Principles for Integrative Structural Biology Studies.

Integrative structure determination is a powerful approach to modeling the structures of biological systems based on data produced by multiple experimental and theoretical methods, with implications for our understanding of cellular biology and drug discovery. This Primer introduces the theory and methods of integrative approaches, emphasizing the kinds of data that can be effectively included in developing models and using the nuclear pore complex as an example to illustrate the practice and challenges involved. These guidelines are intended to aid the researcher in understanding and applying integrative structural methods to systems of their interest and thus take advantage of this rapidly evolving field.

An Integrated View of Immunometabolism.

The worldwide obesity epidemic has emerged as a major cause of insulin resistance and Type 2 diabetes. Chronic tissue inflammation is a well-recognized feature of obesity, and the field of immunometabolism has witnessed many advances in recent years. Here, we review the major features of our current understanding with respect to chronic obesity-related inflammation in metabolic tissues and focus on how these inflammatory changes affect insulin sensitivity, insulin secretion, food intake, and glucose homeostasis. There is a growing appreciation of the varied and sometimes integrated crosstalk between cells within a tissue (intraorgan) and tissues within an organism (interorgan) that supports inflammation in the context of metabolic dysregulation. Understanding these pathways and modes of communication has implications for translational studies. We also briefly summarize the state of this field with respect to potential current and developing therapeutics.

New horizons in the stormy sea of multimodal single-cell data integration.

While measurements of RNA expression have dominated the world of single-cell analyses, new single-cell techniques increasingly allow collection of different data modalities, measuring different molecules, structural connections, and intermolecular interactions. Integrating the resulting multimodal single-cell datasets is a new bioinformatics challenge. Equally important, it is a new experimental design challenge for the bench scientist, who is not only choosing from a myriad of techniques for each data modality but also faces new challenges in experimental design. The ultimate goal is to design, execute, and analyze multimodal single-cell experiments that are more than just descriptive but enable the learning of new causal and mechanistic biology. This objective requires strict consideration of the goals behind the analysis, which might range from mapping the heterogeneity of a cellular population to assembling system-wide causal networks that can further our understanding of cellular functions and eventually lead to models of tissues and organs. We review steps and challenges toward this goal. Single-cell transcriptomics is now a mature technology, and methods to measure proteins, lipids, small-molecule metabolites, and other molecular phenotypes at the single-cell level are rapidly developing. Integrating these single-cell readouts so that each cell has measurements of multiple types of data, e.g., transcriptomes, proteomes, and metabolomes, is expected to allow identification of highly specific cellular subpopulations and to provide the basis for inferring causal biological mechanisms.

Active Interaction Mapping Reveals the Hierarchical Organization of Autophagy.

We have developed a general progressive procedure, Active Interaction Mapping, to guide assembly of the hierarchy of functions encoding any biological system. Using this process, we assemble an ontology of functions comprising autophagy, a central recycling process implicated in numerous diseases. A first-generation model, built from existing gene networks in Saccharomyces, captures most known autophagy components in broad relation to vesicle transport, cell cycle, and stress response. Systematic analysis identifies synthetic-lethal interactions as most informative for further experiments; consequently, we saturate the model with 156,364 such measurements across autophagy-activating conditions. These targeted interactions provide more information about autophagy than all previous datasets, producing a second-generation ontology of 220 functions. Approximately half are previously unknown; we confirm roles for Gyp1 at the phagophore-assembly site, Atg24 in cargo engulfment, Atg26 in cytoplasm-to-vacuole targeting, and Ssd1, Did4, and others in selective and non-selective autophagy. The procedure and autophagy hierarchy are at http://atgo.ucsd.edu/.

Integrating and Adopting AI in the Radiology Workflow: A Primer for Standards and Integrating the Healthcare Enterprise (IHE) Profiles.

The deployment of artificial intelligence (AI) solutions in radiology practice creates new demands on existing imaging workflow. Accommodating custom integrations creates a substantial operational and maintenance burden. These custom integrations also increase the likelihood of unanticipated problems. Standards-based interoperability facilitates AI integration with systems from different vendors into a single environment by enabling seamless exchange between information systems in the radiology workflow. Integrating the Healthcare Enterprise (IHE) is an initiative to improve how computer systems share information across health care domains, including radiology. IHE integrates existing standards-such as Digital Imaging and Communications in Medicine, Health Level Seven, and health care lexicons and ontologies (ie, LOINC, RadLex, SNOMED Clinical Terms)-by mapping data elements from one standard to another. IHE Radiology manages profiles (standards-based implementation guides) for departmental workflow and information sharing across care sites, including profiles for scaling AI processing traffic and integrating AI results. This review focuses on the need for standards-based interoperability to scale AI integration in radiology, including a brief review of recent IHE profiles that provide a framework for AI integration. This review also discusses challenges and additional considerations for AI integration, including technical, clinical, and policy perspectives.

Surveillance system integration: reporting the results of a global multicountry survey.

OBJECTIVES: Currently, there is no comprehensive picture of the global surveillance landscape. This survey examines the current state of surveillance systems, levels of integration, barriers and opportunities for the integration of surveillance systems at the country level, and the role of national public health institutes (NPHIs). STUDY DESIGN: This was a cross-sectional survey of NPHIs. METHODS: A web-based survey questionnaire was disseminated to 110 NPHIs in 95 countries between July and August 2022. Data were descriptively analysed, stratified by World Health Organization region, World Bank Income Group, and self-reported Integrated Disease Surveillance (IDS) maturity status. RESULTS: Sixty-five NPHIs responded. Systems exist to monitor notifiable diseases and vaccination coverage, but less so for private, pharmaceutical, and food safety sectors. While Ministries of Health usually lead surveillance, in many countries, NPHIs are also involved. Most countries report having partially developed IDS. Surveillance data are frequently inaccessible to the lead public health agency and seldomly integrated into a national public health surveillance system. Common challenges to establishing IDS include information technology system issues, financial constraints, data sharing and ownership limitations, workforce capacity gaps, and data availability. CONCLUSIONS: Public health surveillance systems across the globe, although built on similar principles, are at different levels of maturity but face similar developmental challenges. Leadership, ownership and governance, supporting legal mandates and regulations, as well as adherence to mandates, and enforcement of regulations are critical components of effective surveillance. In many countries, NPHIs play a significant role in integrated disease surveillance.

[Interoperability Working Group: core dataset and information systems for data integration and data exchange in the Medical Informatics Initiative]. / Arbeitsgruppe Interoperabilität: Kerndatensatz und Informationssysteme für Integration und Austausch von Daten in der Medizininformatik-Initiative.

The interoperability Working Group of the Medical Informatics Initiative (MII) is the platform for the coordination of overarching procedures, data structures, and interfaces between the data integration centers (DIC) of the university hospitals and national and international interoperability committees. The goal is the joint content-related and technical design of a distributed infrastructure for the secondary use of healthcare data that can be used via the Research Data Portal for Health. Important general conditions are data privacy and IT security for the use of health data in biomedical research. To this end, suitable methods are used in dedicated task forces to enable procedural, syntactic, and semantic interoperability for data use projects. The MII core dataset was developed as several modules with corresponding information models and implemented using the HL7® FHIR® standard to enable content-related and technical specifications for the interoperable provision of healthcare data through the DIC. International terminologies and consented metadata are used to describe these data in more detail. The overall architecture, including overarching interfaces, implements the methodological and legal requirements for a distributed data use infrastructure, for example, by providing pseudonymized data or by federated analyses. With these results of the Interoperability Working Group, the MII is presenting a future-oriented solution for the exchange and use of healthcare data, the applicability of which goes beyond the purpose of research and can play an essential role in the digital transformation of the healthcare system.

[Data integration centers-from a concept in the Medical Informatics Initiative to its local implementation in the Network of University Medicine]. / Die Datenintegrationszentren ­ Von der Konzeption in der Medizininformatik-Initiative zur lokalen Umsetzung in einem Netzwerk Universitätsmedizin.

As part of the Medical Informatics Initiative (MII), data integration centers (DICs) have been established at 38 university and 3 non-university locations in Germany since 2018. At DICs, research and healthcare data are collected. The DICs represent an important pillar in research and healthcare. They establish the technical, organizational, and (ethical) data protection requirements to enable cross-site research with the available routine clinical data.This article presents the three main pillars of DICs: ethical-legal framework, organization, and technology. The organization of DICs and their organizational embedding and interaction are presented, as well as the technical infrastructure. The services that a DIC provides for its own location and for external researchers are explained, and the role of the DIC as an internal and external interface for strengthening cooperation and collaboration is outlined.Legal conformity, organization, and technology form the basis for the processes and structures of a DIC and are decisive for how it is integrated into the healthcare and research landscape of a location, but also for how it can react to national and European requirements and act and function as an interface to the outside world. In this context and with regard to national developments (e.g., introduction of the electronic patient file-ePA), but also international and European initiatives (e.g., European Health Data Space-EHDS), the DIC will play a central role in the future.

Integrated morphodynamic signalling of the mammary gland.

The mammary gland undergoes a spectacular series of changes as it develops, and maintains a remarkable capacity to remodel and regenerate for several decades. Mammary morphogenesis has been investigated for over 100 years, motivated by the dairy industry and cancer biologists. Over the past decade, the gland has emerged as a major model system in its own right for understanding the cell biology of tissue morphogenesis. Multiple signalling pathways from several cell types are orchestrated together with mechanical cues and cell rearrangements to establish the pattern of the mammary gland. The integrated mechanical and molecular pathways that control mammary morphogenesis have implications for the developmental regulation of other epithelial organs.

Initial Experience of 10 Imaging Vendors with the IHE SHARAZONE: a New Multivendor Peer-to-Peer Test Service for DICOM Objects.

Alignment of DICOM (Digital Imaging and Communications in Medicine) capabilities among vendors is crucial to improve interoperability in the healthcare industry and advance medical imaging 2. However, a sustainable model for sharing DICOM samples is not available. To address this issue, Integrating the Healthcare Enterprise (IHE) has introduced the IHE SHARAZONE, a continuous cross-vendor DICOM data sharing test service. IHE is a highly regarded organization known for profiling standards such as DICOM, HL7 v2 (Health Level Seven, version 2), HL7 CDA (Clinical Document Architecture), and HL7 FHIR (Fast Healthcare Interoperability Resources) into practical solutions for clinical practice. The primary goal of the IHE SHARAZONE is to provide a reliable and consistent cross-vendor DICOM data sharing system. To evaluate its effectiveness, a 5-month pilot was conducted with ten imaging vendors. The pilot concluded with a participant survey, which yielded valuable insights into the initial experience with the IHE SHARAZONE. These findings can inform future improvements and developments to this important service.

Anaerobic-aerobic treatment of wastewater and leachate: A review of process integration, system design, performance and associated energy revenue.

Hybrid anaerobic-aerobic biological systems are an environmentally sustainable way of recovering bioenergy during the treatment of high-strength wastewaters and landfill leachate. This study provides a critical review of three major categories of anaerobic-aerobic processes such as conventional wetland, high-rate and integrated bioreactor systems applied for treatment of wastewaters and leachate. A comparative assessment of treatment mechanisms, critical operating parameters, bioreactor configurations, process control strategies, efficacies, and microbial dynamics of anaerobic-aerobic systems is provided. The review also explores the influence of wastewater composition on treatment performance, ammonium nitrogen removal efficacy, impact of mixing leachate, energy consumption, coupled bioenergy production and economic aspects of anaerobic-aerobic systems. Furthermore, the operational challenges, prospective modifications, and key future research directions are discussed. This review will provide in-depth understanding to develop sustainable engineering applications of anaerobic-aerobic processes for effective co-treatment of wastewaters and leachate.

Privacy-preserving harmonization via distributed ComBat.

Challenges in clinical data sharing and the need to protect data privacy have led to the development and popularization of methods that do not require directly transferring patient data. In neuroimaging, integration of data across multiple institutions also introduces unwanted biases driven by scanner differences. These scanner effects have been shown by several research groups to severely affect downstream analyses. To facilitate the need of removing scanner effects in a distributed data setting, we introduce distributed ComBat, an adaptation of a popular harmonization method for multivariate data that borrows information across features. We present our fast and simple distributed algorithm and show that it yields equivalent results using data from the Alzheimer's Disease Neuroimaging Initiative. Our method enables harmonization while ensuring maximal privacy protection, thus facilitating a broad range of downstream analyses in functional and structural imaging studies.

Diffusion enables integration of heterogeneous data and user-driven learning in a desktop knowledge-base.

Integrating reference datasets (e.g. from high-throughput experiments) with unstructured and manually-assembled information (e.g. notes or comments from individual researchers) has the potential to tailor bioinformatic analyses to specific needs and to lead to new insights. However, developing bespoke analysis pipelines from scratch is time-consuming, and general tools for exploring such heterogeneous data are not available. We argue that by treating all data as text, a knowledge-base can accommodate a range of bioinformatic data types and applications. We show that a database coupled to nearest-neighbor algorithms can address common tasks such as gene-set analysis as well as specific tasks such as ontology translation. We further show that a mathematical transformation motivated by diffusion can be effective for exploration across heterogeneous datasets. Diffusion enables the knowledge-base to begin with a sparse query, impute more features, and find matches that would otherwise remain hidden. This can be used, for example, to map multi-modal queries consisting of gene symbols and phenotypes to descriptions of diseases. Diffusion also enables user-driven learning: when the knowledge-base cannot provide satisfactory search results in the first instance, users can improve the results in real-time by adding domain-specific knowledge. User-driven learning has implications for data management, integration, and curation.

A multi-objective genetic algorithm to find active modules in multiplex biological networks.

The identification of subnetworks of interest-or active modules-by integrating biological networks with molecular profiles is a key resource to inform on the processes perturbed in different cellular conditions. We here propose MOGAMUN, a Multi-Objective Genetic Algorithm to identify active modules in MUltiplex biological Networks. MOGAMUN optimizes both the density of interactions and the scores of the nodes (e.g., their differential expression). We compare MOGAMUN with state-of-the-art methods, representative of different algorithms dedicated to the identification of active modules in single networks. MOGAMUN identifies dense and high-scoring modules that are also easier to interpret. In addition, to our knowledge, MOGAMUN is the first method able to use multiplex networks. Multiplex networks are composed of different layers of physical and functional relationships between genes and proteins. Each layer is associated to its own meaning, topology, and biases; the multiplex framework allows exploiting this diversity of biological networks. We applied MOGAMUN to identify cellular processes perturbed in Facio-Scapulo-Humeral muscular Dystrophy, by integrating RNA-seq expression data with a multiplex biological network. We identified different active modules of interest, thereby providing new angles for investigating the pathomechanisms of this disease. Availability: MOGAMUN is available at https://github.com/elvanov/MOGAMUN and as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/MOGAMUN.html. Contact: anais.baudot@univ-amu.fr.

Heterogeneous Autonomous Robotic System in Viticulture and Mariculture: Vehicles Development and Systems Integration.

There are activities in viticulture and mariculture that require extreme physical endurance from human workers, making them prime candidates for automation and robotization. This paper presents a novel, practical, heterogeneous, autonomous robotic system divided into two main parts, each dealing with respective scenarios in viticulture and mariculture. The robotic components and the subsystems that enable collaboration were developed as part of the ongoing HEKTOR project, and each specific scenario is presented. In viticulture, this includes vineyard surveillance, spraying and suckering with an all-terrain mobile manipulator (ATMM) and a lightweight autonomous aerial robot (LAAR) that can be used in very steep vineyards where other mechanization fails. In mariculture, scenarios include coordinated aerial and subsurface monitoring of fish net pens using the LAAR, an autonomous surface vehicle (ASV), and a remotely operated underwater vehicle (ROV). All robotic components communicate and coordinate their actions through the Robot Operating System (ROS). Field tests demonstrate the great capabilities of the HEKTOR system for the fully autonomous execution of very strenuous and hazardous work in viticulture and mariculture, while meeting the necessary conditions for the required quality and quantity of the work performed.

Guidance for Systematic Integration of Undernutrition in Attributing Cause of Death in Children.

Minimally invasive tissue sampling (MITS) is increasingly being used to better understand causes of death in low-resource settings. Undernutrition (eg, wasting, stunting) is prevalent among children globally and yet not consistently coded or uniformly included on death certificates in MITS studies when present. Consistent and accurate attribution of undernutrition is fundamental to understanding its contribution to child deaths. In May 2020, members of the MITS Alliance Cause of Death Technical Working Group convened a panel of experts in public health, child health, nutrition, infectious diseases, and MITS to develop guidance for systematic integration of undernutrition, as assessed by anthropometry, in cause of death coding, including as part of the causal chain or as a contributing condition, in children <5 years of age. The guidance presented here will support MITS and other researchers, public health practitioners, and clinicians with a systematic approach to assigning and interpreting undernutrition in death certification.

WeBrain: A web-based brainformatics platform of computational ecosystem for EEG big data analysis.

The current evolution of 'cloud neuroscience' leads to more efforts with the large-scale EEG applications, by using EEG pipelines to handle the rapidly accumulating EEG data. However, there are a few specific cloud platforms that seek to address the cloud computational challenges of EEG big data analysis to benefit the EEG community. In response to the challenges, a WeBrain cloud platform (https://webrain.uestc.edu.cn/) is designed as a web-based brainformatics platform and computational ecosystem to enable large-scale EEG data storage, exploration and analysis using cloud high-performance computing (HPC) facilities. WeBrain connects researchers from different fields to EEG and multimodal tools that have become the norm in the field and the cloud processing power required to handle those large EEG datasets. This platform provides an easy-to-use system for novice users (even no computer programming skills) and provides satisfactory maintainability, sustainability and flexibility for IT administrators and tool developers. A range of resources are also available on https://webrain.uestc.edu.cn/, including documents, manuals, example datasets related to WeBrain, and collected links to open EEG datasets and tools. It is not necessary for users or administrators to install any software or system, and all that is needed is a modern web browser, which reduces the technical expertise required to use or manage WeBrain. The WeBrain platform is sponsored and driven by the China-Canada-Cuba international brain cooperation project (CCC-Axis, http://ccc-axis.org/), and we hope that WeBrain will be a promising cloud brainformatics platform for exploring brain information in large-scale EEG applications in the EEG community.

PATRIC as a unique resource for studying antimicrobial resistance.

The Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org) is designed to provide researchers with the tools and services that they need to perform genomic and other 'omic' data analyses. In response to mounting concern over antimicrobial resistance (AMR), the PATRIC team has been developing new tools that help researchers understand AMR and its genetic determinants. To support comparative analyses, we have added AMR phenotype data to over 15 000 genomes in the PATRIC database, often assembling genomes from reads in public archives and collecting their associated AMR panel data from the literature to augment the collection. We have also been using this collection of AMR metadata to build machine learning-based classifiers that can predict the AMR phenotypes and the genomic regions associated with resistance for genomes being submitted to the annotation service. Likewise, we have undertaken a large AMR protein annotation effort by manually curating data from the literature and public repositories. This collection of 7370 AMR reference proteins, which contains many protein annotations (functional roles) that are unique to PATRIC and RAST, has been manually curated so that it projects stably across genomes. The collection currently projects to 1 610 744 proteins in the PATRIC database. Finally, the PATRIC Web site has been expanded to enable AMR-based custom page views so that researchers can easily explore AMR data and design experiments based on whole genomes or individual genes.

Drug knowledge bases and their applications in biomedical informatics research.

Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives have been taken to help researchers make good use of them. As the results of these initiatives, many drug knowledge bases have been constructed. They range from simple ones with specific focuses to comprehensive ones that contain information on almost every aspect of a drug. These curated drug knowledge bases have made significant contributions to the development of efficient and effective health information technologies for better health-care service delivery. Understanding and comparing existing drug knowledge bases and how they are applied in various biomedical studies will help us recognize the state of the art and design better knowledge bases in the future. In addition, researchers can get insights on novel applications of the drug knowledge bases through a review of successful use cases. In this study, we provide a review of existing popular drug knowledge bases and their applications in drug-related studies. We discuss challenges in constructing and using drug knowledge bases as well as future research directions toward a better ecosystem of drug knowledge bases.

Multi-omics integration-a comparison of unsupervised clustering methodologies.

With the recent developments in the field of multi-omics integration, the interest in factors such as data preprocessing, choice of the integration method and the number of different omics considered had increased. In this work, the impact of these factors is explored when solving the problem of sample classification, by comparing the performances of five unsupervised algorithms: Multiple Canonical Correlation Analysis, Multiple Co-Inertia Analysis, Multiple Factor Analysis, Joint and Individual Variation Explained and Similarity Network Fusion. These methods were applied to three real data sets taken from literature and several ad hoc simulated scenarios to discuss classification performance in different conditions of noise and signal strength across the data types. The impact of experimental design, feature selection and parameter training has been also evaluated to unravel important conditions that can affect the accuracy of the result.