The Demographic Imperative in Religious Change in the United States

U.S. Protestants are less likely to belong to “mainline” denominations and more likely to belong to “conservative” ones than used to be the case. Evidence from the General Social Survey indicates that higher fertility and earlier childbearing among women from conservative denominations explains 76% of the observed trend for cohorts born between 1903 and 1973: conservative denominations have grown their own. Mainline decline would have slowed in recent cohorts, but a drop‐off in conversions from conservative to mainline denominations prolonged the decline. A recent rise in apostasy added a few percentage points to mainline decline. Conversions from mainline to conservative denominations have not changed, so they played no role in the restructuring

First-principles extrapolation method for accurate CO adsorption energies on metal surfaces

We show that a simple first-principles correction based on the difference between the singlet-triplet CO excitation energy values obtained by DFT and high-level quantum chemistry methods yields accurate CO adsorption properties on a variety of metal surfaces. We demonstrate a linear relationship between the CO adsorption energy and the CO singlet-triplet splitting, similar to the linear dependence of CO adsorption energy on the energy of the CO 2$\pi$* orbital found recently {[Kresse {\em et al.}, Physical Review B {\bf 68}, 073401 (2003)]}. Converged DFT calculations underestimate the CO singlet-triplet excitation energy $\Delta E_{\rm S-T}$, whereas coupled-cluster and CI calculations reproduce the experimental $\Delta E_{\rm S-T}$. The dependence of $E_{\rm chem}$ on $\Delta E_{\rm S-T}$ is used to extrapolate $E_{\rm chem}$ for the top, bridge and hollow sites for the (100) and (111) surfaces of Pt, Rh, Pd and Cu to the values that correspond to the coupled-cluster and CI $\Delta E_{\rm S-T}$ value. The correction reproduces experimental adsorption site preference for all cases and obtains $E_{\rm chem}$ in excellent agreement with experimental results.Comment: Table sent as table1.eps. 3 figure

Best practice data standards for discrete chemical oceanographic observations

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Jiang, L.-Q., Pierrot, D., Wanninkhof, R., Feely, R. A., Tilbrook, B., Alin, S., Barbero, L., Byrne, R. H., Carter, B. R., Dickson, A. G., Gattuso, J.-P., Greeley, D., Hoppema, M., Humphreys, M. P., Karstensen, J., Lange, N., Lauvset, S. K., Lewis, E. R., Olsen, A., Pérez, F. F., Sabine, C., Sharp, J. D., Tanhua, T., Trull, T. W., Velo, A., Allegra, A. J., Barker, P., Burger, E., Cai, W-J., Chen, C-T. A., Cross, J., Garcia, H., Hernandez-Ayon J. M., Hu, X., Kozyr, A., Langdon, C., Lee., K, Salisbury, J., Wang, Z. A., & Xue, L. Best practice data standards for discrete chemical oceanographic observations. Frontiers in Marine Science, 8, (2022): 705638, https://doi.org/10.3389/fmars.2021.705638.Effective data management plays a key role in oceanographic research as cruise-based data, collected from different laboratories and expeditions, are commonly compiled to investigate regional to global oceanographic processes. Here we describe new and updated best practice data standards for discrete chemical oceanographic observations, specifically those dealing with column header abbreviations, quality control flags, missing value indicators, and standardized calculation of certain properties. These data standards have been developed with the goals of improving the current practices of the scientific community and promoting their international usage. These guidelines are intended to standardize data files for data sharing and submission into permanent archives. They will facilitate future quality control and synthesis efforts and lead to better data interpretation. In turn, this will promote research in ocean biogeochemistry, such as studies of carbon cycling and ocean acidification, on regional to global scales. These best practice standards are not mandatory. Agencies, institutes, universities, or research vessels can continue using different data standards if it is important for them to maintain historical consistency. However, it is hoped that they will be adopted as widely as possible to facilitate consistency and to achieve the goals stated above.Funding for L-QJ and AK was from NOAA Ocean Acidification Program (OAP, Project ID: 21047) and NOAA National Centers for Environmental Information (NCEI) through NOAA grant NA19NES4320002 [Cooperative Institute for Satellite Earth System Studies (CISESS)] at the University of Maryland/ESSIC. BT was in part supported by the Australia’s Integrated Marine Observing System (IMOS), enabled through the National Collaborative Research Infrastructure Strategy (NCRIS). AD was supported in part by the United States National Science Foundation. AV and FP were supported by BOCATS2 Project (PID2019-104279GB-C21/AEI/10.13039/501100011033) funded by the Spanish Research Agency and contributing to WATER:iOS CSIC interdisciplinary thematic platform. MH was partly funded by the European Union’s Horizon 2020 Research and Innovation Program under grant agreement N°821001 (SO-CHIC)

Methods of nutrition surveillance in low-income countries

Background In 1974 a joint FAO/UNICEF/WHO Expert Committee met to develop methods for nutrition surveillance. There has been much interest and activity in this topic since then, however there is a lack of guidance for practitioners and confusion exists around the terminology of nutrition surveillance. In this paper we propose a classification of data collection activities, consider the technical issues for each category, and examine the potential applications and challenges related to information and communication technology. Analysis There are three major approaches used to collect primary data for nutrition surveillance: repeated cross-sectional surveys; community-based sentinel monitoring; and the collection of data in schools. There are three major sources of secondary data for surveillance: from feeding centres, health facilities, and community-based data collection, including mass screening for malnutrition in children. Surveillance systems involving repeated surveys are suitable for monitoring and comparing national trends and for planning and policy development. To plan at a local level, surveys at district level or in programme implementation areas are ideal, but given the usually high cost of primary data collection, data obtained from health systems are more appropriate provided they are interpreted with caution and with contextual information. For early warning, data from health systems and sentinel site assessments may be valuable, if consistent in their methods of collection and any systematic bias is deemed to be steady. For evaluation purposes, surveillance systems can only give plausible evidence of whether a programme is effective. However the implementation of programmes can be monitored as long as data are collected on process indicators such as access to, and use of, services. Surveillance systems also have an important role to provide information that can be used for advocacy and for promoting accountability for actions or lack of actions, including service delivery. Conclusion This paper identifies issues that affect the collection of nutrition surveillance data, and proposes definitions of terms to differentiate between diverse sources of data of variable accuracy and validity. Increased interest in nutrition globally has resulted in high level commitments to reduce and prevent undernutrition. This review helps to address the need for accurate and regular data to convert these commitments into practice

Long-term follow-up of glycemic and neurological outcomes in an international series of patients with sulfonylurea-treated ABCC8 permanent neonatal diabetes

This is the author accepted manuscript. The final version is available from the American Diabetes Association via the DOI in this record OBJECTIVE ABCC8 mutations cause neonatal diabetes mellitus that can be transient (TNDM) or, less commonly, permanent (PNDM); ∼90% of individuals can be treated with oral sulfonylureas instead of insulin. Previous studies suggested that people with ABCC8-PNDM require lower sulfonylurea doses and have milder neurological features than those with KCNJ11-PNDM. However, these studies were short-term and included combinations of ABCC8-PNDM and ABCC8-TNDM. We aimed to assess the long-term glycemic and neurological outcomes in sulfonylurea-treated ABCC8-PNDM. RESEARCH DESIGN AND METHODS We studied all 24 individuals with ABCC8-PNDM diagnosed in the U.K., Italy, France, and U.S. known to transfer from insulin to sulfonylureas before May 2010. Data on glycemic control, sulfonylurea dose, adverse effects including hypoglycemia, and neurological features were analyzed using nonparametric statistical methods. RESULTS Long-term data were obtained for 21 of 24 individuals (median follow-up 10.0 [range 4.1–13.2] years). Eighteen of 21 remained on sulfonylureas without insulin at the most recent follow-up. Glycemic control improved on sulfonylureas (presulfonylurea vs. 1-year posttransfer HbA1c 7.2% vs. 5.7%, P = 0.0004) and remained excellent long-term (1-year vs. 10-year HbA1c 5.7% vs. 6.5%, P = 0.04), n = 16. Relatively high doses were used (1-year vs. 10-year dose 0.37 vs. 0.25 mg/kg/day glyburide, P = 0.50) without any severe hypoglycemia. Neurological features were reported in 13 of 21 individuals; these improved following sulfonylurea transfer in 7 of 13. The most common features were learning difficulties (52%), developmental delay (48%), and attention deficit hyperactivity disorder (38%). CONCLUSIONS Sulfonylurea treatment of ABCC8-PNDM results in excellent long-term glycemic control. Overt neurological features frequently occur and may improve with sulfonylureas, supporting early, rapid genetic testing to guide appropriate treatment and neurodevelopmental assessment.Wellcome Trus