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Multi-layer low Earth orbit constellations (ML-LEOs) have become a pivotal trend in the development of satellite network systems, where their layered orbital architecture improves system performance by strategically deploying satellites in distinct orbital layers. However, two critical issues remain open: how does the configuration of ML-LEO affect its performance, and how many layers are required to achieve optimal performance? This paper first investigates the impact of the number of layers L on the capacity of ML-LEOs. By analyzing the distribution of inter-layer inter-satellite links (ISLs) and the flow count on bottleneck links, we derive a closed-form mathematical expression for ML-LEO capacity under different values of L. In particular, we show that when each layer adopts an identical constellation topology and the number of satellites per orbit equals the number of orbits, the capacity of the ML-LEO is L times that of a single-layer low Earth orbit constellation (SL-LEO). Furthermore, we present the optimal parameter configuration for ML-LEOs: the number of orbits per layer should equal the number of satellites per orbit, the number of layers should be half the number of satellites per orbit, and the optimal number of inter-layer ISLs is twice the product of the number of orbits per layer and the number of layers. Finally, extensive simulations are carried out to thoroughly verify the accuracy of the analytical results. Our analysis reveals the performance benefits of multi-layer topology and establishes a theoretical framework for parameter optimization in ML-LEO.

Horizontal gene transfer (HGT) is a driving force in microbial evolution that allows community members to rapidly evolve to cope with environmental stressors and competition. Despite the importance of HGT for the generation of genetic diversity, little is known about the specific mechanisms or dynamics of transfer in complex communities. Transductomics is a sequencing based technique which identifies potential HGT by bacteriophages (transduction) through sequencing of the transductome - the DNA carried by bacteriophages and other virus-like particles in a sample. We analyzed the murine gut transductome before and after perturbations with antibiotics and Clostridioides difficile infection (CDI). We found that several bacterial families - the Oscillospiraceae, Butyricoccaceae, and Turicibactericeae - disproportionally contributed to the transductome. Some families, like the Butyricicoccaceae, were frequent transducers in both the baseline and perturbed murine gut microbiome while other taxa displayed condition-specific transduction indicating that there may be specific transducing subpopulations or regulatory mechanisms controlling transduction frequency. Additionally, we found a diversity of highly abundant and enriched mobile genetic elements (MGEs) in the transductome including plasmids, integrative conjugative elements, phage satellites and transposons. The detection of MGEs containing conjugative elements suggest that some MGEs may spread through both transduction and conjugation. Overall, our work reveals a complex network of gene exchange occurring through transduction in the gut microbiome.

6G promises ultra-low latency, high data throughput, and seamless global connectivity. However, providing uninterrupted connectivity in remote and underserved regions remains a critical challenge for Terrestrial Networks (TNs), where the cost of deploying infrastructure is difficult to justify against sparse user density. Standardized under 3GPP Release 17, Non-Terrestrial Networks (NTNs) have emerged as a viable solution to close this digital divide. Among NTN platforms, High-Altitude Platform Stations (HAPS) occupy a strategic middle ground, as they deliver lower propagation delays than Low-Earth Orbit (LEO) satellites while achieving far broader coverage than TN-based Base Stations (BS). Despite these advantages, battery-powered Internet of Things (IoT) devices communicating via HAPS face a fundamental energy efficiency (EE) challenge: transmit power must be carefully managed to maximize data throughput while preserving battery life and minimizing packet queuing delays. To address this, we propose a Q-learning-based Reinforcement Learning (RL) framework. The RL agent observes the instantaneous battery level and queue state of the IoT device, and dynamically selects optimal power levels from a discrete action space across successive time slots. Unlike traditional heuristic algorithms, such as Round Robin (RR), Max Single-to-Noise Ratio (Max-SNR), and fixed-power allocation, which rely on static rules or greedy channel-based decisions, the proposed Q-learning agent learns adaptive, long-term optimal policies through direct interaction with the environment, without requiring explicit mathematical modeling of the channel or traffic dynamics. Extensive simulations demonstrate that the proposed framework achieves up to 40% higher average EE compared to all benchmark schemes, maintains consistently lower power consumption, and exhibits superior statistical reliability as evidenced by a right-shifted Cumulative Distribution Function (CDF) of EE. These results demonstrate Q-learning as a promising candidate for scalable, energy-aware power control of next-generation HAPS-assisted IoT deployments in 6G NTN ecosystems.

Centriolar satellites (CS) are dynamic and heterogeneous granular assemblies that concentrate around centrosomes and contribute to ciliogenesis. In this issue, Begar et al. (https://doi.org/10.1083/jcb.202509238) examine the CS scaffold protein PCM1 to dissect CS assembly and structure during the cell cycle and ciliogenesis.

The Sea and Land Surface Temperature Radiometer (SLSTR) is a dual-view scanning radiometer onboard the Sentinel-3A and Sentinel-3B satellites. This sensor provides data from the visible to the thermal infrared, with a temporal resolution of approximately 12 h. In this work, we present an automated system using shortwave infrared (SWIR) bands at 500 m spatial resolution to monitor active volcanoes in near real time. The system implements a normalized hotspot index (NHI) to detect and characterize high-temperature volcanic features in daylight and nighttime conditions. During the first three months of operation (i.e., August-October 2025), the system successfully identified several eruptive activities, with a false positive rate around 2.0%. The latter includes also true hot pixels associated with vegetation fires and other high-temperature sources. Results were assessed through comparison with the Fire Information for Resource Management System (FIRMS), the Middle Infrared Observations of Volcanic Activity (MIROVA), MODVOLC, and the S3-L2 FRP product. The preliminary comparison with the MIROVA-MODIS dataset reveals a good correlation in the estimates of fire radiative power over Etna (Italy) and Kilauea (Hawaii, USA), although discrepancies in the magnitude of this parameter remain significant also because of the SWIR retrieval method, which was optimized for gas flares. Despite the impact of snow-covered surfaces and band co-registration on the accuracy of hotspot detection, this study shows that the NHI-SLSTR system may provide a relevant contribution to the surveillance of active volcanoes from space, integrating information from other systems performing globally.

Long-term suspended sediment concentration data are essential for understanding the response of rivers in a changing world. Here, we trained and evaluated a model that uses surface reflectance from multiple Landsat satellites (TM, ETM+, and OLI) to estimate suspended sediment concentration (SSC) in rivers over multiple decades on a global scale. First, we built the largest global matchup database recorded, consisting of remote sensing surface reflectance data and in-situ SSC (N = 240,224). Second, we derived empirical models for harmonizing water surface reflectance across Landsat TM, ETM+, and OLI using ~ 88 million riverine surface reflectance observations from 1984 to 2021. After applying the harmonization to water surface reflectance in the matchup database, we developed the SSC prediction model using an extreme gradient boosting algorithm (XGBoost). The model was trained and evaluated with in-situ SSC data ranging from low to extremely turbid water (min = 0.1 mg/L and max = 5,760 mg/L). Our model achieved accuracy in predicting SSC comparable to or better than existing models (RMSE = 5.22 mg/L, RMSLE = 0.24, MAPLE = 22.53%, and relative error = 0.75). Our model shows consistent spatial and temporal predictions despite being a single, global SSC algorithm, suggesting successful spatial-temporal cross-validation during model development. The model will support the development of long-term, global riverine SSC records across Landsat missions.

Nighttime light (NTL) remote sensing data serves as a vital data source for monitoring urbanization processes and assessing the level of sustainable development from multidimensional aspects. Predicting NTL data offers a more comprehensive reflection of future urbanization, with the potential to provide deeper insights into future human activities and capture environmental indicators. To address the lack of globally consistent and dynamically evolving SSP-based NTL projections, we generated a global future NTL dataset (FUT-NTL) from 2025 to 2050 (at 5-year intervals) under five Shared Socio-economic Pathways (SSPs) with a spatial resolution of 1 km through the random forest regression models. The prediction models perform well globally, achieving the highest regional R2 of 0.92 compared to the observed NTL intensity in 2020, with RMSE values ranging from 2.73 to 7.83 nWcm-2sr-1. Predicted NTL datasets align well with SSP narratives, showing the highest growth rates of NTL in scenarios of rapid development, particularly under SSP5. Sub-Saharan Africa stands out with the highest growth rates in NTL intensity across scenarios, except SSP4. Generally, our predicted datasets can be easily updated and provide valuable proxies for analyzing future urbanization, socioeconomic activities, and environmental indicators.

This work proposes a multiscale spatial and temporal approach to assess the impacts of the ionosphere and neutrosphere (neutral atmosphere including both tropospheric and stratospheric) through an independent analysis of each component on Precise Point Positioning (PPP) accuracy and stability during selected representative geomagnetic events of Solar Cycle 25. Geomagnetically quiet and disturbed days were selected using the Kp index, with 21 multi-GNSS stations distributed across latitude bands. Kinematic PPP processing was performed using APPPOLO software (v1.0) with ionosphere-free dual-frequency combinations, precise products, and robust filtering, totaling 924 solutions. Results show improvements in geometry and satellite availability with multi-GNSS, achieving discrepancies within 0-10 cm in more than 89% of the solutions. The VMF3 model confirmed the deterministic behavior of ZHD and the latitudinal variability of ZWD, with increased stability in multi-GNSS solutions. Greater degradation was observed at high latitudes under disturbed geomagnetic conditions, particularly for GPS-only processing. Residual analysis indicated elevation-dependent effects and constellation-related differences. The analysis of ionospheric irregularities using ROTI revealed that PPP degradation is strongly associated with spatial distribution and satellite geometry, with enhanced effects at high latitudes and low elevation angles.

Large quantities of plastic pollution are accumulating at small island nations across the western Indian Ocean. Despite a historical focus on terrestrial inputs, recent research suggests that most pollution arriving at some islands may come from fishing and shipping activity. We use a 2D Lagrangian particle-tracking model, combined with satellite-tracked shipping and fishing data, to identify major fisheries and shipping lanes which are responsible for plastic debris beaching at the Seychelles. Virtual particles, representing plastic debris, are released monthly over multiple decades and are advected by currents from a 1/50°(∼2 km) regional ocean model. We find most fishing debris originates from within the Seychelles' own exclusive economic zone, and sources of shipping debris are concentrated along major shipping routes. Sources vary seasonally, due to wind-induced reversals of surface currents between monsoons. Finally, we find variation in the quantity and seasonality of debris accumulation on a sub-island scale. Therefore, higher-resolution models that resolve local currents and kilometre scale islands may play a vital role in clean-up and management efforts.

Cotton leaf curl disease (CLCuD) is a serious threat to cotton production across the Indian subcontinent, especially in Rajasthan, Haryana, and Punjab. The disease is caused by monopartite single-stranded DNA begomoviruses along with their associated satellite DNAs. Among these viruses, Begomovirus gossypimultanense (Cotton leaf curl Multan virus; CLCuMuV) is one of the primary viruses responsible for widespread infections and significant yield losses. Twenty-six symptomatic cotton leaf samples collected from diverse agro-ecological regions of northwestern India were screened and confirmed as CLCuMuV through molecular analyses. Given that the full-length replication-associated protein (Rep) is widely used as a representative phylogenetic marker across ssDNA viruses, we hypothesized that the conserved CRESS domain (~ 300 bp) within the Rep protein, which is functionally indispensable, could serve as an alternative molecular marker for molecular characterization of CLCuMuV. To test this hypothesis, in addition to sequences generated from field samples, a dataset of 278 publicly available CLCuMuV CRESS domain sequences retrieved from the NCBI database was assembled. Comparative phylogenetic analyses showed that both the full-length Rep and the CRESS domain resolved five major groups (Group 1-5) with consistent clustering, and tanglegram analysis demonstrated one-to-one correspondence between group compositions, indicating strong phylogenetic congruence. Population genetic analyses revealed high haplotype diversity in both regions, while neutrality and selection pressure analyses indicated predominant purifying selection, with stronger functional constraint in the CRESS domain. Our findings demonstrate that the conserved CRESS domain within the Rep gene reliably recapitulates the phylogenetic and population genetic structure inferred from the full-length Rep protein. The strong topological congruence, high haplotype resolution, and evidence of functional constraint support the CRESS domain as a robust, reliable, and cost-effective molecular marker for epidemiological characterization and evolutionary studies of CLCuMuV.

Aging is associated with a progressive decline in skeletal muscle mass and function, contributing to reduced physical capacity in older adults. Central is the deterioration of satellite cells, our tissue-resident muscle stem cells, which participate in adaptation, repair, and regeneration of skeletal muscle. Evidence from in vitro, murine, and human studies indicates an age-related reduction in satellite cell content, notably within type II muscle fibers, alongside impairments in myogenic potential. There is no single causative mechanism behind satellite cell age-related dysfunction, but a convergence of morphological changes and intrinsic and extrinsic factors that affect satellite cell dynamics and its niche. Intrinsic factors such as signalling pathways, cellular senescence, impaired autophagy, mitochondrial dysfunction, and epigenetic modifications can impact satellite cell function. Concurrently, extrinsic factors can impact the satellite cell niche and their function such as systemic circulating factors and vasculature and extracellular matrix remodeling. These age-related alterations can diminish regenerative capacity, blunt hypertrophic responses, and impair recovery from disuse or injury. Satellite cell dysfunction is a pivotal contributor to age-related skeletal muscle decline, frailty, and the quality of life in older adults. Despite growing insights from in vitro and animal models, the key mechanistic changes that underlie human satellite cell dysfunction with age are not fully understood. Improved characterization of age-related satellite cell changes in humans is essential to preserving muscle health across the lifespan.

Pine wilt disease (PWD) can spread rapidly after the disease occurs and often causes large-scale death of the pine. Therefore, the timely identification of infected trees is critical for forest conservation and effective disease management. However, early infected trees are difficult to distinguish in satellite remote sensing images. Their visual differences from healthy trees and complex background features are often subtle, and existing image-processing methods do not fully exploit heterogeneous information. To address this problem, we constructed the Naro dataset for satellite-based PWD detection and proposed SGM-RTDETR based on Real-Time Detection Transformer (RT-DETR). The proposed model consists of a Semantic-Visual Fusion Module (SVFM) and a Disease Feature Refinement Module (DFRM). In SVFM, ExG, VARI, and GLI are concatenated with RGB imagery to form a six-channel visual input, which enhances the spectral differences between diseased and non-diseased targets. In addition, textual prior knowledge is introduced into the decoder input through a Stackelberg game-based visual-text fusion strategy. This strategy helps the encoded memory features maintain clearer disease-related semantics in complex backgrounds. DFRM then performs channel recalibration, feature refinement, and residual enhancement on the fused memory features to better extract fine-grained disease cues in remote sensing scenes. Experiments on the Naro dataset show that SGM-RTDETR achieves 80.75% mAP@0.5 and 35.43% mAP@0.5:0.95, which is 2.74 percentage points higher than RT-DETR-L on mAP@0.5:0.95. Overall, the results indicate that the dual-module structure improves the precision and robustness of PWD detection in satellite remote sensing images.

The atmospheric weighted mean temperature (Tm) is critical for converting a zenith wet delay (ZWD) to precipitable water vapor (PWV). However, the existing Tm models still have certain limitations: Those driven by surface-measured parameters achieve high accuracy but depend heavily on in situ instruments, incurring high costs and lacking forecasting capability. Empirical models avoid measured data but fail to capture short-term Tm variations, leading to lower accuracy. Daily weather forecast data-which are low-cost, readily available, and reflective of short-term changes-offer a promising alternative. This study develops a gridded Tm model named CGTV-Tm, which couples temperature and water vapor pressure, using ERA5 reanalysis data over China (2019-2023). The model can be driven by daily weather forecast data. A dual vertical correction method is also proposed to improve performance. Validation against 2024 ERA5 and radiosonde data shows that CGTV-Tm achieves RMSEs of 2.38 K (vs. ERA5) and 2.64 K (vs. radiosonde), significantly outperforming the Bevis (3.61 K, 3.67 K), PTm (3.19 K, 2.94 K), and CGT-Tm (2.71 K, 3.08 K) models. When driven by daily weather forecast data, CGTV-Tm achieves an RMSE of 2.90 K, improving accuracy by 29.6% and 21.2% over the state-of-the-art empirical models GPT3 and HGPT2, respectively. These results demonstrate that CGTV-Tm not only surpasses traditional linear Tm models that rely solely on surface temperature but also, by using weather forecast data, it removes dependence on in situ instruments, offering a superior low-cost solution for real-time GNSS (Global Navigation Satellite System) PWV retrieval.

Global Navigation Satellite Systems (GNSS) provide essential positioning, navigation, and timing (PNT) services for a wide range of safety-critical applications. However, GNSS performance degrades significantly in satellite-deficient or interference-prone environments. To address this limitation, this study proposes a hybrid GNSS/eLoran integrity monitoring framework based on a simplified Receiver Autonomous Integrity Monitoring (RAIM) architecture. In the proposed method, GNSS observations from satellite constellations and range-equivalent measurements from the enhanced Loran (eLoran) terrestrial system are jointly processed using a weighted least-squares estimator. Integrity monitoring is performed through a global chi-square consistency test combined with a solution separation strategy for fault identification and exclusion. Horizontal Protection Level (HPL) is derived from the covariance of the estimation process to ensure bounded positioning error under nominal and fault conditions. Unlike conventional GNSS-only RAIM, the proposed framework enables improved redundancy and fault observability in satellite-deficient scenarios by incorporating heterogeneous terrestrial measurements. Simulation experiments consider satellite faults, eLoran measurement disturbances, and inter-system clock bias effects. Results demonstrate that the proposed method maintains reliable fault detection capability and ensures that positioning errors remain consistently bounded by the protection level under all tested scenarios.

Smartphone global navigation satellite system (GNSS) positioning is degraded by low-cost antennas, limited receiver hardware, multipath propagation, and noisy code pseudorange observations. Existing correction methods often improve stochastic weighting, estimate coordinate-domain corrections, or smooth receiver trajectories, but they rarely estimate how each satellite contributes to the horizontal position error while preserving line-of-sight (LOS) geometry. This study presents a random-forest-assisted geometry-aware correction method that combines satellite-wise LOS projection error estimation with exponential temporal weighted least squares (Temporal WLS). The horizontal error between the smartphone National Marine Electronics Association (NMEA) solution and the F9P reference position is projected onto each satellite LOS direction and used as the learning target. A random forest model is trained using 26 smartphone GNSS features, including geometry, signal strength, code-derived variation, uncertainty, automatic gain control, and state flags. The predicted LOS errors are fused with satellite geometry through epoch-wise WLS and Temporal WLS. In same-session front-70/back-30 validation, the horizontal root mean square (RMS) error decreased from 2.747 m to 1.033 m. Excluding one suspected non-co-located reference session further reduced the RMS error from 2.867 m to 0.362 m.

Autonomous Haulage Systems (AHS) have significantly transformed surface mining operations by improving safety, productivity, and operational consistency. Currently, AHS predominantly rely on vehicle-centric perception architectures. Onboard LiDAR, radar, cameras, and Global Navigation Satellite Systems (GNSS) perform sensing, interpretation, and decision-making within individual systems. These processes enable collision avoidance and path tracking. However, they are limited in their ability to consider the broader, dynamic mining environment characterized by dust, terrain degradation, geotechnical instability, heterogeneous traffic, and rapidly evolving operational conditions. This paper presents a systematic review of dynamic vision systems of AHS in surface mining. It critically analyzes the transition from autonomy to interconnected, ecosystem-aware intelligence. The review synthesizes literature from mining automation, robotics, intelligent transportation systems, and multi-agent perception. It assesses sensing technologies, perception algorithms, sensor fusion strategies, and environmental robustness techniques. Attention is focused on the limitations of egocentric perception models in complex surface mining ecosystems. Building on identified gaps, the paper proposes a conceptual framework for Ecosystem-Centric Dynamic Vision (ECDV). Perception is enhanced through integration with fleet communication networks, dispatch systems, digital twins, geotechnical monitoring platforms, and environmental sensing infrastructure. The framework outlines a multi-layer architecture enabling cooperative perception, predictive hazard modeling, and risk-aware decision support at the mine-wide level. The review concludes by outlining a research agenda to transition from vehicle autonomy to ecosystem intelligence in surface mining. It highlights opportunities in cooperative perception, adaptive sensor fusion under degraded visibility, and digital-twin-integrated predictive safety systems.

Oligofructans are a category of non-digestible carbohydrates with beneficial effects on gut health and microbiota modulation. In this study, oligofructans were produced from raw sugar using Bacillus subtilis TISTR 001, and their safety and effects on the gut microbiota were assessed in rats. The acute toxicity assessment consisted of administering a single oral dose of 2000 mg/kg body weight (bw), whereas the subchronic toxicity assessment included oral dosages of 200, 600, and 2000 mg/kg/day for 90 days. In the acute toxicity test, no mortality or toxicity was observed in the rats treated with a single dose of oligofructans during the 14-day observation period. The median lethal dose (LD50) of the oligofructans was >2000 mg/kg bw. In the subchronic toxicity study, daily oligofructans doses of 200, 600, and 2000 mg/kg bw for 90 days did not cause lethality or toxic clinical symptoms in rats of either sex. Furthermore, no treatment-related adverse effects of oligofructans on the hematological and biochemical parameters or organ histopathology were observed in the treatment and satellite groups. Hence, the no-observed-adverse-effect level (NOAEL) of oligofructans under the study's test conditions was confirmed as 2000 mg/kg/day. No adverse effects were observed in either acute or subchronic toxicity studies at doses up to 2000 mg/kg/day. Moreover, oligofructans modulated the gut microbiota by promoting the growth of potentially beneficial commensal bacteria and reducing the taxa associated with inflammation or metabolic dysfunction. However, further studies are required to confirm these microbiome-related changes in humans.

Accurate monitoring of fine particulate matter (PM2.5) in North-East India is severely constrained by sparse ground-based observations, complex terrain, high humidity and persistent cloud cover, particularly during the monsoon season, when satellite aerosol retrievals are frequently missing or unreliable. To address these limitations, this study develops an integrated multi-sensor satellite and machine learning (ML) framework for estimating daily surface PM2.5 concentrations at 1 km spatial resolution over the Guwahati-Kamrup region. A key contribution of this study is the integration of reconstructed multi-sensor aerosol optical depth within a high-resolution PM2.5 estimation framework tailored for a cloud-dominated, data-scarce region of North-East India. The reconstructed aerosol optical depth is combined with meteorological variables, land-surface characteristics and limited CPCB ground measurements within a unified modelling architecture. Multiple ML models are evaluated, and a multi-layer perceptron is identified as the best-performing estimator, achieving a coefficient of determination (R2) of 0.82 with substantially reduced prediction errors compared to models driven by raw satellite inputs. The resulting PM2.5 fields reveal pronounced urban-rural gradients, persistent transport-aligned hotspots and strong seasonal modulation consistent with the influence of boundary-layer dynamics and wet scavenging processes. Winter and post-monsoon periods exhibit the highest pollution accumulation, while monsoon conditions consistently suppress near-surface concentrations. Long-term hotspot analysis and statistical projections indicate a gradual intensification of urban PM2.5 exposure under observed emission trajectories.

Pericentromeres are heterochromatic regions adjacent to centromeres that ensure accurate chromosome segregation. Despite their conserved function, they are composed of rapidly evolving A/T-rich satellite DNA. To test the functional consequences of this rapid sequence evolution, we establish hybrid mouse embryos as a model system to compare divergent satellite arrays from distinct species in a common cytoplasm. We show that variation in satellite sequence impacts heterochromatin formation, recruitment of the Chromosome Passenger Complex (CPC), and interactions with the mitotic spindle. Differences in satellite DNA sequence alter pericentromere packaging by Polycomb Repressive Complex 1 (PRC1), as satellite arrays that recruit PRC1 are enriched for specific A/T sequences that the PRC1 AT-hook preferentially binds. Furthermore, PRC1 heterochromatin modifies pericentromere function by inhibiting recruitment of the CPC, increasing microtubule forces on kinetochores during mitosis. Our results provide a direct link between satellite DNA composition and mitotic chromosome behavior and highlight early embryogenesis as a critical point in development that is sensitive to satellite DNA evolution.