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Parkinson’s disease (PD) is one of the most prevalent movement disorders and its diagnosis relies heavily on the typical clinical manifestations in the late stages. This study aims to screen and identify biomarkers of PD for earlier intervention. We performed a differential analysis of postmortem brain transcriptome studies. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify biomarkers related to Braak stage. We found 58 genes with significantly different expression in both PD brain tissue and blood samples. PD gene signature and risk score model consisting of nine genes were constructed using least absolute shrinkage and selection operator regression (LASSO) and logistic regression. PLOD3 and LRRN3 in gene signature were identified to serve as key genes as well as potential risk factors in PD. Gene function enrichment analysis and evaluation of immune cell infiltration revealed that PLOD3 was implicated in suppression of cellular metabolic function and inflammatory cell infiltration, whereas LRRN3 exhibited an inverse trend. The cellular subpopulation expression of the PLOD3 and LRRN3 has significant distributional variability. The expression of PLOD3 was more enriched in inflammatory cell subpopulations, such as microglia, whereas LRRN3 was more enriched in neurons and oligodendrocyte progenitor cells clusters (OPC). Additionally, the expression of PLOD3 and LRRN3 in Qilu cohort was verified to be consistent with previous results. Collectively, we screened and identified the functions of PLOD3 and LRRN3 based the integrated study. The combined detection of PLOD3 and LRRN3 expression in blood samples can improve the early detection of PD.

Seismic fault displacement is the main factor leading to the local buckling failure of the buried pipeline, especially crossing the oblique-reverse fault. The 3-D displacement of the oblique-reverse fault makes the local buckling behavior of the buried pipeline more complex. It may be inaccurate to evaluate the local buckling location and degree using the pipe local buckling results under the single fault displacement, affecting the monitoring and safety assessment of the pipeline. In this paper, to determine the pipe local buckling behavior (potential local buckling locations, developing process) under the oblique-reverse fault displacement, a shell and solid element nonlinear contact coupling model of the pipeline crossing oblique-reverse fault is established using ABAQUS. The results reveal two potential local buckling areas and three stages of the local buckling developing process under the oblique-reverse fault displacement. Subsequently, the potential local buckling locations and three stages in the local buckling under different operating conditions is obtained. It proves that using the results of pipe local buckling under single fault to evaluate the pipeline local buckling under composed fault is not safe enough. Consequently, local buckling behavior of the pipeline crossing oblique-reverse fault provides a reference for the preliminary pipe design, detection, and evaluation.

T cell exhaustion plays critical roles in tumor immune evasion. Novel strategies to suppress immune evasion are in urgent need. We aimed to identify potential compounds to target T cell exhaustion and increase response to immune checkpoint inhibitors (ICIs). Differentially expressed genes (DEGs) were identified between tumors with different immune evasion potential by comparing the transcriptome data. DEGs were then analyzed in the Connectivity Map (CMap) platform to identify potential compounds to increase response to ICIs. Gene set enrichment analysis, LDH release assay, Chromatin immunoprecipitation (ChIP), and Co-IP were performed to explore the potential mechanisms in vitro. Patients derived organoids and humanized xenograft mouse model were utilized to validate the finding ex vivo and in vivo. We identified 25 potential compounds that may play critical roles in regulating tumor immune evasion. We further pinpointed a specific compound, dexamethasone, which shows potent anti-tumor effect in multiple cancer cell lines when cocultured with T cells. Dexamethasone can suppress T cell exhaustion by decreasing the activity of two immune checkpoints simultaneously, including PD-L1 and IDO1. Functional study shows dexamethasone can increase the sensitivity of ICIs in coculture system, 3D organoid model and humanized mouse model. Mechanism study shows dexamethasone mediated transcriptional suppression of PD-L1 and IDO1 depends on the nuclear translocation of GR/STAT3 complex. These findings demonstrate dexamethasone can suppress immune evasion by inducing GR/STAT3 mediated downregulation of PD-L1 and IDO1 pathways.

Motor imagery (MI) EEG signal classification is a critical issue for brain–computer interface (BCI) systems. In traditional MI EEG machine learning algorithms, feature extraction and classification often have different objective functions, thus resulting in information loss. To solve this problem, a novel spatial-frequency-temporal (SFT) 3D CNN model is proposed. Specifically, the energies of EEG signals located in multiple local SFT ranges are extracted to obtain a novel 3D MI EEG feature representation, and a novel 3D CNN model is designed to simultaneously learn the complex MI EEG features in the entire SFT domains and carry out classification. An extensive experimental study is implemented on two public EEG datasets to evaluate the effectiveness of our method. For BCI Competition III Dataset IVa, the average accuracy rate of five subjects obtained by the proposed method reaches 86.6% and yields 4.1% improvement over the state-of-the-art filter band common spatial pattern (FBCSP) method. For BCI Competition III dataset IIIa, by achieving an average accuracy rate of 91.85%, the proposed method outperforms the state-of-the-art dictionary pair learning (DPL) method by 4.44%.

Multi-view spectral clustering is a hot research area which has attracted increasing attention. Most existing multi-view spectral clustering methods utilize a two-step strategy. The first step obtains a common embedding by fusing spectral embeddings of different views, and the second step conducts hard clustering, such as K-means or spectral rotation, on the common embedding. Because the goal of the first step is not obtaining optimal clustering result, and the requirement to post-processing makes the final clustering result uncertain. In this paper, we propose a novel one-step multi-view spectral clustering method, in which the spectral embedding and nonnegative embedding are unified into one framework. Therefore, our method can avoid the uncertainty brought by post-processing and obtain optimal clustering result. Moreover, the nonnegative embedding is divided into two parts. The common nonnegative embedding indicates the shared cluster structure, and the specific nonnegative embedding indicates the exclusive cluster structure of each view. Hence, our method can well tackle with noises and outliers of different views. Furthermore, an alternating iterative algorithm is used to solve the joint optimization problem. Extensive experimental results on four real-world datasets have demonstrated the effectiveness of the proposed method.

Background Biliary adenofibroma (BAF) is a rare primary hepatic tumor with the potential risk of malignant transformation. Given the extreme rarity of the disease, the imaging features of BAF are unclear. We presented a case of malignant BAF and conducted a systematic literature review. We highlighted the key imaging features in the diagnosis and aggressiveness assessment of BAF, as well as the role of various imaging modalities in evaluating BAF. Case presentation We reported a 64-year-old woman with a 5-months history of pain in the right upper quadrant abdomen. US of the liver showed a hypoechoic subcapsular nodule. CT scan revealed a subcapsular solid-cystic mass in segment V of the liver. The mass showed a marked enhancement in the arterial phase followed by wash-out in the venous phase. The patient underwent partial resection of liver’s right lobe. The mass was diagnosed as BAF with malignant transformation by postoperative pathology. Conclusions CT and MRI are helpful in recognizing and characterizing BAF. The imaging features of BAF include a solitary, large solid-cystic mass with a well-defined margin, lobulated shape, and internal septa; subcapsular location; no intrahepatic bile duct communication; the presence of von Meyenberg complexes in background liver. The enhancement patterns may have the potential to assess the aggressiveness of BAF, and that marked enhancement in the arterial phase followed by wash-out in the venous phase is suggestive of malignant BAF.

Utilizing the experimental and modeling approaches, the Gamma radiation effects on stress responses of the silicon rubber foam under quasistatic compression are investigated. In the experimental work, the samples of the silicon rubber and the silicon rubber foams are quasistatically compressed before and after the Gamma radiation (a dose of 500 kGy and a dose rate of 100 Gy/min). The data reveal that the Gamma radiation obviously increases the material hardness, e.g., the compressive stresses of the silicon rubber and the silicon rubber foams both increase over 5 times as the strain is 20%. In the simulation work, a multiscale method combined with finite element method is developed to numerically predict the compressive stress of the silicon rubber foams. The microscale models are first constructed based on the real microstructures of the silicon rubber foams. The compressive stress and strain relation before and after the Gamma radiation is then simulated and obtained utilizing the phenomenological constitutive models based on the testing data of the silicon rubber. The simulation reveals that the Gamma radiation strongly affects the compressive response of the microscale models. The stress responses of the microscale models are then transferred into the macroscale models. The results also prove that the Gamma radiation obviously increases the hardness of the macroscale models. Data comparison shows that the numerical results agree with the testing data well, which verifies the developed method. The present work develops a new method to predict the radiation effects on mechanical properties of rubber foams.

High-accuracy traffic flow forecasting is vital to the development of intelligent city transportation systems. Recently, traffic flow forecasting models based on the kernel method have been widely applied due to their great generalization capability. The aim of this article is twofold: A novel kernel learning method, relevance vector machine, is employed to short-term traffic flow forecasting so as to capture the inner correlation between sequential traffic flow data, it is a type of nonlinear model which is accurate and using only a small number of relevant basis functions automatically selected. So that it can find concise data representations which are adequate for the learning task retaining as much information as possible. On the other hand, the sample size for learning has a significant impact on forecasting accuracy. How to balancing the relationship between the sample size and the forecasting accuracy is an important research topic. A randomized controlled statistical testing is layout to evaluating the impacts of sample size of the new proposed traffic flow forecasting model. The experimental results show that the new model achieves similar or better forecasting and generalization performance compared to some old ones; besides, it is less sensitive to the size of learning sample.

A complex rubber foam under quasi-static compression is simulated using the finite element method (FEM). The present work sets up the phenomenological constitutive model for the silicon rubber. The computerized tomography (CT) technique is utilized to reconstruct the real complex foam geometries. The quasi-static uniaxial compression on the foam is simulated in ABAQUS. The present work obtains the stress response as the nominal strain nearly reaches 80% and the foam exhibits hyper-elastic behavior. The FEM results achieve good agreements with the data obtained from the multi-scale simulation and the tests as the nominal strain is less than 60%.

Nonnegative matrix factorization (NMF) has been successfully used in different applications including computer vision, pattern recognition and text mining. NMF aims to decompose a data matrix into the product of two matrices (respectively denoted as the basis vectors and the encoding vectors), whose entries are constrained to be nonnegative. Unlike the ordinary NMF, we propose a novel NMF, denoted as MMNMF, which considers both geometrical information and discriminative information hidden in the data. The geometrical information is discovered by minimizing the distance among the encoding vectors, while the discriminative information is uncovered by maximizing the distance among base vectors. Clustering experiments are performed on the real-world data sets of faces, images, and documents to demonstrate the effectiveness of the proposed algorithm.

Background Growing evidence suggests that hepatitis C virus (HCV) contributes to hepatocellular carcinoma (HCC) by directly modulating oncogenic signaling pathways. Protein phosphatase magnesium-dependent 1A (PPM1A) has recently emerged as an important tumor suppressor as it can block a range of tumor-centric signaling pathways through protein dephosphorylation. However, the role and regulatory mechanisms of PPM1A in HCV-infected cells have not been reported. Methods Total, cytoplasmic, and nuclear PPM1A protein after HCV infection or overexpression of HCV nonstructural protein 3 (NS3) were detected by western blotting. The expression of PPM1A in normal liver and HCV-related HCC tissues was quantified by immunohistochemistry. The effects of HCV infection and NS3 expression on the PPM1A protein level were systematically analyzed, and the ubiquitination level of PPM1A was determined by precipitation with anti-PPM1A and immunoblotting with either anti-ubiquitin or anti-PPM1A antibody. Finally, the roles of NS3 and PPM1A in hepatoma cell migration and invasion were assessed by wound healing and transwell assays, respectively. Results HCV infection and replication decreased PPM1A abundance, mediated by NS3, in hepatoma cells. Compared to normal liver tissues, the expression of PPM1A was significantly decreased in the HCC tumor tissues and adjacent non-tumor tissues. NS3 directly interacted with PPM1A to promote PPM1A ubiquitination and degradation, which was dependent on its protease domain. Blockade of PPM1A through small interfering RNA significantly promoted HCC cell migration, invasion, and epithelial mesenchymal transition (EMT), which were further intensified by TGF-β1 stimulation, in vitro. Furthermore, restoration of PPM1A abrogated the NS3-mediated promotion of HCC migration and invasion to a great extent, which was dependent on its protein phosphatase function. Conclusions Our findings demonstrate that the HCV protein NS3 can downregulate PPM1A by promoting its ubiquitination and proteasomal degradation, which might contribute to the migration and invasion of hepatoma cells and may represent a new strategy of HCV in carcinogenesis. Electronic supplementary material The online version of this article (doi:10.1186/s13046-017-0510-8) contains supplementary material, which is available to authorized users.

A novel method of linear approximation of a matrix is proposed for designing the linear-phase and non-linear-phase extrapolated impulse response (EIR) filters. There are zero-valued and one-valued coefficients in the scale vectors of the proposed EIR filters. These zero-valued coefficients do not require multipliers or adders, and these one-valued coefficients do not require multipliers, in the filter implementation. Additionally, the optimal design of the EIR filters in the mini-max sense and the least-square sense by the iterative gradient searching (IGS) technique is investigated. In implementing the IGS technique, the original EIR filter, the principal component analysis based EIR filter and the proposed EIR filter are set as the initial solutions, separately. If the proposed EIR filter is set as the initial solution, the IGS technique can synthesize the prototype finite impulse response (FIR) filter satisfactorily, with the lowest hardware implementation complexity. Further, experimental comparison of the proposed EIR method with the two most popular non-EIR low-complexity FIR filter design methods also indicates some superiority of the proposed EIR method. Finally, the proposed method of linear approximation of a matrix may also be useful in other areas of signal processing.

Support vector data description (SVDD) has been widely used in novelty detection applications. Since the decision function of SVDD is expressed through the support vectors which contain sensitive information, the support vectors will be disclosed when SVDD is used to detect the unknown samples. Accordingly, privacy concerns arise. In addition, when it is applied to large datasets, SVDD does not scale well as its complexity is linear with the size of the training dataset (actually the number of support vectors). Our work here is distinguished in two aspects. First, by decomposing the kernel mapping space into three subspaces and exploring the pre-image of the center of SVDD's sphere in the original space, a fast decision approach of SVDD, called FDA-SVDD, is derived, which includes three implementation versions, called FDA-SVDD-I, FDA-SVDD-II and FDA-SVDD-III. The decision complexity of the proposed method is reduced to only $$O$$ O (1). Second, as the decision function of FDA-SVDD only refers to the pre-image of the sphere center, the privacy of support vectors can be preserved. Therefore, the proposed FDA-SVDD is particularly attractive in privacy-preserving novelty detection applications. Empirical analysis conducted on UCI and USPS datasets demonstrates the effectiveness of the proposed approach and verifies the derived theoretical results.

This paper explores how a kind of probabilistic systems, namely, Gaussian kernel density estimation (GKDE), can be used to interpret several classical kernel methods, including the well-known support vector machine (SVM), support vector regression (SVR), one-class kernel classifier, i.e., support vector data description (SVDD) or equivalently minimal enclosing ball (MEB), and the fuzzy systems (FS). For the SVM, we reveal that the classical SVM with Gaussian density kernel attempts to find a noisy GKDE based Bayesian classifier with equal prior probabilities for each class. For the SVR, the classification based e-SVR attempts to obtain two noisy GKDEs for each class in the constructed binary classification dataset, and the decision boundary just corresponds to the mapping function of the original regression problem. For the MEB or SVDD, we reveal the equivalence between it and the integrated-squared-errors (ISE) criterion based GKDE and by using this equivalence a MEB based classifier with privacy-preserving function is proposed for one kind of classification tasks where the datasets contain privacy-preserving clouds. For the FS, we show that the GKDE for a regression dataset is equivalent to the construction of a zero-order Takagi–Sugeno–Kang (TSK) fuzzy system based on the same dataset. Our extensive experiments confirm the obtained conclusions and demonstrated the effectiveness of the proposed new machine learning and modeling methods.