3D MHD Modeling of the Gaseous Structure of the Galaxy: Synthetic Observations

We generated synthetic observations from the four-arm model presented in Gomez & Cox (2004) for the Galactic ISM in the presence of a spiral gravitational perturbation. We found that velocity crowding and diffusion have a strong effect in the l-v diagram. The v-b diagram presents structures at the expected spiral arm velocities, that can be explained by the off-the-plane structure of the arms presented in previous papers of this series. Such structures are observed in the Leiden/Dwingeloo HI survey. The rotation curve, as measured from the inside of the modeled galaxy, shows similarities with the observed one for the Milky Way Galaxy, although it has large deviations from the smooth circular rotation corresponding to the background potential. The magnetic field inferred from a synthetic synchrotron map shows a largely circular structure, but with interesting deviations in the midplane due to distortion of the field from circularity in the interarm regions.Comment: Accepted for publication in ApJ. Better quality figures in http://www.astro.umd.edu/~gomez/publica/3d_galaxy-3.pd

Tracking Cyber Adversaries with Adaptive Indicators of Compromise

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, US

Status of National Open Spatial Data Infrastructures: a Comparison Across Continents

The increasing need for geospatial information demands for well-organised management among all levels of society. A Spatial Data Infrastructure (SDI) is a multidisciplinary and dynamic instrument that facilitates access and sharing of geospatial information. The current trend towards open data initiatives is influencing the development of these infrastructures. In order to examine this effect, this article addresses the following question: what is the current state of SDI openness of four best practice open data countries Canada, The Netherlands, Australia and Brazil, and how do they compare? The question is answered through a qualitative literature study and the application of a newly developed Open SDI Assessment Framework to the countries. The Netherlands and Canada show a high performance on all assessment dimensions; data discovery, data access and data properties. Australia and Brazil show a poor open SDI performance, as they could not meet the requirements set for the assessed datasets. General conclusions of the assessment are that data is currently fragmented and scattered among the web in all four countries, which strongly negatively influences the user experience. It is crucial that a strict legal framework is embedded in a country, which ensures that current SDI objectives and propositions regarding an user-centred approach and open data availability are achieved

ANIMA: Association Network Integration for Multiscale Analysis

Contextual functional interpretation of -omics data derived from clinical samples is a classical and difficult problem in computational systems biology. The measurement of thousands of datapoints on single samples has become routine but relating ‘big data’ datasets to the complexities of human pathobiology is an area of ongoing research. Complicating this is the fact that many publically available datasets use bulk transcriptomics data from complex tissues like blood. The most prevalent analytic approaches derive molecular ‘signatures’ of disease states or apply modular analysis frameworks to the data. Here we show, using a network-based data integration method using clinical phenotype and microarray data as inputs, that we can reconstruct multiple features (or endophenotypes) of disease states at various scales of organization, from transcript abundance patterns of individual genes through co-expression patterns of groups of genes to patterns of cellular behavior in whole blood samples, both in single experiments as well as in a meta-analysis of multiple datasets

Gene expression patterns in anterior pituitary associated with quantitative measure of oestrous behaviour in dairy cows

Intensive selection for high milk yield in dairy cows has raised production levels substantially but at the cost of reduced fertility, which manifests in different ways including reduced expression of oestrous behaviour. The genomic regulation of oestrous behaviour in bovines remains largely unknown. Here, we aimed to identify and study those genes that were associated with oestrous behaviour among genes expressed in the bovine anterior pituitary either at the start of oestrous cycle or at the mid-cycle (around day 12 of cycle), or regardless of the phase of cycle. Oestrous behaviour was recorded in each of 28 primiparous cows from 30 days in milk onwards till the day of their sacrifice (between 77 and 139 days in milk) and quantified as heat scores. An average heat score value was calculated for each cow from heat scores observed during consecutive oestrous cycles excluding the cycle on the day of sacrifice. A microarray experiment was designed to measure gene expression in the anterior pituitary of these cows, 14 of which were sacrificed at the start of oestrous cycle (day 0) and 14 around day 12 of cycle (day 12). Gene expression was modelled as a function of the orthogonally transformed average heat score values using a Bayesian hierarchical mixed model on data from day 0 cows alone (analysis 1), day 12 cows alone (analysis 2) and the combined data from day 0 and day 12 cows (analysis 3). Genes whose expression patterns showed significant linear or non-linear relationships with average heat scores were identified in all three analyses (177, 142 and 118 genes, respectively). Gene ontology terms enriched among genes identified in analysis 1 revealed processes associated with expression of oestrous behaviour whereas the terms enriched among genes identified in analysis 2 and 3 were general processes which may facilitate proper expression of oestrous behaviour at the subsequent oestrus. Studying these genes will help to improve our understanding of the genomic regulation of oestrous behaviour, ultimately leading to better management strategies and tools to improve or monitor reproductive performance in bovines

Seasonal adjustment of daily data with CAMPLET

In the last decade large data sets have become available, both in terms of the number of time series and with higher frequencies (weekly, daily and even higher). All series may suffer from seasonality, which hides other important fluctuations. Therefore time series are typically seasonally adjusted. However, standard seasonal adjustment methods cannot handle series with higher than monthly frequencies. Recently, Abeln et al. (2019) presented CAMPLET, a new seasonal adjustment method, which does not produce revisions when new observations become available. The aim of this paper is to show the attractiveness of CAMPLET for seasonal adjustment of daily time series. We apply CAMPLET to daily data on the gas system in the Netherlands

The Effect of Electronic Cigarette User Modifications and E-liquid Adulteration on the Particle Size Profile of an Aerosolized Product

Electronic cigarettes (e-cigarettes) are an alternate nicotine delivery system that generate a condensation aerosol to be inhaled by the user. The size of the droplets formed in the aerosol can vary and contributes to drug deposition and ultimate bioavailability in the lung. The growing popularity of e-cigarette products has caused an increase in internet sources promoting the use of drugs other than nicotine (DOTNs) in e-cigarettes. The purpose of this study was to determine the effect of various e-cigarette and e-liquid modifications, such as coil resistance, battery voltage, and glycol and drug formulation, on the aerosol particle size. E-liquids containing 12 mg/mL nicotine prepared in glycol compositions of 100% propylene glycol (PG), 100% vegetable glycerin (VG), or 50:50 PG:VG were aerosolized at three voltages and three coil resistances. Methamphetamine and methadone e-liquids were prepared at 60 mg/mL in 50:50 PG:VG and all e-liquids were aerosolized onto a 10 stage Micro-Orifice Uniform Deposit Impactor. Glycol deposition correlated with drug deposition, and the majority of particles centered between 0.172–0.5 μm in diameter, representing pulmonary deposition. The 100% PG e-liquid produced the largest aerosol particles and the 100% VG and 50:50 PG:VG e-liquids produced ultra-fine particles \u3c0.3 μm. The presence of ultrafine particles indicates that drugs can be aerosolized and reach the pulmonary alveolar regions, highlighting a potential for abuse and risk of overdose with DOTNs aerosolized in an e-cigarette system