Quantifying Privacy: A Novel Entropy-Based Measure of Disclosure Risk

It is well recognised that data mining and statistical analysis pose a serious treat to privacy. This is true for financial, medical, criminal and marketing research. Numerous techniques have been proposed to protect privacy, including restriction and data modification. Recently proposed privacy models such as differential privacy and k-anonymity received a lot of attention and for the latter there are now several improvements of the original scheme, each removing some security shortcomings of the previous one. However, the challenge lies in evaluating and comparing privacy provided by various techniques. In this paper we propose a novel entropy based security measure that can be applied to any generalisation, restriction or data modification technique. We use our measure to empirically evaluate and compare a few popular methods, namely query restriction, sampling and noise addition.Comment: 20 pages, 4 figure

Linear-Time Superbubble Identification Algorithm for Genome Assembly

DNA sequencing is the process of determining the exact order of the nucleotide bases of an individual's genome in order to catalogue sequence variation and understand its biological implications. Whole-genome sequencing techniques produce masses of data in the form of short sequences known as reads. Assembling these reads into a whole genome constitutes a major algorithmic challenge. Most assembly algorithms utilize de Bruijn graphs constructed from reads for this purpose. A critical step of these algorithms is to detect typical motif structures in the graph caused by sequencing errors and genome repeats, and filter them out; one such complex subgraph class is a so-called superbubble. In this paper, we propose an O(n+m)-time algorithm to detect all superbubbles in a directed acyclic graph with n nodes and m (directed) edges, improving the best-known O(m log m)-time algorithm by Sung et al

Environmental study of some metals on several aquatic macrophytes

Aquatic macrophytes can be used in the study of quality of water ecosystems and in monitoring of metals and other pollutants. This study was focused on assessment of metals accumulation in certain aquatic macrophytes (biomonitors), in comparison with water and sediment (abiotic monitors) of the lake. Concentrations of Fe, Mn, Cu and Pb were measured in water, sediment and plant samples, namely in stems and leaves of Bidens tripartitus L., Polygonum amphibium L., Lycopus europaeus L. and in roots, stems and leaves of two aquatic plants, Typha angustifolia L. and Typha latifolia L. The concentrations of all investigated metals were higher in sediment than in water. The mean concentrations of metals in macrophytes were sequenced: Fe > Mn > Cu > Pb. This study exhibited different metals concentration in aquatic plants, depending on the plant organ. The highest concentrations of Fe and Pb were recorded in root of T.latifolia L. As means of Mn and Cu, their concentrations were higher in stems and leaves of different investigated species. The application of macrophytes can be possible in finding of solutions for problems of protection, sanation and revitalization of different aquatic ecosystems.Key words: Aquatic macrophytes, metals (Fe, Mn, Cu and Pb), lake contamination

Quantitative analysis of the dystrophin gene by real-time PCR

Duchenne and Becker muscular dystrophy (DMD/BMD) are severe X-linked neuromuscular disorders caused by mutations in the dystrophin gene. Our aim was to optimize a quantitative real-time PCR method based on SYBR® Green I chemistry for routine diagnostics of DMD/BMD deletion carriers. Twenty female relatives of DMD/BMD patients with previously detected partial gene deletions were studied. The relative quantity of the target exons was calculated by a comparative threshold cycle method (ΔΔCt). The carrier status of all subjects was successfully determined. The gene dosage ratio for non-carriers was 1.07±0.20, and for carriers 0.56±0.11. This assay proved to be simple, rapid, reliable and cost-effective

Developing textile entrepreneurial inclination model by integrating experts mining and ISM-MICMAC

The Indian textile industry is lacking in an entrepreneurial inclination of a skilled young generation; because of this, the industry is facing a challenge to achieve sustainable development and growth. To overcome this problem, the goal of this work is to build an entrepreneurial inclination model in the context of the textile industry. For achieving this goal, a combined approach of an extensive literature review and experts mining has been used to establish the entrepreneurial inclination factors in phased of the study. In the second phase, an Interpretive Structural Modelling (ISM) with Matrice d'Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) has been applied to build a structural model and to find the driving force factors and dependence power. The results show that effective entrepreneurship courses, institutional policy, training and internship, institutional corporation and the involvement of institutional heads play a very significant role in encouraging youth towards entrepreneurship. The outcomes of the study can help both the government and academic institutes to draw up effective policy and develop an entrepreneurial culture which can help to create more entrepreneurs in the textile field.N

Evolution of renal function and predictive value of serial renal assessments among patients with acute coronary syndrome:BIOMArCS study

Background: Impaired renal function predicts mortality in acute coronary syndrome (ACS), but its evolution immediately following index ACS and preceding next ACS has not been described in detail. We aimed to describe this evolution using serial measurements of creatinine, glomerular filtration rate [eGFRCr] and cystatin C [CysC]. Methods: From 844 ACS patients included in the BIOMArCS study, we analysed patient-specific longitudinal marker trajectories from the case-cohort of 187 patients to determine the risk of the endpoint (cardiovascular death or hospitalization for recurrent non-fatal ACS) during 1-year follow-up. Study included only patients with eGFRCr ≥ 30 ml/min/1.73 m2. Survival analyses were adjusted for GRACE risk score and based on data >30 days after the index ACS (mean of 8 sample per patient). Results: Mean age was 63 years, 79% were men, 43% had STEMI, and 67% were in eGFR stages 2–3. During hospitalization for index ACS (median [IQR] duration: 5 (3–7) days), CysC levels indicated deterioration of renal function earlier than creatinine did (CysC peaked on day 3, versus day 6 for creatinine), and both stabilized after two weeks. Higher CysC levels, but not creatinine, predicted the endpoint independently of the GRACE score within the first year after index ACS (adjusted HR [95% CI] per 1SD increase: 1.68 [1.03–2.74]). Conclusion: Immediately following index ACS, plasma CysC levels deteriorate earlier than creatinine-based indices do, but neither marker stabilizes during hospitalization but on average two weeks after ACS. Serially measured CysC levels predict mortality or recurrence of ACS during 1-year follow-up independently of patients' GRACE risk score

Understanding of interaction (subgroup) analysis in clinical trials

Background: When the treatment effect on the outcome of interest is influenced by a baseline/demographic factor, investigators say that an interaction is present. In randomized clinical trials (RCTs), this type of analysis is typically referred to as subgroup analysis. Although interaction (or subgroup) analyses are usually stated as a secondary study objective, it is not uncommon that these results lead to changes in treatment protocols or even modify public health policies. Nonetheless, recent reviews have indicated that their proper assessment, interpretation and reporting remain challenging. Results: Therefore, this article provides an overview of these challenges, to help investigators find the best strategy for application of interaction analyses on binary outcomes in RCTs. Specifically, we discuss the key points of formal interaction testing, including the estimation of both additive and multiplicative interaction effects. We also provide recommendations that, if adhered to, could increase the clarity and the completeness of reports of RCTs. Conclusion: Altogether, this article provides a brief non-statistical guide for clinical investigators on how to perform, interpret and report interaction (subgroup) analyses in RCTs

Formation of Nanoclusters and Nanopillars in Nonequilibrium Surface Growth for Catalysis Applications: Growth by Diffusional Transport of Matter in Solution Synthesis

Growth of nanoclusters and nanopillars is considered in a model of surface deposition of building blocks (atoms) diffusionally transported from solution to the forming surface structure. Processes of surface restructuring are also accounted for in the model, which then yields morphologies of interest in catalysis applications. Kinetic Monte Carlo numerical approach is utilized to explore the emergence of FCC-symmetry surface features in Pt-type metal nanostructures. Available results exemplify evaluation of the fraction of the resulting active sites with desirable properties for catalysis, such as (111)-like coordination, as well as suggest optimal growth regimes

Sol-gel as a Method to Tailor the Magnetic Properties of Co1+yAl2-yO4

The magnetic properties of mesoscopic materials are modified by size and surface effects. We present a sol-gel method used to tailor these effects, and illustrate it on Co1+yAl2-yO4 spinel. Nanocomposites made of spinel oxide Co1+yAl2-yO4 particles dispersed in an amorphous SiO2 matrix were synthesized. Samples with various mass fractions -x of Co1+yAl2-yO4 in composite, ranging from predominantly SiO2 (x = 10 wt%) to predominantly spinel (x = 95 wt%), and with various Co concentrations in spinel y were studied. The spinel grain sizes were below 100 nm with a large size distribution, for samples with predominant spinel phase. Those samples showed Curie-Weiss paramagnetic behavior with antiferromagnetically interacting Co ions (theta approximate to -100 K). The grain sizes of spinel stays confined in 100 nm range even in the spinel samples diluted with as low as 5 wt% concentration of amorphous SiO2. For the samples with predominant SiO2 the crystalline nanoparticles are well separated and of size of around 100 nm, but with presence of much smaller spinel nanoparticles of about 10 nm. The magnetic properties of the samples with predominant silica phase showed complex behavior, spin-glass magnetic freezing at the lowest temperatures and lower absolute value of theta and consequently lower exchange constant