Blogging: self presentation and privacy

Blogs are permeating most niches of social life, and addressing a wide range of topics from scholarly and political issues1 to family and children’s daily lives. By their very nature, blogs raise a number of privacy issues as they are easy to produce and disseminate, resulting in large amounts of sometimes personal information being broadcast across the Internet in a persistent and cumulative manner. This article reports the preliminary findings of an online survey of bloggers from around the world. The survey explored bloggers’ subjective sense of privacy by examining their blogging practices and their expectations of privacy when publishing online. The findings suggest that blogging offers individuals a unique opportunity to work on their self-identity via the degree of self-expression and social interaction that is available in this medium. This finding helps to explain why bloggers consciously bring the ‘private’ to the public realm, despite the inherent privacy risks they face in doing so

Implicit solvation using the superposition approximation (IS-SPA): extension to polar solutes in chloroform

Efficient, accurate, and adaptable implicit solvent models remain a significant challenge in the field of molecular simulation. A recent implicit solvent model, IS-SPA, based on approximating the mean solvent force using the superposition approximation, provides a platform to achieve these goals. IS-SPA was originally developed to handle non-polar solutes in the TIP3P water model but can be extended to accurately treat polar solutes in other polar solvents. In this manuscript, we demonstrate how to adapt IS-SPA to include the treatment of solvent orientation and long ranged electrostatics in a solvent of chloroform. The orientation of chloroform is approximated as that of an ideal dipole aligned in a mean electrostatic field. The solvent–solute force is then considered as an averaged radially symmetric Lennard-Jones component and a multipole expansion of the electrostatic component through the octupole term. Parameters for the model include atom-based solvent density and mean electric field functions that are fit from explicit solvent simulations of independent atoms or molecules. Using these parameters, IS-SPA accounts for asymmetry of charge solvation and reproduces the explicit solvent potential of mean force of dimerization of two oppositely charged Lennard-Jones spheres with high fidelity. Additionally, the model more accurately captures the effect of explicit solvent on the monomer and dimer configurations of alanine dipeptide in chloroform than a generalized Born or constant density dielectric model. The current version of the algorithm is expected to outperform explicit solvent simulations for aggregation of small peptides at concentrations below 150 mM, well above the typical experimental concentrations for these materials.Chemistr

Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction

Longitudinal analysis is important in many disciplines, such as the study of behavioral transitions in social science. Only very recently, feature selection has drawn adequate attention in the context of longitudinal modeling. Standard techniques, such as generalized estimating equations, have been modified to select features by imposing sparsity-inducing regularizers. However, they do not explicitly model how a dependent variable relies on features measured at proximal time points. Recent graphical Granger modeling can select features in lagged time points but ignores the temporal correlations within an individual's repeated measurements. We propose an approach to automatically and simultaneously determine both the relevant features and the relevant temporal points that impact the current outcome of the dependent variable. Meanwhile, the proposed model takes into account the non-{\em i.i.d} nature of the data by estimating the within-individual correlations. This approach decomposes model parameters into a summation of two components and imposes separate block-wise LASSO penalties to each component when building a linear model in terms of the past $\tau$ measurements of features. One component is used to select features whereas the other is used to select temporal contingent points. An accelerated gradient descent algorithm is developed to efficiently solve the related optimization problem with detailed convergence analysis and asymptotic analysis. Computational results on both synthetic and real world problems demonstrate the superior performance of the proposed approach over existing techniques.Comment: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 201

Implicit Solvation Using the Superposition Approximation (IS-SPA): Extension to Polar Solutes in Chloroform

Efficient, accurate, and adaptable implicit solvent models remain a significant challenge in the field of molecular simulation. A recent implicit solvent model, IS-SPA, based on approximating the mean solvent force using the superposition approximation, provides a platform to achieve these goals. IS-SPA was originally developed to handle non-polar solutes in the TIP3P water model but can be extended to accurately treat polar solutes in other polar solvents. In this manuscript, we demonstrate how to adapt IS-SPA to include the treatment of solvent orientation and long ranged electrostatics in a solvent of chloroform. The orientation of chloroform is approximated as that of an ideal dipole aligned in a mean electrostatic field. The solvent--solute force is then considered as an averaged radially symmetric Lennard-Jones component and a multipole expansion of the electrostatic component through the octupole term. Parameters for the model include atom-based solvent density and mean electric field functions that are fit from explicit solvent simulations of independent atoms or molecules. Using these parameters, IS-SPA accounts for asymmetry of charge solvation and reproduces the explicit solvent potential of mean force of dimerization of two oppositely charged Lennard-Jones spheres with high fidelity. Additionally, the model more accurately captures the effect of explicit solvent on the monomer and dimer configurations of alanine dipeptide in chloroform than a generalized Born or constant density dielectric model. The current version of the algorithm is expected to outperform explicit solvent simulations for aggregation of small peptides at concentrations below 150 mM, well above the typical experimental concentrations for these materials

Investigation of polyhydroxyalkanoate production by an activated sludge microbial consortium treating artificial dairy wastewater

Petrochemical plastics/polymers are a common feature of day to day living as they occur in packaging, furniture, mobile phones, computers, construction equipment etc. However, these materials are produced from non-renewable materials and are resistant to microbial degradation in the environment. Considerable research has therefore been carried out into the production of sustainable, biodegradable polymers, amenable to microbial catabolism to CO2 and H2O. A key group of microbial polyesters, widely considered as optimal replacement polymers, are the Polyhydroxyalkaonates (PHAs). Primary research in this area has focused on using recombinant pure cultures to optimise PHA yields, however, despite considerable success, the high costs of pure culture fermentation have thus far hindered the commercial viability of PHAs thus produced. In more recent years work has begun to focus on mixed cultures for the optimisation of PHA production, with waste incorporations offering optimal production cost reductions. The scale of dairy processing in Ireland, and the high organic load wastewaters generated, represent an excellent potential substrate for bioconversion to PHAs in a mixed culture system. The current study sought to investigate the potential for such bioconversion in a laboratory scale biological system and to establish key operational and microbial characteristics of same. Two sequencing batch reactors were set up and operated along the lines of an enhanced biological phosphate removal (EBPR) system, which has PHA accumulation as a key step within repeated rounds of anaerobic/aerobic cycling. Influents to the reactors varied only in the carbon sources provided. Reactor 1 received artificial wastewater with acetate alone, which is known to be readily converted to PHA in the anaerobic step of EBPR. Reactor 2 wastewater influent contained acetate and skim milk to imitate a dairy processing effluent. Chemical monitoring of nutrient remediation within the reactors as continuously applied and EBPR consistent performances observed. Qualitative analysis of the sludge was carried out using fluorescence microscopy with Nile Blue A lipophillic stain and PHA production was confirmed in both reactors. Quantitative analysis via HPLC detection of crotonic acid derivatives revealed the fluorescence to be short chain length Polyhydroxybutyrate, with biomass dry weight accumulations of 11% and 13% being observed in reactors 1 and 2, respectively. Gas Chromatography-Mass Spectrometry for medium chain length methyl ester derivatives revealed the presence of hydroxyoctanoic, -decanoic and -dodecanoic acids in reactor 1. Similar analyses in reactor 2 revealed monomers of 3-hydroxydodecenoic and 3-hydroxytetradecanoic acids. Investigation of the microbial ecology of both reactors as conducted in an attempt to identify key species potentially contributing to reactor performance. Culture dependent investigations indicated that quite different communities were present in both reactors. Reactor 1 isolates demonstrated the following species distributions Pseudomonas (82%), Delftia acidovorans (3%), Acinetobacter sp. (5%) Aminobacter sp., (3%) Bacillus sp. (3%), Thauera sp., (3%) and Cytophaga sp. (3%). Relative species distributions among reactor 2 profiled isolates were more evenly distributed between Pseudoxanthomonas (32%), Thauera sp (24%), Acinetobacter (24%), Citrobacter sp (8%), Lactococcus lactis (5%), Lysinibacillus (5%) and Elizabethkingia (2%). In both reactors Gammaproteobacteria dominated the cultured isolates. Culture independent 16S rRNA gene analyses revealed differing profiles for both reactors. Reactor 1 clone distribution was as follows; Zooglea resiniphila (83%), Zooglea oryzae (2%), Pedobacter composti (5%), Neissericeae sp. (2%) Rhodobacter sp. (2%), Runella defluvii (3%) and Streptococcus sp. (3%). RFLP based species distribution among the reactor 2 clones was as follows; Runella defluvii (50%), Zoogloea oryzae (20%), Flavobacterium sp. (9%), Simplicispira sp. (6%), Uncultured Sphingobacteria sp. (6%), Arcicella (6%) and Leadbetterella bysophila (3%). Betaproteobacteria dominated the 16S rRNA gene clones identified in both reactors. FISH analysis with Nile Blue dual staining resolved these divergent findings, identifying the Betaproteobacteria as dominant PHA accumulators within the reactor sludges, although species/strain specific allocations could not be made. GC analysis of the sludge had indicated the presence of both medium chain length as well short chain length PHAs accumulating in both reactors. In addition the cultured isolates from the reactors had been identified previously as mcl and scl PHA producers, respectively. Characterisations of the PHA monomer profiles of the individual isolates were therefore performed to screen for potential novel scl-mcl PHAs. Nitrogen limitation driven PHA accumulation in E2 minimal media revealed a greater propensity among isoates for mcl-pHA production. HPLC analysis indicated that PHB production was not a major feature of the reactor isolates and this was supported by the low presence of scl phaC1 genes among PCR screened isolates. A high percentage distribution of phaC2 mcl-PHA synthase genes was recorded, with the majority sharing high percentage homology with class II synthases from Pseudomonas sp. The common presence of a phaC2 homologue was not reflected in the production of a common polymer. Considerable variation was noted in both the monomer composition and ratios following GC analysis. While co-polymer production could not be demonstrated, potentially novel synthase substrate specificities were noted which could be exploited further in the future

The influence of heavy goods vehicle traffic on accidents on different types of Spanish interurban roads

This paper illustrates a methodology developed to analyze the influence of traffic conditions, i.e. volume and composition on accidents on different types of interurban roads in Spain, by applying negative binomial models. The annual average daily traffic was identified as the most important variable, followed by the percentage of heavy goods vehicles, and different covariate patterns were found for each road type. The analysis of hypothetical scenarios of the reduction of heavy goods vehicles in two of the most representative freight transportation corridors, combined with hypotheses of total daily traffic mean intensity variation, produced by the existence or absence of induced traffic gives rise to several scenarios. In all cases a reduction in the total number of accidents would occur as a result of the drop in the number of heavy goods transport vehicles, However the higher traffic intensity, resulting of the induction of other vehicular traffic, reduces the effects on the number of accidents on single carriageway road segments compared with high capacity roads, due to the increase in exposure. This type of analysis provides objective elements for evaluating policies that encourage modal shifts and road safety enhancements

Binary Models for Marginal Independence

Log-linear models are a classical tool for the analysis of contingency tables. In particular, the subclass of graphical log-linear models provides a general framework for modelling conditional independences. However, with the exception of special structures, marginal independence hypotheses cannot be accommodated by these traditional models. Focusing on binary variables, we present a model class that provides a framework for modelling marginal independences in contingency tables. The approach taken is graphical and draws on analogies to multivariate Gaussian models for marginal independence. For the graphical model representation we use bi-directed graphs, which are in the tradition of path diagrams. We show how the models can be parameterized in a simple fashion, and how maximum likelihood estimation can be performed using a version of the Iterated Conditional Fitting algorithm. Finally we consider combining these models with symmetry restrictions

Measures of User experience in a Streptococcal pharyngitis and Pneumonia Clinical Decision Support Tools

Objective: To understand clinician adoption of CDS tools as this may provide important insights for the implementation and dissemination of future CDS tools. Materials and Methods: Clinicians (n=168) at a large academic center were randomized into intervention and control arms to assess the impact of strep and pneumonia CDS tools. Intervention arm data were analyzed to examine provider adoption and clinical workflow. Electronic health record data were collected on trigger location, the use of each component and whether an antibiotic, other medication or test was ordered. Frequencies were tabulated and regression analyses were used to determine the association of tool component use and physician orders. Results: The CDS tool was triggered 586 times over the study period. Diagnosis was the most frequent workflow trigger of the CDS tool (57%) as compared to chief complaint (30%) and diagnosis/antibiotic combinations (13%). Conversely, chief complaint was associated with the highest rate (83%) of triggers leading to an initiation of the CDS tool (opening the risk prediction calculator). Similar patterns were noted for initiation of the CDS bundled ordered set and completion of the entire CDS tool pathway. Completion of risk prediction and bundled order set components were associated with lower rates of antibiotic prescribing (OR 0.5; CI 0.2-1.2 and OR 0.5; CI 0.3-0.9, respectively). Discussion: Different CDS trigger points in the clinician user workflow lead to substantial variation in downstream use of the CDS tool components. These variations were important as they were associated with significant differences in antibiotic ordering. Conclusions: These results highlight the importance of workflow integration and flexibility for CDS success