Search Results (126)

Bacterial two-component systems (TCSs) mediate environmental sensing and adaptive responses through signal transduction between histidine kinases (HKs) and response regulators (RRs), thereby regulating biochemical processes essential for survival and, in pathogenic species, infection. How signaling specificity and insulation are maintained in organisms encoding multiple paralogous two-component systems remains an open question. Here, we investigate specificity in the Actinobacillus pleuropneumoniae TCS signaling network using an integrated computational framework that combines coevolutionary analysis, structural modeling, molecular dynamics simulations, and free-energy calculations. We show that cognate HK-RR recognition is established locally through clusters of coevolving interface residues, termed the orthologue interface specificity core (OISC), which mediate symmetric molecular recognition at individual interaction interfaces. However, interface-level recognition alone is insufficient to explain signaling fidelity across the network. Instead, system-wide specificity and pathway insulation emerge in this network from asymmetric energetic discrimination among cognate and non-cognate interactions across the ensemble of paralogous interfaces. Graded free-energy profiles reveal that broadly compatible interfaces can coexist with robust signaling insulation, reconciling interface promiscuity with stable network organization. Together, these findings support a two-tiered model for the TCS network analyzed here, in which symmetric interface constraints enable cognate recognition, while asymmetric network-level energetics govern signaling specificity. This framework may extend to other paralogous TCS networks. Full article

This Special Issue based on eight Articles/Reviews focuses on low-cost chemical sensor technologies, bio-chemical sensors, advanced active materials, sensing nanomaterials, sensor nodes, wireless sensor networks for chemical sensing, functional characterization, miniaturized transducers, advanced proofs of concept, and chemical detection applications. Promising advanced materials such as metal oxide nanostructures, carbon nanomaterials, composite heterostructures, multilayered coatings, and more have been explored for chemical sensing applications and environmental sustainability. Sensing solutions have been applied in the context of bio-chemical detection and gas monitoring, representing the current state of the art. Full article

Given the increasing frequency, severity, and socioecological impacts of wildfires, there is an urgent need for robust frameworks to better characterize fire behavior and flammability patterns across ecosystems to support early warning, mitigation, and management strategies. However, flammability remains difficult to quantify and scale, as it involves multiple interacting components that are typically measured at the bench scale. This study aimed to establish empirical links between spectral information, plant traits, and flammability metrics, and to scale these relationships to satellite imagery to translate these metrics into a spatial context. We combined laboratory spectroscopy, plant trait measurements including leaf mass per area, carbon, and cellulose, and combustion experiments using a simple and reproducible burning device. In total, 84 samples were collected and analysed, allowing us to characterise how spectral signatures relate to vegetation traits and fire behaviour. Spectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models. These models were then transferred to Environmental Mapping and Analysis Program (EnMAP) hyperspectral imagery to derive spatial estimates across eucalypt forests and grasslands of the Australian Capital Territory (ACT). Spectral information distinguished fuel types and captured variability of the plant traits, while these traits showed associations with combustion behaviour. Based on these links, the best-performing model predicted the rate of temperature increase, a combustibility metric, in eucalypt forests (R² = 0.70; Root Mean Square Error = 32.48 °C/s). In contrast, grassland models showed limited predictive performance, likely due to weaker relationships between plant traits and flammability metrics. Overall, this study demonstrates a practical and scalable approach for deriving flammability maps from hyperspectral and in situ data, highlighting the potential of plant-trait-based remote sensing. The resulting maps should not be interpreted as standalone fire risk products, but rather as a characterization of the structural and biochemical drivers of flammability. The main constraint of this work is the limited sample size. Future research should expand spatial and temporal coverage to better capture vegetation variability and enable the inclusion of independent validation datasets. Exploring alternative combustion protocols and testing more advanced spectral modelling approaches for trait estimation would provide additional insights. Full article

Gel-based flexible electronic sensors have emerged as a transformative class of materials for next-generation applications. These applications are wearable electronics, soft robotics, electronic skin (e-skin), and healthcare monitoring systems. Owing to their intrinsic softness, stretchability, and biocompatibility, gels provide an ideal platform for constructing highly deformable and skin-conformable sensing devices. This paper provides insight into emerging fabrication techniques, including 3D printing, bioprinting, and microfabrication. These techniques have facilitated the creation of complex architectures with improved sensitivity and scalability. The review also focuses on recent advancements that have focused on overcoming traditional limitations. These limitations are poor mechanical strength, dehydration, limited environmental stability, and low sensitivity. In particular, the incorporation of conductive fillers and ionic species has enabled a range of sensing mechanisms. These mechanisms include piezoresistive, capacitive, piezoelectric, and ionotronic responses. Therefore, it allows for the accurate detection of strain, pressure, temperature, and biochemical signals. Finally, this review provides a summary of future research, which is expected to focus on multifunctional integration, sustainable materials, and intelligent data processing. It provides pathways to the widespread adoption of gel-based flexible electronic sensors in both consumer and clinical applications. Full article

The development of self-powered environmental sensors is of great practical significance for addressing the power supply dilemma of traditional sensors in remote areas and avoiding environmental pollution from waste batteries. Given that the majority of the self-powered environmental sensors are based on the TENG principle, especially the active self-powered sensors, this paper reviews recent advances in triboelectric nanogenerator (TENG)-based self-powered environmental sensors. What distinguishes this review from the previous ones published on TENG is that it systematically discusses the application of TENG-based self-powered sensors for environmental monitoring. TENG-based self-powered sensors are classified into two types: TENG as a power supply for professional biochemical sensors and active self-powered sensors where TENG acts as both power source and sensing unit. This paper illustrates the applications of these devices in detecting targets in the environment, such as heavy metal ions, toxic gases, bacterial DNA, and bacteria, and summarizes the relevant performance parameters. It also analyzes key challenges including efficient mechanical energy harvesting, material durability and sensing specificity. Finally, the outlook notes that TENG-based sensors will expand detection ranges and integrate with other technologies, providing valuable guidance for their environmental monitoring applications. Full article

This study uses a bismuth ferrite (BiFeO₃) and MXene (Ti₃C₂Tx) to design a surface plasmon resonance (SPR) biosensor for the sensitivity enhancement at a 633 nm wavelength. Here, MXene serves as a biorecognition element (BRE) layer to ensure stable and reliable biomolecule adsorption. The MXene is a family of two-dimensional (2D) materials with metallic-like conductivity, a large surface area that can attach biomolecules, and improve biocompatibility. The addition of a conductive 2D MXene layer and a high-index BiFeO₃ dielectric layer greatly improves light–matter interaction and evanescent field penetration at the sensing interface. Strong plasmonic coupling is indicated by the reflectance analysis, which shows a distinct and consistent shift in the resonance angle as analyte RI increases. This study examined the sensitivity at optimized Ag and BiFeO₃ layer thickness. At an Ag of 39 nm and BiFeO₃ of 3 nm thickness, the maximal sensitivity of 340.68°/RIU with a remarkable figure of merit (FoM) of 47.38/RIU is obtained. The overall detection accuracy (DA) and FoM are significantly improved by the large sensitivity enhancement, despite a slight increase in full width at half maximum (FWHM). Furthermore, the penetration depth (PD) of 198.50 nm (at RI:1.330) and 199.52 nm (at RI:1.335) is attained with the proposed structure. Due to its high sensitivity, reusability, and reproducibility, the SPR biosensor has the potential to be used in biochemical, environmental, and medical detection. Full article

Photonic crystal waveguides (PhCWs) have emerged as a leading platform for integrated optical sensing due to their ability to engineer dispersion, enhance light–matter interaction, and exploit slow-light effects. This review provides a comprehensive analysis of the fundamental physics, performance metrics, device architectures, and application domains that define the current state of PhCW-based sensing. Key mechanisms governing sensitivity, figure of merit, detection limit, and dynamic range are examined, with emphasis on the intrinsic trade-offs introduced by slow-light operation, including disorder-induced scattering, linewidth broadening, and thermal susceptibility. Advances in dispersion engineering, such as hole shifting, gentle confinement, and width modulation, are highlighted alongside novel architectures including slot PhCWs, hybrid material platforms, and plasmonic–photonic configurations. Their respective capabilities in enhancing analyte overlap, improving spectral stability, and expanding functional integration are critically assessed. Emerging applications in biochemical detection, environmental monitoring, and nanoscale particle sensing further illustrate the versatility of PhCWs within modern optofluidic and lab-on-chip systems. The review concludes by outlining key challenges and future directions, including disorder-resilient slow-light design, inverse-engineered structures, and platform-level integration, which collectively chart a path toward next-generation high-performance photonic crystal sensing technologies. Full article

Considering the present environmental concerns, nanomaterial-based methods should be applied to achieve the bioeconomic sustainability initiatives and climate change mitigation. Plants and plant extracts are one of the most underused biomass and bioactive ingredients resources. Moreover, nowadays crop loss is one of the main problems that the world faces, together with the depletion of natural resources, increasing population and limited arable land, leading to increased food scarcity and demand. To correctly attribute/use plant-based bioresources or to rapidly decide which farming operations should be performed before crop loss, we should be able to properly characterize plants or plant-based resources by the desired useful characteristics, such as (bio)chemical characteristics, rather than simply observing physical traits of plants (because, when these traits become visible, it may be too late for crop loss mitigation). Plant crops could be optimized, for example, using electrochemical methods that assess the nutrient uptake and nutrient use efficiency (NUE) or the oxidative stress burst encountered before crop loss, in order to improve crop yields and crop quality. Other different important analytes (such as hormones, pathogens, metabolites, etc.) or plant characteristics (such as genus, species, phylogenetic analysis, etc.) can be evaluated with these electrochemical sensors and methods. In the present review, we focus on the application of nanomaterials/nanotechnologies for the development of fast, accurate, accessible, cost-effective, sensitive and selective analytical electrochemical methods for the detection of different relevant biomolecules in plants or plant-related samples (plant extracts, plant cells, plant tissues, and/or plant-derived natural drinks/foods, as well as entire plants/plant parts), both in vivo vs. ex vivo and in situ vs. ex situ. This review systematically presents and critically discusses the outcomes of current electrochemical methods (both applied in the lab or as wearable/implantable sensors) and the future perspectives of these nanotechnology-based sensors, with an accent on wearable sensors for smart and precision agriculture, as real-world sensing technologies with significant practical impact. The novelty of this article is the abundance of electrochemical analytical parameters gathered and discussed, for such a large number of analyte categories. Full article

The tardigrade Ramazzottius varieornatus exhibits extraordinary resilience to extreme environmental stresses, yet the functional diversity of its proteome remains largely unexplored. In this study, the structural and biochemical characterization of RvCA5, an atypical α-carbonic anhydrase (CA) identified in R. varieornatus, is presented. Expression analysis in E. coli revealed the spontaneous formation of a truncated RvCA5 species, which was confirmed to be unrelated to signal peptide cleavage. RvCA5 exhibited distinct structural features, including extended intrinsically disordered regions (IDRs) at both termini. Unlike canonical α-CAs, RvCA5 exhibited negligible CO₂ hydration activity, which was partially enhanced by the removal of the N-terminal IDR, suggesting that this region acts as a dynamic entropic barrier hindering substrate diffusion. RvCA5 possesses multiple surface-exposed reactive cysteine residues, resembling the redox-sensing human CA 3. Notably, consistent with a predicted nuclear localization signal, in silico modeling predicted that RvCA5 can bind DNA via a positively charged patch near the C-terminal IDR. The DNA-binding capability of RvCA5 was experimentally demonstrated by electrophoretic mobility shift assays. Collectively, these findings suggest that RvCA5 potentially functions as a redox-responsive transcriptional regulator. Full article

Previous studies have demonstrated that catecholamines, including epinephrine (Epi), norepinephrine (NE), and dopamine (DA), function both as xenosiderophores for bacteria possessing dedicated transport channels and as potential quorum-sensing signaling molecules or regulatory factors. However, current research on the interactions between dopamine and bacteria remains relatively limited. In this study, treatment of Escherichia coli (E. coli) ATCC 11303 with a specific concentration of dopamine resulted in a 33.63% increase in the maximum growth biomass, a 47.32% enhancement in biofilm formation, a 24.60% increase in protease activity, a 68.81% improvement in swimming motility, and increases of 33.77% and 47.67% in chemotaxis and swarming motility, respectively. Transcriptome analysis revealed that dopamine promoted the expression of numerous iron uptake-related genes, while biofilm formation-related genes and virulence genes were concomitantly upregulated. High-performance liquid chromatography–mass spectrometry (HPLC-MS) and isotope ratio mass spectrometry (IRMS) analyses confirmed that E. coli ATCC 11303 can uptake dopamine, suggesting the existence of specific transport pathways. Multi-omics analysis revealed significant regulatory effects on metal ion transport, amino acid metabolism, purine metabolism, environmental adaptation, quorum sensing, two-component systems, and xylene degradation pathways. Dopamine may act as both a xenosiderophore and a signaling molecule, thereby modulating multiple critical physiological and biochemical processes and promoting bacterial growth. These findings provide valuable insights into the development of novel exogenous xenosiderophores and signaling modulators, advancing our understanding of microbial interactions with their host environment and contributing to the field of microbial endocrinology. Full article

The health and operational continuity of emergency responders are fundamental pillars of sustainable and resilient disaster management systems. These personnel operate in high-risk environments, exposed to intense physical, environmental, and psychological stress. This makes it crucial to monitor their health to safeguard their well-being and performance. Traditional methods, which rely on intermittent, voice-based check-ins, are reactive and create a dangerous information gap regarding a responder’s real-time health and safety. To address this sustainability challenge, the convergence of the Internet of Things (IoT) and wearable biosensors presents a transformative opportunity to shift from reactive to proactive safety monitoring, enabling the continuous capture of high-resolution physiological and environmental data. However, realizing a field-deployable system is a complex “system-of-systems” challenge. This review contributes to the field of sustainable emergency management by analyzing the complete technological chain required to build such a solution, structured along the data workflow from acquisition to action. It examines: (1) foundational health sensing technologies for bioelectrical, biophysical, and biochemical signals; (2) powering strategies, including low-power design and self-powering systems via energy harvesting; (3) ad hoc communication networks (terrestrial, aerial, and space-based) essential for infrastructure-denied disaster zones; (4) data processing architectures, comparing edge, fog, and cloud computing for real-time analytics; and (5) visualization tools, such as augmented reality (AR) and heads-up displays (HUDs), for decision support. The review synthesizes these components by discussing their integrated application in scenarios like firefighting and urban search and rescue. It concludes that a robust system depends not on a single component but on the seamless integration of this entire technological chain, and highlights future research directions crucial for quantifying and maximizing its impact on sustainable development goals (SDGs 3, 9, and 11) related to health, sustainable cities, and resilient infrastructure. Full article

The escalating frequency and severity of drought events pose significant threats to agricultural productivity and food security. Drought stress not only restricts crop growth and yields but also destabilizes agricultural ecosystems. Over evolutionary timescales, plants have developed intricate adaptive strategies, encompassing drought escape (accelerated phenology), avoidance (water-conserving morphology) and tolerance (cellular protection), which involve complex biological mechanisms spanning molecular signaling, metabolic reprogramming and organ morphological remodeling. To mitigate drought risks, breeding drought-tolerant and water-efficient crops is imperative. Currently, drought resistance breeding is undergoing a paradigm shift, transitioning from traditional phenotypic selection toward genomics-assisted selection, molecular design and artificial intelligence (AI)-driven predictive modeling. This review provides a comprehensive analysis of drought stress response mechanisms in crops, integrating three key dimensions: physiological/biochemical adaptations, hormonal signaling networks and morphological/structural modifications. Furthermore, it critically evaluates recent advances in genetic improvement approaches for drought resistance, such as marker-assisted selection, transgenic technology and gene editing. It also explores the integration of multi-omics data and AI to enhance precision molecular breeding and overcome the inherent trade-off between drought resistance and yield potential. By synthesizing advancements in molecular breeding and smart agriculture, this work provides a roadmap for developing climate-resilient crops optimized through synergistic trait engineering and intelligent environmental sensing. Full article

Vegetation heterogeneity supports biodiversity, while homogeneity limits it. In the Great Plains, fire and herbivory enhance ecosystem function by increasing spatial heterogeneity. However, quantifying their effects on plant functional traits and spectral diversity remains challenging due to landscape complexity and scaling limitations. Hyperspectral remote sensing offers a high-resolution approach to assessing these dynamics, improving the evaluations of post-fire recovery and vegetation function. This study examines the impact of fire on plant functional traits and spectral diversity within a savanna landscape in the Edwards Plateau, Texas, using airborne hyperspectral and multispectral imagery. Specifically, it aims to (1) quantify the spatial patterns of plant functional traits and spectral diversity, (2) assess fire’s effects on these patterns, and (3) evaluate how soil type, woody structure, and burn patterns mediate fire responses. High-resolution airborne images from 2018 (pre-fire) and 2020 (post-fire) were analyzed to classify burned and unburned areas, pre-fire woody cover, and derive spectral indices representing plant functional traits, β-diversity components, and spectral evenness. The results indicate that temporal patterns in spectral diversity were driven primarily by soil properties and weather, with limited evidence that fire altered spectral evenness or β-diversity across soils. In contrast, spectral indices showed clearer—but still soil-dependent—fire effects: declines in canopy structure, greenness, and chlorophyll content were less pronounced in burned areas, indicating that fire partially moderated late-season senescence. Fire had a substantial influence on spatial patterns of spectral evenness (but not β-diversity) and vegetation spectral functional traits, and fire and dry-down increased spatial heterogeneity in spectral evenness and in spectral indices indicative of biophysical and biochemical traits across scales. These findings demonstrate that environmental drivers, particularly soil–moisture interactions and interannual moisture variability, exert a stronger control over post-fire spectral diversity than fire alone. Hyperspectral imaging effectively captured these dynamics, supporting its role in monitoring post-fire vegetation responses. In addition to the use of hyperspectral imaging, fire management strategies should consider broader ecological drivers, including soil and weather interactions, to improve the assessments of ecosystem resilience and recovery. Full article

Hyperspectral imaging (HSI) is a swiftly developing intraoperative and diagnostic technique in several clinical specialties. By monitoring oxygenation and biochemical markers, it helps with tissue viability, burn depth measurement, wound healing, and tumor detection. HSI facilitates real-time, harmless diagnosis throughout surgeries or outpatient settings, and allows for the detection of tumor boundaries with over 90% accuracy, according to clinical studies. Originally developed for remote sensing and aerospace applications, HSI has rapidly evolved and found increasing relevance across diverse sectors, including agriculture, environmental monitoring, food safety, pharmaceuticals, defense, and especially medical diagnostics. This review explores the origins, development, and expanding applications of HSI, with a particular emphasis on its role in healthcare. It discusses the operational principles and unique features of hyperspectral systems, such as their ability to produce spectral data cubes, perform non-destructive analysis, and integrate with emerging technologies like artificial intelligence and drone-based platforms. By comparing hyperspectral imaging to traditional and multispectral techniques, the review highlights its superior spectral resolution and versatility. Key challenges, including data volume, sensor calibration, and real-time processing, are also addressed. Finally, emerging trends such as miniaturization, integration with the Internet of Things, and sustainable system designs are examined, offering insights into the future directions and interdisciplinary potentials of HSI in both scientific research and practical applications. Full article

The review article critically evaluates the application of edible plant extracts as natural preservatives in food systems, with a particular focus on environmentally sustainable extraction methodologies. It examines green extraction methods designed to enhance the yield of bioactive compounds responsible for plants’ strong antibacterial properties. The biochemical mechanisms underlying antibacterial activity are studied, namely disruption of bacterial cell walls and membranes; inhibition of metabolic enzymes; interference with nucleic acid synthesis; induction of oxidative stress; and suppression of quorum sensing, biofilm formation, efflux pumps, and β-lactamase activity, along with standardized methodologies for efficacy assessment and extracts’ incorporation into food matrices. Recent research demonstrates the potential of plant extracts to extend the shelf life of meat, seafood, dairy, and fresh products while meeting consumer demand for clean-label products. Although large-scale application remains limited due to challenges, future research should focus on optimizing green extraction approaches, establishing standardized evaluation protocols, and developing regulatory frameworks to facilitate their safe and sustainable use in the food industry. Full article