A steganalysis-based approach to comprehensive identification and characterization of functional regulatory elements

The comprehensive identification of cis-regulatory elements on a genome scale is a challenging problem. We develop a novel, steganalysis-based approach for genome-wide motif finding, called WordSpy, by viewing regulatory regions as a stegoscript with cis-elements embedded in 'background' sequences. We apply WordSpy to the promoters of cell-cycle-related genes of Saccharomyces cerevisiae and Arabidopsis thaliana, identifying all known cell-cycle motifs with high ranking. WordSpy can discover a complete set of cis-elements and facilitate the systematic study of regulatory networks

An Iterative Learning Algorithm for Deciphering Stegoscripts: a Grammatical Approach for Motif Discovery

Steganography, or information hiding, is to conceal the existence of messages so as to protect their confidentiality. We consider de-ciphering a stegoscript, a text with secret messages embedded within a covertext, and identifying the vocabularies used in the mes-sages, with no knowledge of the vocabularies and grammar in which the script was writ-ten. Our research was motivated by the prob-lem of identifying conserved non-coding func-tional elements (motifs) in regulatory regions of genome sequences, which we view as stego-scripts constructed by nature with a statis-tical model consisting of a dictionary and a grammar. We develop an iterative learning algorithm, WordSpy, to learn such a model from a stegoscript. The model then can be applied to identify the embedded secret mes-sages, i.e., the functional motifs. Our algo-rithm can successfully recover the most pos-sible text of the first ten chapters of a novel embedded in a stegoscript and identify the transcription factor binding motifs in the up-stream regions of ∼ 800 yeast genes

WordSpy: identifying transcription factor binding motifs by building a dictionary and learning a grammar

Transcription factor (TF) binding sites or motifs (TFBMs) are functional cis-regulatory DNA sequences that play an essential role in gene transcriptional regulation. Although many experimental and computational methods have been developed, finding TFBMs remains a challenging problem. We propose and develop a novel dictionary based motif finding algorithm, which we call WordSpy. One significant feature of WordSpy is the combination of a word counting method and a statistical model which consists of a dictionary of motifs and a grammar specifying their usage. The algorithm is suitable for genome-wide motif finding; it is capable of discovering hundreds of motifs from a large set of promoters in a single run. We further enhance WordSpy by applying gene expression information to separate true TFBMs from spurious ones, and by incorporating negative sequences to identify discriminative motifs. In addition, we also use randomly selected promoters from the genome to evaluate the significance of the discovered motifs. The output from WordSpy consists of an ordered list of putative motifs and a set of regulatory sequences with motif binding sites highlighted. The web server of WordSpy is available at

UV-B responsive microRNA genes in Arabidopsis thaliana

MicroRNAs (miRNAs) are small, non-coding RNAs that play critical roles in post-transcriptional gene regulation. In plants, mature miRNAs pair with complementary sites on mRNAs and subsequently lead to cleavage and degradation of the mRNAs. Many miRNAs target mRNAs that encode transcription factors; therefore, they regulate the expression of many downstream genes. In this study, we carry out a survey of Arabidopsis microRNA genes in response to UV-B radiation, an important adverse abiotic stress. We develop a novel computational approach to identify microRNA genes induced by UV-B radiation and characterize their functions in regulating gene expression. We report that in A. thaliana, 21 microRNA genes in 11 microRNA families are upregulated under UV-B stress condition. We also discuss putative transcriptional downregulation pathways triggered by the induction of these microRNA genes. Moreover, our approach can be directly applied to miRNAs responding to other abiotic and biotic stresses and extended to miRNAs in other plants and metazoans

The role of blockchain technology in advancing sustainable energy with security settlement: enhancing security and efficiency in China’s security market

Blockchain technology has the potential to revolutionize securities settlement systems, offering an efficient, reliable, and cost-effective alternative to traditional methods. Its features, including distributed data authenticity, programmability, and scalability, can enhance security and efficiency in China’s securities market, while promoting a sustainable energy future. However, to fully leverage the benefits of blockchain in securities registration and settlement, it is crucial to address algorithmic loopholes and operational risks associated with smart contracts. Establishing blockchain technical standards and rules is also necessary to ensure smooth system operation. Furthermore, given the uncertainty of the final settlement time point, adherence to decentralization principles and the incorporation of embedded technology for supervision are essential. Legislative measures are required to regulate smart contracts and mitigate systemic risk effectively. This will ensure a stable settlement time expectation and enable fair allocation of legal responsibility among the involved parties. Additionally, other regulatory approaches should be implemented to provide effective supervision and adapt to the rapid development of blockchain. By addressing challenges and risks, blockchain’s full potential can be realized, enabling a sustainable energy future, while enhancing security and efficiency in China’s securities market

Characterization and Identification of MicroRNA Core Promoters in Four Model Species

MicroRNAs are short, noncoding RNAs that play important roles in post-transcriptional gene regulation. Although many functions of microRNAs in plants and animals have been revealed in recent years, the transcriptional mechanism of microRNA genes is not well-understood. To elucidate the transcriptional regulation of microRNA genes, we study and characterize, in a genome scale, the promoters of intergenic microRNA genes in Caenorhabditis elegans, Homo sapiens, Arabidopsis thaliana, and Oryza sativa. We show that most known microRNA genes in these four species have the same type of promoters as protein-coding genes have. To further characterize the promoters of microRNA genes, we developed a novel promoter prediction method, called common query voting (CoVote), which is more effective than available promoter prediction methods. Using this new method, we identify putative core promoters of most known microRNA genes in the four model species. Moreover, we characterize the promoters of microRNA genes in these four species. We discover many significant, characteristic sequence motifs in these core promoters, several of which match or resemble the known cis-acting elements for transcription initiation. Among these motifs, some are conserved across different species while some are specific to microRNA genes of individual species

Endogenous small-noncoding RNAs and potential functions in desiccation tolerance in Physcomitrella patens

Early land plants like moss Physcomitrella patens have developed remarkable drought tolerance. Phytohormone abscisic acid (ABA) protects seeds during water stress by activating genes through transcription factors such as ABSCISIC ACID INSENSITIVE (ABI3). Small noncoding RNA (sncRNA), including microRNAs (miRNAs) and endogenous small-interfering RNAs (endo-siRNAs), are key gene regulators in eukaryotes, playing critical roles in stress tolerance in plants. Combining next-generation sequencing and computational analysis, we profiled and characterized sncRNA species from two ABI3 deletion mutants and the wild type P. patens that were subject to ABA treatment in dehydration and rehydration stages. Small RNA profiling using deep sequencing helped identify 22 novel miRNAs and 6 genomic loci producing trans-acting siRNAs (ta-siRNAs) including TAS3a to TAS3e and TAS6. Data from degradome profiling showed that ABI3 genes (ABI3a/b/c) are potentially regulated by the plant-specific miR536 and that other ABA-relevant genes are regulated by miRNAs and ta-siRNAs. We also observed broad variations of miRNAs and ta-siRNAs expression across different stages, suggesting that they could potentially influence desiccation tolerance. This study provided evidence on the potential roles of sncRNA in mediating desiccation-responsive pathways in early land plants

Abnormal Changes of Brain Cortical Anatomy and the Association with Plasma MicroRNA107 Level in Amnestic Mild Cognitive Impairment

MicroRNA107 (Mir107) has been thought to relate to the brain structure phenotype of Alzheimer’s disease. In this study, we evaluated the cortical anatomy in amnestic mild cognitive impairment (aMCI) and the relation between cortical anatomy and plasma levels of Mir107 and beta-site amyloid precursor protein (APP) cleaving enzyme 1 (BACE1). Twenty aMCI (20 aMCI) and 24 cognitively normal control (NC) subjects were recruited, and T1-weighted MR images were acquired. Cortical anatomical measurements, including cortical thickness (CT), surface area (SA), and local gyrification index (LGI), were assessed. Quantitative RT-PCR was used to examine plasma expression of Mir107, BACE1 mRNA. Thinner cortex was found in aMCI in areas associated with episodic memory and language, but with thicker cortex in other areas. SA decreased in aMCI in the areas associated with working memory and emotion. LGI showed a significant reduction in aMCI in the areas involved in language function. Changes in Mir107 and BACE1 messenger RNA plasma expression were correlated with changes in CT and SA. We found alterations in key left brain regions associated with memory, language, and emotion in aMCI that were significantly correlated with plasma expression of Mir107 and BACE1 mRNA. This combination study of brain anatomical alterations and gene information may shed lights on our understanding of the pathology of AD

PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering

In this paper, we focus on the problem of Medical Visual Question Answering (MedVQA), which is crucial in efficiently interpreting medical images with vital clinic-relevant information. Firstly, we reframe the problem of MedVQA as a generation task that naturally follows the human-machine interaction, we propose a generative-based model for medical visual understanding by aligning visual information from a pre-trained vision encoder with a large language model. Secondly, we establish a scalable pipeline to construct a large-scale medical visual question-answering dataset, named PMC-VQA, which contains 227k VQA pairs of 149k images that cover various modalities or diseases. Thirdly, we pre-train our proposed model on PMC-VQA and then fine-tune it on multiple public benchmarks, e.g., VQA-RAD and SLAKE, outperforming existing work by a large margin. Additionally, we propose a test set that has undergone manual verification, which is significantly more challenging, even the best models struggle to solve