Vibrio cholerae motility is mediated by a single polar flagellum, composed of four flagellin subunits (FlaABCD) in the filament; however, only FlaA is required for motility. Class III flagellar genes, which include flaA, are controlled by the two-component FlrBC system. FlrB is a histidine kinase that phosphorylates FlrC, which activates Class III promoters. The signal(s) that control phosphotransfer between FlrB and FlrC are unknown. A V. cholerae strain lacking the “non-essential” flagellin genes (ΔflaCEDB) is non-motile. Selection for spontaneous motile strains resulted in mutations localized to a Per-Arnt-Sim (PAS) domain in the FlrB N-terminus, including the mutation L36F. The X-ray crystal structure of FlrB revealed an asymmetric dimer with a unique fold of the PAS domain. Transcriptome analysis showed that class III transcription is increased with the addition of the L36F PAS mutation to FlrB, while class III gene transcription was eliminated with a mutation at the site of phosphorylation (H135N). H135N prevents phosphorylation of purified FlrB, whereas L36F increases phosphorylation, indicating these mutations represent “off” and “on” forms of FlrB. FlrB localizes to the V. cholerae cell pole, and localization is dependent on the flagellar polar targeting protein FlhF. V. cholerae strains containing either “off” or “on” forms of FlrB were defective for intestinal colonization in infant mice (10- to 40-fold defect). Our results demonstrate that the PAS domain controls FlrB activity and class III flagellar gene expression, and FlrB must switch between inactive and active forms in order for V. cholerae to successfully colonize the intestine. IMPORTANCE Vibrio cholerae causes the severe diarrheal disease cholera when it colonizes the human intestine. The bacteria are able to swim due to a polar flagellum, and motility is linked to disease, as well as environmental persistence. This study demonstrates that FlrB, a key regulatory protein, localizes to the cell pole and controls flagellar gene transcription via a PAS domain that regulates autophosphorylation. The ability of FlrB to switch between active and inactive forms is critical for motility, as well as intestinal colonization, emphasizing the importance of V. cholerae swimming for its ability to cause disease.

Amid the current multi-country mpox outbreak, analyzing monkeypox virus (MPXV) and vaccinia virus (VACV) genomes is vital for understanding evolutionary processes that may impact vaccine efficacy and design. This study aimed to elucidate the phylogenetic relationships and structural features of viral antigens, which are crucial for developing effective vaccines. By aligning 1,903 MPXV genomes from the NCBI Virus repository (released between 2022 and 2024), an increase in phylogenetic diversity was observed compared to previous studies. These genomes grouped into Clade I (25 genomes) and Clade IIB (1,898 genomes), with a new Clade I sub-lineage emerging from samples collected in Sud-Kivu province, Democratic Republic of the Congo. Homology-based modeling of six key neutralization determinants encoded by the highly conserved Clade I MPXV genes revealed several amino acid variants near potential antibody binding sites compared to those of vaccinia and variola virus templates. Despite these structural differences, the long-standing vaccinia vaccines, initially created for smallpox, still play a crucial role in safeguarding humanity against mpox today. The findings have deepened our understanding of disparate evolutionary mechanisms for MPXV and VACV, influencing vaccine efficacy. The workflows for viral genomic analysis and structural modeling in CLC Genomics Workbench will facilitate MPXV research and vaccinology. Finally, this study highlights the critical role of MPXV metagenomic surveillance in identifying viral evolution and potential vaccine resistance.

Monkeypox has been a neglected, zoonotic tropical disease for over 50 years. Since the 2022 global outbreak, hundreds of human clinical samples have been subjected to next-generation sequencing (NGS) worldwide with raw data deposited in public repositories. However, sequence analysis for in-depth investigation of viral evolution remains hindered by the lack of a curated, whole genome Monkeypox virus (MPXV) database (DB) and efficient bioinformatics pipelines. To address this, we developed a customized MPXV DB for integration with “ready-to-use” workflows in the CLC Microbial Genomics Module for whole genomic and metagenomic analysis. After database construction (218 MPXV genomes), whole genome alignment, pairwise comparison, and evolutionary analysis of all genomes were analyzed to autogenerate tabular outputs and visual displays (collective runtime: 16 min). The clinical utility of the MPXV DB was demonstrated by using a Chimpanzee fecal, hybrid-capture NGS dataset (publicly available) for metagenomic, phylogenomic, and viral/host integration analysis. The clinically relevant MPXV DB embedded in CLC workflows proved to be a rapid method of sequence analysis useful for phylogenomic exploration and a wide range of applications in translational science.

The field of mitochondrial genomics has advanced rapidly and has revolutionized disciplines such as molecular anthropology, population genetics, and medical genetics/oncogenetics. However, mtDNA next-generation sequencing (NGS) analysis for matrilineal haplotyping and phylogeographic inference remains hindered by the lack of a consolidated mitogenome database and an efficient bioinformatics pipeline. To address this, we developed a customized human mitogenome database (hMITO DB) embedded in a CLC Genomics workflow for read mapping, variant analysis, haplotyping, and geo-mapping. The database was constructed from 4286 mitogenomes. The macro-haplogroup (A to Z) distribution and representative phylogenetic tree were found to be consistent with published literature. The hMITO DB automated workflow was tested using mtDNA-NGS sequences derived from Pap smears and cervical cancer cell lines. The auto-generated read mapping, variants track, and table of haplotypes and geo-origins were completed in 15 min for 47 samples. The mtDNA workflow proved to be a rapid, efficient, and accurate means of sequence analysis for translational mitogenomics.

Precision-cut liver tissue slice (PCLS) contains all major cell types of the liver parenchyma and preserves the original cell-cell and cell-matrix contacts. It represents a promising ex vivo model to study liver fibrosis and test the antifibrotic effect of experimental compounds in a physiological environment. In this study using RNA sequencing, we demonstrated that various pathways functionally related to fibrotic mechanisms were dysregulated in PCLSs derived from rats subjected to bile duct ligation. The activin receptor-like kinase-5 (Alk5) inhibitor SB525334, nintedanib, and sorafenib each reversed a subset of genes dysregulated in fibrotic PCLSs, and of those genes we identified 608 genes whose expression was reversed by all three compounds. These genes define a molecular signature characterizing many aspects of liver fibrosis pathology and its attenuation in the model. A panel of 12 genes and 4 secreted biomarkers including procollagen I, hyaluronic acid (HA), insulin-like growth factor binding protein 5 (IGFBP5), and WNT1-inducible signaling pathway protein 1 (WISP1) were further validated as efficacy end points for the evaluation of antifibrotic activity of experimental compounds. Finally, we showed that blockade of αV-integrins with a small molecule inhibitor attenuated the fibrotic phenotype in the model. Overall, our results suggest that the rat fibrotic PCLS model may represent a valuable system for target validation and determining the efficacy of experimental compounds. NEW & NOTEWORTHY We investigated the antifibrotic activity of three compounds, the activin receptor-like kinase-5 (Alk5) inhibitor SB525334, nintedanib, and sorafenib, in a rat fibrotic precision-cut liver tissue slice model using RNA sequencing analysis. A panel of 12 genes and 4 secreted biomarkers including procollagen I, hyaluronic acid (HA), insulin-like growth factor binding protein 5 (IGFBP5), and WNT1-inducible signaling pathway protein 1 (WISP1) were then established as efficacy end points to validate the antifibrotic activity of the αV-integrin inhibitor CWHM12. This study demonstrated the value of the rat fibrotic PCLS model for the evaluation of antifibrotic drugs.

The selection and design of appropriate kernel functions play a key role in effective support vector machine (SVM) leaning. A general strategy is to customize the existent kernel functions to fit into the data property and structure. Wavelet kernels have been developed to approximate arbitrary nonlinear functions for signal processing. In this paper, we propose novel wavelet kernels based on the Riemannian geometrical structure theory, by constructing a hyperplane with better spatial resolution. This wavelet kernel SVM approach was applied to the yeast time course microarray dataset and outperformed the traditional Gaussian kernel and polynomial kernel.

The classification performance using support vector machines (SVMs) for transcriptomic analysis can be limited due to the high dimensionality of the data. This limitation is most problematic in the case of small training sets. A general solution is to employ a dimension reduction method before SVM classification. In this paper, we propose a novel singular value decomposition (SVD) based method for dual purposes: firstly, to reduce the dimensionality, and secondly to cluster the transcriptional profiles. The kernel functions of SVM were modified based on the Riemannian geometrical structure which can achieve a better spatial resolution. The proposed approach was applied to the yeast time series microarray dataset and outperformed the traditional SVM kernels.

DCT and wavelet based techniques have been widely used in image processing, for example, the applications involving JPEG, MPEG and JPEG2000. To combine the advantages of DCT and wavelet, we introduce in this chapter a novel multilevel DCT decomposition method by exploiting the modified inverse Hilbert curve. The experimental results showed that the proposed multilevel decomposition can extract characteristic DCT coefficients and assign the coefficients to new neighborhoods with distinct frequency properties. We discuss a powerful reversible data hiding algorithm in JPEG images based on this new multilevel DCT. This lossless data hiding algorithm features a key-dependent (multilevel structure) coefficient-extension technique and an embedding location selector, and it can achieve high quality reconstructed images with disparate content types.

Steganalysis has becoming an emerging important technique for detecting secret messages that are embedded in a clean-image. Universal steganalysis is especially useful due to its independence of prior knowledge of the embedding procedure. However, the detection results from the majority of universal methods are largely determined by the training procedure on a mixture of clean-images and stego-images, and therefore not practically feasible. Moreover, many color steganalysis methods do not take color coefficients into special consideration and thus they can be viewed as a simple extension of the analysis for grayscale images. To capture the distinct features of the clean images, we propose a novel predictor based on the intra- and inter- color correlations of wavelet coefficients. This method achieves higher detection rates, under a blind condition that only involves clean images at the training stage.

A projection aligner wherein light from a light source is passed through a mask so as to focus an image of a pattern of the mask on a wafer, characterized in that at least one sensor for monitoring a luminosity and a distribution thereof is disposed in an optical path between the light source and the mask, whereby a luminosity and a distribution thereof on the wafer can be controlled to proper values. The projection aligner is effective for application to minute processing technologies for the production of semiconductor devices, etc.

Proteases play diverse and important roles in metabolic regulation, gene regulatory networks and protein-protein interactions. The complete sequencing of the genomes of three unicellular organisms: malaria parasite Plasmodium falciparum, and the two free-living ciliates Tetrahymena thermophila and Paramecium tetraurelia has opened a new window to study the proteolytic machinery in a large scale systems context. This paper reports a comprehensive survey and analysis of the degradomes (the complete repertoire of proteases) of these organisms. The catalog of proteases with specific enzymatic functions can serve as a starting point for a systems level understanding of the cellular networks involving proteolytic activities.

A significant roadblock to the use of genomic data for understanding gene networks in infectious pathogens is our inability to assign functionality to a large fraction of the genes. Nowhere is this more problematic than in the malaria parasite Plasmodium falciparum, in which 60% of the genes are annotated as "hypothetical". To circumvent this problem we proposed to employ wavelets, feature extraction, kernel based supervised learning, and pattern recognition algorithms to explore temporal expression profiles from the complex and dynamic developmental cycle in the parasite and discover crucial network components.

This paper presents an algorithm for breaking the JPEG based steganographical algorithms such as F5, one of the most robust information hiding systems. The detection technique is based on spatial-frequency feature vector fusion and SVM classification. First, the proposed method extracts features from spatial domain and DCT domain respectively. Second, the data fusion technique is employed to combine their features. Finally, SVM is used to classify the stego images and non-stego images based on the combined features. Owing to the unique concatenation of two domain features, the proposed algorithm shows high sensitivity to the secret messages of small sizes, allowing a more effective attack.

By thwarting visual and chi2 attacks, the F5 steganographic algorithm is viewed as a challenge to steganalysis. This paper presents a novel algorithm that can break F5, even with low embedding rates. The test results show that the proposed method can accurately break F5 when relatively short messages (82 bytes) are embedded into a 256times256 gray image

The F5 steganographic algorithm poses a challenge to steganalysis by successfully avoiding visual and χ attacks. This paper presents a novel algorithm that efficiently breaks F5 in color images, even with low embedding rates. The test results show that the proposed method can accurately break F5 when relatively short messages (4×4 color image) are embedded into a 256 × 256 color image.

Universal blind steganalysis can detect hidden messages without using prior information about the steganographic system. Recently, Farid developed a wavelet coefficient, higher-order statistics based, universal blind steganalysis method. This approach is a global method which demonstrated a high-quality in performance standards. Fridrich and Goljan also presented a DCT based local targeted steganalysis method to break the F5 algorithm. However, both Farid's and Fridrich and Goljan's methods have some limitations. This paper presents a local universal steganalysis technique combining the advantages of both methods. The basic components of the presented method are: novel DCT multilevel decomposition with wavelet structure; a new set of feature vectors; and a modified kernel function in the Kernel Fisher Discriminant. Experimental results show the presented method offers better performance than commonly used schemes. Inherently, the presented method has the ability to localize the hidden information, it can capture stego information in small blocks, and it is functional using only a small training set.

This paper presents a universal blind steganalysis method for color images. The key new components of the presented method are color wavelet decompositio n and a set of feature vectors. In addition, it is ba sed on simple Fisher Linear Discriminatant approach for classifying the received image as containing steganographic content or not. The presented class of color wavelets has several useful properties and ha s been shown to be effective at detecting stego data withi n digital color images. Testing and analysis was cond ucted with 200 color images varying in size and style. Experimental results have shown that the performance of the presented method is better than some popular steganalysis methods.