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Background Psychiatric disorders are among the largest and fastest-growing categories of the global disease burden. However, limited effort has been made to further elucidate associations between socioeconomic factors and psychiatric disorders from a genetic perspective. Methods We randomly divided 501,882 participants in the UK Biobank cohort with socioeconomic Townsend deprivation index (TDI) data into a discovery cohort and a replication cohort. For both cohorts, we first conducted regression analyses to evaluate the associations between the TDI and common psychiatric disorders or traits, including anxiety, bipolar disorder, self-harm, and depression (based on self-reported depression and Patient Health Questionnaire scores). We then performed a genome-wide gene-by-environment interaction study using PLINK 2.0 with the TDI as an environmental factor to explore interaction effects. Results In the discovery cohort, significant associations were observed between the TDI and psychiatric disorders (p Conclusions Our findings suggest the relevance of the TDI to psychiatric disorders. The genome-wide gene-by-environment interaction study identified several candidate genes interacting with the TDI, providing novel clues for understanding the biological mechanism of associations between the TDI and psychiatric disorders.

In the reconstruction and extension project of mountainous highway, it is difficult to choose the route scheme for some sections. The main reason is the comprehensive choice of alignment parameters, project cost and environmental protection. In this paper, a TOPSIS evaluation method based on entropy weight method is proposed. From the most concerned aspects of route scheme selection, 11 indexes are selected, including minimum horizontal curve radius, longitudinal slope value, horizontal and vertical combination, first part cost, filling and excavation balance, proportion of high filling and deep excavation section, bridge tunnel ratio, space type, slope masonry ratio, roadside safety assurance degree and number of access points, the entropy weight method is used to determine the weight of the index, and then the TOPSIS method is used to calculate the distance between each scheme and the ideal solution, so as to judge the advantages and disadvantages of each scheme. The method is applied in practical engineering. The evaluation method provides a new idea for the selection of route scheme in the reconstruction and expansion project.

Background: Cerebral palsy (CP) is a spectrum of non-progressive motor disorders caused by brain injury during fetal or postnatal periods. Current diagnosis of CP mainly relies on neuroimaging and motor assessment. Here, we aimed to explore novel biomarkers for early diagnosis of CP. Methods: Blood plasma from five CP children and their healthy twin brothers/sisters was analyzed by gene microarray to screen out differentially expressed RNAs. Selected differentially expressed circular RNAs (circRNAs) were further validated using quantitative real-time PCR. Receiver operating characteristic (ROC) curve analysis was used to evaluate the value of using hsa_circ_0086354 as a biomarker of CP.Results: 43 up-regulated circRNAs and 2 down-regulated circRNAs were obtained by difference analysis (fold change>2, pConclusion: Hsa_circ_0086354 was significantly down-regulated in blood plasma of CP children, which may be a novel competent biomarker for early diagnosis of CP.

IntroductionDiagnosing Kashin-Beck disease (KBD) involves damages to multiple joints and carries variable clinical symptoms, posing great challenge to the diagnosis of KBD for clinical practitioners. However, it is still unclear which clinical features of KBD are more informative for the diagnosis of Kashin-Beck disease among adolescent.MethodsWe first manually extracted 26 possible features including clinical manifestations, and pathological changes of X-ray images from 400 KBD and 400 non-KBD adolescents. With such features, we performed four classification methods, i.e., random forest algorithms (RFA), artificial neural networks (ANNs), support vector machines (SVMs) and linear regression (LR) with four feature selection methods, i.e., RFA, minimum redundancy maximum relevance (mRMR), support vector machine recursive feature elimination (SVM—RFE) and Relief. The performance of diagnosis of KBD with respect to different classification models were evaluated by sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC).ResultsOur results demonstrated that the 10 out of 26 discriminative features were displayed more powerful performance, regardless of the chosen of classification models and feature selection methods. These ten discriminative features were distal end of phalanges alterations, metaphysis alterations and carpals alterations and clinical manifestations of ankle joint movement limitation, enlarged finger joints, flexion of the distal part of fingers, elbow joint movement limitation, squatting limitation, deformed finger joints, wrist joint movement limitation.ConclusionsThe selected ten discriminative features could provide a fast, effective diagnostic standard for KBD adolescents.

Objective To explore the effect of peer support on exercise self-efficacy, physical activity level, and neuropathic symptoms in patients with diabetes-related peripheral neuropathy (DPN). Methods A total of 60 adults with DPN were assigned to groups. Patients in the control group received routine education (RE), while patients in the intervention group received peer support (PS) combined with routine diabetes education. Data were collected at baseline and after intervention (12 weeks). Results At 12 weeks, better outcomes were found in the PS group compared to the RE group for the following aspects: exercise self-efficacy, steps, total physical activity, fasting blood glucose and 2-hour postprandial blood glucose. Decreases in scores on Toronto Clinical Scoring System occurred between baseline and post-intervention in both groups. Conclusion Peer support is an effective way to improve exercise self-efficacy, number of steps, and general physical activity and to reduce blood glucose for patients with DPN. But the effects of peer support on neuropathic symptoms is are obvious. Further research is needed. Practice implications As a low-cost, effective education approach, peer support strategies should be integrated into our healthcare system to meet the minimum needs of patients with DPN.

Recent development of various spatially resolved transcriptomic techniques has enabled gene expression profiling on complex tissues with spatial localization information. Identifying genes that display spatial expression pattern in these studies is an important first step towards characterizing the spatial transcriptomic landscape. Detecting spatially expressed genes requires the development of statistical methods that can properly model spatial count data, provide effective type I error control, have sufficient statistical power, and are computationally efficient. Here, we developed such a method, SPARK. SPARK directly models count data generated from various spatial resolved transcriptomic techniques through generalized linear spatial models. With a new efficient penalized quasi-likelihood based algorithm, SPARK is scalable to data sets with tens of thousands of genes measured on tens of thousands of samples. Importantly, SPARK relies on newly developed statistical formulas for hypothesis testing, producing well-calibrated p-values and yielding high statistical power. We illustrate the benefits of SPARK through extensive simulations and in-depth analysis of four published spatially resolved transcriptomic data sets. In the real data applications, SPARK is up to ten times more powerful than existing approaches. The high power of SPARK allows us to identify new genes and pathways that reveal new biology in the data that otherwise cannot be revealed by existing approaches.

// Chuang Chen 1, 2, * , Hui Zhao 3, * , Xu Fu 4, * , LuoShun Huang 4 , Min Tang 5 , XiaoPeng Yan 4 , ShiQuan Sun 4 , WenJun Jia 4 , Liang Mao 4 , Jiong Shi 6 , Jun Chen 6 , Jian He 5 , Jin Zhu 7, 8 , YuDong Qiu 1, 4 1 Department of Hepatopancreatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, Jiangsu, China 2 Department of Hepatopancreatobiliary Surgery, Huai'an Hospital Affiliated to Xuzhou Medical University, Second People's Hospital of Huai'an City, Huai'an 223002, Jiangsu, China 3 Department of Hepatopancreatobiliary Surgery, Nanjing Medical University Affiliated Wuxi Second Hospital, Wuxi 214001, Jiangsu, China 4 Department of Hepatopancreatobiliary Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, Jiangsu, China 5 Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, Jiangsu, China 6 Department of Pathology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, Jiangsu, China 7 Key Laboratory of Antibody Technique of Ministry of Health, Nanjing Medical University, Nanjing 210029, Jiangsu, China 8 Huadong medical Institute of Biotechniques, Nanjing 210029, Jiangsu, China * These authors contributed equally to this work Correspondence to: Jin Zhu, email: zhujin1968@njmu.edu.cn YuDong Qiu, email: yudongqiu510@163.com Keywords: hepatocellular carcinoma, CE-CT, EOB-MRI, gross classification, imaging Received: October 04, 2016 Accepted: February 12, 2017 Published: February 24, 2017 ABSTRACT Accurate gross classification through imaging is critical for determination of hepatocellular carcinoma (HCC) patient prognoses and treatment strategies. The present retrospective study evaluated the utility of contrast-enhanced computed tomography (CE-CT) combined with gadolinium-ethoxybenzyl diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (EOB-MRI) for diagnosis and classification of HCCs prior to surgery. Ninety-four surgically resected HCC nodules were classified as simple nodular (SN), SN with extranodular growth (SN-EG), confluent multinodular (CMN), or infiltrative (IF) types. SN-EG, CMN and IF samples were grouped as non-SN. The abilities of the two imaging modalities to differentiate non-SN from SN HCCs were assessed using the EOB-MRI hepatobiliary phase and CE-CT arterial, portal, and equilibrium phases. Areas under the ROC curves for non-SN diagnoses were 0.765 (95% confidence interval [CI]: 0.666–0.846) for CE-CT, 0.877 (95% CI: 0.793–0.936) for EOB-MRI, and 0.908 (95% CI: 0.830–0.958) for CE-CT plus EOB-MRI. Sensitivities, specificities, and accuracies with respect to identification of non-SN tumors of all sizes were 71.4%, 81.6%, and 75.5% for CE-CT; 96.4%, 78.9%, and 89.3% for EOB-MRI; and 98.2%, 84.2%, and 92.5% for CE-CT plus EOB-MRI. These results show that CE-CT combined with EOB-MRI offers a more accurate imaging evaluation for HCC gross classification than either modality alone.

Background: Despite advances in surgical techniques and peri-operative management, post-operative infectious complications still are common after perihilar cholangiocarcinoma (PHCC). This study inv...

Single-cell RNA sequencing (scRNAseq) data always involves various unwanted variables, which would be able to mask the true signal to identify cell-types. More efficient way of dealing with this issue is to extract low dimension information from high dimensional gene expression data to represent cell-type structure. In the past two years, several powerful matrix factorization tools were developed for scRNAseq data, such as NMF, ZIFA, pCMF and ZINB-WaVE. But the existing approaches either are unable to directly model the raw count of scRNAseq data or are really time-consuming when handling a large number of cells (e.g. n>500). In this paper, we developed a fast and efficient count-based matrix factorization method (single-cell negative binomial matrix factorization, scNBMF) based on the TensorFlow framework to infer the low dimensional structure of cell types. To make our method scalable, we conducted a series of experiments on three public scRNAseq data sets, brain, embryonic stem, and pancreatic islet. The experimental results show that scNBMF is more powerful to detect cell types and 10 - 100 folds faster than the scRNAseq bespoke tools. In this paper, we proposed a fast and efficient count-based matrix factorization method, scNBMF, which is more powerful for detecting cell type purposes. A series of experiments were performed on three public scRNAseq data sets. The results show that scNBMF is a more powerful tool in large-scale scRNAseq data analysis. scNBMF was implemented in R and Python, and the source code are freely available at https://github.com/sqsun .

To fulfil the requirement of zero liquid discharge in the coal gasification wastewater treatment plant, evaporation and crystallization are usually adopted to treat reverse osmosis concentrate (ROC). However, their efficacies and the quality of their products are greatly affected by the organic matter concentration in coal gasification-ROC (CG-ROC). In the present study, ozonation with a commercial activated carbon (AC) as catalyst was conducted to further remove the organic matter in the concentrate originated from a three-stage RO after coagulation pretreatment. The kinetic model calculations were used for the first time to quantitatively evaluate the effect of matrix components on the performance of ozonation of CG-ROC at initial pH 9 and 11. The TOC removal difference among ozonation alone, ozonation alone followed by AC adsorption, and AC catalytic ozonation indicates that AC has a certain catalytic effect during ozonation of CG-ROC. The ΔO3/ΔTOC can be significantly reduced from 43.2 mg O3/mg TOC of ozonation alone to 16.2 mg O3/mg TOC of AC catalytic ozonation under the goal of achieving a similar TOC removal. The change of initial pH has little effect on the TOC removal during ozonation with AC, however, it has a remarkable effect on the TOC removal during ozonation alone. According to the results obtained in kinetic model calculations, high concentration of CO3•- would be generated from the reaction between HO• and CO32− with a high concentration in solution, leads to the delay on the TOC removal in the early reaction stage during ozonation alone at initial pH 11. The results of A254 and molecular weight (MW) distribution indicate that ozonation with AC is more efficient in removing the low MW intermediates absorption at 254 nm compared to that of ozonation alone. Ozonation with AC is more suitable for CG-ROC treatment since it is more energy saving, more efficient in organic matter removal, and more tolerated pH change and wastewater matrix compared to ozonation alone.

The core of computational identification of transcription factor binding sites (TFBSs) is to deal with high dimensional and small sample size data and to handle the complex nonlinear relationships between features. Partial least squares (PLS) performs well in reducing dimensionality as well as explaining relations between multiple variables. Besides, kernel methods are widely applied to non-linear relationship process. Therefore, we reasonably introduce kernel partial least squares(KPLS) as a feature selection method for TFBS identification. Moreover, to lower the instability caused by the choice of kernel functions in conventional kernel-based methods, we combine multiple kernel methods with KPLS to develop a new method named multiple kernel PLS (MKPLS) to perform feature selection. PSO is utilized to estimate the parameters of linear combination of multiple kernels, furthermore, SVM is applied here to build an identification framework. 52 Escherichia coli k-12 TFBS datasets are used to test MKPLS as well as KPLS and Mutual Information(MI). Results demonstrate that MKPLS acquires a better ability to pick out the key features and can obtain a noticeable identification accuracy.

Background Identifying transcription factors binding sites (TFBSs) plays an important role in understanding gene regulatory processes. The underlying mechanism of the specific binding for transcription factors (TFs) is still poorly understood. Previous machine learning-based approaches to identifying TFBSs commonly map a known TFBS to a one-dimensional vector using its physicochemical properties. However, when the dimension-sample rate is large (i.e., number of dimensions/number of samples), concatenating different physicochemical properties to a one-dimensional vector not only is likely to lose some structural information, but also poses significant challenges to recognition methods. Materials and method In this paper, we introduce a purely geometric representation method, tensor (also called multidimensional array), to represent TFs using their physicochemical properties. Accompanying the multidimensional array representation, we also develop a tensor-based recognition method, tensor partial least squares classifier (abbreviated as TPLSC). Intuitively, multidimensional arrays enable borrowing more information than one-dimensional arrays. The performance of each method is evaluated by average F -measure on 51 Escherichia coli TFs from RegulonDB database. Results In our first experiment, the results show that multiple nucleotide properties can obtain more power than dinucleotide properties. In the second experiment, the results demonstrate that our method can gain increased prediction power, roughly 33% improvements more than the best result from existing methods. Conclusion The representation method for TFs is an important step in TFBSs recognition. We illustrate the benefits of this representation on real data application via a series of experiments. This method can gain further insights into the mechanism of TF binding and be of great use for metabolic engineering applications.

Time series analysis, an important domain of machine learning and data mining, has attracted much research interest in the previous decade. Part of the research efforts have focused on analyzing stock data, which is a major application of time series analysis. However stock data is always considered as a unidimensional sequence in recent research lacking of the whole market perspective. This paper regards the stock data as a multi-dimensional time series and proposes a novel method of stock trend analysis based on feature reduction method. By taking advantage of the partial least squares, we rank the dimensions of stock data firstly. Then a feature selection algorithm based on the dimension rank is developed referring to the wrapper algorithm. Finally the algorithm is utilized to analyze the stock trend in both single stock and the whole market perspectives on the real stock data. Experimental results show the potential of the feature reduction based trend analysis method of multi-dimensional series, which performs better to the unidimensional-based methods and obtains the global information from the whole market view.

Gene selection prior to classification has been an important topic in bioinformatics, since last decade. Small sample size and high dimensionality in microarray data pose great challenges for performing efficient classification. In this paper we propose efficient hybrid method (GT-kernelPLS) with a combination of wrapper like technique coalitional game theory and kernel partial least square (kernelPLS) filter method. Experimental results on ten microarray data sets ensure that GT-kernelPLS achieve higher accuracy than several state of the art feature selection methods, and it exhibits a reasonable execution time, even for the data sets having more than twenty thousand genes.

Alternative splicing (AS) is a mechanism for generating different gene transcripts (called isoforms) from the same genomic sequence. Accurate classification of alternative splicing is important for understanding the mechanism of gene regulation. Although the different algorithms are applied to classify AS, the accuracy of these algorithms are still unsatisfactory. In this paper, we propose a new classification algorithm to classify the cassette exons and constitutive exons with existed and new features based on the AdaBoost algorithm. Experimental results show that the accuracy is higher than current algorithms. The sensitivity is 81.92% and the specificity is 74.06%, namely that 81.92% of cassette exons and 74.06% of constitutive exons were correctly classified.

High-dimensional data analysis and its great chances of overfitting result in great challenges for constructing efficient models in practical applications. To overcome these problems swarm intelligence algorithms can be utilized. However, the balance between global and local search throughout the course of a run is critical to the success of an intelligence optimization algorithm. Moreover, almost all the available algorithms are still having issues like premature convergence to local optimum and slow convergence rate, especially in high-dimensional space. As motivated above, a new hybrid optimization algorithm integrating particle swarm optimization(PSO) with gravitational search algorithm(GSA) is presented (denoted as PSOGSA). Based on the analysis of the compensatory advantages of the PSO and the GSA, in this paper, we integrate the ability of exploitation in PSO with the ability of exploration in GSA to update velocity equations. To update position equations a mobility factor is used which is guided by diversity of population to improve the final accuracy and the convergence speed of the PSOGSA. We also apply proposed algorithm to the cluster analysis of microarray data. Experiments are conducted on six benchmark test functions, four artificial data sets and three microarray data sets, and the results demonstrate that the proposed algorithm possess better robustness.