65 research outputs found

    Smart Meter Privacy: A Utility-Privacy Framework

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    End-user privacy in smart meter measurements is a well-known challenge in the smart grid. The solutions offered thus far have been tied to specific technologies such as batteries or assumptions on data usage. Existing solutions have also not quantified the loss of benefit (utility) that results from any such privacy-preserving approach. Using tools from information theory, a new framework is presented that abstracts both the privacy and the utility requirements of smart meter data. This leads to a novel privacy-utility tradeoff problem with minimal assumptions that is tractable. Specifically for a stationary Gaussian Markov model of the electricity load, it is shown that the optimal utility-and-privacy preserving solution requires filtering out frequency components that are low in power, and this approach appears to encompass most of the proposed privacy approaches.Comment: Accepted for publication and presentation at the IEEE SmartGridComm. 201

    Understanding how the crowded interior of cells stabilizes DNA/DNA and DNA/RNA hybrids–in silico predictions and in vitro evidence

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    Amplification of DNA in vivo occurs in intracellular environments characterized by macromolecular crowding (MMC). In vitro Polymerase-chain-reaction (PCR), however, is non-crowded, requires thermal cycling for melting of DNA strands, primer-template hybridization and enzymatic primer-extension. The temperature-optima for primer-annealing and extension are strikingly disparate which predicts primers to dissociate from template during extension thereby compromising PCR efficiency. We hypothesized that MMC is not only important for the extension phase in vivo but also during PCR by stabilizing nucleotide hybrids. Novel atomistic Molecular Dynamics simulations elucidated that MMC stabilizes hydrogen-bonding between complementary nucleotides. Real-time PCR under MMC confirmed that melting-temperatures of complementary DNA–DNA and DNA–RNA hybrids increased by up to 8°C with high specificity and high duplex-preservation after extension (71% versus 37% non-crowded). MMC enhanced DNA hybrid-helicity, and drove specificity of duplex formation preferring matching versus mismatched sequences, including hair-pin-forming DNA- single-strands

    Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

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    Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

    Genetics ignite focus on microglial inflammation in Alzheimer’s disease

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    In the past five years, a series of large-scale genetic studies have revealed novel risk factors for Alzheimer’s disease (AD). Analyses of these risk factors have focused attention upon the role of immune processes in AD, specifically microglial function. In this review, we discuss interpretation of genetic studies.  We then focus upon six genes implicated by AD genetics that impact microglial function: TREM2, CD33, CR1, ABCA7, SHIP1, and APOE. We review the literature regarding the biological functions of these six proteins and their putative role in AD pathogenesis. We then present a model for how these factors may interact to modulate microglial function in AD

    β-hairpin forms by rolling up from C-terminal : topological guidance of early folding dynamics

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    That protein folding is a non-random, guided process has been known even prior to Levinthal's paradox; yet, guided searches, attendant mechanisms and their relation to primary sequence remain obscure. Using extensive molecular dynamics simulations of a β-hairpin with key sequence features similar to those of >13,000 β-hairpins in full proteins, we provide significant insights on the entire pre-folding dynamics at single-residue levels and describe a single, highly coordinated roll-up folding mechanism, with clearly identifiable stages, directing structural progression toward native state. Additional simulations of single-site mutants illustrate the role of three key residues in facilitating this roll-up mechanism. Given the many β-hairpins in full proteins with similar residue arrangements and since β-hairpins are believed to act as nucleation sites in early-stage folding dynamics of full proteins, the topologically guided mechanism seen here may represent one of Nature's strategies for reducing early-stage folding complexity

    Utility-Privacy Tradeoffs in Databases: An Information-Theoretic Approach

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    Ensuring the usefulness of electronic data sources while providing necessary privacy guarantees is an important unsolved problem. This problem drives the need for an analytical framework that can quantify the safety of personally identifiable information (privacy) while still providing a quantifable benefit (utility) to multiple legitimate information consumers. This paper presents an information-theoretic framework that promises an analytical model guaranteeing tight bounds of how much utility is possible for a given level of privacy and vice-versa. Specific contributions include: i) stochastic data models for both categorical and numerical data; ii) utility-privacy tradeoff regions and the encoding (sanization) schemes achieving them for both classes and their practical relevance; and iii) modeling of prior knowledge at the user and/or data source and optimal encoding schemes for both cases.Comment: Revised following submission to the IEEE Transactions on Information Forensics and Security: Special Issue on Privacy and Trust Management in Cloud and Distributed Systems; updated with missing reference
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