MNMDCDA: prediction of circRNA-disease associations by learning mixed neighborhood information from multiple distances.

Emerging evidence suggests that circular RNA (circRNA) is an important regulator of a variety of pathological processes and serves as a promising biomarker for many complex human diseases. Nevertheless, there are relatively few known circRNA-disease associations, and uncovering new circRNA-disease associations by wet-lab methods is time consuming and costly. Considering the limitations of existing computational methods, we propose a novel approach named MNMDCDA, which combines high-order graph convolutional networks (high-order GCNs) and deep neural networks to infer associations between circRNAs and diseases. Firstly, we computed different biological attribute information of circRNA and disease separately and used them to construct multiple multi-source similarity networks. Then, we used the high-order GCN algorithm to learn feature embedding representations with high-order mixed neighborhood information of circRNA and disease from the constructed multi-source similarity networks, respectively. Finally, the deep neural network classifier was implemented to predict associations of circRNAs with diseases. The MNMDCDA model obtained AUC scores of 95.16%, 94.53%, 89.80% and 91.83% on four benchmark datasets, i.e., CircR2Disease, CircAtlas v2.0, Circ2Disease and CircRNADisease, respectively, using the 5-fold cross-validation approach. Furthermore, 25 of the top 30 circRNA-disease pairs with the best scores of MNMDCDA in the case study were validated by recent literature. Numerous experimental results indicate that MNMDCDA can be used as an effective computational tool to predict circRNA-disease associations and can provide the most promising candidates for biological experiments.

A pyroptosis-related gene expression signature predicts immune microenvironment and prognosis in head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous and aggressive malignancy with a poor prognosis. Pyroptosis triggered by gasdermins family proteins is reported vital for tumor microenvironment and cancer progression. However, pyroptosis-related gene expression and its relationship with immune infiltration and prognosis of HNSCC have not been fully defined. MATERIAL AND METHODS: RNA-sequencing data of HNSCC patients were acquired from The Cancer Genome Atlas (TCGA) database. A pyroptosis-related gene expression signature and infiltrated immune cells were analyzed. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression and nomogram analyses were used to construct a clinical-molecular risk model for survival prognosis. RESULTS: HNSCC was classified into three different molecular subtypes based on the expression information of pyroptosis-related genes. Immune cell infiltration was demonstrated to be distinct between the three subtypes. The segregation of patients into the high-risk group and low-risk group, were carried out using the signature of differential expression genes (DEGs) signature among the three molecular subtypes. The precision of this signature was corroborated by Receiver operating characteristic curve (ROC) analysis with the 3-year area under time-dependent ROC curve (AUC) reaching 0.711. The risk model was validated in another dataset from the Gene Expression Omnibus (GEO) database. Subsequently we established a clinical-molecular nomogram which combined the risk score with age and stage. The calibration plots for predicting the overall survival rate of 1-, 3-, and 5-years indicated that the nomogram performs well. CONCLUSION: The expression signature that encompasses pyroptosis-related genes could be used as molecular classification for HNSCC and pyroptosis might be a promising therapeutic target in HNSCC.

A novel and independent survival prognostic model for OSCC: the functions and prognostic values of RNA-binding proteins.

BACKGROUND: Oral squamous cell carcinoma (OSCC), exhibiting high morbidity and malignancy, is the most common type of oral cancer. The abnormal expression of RNA-binding proteins (RBPs) plays important roles in the occurrence and progression of cancer. The objective of the present study was to establish a prognostic assessment model of RBPs and to evaluate the prognosis of OSCC patients. METHODS: Gene expression data in The Cancer Genome Atlas (TCGA) were analyzed by univariate Cox regression analysis model that established a novel nine RBPs, which were used to build a prognostic risk model. A multivariate Cox proportional regression model and the survival analysis were used to evaluate the prognostic risk model. Moreover, the receive operator curve (ROC) analysis was tested further the efficiency of prognostic risk model based on data from TCGA database and Gene Expression Omnibus (GEO). RESULTS: Nine RBPs' signatures (ACO1, G3BP1, NMD3, RNGTT, ZNF385A, SARS, CARS2, YARS and SMAD6) with prognostic value were identified in OSCC patients. Subsequently, the patients were further categorized into high-risk group and low-risk in the overall survival (OS) and disease-free survival (DFS), and external validation dataset. ROC analysis was significant for both the TCGA and GEO. Moreover, GSEA revealed that patients in the high-risk group significantly enriched in many critical pathways correlated with tumorigenesis than the low, including cell cycle, adheres junctions, oocyte meiosis, spliceosome, ERBB signaling pathway and ubiquitin-mediated proteolysis. CONCLUSIONS: Collectively, we developed and validated a novel robust nine RBPs for OSCC prognosis prediction. The nine RBPs could serve as an independent and reliable prognostic biomarker and guiding clinical therapy for OSCC patients.

A Fast Attention-Guided Hierarchical Decoding Network for Real-Time Semantic Segmentation.

Semantic segmentation provides accurate scene understanding and decision support for many applications. However, many models strive for high accuracy by adopting complex structures, decreasing the inference speed, and making it challenging to meet real-time requirements. Therefore, a fast attention-guided hierarchical decoding network for real-time semantic segmentation (FAHDNet), which is an asymmetric U-shaped structure, is proposed to address this issue. In the encoder, we design a multi-scale bottleneck residual unit (MBRU), which combines the attention mechanism and decomposition convolution to design a parallel structure for aggregating multi-scale information, making the network perform better at processing information at different scales. In addition, we propose a spatial information compensation (SIC) module that effectively uses the original input to make up for the spatial texture information lost during downsampling. In the decoder, the global attention (GA) module is used to process the feature map of the encoder, enhance the feature interaction in the channel and spatial dimensions, and enhance the ability to mine feature information. At the same time, the lightweight hierarchical decoder integrates multi-scale features to better adapt to different scale targets and accurately segment objects of different sizes. Through experiments, FAHDNet performs outstandingly on two public datasets, Cityscapes and Camvid. Specifically, the network achieves 70.6% mean intersection over union (mIoU) at 135 frames per second (FPS) on Cityscapes and 67.2% mIoU at 335 FPS on Camvid. Compared to the existing networks, our model maintains accuracy while achieving faster inference speeds, thus enhancing its practical usability.

Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier.

BACKGROUND: Self-interacting proteins (SIPs), two or more copies of the protein that can interact with each other expressed by one gene, play a central role in the regulation of most living cells and cellular functions. Although numerous SIPs data can be provided by using high-throughput experimental techniques, there are still several shortcomings such as in time-consuming, costly, inefficient, and inherently high in false-positive rates, for the experimental identification of SIPs even nowadays. Therefore, it is more and more significant how to develop efficient and accurate automatic approaches as a supplement of experimental methods for assisting and accelerating the study of predicting SIPs from protein sequence information. RESULTS: In this paper, we present a novel framework, termed GLCM-WSRC (gray level co-occurrence matrix-weighted sparse representation based classification), for predicting SIPs automatically based on protein evolutionary information from protein primary sequences. More specifically, we firstly convert the protein sequence into Position Specific Scoring Matrix (PSSM) containing protein sequence evolutionary information, exploiting the Position Specific Iterated BLAST (PSI-BLAST) tool. Secondly, using an efficient feature extraction approach, i.e., GLCM, we extract abstract salient and invariant feature vectors from the PSSM, and then perform a pre-processing operation, the adaptive synthetic (ADASYN) technique, to balance the SIPs dataset to generate new feature vectors for classification. Finally, we employ an efficient and reliable WSRC model to identify SIPs according to the known information of self-interacting and non-interacting proteins. CONCLUSIONS: Extensive experimental results show that the proposed approach exhibits high prediction performance with 98.10% accuracy on the yeast dataset, and 91.51% accuracy on the human dataset, which further reveals that the proposed model could be a useful tool for large-scale self-interacting protein prediction and other bioinformatics tasks detection in the future.

Development and validation of a novel survival model for head and neck squamous cell carcinoma based on autophagy-related genes.

BACKGROUND: In view of the critical role of autophagy-related genes (ARGs) in the pathogenesis of various diseases including cancer, this study aims to identify and evaluate the potential value of ARGs in head and neck squamous cell carcinoma (HNSCC). METHODS: RNA sequencing and clinical data in The Cancer Genome Atlas (TCGA) were analyzed by univariate Cox regression analysis and Lasso Cox regression analysis model established a novel 13- autophagy related prognostic genes, which were used to build a prognostic risk model. A multivariate Cox proportional regression model and the survival analysis were used to evaluate the prognostic risk model. Moreover, the efficiency of prognostic risk model was tested by receiver operating characteristic (ROC) curve analysis based on data from TCGA database and Gene Expression Omnibus (GEO). Besides, the other independent datasets from Human Protein Atlas dataset (HPA) also applied. RESULTS: 13 ARGs (GABARAPL1, ITGA3, USP10, ST13, MAPK9, PRKN, FADD, IKBKB, ITPR1, TP73, MAP2K7, CDKN2A, and EEF2K) with prognostic value were identified in HNSCC patients. Subsequently, a prognostic risk model was established based on 13 ARGs, and significantly stratified HNSCC patients into high- and low-risk groups in terms of overall survival (OS) (HR = 0.379，95% CI: 0.289-0.495, p < 0.0001). The multivariate Cox analysis revealed that this model was an independent prognostic factor (HR = 1.506, 95% CI = 1.330-1.706, P < 0.001). The areas under the ROC curves (AUC) were significant for both the TCGA and GEO, with AUC of 0.685 and 0.928 respectively. Functional annotation revealed that model significantly enriched in many critical pathways correlated with tumorigenesis, including the p53 pathway, IL2 STAT5 signaling, TGF beta signaling, PI3K Ak mTOR signaling by gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). In addition, we developed a nomogram shown some clinical net could be used as a reference for clinical decision-making. CONCLUSIONS: Collectively, we developed and validated a novel robust 13-gene signatures for HNSCC prognosis prediction. The 13 ARGs could serve as an independent and reliable prognostic biomarkers and therapeutic targets for the HNSCC patients.

Involvement of miR-146a-5p/neurogenic locus notch homolog protein 1 in the proliferation and differentiation of STRO-1+ human dental pulp stem cells.

Dental pulp stem cells (DPSCs) and stem cells from the apical papilla (SCAPs) are oral mesenchymal stem cells capable of self-renewal and have a potential for multilineage differentiation. Increasing evidence shows that microRNAs (miRNAs) play important roles in stem cell biology. Here, we focused on exploring miR-146a-5p and its relationship to the undifferentiated status of STRO-1+ SCAPs and STRO-1+ DPSCs, as well as its role during STRO-1+ DPSC differentiation and proliferation. Our data indicated that baseline miR-146a-5p expression is significantly lower in STRO-1+ SCAPs than in STRO-1+ DPSCs and increased in the latter during osteogenic induction. Moreover, we identified miR-146a-5p as a key miRNA that promotes osteo/odontogenic differentiation of STRO-1+ DPSCs and attenuates cell proliferation. Additionally, it was observed that STRO-1+ DPSC mineralization results in the downregulation of notch receptor 1 (NOTCH1) and hes family bHLH transcription factor 1 (HES1). Interference with neurogenic locus notch homolog protein 1 (Notch 1) signaling was verified to enhance differentiation and suppress STRO-1+ DPSC proliferation. It was further observed that miR-146a-5p directly targets the 3'-untranslated region (3'-UTR) of NOTCH1 and inhibits expression of both NOTCH1 and HES1mRNAs and Notch 1 and transcription factor HES-1 (HES-1) proteins in STRO-1+ DPSCs. We conclude that miR-146a-5p exerts its regulatory effect on STRO-1+ DPSC differentiation and proliferation partially by suppressing Notch signaling.

Attribute Selection Based on Constraint Gain and Depth Optimal for a Decision Tree.

Uncertainty evaluation based on statistical probabilistic information entropy is a commonly used mechanism for a heuristic method construction of decision tree learning. The entropy kernel potentially links its deviation and decision tree classification performance. This paper presents a decision tree learning algorithm based on constrained gain and depth induction optimization. Firstly, the calculation and analysis of single- and multi-value event uncertainty distributions of information entropy is followed by an enhanced property of single-value event entropy kernel and multi-value event entropy peaks as well as a reciprocal relationship between peak location and the number of possible events. Secondly, this study proposed an estimated method for information entropy whose entropy kernel is replaced with a peak-shift sine function to establish a decision tree learning (CGDT) algorithm on the basis of constraint gain. Finally, by combining branch convergence and fan-out indices under an inductive depth of a decision tree, we built a constraint gained and depth inductive improved decision tree (CGDIDT) learning algorithm. Results show the benefits of the CGDT and CGDIDT algorithms.

Antiprotozoal activity of medicinal plants against Ichthyophthirius multifiliis in goldfish (Carassius auratus).

Ichthyophthiriasis is a widespread disease in aquaculture and causes mass mortalities of fish. The development of new antiprotozoal agents for the treatment of Ichthyophthirius multifiliis infections is of increasing interest. The aim of the present study was to investigate the efficacy of 30 medicinal plants against I. multifiliis. The results showed that the methanol extracts of Magnolia officinalis and Sophora alopecuroides displayed the highest antiprotozoal activity against theronts, with 4-h LC(50) values estimated to be 2.45 and 3.43 mg L(-1), respectively. Concentrations of 2.5, 5.0, 10.0, and 20.0 mg L(-1) of M. officinalis extracts resulted in tomont mortality of 9.7, 43.7, 91.3, and 100% at 20 h, respectively. From 40 to 320 mg L(-1) of S. alopecuroides extracts, tomont mortality increased from 29.7 to 100%. Antiprotozoal efficacy against settled tomonts (2 and 10 h) was also applied; the results indicated that encysted I. multifiliis tomonts were less susceptible to these plant extract treatments. In vivo experiments demonstrated that high concentrations of M. officinalis and S. alopecuroides extracts could kill tomonts, and M. officinalis significantly reduced its reproduction (P < 0.05). These results suggested that the methanol extracts of M. officinalis and S. alopecuroides have the potential to be used as an eco-friendly approach for the control of I. multifiliis.

Predicting Protein-Protein Interactions via Random Ferns with Evolutionary Matrix Representation.

Protein-protein interactions (PPIs) play a crucial role in understanding disease pathogenesis, genetic mechanisms, guiding drug design, and other biochemical processes, thus, the identification of PPIs is of great importance. With the rapid development of high-throughput sequencing technology, a large amount of PPIs sequence data has been accumulated. Researchers have designed many experimental methods to detect PPIs by using these sequence data, hence, the prediction of PPIs has become a research hotspot in proteomics. However, since traditional experimental methods are both time-consuming and costly, it is difficult to analyze and predict the massive amount of PPI data quickly and accurately. To address these issues, many computational systems employing machine learning knowledge were widely applied to PPIs prediction, thereby improving the overall recognition rate. In this paper, a novel and efficient computational technology is presented to implement a protein interaction prediction system using only protein sequence information. First, the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST) was employed to generate a position-specific scoring matrix (PSSM) containing protein evolutionary information from the initial protein sequence. Second, we used a novel data processing feature representation scheme, MatFLDA, to extract the essential information of PSSM for protein sequences and obtained five training and five testing datasets by adopting a five-fold cross-validation method. Finally, the random fern (RFs) classifier was employed to infer the interactions among proteins, and a model called MatFLDA_RFs was developed. The proposed MatFLDA_RFs model achieved good prediction performance with 95.03% average accuracy on Yeast dataset and 85.35% average accuracy on H. pylori dataset, which effectively outperformed other existing computational methods. The experimental results indicate that the proposed method is capable of yielding better prediction results of PPIs, which provides an effective tool for the detection of new PPIs and the in-depth study of proteomics. Finally, we also developed a web server for the proposed model to predict protein-protein interactions, which is freely accessible online at http://120.77.11.78:5001/webserver/MatFLDA_RFs.

Potential Novel Modules and Hub Genes as Prognostic Candidates of Thyroid Cancer by Weighted Gene Co-Expression Network Analysis.

BACKGROUND: Although thyroid cancer (THCA) is one of the most common type of endocrine malignancy, its highly complex molecular mechanisms of carcinogenesis are not completely known. MATERIALS AND METHODS: In this study, weighted gene co-expression network analysis (WGCNA) was utilized to construct gene co-expression networks and evaluate the relations between modules and clinical traits to identify potential prognostic biomarkers for THCA patients. RNA-seq data and clinical data were downloaded from The Cancer Genome Atlas (TCGA). Other independent datasets from the Gene Expression Omnibus (GEO) database and the Human Protein Atlas database were performed to validate findings. RESULTS: Finally, 11 co-expression modules were constructed and four hub genes, CCDC146, SLC4A4, TDRD9 and MUM1L1, were identified and validated statistically, which were considerably interrelated to worse survival of THCA patients. CONCLUSION: This research study revealed four hub genes may be considered candidate prognostic biomarkers and potential therapeutic targets for THCA patients in the future.

Identification and validation of key modules and hub genes associated with the pathological stage of oral squamous cell carcinoma by weighted gene co-expression network analysis.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is a major lethal malignant cancer of the head and neck region, yet its molecular mechanisms of tumourigenesis are still unclear. PATIENTS AND METHODS: We performed weighted gene co-expression network analysis (WGCNA) on RNA-sequencing data with clinical information obtained from The Cancer Genome Atlas (TCGA) database. The relationship between co-expression modules and clinical traits was investigated by Pearson correlation analysis. Furthermore, the prognostic value and expression level of the hub genes of these modules were validated based on data from the TCGA database and other independent datasets from the Gene Expression Omnibus (GEO) database and the Human Protein Atlas database. The significant modules and hub genes were also assessed by functional analysis and gene set enrichment analysis (GSEA). RESULTS: We found that the turquoise module was strongly correlated with pathologic T stage and significantly enriched in critical functions and pathways related to tumourigenesis. PPP1R12B, CFD, CRYAB, FAM189A2 and ANGPTL1 were identified and statistically validated as hub genes in the turquoise module and were closely implicated in the prognosis of OSCC. GSEA indicated that five hub genes were significantly involved in many well-known cancer-related biological functions and signaling pathways. CONCLUSION: In brief, we systematically discovered a co-expressed turquoise module and five hub genes associated with the pathologic T stage for the first time, which provided further insight that WGCNA may reveal the molecular regulatory mechanism involved in the carcinogenesis and progression of OSCC. In addition, the five hub genes may be considered candidate prognostic biomarkers and potential therapeutic targets for the precise early diagnosis, clinical treatment and prognosis of OSCC in the future.

Downregulation of miR-224-5p Promotes Migration and Proliferation in Human Dental Pulp Stem Cells.

INTRODUCTION: Pulp regeneration, as a treatment for pulp necrosis, has significant advantages over root canal therapy for the preservation of living pulp. To date, research on pulp regeneration has mainly focused on the transplantation of pulp stem cells into the root canal, but there is still a lack of research on the migration of pulp cells into the root canal via cell homing. Stem cells from the apical tooth papilla (SCAP) are recognized as multidirectional stem cells, but these cells are difficult to obtain. MicroRNAs are small noncoding RNAs that play crucial roles in regulating normal and pathologic functions. We hypothesized that some types of microRNAs might improve the migration and proliferation function of dental pulp stem cells (DPSCs), which are easily obtained in clinical practice, and as a result, DPSCs might replace SCAP and provide valuable information for regenerative endodontics. METHODS: Magnetic activated cell sorting of DPSCs and SCAP was performed. Next-generation sequencing was performed to examine DPSCs and SCAP miRNAs expression and to identify the most significant differentially expressed miRNA. CCK-8 and transwell assays were used to determine the impact of this miRNA on DPSCs proliferation and migration. RESULTS: The most significant differentially expressed miRNA between DPSCs and SCAP was miR-224-5p. Downregulating miR-224-5p promoted DPSCs proliferation and migration; the opposite results were observed when miR-224-5p was upregulated. CONCLUSION: MiR-224-5p promotes proliferation and migration in DPSCs, a finding that is of great significance for further exploring the role of dental pulp stem cells in regenerative endodontics.

A novel long non-coding RNA, AC012456.4, as a valuable and independent prognostic biomarker of survival in oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is a major malignant cancer of the head and neck. Long non-coding RNAs (lncRNAs) have emerged as critical regulators during the development and progression of cancers. This study aimed to identify a lncRNA-related signature with prognostic value for evaluating survival outcomes and to explore the underlying molecular mechanisms of OSCC. Associations between overall survival (OS), disease-free survival (DFS) and candidate lncRNAs were evaluated by Kaplan-Meier survival analysis and univariate and multivariate Cox proportional hazards regression analyses. The robustness of the prognostic significance was shown via the Gene Expression Omnibus (GEO) database. A total of 2,493 lncRNAs were differentially expressed between OSCC and control samples (fold change >2, p < 0.05). We used Kaplan-Meier survival analysis to identify 21 lncRNAs for which the expression levels were associated with OS and DFS of OSCC patients (p < 0.05) and found that down-expression of lncRNA AC012456.4 especially contributed to poor DFS (p = 0.00828) and OS (p = 0.00987). Furthermore, decreased expression of AC012456.4 was identified as an independent prognostic risk factor through multivariate Cox proportional hazards regression analyses (DFS: p = 0.004, hazard ratio (HR) = 0.600, 95% confidence interval(CI) [0.423-0.851]; OS: p = 0.002, HR = 0.672, 95% CI [0.523-0.863). Gene Set Enrichment Analysis (GSEA) indicated that lncRNA AC012456.4 were significantly enriched in critical biological functions and pathways and was correlated with tumorigenesis, such as regulation of cell activation, and the JAK-STAT and MAPK signal pathway. Overall, these findings were the first to evidence that AC012456.4 may be an important novel molecular target with great clinical value as a diagnostic, therapeutic and prognostic biomarker for OSCC patients.

Learning From Short Text Streams With Topic Drifts.

Short text streams such as search snippets and micro blogs have been popular on the Web with the emergence of social media. Unlike traditional normal text streams, these data present the characteristics of short length, weak signal, high volume, high velocity, topic drift, etc. Short text stream classification is hence a very challenging and significant task. However, this challenge has received little attention from the research community. Therefore, a new feature extension approach is proposed for short text stream classification with the help of a large-scale semantic network obtained from a Web corpus. It is built on an incremental ensemble classification model for efficiency. First, more semantic contexts based on the senses of terms in short texts are introduced to make up of the data sparsity using the open semantic network, in which all terms are disambiguated by their semantics to reduce the noise impact. Second, a concept cluster-based topic drifting detection method is proposed to effectively track hidden topic drifts. Finally, extensive studies demonstrate that as compared to several well-known concept drifting detection methods in data stream, our approach can detect topic drifts effectively, and it enables handling short text streams effectively while maintaining the efficiency as compared to several state-of-the-art short text classification approaches.

Concurrent preoperative chemotherapy and three-dimensional conformal radiotherapy followed by surgery for oral squamous cell carcinoma: a retrospective analysis of 104 cases.

OBJECTIVES: The objectives of this study were to assess the clinical effects of an integrated program consisting of concurrent preoperative combined paclitaxel and nedaplatin chemotherapy and three-dimensional conformal radiotherapy followed by surgery intended to cure oral squamous cell carcinoma and to determine whether this integrated program is feasible and effective with respect to the treatment of oral squamous cell carcinoma. METHODS: A total of 104 biopsy-confirmed patients who presented with oral squamous cell carcinoma for the first time were included in this study. Concurrent preoperative combined paclitaxel and nedaplatin chemotherapy and three-dimensional conformal radiotherapy were administered to these patients. The most common treatment regimen consisted of infusions of paclitaxel (135-175 mg/m2/day), infusions of nedaplatin (150 mg; 80-100 mg/m2/day), and irradiation at doses ranging from 1.5 Gy twice daily to 30-40 Gy over 3-4 weeks. The clinical variables evaluated herein included the local recurrence rate, distant metastasis rate, postoperative survival rate, and degree of mouth opening restriction. RESULTS: The median follow-up time for surviving patients was 60 months, and the median time to progression for all patients was 57.69 months (95% confidence interval, 56.09 to 59.29 months, and the 3-year disease-free survival probability was 97.11%). The effectiveness rate of the integrated program was 98.08%, and the surgery resection rate was 100%. Only a few postoperative adverse reactions were observed. The local recurrence and distant metastasis rates were 1.92% (2 patients) and 2.88% (3 patients), respectively. The titanium rejection and infection reaction rate that led to restriction of mouth opening was only 2.88% (3 patients). Finally, the 5-year post-surgery survival rate was 91.35% (95 patients). CONCLUSION: Preoperative combined paclitaxel and nedaplatin chemotherapy and three-dimensional conformal radiotherapy have significant clinical effects leading to positive anti-tumor results in patients with oral squamous cell carcinoma. These treatments also increase the likelihood that patients will undergo successful surgical treatment. The integrated program described herein can increase long-term survival and surgery resection rates and is associated with only a limited number of adverse reactions.

Robust face recognition via adaptive sparse representation.

Sparse representation (or coding)-based classification (SRC) has gained great success in face recognition in recent years. However, SRC emphasizes the sparsity too much and overlooks the correlation information which has been demonstrated to be critical in real-world face recognition problems. Besides, some paper considers the correlation but overlooks the discriminative ability of sparsity. Different from these existing techniques, in this paper, we propose a framework called adaptive sparse representation-based classification (ASRC) in which sparsity and correlation are jointly considered. Specifically, when the samples are of low correlation, ASRC selects the most discriminative samples for representation, like SRC; when the training samples are highly correlated, ASRC selects most of the correlated and discriminative samples for representation, rather than choosing some related samples randomly. In general, the representation model is adaptive to the correlation structure that benefits from both l1-norm and l2-norm. Extensive experiments conducted on publicly available data sets verify the effectiveness and robustness of the proposed algorithm by comparing it with the state-of-the-art methods.

Developmental toxicity of 3,4-dichloroaniline on rare minnow (Gobiocypris rarus) embryos and larvae.

Gobiocypris rarus is a freshwater cyprinid, which possesses lots of attractive features (short life cycle, high fecundity, and especially the transparent trait during early life stage) that make it a suitable model in aquatic toxicity tests. In this study, the effects of 3,4-dichloroaniline (3,4-DCA) on the early life stages of G. rarus were measured. As endpoints, normal developmental parameters (survival rate, malformation rate, total body length and average heart rate) as well as biomarker genes (stress response (hsp70), organizer function and axis formation (wnt8a), vascular system development (vezf1), detoxification (cyp1a) and endocrine disruption (erα)) in the developing embryos and larvae were recorded during a 72 h exposure. The results revealed that reduced survival rate, increased malformation, changes in heart rate and total body length provide a gradual dose-response relationship, values of 72 h LC(50) were 4.146 (3.665-4.713) mg L(-1) for embryos and 1.088 (0.832-1.432) mg L(-1) for larvae. The developmental biochemical biomarkers are very promising tools to determine the severity of toxicants in the growing G. rarus embryos and larvae, even at a concentration of 1% for LC(50). Gene expressions of wnt8a and cyp1a in embryos were highly up-regulated (more than 100-fold) after exposure to 3,4-DCA. Overall, the present study points out that 3,4-DCA is high toxic to the early development of G. rarus, and offers a practicable and highly sensitive bioassay for the general assessment of chemical toxicity.

MAIL: mining sequential patterns with wildcards.

Sequential pattern mining is an important research task in many domains, such as biological science. In this paper, we study the problem of mining frequent patterns from sequences with wildcards. The user can specify the gap constraints with flexibility. Given a subject sequence, a minimal support threshold and a gap constraint, we aim to find frequent patterns whose supports in the sequence are no less than the given support threshold. We design an efficient mining algorithm MAIL. Two pattern growth strategies are proposed to improve the completeness and the time efficiency. One is based on the candidate occurrence pruning, and the other uses an occurrence graph. A random data generator is designed to test the completeness on artificial data. Experiments on DNA sequences show that MAIL mines four times more patterns than one of its peers and the time performance is six times faster on average than its another peer. We also give a concrete example in which our algorithm is applied on DNA sequences to find interesting patterns.

Bioassay-guided isolation and identification of active compounds from Semen pharbitidis against Dactylogyrus intermedius (Monogenea) in goldfish (Carassius auratus).

The present study was undertaken to isolate the active compounds responsible for the anthelmintic activity of methanol extract of Semen pharbitidis against Dactylogyrus intermedius in goldfish (Carassius auratus). The active methanol extract was fractionated on silica gel column chromatography in a bioassay-guided fractionation, eventually yielding two bioactive compounds: palmitic acid and pharnilatin A by comparing spectral data (NMR and ESI-MS) with literature values. According to in vivo anthelmintic assays, they were found to be 50% effective at the concentrations (EC(50)) of 5.3 and 1.4 mg L(-1), respectively. The promising palmitic acid and pharnilatin A from S. pharbitidis were also subjected to acute toxicity tests for the evaluation of their safety to the host (goldfish). After 48h exposure, the mortalities of goldfish were recorded, and the established LC(50) values were 2.45- and 5.29-fold higher than the corresponding EC(50), demonstrating that pharnilatins A may have better application potential than palmitic acid. The present results provide evidence that pharnilatins A might be potential source of new anti-parasitic drug for the control of Dactylogyrus.