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Introduction Accumulation of polyglutamine (polyQ) ataxin-3 (ATXN3) contributes to the pathobiology of spinocerebellar ataxia type 3 (SCA3). Recently, we showed that polyQ ATXN3 is elevated in the plasma and cerebrospinal fluid (CSF) of SCA3 patients, and has the potential to serve as a biological marker for this disease [1]. Based on these findings, we investigated whether polyQ ATXN3 can also be detected in urine samples from SCA3 patients. Methods We analyzed urine samples from 30 SCA3 subjects (including one pre-symptomatic subject), 35 subjects with other forms of ataxia, and 37 healthy controls. To quantify polyQ ATXN3 protein levels, we used our previously developed immunoassay. Results PolyQ ATXN3 can be detected in the urine of SCA3 patients, but not in urine samples from healthy controls or other forms of ataxia. There was a significant statistical association between polyQ ATXN3 levels in urine samples and those in plasma. Further, the levels of polyQ ATXN3 urine associated with an earlier age of SCA3 disease onset. Conclusion As clinical trials for SCA3 advance, urine polyQ ATXN3 protein has potential to be a useful, non-invasive and inexpensive biomarker for SCA3.

OBJECTIVE To explore the predictive value of serum gastrin (GAS), epidermal growth factor (EGF) levels in gastric ulcer complicated with acute upper gastrointestinal bleeding. STUDY DESIGN A descriptive study. PLACE AND DURATION OF STUDY Department of Emergency, Beijing Jiangong Hospital, China, from January 2019 to June 2020. METHODOLOGY One hundred and twenty-five patients with gastric ulcer and acute upper gastrointestinal bleeding were selected as Group A. One hundred and twenty-five patients with gastric ulcer and no upper gastrointestinal bleeding were selected as Group B. Logistic regression analysis was used to analyse the risk factors of gastric ulcer complicated with acute upper gastrointestinal bleeding. The value of serum GAS, EGF in early diagnosis of gastric ulcer with upper gastrointestinal bleeding was evaluated by receiver operating characteristic (ROC) curve. RESULTS Univariate analysis showed statistically significant differences between Group A and Group B in taking non-steroidal anti-inflammatory drugs (NSAIDs), helicobacter pylori (Hp) infection, serum GAS and EGF (all p

BackgroundHuman papillomavirus (HPV) infection is associated with multiple types of cancer, but the evidence has not yet been fully elucidated in bladder cancer.MethodsFrozen tissue samples collected from 146 patients aged 32 to 89 years with bladder cancer pathological diagnosis between 2015 and 2019 were analyzed. HPV genotyping and integration status determination were performed by capture-based next generation sequencing. Statistical analysis of HPV type distributions was performed according to stage, grade, sex, and age group of patients.ResultsMean (SD) age of the 146 patients was 66.64 ± 10.06 years and 83.56% were men. Overall HPV infection rate was 28.77% (37.50% in women and 27.05% in men), with 11.90% HPV integration events. Among them, 17.12% single and 11.65% coinfections were observed. HPV18 (24.66%) was the most prevalent genotype, followed by HPV33, 16, and 39. All HPV were European lineage (A). HPV16 was more prevalent in women (P = .04).ConclusionsHPV infection may contribute to the etiology both in men and women with bladder cancer. HPV18, followed by HPV33, 16, and 39 genotypes, potentially represent the predominant oncogenic risk types for bladder carcinogenesis.

No treatment for frontotemporal dementia (FTD), the second most common type of early-onset dementia, is available, but therapeutics are being investigated to target the 2 main proteins associated with FTD pathological subtypes: TDP-43 (FTLD-TDP) and tau (FTLD-tau). Testing potential therapies in clinical trials is hampered by our inability to distinguish between patients with FTLD-TDP and FTLD-tau. Therefore, we evaluated truncated stathmin-2 (STMN2) as a proxy of TDP-43 pathology, given the reports that TDP-43 dysfunction causes truncated STMN2 accumulation. Truncated STMN2 accumulated in human induced pluripotent stem cell-derived neurons depleted of TDP-43, but not in those with pathogenic TARDBP mutations in the absence of TDP-43 aggregation or loss of nuclear protein. In RNA-Seq analyses of human brain samples from the NYGC ALS cohort, truncated STMN2 RNA was confined to tissues and disease subtypes marked by TDP-43 inclusions. Last, we validated that truncated STMN2 RNA was elevated in the frontal cortex of a cohort of patients with FTLD-TDP but not in controls or patients with progressive supranuclear palsy, a type of FTLD-tau. Further, in patients with FTLD-TDP, we observed significant associations of truncated STMN2 RNA with phosphorylated TDP-43 levels and an earlier age of disease onset. Overall, our data uncovered truncated STMN2 as a marker for TDP-43 dysfunction in FTD.

For the selection of fund managers in fund investment, traditional measurement methods were mainly based on descriptive analysis and regression modeling for a small sample of numerical data. They did not make full use of the relevant big data information of fund managers and ignored the nonlinearity between data. As a result, the prediction error was large. In this paper, we use the heterogeneous data of fund managers, such as numerical data and textual data, to fully explore the characteristic factors of fund performance, and further employ the artificial intelligence algorithms including the traditional machine learning models and deep learning model to consider the nonlinear characteristics between the data to identify the style of fund managers. It is found that there is a nonlinear characteristic between the characteristic factor and the performance of the fund. The artificial intelligence algorithm, especially, the deep learning method has a better performance in the classification and prediction of fund managers, especially, after adding text information. The research in this paper has enriched the application of intelligent investment advisor in fund investment to a certain extent, and provided a scientific basis for investors’ choice.

Through empirical research, it is found that the traditional autoregressive integrated moving average (ARIMA) model has a large deviation for the forecasting of high‐frequency financial time series. With the improvement in storage capacity and computing power of high‐frequency financial time series, this paper combines the traditional ARIMA model with the deep learning model to forecast high‐frequency financial time series. It not only preserves the theoretical basis of the traditional model and characterizes the linear relationship, but also can characterize the nonlinear relationship of the error term according to the deep learning model. The empirical study of Monte Carlo numerical simulation and CSI 300 index in China show that, compared with ARIMA, support vector machine (SVM), long short‐term memory (LSTM) and ARIMA‐SVM models, the improved ARIMA model based on LSTM not only improves the forecasting accuracy of the single ARIMA model in both fitting and forecasting, but also reduces the computational complexity of only a single deep learning model. The improved ARIMA model based on deep learning not only enriches the models for the forecasting of time series, but also provides effective tools for high‐frequency strategy design to reduce the investment risks of stock index.

Granular computing has advantage of knowledge discovery for complex data. In the paper, we present Fuzzy Granular Hyperplane Classifiers (FGHCs) for data classification from a new angle of Granular Computing. First, we introduce a fuzzy granular hyperplane concept by defining fuzzy granule, fuzzy granular vector, metrics and operators. Next, for binary classification problem, we present solving optimal fuzzy granular hyperplane through evolution strategy; the learning algorithm of parameters and the prediction algorithm of instances are also proposed. Finally, a multi-classification prediction model is designed by combining a set of Fuzzy Granular Hyperplane Classifiers based on vote strategy. In order to evaluate performance, we employed 10-fold cross validation to verify on UCI dataset and Alzheimer's Disease Voice dataset. Theoretical analysis and experiments demonstrated that FGHCs have good performance.

Objective Recent studies have shown that neuregulin 4 (Nrg4), a member of the epidermal growth factor (EGF) family of extracellular ligands, plays an important role in the prevention of obesity, insulin resistance and nonalcoholic fatty liver disease (NAFLD). Considering that thyroid hormone (TH) has profound effects on whole-body energy metabolism, we speculate that circulating Nrg4 levels might be altered in patients with hyperthyroidism. Design and methods A total of 129 hyperthyroid patients and 100 healthy subjects were recruited. Of them, 39 hyperthyroid patients received thionamide treatment for 3 months until euthyroidism. Serum Nrg4 levels were determined using the ELISA method. To further confirm the relationship between TH and Nrg4, C57BL/6 mice were treated with T3 and quantitative real-time PCR was performed to detect Nrg4 gene expression. Results Serum Nrg4 levels were significantly elevated in hyperthyroid patients as compared with normal controls (3.84 ± 1.63 vs 2.21 ± 1.04 ng/mL, P P 3 was further confirmed by animal and cell culture experiments. Conclusions Serum Nrg4 levels were increased in patients with hyperthyroidism. The liver and white adipose tissue might be primary sources contributing to elevated serum Nrg4 concentrations.

Hospitalized patients with COVID-19 show increased serum concentrations of neurofilament light chain that correlate with worse clinical outcomes., Brain damage marker in COVID-19 SARS-CoV-2 infection, the cause of coronavirus disease 2019 (COVID-19), causes neurological manifestations in a substantial proportion of patients. Determining the extent of neuronal injury is essential to better understand disease pathophysiology and to evaluate potential therapies. Prudencio et al. analyzed serum from 142 patients hospitalized with COVID-19 and showed that the expression of the neurofilament light protein (NFL), a marker of neuroaxonal injury, was elevated compared to healthy controls. In addition, serum NFL expression correlated with disease severity and tended to be reduced in subjects treated with remdesivir. The results suggest that serum NFL analysis should be incorporated when evaluating therapeutic trials for COVID-19., Brain imaging studies of patients with COVID-19 show evidence of macro- and microhemorrhagic lesions, multifocal white matter hyperintensities, and lesions consistent with posterior reversible leukoencephalopathy. Imaging studies, however, are subject to selection bias, and prospective studies are challenging to scale. Here, we evaluated whether serum neurofilament light chain (NFL), a neuroaxonal injury marker, could predict the extent of neuronal damage in a cohort of 142 hospitalized patients with COVID-19. NFL was elevated in the serum of patients with COVID-19 compared to healthy controls, including those without overt neurological manifestations. Higher NFL serum concentrations were associated with worse clinical outcomes. In 100 hospitalized patients with COVID-19 treated with remdesivir, a trend toward lower NFL serum concentrations was observed. These data suggest that patients with COVID-19 may experience neuroaxonal injury and may be at risk for long-term neurological sequelae. Neuroaxonal injury should be considered as an outcome in acute pharmacotherapeutic trials for COVID-19.

Spinocerebellar ataxia type 3 (SCA3), caused by a CAG repeat expansion in the ataxin-3 gene (ATXN3), is characterized by neuronal polyglutamine (polyQ) ATXN3 protein aggregates. Although there is no cure for SCA3, gene-silencing approaches to reduce toxic polyQ ATXN3 showed promise in preclinical models. However, a major limitation in translating putative treatments for this rare disease to the clinic is the lack of pharmacodynamic markers for use in clinical trials. Here, we developed an immunoassay that readily detects polyQ ATXN3 proteins in human biological fluids and discriminates patients with SCA3 from healthy controls and individuals with other ataxias. We show that polyQ ATXN3 serves as a marker of target engagement in human fibroblasts, which may bode well for its use in clinical trials. Last, we identified a single-nucleotide polymorphism that strongly associates with the expanded allele, thus providing an exciting drug target to abrogate detrimental events initiated by mutant ATXN3. Gene-silencing strategies for several repeat diseases are well under way, and our results are expected to improve clinical trial preparedness for SCA3 therapies.

Background Atherosclerosis-related cardiovascular diseases are among the leading causes of mortality worldwide. Uptake of modified lipoproteins by monocyte-derived macrophages leads to the formation of foam cells, which by secreting pro-inflammatory cytokines accelerate plaque development. Sorting Nexin 10 (SNX10) plays a critical role in the regulation of macrophage functions as well as in the lipid metabolism. This study aims to explore the precise effects and the underlying molecular mechanisms of SNX10 mediated processes in atherosclerotic diseases. Methods SNX10 knockout mice were generated and bone marrow-derived macrophages (BMDMs) were studied in vitro. Mice were crossed with apolipoprotein E-deficient (ApoE-/-) mice and atherogenesis was monitored for 16 weeks. Results Expression of SNX10 was increased in atherosclerotic plaques from both humans and ApoE-/- mice. SNX10 deficiency decreased foam cell abundance and alleviated atherosclerotic plaque progression in ApoE-/- mice. In addition, high fat, high cholesterol diet fed ApoE-/- mice contained more Ly6ChiCX3CR1lo monocytes than ApoE-/- SNX10-/- (DKO) littermates. ApoE-/- mice showed a higher level of inflammatory macrophages infiltrating into atherosclerotic lesions. SNX10 deficiency reduced the interactions between CD36 and Lyn, thus inhibiting CD36-mediated lipoprotein uptake and foam cell formation. Conclusions Our findings demonstrate CD36-Lyn dependent non-redundant atherogenic role for SNX10 in diet-induced atherogenesis. We propose SNX10 as a potential therapeutic target for the prevention and treatment of atherosclerosis.

The existing index system for volatility forecasting only focuses on asset return series or historical volatility, and the prediction model cannot effectively describe the highly complex and nonlinear characteristics of the stock market. In this study, we construct an investor attention factor through a Baidu search index of antecedent keywords, and then combine other trading information such as the trading volume, trend indicator, quote change rate, etc., as input indicators, and finally employ the deep learning model via temporal convolutional networks (TCN) to forecast the volatility under high-frequency financial data. We found that the prediction accuracy of the TCN model with investor attention is better than those of the TCN model without investor attention, the traditional econometric model as the generalized autoregressive conditional heteroscedasticity (GARCH), the heterogeneous autoregressive model of realized volatility (HAR-RV), autoregressive fractionally integrated moving average (ARFIMA) models, and the long short-term memory (LSTM) model with investor attention. Compared with the traditional econometric models, the multi-step prediction results for the TCN model remain robust. Our findings provide a more accurate and robust method for volatility forecasting for big data and enrich the index system of volatility forecasting.

K-nearest neighbor (KNN) is a classic classifier, which is simple and effective. Adaboost is a combination of several weak classifiers as a strong classifier to improve the classification effect. These two classifiers have been widely used in the field of machine learning. In this paper, based on information fuzzy granulation, KNN and Adaboost, we propose two algorithms, a fuzzy granule K-nearest neighbor (FGKNN) and a boosted fuzzy granule K-nearest neighbor (BFGKNN), for classification. By introducing granular computing, we normalize the process of solving problem as a structured and hierarchical process. Structured information processing is focused, so the performance including accuracy and robust can be enhanced to data classification. First, a fuzzy set is introduced, and an atom attribute fuzzy granulation is performed on samples in the classified system to form fuzzy granules. Then, a fuzzy granule vector is created by multiple attribute fuzzy granules. We design the operators and define the measure of fuzzy granule vectors in the fuzzy granule space. And we also prove the monotonic principle of the distance of fuzzy granule vectors. Furthermore, we also give the definition of the concept of K-nearest neighbor fuzzy granule vector and present FGKNN algorithm and BFGKNN algorithm. Finally, we compare the performance among KNN, Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Logistic Regression (LR), FGKNN and BFGKNN on UCI data sets. Theoretical analysis and experimental results show that FGKNN and BFGKNN have better performance than that of the methods mentioned above if the appropriate parameters are given.

Cartoon alignment is a key to retrieve cartoon characters and synthesise new cartoon clips. To successfully achieve the tasks, it is necessary to extract visual features that comprehensively denote cartoon characters and to align the feature points accurately between cartoon characters. In this paper, Speed Up Robust Feature (SURF) and Shape Context (SC) are introduced to characterise the cartoon character from multi-view. To increase accuracy rate of cartoon character alignment, semi-supervised alignment and Procrustes alignment require predetermining the correspondence. To overcome the flaw, we propose a Manifold Multi-View Learning (MML) to align cartoon characters. MML learns a projection that maps data instance (from cartoon characters with different dimensionality) to a lower-dimensional space, which simultaneously matches the local geometry and preserves the neighbourhood relationship within each cartoon character. The matching relationship can be obtained from local geometry structure. Experimental results show the good performance.

We present an approach for synthesizing Chinese calligraphy with a similar topological style from learning author's written works. Our first contribution is an algorithm to match the trajectory. Second contribution is a method to represent Chinese character topology via WF-histogram. Third contribution is an algorithm to take topological features as features and feed them into the evaluation model-that is Adaboost composed of support vector regressions (SVRs). Fourth contribution is a Genetic Algorithm (GA) introduced in the optimization glyph phase. Moreover, we introduce hypothesis testing and the decay function of transformation amplitude to improve the converge speed. The experiments demonstrate that our approach can obtain a similar topological style Chinese calligraphy with training samples.

There is no effective treatment for amyotrophic lateral sclerosis (ALS), a devastating motor neuron disease. However, discovery of a G(4)C(2) repeat expansion in the C9ORF72 gene as the most common genetic cause of ALS has opened up new avenues for therapeutic intervention for this form of ALS. G(4)C(2) repeat expansion RNAs and proteins of repeating dipeptides synthesized from these transcripts are believed to play a key role in C9ORF72-associated ALS (c9ALS). Therapeutics that target G(4)C(2) RNA, such as antisense oligonucleotides (ASOs) and small molecules, are thus being actively investigated. A limitation in moving such treatments from bench to bedside is a lack of pharmacodynamic markers for use in clinical trials. We explored whether poly(GP) proteins translated from G(4)C(2) RNA could serve such a purpose. Poly(GP) proteins were detected in cerebrospinal fluid (CSF) and in peripheral blood mononuclear cells from c9ALS patients and, notably, from asymptomatic C9ORF72 mutation carriers. Moreover, CSF poly(GP) proteins remained relatively constant over time, boding well for their use in gauging biochemical responses to potential treatments. Treating c9ALS patient cells or a mouse model of c9ALS with ASOs that target G(4)C(2) RNA resulted in decreased intracellular and extracellular poly(GP) proteins. This decrease paralleled reductions in G(4)C(2) RNA and downstream G(4)C(2) RNA-mediated events. These findings indicate that tracking poly(GP) proteins in CSF could provide a means to assess target engagement of G(4)C(2) RNA-based therapies in symptomatic C9ORF72 repeat expansion carriers and presymptomatic individuals who are expected to benefit from early therapeutic intervention.

In this paper, we design a recognition system of the handwritten numerals and English characters based on BP neural network. In the system, we first make some preprocess to the image. Secondly, we extract the structural and statistical features of the image. Thirdly, we train a model on the data sets via BP neural network. Finally, we can predict the test image using the trained model. The experiments show that the handwritten numbers recognition rate can reach more than 94 % and the handwritten English characters is 44 % respectively. Besides that, the recognition rate of Handwritten number and English together is 78 %. It has been proved that our method is effective and robust.

BACKGROUND The objective of the study was to analyze the association of apelin gene (APLN) single nucleotide polymorphisms (SNPs) and type 2 diabetes mellitus (T2DM). METHODS A total of 1966 subjects were enrolled in this study, including 168 cases (first batch), 330 cases (second batch), and 1468 nondiabetic controls. The SNPs in the HapMap-HCB of APLN were detected using Sequenom MassARRAY SNP technology and included rs2281068, rs3115757, rs2235309, and rs2235310. The related clinical characteristics with glucose metabolism were tested, including the fasting blood glucose (FPG), insulin (INS), C-peptide, glycosylated hemoglobin (HbA1c), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C). RESULTS A correlation between rs3115757 and rs2281068 and diabetes was observed in first batch. Thus, we compared the SNPs (rs3115757 and rs2281068) between the cases and controls after more cases were enrolled. In addition, the results showed a significant correlation between APLN rs2281068 and diabetes (p

Yujuan Fan,* Yu Zhang,* Xuesong Li,* Hui Zheng, Yuping Song, Ning Zhang, Chunfang Shen, Xiaofang Fan, Fengdong Ren, Jiayi Shen, Guoguang Ren, Jialin Yang Department of Endocrinology, Central Hospital of Minhang District, Minhang Hospital affiliated to Fudan University, Shanghai, People’s Republic of China *These authors contributed equally to this work Background: The objective of this study was to assess the effects of metformin monotherapy or combined treatment with a dipeptidyl peptidase-4 inhibitor (vildagliptin) on apelin levels in patients with type 2 diabetes mellitus. Methods: Twenty-five patients with poor glycemic control (glycosylated hemoglobin >6.5% [48 mmol/mol]) taking 1,000 mg of metformin daily and 25 healthy controls matched for age and body mass index were enrolled in this study. Anthropometric parameters, glycemic and lipid profile, insulin resistance (homeostasis model assessment of insulin resistance index), and apelin levels were measured at baseline and at 12-week and 24-week visits. Results: At baseline, apelin levels were higher in the T2DM patients than in the controls (1.93±1.81 ng/mL versus 6.09±4.90 ng/mL; P