MicroRNAs (miRNAs) are endogenous ∼23 nt RNAs that play important gene-regulatory roles in animals and plants by pairing to the mRNAs of protein-coding genes to direct their posttranscriptional repression. This review outlines the current understanding of miRNA target recognition in animals and discusses the widespread impact of miRNAs on both the expression and evolution of protein-coding genes.

MicroRNAs: Target Recognition and Regulatory Functions

Image Steganography is a thriving research area of information security where secret data is embedded in images to hide its existence while getting the minimum possible statistical detectability. This paper proposes a novel magic least significant bit substitution method (M-LSB-SM) for RGB images. The proposed method is based on the achromatic component (I-plane) of the hue-saturation-intensity (HSI) color model and multi-level encryption (MLE) in the spatial domain. The input image is transposed and converted into an HSI color space. The I-plane is divided into four sub-images of equal size, rotating each sub-image with a different angle using a secret key. The secret information is divided into four blocks, which are then encrypted using an MLE algorithm (MLEA). Each sub-block of the message is embedded into one of the rotated sub-images based on a specific pattern using magic LSB substitution. Experimental results validate that the proposed method not only enhances the visual quality of stego images but also provides good imperceptibility and multiple security levels as compared to several existing prominent methods.

A novel magic LSB substitution method (M-LSB-SM) using multi-level encryption and achromatic component of an image

Identifying interesting relationships between pairs of variables in large data sets is increasingly important. Here, we present a measure of dependence for two-variable relationships: the maximal information coefficient (MIC). MIC captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination (R2) of the data relative to the regression function. MIC belongs to a larger class of maximal information-based nonparametric exploration (MINE) statistics for identifying and classifying relationships. We apply MIC and MINE to data sets in global health, gene expression, major-league baseball, and the human gut microbiota and identify known and novel relationships.

Detecting Novel Associations in Large Data Sets

Steganography is a method of hiding secret messages in a cover object while communication takes place between sender and receiver. Security of confidential information has always been a major issue from the past times to the present time. It has always been the interested topic for researchers to develop secure techniques to send data without revealing it to anyone other than the receiver. Therefore from time to time researchers have developed many techniques to fulfill secure transfer of data and steganography is one of them. In this paper we have proposed a new technique of image steganography i.e. Hash-LSB with RSA algorithm for providing more security to data as well as our data hiding method. The proposed technique uses a hash function to generate a pattern for hiding data bits into LSB of RGB pixel values of the cover image. This technique makes sure that the message has been encrypted before hiding it into a cover image. If in any case the cipher text got revealed from the cover image, the intermediate person other than receiver can't access the message as it is in encrypted form.

A Secure Image Steganography Based on RSA Algorithm and Hash-LSB Technique

This paper introduces a best approach for Least Significant Bit (LSB) based on image steganography that enhances the existing LSB substitution techniques to improve the security level of hidden information. It is a new approach to substitute LSB of RGB true color image. The new security conception hides secret information within the LSB of image where a secret key encrypts the hidden information to protect it from unauthorized users. In general, in LSB methods, hidden information is stored into a specific position of LSB of image. For this reason, knowing the retrieval methods, anyone can extract the hidden information. In our paper, hidden information is stored into different position of LSB of image depending on the secret key. As a result, it is difficult to extract the hidden information knowing the retrieval methods. We have used the Peak Signal-to-Noise Ratio (PSNR) to measure the quality of the stego images. The value of PSNR gives better result because our proposed method changes very small number of bits of the image. The obtained results show that the proposed method results in LSB based image steganography using secret key which provides good security issue and PSNR value than general LSB based image steganography methods.

A new approach for LSB based image steganography using secret key

microRNAs (miRNAs) play an essential role in the post-transcriptional gene regulation in plants and animals. They regulate a wide range of biological processes by targeting messenger RNAs (mRNAs). Evidence suggests that miRNAs and mRNAs interact collectively in gene regulatory networks. The collective relationships between groups of miRNAs and groups of mRNAs may be more readily interpreted than those between individual miRNAs and mRNAs, and thus are useful for gaining insight into gene regulation and cell functions. Several computational approaches have been developed to discover miRNA-mRNA regulatory modules (MMRMs) with a common aim to elucidate miRNA-mRNA regulatory relationships. However, most existing methods do not consider the collective relationships between a group of miRNAs and the group of targeted mRNAs in the process of discovering MMRMs. Our aim is to develop a framework to discover MMRMs and reveal miRNA-mRNA regulatory relationships from the heterogeneous expression data based on the collective relationships. We propose DIscovering COllective group RElationships (DICORE), an effective computational framework for revealing miRNA-mRNA regulatory relationships. We utilize the notation of collective group relationships to build the computational framework. The method computes the collaboration scores of the miRNAs and mRNAs on the basis of their interactions with mRNAs and miRNAs, respectively. Then it determines the groups of miRNAs and groups of mRNAs separately based on their respective collaboration scores. Next, it calculates the strength of the collective relationship between each pair of miRNA group and mRNA group using canonical correlation analysis, and the group pairs with significant canonical correlations are considered as the MMRMs. We applied this method to three gene expression datasets, and validated the computational discoveries. Analysis of the results demonstrates that a large portion of the regulatory relationships discovered by DICORE is consistent with the experimentally confirmed databases. Furthermore, it is observed that the top mRNAs that are regulated by the miRNAs in the identified MMRMs are highly relevant to the biological conditions of the given datasets. It is also shown that the MMRMs identified by DICORE are more biologically significant and functionally enriched.

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Identification of miRNA-mRNA regulatory modules by exploring collective group relationships

In many real-world situations, individual components of complex systems tend to form groups to interact collectively. The grouping effectuates collective relationships. On the other hand, collective relationshsips stimulate individual components to form groups. To gain clear understanding of the structure and functioning of these systems, it is necessary to identify both group formation and collective relationships at the same time. In this paper, we define the notation of collective group relationships (CGRs) between two sets of individual components and propose a method to discover CGRs from heterogeneous datasets. The method integrates canonical correlation analysis (CCA) with graph mining to find top-k CGRs. Several experimental studies are conducted on both synthetic and real-world datasets to demonstrate the effectiveness and efficiency of the proposed method.

Discovering Collective Group Relationships

Automated Theorem Proving systems are enormously powerful computer programs capable of solving immensely difficult problems. The extreme capabilities of these systems lie on some well-established proof systems. Semantic tableau is such a proof system used to prove the validity of a formula by contradiction and can produce a counterexample if it fails. It can also be used to prove whether a formula is a logical consequence of a set of formulas. Tableau can be used in propositional logic, predicate logic, modal logic, temporal logic, and in other non-classical logics. In this paper, we describe the implementation of a sequential tableau algorithm for propositional logic using a procedural programming language rather then logic programming language. We also propose a tableau based proof system in a distributed environment using the message passing interface. Successful implementation of the proposed high performance approach will un-wrap an efficient paradigm for automated theorem proving.

Towards a tableau based high performance automated theorem prover

Automated Theorem Proving systems are enormously powerful computer programs capable of solving immensely difficult problems. The extreme capabilities of these systems lie on some well-established proof systems, such as Semantic tableau. It is used to prove the validity of a formula by contradiction and it can produce a counterexample if it fails. It can also be used to prove whether a formula is a logical consequence of a set of formulas. Tableau can be used in propositional logic, predicate logic, modal logic, temporal logic, and in other non-classical logics. In this article, we describe a detailed implementation of a sequential tableau algorithm for propositional and first order logic using a procedural language rather then logic programming language. We also illustrate a tableau based proof system in a distributed environment using the Message Passing Interface. This paper also investigates two distinct approaches for parallel and distributed implementation and describes the experimental formula generation procedure. The proposed high performance approach will un-wrap an efficient paradigm for automated theorem proving.

S. M. Masud Karim

Papers

A new approach for LSB based image steganography using secret key

Identification of miRNA-mRNA regulatory modules by exploring collective group relationships

Discovering Collective Group Relationships

Towards a tableau based high performance automated theorem prover

A Tableau Based Automated Theorem Prover Using High Performance Computing