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Highly active and robust eletcrocatalysts based on earth-abundant elements are desirable to generate hydrogen and oxygen as fuels from water sustainably to replace noble metal materials. Here we report an approach to synthesize porous hybrid nanostructures combining amorphous nickel-cobalt complexes with 1T phase molybdenum disulfide (MoS 2 ) via hydrazine-induced phase transformation for water splitting. The hybrid nanostructures exhibit overpotentials of 70 mV for hydrogen evolution and 235 mV for oxygen evolution at 10 mA cm −2 with long-term stability, which have superior kinetics for hydrogen- and oxygen-evolution with Tafel slope values of 38.1 and 45.7 mV dec −1 . Moreover, we achieve 10 mA cm −2 at a low voltage of 1.44 V for 48 h in basic media for overall water splitting. We propose that such performance is likely due to the complete transformation of MoS 2 to metallic 1T phase, high porosity and stabilization effect of nickel-cobalt complexes on 1T phase MoS 2 . Electrocatalysts based on earth-abundant elements have emerged as promising candidates to replace noble metal materials. Here, the authors develop porous hybrid nanostructures combining amorphous Ni-Co complexes with 1T phase MoS 2 for enhanced electrocatalytic activity for overall water splitting.

Deep learning-based software defect prediction has been popular these days. Recently, the publishing of the CodeBERT model has made it possible to perform many software engineering tasks. We propose various CodeBERT models targeting software defect prediction, including CodeBERT-NT, CodeBERT-PS, CodeBERT-PK, and CodeBERT-PT. We perform empirical studies using such models in cross-version and cross-project software defect prediction to investigate if using a neural language model like CodeBERT could improve prediction performance. We also investigate the effects of different prediction patterns in software defect prediction using CodeBERT models. The empirical results are further discussed.

To improve software reliability, software defect prediction is used to find software bugs and prioritize testing efforts. Recently, some researchers introduced deep learning models, such as the deep belief network (DBN) and the state-of-the-art convolutional neural network (CNN), and used automatically generated features extracted from abstract syntax trees (ASTs) and deep learning models to improve defect prediction performance. However, the research on the CNN model failed to reveal clear conclusions due to its limited dataset size, insufficiently repeated experiments, and outdated baseline selection. To solve these problems, we built the PROMISE Source Code (PSC) dataset to enlarge the original dataset in the CNN research, which we named the Simplified PROMISE Source Code (SPSC) dataset. Then, we proposed an improved CNN model for within-project defect prediction (WPDP) and compared our results to existing CNN results and an empirical study. Our experiment was based on a 30-repetition holdout validation and a 10 * 10 cross-validation. Experimental results showed that our improved CNN model was comparable to the existing CNN model, and it outperformed the state-of-the-art machine learning models significantly for WPDP. Furthermore, we defined hyperparameter instability and examined the threat and opportunity it presents for deep learning models on defect prediction.

This study aimed to explore the relationship between the blood urea nitrogen/creatinine (BUN/Cre) ratio and all-cause or cause-specific mortality in the general population. Participants were enrolled from the National Health and Nutrition Examination Survey (NHANES) during 1999 to 2014. Baseline variables were acquired from questionnaires and examinations. Death status were ascertained from National Death Index records. Cox proportional hazards models with cubic spines were used to estimate hazard ratios (HRs) and 95% confidence interval (CI) of all-cause mortality, cardiovascular and cancer mortality. A total of 42038 participants were enrolled in the study with a median 8.13 years of follow-up. Older people and women tend to have a higher BUN/Cre ratio. After multivariable adjustment, BUN/Cre ratio between 11.43 and 14.64 was associated with the lowest all-cause mortality compared with the participants with the lowest quartile (HR 0.83 [0.76, 0.91]; p < 0.001). The highest quartile of BUN/Cre ratio was associated with the lowest risk of cancer mortality (HR 0.64 [0.53, 0.78]; p < 0.001). Restricted cubic splines showed BUN/Cre was nonlinearly associated with all-cause mortality and linearly associated with cancer mortality. This study confirmed a U-shape relationship between BUN/Cre ratio and all-cause mortality in the general population.

Background Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by an abnormal accumulation of amyloid-β (Aβ) plaques, neuroinflammation, and impaired neurogenesis. Urolithin A (UA), a gut-microbial metabolite of ellagic acid, has been reported to exert anti-inflammatory effects in the brain. However, it is unknown whether UA exerts its properties of anti-inflammation and neuronal protection in the APPswe/PS1ΔE9 (APP/PS1) mouse model of AD. Methods Morris water maze was used to detect the cognitive function. Terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) assay was performed to detect neuronal apoptosis. Immunohistochemistry analyzed the response of glia, Aβ deposition, and neurogenesis. The expression of inflammatory mediators were measured by enzyme-linked immunosorbent assay (ELISA) and quantitative real-time polymerase chain reaction (qRT-PCR). The modulating effects of UA on cell signaling pathways were assayed by Western blotting. Results We demonstrated that UA ameliorated cognitive impairment, prevented neuronal apoptosis, and enhanced neurogenesis in APP/PS1 mice. Furthermore, UA attenuated Aβ deposition and peri-plaque microgliosis and astrocytosis in the cortex and hippocampus. We also found that UA affected critical cell signaling pathways, specifically by enhancing cerebral AMPK activation, decreasing the activation of P65NF-κB and P38MAPK, and suppressing Bace1 and APP degradation. Conclusions Our results indicated that UA imparted cognitive protection by protecting neurons from death and triggering neurogenesis via anti-inflammatory signaling in APP/PS1 mice, suggesting that UA might be a promising therapeutic drug to treat AD.

Due to the shortage of defect samples and the high cost of labelling during the process of hot-rolled strip production in the metallurgical industry, it is difficult to obtain a large quantity of defect data with diversity, which seriously affects the identification accuracy of different types of defects on the steel surface. To address the problem of insufficient defect sample data in the task of strip steel defect identification and classification, this paper proposes the Strip Steel Surface Defect-ConSinGAN (SDE-ConSinGAN) model for strip steel defect identification which is based on a single-image model trained by the generative adversarial network (GAN) and which builds a framework of image-feature cutting and splicing. The model aims to reduce training time by dynamically adjusting the number of iterations for different training stages. The detailed defect features of training samples are highlighted by introducing a new size-adjustment function and increasing the channel attention mechanism. In addition, real image features will be cut and synthesized to obtain new images with multiple defect features for training. The emergence of new images is able to richen generated samples. Eventually, the generated simulated samples can be directly used in deep-learning-based automatic classification of surface defects in cold-rolled thin strips. The experimental results show that, when SDE-ConSinGAN is used to enrich the image dataset, the generated defect images have higher quality and more diversity than the current methods do.

The aim of the study was to specify prevalence and severity of menopausal symptoms among middle-aged women and to understand the factors associated with women’s perimenopausal healthcare-seeking behavior in Shanghai, China. A community-based cross-sectional study was carried out involving 3147 participants aged 40–60 years. A combination of stratified sampling and quota sampling was used. Out of the total 16 districts in Shanghai, 7 were purposefully selected in consideration of covering both central and suburban areas, population distribution, and willingness to participate. Two communities were randomly selected in each of six districts. Four communities were randomly selected in the 7th district considering the relatively low coverage of central population in the sampling frame. Eligible women were recruited continuously according to the house number and invited to participate in the study until 200 participants were recruited in each community. A structured questionnaire was designed to collect information including sociodemographic data, menopausal symptoms, and experiences in seeking perimenopausal healthcare. The severity of menopausal symptoms was assessed with the modified Kupperman menopausal index (mKMI). The mean age of all the participants was 51 years. 33.13% of the participants were premenopausal, 14.52% were perimenopausal, and 52.35% were postmenopausal. The total prevalence of menopausal symptoms was 73.8%, while among the perimenopausal women, the symptoms were the most common (81.70%). The top three reported symptoms were fatigue (38.08%), hot flushes and sweating (33.65%), and joint ache (28.81%). Perimenopausal and postmenopausal participants had a higher score of the mKMI than premenopausal women (p < 0.01). Of the women who had symptoms, 25.97% had sought healthcare. A logistic regression model revealed that employment, menstruation status, and the mKMI were significantly associated with healthcare-seeking behaviors (p < 0.01). We concluded that prevalence of menopausal symptoms was relatively high among middle-aged women, with perimenopausal women showing the highest level. However, only a small percentage of the participants sought healthcare. Carrying out health education may be a measure to improve the healthcare-seeking behavior.

Compared to other types of qubits, photon is one of a kind due to its unparalleled advantages in long-distance quantum information exchange. Therefore, photon is a natural candidate for building a large-scale, modular optical quantum computer operating at room temperature. However, low-fidelity two-photon quantum logic gates and their probabilistic nature result in a large resource overhead for fault tolerant quantum computation. While the probabilistic problem can, in principle, be solved by employing multiplexing and error correction, the fidelity of linear-optical quantum logic gate is limited by the imperfections of single photons. Here, we report the demonstration of a linear-optical quantum logic gate with truth table fidelity of 99.84(3)% and entangling gate fidelity of 99.69(4)% post-selected upon the detection of photons. The achieved high gate fidelities are made possible by our near-optimal Rydberg single-photon source. Our work paves the way for scalable photonic quantum applications based on near-optimal single-photon qubits and photon-photon gates. The current main source of errors for photonic quantum logic gates is the imperfections of the single photons. Here, by using high-quality photons from Rydberg atoms, the authors are able to reach 99.7% entangling gate fidelity in a photonic CNOT gate.

Supported nanoparticles are broadly employed in industrial catalytic processes, where the active sites can be tuned by metal-support interactions (MSIs). Although it is well accepted that supports can modify the chemistry of metal nanoparticles, systematic utilization of MSIs for achieving desired catalytic performance is still challenging. The developments of supports with appropriate chemical properties and identification of the resulting active sites are the main barriers. Here, we develop two-dimensional transition metal carbides (MXenes) supported platinum as efficient catalysts for light alkane dehydrogenations. Ordered Pt 3 Ti and surface Pt 3 Nb intermetallic compound nanoparticles are formed via reactive metal-support interactions on Pt/Ti 3 C 2 T x and Pt/Nb 2 CT x catalysts, respectively. MXene supports modulate the nature of the active sites, making them highly selective toward C–H activation. Such exploitation of the MSIs makes MXenes promising platforms with versatile chemical reactivity and tunability for facile design of supported intermetallic nanoparticles over a wide range of compositions and structures. The performance of supported metal nanoparticle catalysts can be tailored by metal-support interactions, but their use in catalyst design is still challenging. Here, the authors develop two-dimensional transition metal carbides as platforms for designing intermetallic compound catalysts that are efficient for light alkane dehydrogenations.

Acute lung injury (ALI), a common and critical illness with high morbidity and mortality, is caused by multiple causes. It has been confirmed that oxidative stress plays an important role in the development of ALI. Ferroptosis, a newly discovered programmed cell death in 2012, is characterized by iron-dependent lipid peroxidation and involved in many diseases. To date, compelling evidence reveals the emerging role of ferroptosis in the pathophysiological process of ALI. Here, we review the role of ferroptosis in the pathogenesis of ALI and its therapeutic potential in ALI.