Language about salvation: an analysis of part of the vocabulary of the Old Testament

It may seem odd that, after many centuries of translation and exegesis, the meaning of a common Old Testament Hebrew word like HOSIA can still be taken as the subject of a doctoral dissertation. There are several answers to this charge. First, there have, broadly speaking, been only two approaches to the problem of the meaning of HOSIA, the one based on simple translation (e.g. 'HOSIA means "save "), and the other on comparative philology (e.g. 'the root of HOSIA means "spaciousness". Cf. Arabic wasia "be spacious "'). Even without analysing the obvious inadequacy of these two methods, it is clear that there is still room for a systematic definition of the meaning of HOSIA from within the Hebrew language. How is it distinguished, for example, from HISSIL which also 'means "save", and from HIRHIB whose root also 'means "spaciousness "? Monolingual definition, in terms of meaning-relations contracted within the language, and semantic components identifiable in lexical groups, is, to the best of my knowledge, unknown in the field of Old Testament Hebrew lexicography.This leads to a second, more general answer. The gap between the semantics of Biblical language and modern linguistic theory has still to be bridged. My interests in this direction began in 1961 at New College, Edinburgh, under the stimulus of Professor James Barr whose famous book on the subject was published in that year, and were further encouraged by Professor Chaim Rabin in Jerusalem, whose course in semantica mictra'it at the Hebrew University in 1962, in a way marked the beginning of a new era for the semantics of the Hebrew language. More recently, my participation in the activities of the Linguistic Section of the University of Newcastle upon Tyne Philosophical Society, and some valuable assistance from Professor John Lyons in the University of Edinburgh, have made me aware of the immense contribution still to be made by general linguistics to Old Testament lexicography and interpretation.In this short essay I have tried to work out a general semantic theory applicable to a religious text like the Old Testament. In the field of Biblical research, semanticists - and this includes philologists, lexicographers, exegetes and theologians - have a distinct advantage over their colleagues in other branches of linguistic science in having a closed literary corpus to work with. Our first step is to define this corpus and the context or contexts in which it has meaning (Chapter I). There are varieties of language within the corpus and distinctions must be drawn in terms of style or literary form (Chapter II). A third chapter presents some of the more important historical factors operating in the associative field to which HOSIA , HISSIL, etc. belong; while the next chapter is a synchronic analysis of the meaning of these terms as they are used in a selected variety of Old Testament Hebrew, namely language addressed to God. The results of this analysis can then be correlated, compared with the historical data, and set forth as dictionary definitions (Chapter V). A final chapter attempts to draw up a modest blue -print for semantic studies of Old Testament terms, based on the experience of handling the lexical material involved in the foregoing chapters.This outline suggests a third answer to the charge that there can hardly be anything left to say on the meaning of HOSIA: a problem like this cannot properly be studied in isolation. Questions about the context of the Old Testament, the nature of religious language, and the relation between "word -studies" and "concept- studies ", on which there is still a great deal to be said, arise at every stage. Which words belong to language about salvation and which do not? What is the relation between "the meaning of HOSIA and "the meaning of salvation"? How is it possible to move from semantic analysis to Biblical Theology? What theological norms are there in cases of diversity of meaning? In short, there are theological and religious issues in this kind of study which point beyond the relatively circumscribed context of linguistic description

Effects of X-Linkage and Sex-Biased Gene Expression on the Rate of Adaptive Protein Evolution in Drosophila

Patterns of polymorphism and divergence in Drosophila protein-coding genes suggest that a considerable fraction of amino acid differences between species can be attributed to positive selection and that genes with sex-biased expression, that is, those expressed predominantly in one sex, have especially high rates of adaptive evolution. Previous studies, however, have been restricted to autosomal sex-biased genes and, thus, do not provide a complete picture of the evolutionary forces acting on sex-biased genes across the genome. To determine the effects of X-linkage on sex-biased gene evolution, we surveyed DNA sequence polymorphism and divergence in 45 X-linked genes, including 17 with male-biased expression, 13 with female-biased expression, and 15 with equal expression in the 2 sexes. Using both single- and multilocus tests for selection, we found evidence for adaptive evolution in both groups of sex-biased genes. The signal of adaptive evolution was particularly strong for X-linked male-biased genes. A comparison with data from 91 autosomal genes revealed a ‘‘fast-X’’ effect, in which the rate of adaptive evolution was greater for X-linked than for autosomal genes. This effect was strongest for male-biased genes but could be seen in the other groups as well. A genome-wide analysis of coding sequence divergence that accounted for sex-biased expression also uncovered a fast-X effect for male-biased and unbiased genes, suggesting that recessive beneficial mutations play an important role in adaptation.Organismic and Evolutionary Biolog

DeWitt Wallace Library Annual Report 2015-2016

Summary of library and media services activities for 2015-201

Improving an Active-Optical Reflectance Sensor Algorithm Using Soil and Weather Information

Active-optical reflectance sensors (AORS) use light reflectance characteristics from a crop canopy as an indicator of the plant’s N health. However, studies have shown AORS algorithms used in conjunction with measured reflectance characteristics for corn (Zea maysL.) N fertilizer rate recommendations are not consistently accurate. Our objective was to determine if soil and weather information could be utilized with an AORS algorithm developed at the University of Missouri (ALGMU) to improve in-season (∼V9 corn development stage) N fertilizer recommendations. Nitrogen response trials were conducted across eight states over three growing seasons, totaling 49 sites with soils ranging in productivity. Nitrogen fertilizer rates according to the ALGMU were compared to economic optimal nitrogen rate (EONR). Without soil and weather information included, the root mean square error (RMSE) of the difference between ALGMU and EONR (MUDIFF) was 81 and 74 kg N ha–1 for treatments receiving 0 and 45 kg N ha–1 applied at planting, respectively. When ALGMU was adjusted using weather (seasonal precipitation and distribution prior to sidedress) and soil clay content, the RMSE was reduced by 24 to 26 kg N ha–1. Without adjustment, 20 and 29% of sites were within 34 kg N ha–1 of EONR with 0 and 45 kg N ha–1 at planting, respectively. But with adjustment for soil and weather data, 45 and 51% of sites were within 34 kg N ha–1 of EONR. These results show that weather and soil information could be used to improve ALGMU N recommendation performance

Methane Emissions from Process Equipment at Natural Gas Production Sites in the United States: Liquid Unloadings

Methane emissions from liquid unloadings were measured at 107 wells in natural gas production regions throughout the United States. Liquid unloadings clear wells of accumulated liquids to increase production, employing a variety of liquid lifting mechanisms. In this work, wells with and without plunger lifts were sampled. Most wells without plunger lifts unload less than 10 times per year with emissions averaging 21 000–35 000 scf methane (0.4–0.7 Mg) per event (95% confidence limits of 10 000–50 000 scf/event). For wells with plunger lifts, emissions averaged 1000–10 000 scf methane (0.02–0.2 Mg) per event (95% confidence limits of 500–12 000 scf/event). Some wells with plunger lifts are automatically triggered and unload thousands of times per year and these wells account for the majority of the emissions from all wells with liquid unloadings. If the data collected in this work are assumed to be representative of national populations, the data suggest that the central estimate of emissions from unloadings (270 Gg/yr, 95% confidence range of 190–400 Gg) are within a few percent of the emissions estimated in the EPA 2012 Greenhouse Gas National Emission Inventory (released in 2014), with emissions dominated by wells with high frequencies of unloadings

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest

Soil sample timing, nitrogen fertilization, and incubation length influence anaerobic potentially mineralizable nitrogen

Understanding the variables that affect the anaerobic potentially mineralizable N (PMNan) test should lead to a standard procedure of sample collection and incubation length, improving PMNan as a tool in corn (Zea mays L.) N management. We evaluated the effect of soil sample timing (preplant and V5 corn development stage [V5]), N fertilization (0 and 180 kg ha−1) and incubation length (7, 14, and 28 d) on PMNan (0–30 cm) across a range of soil properties and weather conditions. Soil sample timing, N fertilization, and incubation length affected PMNan differently based on soil and weather conditions. Preplant vs. V5 PMNan tended to be greater at sites that received \u3c 183 mm of precipitation or \u3c 359 growing degree-days (GDD) between preplant and V5, or had soil C/N ratios \u3e 9.7:1; otherwise, V5 PMNan tended to be greater than preplant PMNan. The PMNan tended to be greater in unfertilized vs. fertilized soil in sites with clay content \u3e 9.5%, total C \u3c 24.2 g kg−1, soil organic matter (SOM) \u3c 3.9 g kg−1, or C to N ratios \u3c 11.0:1; otherwise, PMNan tended to be greater in fertilized vs. unfertilized soil. Longer incubation lengths increased PMNan at all sites regardless of sampling methods. Since PMNan is sensitive to many factors (sample timing, N fertilization, incubation length, soil properties, and weather conditions), it is important to follow a consistent protocol to compare PMNan among sites and potentially use PMNan to improve corn N management

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 \u3c 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions