Bio-integrated wearable systems can measure a broad range of biophysical, biochemical, and environmental signals to provide critical insights into overall health status and to quantify human performance. Recent advances in material science, chemical analysis techniques, device designs, and assembly methods form the foundations for a uniquely differentiated type of wearable technology, characterized by noninvasive, intimate integration with the soft, curved, time-dynamic surfaces of the body. This review summarizes the latest advances in this emerging field of "bio-integrated" technologies in a comprehensive manner that connects fundamental developments in chemistry, material science, and engineering with sensing technologies that have the potential for widespread deployment and societal benefit in human health care. An introduction to the chemistries and materials for the active components of these systems contextualizes essential design considerations for sensors and associated platforms that appear in following sections. The subsequent content highlights the most advanced biosensors, classified according to their ability to capture biophysical, biochemical, and environmental information. Additional sections feature schemes for electrically powering these sensors and strategies for achieving fully integrated, wireless systems. The review concludes with an overview of key remaining challenges and a summary of opportunities where advances in materials chemistry will be critically important for continued progress.
Parthasarathy Gandeepan, T. Müller, D. Zell
et al.
C-H activation has surfaced as an increasingly powerful tool for molecular sciences, with notable applications to material sciences, crop protection, drug discovery, and pharmaceutical industries, among others. Despite major advances, the vast majority of these C-H functionalizations required precious 4d or 5d transition metal catalysts. Given the cost-effective and sustainable nature of earth-abundant first row transition metals, the development of less toxic, inexpensive 3d metal catalysts for C-H activation has gained considerable recent momentum as a significantly more environmentally-benign and economically-attractive alternative. Herein, we provide a comprehensive overview on first row transition metal catalysts for C-H activation until summer 2018.
Stress can influence health throughout the lifespan, yet there is little agreement about what types and aspects of stress matter most for human health and disease. This is in part because “stress” is not a monolithic concept but rather, an emergent process that involves interactions between individual and environmental factors, historical and current events, allostatic states, and psychological and physiological reactivity. Many of these processes alone have been labeled as “stress.” Stress science would be further advanced if researchers adopted a common conceptual model that incorporates epidemiological, affective, and psychophysiological perspectives, with more precise language for describing stress measures. We articulate an integrative working model, highlighting how stressor exposures across the life course influence habitual responding and stress reactivity, and how health behaviors interact with stress. We offer a Stress Typology articulating timescales for stress measurement – acute, event-based, daily, and chronic – and more precise language for dimensions of stress measurement.
Daniel McDonald, Embriette R. Hyde, Justine W. Debelius
et al.
We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples. ABSTRACT Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the United States, United Kingdom, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes compared to environmental samples; demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations; uncover new molecules and kinds of molecular communities in the human stool metabolome; and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education. IMPORTANCE We show that a citizen science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n = 1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet, which we confirm with untargeted metabolomics on hundreds of samples.
Ecological and environmental citizen science projects have enormous potential to advance science, influence policy, and guide resource management by producing datasets that are otherwise infeasible to generate. This potential can only be realized, though, if the datasets are of high quality. While scientists are often skeptical of the ability of unpaid volunteers to produce accurate datasets, a growing body of publications clearly shows that diverse types of citizen science projects can produce data with accuracy equal to or surpassing that of professionals. Successful projects rely on a suite of methods to boost data accuracy and account for bias, including iterative project development, volunteer training and testing, expert validation, replication across volunteers, and statistical modeling of systematic error. Each citizen science dataset should therefore be judged individually, according to project design and application, rather than assumed to be substandard simply because volunteers generated it.
Andrea Petrone, Paulo Borges, Fernando Pereira
et al.
The Azores Archipelago is known for its important natural heritage, yet its ecosystems face a “green tsunami” in the form of numerous exotic and invasive species. This influx has wrought serious biodiversity loss and degradation of ecosystem services, representing one of the greatest threats to conservation across the islands. Originating from accelerated global trade and travel, these invasions impact human activities, public health and economic sectors alike. The PRIBES project intends to contribute to "The Regional Strategy for the Management of Terrestrial and Freshwater Exotic and Invasive Species in the Azores" (PRIBES-LIFE-IP- Estratégia regional para o controlo e prevenção de espécies exóticas invasoras - no âmbito do projeto LIFE IP AZORES NATURA, LIFE17 IPE/PT/000010). Recently, a plan was delivered to the Azorean government that proposes as key strategy: an unified Azores Invasive Species Task Force, a central coordination unit and island‐level focal points defined clear leadership roles for agencies and stakeholders (Axis 1), while stringent pre‐export controls, quarantine measures and risk analyses blocked new arrivals (Axis 2); parallel early‐detection teams and citizen‐science networks screened ports, airports and nurseries and triggered rapid eradication protocols (Axis 3), guided by a tiered framework of eradication, containment, control and mitigation chosen on feasibility and cost–benefit grounds (Axis 4). Simultaneously, national and international partnerships with IUCN (International Union for Conservation of Nature) ISSG (Invasive Species Specialist Group), CABI (Commonwealth Agricultural Bureaux International) and other island regions fostered data exchange (Axis 5), targeted scientific research investigated invasion pathways and management efficacy (Axis 6) and a central observatory consolidated occurrence records and risk assessments (Axis 7). Meanwhile, outreach campaigns, industry training and school programmes rallied public awareness (Axis 8). The AZORES BIOPORTAL (ABP) is a regional e-infrastructure dedicated to the mobilisation, curation and dissemination of biodiversity data from the Azores. It provides centralised data repository for researchers, policy-makers and educators; validated species checklists, including endemic, native and introduced species; integration with national and international biodiversity networks, including PORBIOTA, GBIF and LifeWatch ERIC; and tools for data visualisation and access, supporting conservation, ecological research and environmental management. ABP follows the FAIR (Findable, Accessible, Interoperable, Reusable) and supports open science. Mapping the occurrence of both native (endemic and non endemic) and exotic species is of key importance for the PRIBES project and the ABP intiative.A total of 243 vascular plant taxa were recorded across São Jorge Island, encompassing 89 families. These records correspond to 4,524 individual plant occurrences, including repeated observations of the same species across different sites. As each photographic observation is tied to unique geographic coordinates, all recorded specimens represent new spatial records for the Island’s flora. Amongst the taxa, 53 are considered endemic to the Azores, 131 are introduced, 58 are native and one species (Dracaena draco (L.) L.) is of indeterminate status. These correspond to 1,773 individual occurrences of endemic taxa, 1779 introduced, 970 native and one with indeterminate status. At the family level, 31 families include endemic taxa, 63 include introduced taxa, 34 include native taxa and one family contains a taxon of indeterminate status.The inventory includes several noteworthy Azorean endemics, spanning both ferns and flowering plants. Amongst the ferns, notable records include Crisped Buckler Fern Dryopteris crispifolia Rasbach, Reichst. & Vida, Azorean Buckler Fern Dryopteris azorica (Christ) Alston and Azorean Rockcap Fern Polypodium macaronesicum subsp. azoricum (Vasc.) Rumsey, Carine & Robba. Iconic flowering species and woody endemics recorded during the survey comprise Azorean Cherry Prunus lusitanica subsp. azorica (Mouill.) Franco, Azorean Buckthorn Frangula azorica Grubov, Azorean Eyebright Euphrasia grandiflora Hochst. ex Seub., Azorean Greater-hawkbit Leontodon filii (Hochst. ex Seub.) Paiva & Ormonde and Narrow-lipped Butterfly Orchid Platanthera micrantha (Hochst. ex Seub.) Schltr. Additional endemic taxa include Azorean Dock Rumex azoricus Rech.f., Azorean Holly Ilex azorica Gand., Azorean Umbrella Milkwort Tolpis azorica (Nutt.) P. Silva and the hemiparasitic Azorean Dwarf Mistletoe Arceuthobium azoricum Wiens & Hawksw. Other significant native species recorded include the ferns Wilson's Filmy-fern Hymenophyllum wilsonii Hook., Killarney Fern Vandenboschia speciosa (Willd.) G.Kunkel and Scaly Tongue-fern Elaphoglossum hirtum (Sw.) C.Chr., Cretan Thyme Thymus caespititius Brot., Many-stalked Spike-rush Eleocharis multicaulis (Sm.) Desv. and the more common native Firetree Morella faya (Aiton) Wilbur.Amongst the most problematic surveyed exotic invasive plant species are the Ginger Lily Hedychium gardnerianum Sheppard ex Ker-Gawl., Knotweed Persicaria capitata (Buch.-Ham. ex D.Don) H.Gross, Bigleaf Hydrangea Hydrangea macrophylla (Thunb.) Ser., Crofton Weed Ageratina adenophora (Spreng.) R.M.King & H.Rob., Australian Cheesewood Pittosporum undulatum Vent. and the Wandering Jew Tradescantia fluminensis Vell., as well as the American Pokeweed Phytolacca americana L.
Philip Wiese, Victor Kartsch, Marco Guermandi
et al.
The widespread adoption of Internet of Things (IoT) technologies has significantly advanced environmental monitoring (EM) by enabling cost-effective and scalable sensing solutions. Concurrently, machine learning (ML) and artificial intelligence (AI) are introducing powerful tools for the efficient and accurate analysis of complex environmental data. However, current IoT platforms for environmental sensing are typically limited to a narrow set of sensors, preventing a comprehensive assessment of environmental conditions and lacking sufficient computational capabilities to support the deployment of advanced ML and AI algorithms on the edge. To overcome these limitations, we introduce a compact (17x38 mm2), multi-modal, MCU-based environmental IoT node integrating 11 sensors, including CO2 concentration, volatile organic compounds (VOCs), light intensity, UV radiation, pressure, temperature, humidity, visual sensing via an RGB camera, and precise geolocation through a GNSS module. It features GAP9, a parallel ultra-low-power system-on-chip, enabling real-time, energy-efficient edge processing of advanced ML models directly on-device. We implemented a YOLOv5-based occupancy detection pipeline (0.3 M parameters, 42 MOP per inference), demonstrating 42% energy savings over raw data streaming. Additionally, we present a smart indoor air quality (IAQ) monitoring setup that combines occupancy detection with adaptive sample rates, achieving operational times of up to 143 h on a single compact 600 mAh, 3.7 V battery. Our platform lays the groundwork for innovative applications such as predictive indoor IAQ, enabling efficient AI-driven on-edge forecasting for energy-efficient and autonomous, proactive pollution-mitigation control strategies
Integrated Sensing and Communications (ISAC) is poised to become one of the defining capabilities of the sixth generation (6G) wireless communications systems, enabling the network infrastructure to jointly support high-throughput communications and situational awareness. While recent advances have explored ISAC for both human-centric applications and environmental monitoring, existing research remains fragmented across these domains. This paper provides the first unified review of ISAC-enabled sensing for both human activities and environment, focusing on signal-level mechanisms, sensing features, and real-world feasibility. We begin by characterising how diverse physical phenomena, ranging from human vital sign and motion to precipitation and flood dynamics, impact wireless signal propagation, producing measurable signatures in channel state information (CSI), Doppler profiles, and signal statistics. A comprehensive analysis is then presented across two domains: human sensing applications including localisation, activity recognition, and vital sign monitoring; and environmental sensing for rainfall, soil moisture, and water level. Experimental results from Long-Term Evolution (LTE) sensing under non-line-of-sight (NLOS) conditions are incorporated to highlight the feasibility in infrastructure-limited scenarios. Open challenges in signal fusion, domain adaptation, and generalisable sensing architectures are discussed to facilitate future research toward scalable and autonomous ISAC.
Giordano d'Aloisio, Tosin Fadahunsi, Jay Choy
et al.
Background: Text-to-image generation models are widely used across numerous domains. Among these models, Stable Diffusion (SD) - an open-source text-to-image generation model - has become the most popular, producing over 12 billion images annually. However, the widespread use of these models raises concerns regarding their social and environmental sustainability. Aims: To reduce the harm that SD models may have on society and the environment, we introduce SustainDiffusion, a search-based approach designed to enhance the social and environmental sustainability of SD models. Method: SustainDiffusion searches the optimal combination of hyperparameters and prompt structures that can reduce gender and ethnic bias in generated images while also lowering the energy consumption required for image generation. Importantly, SustainDiffusion maintains image quality comparable to that of the original SD model. Results: We conduct a comprehensive empirical evaluation of SustainDiffusion, testing it against six different baselines using 56 different prompts. Our results demonstrate that SustainDiffusion can reduce gender bias in SD3 by 68%, ethnic bias by 59%, and energy consumption (calculated as the sum of CPU and GPU energy) by 48%. Additionally, the outcomes produced by SustainDiffusion are consistent across multiple runs and can be generalised to various prompts. Conclusions: With SustainDiffusion, we demonstrate how enhancing the social and environmental sustainability of text-to-image generation models is possible without fine-tuning or changing the model's architecture.
Environmental modeling faces critical challenges in predicting ecosystem dynamics across unmonitored regions due to limited and geographically imbalanced observation data. This challenge is compounded by spatial heterogeneity, causing models to learn spurious patterns that fit only local data. Unlike conventional domain generalization, environmental modeling must preserve invariant physical relationships and temporal coherence during augmentation. In this paper, we introduce Generalizable Representation Enhancement via Auxiliary Transformations (GREAT), a framework that effectively augments available datasets to improve predictions in completely unseen regions. GREAT guides the augmentation process to ensure that the original governing processes can be recovered from the augmented data, and the inclusion of the augmented data leads to improved model generalization. Specifically, GREAT learns transformation functions at multiple layers of neural networks to augment both raw environmental features and temporal influence. They are refined through a novel bi-level training process that constrains augmented data to preserve key patterns of the original source data. We demonstrate GREAT's effectiveness on stream temperature prediction across six ecologically diverse watersheds in the eastern U.S., each containing multiple stream segments. Experimental results show that GREAT significantly outperforms existing methods in zero-shot scenarios. This work provides a practical solution for environmental applications where comprehensive monitoring is infeasible.
Features of decorative plants within urban ecosystems can be used to create highly decorative plantings with a long-lasting aesthetic effect that interacts harmoniously with urban systems. The study aims to determine the degree of decorativeness of dendrosozoexotics of the family Araliaceae Juss. Represented in the collection plantations of botanical gardens in Kyiv. A comprehensive assessment of the decorative effect of 8 species of woody plants of the Araliaceae family was conducted. The results of the assessment of the degree of decorativeness of the studied plant species were presented, noting that 2 species have a high degree of decorativeness (Aralia. elata (Miq.) Seem. and Eleutherococcus lasiogyne (Harms) S.Y.Hu), 5 species are characterised by a high degree of decorativeness (Kalopanax septemlobus (Thunb.) Koidz, Eleutherococcus senticosus (Rupr. & Maxim.) Maxim., Eleutherococcus sessiliflorus (Rupr. & Maxim.) S.Y. Hu, Eleutherococcus trifoliatus (L. f.) S.Y. Hu, Eleutherococcus wardii (W.W.Sm.) S.Y.Hu.) Mediocre decorativeness was noted in one species – Eleutherococcus sieboldianus (Makino) Koidz. The dynamics of seasonal decorativeness of dendrosozoan exotics of the family Araliaceae were analysed, determining a high degree of decorativeness in 87.5 % of the studied plant species. The peak of the decorative effect of dendrosozoexotics occurs in summer and lasts approximately 120 days (from 3 decades of May to 2 decades of September), which is associated with biological characteristics and phases of the ontogeny of introductions, in particular, long periods of flowering, fruit formation and ripening. The results obtained are the basis for optimising the species composition of urban and park plantations in megacities and increasing their decorative effect in an urban environment, accounting for the resistance of the studied plant species to anthropogenic load
Genoveva Vargas-Solar, Jérôme Darmont, Alejandro Adorjan
et al.
This vision paper introduces a pioneering data lake architecture designed to meet Life \& Earth sciences' burgeoning data management needs. As the data landscape evolves, the imperative to navigate and maximize scientific opportunities has never been greater. Our vision paper outlines a strategic approach to unify and integrate diverse datasets, aiming to cultivate a collaborative space conducive to scientific discovery.The core of the design and construction of a data lake is the development of formal and semi-automatic tools, enabling the meticulous curation of quantitative and qualitative data from experiments. Our unique ''research-in-the-loop'' methodology ensures that scientists across various disciplines are integrally involved in the curation process, combining automated, mathematical, and manual tasks to address complex problems, from seismic detection to biodiversity studies. By fostering reproducibility and applicability of research, our approach enhances the integrity and impact of scientific experiments. This initiative is set to improve data management practices, strengthening the capacity of Life \& Earth sciences to solve some of our time's most critical environmental and biological challenges.