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Electrochemical nitrate reduction method (NitRR) is a low‐carbon, environmentally friendly, and efficient method for synthesizing ammonia, which has received widespread attention in recent years. Copper‐based catalysts have a leading edge in nitrate reduction due to their good adsorption of *NO3. However, the formation of active hydrogen (*H) on Cu surfaces is difficult and insufficient, resulting in a large amount of the by‐product NO2−. In this work, Pd single atoms suspended on the interlayer unsaturated bonds of CuO atoms formed due to dislocations (Pd−CuO) were prepared by low temperature treatment, and the Pd single atoms located on the dislocations were subjected to shear stress and the dynamic effect of support formation to promote the conversion of nitrate into ammonia. The catalysis had an ammonia yield of 4.2 mol. gcat−1. h−1, and a Faraday efficiency of 90 % for ammonia production at −0.5 V vs. RHE. Electrochemical in situ characterization and theoretical calculations indicate that the dynamic effects of Pd single atoms and carriers under shear stress obviously promote the production of active hydrogen, reduce the reaction energy barrier of the decision‐making step for nitrate conversion to ammonia, further promote ammonia generation. [ABSTRACT FROM AUTHOR]

Deutenzalutamide is a new molecular entity androgen receptor antagonist. The primary aim of this study was to develop a population pharmacokinetic model of deutenzalutamide and evaluate effects of intrinsic and extrinsic factors on pharmacokinetics. A nonlinear mixed‐effects modeling approach was performed to develop the population pharmacokinetic of deutenzalutamide using data from 1 Phase I trial of deutenzalutamide. Goodness‐of‐fit plots, prediction‐corrected visual predictive check, and bootstrap analysis were carried out to evaluate the final model. Simulation for the developed model was used to evaluate the covariate effects on the pharmacokinetics of deutenzalutamide. A 2‐compartment model with first‐order absorption and elimination from the central compartment was established for deutenzalutamide. The final covariate included body weight on peripheral compartment volume. This is the first research developing the population pharmacokinetic model of deutenzalutamide in patients with metastatic castration‐resistant prostate cancer, and it is expected to support the future clinical administration of deutenzalutamide. [ABSTRACT FROM AUTHOR]

Context Current metabolomics studies in diabetes have focused on the fasting state, while only a few have addressed the satiated state. Objective We combined the oral glucose tolerance test (OGTT) and metabolomics to examine metabolite-level changes in populations with different glucose tolerance statuses and to evaluate the potential risk of these changes for diabetes. Methods We grouped participants into those with normal glucose tolerance (NGT), impaired glucose regulation (IGR), and newly diagnosed type 2 diabetes (NDM). During the OGTT, serum was collected at 0, 30, 60, 120, and 180 minutes. We evaluated the changes in metabolite levels during the OGTT and compared metabolic profiles among the 3 groups. The relationship between metabolite levels during the OGTT and risk of diabetes and prediabetes was analyzed using a generalized estimating equation (GEE). The regression results were adjusted for sex, body mass index, fasting insulin levels, heart rate, smoking status, and blood pressure. Results Glucose intake altered metabolic profile and induced an increase in glycolytic intermediates and a decrease in amino acids, glycerol, ketone bodies, and triglycerides. Isoleucine levels differed between the NGT and NDM groups and between the NGT and IGR groups. Changes in sarcosine levels during the OGTT in the diabetes groups were opposite to those in glycine levels. GEE analysis revealed that during OGTT, isoleucine, sarcosine, and acetic acid levels were associated with NDM risks, and isoleucine and acetate levels with IGR risks. Conclusion Metabolic profiles differ after glucose induction in individuals with different glucose tolerance statuses. Changes in metabolite levels during OGTT are potential risk factors for diabetes development. [ABSTRACT FROM AUTHOR]

Powdery mildew significantly impacts the yield of natural rubber by being one of the predominant diseases that affect rubber trees. Accurate, non-destructive recognition of powdery mildew in the early stage is essential for the cultivation management of rubber trees. The objective of this study is to establish a technique for the early detection of powdery mildew in rubber trees by combining spectral and physicochemical parameter features. At three field experiment sites and in the laboratory, a spectroradiometer and a hand-held optical leaf-clip meter were utilized, respectively, to measure the hyperspectral reflectance data (350–2500 nm) and physicochemical parameter data of both healthy and early-stage powdery-mildew-infected leaves. Initially, vegetation indices were extracted from hyperspectral reflectance data, and wavelet energy coefficients were obtained through continuous wavelet transform (CWT). Subsequently, significant vegetation indices (VIs) were selected using the ReliefF algorithm, and the optimal wavelengths (OWs) were chosen via competitive adaptive reweighted sampling. Principal component analysis was used for the dimensionality reduction of significant wavelet energy coefficients, resulting in wavelet features (WFs). To evaluate the detection capability of the aforementioned features, the three spectral features extracted above, along with their combinations with physicochemical parameter features (PFs) (VIs + PFs, OWs + PFs, WFs + PFs), were used to construct six classes of features. In turn, these features were input into support vector machine (SVM), random forest (RF), and logistic regression (LR), respectively, to build early detection models for powdery mildew in rubber trees. The results revealed that models based on WFs perform well, markedly outperforming those constructed using VIs and OWs as inputs. Moreover, models incorporating combined features surpass those relying on single features, with an overall accuracy (OA) improvement of over 1.9% and an increase in F1-Score of over 0.012. The model that combines WFs and PFs shows superior performance over all the other models, achieving OAs of 94.3%, 90.6%, and 93.4%, and F1-Scores of 0.952, 0.917, and 0.941 on SVM, RF, and LR, respectively. Compared to using WFs alone, the OAs improved by 1.9%, 2.8%, and 1.9%, and the F1-Scores increased by 0.017, 0.017, and 0.016, respectively. This study showcases the viability of early detection of powdery mildew in rubber trees. [ABSTRACT FROM AUTHOR]

High-pressure die casting (HPDC) has been extensively used to manufacture aluminum alloy heat dissipation components in the fields of vehicles, electronics, and communication. With the increasing demand for HPDC heat dissipation components, the thermal conductivity of die-cast aluminum alloys is paid more attention. In this paper, a comprehensive review of the research progress on the thermal conductivity of HPDC aluminum alloys is provided. First of all, we introduce the general heat transport mechanism in aluminum alloys, including electrical transport and phonon transport. Secondly, we summarize several common die-cast aluminum alloy systems utilized for heat dissipation components, such as an Al–Si alloy system and silicon-free aluminum alloy systems, along with the corresponding composition optimizations for these alloy systems. Thirdly, the effect of processing parameters, which are significant for the HPDC process, on the thermal conductivity of HPDC aluminum alloys is discussed. Moreover, some heat treatment strategies for enhancing the thermal conductivity of die-cast aluminum alloys are briefly discussed. Apart from experimental findings, a range of theoretical models used to calculate the thermal conductivity of die-cast aluminum alloys are also summarized. This review aims to guide the development of new high-thermal-conductivity die-cast aluminum alloys. [ABSTRACT FROM AUTHOR]