A blueprint for contemporary studies of microbiomes

The One Health concept stresses the ecological relationships between human, animal, and environmental health. Much of the One Health literature to date has examined the transfer of pathogens from animals (e.g., emerging zoonoses) and the environment to humans. The recent rapid development of technology to perform high throughput DNA sequencing has expanded this view to include the study of entire microbial communities. Applying the One Health approach to the microbiome allows for consideration of both pathogenic and non-pathogenic microbial transfer between humans, animals, and the environment. We review recent research studies of such transmission, the molecular and statistical methods being used, and the implications of such microbiome relationships for human health. Our review identified evidence that the environmental microbiome as well as the microbiome of animals in close contact can affect both the human microbiome and human health outcomes. Such microbiome transfer can take place in the household as well as the workplace setting. Urbanization of built environments leads to changes in the environmental microbiome which could be a factor in human health. While affected by environmental exposures, the human microbiome also can modulate the response to environmental factors through effects on metabolic and immune function. Better understanding of these microbiome interactions between humans, animals, and the shared environment will require continued development of improved statistical and ecological modeling approaches. Such enhanced understanding could lead to innovative interventions to prevent and manage a variety of human health and disease states.

Over the past decade, it has become exceedingly clear that the microbiome is a critical factor in human health and disease and thus should be investigated to develop innovative treatment strategies. The field of metagenomics has come a long way in leveraging the advances of next-generation sequencing technologies resulting in the capability to identify and quantify all microorganisms present in human specimens. However, the field of metagenomics is still in its infancy, specifically in regard to the limitations in computational analysis, statistical assessments, standardization, and validation due to vast variability in the cohorts themselves, experimental design, and bioinformatic workflows. This review summarizes the methods, technologies, computational tools, and model systems for characterizing and studying the microbiome. We also discuss important considerations investigators must make when interrogating the involvement of the microbiome in health and disease in order to establish robust results and mechanistic insights before moving into therapeutic design and intervention.

Cardiology as a medical discipline has reached a critical stretch of white water unlike any it has navigated over the past 40 years. The changes our specialty now faces have been brought about not, as in the past, by conceptual or technical advances but rather by cost barriers that will govern every aspect of our work, including education, research, and clinical practice, for as long as anyone can predict. A little more than 40 years ago, when I first entered Dr. Lewis Dexter's laboratory as a cardiology fellow at Harvard Medical School and the Peter Bent Brigham Hospital, cardiac catheterization had been in use for only about 3 or 4 years. It dominated our work in unraveling the questions of circulatory physiology and biochemistry. Shortly thereafter, we began to focus on how the heart functions both as a muscle and as a pump. That was an era of both technological innovation and conceptual advancement best exemplified by new, accurate radiographic imaging techniques. These methods augmented and complemented the physiological pressure-flow measurements and revolutionized the field. They showed us the cardiac chambers and the coronary and other arteries in ways never before possible, thus refining the concept and role of cardiovascular dimension and volume in heart function. All of this culminated in the 1980s with the advent of focused intervention and remarkably effective surgery. Molecular biology, which now dominates academic cardiology, was beginning to find its place in the field. We now have unique drugs for clinical care, and large clinical trials have become commonplace (Fig 1⇓). Figure 1. Until recently, the development of cardiology has been influenced more by conceptual and technological advances than by other forces, but in the 1990s cost has become the chief governing factor and is likely to remain so for as long as anyone can …

In bacteria, the primary organelle of energy transduction is the cytoplasmic membrane. As in all cells, the membrane is the locus of the permeability barrier and of transport systems; but it also houses the respiratory chain and ancillary enzymes of oxidative phosphorylation, photosynthetic pigments, the flagellar motor, and other energy-linked functions. The recent surge of research into the mechanisms of bacterial energy coupling can be attributed to both technical and conceptual advances. Among the technical advances are the application of ionophores, the isolation of mutants defective in energy coupling, and especially the development of membrane vesicles into a major research tool. Chief among the conceptual advances is the growing recognition that Peter Mitchell’s chemiosmotic theory provides a rational and comprehensive framework upon which the experimental observations can be assembled. A guide to some and a goad to others, chemiosmotic theory has lent direction to the revolution that is now transforming bioenergetics.

Background: The human microbiome has emerged as a pivotal factor in health and disease, significantly influencing various physiological processes and disease outcomes. Despite advances in microbiome research, the integration of microbiome knowledge into clinical practice remains limited. This review aims to elucidate the role of the microbiome in health and disease, emphasizing its potential for disease prevention, diagnosis, and treatment. Aim: To provide a comprehensive overview of the human microbiome’s structure, function, and impact on overall health and disease prevention. The review seeks to bridge the gap between microbiome research and clinical application, facilitating a better understanding among medical professionals. Methods: The review synthesizes findings from recent microbiome studies, including those from large-scale initiatives such as the Human Microbiome Project and the MetaHIT consortium. It examines various methodologies used to study microbiome structure and function, including 16S rRNA sequencing, metagenomics, metatranscriptomics, proteomics, and metabolomics. Results: The review highlights the diverse roles of the microbiome in health, such as its impact on immune system development, metabolic processes, and disease prevention. It also discusses the implications of microbiome research for various diseases, including infectious diseases, inflammatory bowel diseases, obesity, and cardiovascular conditions.

The EU Response to COVID-19: From Reactive Policies to Strategic Decision-Making.

<p><strong>Abstract</strong>:Rapid assessment has been done in Tangerang Distric Health Office to assess the availability and quality of routine facility-based data for Sub-National Health System Performance Assessment (SNHSPA) indicators. Also to assess the need of community based swvey as a complementary data and attendance to organize Surkesda.</p><p><strong>Metodology</strong>: Assessment activities composed of 2-day workshop, dan field visits (3-4 days), and analysis. The workshop was used to introduce the conceptual framework of SNHSPA to the potential users of SNHSPA. The workshop also served as an opportunity to receive feedback on the framework as well as indicators to be used for the SNHSPA in Distric Health Offices of Tangerang. During the field visit, assessment was conducted in 1 public hospital, 1 private hospital and 6 Health Centers with different stages of development (good, fair and poor). Assessment was conducted on data availability and reliability and consistency. The result indicated there were not available data from private health practices on outpatient visits, pneumonia, and tuberculosis (TB) in all health centers visited. All of staff stated that subordinate health facilities' data on outpatient visit and pneumonia were available. In the case of lB, half of staff indicated that the data on TB were not available from the sub-ordinate facilities. The quality of the data is questionable. Not all the patient with suspect TB sent to Health Centers. Hence, the data of TB can not reflect situation on community. From 6 health centers, half of the staff stated the different number of TB cases among forms. It indicated inconsistency between form of "SP3" <em>(S</em><em>istim </em><em>Pe</em><em>n</em><em>cata</em><em>tan </em><em>dan </em><em>Pel</em><em>aporan </em><em>Puske</em><em>s</em><em>mas</em><em>/"</em>LBl<em>"</em><em> </em><em>(</em><em>Laporan </em><em>Bulanan </em><em>1</em><em>) </em>report and form "TB01 " <em>(T</em><em>ubercul</em><em>osis </em><em>1</em><em>). </em>All of health center staff stated that the pneumonia data and outpatient visit data showed the number of cases in all health center districts, except data from private health practices. One of six health centers reported inconsistency data among the number of pneumonia cases, and 5 of them stated that only once a month counted the new cases of pneumonia. It means that the quality of data was low. There are different numbers of health facility and village, which was reported by head of health centers and which was received by district health offices. The private and public hospitals sent report routinely to district health office, but not routinely to regional government.</p> <p> </p> <p><strong>Keywords</strong>: SNHSPA, facility-based data</p>

Environmental microbiome research shows that microbial communities help to shape complex ecological processes that influence human and environmental health. Researchers are currently investigating the potential health-inducing interactions between humans and the environmental microbiome, particularly in urban areas. However, not only are there inherent technical issues to overcome in implementing the findings of this research, but there are also complex social, political and economic factors that affect the design, construction and management phases. Drawing on a recent paper discussing opportunities for Microbiome-Inspired Green Infrastructure (MIGI) (Robinson et al. 2018), we set out criteria for an ‘Environmental Microbiome Toolkit for Urban Designers’ that could be used by planners, architects, landscape architects and civil engineers. We provide a worked example of this toolkit to design a public space in Sheffield, demonstrating practical design techniques to consider the environmental microbiome and its role in human and ecosystem health. The Landscape Institute (LI) is the professional body that regulates and represents landscape architects, providing guidance across all spheres of the profession. One of the key functions of the LI is to develop and maintain the LI Plan of Work, regulating the work that landscape architects undertake at each stage of a project, from landscape assessment through to conceptual and detailed design, contract administration and landscape management. We apply these industry-standards to show how the environmental microbiome should be considered in landscape assessment, design and management, bridging the gap between research and practice and providing a common reference point for future policy development and industry regulation.

Beyond correlation: Understanding the causal link between microbiome and plant health

Numerous microbiome studies have revealed significant associations between the microbiome and human health and disease. These findings have motivated researchers to explore the causal role of the microbiome in human complex traits and diseases. However, the complexities of microbiome data pose challenges for statistical analysis and interpretation of causal effects. We introduced a novel statistical framework, CRAmed, for inferring the mediating role of the microbiome between treatment and outcome. CRAmed improved the interpretability of the mediation analysis by decomposing the natural indirect effect into two parts, corresponding to the presence-absence and abundance of a microbe, respectively. Comprehensive simulations demonstrated the superior performance of CRAmed in Recall, precision, and F1 score, with a notable level of robustness, compared to existing mediation analysis methods. Furthermore, two real data applications illustrated the effectiveness and interpretability of CRAmed. Our research revealed that CRAmed holds promise for uncovering the mediating role of the microbiome and understanding of the factors influencing host health. The R package CRAmed implementing the proposed methods is available online at https://github.com/liudoubletian/CRAmed.

Ecological momentary assessment (EMA) is a widely used methodology in psychological sciences; however, more broadly, environmental scientists have yet to fully capitalize on the benefits this method offers for gaining a critical understanding of subjective and behavioral responses to environmental factors. EMA enables the collection of experiences and actions occurring in one's natural environment as they unfold over time, allowing researchers to gain contextually informed, dynamic, and longitudinal insights. EMA can provide an accurate understanding of experiences and behaviors relevant to environmental science. To share this perspective, first, we describe current limitations in environmental research that could be addressed through the integration of EMA. Second, we discuss several benefits of adopting EMA in environmental sciences. Finally, we highlight the challenges and considerations involved in integrating EMA. The overarching implication of this work is to foster the interdisciplinary potential and promise of EMA methodology in advancing environmental science research.

A number of R/Bioconductor packages for microbiome data science have been released in the recent years. The majority of the existing frameworks and packages focus on the analysis of taxonomic profiling data generated by phylogenetic microarrays, 16S amplicon sequencing, or metagenome analysis. However, there is an increasing need to integrate taxonomic profiles with other measurement types, such as transcriptomics, metabolomics, host genomics, cross-kingdom analysis, and hierarchical side information of the features and samples. Modern data containers, such as the MultiAssayExperiment, have opened up new opportunities for developing a systematic approach to multi-table data integration in microbiome studies. A dedicated ecosystem of tools for microbiome analysis can provide valuable, targeted tools for those focusing on microbiome and metagenome profiling data sets. We will discuss recent advances in microbiome data integration and the associated package ecosystem in R/Bioconductor, highlighting links to related work in other areas such as transcriptomics and single-cell studies that are greatly accelerating these community-driven efforts.

Theme: Data and Linkage QualityObjectives:
 
 To define health data quality from clinical, data science, and health system perspectives
 To describe some of the international best practices related to quality and how they are being applied to Canada’s administrative health data.
 To compare methods for health data quality assessment and improvement in Canada (automated logical checks, chart quality indicators, reabstraction studies, coding manager perspectives)
 To highlight how data linkage can be used to provide new insights into the quality of original data sources
 To highlight current international initiatives for improving coded data quality including results from current ICD-11 field trials
 
 Dr. Keith Denny: Director of Clinical Data Standards and Quality, Canadian Insititute for Health Information (CIHI), Adjunct Research Professor, Carleton University, Ottawa, ON. He provides leadership for CIHI’s information quality initiatives and for the development and application of clinical classifications and terminology standards.
 Maureen Kelly: Manager of Information Quality at CIHI, Ottawa, ON. She leads CIHI’s corporate quality program that is focused on enhancing the quality of CIHI’s data sources and information products and to fostering CIHI’s quality culture.
 Dr. Cathy Eastwood: Scientific Manager, Associate Director of Alberta SPOR Methods & Development Platform, Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB. She has expertise in clinical data collection, evaluation of local and systemic data quality issues, disease classification coding with ICD-10 and ICD-11.
 Dr. Hude Quan: Professor, Community Health Sciences, Cumming School of Medicine, University of Calgary, Director Alberta SPOR Methods Platform; Co-Chair of Hypertension Canada, Co-Chair of Person to Population Health Collaborative of the Libin Cardiovascular Institute in Calgary, AB. He has expertise in assessing, validating, and linking administrative data sources for conducting data science research including artificial intelligence methods for evaluating and improving data quality.
 Intended Outcomes:“What is quality health data?” The panel of experts will address this common question by discussing how to define high quality health data, and measures being taken to ensure that they are available in Canada. Optimizing the quality of clinical-administrative data, and their use-value, first requires an understanding of the processes used to create the data.
 Subsequently, we can address the limitations in data collection and use these data for diverse applications. Current advances in digital data collection are providing more solutions to improve health data quality at lower cost.
 This panel will describe a number of quality assessment and improvement initiatives aimed at ensuring that health data are fit for a range of secondary uses including data linkage. It will also discuss how the need for the linkage and integration of data sources can influence the views of the data source’s fitness for use.
 CIHI content will include:
 
 Methods for optimizing the value of clinical-administrative data
 CIHI Information Quality Framework
 Reabstraction studies (e.g. physician documentation/coders’ experiences)
 Linkage analytics for data quality
 
 University of Calgary content will include:
 
 Defining/measuring health data quality
 Automated methods for quality assessment and improvement
 ICD-11 features and coding practices
 Electronic health record initiatives

Triose phosphates are the principal product of photosynthesis. They are used within the chloroplast for starch synthesis, or translocated to the cytosol where they are used to fuel sucrose synthesis. Use of triose phosphate releases inorganic phosphate, and is under strict metabolic control that matches the supply of triose phosphate from the Calvin-Benson cycle to the demand for carbon by sinks (Heldt & Heldt, 2011; McClain & Sharkey, 2019). However, a low rate of triose phosphate utilization (TPU) can deplete the phosphate pool, restrict ATP synthesis and reduce the availability of ATP to power the Calvin-Benson cycle, thereby limiting photosynthesis (Sharkey, 1985).

Conflict is a major facet of many environmental challenges of our time. However, growing conflict complexity makes it more difficult to identify win-win strategies for sustainable conflict resolution. Innovative methods are needed to help predict, understand, and resolve conflicts in cooperative ways. Agent-Based Modeling of Environmental Conflict and Cooperation examines computer modeling techniques as an important set of tools for assessing environmental and resource-based conflicts and, ultimately, for finding pathways to conflict resolution and cooperation. This book has two major goals. First, it argues that complexity science can be a unifying framework for professions engaged in conflict studies and resolution, including anthropology, law, management, peace studies, urban planning, and geography. Second, this book presents an innovative framework for approaching conflicts as complex adaptive systems by using many forms of environmental analysis, including system dynamics modeling, agent-based modeling, evolutionary game theory, viability theory, and network analysis. Known as VIABLE (Values and Investments from Agent-Based interaction and Learning in Environmental systems), this framework allows users to model advanced facets of conflicts—including institution building, coalition formation, adaptive learning, and the potential for future conflict—and conflict resolution based on the long-term viability of the actors' strategies. Written for scholars, students, practitioners, and policy makers alike, this book offers readers an extensive introduction to environmental conflict research and resolution techniques. As the result of decades of research, the text presents a strong argument for conflict modeling and reviews the most popular and advanced techniques, including system dynamics modeling, agent-based modeling, and participatory modeling methods. This indispensable guide uses NetLogo, a widely used and free modeling software package, to implement the VIABLE modeling approach in three case study applications around the world. Readers are invited to explore, adapt, modify, and expand these models to conflicts they hope to better understand and resolve.