Showing papers by "Saarland University published in 2017"

Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel

Abstract: Aims To appraise the clinical and genetic evidence that low-density lipoproteins (LDLs) cause atherosclerotic cardiovascular disease (ASCVD).

Diverse Applications of Nanomedicine

Abstract: The design and use of materials in the nanoscale size range for addressing medical and health-related issues continues to receive increasing interest. Research in nanomedicine spans a multitude of areas, including drug delivery, vaccine development, antibacterial, diagnosis and imaging tools, wearable devices, implants, high-throughput screening platforms, etc. using biological, nonbiological, biomimetic, or hybrid materials. Many of these developments are starting to be translated into viable clinical products. Here, we provide an overview of recent developments in nanomedicine and highlight the current challenges and upcoming opportunities for the field and translation to the clinic.

Pleiotropic Effects of Statins on the Cardiovascular System.

Abstract: The statins have been used for 30 years to prevent coronary artery disease and stroke. Their primary mechanism of action is the lowering of serum cholesterol through inhibiting hepatic cholesterol biosynthesis thereby upregulating the hepatic low-density lipoprotein (LDL) receptors and increasing the clearance of LDL-cholesterol. Statins may exert cardiovascular protective effects that are independent of LDL-cholesterol lowering called pleiotropic effects. Because statins inhibit the production of isoprenoid intermediates in the cholesterol biosynthetic pathway, the post-translational prenylation of small GTP-binding proteins such as Rho and Rac, and their downstream effectors such as Rho kinase and nicotinamide adenine dinucleotide phosphate oxidases are also inhibited. In cell culture and animal studies, these effects alter the expression of endothelial nitric oxide synthase, the stability of atherosclerotic plaques, the production of proinflammatory cytokines and reactive oxygen species, the reactivity of platelets, and the development of cardiac hypertrophy and fibrosis. The relative contributions of statin pleiotropy to clinical outcomes, however, remain a matter of debate and are hard to quantify because the degree of isoprenoid inhibition by statins correlates to some extent with the amount of LDL-cholesterol reduction. This review examines some of the currently proposed molecular mechanisms for statin pleiotropy and discusses whether they could have any clinical relevance in cardiovascular disease.

Simple Does It: Weakly Supervised Instance and Semantic Segmentation

Abstract: Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annotations, we propose a new approach that does not require modification of the segmentation training procedure. We show that when carefully designing the input labels from given bounding boxes, even a single round of training is enough to improve over previously reported weakly supervised results. Overall, our weak supervision approach reaches ~95% of the quality of the fully supervised model, both for semantic labelling and instance segmentation.

Adversarial examples for malware detection

Abstract: Machine learning models are known to lack robustness against inputs crafted by an adversary. Such adversarial examples can, for instance, be derived from regular inputs by introducing minor—yet carefully selected—perturbations.

DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis

Abstract: Summary Background The WHO classification of brain tumours describes 15 subtypes of meningioma. Nine of these subtypes are allotted to WHO grade I, and three each to grade II and grade III. Grading is based solely on histology, with an absence of molecular markers. Although the existing classification and grading approach is of prognostic value, it harbours shortcomings such as ill-defined parameters for subtypes and grading criteria prone to arbitrary judgment. In this study, we aimed for a comprehensive characterisation of the entire molecular genetic landscape of meningioma to identify biologically and clinically relevant subgroups. Methods In this multicentre, retrospective analysis, we investigated genome-wide DNA methylation patterns of meningiomas from ten European academic neuro-oncology centres to identify distinct methylation classes of meningiomas. The methylation classes were further characterised by DNA copy number analysis, mutational profiling, and RNA sequencing. Methylation classes were analysed for progression-free survival outcomes by the Kaplan-Meier method. The DNA methylation-based and WHO classification schema were compared using the Brier prediction score, analysed in an independent cohort with WHO grading, progression-free survival, and disease-specific survival data available, collected at the Medical University Vienna (Vienna, Austria), assessing methylation patterns with an alternative methylation chip. Findings We retrospectively collected 497 meningiomas along with 309 samples of other extra-axial skull tumours that might histologically mimic meningioma variants. Unsupervised clustering of DNA methylation data clearly segregated all meningiomas from other skull tumours. We generated genome-wide DNA methylation profiles from all 497 meningioma samples. DNA methylation profiling distinguished six distinct clinically relevant methylation classes associated with typical mutational, cytogenetic, and gene expression patterns. Compared with WHO grading, classification by individual and combined methylation classes more accurately identifies patients at high risk of disease progression in tumours with WHO grade I histology, and patients at lower risk of recurrence among WHO grade II tumours (p=0·0096) from the Brier prediction test). We validated this finding in our independent cohort of 140 patients with meningioma. Interpretation DNA methylation-based meningioma classification captures clinically more homogenous groups and has a higher power for predicting tumour recurrence and prognosis than the WHO classification. The approach presented here is potentially very useful for stratifying meningioma patients to observation-only or adjuvant treatment groups. We consider methylation-based tumour classification highly relevant for the future diagnosis and treatment of meningioma. Funding German Cancer Aid, Else Kroner-Fresenius Foundation, and DKFZ/Heidelberg Institute of Personalized Oncology/Precision Oncology Program.

DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset

Abstract: We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems. The dataset is available on http://yanran.li/dailydialog

Ascertaining Uncertainty for Efficient Exact Cache Analysis

Abstract: Static cache analysis characterizes a program's cache behavior by determining in a sound but approximate manner which memory accesses result in cache hits and which result in cache misses. Such information is valuable in optimizing compilers, worst-case execution time analysis, and side-channel attack quantification and mitigation.Cache analysis is usually performed as a combination of `must' and `may' abstract interpretations, classifying instructions as either `always hit', `always miss', or `unknown'. Instructions classified as `unknown' might result in a hit or a miss depending on program inputs or the initial cache state. It is equally possible that they do in fact always hit or always miss, but the cache analysis is too coarse to see it.Our approach to eliminate this uncertainty consists in (i) a novel abstract interpretation able to ascertain that a particular instruction may definitely cause a hit and a miss on different paths, and (ii) an exact analysis, removing all remaining uncertainty, based on model checking, using abstract-interpretation results to prune down the model for scalability.We evaluated our approach on a variety of examples; it notably improves precision upon classical abstract interpretation at reasonable cost.

Predicting process behaviour using deep learning

Abstract: Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real datasets and our results surpass the state-of-the-art in prediction precision.

Rare earth and transition metal based entropy stabilised perovskite type oxides

Abstract: Multicomponent oxides with perovskite type of structure containing up to 10 different cations in equiatomic amounts have been synthesised for the first time. Out of eleven systems synthesised, only six systems crystallised as single phase perovskite type compounds with random and homogenous cation distribution on the respective sites. The formation of phase pure 10-cationic system, (Gd0.2La0.2Nd0.2Sm0.2Y0.2)(Co0.2Cr0.2Fe0.2Mn0.2Ni0.2)O3, in contrast to the multiphase mixtures observed in five of the lower entropy systems (containing 6 cations) indicates a possible role of entropy in the stabilisation of a single phase crystal structure. The entropy driven structural stabilisation effect is further supported by the reversible phase transformation, from single phase to multiple phase upon cyclic heat treatment, observed in the (Gd0.2La0.2Nd0.2Sm0.2Y0.2)MnO3 system. This type of entropic signature has been observed in rocksalt based high entropy oxide systems. However, it has not been reported before for perovskite based compounds, as shown in this study.

Mapping and phasing of structural variation in patient genomes using nanopore sequencing

Abstract: Despite improvements in genomics technology, the detection of structural variants (SVs) from short-read sequencing still poses challenges, particularly for complex variation. Here we analyse the genomes of two patients with congenital abnormalities using the MinION nanopore sequencer and a novel computational pipeline-NanoSV. We demonstrate that nanopore long reads are superior to short reads with regard to detection of de novo chromothripsis rearrangements. The long reads also enable efficient phasing of genetic variations, which we leveraged to determine the parental origin of all de novo chromothripsis breakpoints and to resolve the structure of these complex rearrangements. Additionally, genome-wide surveillance of inherited SVs reveals novel variants, missed in short-read data sets, a large proportion of which are retrotransposon insertions. We provide a first exploration of patient genome sequencing with a nanopore sequencer and demonstrate the value of long-read sequencing in mapping and phasing of SVs for both clinical and research applications.

Review of Portable and Low-Cost Sensors for the Ambient Air Monitoring of Benzene and Other Volatile Organic Compounds

Abstract: This article presents a literature review of sensors for the monitoring of benzene in ambient air and other volatile organic compounds. Combined with information provided by stakeholders, manufacturers and literature, the review considers commercially available sensors, including PID-based sensors, semiconductor (resistive gas sensors) and portable on-line measuring devices as for example sensor arrays. The bibliographic collection includes the following topics: sensor description, field of application at fixed sites, indoor and ambient air monitoring, range of concentration levels and limit of detection in air, model descriptions of the phenomena involved in the sensor detection process, gaseous interference selectivity of sensors in complex VOC matrix, validation data in lab experiments and under field conditions.

Meeting the Contact-Mechanics Challenge

Abstract: This paper summarizes the submissions to a recently announced contact-mechanics modeling challenge. The task was to solve a typical, albeit mathematically fully defined problem on the adhesion between nominally flat surfaces. The surface topography of the rough, rigid substrate, the elastic properties of the indenter, as well as the short-range adhesion between indenter and substrate, were specified so that diverse quantities of interest, e.g., the distribution of interfacial stresses at a given load or the mean gap as a function of load, could be computed and compared to a reference solution. Many different solution strategies were pursued, ranging from traditional asperity-based models via Persson theory and brute-force computational approaches, to real-laboratory experiments and all-atom molecular dynamics simulations of a model, in which the original assignment was scaled down to the atomistic scale. While each submission contained satisfying answers for at least a subset of the posed questions, efficiency, versatility, and accuracy differed between methods, the more precise methods being, in general, computationally more complex. The aim of this paper is to provide both theorists and experimentalists with benchmarks to decide which method is the most appropriate for a particular application and to gauge the errors associated with each one.

The systemic nature of CKD

Abstract: The accurate definition and staging of chronic kidney disease (CKD) is one of the major achievements of modern nephrology. Intensive research is now being undertaken to unravel the risk factors and pathophysiologic underpinnings of this disease. In particular, the relationships between the kidney and other organs have been comprehensively investigated in experimental and clinical studies in the last two decades. Owing to technological and analytical limitations, these links have been studied with a reductionist approach focusing on two organs at a time, such as the heart and the kidney or the bone and the kidney. Here, we discuss studies that highlight the complex and systemic nature of CKD. Energy balance, innate immunity and neuroendocrine signalling are highly integrated biological phenomena. The diseased kidney disrupts such integration and generates a high-risk phenotype with a clinical profile encompassing inflammation, protein-energy wasting, altered function of the autonomic and central nervous systems and cardiopulmonary, vascular and bone diseases. A systems biology approach to CKD using omics techniques will hopefully enable in-depth study of the pathophysiology of this systemic disease, and has the potential to unravel critical pathways that can be targeted for CKD prevention and therapy.

Advanced Steel Microstructural Classification by Deep Learning Methods

Abstract: The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Networks (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.

Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation

Abstract: Recent work has shown that state-of-the-art classifiers are quite brittle, in the sense that a small adversarial change of an originally with high confidence correctly classified input leads to a wrong classification again with high confidence. This raises concerns that such classifiers are vulnerable to attacks and calls into question their usage in safety-critical systems. We show in this paper for the first time formal guarantees on the robustness of a classifier by giving instance-specific \emph{lower bounds} on the norm of the input manipulation required to change the classifier decision. Based on this analysis we propose the Cross-Lipschitz regularization functional. We show that using this form of regularization in kernel methods resp. neural networks improves the robustness of the classifier without any loss in prediction performance.

Bromocriptine for the treatment of peripartum cardiomyopathy: a multicentre randomized study.

Abstract: Aims An anti-angiogenic cleaved prolactin fragment is considered causal for peripartum cardiomyopathy (PPCM). Experimental and first clinical observations suggested beneficial effects of the prolactin release inhibitor bromocriptine in PPCM. Methods and results In this multicentre trial, 63 PPCM patients with left ventricular ejection fraction (LVEF) ≤35% were randomly assigned to short-term (1W: bromocriptine, 2.5 mg, 7 days) or long-term bromocriptine treatment (8W: 5 mg for 2 weeks followed by 2.5 mg for 6 weeks) in addition to standard heart failure therapy. Primary end point was LVEF change (delta) from baseline to 6 months assessed by magnetic resonance imaging. Bromocriptine was well tolerated. Left ventricular ejection fraction increased from 28 ± 10% to 49 ± 12% with a delta-LVEF of + 21 ± 11% in the 1W-group, and from 27 ± 10% to 51 ± 10% with a delta-LVEF of + 24 ± 11% in the 8W-group (delta-LVEF: P = 0.381). Full-recovery (LVEF ≥ 50%) was present in 52% of the 1W- and in 68% of the 8W-group with no differences in secondary end points between both groups (hospitalizations for heart failure: 1W: 9.7% vs. 8W: 6.5%, P = 0.651). The risk within the 8W-group to fail full-recovery after 6 months tended to be lower. No patient in the study needed heart transplantation, LV assist device or died. Conclusion Bromocriptine treatment was associated with high rate of full LV-recovery and low morbidity and mortality in PPCM patients compared with other PPCM cohorts not treated with bromocriptine. No significant differences were observed between 1W and 8W treatment suggesting that 1-week addition of bromocriptine to standard heart failure treatment is already beneficial with a trend for better full-recovery in the 8W group. Clinical trial registration ClinicalTrials.gov, study number: NCT00998556.

Does Self-Control Training Improve Self-Control? A Meta-Analysis.

Abstract: Self-control is positively associated with a host of beneficial outcomes. Therefore, psychological interventions that reliably improve self-control are of great societal value. A prominent idea suggests that training self-control by repeatedly overriding dominant responses should lead to broad improvements in self-control over time. Here, we conducted a random-effects meta-analysis based on robust variance estimation of the published and unpublished literature on self-control training effects. Results based on 33 studies and 158 effect sizes revealed a small-to-medium effect of g = 0.30, confidence interval (CI95) [0.17, 0.42]. Moderator analyses found that training effects tended to be larger for (a) self-control stamina rather than strength, (b) studies with inactive compared to active control groups, (c) males than females, and (d) when proponents of the strength model of self-control were (co)authors of a study. Bias-correction techniques suggested the presence of small-study effects and/or publication bias and arrived at smaller effect size estimates (range: gcorrected = .13 to .24). The mechanisms underlying the effect are poorly understood. There is not enough evidence to conclude that the repeated control of dominant responses is the critical element driving training effects.

Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Security

Abstract: Online programming discussion platforms such as Stack Overflow serve as a rich source of information for software developers. Available information include vibrant discussions and oftentimes ready-to-use code snippets. Previous research identified Stack Overflow as one of the most important information sources developers rely on. Anecdotes report that software developers copy and paste code snippets from those information sources for convenience reasons. Such behavior results in a constant flow of community-provided code snippets into production software. To date, the impact of this behaviour on code security is unknown. We answer this highly important question by quantifying the proliferation of security-related code snippets from Stack Overflow in Android applications available on Google Play. Access to the rich source of information available on Stack Overflow including ready-to-use code snippets provides huge benefits for software developers. However, when it comes to code security there are some caveats to bear in mind: Due to the complex nature of code security, it is very difficult to provide ready-to-use and secure solutions for every problem. Hence, integrating a security-related code snippet from Stack Overflow into production software requires caution and expertise. Unsurprisingly, we observed insecure code snippets being copied into Android applications millions of users install from Google Play every day. To quantitatively evaluate the extent of this observation, we scanned Stack Overflow for code snippets and evaluated their security score using a stochastic gradient descent classifier. In order to identify code reuse in Android applications, we applied state-of-the-art static analysis. Our results are alarming: 15.4% of the 1.3 million Android applications we analyzed, contained security-related code snippets from Stack Overflow. Out of these 97.9% contain at least one insecure code snippet.

Altered Gut Microbiome Composition and Tryptic Activity of the 5xFAD Alzheimer's Mouse Model.

Abstract: The regulation of physiological gut functions such as peristalsis or secretion of digestive enzymes by the central nervous system via the Nervus vagus is well known. Recent investigations highlight that pathological conditions of neurological or psychiatric disorders might directly interfere with the autonomous neuronal network of the gut - the enteric nervous system, or even derive from there. By using a murine Alzheimer's disease model, we investigated a potential influence of disease-associated changes on gastrointestinal properties. 5xFAD mice at three different ages were compared to wild type littermates in regard to metabolic parameters and enzymes of the gut by fluorimetric enzyme assay and western blotting. Overexpression of human amyloid-β protein precursor (AβPP) within the gut was assessed by qPCR and IHC; fecal microbiome analysis was conducted by 16SrRNA quantitation of selected phyla and species. While general composition of fecal samples, locomotion, and food consumption of male 5xFAD animals were not changed, we observed a reduced body weight occurring at early pathological stages. Human AβPP was not only expressed within the brain of these mice but also in gut tissue. Analysis of fecal proteins revealed a reduced trypsin amount in the 5xFAD model mice as compared to the wild type. In addition, we observed changes in fecal microbiota composition along with age. We therefore suggest that the presence of the mutated transgenes (AβPP and PS1), which are per se the basis for the genetic form of Alzheimer's disease in humans, directly interferes with gut function as shown here for the disease model mice.

Comparing the Usability of Cryptographic APIs

Abstract: Potentially dangerous cryptography errors are well-documented in many applications. Conventional wisdom suggests that many of these errors are caused by cryptographic Application Programming Interfaces (APIs) that are too complicated, have insecure defaults, or are poorly documented. To address this problem, researchers have created several cryptographic libraries that they claim are more usable, however, none of these libraries have been empirically evaluated for their ability to promote more secure development. This paper is the first to examine both how and why the design and resulting usability of different cryptographic libraries affects the security of code written with them, with the goal of understanding how to build effective future libraries. We conducted a controlled experiment in which 256 Python developers recruited from GitHub attempt common tasks involving symmetric and asymmetric cryptography using one of five different APIs. We examine their resulting code for functional correctness and security, and compare their results to their self-reported sentiment about their assigned library. Our results suggest that while APIs designed for simplicity can provide security benefits – reducing the decision space, as expected, prevents choice of insecure parameters – simplicity is not enough. Poor documentation, missing code examples, and a lack of auxiliary features such as secure key storage, caused even participants assigned to simplified libraries to struggle with both basic functional correctness and security. Surprisingly, the availability of comprehensive documentation and easy-to-use code examples seems to compensate for more complicated APIs in terms of functionally correct results and participant reactions, however, this did not extend to security results. We find it particularly concerning that for about 20% of functionally correct tasks, across libraries, participants believed their code was secure when it was not. Our results suggest that while new cryptographic libraries that want to promote effective security should offer a simple, convenient interface, this is not enough: they should also, and perhaps more importantly, ensure support for a broad range of common tasks and provide accessible documentation with secure, easy-to-use code examples.