Recruitment, growth and mortality of an Antarctic hexactinellid sponge, Anoxycalyx joubini.

Polar ecosystems are sensitive to climate forcing, and we often lack baselines to evaluate changes. Here we report a nearly 50-year study in which a sudden shift in the population dynamics of an ecologically important, structure-forming hexactinellid sponge, Anoxycalyx joubini was observed. This is the largest Antarctic sponge, with individuals growing over two meters tall. In order to investigate life history characteristics of Antarctic marine invertebrates, artificial substrata were deployed at a number of sites in the southern portion of the Ross Sea between 1967 and 1975. Over a 22-year period, no growth or settlement was recorded for A. joubini on these substrata; however, in 2004 and 2010, A. joubini was observed to have settled and grown to large sizes on some but not all artificial substrata. This single settlement and growth event correlates with a region-wide shift in phytoplankton productivity driven by the calving of a massive iceberg. We also report almost complete mortality of large sponges followed over 40 years. Given our warming global climate, similar system-wide changes are expected in the future

Inflammatory Cytokine Profiles During Exercise in Obese, Diabetic, and Healthy Children

Objective: Modulation of inflammatory status is considered a key component of the overall health effects of exercise. This may be especially relevant in children with obesity (Ob) or type 1 diabetes (T1DM), in which an imbalance between pro- and anti-inflammatory mediators could accelerate onset and progression of cardiovascular complications. To date, exercise-induced alterations in immuno-modulatory mediators in Ob and T1DM children remain largely unknown

Self-harm and suicidal ideation among young people is more often recorded by child protection than health services in an Australian population cohort

OBJECTIVE: We investigated patterns of service contact for self-harm and suicidal ideation recorded by a range of human service agencies - including health, police and child protection - with specific focus on overlap and sequences of contacts, age of first contact and demographic and intergenerational characteristics associated with different service responses to self-harm.METHODS: Participants were 91,597 adolescents for whom multi-agency linked data were available in a longitudinal study of a population cohort in New South Wales, Australia. Self-harm and suicide-related incidents from birth to 18 years of age were derived from emergency department, inpatient hospital admission, mental health ambulatory, child protection and police administrative records. Descriptive statistics and binomial logistic regression were used to examine patterns of service contacts.RESULTS: Child protection services recorded the largest proportion of youth with reported self-harm and suicidal ideation, in which the age of first contact for self-harm was younger relative to other incidents of self-harm recorded by other agencies. Nearly 40% of youth with a health service contact for self-harm also had contact with child protection and/or police services for self-harm. Girls were more likely to access health services for self-harm than boys, but not child protection or police services.CONCLUSION: Suicide prevention is not solely the responsibility of health services; police and child protection services also respond to a significant proportion of self-harm and suicide-related incidents. High rates of overlap among different services responding to self-harm suggest the need for cross-agency strategies to prevent suicide in young people.</p

Extreme Ultraviolet Reflective Grating Characterization and Simulationsfor the Aspera SmallSat Mission

The Aspera SmallSat mission is designed to detect and map the warm-hot gaseous component of the halos of nearby galaxies through long-slit spectroscopy of the ionized O VI emission line (103.2 nm) for the first time. The Aspera Rowland circle type spectrograph uses a toroidal grating coated with a multilayer film consisting of aluminum, lithium fluoride, and magnesium fluoride capping to optimize reflectivity in the extreme ultraviolet (EUV) waveband from 103 to 104nm. We discuss the grating characterization test setup at the University of Arizona (UA), which will validate the multilayer coating and grating efficiency in a UV vacuum chamber. We also simulate the reflectivity of the multilayer thin film coating using IMD IDL software to compare simulated results with measured reflectivity. Additionally, non-sequential ray trace simulations and 3D CAD modeling are used for verification of the test setup. Finally, the implications of the differences between the measured and simulated reflectivity and grating efficiencies are considered, including impact to the mission

Recruitment, Growth and Mortality of an Antarctic Hexactinellid Sponge, Anoxycalyx joubini

Polar ecosystems are sensitive to climate forcing, and we often lack baselines to evaluate changes. Here we report a nearly 50-year study in which a sudden shift in the population dynamics of an ecologically important, structure-forming hexactinellid sponge, Anoxycalyx joubini was observed. This is the largest Antarctic sponge, with individuals growing over two meters tall. In order to investigate life history characteristics of Antarctic marine invertebrates, artificial substrata were deployed at a number of sites in the southern portion of the Ross Sea between 1967 and 1975. Over a 22-year period, no growth or settlement was recorded for A. joubini on these substrata; however, in 2004 and 2010, A. joubini was observed to have settled and grown to large sizes on some but not all artificial substrata. This single settlement and growth event correlates with a region-wide shift in phytoplankton productivity driven by the calving of a massive iceberg. We also report almost complete mortality of large sponges followed over 40 years. Given our warming global climate, similar system-wide changes are expected in the future

Problems of multi-species organisms: endosymbionts to holobionts

The organism is one of the fundamental concepts of biology and has been at the center of many discussions about biological individuality, yet what exactly it is can be confusing. The definition that we find generally useful is that an organism is a unit in which all the subunits have evolved to be highly cooperative, with very little conflict. We focus on how often organisms evolve from two or more formerly independent organisms. Two canonical transitions of this type—replicators clustered in cells and endosymbiotic organelles within host cells—demonstrate the reality of this kind of evolutionary transition and suggest conditions that can favor it. These conditions include co-transmission of the partners across generations and rules that strongly regulate and limit conflict, such as a fair meiosis. Recently, much attention has been given to associations of animals with microbes involved in their nutrition. These range from tight endosymbiotic associations like those between aphids and Buchnera bacteria, to the complex communities in animal intestines. Here, starting with a reflection about identity through time (which we call “Theseus’s fish”), we consider the distinctions between these kinds of animal–bacteria interactions and describe the criteria by which a few can be considered jointly organismal but most cannot

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI