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We propose a novel linear learning system based on the peak price tracking (PPT) strategy for portfolio selection (PS). Recently, the topic of tracking control attracts intensive attention and some novel models are proposed based on backstepping methods, such that the system output tracks a desired trajectory. The proposed system has a similar evolution with a transform function that aggressively tracks the increasing power of different assets. As a result, the better performing assets will receive more investment. The proposed PPT objective can be formulated as a fast backpropagation algorithm, which is suitable for large-scale and time-limited applications, such as high-frequency trading. Extensive experiments on several benchmark data sets from diverse real financial markets show that PPT outperforms other state-of-the-art systems in computational time, cumulative wealth, and risk-adjusted metrics. It suggests that PPT is effective and even more robust than some defensive systems in PS.

Homeobox b13 (HOXB13) is considered to be a tumor suppressor gene in multiple types of human cancer. The present study aimed to identify the difference in expression of HOXB13 mRNA between gastric cancer (GC) tissues and corresponding non-malignant gastric tissues. The clinical significance of HOXB13 mRNA expression was also assessed in GC and a potential association between HOXB13 mRNA expression and DNA promoter methylation was observed. The expression of HOXB13 mRNA was assessed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and HOXB13 methylation status was assessed by methylation-specific PCR (MSP) in 5 GC cell lines and 85 paired GC and normal gastric tissues. Kaplan-Meier survival curves were used to assess the survival of patients with GC. HOXB13 mRNA expression was significantly lower in primary GC tissues than in corresponding nonmalignant gastric tissues, and decreased HOXB13 expression was associated with poorer differentiation, lymph node metastasis, invasion depth and Tumor-Node-Metastasis (TNM) stage. Kaplan-Meier survival analysis demonstrated that HOXB13 mRNA expression was a significant prognostic indicator of GC patient survival. Furthermore, MSP revealed that the proportion of GC samples with hypermethylated HOXB13 (60.0%, 51/85) was increased compared with the corresponding nonmalignant gastric tissues (11.8%, 10/85). Decreased HOXB13 mRNA expression was due to DNA hypermethylation as following treatment with the DNA methyltransferase inhibitor 5-Aza-dC, HOXB13 expression in the GC MKN-45 cell line was upregulated. The results of the present study indicate that decreased expression of HOXB13 mRNA was associated with tumor differentiation, depth of invasion, lymph node metastases and TNM stage in GC, and it was a significant poor prognostic factor for patients with GC. Aberrant DNA promoter methylation was a crucial reason for the downregulation of HOXB13 mRNA expression.

Background: Accumulating evidence has confirmed the potential prognostic value of LVI in patients with cancers. This aim of the current study was to clarify the potential relationship between LVI and lymph node metastasis, establish predictive clinicopathologic prognostic factors for LVI and lymph node metastasis, and determine the prognostic significance of LVI for patients younger than 70 years with resected gastric cancer., Methods: Overall survival rates were calculated using Kaplan-Meier analysis. Differences in proportions of patients were tested with the χ 2 test. Univariate and multivariate analyses were applied to identify independent prognostic factors. Logistic regression analysis was employed to identify the risk factors predicting the presence of LVI and LN metastasis., Results: Univariate analysis led to the identification of tumor size, LVI and pN stage as factors significantly correlated with prognosis. Multivariate analysis demonstrated that tumor size, LVI, pN stage, and number of LNs retrieved are independent prognostic factors for the entire population. Logistic regression analysis proved that LVI and pT stage were significantly associated with LN metastasis., Conclusion: LVI is an independent prognostic factor predicting LN metastasis and a strongly independent predictor of survival for patients with resected gastric cancer. We recommend that LVI should be taken into account as an important adjuvant prognostic factor, specially for pN0 cases with inadequate LNs retrieved. And the maximum number of LNs possible should be retrieved for optimal staging, especially for patients with higher cT stage., (Copyright © 2018 IJS Publishing Group Ltd. Published by Elsevier Ltd. All rights reserved.)

Background: MicroRNAs deregulation are common in human tumor progression. miR-1236-3p has been reported to function as tumor suppressor microRNA in various malignancies. The aim of this study was to demonstrate the downregulated expression of miR-1236-3p in gastric cancer (GC) tissues and cell lines, and clarify its biological function in GC., Methods: Real-time polymerase chain reaction was used to measure the mRNA level of miR-1236-3p in GC. Dual luciferase assay was used to demonstrate that MTA2 was one of the candidate target genes of miR-1236-3p. Western blots were utilized to detect the protein levels. Cell function assays were also performed to determine the function of miR-1236-3p in GC., Results: miR-1236-3p expression, which was associated with lymph node metastasis, differentiation and clinical stage, was significantly reduced in GC tissues and cell lines. miR-1236-3p over-expression could inhibit GC cell proliferation, migration and invasion, and inhibition of miR-1236-3p expression had opposite effects. Furthermore, we demonstrated that MTA2 was a candidate target of miR-1236-3p, and miR-1236-3p over-expression significantly inhibited the process of epithelial-mesenchymal transition. We also found that miR-1236-3p could suppress the PI3K/Akt signaling pathway in GC cells., Conclusions: Our results suggest that miR-1236-3p functions as a tumor suppressor in GC and could be a promising therapeutic target for GC.

Local feature descriptor plays a key role in different image classification applications. Some of these methods such as local binary pattern and image gradient orientations have been proven effective to some extent. However, such traditional descriptors which only utilize single-type features, are deficient to capture the edges and orientations information and intrinsic structure information of images. In this paper, we propose a kernel embedding multiorientation local pattern (MOLP) to address this problem. For a given image, it is first transformed by gradient operators in local regions, which generate multiorientation gradient images containing edges and orientations information of different directions. Then the histogram feature which takes into account the sign component and magnitude component, is extracted to form the refined feature from each orientation gradient image. The refined feature captures more information of the intrinsic structure, and is effective for image representation and classification. Finally, the multiorientation refined features are automatically fused in the kernel embedding discriminant subspace learning model. The extensive experiments on various image classification tasks, such as face recognition, texture classification, object categorization, and palmprint recognition show that MOLP could achieve competitive performance with those state-of-the art methods.

Background: The aim of this study was to conduct a meta-analysis comparing the oncological, intraoperative and safety outcomes in sigmoid and rectal cancer surgery with and without preservation of the left colic artery (LCA)., Method: We searched Medline, Embase, China National Knowledge Infrastructure (CNKI), and PubMed databases for relevant articles published between 1962 and 2017. Randomized and non-randomized clinical trials were identified and included in the study. End-points evaluated included 5-year mortality, number of patients with retrieved positive metastatic lymph nodes (LN) at the root of inferior mesenteric artery (IMA), number of retrieved LNs, morbidity, mortality, recurrence, bowel obstruction, intraoperative blood loss, anastomotic leakage, operation time, surgical site infection and postoperative bleed. Meta-analysis was conducted using RevMan 5.3 software. The odds ratio (OR) with 95% confidence intervals (CI) was used to analyze dichotomous data., Results: Seventeen studies including 6247 patients were identified for the meta-analysis. Meta-analysis revealed that preserving the LCA was associated with reduced anastomotic leakage rate (OR, 0.78; 95% CI, 0.62-0.98; P = 0.03). There were no significant differences between the two groups with respect to the 5-year mortality, number of retrieved LNs, number of patients with retrieved positive metastatic LNs at the root of IMA, morbidity, mortality, recurrence, bowel obstruction, intraoperative blood loss, operation time, surgical site infection and postoperative bleed., Conclusion: In comparison with ligating the left colic artery, preserving the left colic artery seems to achieve comparable success with acceptable safety outcomes and we suggest to preserve the LCA in the sigmoid and rectal cancer surgeries. However, more multicenter randomized controlled trials are required to further evaluate the efficacy and safety of preserving the left colic artery in surgeries., (Copyright © 2018. Published by Elsevier Ltd.)

Aim: To identify multiple microRNAs (miRNAs) for predicting the prognosis of gastric cancer (GC) patients by bioinformatics analysis., Methods: The original microarray dataset GSE93415, which included 20 GC and 20 tumor adjacent normal gastric mucosal tissues, was downloaded from the Gene Expression Omnibus database and used for screening differentially expressed miRNAs (DEMs). The cut-off criteria were P < 0.05 and fold change > 2.0. In addition, we acquired the miRNA expression profiles and clinical information of 361 GC patients from The Cancer Genome Atlas database to assess the prognostic role of the DEMs. The target genes of miRNAs were predicted using TargetScan, miRDB, miRWalk, and DIANA, and then the common target genes were selected for functional enrichment analysis., Results: A total of 110 DEMs including 19 up-regulated and 91 down-regulated miRNAs were identified between 20 pairs of GC and tumor adjacent normal tissues, and the Kaplan-Meier survival analysis found that a three-miRNA signature (miR-145-3p, miR-125b-5p, and miR-99a-5p) had an obvious correlation with the survival of GC patients. Furthermore, univariate and multivariate Cox regression analyses indicated that the three-miRNA signature could be a significant prognostic marker in GC patients. The common target genes of the three miRNAs are added up to 108 and used for Gene Functional Enrichment analysis. Biological Process and Molecular Function analyses showed that the target genes are involved in cell recognition, gene silencing and nucleic acid binding, transcription factor activity, and transmembrane receptor activity. Cellular Component analysis revealed that the genes are portion of nucleus, chromatin silencing complex, and TORC1/2 complex. Biological Pathway analysis indicated that the genes participate in several cancer-related pathways, such as the focal adhesion, PI3K, and mTOR signaling pathways., Conclusion: This study justified that a three-miRNA signature could play a role in predicting the survival of GC patients., Competing Interests: Conflict-of-interest statement: The authors declare that there is no conflict of interest related to this study.

This study aims to observe the expression of microRNA (miR)-634 in different gastric cancer cell lines and tissues, and to study the effects of miR-634 on the proliferation, migration, and invasion of the gastric cancer cells. The miR-634 mimics and miR-634 inhibitors were transfected by lentivirus into human gastric cancer SGC-7901 and MGC-803 cells, and the miR-634 cells without transfection were used as the control group (NC group). The expression of miR-634 in the transfected cells was detected by qRT-PCR. Cell viability was measured by the CCK8 assay. The migration and invasion ability of the cells were detected by scratch assays and Transwell ® chamber assays, respectively, and the luciferase assay verified the binding of miR-634 to the target gene JAG1. The expression level of miR-634 in gastric cancer tissues and cell lines was significantly lower than that in normal adjacent tissues and control cells. The survival of cells was significantly decreased, and number of cells migrating and invading was decreased in the miR-634 mimics group. However, in the miR-634 inhibitor group, the opposite results were observed. Over-expression of miR-634 inhibited the proliferation, migration, and invasion of gastric cancer cell lines, and the miR-634 target gene was JAG1., (© 2017 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Background/aims: MicroRNAs have a significant role in the tumorigenesis and progression of cancers, including gastric cancer (GC). Our study aimed to identify a novel biomarker to predict the prognosis of patients with GC., Methods: The GC microarray dataset, GSE28700, was downloaded from the Gene Expression Omnibus (GEO) database and screened for differentially expressed miRNAs (DEMs). The downregulation of miR-376a expression was verified in GC cell lines and 82 paired GC tissues by performing RT-qPCR and the correlation between its expression and clinicopathological characteristics was also explored. The target genes of miR-376a were predicted using TargetScan7.1, miRDB, and DIANA website tools. A functional enrichment analysis was performed to explore the biological role of the common target genes., Results: Bioinformatics analysis found that miR-376a was downregulated in GC tissues. Compared with the control group, RT-qPCR results showed that the expression of miR-376a in GC cell lines and tissues were also significantly decreased. The expression of miR-376a was statistically associated with T and N stage. Survival analysis with Kaplan-Meier showed that GC patients in the low expression group had a poorer prognosis than those in the high expression group (median survival of 26.4 and 46.9 months, respectively). Univariate and multivariate analysis demonstrated that low miR-376a expression was an independent prognostic marker for poor survival. Functional enrichment analysis indicated that the common targets genes were involved in cell-cell communication, VEGF and mTOR1-mediated signaling, and epithelial-to-mesenchymal transition (EMT)., Conclusion: The results suggest that miR-376a could play an important role in the tumorigenesis and progression of GC and act as a novel therapeutic target and prognostic indicator in patients with GC., (© 2018 The Author(s). Published by S. Karger AG, Basel.)

The present study was conducted to investigate the prognosis and survival of patients with locally advanced gastric cancer who underwent distal subtotal gastrectomy with modified D2 (D1+) and D2 lymphadenectomy, under 70 years of age. The five-year overall survival rates of 390 patients were compared between those receiving D1+ and D2 lymphadenectomy. Univariate and multivariate analyses were used to identify factors that correlated with prognosis and lymph node metastasis. Tumor size (P=0.039), pT stage (P=0.011), pN stage (P<0.001), and lymphadenectomy (P=0.004) were identified as independent prognostic factors. Furthermore, tumor size (P=0.022), pT stage (P=0.012), and lymphadenectomy (P=0.028) were proven as independent factors predicting lymph node metastasis. In conclusion, cancers of larger size, higher pT stage, and with D1+ lymphadenectomy had a higher risk of lymph node metastasis. Standard D2 lymphadenectomy removes sufficient lymph nodes to improve staging accuracy and survival. Therefore, D2 lymphanectomy is recommended in distal subtotal gastrectomy for locally advanced gastric cancer, especially for cancers of larger size and higher pT stage.

Background: To compare the efficacy and safety of endoscopic resection (ER) and radical gastrectomy (RG) for early gastric cancer (EGC) in Asia., Materials and Methods: We systematically searched relevant articles published before September 1, 2017. We evaluated the quality of the included non-randomized studies using the Newcastle-Ottawa Scale (NOS). Meta-analysis was carried out using RevMan 5.3 software. The odds ratio (OR) with 95% confidence intervals (CI) were used for the dichotomous data., Results: Fifteen retrospective studies were included in this analysis (3737 patients in the ER group and 4246 patients in RG group). No significant differences in the three-year survival rate (OR, 0.87; 95%CI, 0.50-1.53) and five-year survival rate (OR, 0.81; 95%CI, 0.58-1.13) between the ER and RG groups were observed. Although patients undergoing ER had a higher risk of recurrence (OR, 6.07; 95%CI 4.17-8.84) and the occurrence of metachronous cancer (OR, 8.35; 95%CI, 5.48-12.75), recurrent or metachronous gastric cancers following ER were successfully detected and removed using the endoscopic technique. Higher recurrence in the ER group may be associated with its lower en bloc resection rate (OR, 0.05; 95%CI, 0.02-0.14) and complete resection rate (OR, 0.03; 95%CI, 0.01-0.08). Importantly, although the three-year survival and five-year survival were similar in the two groups, the complication rate in the ER group was significantly lower than that in the RG group., Conclusion: ER is a good choice for patients with small EGC lesions (≤2 cm) without lymph node metastasis, especially in elderly patients with various medical comorbidities and in patients who cannot tolerate abdominal surgery or who meet the criteria but decline surgery. In contrast, RG is recommended when the diameter of the tumor is large (>2 cm) and preoperative examination suggests the possible presence of lymph node metastasis., (Copyright © 2017. Published by Elsevier Ltd.)

A sparse representation classifier (SRC) and a kernel discriminant analysis (KDA) are two successful methods for face recognition. An SRC is good at dealing with occlusion, while a KDA does well in suppressing intraclass variations. In this paper, we propose kernel extended dictionary (KED) for face recognition, which provides an efficient way for combining KDA and SRC. We first learn several kernel principal components of occlusion variations as an occlusion model, which can represent the possible occlusion variations efficiently. Then, the occlusion model is projected by KDA to get the KED, which can be computed via the same kernel trick as new testing samples. Finally, we use structured SRC for classification, which is fast as only a small number of atoms are appended to the basic dictionary, and the feature dimension is low. We also extend KED to multikernel space to fuse different types of features at kernel level. Experiments are done on several large-scale data sets, demonstrating that not only does KED get impressive results for nonoccluded samples, but it also handles the occlusion well without overfitting, even with a single gallery sample per subject.

Background: MicroRNAs have been suggested to play a vital role in regulating carcinogenesis, tumor progression and invasion. MiR-335 is involved in suppressing metastasis and invasion in various human cancers. However, the mechanisms responsible for the aberrant expression of miR-335 in gastric cancer (GC) remain unknown., Methods: Expression of miR-335 in four GC cell lines and 231 GC tissues was determined by real-time quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR). DNA methylation status in the CpG islands upstream of miR-335 in GC cell lines and tissues was determined by methylation-specific PCR and bisulfite sequence-PCR. The effects of the demethylating agent 5-aza-2'-deoxycytidine on cell proliferation, apoptosis, cell cycle, migration, and invasion were investigated in GC cell lines., Results: Cancer-specific methylation was detected in the upstream CpG-rich regions of miR-335, which dramatically silenced its transcriptional activity in GC cell lines and tissues. Low levels of miR-335 expression and high levels of miR-335 methylation in GC tissues were associated with poor clinical features and prognosis. Restoration of miR-335 expression in GC cells promoted cell apoptosis, inhibited tumor cell migration, invasion, and proliferation, and arrested the cell cycle at G0/G1 phase. Overexpression of miR-335 significantly reduced the activity of a luciferase reporter containing the 3' untranslated region of V-crk avian sarcoma virus CT10 oncogene homolog-like (CRKL)., Conclusions: MiR-335 functions as a tumor suppressor and may be silenced by promoter hypermethylation. It plays a role in inhibiting tumor cell migration, invasion, and proliferation, arresting the cell cycle at G0/G1 phase, and promoting apoptosis in GC cells through targeting CRKL.

Robust descriptor-based subspace learning with complex data is an active topic in pattern analysis and machine intelligence. A few researches concentrate the optimal design on feature representation and metric learning. However, traditionally used features of single-type, e.g., image gradient orientations (IGOs), are deficient to characterize the complete variations in robust and discriminant subspace learning. Meanwhile, discontinuity in edge alignment and feature match are not been carefully treated in the literature. In this paper, local order constrained IGOs are exploited to generate robust features. As the difference-based filters explicitly consider the local contrasts within neighboring pixel points, the proposed features enhance the local textures and the order-based coding ability, thus discover intrinsic structure of facial images further. The multimodal features are automatically fused in the most discriminant subspace. The utilization of adaptive interaction function suppresses outliers in each dimension for robust similarity measurement and discriminant analysis. The sparsity-driven regression model is modified to adapt the classification issue of the compact feature representation. Extensive experiments are conducted by using some benchmark face data sets, e.g., of controlled and uncontrolled environments, to evaluate our new algorithm.

Background: Protein complexes are the key molecular entities to perform many essential biological functions. In recent years, high-throughput experimental techniques have generated a large amount of protein interaction data. As a consequence, computational analysis of such data for protein complex detection has received increased attention in the literature. However, most existing works focus on predicting protein complexes from a single type of data, either physical interaction data or co-complex interaction data. These two types of data provide compatible and complementary information, so it is necessary to integrate them to discover the underlying structures and obtain better performance in complex detection., Results: In this study, we propose a novel multi-view clustering algorithm, called the Partially Shared Multi-View Clustering model (PSMVC), to carry out such an integrated analysis. Unlike traditional multi-view learning algorithms that focus on mining either consistent or complementary information embedded in the multi-view data, PSMVC can jointly explore the shared and specific information inherent in different views. In our experiments, we compare the complexes detected by PSMVC from single data source with those detected from multiple data sources. We observe that jointly analyzing multi-view data benefits the detection of protein complexes. Furthermore, extensive experiment results demonstrate that PSMVC performs much better than 16 state-of-the-art complex detection techniques, including ensemble clustering and data integration techniques., Conclusions: In this work, we demonstrate that when integrating multiple data sources, using partially shared multi-view clustering model can help to identify protein complexes which are not readily identifiable by conventional single-view-based methods and other integrative analysis methods. All the results and source codes are available on https://github.com/Oyl-CityU/PSMVC .

Background: Several recent studies have used the Minimum Dominating Set (MDS) model to identify driver nodes, which provide the control of the underlying networks, in protein interaction networks. There may exist multiple MDS configurations in a given network, thus it is difficult to determine which one represents the real set of driver nodes. Because these previous studies only focus on static networks and ignore the contextual information on particular tissues, their findings could be insufficient or even be misleading., Results: In this study, we develop a Collective-Influence-corrected Minimum Dominating Set (CI-MDS) model which takes into account the collective influence of proteins. By integrating molecular expression profiles and static protein interactions, 16 tissue-specific networks are established as well. We then apply the CI-MDS model to each tissue-specific network to detect MDS proteins. It generates almost the same MDSs when it is solved using different optimization algorithms. In addition, we classify MDS proteins into Tissue-Specific MDS (TS-MDS) proteins and HouseKeeping MDS (HK-MDS) proteins based on the number of tissues in which they are expressed and identified as MDS proteins. Notably, we find that TS-MDS proteins and HK-MDS proteins have significantly different topological and functional properties. HK-MDS proteins are more central in protein interaction networks, associated with more functions, evolving more slowly and subjected to a greater number of post-translational modifications than TS-MDS proteins. Unlike TS-MDS proteins, HK-MDS proteins significantly correspond to essential genes, ageing genes, virus-targeted proteins, transcription factors and protein kinases. Moreover, we find that besides HK-MDS proteins, many TS-MDS proteins are also linked to disease related genes, suggesting the tissue specificity of human diseases. Furthermore, functional enrichment analysis reveals that HK-MDS proteins carry out universally necessary biological processes and TS-MDS proteins usually involve in tissue-dependent functions., Conclusions: Our study uncovers key features of TS-MDS proteins and HK-MDS proteins, and is a step forward towards a better understanding of the controllability of human interactomes.

Context: The associations between Type D personality and poor quality of life, overall survival, and mental health in gastric cancer survivors., Objectives: The aim of this research was to explore quality of life (QoL), mental health status, Type D personality, symptom duration, and emergency admissions of Chinese gastric cancer patients, as well as the relationship between these factors., Methods: Eight hundred thirty eligible Chinese patients newly diagnosed with gastric cancer between July 2009 and July 2011 were enrolled in this prospective study. Type D personality was measured with the 14-item Type D Personality Scale (DS14). Mental health status was measured with the Hospital Anxiety and Depression Scale. The QoL outcomes were assessed longitudinally using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 and Quality of Life Questionnaire-STO22 at baseline and six months after diagnosis., Results: The proportion of patients with symptom duration of more than one month and who were diagnosed after emergency admissions in the Type D group was significantly higher than that in the non-Type D personality group. At both of the time points, Type D patients reported statistically significant lower scores on role, emotional, cognitive, and social functioning (all Ps < 0.001) functional scales, global health status/QoL scales (P < 0.001), and worse symptom scores compared to patients without a Type D personality. During the six-month time frame, a higher percentage of patients in the Type D group demonstrated a considerable QoL deterioration. Clinically elevated levels of anxiety and depression were more prevalent in Type D than in non-Type D survivors (both Ps < 0.001). There was a statistically significant difference in three-year overall survival between the patients in the Type D group and the non-Type D personality group., Conclusion: Type D personality is associated with poor QoL, three-year overall survival and mental health status among survivors of gastric cancer, even after adjustment of confounding background variables. The Type D personality group experienced increased levels of pain and fatigue compared to non-Type D patients. Type D personality might be a general vulnerability factor to screen for subgroups at risk of longer symptom duration and emergency admissions in clinical practice., (Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.)

A coelomycete with characters resembling the asexual morphs in the family Botryosphaeriaceae was isolated from a fallen leaf of an orchid collected in Thailand. Morphological and phylogenetic analyses placed the strain in Neoscytalidium. Phylogenetic relationships among Neoscytalidium species were inferred by analyzing internal transcribed spacers and large subunit of rRNA sequence data and indicate that our strain is a new species, which is introduced and illustrated herein as Neoscytalidium orchidacearum sp. nov.

Background: To facilitate advances in personalized medicine, it is important to detect predictive, stable and interpretable biomarkers related with different clinical characteristics. These clinical characteristics may be heterogeneous with respect to underlying interactions between genes. Usually, traditional methods just focus on detection of differentially expressed genes without taking the interactions between genes into account. Moreover, due to the typical low reproducibility of the selected biomarkers, it is difficult to give a clear biological interpretation for a specific disease. Therefore, it is necessary to design a robust biomarker identification method that can predict disease-associated interactions with high reproducibility., Results: In this article, we propose a regularized logistic regression model. Different from previous methods which focus on individual genes or modules, our model takes gene pairs, which are connected in a protein-protein interaction network, into account. A line graph is constructed to represent the adjacencies between pairwise interactions. Based on this line graph, we incorporate the degree information in the model via an adaptive elastic net, which makes our model less dependent on the expression data. Experimental results on six publicly available breast cancer datasets show that our method can not only achieve competitive performance in classification, but also retain great stability in variable selection. Therefore, our model is able to identify the diagnostic and prognostic biomarkers in a more robust way. Moreover, most of the biomarkers discovered by our model have been verified in biochemical or biomedical researches., Conclusions: The proposed method shows promise in the diagnosis of disease pathogenesis with different clinical characteristics. These advances lead to more accurate and stable biomarker discovery, which can monitor the functional changes that are perturbed by diseases. Based on these predictions, researchers may be able to provide suggestions for new therapeutic approaches.

Background: Protein complexes carry out nearly all signaling and functional processes within cells. The study of protein complexes is an effective strategy to analyze cellular functions and biological processes. With the increasing availability of proteomics data, various computational methods have recently been developed to predict protein complexes. However, different computational methods are based on their own assumptions and designed to work on different data sources, and various biological screening methods have their unique experiment conditions, and are often different in scale and noise level. Therefore, a single computational method on a specific data source is generally not able to generate comprehensive and reliable prediction results., Results: In this paper, we develop a novel Two-layer INtegrative Complex Detection (TINCD) model to detect protein complexes, leveraging the information from both clustering results and raw data sources. In particular, we first integrate various clustering results to construct consensus matrices for proteins to measure their overall co-complex propensity. Second, we combine these consensus matrices with the co-complex score matrix derived from Tandem Affinity Purification/Mass Spectrometry (TAP) data and obtain an integrated co-complex similarity network via an unsupervised metric fusion method. Finally, a novel graph regularized doubly stochastic matrix decomposition model is proposed to detect overlapping protein complexes from the integrated similarity network., Conclusions: Extensive experimental results demonstrate that TINCD performs much better than 21 state-of-the-art complex detection techniques, including ensemble clustering and data integration techniques.

Objective: The aim of this research was to explore quality of life (QoL), mental health status, type D personality, symptom duration, and emergency admissions of Chinese rectal cancer patients as well as the relationship between these factors., Methods: Type D personality was measured with the 14-item Type D Personality Scale (DS14). Mental health status was measured with the Hospital Anxiety and Depression Scale (HADS). The QoL outcomes were assessed longitudinally using the European Organization for Research and Treatment of Cancer QLQ-C30 and QLQ-CR38 questionnaires at the baseline and 6 months after diagnosis., Results: Of the 852 survivors who responded (94 %), 187 (22 %) had a type D personality. The proportion of patients with duration of symptoms >1 month and being diagnosed after emergency admissions in type D group is significantly higher than that in non-type D group. At both of the time points, type D patients reported statistically significant lower scores on most of the functional scales, global health status/QoL scales, and worse symptom scores compared to patients without a type D personality. At the 6-month time point, a higher percentage of patients in the type D group demonstrated QoL deterioration. Clinically elevated levels of anxiety and depression were more prevalent in type D than in non-type D survivors., Conclusions: Type D personality was associated with poor QoL and mental health status among survivors of rectal cancer, even after adjustment for confounding background variables. Type D personality might be a general vulnerability factor to screen for subgroups at risk for longer symptom duration and emergency admissions in clinical practice.

No ideal prognostic model has been applied to clearly identify which suitable high-risk stage II colon cancer patients with negative margins undergoing nonemergent surgery should receive adjuvant chemotherapy routinely. Clinicopathologic and prognostic data of 333 stage II colon cancer patients who underwent D2 or D3 lymphadenectomy during nonemergent surgery were retrospectively analyzed. Four pathologically determined factors, including adjacent organ involvement (RR 2.831, P = 0.001), histologic differentiation (RR 2.151, P = 0.009), lymphovascular invasion (RR 4.043, P < 0.001), and number of lymph nodes retrieved (RR 2.161, P = 0.011), were identified as independent prognostic factors on multivariate analysis. Importantly, a simple cumulative scoring system clearly categorizing prognostic risk groups was generated: risk score = ∑ coefficient' × status (AOI + histological differentiated + lymphovascular invasion + LNs retrieved). Our new prognostic model may provide valuable information on the impact of lymphovascular invasion, as well as powerfully and reliably predicting prognosis and recurrence for this particular cohort of patients. This model may identify suitable patients with an R0 resection who should receive routine postoperative adjuvant therapy and may help clinicians to facilitate individualized treatment. In this study, we aim to provide an ideal and quantifiable method for clinical decision making in the nonemergent surgical treatment of stage II colon cancer. Our prognostic and predictive model should be applied in multicenter, prospective studies with large sample sizes, in order to obtain a more reliable clinical recommendation.

This paper provides recommendations of one name for use among pleomorphic genera in Dothideomycetes by the Working Group on Dothideomycetes established under the auspices of the International Commission on the Taxonomy of Fungi (ICTF). A number of these generic names are proposed for protection because they do not have priority and/or the generic name selected for use is asexually typified. These include: Acrogenospora over Farlowiella; Alternaria over Allewia, Lewia, and Crivellia; Botryosphaeria over Fusicoccum; Camarosporula over Anthracostroma; Capnodium over Polychaeton; Cladosporium over Davidiella; Corynespora over Corynesporasca; Curvularia over Pseudocochliobolus; Elsinoë over Sphaceloma; Excipulariopsis over Kentingia; Exosporiella over Anomalemma; Exserohilum over Setosphaeria; Gemmamyces over Megaloseptoria; Kellermania over Planistromella; Kirschsteiniothelia over Dendryphiopsis; Lecanosticta over Eruptio; Paranectriella over Araneomyces; Phaeosphaeria over Phaeoseptoria; Phyllosticta over Guignardia; Podonectria over Tetracrium; Polythrincium over Cymadothea; Prosthemium over Pleomassaria; Ramularia over Mycosphaerella; Sphaerellopsis over Eudarluca; Sphaeropsis over Phaeobotryosphaeria; Stemphylium over Pleospora; Teratosphaeria over Kirramyces and Colletogloeopsis; Tetraploa over Tetraplosphaeria; Venturia over Fusicladium and Pollaccia; and Zeloasperisporium over Neomicrothyrium. Twenty new combinations are made: Acrogenospora carmichaeliana (Berk.) Rossman & Crous, Alternaria scrophulariae (Desm.) Rossman & Crous, Pyrenophora catenaria (Drechsler) Rossman & K.D. Hyde, P. dematioidea (Bubák & Wróbl.) Rossman & K.D. Hyde, P. fugax (Wallr.) Rossman & K.D. Hyde, P. nobleae (McKenzie & D. Matthews) Rossman & K.D. Hyde, P. triseptata (Drechsler) Rossman & K.D. Hyde, Schizothyrium cryptogamum (Batzer & Crous) Crous & Batzer, S. cylindricum (G.Y. Sun et al.) Crous & Batzer, S. emperorae (G.Y. Sun & L. Gao) Crous & Batzer, S. inaequale (G.Y. Sun & L. Gao) Crous & Batzer, S. musae (G.Y. Sun & L. Gao) Crous & Batzer, S. qianense (G.Y. Sun & Y.Q. Ma) Crous & Batzer, S. tardecrescens (Batzer & Crous) Crous & Batzer, S. wisconsinense (Batzer & Crous) Crous & Batzer, Teratosphaeria epicoccoides (Cooke & Massee) Rossman & W.C. Allen, Venturia catenospora (Butin) Rossman & Crous, V. convolvularum (Ondrej) Rossman & Crous, V. oleaginea (Castagne) Rossman & Crous, and V. phillyreae (Nicolas & Aggéry) Rossman & Crous, combs. nov. Three replacement names are also proposed: Pyrenophora grahamii Rossman & K.D. Hyde, Schizothyrium sunii Crous & Batzer, and Venturia barriae Rossman & Crous noms. nov.

This study was conducted to investigate prognosis and survival of patients undergoing distal subtotal gastrectomy with D2 and D2+ lymphadenectomy for patients with locally advanced gastric cancer. Overall survival rates of 416 patients with locally advanced gastric cancer were compared between D2 and D2+ lymphadenectomy. Univariate analysis and multivariate analysis was used to identify significant prognostic factors correlated with LN metastasis and prognosis. Univariate analysis identified tumor size, lymphatic vessel invasion, pT stage, pN stage, TNM stage, locoregional recurrence, and distant recurrence, to significantly correlate with prognosis; Tumor size, LVI, and pT stage were identified as independent factors correlating with LN metastasis. Multivariate analysis demonstrated that tumor size, pT stage, pN stage, locoregional recurrence, and distant recurrence were independent prognostic factors; Tumor size and pT stage were independent prognostic factors predicting LN metastasis. When comparing 5-year survival rates of patients who underwent D2 and D2+ lymphadenectomy, as stratified by pT stage and pN stage, a significant difference was found in pN3 patients, but not for pT2-4 and pN0-2 patients, or the patient cohort as a whole. In conclusion, D2 lymphadenectomy for patients with locally advanced gastric cancer undergoing distal subtotal gastrectomy was recommended, especially in eastern Asia.

Identification of protein complexes is fundamental for understanding the cellular functional organization. With the accumulation of physical protein-protein interaction (PPI) data, computational detection of protein complexes from available PPI networks has drawn a lot of attentions. While most of the existing protein complex detection algorithms focus on analyzing the physical protein-protein interaction network, none of them take into account the "signs"  (i.e., activation-inhibition relationships) of physical interactions. As the "signs"  of interactions reflect the way proteins communicate, considering the "signs"  of interactions can not only increase the accuracy of protein complex identification, but also deepen our understanding of the mechanisms of cell functions. In this study, we proposed a novel Signed Graph regularized Nonnegative Matrix Factorization (SGNMF) model to identify protein complexes from signed PPI networks. In our experiments, we compared the results collected by our model on signed PPI networks with those predicted by the state-of-the-art complex detection techniques on the original unsigned PPI networks. We observed that considering the "signs"  of interactions significantly benefits the detection of protein complexes. Furthermore, based on the predicted complexes, we predicted a set of signed complex-complex interactions for each dataset, which provides a novel insight of the higher level organization of the cell. All the experimental results and codes can be downloaded from http://mail.sysu.edu.cn/home/stsddq@mail.sysu.edu.cn/dai/others/SGNMF.zip.

Background: The identification of protein-protein interactions contributes greatly to the understanding of functional organization within cells. With the development of affinity purification-mass spectrometry (AP-MS) techniques, several computational scoring methods have been proposed to detect protein interactions from AP-MS data. However, most of the current methods focus on the detection of co-complex interactions and do not discriminate between direct physical interactions and indirect interactions. Consequently, less is known about the precise physical wiring diagram within cells., Results: In this paper, we develop a Binary Interaction Network Model (BINM) to computationally identify direct physical interactions from co-complex interactions which can be inferred from purification data using previous scoring methods. This model provides a mathematical framework for capturing topological relationships between direct physical interactions and observed co-complex interactions. It reassigns a confidence score to each observed interaction to indicate its propensity to be a direct physical interaction. Then observed interactions with high confidence scores are predicted as direct physical interactions. We run our model on two yeast co-complex interaction networks which are constructed by two different scoring methods on a same combined AP-MS data. The direct physical interactions identified by various methods are comprehensively benchmarked against different reference sets that provide both direct and indirect evidence for physical contacts. Experiment results show that our model has a competitive performance over the state-of-the-art methods., Conclusions: According to the results obtained in this study, BINM is a powerful scoring method that can solely use network topology to predict direct physical interactions from AP-MS data. This study provides us an alternative approach to explore the information inherent in AP-MS data. The software can be downloaded from https://github.com/Zhangxf-ccnu/BINM.

The aims of this study were (a) to compare the clinical presentations and outcomes of patients referred to the hospital from the emergency department (ED) and those referred from non-ED facilities in a Chinese population and (b) to identify the factors influencing delays in medical assessment and prognosis for patients with rectal cancer (RC). Eligible Chinese patients newly diagnosed with RC, admitted to the hospital from the ED, or referred from non-ED sources between 1 January 2008 and 31 December 2009 were enrolled in this prospective study. Associations between symptoms, symptom duration, tumor stage at diagnosis, and 3-year survival were proposed to identify factors associated with delay in the diagnosis of RC and emergency admission. Compared with patients in the non-ED group, patients in the ED group had a significantly longer hospital stay, greater in-hospital mortality, a higher proportion of advanced stage tumor, lower rate of undergoing potentially curative surgery, and a higher proportion of symptom duration longer than 1 month. There was a statistically significant difference in the 3-year overall survival between the patients who underwent emergency operation within 24 h of admission and the patients presenting as emergency who underwent an operation longer than 24 h from admission to operation. Patients who endured symptoms longer than 1 month had a significantly higher proportion of emergency admissions, higher proportion of advanced stage tumor, and lower rate of undergoing potentially curative surgery compared with patients whose symptom duration was less than 1 month. In conclusion, (a) ED referral patients endured significantly longer symptom duration before diagnosis. (b) Emergency operation within 24 h of admission was an independent prognostic indicator of overall survival in patients with RC. A two-stage approach for the management of patients with RC who presented as emergency could enable patients to be transferred to a specialist department of surgical oncology for a definitive radical oncological operation and improve the prognosis.

In this paper, we propose a novel discriminative and compact coding (DCC) for robust face recognition. It introduces multiple error measurements into regression model. They collaborate to tune regression codes of different properties (sparsity, compactness, high discriminating ability, etc.), to further improve robustness and adaptivity of the regression model. We propose two types of coding models: 1) multiscale error measurements that produces sparse and highly discriminative codes and 2) inspires within-class collaborative representation that produces sparse and compact codes. The update of codes and the combination of different errors are automatically processed. DCC is also robust to the choice of parameters, producing stable regression residuals which are crucial to classification. Extensive experiments on benchmark datasets show that DCC has promising performance and outperforms other state-of-the-art regression models.

More than 100 recent collections of Valsaria sensu lato mostly from Europe were used to elucidate the species composition within the genus. Multigene phylogeny based on SSU, LSU, ITS, rpb2 and tef1 sequences revealed a monophyletic group of ten species within the Dothideomycetes , belonging to three morphologically similar genera. This group could not be accommodated in any known family and are thus classified in the new family Valsariaceae and the new order Valsariales. The genus Valsaria sensu stricto comprises V. insitiva , V. robiniae , V. rudis , V. spartii , V. lopadostomoides sp. nov. and V. neotropica sp. nov., which are phylogenetically well-defined, but morphologically nearly indistinguishable species. The new monotypic genus Bambusaria is introduced to accommodate Valsaria bambusae. Munkovalsaria rubra and Valsaria fulvopruinata are combined in Myrmaecium , a genus traditionally treated as a synonym of Valsaria , which comprises three species, with M. rubricosum as its generic type. This work is presented as a basis for additional species to be detected in future.

Lambertian model is a classical illumination model consisting of a surface albedo component and a light intensity component. Some previous researches assume that the light intensity component mainly lies in the large-scale features. They adopt holistic image decompositions to separate it out, but it is difficult to decide the separating point between large-scale and small-scale features. In this paper, we propose to take a logarithm transform, which can change the multiplication of surface albedo and light intensity into an additive model. Then, a difference (substraction) between two pixels in a neighborhood can eliminate most of the light intensity component. By dividing a neighborhood into subregions, edgemaps of multiple scales can be obtained. Then, each edgemap is multiplied by a weight that can be determined by an independent training scheme. Finally, all the weighted edgemaps are combined to form a robust holistic feature map. Extensive experiments on four benchmark data sets in controlled and uncontrolled lighting conditions show that the proposed method has promising results, especially in uncontrolled lighting conditions, even mixed with other complicated variations.

Aim: To investigate remnant gastric cancer (RGC) at various times after gastrectomy, and lay a foundation for the management of RGC., Methods: Sixty-five patients with RGC > 2 years and < 10 years after gastrectomy (RGC I) and forty-nine with RGC > 10 years after gastrectomy (RGC II) who underwent curative surgery were enrolled in the study. The clinicopathologic factors, surgical outcomes, and prognosis were compared between RGC I and RGC II., Results: There was no significant difference in surgical outcomes between RGC I and RGC II. For patients reconstructed with Billroth II, significantly more patients were RGC II compared with RGC (71.9% vs 21.2%, P < 0.001), and more RGC II patients had anastomotic site locations compared to RGC I (31.0% vs 56.3%, P = 0.038). The five-year survival rates for the patients with RGC I and RGC II were 37.6% and 47.9%, respectively, but no significant difference was observed. Borrmann type and tumor stage were confirmed to be independent prognostic factors in both groups., Conclusion: RGC II is located on the anastomotic site in higher frequency and more cases develop after Billroth II reconstruction than RGC I.

Background: Recently, several studies have drawn attention to the determination of a minimum set of driver proteins that are important for the control of the underlying protein-protein interaction (PPI) networks. In general, the minimum dominating set (MDS) model is widely adopted. However, because the MDS model does not generate a unique MDS configuration, multiple different MDSs would be generated when using different optimization algorithms. Therefore, among these MDSs, it is difficult to find out the one that represents the true driver set of proteins., Results: To address this problem, we develop a centrality-corrected minimum dominating set (CC-MDS) model which includes heterogeneity in degree and betweenness centralities of proteins. Both the MDS model and the CC-MDS model are applied on three human PPI networks. Unlike the MDS model, the CC-MDS model generates almost the same sets of driver proteins when we implement it using different optimization algorithms. The CC-MDS model targets more high-degree and high-betweenness proteins than the uncorrected counterpart. The more central position allows CC-MDS proteins to be more important in maintaining the overall network connectivity than MDS proteins. To indicate the functional significance, we find that CC-MDS proteins are involved in, on average, more protein complexes and GO annotations than MDS proteins. We also find that more essential genes, aging genes, disease-associated genes and virus-targeted genes appear in CC-MDS proteins than in MDS proteins. As for the involvement in regulatory functions, the sets of CC-MDS proteins show much stronger enrichment of transcription factors and protein kinases. The results about topological and functional significance demonstrate that the CC-MDS model can capture more driver proteins than the MDS model., Conclusions: Based on the results obtained, the CC-MDS model presents to be a powerful tool for the determination of driver proteins that can control the underlying PPI networks. The software described in this paper and the datasets used are available at https://github.com/Zhangxf-ccnu/CC-MDS .

Aim: To investigate the prognostic significance of lymph node micrometastasis (LNMM) in patients with gastric carcinoma., Methods: Two reviewers independently searched electronic databases including PubMed, EMBASE, the Cochrane Database of Systematic Reviews, the Cochrane Controlled Studies Register, and the China National Knowledge Infrastructure electronic database between January 1996 and January 2014. Strict literature retrieval and data extraction were performed to extract relevant data. Data analysis was conducted using RevMan 5.2.4 software, and relative risks (RRs) for patient death in five years and recurrence were calculated. A fixed- or random-effects model was selected to pool and a forest plot was used to display RRs., Results: Twelve cohort studies containing a total of 1684 patients were identified. LNMM positivity was worse than LNMM negativity with regards to the number of patients who died in five years. The effects of LNMM positivity in patients with gastric cancer of different T-stages remain unclear. LNMM in patients with gastric carcinoma was also associated with a higher recurrence rate. With regards to the number of patients who died in five years, Asian patients were worse than European and Australian patients., Conclusion: We recommend that LNMM should not be used as a gold standard for prognosis evaluation in patients with gastric cancer in clinical settings until more high quality trials are available.

Purpose: Transcription factor 21 (TCF21) has been identified as a candidate tumor suppressor at 6q23-q24 that is epigenetically inactivated in many types of human cancers. This study aimed to determine the expression of TCF21 mRNA and protein in gastric cancer cell lines and tissue specimens and then investigate the prognostic impact of TCF21 expression in gastric cancer and analyze the relationship between TCF21 expression and methylation level., Methods: We used real-time PCR and immunohistochemical staining to detect the expression of TCF21 and used methylation-specific-PCR to determine the methylation status of TCF21 in gastric cancer samples and gastric cancer cell lines., Results: The results showed that TCF21 expression level in gastric cancer samples was significantly lower than in normal adjacent tissue samples. The Kaplan-Meier survival analysis demonstrated that TCF21 was a significant prognosticator of cancer-specific survival (p = 0.001). Furthermore, the methylation level of TCF21 in gastric cancer samples was much higher than the samples in normal adjacent tissue. Treatment with the DNA methyltransferase inhibitor 5-Aza-2'-deoxy-cytidine can upregulate the expression of TCF21 in gastric cancer cells., Conclusions: These results suggest that the low expression of TCF21 was an independent prognostic factor for poor survival in patients with gastric cancer. Aberrant methylation was an important reason for the downregulation of TCF21 and may be associated with tumorigenesis in gastric cancer.

Face recognition under uncontrolled conditions, e.g., complex backgrounds and variable resolutions, is still challenging in image processing and computer vision. Although many methods have been proved well-performed in the controlled settings, they are usually of weak generality across different data sets. Meanwhile, several properties of the source domain, such as background and the size of subjects, play an important role in determining the final classification results. A transferrable representation learning model is proposed in this paper to enhance the recognition performance. To deeply exploit the discriminant information from the source domain and the target domain, the bioinspired face representation is modeled as structured and approximately stable characterization for the commonality between different domains. The method outputs a grouped boost of the features, and presents a reasonable manner for highlighting and sharing discriminant orientations and scales. Notice that the method can be viewed as a framework, since other feature generation operators and classification metrics can be embedded therein, and then, it can be applied to more general problems, such as low-resolution face recognition, object detection and categorization, and so forth. Experiments on the benchmark databases, including uncontrolled Face Recognition Grand Challenge v2.0 and Labeled Faces in the Wild show the efficacy of the proposed transfer learning algorithm.

Article 59.1, of the International Code of Nomenclature for Algae, Fungi, and Plants (ICN; Melbourne Code), which addresses the nomenclature of pleomorphic fungi, became effective from 30 July 2011. Since that date, each fungal species can have one nomenclaturally correct name in a particular classification. All other previously used names for this species will be considered as synonyms. The older generic epithet takes priority over the younger name. Any widely used younger names proposed for use, must comply with Art. 57.2 and their usage should be approved by the Nomenclature Committee for Fungi (NCF). In this paper, we list all genera currently accepted by us in Dothideomycetes (belonging to 23 orders and 110 families), including pleomorphic and nonpleomorphic genera. In the case of pleomorphic genera, we follow the rulings of the current ICN and propose single generic names for future usage. The taxonomic placements of 1261 genera are listed as an outline. Protected names and suppressed names for 34 pleomorphic genera are listed separately. Notes and justifications are provided for possible proposed names after the list of genera. Notes are also provided on recent advances in our understanding of asexual and sexual morph linkages in Dothideomycetes . A phylogenetic tree based on four gene analyses supported 23 orders and 75 families, while 35 families still lack molecular data.

In this paper, we propose a multilayer surface albedo (MLSA) model to tackle face recognition in bad lighting conditions, especially with reference images in bad lighting conditions. Some previous researches conclude that illumination variations mainly lie in the large-scale features of an image and extract small-scale features in the surface albedo (or surface texture). However, this surface albedo is not robust enough, which still contains some detrimental sharp features. To improve robustness of the surface albedo, MLSA further decomposes it as a linear sum of several detailed layers, to separate and represent features of different scales in a more specific way. Then, the layers are adjusted by separate weights, which are global parameters and selected for only once. A criterion function is developed to select these layer weights with an independent training set. Despite controlled illumination variations, MLSA is also effective to uncontrolled illumination variations, even mixed with other complicated variations (expression, pose, occlusion, and so on). Extensive experiments on four benchmark data sets show that MLSA has good receiver operating characteristic curve and statistical discriminating capability. The refined albedo improves recognition performance, especially with reference images in bad lighting conditions.

Background: Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization., Results: In this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points., Conclusions: Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

Background: Identification of protein complexes can help us get a better understanding of cellular mechanism. With the increasing availability of large-scale protein-protein interaction (PPI) data, numerous computational approaches have been proposed to detect complexes from the PPI networks. However, most of the current approaches do not consider overlaps among complexes or functional annotation information of individual proteins. Therefore, they might not be able to reflect the biological reality faithfully or make full use of the available domain-specific knowledge., Results: In this paper, we develop a Generative Model with Functional and Topological Properties (GMFTP) to describe the generative processes of the PPI network and the functional profile. The model provides a working mechanism for capturing the interaction structures and the functional patterns of proteins. By combining the functional and topological properties, we formulate the problem of identifying protein complexes as that of detecting a group of proteins which frequently interact with each other in the PPI network and have similar annotation patterns in the functional profile. Using the idea of link communities, our method naturally deals with overlaps among complexes. The benefits brought by the functional properties are demonstrated by real data analysis. The results evaluated using four criteria with respect to two gold standards show that GMFTP has a competitive performance over the state-of-the-art approaches. The effectiveness of detecting overlapping complexes is also demonstrated by analyzing the topological and functional features of multi- and mono-group proteins., Conclusions: Based on the results obtained in this study, GMFTP presents to be a powerful approach for the identification of overlapping protein complexes using both the PPI network and the functional profile. The software can be downloaded from http://mail.sysu.edu.cn/home/stsddq@mail.sysu.edu.cn/dai/others/GMFTP.zip.

The present study aimed to investigate the role of histone modification and DNA methylation in epidermal growth factor-containing fibulin-like extracellular matrix protein 1 (EFEMP1) silencing in gastric cancer (GC). In the present study, four GC cell lines, and 45 paired normal and GC tissue samples were used to assess EFEMP1 expression using quantitative polymerase chain reaction (PCR), and EFEMP1 gene methylation status was evaluated by methylation-specific PCR. The involvement of histone modification in GC cell lines was examined by a chromatin immunoprecipitation (ChIP) assay. The results demonstrated that EFEMP1 mRNA level and methylation status in the EFEMP1 promoter region was associated with tumor differentiation, depth of tumor invasion and lymph node metastasis. DNA methyltransferase inhibitor 5-aza-2'-deoxycytidine (DAC) rapidly reduced DNA methylation and histone H3-K9 trimethylation at the silenced loci and reactivated EFEMP1 expression. By contrast, the histone deacetylase inhibitor trichostatin A markedly increased histone H3-K9 acetylation. However, it had no effect on DNA methylation, histone H3-K9 trimethylation or gene expression. In conclusion, the results suggested that EFEMP1 may function as a tumor suppressor in GC. Aberrant DNA methylation and histone H3-K9 trimethylation of EFEMP1 may be responsible for its downregulation in GC, and thus have an important role in tumor invasion and metastasis.

Face recognition with illumination or pose variation is a challenging problem in image processing and pattern recognition. A novel algorithm using band-reweighed Gabor kernel embedding to deal with the problem is proposed in this paper. For a given image, it is first transformed by a group of Gabor filters, which output Gabor features using different orientation and scale parameters. Fisher scoring function is used to measure the importance of features in each band, and then, the features with the largest scores are preserved for saving memory requirements. The reduced bands are combined by a vector, which is determined by a weighted kernel discriminant criterion and solved by a constrained quadratic programming method, and then, the weighted sum of these nonlinear bands is defined as the similarity between two images. Compared with existing concatenation-based Gabor feature representation and the uniformly weighted similarity calculation approaches, our method provides a new way to use Gabor features for face recognition and presents a reasonable interpretation for highlighting discriminant orientations and scales. The minimum Mahalanobis distance considering the spatial correlations within the data is exploited for feature matching, and the graphical lasso is used therein for directly estimating the sparse inverse covariance matrix. Experiments using benchmark databases show that our new algorithm improves the recognition results and obtains competitive performance.

Objective: To perform a meta-analysis comparing laparoscopic versus open gastrectomy (LG vs. OG) for early gastric cancer (EGC) in Asia., Methods: PubMed, Embase, CINAHL, AMED, the Cochrane database of Systematic Reviews, the Cochrane Controlled Trials Register, and the China National Knowledge Infrastructure electronic databases were systematically searched for studies published between January 1, 1992 and July 1, 2012. A series of clinical indices, including operative time, incision length, blood loss, harvested lymph nodes, time to flatus postoperatively, time to first oral intake postoperatively, use of analgesics, complications, duration of hospital stay, recurrence, and mortality were compared using weighted mean differences (WMDs) and odds ratios (ORs)., Results: Five randomized controlled trials and 11 case controls were included, including 1665 patients with EGC (919 LG, 746 OG). LG was associated with less trauma (incision length: WMD -12.91 cm; P<0.00001), less blood loss (WMD -121.04 mL, P<0.00001), less postoperative pain (number of times to use analgesics: WMD -1.64; P=0.001), faster bowel recovery (time to flatus: WMD -0.62 d; P=0.0001), fewer serious complications (OR 0.57; P=0.01), and shorter postoperative hospital stay (WMD -3.73 d; P=0.0007). However, LG had longer operative times (WMD 44.09 min; P<0.00001). LG also had fewer harvested lymph nodes, although this difference was not statistically significant (WMD -3.43 lymph nodes; P=0.04). There was no difference in recurrence rates (OR 0.58; P=0.33) and mortality between LG and OG., Conclusions: For the treatment of EGC in Asia, LG has several advantages, including safety, less trauma, and faster recovery. Our results should be validated in western studies.

Interactions between biomolecules play an essential role in various biological processes. For predicting DNA-binding or protein-binding proteins, many machine-learning-based techniques have used various types of features to represent the interface of the complexes, but they only deal with the properties of a single atom in the interface and do not take into account the information of neighborhood atoms directly. This paper proposes a new feature representation method for biomolecular interfaces based on the theory of graph wavelet. The enhanced graph wavelet features (EGWF) provides an effective way to characterize interface feature through adding physicochemical features and exploiting a graph wavelet formulation. Particularly, graph wavelet condenses the information around the center atom, and thus enhances the discrimination of features of biomolecule binding proteins in the feature space. Experiment results show that EGWF performs effectively for predicting DNA-binding and protein-binding proteins in terms of Matthew's correlation coefficient (MCC) score and the area value under the receiver operating characteristic curve (AUC).

Objective: A systematic review and meta-analysis was performed to investigate the efficacy of neoadjuvant chemotherapy for nonmetastatic esophago-gastric adenocarcinomas., Methods: Electronic databases were searched systematically from January 1980 to July 2012 and a total of 2,587 patients from 17 randomized controlled trials were subjected to meta-analysis. The odds ratios (ORs) for overall survival (OS) and progression-free survival (PFS) were calculated., Results: Seventeen randomized controlled trials were obtained and various comparisons of treatment approaches were performed. Randomized controlled trials detected no differences in these comparisons: R0 resection for neoadjuvant chemotherapy versus none; Preoperative chemotherapy versus surgery alone: 3-year OS, 5-year OS, 5-year OS in Europe, 3-year PFS; Preoperative chemotherapy plus postoperative chemotherapy versus postoperative chemotherapy: 1-year OS, 5-year OS; Preoperative chemotherapy versus preoperative chemoradiotherapy: 3-year OS. Randomized controlled trials detected significant differences in these comparisons: Preoperative chemotherapy plus postoperative chemotherapy versus surgery alone: 3-year and 5-year PFS, 5-year OS; Subgroup analysis examining preoperative chemotherapy versus surgery alone: 5-year OS in Asia; Preoperative chemotherapy versus postoperative chemotherapy: 1-year OS., Conclusion: The current limited evidence suggests that preoperative chemotherapy can be applied to patients with nonmetastatic esophago-gastric adenocarcinomas (specifically, advanced esophago-gastric cancer). However, the results should be interpreted with caution because of the statistically low power and the heterogeneity among study designs; therefore, our results need validations in future studies.

In cancer biology, it is very important to understand the phenotypic changes of the patients and discover new cancer subtypes. Recently, microarray-based technologies have shed light on this problem based on gene expression profiles which may contain outliers due to either chemical or electrical reasons. These undiscovered subtypes may be heterogeneous with respect to underlying networks or pathways, and are related with only a few of interdependent biomarkers. This motivates a need for the robust gene expression-based methods capable of discovering such subtypes, elucidating the corresponding network structures and identifying cancer related biomarkers. This study proposes a penalized model-based Student's t clustering with unconstrained covariance (PMT-UC) to discover cancer subtypes with cluster-specific networks, taking gene dependencies into account and having robustness against outliers. Meanwhile, biomarker identification and network reconstruction are achieved by imposing an adaptive [Formula: see text] penalty on the means and the inverse scale matrices. The model is fitted via the expectation maximization algorithm utilizing the graphical lasso. Here, a network-based gene selection criterion that identifies biomarkers not as individual genes but as subnetworks is applied. This allows us to implicate low discriminative biomarkers which play a central role in the subnetwork by interconnecting many differentially expressed genes, or have cluster-specific underlying network structures. Experiment results on simulated datasets and one available cancer dataset attest to the effectiveness, robustness of PMT-UC in cancer subtype discovering. Moveover, PMT-UC has the ability to select cancer related biomarkers which have been verified in biochemical or biomedical research and learn the biological significant correlation among genes.

Detecting protein complexes from protein-protein interaction (PPI) networks is a challenging task in computational biology. A vast number of computational methods have been proposed to undertake this task. However, each computational method is developed to capture one aspect of the network. The performance of different methods on the same network can differ substantially, even the same method may have different performance on networks with different topological characteristic. The clustering result of each computational method can be regarded as a feature that describes the PPI network from one aspect. It is therefore desirable to utilize these features to produce a more accurate and reliable clustering. In this paper, a novel Bayesian Nonnegative Matrix Factorization (NMF)-based weighted Ensemble Clustering algorithm (EC-BNMF) is proposed to detect protein complexes from PPI networks. We first apply different computational algorithms on a PPI network to generate some base clustering results. Then we integrate these base clustering results into an ensemble PPI network, in the form of weighted combination. Finally, we identify overlapping protein complexes from this network by employing Bayesian NMF model. When generating an ensemble PPI network, EC-BNMF can automatically optimize the values of weights such that the ensemble algorithm can deliver better results. Experimental results on four PPI networks of Saccharomyces cerevisiae well verify the effectiveness of EC-BNMF in detecting protein complexes. EC-BNMF provides an effective way to integrate different clustering results for more accurate and reliable complex detection. Furthermore, EC-BNMF has a high degree of flexibility in the choice of base clustering results. It can be coupled with existing clustering methods to identify protein complexes.

Face recognition with occlusion is common in the real world. Inspired by the works of structured sparse representation, we try to explore the structure of the error incurred by occlusion from two aspects: the error morphology and the error distribution. Since human beings recognize the occlusion mainly according to its region shape or profile without knowing accurately what the occlusion is, we argue that the shape of the occlusion is also an important feature. We propose a morphological graph model to describe the morphological structure of the error. Due to the uncertainty of the occlusion, the distribution of the error incurred by occlusion is also uncertain. However, we observe that the unoccluded part and the occluded part of the error measured by the correntropy induced metric follow the exponential distribution, respectively. Incorporating the two aspects of the error structure, we propose the structured sparse error coding for face recognition with occlusion. Our extensive experiments demonstrate that the proposed method is more stable and has higher breakdown point in dealing with the occlusion problems in face recognition as compared to the related state-of-the-art methods, especially for the extreme situation, such as the high level occlusion and the low feature dimension.

Aim: To investigate the efficacy of extended antimicrobial prophylaxis (EAP) after gastrectomy by systematic review of literature and meta-analysis., Methods: Electronic databases of PubMed, Embase, CINAHL, the Cochrane Database of Systematic Reviews, the Cochrane Controlled Trials Register and the China National Knowledge Infrastructure were searched systematically from January 1980 to October 2012. Strict literature retrieval and data extraction were carried out independently by two reviewers and meta-analyses were conducted using RevMan 5.0.2 with statistics tools risk ratios (RRs) and intention-to-treat analyses to evaluate the items of total complications, surgical site infection, incision infection, organ (or space) infection, remote site infection, anastomotic leakage (or dehiscence) and mortality. Fixed model or random model was selected accordingly and forest plot was conducted to display RR. Likewise, Cochrane Risk of Bias Tool was applied to evaluate the quality of randomized controlled trials (RCTs) included in this meta-analysis., Results: A total of 1095 patients with gastric cancer were enrolled in four RCTs. No statistically significant differences were detected between EAP and intraoperative antimicrobial prophylaxis (IAP) in total complications (RR of 0.86, 95%CI: 0.63-1.16, P = 0.32), surgical site infection (RR of 1.97, 95%CI: 0.86-4.48, P = 0.11), incision infection (RR of 4.92, 95%CI: 0.58-41.66, P = 0.14), organ or space infection (RR of 1.55, 95%CI: 0.61-3.89, P = 0.36), anastomotic leakage or dehiscence (RR of 3.85, 95%CI: 0.64-23.17, P = 0.14) and mortality (RR of 1.14, 95%CI: 0.10-13.12; P = 0.92). Likewise, multiple-dose antimicrobial prophylaxis showed no difference compared with single-dose antimicrobial prophylaxis in surgical site infection (RR of 1.10, 95%CI: 0.62-1.93, P = 0.75). Nevertheless, EAP showed a decreased remote site infection rate compared with IAP alone (RR of 0.54, 95%CI: 0.34-0.86, P = 0.01), which is the only significant finding. Unfortunately, EAP did not decrease the incidence of surgical site infections after gastrectomy; likewise, multiple-dose antimicrobial prophylaxis failed to decrease the incidence of surgical site infection compared with single-dose antimicrobial prophylaxis., Conclusion: We recommend that EAP should not be used routinely after gastrectomy until more high-quality RCTs are available.

With the rapid development of high-throughput experiment techniques for protein-protein interaction (PPI) detection, a large amount of PPI network data are becoming available. However, the data produced by these techniques have high levels of spurious and missing interactions. This study assigns a new reliably indication for each protein pairs via the new generative network model (RIGNM) where the scale-free property of the PPI network is considered to reliably identify both spurious and missing interactions in the observed high-throughput PPI network. The experimental results show that the RIGNM is more effective and interpretable than the compared methods, which demonstrate that this approach has the potential to better describe the PPI networks and drive new discoveries.