Superconductivity Above 100 K Predicted in Carbon-Cage Network.

To explore carbide superconductors with higher transition temperature, two novel carbon structures of cage-network are designed and their superconductivity is studied by doping metals. MC6 and MC10 are respectively identified as C24 and C32 cage-network structures. This study finds that both carbon structures drive strong electron-phonon interaction and can exhibit superconductivity above liquid nitrogen temperature. Importantly, the superconducting transition temperatures above 100 K are predicted to be achieved in C24 -cage-network systems doped by Na, Mg, Al, In, and Tl at ambient pressure, which is far higher than those in graphite, fullerene, and other carbides. Meanwhile, the superconductivity of cage-network carbides is also found to be sensitive to the electronegativity and concentration of dopant M. The result indicates that the higher transition temperatures can be obtained by optimizing the carbon-cage-network structures and the doping conditions. The study suggests that the carbon-cage-network structure is a direction to explore high-temperature superconducting carbides.

Hierarchical NiOx nanotube arrays/CoP nanosheets heterostructure enables robust alkaline hydrogen evolution reaction.

Rational design of low-cost and high-efficiency electrocatalysts for hydrogen evolution reaction (HER) is critical for scalable and sustainable hydrogen production from economical water-alkali splitting. Herein, density functional theory (DFT) calculations reveal that coupling NiOx and CoP could effectively boost overall HER kinetics through lowing the H2O dissociation barrier, accelerating the OH* transfer process, and providing the rapid H* migration kinetics as well as the appropriate H* energetics. Based on these findings, we successfully prepared a three-dimensional (3D) self-supported electrode of ultrathin CoP nanosheets directly grown on the surface-oxidized Ni nanotube arrays via a simple and scalable electrochemical synthesis method. As expected, such a heterostructure electrode exhibits superior alkaline HER performance with low overpotentials of 51 and 164 mV to drive the current densities of 10 and 500 mA cm-2, respectively, outperforming most of the efficient alkaline HER electrocatalysts.

A Deep Neural Network-Based Model for Quantitative Evaluation of the Effects of Swimming Training.

This paper analyzes the quantitative assessment model of the swimming training effect based on the deep neural network by constructing a deep neural network model and designing a quantitative assessment model of the swimming training effect. This paper addresses the problem of not considering the influence of the uncertainties existing in the virtual environment when evaluating swimming training and adds the power of the delays in the actual training operation environment, which is used to improve the objectivity and usability of swimming training evaluation results. To better measure the degree of influence of uncertainties, a training evaluation software module is developed to validate the usability of the simulated training evaluation method using simulated case data and compare it with the data after training evaluation using the unimproved evaluation method to verify the correctness and objectivity of the evaluation method in this paper. In the experiments, the feature extractor is a deep neural network, and the classifier is a gradient-boosting decision tree with integrated learning advantages. In the experimental comparison, we can achieve more than 60% accuracy and no more than a 1.00% decrease in recognition rate on DBPNN + GBDT, 78.5% parameter reduction, and 54.5% floating-point reduction on DPBNN. We can effectively reduce 32.1% of video memory occupation. It can be concluded from the experiments that deep neural network models are more effective and easier to obtain relatively accurate experimental results than shallow learning when facing high-dimensional sparse features. At the same time, deep neural networks can also improve the prediction results of external learning models. Therefore, the experimental results of this model are most intuitively accurate when combining deep neural networks with gradient boosting decision trees.

Rationale and study design for one-stop assessment of renal artery stenosis and renal microvascular perfusion with contrast-enhanced ultrasound for patients with suspected renovascular hypertension / 中华医学杂志(英文版)

BACKGROUND@#Renal artery stenosis (RAS) is always associated with abnormalities in renal microvascular perfusion (RMP). However, few imaging methods can simultaneously evaluate the degree of luminal stenosis and RMP. Thus, this study will aim to evaluate the feasibility of using contrast-enhanced ultrasound (CEUS) for assessing both RAS and RMP to achieve a one-stop assessment of patients with suspected renovascular hypertension.@*METHODS@#This will be a single-center diagnostic study with a sample size of 440. Patients with chronic kidney disease (CKD) and suspected of having resistant hypertension will be eligible. Patients with Stages 1-3 CKD will undergo CEUS and computed tomography (CT) angiography (CTA). Values obtained by CEUS and CTA for diagnosing low-grade (lumen reduced by <60%) and high-grade (lumen reduced by ≥60%) RAS will be compared. Moreover, all patients will also undergo radionuclide imaging. The diagnostic value for RAS will be assessed by the receiver operating characteristic curve, including the accuracy, sensitivity, specificity, positive predictive values, negative predictive values, and area under the ROC. Pearson correlation analysis will be performed to assess the association between CEUS findings for RMP and glomerular filtration rate measured by a radionuclide imaging method.@*CONCLUSION@#The data gathered from this study will be used to evaluate the feasibility of expanding clinical applications of CEUS for evaluation of patients with suspected renovascular hypertension.@*TRIAL REGISTRATION@#Chinese Clinical Trial Registry, ChiCTR1800016252; https://www.chictr.org.cn.

[Development of indel markers for molecular authentication of Panax ginseng and P. quinquefolius].

Panax ginseng and P. quinquefolius are two kinds of important medicinal herbs. They are morphologically similar but have different pharmacological effects. Therefore, botanical origin authentication of these two ginsengs is of great importance for ensuring pharmaceutical efficacy and food safety. Based on the fact that intron position in orthologous genes is highly conserved across plant species, intron length polymorphisms were exploited from unigenes of ginseng. Specific primers were respectively designed for these two species based on their insertion/deletion sequences of cytochrome P450 and glyceraldehyde 3-phosphate dehydrogenase, and multiplex PCR was conducted for molecular authentication of P.ginseng and P. quinquefolius. The results showed that the developed multiplex PCR assay was effective for molecular authentication of P.ginseng and P. quinquefolius without strict PCR condition and the optimization of reaction system.This study provides a preferred ideal marker system for molecular authentication of ginseng,and the presented method can be employed in origin authentication of other herbal preparations.