Plant-parasitic nematodes pose a silent yet devastating threat to global agriculture, causing significant yield losses and economic damage. Traditional detection methods such as soil sampling, microscopy, and molecular diagnostics are slow, labor-intensive, and often ineffective in early-stage infestations. Nano biosensors: cuttingedge analytical tools that leverage nanomaterials like carbon nanotubes, graphene, and quantum dots to detect nematode-specific biochemical markers such as volatile organic compounds (VOCs) and oesophageal gland secretions, with unprecedented speed and accuracy. The real breakthrough lies in the fusion of artificial intelligence (AI) and nano-biosensor technology, forging a new frontier in precision agriculture. By integrating AI's powerful data analysis, pattern recognition, and predictive capabilities with the extraordinary sensitivity and specificity of nano-biosensors, it becomes possible to detect biomolecular changes in real-time, even at the earliest stages of disease progression. AI-driven nano biosensors can analyze real-time data, enhance detection precision, and provide actionable insights for farmers, enabling proactive and targeted pest management. This synergy revolutionizes nematode monitoring, paving the way for smarter, more sustainable agricultural practices. This review explores the transformative potential of AI-powered nano-biosensors in advancing precision agriculture. By integrating these technologies with smart farming systems, we move closer to real-time, costeffective, and field-deployable solutions, ushering in a new era of high-tech, eco-friendly crop protection.

This study examines permafrost thermal regimes and hydrological responses to climate change in the Navarro Valley, Chile's Dry Central Andes, using a decade of ground temperature data (2013-2022) from two rock glaciers-RG1 (3805 m) and RG2 (4047 m)-alongside short-term meltwater conductivity records, meteorological data, and long-term streamflow records. We assess permafrost stability and climatic sensitivity by analyzing thermal offset data (2017-2022) and ground temperature trends. Both sites show sustained warming, but RG1 exhibits accelerated warming (+ 2.84 degrees C/decade), frequent freeze-thaw cycles, and extended thaw periods, indicating a transitional regime. In contrast, RG2 shows fewer freeze-thaw cycles and greater thermal buffering, consistent with cold permafrost. The statistical model overestimated thaw dynamics at RG2, highlighting the importance of field-based data for accurate classification. Hydrological signals at RG1-including cold, mineralized meltwater and rapid ground surface temperature stream coupling-are attributed to thawing and deeper flowpaths. Conductivity data (2014-2015) reveal solute pulses consistent with early melt events and debris interaction. Meanwhile, long-term streamflow trends indicate declining discharge. These findings suggest feedback between permafrost loss and water availability. This study underscores the divergent evolution of adjacent rock glaciers under warming by integrating thermal, hydrological, and climatic data. RG1 shows signs of degradation, while RG2 may act as a temporary refuge. Continued monitoring is essential for managing water security in vulnerable mountain regions like the Dry Andes.Graphical AbstractThis graphical abstract visually summarizes a ten-year monitoring effort of mountain permafrost and glacier hydrology in the Navarro Valley, Dry Andes (32 degrees S), with implications for water security under climate change. The left panel situates the study area within the upper Aconcagua Basin, identifying two instrumented sites within the Tres Gemelos rock glacier complex-RG1 (3805 m) and RG2 (4047 m)-and an automatic weather station. These sites were selected for continuous monitoring of ground temperature and streamflow to assess permafrost behavior in a water-stressed mountain catchment. Moving to the center, the image presents an integrated monitoring framework that links temperature-depth profiles, surface-subsurface thermal dynamics, and discharge records. Key indicators such as freeze-thaw cycle counts and thawed-day metrics are used to classify thermal regimes and detect warming trends. The upper-right panel features a conceptual model that connects permafrost degradation to hydrological responses: RG1, characterized as transitional, shows signs of enhanced shallow flow and seasonal meltwater pulses, while RG2 retains cold, thermally buffered conditions that support greater storage stability. These contrasts are further illustrated by temperature trend graphs, which reveal faster warming at RG1 (+ 2.84 degrees C/decade) compared to RG2 (+ 1.92 degrees C/decade), as well as increased thaw metrics. Below, a long-term streamflow graph (1970-2023) documents declining discharge, visually supported by a field photo of a dry riverbed. The bottom panel summarizes the study's key finding: RG1 and RG2 are evolving along divergent thermal and hydrological trajectories, underscoring the need for high-resolution monitoring to guide water resource planning in an era of warming and drought.

Hypochlorite (ClO-) is a highly reactive chemical extensively used in households, public areas, and various industries due to its multiple functions of disinfection, bleaching, and sterilization. However, overuse of ClO- may contaminate the water, soil, air and food, leading to negative impacts on the environments, ecosystems and food safety. Meanwhile, excessive ClO- in human body can also cause severe damage to the immune system. Thus, the development of effective and precise detection tools for ClO- is of great significance to better understand its complicated roles in environments and biosystems. Herein, a new high-performance ratiometric fluorescent probe 2-amino-3-((10-propyl-10H-phenothiazin-3-yl)methylene)-amino)maleonitrile (PD) was developed for effective detection of ClO- in various bio/environmental and food samples. Probe PD exhibits highly-specific ratiometric fluorescent response to ClO- with rapid response (< 1 min), excellent sensitivity (detection limit, 47.4 nM), wide applicable pH range (4 -12), and excellent versatility in practical applications. In practical applications, PD enables the sensitive and quantitative detection of ClO- levels in various water samples, bio-fluids, dairy products, fruits and vegetables with high-precision (recoveries, 97.00 -104.40 %), as well as the successful application for visual tracking ClO- in fresh fruits and vegetables. Furthermore, test strips containing PD offer a visual and convenient tool for quick identification of ClO- in aqueous media by the naked eye. Importantly, the good biocompatibility of PD enables its practical applications in real-time bioimaging of endogenous/exogenous ClO- levels in living cells, bacteria, onion cells, Arabidopsis, as well as zebrafish. This study provided an effective method for visual monitoring and bioimaging of ClO- levels in various environments, foods and living biosystems.

The European rabbit (Oryctolagus cuniculus) is a keystone species in Mediterranean ecosystems but also considered a pest in some agricultural areas. Despite its threatened status due to diseases and habitat loss, rabbit populations thrive in motorway verges, causing conflicts with human activities. In this study we examine the factors affecting rabbit warren abundance in motorway verges in central Spain, with implications for conservation and management. The research aimed to assess the importance of infrastructure (e.g. motorway slopes) and landscape (e.g. land use, soil depth) factors on rabbit warren abundance along 1631 km of motorway verges and to develop an index for broader-scale abundance and risk assessment. Using generalized linear mixed models, the study revealed that both infrastructure (slope) and landscape factors (soil depth, vegetation structure and land cover gradients) significantly influenced warren abundance. Rabbit warrens were more abundant in agricultural landscapes with deep soils and in intermediate slope ranges. The findings suggest that rabbit abundance in motorway verges is driven by a combination of factors involving both infrastructure features but also land use in surrounding areas. The derived model predictions were able to correctly discriminate between crop damaged and non-damaged areas, highlighting its potential as a tool for conflict mitigation and conservation planning. The study underscores the need to integrate landscape and infrastructure features into wildlife management strategies to address human-wildlife conflicts effectively. Future work should include direct population monitoring and explore broader ecological impacts, such as predator dynamics and wildlife-vehicle collisions.

The seasonal freeze-thaw cycle of frozen soil regulates soil hydrothermal processes and serves as a crucial indicator of climate change in high-latitude cold regions. Monitoring the dynamic evolution of frozen soil structure and composition is essential for infrastructure development, soil conservation and carbon storage regulation. Compared to in-situ borehole measurements and remote sensing, near-surface geophysical methods offer spatially resolved insights into freeze-thaw processes at different depths. In this study, we applied electrical resistivity tomography and ambient noise seismic monitoring to investigate seasonal freeze-thaw cycles at a frozen soil test site in Northeast China. Geophysical data collected over a complete freeze-thaw cycle reveal the coupling between soil structure and hydrothermal properties, with strong consistency observed between physical parameters and hydrological information. Resistivity variations correlate with temperature, water content, and solute concentration across different freeze-thaw stages. Seismic relative velocity changes (dv/v) and surface wave phase velocity changes (dc/c) were negatively correlated with accumulated temperature and groundwater levels, reflecting soil pore freezing and the hydrothermal state of the deep subsurface environment. Meanwhile, the measured data verify that dc/c offers higher spatiotemporal resolution than dv/v. Sensitivity analysis indicate that resistivity is more responsive to shallow thermal exchange, while seismic velocity changes are more sensitive to deep hydrological variations. Integrating pore geometry and water-ice phase mechanisms, we construct a freeze-thaw evolution model for seasonally frozen soil based on combined hydrological and geophysical data. The results validate the effectiveness of geophysical methods for detecting and monitoring frozen soil, and provide technical support for quantifying phase transition mechanisms in freeze-thaw processes.

This manuscript presents a comprehensive presentation of ground temperature data collected at 16 nodes of the 121 of the Crater Lake Circumpolar Active Layer Monitoring (CALM) site on Deception Island, Antarctica, from 2008 to early 2022. Each one of the 16 shallow boreholes has been equipped with miniature temperature loggers, providing valuable insights into the thermal regime of the ground at a depth of 50 cm, which corresponds to the mean depth of the top of the permafrost table as observed by annual mechanical probing in the CALM site. Despite a 9-month long gap in data collection during 2017 due to persistent snow cover, the time series remains largely intact, with annual measurements taken every 3 h. The manuscript details the methodologies employed for data collection, including the use of iButton loggers, and outlines the challenges faced in retrieving and processing the data in the harsh Antarctic environment. The cleaned dataset, which consolidates data from various nodes while removing erroneous records, is made freely accessible to the scientific community without any additional processing of the data such as offset corrections or gaps interpolation. This resource is expected to facilitate further research into the thermal dynamics of the active layer and permafrost and its implications for climate change since both are influenced by external factors such as snow cover, air temperature and others. Overall, the presented dataset contributes to the limited body of knowledge regarding Antarctic permafrost and provides a foundation for future investigations into the effects of climate change on frozen ground dynamics. The dataset serves as a vital tool for researchers aiming to model ground thermal behaviour and assess the impacts of environmental changes in polar regions.

Component temperature and emissivity are crucial for understanding plant physiology and urban thermal dynamics. However, existing thermal infrared unmixing methods face challenges in simultaneous retrieval and multicomponent analysis. We propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas. TRUST-DART utilizes the DART model and requires inputs including at-surface radiance imagery, downwelling sky irradiance, a 3D mock-up with component classification, and standard DART parameters (e.g., spatial resolution and skylight ratio). This method produces maps of component emissivity and temperature. The accuracy of TRUST-DART is evaluated using both vegetation and urban scenes, employing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images and DART-simulated pseudo-ASTER images. Results show a residual radiance error is approximately 0.05 W/(m2 & sdot;sr). In absence of the co-registration and sensor noise errors, the median residual error of emissivity is approximately 0.02, and the median residual error of temperature is within 1 K. This novel approach significantly advances our ability to analyze thermal properties of urban areas, offering potential breakthroughs in urban environmental monitoring and planning. The source code of TRUSTDART is distributed together with DART (https://dart.omp.eu).

The laboratory experiment is an effective tool for the rapid assessment of the unsaturated soil slopes instability induced by extreme weather events. However, traditional experimental methods for unsaturated soils, including the measurement of the soil-water characteristic curve (SWCC), soil hydraulic conductivity function (SHCF), shear strength envelope, etc., are time-consuming. To overcome this limitation, a rapid testing strategy is proposed. In the experimental design, the water saturation level is selected as the control variable instead of the suction level. In the suction measurement, the suction monitoring method is adopted instead of the suction control method, allowing for simultaneous testing of multiple soil samples. The proposed rapid testing strategy is applied to measure the soil hydro-mechanical properties over a wide suction/saturation range. The results demonstrate that: (1) only 3-4 samples and 2-5 days are in need in the measurement of SWCC; (2) 7 days is enough to determine a complete permeability function; (3) only 3 samples and 3-7 days are in need in the measurement of the shear strength envelope; (4) pore size/water distribution measurement technique is fast and recommended as a beneficial supplement to traditional test methods for unsaturated soils. Our findings suggest that by employing these proposed rapid testing methods, the measurement of pivotal properties for unsaturated soils can be accomplished within one week, thus significantly reducing the temporal and financial costs associated with experiments. The findings provide a reliable experimental approach for the rapid risk assessment of geological disasters induced by extreme climatic events.

Heavy metals (HMs) contamination poses a significant threat to environmental matrices, particularly soil, which is essential for food security, agricultural productivity, and key ecosystem services. Understanding how crops respond to HMs is crucial for developing biomonitoring strategies to assess soil contamination and inform remediation efforts. Plants, including crops, exhibit a range of functional traits (FT) that can indicate HMs stress and contamination levels. In this study, we investigated the response strategies of Zea mays L. var. Limagrain 31455, widely cultivated throughout the region of Land of Fires, a critically polluted area of southern Italy, to different concentrations of Zn, Pb, and Cr, corresponding to moderate to severe soil contamination. Functional traits related to the photosynthetic machinery, including gas exchange, chlorophyll fluorescence and reflectance indices, were examined. Root morpho-histochemical analysis were also conducted to correlate early root alterations with any observed changes in these photosynthetic traits. Results revealed distinct response patterns: tolerance to Zn, without adverse effects on photosynthetic traits; resistance to Pb, mediated by increased RD and photoprotection through change in reflectance indices; and sensitivity to Cr highlighted by severe functional impairments of all the studied photosynthetic traits and structural root damages. Functional traits, such as chlorophyll fluorescence parameters and the photochemical reflectance index or normalized difference vegetation index, demonstrated high potential for monitoring HMs stress responses; in addition, morpho-anatomical traits of the root system provided insights into biomass allocation and the capacity of var. Limagrain 31455 to tolerate and adapt to HMs stress. These findings underscore the importance of integrating physiological, anatomical, and spectral analyses to improve the biomonitoring and management of polluted soils and detecting spatial variability in contamination via remote sensing.

Most Australian vegetable growers apply fumigants or nematicides as a precautionary nematode control measure when crops susceptible to root-knot nematode (RKN, Meloidogyne spp.) are grown in soils and environmental conditions suitable for the nematode. The only way growers can make rational decisions on whether these expensive and environmentally disruptive chemicals are required is to regularly monitor RKN populations and decide whether numbers prior to planting are high enough to cause economic damage. However, such monitoring programs are difficult to implement because nematode quantification methods vary in efficiency and the damage threshold for RKN on highly susceptible vegetable crops is often < 10 root-knot nematodes /200 mL soil. Consequently, five nematode quantification methods were tested to see whether they could reliably detect these very low population densities of RKN. Two novel methods produced consistent results: 1) extracting nematodes from 2 L soil samples using Whitehead trays, quantifying the RKN DNA in the nematode suspension using molecular methods, and generating a standard curve so that the molecular results provided an estimate of the total number of RKN individuals in the sample, and 2) a bioassay in which two tomato seedlings were planted in pots containing 2 L soil and the number of galls produced on roots were counted after 21-25 days. Both methods could be used to quantify low populations of RKN, but bioassays are more practical because expensive equipment and facilities are not required and they can be done at a local level by people lacking nematological or molecular skills.