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Among the different types of massive stars in advanced evolutionary stages is the enigmatic WN8h type. There are only a few Wolf-Rayet (WR) stars with this spectral type in our Galaxy. It has long been suggested that WN8h-type stars are the products of binary evolution that may harbor neutron stars (NS). One of the most intriguing WN8h stars is the runaway WR 124 surrounded by its magnificent nebula M1-67. We test the presence of an accreting NS companion in WR 124 using similar to 100 ks long observations by the Chandra X-ray observatory. The hard X-ray emission from WR 124 with a luminosity of L-X similar to 10(31) erg s(-1) is marginally detected. We use the non-local thermodynamic equilibrium stellar atmosphere code PoWR to estimate the WR wind opacity to the X-rays. The wind of a WN8-type star is effectively opaque for X-rays, hence the low X-ray luminosity of WR 124 does not rule out the presence of an embedded compact object. We suggest that, in general, high-opacity WR winds could prevent X-ray detections of embedded NS, and be an explanation for the apparent lack of WR+NS systems.
OBJECTIVE: Interoceptive awareness is known to be impaired in eating disorders. To date, it has remained unclear whether this variable is related to the construct of interoceptive sensitivity. Interoceptive sensitivity is considered to be an essential variable in emotional processes. The objective of the study was to elucidate this potential relationship and to clarify whether general interoceptive sensitivity is reduced in anorexia nervosa. METHODS: Using a heartbeat perception task, interoceptive sensitivity was assessed in 28 female patients with anorexia nervosa and 28 matched healthy controls. Questionnaires assessing interoceptive awareness (EDI) and several other variables were also administered. RESULTS: Patients with anorexia nervosa displayed significantly decreased interoceptive sensitivity. They also had more difficulties in interoceptive awareness. CONCLUSIONS: In addition to a decreased ability to recognize certain visceral sensations related to hunger, there is a generally reduced capacity to accurately perceive bodily signals in anorexia nervosa. This highlights the potential importance of interoceptive sensitivity in the pathogenesis of eating disorders.
A catalog of genetic loci associated with kidney function from analyses of a million individuals
(2019)
Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through transancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these,147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.
Rabbit associated genotype 3 hepatitis E virus (HEV) strains were detected in feral, pet and farm rabbits in different parts of the world since 2009 and recently also in human patients. Here, we report a serological and molecular survey on 72 feral rabbits, collected along a rural-urban transect in and next to Frankfurt am Main, Central Germany. ELISA investigations revealed in 25 of 72 (34.7%) animals HEV-specific antibodies. HEV derived RNA was detected in 18 of 72 (25%) animals by reverse transcription-polymerase chain reaction assay. The complete genomes from two rabbitHEV-strains, one from a rural site and the other from an inner-city area, were generated by a combination of high-throughput sequencing, a primer walking approach and 5′- and 3′- rapid amplification of cDNA ends. Phylogenetic analysis of open reading frame (ORF)1-derived partial and complete ORF1/ORF2 concatenated coding sequences indicated their similarity to rabbit-associated HEV strains. The partial sequences revealed one cluster of closely-related rabbitHEV sequences from the urban trapping sites that is well separated from several clusters representing rabbitHEV sequences from rural trapping sites. The complete genome sequences of the two novel strains indicated similarities of 75.6–86.4% to the other 17 rabbitHEV sequences; the amino acid sequence identity of the concatenated ORF1/ORF2-encoded proteins reached 89.0–93.1%. The detection of rabbitHEV in an inner-city area with a high human population density suggests a high risk of potential human infection with the zoonotic rabbitHEV, either by direct or indirect contact with infected animals. Therefore, future investigations on the occurrence and frequency of human infections with rabbitHEV are warranted in populations with different contact to rabbits.
The cis-trans isomerisation of N-benzylideneaniline (NBA) and derivatives containing a central C=N bond has been investigated experimentally and theoretically. Eight different NBA molecules in three different solvents were irradiated to enforce a photochemical trans (hv) -> cis isomerisation and the kinetics of the thermal backreaction cis (Delta)-> trans were determined by NMR spectroscopy measurements in the temperature range between 193 and 288 K. Theoretical calculations using density functional theory and Eyring transition-state theory were carried out for 12 different NBA species in the gas phase and three different solvents to compute thermal isomerisation rates of the thermal back reaction. While the computed absolute rates are too large, they reveal and explain experimental trends. Time-dependent density functional theory provides optical spectra for vertical transitions and excitation energy differences between trans and cis forms. Together with isomerisation rates, the latter can be used to identify "optimal switches" with good photochromicity and reasonable thermal stability.
An efficient selection of indexes is indispensable for database performance. For large problem instances with hundreds of tables, existing approaches are not suitable: They either exhibit prohibitive runtimes or yield far from optimal index configurations by strongly limiting the set of index candidates or not handling index interaction explicitly. We introduce a novel recursive strategy that does not exclude index candidates in advance and effectively accounts for index interaction. Using large real-world workloads, we demonstrate the applicability of our approach. Further, we evaluate our solution end to end with a commercial database system using a reproducible setup. We show that our solutions are near-optimal for small index selection problems. For larger problems, our strategy outperforms state-of-the-art approaches in both scalability and solution quality.
E-commerce marketplaces are highly dynamic with constant competition. While this competition is challenging for many merchants, it also provides plenty of opportunities, e.g., by allowing them to automatically adjust prices in order to react to changing market situations. For practitioners however, testing automated pricing strategies is time-consuming and potentially hazardously when done in production. Researchers, on the other side, struggle to study how pricing strategies interact under heavy competition. As a consequence, we built an open continuous time framework to simulate dynamic pricing competition called Price Wars. The microservice-based architecture provides a scalable platform for large competitions with dozens of merchants and a large random stream of consumers. Our platform stores each event in a distributed log. This allows to provide different performance measures enabling users to compare profit and revenue of various repricing strategies in real-time. For researchers, price trajectories are shown which ease evaluating mutual price reactions of competing strategies. Furthermore, merchants can access historical marketplace data and apply machine learning. By providing a set of customizable, artificial merchants, users can easily simulate both simple rule-based strategies as well as sophisticated data-driven strategies using demand learning to optimize their pricing strategies.
Currently available wearables are usually based on a single sensor node with integrated capabilities for classifying different activities. The next generation of cooperative wearables could be able to identify not only activities, but also to evaluate them qualitatively using the data of several sensor nodes attached to the body, to provide detailed feedback for the improvement of the execution. Especially within the application domains of sports and health-care, such immediate feedback to the execution of body movements is crucial for (re-) learning and improving motor skills. To enable such systems for a broad range of activities, generalized approaches for human motion assessment within sensor networks are required. In this paper, we present a generalized trainable activity assessment chain (AAC) for the online assessment of periodic human activity within a wireless body area network. AAC evaluates the execution of separate movements of a prior trained activity on a fine-grained quality scale. We connect qualitative assessment with human knowledge by projecting the AAC on the hierarchical decomposition of motion performed by the human body as well as establishing the assessment on a kinematic evaluation of biomechanically distinct motion fragments. We evaluate AAC in a real-world setting and show that AAC successfully delimits the movements of correctly performed activity from faulty executions and provides detailed reasons for the activity assessment.
Merchants on modern e-commerce platforms face a highly competitive environment. They compete against each other using automated dynamic pricing and ordering strategies. Successfully managing both inventory levels as well as offer prices is a challenging task as (i) demand is uncertain, (ii) competitors strategically interact, and (iii) optimized pricing and ordering decisions are mutually dependent. We show how to derive optimized data-driven pricing and ordering strategies which are based on demand learning techniques and efficient dynamic optimization models. We verify the superior performance of our self-adaptive strategies by comparing them to different rule-based as well as data-driven strategies in duopoly and oligopoly settings. Further, to study and to optimize joint dynamic ordering and pricing strategies on online marketplaces, we built an interactive simulation platform. To be both flexible and scalable, the platform has a microservice-based architecture and allows handling dozens of competing merchants and streams of consumers with configurable characteristics.
Many markets are characterized by pricing competition. Typically, competitors are involved that adjust their prices in response to other competitors with different frequencies. We analyze stochastic dynamic pricing models under competition for the sale of durable goods. Given a competitor’s pricing strategy, we show how to derive optimal response strategies that take the anticipated competitor’s price adjustments into account. We study resulting price cycles and the associated expected long-term profits. We show that reaction frequencies have a major impact on a strategy’s performance. In order not to act predictable our model also allows to include randomized reaction times. Additionally, we study to which extent optimal response strategies of active competitors are affected by additional passive competitors that use constant prices. It turns out that optimized feedback strategies effectively avoid a decline in price. They help to gain profits, especially, when aggressive competitor s are involved.