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Rapid, accurate, and reliable detection of pathogenic bacteria remains a critical need in clinical, food, and environmental monitoring. In today's context, nucleic acid amplification-mediated biosensing have emerged as a prominent approach to improve detection sensitivity, whereas dual-mode signal readout approaches have more enhanced analytical robustness and reliability. This review outlines recent advances in nucleic acid signal amplification strategies, including enzyme-based methods like LAMP, RPA, and RCA, as well as enzyme-free approaches like HCR, CHA, and EDR. Special attention is given to incorporating these amplification methods into dual-mode biosensing systems that combine both optical and electrochemical transduction mechanisms. This integration enables complementary signal generation and improves detection accuracy by reducing false-positive and false-negative results. This study critically examines the advancement of nucleic acid signal amplification strategies (NASAS)-mediated dual-mode sensing systems for detecting major pathogenic bacteria, including Escherichia coli, Salmonella, Listeria monocytogenes, Staphylococcus aureus, and Vibrio species, focusing on selectivity, sensitivity, assay design, and real-sample applicability. Finally, the review highlights present challenges related to system integration, standardization, and point-of-care applications. Additionally, it outlines potential future directions for rendering nucleic acid amplification-based dual-mode probes into practical diagnostic devices. Overall, this study affords a comprehensive synthesis of emerging approaches and design mechanisms for next-generation diagnostic scaffold for pathogen analysis.

This paper introduces a high resolution, machine learning-ready heliophysics dataset derived from NASA's Solar Dynamics Observatory (SDO), specifically designed to advance machine learning (ML) applications in solar physics and space weather forecasting. The dataset includes processed imagery from the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI), spanning a solar cycle from May 2010 to December 2024. To ensure suitability for ML tasks, the data has been preprocessed, including correction of spacecraft roll angles, orbital adjustments, exposure normalization, and degradation compensation. We also provide auxiliary application benchmark datasets complementing the core SDO dataset. These provide benchmark applications for central heliophysics and space weather tasks such as active region segmentation, active region emergence forecasting, coronal field extrapolation, solar flare prediction, solar Extreme Ultraviolet (EUV) spectra prediction, and solar wind speed estimation. By establishing a unified, standardized data collection, this dataset aims to facilitate benchmarking, enhance reproducibility, and accelerate the development of AI-driven models for critical space weather prediction tasks, bridging gaps between solar physics, machine learning, and operational forecasting.

Arctic and boreal regions are experiencing rapid environmental changes that include thawing permafrost and increasing disturbances. The NASA Arctic-Boreal Vulnerability Experiment (ABoVE) sought to better understand these changes through field, airborne, and remote sensing measurements. One key airborne instrument was the Land, Vegetation, and Ice Sensor (LVIS), a wide-swath imaging laser altimeter system. LVIS conducted 32 flights during June-August periods of 2017 and 2019, capturing data across more than 91,000 km² of diverse Arctic and boreal ecosystems. The surface topography and vegetation structure data collected throughout Alaska and Northwestern Canada spans boreal forests to Arctic tundra, crossing 12 distinct ecoregions. This airborne collection enables direct comparison with coincident NASA Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data, extends research beyond the ~52° N limit of NASA's Global Ecosystem Dynamics Investigation (GEDI) sensor, and provides precursor data for future satellite missions, such as NASA's recently selected Earth Dynamics Geodetic Explorer (EDGE). We summarize detailed information on LVIS data records from ABoVE deployments, including access and visualization using custom open source tools.

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Satellite quartet demonstrates record-breaking ability to predict solar explosions' arrivals at Earth.

Species interact with habitat based on their functional traits, but it is unclear how specific traits interact with the multiple components of habitat structure. To address this knowledge gap for forest-dwelling bird communities, we leveraged forest structure information from NASA's Global Ecosystems Dynamics Investigation (GEDI) spaceborne light detection and ranging (LiDAR) mission alongside broad-scale observations of bird occurrence from eBird. Using a custom implementation of the hierarchical modelling of species community (HMSC) approach, we estimated relationships between functional traits and the responses of bird populations to vertical and horizontal forest structure. Although variable importance indicated that climate and elevation explained the most variation in bird occurrence, we discovered two strong signals of trait interactions with species' responses to forest structure: specialized foraging strategies explained birds' responses to vertical structure, while larger bodied birds with high flight efficiency showed weaker responses to both vertical and horizontal structure. Other functional traits such as diet and nest height were also associated with responses to vertical and horizontal structure, but with greater uncertainty. Our results demonstrate the explanatory power of functional traits in describing birds' interactions with habitat and suggest that functional traits may play a role in structuring species' responses to forest structure. The use of broad-scale forest structure and bird occurrence data clarifies the impact of behaviour and morphology on habitat use, better quantifying the relationships between birds and their environment.

This data article presents the CubeSat Cybersecurity Dataset for Intrusion Detection (CuCD-ID), a collection of labelled command and telemetry data designed to support machine learning-based security research for space systems. The data were generated in a high-fidelity software-in-the-loop environment using NASA's Operational Simulator for Small Satellites (NOS3) running the core Flight System (cFS). Telemetry was captured across five scripted scenarios: one nominal case and four adversarial tactics aligned with the Space Attack Research and Tactic Analysis (SPARTA) framework, specifically command flooding, false data injection, storage exhaustion, and defence impairment. All scenarios were driven by commands issued from the COSMOS v4 ground station software. The repository contains two primary tabular datasets in Comma-Separated Values (CSV) format: a raw, balanced dataset with 25,000 records and 31 features, and an augmented, noised dataset with 22,465 records and 23 features. Each record contains features parsed from Consultative Committee for Space Data Systems (CCSDS) packet headers or engineered from a 20-second sliding window, alongside system-level metrics and a numeric class label. The augmented data incorporates nine documented noise categories to emulate plausible in-orbit disturbances and improve model robustness against benign variability: White Noise, Analog Outliers, Gaps, Trends, Signal Shifts, Frequency Changes, Sensor Dropout, Magnitude Warping, and Window Time Warping. The dataset is suitable for developing and benchmarking supervised and unsupervised intrusion detection methods, including on-board and Tiny Machine Learning applications. All COSMOS v4 scripts used to generate the scenarios are also provided to ensure full reproducibility and enable extension of the data collection.

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Atom interferometers deployed in space are excellent tools for high precision measurements, navigation, or Earth observation. In particular, differential interferometric setups feature common-mode noise suppression and enable reliable measurements in the presence of ambient platform noise. Here we report on orbital magnetometry campaigns performed with differential single- and double-loop interferometers in NASA's Cold Atom Lab aboard the International Space Station. By comparing measurements with atoms in magnetically sensitive and insensitive states, we have realized atomic magnetometers mapping magnetic field curvatures. Our results pave the way towards precision quantum sensing missions in space.

Understanding long-term and intra-annual variations in human thermal discomfort is essential for climate-health assessments in climate-sensitive regions. In this paper, thermal comfort dynamics have been analyzed at seven locations in Northwestern India by utilizing Effective Temperature (ET) and Temperature-Humidity Index (THI) data for a period of 54 years (1969-2022). Climatic data for dry-bulb temperature (DBT), wet-bulb temperature (WBT), relative humidity (RH), and wind speed (WS) on a daily basis were acquired from the India Meteorological Department (IMD), while missing data were supplemented from NASA's MERRA-2. Analyses were conducted at annual, seasonal, monthly, and weekly scales. Annual averages of ET and THI indicate generally comfortable conditions; however, higher-resolution analyses reveal substantial thermal stress periods that annual means obscure. At Delhi and Amritsar, ET shows either weak or non-significant trends, while THI exhibits significant warming due to increased WBT, reduced RH, and wind stilling. Trends analyzed on a monthly and weekly basis reveal pronounced seasonal intensification of heat stress. During the pre-summer period, ET shows notable warming, with weekly trends reaching + 0.081 to + 0.086 °C/year in Ambala and Patiala. Pre-monsoon warming is more evident in THI, increasing by + 0.058 to + 0.077 °C/year during mid-May in Delhi, Patiala, and Amritsar. Persistent peak heat from June to mid-July coincides with WBT increases of approximately + 0.05 °C/year at several stations. In contrast, winter and post-monsoon periods exhibit cooling in THI associated with reductions in WBT and DBT, while ET shows weaker declines due to concurrent decreases in wind speed. These contrasting ET and THI responses highlight the importance of multi-index and ventilation-sensitive assessments for thermal risk evaluation, public health preparedness, and climate adaptation planning in Northwestern India.

Plant growth systems tested on the International Space Station (ISS) are small-area growth units that mostly use solid media. With NASA's plan to send astronauts on long-duration exploration missions, there is a need to produce larger amounts of fresh food with limited upmass and resources. The eXposed Root On-Orbit Test System (XROOTS) is an aeroponic-hydroponic nutrient delivery system designed for exploration missions and was tested on the ISS. Post-harvest samples were returned for microbiological analyses of the plant leaves, roots, and fruit from lettuce, mizuna mustard, wheat, radish, tomato, and pea plants grown in the XROOTS. The microbiological food safety of crops was evaluated through culture-based microbial enumeration and identification. The microbial communities were compared between different plants and plant tissues by sequencing the prokaryotic V4 variable region of the 16S ribosomal RNA (rRNA) gene amplicons and fungal internal transcribed spacer (ITS) region. The microbial counts from the root module surface samples demonstrated a reduction after cleansing. The bacterial counts in the nutrient solution ranged from 65 to 3,800 CFU/ml. The bacterial counts in the distal leaf sections were lower than those in the leaf proximal, wick, and roots in all plant samples. The tomato fruit and the pea pod samples had the lowest average counts. The microbial counts from the leaves and wicks harvested from XROOTS were similar to the ranges found on previous Veggie (Vegetable Production System)-grown leafy greens. All screening tests for potential foodborne pathogenic bacteria were negative. Sequencing analyses showed that diversity was low in the leaves and higher in the roots, and the microbial community was more diversified in the XROOTS samples compared with previous Veggie experiments. Pseudomonas had the highest relative abundance in the majority of samples. Although some microbes were shared in the majority of plant tissues, unique microbes were identified for each plant type grown in XROOTS and when compared with previous Veggie demonstrations. Microbial surveys of ISS-grown plants and the associated hardware provide valuable data that can reveal potential challenges in deep-space crop production operations and ensure the quality of crops intended for crew consumption.

Blue Ghost data suggest NASA's growing commercial Moon program can deliver results.

In recent decades, the monitoring of volcanoes has been revolutionized by the launch of Earth-observing satellites and advances in thermal infrared remote sensing. These developments have revealed a wide range of thermal responses of volcanic surfaces to subsurface processes, even demonstrating that eruptions are often preceded by measurable thermal anomalies. This recognition highlights the need for robust tools to systematically detect and track such anomalies, making full use of existing satellite datasets and maximizing the value of current instruments in orbit. To address this challenge, we present the Subtle Surface Thermal Anomalies Recognizer (SSTAR), a versatile and user-friendly application designed to analyze diffuse thermal anomalies, i.e., subtle thermal unrest (~ 1 K) across large areas (several km2). SSTAR leverages data from NASA's Terra and Aqua satellites, which host the Moderate Resolution Imaging Spectroradiometers (MODIS), and builds upon a robust statistical framework. By processing pixel-level data, SSTAR tracks the temporal evolution of diffuse thermal anomalies at specific target sites and maps their spatiotemporal distribution across extended areas. Key features include filtering tools that distinguish between long-term (years) and short-term (weeks) anomalies, as well as uncertainty quantification using bootstrapping. The application is standalone, features an interactive interface for streamlined analysis, and is accessible to newcomers to satellite-based thermal remote sensing. At the same time, specialized users can customize the underlying scripts for other specific research needs. As a demonstration, we apply SSTAR to Shishaldin volcano (Alaska), revealing the emergence of significant thermal anomalies around the summit crater and flanks prior to eruptions. We envision SSTAR as a valuable resource for studying subtle thermal unrest at active volcanoes and hydrothermal systems, where the detection of faint and spatially coherent anomalies may help identify subsurface fluid pathways. Its flexible design enables integration with additional satellite datasets, positioning SSTAR as a forward-looking tool for advancing space-based volcanic thermal monitoring. Building on this capability, daily updated diffuse thermal anomalies are provided for target volcanoes through an open web platform hosted at Geosciences Barcelona-CSIC (https://sstar.geo3bcn.csic.es/), to support surveillance agencies and expert committees in alert-level assessments. The online version contains supplementary material available at 10.1186/s40623-026-02497-6.

Inspired by NASA's In-Space Manufacturing initiative, this concept proposes a paradigm shift to enhance combat medical mission flexibility and sustainability. In an era of contested logistics and limited aeromedical evacuation, leveraging the established additive manufacturing expertise of military dental teams can mitigate critical supply chain vulnerabilities. This approach transforms dental personnel into versatile force multipliers, improving warfighter readiness and accelerating return-to-duty rates across medical specialties. This conceptual framework outlines the integration of advanced technologies at Role 2 medical facilities. Dental teams, acting as pseudo-biomedical engineers, can utilize portable 3D optical scanners, computer-aided design software, and 3D printers to fabricate essential medical devices on demand. This capability enables just-in-time production of items ranging from dental prosthetics to non-durable medical equipment, directly addressing supply shortfalls in the field. Proof-of-concept validations demonstrated significant logistical and clinical benefits. A forward-deployed digital workflow successfully replaced a 1,200-pound legacy dental unit with a sub-100-pound portable package. A formal modernization exercise confirmed that a digital workflow for prosthetics reduced equipment weight by 60% (a 649-pound reduction), cut fabrication time by 50%, and lowered acquisition costs by $15,000. Field applications included the successful on-demand printing of custom orthopedic splints, overcoming equipment shortfalls and enabling rapid return to duty for injured personnel. Dental teams equipped with additive manufacturing capabilities represent a transformative asset for the entire deployed medical force. They can provide crucial logistical reinforcement and reduce dependence on fragile supply chains. To realize this potential, expeditionary decision-makers must act to integrate this capability through updated doctrine, targeted training, and validated materiel solutions, ensuring a more resilient and adaptive medical response in future Large-Scale Combat Operations.

NASA's Mars Atmosphere and Volatile EvolutioN spacecraft carries an extensive suite of instruments for characterizing the Mars upper atmosphere. Of these, the NGIMS, IUVS, and EUVM instruments produce datasets for CO2 density and neutral atmosphere temperature. The different instruments and retrieval methods utilized provide an expansive view of the Mars upper atmosphere. To make full use of the geophysical coverage offered by these datasets, we undertake a systematic comparison of the datasets to understand where they are different and how any biases between datasets can be removed. We conduct pairwise comparisons between datasets, binning the data by geophysical and forcing parameters, to develop adjustment factors that can be used to adjust the measured CO2 density and neutral temperature of one dataset to nominal agreement with another. The determined adjustment factors are reported for use by the wider Mars aeronomy community.

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NASA's OSIRIS-REx mission demonstrated the potential for robotic spacecraft to probe physical properties of planetary bodies. In 2020, the spacecraft autonomously collected granular material from Bennu, a small unconsolidated asteroid, leaving behind a region excavated by the combined effects of the gas-driven sampler and backaway thrusters. Disambiguating the physical responses to these two energy-injection events offers an opportunity to characterize a microgravity asteroid's near-surface properties and understand how thruster-surface interactions could be utilized by future missions. We do so here using data from the spacecraft's instruments and telemetry in conjunction with detailed modeling of the thrusters. The sampler initially formed a crater 0.5-0.7 meters in radius before thruster activation. The thrusters deposited four regions of high-pressure gas ∼1-6 meters from the contact location, with lower pressures inside and beyond this region. As a result, the crater expanded into a region undergoing active erosion from the thrusters, and its final dimensions were increased by thruster effects. The total erosion and redeposition depend on the pre-existing mass distribution and topography of the area. Varying erosion responses to the thrusters indicate variability in material properties laterally, and, combined with accelerometer data, as a function of depth. The efficacy of the thrusters to mobilize material over a broad area (>100 square meters), and at very small pressures (perhaps as low as 0.005 Pa), motivates their use to interrogate small-body surface properties, particularly in the spacecraft's planned 2029 encounter with asteroid Apophis. The online version contains supplementary material available at 10.1007/s11214-026-01285-8.

Individuals working in extreme operational environments face sustained psychological and physiological stressors that can degrade wellbeing and performance. This study presents the development and validation of the Meaningful Work and Enjoyment Scale (MeWES), a brief measure designed to assess the extent to which individuals perceive their work as meaningful and enjoyable in isolated, confined, and high-demand settings. Grounded in the job demands-resources model and theories of resilience and engagement, the MeWES was evaluated across three populations: Antarctic expedition personnel, senior US Navy officers, and participants in NASA's Human Exploration Research Analog. The final six-item version of the MeWES demonstrated strong internal consistency, unidimensional structure, and convergent validity with the Work and Meaning Inventory. Predictive validity was established through significant relationships with morale, career satisfaction, affect, team cohesion, and performance. Incremental validity analyses indicated that the MeWES explains unique variance in key psychological and team outcomes beyond existing measures of work meaning. Findings support the role of meaningful work as a psychological resource that promotes resilience and enhances individual and team functioning in extreme contexts. The MeWES provides a practical measure for research and applied interventions aimed at sustaining human performance and wellbeing in operational environments characterized by prolonged stress and isolation.

IntroductionExposure to spaceflight and microgravity environments has been implicated in large reductions in astronaut bone mineral density (BMD). While low BMD is a known risk factor for fracture, there have been no reported cases of in-flight fracture, and very little documentation of post-flight fracture. The present study sought to review the incidence of fractures within 5 years of return to spaceflight.MethodsUsing NASA's Lifetime Surveillance of Astronaut Health epidemiology database, a retrospective cohort study was conducted to identify the incidence of fracture in the 5-year post-flight period. All astronauts who participated in spaceflight with 5-year post-flight medical data were included. Demographics were compared between the fracture and nonfracture cohorts.ResultsOf the 242 astronauts who met the inclusion criteria, 7 (2.9%) sustained fractures within 5 years post-flight. Three post-flight fractures occurred in the hip or spine. Six of the 7 fractures occurred within 2 years of return from spaceflight. Spaceflight length, age, and time from spaceflight were not statistically significantly associated with increased risk of fracture.ConclusionFracture upon return to the gravitational environment is a serious risk for astronauts that can significantly jeopardize astronaut health and mission success. Fractures of the hip and spine, specifically, are associated with decreased BMD. While the incidence of these fractures is roughly 1%, the negative implications, including high 1-year mortality rates and functional implications, emphasize the need to optimize bone health and fracture treatment protocols.