Adaptive immune response to lipoproteins of Staphylococcus aureus in healthy subjects

Staphylococcus aureus is a frequent commensal but also a dangerous pathogen, causing many forms of infection ranging from mild to life-threatening conditions. Among its virulence factors are lipoproteins, which are anchored in the bacterial cell membrane. Lipoproteins perform various functions in colonization, immune evasion, and immunomodulation. These proteins are potent activators of innate immune receptors termed Toll-like receptors 2 and 6. This study addressed the specific B-cell and T-cell responses directed to lipoproteins in human S. aureus carriers and non-carriers. 2D immune proteomics and ELISA approaches revealed that titers of antibodies (IgG) binding to S. aureus lipoproteins were very low. Proliferation assays and cytokine profiling data showed only subtle responses of T cells; some lipoproteins did not elicit proliferation. Hence, the robust activation of the innate immune system by S. aureus lipoproteins does not translate into a strong adaptive immune response. Reasons for this may include inaccessibility of lipoproteins for B cells as well as ineffective processing and presentation of the antigens to T cells.</p

Oil price uncertainty and sectoral stock returns in China: A time-varying approach

This article has been made available through the Brunel Open Access Publishing Fund.This paper investigates the time-varying impact of oil price uncertainty on stock prices in China using weekly data on ten sectoral indices over the period January 1997-February 2014. The estimation of a bivariate VAR-GARCH-in-mean model suggests that oil price volatility affects stock returns positively during periods characterised by demand-side shocks in all cases except the Consumer Services, Financials, and Oil and Gas sectors. The latter two sectors are found to exhibit a negative response to oil price uncertainty during periods with supply-side shocks instead. By contrast, the impact of oil price uncertainty appears to be insignificant during periods with precautionary demand shocks

Forecasting volatility of Bitcoin

Since Bitcoin price is highly volatile, forecasting its volatility is crucial for many applications, such as risk management or hedging. We study which model is the most suitable for forecasting Bitcoin volatility. We consider several GARCH and two heterogeneous autoregressive (HAR) models and compare them. Since we utilize realized variance estimated from high frequency data as a proxy for true volatility, we can draw sharper conclusions than studies which use only daily data. We find that EGARCH and APARCH perform best among the GARCH models. HAR models based on realized variance perform better than GARCH models based on daily data. Superiority of HAR models over GARCH models is strongest for short-term volatility forecasts.publishedVersio

Enhancing In-Memory Spatial Indexing with Learned Search

Spatial data is ubiquitous. Massive amounts of data are generated every day from a plethora of sources such as billions of GPS-enableddevices (e.g., cell phones, cars, and sensors), consumer-based applications (e.g., Uber and Strava), and social media platforms (e.g.,location-tagged posts on Facebook, Twitter, and Instagram). This exponential growth in spatial data has led the research communityto build systems and applications for efficient spatial data processing.In this study, we apply a recently developed machine-learned search technique for single-dimensional sorted data to spatial indexing.Specifically, we partition spatial data using six traditional spatial partitioning techniques and employ machine-learned search withineach partition to support point, range, distance, and spatial join queries. Adhering to the latest research trends, we tune the partitioningtechniques to be instance-optimized. By tuning each partitioning technique for optimal performance, we demonstrate that: (i) grid-basedindex structures outperform tree-based index structures (from 1.23× to 2.47×), (ii) learning-enhanced variants of commonly used spatialindex structures outperform their original counterparts (from 1.44× to 53.34× faster), (iii) machine-learned search within a partitionis faster than binary search by 11.79% - 39.51% when filtering on one dimension, (iv) the benefit of machine-learned search diminishesin the presence of other compute-intensive operations (e.g. scan costs in higher selectivity queries, Haversine distance computation, andpoint-in-polygon tests), and (v) index lookup is the bottleneck for tree-based structures, which could potentially be reduced by linearizingthe indexed partitions.Additional Key Words and Phrases: spatial data, indexing, machine-learning, spatial queries, geospatia

Dark energy survey year 3 results: photometric data set for cosmology

Artículo escrito por un elevado número de autores, solo se referencia el que aparece en primer lugar, el nombre del grupo de colaboración, si lo hubiere, y los autores pertenecientes a la UAMWe describe the Dark Energy Survey (DES) photometric data set assembled from the first three years of science operations to support DES Year 3 cosmologic analyses, and provide usage notes aimed at the broad astrophysics community. Y3 GOLD improves on previous releases from DES, Y1 GOLD, and Data Release 1 (DES DR1), presenting an expanded and curated data set that incorporates algorithmic developments in image detrending and processing, photometric calibration, and object classification. Y3 GOLD comprises nearly 5000 deg2 of grizY imaging in the south Galactic cap, including nearly 390 million objects, with depth reaching a signal-to-noise ratio ∼10 for extended objects up to iAB ∼ 23.0, and top-of-the-atmosphere photometric uniformity 98% and purity >99% for galaxies with 19 < iAB < 22.5. Additionally, it includes per-object quality information, and accompanying maps of the footprint coverage, masked regions, imaging depth, survey conditions, and astrophysical foregrounds that are used to select the cosmologic analysis sample

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change