Sawyerdowd3585

01). selleck inhibitor Because excess nutrient can reduce the marshes' root growth and degrade their root mat, we posit that the long-term nutrient enrichment in the area, which resulted from the diverted Mississippi River water, has increased the marshes' susceptibility to hurricanes. The results highlight the resilience of coastal marsh ecosystems against hurricanes, but also underline the profound synergistic effects of climatic and anthropogenic factors on the sustainability of coastal ecosystems, which have important implications for coastal management under the current climate trend. BACKGROUND AND OBJECTIVE As the most common cardiovascular defect, coronary artery disease (CAD), also called ischemic heart disease, is one of the substantial causes of death globally. Several diagnosis approaches such as baseline electrocardiography, echocardiography, magnetic resonance imaging, and coronary angiography are suggested for screening the suspected patients that may suffer from CAD. However, applying such methods may have health side effects and/or expensive costs. METHODS As an alternative to the available diagnosis tools/methods, this research involves a decision tree learning algorithm called classification and regression tree (CART) for a simple and reliable diagnosis of CAD. Several CART models are developed based on the recently CAD dataset published in the literature. RESULTS Utilizing all the features of the dataset (55 independent parameters), it was found that only 40 independent parameters influence the CAD diagnosis and consequently development of the predictive model. Based on the feature importance obtained from the first CART model, three new CART models are then developed using 18, 10, and 5 selected features. Except for the five-feature CART model, the outcomes of developed CART models demonstrate the maximum achievable accuracy, sensitivity, and specificity for CAD diagnosis (100%), while comparing the predictions with the reported targets. The error analysis reveals that the literature models including sequential minimal optimization (SMO), bagging SMO, Naïve Bayes (NB), artificial neural network (ANN), C4.5, J48, Bagging, and ANN in conjunction with the genetic algorithm (GA) do not outperform the CART methodology in classifying patients as normal or CAD. CONCLUSIONS Hence, the robustness of the tree-based algorithm in accurate and fast predictions is confirmed, implying the proposed classification technique can be successfully utilized to develop a coherent decision-making system for the CAD diagnosis. V.INTRODUCTION The purpose of this study was to explore the relationship between interference control and working memory with academic performance in both female and male high school students using a longitudinal design. METHODS One hundred and eighty-seven grade seventh to ninth students (mean age 13.1 ± 1.0 years old) from a French-Canadian high school located in Montreal, Canada, completed a 3-year prospective study. Interference control (Flanker task), working memory (N-back task) and academic performance (grades in science, mathematics, language and the overall average) were assessed every year during the 3-year study. RESULTS Female students had significantly higher grades than male students for overall average, science and language at year 1 as well as higher grades for overall average and language at year 3 (p  less then  0.05). However, no differences were found between genders for any measures of interference control or working memory at year 1 and 3. Furthermore, we noted that the relations between cognitive control with our academic performance measures differ according to gender. Finally, our results showed that neither interference control nor working memory seem to be the primary predictor for any of our academic performance measures in both female and male students. CONCLUSIONS Results of the present study indicate that cognitive control measures were not able to explain the gender differences in academic performance. Our results also show that interference control and working memory were weakly related to academic performance and that these associations had a poor ability to predict variations in academic performance during a 3-year period. Here, the adsorption of impurity species from triglyceride solvent representing a model vegetable oil is studied using atomistic molecular dynamics simulations. We compare the adsorption of water, glycerol, oleic acid, monoolein, and two types of phospholipids on model silica adsorbents differing in their OH-group density, i.e. hydrogen bonding ability, quartz and cristobalite. We find that the species containing charged groups, phospholipids DOPC and DOPE, adsorb significantly stronger than the nonionic impurities. Secondary contribution to adsorption arises from hydrogen bonding capability of the impurity species, the silica surface, and also the triglyceride solvent in general, more hydrogen bonding sites in impurity species leads to enhanced adsorption but hydrogen bonding with solvent competes for the available sites. Interestingly, adsorption is weaker on cristobalite even though it has a higher hydrogen bonding site density than quartz. This is because the hydrogen bonds can saturate each other on the adsorbent. The finding demonstrates that optimal adsorption response is obtained with intermediate adsorbent hydrogen bonding site densities. Additionally, we find that monoolein and oleic acid show a concentration driven adsorption response and reverse micelle like aggregate formation in bulk triglyceride solvent even in the absence of water. The findings offer insight into adsorption phenomena at inorganic adsorbent - apolar solvent interfaces and provide guidelines for enhanced design of adsorbent materials for example for vegetable oil purification. Electrochemical water splitting to hydrogen fuel is highly desirable yet challenging mainly limited by sluggish cathodic oxygen evolution reaction (OER). Urea electrolysis can produce hydrogen more energy-savingly by replacing OER process with urea oxidation reaction (UOR) due to favorable thermodynamic potential, however lacking efficient UOR catalysts restricts the industrial application. Here we reported novel NiMo-based nanorods, Ni/Ni0.2Mo0.8N/MoO3, by thermal ammonolysis of NiMo-based precursor as excellent catalyst for OER and hydrogen evolution reaction (HER) with small overpotentials of 252 mV, and 103 mV to achieve a current density of 10 mA cm-2 in 1.0 M KOH. Moreover, the Ni/Ni0.2Mo0.8N/MoO3 shows fabulous catalytic UOR activity with a low potential of 1.349 V at 10 mA cm-2, outperforming most recently reported non-noble metal catalysts and commercial RuO2. More importantly, the cell voltage of urea electrolysis using Ni/Ni0.2Mo0.8N/MoO3 as cathode for HER and anode for UOR is significantly reduced from 1.