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Parks Kolding posted an update 6 months ago
Nitrogen nutrient salts are considered the major environmental factors (RNH4+-N0.92, RTN0.85) affecting the structure and distribution of denitrogen bacteria. We aimed to investigate the mechanisms by which wetland bacteria adapt to environmental factors in different types of habitats. High-throughput sequencing technology was used to study the microbial community structure of sediments in three wetland habitats of the Yongding River, China. The microbial community structure differed across different habitats. Species richness of nitrifying bacteria increased, while that of denitrifying bacteria decreased, with ammonium salt and total nitrogen concentrations increasing from surface flow wetland to ditch wetland. The characteristics of the three habitat types and their distribution in the Yongding River wetland are beneficial to the differential distribution of microbial communities across the wetland, and to the existence and denitrification of different dominant bacteria. Overall, these results help explain the natural filtering function of wetlands.
(1) Assess the feasibility of 13N-ammonia cardiac PET (13N-ammonia-PET) imaging in radiotherapy (RT) treatment position in locally-advanced breast cancer (LABC) patients. (2) Correlate pre-/post-RT changes in myocardial flow reserve (MFR) with the corresponding radiation heart dose.
Ten left-sided LABC patients undergoing Volumetric Modulated-Arc-Therapy (VMAT) to chest wall and regional lymph nodes underwent a rest/stress 13N-ammonia-PET at baseline and (median) 13months post-RT. Changes in cardiac functions and coronary artery Ca2+ scoring between baseline and follow-up were correlated with average RT dose to the myocardium,3 coronary territories, and 17 myocardial segments.
Eight (of 10) patients successfully completed the study. The average rest (stress) global MBF (ml.g-1.min-1) for baseline (follow-up) were 0.83±0.25 (2.4±0.79) and 0.92±0.30 (2.76±0.71), respectively. Caspase Inhibitor VI Differences in MBF, heart rate, blood pressure, and rate-pressure product (RPP) between baseline and follow-up were insignificant ( potential index for early detection of cardiotoxicity in BC patients receiving RT to the chest wall.
Pain and affect are generally associated. However, individuals may differ in the magnitude of the coupling between pain and affect, which may have important implications for their mental health. The present study uses ecological momentary assessments (EMA) to examine individual differences in momentary pain-affect coupling and their associations with depressive and anxiety symptoms.
This study is a secondary data analysis of three primary EMA studies. Participants were a total of 290 patients with chronic pain. Results were synthesized across studies using meta-analytic techniques.
Individuals whose pain was more strongly concurrently coupled with affect (positively associated with negative affect or negatively associated with positive affect) reported higher levels of depressive and anxiety symptoms. Results from lagged analyses suggest that individual differences in affect reactivity to pain were not significantly associated with depressive or anxiety symptoms.
These findings suggest that individuals with greater concurrent coupling between pain and affect experience more mental health problems. Potential avenues for future research include intervention strategies that target the decoupling of pain and affect experiences in patients with chronic pain.
These findings suggest that individuals with greater concurrent coupling between pain and affect experience more mental health problems. Potential avenues for future research include intervention strategies that target the decoupling of pain and affect experiences in patients with chronic pain.Accurate calculation of molecular polarizabilities and Raman intensities required high-level correlated wave functions (CCSD) and large basis set with the inclusion of electronic correlation within experimental precision. These requirements, in terms of time and computation, are economically costly. Polarized Gaussian basis sets adapted to effective core potentials (ECPs) for the static and frequency dependent Raman intensities is presented. The results of the proposed basis sets at CCSD and DFT levels in comparison with Sadlej-pVTZ, as reference basis set, show quite a good quantitative agreement in the properties with a valuable reduction in the computational time and resources. Multivariate principal component analysis (PCA) was performed to study the assessment of the efficiency of proposed methodology and diagnose the inherent information related to the kind of normal vibrational mode of each molecule, based on the variations in the computed Raman intensities. The results, in the form of score-plots, explored a clear segregation and classification among the Raman intensities data, revealing its dependence on the excitation frequencies of laser and nature of the vibrational mode of each molecule of interest. Moreover, the projection of the loadings-plots of the PCs successfully enabled to classify the most correlated computational methods in to the same groups, and made isolations of the less efficient basis functions at the corresponding theoretical method.Rapid quantification methods for sugar-free Yangwei granules were developed based on near-infrared (NIR) spectroscopy combined with machine learning approaches as a quality control strategy for Chinese medicine granules (CMGs). Different machine learning approaches-i.e., interval partial least squares optimized by the genetic algorithm (GA-iPLS), the backpropagation artificial neural network (BP-ANN), and the particle swarm optimization-support vector machine (PSO-SVM)-were used to develop prediction models for three active pharmaceutical ingredients (APIs), namely, albiflorin, paeoniflorin, and benzoylpaeoniflorin. The partial least squares (PLS) algorithm was used for linear model calibration and comparison of the prediction performance of these developed models. The performance of the final models was assessed by the correlation coefficient (R), root mean square error of calibration set (RMSEC), and root mean square error of prediction set (RMSEP). All models performed well in model fitting and provided satisfactory prediction accuracy.
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