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Abstract Background Ablation is one of the main methods for local treatment of hepatocellular carcinoma (HCC). Different from radiofrequency ablation (RFA), microwave ablation (MWA) is not limited by tissue conductivity, and can use multiple electrodes at the same time to improve ablation efficiency. In addition, MWA can form a larger ablation area, which makes it possible to completely ablate large HCC. However, MWA may be incomplete due to factors such as larger tumors or tumors in high-risk areas. The mechanism by which the cellular and tumor immune microenvironment (TIME) is involved in the in vitro effects of incomplete microwave ablation (iMWA) needs to be further elucidated. Methods H22 tumor-bearing C57BL/6 mice were treated with iMWA with several combinations of ablation power and time duration. The effects of iMWA on the genes of HCC cancer cells and the TIME were investigated by RNA sequencing, mass cytometry, immunohistochemistry, and immunofluorescence. The effect of iMWA in combination with anti-Gr-1 on HCC tumor growth was also evaluated. Results Thermal stress generated by iMWA induced coagulative necrosis and apoptosis in the region of the ablation center of HCC. RNA sequencing analysis showed that iMWA can boost chemokine CXCL5, which was further confirmed by quantitative real time polymerase chain reaction (qRT-PCR). Mass cytometry results showed that relative to Ctrl group, iMWA-treated led to decreased CD4+ T, CD8+ T, Natural killer (NK), macrophages including both M1 and M2 types but increased monocytes and bone marrow-derived suppressor cells (MDSC). Therefore, inhibiting MDSC is the main target in the later stage of iMWA. In vivo results showed that the tumor volume and weight of iMWA+ anti-Gr-1 group were significantly reduced compared with iMWA+ anti-IgG group. In addition, the merged expressions of CD11b and Gr-1 proteins were found reduced in the iMWA+ anti-Gr-1 group compared with the iMWA+ anti-IgG group by immunofluorescence staining. Immunohistochemistry suggested that CD8 was enriched in the iMWA+ anti-Gr-1 group but not in the iMWA+ anti-IgG group. Conclusion Our data suggests that iMWA and Gr-1 blocking combined therapy can further inhibit HCC growth and significantly improve the CD8+ T cells in the mouse subcutaneous tumor model, which brings good news to HCC patients.

BackgroundLung cancer is a leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of cases. While immune checkpoint inhibitors (ICIs) have transformed treatment for advanced NSCLC, the role of bone metastasis in modulating ICI efficacy remains unclear. Bone metastasis, occurring in 30-40% of advanced NSCLC cases, is associated with worse outcomes. However, how this affects the therapeutic benefit of ICIs has not been fully elucidated, highlighting a critical knowledge gap in optimizing treatment for this patient population.MethodsA comprehensive literature search across multiple databases, including PubMed, Embase, and Cochrane, identified 13 studies with a total of 3,681 patients, of whom 37.6% had bone metastasis. Overall survival (OS) and progression-free survival (PFS) were compared between NSCLC patients with and without bone metastasis. Data were analyzed using a random-effects model to account for study heterogeneity.ResultsThe meta-analysis demonstrated that bone metastasis significantly worsened overall survival (OS) and progression-free survival (PFS) in NSCLC patients treated with ICIs. Specifically, bone metastasis was associated with a 45% increased risk of death (HR: 1.45, 95% CI: 1.30–1.62, p < 0.001) and a 40% increased risk of disease progression (HR: 1.40, 95% CI: 1.25–1.58, p < 0.001). No statistically significant impact on PFS was observed. (HR: 1.28, 95% CI: 0.77–2.10, p = 0.34). High heterogeneity was observed in some subgroup analyses (I² = 72%), indicating variability in the results.ConclusionBone metastasis is a significant negative prognostic factor for NSCLC patients treated with ICIs, associated with a higher risk of mortality and disease progression. These results underscore the importance of tailored treatment approaches for NSCLC patients with bone metastasis and call for further research to optimize therapy outcomes in this group.

Abstract Ferroptosis is an iron-dependent cell death that was discovered recently. For beneficial microbes to establish mutualistic relationships with hosts, precisely controlled cell death in plant cells is necessary. However, whether ferroptosis is involved in the endophyte‒plant system is poorly understood. Here, we reported that endophytic Streptomyces hygroscopicus OsiSh-2, which established a sophisticated and beneficial interaction with host rice plants, caused ferroptotic cell death in rice characterized by ferroptosis- and immune-related markers. Treatments with ferroptosis inhibitors and inducers, different doses of OsiSh-2, and the siderophore synthesis-deficient mutant ΔcchH revealed that only moderate ferroptosis induced by endophytes is essential for the establishment of an optimal symbiont to enhance plant growth. Additionally, ferroptosis involved in a defence-primed state in rice, which contributed to improved resistance against rice blast disease. Overall, our study provides new insights into the mechanisms of endophyte‒plant interactions mediated by ferroptosis and suggests new directions for crop yield promotion.

Safety-critical systems, such as the railway signal system, are subject to potentially high costs from failures, including loss of life and property damage. The use of new technology, including communication-based train control (CBTC) systems with software and computers, has changed the types of accidents that occur. Software-related issues and dysfunctional interactions between system components controlled by the software are increasingly the cause of incidents. Developing a “safe” safety-critical system requires accurate and complete safety requirements, which are the foundation of system development. Traditional hazard analysis techniques are insufficient for identifying the causes of accidents in modern railway signaling systems. Systems-Theoretic Process Analysis (STPA) is a powerful new hazard analysis method designed to address these limitations. Building upon this foundation, a hierarchical approach to safety requirement development has been further developed. This approach combines STPA analysis with a hierarchical modeling approach to establish traceability links from safety requirements to specific architectures, refine and allocate system-level safety requirements to relevant subsystems, and abstract safety requirements at higher hierarchical levels to enable easy changes to lower-level implementations. This paper employs the aforementioned methodology within the context of the CBTC system, thereby enhancing risk management and hazard analysis, enabling early insights, and facilitating the generation of safety requirements of CBTC System.

Study region: This study was carried on Tibetan Plateau (TP), which is recognized as the “Third Pole” and “Asia Water Tower”. Study focus: TP is suffering extreme hydroclimatic change under global climate warming. Quantifying the hydroclimatic change pattern and strength is necessary to protect local to downstream hydrology and water resources. This study promoted an aggregation framework to diagnose the compound hydroclimatic change on TP using several high-resolution reanalysis data. New hydrological insights for the region: The promoted aggregation framework is efficient to diagnose the compound hydroclimatic change on TP. Both individual applied data and compound result indicate a warming and wetting environment on most TP, combined with increased trend of evapotranspiration (ET) and soil moisture (SM), while decreased trend of runoff (ROF). The hotspots with strong hydroclimatic change mainly located at inner-southeast TP and northeast TP, including Changtang Plateau, sources of Yellow-Yangtze-Mekong-Salween-Brahmaputra river basin, and Qaidam Basin. The compound warming strength is above 0.06 Celsius/mon/a in winter while the wetting trend is above 0.5 mm/mon/a in summer at most hotspots. ET is increased with trend above 0.1 mm/mon/a in spring, summer, autumn at most hotspots. SM is drying at south periphery while wetting at the remaining area with annual and seasonal trend above 0.0005 mm3/mm3/mon/a at hotspots. ROF is decreased with trend above 2 mm/mon/a at south-southeast hotspots in summer and autumn.

Hepatocellular carcinoma is one of the leading causes of cancer-related mortality globally. The emergence of immunotherapy has been shown to be a promising therapeutic approach for hepatocellular carcinoma in recent years. It has been well known that T cell plays a key role in current immunotherapy. However, sustained exposure to antigenic stimulation within the tumor microenvironment may lead to T cell exhaustion, which may cause treatment ineffectiveness. Therefore, reversing T cell exhaustion has been an important issue for the clinical application of immunotherapy, and a comprehensive understanding of the intricacies surrounding T cell exhaustion and its underlying mechanisms is imperative for devising strategies to overcome the T cell exhaustion during treatment. In this review, we summarized the reported drivers of T cell exhaustion in hepatocellular carcinoma and delineate potential ways to reverse it. Additionally, we discussed the interplay among metabolic plasticity, epigenetic regulation, and transcriptional factors in exhausted T cells in hepatocellular carcinoma, and their implication for future clinical applications.

Sub-seasonal drought forecasting is crucial for early warning in estimating agricultural production and optimizing irrigation management, as forecasting skills are relatively weak during this period. Soil moisture exhibits stronger persistence compared to other climate system quantities, which makes it especially influential in shaping land-atmosphere feedback, thus supplying a unique insight into drought forecasting. Relying on the soil moisture memory, this study investigates the combination of multiple deep-learning modules for sub-seasonal drought indices hindcast in the Huai River basin of China, using long-term ERA5-Land soil moisture records with a noise-assisted data analysis tool. The inter-compared deep-learning models include a hybrid model and a committee machine framework. The results show that the performance of the committee machine framework can be improved with the help of series decomposition and the forecasting skill is not impaired with the lead time increases. Overall, this study highlights the potential of combining deep-learning models with soil moisture memory analysis to improve sub-seasonal drought forecasting.

The environment of the Mu Us Sandy Land, northern Shaanxi, is fragile, so the temporal and spatial evolution of drought can provide a reference for ecological construction and agricultural production. Based on the daily precipitation data of five meteorological stations from 1967 to 2020, the spatial and temporal evolution characteristics of drought in the Mu Us Sandy Land of northern Shaanxi were analyzed using the methods Standardized Precipitation Index (SPI) and run-length theory. The results show that 1) the smaller the time scale, the higher the sensitivity of the SPI to primary precipitation. 2) The annual, summer, autumn, and winter SPI showed an upward and a wetting trend, and the fastest wetting speed is observed in summer, while spring showed an increasing trend of drought. 3) In the past 54 years, the duration, degree, and intensity of drought events at SPI-3 and SPI-12 scales in the Mu Us Sandy Land of northern Shaanxi showed an insignificant decreasing trend, but the decreasing rate at the SPI-12 scale was faster than that at the SPI-3 scale. The serious periods of drought are November 2018 to May 2019 and April 1999 to July 2021. 4) The duration of drought events at two timescales in each region showed a decreasing trend. The longest durations of drought were in Yulin and Jingbian; Shenmu and Dingbian are the regions with the fastest reduction rate of drought degree at the two scales. Hengshan shows rapid reduction of drought intensity at the two scales and also the region with large average drought intensity. These results are helpful in understanding and describing drought events for drought risk management under the condition of global warming.

Determining sources of uncertainty in the agricultural water supply helps to increase irrigation scheduling accuracy. Evaporation and transpiration, sensitivity coefficient of water tension, cultivation costs, product price and water allocated to plants are among the main sources of uncertainty in the simulation process. This study was conducted to estimate the fluctuations of the response due to the uncertainty of four parameters of soil moisture, water provided for cultivation, cost of cultivation during the growing season, and coefficient of sensitivity to drought. In the concept of water and food nexus, two basic factors, including increasing economic productivity and achieving maximum product production, are known as decision criteria. Therefore, mathematical modeling of net income and the ratio of actual and potential yield production has been prepared according to the four uncertain input variables of the problem. The proposed model has used a multi-objective particle swarm optimization algorithm to estimate the best values of non-deterministic variables. The results of the uncertainty analysis showed that the range of changes in performance and net profit compared to the coefficient of sensitivity to water stress has different ranges and mechanisms and knowing the nature of each helps to improve the responses. HIGHLIGHTS The fluctuations of the response due to the uncertainty of four parameters of soil moisture, water provided for cultivation, and cultivation cost were evaluated.; Mathematical modeling of net income and the ratio of actual and potential yield production has been prepared according to the four uncertain input variables.;

ABSTRACT Endophytes play an important role in shaping plant growth and immunity. However, the mechanisms for endophyte-induced disease resistance in host plants remain unclear. Here, we screened and isolated the immunity inducer ShAM1 from the endophyte Streptomyces hygroscopicus OsiSh-2, which strongly antagonizes the pathogen Magnaporthe oryzae. Recombinant ShAM1 can trigger rice immune responses and induce hypersensitive responses in various plant species. After infection with M. oryzae, blast resistance was dramatically improved in ShAM1-inoculated rice. In addition, the enhanced disease resistance by ShAM1 was found to occur through a priming strategy and was mainly regulated through the jasmonic acid-ethylene (JA/ET)-dependent signaling pathway. ShAM1 was identified as a novel α-mannosidase, and its induction of immunity is dependent on its enzyme activity. When we incubated ShAM1 with isolated rice cell walls, the release of oligosaccharides was observed. Notably, extracts from the ShAM1-digested cell wall can enhance the disease resistance of the host rice. These results indicated that ShAM1 triggered immune defense against pathogens by damage-associated molecular pattern (DAMP)-related mechanisms. Our work provides a representative example of endophyte-mediated modulation of disease resistance in host plants. The effects of ShAM1 indicate the promise of using active components from endophytes as plant defense elicitors for the management of plant disease. IMPORTANCE The specific biological niche inside host plants allows endophytes to regulate plant disease resistance effectively. However, there have been few reports on the role of active metabolites from endophytes in inducing host disease resistance. In this study, we demonstrated that an identified α-mannosidase protein, ShAM1, secreted by the endophyte S. hygroscopicus OsiSh-2 could activate typical plant immunity responses and induce a timely and cost-efficient priming defense against the pathogen M. oryzae in rice. Importantly, we revealed that ShAM1 enhanced plant disease resistance through its hydrolytic enzyme (HE) activity to digest the rice cell wall and release damage-associated molecular patterns. Taken together, these findings provide an example of the interaction mode of endophyte-plant symbionts and suggest that HEs derived from endophytes can be used as environmentally friendly and safe prevention agent for plant disease control.

Drought and salinity are major environmental stresses that impair crop growth and productivity worldwide. Improving drought and salt tolerance of crops with microbial mutualists is an effective and environmentally sound strategy to meet the demands of the ever-growing world population. In the present study, we found that the Streptomyces albidoflavus OsiLf-2, a moderately salt-tolerant endophytic actinomycete, produced abundant osmolytes, including proline, polysaccharides, and ectoine. Inoculation with OsiLf-2 increased the osmotic-adjustment ability of the rice host by increasing the proline content (by 250.3% and 49.4%) and soluble sugar (by 20.9% and 49.4%) in rice under drought and salt conditions, relative to the uninoculated control. OsiLf-2 increased stress responses in the rice host at the physiological and biochemical levels (photosynthesis efficiency, osmolytes and antioxidant content), and the gene level (osmolytes synthesis, stress-responsive and ion-transport related genes), raising rice yields under both greenhouse and saline–alkaline soil conditions. The use of endophytic actinomycetes offers a promising biotechnological approach to developing stress-tolerant plants.

Camellia oleifera (C. oleifera) is a unique edible oil crop in China cultivated in the hilly southern mountains. Although C. oleifera is classified as a drought-tolerant tree species, drought remains the main factor limiting the growth of C. oleifera in summer and autumn. Using endophytes to improve crop drought tolerance is one effective strategy to meet our growing food crop demand. In this study, we showed that endophyte Streptomyces albidoflavus OsiLf-2 could mitigate the negative impact of drought stress on C. oleifera, thus improving seed, oil, and fruit quality. Microbiome analysis revealed that OsiLf-2 treatment significantly affected the microbial community structure in the rhizosphere soil of C. oleifera, decreasing both the diversity and abundance of the soil microbe. Likewise, transcriptome and metabolome analyses found that OsiLf-2 protected plant cells from drought stress by reducing root cell water loss and synthesizing osmoregulatory substances, polysaccharides, and sugar alcohols in roots. Moreover, we observed that OsiLf-2 could induce the host to resist drought stress by increasing its peroxidase activity and synthesizing antioxidants such as cysteine. A multi-omics joint analysis of microbiomes, transcriptomes, and metabolomes revealed OsiLf-2 assists C. oleifera in resisting drought stress. This study provides theoretical and technical support for future research on endophytes application to enhance the drought resistance, yield, and quality of C. oleifera.

Summary: The soil moisture active/passive (SMAP) mission represents a significant advance in measuring soil moisture from satellites. However, its large spatial-temporal data gaps limit the use of its values in near-real-time (NRT) applications. Considering this, the study uses NRT operational metadata (precipitation and skin temperature), together with some surface parameterization information, to feed into a random forest model to retrieve the missing values of the SMAP L3 soil moisture product. This practice was tested in filling the missing points for both SMAP descending (6:00 AM) and ascending orbits (6:00 PM) in a crop-dominated area from 2015 to 2019. The trained models with optimized hyper-parameters show the goodness of fit (R2 ≥ 0.86), and their resulting gap-filled estimates were compared against a range of competing products with in situ and triple collocation validation. This gap-filling scheme driven by low-latency data sources is first attempted to enhance NRT spatiotemporal support for SMAP L3 soil moisture.

Damage from climate change is widespread throughout the world. This change has brought about calamities, the most prevalent of which is the emergence of numerous droughts which are increasingly threatening human lives. In this paper, we studied the spatial and temporal variations of drought under the effect of climate change in the Huaibei Plain, which is a very important agricultural zone in China. Drought has attracted increasing attention in research due to its heavy impact on agriculture, the environment, livelihood, and food security. The SPEI (Standardized Precipitation Evapotranspiration Index) has been used in this study to express and identify drought events in the Huaibei Plain due to climate change. A general circulation model (GCM), HadGEM2-AO, which was the most appropriate for the study area’s precipitation simulation, and three Representative Concentration Pathways (RCP), RCP 2.6, RCP 4.5, and RCP 8.5, were used to analyze and compare the drought effect for the baseline (1985–2017) and the future climate scenarios (2025–2090). At 3 and 6 months, the SPEI successfully detects agricultural drought in temporal and spatial variation. However, according to the analysis, more severe agricultural drought events are foreseen in the future than in the baseline because of climate change. SPEI performed better than SPI in detecting drought in the baseline and simulated data due to increased evapotranspiration. Between the SPEI-3 and SPEI-6, the Pearson coefficient correlation reveals a positive association. The Mann-Kendall test was used to cover the two studied periods in order to establish the drought trend. Both decreasing and increasing trends, in different timescales, were detected by Sen’s Slope in the baseline and future periods with all RCPs.

The Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) V06 product has been widely studied, but the errors and the source of the errors within IMERG over diverse climate regions still need to be quantified. To this end, the final run gauge-calibrated IMERG V06 (V06C) and uncalibrated IMERG V06 (V06UC) products are comprehensively evaluated here against 2088 precipitation gauges acquired between March 2014 and June 2018 over China. Moreover, V06C and V06UC rainfall estimates are compared against the Precipitation Estimation from Remotely Sensed Imagery using Artificial Neural Networks (PERSIANN)-Climate Data Record (CDR) and the Climate Prediction Center morphing technique (CMORPH) gauge-satellite blended (BLD) products. Continuous statistical indices and two error decomposition schemes are used to quantify their performance. Key results are as follows. (1) Except for V06UC’s relatively high underestimation over the Tibetan Plateau (TP) and high overestimation over Xinjiang (XJ), Northeastern China (DB), and Northern China (HB) and CDR’s severe overestimation over TP, all four satellite-based precipitation products can generally capture the spatial pattern of precipitation over China. Moreover, the satellite-based precipitation estimates agree better with gauge observations over humid regions than over semi-humid, semi-arid, and arid regions. (2) All the statistical indicators show that CDR has the worst performance, whereas BLD is the best precipitation product. As for the two IMERG products, V06C has improved V06UC’s precipitation estimate. Results show that the gauge calibration algorithm (GCA) used in IMERG has active effect in terms of r, POD, and CSI. (3) Within all subregions, all four satellite-based precipitation products demonstrate their worst performance over the arid XJ region which exhibits the highest FAR and lowest POD and CSI values among all regions. (4) In terms of intensity distribution, for summer over China, the four satellite-based precipitation products generally overestimate the frequency of moderate precipitation and light precipitation events (42 mm/day). (5) The relative bias ratio (RBR) analysis shows that the contribution of missed precipitation tends to be lower over wetter regions. In addition, for the same climate region, the contribution of missed precipitation is clearly lower in summer than in winter. In summer, false precipitation dominates the total error, whereas missed and false precipitation are the two leading error sources in winter. Future algorithm refinement efforts should focus on decreasing FAR in summer and winter and improving missed snow events during the winter.

Root zone soil moisture (RZSM) is a vital variable for agricultural production, water resource management and runoff prediction. Satellites provide large-scale and long-term near-surface soil moisture retrievals, which can be used to estimate RZSM through various methods. In this study, we tested the utility of an exponential filter (ExpF) using in situ soil moisture by optimizing the optimal characteristic time length T_opt for different soil depths. Furthermore, the parameter analysis showed that T_opt correlated negatively with precipitation and had no significant correlation with selected soil properties. Two approaches were taken to obtain T_opt: (1) optimization of the Nash–Sutcliffe efficiency coefficient (NSE); (2) calculation based on annual average precipitation. The precipitation-based T_pre outperformed the station-specific T_opt and stations-averaged T_opt. To apply the ExpF on grid scale, the precipitation-based T_pre considering spatial variability was adopted in the ExpF to obtain RZSM from a new soil moisture dataset RF_SMAP_L3_P (Random Forest Soil Moisture Active Passive_L3_Passive) continuous in time and space over Huai River Basin. Finally, the performance of RF_SMAP_L3_P RZSM (0–100 cm) was evaluated using in situ measurements and compared with mainstream products, for instance, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity Level 4 (SMOS L4) RZSM. The results indicated that RF_SMAP_L3_P RZSM could captured the temporal variation of measured RZSM best with R value of 0.586, followed by SMAP L4, which had the lowest bias value of 0.03, and SMOS L4 significantly underestimated the measured RZSM with bias value of −0.048 in the basin. Higher accuracy of RF_SMAP_L3_P RZSM was found in the flood period compared with the non-flood period, which indicates a better application for ExpF in wetter weather conditions.

Remotely sensed soil moisture (SM) with the enhanced spatial resolution (several kilometers or even tens of meters) is of value for important local applications like irrigation scheduling and farm-scale water management. The Disaggregation based on Physical and Theoretical scale Change (DISPATCH) algorithm is an established evaporation-based tool for passive-SM downscaling. However, its performance in areas where evapotranspiration is generally energy-limited has not been adequately evaluated. Here, DISPATCH is applied to coarse-scale L-band passive-SM to evaluate its applicability to produce a 1-km downscaled product in the semi-humid/humid Huai River Basin (HRB), which has been identified as challenging for DISPATCH in previous studies. The influence of different calibration modes of linear SEE (soil evaporative efficiency) model on disaggregation results and LST (land surface temperature) inputs at different temporal resolutions are also investigated. The performances of DISPATCH were evaluated using a spatio-temporal statistical analysis against available in-situ SM. Globally, DISPATCH performs poorly in HRB with low correlation and least-square regression slopes (significantly different to unity) between downscaled-SM and in-situ observations. This relatively poor performance is attributed to low background SM spatial variability and weak moisture-evaporation coupling within HRB. However, in spite of those global poor performances, an improvement in the SM spatio-temporal representation was observed under summer-dry conditions over HRB. The uncertainty of DISPATCH estimates can be reduced by the multi-date calibration of linear SEE model within HRB. Likewise, downscaled SM based on merging multiple LST products for three consecutive days has better temporal coverage and lower uncertainty than comparable products only using daily LST retrievals. These configurations are expected to further enlarge the applicability of DISPATCH in more humid climates.