File Structure Research Articles

Investigating the Effectiveness of Natural Analogs and Different Metabolites of Curcumin for the Inhibition of the Influenza Virus Proteins in a Simulated Environment

Background:: Background: Curcumin possesses numerous properties, including anti-influenza effects. Various natural analogs and metabolites of curcumin have been identified. Objective: Objective: To better understand the mechanism of curcumin's anti-influenza effect, the inhibitory effect of natural analogs and secondary metabolites of curcumin on 11 influenza virus proteins was investigated in a simulated environment Methods:: Molecular data and structural files of curcumin, its natural analogs, and secondary metabolites were retrieved from the PubChem database for conversion to PDB files. Viral protein sequences were obtained from uniprot.org, and PDB structures of proteins were predicted using an online protein structure and I-TASSER server. Using AutoDock 4.2 software, the molecular docking studies were performed. Results:: Three natural analogs of curcumin had a greater affinity to the PB1-F2 protein of the virus. The Binding Energies (BEs) of curcumin, bisdemethoxycurcumin, and demethoxycurcumin to the PB1-F2 protein were -8.28, -8.44, and -8.46 kcal/mol, respectively. Although bisdemethoxycurcumin had the lowest BE, it interacted with fewer amino acids in the active site of the protein compared to the other analogs. Curcumin metabolites were less likely to bind to influenza virus protein than curcumin. Conclusion:: Our study indicated curcumin and its analogs to have the greatest affinity to the PB1- F2 protein compared to other viral proteins. Given the role of this protein in increasing inflammation caused by influenza, curcumin may reduce inflammation in patients by affecting the function of this protein.

Conformational Variability Prediction of Influenza Virus Hemagglutinins with Amino Acid Mutations Using Supersecondary Structure Code.

Supersecondary structure code (SSSC), which is represented as a conformation term using the letters "H," "S," "T," and "D" for each amino acid peptide unit, can be utilized to look up supersecondary structure motifs like a helix-hairpin-helix (HhH) motif as character strings from the Protein Data Bank (PDB) structure files. The deep neural network-based conformational variability prediction system of protein structures (SSSCPreds) can simultaneously predict locations of protein flexibility or rigidity and the shapes of those regions with high accuracy. The sequence flexibility/rigidity map obtained from SSSCPreds has the prediction accuracy enough to discuss the correlation with the sequence-to-phenotype ones by mutations. In this chapter, the protocol of conformational variability prediction methods using SSSC, including the analysis of influenza virus hemagglutinins with amino acid mutations, is described. The conformational variability pattern of hemagglutinins for human influenza A H1N1pdm extremely resembles that of avian influenza A H5N1, except for the furin cleavage site of H5N1. The transition of virus variants is visually understandable from the maps, including the sharply increased flexibility with the insight into the recent unseasonal influenza epidemics in Japan. The prediction accuracy of conformational variability is low for proteins at pH5 with very few measurement conditions, so this is the limitation of the conformational variability prediction method.

Adversarial Binaries: AI-guided Instrumentation Methods for Malware Detection Evasion

Adversarial binaries are executable files that have been altered without loss of function by an AI agent in order to deceive malware detection systems. Progress in this emergent vein of research has been constrained by the complex and rigid structure of executable files. Although prior work has demonstrated that these binaries deceive a variety of malware classification models which rely on disparate feature sets, a consensus as to the best approach has not been reached, either in terms of the optimization algorithms or the instrumentation methods. Although inconsistencies in the data sets, target classifiers, and functionality verification methods make head-to-head comparisons difficult, we extract lessons learned and make recommendations for future research.

Ph3pyWF: An automated workflow software package for ceramic lattice thermal conductivity calculation

This paper introduces Ph3pyWF, a Python software package we designed to facilitate high-throughput analysis of lattice thermal conductivity in ceramic materials. The user interface caters to individuals with varying expertise, accommodating both novices and experts in the field. For beginners, only the initial structure file is required as input, as the software automatically populates other necessary parameters. Advanced users can customize numerous procedure parameters to suit their specific research needs. At its core, Ph3pyWF aims to establish an efficient data exchange and task management system. This paper elucidates the design details of the software package and presents several examples of its application to oxide ceramics, showcasing its general applicability and practicality in the analysis of lattice thermal conductivity.

A WFS1 variant disrupting acceptor splice site uncovers the impact of alternative splicing on beta cell apoptosis in a patient with Wolfram syndrome

Aims/hypothesisWolfram syndrome 1 (WS1) is an inherited condition mainly manifesting in childhood-onset diabetes mellitus and progressive optic nerve atrophy. The causative gene, WFS1, encodes wolframin, a master regulator of several cellular responses, and the gene’s mutations associate with clinical variability. Indeed, nonsense/frameshift variants correlate with more severe symptoms than missense/in-frame variants. As achieving a genotype–phenotype correlation is crucial for dealing with disease outcome, works investigating the impact of transcriptional and translational landscapes stemming from such mutations are needed. Therefore, we sought to elucidate the molecular determinants behind the pathophysiological alterations in a WS1 patient carrying compound heterozygous mutations in WFS1: c.316-1G>A, affecting the acceptor splice site (ASS) upstream of exon 4; and c.757A>T, introducing a premature termination codon (PTC) in exon 7.MethodsBioinformatic analysis was carried out to infer the alternative splicing events occurring after disruption of ASS, followed by RNA-seq and PCR to validate the transcriptional landscape. Patient-derived induced pluripotent stem cells (iPSCs) were used as an in vitro model of WS1 and to investigate the WFS1 alternative splicing isoforms in pancreatic beta cells. CRISPR/Cas9 technology was employed to correct ASS mutation and generate a syngeneic control for the endoplasmic reticulum stress induction and immunotoxicity assays.ResultsWe showed that patient-derived iPSCs retained the ability to differentiate into pancreatic beta cells. We demonstrated that the allele carrying the ASS mutation c.316-1G>A originates two PTC-containing alternative splicing transcripts (c.316del and c.316–460del), and two open reading frame-conserving mRNAs (c.271–513del and c.316–456del) leading to N-terminally truncated polypeptides. By retaining the C-terminal domain, these isoforms sustained the endoplasmic reticulum stress response in beta cells. Otherwise, PTC-carrying transcripts were regulated by the nonsense-mediated decay (NMD) in basal conditions. Exposure to cell stress inducers and proinflammatory cytokines affected expression levels of the NMD-related gene SMG7 (>twofold decrease; p<0.001) without eliciting a robust unfolded protein response in WFS1 beta cells. This resulted in a dramatic accumulation of the PTC-containing isoforms c.316del (>100-fold increase over basal; p<0.001) and c.316–460del (>20-fold increase over basal; p<0.001), predisposing affected beta cells to undergo apoptosis. Cas9-mediated recovery of ASS retrieved the canonical transcriptional landscape, rescuing the normal phenotype in patient-derived beta cells.Conclusions/interpretationThis study represents a new model to study wolframin, highlighting how each single mutation of the WFS1 gene can determine dramatically different functional outcomes. Our data point to increased vulnerability of WFS1 beta cells to stress and inflammation and we postulate that this is triggered by escaping NMD and accumulation of mutated transcripts and truncated proteins. These findings pave the way for further studies on the molecular basis of genotype–phenotype relationship in WS1, to uncover the key determinants that might be targeted to ameliorate the clinical outcome of patients affected by this rare disease.Data availabilityThe in silico predicted N-terminal domain structure file of WT wolframin was deposited in the ModelArchive, together with procedures, ramachandran plots, inter-residue distance deviation and IDDT scores, and Gromacs configuration files (doi/10.5452/ma-cg3qd). The deep-sequencing data as fastq files used to generate consensus sequences of AS isoforms of WFS1 are available in the SRA database (BioProject PRJNA1109747).Graphical

Robust malicious software detection and classification using global whale optimization algorithm with deep learning approach

Software malware detection and classification leverage sophisticated procedures and methods from the cybersecurity domain for identifying and categorizing malicious software, generally called malware. This procedure analyses code behaviour, file structures, and other features to distinguish between benign and malicious programs. Machine learning (ML) and artificial intelligence (AI) are vital in this domain, allowing the progress of dynamic and adaptive systems that identify novel and developing malware attacks. By training on massive datasets of benign and malicious instances, these systems learn patterns and signatures indicative of malware. This lets them correctly categorize and respond to potential attacks in real-time. This study presents a Global Whale Optimization Algorithm with Neutrosophic Logic for Software Malware Detection and Classification (GWOANL-SMDC) technique. The GWOANL-SMDC technique secures the software via the Android malware recognition process. Primarily, the GWOANL-SMDC technique employs the Neutrosophic Cognitive Maps (NCM) model for the feature selection process. The GWOANL-SMDC technique uses a convolutional long short-term memory (ConvLSTM) model for software malware detection. At last, the GWOA-based parameter tuning is performed to improve the performance of the ConvLSTM model. The simulation values of the GWOANL-SMDC technique are examined on the malware dataset. The obtained results ensured that the GWOANL-SMDC technique improved capability in detecting software malware.

Improvising the Malware Detection Accuracy in Portable Document Format (PDFs) through Machine Learning Classifiers

Every time a spike is observed in cyber-attacks, a huge financial loss is incurred that has surpassed $2 trillion according to some estimates. Apart from monetary setbacks, the damages in terms of loss of credibility and quality of services are way more difficult to recover. PDF documents have been considered as innocuous source of data sharing with static meta-tags. However, the structure of PDF file contains different objects, features and characteristics that make it easier for the attackers to invade (by attaching the malicious code with tags). Current research is focused on analyzing the structure of PDF files, data-structures exploited and extracting feature tags with associated feature values of PDFs. A total of 17,884 documents were used, having two categories (8942 malicious documents and 8942 benign files).

CADefender: Detection of unknown malicious AutoLISP computer-aided design files using designated feature extraction and machine learning methods

Computer-aided design (CAD) files are used to create digital designs for various structures – from the smallest chips in the high-tech industry to large-scale buildings and bridges in the civil engineering space. We found that most exploits and malicious payloads are deployed through Auto List Processing (AutoLISP) source code (LSP) or Fast Load AutoLISP (FAS) files, which are non-executable files (NEFs) containing scripts in the AutoLISP language that are native to AutoCAD; While antivirus software is capable of detecting many malicious CAD files, the potential to improve protection by using a dedicated machine learning (ML) based detection solution remains, especially against unknown and sophisticated CAD malware. In this study, we are the first to propose designated feature extraction methods and a robust framework aimed at the detection of known and unknown AutoLISP malware using ML algorithms. To accomplish this, we examined the structure, functionality, and ecosystems of AutoLISP files and collected the largest known representative collection of LSP files consisting of 6418 malicious and benign files (labeled and verified). We then explored the use of two novel static-analysis-based feature extraction methods (knowledge-based and structural) designated for LSP files to extract a discriminative set of informative features, which can subsequently be used by ML models to detect malicious LSP files. These two feature extraction methods serve as the basis of the proposed detection framework, whose performance we comprehensively compare to both widely used antiviruses and baseline ML models based on existing feature extraction methods, including MinHash, Bidirectional Encoder Representations from Transformers (BERT), and n-gram. Our results highlight our methods' contributions to the detection of unknown AutoLISP malware and demonstrate their ability to outperform existing methods. The best performance in the task of unknown malicious LSP file detection was obtained by the Artificial Neural Networks (ANN) model trained on 100 knowledge-based features, which obtained a true positive rate (TPR) of 99.49% with a false positive rate (FPR) of 0.57%. Our framework's role in explainability is also highlighted, as we also present the prominent features that contribute most to the model's detection capabilities; this information can be used for explainability purposes. We conclude by evaluating the proposed framework's ability to detect a malicious file from an unknown AutoLISP malware family and by evaluating our framework on an additional independent test set that originated from another source, scenarios that are often faced by malware detection solutions.

Using the Ethereum Blockchain in the Internet of Things Network for IT Diagnostics

The article discusses the use of Ethereum blockchain technology in the Internet of Things (IoT) network for IT diagnostics of patients, which increases data security and user privacy. This integration is proving effective for storing and managing sensitive data of patients with neurological diseases. An integrated system architecture has been developed that combines the IoT network, the IPFS (InterPlanetary File System) file structure with the Ethereum blockchain to create a reliable data storage model. This system ensures efficient, secure and transparent data processing, optimizing the processes of data registration, authorization and verification. Using IPFS for decentralized file storage, along with the Ethereum blockchain to create tamper-proof medical records, provides increased efficiency, scalability and privacy. During the experiments, the process of creating and testing the system was implemented, including setting up the environment, connecting an IPFS node, programming Ethereum smart contracts, sampling voice data and storing their hashes.

A CAD-oriented parallel-computing design framework for shape and topology optimization of arbitrary structures using parametric level set

Recently, the high-resolution topology optimization to promote engineering applicability has gained much more attentions. However, an accurate and highly-efficient design framework for implementing shape and topology optimization of engineering structures with integration of CAD model is still in demand. In the current work, the critical intention is to develop a CAD-oriented parallel-computing design framework for arbitrary structures, where the Parametric Level Set Method (PLSM) is employed for shape and topology optimization. Firstly, an implicit identification model is constructed for generating a signed distance field using the vertex and normal information from the ‘STL’ file of engineering structures. The signed distance field is combined with the compactly supported radial basis functions (CSRBFs) to solve the initial level set function with a parametrization. This method is applied to present all domains, including design domains, Neumann boundary domains, Dirichlet boundary domains, and non-design domains. Secondly, the CPU parallel strategy is considered for allocating partitions of structural stiffness matrix in finite element analysis to different CPU cores for the parallel-computing to save computation costs. Thirdly, a parallel-computing design formulation is developed for performing shape and topology optimization of arbitrary structures, in which the partitioned terms of all design variables and stiffness matrix are concurrently computed on each CPU core. Finally, several classic benchmarks and the critical engineering structure of Virtual Reality (VR) glass part with extremely complex geometries, are discussed to demonstrate the effectiveness and efficiency of the proposed design framework.

Screening of novel disease genes of sepsis-induced myocardial Disfunction by RNA sequencing and bioinformatics analysis

BackgroundThere is still a lack of effective treatment for sepsis-induced myocardial dysfunction (SIMD), while the pathogenesis of SIMD still remains largely unexplained. MethodsRNA sequencing results (GSE267388 and GSE79962) were used for cross-species integrative analysis. Bioinformatic analyses were used to delve into function, tissue- and cell- specificity, and interactions of genes. External datasets and qRT-PCR experiments were used for validation. L1000 FWD was used to predict targeted drugs, and 3D structure files were used for molecular docking. ResultsBased on bioinformatic analyses, ten differentially expressed genes were selected as genes of interest, seven of which were verified to be significantly differential expression. Bucladesine was considered as a potential targeted drug for SIMD, which banded to seven target proteins primarily by forming hydrogen bonds. ConclusionIt was considered that Cebpd, Timp1, Pnp, Osmr, Tgm2, Cp, and Asb2 were novel disease genes, while bucladesine was a potential therapeutic drug, of SIMD.

SSN2: The next generation of spatial stream network modeling in R.

The SSN2 R package provides tools for spatial statistical modeling, parameter estimation, and prediction on stream (river) networks. SSN2 is the successor to the SSN R package (Ver Hoef, Peterson, Clifford, & Shah, 2014), which was archived alongside broader changes in the R-spatial ecosystem (Nowosad, 2023) that included 1) the retirement of rgdal (Bivand, Keitt, & Rowlingson, 2021), rgeos (Bivand & Rundel, 2020), and maptools (Bivand & Lewin-Koh, 2021) and 2) the lack of active development of sp (Bivand, Pebesma, & Gómez-Rubio, 2013). SSN2 maintains compatibility with the input data file structures used by the SSN R package but leverages modern R-spatial tools like sf (Pebesma, 2018). SSN2 also provides many useful features that were not available in the SSN R package, including new modeling and helper functions, enhanced fitting algorithms, and simplified syntax consistent with other R generic functions.

Matrix-based project dataset parsers

There are several existing project datasets, which involve separate data sources for simulated and real projects, individual and multiprojects, and single- and multimodal attributes. In addition, their file structures are heterogeneous; therefore, scholars can usually use only one dataset to test a proposed scheduling or resource allocation algorithm. Since the internal structures of these projects are also very different, it is difficult to ensure that an algorithm optimized for a given type of project will also perform well on projects with other structures. The proposed parsing method supports researchers in:•reading several types of projects: simulated, real, individual, and multiprojects, as well as single- and multimodal attributes;•considering the priorities of activities and the flexibility of their dependencies, which is essential for modeling the structural flexibility employed by agile, hybrid, and extreme project management approaches;•building a large project database for testing and comparing different scheduling and resource allocation algorithms.

Efficient protein structure archiving using ProteStAr.

The introduction of Deep Minds' Alpha Fold 2 enabled the prediction of protein structures at an unprecedented scale. AlphaFold Protein Structure Database and ESM Metagenomic Atlas contain hundreds of millions of structures stored in CIF and/or PDB formats. When compressed with a general-purpose utility like gzip, this translates to tens of terabytes of data, which hinders the effective use of predicted structures in large-scale analyses. Here, we present ProteStAr, a compressor dedicated to CIF/PDB, as well as supplementary PAE files. Its main contribution is a novel approach to predicting atom coordinates on the basis of the previously analyzed atoms. This allows efficient encoding of the coordinates, the largest component of the protein structure files. The compression is lossless by default, though the lossy mode with a controlled maximum error of coordinates reconstruction is also present. Compared to the competing packages, i.e. BinaryCIF, Foldcomp, PDC, our approach offers a superior compression ratio at established reconstruction accuracy. By the efficient use of threads at both compression and decompression stages, the algorithm takes advantage of the multicore architecture of current central processing units and operates with speeds of about 1 GB/s. The presence of Python and C++ API further increases the usability of the presented method. The source code of ProteStAr is available at https://github.com/refresh-bio/protestar.

Describing and Sharing Molecular Visualizations Using the MolViewSpec Toolkit.

With the ever-expanding toolkit of molecular viewers, the ability to visualize macromolecular structures has never been more accessible. Yet, the idiosyncratic technical intricacies across tools and the integration complexities associated with handling structure annotation data present significant barriers to seamless interoperability and steep learning curves for many users. The necessity for reproducible data visualizations is at the forefront of the current challenges. Recently, we introduced MolViewSpec (homepage: https://molstar.org/mol-view-spec/, GitHub project: https://github.com/molstar/mol-view-spec), a specification approach that defines molecular visualizations, decoupling them from the varying implementation details of different molecular viewers. Through the protocols presented herein, we demonstrate how to use MolViewSpec and its 3D view-building Python library for creating sophisticated, customized 3D views covering all standard molecular visualizations. MolViewSpec supports representations like cartoon and ball-and-stick with coloring, labeling, and applying complex transformations such as superposition to any macromolecular structure file in mmCIF, BinaryCIF, and PDB formats. These examples showcase progress towards reusability and interoperability of molecular 3D visualization in an era when handling molecular structures at scale is a timely and pressing matter in structural bioinformatics as well as research and education across the life sciences. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating a MolViewSpec view using the MolViewSpec Python package Basic Protocol 2: Creating a MolViewSpec view with reference to MolViewSpec annotation files Basic Protocol 3: Creating a MolViewSpec view with labels and other advanced features Support Protocol 1: Computing rotation and translation vectors Support Protocol 2: Creating a MolViewSpec annotation file.

Automation of the &amp;lt;i&amp;gt;Ad Hoc&amp;lt;/i&amp;gt; Approach for Derandomization of Proteins: A Tutorial for Undergraduates in Molecular Sciences

Data analysis and manipulation software are vulnerable to user error during data processing and computations take considerable time when handling huge data and multiple repetitive tasks. These problems are usually mitigated by creating an app to repeat any given task reproducibly any number of times. This paper discusses the development of app that systematically automates the &amp;lt;i&amp;gt;ad hoc&amp;lt;/i&amp;gt; approach for derandomization of proteins and, or peptides. Thirty second-year undergraduates with little-to-no prior knowledge of computer programming are (were) asked to create this app with modules that sequentially convert spectra from original units to molar extinction and subtract baseline spectrum from the resultant spectra, derandomize the spectra by removing suspected significant unfolded domains from them, concatenate the generated files to a single file in an acceptable format for structural analysis, process our group structural algorithm output files into a user-friendly format to ease data analysis. In addition, they are (were) asked to prepare protein solution, determine its concentration spectroscopically, collect circular dichroism measurements of the protein, derandomize the protein spectra, and determine the secondary structure of the resultant protein spectra with our structure algorithm. The assessment results demonstrated that the students could prepare samples for CD analysis, collect spectra of proteins, and create an app to automate the &amp;lt;i&amp;gt;ad hoc&amp;lt;/i&amp;gt; approach. The hands-on activities enable students to acquire knowledge in basic programming and circular dichroism, CD spectroscopy.

Evaluating Molecular Complexity with Open-Source Machine Learning Approaches to Predict Process Mass Intensity.

The application of green chemistry is critical for cultivating environmental responsibility and sustainable practices in pharmaceutical manufacturing. Process mass intensity (PMI) is a key metric that quantifies the resource efficiency of a manufacturing process, but determining what constitutes a successful PMI of a specific molecule is challenging. A recent approach correlated molecular features to a crowdsourced definition of molecular complexity to determine PMI targets. While recent machine learning tools show promise in predicting molecular complexity, a more extensive application could significantly optimize manufacturing processes. To this end, we refine and expand upon the SMART-PMI tool by Sheridan et al. to create an open-source model and application. Our solution emphasizes explainability and parsimony to facilitate a nuanced understanding of prediction and ensure informed decision-making. The resulting model uses four descriptors-the heteroatom count, stereocenter count, unique topological torsion, and connectivity index chi4n-to compute molecular complexity with a comparable 82.6% predictive accuracy and 0.349 RMSE. We develop a corresponding app that takes in structured data files (SDF) to rapidly quantify molecular complexity and provide a PMI target that can be used to drive process development activities. By integrating machine learning explainability and open-source accessibility, we provide flexible tools to advance the field of green chemistry and sustainable pharmaceutical manufacturing.

Digital Forensic Research for Analyzing Drone and Mobile Device: Focusing on DJI Mavic 2 Pro

With the frequent occurrence of drone-related criminal cases, drone forensics has become a hot spot of concern. During drone-related criminal investigations, the implicated drones are often forcibly brought down, which poses significant challenges in conducting forensic analysis. In order to restore the truth of criminal cases, it is necessary to extract data not only from the external TF card but also from internal chip memory in drone forensics. To address this issue, a drone data parser (DRDP) is proposed to extract internal and external data from criminal-implicated drones. In this paper, we present comprehensive forensics on the DJI Mavic 2 Pro, analyzing the main file structure and encryption model. According to its file structures, three case studies are conducted on various file types (DAT files, TXT files, and default files) to verify the effectiveness and applicability of the designed procedure. The results show that the encrypted data of the implicated drone, such as GPS information, flight time, flight altitude, flight distance, three velocity components (x, y, z) and other information can be extracted and decrypted correctly, which provides evidence for the identification of the case facts.

Analysis and Design of Responsive Web-Based Learning Media Equipped with Pomodoro Feature in the Course of Software Engineering

The development of information and communication technology has brought about changes in the field of education, one of which is the emergence of web-based learning. Based on preliminary observation data collected through questionnaires, students still expressed uncertainty about their understanding of software engineering (SE) materials. The current learning media for SE include PowerPoint (PPT), e-modules, and instructional videos, which students find less engaging and challenging to comprehend. Additionally, students face distractions like games and social media, leading to difficulty maintaining focus while studying. The objectives of this research are (1) to analyze user and system needs and (2) to design web-based responsive learning media for the software engineering course, equipped with a Pomodoro feature. The research method used is research and development (R&amp;D) with the 4D Thiagarajan development model, consisting of four stages: define, design, develop, and disseminate. However, only the definition and design stages were carried out due to research limitations. The presentation of data analysis results involves (1) needs analysis, which includes frontend analysis, learner analysis, task analysis, concept analysis, and specifying instructional objectives; (2) system design, which includes context diagrams and data flow diagrams; (3) database design, which includes Entity Relationship Diagram (ERD) and database file structure; (4) initial design in the form of a prototype; (5) technology design for development and implementation; and (6) instructional media testing design

Adaptive optics telemetry standard

Context. The amount of adaptive optics (AO) telemetry generated by visible/near-infrared ground-based observatories is ever greater, leading to a growing need for a standardised data exchange format to support performance analysis, AO research, and development activities that involve large-scale telemetry mining, processing, and curation.Aims. This paper introduces the Adaptive Optics Telemetry (AOT) data exchange format as a standard for sharing AO telemetry from visible/infrared ground-based observatories. AOT is based on the flexible image transport system (FITS) and aims to provide unambiguous and consistent data access across various systems and configurations, including natural and single- or multiple-laser guide-star AO systems.Methods. We designed AOT with a focus on two key use cases: atmospheric turbulence parameter estimation and point-spread function reconstruction. We prototyped and tested the design using existing AO telemetry datasets from multiple systems: single conjugate with natural and laser guide stars, tomographic systems with multi-channel wavefront sensors, and single- and multi-wavefront correctors in systems featuring either a Shack-Hartmann or Pyramid as the main wavefront sensor.Results. The AOT file structure has been thoroughly defined, with specified data fields, descriptions, data types, units, and expected dimensions. To support this format, we have developed a Python package that enables the data conversion, reading, writing, and exploration of AOT files; it has been made publicly available and is compatible with a general-purpose Python package manager. We have demonstrated the flexibility of the AOT format by packaging data from five different instruments, installed on different telescopes.