Structural Information Research Articles

VISTA Enhancer browser: an updated database of tissue-specific developmental enhancers

Abstract Regulatory elements (enhancers) are major drivers of gene expression in mammals and harbor many genetic variants associated with human diseases. Here, we present an updated VISTA Enhancer Browser (https://enhancer.lbl.gov), a database of transgenic enhancer assays conducted in developing mouse embryos in vivo. Since the original publication in 2007, the database grew nearly 20-fold from 250 to over 4500 experiments and currently harbors over 23 500 images. The updated database provides structured information on experiments conducted at different stages of embryonic development, including enhancer activities of human pathogenic and synthetic variants and sequences derived from a variety of species. In addition to manually curated results of thousands of individual experiments, the new database also features hundreds of manually curated comparisons between alleles. The VISTA Enhancer Browser provides a crucial resource for study of human genetic variation, gene regulation and developmental biology.

Electronic Health Record Phenotyping of Pediatric Suicide-Related Emergency Department Visits.

Suicide is a leading cause of death among young people. Accurate detection of self-injurious thoughts and behaviors (SITB) underpins equity in youth suicide prevention. To compare methods of detecting SITB using structured electronic health information and measure algorithmic performance across demographics. This cross-sectional study used medical records among youths aged 6 to 17 years with at least 1 mental health-related emergency department (ED) visit in 2017 to 2019 to an academic health system in Southern California serving 787 000 unique individuals each year. Analyses were conducted between January and September 2023. Multiexpert electronic health record review ascertained the presence of SITB using the Columbia Classification Algorithm of Suicide Assessment. Random forest classifiers with nested cross-validation were developed using (1) International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for nonfatal suicide attempt and self-harm and chief concern and (2) all available structured data, including diagnoses, medications, and laboratory tests. Detection performance was assessed overall and stratified by age group, sex, and race and ethnicity. The sample comprised 2702 unique youths with an MH-related ED visit (1384 youths who identified as female [51.2%]; 131 Asian [4.8%], 266 Black [9.8%], 719 Hispanic [26.6%], 1319 White [48.8%], and 233 other race [8.6%]; median [IQR] age, 14 [12-16] years), including 898 children and 1804 adolescents. Approximately half of visits were related to SITB (1286 visits [47.6%]). Sensitivity of SITB detection using only codes and chief concern varied by age group and increased until age 15 years (6-9 years: 59.3% [95% CI, 48.5%-69.5%]; 10-12 years: 69.0% [95% CI, 63.8%-73.9%]; 13-15 years: 88.4% [95% CI, 85.1%-91.2%]; 16-17 years: 83.1% [95% CI, 79.1%-86.6%]), while specificity remained constant. The area under the receiver operating characteristic curve (AUROC) was lower among preadolescents (0.841 [95% CI, 0.815-0.867]) and male (0.869 [95% CI, 0.848-0.890]), Black (0.859 [95% CI, 0.813-0.905]), and Hispanic (0.861 [95% CI, 0.831-0.891]) youths compared with adolescents (0.925 [95% CI, 0.912-0.938]), female youths (0.923 [95% CI, 0.909-0.937]), and youths of other races and ethnicities (eg, White: 0.901 [95% CI, 0.884-0.918]). Augmented classification (ie, using all available structured data) outperformed classification with codes and chief concern alone (AUROC, 0.975 [95% CI, 0.968-0.980] vs 0.894 [95% CI, 0.882-0.905]; P < .001). In this study, diagnostic codes and chief concern underestimated SITB prevalence, particularly among minoritized youths. These results suggest that priority on algorithmic fairness in suicide prevention strategies must extend to accurate detection of youths with suicide-related emergencies.

MOBIDB in 2025: integrating ensemble properties and function annotations for intrinsically disordered proteins.

The MobiDB database (URL: https://mobidb.org/) aims to provide structural and functional information about intrinsic protein disorder, aggregating annotations from the literature, experimental data, and predictions for all known protein sequences. Here, we describe the improvements made to our resource to capture more information, simplify access to the aggregated data, and increase documentation of all MobiDB features. Compared to the previous release, all underlying pipeline modules were updated. The prediction module is ten times faster and can detect if a predicted disordered region is structurally extended or compact. The PDB component is now able to process large cryo-EM structures extending the number of processed entries. The entry page has been restyled to highlight functional aspects of disorder and all graphical modules have been completely reimplemented for better flexibility and faster rendering. The server has been improved to optimise bulk downloads. Annotation provenance has been standardised by adopting ECO terms. Finally, we propagated disorder function (IDPO and GO terms) from the DisProt database exploiting sequence similarity and protein embeddings. These improvements, along with the addition of comprehensive training material, offer a more intuitive interface and novel functional knowledge about intrinsic disorder.

NIR-II scattering gold superclusters for intravascular optical coherence tomography molecular imaging.

Currently, intravascular optical coherence tomography (IV-OCT) is limited to anatomical imaging, providing structural information about atherosclerotic plaque morphology, thrombus and dissection. Earlier detection and risk stratification would be possible through molecular characterization of endothelium but necessitates a purpose-engineered IV-OCT contrast agent. Here we developed gold superclusters (AuSCs) tailored to clinical instrumentation and integrated into clinically relevant workflows. AuSCs are aqueously dispersible clusters of closely packed small gold nanoparticles, affording plasmon hybridization to maximize light scattering at the IV-OCT laser line (~1,350 nm). A polymer coating fosters AuSC uniformity and provides a functionalizable handle, which we targeted to intravascular P-selectin, an early vascular endothelial marker of inflammation. In a rat model of intravascular inflammation, P-selectin-targeted AuSC facilitated IV-OCT molecular imaging, where the strength of the signal correlates with the severity of vascular inflammation.

Discovery of Highly Bioactive Peptides through Hierarchical Structural Information and Molecular Dynamics Simulations.

Peptide drugs play an essential role in modern therapeutics, but the computational design of these molecules is hindered by several challenges. Traditional methods like molecular docking and molecular dynamics (MD) simulation, as well as recent deep learning approaches, often face limitations related to computational resource demands, complex binding affinity assessments, extensive data requirements, and poor model interpretability. Here, we introduce PepHiRe, an innovative methodology that utilizes the hierarchical structural information in peptide sequences and employs a novel strategy called Ladderpath, rooted in algorithmic information theory, to rapidly generate and enhance the efficiency and clarity of novel peptide design. We applied PepHiRe to develop BH3-like peptide inhibitors targeting myeloid cell leukemia-1, a protein associated with various cancers. By analyzing just eight known bioactive BH3 peptide sequences, PepHiRe effectively derived a hierarchy of subsequences used to create new BH3-like peptides. These peptides underwent screening through MD simulations, leading to the selection of five candidates for synthesis and subsequent in vitro testing. Experimental results demonstrated that these five peptides possess high inhibitory activity, with IC50 values ranging from 28.13 ± 7.93 to 167.42 ± 22.15 nM. Our study explores a white-box model driven technique and a structured screening pipeline for identifying and generating novel peptides with potential bioactivity.

Improving ncRNA family prediction using Multi-Modal contrastive learning of sequence and structure.

Recent advancements in high-throughput sequencing technology have significantly increased the focus on non-coding RNA (ncRNA) research within the life sciences. Despite this, the functions of many ncRNAs remain poorly understood. Research suggests that ncRNAs within the same family typically share similar functions, underlining the importance of understanding their roles. There are two primary methods for predicting ncRNA families: biological and computational. Traditional biological methods are not suitable for large-scale data prediction due to the significant human and resource requirements. Concurrently, most existing computational methods either rely solely on ncRNA sequence data or are exclusively based on the secondary structure of ncRNA molecules. These methods fail to fully utilize the rich multimodal information available from ncRNAs, thereby preventing them from learning more comprehensive and in-depth feature representations. To tackle these problems, we proposed MM-ncRNAFP, a multi-modal contrastive learning framework for ncRNA family prediction. We first used a pre-trained language model to encode the primary sequences of a large mammalian ncRNA dataset. Then, we adopted a contrastive learning framework with an attention mechanism to fuse the secondary structure information obtained by graph neural networks. The MM-ncRNAFP method can effectively fuse multi-modal information. Experimental comparisons with several competitive baselines demonstrated that MM-ncRNAFP can achieve more comprehensive representations of ncRNA features by integrating both sequence and structural information. This integration significantly enhances the performance of ncRNA family prediction. Ablation experiments and qualitative analyses were performed to verify the effectiveness of each component in our model. Moreover, since our model is pre-trained on a large amount of ncRNA data, it has the potential to bring significant improvements to other ncRNA-related tasks. MM-ncRNAFP and the datasets are available at https://github.com/xuruiting2/MM-ncRNAFP.

Investigating the role of keratin proteins and microbial associations in hereditary and pathogenic alopecia.

The purpose of this research was to identify the role of keratin proteins in causing inherited as well as pathogenic alopecia, pinpoint deleterious SNPs, and predict structural changes affecting protein-protein interactions in hair disorders. To elucidate the role of keratin proteins and genetic mutations in alopecia by analyzing protein structures through bioinformatics and identifying a mutation in the LPAR6 gene. It sought to identify the microorganisms linked to alopecia and conducted a comprehensive bioinformatics analysis of proteins with unknown experimental structures and molecular simulation analysis. The study identified a genetic mutation (c.188A > T, p.Asp63Val) in the LPAR6 gene associated with hereditary hair loss. Pathogenic alopecia was identified to be associated with S. aureus and two ic keratinophilic fungi namely M. canis, and T. violaceum. Additionally, among 14 proteins lacking prior structural information, four proteins namely Keratin, type II cuticular Hb3 (KR1), Keratin, type II cuticular Hb6 (KR2), Keratin, type II cytoskeletal 74 (KR3) and Keratin, type II cuticular Hb1 (KR4) exhibited common 'K-head' and 'F' domains. Docking analysis revealed five distinct binding sites (C1-C5) for each protein. The 'K-head' displayed the highest predicted binding affinities with Vina scores of -5.6 for KR2 and - 4.7 for KR4 whereas the 'F' domain showed Vina scores of -6.0 for KR3 and - 5.7 for KR2. This research underscores the crucial role of keratin proteins in both hereditary and pathogenic alopecia, emphasizing their significance for future investigations.

Entire loosening stage monitoring of bolted joints via nonlinear electro-mechanical impedance spectroscopy

This article proposes a nonlinear electro-mechanical impedance spectroscopy (NEMIS) methodology for the entire loosening stage monitoring of bolted joints. Unlike the conventional electro-mechanical impedance spectroscopy (EMIS) implemented in the frequency domain, NEMIS utilizes a temporal interrogating signal to procure the developed nonlinear impedance spectrum. It can harness the structural resonance information akin to the conventional EMIS, while concurrently capturing the contact acoustic nonlinearity (CAN) features with only one solitary Piezoelectric Wafer Active Sensor (PWAS). To elucidate the underlying principles of NEMIS, a comparative analysis juxtaposing the conventional EMIS, nonlinear ultrasonic techniques, and the proposed NEMIS was conducted. Subsequently, a reduced-order analytical model was established to simulate the rough contact interfaces of the bolted joints under various looseness states. Parametric studies were carried out to demonstrate the resonance deviation and nonlinear features resulting from the bolt looseness. Ultimately, the experimental implementation of NEMIS for the entire loosening stage monitoring of the bolted joint was performed compared with the conventional EMIS method. It was proved that NEMIS could detect both the incipient bolt loosening at an embryo stage and severe loosening of the bolted joint at the terminal period, integrating the continuous monitoring capability of conventional EMIS and the sensitivity of nonlinear ultrasonic methodology. The quantification on the severity of bolt loosening was accomplished via both linear and nonlinear damage indices, exhibiting a consistent trend with the gradations of bolt looseness. The article culminates in a summary, concluding remarks, and suggestions for future work.

Advancing Native Mass Spectrometry Toward Cellular Biology.

Protein structure, including various post-translational modifications and higher-order structures, regulates diverse biological functions. Native mass spectrometry (native MS) is a powerful analytical technique used to determine the masses of biomolecules, such as proteins and their complexes, while preserving their native folding in solution. This method provides structural information on the composition of monomers or complexes and the stoichiometry of subunits within each complex, significantly contributing to protein structural analysis. Native MS has evolved to incorporate top-down approaches, enabling the characterization of proteoforms and non-covalent interactions between metabolites or proteins and specific targets. This perspective highlights the advancements in native MS for intracellular proteins and protein complexes, and discusses future research directions toward cellular biology.

FFS-Net: Fourier-based segmentation of colon cancer glands using frequency and spatial edge interaction

The morphological features of glands provide a reliable basis for pathologists to diagnose colon cancer correctly. Currently, most methods are limited in their ability to address blurred edges, adhesions, and morphological differences in glands when working in the spatial domain. In this study, a new perspective is proposed to segment glands by utilizing the interaction between Fourier and spatial domain features. Specifically, a Fourier transform is used to analyze the frequency characteristics of pathological images in which the phase component is related to image structural information and the amplitude component contains image intensity information. A Fourier-based colon cancer gland segmentation network (FFS-Net) is proposed, which gradually constructs feature representations in both the spatial and frequency domains through spatial frequency edge interactions. This network comprises spatial subnetworks, frequency subnetworks, and an edge phase fusion module (EPFM). To alleviate the interference of complex background information in feature discrimination, the spatial subnetwork collects high-frequency contour information regarding the glands. Then, the EPFM completes the spatial-frequency feature interaction to add edge-related constraints to the extracted features, enhance edge representation, and obtain global features from the frequency domain, thereby, addressing significant differences in gland morphology. Finally, learnable Gabor filters are introduced to enhance edge texture detail information in the frequency subnetwork, which further improves the segmentation of glands exhibiting blurred edges. The experimental results demonstrate that FFS-Net’s performance on two representative datasets was competitive and highly interpretable.

Preparation, identification and α-glucosidase inhibitory activity of a high-methoxyl HG-type pectin from Liangping pomelo (Citrus maxima cv. Liangpin Yu) peel.

The peel from Liangping pomelo (Citrus maxima cv. Liangpin Yu) is generally discarded as waste during post-harvest handling and process, resulting in environmental pollution and waste. Pectin is the major component in pomelo peels and yields significant economic advantages. Thus, developing pomelo peel pectin (PPs) might be a feasible strategy to reduce environmental pollution caused by pomelo peel. The optimized PPs yield was 156.5 ± 2.5 g kg-1 under the inoculum size of 100 mg g-1, liquid-solid ratio of 31 mL g-1, fermentation time of 64 h, and fermentation temperature of 39 °C. PPs-6Aa, a pectin fraction from PPs purified with DEAE-52 cellulose, Sephadex G-100 and Sephadex G-75 column chromatography, showed higher α-glucosidase inhibitory activity, with an IC50 of 0.12 ± 0.03 mg mL-1. It was a high-methoxyl HG-type pectin of 42.8 kDa, and its repeat unit was →4)-α-GalpA-6-OMe-(1→4)-α-GalpA-6-OMe-(1→. Additionally, its α-glucosidase inhibitory activity might be related to hydrogen bonds formed with Lys-156, Glu-277, His-280, Asp-307, Arg-315, Asn-350, Asp-352 and Glu-411, and to hydrophobic interactions formed with Ser-157, Tyr-158, Asp-233, Gln-239, Ser-240, Phe-303, Thr-306, Leu-313, Phe-314, Gln-353 and Arg-442. These findings provide structural and bioactivity information on pectin from Liangping pomelo peel, which could be beneficial for the development of functional foods and pharmaceuticals. © 2024 Society of Chemical Industry.

SChemNET: a deep learning framework for predicting small molecules targeting microRNA function

MicroRNAs (miRNAs) have been implicated in human disorders, from cancers to infectious diseases. Targeting miRNAs or their target genes with small molecules offers opportunities to modulate dysregulated cellular processes linked to diseases. Yet, predicting small molecules associated with miRNAs remains challenging due to the small size of small molecule-miRNA datasets. Herein, we develop a generalized deep learning framework, sChemNET, for predicting small molecules affecting miRNA bioactivity based on chemical structure and sequence information. sChemNET overcomes the limitation of sparse chemical information by an objective function that allows the neural network to learn chemical space from a large body of chemical structures yet unknown to affect miRNAs. We experimentally validated small molecules predicted to act on miR-451 or its targets and tested their role in erythrocyte maturation during zebrafish embryogenesis. We also tested small molecules targeting the miR-181 network and other miRNAs using in-vitro and in-vivo experiments. We demonstrate that our machine-learning framework can predict bioactive small molecules targeting miRNAs or their targets in humans and other mammalian organisms.

Use of Standard Operating Procedures for Supporting Cross-Organizational Emergency Management: Challenges and Opportunities Identified from a Tabletop Exercise

AbstractThis study investigated the crucial role of formulating and applying standard operating procedures (SOPs) in the context of emergency planning and response. The effective application of SOPs during operations is essential in order to address challenges that surpass individual organizational capacities. A specific focus on fostering efficient information sharing and executing specified actions maximizes the prospect of success. To explore the collective functionality of SOPs across organizations, we conducted a comprehensive tabletop exercise involving 10 organizations spanning tactical, operational, and strategic levels. Our analysis identified six key challenges, primarily related to the structures for information sharing, communication pathways, and the complex integration and application of common SOPs within these diverse organizational contexts. This research contributes insights into the use of SOPs in large-scale, cross-organizational scenarios.

Graph Curvature Flow-Based Masked Attention.

Graph neural networks (GNNs) have revolutionized drug discovery in chemistry and biology, enhancing efficiency and reducing resource demands. However, classical GNNs often struggle to capture long-range dependencies due to challenges like oversmoothing and oversquashing. Graph Transformers address these issues by employing global self-attention mechanisms that allow direct information exchange between any pair of nodes, enabling the modeling of long-range interactions. Despite this, Graph Transformers often face difficulties in capturing the nuanced structural information on graphs. To overcome these challenges, we introduce the CurvFlow-Transformer, a novel graph Transformer model incorporating a curvature flow-based masked attention mechanism. By leveraging a topologically enhanced mask matrix, the attention layer can effectively detect subtle structural differences within graphs, balancing the focus between global mutual information and local structural details of molecules. The CurvFlow-Transformer demonstrates superior performance on the MoleculeNet data set, surpassing several state-of-the-art models across various tasks. Moreover, the model provides unique insights into the relationship between molecular structure and chemical properties by analyzing the attention heat coefficients of individual atoms.

The Present State and Impact of AI-Driven Computational Tools for Predicting Plant Protein Structures.

Several key functions of plants, such as photosynthesis, nutrient transport, disease resistance, and abiotic tolerance, are manifested by several classes of proteins. Prediction of 3- dimensional (3-D) structures of proteins and their working mechanisms can have a profound impact on plant proteomics research and could help improve key agricultural traits in crop plants. This review aims to present the current status of plant protein structure determination and discuss the way forward. Most experimentally proven protein structures are available only for the model plant Arabidopsis thaliana. Most of the key crop plants have only a few hundred or fewer experimentally proven 3-D structures. Fewer than 1% of the protein sequences in the majority of plants have had their 3D structures experimentally determined, and A. thaliana is the sole plant with the highest percentage of 1.4 % of protein sequences with experimentally determined structures. AI-based protein structure prediction tool AlphaFold has predicted models of several thousand proteins for many crop plants. In AlphaFold predicted protein models, soybean has the highest percentage (65%) of its UniProt protein sequences with predicted models, and a few other crop plants have also a considerable percentage of its UniProt sequences with AlphaFold predicted models. AlphaFold might help predict models and bridge the gap in plant structure determination studies. Protein structure information might lead to engineering key residues to improve the agronomical performance of crop plants.

Tripartite interaction representation algorithm for crystal graph neural networks

Driven by emerging research paradigms, the application of artificial intelligence models presents innovative tools for designing materials and optimizing their performance. In the field of materials science, there is a current research emphasis on exploring techniques for characterizing material structures to achieve precise descriptions. This paper proposes a crystal graph convolution neural network model that incorporates a tripartite interaction approach. The model not only incorporates atomic information, bond lengths, and bond angles but also offers a method for updating atoms and bond lengths, facilitating accurate descriptions of crystal structures by capturing implicit structural information. Focusing on predicting the formation energy of crystalline compounds, our results demonstrate improved predictive accuracy compared to existing representation algorithms. The average error of the formation energy in the random dataset, demonstrating robust generalization, is merely 0.048eV/atom, with an impressive R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^{2}$$\\end{document} value of 0.994. Additionally, this paper establishes a crystal graph neural network framework for predicting algorithm performance. By integrating automatic parallel algorithms and an automated process, we achieve a synthesis of these techniques, enhancing computational efficiency and streamlining algorithm usage.

Spatial domains identification in spatial transcriptomics using modality-aware and subspace-enhanced graph contrastive learning

Spatial transcriptomics (ST) technologies have emerged as an effective tool to identify the spatial architecture of tissues, facilitating a comprehensive understanding of organ function and the tissue microenvironment. Spatial domain identification is the first and most critical step in ST data analysis, which requires thoughtful utilization of tissue microenvironment and morphological priors. Here, we propose a graph contrastive learning framework, GRAS4T, which combines contrastive learning and a subspace analysis model to accurately distinguish different spatial domains by capturing the tissue microenvironment through self-expressiveness of spots within the same domain. To uncover the pertinent features for spatial domain identification, GRAS4T employs a graph augmentation based on histological image priors, preserving structural information crucial for the clustering task. Experimental results on eight ST datasets from five different platforms show that GRAS4T outperforms five state-of-the-art competing methods. Significantly, GRAS4T excels at separating distinct tissue structures and unveiling more detailed spatial domains. GRAS4T combines the advantages of subspace analysis and graph representation learning with extensibility, making it an ideal framework for ST domain identification.

The development and applications of multidimensional biomolecular spectroscopy illustrated by photosynthetic light harvesting.

The parallel and synergistic developments of atomic resolution structural information, new spectroscopic methods, their underpinning formalism, and the application of sophisticated theoretical methods have led to a step function change in our understanding of photosynthetic light harvesting, the process by which photosynthetic organisms collect solar energy and supply it to their reaction centers to initiate the chemistry of photosynthesis. The new spectroscopic methods, in particular multidimensional spectroscopies, have enabled a transition from recording rates of processes to focusing on mechanism. We discuss two ultrafast spectroscopies - two-dimensional electronic spectroscopy and two-dimensional electronic-vibrational spectroscopy - and illustrate their development through the lens of photosynthetic light harvesting. Both spectroscopies provide enhanced spectral resolution and, in different ways, reveal pathways of energy flow and coherent oscillations which relate to the quantum mechanical mixing of, for example, electronic excitations (excitons) and nuclear motions. The new types of information present in these spectra provoked the application of sophisticated quantum dynamical theories to describe the temporal evolution of the spectra and provide new questions for experimental investigation. While multidimensional spectroscopies have applications in many other areas of science, we feel that the investigation of photosynthetic light harvesting has had the largest influence on the development of spectroscopic and theoretical methods for the study of quantum dynamics in biology, hence the focus of this review. We conclude with key questions for the next decade of this review.

Blockchain for video watermarking: An enhanced copyright protection approach for video forensics based on perceptual hash function.

As a direct result of advancements in digital technology and the Internet, the copyright protection and information integrity of multimedia that are being published across the Internet have emerged as a major and urgent issue that needs to be addressed. The technique of digital watermarking may be used to protect intellectual property. In terms of authentication, resilience, storage, and capacity of digital watermarking information, there is still room for development. Blockchain's potential in video copyright protection and management applications has motivated researchers. Copyright owners and consumers may now communicate directly via copyright protection apps built on the blockchain, eliminating the need for distributers and the associated fees. Nonetheless, the current blockchain-based video watermarking solutions require storing a significant number of coordinates depending on the watermark size and are susceptible to video frame attacks on the video frame texture region. This study proposes an enhanced video copyright management approach that incorporates digital watermarking, the blockchain, and a perceptual hash function. Watermark information is stored on a blockchain structure, which also acts as a timestamp for verification purposes. To verify watermark data without the source video, a perceptual hash function is employed to compute a hash value based on the structural information of video frames. The contribution is in learning how to extract a hash function from a small number of video frames that still adequately represent a large amount of video content while also reducing the number of unnecessary video frames and the amount of computation required to summarize and index that content in a blockchain. This expedites the dissemination of copyrighted works and increases their security and readability, hence facilitating their circulation on the Internet. Our experimental results demonstrate that this approach is memory efficient, as it only needs to store one key for each key frame, regardless of the size of the watermark. Additionally, the overall robustness is greatly improved by using the blockchain's random hash function. Therefore, new and important advancements in video watermarking have been realized because of this effort.

A comparative of theosophical sufism in Syarah al-Hikam by Sholeh Darat and ‘Abdullah Gangohi

This study elaborated on the explanation of theosophical Sufism in the book of al-Hikam by two well-known Islamic academics, Kyai Sholeh Darat (Indonesia) and Shaykh ‘Abdullah Gangohi (India). The data in this study are the text of Syarah al-Hikam, written by Kyai Sholeh Darat and the text of Syarah Al-Hikam in the perspective of Shaykh ‘Abdullah Gangohi. This qualitative research study used a Systemic Functional Linguistic (SFL) approach from M.A.K. Halliday. Data analysis in this research applied the Spradley model, namely: (1) domain analysis, (2) taxonomy analysis, (3) componential analysis and (4) analysis of cultural themes. The domain analysis tried to map the information structure in the explanation of al-Hikam based on the theory of SFL. This information structure is the basis for the genre analysis as a semiotic representation of social society. Meanwhile, the taxonomic analysis tried to elaborate on the Sufism thoughts of both the thinkers based on the Syarah al-Hikam text. Componential analysis synergised the results of the information structure and the theosophical Sufism thought in al-Hikam. Finally, the analysis of cultural themes endeavoured to elaborate on the theosophical Sufism thoughts of the two Sufism thinkers and to draw the relationships and the contributions to the universal Sufism. The conditions of theosophical Sufism can be elaborated based on the information structure. The limitation of this study is the lack of researcher’s efforts to investigate the substance of theosophical Sufism, because this research is only limited to the outer layer of language.Contribution: This article shows that the thought of theosophical Sufism showed that Islam in Southeast Asia is Islam that carries the spirit of Islam Rahmatan lil-alamin. In addition, the thought of theosophical Sufism in Syarah al-Hikam has had an impact on the socio-cultural transformation, especially on the Sufism movement [thariqah] spread across Indonesia and India.