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Polymerized ionic liquids (PILs) with anions of bis(trifluoromethylsulfonyl)imide (TFSI−), hexafluorophosphate (PF6−), and tetrafluoroborate (BF4−); and cations of poly[1-(4-vinylbenzyl)-3-alkyl-imidazolium] P[VBCnIM]+ with alkyl lengths C1, C2, C4 were successfully synthesized and characterized. X-ray scattering showed an increase of backbone-to-backbone spacing (db) by 0.8 Å per CH2 added to the alkyl side chain. Rheological and dielectric measurements were used to measure rates of chain relaxation and ion dissociation/association. The glass transition temperatures Tg follow the trend: PC4-TFSI < PC2-TFSI < PC1-TFSI< PC1-BF4 < PC1-PF6, which correlates well with their dielectric behaviors. However, the fragility mDR from dielectric relaxation increases with decreasing Tg, which is the opposite of the dependence of the fragility mRheo from rheology for both our PILs and of neutral polymers. The dielectric and rheological relaxations of our PIL’s are expected to be influenced by both their anion-cation binding energies and their relative free volumes, while for neutral polymers, only free volume influences relaxation. The increase of fragility of mDR with decreasing Tg, therefore suggests that dielectric relaxation is influenced more by anion-cation binding energy than by free volume, while the reverse is true for mRheo. The plateau modulus GN and entanglement molecular weight Me estimated from rheological measurements agree with predictions of the packing model, using only a small modification of the Flory characteristic ratio C∞ from that of a neutral polymer. Packing lengths of p= 6.0 ~ 9.3 Å and tube diameters dt= 11 ~ 17 nm are found, depending on specific cation and anion structures. 

This paper studies privacy in the context of decision-support queries that classify objects as either true or false based on whether they satisfy the query. Mechanisms to ensure privacy may result in false positives and false negatives. In decision-support applications, often, false negatives have to remain bounded. Existing accuracy-aware privacy preserving techniques cannot directly be used to support such an accuracy requirement and their naive adaptations to support bounded accuracy of false negatives results in significant privacy loss depending upon distribution of data. This paper explores the concept of minimally-invasive data exploration for decision support that attempts to minimize privacy loss while supporting bounded guarantee on false negatives by adaptively adjusting privacy based on data distribution. Our experimental results show that the MIDE algorithms perform well and are robust over variations in data distributions. 

We study the problem of answering queries when (part of) the data may be sensitive and should not be leaked to the querier. Simply restricting the computation to non-sensitive part of the data may leak sensitive data through inference based on data dependencies. While inference control from data dependencies during query processing has been studied in the literature, existing solution either detect and deny queries causing leakage, or use a weak security model that only protects against exact reconstruction of the sensitive data. In this paper, we adopt a stronger security model based on full deniability that prevents any information about sensitive data to be inferred from query answers. We identify conditions under which full deniability can be achieved and develop an efficient algorithm that minimally hides non-sensitive cells during query processing to achieve full deniability. We experimentally show that our approach is practical and scales to increasing proportion of sensitive data, as well as, to increasing database size. 

Abstract Proper cell-type identity relies on highly coordinated regulation of gene expression. Regulatory elements such as enhancers can produce cell type-specific expression patterns, but the mechanisms underlying specificity are not well understood. We previously identified an enhancer region capable of driving specific expression in giant cells, which are large, highly endoreduplicated cells in the Arabidopsis thaliana sepal epidermis. In this study, we use the giant cell enhancer as a model to understand the regulatory logic that promotes cell type-specific expression. Our dissection of the enhancer revealed that giant cell specificity is mediated primarily through the combination of two activators and one repressor. HD-ZIP and TCP transcription factors are involved in the activation of expression throughout the epidermis. High expression of HD-ZIP transcription factor genes in giant cells promoted higher expression driven by the enhancer in giant cells. Dof transcription factors repressed the activity of the enhancer such that only giant cells maintained enhancer activity. Thus, our data are consistent with a conceptual model whereby cell type-specific expression emerges from the combined activities of three transcription factor families activating and repressing expression in epidermal cells. 

Knowledge of locations and activities ofcis-regulatory elements (CREs) is needed to decipher basic mechanisms of gene regulation and to understand the impact of genetic variants on complex traits. Previous studies identified candidate CREs (cCREs) using epigenetic features in one species, making comparisons difficult between species. In contrast, we conducted an interspecies study defining epigenetic states and identifying cCREs in blood cell types to generate regulatory maps that are comparable between species, using integrative modeling of eight epigenetic features jointly in human and mouse in our Validated Systematic Integration (VISION) Project. The resulting catalogs of cCREs are useful resources for further studies of gene regulation in blood cells, indicated by high overlap with known functional elements and strong enrichment for human genetic variants associated with blood cell phenotypes. The contribution of each epigenetic state in cCREs to gene regulation, inferred from a multivariate regression, was used to estimate epigenetic state regulatory potential (esRP) scores for each cCRE in each cell type, which were used to categorize dynamic changes in cCREs. Groups of cCREs displaying similar patterns of regulatory activity in human and mouse cell types, obtained by joint clustering on esRP scores, harbor distinctive transcription factor binding motifs that are similar between species. An interspecies comparison of cCREs revealed both conserved and species-specific patterns of epigenetic evolution. Finally, we show that comparisons of the epigenetic landscape between species can reveal elements with similar roles in regulation, even in the absence of genomic sequence alignment. 

Abstract Organizations often collect private data and release aggregate statistics for the public’s benefit. If no steps toward preserving privacy are taken, adversaries may use released statistics to deduce unauthorized information about the individuals described in the private dataset. Differentially private algorithms address this challenge by slightly perturbing underlying statistics with noise, thereby mathematically limiting the amount of information that may be deduced from each data release. Properly calibrating these algorithms—and in turn the disclosure risk for people described in the dataset—requires a data curator to choose a value for a privacy budget parameter, ɛ . However, there is little formal guidance for choosing ɛ , a task that requires reasoning about the probabilistic privacy–utility tradeoff. Furthermore, choosing ɛ in the context of statistical inference requires reasoning about accuracy trade-offs in the presence of both measurement error and differential privacy (DP) noise. We present Vi sualizing P rivacy (ViP), an interactive interface that visualizes relationships between ɛ , accuracy, and disclosure risk to support setting and splitting ɛ among queries. As a user adjusts ɛ , ViP dynamically updates visualizations depicting expected accuracy and risk. ViP also has an inference setting, allowing a user to reason about the impact of DP noise on statistical inferences. Finally, we present results of a study where 16 research practitioners with little to no DP background completed a set of tasks related to setting ɛ using both ViP and a control. We find that ViP helps participants more correctly answer questions related to judging the probability of where a DP-noised release is likely to fall and comparing between DP-noised and non-private confidence intervals.