Mississippian stratotypes

Working Group on the Mississippian of the U.S.A.Ope

Alumina content of carbonate rocks as an index to sodium sulfate soundness : a preliminary report

Ope

A geologic excursion to fluorspar mines in Hardin and Pope Counties, Illinois; Illinois-Kentucky mining district and adjacent Upper Mississippi Embayment

"Prepared for the Ninth Annual Forum on the Geology of Industrial Minerals, Paducah, Kentucky, April 27-28, 1973."Bibliography: p. 27-28

Areal geology of the Illinois fluorspar district : Part III, Herod and Shetlerville Quadrangles

Ope

Children's body mass index, participation in school meals, and observed energy intake at school meals

<p>Abstract</p> <p>Background</p> <p>Data from a dietary-reporting validation study with fourth-grade children were analyzed to investigate a possible relationship of body mass index (BMI) with daily participation in school meals and observed energy intake at school meals, and whether the relationships differed by breakfast location (classroom; cafeteria).</p> <p>Methods</p> <p>Data were collected in 17, 17, and 8 schools during three school years. For the three years, six, six, and seven of the schools had breakfast in the classroom; all other schools had breakfast in the cafeteria. Information about 180 days of school breakfast and school lunch participation during fourth grade for each of 1,571 children (90% Black; 53% girls) was available in electronic administrative records from the school district. Children were weighed and measured, and BMI was calculated. Each of a subset of 465 children (95% Black; 49% girls) was observed eating school breakfast and school lunch on the same day. Mixed-effects regression was conducted with BMI as the dependent variable and school as the random effect; independent variables were breakfast participation, lunch participation, combined participation (breakfast and lunch on the same day), average observed energy intake for breakfast, average observed energy intake for lunch, sex, age, breakfast location, and school year. Analyses were repeated for BMI category (underweight/healthy weight; overweight; obese; severely obese) using pooled ordered logistic regression models that excluded sex and age.</p> <p>Results</p> <p>Breakfast participation, lunch participation, and combined participation were not significantly associated with BMI or BMI category irrespective of whether the model included observed energy intake at school meals. Observed energy intake at school meals was significantly and positively associated with BMI and BMI category. For the total sample and subset, breakfast location was significantly associated with BMI; average BMI was larger for children with breakfast in the classroom than in the cafeteria. Significantly more kilocalories were observed eaten at breakfast in the classroom than in the cafeteria.</p> <p>Conclusions</p> <p>For fourth-grade children, results provide evidence of a positive relationship between BMI and observed energy intake at school meals, and between BMI and school breakfast in the classroom; however, BMI and participation in school meals were not significantly associated.</p

Design and Development of a Human Building Interaction Laboratory

A future is envisioned where buildings are assembled on-site from factory manufactured modular elements that integrate the smart technology needed to enable scalable, cost-effective solutions with autonomous, occupant-responsive, healthy, and sustainable features. The use of modular elements would mean that buildings are assembled rather than constructed on-site with better quality control, less material waste, and more predictable schedules. The use of manufactured building elements can enable more cost-effective integration of new sensors, embedded intelligence, networking, adaptive interfaces, renewable energy, energy recovery, comfort delivery, and resiliency technologies, making high-performance buildings more affordable. To explore and evaluate these modular and intelligent comfort delivery concepts and advanced approaches for interaction with occupants, a new human-building interaction laboratory (HBIL) has been designed and is under development. The facility has a modular construction layout with thermally active panels. The interior surface temperature of each panel can be individually controlled using a hydronic system. Such configuration allows us to emulate different climate zones and building type conditions and perform studies such as the effect of different active building surfaces on thermal comfort, localized comfort delivery, and occupant comfort control, among others. Moreover, each panel is reconfigurable to allow investigating different interior surface treatments for different visual and acoustic comfort conditions. In this paper, the overall design approach of the facility is presented. Furthermore, a prototype panel has been constructed to validate the design and assess the dynamic and steady-state thermal performance. Test results for the prototype panel are also presented here with a discussion on their agreement with design phase modeling results

Major improvements to the Heliconius melpomene genome assembly used to confirm 10 chromosome fusion events in 6 million years of butterfly evolution

The Heliconius butterflies are a widely studied adaptive radiation of 46 species spread across Central and South America, several of which are known to hybridize in the wild. Here, we present a substantially improved assembly of the Heliconius melpomene genome, developed using novel methods that should be applicable to improving other genome assemblies produced using short read sequencing. First, we whole-genome-sequenced a pedigree to produce a linkage map incorporating 99% of the genome. Second, we incorporated haplotype scaffolds extensively to produce a more complete haploid version of the draft genome. Third, we incorporated ~20x coverage of Pacific Biosciences sequencing, and scaffolded the haploid genome using an assembly of this long-read sequence. These improvements result in a genome of 795 scaffolds, 275 Mb in length, with an N50 length of 2.1 Mb, an N50 number of 34, and with 99% of the genome placed, and 84% anchored on chromosomes. We use the new genome assembly to confirm that the Heliconius genome underwent 10 chromosome fusions since the split with its sister genus Eueides, over a period of about 6 million yr

Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a "Second Solubility Challenge"in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019 but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms and were trained on a relatively small data set of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility data sets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge data sets, with the best model, a graph convolutional neural network, resulting in an RMSE of 0.86 log units. Critical analysis of the models reveals systematic differences between the performance of models using certain feature sets and training data sets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modeling complex chemical spaces from sparse training data sets