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Abstract Background The association of a combination of the TyG index and obesity markers, specifically waist circumference (WC), with cognitive function is unknown. This research investigated the relationship between TyG-WC measurements and cognitive impairment in a low-income population in China; moreover, this study evaluated the role of diabetes mellitus and body mass index (BMI) in modulating this relationship. Methods 1125 eligible individuals aged ≥ 60 years participated in this study. The TyG index and obesity indicators (BMI, WC, and waist-to-height ratio) were calculated for individual participants and categorized into quartiles. Multivariate logistic regression analysis was used to evaluate the correlation between TyG-WC values and cognitive impairment; the possibility of a nonlinear relationship was explored using constrained cubic spline analysis. The participants were divided into different groups according to their diabetes status and BMI category for subgroup analyses. Linear regression was used to investigate the correlation between TyG-WC values and MMSE scores. Results The prevalence of cognitive impairment in the study participants was 47.3%, with a significant negative association between TyG-WC values and cognitive impairment, (odds ratio [OR] = 0.999; 95% confidence interval [CI], 0.997–1.00, P = 0.009). A U-shaped correlation was observed between the TyG-WC values and cognitive impairment (P = 0.008). Subgroup analyses showed that the inverse association between TyG-WC values and cognitive impairment was stronger in non-diabetic individuals (OR = 0.998; 95% CI, 0.997–0.999; P = 0.002) and in those with a lower BMI (

Abstract The expression and biological function of junctional adhesion molecule-like protein (JAML) in colorectal cancer (CRC) remain unclear. Paraffin tissue samples from 50 cases of CRC were collected to determine the expression of JAML. JAML was overexpressed or knock-down in CRC cells to evaluated the proliferation, migration and invasion in vitro and in vivo. Western-blot and others were applied to explore the mechanisms. The study showed that JAML was highly expressed within cancer tissues in 50% (25/50) of patients with CRC, and was correlated with higher TNM stage (p

AimThe Triglyceride-Glucose (TyG) index, an indicator of insulin resistance, has been proposed as a predictor of cardiovascular diseases. However, its role in predicting stroke risk, particularly in low-income populations, is not well understood. This study aimed to investigate the predictive value of the TyG index for stroke incidence in a low-income Chinese population, with a focus on gender and age-specific differences.MethodsThis 10-year prospective cohort study included 3,534 participants aged ≥45 years from rural areas in northern China. Baseline data on demographic characteristics, lifestyle factors, and clinical measurements were collected. Participants were followed for stroke incidence, categorized into ischemic and hemorrhagic stroke. Multivariate logistic regression models were used to assess the association between the TyG index and stroke incidence, adjusting for potential confounders.ResultsDuring the follow-up period, 368 participants (10.4%) experienced a stroke, with 327 ischemic and 31 hemorrhagic strokes. TyG index was significantly associated with total and ischemic stroke incidence but not hemorrhagic stroke. After adjusting for confounding factors, for every one standard deviation increase in TyG index, the risk of stroke increased by 32% for overall stroke (RR: 1.32; 95% CI: 1.08-1.61; P=0.006) and 39% for ischemic stroke (RR: 1.39; 95% CI: 1.12-1.73; P=0.003). The risk of stroke in the highest TyG tertile levels (tertile 3) increased by 49% (RR: 1.49; 95% CI 1.11-1.99; P=0.007) for overall stroke, compared to those in the lowest tertile levels (tertile 1). For ischemic stroke, the risk of stroke increased by 53% (RR: 1.53; 95% CI 1.12-2.11; P=0.008) in the highest TyG tertile levels (tertile 3) compared to those in the lowest tertile levels (tertile 1).ConclusionThis 10-year prospective cohort study has established the TyG index as an independent predictor of both total and ischemic stroke incidence in a low-income Chinese population. The findings indicate that the TyG index is particularly effective in predicting stroke risk among women and older adults (≥60 years), but not for hemorrhagic stroke. These insights are crucial for improving clinical practice and stroke prevention strategies.

Abstract This comprehensive review aims to clarify the growing impact of Transformer‐based models in the fields of neuroscience, neurology, and psychiatry. Originally developed as a solution for analyzing sequential data, the Transformer architecture has evolved to effectively capture complex spatiotemporal relationships and long‐range dependencies that are common in biomedical data. Its adaptability and effectiveness in deciphering intricate patterns within medical studies have established it as a key tool in advancing our understanding of neural functions and disorders, representing a significant departure from traditional computational methods. The review begins by introducing the structure and principles of Transformer architectures. It then explores their applicability, ranging from disease diagnosis and prognosis to the evaluation of cognitive processes and neural decoding. The specific design modifications tailored for these applications and their subsequent impact on performance are also discussed. We conclude by providing a comprehensive assessment of recent advancements, prevailing challenges, and future directions, highlighting the shift in neuroscientific research and clinical practice towards an artificial intelligence‐centric paradigm, particularly given the prominence of Transformer architecture in the most successful large pre‐trained models. This review serves as an informative reference for researchers, clinicians, and professionals who are interested in understanding and harnessing the transformative potential of Transformer‐based models in neuroscience, neurology, and psychiatry.

Multiple sclerosis (MS) was defined as a rare disease in China due to its low prevalence. For a long time, interferon β was the only approved disease-modifying therapy (DMT). Since the first oral DMT was approved in 2018, DMT approval accelerated, and seven DMTs were approved within 5 years. With an increasing number of DMTs being prescribed in clinical practice, it is necessary to discuss the standardized MS treatment algorithms depending on the disease activity and DMT availability. In this review paper, more than 20 Chinese experts in MS have reviewed the therapeutic progress of MS in China and worldwide and discussed algorithms for treating relapsing MS (RMS) based on the available DMTs in China, providing insights for establishing the standardized RMS treatment algorithms in this country.

Abstract One of the most important therapeutic interventions for non-small cell lung cancer is radiotherapy. Ionizing radiation (IR) is classified by traditional radiobiology principles as a direct cytocidal therapeutic agent against cancer, although there is growing recognition of other antitumor immunological responses induced by this modality. The most effective therapeutic combinations to harness radiation-generated antitumor immunity and enhance treatment results for malignancies resistant to existing radiotherapy regimens could be determined by a more sophisticated understanding of the immunological pathways created by radiation. Innate immune signaling is triggered by the activation of cGAS-STING, and this promotes adaptive immune responses to help fight cancer. This identifies a molecular mechanism radiation can use to trigger antitumor immune responses by bridging the DNA-damaging ability of IR with the activation of CD8 + cytotoxic T cell-mediated killing of tumors. We also discuss radiotherapy-related parameters that affect cGAS-STING signaling, negative consequences of cGAS-STING activation, and intriguing treatment options being tested in conjunction with IR to support immune activation by activating STING-signaling. Improved therapeutic outcomes will result from a better understanding of how IR promotes cGAS-STING signaling in immune-based treatment regimens that maximize radiotherapy’s anticancer effectiveness.

Point cloud-based detection focuses on land traffic, rarely marine, facing issues with ships: it struggles in bad weather due to reliance on adverse weather data and fails to detect ships effectively due to overlooking size and appearance differences. Addressing the above challenges, our work introduces point cloud data of marine scenarios under realistically simulated adverse weather conditions and a dedicated Ship Detector tailored for marine environments. To adapt to various maritime weather conditions, we simulate realistic rain and fog in collected marine scene point cloud data. Additionally, addressing the issue of losing geometric and height information during feature extraction for large objects, we propose a Ship Detector. It employs a dual-branch sparse convolution layer for extracting multi-scale 3D feature maps, effectively minimizing height information loss. Additionally, a multi-scale 2D convolution module is utilized, which encodes and decodes feature maps and directly employs 3D feature maps for target prediction. To reduce dependency on existing data and enhance model robustness, our training dataset includes simulated point cloud data representing adverse weather conditions. In maritime point cloud ship detection, our Ship Detector, compared to adjusted small object detectors, demonstrates the best performance.

Digital transformation has been prioritized in the railway industry to bring automation to railway operations. Digital Twin (DT) technology has recently gained attention in the railway industry to fulfill this goal. Contemporary researchers argue that DT can be advantageous in Railway manufacturing logistics to planning and scheduling. Although underlying technologies of DT, e.g., modelling, computer vision, and the Internet of Things, have been studied for various railway industry applications, the DT has been least explored in the context of railways. Thus, in this paper, we aim to understand the state-of-the-art of DT for railway (DTR), for advanced railway systems. Besides, this survey clarifies how DT can serve the railway twin system designers and developers. As DTR is still in its early adoption stage, there is hardly any clear direction to identify the technologies for specific DTR applications. Therefore, based on our findings we present a taxonomy for DTR for designers and developers. Finally, we describe potential challenges, pitfalls, and opportunities in DTR for future researchers.

Summary: Background: Laboratory determination of autoantibodies against acetylcholine receptor (AChR), muscle-specific kinase (MuSK) and other autoantigens have been integrated into the diagnosis of myasthenia gravis (MG). However, evidence supporting the selection of methodologies is lacking. Methods: In this prospective, multicentre cohort study, we recruited patients with suspected MG to evaluate the diagnostic accuracy of cell-based assay (CBA), radioimmunoprecipitation assay (RIPA) and enzyme-linked immunosorbent assay (ELISA) in detecting AChR and MuSK autoantibodies. This study is registered with www.clinicaltrials.gov, number NCT05219097. Findings: 2272 eligible participants were recruited, including 2043 MG, 229 non-MG subjects. AChR antibodies were detected in 1478, 1310, and 1280 out of a total of 2043 MG patients by CBA, RIPA, and ELISA, respectively; sensitivity, 72.3% (95% CI, 70.3–74.3), 64.1% (95% CI, 62.0–66.2), 62.7% (95% CI, 60.5–64.8); specificity, 97.8% (95% CI, 95.0–99.3), 97.8% (95% CI, 95.0–99.3), 94.8% (95% CI, 91.9–97.7). MuSK antibodies were found in 59, 50, and 54 from 2043 MG patients by CBA, RIPA and ELISA, respectively; sensitivity, 2.9% (95% CI, 2.2–3.7), 2.4% (95% CI, 1.8–3.2), 2.6% (95% CI, 2.0–3.4); specificity, 100% (95% CI, 98.4–100), 100% (95% CI, 98.4–100), and 99.1% (95% CI, 96.9–99.9). The area under the curve of AChR antibodies tested by CBA was 0.858, and there were statistical differences with RIPA (0.843; p = 0.03) and ELISA (0.809; p

Dimethyl fumarate (DMF) is a widely used oral disease-modifying therapy for multiple sclerosis (MS). Its efficacy and safety profiles are supported by over a decade of experience. Differences exist between Asia and Europe/United States in the prevalence and characteristics of MS; most data for DMF are derived from populations outside Asia. DMF was recently (2021) approved for use in China. The objectives of this review were to evaluate the evidence for DMF’s profile, to provide an update to healthcare providers on current knowledge surrounding its use and to assess the relevance of existing data to use in China. This study used a modified Delphi method based on the insights of a scientific Steering Committee (SC), with a structured literature review conducted to assess the data of DMF. The literature review covered all papers in English (from 01 January 2011 to 21 February 2022) that include ‘dimethyl fumarate’ and ‘multiple sclerosis’, and their MeSH terms, on PubMed, supplemented by EMBASE and Citeline searches. Papers were categorized by topic and assessed for relevance and quality, before being used to formulate statements summarizing the literature on each subject. SC members voted on/revised statements, requiring ⩾80% agreement and ⩽10% disagreement for inclusion. Statements not reaching this level were discussed further until agreement was reached or until there was agreement to remove the statement. A total of 1030 papers were retrieved and used to formulate the statements and evidence summaries considered by the SC members. A total of 45 statements were agreed by the SC members. The findings support the positive efficacy and safety profile of DMF in treating patients with MS. Limited Chinese patient data are an ongoing consideration; however, based on current evidence, the statements are considered applicable to both the global and Chinese populations. DMF is a valuable addition to address unmet MS treatment needs in China. Registration: Not applicable

Abstract The aphasia quotient of Western Aphasia Battery (WAB-AQ) has been used as an inclusion criterion and as an outcome measure in clinical, research, or community settings. The WAB-AQ is also commonly used to measure recovery. This study aimed to quantitatively determine levels of the linguistic deficit by using a cluster analysis of the WAB-AQ in post-stroke aphasia (PSA). 308 patients were extracted from the database. Cutoff scores are defined by mean overlap WAB-AQ scores of clusters by systematic cluster analysis, the method of which is the farthest neighbor element, and the metrics are square Euclidean distance and Pearson correlation, performed on the full sample of WAB-AQ individual subitem scores. A 1-way analysis of variance, with post hoc comparisons conducted, was used to determine whether clusters had significant differences. Three clusters were identified. The scores for severe, moderate, and mild linguistic deficit levels ranged from 0 to 30, 30.1 to 50.3, and 50.4 to 93.7, respectively. For PSA, the cluster analysis of WAB-AQ supports a 3-impairment level classification scheme.

Energy band alignment theory is used to understand interface charge transfer in semiconductor/semiconductor junctions but many abnormal results cannot be well explained. Here, the authors demonstrate a Faradaic junction theory with isoenergetic charge transfer at semiconductor/semiconductor interfaces to explain these abnormal results.

The highly endangered crocodile lizard (Shinisaurus crocodilurus) continues to be impacted by disease, especially in captive breeding populations. In this paper, based on high-throughput sequencing, we investigated parasitic infections in captive and wild crocodile lizard populations in the Daguishan National Nature Reserve and Guangdong Luokeng Shinisaurus crocodilurus National Nature Reserve. The results show that the overall parasitic infection rate in crocodile lizards was 33.33% (23/69). Four parasite genera were detected, including Eimeria, Cryptosporidium, Nematopsis, and Acanthamoeba, with infection rates of 15.94% (11/69), 17.39% (12/69), 7.25% (5/69), and 4.35% (3/69), respectively. Significant differences in the infection rate were found between the different parasite species (χ2 = 8.54, p < 0.05, chi-squared test). The parasitic infection rates in the captive and wild populations were 39.29% (22/56) and 7.69% (1/13), respectively, which were significantly different (p < 0.05, Fisher’s exact test). However, no significant differences in the infection rates of the four parasite genera were found between the captive and wild populations (p > 0.05, Fisher’s exact test). The parasitic infection rates in Daguishan and Luokeng were 34.09% (15/44) and 32.00% (8/25), respectively, which were not significantly different (p > 0.05, Fisher’s exact test). However, significant differences in terms of species were found in the two reserves (p < 0.01, Fisher’s exact test). Only Cryptosporidium infection showed a significant difference between the two regions (p < 0.01, Fisher’s exact test). Our results suggest that captive crocodile lizards are more susceptible to parasitic diseases than wild crocodile lizards and that Cryptosporidium infection varies by geographical region. This study provides basic information about the parasites of endangered crocodile lizards, as well as a reference for disease control and conservation.

A mechanical analysis model for the floating of buried pipelines in soil liquefaction areas is established in this paper. In order to improve the inherent defects of the elastic foundation beam method based on the Winkler model and increase the calculation accuracy, the Pasternak model is introduced and the interaction between soil and spring is considered. A mechanical analysis method for buried pipelines in liquefaction zone considering axial load is proposed in present paper. According to the Pasternak model and the deflection curve differential equation, the pipe bending deformation curve equation and the deformation coordination equation are derived. The analytical calculation method of the pipe mechanical response is established. A new method of the mechanical analysis of the floating of buried pipelines in the liquefaction zone is provided. The mechanical response of the pipeline under the conditions of different pipeline parameters and liquefaction zone length is analyzed. The reliability of the analysis method in this paper is verified by the comparison of finite element method (FEM). Considering that the previous researches of scholars mainly focused on straight pipes, there are few studies on the pipe structure nodes in liquefied soil. The mechanical properties of the three-way pipe structure in the soil liquefaction zone are analyzed by the finite element method (FEM). The influence of pipe diameter, wall thickness, liquefied soil density, transition zone length, buried depth, and pipeline internal pressure on the mechanical response of the pipeline is analyzed.

A class-incremental scheme of fisher discriminant analysis is proposed to improve the performance ofprocess fault diagnosis. Fisher discriminant analysis seeks directions which are efficient for discrimination and has excellent fault detection and diagnostic performance for the sample set with the tag. However, due to the property of the model, it has no detection and diagnostic capabilities forun-seen faults. In order to address this issue, the $F$ direction, which is based on a partial $F$ -values with the principle component analysis, is proposed in this paper. After a new fault being detected and added into the known fault collection, a class-incremental scheme is used to update the fisher discriminant analysis model to enhance the model's ability for continuous fault identification. The proposed approach is validated by the Tennessee Eastman process for the fault diagnosis. The results demonstrate that the proposed class-incremental fisher discriminant analysis method outperforms other conventional fisher discriminant analysis methods.

Many similarity measure algorithms of nodes in weighted graph data have been proposed by employing the degree of nodes in recent years. Despite these algorithms obtaining great results, there may be still some limitations. For instance, the strength of nodes is ignored. Aiming at this issue, the relative entropy of the distance distribution based similarity measure of nodes is proposed in this paper. At first, the structural weights of nodes are given by integrating their degree and strength. Next, the distance between any two nodes is calculated with the help of their structural weights and the Euclidean distance formula to further obtain the distance distribution of each node. After that, the probability distribution of nodes is constructed by normalizing their distance distributions. Thus, the relative entropy can be applied to measure the difference between the probability distributions of the top d important nodes and all nodes in graph data. Finally, the similarity of two nodes can be measured in terms of this above-mentioned difference calculated by relative entropy. Experimental results demonstrate that the algorithm proposed by considering the strength of node in the relative entropy has great advantages in the most similar node mining and link prediction.

Objective: The prevalence of multiple sclerosis (MS) in China is low, although it has been increasing recently. Owing to the paucity of data on immunotherapy acceptance in the Chinese population, we conducted this study to analyze factors affecting the acceptance of immunotherapy and selection of disease-modifying therapies (DMTs) based on personal and clinical data of patients with MS.Methods: In this study, data were obtained from the Multiple Sclerosis Patient Survival Report 2018, which was the first national survey of patients with MS in China. There were 1,212 patients with MS from 31 provinces who were treated at 49 Chinese hospitals over a 4-month period from May 2018 to August 2018, and the patients were asked to complete online questionnaires to assess their understanding of the disease.Results: In general, highly educated patients with frequent relapses were more willing to receive treatment regardless of DMTs or other immunotherapy, and patients with more understanding of the disease opted to be treated. Younger patient population, patients with severe disease course, and those with more symptoms were likely to choose the treatment. Moreover, a higher proportion of women chose to be treated with DMTs than with other immunotherapies.Conclusions: Education status and patient awareness of the disease impact the treatment acceptance in Chinese patients with MS. Therefore, we call for improving the awareness of MS disease and social security to help patients to improve their quality of life.

Fibroblast growth factor 23 (FGF23), which is involved in the regulation of vitamin D, is an emerging independent risk factor for cardiovascular diseases. Previous studies have demonstrated a positive association between FGF23 and stroke. In this study, we aimed to assess the association of FGF23 with ischemic stroke and its subtypes by applying a Mendelian randomization (MR) framework. Five genetic variants obtained from a genome-wide association study involving 16,624 European subjects were used as valid instruments of circulating FGF23 levels. MR was applied to infer the causality of FGF23 levels and the risk of ischemic stroke using data from the MEGASTROKE consortium. Subsequently, several MR analyses, including inverse-variance weighted meta-analysis, MR-Egger, weighted median estimate (WME), MR Pleiotropy Residual Sum and Outlier were performed. The heterogeneity test analysis, including Cochran’s Q, I2 test and leave-one-out analysis were also applied. Furthermore, potential horizontal/vertical pleiotropy was assessed. Lastly, the power of MR analysis was tested. Three validated variants were found to be associated with circulating FGF23 levels and were used for further investigation. We found that high expression level of FGF23 was not associated with any ischemic stroke. However, a causal association between genetically predicted FGF23 levels and the risk of large-artery atherosclerotic stroke (LAS) was significant, with an odds ratio of 1.74 (95% confidence interval = 1.08–2.81) per standard deviation increase in circulating FGF23 levels. Our findings provide support for the causal association between FGF23 and LAS, and therefore, offer potential therapeutic targets for LAS. The specific roles of FGF23 in LAS and associated molecules require further investigation.

Abstract Tumor treatment is still complicated in the field of medicine. Tumor immunotherapy has been the most interesting research field in cancer therapy. Application of chimeric antigen receptor T (CAR-T) cell therapy has recently achieved excellent clinical outcome in patients, especially those with CD19-positive hematologic malignancies. This phenomenon has induced intense interest to develop CAR-T cell therapy for cancer, especially for solid tumors. However, the performance of CAR-T cell treatment in solid tumor is not as satisfactory as that in hematologic disease. Clinical studies on some neoplasms, such as glioblastoma, ovarian cancer, and cholangiocarcinoma, have achieved desirable outcome. This review describes the history and evolution of CAR-T, generalizes the structure and preparation of CAR-T, and summarizes the latest advances on CAR-T cell therapy in different tumor types. The last section presents the current challenges and prospects of CAR-T application to provide guidance for subsequent research.

This survey study aims to provide an overview of Chinese as a second language (CSL) pronunciation teaching in the higher educational institutions in the US. 80 respondents participated in and completed an online survey. The survey elicited the participants’ responses in the following constructs: the context of Chinese pronunciation teaching, the status of Chinese pronunciation teaching, the CSL learners’ motivation and goal of Chinese pronunciation teaching, approach of pinyin teaching, difficulties in segments and suprasegmentals, and issues related to the training of Chinese pronunciation teaching. Not only have the findings from the study shed new light on many important issues in Chinese pronunciation teaching, they also have important pedagogical implications for all parties in CSL teaching: learners, instructors, researchers, textbook compilers, and administrators. While this study raises more questions than answers in Chinese pronunciation teaching, the survey highlights some critical issues that entail the joint efforts of the CSL field and points to the direction of further studies on Chinese pronunciation and Chinese pronunciation teaching.

Traditional tracking-by-detection trackers may fail to track targets owing to interferences, such as deformation, occlusion, fast or irregular motion, and background clutter. To improve the process of feature extraction, sample training, and the performance of the traditional kernelized correlation filter (KCF), this paper proposes a new joint multi-feature and scale-adaptive correlation filter tracking algorithm based on histogram of oriented gradients (HOG) and color-naming features. The new algorithm is composed of a position correlation filter tracker and a scale correlation filter tracker. It first uses a mask matrix to cut the training samples, obtains a higher proportion of real samples, and then trains the two correlation filter trackers. One is a position tracker that uses HOG and color-naming features to train two correlation filters, and then combines the results of the two correlation filters to calculate the target position. The second tracker is used to identify the scale with the maximum response size to select the target scale. The proposed algorithm achieves a competitive performance compared with algorithms, such as KCF, compressive tracking, and circulant structure kernel. The results show that this algorithm achieves a better performance when faced with occlusion, deformation, background clutter, and fast or irregular motion, and is able to track changes in target scale.

Smart railway maintenance is crucial to the safety and efficiency of railway operations. Successful deployment of technologies such as condition-based monitoring and predictive maintenance will enable railway companies to conduct proactive maintenance before defects and failures take place to improve operation safety and efficiency. In this paper, we first propose to develop a classification-based method to detect rail defects such as localized surface collapse, rail end batter, or rail components—such as joints, turning points, crossings, etc.—by using acceleration data. In order to improve the performance of the classification-based models and enhance their applicability in practice, we further propose a deep learning-based approach for the detection of rail joints or defects by deploying convolutional neural networks (CNN). CNN-based models can work directly with raw data to reduce the heavy preprocessing of feature engineering and directly detect joints located on either the left or the right rail. Two convolutional networks, ResNet and fully convolutional networks (FCN), are investigated and evaluated with the collected acceleration data. The experimental results show both deep neural networks obtain good performance, which demonstrate that the deep learning-based methods are effective for detecting rail joints or defects with the expected performance.

This paper proposes a particle filter (PF)-based electricity load prediction method to improve the accuracy of the microgrid day-ahead scheduling. While most of the existing prediction methods assume electricity loads follow normal distributions, we consider it is a nonlinear and non-Gaussian process which is closer to the reality. To handle the nonlinear and non-Gaussian characteristics of electricity load profile, the PF-based method is implemented to improve the prediction accuracy. These load predictions are used to provide the microgrid day-ahead scheduling. The impact of load prediction error on the scheduling decision is analyzed based on actual data. Comparison results on a distribution system show that the estimation precision of electricity load based on the PF method is the highest among several conventional intelligent methods such as the Elman neural network (ENN) and support vector machine (SVM). Furthermore, the impact of the different parameter settings are analyzed for the proposed PF based load prediction. The management efficiency of microgrid is significantly improved by using the PF method.

OBJECTIVE:Many studies have previously investigated the potential association between mobile phone use and the risk of glioma. However, results from these individual studies are inconclusive and controversial. The objective of our study was to investigate the potential association between mobile phone use and subsequent glioma risk using meta-analysis. METHODS:We performed a systematic search of the Science Citation Index Embase and PubMed databases for studies reporting relevant data on mobile phone use and glioma in 1980-2016. The data were extracted and measured in terms of the odds ratio (OR) and 95% confidence interval (CI) using the random effects model. Subgroup analyses were also carried out. This meta-analysis eventually included 11 studies comprising a total 6028 cases and 11488 controls. RESULTS:There was a significant positive association between long-term mobile phone use (minimum, 10 years) and glioma (OR = 1.44, 95% CI = 1.08-1.91). And there was a significant positive association between long-term ipsilateral mobile phone use and the risk of glioma (OR = 1.46, 95% CI = 1.12-1.92). Long-term mobile phone use was associated with 2.22 times greater odds of low-grade glioma occurrence (OR = 2.22, 95% CI = 1.69-2.92). Mobile phone use of any duration was not associated with the odds of high-grade glioma (OR = 0.81, 95% CI = 0.72-0.92). Contralateral mobile phone use was not associated with glioma regardless of the duration of use. Similarly, this association was not observed when the analysis was limited to high-grade glioma. CONCLUSIONS:Our results suggest that long-term mobile phone use may be associated with an increased risk of glioma. There was also an association between mobile phone use and low-grade glioma in the regular use or long-term use subgroups. However, current evidence is of poor quality and limited quantity. It is therefore necessary to conduct large sample, high quality research or better characterization of any potential association between long-term ipsilateral mobile phone use and glioma risk.

Objective To study clinical characteristics of ocular myasthenia gravis (OMG) to provide evidence for diagnosis and treatment of this disease. Methods Clinical data of 113 OMG patients were retrospectively analysed. Immunofluoresence cell staining was applied to detect the acetylcholine receptor (AChR) antibody and muscle ⁃ specific receptor tyrosine kinase (MuSK) antibody, and their predictive value for developing to generalized myasthenia gravis (MG) was assessed. Results Adult⁃onset of OMG was often happened in males over age 40. The most common initial presentation was ptosis (n = 95, 84.07% ), and a few started with diplopia (n = 18, 15.93% ). The positive ratio of fatigue test and neostigmine test was 79.44% (85/107) and 84.85% (84/99) respectively, abnormal rate of repetitive nerve stimulation (RNS) and serum thyroid antibodies was 44.32% (39/88) and 28% (14/50) respectively. The incidence of thymic hyperplasia and thymoma was 16.67% (17/102) and 11.76% (12/102), respectively. The positive rate of serum AChR antibody was 62.83% (71/113), but the serum MuSK antibody was negative in all patients. There were 12.39% (14/113) OMG patients progressed to generalized MG. Patients with higher AChR antibody expression were with more risk to progress to generalized MG (χ2 = 4.587, P = 0.032). Conclusion Adult ⁃ onset OMG often begins in middle ⁃ aged males. The main manifestation of OMG are ptosis and diplopia. Most patients are associated with abnormality of thymus and thyroid. Patients with higher AChR antibody expression are more at risk for progressing to generalized MG. MuSK antibody is uncommon in OMG. DOI：10.3969/j.issn.1672-6731.2011.01.018

An emerging prognostic and health management (PHM) technology has recently attracted a great deal of attention from academies, industries, and governments. The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management systems. PHM models depend on the smart sensors and data generated from sensors. This paper proposed a machine learning-based methods for developing PHM models from sensor data to perform fault diagnostic for transformer systems in a smart grid. In particular, we apply the Cuckoo Search (CS) algorithm to optimize the Back-propagation (BP) neural network in order to build high performance fault diagnostics models. The models were developed using sensor data called dissolved gas data in oil of the power transformer. We validated the models using real sensor data collected from power transformers in China. The results demonstrate that the developed meta heuristic algorithm for optimizing the parameters of the neural network is effective and useful; and machine learning-based models significantly improved the performance and accuracy of fault diagnosis/detection for power transformer PHM.

In the past decades, machine learning techniques or algorithms, particularly, classifiers have been widely applied to various real-world applications such as PHM. In developing high-performance classifiers, or machine learning-based models, i.e. predictive model for PHM, the predictive model evaluation remains a challenge. Generic methods such as accuracy may not fully meet the needs of models evaluation for prognostic applications. This paper addresses this issue from the point of view of PHM systems. Generic methods are first reviewed while outlining their limitations or deficiencies with respect to PHM. Then, two approaches developed for evaluating predictive models are presented with emphasis on specificities and requirements of PHM. A case of real prognostic application is studies to demonstrate the usefulness of two proposed methods for predictive model evaluation. We argue that predictive models for PHM must be evaluated not only using generic methods, but also domain-oriented approaches in order to deploy the models in real-world applications.

There is a growing use of carbon fiber reinforced polymers (CFRPs) in modern airframes with still a limited understanding of the in-service behavioral characteristics of these structures. Structural Health Monitoring (SHM) technologies that use surface-bonded piezoceramic (PZT) transducers to generate and measure guided waves within these structures have demonstrated promising damage detection and localization results and potential for data gathering in data-driven damage prognosis. This paper investigates the development of a data-driven SHM based damage prognosis system for estimating remaining useful life (RUL) of CFRP coupons following damage initiation. A robust and realistic laboratory data gathering methodology is introduced as a building block for evaluating the feasibility of data-driven damage prognosis for in-service aerospace structures. Data are gathered using a PZT-based SHM system. Using the gathered raw guided wave signals, a number of time and frequency domain features are first extracted which are derived from existing damage imaging and detection algorithms. Then, using various combinations of the feature sets as inputs to generic data mining algorithms, the paper presents estimates of the predicted RUL against actual damage diameter progression.