DTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation

One of the major challenges in single-cell data analysis is the determination of cellular developmental trajectories using single-cell data. Although substantial studies have been conducted in recent years, more effective methods are still strongly needed to infer the developmental processes accurately. This work devises a new method, named DTFLOW, for determining the pseudotemporal trajectories with multiple branches. DTFLOW consists of two major steps: a new method called Bhattacharyya kernel feature decomposition(BKFD) to reduce the data dimensions, and a novel approach named Reverse Searching on k-nearest neighbor graph(RSKG) to identify the multi-branching processes of cellular differentiation. In BKFD, we first establish a stationary distribution for each cell to represent the transition of cellular developmental states based on the random walk with restart algorithm, and then propose a new distance metric for calculating pseudotime of single cells by introducing the Bhattacharyya kernel matrix. The effectiveness of DTFLOW is rigorously examined by using four single-cell datasets. We compare the efficiency of DTFLOW with the published state-of-the-art methods. Simulation results suggest that DTFLOW has superior accuracy and strong robustness properties for constructing pseudotime trajectories. The Python source code of DTFLOW can be freely accessed at https://github.com/statway/DTFLOW.     

Rotationally resliced 3D prostate segmentation of MR images using Bhattacharyya similarity and active band theory

PurposeIn this article, we propose a novel, semi-automatic segmentation method to process 3D MR images of the prostate using the Bhattacharyya coefficient and active band theory with the goal of providing technical support for computer-aided diagnosis and surgery of the prostate.MethodsOur method consecutively segments a stack of rotationally resectioned 2D slices of a prostate MR image by assessing the similarity of the shape and intensity distribution in neighboring slices. 2D segmentation is first performed on an initial slice by manually selecting several points on the prostate boundary, after which the segmentation results are propagated consecutively to neighboring slices. A framework of iterative graph cuts is used to optimize the energy function, which contains a global term for the Bhattacharyya coefficient with the help of an auxiliary function. Our method does not require previously segmented data for training or for building statistical models, and manual intervention can be applied flexibly and intuitively, indicating the potential utility of this method in the clinic.ResultsWe tested our method on 3D T2-weighted MR images from the ISBI dataset and PROMISE12 dataset of 129 patients, and the Dice similarity coefficients were 90.34 ± 2.21% and 89.32 ± 3.08%, respectively. The comparison was performed with several state-of-the-art methods, and the results demonstrate that the proposed method is robust and accurate, achieving similar or higher accuracy than other methods without requiring training.ConclusionThe proposed algorithm for segmenting 3D MR images of the prostate is accurate, robust, and readily applicable to a clinical environment for computer-aided surgery or diagnosis.     

Study on specificity of colon carcinoma-associated serum markers and establishment of SVM prediction model

We aimed to evaluate the specificity of 12 tumor markers related to colon carcinoma and identify the most sensitive index. Logistic regression and Bhattacharyya distance were used to evaluate the index. Then, different index combinations were used to establish a support vector machine (SVM) diagnosis model of malignant colon carcinoma. The accuracy of the model was checked. High accuracy was assumed to indicate the high specificity of the index. Through Logistic regression, three indexes, CEA, HSP60 and CA199, were screened out. Using Bhattacharyya distance, four indexes with the largest Bhattacharyya distance were screened out, including CEA, NSE, AFP, and CA724. The specificity of the combination of the above six indexes was higher than that of other combinations, so did the accuracy of the established SVM identification model. Using Logistic regression and Bhattacharyya distance for detection and establishing an SVM model based on different serum marker combinations can increase diagnostic accuracy, providing a theoretical basis for application of mathematical models in cancer diagnosis.     

WEBnm@ v2.0: Web server and services for comparing protein flexibility

Background

Normal mode analysis (NMA) using elastic network models is a reliable and cost-effective computational method to characterise protein flexibility and by extension, their dynamics. Further insight into the dynamics–function relationship can be gained by comparing protein motions between protein homologs and functional classifications. This can be achieved by comparing normal modes obtained from sets of evolutionary related proteins.

Results

We have developed an automated tool for comparative NMA of a set of pre-aligned protein structures. The user can submit a sequence alignment in the FASTA format and the corresponding coordinate files in the Protein Data Bank (PDB) format. The computed normalised squared atomic fluctuations and atomic deformation energies of the submitted structures can be easily compared on graphs provided by the web user interface. The web server provides pairwise comparison of the dynamics of all proteins included in the submitted set using two measures: the Root Mean Squared Inner Product and the Bhattacharyya Coefficient. The Comparative Analysis has been implemented on our web server for NMA, WEBnm@, which also provides recently upgraded functionality for NMA of single protein structures. This includes new visualisations of protein motion, visualisation of inter-residue correlations and the analysis of conformational change using the overlap analysis. In addition, programmatic access to WEBnm@ is now available through a SOAP-based web service. Webnm@ is available at http://apps.cbu.uib.no/webnma.

Conclusion

WEBnm@ v2.0 is an online tool offering unique capability for comparative NMA on multiple protein structures. Along with a convenient web interface, powerful computing resources, and several methods for mode analyses, WEBnm@ facilitates the assessment of protein flexibility within protein families and superfamilies. These analyses can give a good view of how the structures move and how the flexibility is conserved over the different structures.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0427-6) contains supplementary material, which is available to authorized users.     

Estimation of spectral similarities utilizing segmented regions' probability distribution in the block-optimized pan-sharpened image for material classification

Pan-sharpening is an image fusion approach that combines the spectral information in multispectral (MS) images with the spatial properties of PAN (Panchromatic) images. This vital technique is used in categorization, detection, and other remote sensing applications. In the first step, the article focuses on increasing the finer spatial details in the MS image with PAN images using two levels of fusion without causing spectral deterioration. The suggested fusion method efficiently utilizes image transformation techniques and spatial domain image fusion methods. The luminance component of MS images typically contains spatial features that are not as detailed as the PAN images. A multiscale transform is applied to the intensity/luminance component and PAN image to introduce features into the intensity component. In the first level of processing, coefficients obtained from the non-subsampled contourlet transform are subjected to particle swarm optimization weighted block-based fusion. The second level of fusion is carried out using the concept of spatial frequency to reduce spectral distortion. Numerous reference and non-reference parameters are used to evaluate the sharpened image's quality. In the next step, the article focuses on designing an evaluation metric for analysing spectral distortion based on the Bhattacharyya coefficient and distance. The Bhattacharyya coefficient and distance are calculated for each segmented region to assess the sharpened images' quality. Spectral degradation analysis using proposed techniques can also be useful for analysing materials in the segmented regions. The research findings demonstrate that the spatial features of fused images obtained from the proposed technique increased with the least spectral degradation.     

On a property of the exponential distribution

If t is an independent exponentially distributed random variable, the distribution p = [t - x] is a modified geometric distribution, similar to the result of HAWKINS and KOTZ (1976), x is uniform.     

Reconstructing the Pleistocene geography of the Aphelocoma jays (Corvidae)

Understanding historical distributions of species and evolving lineages has been a topic of considerable interest, yet methods used to date have not provided detailed, quantitative distributional hypotheses. Here, we present a technique based on models of species’ ecological niches and Pleistocene climate reconstructions that provides such hypotheses, providing the example of reconstructions for the Aphelocoma jays. We demonstrate in general a greater degree than expected of stability in jay species’ distributional areas back through at least the most recent glaciation event, and that existing patterns of genetic differentiation may date to before the Late Pleistocene glaciations. More generally, the method offers the potential for reconstructing historical distributions of species or lineages, and providing a detailed geographic framework for addressing many biogeographic and systematic questions.     

Compound Intervened Poisson Distribution

The nature and characteristics of Intervened Poisson Distribution (IPD) has been well discussed by Shanmugam (1985). In this paper, Compound Intervened Poisson Distribution (CIPD) is introduced and its properties are studied.     

Modelling potential biotope composition on a regional scale revealed that climate variables are stronger drivers than soil variables

Aim

Environmental conditions define the suitability of an area for biotopes, and any area can be suitable for several biotopes. However, most previous studies modelled the distribution of single biotopes ignoring the potential co-occurrence of biotopes in one area, which limits the usefulness of such models for conservation and restoration planning. In this study, we described the potential biotope composition of an area in response to environmental conditions.

Location

Bavaria, Federal State of Germany.

Methods

Based on the Bavarian biotope mapping data, we modelled the distribution of 29 terrestrial biotopes based on six climate variables and six chemical and four physical soil properties using the species distribution modelling algorithm Maxent.

Results

For most biotopes, we found that climate variables were more important than soil variables for the biotope distribution and that the area of the predicted biotope distribution was larger than the observed distribution. The potential biotope composition illustrated that while 8% of the area in Bavaria was not sufficiently suitable for any analysed biotope, 92% of the modelled area in Bavaria was suitable for at least one biotope, 84% for two and 77% for at least three biotopes. The difference in suitability between the most suitable biotopes in composition was minor. Further, over one-quarter of the modelled area was suitable for 6–8 different biotopes.

Main Conclusions

Our study showed that considering a composition of potentially suitable biotopes in a raster cell, instead of only the most suitable biotope, provides valuable information to identify conservation priorities and restoration opportunities.     

Evaluation of the spatial distribution of Anoplophora glabripennis (Motschulsky) (Coleoptera: Cerambycidae) in South Korea combining climate and host plant distribution

Anoplophora glabripennis (Motschulsky) (Coleoptera: Cerambycidae), a global forest pest, has a potential to damage forests in South Korea, requiring an effective tool for evaluating its potential distribution. This study aimed to evaluate the spatial distribution of A. glabripennis in South Korea by simultaneously considering climate and host plants. Climatic suitability was firstly evaluated using a CLIMEX model; then, it was combined with the areal distribution of host plants using a simple mathematical formulation. We finally projected the spatial distribution of A. glabripennis onto the map of administrative districts to identify hazardous areas to watch. As a result, the developed model predicted that over 40% of areas in South Korea could be exposed to A. glabripennis damage, and most of them were located in mountainous areas with abundant host plants. In addition, climatic suitability was higher in coastal areas, which was different than a previous record of A. glabripennis occurrence, while the prediction by a comprehensive model was consistent with the record. In conclusion, the model including both climate and host plant occurrence was more reliable than the model which only included climate, and could provide useful data for determining areas for monitoring and control.     

On the Discrete POISSON-Inverse GAUSSian Distribution

The discrete POISSON-inverse GAUssian distribution is obtained by compounding the POISSON distribution with the inverse-GAUssian distribution. The maximum likelihood of the parameter is discussed. Comparison with the POISSON, negative binomial, POISSON-LINDLEY and the generalized WARING distribution when fitting a distribution to accident statistics data is given.     

动物种群空间分布Matlab源程序

动物种群空间分布因物种、种群、生境等特点的不同可以分为均匀分布、随机分布、聚集分布（负二项分布、Neyman分布、Poisson-二项分布）等类型,根据上述各分布函数用Matlab语言分别编程,以达到各分布函数理论值计算简化的目的。