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Abstract Background Telerehabilitation is a promising avenue for improving patient outcomes and expanding accessibility. However, there is currently no spine-related assessment for telerehabilitation that covers multiple exercises. Methods We propose a wearable system with two inertial measurement units (IMUs) to identify IMU locations and estimate spine angles for ten commonly prescribed spinal degeneration rehabilitation exercises (supine chin tuck head lift rotation, dead bug unilateral isometric hold, pilates saw, catcow full spine, wall angel, quadruped neck flexion/extension, adductor open book, side plank hip dip, bird dog hip spinal flexion, and windmill single leg). Twelve healthy subjects performed these spine-related exercises, and wearable IMU data were collected for spine angle estimation and IMU location identification. Results Results demonstrated average mean absolute spinal angle estimation errors of 2.59 $$^\circ$$ ∘ and average classification accuracy of 92.97%. The proposed system effectively identified IMU locations and assessed spine-related rehabilitation exercises while demonstrating robustness to individual differences and exercise variations. Conclusion This inexpensive, convenient, and user-friendly approach to spine degeneration rehabilitation could potentially be implemented at home or provide remote assessment, offering a promising avenue to enhance patient outcomes and improve accessibility for spine-related rehabilitation. Trial registration: No. E2021013P in Shanghai Jiao Tong University.

Abstract Adatom engineering represents a highly promising opportunity for enhancing electrochemical CO reduction reaction (CORR). However, the aggregation of adatoms under typical reaction conditions often leads to a decline in catalyst activity. Recent studies have revealed that N-heterocyclic carbene (NHC) can stabilize surface adatoms. Herein, based on density functional theory calculations, we reveal a significant enhancement in the catalytic activity of Cu adatoms decorated with NHC molecules for CORR. The NHC decoration strengthens the interaction between the d xy orbital of the Cu adatom and the p x orbital of the C atom, reducing the energy barriers in both CO hydrogenation and C-C coupling steps. Moreover, the CORR catalytic activity of the NHC decorated adatom can be further improved by tuning the side groups of NHC molecules. These results provide insights for the design of efficient CORR catalysts and offer a theoretical framework that can be extended to other hydrogenation reactions.

Abstract Necroptosis has been reported to be involved in cancer progression and associated with cancer prognosis. However, the prognostic values of necroptosis-related genes (NRGs) in hepatocellular carcinoma (HCC) remain largely unknown. This study aimed to build a signature on the basis of NRGs to evaluate the prognosis of HCC patients. In this study, using bioinformatic analyses of transcriptome sequencing data of HCC (n = 370) from The Cancer Genome Atlas (TCGA) database, 63 differentially expressed NRGs between HCC and adjacent normal tissues were determined. 24 differentially expressed NRGs were found to be related with overall survival (OS). Seven optimum NRGs, determined using Lasso regression and multivariate Cox regression analysis, were used to construct a new prognostic risk signature for predicting the prognosis of HCC patients. Then survival status scatter plots and survival curves demonstrated that the prognosis of patients with high-Riskscore was worse. The prognostic value of this 7-NRG signature was validated by the International Cancer Genome Consortium (ICGC) cohort and a local cohort (Wenzhou, China). Notably, Riskscore was defined as an independent risk factor for HCC prognosis using multivariate cox regression analysis. Immune infiltration analysis suggested that higher macrophage infiltration was found in patients in the high-risk group. Finally, enhanced 7 NRGs were found in HCC tissues by immunohistochemistry. In conclusion, a novel 7-NRG prognostic risk signature is generated, which contributes to the prediction in the prognosis of HCC patients for the clinicians.

Background: Ginsenoside Rg1, a bioactive component of Ginseng, has demonstrated anti-inflammatory, anti-cancer, and hepatoprotective effects. It is known that the epithelial–mesenchymal transition (EMT) plays a key role in the activation of hepatic stellate cells (HSCs). Recently, Rg1 has been shown to reverse liver fibrosis by suppressing EMT, although the mechanism of Rg1-mediated anti-fibrosis effects is still largely unclear. Interestingly, Smad7, a negative regulator of the transforming growth factor β (TGF-β) pathway, is often methylated during liver fibrosis. Whether Smad7 methylation plays a vital role in the effects of Rg1 on liver fibrosis remains unclear. Methods: Anti-fibrosis effects were examined after Rg1 processing in vivo and in vitro. Smad7 expression, Smad7 methylation, and microRNA-152 (miR-152) levels were also analyzed. Results: Rg1 significantly reduced the liver fibrosis caused by carbon tetrachloride, and reduced collagen deposition was also observed. Rg1 also contributed to the suppression of collagenation and HSC reproduction in vitro. Rg1 caused EMT inactivation, reducing Desmin and increasing E-cadherin levels. Notably, the effect of Rg1 on HSC activation was mediated by the TGF-β pathway. Rg1 induced Smad7 expression and demethylation. The over-expression of DNA methyltransferase 1 (DNMT1) blocked the Rg1-mediated inhibition of Smad7 methylation, and miR-152 targeted DNMT1. Further experiments suggested that Rg1 repressed Smad7 methylation via miR-152-mediated DNMT1 inhibition. MiR-152 inhibition reversed the Rg1-induced promotion of Smad7 expression and demethylation. In addition, miR-152 silencing led to the inhibition of the Rg1-induced EMT inactivation. Conclusion: Rg1 inhibits HSC activation by epigenetically modulating Smad7 expression and at least by partly inhibiting EMT.

Abstract Kramers nodal lines (KNLs) have recently been proposed theoretically as a special type of Weyl line degeneracy connecting time-reversal invariant momenta. KNLs are robust to spin orbit coupling and are inherent to all non-centrosymmetric achiral crystal structures, leading to unusual spin, magneto-electric, and optical properties. However, their existence in in real quantum materials has not been experimentally established. Here we gather the experimental evidence pointing at the presence of KNLs in SmAlSi, a non-centrosymmetric metal that develops incommensurate spin density wave order at low temperature. Using angle-resolved photoemission spectroscopy, density functional theory calculations, and magneto-transport methods, we provide evidence suggesting the presence of KNLs, together with observing Weyl fermions under the broken inversion symmetry in the paramagnetic phase of SmAlSi. We discuss the nesting possibilities regarding the emergent magnetic orders in SmAlSi. Our results provide a solid basis of experimental observations for exploring correlated topology in SmAlSi

ABSTRACT: While the Harris-Todaro model is a traditional approach used in researching the urban-rural dichotomy, it fails to explain families’ goals to maximize their current utility in terms of intertemporal decision-making conditions. To fill this gap, in this paper, an urban-rural dichotomy model involving labor migration and education is established, in which it is assumed that family utility derives from consumption and children’s educational achievement. The steady-state path derived through the Bellman equation suggests that increasing educational investment and family education intensity leads to a significant urban-rural difference in children’s educational achievement. Compared with the traditional Harris-Todaro model, the transversality condition is loosened in this model, while the unavailability of loans constrains migrant families. Four hypotheses are made and tested using an empirical study. An ordinary least squares regression was used in the analysis, but due to the endogeneity caused by missing variables, the instrumental variable method and two-stage least squares regression were used. The results demonstrate that the household registration system can explain 44.5% of the educational achievement difference, and the initial difference is inflated 4.73 times after nine years of compulsory education. This divergence could increase the differences caused by household registration status, resulting in larger income gaps and intergenerational heredity of identities.

The performance of software defect prediction (SDP) models determines the priority of test resource allocation. Researchers also use interpretability techniques to gain empirical knowledge about software quality from SDP models. However, SDP methods designed in the past research rarely consider the impact of data transformation methods, simple but commonly used preprocessing techniques, on the performance and interpretability of SDP models. Therefore, in this paper, we investigate the impact of three data transformation methods (Log, Minmax, and Z-score) on the performance and interpretability of SDP models. Through empirical research on (i) six classification techniques (random forest, decision tree, logistic regression, Naive Bayes, K-nearest neighbors, and multilayer perceptron), (ii) six performance evaluation indicators (Accuracy, Precision, Recall, F1, MCC, and AUC), (iii) two interpretable methods (permutation and SHAP), (iv) two feature importance measures (Top-k feature rank overlap and difference), and (v) three datasets (Promise, Relink, and AEEEM), our results show that the data transformation methods can significantly improve the performance of the SDP models and greatly affect the variation of the most important features. Specifically, the impact of data transformation methods on the performance and interpretability of SDP models depends on the classification techniques and evaluation indicators. We observe that log transformation improves NB model performance by 7%–61% on the other five indicators with a 5% drop in Precision. Minmax and Z-score transformation improves NB model performance by 2%–9% across all indicators. However, all three transformation methods lead to substantial changes in the Top-5 important feature ranks, with differences exceeding 2 in 40%–80% of cases (detailed results available in the main content). Based on our findings, we recommend that (1) considering the impact of data transformation methods on model performance and interpretability when designing SDP approaches as transformations can improve model accuracy, and potentially obscure important features, which lead to challenges in interpretation, (2) conducting comparative experiments with and without the transformations to validate the effectiveness of proposed methods which are designed to improve the prediction performance, and (3) tracking changes in the most important features before and after applying data transformation methods to ensure precise and traceable interpretability conclusions to gain insights. Our study reminds researchers and practitioners of the need for comprehensive considerations even when using other similar simple data processing methods.

Acute myeloid leukemia (AML) is one of the most common hematopoietic malignancies and exhibits a high rate of relapse and unfavorable outcomes. Ferroptosis, a relatively recently described type of cell death, has been reported to be involved in cancer development. However, the prognostic value of ferroptosis-related genes (FRGs) in AML remains unclear. In this study, we found 54 differentially expressed ferroptosis-related genes (DEFRGs) between AML and normal marrow tissues. 18 of 54 DEFRGs were correlated with overall survival (OS) (P

An error-related potential (ErrP) occurs when people’s expectations are not consistent with the actual outcome. Accurately detecting ErrP when a human interacts with a BCI is the key to improving these BCI systems. In this paper, we propose a multi-channel method for error-related potential detection using a 2D convolutional neural network. Multiple channel classifiers are integrated to make final decisions. Specifically, every 1D EEG signal from the anterior cingulate cortex (ACC) is transformed into a 2D waveform image; then, a model named attention-based convolutional neural network (AT-CNN) is proposed to classify it. In addition, we propose a multi-channel ensemble approach to effectively integrate the decisions of each channel classifier. Our proposed ensemble approach can learn the nonlinear relationship between each channel and the label, which obtains 5.27% higher accuracy than the majority voting ensemble approach. We conduct a new experiment and validate our proposed method on a Monitoring Error-Related Potential dataset and our dataset. With the method proposed in this paper, the accuracy, sensitivity and specificity were 86.46%, 72.46% and 90.17%, respectively. The result shows that the AT-CNNs-2D proposed in this paper can effectively improve the accuracy of ErrP classification, and provides new ideas for the study of classification of ErrP brain–computer interfaces.

Kagome lattice material Co3Sn2S2 is identified as a magnetic Weyl semimetal and its magnetic properties are less studied. Here, the authors observe localized spin-orbit polarons nucleated around single S-vacancies carrying a large diamagnetic orbital magnetism in Co3Sn2S2.

As mobile robots are increasingly introduced into our daily lives, it grows ever more imperative that these robots navigate with and among people in a safe and socially acceptable manner, particularly in shared spaces. While research on enabling socially-aware robot navigation has expanded over the years, there are no agreed-upon evaluation protocols or benchmarks to allow for the systematic development and evaluation of socially-aware navigation. As an effort to aid more productive development and progress comparisons, in this paper we review the evaluation methods, scenarios, datasets, and metrics commonly used in previous socially-aware navigation research, discuss the limitations of existing evaluation protocols, and highlight research opportunities for advancing socially-aware robot navigation.

The construction of desert railways inevitably destructs the environment and aggravates the wind–sand damage along the line. A reasonable railway route is an effective measure to avoid blown sand hazards, save construction costs, and reduce environmental damage. Currently, the selection methods for the railway route scheme are to analyze the qualitative indicators and quantitative indicators separately, and there are few decision-making models for the desert railway scheme. Therefore, this study aims to propose a comprehensive quantitative optimization model of the route scheme for the desert railway. Based on the design principles of hazard reduction, the evaluation index system of the desert railway route is first constructed, including railway design factors, wind-blown sand hazard factors, environmental impact factors, and operation condition factors. Subsequently, the subjective weights and objective weights are combined to obtain the comprehensive weights of the index by utilizing the principle of minimum discrimination information. Finally, the interval number is employed to quantify the linguistic fuzzy number of qualitative indicators, and the optimization model of the route scheme for the desert railway is constructed based on the technique for order preference by similarity to an ideal solution (TOPSIS). The model is verified using the Minfeng-Yuhu section in the Hotan–Ruoqiang railway as the case study. The achieved results reveal that this model enhances the accuracy and efficiency of the railway scheme decision-making and provides a theoretical basis for the optimal design and sand damage control of the desert railway.

Clear cell renal cell carcinoma (ccRCC) is the most common renal cell carcinoma and has poor prognosis in the locally advanced stage. Ferroptosis, a relatively new type of cell death, has gained significant attention in recent years. This study aimed to explore the prognostic value of ferroptosis-related genes (FRGs) in ccRCC. In this study, 50 differentially expressed FRGs between ccRCC and adjacent normal kidney tissues were identified, 26 of them correlated with overall survival (OS) (P

Objective To evaluate the impact of treatment with new direct-acting antivirals (DAAs) on the prevalent hepatitis C virus (HCV) population in England.Design A repeated cross-sectional analysis.Setting Four secondary care hospitals in England.Participants Patients who, in 2015 and/or 2016, had chronic HCV infection and were alive were eligible, regardless of the type of HCV intervention received.Outcome measures Data including intravenous drug use (IVDU) status, HCV genotype, cirrhosis status, HCV treatment history, vital status and treatment outcomes were collected at two time points in 2015 and 2016 using electronic case report forms.Results There were 1605 and 1355 patients with active chronic HCV in 2015 and 2016, respectively. Between 2015 and 2016, the proportion of patients with current IVDU increased (10.3% vs 14.5%, respectively), while that of patients with cirrhosis (28.2% vs 22.4%) and treatment-experienced patients (31.2% vs 27.1%) decreased. Among patients whose treatment outcome was known by 2016, high cure rates were observed, with an overall sustained virological response rate of 93.2%. From 2015 to 2016, there was a progressive increase in the proportion of treated patients who were non-cirrhotic, with current IVDU and non-liver transplant recipients.Conclusions The characteristics of patients with HCV remaining in contact with specialised care evolved with a changing landscape of treatment and related health policy. With increasing access to DAAs in UK, high cure rates were achieved in the study cohort.

Based on the monitoring data of ambient air quality and meteorological observation data, the characteristics and meteorological influencing factors of air pollution in Luojiang District of Deyang City from 2018 to 2022 were analyzed. The results show that from 2018 to 2022, the main air pollutants affecting the air quality of Luojiang District of Deyang City were PM2.5 and PM10, and the primary pollutant on heavy pollution days was basically PM2.5. PM2.5 and PM10 pollution showed obvious seasonal differences, and PM2S concentration exceeded the limit mainly in spring and winter, among which it was the most serious in early spring, especially in January and February, followed by December. PMW exceeding the standard had a high seasonal correlation with PM2S exceeding the standard, mainly in spring and winter, among which it was the most serious in winter, especially in December, followed by January. PM2.5 and PM1.0 pollution showed an overall weakening trend. PM2.5 and PM1.0 concentration were closely related to meteorological factors such as temperature, relative humidity, wind speed, precipitation and air pressure, and were mainly affected by rainfall. [ABSTRACT FROM AUTHOR]