Rainforest transformation reallocates energy from green to brown food webs.

Terrestrial animal biodiversity is increasingly being lost because of land-use change1,2. However, functional and energetic consequences aboveground and belowground and across trophic levels in megadiverse tropical ecosystems remain largely unknown. To fill this gap, we assessed changes in energy fluxes across 'green' aboveground (canopy arthropods and birds) and 'brown' belowground (soil arthropods and earthworms) animal food webs in tropical rainforests and plantations in Sumatra, Indonesia. Our results showed that most of the energy in rainforests is channelled to the belowground animal food web. Oil palm and rubber plantations had similar or, in the case of rubber agroforest, higher total animal energy fluxes compared to rainforest but the key energetic nodes were distinctly different: in rainforest more than 90% of the total animal energy flux was channelled by arthropods in soil and canopy, whereas in plantations more than 50% of the energy was allocated to annelids (earthworms). Land-use change led to a consistent decline in multitrophic energy flux aboveground, whereas belowground food webs responded with reduced energy flux to higher trophic levels, down to -90%, and with shifts from slow (fungal) to fast (bacterial) energy channels and from faeces production towards consumption of soil organic matter. This coincides with previously reported soil carbon stock depletion3. Here we show that well-documented animal biodiversity declines with tropical land-use change4-6 are associated with vast energetic and functional restructuring in food webs across aboveground and belowground ecosystem compartments

Anthropogenic organic micro-pollutants and pathogens in the urban water cycle: assessment, barriers and risk communication (ASKURIS)

In urban areas, water often flows along a partially closed water cycle in which treated municipal wastewater is discharged into surface waters which are one source of raw waters used for drinking water supply. A number of organic micro-pollutants (OMP) can be found in different water compartments. In the near future, climatic and demographic changes will probably contribute to an increase of OMP and antibiotic-resistant pathogens in aquatic ecosystems. The occurrence of OMP, possible adverse effects on aquatic organisms and human health and the public perception must be carefully assessed to properly manage and communicate potentially associated risks and to implement appropriate advanced treatment options at the optimum location within the water cycle. Therefore, the interdisciplinary research project ASKURIS focuses on identification and quantification, toxicological assessment and removal of organic micro-pollutants and antibiotic-resistant pathogens in the Berlin water cycle, life cycle-based economic and environmental assessment, public perception and management of potential risks

Women's gambling behaviour, product preferences, and perceptions of product harm: Differences by age and gambling risk status

Background: Women's participation in, and harm from gambling, is steadily increasing. There has been very limited research to investigate how gambling behaviour, product preferences, and perceptions of gambling harm may vary across subgroups of women. Methods: This study surveyed a convenience sample of 509 women from Victoria and New South Wales, Australia. Women were asked a range of questions about their socio-demographic characteristics and gambling behaviour. Focusing on four gambling products in Australia-casino gambling, electronic gambling machines (EGMs), horse betting, and sports betting-women were asked about their frequency of participation, their product preferences, and perceptions of product harms. The sample was segmented a priori according to age and gambling risk status, and differences between groups were identified using Chi-square tests and ANOVAs. Thematic analysis was used to interpret qualitative data. Results: Almost two thirds (n=324, 63.7%) of women had engaged with one of the four products in the previous 12 months. Compared to other age groups, younger women aged 16-34 years exhibited a higher proportion of problem gambling, gambled more frequently, and across more products. While EGMs were the product gambled on most frequently by women overall, younger women were significantly more likely to bet on sports and gamble at casinos relative to older women. Qualitative data indicated that younger women engaged with gambling products as part of a 'night out', 'with friends', due to their 'ease of access' and perceived 'chance of winning big'. There were significant differences in the perceptions of the harms associated with horse and sports betting according to age and gambling risk status, with younger women and gamblers perceiving these products as less harmful. Conclusions: This study highlights that there are clear differences in the gambling behaviour, product preferences, and perceptions of product harms between subgroups of women. A gendered approach will enable public health researchers and policymakers to ensure that the unique factors associated with women's gambling are taken into consideration in a comprehensive public health approach to reducing and preventing gambling harm

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