Herschel Gould Belt Survey Observations of Dense Cores in the Cepheus Flare Clouds

Abstract: We present Herschel SPIRE and PACS maps of the Cepheus Flare clouds L1157, L1172, L1228, L1241, and L1251, observed by the Herschel Gould Belt Survey of nearby star-forming molecular clouds. Through modified blackbody fits to the SPIRE and PACS data, we determine typical cloud column densities of (0.5–1.0) × 1021 cm−2 and typical cloud temperatures of 14–15 K. Using the getsources identification algorithm, we extract 832 dense cores from the SPIRE and PACS data at 160–500 μm. From placement in a mass versus size diagram, we consider 303 to be candidate prestellar cores, and 178 of these to be “robust” prestellar cores. From an independent extraction of sources at 70 μm, we consider 25 of the 832 dense cores to be protostellar. The distribution of background column densities coincident with candidate prestellar cores peaks at (2–4) × 1021 cm−2. About half of the candidate prestellar cores in Cepheus may have formed as a result of the widespread fragmentation expected to occur within filaments of “transcritical” line mass. The lognormal robust prestellar core mass function (CMF) drawn from all five Cepheus clouds peaks at 0.56 M⊙ and has a width of ∼0.5 dex, similar to that of Aquila’s CMF. Indeed, the width of Cepheus’s aggregate CMF is similar to the stellar system initial mass function (IMF). The similarity of CMF widths in different clouds and the system IMF suggests a common, possibly turbulent origin for seeding the fluctuations that evolve into prestellar cores and stars

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Institutional Data Repository Development, a Moving Target

At the end of 2019, the Research Data Service (RDS) at the University of Illinois at Urbana-Champaign (UIUC) completed its fifth year as a campus-wide service. In order to gauge the effectiveness of the RDS in meeting the needs of Illinois researchers, RDS staff developed a five-year review consisting of a survey and a series of in-depth focus group interviews. As a result, our institutional data repository developed in-house by University Library IT staff, Illinois Data Bank, was recognized as the most useful service offering by our unit. When launched in 2016, storage resources and web servers for Illinois Data Bank and supporting systems were hosted on-premises at UIUC. As anticipated, researchers increasingly need to share large, and complex datasets. In a responsive effort to leverage the potentially more reliable, highly available, cost-effective, and scalable storage accessible to computation resources, we migrated our item bitstreams and web services to the cloud. Our efforts have met with success, but also with painful bumps along the way. This article describes how we supported data curation workflows through transitioning from on-premises to cloud resource hosting. It details our approaches to ingesting, curating, and offering access to dataset files up to 2TB in size--which may be archive type files (e.g., .zip or .tar) containing complex directory structures

OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services

This paper presents a cross-cultural crowdsourcing platform, called OurPlaces, where people from different cultures can share their spatial experiences. We built a three-layered architecture composed of: (i) places (locations where people have visited); (ii) cognition (how people have experienced these places); and (iii) users (those who have visited these places). Notably, cognition is represented as a paring of two similar places from different cultures (e.g., Versailles and Gyeongbokgung in France and Korea, respectively). As a case study, we applied the OurPlaces platform to a cross-cultural tourism recommendation system and conducted a simulation using a dataset collected from TripAdvisor. The tourist places were classified into four types (i.e., hotels, restaurants, shopping malls, and attractions). In addition, user feedback (e.g., ratings, rankings, and reviews) from various nationalities (assumed to be equivalent to cultures) was exploited to measure the similarities between tourism places and to generate a cognition layer on the platform. To demonstrate the effectiveness of the OurPlaces-based system, we compared it with a Pearson correlation-based system as a baseline. The experimental results show that the proposed system outperforms the baseline by 2.5% and 4.1% in the best case in terms of MAE and RMSE, respectively

DaGzang: a synthetic data generator for cross-domain recommendation services

Research on cross-domain recommendation systems (CDRS) has shown efficiency by leveraging the overlapping associations between domains in order to generate more encompassing user models and better recommendations. Nonetheless, if there is no dataset belonging to a specific domain, it is a challenge to generate recommendations in CDRS. In addition, finding these overlapping associations in the real world is generally tricky, and it makes its application to actual services hard. Considering these issues, this study aims to present a synthetic data generation platform (called DaGzang) for cross-domain recommendation systems. The DaGzang platform works according to the complete loop, and it consists of the following three steps: (i) detecting the overlap association (data distribution pattern) between the real-world datasets, (ii) generating synthetic datasets based on these overlap associations, and (iii) evaluating the quality of the generated synthetic datasets. The real-world datasets in our experiments were collected from Amazon’s e-commercial website. To validate the usefulness of the synthetic datasets generated from DaGzang, we embed these datasets into our cross-domain recommender system, called DakGalBi. We then evaluate the recommendations generated from DakGalBi with collaborative filtering (CF) algorithms, user-based CF, and item-based CF. Mean absolute error (MAE) and root mean square error (RMSE) metrics are measured to evaluate the performance of collaborative filtering (CF) CDRS. In particular, the highest performance of the three recommendation methods is user-based CF when using 10 synthetic datasets generated from DaGzang (0.437 at MAE and 0.465 at RMSE)

Deletion of the nicotinic acetylcholine receptor subunit gene Dα1 confers insecticide resistance, but at what cost?

Nicotinic acetylcholine receptors (nAChRs) have vital functions in processes of neurotransmission that underpin key behaviors. These pentameric ligand-gated ion channels have been used as targets for insecticides that constitutively activate them, causing the death of insect pests. In examining a knockout of the Dα1 nAChR subunit gene, our study linked this one subunit with multiple traits. We were able to confirm previous work that had identified Dα1 as a target of the neonicotinoid class of insecticides. Further, we uncovered roles for the gene in influencing mating behavior and patterns of sleep. The knockout mutant was also observed to have a significant reduction in longevity. This study highlighted the severe fitness costs that appear to be associated with the loss of function of this gene in natural populations in the absence of insecticides targeting the Dα1 subunit. Such a fitness cost could explain why target site resistances to neonicotinoids in pest insect populations have been associated specific amino acid replacement mutations in nAChR subunits, rather than loss of function. That mutant phenotypes were observed for the two behaviors examined indicates that the functions of Dα1, and other nAChR subunits, need to be explored more broadly. It also remains to be established whether these phenotypes were due to loss of the Dα1 receptor and/or to compensatory changes in the expression levels of other nAChR subunits

A comparison of carbon footprints of magnesium oxide and magnesium hydroxide produced from conventional processes

In this study, modelling the carbon footprints of magnesium oxide and magnesium hydroxide (>99% purity) production based on technologies treating bischofite brines (e.g. Aman process) and serpentinite ores (e.g. Magnifin process) was performed. The two technologies have been utilised by many producers around the world to deliver specialty magnesium products. Using theoretical values of heat of reaction obtained from HSC (H-enthalpy, S-entropy and Cp-heat capacity) software simulations and the practical thermal efficiency of roasting and pyrohydrolysis equipment, greenhouse gas (GHG) emissions of 2.7 e5.6 kg CO2eq/kg MgO and 1.6e3.3 kg CO2eq/kg Mg(OH)2 were estimated for the process treating a bischofite brine. The corresponding figures calculated for the process recovering magnesium values from a serpentinite ore were determined as 3.8e7.5 kg CO2eq/kg MgO and 2.6e5.2 kg CO2eq/kg Mg(OH)2. They are somewhat comparable to MgO's carbon footprint of 3.1e4.5 kg CO2eq/kg MgO from Chinese producers using one-stage magnesite calcination to produce caustic calcined magnesia (~92% purity). From a carbon footprint perspective, it is apparent that the brine process provides the lowest environmental burdens compared to the serpentinite and magnesite routes