Fucosyltransferase 9 promotes neuronal differentiation and functional recovery after spinal cord injury by suppressing the activation of Notch signaling.

Individuals with spinal cord injury (SCI) suffer from permanent disabilities such as severe motor, sensory and autonomic dysfunction. Neural stem cell transplantation has proven to be a potential strategy to promote regeneration of the spinal cord, since NSCs can produce neurotrophic growth factors and differentiate into mature neurons to reconstruct the injured site. However, it is necessary to optimize the differentiation of NSCs before transplantation to achieve a better regenerative outcome. Inhibition of Notch signaling leads to a transition from NSCs to neurons, while the underlying mechanism remains inadequately understood. Our results demonstrate that overexpression of fucosyltransferase 9 (Fut9), which is upregulated by Wnt4, promotes neuronal differentiation by suppressing the activation of Notch signaling through disruption of furin-like enzyme activity during S1 cleavage. In an in vivo study, Fut9-modified NSCs efficiently differentiates into neurons to promote functional and histological recovery after SCI. Our research provides insight into the mechanisms of Notch signaling and a potential treatment strategy for SCI.

Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 to Close Contacts, China, January-February 2020.

We estimated the symptomatic, PCR-confirmed secondary attack rate (SAR) for 2,382 close contacts of 476 symptomatic persons with coronavirus disease in Yichang, Hubei Province, China, identified during January 23-February 25, 2020. The SAR among all close contacts was 6.5%; among close contacts who lived with an index case-patient, the SAR was 10.8%; among close-contact spouses of index case-patients, the SAR was 15.9%. The SAR varied by close contact age, from 3.0% for those <18 years of age to 12.5% for those >60 years of age. Multilevel logistic regression showed that factors significantly associated with increased SAR were living together, being a spouse, and being >60 years of age. Multilevel regression did not support SAR differing significantly by whether the most recent contact occurred before or after the index case-patient's onset of illness (p = 0.66). The relatively high SAR for coronavirus disease suggests relatively high virus transmissibility.

Disparities in end-of-life care, expenditures, and place of death by health insurance among cancer patients in China: a population-based, retrospective study.

BACKGROUND: Disparities in the utilization, expenditures, and quality of care by insurance types have been well documented. Such comparisons have yet to be investigated in end-of-life (EOL) settings in China, where public insurance covers over 95% of the Chinese population. This study examined the associations between health insurance and EOL care in the last six months of life: outpatient visits, emergency department (ED) visits, inpatient services, intensive care unit (ICU) admissions, expenditures, and place of death among the cancer patients. METHODS: A total of 398 patients diagnosed with cancer who survived more than 6 months after diagnosis and died from July 2015 to June 2017 in urban Yichang, China, were included. Descriptive analysis and multivariate regression models were used to investigate the bivariate and independent associations, respectively, between health insurance with EOL healthcare utilization, expenditures and place of death. RESULTS: Urban Employee Basic Medical Insurance (UEBMI) beneficiaries visited EDs more frequently than Urban Resident-based Basic Medical Insurance (URBMI) and New Rural Cooperative Medical Scheme (NRCMS) beneficiaries (marginal effects [95% Confidence Interval]: 2.15 [1.81-2.48] and 1.92 [1.59-2.26], respectively). NRCMS and UEBMI beneficiaries had more hospitalizations than URBMI beneficiaries (1.01 [0.38-1.64] and 0.71 [0.20-1.22], respectively). Compared to URBMI beneficiaries, NRCMS beneficiaries and UEBMI beneficiaries had ¥15,722 and ¥43,241 higher expenditures. Similarly, UEBMI beneficiaries were most likely to die in hospitals, followed by NRCMS (UEBMI vs. NRCMS: 0.23 [0.11-0.36]) and URBMI (UEBMI vs. URBMI: 0.67 [0.57-0.78]) beneficiaries. CONCLUSIONS: The disproportionately lower utilization of EOL care among NRCMS and URBMI beneficiaries, compared to UEBMI beneficiaries, raised concerns regarding quality of EOL care and financial burdens of NRCMS and URBMI beneficiaries. Purposive hospice care intervention might be warranted to address EOL care for these beneficiaries in China.

Determinants of place of death for end-stage cancer patients: evidence from China.

OBJECTIVE: To determine factors influence place of death (POD) for end-stage cancer patients and investigate how the healthcare utilization mediates on the effect of socioeconomic status (SES) on POD. DESIGN: A population-based, retrospective study from July 2015 to June 2017. SETTING: Yichang, China. PARTICIPANTS: 894 end-stage cancer patients. MAIN OUTCOME MEASURE: POD. RESULTS: Patients of hospital death experience more inpatient hospitalization services (IHS) and emergency department visits. Patients enrolled in the New Rural Cooperative Medical Scheme (OR = 7.60, P < 0.001) and Urban Employee Basic Medical Insurance (OR = 28.0, P < 0.001) have higher rates of hospital death than those in the Urban Resident-based Basic Medical Insurance. Living with spouse (OR = 1.72, P = 0.019) and receiving higher education (OR = 1.92, P = 0.004), increase the likelihood of hospital death by 72% and 92%, respectively. The probability of hospital death will increase by 14% and decrease by 4% per IHS and outpatient services occur, respectively. Outpatient services (Z = -2.28, P < 0.001), and IHS (Z = 2.17, P < 0.001) mediate 1.81% and 1.89%, respectively, of the effect of health insurance on POD. The overall effect of the mediators is non-statistically significant (Z = 0.09, P = 0.825). CONCLUSION: POD is mainly driven by SES. The relationship between health insurance and POD is partly mediated by outpatient services and IHS, respectively. The results corroborated that hospital and home services should be coherently bridged. Furthermore, benefit packages for end-stage cancer patients could be redesigned.

Carbon monoxide and risk of outpatient visits due to cause-specific diseases: a time-series study in Yichang, China.

BACKGROUND: Previous studies showed inconsistent results on risk of increased outpatient visits for cause-specific diseases associated with ambient carbon monoxide (CO). METHODS: Daily data for CO exposure and outpatient visits for all-causes and five specific diseases in Yichang, China from 1st January 2016 to 31st December 2017 were collected. Generalised additive models with different lag structures were used to examine the short-term effects of ambient CO on outpatient visits. Potential effect modifications by age, sex and season were examined. RESULTS: A total of 5,408,021 outpatient visits were recorded. We found positive and statistically significant associations between CO and outpatient visits for multiple outcomes and all the estimated risks increased with longer moving average lags. An increase of 1 mg/m3 of CO at lag06 (a moving average of lag0 to lag6), was associated with 24.67% (95%CI: 14.48, 34.85%), 21.79% (95%CI: 12.24, 31.35%), 39.30% (95%CI: 25.67, 52.92%), 25.83% (95%CI: 13.91, 37.74%) and 19.04% (95%CI: 8.39, 29.68%) increase in daily outpatient visits for all-cause, respiratory, cardiovascular, genitourinary and gastrointestinal diseases respectively. The associations for all disease categories except for genitourinary diseases were statistically significant and stronger in warm seasons than cool seasons. CONCLUSION: Our analyses provide evidences that the CO increased the total and cause-specific outpatient visits and strengthen the rationale for further reduction of CO pollution levels in Yichang. Ambient CO exerted adverse effect on respiratory, cardiovascular, genitourinary, gastrointestinal and neuropsychiatric diseases especially in the warm seasons.

GSB: Group superposition binarization for vision transformer with limited training samples.

Vision Transformer (ViT) has performed remarkably in various computer vision tasks. Nonetheless, affected by the massive amount of parameters, ViT usually suffers from serious overfitting problems with a relatively limited number of training samples. In addition, ViT generally demands heavy computing resources, which limit its deployment on resource-constrained devices. As a type of model-compression method, model binarization is potentially a good choice to solve the above problems. Compared with the full-precision one, the model with the binarization method replaces complex tensor multiplication with simple bit-wise binary operations and represents full-precision model parameters and activations with only 1-bit ones, which potentially solves the problem of model size and computational complexity, respectively. In this paper, we investigate a binarized ViT model. Empirically, we observe that the existing binarization technology designed for Convolutional Neural Networks (CNN) cannot migrate well to a ViT's binarization task. We also find that the decline of the accuracy of the binary ViT model is mainly due to the information loss of the Attention module and the Value vector. Therefore, we propose a novel model binarization technique, called Group Superposition Binarization (GSB), to deal with these issues. Furthermore, in order to further improve the performance of the binarization model, we have investigated the gradient calculation procedure in the binarization process and derived more proper gradient calculation equations for GSB to reduce the influence of gradient mismatch. Then, the knowledge distillation technique is introduced to alleviate the performance degradation caused by model binarization. Analytically, model binarization can limit the parameter's search space during parameter updates while training a model. Therefore, the binarization process can actually play an implicit regularization role and help solve the problem of overfitting in the case of insufficient training data. Experiments on three datasets with limited numbers of training samples demonstrate that the proposed GSB model achieves state-of-the-art performance among the binary quantization schemes and exceeds its full-precision counterpart on some indicators. Code and models are available at: https://github.com/IMRL/GSB-Vision-Transformer.

Efficient and robust estimation of single-vehicle crash severity: A mixed logit model with heterogeneity in means and variances.

This study delves into the factors that contribute to the severity of single-vehicle crashes, focusing on enhancing both computational speed and model robustness. Utilizing a mixed logit model with heterogeneity in means and variances, we offer a comprehensive understanding of the complexities surrounding crash severity. The analysis is grounded in a dataset of 39,788 crash records from the UK's STATS19 database, which includes variables such as road type, speed limits, and lighting conditions. A comparative evaluation of estimation methods, including pseudo-random, Halton, and scrambled and randomized Halton sequences, demonstrates the superior performance of the latter. Specifically, our estimation approach excels in goodness-of-fit, as measured by ρ2, and in minimizing the Akaike Information Criterion (AIC), all while optimizing computational resources like run time and memory usage. This strategic efficiency enables more thorough and credible analyses, rendering our model a robust tool for understanding crash severity. Policymakers and researchers will find this study valuable for crafting data-driven interventions aimed at reducing road crash severity.

Learning Disentangled Representation for One-Shot Progressive Face Swapping.

Although face swapping has attracted much attention in recent years, it remains a challenging problem. Existing methods leverage a large number of data samples to explore the intrinsic properties of face swapping without considering the semantic information of face images. Moreover, the representation of the identity information tends to be fixed, leading to suboptimal face swapping. In this paper, we present a simple yet efficient method named FaceSwapper, for one-shot face swapping based on Generative Adversarial Networks. Our method consists of a disentangled representation module and a semantic-guided fusion module. The disentangled representation module comprises an attribute encoder and an identity encoder, which aims to achieve the disentanglement of the identity and attribute information. The identity encoder is more flexible, and the attribute encoder contains more attribute details than its competitors. Benefiting from the disentangled representation, FaceSwapper can swap face images progressively. In addition, semantic information is introduced into the semantic-guided fusion module to control the swapped region and model the pose and expression more accurately. Experimental results show that our method achieves state-of-the-art results on benchmark datasets with fewer training samples. Our code is publicly available at https://github.com/liqi-casia/FaceSwapper.

Secure State Estimation for Artificial Neural Networks With Unknown-But-Bounded Noises: A Homomorphic Encryption Scheme.

This article is concerned with the secure state estimation problem for artificial neural networks (ANNs) subject to unknown-but-bounded noises, where sensors and the remote estimator are connected via open and bandwidth-limited communication networks. Using the encoding-decoding mechanism (EDM) and the Paillier encryption technique, a novel homomorphic encryption scheme (HES) is introduced, which aims to ensure the secure transmission of measurement information within communication networks that are constrained by bandwidth. Under this encoding-decoding-based HES, the data being transmitted can be encrypted into ciphertexts comprising finite bits. The emphasis of this research is placed on the development of a secure set-membership state estimation algorithm, which allows for the computation of estimates using encrypted data without the need for decryption, thereby ensuring data security throughout the entire estimation process. Taking into account the unknown-but-bounded noises, the underlying ANN, and the adopted HES, sufficient conditions are determined for the existence of the desired ellipsoidal set. The related secure state estimator gains are then derived by addressing optimization problems using the Lagrange multiplier method. Lastly, an example is presented to verify the effectiveness of the proposed secure state estimation approach.

Asymmetric Convolution: An Efficient and Generalized Method to Fuse Feature Maps in Multiple Vision Tasks.

Fusing features from different sources is a critical aspect of many computer vision tasks. Existing approaches can be roughly categorized as parameter-free or learnable operations. However, parameter-free modules are limited in their ability to benefit from offline learning, leading to poor performance in some challenging situations. Learnable fusing methods are often space-consuming and timeconsuming, particularly when fusing features with different shapes. To address these shortcomings, we conducted an in-depth analysis of the limitations associated with both fusion methods. Based on our findings, we propose a generalized module named Asymmetric Convolution Module (ACM). This module can learn to encode effective priors during offline training and efficiently fuse feature maps with different shapes in specific tasks. Specifically, we propose a mathematically equivalent method for replacing costly convolutions on concatenated features. This method can be widely applied to fuse feature maps across different shapes. Furthermore, distinguished from parameter-free operations that can only fuse two features of the same type, our ACM is general, flexible, and can fuse multiple features of different types. To demonstrate the generality and efficiency of ACM, we integrate it into several state-of-the-art models on three representative vision tasks: visual object tracking, referring video object segmentation, and monocular 3D object detection. Extensive experimental results on three tasks and several datasets demonstrate that our new module can bring significant improvements and noteworthy efficiency.

Model-Free λ-Policy Iteration for Discrete-Time Linear Quadratic Regulation.

This article presents a model-free λ -policy iteration ( λ -PI) for the discrete-time linear quadratic regulation (LQR) problem. To solve the algebraic Riccati equation arising from solving the LQR in an iterative manner, we define two novel matrix operators, named the weighted Bellman operator and the composite Bellman operator. Then, the λ -PI algorithm is first designed as a recursion with the weighted Bellman operator, and its equivalent formulation as a fixed-point iteration with the composite Bellman operator is shown. The contraction and monotonic properties of the composite Bellman operator guarantee the convergence of the λ -PI algorithm. In contrast to the PI algorithm, the λ -PI does not require an admissible initial policy, and the convergence rate outperforms the value iteration (VI) algorithm. Model-free extension of the λ -PI algorithm is developed using the off-policy reinforcement learning technique. It is also shown that the off-policy variants of the λ -PI algorithm are robust against the probing noise. Finally, simulation examples are conducted to validate the efficacy of the λ -PI algorithm.

Mitigating the impact of outliers in traffic crash analysis: A robust Bayesian regression approach with application to tunnel crash data.

Traffic crash datasets are often marred by the presence of anomalous data points, commonly referred to as outliers. These outliers can have a profound impact on the results obtained through the application of traditional methods such as logit and probit models, commonly used in the domain of traffic safety analysis, resulting in biased and unreliable estimates. To mitigate this issue, this study introduces a robust Bayesian regression approach, the robit model, which utilizes a heavy-tailed Student's t distribution to replace the link function of these thin-tailed distributions, effectively reducing the influence of outliers on the analysis. Furthermore, a sandwich algorithm based on data augmentation is proposed to enhance the estimation efficiency of posteriors. The proposed model is rigorously tested using a dataset of tunnel crashes, and the results demonstrate its efficiency, robustness, and superior performance compared to traditional methods. The study also reveals that several factors such as night and speeding have a significant impact on the injury severity of tunnel crashes. This research provides a comprehensive understanding of the outliers treatment methods in traffic safety studies and offers valuable recommendations for the development of appropriate countermeasures to effectively prevent severe injuries in tunnel crashes.

Federated Noisy Client Learning.

Federated learning (FL) collaboratively trains a shared global model depending on multiple local clients, while keeping the training data decentralized to preserve data privacy. However, standard FL methods ignore the noisy client issue, which may harm the overall performance of the shared model. We first investigate the critical issue caused by noisy clients in FL and quantify the negative impact of the noisy clients in terms of the representations learned by different layers. We have the following two key observations: 1) the noisy clients can severely impact the convergence and performance of the global model in FL and 2) the noisy clients can induce greater bias in the deeper layers than the former layers of the global model. Based on the above observations, we propose federated noisy client learning (Fed-NCL), a framework that conducts robust FL with noisy clients. Specifically, Fed-NCL first identifies the noisy clients through well estimating the data quality and model divergence. Then robust layerwise aggregation is proposed to adaptively aggregate the local models of each client to deal with the data heterogeneity caused by the noisy clients. We further perform label correction on the noisy clients to improve the generalization of the global model. Experimental results on various datasets demonstrate that our algorithm boosts the performances of different state-of-the-art systems with noisy clients. Our code is available at https://github.com/TKH666/Fed-NCL.

Improving Pretrained Language Model Fine-Tuning With Noise Stability Regularization.

The advent of large-scale pretrained language models (PLMs) has contributed greatly to the progress in natural language processing (NLP). Despite its recent success and wide adoption, fine-tuning a PLM often suffers from overfitting, which leads to poor generalizability due to the extremely high complexity of the model and the limited training samples from downstream tasks. To address this problem, we propose a novel and effective fine-tuning framework, named layerwise noise stability regularization (LNSR). Specifically, our method perturbs the input of neural networks with the standard Gaussian or in-manifold noise in the representation space and regularizes each layer's output of the language model. We provide theoretical and experimental analyses to prove the effectiveness of our method. The empirical results show that our proposed method outperforms several state-of-the-art algorithms, such as [Formula: see text] norm and start point (L2-SP), Mixout, FreeLB, and smoothness inducing adversarial regularization and Bregman proximal point optimization (SMART). In addition to evaluating the proposed method on relatively simple text classification tasks, similar to the prior works, we further evaluate the effectiveness of our method on more challenging question-answering (QA) tasks. These tasks present a higher level of difficulty, and they provide a larger amount of training examples for tuning a well-generalized model. Furthermore, the empirical results indicate that our proposed method can improve the ability of language models to domain generalization.

Highly Stretchable and Biocompatible Wrinkled Nanoclay-Composite Hydrogel With Enhanced Sensing Capability for Precise Detection of Myocardial Infarction.

It is challenging to balance high biocompability with good mechanical-electrical sensing performance, especially when triggering inflammatory stress response after in vivo implantation. Herein, a bioinspired wrinkle-reinforced adaptive nanoclay-interlocked soft strain-sensor based on a highly stretchable and elastic ionic-conductive hydrogel is reported. This novel nanoclay-composite hydrogel exhibits excellent tensile properties and high sensing capacity with steady and reliable sensing performance due to the structural-mechanical-electrical integrity of the nanoclay crosslinked and nano-reinforced interpenetrating network. The incorporation of amphiphilic ions provides the hydrogel with significant protein resistance, reducing its non-specific adsorption to proteins upon implantation, improving its biosafety as an implanted device, and maintaining the authenticity of the sensing results. Based on the revealed sensing enhanced mechanism based on hierarchical ordered structures as a proof-of-concept application, this hydrogel sensor is demonstrated to be able to accurately localize the region where myocardial infarction occurs and may become a novel strategy for real-time monitoring of pathological changes in heart disease.

Prediction of Outlet Pressure for the Sulfur Dioxide Blower Based on Conv1D-BiGRU Model and Genetic Algorithm.

The sulfur dioxide blower is a centrifugal blower that transports various gases in the process of acid production with flue gas. Accurate prediction of the outlet pressure of the sulfur dioxide blower is quite significant for the process of acid production with flue gas. Due to the internal structure of the sulfur dioxide blower being complex, its mechanism model is difficult to establish. A novel method combining one-dimensional convolution (Conv1D) and bidirectional gated recurrent unit (BiGRU) is proposed for short-term prediction of the outlet pressure of sulfur dioxide blower. Considering the external factors such as inlet pressure and inlet flow rate of the blower, the proposed method first uses Conv1D to extract periodic and local correlation features of these external factors and the blower's outlet pressure data. Then, BiGRU is used to overcome the complexity and nonlinearity in prediction. More importantly, genetic algorithm (GA) is used to optimize the important hyperparameters of the model. Experimental results show that the combined model of Conv1D and BiGRU optimized by GA can predict the outlet pressure of sulfur dioxide blower accurately in the short term, in which the root mean square error (RMSE) is 0.504, the mean absolute error (MAE) is 0.406, and R-square (R 2) is 0.993. Also, the proposed method is superior to LSTM, GRU, BiLSTM, BiGRU, and Conv1D-BiLSTM.

Hamiltonian-Driven Adaptive Dynamic Programming With Efficient Experience Replay.

This article presents a novel efficient experience-replay-based adaptive dynamic programming (ADP) for the optimal control problem of a class of nonlinear dynamical systems within the Hamiltonian-driven framework. The quasi-Hamiltonian is presented for the policy evaluation problem with an admissible policy. With the quasi-Hamiltonian, a novel composite critic learning mechanism is developed to combine the instantaneous data with the historical data. In addition, the pseudo-Hamiltonian is defined to deal with the performance optimization problem. Based on the pseudo-Hamiltonian, the conventional Hamilton-Jacobi-Bellman (HJB) equation can be represented in a filtered form, which can be implemented online. Theoretical analysis is investigated in terms of the convergence of the adaptive critic design and the stability of the closed-loop systems, where parameter convergence can be achieved under a weakened excitation condition. Simulation studies are investigated to verify the efficacy of the presented design scheme.

Hamiltonian-Driven Adaptive Dynamic Programming With Approximation Errors.

In this article, we consider an iterative adaptive dynamic programming (ADP) algorithm within the Hamiltonian-driven framework to solve the Hamilton-Jacobi-Bellman (HJB) equation for the infinite-horizon optimal control problem in continuous time for nonlinear systems. First, a novel function, "min-Hamiltonian," is defined to capture the fundamental properties of the classical Hamiltonian. It is shown that both the HJB equation and the policy iteration (PI) algorithm can be formulated in terms of the min-Hamiltonian within the Hamiltonian-driven framework. Moreover, we develop an iterative ADP algorithm that takes into consideration the approximation errors during the policy evaluation step. We then derive a sufficient condition on the iterative value gradient to guarantee closed-loop stability of the equilibrium point as well as convergence to the optimal value. A model-free extension based on an off-policy reinforcement learning (RL) technique is also provided. Finally, numerical results illustrate the efficacy of the proposed framework.

A Distributional Perspective on Multiagent Cooperation With Deep Reinforcement Learning.

Among various value decomposition-based multiagent reinforcement learning (MARL) algorithms, the overall performance of the multiagent system is represented by a scalar global Q value and optimized by minimizing the temporal difference (TD) error with respect to that global Q value. However, the global Q value cannot accurately model the distributed dynamics of the multiagent system, since it is only a simplified representation for different individual Q values of agents. To explicitly consider the correlations between different cooperative agents, in this article, we propose a distributional framework and construct a practical model called distributional multiagent cooperation (DMAC) from a novel distributional perspective. Specifically, in DMAC, we view the individual Q value for the executed action of a random agent as a value distribution, whose expectation can further represent the overall performance. Then, we employ distributional RL to minimize the difference between the estimated distribution and its target for the optimization. The advantage of DMAC is that the distributed dynamics of agents can be explicitly modeled, and this results in better performance. To verify the effectiveness of DMAC, we conduct extensive experiments under nine different scenarios of the StarCraft Multiagent Challenge (SMAC). Experimental results show that the DMAC can significantly outperform the baselines with respect to the average median test win rate.

Economic Burden of Malignant Tumors - Yichang City, Hubei Province, China, 2019.

What is already known about this topic?: Malignant tumors are common chronic non-communicable disease and have caused serious health hazards to residents and heavy economic burden of disease to the society. What is added by this report?: This is the first report on the economic burden of multiple types of malignant tumors in Yichang City. In 2019, the direct medical burden of lung cancer in Yichang was the highest, reaching 561.67 million CNY, and the indirect economic burden of lung cancer in Yichang was higher than that of other malignant tumors, costing 326.49 million CNY. What are the implications for public health practice?: The results can provide evidence for the formulation of local cancer prevention and control strategies and public health decision-making.