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Background: Vitamin D deficiency is common among the population, but its relationship with mortality of postmenopausal females is unclear. The aim of this study is to explore the association between serum 25-Hydroxyvitamin D (25(OH)D) and all-cause and cause-specific mortality among postmenopausal women in the United States. Methods: 6812 participants of postmenopausal females from the National Health and Nutrition Examination Survey (2001–2018) were included in this study. The mortality status of the follow-up was ascertained by linkage to National Death Index (NDI) records through 31 December 2019. We used cox proportional hazards models to estimate the association of serum 25(OH)D concentrations and mortality of postmenopausal females. Results: The mean level of serum 25(OH)D was 72.57 ± 29.93 nmol/L, and 65.34% had insufficient vitamin D. In postmenopausal females, low serum 25(OH)D concentrations were significantly associated with higher levels of glycohemoglobin, glucose, and lower levels of HDL. During follow-up, 1448 all-cause deaths occurred, including 393 cardiovascular disease (CVD)-related deaths and 263 cancer deaths. After multivariate adjustment, higher serum 25(OH)D levels were significantly related with lower all-cause and CVD mortality. In addition, serum 25(OH)D presented a L-shaped relationship with all-cause mortality, while appeared a U-shaped with CVD mortality, and the cut-off value is 73.89 nmol/L and 46.75 nmol/L respectively. Conclusions: Low serum 25(OH)D levels are associated with the higher risk of all-cause and CVD mortality in postmenopausal females. These findings provide new ideas and targets for the health management of postmenopausal women. [ABSTRACT FROM AUTHOR]

In general, the demand rate moving through a product life cycle can be reasonably depicted by a trapezoidal-type pattern: it initially increases during the introduction and growth phases, then remains reasonably constant in the maturity phase, and finally decreases in the decline phase. It is evident that perishable products deteriorate continuously over time and can not be sold after its maximum lifetime. Thus, the deterioration rate of a product is increasing with time and closely related to its maximum lifetime. Furthermore, it has been hard to obtain loans from banks since the global financial meltdown in 2008. Hence, over 80% of firms in the United Kingdom and the United States sell their products on various short-term, interest-free loans (i.e., trade credit) to customers. To incorporate those important facts, we develop an inventory model by (1) assuming the demand pattern is trapezoidal, (2) extending the deterioration rate to 100% as its maximum lifetime is approaching, (3) using discounted cash-flow analysis to calculate all relevant costs considering the effects of upstream and downstream trade credits, and (4) including the costly purchase cost into the total cost, which is omitted in previous studies. Then, the order quantity that maximizes the present value of the profit is uniquely determined. Finally, through numerical examples, managerial insights are provided. [ABSTRACT FROM AUTHOR]

In Punta del Este, a resort town in Uruguay, real-estate property is in demand by both domestic and foreign buyers. There are several stages of selling residential units: before, during, and after the actual construction. Different pricing strategies are used at every stage. Our goal in this paper is to derive, under various scenarios of practical relevance, optimal strategies for setting prices within two-stage selling framework, as well as to explore the optimal timing for accomplishing these tasks in order to maximize the overall seller's expected revenue. Specifically, we put forward a general two-period pricing model and explore pricing strategies from the seller's perspective, when the buyer's decisions in the two periods are uncertain: commodity valuations may or may not be independent, may or may not follow the same distribution, be heavily or just lightly influenced by exogenous economic conditions, and so on. Our theoretical findings are illustrated with numerical and graphical examples using appropriately constructed parametric models. [ABSTRACT FROM AUTHOR]

• We have constructed the latest dataset that contains diverse data of the most influential AI scholars (as of 2020) by integrating a variety of web resources. • We provided an in-depth analysis of the overall educational characteristics of AI scholars. • We analyzed the differences in the effect of different educational factors on the research productivity of AI researchers in the industry and academia, gaining an extensive understanding of the factors that drive the success of AI talent. The early academic beginning is critical in the development of a researcher's academic career because it helps determine one's further success. We aim to shed light on the path that drives the success of talents in the field of artificial intelligence (AI) by investigating the academic education background of distinguished AI researchers and analyzing the contribution of different educational factors to their research performance. In this study, we collected and coded the curriculum vitae of 1832 AI researchers. Results show that most AI researchers were educated in the United States and obtained their highest degrees from top universities. As for their educational background, approximately 18.27% of AI researchers chose non-AI majors, such as mathematics, physics, and chemistry, instead of AI-related majors, such as computer science. Furthermore, negative binomial regression analysis demonstrates that individuals who publish more during study period will have better research output, whether they are currently in academia or industry. Researchers in academia with overseas degrees published more articles than those without overseas degrees. In terms of interdisciplinary education, a mathematics background leads to increased research visibility of AI researchers in the industry but depresses the scholarly productivity of AI researchers in academia. Academic qualification is the main factor determining the scientific performance of AI researchers in industry, which is not the case in academia. The analysis also showed that individuals who graduated from more prestigious universities tended to receive more citations than those graduating from less famous universities. Moreover, AI researchers in academia who have graduated from prestigious universities seem to pay more attention to the quality of the papers rather than the quantity. [ABSTRACT FROM AUTHOR]

The 2018 12 th Workshop on Recent Issues in Bioanalysis took place in Philadelphia, PA, USA on April 9-13, 2018 with an attendance of over 900 representatives from pharmaceutical/biopharmaceutical companies, biotechnology companies, contract research organizations and regulatory agencies worldwide. WRIB was once again a 5-day, week-long event - a full immersion week of bioanalysis, biomarkers and immunogenicity. As usual, it was specifically designed to facilitate sharing, reviewing, discussing and agreeing on approaches to address the most current issues of interest including both small- and large-molecule bioanalysis involving LCMS, hybrid LBA/LCMS and LBA/cell-based assays approaches. This 2018 White Paper encompasses recommendations emerging from the extensive discussions held during the workshop and is aimed to provide the bioanalytical community with key information and practical solutions on topics and issues addressed, in an effort to enable advances in scientific excellence, improved quality and better regulatory compliance. Due to its length, the 2018 edition of this comprehensive White Paper has been divided into three parts for editorial reasons. This publication (Part 2) covers the recommendations for PK, PD and ADA assays by hybrid LBA/LCMS and regulatory agencies' input. Part 1 (LCMS for small molecules, peptides, oligonucleotides and small molecule biomarkers) and Part 3 (LBA/cell-based assays: immunogenicity, biomarkers and PK assays) are published in volume 10 of Bioanalysis , issues 22 and 24 (2018), respectively.

The expression patterns and functional roles of three osteopontin splice variants (OPNa, b, and c) in cancer metastasis and progression are not well understood due to the lack of reliable assays to differentiate the isoforms. We have developed a mass spectrometric method to quantify OPN isoforms in human plasma. The method is based on the immunocapture of all OPN isoforms, followed by MRM-MS analysis of isoform-specific tryptic peptides. We were able to simultaneously identify and quantify all three isoforms in the plasma of 10 healthy individuals and 10 non-small cell lung cancer (NSCLC) patients. Our results show that none of the OPN splice variants is cancer specific. However, OPNa, the major isoform in healthy and NSCLC plasma, is substantially elevated in NSCLC patients, whereas OPNb and OPNc are at equivalent levels in two populations.

Background: A large amount of cohort studies addressed coffee consumption and risk of coronary heart disease (CHD) and yielded inconsistent results. We conducted a meta-analysis to estimate the pooling effects., Methods: We searched for all published English articles indexed in MEDLINE or PubMed from January 1966 to January 2008. Twenty-one independent prospective cohort studies, which tested CHD risk by coffee consumption, were identified. A general variance-based method was used to pool the relative risks (RR). 15,599 cases from 407,806 participants were included in pooling the overall effects., Results: As compared to the light coffee consumption (<1 cup/d in US or or=6 or >or=7 cups/d) categories of coffee consumption (all p>0.05); Moderate coffee consumption showed significantly lower CHD RR (95% CI) of 0.82 (0.73-0.92) (p<0.001) in women, and of 0.87 (0.80-0.86) (p=0.001) in men and women followed