Zhongmeng Zhao

Chaotic image encryption based on circular substitution box and key stream buffer

Abstract A new image encryption algorithm based on spatiotemporal chaotic system is proposed, in which the circular S-box and the key stream buffer are introduced to increase the security. This algorithm is comprised of a substitution process and a diffusion process. In the substitution process, the S-box is considered as a circular sequence with a head pointer, and each image pixel is replaced with an element of S-box according to both the pixel value and the head pointer, while the head pointer varies with the previous substituted pixel. In the diffusion process, the key stream buffer is used to cache the random numbers generated by the chaotic system, and each image pixel is then enciphered by incorporating the previous cipher pixel and a random number dependently chosen from the key stream buffer. A series of experiments and security analysis results demonstrate that this new encryption algorithm is highly secure and more efficient for most of the real image encryption practices.

A probabilistic method for identifying rare variants underlying complex traits

BackgroundIdentifying the genetic variants that contribute to disease susceptibilities is important both for developing methodologies and for studying complex diseases in molecular biology. It has been demonstrated that the spectrum of minor allelic frequencies (MAFs) of risk genetic variants ranges from common to rare. Although association studies are shifting to incorporate rare variants (RVs) affecting complex traits, existing approaches do not show a high degree of success, and more efforts should be considered.ResultsIn this article, we focus on detecting associations between multiple rare variants and traits. Similar to RareCover, a widely used approach, we assume that variants located close to each other tend to have similar impacts on traits. Therefore, we introduce elevated regions and background regions, where the elevated regions are considered to have a higher chance of harboring causal variants. We propose a hidden Markov random field (HMRF) model to select a set of rare variants that potentially underlie the phenotype, and then, a statistical test is applied. Thus, the association analysis can be achieved without pre-selection by experts. In our model, each variant has two hidden states that represent the causal/non-causal status and the region status. In addition, two Bayesian processes are used to compare and estimate the genotype, phenotype and model parameters. We compare our approach to the three current methods using different types of datasets, and though these are simulation experiments, our approach has higher statistical power than the other methods. The software package, RareProb and the simulation datasets are available at: http://www.engr.uconn.edu/~jiw09003.

Self-embedding fragile watermarking based on DCT and fast fractal coding

A self-embedding fragile watermarking scheme is proposed in this paper, which is based on Discrete Cosine Transform and fractal compression coding. To overcome the high computational complexity of fractal coding, a fast coding method is also presented that improves the efficiency of fractal block coding in the watermarking procedure. In our algorithm, three kinds of watermarks are generated for image authentication and recovery, which is based on an interleaved and overlapped 8 ×8 image block structure. This makes our method obtain an authentication granularity of 4 ×4 approximately. At the same time, we take advantage of two levels of mapping to select mapping block for every image block. Three versions of recovery watermarks for each block are embedded in different quadrants, which provides another two chances for block recovery in case one is destroyed. Experimental results demonstrate that the proposed scheme not only outperforms conventional self-embedding fragile watermarking algorithms in tamper recovery, but also improves the security against the various counterfeiting attacks.

A chaos-based image encryption scheme using 2D rectangular transform and dependent substitution

Chaos-based image cryptosystems usually adopt the traditional confusion-diffusion architecture which is considered insecure against known/chosen plaintext attacks. To overcome this drawback, this paper proposes a novel chaos-based image encryption scheme, in which the two-dimensional rectangular transform is employed to directly scramble the image of any rectangular size, and the dependent substitution is introduced to substitute for each pixel according to the image pixels. This scheme comprises two stages of encryption processes. Each stage provides the confusion and diffusion simultaneously in one traverse of image pixels. As a result, the proposed scheme has high speed and achieves a satisfactory security performance. Experimental results and various types of security analysis indicate that this scheme is efficient and secure enough to be used for practical image encryption and transmission.

An improved burden-test pipeline for identifying associations from rare germline and somatic variants

BackgroundIdentifying rare germline and somatic variants associated with cancer progression is an important research topic in cancer genomics. Although many approaches are proposed for rare variant association study, they are not fit for cancer sequencing data due to multiple issues, such as overly relying on pre-selection, losing sight of interacting hotspots, etc.ResultsIn this article, we propose an improved pipeline to identify germline variant and somatic mutation interactions influencing cancer susceptibility from pair-wise cancer sequencing data. The proposed pipeline, RareProb-C performs an algorithmic selection on the given variants by incorporating the variant allelic frequencies. The interactions among the variants are considered within the regions which are limited by a four-gamete test. Then it filters singular cases according to the posterior probability at each site. Finally, it outputs the selected candidates that pass a collapse test.ConclusionsWe apply RareProb-C on a series of carefully constructed simulation cases and it outperforms six existing genetic model-free approaches. We also test RareProb-C on 429 TCGA ovarian cancer cases, and RareProb-C successfully identifies the known highlighted variants which are considered increasing disease susceptibilities.

Identifying Heterogeneity Patterns of Allelic Imbalance on Germline Variants to Infer Clonal Architecture

It is suggested that the evolution of somatic mutations may be significant impacted by inherited polymorphisms, while the clonal somatic copy-number mutations may contribute to the potential selective advantages of heterozygous germline variants. A fine resolution on clonal architecture of such cooperative germline-somatic dynamics provides insight into tumour heterogeneity and offers clinical implications. Although it is reported that germline allelic imbalance patterns often play important roles, existing approaches for clonal analysis mainly focus on single nucleotide sites. To address this need, we propose a computational method, GLClone that identifies and estimates the clonal patterns of the copy-number alterations on germline variants. The core of GLClone is a hierarchical probabilistic model. The variant allelic frequencies on germline variants are modeled as observed variables, while the cellular prevalence is designed as hidden states and estimated by Bayesian posteriors. A variational approximation algorithm is proposed to train the model and estimate the unknown variables and model parameters. We examine GLClone on several groups of simulation datasets, which are generated by different configurations, and compare to three popular state-of-the-art approaches, and GLClone outperforms on accuracy, especially a complex clonal structure exists.

Accurately Estimating Tumor Purity of Samples with High Degree of Heterogeneity from Cancer Sequencing Data

Tumor purity is the proportion of tumor cells in the sampled admixture. Estimating tumor purity is one of the key steps for both understanding the tumor micro-environment and reducing false positives and false negatives in the genomic analysis. However, existing approaches often lose some accuracy when analyzing the samples with high degree of heterogeneity. The patterns of clonal architecture shown in sequencing data interfere with the data signals that the purity estimation algorithms expect. In this article, we propose a computational method, EMPurity, which is able to accurately infer the tumor purity of the samples with high degree of heterogeneity. EMPurity captures the patterns of both the tumor purity and clonal structure by a probabilistic model. The model parameters are directly calculated from aligned reads, which prevents the errors transferring from the variant calling results. We test EMPurity on a series of datasets comparing to three popular approaches, and EMPurity outperforms them on different simulation configurations.

An Expanded Association Approach for Rare Germline Variants with Copy-Number Alternation

Tumorigenesis is considered as a complex process that is often driven by close interactions between germline variants and accumulated somatic mutational events. Recent studies report that some somatic copy-number alternations show such interactions by harboring germline susceptibility variants under potential selection in clonal expansions. Incorporating these interactions into genetic association approach could be valuable in not only discovering novel susceptibility variants, but providing insight into tumor heterogeneity and clinical implications. To address this need, in this article, we propose RareProb-G, an expanded version of a computational method, which is designed for identifying rare germline susceptibility variants located in the somatic allelic amplification or loss of heterozygosity regions. RareProb-G is based on a hidden Markov random field model. The interactions among germline variants and somatic events are modeled by a neighborhood system, which is bounded by a t-test on variant allelic frequencies. Each variant is assigned four hidden states, which represent the regional status and causal/neutral status, respectively. A hidden Markov model is also introduced to estimate the initial values of the hidden states and unknown model parameters. To verify this approach, we conduct a series of simulation experiments under different configurations, and RareProb-G outperforms than RareProb on both sensitivity and specificity.

Identifying interacting genetic variations by fish-swarm logic regression.

Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR) is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR), which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds.

TNSim: A Tumor Sequencing Data Simulator for Incorporating Clonality Information

In recent years, the next generation sequencing enables us to obtain high resolution landscapes of the genetic changes at single-nucleotide level. More and more novel methods are proposed for efficient and effective analyses on cancer sequencing data. To facilitate such development, data simulator is a crucial tool, which not only tests and evaluates proposed approaches, but provides the feedbacks for further improvements as well. Several simulators are released to generate the next generation sequencing data. However, based on our best knowledge, none of them considers clonality information. It is suggested that clonal heterogeneity does widely exist in tumor samples. The patterns of somatic mutational events usually expose a wide spectrum of variant allelic frequencies, while some of them are only detectable in one or multiple clonal lineages. In this article, we introduce a Tumor-Normal sequencing Simulator, TNSim, to generate the next generation sequencing data by involving clonality information. The simulator is able to mimic a tumor sample and the paired normal sample, where the germline variants and somatic mutations can be settled respectively. Tumor purity is adjustable. Clonal architecture is preassigned as one or more clonal lineages, where each lineage consists of a set of somatic mutations whose variant allelic frequencies are similar. A group of experiments are conducted to evaluate its performance. The statistical features of the artificial sequencing reads are comparable to the real tumor sequencing data whose sample consists of multiple sub-clones. The source codes are available at http://github.com/lnmxgy/TNSim and for academic use only.