User Interface Engineering (KSU)

This Grants Collection for User Interface Engineering was created under a Round Twelve ALG Textbook Transformation Grant. Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process. Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials: Linked Syllabus Initial Proposal Final Reporthttps://oer.galileo.usg.edu/compsci-collections/1035/thumbnail.jp

Mobile Software Development (KSU)

This Grants Collection for Mobile Software Development was created under a Round Twelve ALG Textbook Transformation Grant. Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process. Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials: Linked Syllabus Initial Proposal Final Reporthttps://oer.galileo.usg.edu/compsci-collections/1036/thumbnail.jp

Divergent DNA Methylation Provides Insights into the Evolution of Duplicate Genes in Zebrafish

The evolutionary mechanism, fate and function of duplicate genes in various taxa have been widely studied; however, the mechanism underlying the maintenance and divergence of duplicate genes in Danio rerio remains largely unexplored. Whether and how the divergence of DNA methylation between duplicate pairs is associated with gene expression and evolutionary time are poorly understood. In this study, by analyzing bisulfite sequencing (BS-seq) and RNA-seq datasets from public data, we demonstrated that DNA methylation played a critical role in duplicate gene evolution in zebrafish. Initially, we found promoter methylation of duplicate genes generally decreased with evolutionary time as measured by synonymous substitution rate between paralogous duplicates (Ks). Importantly, promoter methylation of duplicate genes was negatively correlated with gene expression. Interestingly, for 665 duplicate gene pairs, one gene was consistently promoter methylated, while the other was unmethylated across nine different datasets we studied. Moreover, one motif enriched in promoter methylated duplicate genes tended to be bound by the transcription repression factor FOXD3, whereas a motif enriched in the promoter unmethylated sequences interacted with the transcription activator Sp1, indicating a complex interaction between the genomic environment and epigenome. Besides, body-methylated genes showed longer length than body-unmethylated genes. Overall, our results suggest that DNA methylation is highly important in the differential expression and evolution of duplicate genes in zebrafish.</p

Comparison and analysis of bare soil evaporation models combined with ASTER data in Heihe River Basin

AbstractBased on ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) remote sensing data, bare soil evaporation was estimated with the Penman-Monteith model, the Priestley-Taylor model, and the aerodynamics model. Evaporation estimated by each of the three models was compared with actual evaporation, and error sources of the three models were analyzed. The mean absolute relative error was 9% for the Penman-Monteith model, 14% for the Priestley-Taylor model, and 32% for the aerodynamics model; the Penman-Monteith model was the best of these three models for estimating bare soil evaporation. The error source of the Penman-Monteith model is the neglect of the advection estimation. The error source of the Priestley-Taylor model is the simplification of the component of aerodynamics as 0.72 times the net radiation. The error source of the aerodynamics model is the difference of vapor pressure and neglect of the radiometric component. The spatial distribution of bare soil evaporation is evident, and its main factors are soil water content and elevation

Biosynthesis of thiocarboxylic acid-containing natural products.

Thiocarboxylic acid-containing natural products are rare and their biosynthesis and biological significance remain unknown. Thioplatensimycin (thioPTM) and thioplatencin (thioPTN), thiocarboxylic acid congeners of the antibacterial natural products platensimycin (PTM) and platencin (PTN), were recently discovered. Here we report the biosynthetic origin of the thiocarboxylic acid moiety in thioPTM and thioPTN. We identify a thioacid cassette encoding two proteins, PtmA3 and PtmU4, responsible for carboxylate activation by coenzyme A and sulfur transfer, respectively. ThioPTM and thioPTN bind tightly to β-ketoacyl-ACP synthase II (FabF) and retain strong antibacterial activities. Density functional theory calculations of binding and solvation free energies suggest thioPTM and thioPTN bind to FabF more favorably than PTM and PTN. Additionally, thioacid cassettes are prevalent in the genomes of bacteria, implicating that thiocarboxylic acid-containing natural products are underappreciated. These results suggest that thiocarboxylic acid, as an alternative pharmacophore, and thiocarboxylic acid-containing natural products may be considered for future drug discovery

Experimental cyclic inter-conversion between Coherence and Quantum Correlations

Quantum resource theories seek to quantify sources of non-classicality that bestow quantum technologies their operational advantage. Chief among these are studies of quantum correlations and quantum coherence. The former to isolate non-classicality in the correlations between systems, the latter to capture non-classicality of quantum superpositions within a single physical system. Here we present a scheme that cyclically inter-converts between these resources without loss. The first stage converts coherence present in an input system into correlations with an ancilla. The second stage harnesses these correlations to restore coherence on the input system by measurement of the ancilla. We experimentally demonstrate this inter-conversion process using linear optics. Our experiment highlights the connection between non-classicality of correlations and non-classicality within local quantum systems, and provides potential flexibilities in exploiting one resource to perform tasks normally associated with the other.Comment: 8 pages, 4 figures, comments welcom

Large Water Management Projects and Schistosomiasis Control, Dongting Lake Region, China

Two large water projects will likely extend the range of snail habitats and increase schistosome transmission

Machine learning classification models for fetal skeletal development performance prediction using maternal bone metabolic proteins in goats

Background: In developing countries, maternal undernutrition is the major intrauterine environmental factor contributing to fetal development and adverse pregnancy outcomes. Maternal nutrition restriction (MNR) in gestation has proven to impact overall growth, bone development, and proliferation and metabolism of mesenchymal stem cells in offspring. However, the efficient method for elucidation of fetal bone development performance through maternal bone metabolic biochemical markers remains elusive. Methods: We adapted goats to elucidate fetal bone development state with maternal serum bone metabolic proteins under malnutrition conditions in mid- and late-gestation stages. We used the experimental data to create 72 datasets by mixing different input features such as one-hot encoding of experimental conditions, metabolic original data, experimental-centered features and experimental condition probabilities. Seven Machine Learning methods have been used to predict six fetal bone parameters (weight, length, and diameter of femur/humerus). Results: The results indicated that MNR influences fetal bone development (femur and humerus) and fetal bone metabolic protein levels (C-terminal telopeptides of collagen I, CTx, in middle-gestation and N-terminal telopeptides of collagen I, NTx, in late-gestation), and maternal bone metabolites (low bone alkaline phosphatase, BALP, in middle-gestation and high BALP in late-gestation). The results show the importance of experimental conditions (ECs) encoding by mixing the information with the serum metabolic data. The best classification models obtained for femur weight (Fw) and length (FI), and humerus weight (Hw) are Support Vector Machines classifiers with the leave-one-out cross-validation accuracy of 1. The rest of the accuracies are 0.98, 0.946 and 0.696 for the diameter of femur (Fd), diameter and length of humerus (Hd, Hl), respectively. With the feature importance analysis, the moving averages mixed ECs are generally more important for the majority of the models. The moving average of parathyroid hormone (PTH) within nutritional conditions (MA-PTH-experim) is important for Fd, Hd and Hl prediction models but its removal for enhancing the Fw, Fl and Hw model performance. Further, using one feature models, it is possible to obtain even more accurate models compared with the feature importance analysis models. In conclusion, the machine learning is an efficient method to confirm the important role of PTH and BALP mixed with nutritional conditions for fetal bone growth performance of goats. All the Python scripts including results and comments are available into an open repository at https://gitlab.com/muntisa/goat-bones-machine-learning