Intrusion detection in unlabeled data with quarter-sphere Support Vector Machines

Practical application of data mining and machine learning techniques to intrusion detection is often hindered by the difficulty to produce clean data for the training. To address this problem a geometric framework for unsupervised anomaly detection has been recently proposed. In this framework, the data is mapped into a feature space, and anomalies are detected as the entries in sparsely populated regions. In this contribution we propose a novel formulation of a one-class Support Vector Machine (SVM) specially designed for typical IDS data features. The key idea of our ”quarter-sphere” algorithm is to encompass the data with a hypersphere anchored at the center of mass of the data in feature space. The proposed method and its behavior on varying percentages of attacks in the data is evaluated on the KDDCup 1999 dataset

Automatic Identification of Faked and Fraudulent Interviews in Surveys by Two Different Methods

This paper presents two new tools for the identification of faking interviewers in surveys. One method is based on Benford's Law, and the other exploits the empirical observation that fakers most often produce answers with less variability than could be expected from the whole survey. We focus on fabricated data, which were taken out of the survey before the data were disseminated in the German Socio-Economic Panel (SOEP). For two samples, the resulting rankings of the interviewers with respect to their cheating behavior are given. For both methods all of the evident fakers are identified.

Differentielle Expression von Monozytensubpopulationen im Rahmen von Inflammation und Remodelling bei der koronaren Herzkrankheit

Die koronare Herzerkrankung ist eine Erkrankung von weltweiter Bedeutung. Die Inflammation sowie die zentrale Rolle der Monozyten sind seit Jahren Inhalt wissenschaftlicher Studien zum besseren Verständnis der arteriosklerotischen Pathogenese. Ziel der durchgeführten Untersuchung war daher der Nachweis einer differenziellen Monozytenverteilung in Patienten mit koronarer Herzerkrankung im Vergleich zu gesunden Kontrollprobanden. Zur Erreichung dieses Ziels wurde eine Kohortenstudie mit drei Patientenkohorten, definiert nach der Krankheitsaktivität zum Zeitpunkt des Studieneinschlusses und einer Kontrollkohorte aus kardial nicht vorerkrankten Probanden, durchgeführt. Die Krankheitsaktivität war definiert nach: 1. Akutes Ereignis (Kohorte: Myokardinfarkt MI) 2. Chronische KHK, ohne akutes Ereignis in den letzten sechs Monaten (Kohorte: Chronical artery disease = CAD) 3. Chronische KHK, mit akut auftretendem Ereignis (Kohorte: CAD-MI) 4. Kontroll-Kohorte Es erfolgte die durchflusszytometrische Analyse von Leukozyten, die aus Vollblutproben isoliert wurden. Dieses Verfahren ermöglichte sowohl die exakte Differenzierung sowie die Quantifizierung der gesuchten Monozytensubpopulationen. Die erhaltenen Messdaten der einzelnen Kohorten wurden in statistischen Verfahren ausgewertet. Die Ergebnisse der Untersuchungen ergaben signifikant höhere Vorkommen von inflammatorischen Monozyten bei Patienten mit akuten koronaren Ereignissen (pCAD<0,01). Die intermediären Monozyten waren bei allen Patientengruppen gegenüber der Kontrollkohorte erhöht (pMI<0,05, pCAD<0,05, pCAD-MI< 0,01). Die Ergebnisse lassen sich mit bisher durchgeführten Studien, im Bezug zu der Fragestellung Monozytenvorkommen und Funktion bei Patienten mit KHK vereinbaren. Auch in der dritten Subpopulation (CD14loCD16hi), den reparativen Monozyten konnten signifikant höhere Monozytenvorkommen für die Kontrollgruppe (p<0,05) sowie für die Patientengruppe CAD (p< 0,001) gegenüber den Patienten mit akuten Myokardinfarkten nachgewiesen werden. Dieses Ergebnis spricht für ein eventuelles Verdrängen der reparativen Monozyten durch die Monozytenpopulationen, die in Zusammenhang mit einer aktiven KHK und den klinischen akuten Ereignissen stehen könnten. Der Nachweis eines differenziellen monozytären Verteilungsmusters bei Patienten mit unterschiedlichen Krankheitsaktivitäten kann weitere Studien zur Untersuchung in Frage kommender Biomarker zu Frühdiagnostik oder neuer immunmodulatorischer Therapien unterstützen

Societal effects of transdisciplinary sustainability research—How can they be strengthened during the research process?

Transdisciplinary sustainability research aims to mitigate or to solve complex societal problems and advance the production of scientific knowledge. Reflexive approaches to transdisciplinary research processes are outlined to systematically strengthen the potential for societal effectiveness. So far, it is rare to find empirically based analyses of the links between the quality of the research process and the methods applied on the one hand and the effects achieved on the other. This paper thus addresses the issue of heightening the societal effects of transdisciplinary sustainability research. The objective is to explore ways of consciously promoting societal effectiveness in transdisciplinary research. We argue that these possibilities evolve at the intersection between the general project framework and an adaptive shaping of transdisciplinary research processes. A reflexive approach of this kind proactively considers the dynamics of interests and concerns, roles and responsibilities, the collaboration culture within a project, and the connectivity to the context of action addressed. Its deployment presupposes an appreciation of the basic conditions, i.e. the historical development of the respective problem, the heterogeneity of actors involved, the general environment and, finally, the funding conditions

Oil droplet breakup during pressure swirl atomization of food emulsions: Influence of atomization pressure and initial oil droplet size

Atomization of emulsions with pressure swirl atomizers is a common task in food process engineering. Especially in spray drying processes for food materials like dairy products, it is the technology of choice. During atomization, emulsions are subjected to high stresses, which can lead to deformation and breakup of the dispersed droplets. In this study, the influence of atomization pressure (5–20 MPa) and initial oil droplet size (0.26, 3.1, and 20.8 μm) on the oil droplet breakup during atomization of food based oil‐in‐water emulsions with pressure swirl atomizers was investigated. It was shown that a significant oil droplet breakup takes place upon atomization. The size of oil droplets with an initial value of 3.1 and 20 μm was reduced up to 0.36 μm. No breakup of oil droplets with an initial value of 0.26 μm was observed. The breakup was highly dependent on the atomization pressure. The results were analyzed based on existing knowledge on droplet breakup in laminar flow. A concept to estimate capillary numbers during atomization was developed based on common models from different applications. The results of this study can be used to control the resulting oil droplet size after atomization with pressure swirl atomizers. Practical application: Spray drying of emulsions is a widely used process in the food industry to produce products with encapsulated oily components. Product examples include infant formula, milk powder, and the encapsulation of aroma and coloring compounds. Breakup of the oil droplets during the atomization step of spray drying can change a previously adjusted and desired oil droplet size. As the oil droplet size in the final product can be responsible for several properties like sensorial aspects and stability, a control of oil droplet breakup is essential. Pressure swirl atomizers are widely used in industrial applications as atomization devices. In this study, oil droplet breakup during atomization with these atomizers was investigated. The findings in this study allow a better control of the oil droplet size during atomization in practical applications

Point-of-care detection and differentiation of anticoagulant therapy - development of thromboelastometry-guided decision-making support algorithms

BACKGROUND DOAC detection is challenging in emergency situations. Here, we demonstrated recently, that modified thromboelastometric tests can reliably detect and differentiate dabigatran and rivaroxaban. However, whether all DOACs can be detected and differentiated to other coagulopathies is unclear. Therefore, we now tested the hypothesis that a decision tree-based thromboelastometry algorithm enables detection and differentiation of all direct Xa-inhibitors (DXaIs), the direct thrombin inhibitor (DTI) dabigatran, as well as vitamin K antagonists (VKA) and dilutional coagulopathy (DIL) with high accuracy. METHODS Following ethics committee approval (No 17-525-4), and registration by the German clinical trials database we conducted a prospective observational trial including 50 anticoagulated patients (n = 10 of either DOAC/VKA) and 20 healthy volunteers. Blood was drawn independent of last intake of coagulation inhibitor. Healthy volunteers served as controls and their blood was diluted to simulate a 50% dilution in vitro. Standard (extrinsic coagulation assay, fibrinogen assay, etc.) and modified thromboelastometric tests (ecarin assay and extrinsic coagulation assay with low tissue factor) were performed. Statistical analyzes included a decision tree analyzes, with depiction of accuracy, sensitivity and specificity, as well as receiver-operating-characteristics (ROC) curve analysis including optimal cut-off values (Youden-Index). RESULTS First, standard thromboelastometric tests allow a good differentiation between DOACs and VKA, DIL and controls, however they fail to differentiate DXaIs, DTIs and VKAs reliably resulting in an overall accuracy of 78%. Second, adding modified thromboelastometric tests, 9/10 DTI and 28/30 DXaI patients were detected, resulting in an overall accuracy of 94%. Complex decision trees even increased overall accuracy to 98%. ROC curve analyses confirm the decision-tree-based results showing high sensitivity and specificity for detection and differentiation of DTI, DXaIs, VKA, DIL, and controls. CONCLUSIONS Decision tree-based machine-learning algorithms using standard and modified thromboelastometric tests allow reliable detection of DTI and DXaIs, and differentiation to VKA, DIL and controls. TRIAL REGISTRATION Clinical trial number: German clinical trials database ID: DRKS00015704

Selection effects may account for better outcomes of the German Disease Management Program for type 2 diabetes

<p>Abstract</p> <p>Background</p> <p>The nationwide German disease management program (DMP) for type 2 diabetes was introduced in 2003. Meanwhile, results from evaluation studies were published, but possible baseline differences between DMP and usual-care patients have not been examined. The objective of our study was therefore to find out if patient characteristics as socio-demographic variables, cardiovascular risk profile or motivation for life style changes influence the chance of being enrolled in the German DMP for type 2 diabetes and may therefore account for outcome differences between DMP and usual-care patients.</p> <p>Methods</p> <p>Case control study comparing DMP patients with usual-care patients at baseline and follow up; mean follow-up period of 36 ± 14 months. We used chart review data from 51 GP surgeries. Participants were 586 DMP and 250 usual-care patients with type 2 diabetes randomly selected by chart registry. Data were analysed by multivariate logistic and linear regression analyses. Significance levels were p ≤ 0.05.</p> <p>Results</p> <p>There was a better chance for enrolment if patients a) had a lower risk status for diabetes complications, i.e. non-smoking (odds ratio of 1.97, 95% confidence interval of 1.11 to 3.48) and lower systolic blood pressure (1.79 for 120 mmHg vs. 160 mmHg, 1.15 to 2.81); b) had higher activity rates, i.e. were practicing blood glucose self-monitoring (1.67, 1.03 to 2.76) and had been prescribed a diabetes patient education before enrolment (2.32, 1.29 to 4.19) c) were treated with oral medication (2.17, 1.35 to 3.49) and d) had a higher GP-rated motivation for diabetes education (4.55 for high motivation vs. low motivation, 2.21 to 9.36).</p> <p>Conclusions</p> <p>At baseline, future DMP patients had a lower risk for diabetes complications, were treated more intensively and were more active and motivated in managing their disease than usual-care patients. This finding a) points to the problem that the German DMP may not reach the higher risk patients and b) selection bias may impair the assessment of differences in outcome quality between enrolled and usual-care patients. Suggestions for dealing with this bias in evaluation studies are being made.</p

Which chronic diseases and disease combinations are specific to multimorbidity in the elderly? Results of a claims data based cross-sectional study in Germany

<p>Abstract</p> <p>Background</p> <p>Growing interest in multimorbidity is observable in industrialized countries. For Germany, the increasing attention still goes still hand in hand with a small number of studies on multimorbidity. The authors report the first results of a cross-sectional study on a large sample of policy holders (n = 123,224) of a statutory health insurance company operating nationwide. This is the first comprehensive study addressing multimorbidity on the basis of German claims data. The main research question was to find out which chronic diseases and disease combinations are specific to multimorbidity in the elderly.</p> <p>Methods</p> <p>The study is based on the claims data of all insured policy holders aged 65 and older (n = 123,224). Adjustment for age and gender was performed for the German population in 2004. A person was defined as multimorbid if she/he had at least 3 diagnoses out of a list of 46 chronic conditions in three or more quarters within the one-year observation period. Prevalences and risk-ratios were calculated for the multimorbid and non-multimorbid samples in order to identify diagnoses more specific to multimorbidity and to detect excess prevalences of multimorbidity patterns.</p> <p>Results</p> <p>62% of the sample was multimorbid. Women in general and patients receiving statutory nursing care due to disability are overrepresented in the multimorbid sample. Out of the possible 15,180 combinations of three chronic conditions, 15,024 (99%) were found in the database. Regardless of this wide variety of combinations, the most prevalent individual chronic conditions do also dominate the combinations: Triads of the six most prevalent individual chronic conditions (hypertension, lipid metabolism disorders, chronic low back pain, diabetes mellitus, osteoarthritis and chronic ischemic heart disease) span the disease spectrum of 42% of the multimorbid sample. Gender differences were minor. Observed-to-expected ratios were highest when purine/pyrimidine metabolism disorders/gout and osteoarthritis were part of the multimorbidity patterns.</p> <p>Conclusions</p> <p>The above list of dominating chronic conditions and their combinations could present a pragmatic start for the development of needed guidelines related to multimorbidity.</p

Multimorbidity Patterns in the Elderly: A New Approach of Disease Clustering Identifies Complex Interrelations between Chronic Conditions

Objective: Multimorbidity is a common problem in the elderly that is significantly associated with higher mortality, increased disability and functional decline. Information about interactions of chronic diseases can help to facilitate diagnosis, amend prevention and enhance the patients ’ quality of life. The aim of this study was to increase the knowledge of specific processes of multimorbidity in an unselected elderly population by identifying patterns of statistically significantly associated comorbidity. Methods: Multimorbidity patterns were identified by exploratory tetrachoric factor analysis based on claims data of 63,104 males and 86,176 females in the age group 65+. Analyses were based on 46 diagnosis groups incorporating all ICD-10 diagnoses of chronic diseases with a prevalence $ 1%. Both genders were analyzed separately. Persons were assigned to multimorbidity patterns if they had at least three diagnosis groups with a factor loading of 0.25 on the corresponding pattern. Results: Three multimorbidity patterns were found: 1) cardiovascular/metabolic disorders [prevalence female: 30%; male: 39%], 2) anxiety/depression/somatoform disorders and pain [34%; 22%], and 3) neuropsychiatric disorders [6%; 0.8%]. The sampling adequacy was meritorious (Kaiser-Meyer-Olkin measure: 0.85 and 0.84, respectively) and the factors explained a large part of the variance (cumulative percent: 78 % and 75%, respectively). The patterns were largely age-dependent an