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The increasing prevalence of hazardous chemical incidents in the United States necessitates the implementation of analytically robust, rapid, and reliable screening techniques for toxicant mixture analysis to understand short- and long-term health impacts of environmental exposures. A recent chemical disaster in East Palestine, Ohio has underscored the importance of thorough contamination assessment. On February 03, 2023, a Norfolk Southern train derailment prompted a chemical spill and fires. An open burn involving over 100,000 gal of vinyl chloride was conducted three days later. Hazardous compounds were released into air, water, and soil. To provide time-sensitive exposure data for emergency response, this study outlines a novel methodology for rapid characterization of chemical contamination of environmental media to support disaster response efforts. A controlled static headspace sampling system, in conjunction with a high-resolution proton transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS), was developed to characterize volatile organic compounds (VOCs) in surface water samples collected near the East Palestine train derailment site. Spatial variations were observed in the chemical composition of surface water samples collected at different locations. Hydrocarbons were found to be the most abundant chemical group of all surface water samples, contributing 50 % to 97 % to the total headspace VOC mass. Compounds commonly detected in surface water samples, including benzene, styrene, xylene, and methyl tert-butyl ether (MTBE) were also observed in most surface water samples, with aqueous concentrations typically at ng/L levels. This study demonstrated the potential of the proposed methodology to be applied for rapid field screening of volatile chemicals in water samples in order to enable fast emergency response to chemical disasters and environmental hazards.

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In the face of rising population, erratic climate, resource depletion, and increased exposure to natural hazards, environmental monitoring is increasingly important. Satellite data form most of our observations of Earth. On-the-ground observations based on in situ sensor systems are crucial for these remote measurements to be dependable. Providing open-source options to rapidly prototype environmental datalogging systems allows quick advancement of research and monitoring programs. This paper introduces Loom, a development environment for low-power Arduino-programmable microcontrollers. Loom accommodates a range of integrated components including sensors, various datalogging formats, internet connectivity (including Wi-Fi and 4G Long Term Evolution (LTE)), radio telemetry, timing mechanisms, debugging information, and power conservation functions. Additionally, Loom includes unique applications for science, technology, engineering, and mathematics (STEM) education. By establishing modular, reconfigurable, and extensible functionality across components, Loom reduces development time for prototyping new systems. Bug fixes and optimizations achieved in one project benefit all projects that use Loom, enhancing efficiency. Although not a one-size-fits-all solution, this approach has empowered a small group of developers to support larger multidisciplinary teams designing diverse environmental sensing applications for water, soil, atmosphere, agriculture, environmental hazards, scientific monitoring, and education. This paper not only outlines the system design but also discusses alternative approaches explored and key decision points in Loom's development.

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Layered perovskites, a novel class of two-dimensional (2D) layered materials, exhibit versatile photophysical properties of great interest in photovoltaics and optoelectronics. However, their instability to environmental factors, particularly water, has limited their utility. In this study, we introduce an innovative solution to the problem by leveraging the unique properties of natural beeswax as a protective coating of 2D-fluorinated phenylethylammonium lead iodide perovskite. These photodetectors show outstanding figures of merit, such as a responsivity of >2200 A/W and a detectivity of 2.4 × 1018 Jones. The hydrophobic nature of beeswax endows the 2D perovskite sensors with an unprecedented resilience to prolonged immersion in contaminated water, and it increases the lifespan of devices to a period longer than one year. At the same time, the biocompatibility of the beeswax and its self-cleaning properties make it possible to use the very same turbidity sensors for healthcare in photoplethysmography and monitor the human heartbeat with clear systolic and diastolic signatures. Beeswax-enabled multipurpose optoelectronics paves the way to sustainable electronics by ultimately reducing the need for multiple components.

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The prediction of domestic electricity consumption is relevant because it helps to plan energy production, among many other benefits. In this work a dataset was collected from one house in an urban city of north-east of Mexico. An ad-hoc acquisition system was implemented to collect the data using a smart meter and the open weather API. The data was collected every minute over a period of 14 months since November 5, 2022, to January 5, 2024. The dataset contains 605,260 samples of 19 variables related with energy consumption and weather data. This dataset is specifically tailored for predicting domestic energy consumption and understanding consumption behaviours, filling a void in the existing literature where such datasets for Mexico are scarce. Moreover, the multivariate nature of the dataset allows researchers to investigate and propose new techniques for forecasting or pattern classification using multivariate data collected in a real scenario.

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In the context of improving aircraft safety, this work focuses on creating and testing a graphene-based ice detection system in an environmental chamber. This research is driven by the need for more accurate and efficient ice detection methods, which are crucial in mitigating in-flight icing hazards. The methodology employed involves testing flat graphene-based sensors in a controlled environment, simulating a variety of climatic conditions that could be experienced in an aircraft during its entire flight. The environmental chamber enabled precise manipulation of temperature and humidity levels, thereby providing a realistic and comprehensive test bed for sensor performance evaluation. The results were significant, revealing the graphene sensors' heightened sensitivity and rapid response to the subtle changes in environmental conditions, especially the critical phase transition from water to ice. This sensitivity is the key to detecting ice formation at its onset, a critical requirement for aviation safety. The study concludes that graphene-based sensors tested under varied and controlled atmospheric conditions exhibit a remarkable potential to enhance ice detection systems for aircraft. Their lightweight, efficient, and highly responsive nature makes them a superior alternative to traditional ice detection technologies, paving the way for more advanced and reliable aircraft safety solutions.

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Malaria parasites have coevolved with humans over thousands of years, mirroring their migration out of Africa. They persist to this day, despite continuous elimination efforts worldwide. These parasites can adapt to changing environments during infection of human and mosquito, and when expanding the geographical range by switching vector species. Recent studies in the human malaria parasite, Plasmodium falciparum, identified determinants governing the plasticity of sexual conversion rates, sex ratio, and vector competence. Here we summarize the latest literature revealing environmental, epigenetic, and genetic determinants of malaria transmission.

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Early development stages in marine bivalve are critical periods where larvae transition from pelagic free-life to sessile mature individuals. The successive metamorphosis requires the expression of key genes, the functions of which might be under high selective pressure, hence understanding larval development represents key knowledge for both fundamental and applied research. Phenotypic larvae development is well known, but the underlying molecular mechanisms such as associated gene expression dynamic and molecular cross-talks remains poorly described for several nonmodel species, such as P. margaritifera. We designed a whole transcriptome RNA-sequencing analysis to describe such gene expression dynamics following four larval developmental stages:  d-shape, Veliger, Umbo and Eye-spot. Larval gene expression and annotated functions drastically diverge. Metabolic function (gene expression related to lipid, amino acid and carbohydrate use) is highly upregulated in the first development stages, with increasing demand from  d-shape to umbo. Morphogenesis and larval transition are partly ordered by Thyroid hormones and Wnt signaling. While larvae shells show some similar characteristic to adult shells, the cause of initialization of biomineralization differ from the one found in adults. The present study provides a global overview of Pinctada margaritifera larval stages transitioning through gene expression dynamics, molecular mechanisms and ontogeny of biomineralization, immune system, and sensory perception processes.

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Noelia Lander works on cell signaling in American trypanosomes and studies the role of cyclic adenosine monophosphate (cAMP) microdomains in environmental sensing and differentiation. In this mSphere of Influence, Dr. Lander reflects on three research articles in different eukaryotic models that had impacted on the way she thinks about the regulation of cAMP signals in Trypanosoma cruzi, the etiologic agent of Chagas disease. The articles "FRET biosensor uncovers cAMP nano-domains at ß-adrenergic targets that dictate precise tuning of cardiac contractility" (N. C. Surdo, M. Berrera, A. Koschinski, M. Brescia, et al., Nat Commun 8:15031, 2017, https://doi.org/10.1038/ncomms15031), "Cyclic AMP signaling and glucose metabolism mediate pH taxis by African trypanosomes" (S. Shaw, S. Knüsel, D. Abbühl, A. Naguleswaran, et al., Nat Commun 13:603, 2022, https://doi.org/10.1038/s41467-022-28293-w), and "Encystation stimuli sensing is mediated by adenylate cyclase AC2-dependent cAMP signaling in Giardia" (H. W. Shih, G. C. M. Alas, and A. R. Paredez, Nat Commun 14:7245, 2023, https://doi.org/10.1038/s41467-023-43028-1) influenced her current hypothesis that cAMP signals are generated in response to environmental cues leading to changes in membrane fluidity at the flagellar tip and the contractile vacuole complex of T. cruzi, structures where cAMP mediates key cellular processes for developmental progression.

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Stretchable sensors have been widely investigated and developed for the purpose of human motion detection, touch sensors, and healthcare monitoring, typically converting mechanical/structural deformation into electrical signals. The viscoelastic strain of stretchable materials often results in nonlinear stress-strain characteristics over a broad range of strains, consequently making the stretchable sensors at the body joints less accurate in predicting and recognizing human gestures. Accurate recognition of human gestures can be further deteriorated by environmental changes such as temperature and humidity. Here, we demonstrated an environment-adaptable high stress-strain linearity (up to ε = 150%) and high-durability (>100,000 cycles) stretchable sensor conformally laminated onto the body joints for human gesture recognition. The serpentine configuration of our ionic liquid-based stretchable film enabled us to construct broad data sets of mechanical strain and temperature changes for machine learning-based gesture recognition. Signal recognition and training of distinct strains and environmental stimuli using a machine learning-based algorithm analysis successfully measured and predicted the joint motion in a temperature-changing environment with an accuracy of 92.86% (R-squared). Therefore, we believe that our serpentine-shaped ion gel-based stretchable sensor harmonized with machine-learning analysis will be a significant achievement toward environmentally adaptive and multianalyte sensing applications. Our proposed machine learning-enabled multisensor system may enable the development of future electronic devices such as wearable electronics, soft robotics, electronic skin, and human-machine interaction systems.

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Internet-of-Things systems are increasingly being installed in buildings to transform them into smart ones and to assist in the transition to a greener future. A common feature of smart buildings, whether commercial or residential, is environmental sensing that provides information about temperature, dust, and the general air quality of indoor spaces, assisting in achieving energy efficiency. Environmental sensors though, especially when combined, can also be used to detect occupancy in a space and to increase security and safety. The most popular methods for the combination of environmental sensor measurements are concatenation and neural networks that can conduct fusion in different levels. This work presents an evaluation of the performance of multiple late fusion methods in detecting occupancy from environmental sensors installed in a building during its construction and provides a comparison of the late fusion approaches with early fusion followed by ensemble classifiers. A novel weighted fusion method, suitable for imbalanced samples, is also tested. The data collected from the environmental sensors are provided as a public dataset.

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Ionotropic glutamate receptors (iGluRs), pivotal in mediating excitatory neurosignals within the central nervous system, are instrumental in environmental stress responses. In this investigation, 12 iGluRs identified in the Pacific oyster are herein designated as CgiGluRs, and further categorized into three distinct subfamilies based on their transmembrane domains. Cross-species evolutionary analysis unveiled a high degree of conservation in the sequence and structural attributes of these CgiGluRs. These receptors are ubiquitously distributed across various tissues, with pronounced expression in the oyster's mantle, labial palps, and gills, underlining their integral role in the oyster's environmental sensing mechanisms. Post the D-shaped larval stage, a marked upward trend in CgiGluRs expression was observed, denoting their critical involvement in oyster development beyond this phase. Exposure to five metals-cadmium (Cd), copper (Cu), zinc (Zn), mercury (Hg), and lead (Pb)-elicited a significant upregulation of CgGRIA4 expression, indicating a robust response to metal stress. A KEGG enrichment analysis on 142 genes, exhibiting parallel expression trends with CgGRIA4 under metal stress, suggests that CgGRIA4 could augment excitatory signal transmission by activating glutamatergic and dopaminergic synapses, thereby contributing to the metal stress response in the oyster. This inquiry not only bolsters our comprehension of the iGluRs gene family in metal stress response but also paves the way for future exploration of its cardinal role in cellular signaling and environmental adaptability.

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The cluster of differentiation 36 (CD36) domain defines the characteristic ectodomain associated with class B scavenger receptor (SR-B) proteins. In bilaterians, SR-Bs play critical roles in diverse biological processes including innate immunity functions such as pathogen recognition and apoptotic cell clearance, as well as metabolic sensing associated with fatty acid uptake and cholesterol transport. Although previous studies suggest this protein family is ancient, SR-B diversity across Eukarya has not been robustly characterized. We analyzed SR-B homologs identified from the genomes and transcriptomes of 165 diverse eukaryotic species. The presence of highly conserved amino acid motifs across major eukaryotic supergroups supports the presence of a SR-B homolog in the last eukaryotic common ancestor. Our comparative analyses of SR-B protein structure identify the retention of a canonical asymmetric beta barrel tertiary structure within the CD36 ectodomain across Eukarya. We also identify multiple instances of independent lineage-specific sequence expansions in the apex region of the CD36 ectodomain-a region functionally associated with ligand-sensing. We hypothesize that a combination of both sequence expansion and structural variation in the CD36 apex region may reflect the evolution of SR-B ligand-sensing specificity between diverse eukaryotic clades.

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Recent reports highlighted several novel applications for the Bombyx mori silk fibroin (SF), as edible coatings for the preservation of food freshness, smart labels, or packaging materials. This study complements these reports and additionally describes the colorimetric sensing properties of the natural protein that could be explored to enhance the practical value of such applications. Our data show that in response to pH changes, reconstituted SF is able to undergo visible color changes that correlate with the intensity of the stimuli, regardless of its physical format or physical cross-linking state. The intensity of the developed color was proportional to the extent of the protein's hydrolytic degradation. We also found that these pH-driven color changes were reversible and interchangeable, with colorless samples at neutral pH, purple in acidic environments, and yellow under basic conditions. Our mechanistic studies identified tryptophan as being responsible for these colorimetric responses, which could be further intensified by the presence of ionized tyrosine functionalities. In addition, we determined that SF's sensing properties also applied to ultraviolet light exposure. Finally, we showed that the innate sensing capabilities of activated SF can be enhanced via the covalent incorporation of additional tryptophan into the protein. Overall, our results further support the utility of SF for sensing applications.

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Air pollution remains a major public health risk. People living in urban spaces are among those most affected by exposure to unhealthy levels of air pollution. However, many urban spaces especially in low- and middle-income countries lack high resolution and long-term data on the state of air quality. Without high resolution air quality data on the different spaces in a city, citizens and authorities are unable to quantify the challenge and act. This is in part attributed to the high cost of air quality monitoring equipment that are expensive to set up, maintain and not designed for local operating conditions that characterise environments in such contexts. In this paper, we describe AirQo sensor kit, a low-cost sensing hardware system designed for and custom made to work in low-resource settings and outdoor urban environments. We describe the design of the air quality sensing device, 3D-printed enclosure, installation-mount, fabrication and deployment configurations. We demonstrate that the low-cost sensing hardware provides a complete solution comparable to the traditional monitoring system and inspires action to tackle air pollution issues. The sensor kit presented in this paper has been widely deployed in cities in Eastern, Western and Central African countries.

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Although there is a growing demand for new sensors for environmental monitoring, biofouling continues to plague current sensors and sensing networks. As soon as a sensor is placed in water, the formation of a biofilm begins. Once a biofilm is established, reliable measurements are often no longer possible. Although current biofouling mitigation strategies can slow the biofouling process, a biofilm will eventually develop on or near the sensing surface. While antibiofouling strategies are being continuously developed, the complexity of the biofilm community structure and the surrounding environment means that there is unlikely to be a single solution that will minimize biofilms on all environmental sensors. Thus, antibiofouling research often focuses on optimizing a specific biofilm mitigation approach for a given sensor, application, and environmental condition. While this is practical from the standpoint of a sensor developer, it makes the comparison of different mitigation strategies difficult. In this Perspective, we discuss the application of different biofouling mitigation strategies to sensing and then explore the need for the sensor community to adopt standard protocols to increase the comparability of the biofouling mitigation approaches and help sensor developers identify the most appropriate strategy for their system.

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With the Internet of Things expanding to more locations across our planet, power becomes the main factor affecting device longevity. There is a need for more novel energy harvesting systems that are able to power remote devices for sustained periods. This publication presents one such device. Based on a novel actuator that utilises off-the-shelf gas mixtures to generate a variable force from temperature change, this publication presents a device capable of generating up to 150mJ per diurnal temperature cycle; this is enough electrical energy to send up to three LoRaWAN transmissions per day using slowly changing environmental temperatures.

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Potentiometric ion-selective electrodes (ISEs) have broad applications in personalized healthcare, smart agriculture, oil/gas exploration, and environmental monitoring. However, high-precision potentiometric sensing is difficult with field-deployed sensors due to time-dependent voltage drift and the need for frequent calibration. In the laboratory setting, these issues are resolved by repeated calibration by measuring the voltage response at multiple standard solutions at a constant temperature. For field-deployed sensors, it is difficult to frequently interrupt operation and recalibrate with standard solutions. Moreover, the constant surrounding temperature constraint imposed by the traditional calibration process makes it unsuitable for temperature-varying field use. To address the challenges of traditional calibration for field-deployed sensors, in this study, we propose a novel in situ calibration approach in which we use natural/external temperature variation in the field to obtain the time-varying calibration parameters, without having to relocate the sensors or use any complex system. We also develop a temperature-supervised monitoring method to detect the drift of the sensor during operation. Collectively, the temperature-based drift monitoring and in situ calibration methods allow us to monitor the drift of sensors and correct them periodically to achieve high-precision sensing. We demonstrate our approach in three testbeds: (1) under controlled temperature variation in the lab, (2) under natural temperature variation in a greenhouse, and (3) in the field to monitor nitrate activity of an agricultural site. In the laboratory study, we validate that the calibration parameters of printed nitrate ISEs can be reproduced by our proposed calibration process; therefore, it can serve as an alternative to traditional calibration processes. In the greenhouse, we show the use of natural temperature variation to calibrate the sensors and detect the drift in a fixed concentration nitrate solution. Finally, we demonstrate the use of the method to monitor the nitrate activity of an agricultural field within 10% of laboratory-based measurements (i.e., a sensitivity of 0.03 mM) for a period of 22 days. The findings highlight the prospect of temperature-based calibration and drift monitoring for high-precision sensing with field-deployed ISEs.

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Per- and polyfluoroalkyl substances (PFAS) have gained significant attention as emerging contaminants due to their persistence, abundance, and adverse health effects. Consequently, the urgent need for ubiquitous and effective sensors capable of detecting and quantifying PFAS in complex environmental samples has become a priority. In this study, we present the development of an ultrasensitive molecularly imprinted polymer (MIP) electrochemical sensor tailored by chemically vapour-deposited boron and nitrogen codoped diamond-rich carbon nanoarchitectures for the selective determination of perfluorooctanesulfonic acid (PFOS). This approach allows for a multiscale reduction of MIP heterogeneities, leading to improved selectivity and sensitivity in PFOS detection. Interestingly, the peculiar carbon nanostructures induce a specific distribution of binding sites in the MIPs that exhibit a strong affinity for PFOS. The designed sensors demonstrated a low limit of detection (1.2 µg L-1) and exhibited satisfactory selectivity and stability. To gain further insights into the molecular interactions between diamond-rich carbon surfaces, electropolymerised MIP, and the PFOS analyte, a set of density functional theory (DFT) calculations was performed. Validation of the sensor's performance was carried out by successfully determining PFOS concentrations in real complex samples, such as tap water and treated wastewater, with average recovery rates consistent with UHPLC-MS/MS results. These findings demonstrate the potential of MIP-supported diamond-rich carbon nanoarchitectures for water pollution monitoring, specifically targeting emerging contaminants. The proposed sensor design holds promise for the development of in situ PFOS monitoring devices operating under relevant environmental concentrations and conditions.

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Integral to the protein structure/function paradigm, oligomeric state is typically conserved along with function across evolution. However, notable exceptions such as the hemoglobins show how evolution can alter oligomerization to enable new regulatory mechanisms. Here, we examine this linkage in histidine kinases (HKs), a large class of widely distributed prokaryotic environmental sensors. While the majority of HKs are transmembrane homodimers, members of the HWE/HisKA2 family can deviate from this architecture as exemplified by our finding of a monomeric soluble HWE/HisKA2 HK (EL346, a photosensing light-oxygen-voltage [LOV]-HK). To further explore the diversity of oligomerization states and regulation within this family, we biophysically and biochemically characterized multiple EL346 homologs and found a range of HK oligomeric states and functions. Three LOV-HK homologs are primarily dimeric with differing structural and functional responses to light, while two Per-ARNT-Sim-HKs interconvert between differentially active monomers and dimers, suggesting dimerization might control enzymatic activity for these proteins. Finally, we examined putative interfaces in a dimeric LOV-HK, finding that multiple regions contribute to dimerization. Our findings suggest the potential for novel regulatory modes and oligomeric states beyond those traditionally defined for this important family of environmental sensors.

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