Attitudes of U.S. Wind Turbine Neighbors: Analysis of a Nationwide Survey

Experts predict continuing deployment of wind turbines in the United States, which will create more interactions between turbines and surrounding communities. Policymakers can benefit from analyses of existing wind projects that enable them to better understand likely effects on residents around proposed projects. Our analysis of a randomly drawn, representative national survey of 1705 existing U.S. wind project neighbors provides previously unavailable detail about factors influencing the attitudes of these neighbors toward their local wind projects. Overall, we find positive-leaning attitudes, which improve over time as individuals self-select into communities near existing wind projects. Hearing wind turbines leads to less-positive attitudes, although living very near to turbines does not, nor does seeing wind turbines. In fact, our findings suggest complex relationships among nearby residents’ attitudes, their perceptions about the particular fit of turbines within their landscape and community, and their perceptions of wind project impacts on property values. These findings—along with the positive correlation between perceived planning-process fairness and attitude—suggest areas of focus for wind project development that may influence social outcomes and acceptance of wind energy. The concluding discussion provides a number of policy and future research recommendations based on the research

Quantum resource estimates for computing elliptic curve discrete logarithms

We give precise quantum resource estimates for Shor's algorithm to compute discrete logarithms on elliptic curves over prime fields. The estimates are derived from a simulation of a Toffoli gate network for controlled elliptic curve point addition, implemented within the framework of the quantum computing software tool suite LIQ$Ui|\rangle$. We determine circuit implementations for reversible modular arithmetic, including modular addition, multiplication and inversion, as well as reversible elliptic curve point addition. We conclude that elliptic curve discrete logarithms on an elliptic curve defined over an $n$-bit prime field can be computed on a quantum computer with at most $9n + 2\lceil\log_2(n)\rceil+10$ qubits using a quantum circuit of at most $448 n^3 \log_2(n) + 4090 n^3$ Toffoli gates. We are able to classically simulate the Toffoli networks corresponding to the controlled elliptic curve point addition as the core piece of Shor's algorithm for the NIST standard curves P-192, P-224, P-256, P-384 and P-521. Our approach allows gate-level comparisons to recent resource estimates for Shor's factoring algorithm. The results also support estimates given earlier by Proos and Zalka and indicate that, for current parameters at comparable classical security levels, the number of qubits required to tackle elliptic curves is less than for attacking RSA, suggesting that indeed ECC is an easier target than RSA.Comment: 24 pages, 2 tables, 11 figures. v2: typos fixed and reference added. ASIACRYPT 201

Non-Negative Matrix Factorization for Learning Alignment-Specific Models of Protein Evolution

Models of protein evolution currently come in two flavors: generalist and specialist. Generalist models (e.g. PAM, JTT, WAG) adopt a one-size-fits-all approach, where a single model is estimated from a number of different protein alignments. Specialist models (e.g. mtREV, rtREV, HIVbetween) can be estimated when a large quantity of data are available for a single organism or gene, and are intended for use on that organism or gene only. Unsurprisingly, specialist models outperform generalist models, but in most instances there simply are not enough data available to estimate them. We propose a method for estimating alignment-specific models of protein evolution in which the complexity of the model is adapted to suit the richness of the data. Our method uses non-negative matrix factorization (NNMF) to learn a set of basis matrices from a general dataset containing a large number of alignments of different proteins, thus capturing the dimensions of important variation. It then learns a set of weights that are specific to the organism or gene of interest and for which only a smaller dataset is available. Thus the alignment-specific model is obtained as a weighted sum of the basis matrices. Having been constrained to vary along only as many dimensions as the data justify, the model has far fewer parameters than would be required to estimate a specialist model. We show that our NNMF procedure produces models that outperform existing methods on all but one of 50 test alignments. The basis matrices we obtain confirm the expectation that amino acid properties tend to be conserved, and allow us to quantify, on specific alignments, how the strength of conservation varies across different properties. We also apply our new models to phylogeny inference and show that the resulting phylogenies are different from, and have improved likelihood over, those inferred under standard models

Koinonia: verifiable e-voting with long-term privacy

Despite years of research, many existing e-voting systems do not adequately protect voting privacy. In most cases, such systems only achieve "immediate privacy", that is, they only protect voting privacy against today's adversaries, but not against a future adversary, who may possess better attack technologies like new cryptanalysis algorithms and/or quantum computers. Previous attempts at providing long-term voting privacy (dubbed "everlasting privacy" in the literature) often require additional trusts in parties that do not need to be trusted for immediate privacy. In this paper, we present a framework of adversary models regarding e-voting systems, and analyze possible threats to voting privacy under each model. Based on our analysis, we argue that secret-sharing based voting protocols offer a more natural and elegant privacy-preserving solution than their encryption-based counterparts. We thus design and implement Koinonia, a voting system that provides long-term privacy against powerful adversaries and enables anyone to verify that each ballot is well-formed and the tallying is done correctly. Our experiments show that Koinonia protects voting privacy with a reasonable performance

MMP-2 siRNA Inhibits Radiation-Enhanced Invasiveness in Glioma Cells

Our previous work and that of others strongly suggests a relationship between the infiltrative phenotype of gliomas and the expression of MMP-2. Radiation therapy, which represents one of the mainstays of glioma treatment, is known to increase cell invasion by inducing MMP-2. Thus, inhibition of MMP-2 provides a potential means for improving the efficacy of radiotherapy for malignant glioma.We have tested the ability of a plasmid vector-mediated MMP-2 siRNA (p-MMP-2) to modulate ionizing radiation-induced invasive phenotype in the human glioma cell lines U251 and U87. Cells that were transfected with p-MMP-2 with and without radiation showed a marked reduction of MMP-2 compared to controls and pSV-transfected cells. A significant reduction of proliferation, migration, invasion and angiogenesis of cells transfected with p-MMP-2 and in combination with radiation was observed compared to controls. Western blot analysis revealed that radiation-enhanced levels of VEGF, VEGFR-2, pVEGFR-2, p-FAK, and p-p38 were inhibited with p-MMP-2-transfected cells. TUNEL staining showed that radiation did not induce apoptosis in U87 and U251 cells while a significant increase in TUNEL-positive cells was observed when irradiated cells were simultaneously transfected with p-MMP-2 as compared to controls. Intracranial tumor growth was predominantly inhibited in the animals treated with p-MMP-2 alone or in combination with radiation compared to controls.MMP-2 inhibition, mediated by p-MMP-2 and in combination with radiation, significantly reduced tumor cell migration, invasion, angiogenesis and tumor growth by modulating several important downstream signaling molecules and directing cells towards apoptosis. Taken together, our results demonstrate the efficacy of p-MMP-2 in inhibiting radiation-enhanced tumor invasion and progression and suggest that it may act as a potent adjuvant for radiotherapy in glioma patients

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

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