Systematic review of comparing single-incision versus conventional laparoscopic right hemicolectomy for right colon cancer.

BACKGROUND: Single-incision laparoscopic right hemicolectomy (SILS) has long used in surgery for a long time. However, there is barely a systemic review related to the comparison between the SILS and the conventional laparoscopic right hemicolectomy (CLS) for the right colon cancer in the long term follow-up. Herein, we used the most recent articles to compare these two techniques by meta-analysis. METHODS: We searched PubMed, Web of Science, Cochrane Library and Wanfang databases to compare SILS with CLS for right colon cancer up to May 2019. The operative, postoperative, pathological and mid-term follow-up outcomes of nine studies were extracted and compared. RESULTS: A total of 1356 patients participated in 9 studies, while 653 patients were assigned to the SILS group and 703 patients were assigned to the CLS group. The patients' baselines in the SILS group were consistent with those in the CLS group. Compared to the CLS group, the SILS group had a shorter operation duration (SMD - 23.49, 95%CI - 36.71 to - 10.27, P < 0.001, chi-square = 24.11), shorter hospital stay (SMD - 0.76, 95% `CI - 1.07 to - 0.45, P < 0.001, chi-square = 9.85), less blood loss (SMD - 8.46, 95% CI - 14.59 to - 2.34; P < 0.05; chi-square = 2.26), smaller incision length (SMD - 1.60, 95% CI - 2.66 to - 0.55, P < 0.001; chi-square = 280.44), more lymph node harvested (SMD - 0.98, 95% CI - 1.79 to - 0.16, P < 0.05; chi-square = 4.61), and a longer proximal surgical edge (SMD - 0.51, 95% CI - 0.93 to - 0.09, P < 0.05; chi-square = 2.42). No significant difference was found in other indexes. After we removed a single large study, we performed another meta-analysis again. The operation duration in the SILS group was still better than that in the CLS group. CONCLUSION: SILS could be a faster and more reliable approach than CLS for the right colon cancer and could accelerate patient recovery, especially for patients with a low BMI.

[A late-type star spectra outlier data mining system].

In M star population, some special objects, which may be of magnetic activity, may be giant stars, or may be of other rare properties, are very important for the follow-up observation and the scientific research on galactic structure and evolution. For local bias of M-type star spectral characteristic lines contained in subspace, a late-type star spectra outlier data mining system is given in the present paper. Firstly, for the sample of M stellar spectral characteristic lines indices, its distribution characteristics in attribute spaces are measured by using the sparse factor and sparsity coefficient, and then this sample is discretized and dimension-reduced to the spectral subspace. Secondly, local outlier subspaces are extracted by PSO (particle swarm optimization) algorithm and identified. Additionally, the effects of sparse coefficient and sparse factor on the number of outliers are discussed by experiments on the sample of SDSS M stellar spectral line index set, and the outliers are compared with spectral type provided by SDSS. In this way, the feasibility and value of this system were validated.

[An abnormal characteristic line mining method of celestial spectrum based on attribute weight and w(k)-distance].

Outlier mining is one of the effective methods to find the abnormal celestial spectrum data, and is also one of effective ways to discover the special and unknown celestial bodies. In the present paper, an abnormal characteristic line mining method of celestial spectrum is presented based on the attribute weight and w(k)-distance by using the idea of information entropy. Based on it, an abnormal characteristic line mining system of celestial spectrum was designed and implemented. Firstly, attribute weight of characteristic line was determined by using the idea of information entropy, so that important degree was effectively reflected for each characteristic line. Secondly, massive characteristic line data set of celestial spectrum was reduced by utilizing pruning technique based on neighborhood radius, so that candidate set of abnormal characteristic line was obtained by deleting data objects in which there may not be abnormal characteristic lines. Thirdly, w(k)-distance sum was computed according to the deviation between the data objects in the candidate set, and the objects whose w(k)-distance sum value ranks the first top n were regarded as abnormal characteristic line data objects. In the end, the experimental and the system's running results validated the effectiveness and feasibility of the method by using the SDSS star spectral data set.

[A post-processing method of classification rule on stellar spectra].

Automatic classification and analysis of observational data is of great significance along with the gradual implementation of LAMOST Survey, which will obtain a large number of spectra data. In classification rules extracted, there is often a great deal of redundancy which will reduce the classification efficiency and quality seriously. In the present paper, a post-processing method of star spectra classification rule based on predicate logic is presented by using predication to describe the classification rules and logical reasoning to eliminate redundant rules. In the end, some experimental results on LAMOST's stellar spectra data show that, with no classification accuracy reduction, the efficiency of auto classification is significantly improved.

[Automatic classification method of star spectrum data based on classification pattern tree].

Frequent pattern, frequently appearing in the data set, plays an important role in data mining. For the stellar spectrum classification tasks, a classification rule mining method based on classification pattern tree is presented on the basis of frequent pattern. The procedures can be shown as follows. Firstly, a new tree structure, i. e., classification pattern tree, is introduced based on the different frequencies of stellar spectral attributes in data base and its different importance used for classification. The related concepts and the construction method of classification pattern tree are also described in this paper. Then, the characteristics of the stellar spectrum are mapped to the classification pattern tree. Two modes of top-to-down and bottom-to-up are used to traverse the classification pattern tree and extract the classification rules. Meanwhile, the concept of pattern capability is introduced to adjust the number of classification rules and improve the construction efficiency of the classification pattern tree. Finally, the SDSS (the Sloan Digital Sky Survey) stellar spectral data provided by the National Astronomical Observatory are used to verify the accuracy of the method. The results show that a higher classification accuracy has been got.

[A soft discretization method of celestial spectrum characteristic line based on fuzzy C-means clustering].

Discretization of continuous numerical attribute is one of the important research works in the preprocessing of celestial spectrum data. For characteristic line of celestial spectrum, a soft discretization algorithm is presented by using improved fuzzy C-means clustering. Firstly, candidate fuzzy clustering centers of characteristic line are chosen by using density values of sample data, so that its anti-noise ability is improved. Secondly, parameters in the fuzzy clustering are dynamically adjusted by taking compatibility of decision table as criteria, so that optimal discretization effect of the characteristic line is achieved. In the end, experimental results effectively validate that the algorithm has higher correct recognition rate of the algorithm by using three SDSS celestial spectrum data sets of high-redshift quasars, late-type star and quasars.

[Rapid identification of variable star spectrum based on information entropy].

Variable star is very important for mankind studying cosmic origin and evolution. For studying variable star, the chief difficulty results from the filtration and identification of variable star, that is how to validly identify variable star spectra from large high-dimensional star spectra data. The traditional outlier definition tries to find the difference between the outlier data and the general model by different ways, and then the result is quantitatively analyzed and filtrated. However, the time complexity of this method is over size and its results are inscrutable and unaccountable. Information entropy is a measure of the uncertainty associated with a random variable. In the present paper, information entropy is imported as the standard of dataset common mode. A novel method is proposed to identify the variable star spectrum rapidly based on information entropy. The time complexity of this method is observably reduced and the man-made impact is effectively overcome. The preliminary experimental results based on Sloan star spectrum data show that the method is workable for rapid identification of variable star spectrum.

[Automatic classification method of star spectrum data based on constrained concept lattice].

Concept lattice is an effective formal tool for data analysis and knowledge extraction. Constrained concept lattice, with the characteristics of higher constructing efficiency, practicability and pertinency, is a new concept lattice structure. For the automatic classification task of star spectrum, a classification rule mining method based on constrained concept lattice is presented by using the concepts of partition and extant supports. In the end, the experimental results validate the higher classification efficiency and correctness of the method by taking the star spectrum data as the formal context, so that an effective way is provided for the automatic classification of massive star spectrum.

[Research on two-stage fuzzy clustering method for spectrum data based on PSO].

A novel high-dimensional clustering algorithm is proposed. On the basis of this, a two-stage fuzzy clustering approach, named TSPFCM, is presented. On the first stage, data is clustered by a new clustering method. On the second stage, the result of the first stage is taken as the initial cluster centers, and PSO mechanism is inducted into fuzzy clustering to solve the locality and the sensitiveness of the initial condition of Fuzzy C-means Clustering. The running results of the system show that it is feasible and valuable to apply this method to mining the clustering in spectrum data.

[The local outliers mining system of celestial body spectrum based on constrained concept lattice].

It is one of the main goals in mankinds universe exploration to find unknown and particular celestial bodies. Data mining is an effective way of finding the spectrum data of unknown and particular celestial body in mass celestial body spectrum data. Constrained concept lattice, with characteristics of higher constructing efficiency, practicability and pertinency, is a new concept lattice structure. For local bias data of celestial body spectrum in characteristic subspace, the local outlier mining system of celestial body spectrum based on constrained concept lattice was designed and implemented by using VC++ 6.0 and Oracle 9i as developing tools. At the same time, its software architecture and function modules were outlined. Such key techniques for preprocessing celestial body spectrum data, the constructing method of constrained concept lattice, and the local outlier mining method were discussed in details. The running results show that the system is feasible and valuable for mining local bias data of celestial body spectrum in low dimensional characteristic subspace. Therefore, the system provides an effective means for finding the unknown and particular celestial bodies.

[Logistic regression analysis on therapeutic effect of acupuncture on neck pain caused by cervical spondylosis and factors influencing therapeutic effect].

OBJECTIVE: To explore the main factors influencing the therapeutic effect of acupuncture on neck pain caused by cervical spondylosis, so as to provide references for further increasing the therapeutic effect of acupuncture. METHODS: One hundred and six cases were randomly divided into an observation group and a control group, 53 cases in each group. The observation group was treated with routine acupuncture at Dazhui (GV 14), Jingbailao (Ex-HN 15) and Jianzhongshu (SI 15); and the control group was treated with sham acupuncture at 1 cm lateral to Bailao (Ex-HN 15) and Jianzhongshu (SI 15). The needles were retained for 20 min in the two groups and infrared radiation was used for adjuvant treatment in this period. Northwick Park Neck Pain Questionnaire (NPQ) was used to assess the patients' quality of life before and after the treatment. The potential influential factors were analyzed by Logistic regression. RESULTS: The effective rate was 75.5% in the observation group and 52.8% in the control group with a significant difference between the two groups (P<0.05). The Logistic regression analysis indicated that the therapeutic effect in the observation group was superior to the control group (OR = 2.670), and both the attack frequency and duration of the neck pain will influence the therapeutic effect (OR = 1.055 and OR = 2.446). CONCLUSION: Acupuncture has a better therapeutic effect on neck pain caused by cervical spondylosis, and patient's clinical history about attack frequency and duration of neck pain are factors influencing clinical therapeutic effect.

Analysis on the effect of acupuncture in treating cervical spondylosis with different syndrome types.

OBJECTIVE: To observe the clinical effect of acupuncture in treating cervical spondylosis with different syndrome types. METHODS: One hundred and seventeen patients were randomized into the treated group: (59 cases), treated with normal acupuncture, and the control group (58 cases), treated with sham acupuncture, operated once every other day, 9 times in total (in 18 days) as one therapeutic course, and a succeeding 3-month follow-up study was carried out after terminating the therapy. The efficacy of treatment was evaluated with the Northwick Park Neck Pain Questionnaire (NPQ) and Visual Analogue Scale (VAS), and the scores gained in patients with different syndrome types were analyzed with a general linear model. RESULTS: The NPQ and VAS: scores showed a linear decreasing tendency in both groups at the time of ending treatment and the 1st month of follow-up, but showed a secondary curve increasing tendency in the 3rd month of follow-up. Multivariate analysis showed the difference was of statistical significance (P<0.05). However, the analysis through lead-in of syndrome type as an individual influencing factor showed that syndrome type exerts a significant influence on VAS score (P<0.05), but has insignificant influence on the NPQ score (P>0.05). CONCLUSION: Acupuncture shows: good immediate effect in treating cervical spondylosis, but its long-term effect is not satisfactory. The difference in syndrome type may have some impact on the effects of acupuncture in alleviating pain, but exerts no evident influence on the comprehensive effect.

[Research on the interrelation analysis system of celestial spectrum data based on constraint FP tree].

It is an effective method of the mankind seeking after the celestial law that the inherent and unknown interrelationships between characteristics of celestial spectrum data and its physical and chemical properties are mined from the mass celestial body spectrum data. In the present paper, the interrelation analysis system of celestial body spectrum data based on constraint FP tree is designed and implemented by using the association rule based constraint FP tree as the way of analyzing celestial spectrum data, and adopting VC++ and Oracle9i as the development tools. At the same time, its software architecture and function modules are outlined. Its key techniques such as preprocessing of celestial body spectrum data,background knowledge representing, constraint FP-tree constructing, constraint frequent patterns and association rules mining etc are discussed in details. The running results show that the system is feasible and valuable for adopting association rule to describe the above interrelationships. Therefore, the interrelation analysis system of celestial body spectrum data provides an effective means for seeking after the inherent and unknown celestial law.

[A study on the outlier mining system for LAMOST spectra].

To find unknown celestial bodies is one of main goals in mankind's universe exploration, and outlier mining is a kind of effective way of finding unknown celestial bodies from mass spectrum data. In the present work, using VC++ and Oracle9i as development tools, an outlier mining system for star spectra is designed and realized, and its software architecture and function modules are outlined. At the same time, the system's key components such as star spectrum data preprocessing based on median filters, clustering of star spectrum data based on distance, outlier mining of star spectrum data based on distance support and three-dimensional visualization of star spectrum outlier based on PCA, are elaborated. The preliminary experimental results based on SDSS star spectrum data show that the system is workable for outlier mining of celestial body spectrum data, and a new kind of effective way of finding unknown and peculiar celestial body spectrum data.