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With the rapid growth of information technology and sports, a large amount of sports social network data has emerged. Sports social network data contains rich entity information about athletes, coaches, sports teams, football, basketball, and other sports. Understanding the interaction among these entities is meaningful and challenging. To this end, we first introduce the background of sports social networks. Secondly, we review and categorize the recent research efforts in sports social networks and sports social network analysis based on passing networks, from the centrality and its variants to entropy, and several other metrics. Thirdly, we present and compare different sports social network models that have been used for sports social network analysis, modeling, and prediction. Finally, we present promising research directions in the rapidly growing field, including mining the genes of sports team success with multiview learning, evaluating the impact of sports team collaboration with motif-based graph networks, finding the best collaborative partners in a sports team with attention-aware graph networks, and finding the rising star for a sports team with attribute-based convolutional neural networks. This paper aims to provide the researchers with a broader understanding of the sports social networks, especially valuable as a concise introduction for budding researchers interested in this field.

PURPOSE: To compare the performance of Barrett toric calculator incorporated with measured posterior corneal astigmatism (PCA) derived from IOL Master 700 and Pentacam HR versus predicted PCA. METHODS: The predicted residual astigmatism using Barrette toric IOL calculator with predicted PCA, measured PCA from IOL Master 700 and measured PCA from Pentacam were calculated with the preoperative keratometry and intended IOL axis with modification. The vector analysis was performed to calculate the mean absolute prediction error (MAE), the centroid of the prediction error and the percentage of eyes with a prediction error within ±0.50 D, ± 0.75 D, and ± 1.00 D. RESULTS: In 57 eyes of 57 patients with mean age of 70.42±10.75 years, the MAE among the three calculation methods were 0.59±0.38 D (Predicted PCA) , 0.60±0.38 D (Measured PCA from IOL Master 700) and 0.60±0.36 D (Measured PCA from Pentacam) with no significant difference, either in the whole sample, the WTR eyes and the ATR eyes (F = 0.078, 0.306 and 0.083, p=0.925, 0.739 and 0.920, respectively). Measured PCA obtained from IOL Master 700 resulted in one level reduction (from Tn to Tn-1) in 49.12% eyes in cylindrical model selection, while measured PCA obtained from Pentacam resulted in one level reduction of toric model selection in 18.18% eyes. CONCLUSION: The present study suggested that the incorporation of measured PCA values derived from IOL Master 700 and Pentacam produce comparable clinical outcome with the predicted PCA mode in Barrette toric calculator.

The failure of obtaining employment could lead to serious psychosocial outcomes such as depression and substance abuse, especially for college students who may be less cognitively and emotionally mature. In addition to academic performance, employers’ unconscious biases are a potential obstacle to graduating students in becoming employed. Thus, it is necessary to understand the nature of such unconscious biases to assist students at an early stage with personalized intervention. In this paper, we analyze the existing bias in college graduate employment through a large-scale education dataset and develop a framework called SUMMER (bia S -aware grad U ate e M ploy ME nt p R ediction) to predict students’ employment status and employment preference while considering biases. The framework consists of four major components. Firstly, we resolve the heterogeneity of student courses by embedding academic performance into a unified space. Next, we apply a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to overcome the label imbalance problem of employment data. Thirdly, we adopt a temporal convolutional network to comprehensively capture sequential information of academic performance across semesters. Finally, we design a bias-based regularization to smooth the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework.

Knowledge Graphs (KGs) which can encode structural relations connecting two objects with one or multiple related attributes have become an increasingly popular research direction. Given the superiority of deep learning in representing complex data in continuous space, it is handy to represent KGs data, thus promoting KGs construction, representation, and application. This survey article provides a comprehensive overview of deep learning technologies and KGs by exploring research topics from diverse phases of the KGs lifecycle, such as construction, representation, and knowledge-aware application. We propose new taxonomies on these research topics for motivating cross-understanding between deep learning and KGs. Based on the above three phases, we classify the different tasks of KGs and task-related methods. Afterwards, we explain the principles of combing deep learning in various KGs steps like KGs embedding. We further discuss the contribution and advantages of deep learning applied to the different application scenarios. Finally, we summarize some critical challenges and open issues deep learning approaches face in KGs.

Citation network analysis attracts increasing attention from disciplines of complex network analysis and science of science. One big challenge in this regard is that there are unreasonable citations in citation networks, i.e., cited papers are not relevant to the citing paper. Existing research on citation analysis has primarily concentrated on the contents and ignored the complex relations between academic entities. In this paper, we propose a novel research topic, that is, how to detect anomalous citations. To be specific, we first define anomalous citations and propose a unified framework, named ACTION, to detect anomalous citations in a heterogeneous academic network. ACTION is established based on non-negative matrix factorization and network representation learning, which considers not only the relevance of citation contents but also the relationships among academic entities including journals, papers, and authors. To evaluate the performance of ACTION, we construct two anomalous citation datasets. Experimental results demonstrate the effectiveness of the proposed method. Detecting anomalous citations carry profound significance for academic fairness.

With the rapid growth of information technology and sports, analyzing sports information has become an increasingly challenging issue. Sports big data come from the Internet and show a rapid growth trend. Sports big data contain rich information such as athletes, coaches, athletics, and swimming. Nowadays, various sports data can be easily accessed, and amazing data analysis technologies have been developed, which enable us to further explore the value behind these data. In this paper, we first introduce the background of sports big data. Secondly, we review sports big data management such as sports big data acquisition, sports big data labeling, and improvement of existing data. Thirdly, we show sports data analysis methods, including statistical analysis, sports social network analysis, and sports big data analysis service platform. Furthermore, we describe the sports big data applications such as evaluation and prediction. Finally, we investigate representative research issues in sports big data areas, including predicting the athletes’ performance in the knowledge graph, finding a rising star of sports, unified sports big data platform, open sports big data, and privacy protections. This paper should help the researchers obtaining a broader understanding of sports big data and provide some potential research directions.

Ovarian cancer is one of the most common gynecological diseases with high mortality rates. Previous studies have shown that microRNA (miR)-638 is associated with tumorigenesis. The present study aimed to assess the role and underlying mechanisms of miR-638 in ovarian cancer. miR-638 expression was detected in ovarian cancer tissues and miR-638 was overexpressed or knocked down in ovarian cancer OVCAR-3 and Caov-3 cells. The clinical results revealed that miR-638 expression was downregulated in ovarian cancer tissues compared with in adjacent normal tissues. miR-638 expression was also found to be relatively low in OVCAR-3 cells whilst being relatively high in Caov-3 cells among the five ovarian cancer cell lines tested. miR-638 overexpression inhibited cell viability, arrested the cell cycle at the G1 phase and promoted apoptosis in OVCAR-3 cells. By contrast, miR-638 knockdown increased Caov-3 cell viability, facilitated cell cycle progression and inhibited apoptosis. miR-638 reduced the expression of high mobility group A1 (HMGA1) by directly targeting its 3' untranslated region. HMGA1 overexpression reversed the inhibition of proliferation induced by miR-638 overexpression in OVCAR-3 cells. These results suggest that miR-638 may serve to be a suppressor of ovarian cancer by regulating HMGA1, which may provide a potential therapeutic target for ovarian cancer.

This paper investigates the impact of institutes and papers over time based on the heterogeneous institution-citation network. A new model, IPRank, is introduced to measure the impact of institution and paper simultaneously. This model utilises the heterogeneous structural measure method to unveil the impact of institution and paper, reflecting the effects of citation, institution, and structural measure. To evaluate the performance, the model first constructs a heterogeneous institution-citation network based on the American Physical Society (APS) dataset. Subsequently, PageRank is used to quantify the impact of institution and paper. Finally, impacts of same institution are merged, and the ranking of institutions and papers is calculated. Experimental results show that the IPRank model better identifies universities that host Nobel Prize laureates, demonstrating that the proposed technique well reflects impactful research. Refereed/Peer-reviewed

The peri-urban rivers are one of the critical interfaces between urban-rural symbiotic ecosystems and appear to be a reservoir of antibiotic resistance genes (ARGs) in the environment. To prevent the transmission risks of ARGs between peri-urban river and human, it is essential to explore the prevalence and source of ARGs in the environment for designing potential mitigation strategies. In this study, we focused on the characterization and source-tracking of ARGs in the sediments of a typical peri-urban river in Beijing, Chaobai River. Twenty-seven ARGs frequently reported in the environment, and two integrons (intI1 and intI2) were detected using high-throughput quantitative PCR. The profile of bacterial community was determined by performing 16S rRNA gene sequencing. Meanwhile, crAssphage, a novel recently-discovered DNA bacteriophage, was employed for tracking the contribution of human fecal pollution to the prevalence of ARGs. Results showed that the targeted ARGs were detected widely in the sediments of Chaobai River. Relatively, the abundances of ARGs in downstream were higher than those in the upstream, likely suggesting a gradient impact of anthropogenic activities along the river. Remarkably, the int1 gene was correlated significantly with most of the ARGs and might be the key factor influencing the shaping of ARGs in the river sediments. However, no significant correlations were observed between the ARGs and selective pressure factors, including antibiotics and metals. Of the identified 1039 genera, Escherichia-Shigella, Bacteroides, Arcobacter, Dechloromonas and Pseudomonas were the top most abundant organisms. Microbial source tracking based on the crAssphage annotation suggested that human sewage might be one of the potential sources of resistance bacteria in the river sediments. The study can advance our knowledge about ARGs in the peri-urban river and provides a management reference for ARG pollution control.

The over-use of antibiotics causes growing concerns about human health risks induced by increasing rates of antimicrobial resistance. Riverine systems are considered generally as a natural reservoir of antibiotic resistance genes (ARGs). In this study, several methods including high-throughput sequencing-based metagenomics approach, statistical analysis and network analysis were applied jointly to characterize the wide-spectrum profile of ARGs in the sediments of an urban river in Beijing. Furthermore, contribution of human activities for the presence of ARGs was identified through comparative studies on the metagenomic profiling of ARGs between the river sediments and pristine niches (remote Antarctic soils and deep sea sediments). In total, 442 ARG subtypes belonging to 22 ARG types were detected in the human-impacted river sediments with an abundance range of 1.1 × 10−1–8.1 × 10−1 copy of ARG per copy of 16S-rRNA gene. The most abundant and diverse ARGs were commonly associated with antibiotics that have been extensively used in that area, likely indicating the spread of ARGs in river environments because of the selective pressure resulting from antibiotic use. As a whole, anthropogenic activities were the dominant contributor of major ARG types, for example, occupying 100% for sulfonamide-ARGs, 97% for beta-lactam-ARGs, 94% for aminoglycoside-ARGs and 64% for tetracycline-ARGs. This study provides insights into the role of human activities in accelerating the dissemination and proliferation of ARGs in urban river environment and draws attention to controlling the use and discharge of antibiotics for protection of public health.

Author impact evaluation and prediction play a key role in determining rewards, funding, and promotion. In this paper, we first introduce the background of the author impact evaluation and prediction. Then, we review the recent developments of the author impact evaluation, including data collection, data preprocessing, data analysis, feature selection, algorithm design, and algorithm evaluation. Third, we provide an in-depth literature review on the author impact predictive models and the common evaluation metrics. Finally, we look into the representative research issues, including author impact inflation, unified evaluation standards, academic success gene, identification of the origins of hot streaks, and higher-order academic networks analysis. This paper should help the researchers obtain a broader understanding of the author impact evaluation and prediction and provides future research directions. Refereed/Peer-reviewed

Scholarly impact assessment has always been a hot issue. It has played an important role in evaluating researchers, scientific papers, scientific teams, and institutions within science of science. Scholarly impact assessment is also used to address fundamental issues, such as reward evaluation, funding allocation, promotion and recruitment decision. Scholars generally agree that it is more reasonable to use weighted citations to assess the scholarly impact. Although a great number of researchers use weighted citations to access the scholarly impact, there is a lack of a systematic summary of citation weighting methods. To fill the gap, this paper summarizes the existing classical indicators and weighting methods used in measuring scholarly impact from the perspectives of articles, authors and journals. We also summarize the focus of the indicators involved in this paper and the weighting factors that involved in the weighting methods. Finally, we discuss the open issues to try to discover the hidden trends of citation weighting. Through this paper, we can not only have a clearer understanding of the weighting methods in the scholarly impact assessment, but also think more deeply about the weighting factors to be explored. Refereed/Peer-reviewed

Educational big data is becoming a strategic educational asset, exceptionally significant in advancing educational reform. The term educational big data stems from the rapidly growing educational data development, including students' inherent attributes, learning behavior, and psychological state. Educational big data has many applications that can be used for educational administration, teaching innovation, and research management. The representative examples of such applications are student academic performance prediction, employment recommendation, and financial support for low-income students. Different empirical studies have shown that it is possible to predict student performance in the courses during the next term. Predictive research for the higher education stage has become an attractive area of study since it allowed us to predict student behavior. In this survey, we will review predictive research, its applications, and its challenges. We first introduce the significance and background of educational big data. Second, we review the students' academic performance prediction research, such as factors influencing students' academic performance, predicting models, evaluating indices. Third, we introduce the applications of educational big data such as prediction, recommendation, and evaluation. Finally, we investigate challenging research issues in this area. This discussion aims to provide a comprehensive overview of educational big data.

An academic conference is not only a venue for publishing papers but also a nursery room for new scientific encounters. While previous research has investigated scientific collaboration mechanisms based on the triadic closure and focal closure, in this paper, we propose a new collaboration mechanism named conference closure. Conference closure means that scholars involved in a common conference may collaborate with each other in the future. We analyze the extent to which scholars will meet new collaborators from both the individual and community levels by using 22 conferences in the field of data mining extracted from DBLP digital library. Our results demonstrate the existence of conference closure and this phenomenon is more remarkable in conferences with high field rating and large scale attendees. Scholars involved in multiple conferences will encounter more collaborators from the conferences. Another interesting finding is that although most conference attendees are junior scholars with few publications, senior scholars with fruitful publications may gain more collaborations during the conference. Meanwhile, the conference closure still holds if we control the productivity homophily. Our study will shed light on evaluating the impact of a conference from the social function perspective based on the index of conference closure.

OAPA Quantifying the impact of scientific papers objectively is crucial for research output assessment, which subsequently affects institution and country rankings, research funding allocations, academic recruitment and national/international scientific priorities. While most of the assessment schemes based on publication citations may potentially be manipulated through negative citations, in this study, we explore Conflict of Interest (COI) relationships and discover negative citations and subsequently weaken the associated citation strength. PANDORA (Positive And Negative COI- Distinguished Objective Rank Algorithm) has been developed, which captures the positive and negative COI, together with the positive and negative suspected COI relationships. In order to alleviate the influence caused by negative COI relationship, collaboration times, collaboration time span, citation times and citation time span are employed to determine the citing strength; while for positive COI relationship, we regard it as normal citation relationship. Furthermore, we calculate the impact of scholarly papers by PageRank and HITS algorithms, based on a credit allocation algorithm which is utilized to assess the impact of institutions fairly and objectively. Experiments are conducted on the publication dataset from American Physical Society (APS) dataset, and the results demonstrate that our method significantly outperforms the current solutions in Recommendation Intensity of list R at top-K and Spearman & #x2019;s rank correlation coefficient at top-K. Refereed/Peer-reviewed

Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions. Refereed/Peer-reviewed

The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide. In addition to academic performance, unconscious biases can become one key obstacle for hunting jobs for graduating students. Thus, it is necessary to understand these unconscious biases so that we can help these students at an early stage with more personalized intervention. In this paper, we develop a framework, i.e., MAYA (Multi-mAjor emploYment stAtus) to predict students' employment status while considering biases. The framework consists of four major components. Firstly, we solve the heterogeneity of student courses by embedding academic performance into a unified space. Then, we apply a generative adversarial network (GAN) to overcome the class imbalance problem. Thirdly, we adopt Long Short-Term Memory (LSTM) with a novel dropout mechanism to comprehensively capture sequential information among semesters. Finally, we design a bias-based regularization to capture the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework.

River is considered generally as a natural reservoir of antibiotic resistance genes (ARGs) in environments. For the prevention and control of ARG risks, it is critical to comprehensively characterize the antibiotic resistomes and their associations in riverine systems. In this study, we proposed a metagenomic framework for identifying antibiotic resistomes in river sediments from multiple categories, including ARG potential, ARG hosts, pathogenicity potential, co-selection potential and gene transfer potential, and applied it to understand the presence, hosts, and co-occurrence of ARGs in the sediments of an urban river in Beijing. Results showed that a total of 203 ARG subtypes belonging to 21 ARG types were detected in the river sediments with an abundance range of 107.7–1004.1×/Gb, dominated by multidrug, macrolide-lincosamide-streptogramin, bacitracin, quinolone and sulfonamide resistance genes. Host-tracking analysis identified Dechloromonas, Pseudoxanthomonas, Arenimonas, Lysobacter and Pseudomonas as the major hosts of ARGs. A number of ARG-carrying contigs (ACCs) were annotated as fragments of pathogenic bacteria and carried multiple multidrug-ARGs. In addition, various biocide/metal resistance genes (B/MRGs) and mobile genetic elements (MGEs), including prophages, plasmids, integrons and transposons, were detected in the river sediments. More importantly, the co-occurrence analysis via ACCs showed a strong association of ARGs with B/MRGs and MGEs, indicating high potential of co-selection and active horizontal transmission for ARGs in the river environment, likely driven by the frequent impact of anthropogenic activities in that area.

The first question to make a virtual reality system is to create a virtual scene. This virtual scene should be made up with factors which including three-dimensional (3d) model, 3d animation, video, sound, etc. Because people's vision is the most sensitive, the 3d model for the spread of information is the largest, and creating a realistic scenario model and a real-time dynamic display effect for virtual reality system is very important. Unity 3d is a development tool of which often used in the virtual reality system, but the scene model making function and 3d animation production ability are not good, 3DS MAX is powerful and very popular 3d modeling software. It is suitable to make 3d model for virtual reality system. This paper mainly discusses the problems of 3DS MAX in the use of virtual reality modeling.

Virtual reality eventually display need to use the camera, and the camera is an essential element in virtual reality scene, through the camera to present 3d scene. This paper mainly discusses the use of the camera in the virtual reality software Unity 3d.

Innovation diffusion is the process of adopting novel things, which is deemed as the diffusion of human behaviors. Previous studies mainly posit an unclear distinction with information diffusion. However, there exist critical differences in both social and economic areas. Inspired by this observation, we propose a practical method for innovation diffusion based on the process of behavior diffusion, which integrates threshold model with social imitation strategy. The primary goal of our proposed model is to combine individuals' economic as well as social characteristics to study innovation diffusion. The interplay of these two strategies is studied in the context of a coordination game. According to the simulation results on Erdös-Rényi random networks and small-world networks, we find that the conjunction of these two approaches can greatly improve the efficiency of innovation diffusion. In addition, we further study the impacts of varied initial seeds' selection strategies on the overall performance of innovation diffusion using our proposed model.

Recommender systems are becoming increasingly important and prevalent because of the ability of solving information overload. In recent years, researchers are paying increasing attention to aggregate diversity as a key metric beyond accuracy, because improving aggregate recommendation diversity may increase long tails and sales diversity. Trust is often used to improve recommendation accuracy. However, how to utilize trust to improve aggregate recommendation diversity is unexplored. In this paper, we focus on solving this problem and propose a novel trust-aware recommendation method by incorporating time factor into similarity computation. The rationale underlying the proposed method is that, trustees with later creation time of trust relation can bring more diverse items to recommend to their trustors than other trustees with earlier creation time of trust relation. Through relevant experiments on publicly available dataset, we demonstrate that the proposed method outperforms the baseline method in terms of aggregate diversity while maintaining almost the same recall.

Citation recommendation is an interesting and significant research area as it solves the information overload in academia by automatically suggesting relevant references for a research paper. Recently, with the rapid proliferation of information technology, research papers are rapidly published in various conferences and journals. This makes citation recommendation a highly important and challenging discipline. In this paper, we propose a novel citation recommendation method that uses only easily obtained citation relations as source data. The rationale underlying this method is that, if two citing papers are significantly co-occurring with the same citing paper(s), they should be similar to some extent. Based on the above rationale, an association mining technique is employed to obtain the paper representation of each citing paper from the citation context. Then, these paper representations are pairwise compared to compute similarities between the citing papers for collaborative filtering. We evaluate our proposed method through two relevant real-world data sets. Our experimental results demonstrate that the proposed method significantly outperforms the baseline method in terms of precision, recall, and F1, as well as mean average precision and mean reciprocal rank, which are metrics related to the rank information in the recommendation list.

A novel poly (trimethylol propane triacrylate-co-ethylene dimethacrylate) [poly (TMPTA-co-EDMA)] monolith was prepared by in situ free-radical polymerization in a 50 mm × 4.6mm i.d. stainless steel column and was investigated for high performance liquid chromatography (HPLC). The porous structure of monolith was optimized by changing the conditions of polymerization. The chemical group of the monolithic column was confirmed by a Fourier transform infrared spectroscopy (FT-IR) method and the morphology of column structure was characterized by scanning electron microscopy (SEM). The mechanical strength and permeability were also studied. Finally, a series of low-molecular-weight organic compounds were utilized to evaluate the retention behaviors of the monolithic column. The result demonstrated that the prepared column exhibited an RP-chromatographic behavior and good separation performance. The method reproducibility was obtained by evaluating the run-to-run and column-to-column with relative standard deviations (RSDs) less than 0.7% (n=6) and 2.9% (n=6), respectively, which indicated that prepared monolithic columns had good reproducibility and stability.

Researchers use various skills in their work, such as writing, data analyzing and experiments design. These research skills have greatly influenced quality of their research outputs, as well as their scientific impact. Although there are many indicators having been proposed to quantify the impact of researchers, studies of evaluating their scientific research skills are very rare. In this paper, we analyze the factors affecting researchers' skill ranking and propose a new model based on hypergraph theory to evaluate the scientific research skills. To validate our skill ranking model, we perform experiments on PLoS One dataset and compare the rank of researchers' skills with their papers citation counts and h-index. Finally, we analyze the patterns about how researchers' skill ranking increased over time. Our studies also show the change patterns of researchers between different skills.

A porous monolith was prepared by in situ free-radical polymerization using N-isopropylacrylamide (NIPAAm) and trimethylol propane triacrylate (TMPTA) as functional monomers, ethylene dimethacrylate (EDMA) as crosslinking agent. The chemical group of the monolith was assayed by a Fourier transform infrared spectroscopy (FT-IR) method and the morphology of optimized monolithic column was characterized by scanning electron microscopy (SEM). The mechanical strength and permeability have been studied in detail as well. The run-to-run and column-to-column reproducibility of the retention times were less than 0.9% and 3.0%, respectively. Furthermore, the influence of temperature and mobile phase composition on the separation of aromatic compounds was investigated. The results indicated that poly (trimethylol propane triacrylate-co-N-isopropylacrylamide-co-ethylenedimethacrylate) (TMPTA-co-NIPAAm-co-EDMA) monolithic column not only had high porosity and strong rigidity, but also was a promising tool for analyzing small molecule compounds with a short analysis time by controlling the column temperature.

This paper proposes a novel method named ScholarRank to evaluate the scientific impact of rising stars. Our proposed ScholarRank integrates the merits of both statistical indicators and influence calculation algorithms in heterogeneous academic networks. The ScholarRank method considers three factors, which are the citation counts of authors, the mutual influence among coauthors and the mutual reinforce process among different entities in heterogeneous academic networks. Through experiments on real datasets, we demonstrate that our ScholarRank can efficiently select more top ranking rising stars than other methods.

In mammals, xylose is found as the first sugar residue of the tetrasaccharide GlcAbeta1-3Galbeta1-3Galbeta1-4Xylbeta1-O-Ser, initiating the formation of the glycosaminoglycans heparin/heparan sulfate and chondroitin/dermatan sulfate. It is also found in the trisaccharide Xylalpha1-3Xylalpha1-3Glcbeta1-O-Ser on epidermal growth factor repeats of proteins, such as Notch. UDP-xylose synthase (UXS), which catalyzes the formation of the UDP-xylose substrate for the different xylosyltransferases through decarboxylation of UDP-glucuronic acid, resides in the endoplasmic reticulum and/or Golgi lumen. Since xylosylation takes place in these organelles, no obvious requirement exists for membrane transport of UDP-xylose. However, UDP-xylose transport across isolated Golgi membranes has been documented, and we recently succeeded with the cloning of a human UDP-xylose transporter (SLC25B4). Here we provide new evidence for a functional role of UDP-xylose transport by characterization of a new Chinese hamster ovary cell mutant, designated pgsI-208, that lacks UXS activity. The mutant fails to initiate glycosaminoglycan synthesis and is not capable of xylosylating Notch. Complementation was achieved by expression of a cytoplasmic variant of UXS, which proves the existence of a functional Golgi UDP-xylose transporter. A approximately 200 fold increase of UDP-glucuronic acid occurred in pgsI-208 cells, demonstrating a lack of UDP-xylose-mediated control of the cytoplasmically localized UDP-glucose dehydrogenase in the mutant. The data presented in this study suggest the bidirectional transport of UDP-xylose across endoplasmic reticulum/Golgi membranes and its role in controlling homeostasis of UDP-glucuronic acid and UDP-xylose production.

Evaluating the impact of an article is a significant topic and has attracted extensive attention. Citation-based assessment methods currently face a limitation, i.e. the anomalous citations patterns still remain poorly understand. To remedy this drawback, we propose a Positive and Negative Conflict of Interest (COI)-based Rank algorithm, named PNCOIRank, to acquire positive COI, negative COI, positive suspected COI and negative suspected COI relationships. We investigate the citation relationships by the following scholarly factors: citing times, the interval of citing time, collaboration times, the interval of collaboration time, and team of citing authors with the purpose of weakening the COI relationships in citation network. A weighted PageRank is finally constructed and employed, with HITS algorithm to assess the impact of articles. Through experiments on American Physical Society (APS) dataset, we show that PNCOIRank significantly outperforms the existing methods in terms of recommendation intensity.

Evaluating the scientific impact of scholars has been studied by researchers from various disciplines for a long time. However, very few efforts have been devoted to evaluate the future potential of researchers based on their performance at the initial stage of scientific careers. Academic rising stars represent junior researchers who may not be very outstanding among the peers at the initial stage of their careers, but tend to become influential scholars in the future. In this paper, we propose a novel method named CocaRank, which integrates our proposed new indicator called the collaboration caliber, the typical indicator citation counts and hybrid calculation results on heterogeneous academic networks, to find academic rising stars. In addition, we investigate the appropriate time interval for the prediction of rising stars. The experimental results on real datasets demonstrate that our method can find more top ranked rising stars with higher average citation counts than other state-of-art methods.

Trust as one of important social relations has attracted much attention from researchers in the field of social network-based recommender systems. In trust network-based recommender systems, there exist normally two roles for users, truster and trustee. Most of trust-based methods generally utilize explicit links between truster and trustee to find similar neighbors for recommendation. However, there possibly exist implicit correlations between users, especially for users with the same role (truster or trustee). In this paper, we propose a novel Collaborative Filtering method called CF-TC, which exploits Trust Context to discover implicit correlation between users with the same role for recommendation. In this method, each user is first represented by the same-role users who are co-occurring with the user. Then, similarities between users with the same role are measured based on obtained user representation. Finally, two variants of our method are proposed to fuse these computed similarities into traditional collaborative filtering for rating prediction. Using two publicly available real-world Epinions and Ciao datasets, we conduct comprehensive experiments to compare the performance of our proposed method with some existing benchmark methods. The results show that CF-TC outperforms other baseline methods in terms of RMSE, MAE, and recall.

To reveal the information hiding in the scholarly big data, relationship analysis among academic entities has been studied from different perspectives in recent years. In this paper, we focus on the problem of collaboration relationship prediction between authors in heterogeneous information networks, and a new method called MACP, i.e., Meta path and author Attribute based Collaboration Prediction model, is proposed to solve this problem. We use a two-phase collaboration probability learning approach. First, topological features with author attributes are extracted from the network, and then a supervised learning algorithm is employed to find the best weight associated with each feature to determine the collaboration relationship. We present the experiments on a real information network, namely the APS network, which shows that our proposed model can generate more accurate results compared with the method only considering structural features.

In the environmental event of hazardous release into river, quick and accurate identification of the contamination source is important for emergence response. Generally, given a noisy and finite set of monitoring information, determining the source items (i.e. location, strength and release time) is an ill-posed inverse problem. In this study, a Markov chain Monte Carlo method combined with advection-dispersion equation (ADE) was proposed for the source identification of contamination event in river system based on a Bayesian probabilistic inferential framework. Case study with analytical solution for one-dimensional ADE showed that the proposed methodology was effective and the mean posterior errors for all source parameters were lower than 3%. Case simulation based on two-dimensional ADE with numerical solution obtained similar results and further demonstrated the utility of the proposed approach for source identification. We hope the study will provide a helpful guidance to develop approach for contamination event source identification to support environmental risk management of river system.

Blue tides at the head of Tokyo Bay are a hydro-environmental phenomenon where seawater appears to be milky blue because of the reflection of the sunlight off surface water containing large amounts of sulphur particles. Its appearance is due to the coastal upwelling of bottom oxygen-depleted water induced by northeasterly wind-driven circulation. Blue tides cause the death of many shellfish and other aquatic animals across the head of Tokyo Bay and consequently result in substantial economic losses to coastal fisheries. This paper examines the occurrence of wind-driven blue tides in Tokyo Bay, based on a simplified hydrodynamic model and observational analysis. The model assumed a two-layer structure with a wind-driven upper layer and an oxygen-depleted lower layer. In this study, we derived a simple analytical solution to determine a critical wind condition for which the lower layer outcrops at the surface if the wind forcing is sufficiently strong, resulting in the mixing of the two layers and giving rise to blue tide. The results of sensitivity analyses of the analytical solution to all incorporated factors were found to be in accordance with a qualitative understanding of the blue tide phenomenon. More importantly, comparisons of observational data with real cases of blue tide during 1978–2016 and without blue tide during 2003–2016 suggested that this analytical solution was mostly valid. This study would be helpful for gaining a better understanding of the hydro-dynamical mechanism of blue tide.

A family of five beta1,3-galactosyltransferases has been characterized that catalyze the formation of Galbeta1,3GlcNAcbeta and Galbeta1,3GalNAcbeta linkages present in glycoproteins and glycolipids (beta3GalT1, -2, -3, -4, and -5). We now report a new member of the family (beta3GalT6), involved in glycosaminoglycan biosynthesis. The human and mouse genes were located on chromosomes 1p36.3 and 4E2, respectively, and homologs are found in Drosophila melanogaster and Caenorhabditis elegans. Unlike other members of the family, beta3GalT6 showed a broad mRNA expression pattern by Northern blot analysis. Although a high degree of homology across several subdomains exists among other members of the beta3-galactosyltransferase family, recombinant enzyme did not utilize glucosamine- or galactosamine-containing acceptors. Instead, the enzyme transferred galactose from UDP-galactose to acceptors containing a terminal beta-linked galactose residue. This product, Galbeta1,3Galbeta is found in the linkage region of heparan sulfate and chondroitin sulfate (GlcAbeta1,3Galbeta1,3Galbeta1,4Xylbeta-O-Ser), indicating that beta3GalT6 is the so-called galactosyltransferase II involved in glycosaminoglycan biosynthesis. Its identity was confirmed in vivo by siRNA-mediated inhibition of glycosaminoglycan synthesis in HeLa S3 cells. Its localization in the medial Golgi indicates that this is the major site for assembly of the linkage region.

We report the characterization of two Chinese hamster ovary cell lines that produce large amounts of sulfated N-linked oligosaccharides. Clones 26 and 489 were derived by stable transfection of the glycosaminoglycan-deficient cell mutant pgsA-745 with a cDNA library prepared from wild-type cells. Peptide:N-glycanase F released nearly all of the sulfate label, indicating that sulfation had occurred selectively on the Asn-linked glycans. Hydrazinolysis followed by nitrous acid treatment at pH 4 and borohydride reduction yielded reduced sulfated disaccharides that comigrated with standard Gal3SO4beta1-4anhydromannitol. The disaccharides were resistant to periodate oxidation but became sensitive after the sulfate group was removed by methanolysis, indicating that the sulfate was located at C3 of the galactose residues. Maackia amurensis lectin bound to the sulfated glycopeptides on the cell surface and in free form, even after sialidase treatment. This finding indicates that the lectin requires only a charged group at C3 of the galactose unit and not an intact sialic acid. Growth of cells with chlorate restored sialidase sensitivity to lectin binding, indicating that sulfation and sialylation occurred largely at the same sites. The enhanced sulfation was due to elevated sulfotransferase activity that catalyzed transfer of sulfate from phosphoadenosine-5'-phosphosulfate to Galbeta1-4(3)GlcNAcbeta-O-naphthalenemethanol.

To study how the pattern of sulfation along a heparan sulfate chain affects its turnover, we examined heparan sulfate catabolism in wild-type Chinese hamster ovary cells and mutant pgsF-17, defective in 2-O-sulfation of uronic acid residues (Bai, X., and Esko, J. D. (1996) J. Biol. Chem. 271, 17711-17717). Heparan sulfate from the mutant contains normal amounts of 6-O-sulfated glucosamine residues and iduronic acid and somewhat higher levels of N-sulfated glucosamine residues but lacks any 2-O-sulfated iduronic or glucuronic acid residues. Pulse-chase experiments showed that both mutant and wild-type cells transport newly synthesized heparan sulfate proteoglycans to the plasma membrane, where they shed into the medium or move into the cell through endocytosis. Internalization of the cell-associated molecules leads to sequential endoglycosidase (heparanase) fragmentation of the chains and eventual lysosomal degradation. In wild-type cells, the chains begin to degrade within 1 h, leading to the accumulation of intermediate (10-20-kDa) and small (4-7-kDa) oligosaccharides. Mutant cells did not generate these intermediates, although internalization and intracellular trafficking of the heparan sulfate chains appeared normal, and the chains degraded with normal kinetics. This difference was not due to defective heparanase activities in the mutant, since cytoplasmic extracts from mutant cells cleaved wild-type heparan sulfate chains in vitro. Instead, the heparan sulfate chains from the mutant were relatively resistant to degradation by cellular heparanases. These findings suggest that 2-O-sulfated iduronic acid residues in heparan sulfate are important for cleavage by endogenous heparanases but not for the overall catabolism of the chains.

The interaction of heparan sulfate with protein ligands depends on unique oligosaccharide sequences containing iduronic acid (IdUA), N-sulfated glucosamine residues, and O-sulfated sugars. To study the role of O-sulfation in greater detail, we isolated a Chinese hamster ovary cell mutant defective in 2-O-sulfation of iduronic acid. The mutant, pgsF-17, was identified by a colony blotting assay in which colonies of mutagen-treated cells were replica plated to two disks of polyester cloth. One disk was blotted with 125I-labeled basic fibroblast growth factor (bFGF) to measure binding to cell surface proteoglycans. The other disk was incubated with 35SO4 to measure proteoglycan biosynthesis. Autoradiography revealed a colony that did not bind 125I-bFGF, but incorporated 35SO4 normally (mutant pgsF-17). Complete deaminative cleavage of heparan sulfate revealed that material from pgsF-17 lacked IdUA(2OSO3)-GlcNSO3 and IdUA(2OSO3)-GlcNSO3(6OSO3), but contained a higher proportion of glucuronic acid GlcUA-GlcNSO3(6OSO3) and IdUA-GlcNSO3(6OSO3). Assay of the 2-O-sulfotransferase that acts on IdUA residues showed that mutant 17 lacked enzyme activity. Interestingly, the alteration resulted in accumulation of GlcNSO3 groups, suggesting that under normal conditions 2-O-sulfation decreases GlcNAc N-deacetylation/N-sulfation, and that the reactions occur simultaneously. The formation of IdUA and 6-O-sulfated glucosaminyl residues appears to be independent of 2-O-sulfation. pgsF-17 also lacks 2-O-sulfated GlcUA residues, suggesting that the same enzyme is responsible for 2-O-sulfation of IdUA and GlcUA residues. Mutant 17 provides a useful tool for studying the regulation of heparan sulfate biosynthesis and the relationship of heparan sulfate fine structure to its biological function.

This paper analyzes the situation of the library digital resources integration in China and abroad, following service oriented criterion. This paper divides resources into three types, which are local special database resource, introduced resource, and Internet resource. Based on these resources, this paper proposes service-oriented integration technology. This system puts forward library digital resources retrieval process design and digital resource integration design. The system has the higher resources retrieval and resources integration ability. Keywords-Library; Digital Resources; Retrieval System; Integration

Evaluating the impact of a scholarly article is of great significance and has attracted great attentions. Although citation-based evaluation approaches have been widely used, these approaches face limitations e.g. in identifying anomalous citations patterns. This negligence would inevitably cause unfairness and inaccuracy to the article impact evaluation. In this study, in order to discover the anomalous citations and ensure the fairness and accuracy of research outcome evaluation, we investigate the citation relationships between articles using the following factors: collaboration times, the time span of collaboration, citing times and the time span of citing to weaken the relationship of Conflict of Interest (COI) in the citation network. Meanwhile, we study a special kind of COI, namely suspected COI relationship. Based on the COI relationship, we further bring forward the COIRank algorithm, an innovative scheme for accurately assessing the impact of an article. Our method distinguishes the citation strength, and utilizes PageRank and HITS algorithms to rank scholarly articles comprehensively. The experiments are conducted on the American Physical Society (APS) dataset. We find that about 80.88% articles contain contributed citations by coauthors in 26,366 articles and 75.55% articles among these articles are cited by the authors belonging to the same affiliation, indicating COI and suspected COI should not be ignored for evaluating impact of scientific papers objectively. Moreover, our experimental results demonstrate COIRank algorithm significantly outperforms the state-of-art solutions. The validity of our approach is verified by using the probability of Recommendation Intensity. Refereed/Peer-reviewed

Due to the insecurity of Internet, many enterprises and schools develop and adopt a Virtual Private Network (VPN) technology for secure intercommunication in this global open network. VPN arouses people's attention with its characteristics of safe, reliable, economical and effective transmission via a public network. This paper introduces the concept of VPN, its technological composition, classification, application and implementation. Because the education network access speed is slow, in addition visiting some data resources of the library limit the IP address range, we can't get access to the digital inner resources of school library outside the university. This article has solved library resources question accessed outside the university. It describes in detail the establishment of PPTP VPN servers, focuses on three VPN users' dialing methods aimed at different operating systems, and presents the approaches to handling common errors.

The human epidermal growth factor (EGF) receptor (HER) family members cooperate in malignancy. Of this family, HER2 does not bind growth factors and HER3 does not encode an active tyrosine kinase. This diversity creates difficulty in creating pan-specific therapeutic HER family inhibitors. We have identified single amino acid changes in epidermal growth factor receptor (EGFR) and HER3 which create high affinity sequestration of the cognate ligands, and may be used as receptor decoys to downregulate aberrant HER family activity. In silico modeling and high throughput mutagenesis were utilized to identify receptor mutants with very high ligand binding activity. A single mutation (T15S; EGFR subdomain I) enhanced affinity for EGF (two-fold), TGF-alpha (twenty-six-fold), and heparin-binding (HB)-EGF (six-fold). This indicates that T15 is an important, previously undescribed, negative regulatory amino acid for EGFR ligand binding. Another mutation (Y246A; HER 3 subdomain II) enhanced neuregulin (NRG)1-beta binding eight-fold, probably by interfering with subdomain II-IV interactions. Further work revealed that the HER3 subunit of an EGFR:HER3 heterodimer suppresses EGFR ligand binding. Optimization required reversing this suppression by mutation of the EGFR tether domain (G564A; subdomain IV). This mutation resulted in enhanced ligand binding (EGF, ten-fold; TGF-alpha, thirty-four-fold; HB-EGF, seventeen-fold; NRG1-beta, thirty-one-fold). This increased ligand binding was reflected in improved inhibition of in vitro tumor cell proliferation and tumor suppression in a human non-small cell lung cancer xenograft model. In conclusion, amino acid substitutions were identified in the EGFR and HER3 ECDs that enhance ligand affinity, potentially enabling a pan-specific therapeutic approach for downregulating the HER family in cancer.

All four members of the human epidermal growth factor (EGF) receptor (HER) family are implicated in human cancers. Although efficacious in a subset of patients, resistance to single-targeted anti-HER therapy [i.e., cetuximab (Erbitux) and trastuzumab (Herceptin)] is often associated with coexpression of other HER family members. This may be overcome by a HER ligand binding molecule that sequesters multiple EGF-like ligands, preventing ligand-dependent receptor activation. Toward this end, we have combined the HER-1/EGFR and HER-3 ligand binding domains, dimerized with fusion of an Fc fragment of human IgG1. This resulted in a mixture of HER-1/Fc homodimer (HFD100), HER-3/Fc homodimer (HFD300), and HER-1/Fc:HER-3/Fc heterodimer (RB200), also termed Hermodulins. The purified first-generation RB200 bound EGF and neuregulin 1 (NRG1)-β1 ligands, determined by cross-linking and direct binding studies. The binding affinity for both was ∼10 nmol/L by dissociation-enhanced lanthanide fluorescence immunoassay using europium (Eu)-labeled ligands. Competition studies with RB200 using Eu-EGF or Eu-NRG1-β1 revealed that RB200 bound HER-1 ligands, including transforming growth factor-α and heparin-binding EGF, and HER-3 ligands NRG1-α and NRG1-β3. RB200 inhibited EGF- and NRG1-β1–stimulated tyrosine phosphorylation of HER family proteins, proliferation of a diverse range of tumor cells in monolayer cell growth assays, tumor cell proliferation as a single agent and in synergy with tyrosine kinase inhibitors, lysophosphatidic acid–stimulated cell proliferation, and tumor growth in two human tumor xenograft nude mouse models. Taken together, the data reveal that RB200 has the potential to sequester multiple HER ligands and interfere with signaling by HER-1, HER-2, and HER-3. [Mol Cancer Ther 2008;7(10):3223–36]