Linear-fitting-based similarity coefficient map for tissue dissimilarity analysis in T2^＊-w magnetic resonance imaging

Similarity coefficient mapping（SCM） aims to improve the morphological evaluation of T＊2weighted magnetic resonance imaging（T＊2-w MRI）. However, how to interpret the generated SCM map is still pending. Moreover, is it probable to extract tissue dissimilarity messages based on the theory behind SCM？ The primary purpose of this paper is to address these two questions. First, the theory of SCM was interpreted from the perspective of linear fitting. Then, a term was embedded for tissue dissimilarity information. Finally, our method was validated with sixteen human brain image series from multiecho T＊2-w MRI. Generated maps were investigated from signal-to-noise ratio（SNR） and perceived visual quality, and then interpreted from intra- and inter-tissue intensity. Experimental results show that both perceptibility of anatomical structures and tissue contrast are improved. More importantly, tissue similarity or dissimilarity can be quantified and cross-validated from pixel intensity analysis. This method benefits image enhancement, tissue classification, malformation detection and morphological evaluation.

Feasibility of similarity coefficient map for improving morphological evaluation of T_2* weighted MRI for renal cancer

The purpose of this paper is to investigate the feasibility of using a similarity coefficient map（SCM） in improving the morphological evaluation of T2＊ weighted（T2＊W） magnatic resonance imaging（MRI） for renal cancer.Simulation studies and in vivo 12-echo T2＊W experiments for renal cancers were performed for this purpose.The results of the first simulation study suggest that an SCM can reveal small structures which are hard to distinguish from the background tissue in T2＊W images and the corresponding T2＊ map.The capability of improving the morphological evaluation is likely due to the improvement in the signal-to-noise ratio（SNR） and the carrier-to-noise ratio（CNR） by using the SCM technique.Compared with T2＊ W images,an SCM can improve the SNR by a factor ranging from 1.87 to 2.47.Compared with T2＊ maps,an SCM can improve the SNR by a factor ranging from 3.85 to 33.31.Compared with T2＊W images,an SCM can improve the CNR by a factor ranging from 2.09 to 2.43.Compared with T2＊ maps,an SCM can improve the CNR by a factor ranging from 1.94 to 8.14.For a given noise level,the improvements of the SNR and the CNR depend mainly on the original SNRs and CNRs in T2＊W images,respectively.In vivo experiments confirmed the results of the first simulation study.The results of the second simulation study suggest that more echoes are used to generate the SCM,and higher SNRs and CNRs can be achieved in SCMs.In conclusion,an SCM can provide improved morphological evaluation of T2＊W MR images for renal cancer by unveiling fine structures which are ambiguous or invisible in the corresponding T2＊W MR images and T2＊ maps.Furthermore,in practical applications,for a fixed total sampling time,one should increase the number of echoes as much as possible to achieve SCMs with better SNRs and CNRs.

Comparisons of Several Similarity Coefficients for Cluster Analysis of Hot Pepper Germplasm Resources

An appropriate genetic similarity coefficient is particularly important for accurately estimating the genetic similarity and phylogenetic relationship between individuals and evaluating the genetic diversity of populations. In this study, five genetic similarity coefficients were compared for analysis of phylogenetic relationship among 31 hot pepper inbred lines based on SRAP. The applicability of different genetic similarity coefficient was investigated by means of SRAP data of hot pepper inbred lines. According to the experimental results, the variation ranges of Nei ＆ Li, Jaceard, Sorensen, Simple matching and Yule coefficients were 0. 598 - 0. 973, 0. 427 - 0. 947, 0. 598 - 0. 973, 0.427 - 0. 947 and 0. 133 - 0. 997, respectively. Results of cluster analysis based on different similarity coefficients varied greatly. To be specific, clustering results based on Nei ＆ Li, Jaccard and Sorensen coefficients were consistent; clustering with Simple matching and Yule coef ficients led to consistent classification of category in different order and slightly different classification of subcategory. Comprehensively comparing the results of cluster analysis and the dendrograms of hot pepper inbred lines, Yule coefficient is suitable for SRAP analysis of hot pepper.

Continuous and Discrete Similarity Coefficient for Identifying Essential Proteins Using Gene Expression Data

Essential proteins play a vital role in biological processes,and the combination of gene expression profiles with Protein-Protein Interaction(PPI)networks can improve the identification of essential proteins.However,gene expression data are prone to significant fluctuations due to noise interference in topological networks.In this work,we discretized gene expression data and used the discrete similarities of the gene expression spectrum to eliminate noise fluctuation.We then proposed the Pearson Jaccard coefficient(PJC)that consisted of continuous and discrete similarities in the gene expression data.Using the graph theory as the basis,we fused the newly proposed similarity coefficient with the existing network topology prediction algorithm at each protein node to recognize essential proteins.This strategy exhibited a high recognition rate and good specificity.We validated the new similarity coefficient PJC on PPI datasets of Krogan,Gavin,and DIP of yeast species and evaluated the results by receiver operating characteristic analysis,jackknife analysis,top analysis,and accuracy analysis.Compared with that of node-based network topology centrality and fusion biological information centrality methods,the new similarity coefficient PJC showed a significantly improved prediction performance for essential proteins in DC,IC,Eigenvector centrality,subgraph centrality,betweenness centrality,closeness centrality,NC,PeC,and WDC.We also compared the PJC coefficient with other methods using the NF-PIN algorithm,which predicts proteins by constructing active PPI networks through dynamic gene expression.The experimental results proved that our newly proposed similarity coefficient PJC has superior advantages in predicting essential proteins.

Comparison of Some Methods on Prediction of Activity Interaction Coefficient

Based on Miedema's semiempirical formation enthalpy model for binary alloys, free volume theory and ageneral solution model, a new model for prediction of activity interaction coefficient ε is proposed. The calculatedresults are better in agreement with the experimental values than the two previous models. The related theories andmodels are discussed according to the degree of agreement with experimental values.

Similarity Analysis of Beetle Communities in Jiuzhaigou,Xiaozhaizigou, Caopo, Ma'anshan and Baihe Nature Reserves of Sichuan Province,China

In this study, beetle communities in Jiuzhaigou, Xiaozhaizigou, Caopo, Ma＇anshan and Baihe nature reserves of Sichuan Province were compared and analyzed. F index, G index and G-F index of beetle communities in five nature reserves were analyzed to calculate the similarity coefficient C between these five na- ture reserves. According to the results, beetle communities exhibited high dissimilarity between Jiuzhaigou and Xiaozhaizigou, Caopo and Jiuzhaigou, Caopo and Xiaozhaizigou, Ma＇anshan and Xiaozhaizigou, Ma＇anshan and Jiuzhaigou, Baihe and Xiaozhaizigou, Baihe and Caopo, Baihe and Ma＇anshan; beetle communities exhibited moderate dissimilarity between Ma＇anshan and Caopo, Baihe and Jiuzhaigou.

Determination of the Drag Coefficient over the Tibetan Plateau

In this paper,a preliminary study is given on the drag (i.e.bulk transfer for momentum) coefficient,on the basis of data from four sets of AWS in Tibet during the first observational year from July 1993 to July 1994 according to China Japan Asian Monsoon Cooperative Research Program.The results show that the drag coefficient over the Tibetan Plateau is 3.3 to 4.4×103.In addition,monthly and diurnal variations of drag coefficient and the relationship among the drag coefficients and the bulk Richardson number,surface roughness length and wind speed at 10 m height are discussed in detail.

Group decision-making method based on entropy and experts cluster analysis

According to the aggregation method of experts＇ evaluation information in group decision-making,the existing methods of determining experts＇ weights based on cluster analysis take into account the expert＇s preferences and the consistency of expert＇s collating vectors,but they lack of the measure of information similarity.So it may occur that although the collating vector is similar to the group consensus,information uncertainty is great of a certain expert.However,it is clustered to a larger group and given a high weight.For this,a new aggregation method based on entropy and cluster analysis in group decision-making process is provided,in which the collating vectors are classified with information similarity coefficient,and the experts＇ weights are determined according to the result of classification,the entropy of collating vectors and the judgment matrix consistency.Finally,a numerical example shows that the method is feasible and effective.

Automatic pickup of arrival time of channel wave based on multi-channel constraints

Accurately detecting the arrival time of a channel wave in a coal seam is very important for in-seam seismic data processing. The arrival time greatly affects the accuracy of the channel wave inversion and the computed tomography （CT） result. However, because the signal-to-noise ratio of in-seam seismic data is reduced by the long wavelength and strong frequency dispersion, accurately timing the arrival of channel waves is extremely difficult. For this purpose, we propose a method that automatically picks up the arrival time of channel waves based on multi-channel constraints. We first estimate the Jaccard similarity coefficient of two ray paths, then apply it as a weight coefficient for stacking the multi- channel dispersion spectra. The reasonableness and effectiveness of the proposed method is verified in an actual data application. Most importantly, the method increases the degree of automation and the pickup precision of the channel-wave arrival time.

Method of fuzzy uniformity analysis for simulation underwater acoustic signals

Since the simulation underwater acoustic signal is used in the semi-object simulation experiment of underwater weapons, it has great impression upon simulation fidelity. It is asked that whether simulation signals can replace the real signal effectually. Considering the randomness of signals, the interval estimation of feature parameters of simulation signals is made. By comparing the obtained confidence interval with the corresponding accept interval, the concept of similarity coefficient of simulation signals is given. By making a statistical analysis for similarity coefficient, the uniformity information of simulation signals is extracted, and the fuzzy number which expresses the fuzzy uniformity level of simu- lation signals is obtained. The analysis method on fuzzy uniformity of simulation underwater acoustic signals is presented. It is indi- cated by the application in simulation of target radiated-noises that the method is suitable and effectual for the simulation research on underwater acoustic signals, and the analysis result may provide support for decision-making relative to perfecting simulation sys- tems and applying simulation signals.

Relationship between Surface Pollen and Modern Vegetation in Southwestern China

Based on the pollen data obtained from thirty-five surface soil samples and investigated vegetation data from seven plant quadrats,the quantitative relationship between surface soil pollen and modern vegetation are studied in the longitudinal range-gorge region (LRGR) in Southwestern China.R-values (referring to pollen assemblages) are calculated with pollen percentage and plant abundance.The coefficients of similarity between pollen and vegetation are analyzed.The results show that the pollen assemblages on surface soil of all vegetation zones can basically represent the native vegetation,but the pollen assemblages are not fully according with the vegetation.This is due to the influencing factors including pollen preservation ability,pollen production,amount of exotic pollen and pollen identification.The pollen representation in surface soil is different in families and genera.The pollen of woody plants such as Pinus,Tsuga,Alnus,Fagus and Castanopsis are over-representative,but those of Quercus,Carpinus Myrica,Elaeocarpaceae,Ericaceae,Theaceae and Llex are underrepresentative;the pollen of herbaceous plants such as Artemisia and Rubiaceae are over-representative,while those of others including Gramineae and Araliaceae are under-representative.The R-values of the same taxon pollen in different vegetation zones are different,depending on their distances from the pollen sources.The coefficients of the similarity between plant communities and pollen assemblages are mostly over 70%,which indicate again that the surface soil pollen and spores assemblages can represent the vegetation.It is concluded that there is a good corresponding relationship between surface pollen assemblages and native vegetation in LRGR,and it is of great significance for reconstructing the past vegetation and paleoclimate using quantitative fossil pollen data in this region.

Multivariate similarity clustering analysis:a new method regarding biogeography and its application in global insects

A new method,multivariate similarity clustering analysis(MSCA)method,was established for biogeographical distribution analyzing.General similarity formula(GSF),the core of MSCA method,can be used to calculate the similarity coefficients between 2 and among any≥3 geographical units.Taking the global insects as example,we introduced the steps to use of GSF and consequent clustering processes of this method in details.Firstly,geographical distributions of certain taxa(e.g.Insecta)were categorized into basic geographical units(BGUs);Secondly,similarity coefficients between 2 and among n BGUs were calculated using GSF.Thirdly,hierarchical clustering was conducted according to values of similarity coefficients(from high to low);then a clustering diagram was generated.Finally,a framework of biogeographical division map was established for the target taxa(e.g.Insecta).We concluded that the MSCA method was efficiently applied in analyzing the biogeographical distribution of given biological taxa;the geographical regions regarding global insects were categorized into 7 Realms with 20 sub-Realms based on the results of MSCA method.

Characteristics of industrial structure evolution and isomorphism in Central Asia

This study undertakes a systematic examination of characteristics of the spatio-temporal evolution of industrial economies in Central Asia from the perspectives of industrial scale,structural rationality,industrial competitiveness,and industrial isomorphism.The results show that industrial structures in Central Asian countries are becoming increasingly advanced,with certain differences among them in the characteristics of this evolution.Kazakhstan has long had a tertiary-secondary-primary industrial pattern,and productive services have played an increasingly prominent role in the development of its tertiary industry.The transformation of the industrial structure in Uzbekistan,from a secondary-tertiary-primary pattern at its independence from the Soviet Union to a tertiary-secondary-primary pattern,is apparent.Tajikistan's industrial structure has also changed significantly in recent times.Its secondary industries shrunk while tertiary industries developed rapidly.In Kyrgyzstan,the ratios of secondary and tertiary industries to total industrial output have fluctuated significantly while considerable progress has been made in the service sector.The industrial structure of Turkmenistan is significantly lower than the other countries,and Turkmenistan is the only country in the Central Asian region which still shows a tertiary-secondary-primary industrial pattern.The feasibility and competitiveness of the industrial structures of these five Central Asian countries have different characteristics.Kazakhstan has structural advantages but lags in competitiveness,Uzbekistan is driven by both structural and competitive advantages,Tajikistan enjoys structural advantages while Kyrgyzstan lags behind in competitiveness,and Turkmenistan has a competitiveness-driven economy.Furthermore,values of the similar coefficient index of the three industrial structures in these countries were mostly above 0.95,the coefficients of the secondary industrial subdivisions in some countries were below 0.85,and those of tertiary industrial subdivisions among most countries were above 0.89,indicating considerable similarities in industrial structure among them.These findings are important in the context of establishing an effective industrial development strategy for the Silk Road Economic Belt,improving international cooperation,and upgrading industrial structures to achieve economic prosperity.