae </it>and 34 <it>K. oxytoca </it>clinical isolates with an ESBL-phenotype was used in a universal CTX-M PCR amplification assay. Identification and differentiation of ^<it>bla</it>CTX-M and ^<it>bla</it>OXY/K1 sequences was obtained by DNA sequencing of M13-sequence-tagged CTX-M PCR-amplicons using a M13-specific sequencing primer.</p> <p>Results</p> <p>Nine out of 20 <it>K. pneumoniae </it>clinical isolates had a ^<it>bla</it>CTX-M genotype. Interestingly, we found that the universal degenerated primers also amplified the chromosomally located K1-gene in all 34 <it>K. oxytoca </it>clinical isolates. Molecular identification and differentiation between ^<it>bla</it>CTX-M and ^<it>bla</it>OXY/K1-genes could only been achieved by sequencing of the PCR-amplicons. <it>In silico </it>analysis revealed that the universal degenerated CTX-M primer-pair used here might also amplify the chromosomally located ^<it>bla</it>OXY and K1-genes in <it>Klebsiella </it>spp. and K1-like genes in other <it>Enterobacteriaceae</it>.</p> <p>Conclusion</p> <p>The PCR-based molecular typing method described here enables a rapid and reliable molecular identification of ^<it>bla</it>CTX-M, and ^<it>bla</it>OXY/K1-genes. The principles used in this study could also be applied to any situation in which antimicrobial resistance genes would need to be sequenced.</p

Molecular characteristics of carbapenemase-producing Enterobacterales in the Netherlands; results of the 2014–2018 national laboratory surveillance

Objectives: Carbapenem resistance mediated by mobile genetic elements has emerged worldwide and has become a major public health threat. To gain insight into the molecular epidemiology of carbapenem resistance in The Netherlands, Dutch medical microbiology laboratories are requested to submit suspected carbapenemase-producing Enterobacterales (CPE) to the National Institute for Public Health and the Environment as part of a national surveillance system. Methods: Meropenem MICs and species identification were confirmed by E-test and MALDI-TOF and carbapenemase production was assessed by the Carbapenem Inactivation Method. Of all submitted CPE, one species/carbapenemase gene combination per person per year was subjected to next-generation sequencing (NGS). Results: In total, 1838 unique isolates were received between 2014 and 2018, of which 892 were unique CPE isolates with NGS data available. The predominant CPE species were Klebsiella pneumoniae (n = 388, 43%), Escherichia coli (n = 264, 30%) and Enterobacter cloacae complex (n = 116, 13%). Various carbapenemase alleles of the same carbapenemase gene resulted in different susceptibilities to meropenem and this effect varied between species. Analyses of NGS data showed variation of prevalence of carbapenemase alleles over time with blaOXA-48 being predominant (38%, 336/892), followed by blaNDM-1 (16%, 145/892). For the first time in the Netherlands, blaOXA-181, blaOXA-232 and blaVIM-4 were detected. The genetic background of K. pneumoniae and E. coli isolates was highly diverse. Conclusions: The CPE population in the Netherlands is diverse, suggesting multiple introductions. The predominant carbapenemase alleles are blaOXA-48 and blaNDM-1. There was a clear association between species, carbapenemase allele and susceptibility to meropenem

National laboratory-based surveillance system for antimicrobial resistance: a successful tool to support the control of antimicrobial resistance in the Netherlands

An important cornerstone in the control of antimicrobial resistance (AMR) is a well-designed quantitative system for the surveillance of spread and temporal trends in AMR. Since 2008, the Dutch national AMR surveillance system, based on routine data from medical microbiological laboratories (MMLs), has developed into a successful tool to support the control of AMR in the Netherlands. It provides background information for policy making in public health and healthcare services, supports development of empirical antibiotic therapy guidelines and facilitates in-depth research. In addition, participation of the MMLs in the national AMR surveillance network has contributed to sharing of knowledge and quality improvement. A future improvement will be the implementation of a new semantic standard together with standardised data transfer, which will reduce errors in data handling and enable a more real-time surveillance. Furthermore, the

Identification of a BRCA2-Specific modifier locus at 6p24 related to breast cancer risk

Common genetic variants contribute to the observed variation in breast cancer risk for BRCA2 mutation carriers; those known to date have all been found through population-based genome-wide association studies (GWAS). To comprehensively identify breast cancer risk modifying loci for BRCA2 mutation carriers, we conducted a deep replication of an ongoing GWAS discovery study. Using the ranked P-values of the breast cancer associations with the imputed genotype of 1.4 M SNPs, 19,029 SNPs were selected and designed for inclusion on a custom Illumina array that included a total of 211,155 SNPs as part of a multi-consortial project. DNA samples from 3,881 breast cancer affected and 4,330 unaffected BRCA2 mutation carriers from 47 studies belonging to the Consortium of Investigators of Modifiers of BRCA1/2 were genotyped and available for analysis. We replicated previously reported breast cancer susceptibility alleles in these BRCA2 mutation carriers and for several regions (including FGFR2, MAP3K1, CDKN2A/B, and PTHLH) identified SNPs that have stronger evidence of association than those previously published. We also identified a novel susceptibility allele at 6p24 that was inversely associated with risk in BRCA2 mutation carriers (rs9348512; per allele HR = 0.85, 95% CI 0.80-0.90, P = 3.9×10−8). This SNP was not associated with breast cancer risk either in the general population or in BRCA1 mutation carriers. The locus lies within a region containing TFAP2A, which encodes a transcriptional activation protein that interacts with several tumor suppressor genes. This report identifies the first breast cancer risk locus specific to a BRCA2 mutation background. This comprehensive update of novel and previously reported breast cancer susceptibility loci contributes to the establishment of a panel of SNPs that modify breast cancer risk in BRCA2 mutation carriers. This panel may have clinical utility for women with BRCA2 mutations weighing options for medical prevention of breast cancer

An original phylogenetic approach identified mitochondrial haplogroup T1a1 as inversely associated with breast cancer risk in BRCA2 mutation carriers

Introduction: Individuals carrying pathogenic mutations in the BRCA1 and BRCA2 genes have a high lifetime risk of breast cancer. BRCA1 and BRCA2 are involved in DNA double-strand break repair, DNA alterations that can be caused by exposure to reactive oxygen species, a main source of which are mitochondria. Mitochondrial genome variations affect electron transport chain efficiency and reactive oxygen species production. Individuals with different mitochondrial haplogroups differ in their metabolism and sensitivity to oxidative stress. Variability in mitochondrial genetic background can alter reactive oxygen species production, leading to cancer risk. In the present study, we tested the hypothesis that mitochondrial haplogroups modify breast cancer risk in BRCA1/2 mutation carriers. Methods: We genotyped 22,214 (11,421 affected, 10,793 unaffected) mutation carriers belonging to the Consortium of Investigators of Modifiers of BRCA1/2 for 129 mitochondrial polymorphisms using the iCOGS array. Haplogroup inference and association detection were performed using a phylogenetic approach. ALTree was applied to explore the reference mitochondrial evolutionary tree and detect subclades enriched in affected or unaffected individuals. Results: We discovered that subclade T1a1 was depleted in affected BRCA2 mutation carriers compared with the rest of clade T (hazard ratio (HR) = 0.55; 95% confidence interval (CI), 0.34 to 0.88; P = 0.01). Compared with the most frequent haplogroup in the general population (that is, H and T clades), the T1a1 haplogroup has a HR of 0.62 (95% CI, 0.40 to 0.95; P = 0.03). We also identified three potential susceptibility loci, including G13708A/rs28359178, which has demonstrated an inverse association with familial breast cancer risk. Conclusions: This study illustrates how original approaches such as the phylogeny-based method we used can empower classical molecular epidemiological studies aimed at identifying association or risk modification effects.Peer reviewe