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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.

Pancreatic cancer is one of the most aggressive cancers with poor prognosis and a low 5-year survival rate. The family of P21-activated kinases (PAKs) appears to modulate many signaling pathways that contribute to pancreatic carcinogenesis. In this work, we demonstrated that PAK1 is a critical regulator in pancreatic cancer cell growth. PAK1-targeted inhibition is therefore a new potential therapeutic strategy for pancreatic cancer. Our small molecule screening identified a relatively specific PAK1-targeted inhibitor, CP734. Pharmacological and biochemical studies indicated that CP734 targets residue V342 of PAK1 to inhibit its ATPase activity. Further in vitro and in vivo studies elucidated that CP734 suppresses pancreatic tumor growth through depleting PAK1 kinase activity and its downstream signaling pathways. Little toxicity of CP734 was observed in murine models. Combined with gemcitabine or 5-fluorouracil, CP734 also showed synergistic effects on the anti-proliferation of pancreatic cancer cells. All these favorable results indicated that CP734 is a new potential therapeutic candidate for pancreatic cancer. Key words: PAK1, Pancreatic cancer, Inhibitor, Structure-based virtual screening, Synergistic effect

In social media, images and texts are used to convey individuals' attitudes and feelings; thus, social media has become an indispensable part of people's lives. To understand social behavior and provide better recommendations, sentiment analysis on social media is helpful. One sentiment analysis task is polarity prediction. Although current research on visual or textual sentiment analysis has achieved quite good progress, multimodal and cross-modal analysis combining visual and textual correlation is still in the exploration stage. To capture a semantic connection between images and captions, this paper proposes a cross-modal approach that considers both images and captions in classifying image sentiment polarity. This method transfers the correlation between textual content to images. First, the image and its corresponding caption are sent into an inner-class mapping model, where they are transformed into vectors in Hilbert space to obtain their labels by calculating the inner-class maximum mean discrepancy (MMD). Then, a class-aware sentence representation (CASR) model assigns the distributed representation to the labels with a class-aware attention-based gated recurrent unit (GRU). Finally, an inner-class dependency LSTM (IDLSTM) classifies the sentiment polarity. Experiments carried out on the Getty Images dataset and Twitter 1269 dataset demonstrate the effectiveness of our approach. Moreover, extensive experimental results show that our model outperforms baseline solutions.

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.

ABSTRACT Plants fine-tune the growth-defense trade-off to survive when facing pathogens. Meanwhile, plant-associated microbes, such as the endophytes inside plant tissues, can benefit plant growth and stress resilience. However, the mechanisms for the beneficial microbes to increase stress resistance with little yield penalty in host plants remain poorly understood. In the present study, we report that endophytic Streptomyces hygroscopicus OsiSh-2 can form a sophisticated interaction with host rice, maintaining cellular homeostasis under pathogen-infection stress, and optimize plant growth and disease resistance in rice. Four-year field trials consistently showed that OsiSh-2 could boost host resistance to rice blast pathogen Magnaporthe oryzae while still maintaining a high yield. The integration of the proteomic, physiological, and transcriptional profiling analysis revealed that OsiSh-2 induced rice defense priming and controlled the expression of energy-consuming defense-related proteins, thus increasing the defense capability with the minimized costs of plant immunity. Meanwhile, OsiSh-2 improved the chloroplast development and optimally maintained the expression of proteins related to plant growth under pathogen stress, thus promoting the crop yield. Our results provided a representative example of an endophyte-mediated modulation of disease resistance and fitness in the host plant. The multilayer effects of OsiSh-2 implicate a promising future of using endophytic actinobacteria for disease control and crop yield promotion. IMPORTANCE Under disease stress, activation of defense response in plants often comes with the cost of a reduction in growth and yield, which is referred as the growth-defense trade-off. The microorganisms which can be recruited by plants to mitigate the growth-defense trade-off are of great value in crop breeding. Here, we reported a rice endophytic actinomycetes Streptomyces hygroscopicus OsiSh-2, which can improve host performances on resistance to rice blast while still sustaining high yield in the 4-year field trials. The proteomic, physiological, and transcriptional profiling data offer insights into the molecular basis underlying the balancing between defense and growth in OsiSh-2-rice symbiont. The findings provide an example for the endophyte-mediated modulation of growth-defense trade-offs in plants and indicated the promising application of endophytic actinobacterial strains in agriculture to breed “microbe-optimized crops.”

Abstract Server workload in the form of cloud-end clusters is a key factor in server maintenance and task scheduling. How to balance and optimize hardware resources and computation resources should thus receive more attention. However, we have observed that the disordered execution of running application and batching seriously cuts down the efficiency of the server. To improve the workload prediction accuracy, this paper proposes an approach using the long short-term memory (LSTM) encoder-decoder network with attention mechanism. First, the approach extracts the sequential and contextual features of the historical workload data through the encoder network. Second, the model integrates the attention mechanism into the decoder network, through which the prediction for batch workloads can be carried out. Third, experiments carried out on Alibaba and Dinda workload traces dataset demonstrate that our method achieves state-of-the-art performance in mixed workload prediction in cloud computing environment. Furthermore, we also propose a scroll prediction method, which splits a long prediction sequence into several small sequences to monitor and control prediction accuracy. This work helps to dynamically guide the configuration for workload balancing.

Abstract The Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement mission (IMERG) aims to deliver the “best” precipitation estimation from space and has attracted much attention. The Version 05 of IMERG products including the near‐real‐time “Early” and “Late” run products (IMERG‐E and IMERG‐L, respectively), and the post‐real‐time “Final” run IMERG product (IMERG‐F) are assessed at both national and basin scales against gauge observations over Mainland China for a 4‐year period (from April 2014 to March 2018). As control products for comparison, their predecessor Tropical Rainfall Measurement Mission (TRMM) Multi‐satellite Precipitation Analysis (TMPA) products (i.e., TMPA‐RT and TMPA‐V7) are also employed. Components analysis confirms the best performance of IMERG‐F among the five SPEs in three different categories. All five SPEs feature increasing bias and root mean square difference (RMSD) with increasing daily gauge total precipitation, and such issue is less pronounced for IMERG‐F—as evidenced by the lowest bias and RMSD across all precipitation rates. Besides, compared to TMPA, IMERG products exhibit better accuracy in detecting real precipitation evens, especially for light‐to‐medium rain (

Summary: Fibrosis, or the accumulation of extracellular matrix, is a common feature of many chronic diseases. To interrogate core molecular pathways underlying fibrosis, we cross-examine human primary cells from various tissues treated with TGF-β, as well as kidney and liver fibrosis models. Transcriptome analyses reveal that genes involved in fatty acid oxidation are significantly perturbed. Furthermore, mitochondrial dysfunction and acylcarnitine accumulation are found in fibrotic tissues. Substantial downregulation of the PGC1α gene is evident in both in vitro and in vivo fibrosis models, suggesting a common node of metabolic signature for tissue fibrosis. In order to identify suppressors of fibrosis, we carry out a compound library phenotypic screen and identify AMPK and PPAR as highly enriched targets. We further show that pharmacological treatment of MK-8722 (AMPK activator) and MK-4074 (ACC inhibitor) reduce fibrosis in vivo. Altogether, our work demonstrate that metabolic defect is integral to TGF-β signaling and fibrosis.

Camellia oleifera Abel. is one of the world’s four famous woody oil trees and has drawn increasing attention because of its high commercial value. Endophytes are microorganisms inhabiting inside plant tissues, and their functions vary with the change of host status and environmental parameters. To deepen our understanding of the interactions between C. oleifera and their endophytic actinobacteria, the present study investigated the four endophytic actinobacterial composition-residing high-yield C. oleifera trees. A total of 156 endophytic actinobacterial isolates were obtained distributed in 17 genera. Among them, Streptomyces was the dominant endophytic actinobacteria, followed by Nocardia, Amycolatopsis, Microbiospora, Micromonospora and other rare actinobacteria genera. Soil characteristics including soil pH and organic matter were found to play crucial roles in shaping the endophytic actinobacterial community composition. Furthermore, all isolates were studied to determine their plant growth-promotion traits, 86.54% could produce Indole 3-Acetic Acid, 16.03% showed nitrogen-fixing, 21.15% showed phosphorus solubilizing, and 35.26% produced siderophore. Under the glasshouse condition, some isolates exhibited growth promotion effects on C. oleifera seedlings with significant increase in spring shoot length and ground diameter. Altogether, this study demonstrated that C. oleifera plants harbored a high diversity and novelty of culturable endophytic actinobacteria, which represent important potential as natural biofertilizers for the high production of C. oleifera.

GPR40 and GPR120 are fatty acid sensors that play important roles in glucose and energy homeostasis. GPR40 potentiates glucose-dependent insulin secretion and demonstrated in clinical studies robust glucose lowering in type 2 diabetes. GPR120 improves insulin sensitivity in rodents, albeit its mechanism of action is not fully understood. Here, we postulated that the antidiabetic efficacy of GPR40 could be enhanced by coactivating GPR120. A combination of GPR40 and GPR120 agonists in db/db mice, as well as a single molecule with dual agonist activities, achieved superior glycemic control compared with either monotherapy. Compared with a GPR40 selective agonist, the dual agonist improved insulin sensitivity in ob/ob mice measured by hyperinsulinemic-euglycemic clamp, preserved islet morphology, and increased expression of several key lipolytic genes in adipose tissue of Zucker diabetic fatty rats. Novel insights into the mechanism of action for GPR120 were obtained. Selective GPR120 activation suppressed lipolysis in primary white adipocytes, although this effect was attenuated in adipocytes from obese rats and obese rhesus, and sensitized the antilipolytic effect of insulin in rat and rhesus primary adipocytes. In conclusion, GPR120 agonism enhances insulin action in adipose tissue and yields a synergistic efficacy when combined with GPR40 agonism.

Precise agricultural predictions of climate change effects on crop water productivity are essential to ensure food security and alleviate water scarcity. In this regard, the present study provides an overview of the future impacts of climate change on the irrigation of agricultural products such as rice, millet, maize, cassava, sorghum, and sugar cane. These crops are some of the most-consumed foodstuffs in countries of the Niger River basin. This study is realized throughout 2020 to 2080, and three Global Climate Models (GCMs) (CSIRO, MIROC5, and ECHAM. MPI-ESM-LR) have been used. The GCMs data have been provided by the IPCC5 database. The irrigation water requirement for each crop was calculated using Smith’s CROPWAT approach. The Penman–Monteith equation recommended by the FAO was used to calculate the potential evapotranspiration. The inter-annual results of the IWR, according to the set of models selected, illustrate that the largest quantities of water used for irrigation are generally observed between January and March, and the lowest quantities are the most often seen between July and September. The majority of models also illustrate a peak in the IWR between March and April. Sorghum and millet are the crops consuming the least amount of water for irrigation; followed by cassava, then rice and corn, and finally sugar cane. The most significant IWRs, which have been predicted, will be between 16.3 mm/day (MIROC5 model, RCP 4.5) and 45.9 mm/day (CSIRO model, RCP 4.5), particularly in Mali, Niger, Algeria, and rarely in Burkina-Faso (CSIRO model, RCP4.5 and 8.5). The lowest IWRs predicted by the models will be from 1.29 mm/day (MIROC5 model, RCP 4.5) to 33.4 mm/day (CSIRO model, RCP 4.5); they will be observed according to the models in Guinea, southern Mali, Ivory Coast, center and southern Nigeria, and Cameroon. However, models predict sugarcane to be the plant with the highest IWR, between 0.25 mm/day (Benin in 2020–2040) and 25.66 mm/day (Chad in 2060–2080). According to the models’ predictions, millet is the crop with the most IWR, between 0.20 mm/day (Benin from 2020 to 2060) and 19.37 mm/day (Chad in 2060–2080). With the results of this study, the countries belonging to the Niger River basin can put in place robust policies in the water resources and agriculture sectors, thus ensuring food security and high-quality production of staple crops, and avoiding water scarcity while facing the negative impacts of climate change.

Understanding the spatiotemporal characteristics of hydrological components and their impacts on vegetation are critical for comprehending hydrological, climatological, and ecological processes under environmental change and solving future water management challenges. Innovative methods need to be developed in semiarid areas to analyze the special hydrological factors in the water resource systems of these areas. Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) were applied with the normalized difference vegetation index (NDVI) data in this paper to analyze spatiotemporal changes of hydrological factors in the Xiliaohe River Basin (XRB). The results showed that precipitation (P), evapotranspiration (ET) and temperature (T) had similar seasonal change patterns at rates of 0.05 cm/yr., 0.01 cm/yr. and −0.05 °C/yr., respectively. Total water storage change (TWSC) was consistent with the change trend of soil moisture change (SMC) and showed a fluctuating trend. Groundwater change (GWC) showed a decreasing trend at a rate of −0.43 cm/yr. P and ET had a greater impact on GLDAS data (R = 0.634, P < 0.05 and R = 0.686, P < 0.01, respectively) than on other factors. GWC was more sensitive to changes in T (R = 0.570, P < 0.05). Furthermore, a lag period of 0 to 1 months was observed for the effects of P and ET on TWSC and GLDAS. NDVI showed an upward trend at a rate of 0.001 yr−1 between 2002 and 2014. A spatial distribution of NDVI was heterogeneous in the study area. ET, GLDAS and GWC in growing season limited vegetation growth and were more important than other factors in XRB. The results may contribute to an understanding of the relationships between the hydrological cycle and climate change and provide scientific support for local environmental management.

The development of multimodal media compensates for the lack of information expression in a single modality and thus gradually becomes the main carrier of sentiment. In this situation, automatic assessment for sentiment information in multimodal contents is of increasing importance for many applications. To achieve this, we propose a joint sentiment part topic regression model (JSP) based on latent Dirichlet allocation (LDA), with a sentiment part, which effectively utilizes the complementary information between the modalities and strengthens the relationship between the sentiment layer and multimodal content. Specifically, a linear regression module is developed to share implicit variables between image–text pairs, so that one modality can predict the other. Moreover, a sentiment label layer is added to model the relationship between sentiment distribution parameters and multimodal contents. Experimental results on several datasets verify the feasibility of our proposed approach for multimodal sentiment analysis.

The sentiment analysis of microblog text has always been a challenging research field due to the limited and complex contextual information. However, most of the existing sentiment analysis methods for microblogs focus on classifying the polarity of emotional keywords while ignoring the transition or progressive impact of words in different positions in the Chinese syntactic structure on global sentiment, as well as the utilization of emojis. To this end, we propose the emotion-semantic-enhanced bidirectional long short-term memory (BiLSTM) network with the multi-head attention mechanism model (EBILSTM-MH) for sentiment analysis. This model uses BiLSTM to learn feature representation of input texts, given the word embedding. Subsequently, the attention mechanism is used to assign the attentive weights of each words to the sentiment analysis based on the impact of emojis. The attentive weights can be combined with the output of the hidden layer to obtain the feature representation of posts. Finally, the sentiment polarity of microblog can be obtained through the dense connection layer. The experimental results show the feasibility of our proposed model on microblog sentiment analysis when compared with other baseline models.

For winter wheat ( L.) that grows during the rainless season, the contribution of groundwater to the root zone (CGWR) is an important water source for growth. Accurately estimating the CGWR is important for making decisions on irrigation and discharge for winter wheat fields and preventing water pollution. Because winter wheat slows and even stops root growth over winter, so the fixed root density distribution function that is suitable for soybean [ (L.) Merr.] may not suit winter wheat calculations. Therefore, when estimating the CGWR of winter wheat with the numerical model HYDRUS-1D, the root density distribution function should first be determined from two types: fixed or piecewise root density distribution functions. Based on field observations and local weather data for 2004–2005 and 2005–2006, HYDRUS-1D was evaluated with different root density distribution functions by comparing simulated and measured root zone soil water contents. The evaluated model with the most suitable distribution function was used to estimate the daily CGWR for six winter wheat hydrological growth seasons. For all seasons, winter wheat growth was assumed to be at its optimal state. The main results were: (i) a piecewise root density distribution function was the most suitable for winter wheat; (ii) simulated seasonal CGWRs were 154, 128, and 136 mm in the dry, normal, and wet seasons, respectively; and (iii) the CGWR for winter wheat transpiration was about 58, 47, and 69% of the total in dry, normal, and wet seasons, respectively. Overall, we concluded that accurate description of the root density distribution was helpful to estimate the CGWR.

The comprehensive assessment of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) V05B is important for benchmarking the product’s continued improvement and future development. The performance of IMERG V05B precipitation products was systematically evaluated using 542 precipitation gauges at multiple spatiotemporal scales from March 2014 to February 2017 over China. Moreover, IMERG V05B was compared with IMERG V04A, the Tropical Rainfall Measuring Mission (TRMM) 3B42, and the Climate Prediction Center Morphing technique (CMORPH)-CRT in this study. Categorical verification techniques and statistical methods are used to quantify their performance. Results illustrate the following. (1) Except for IMERG V04A’s severe underestimation over the Tibetan Plateau (TP) and Xinjiang (XJ) with high negative relative biases (RBs) and CMORPH-CRT’s overestimation over XJ with high positive RB, the four satellite-based precipitation products generally capture the same spatial patterns of precipitation over China. (2) At the annual scale over China, the IMERG products do not show an advantage over its predecessor (TRMM 3B42) in terms of RMSEs, RRMSEs, and Rs; meanwhile, the performance of IMERG products is worse than TRMM 3B42 in spring and summer according to the RMSE, RRMSE, and R metrics. Between the two IMERG products, IMERG V05B shows the anticipated improvement (over IMERG V04A) with a decrease in RMSE from 0.4496 to 0.4097 mm/day, a decrease of RRMSE from 16.95% to 15.44%, and an increase of R from 0.9689 to 0.9759 during the whole study period. Similar results are obtained at the seasonal scale. Among the four satellite products, CMORPH-CRT shows the worst seasonal performance with the highest RMSE (0.6247 mm/day), RRMSE (23.55%), and lowest R (0.9343) over China. (3) Over XJ and TP, IMERG V05B clearly improves the strong underestimation of precipitation in IMERG V04A with the RBs of 5.2% vs. −21.8% over XJ, and 2.78% vs. −46% over TP. Results at the annual scale are similar to those obtained at the seasonal scale, except for summer results over XJ. While, over the remaining subregions, the two IMERG products have a close performance; meanwhile, IMERG V04A slightly improves IMERG V05B’s overestimation according to RBs (except for HN) at the annual scale. However, all four products are unreliable over XJ at both an annual and seasonal scale. (4) Across all products, TRMM 3B42 best reproduces the probability density function (PDF) of daily precipitation intensity. (5) According to the categorical verification technique in this study, both IMERG products yield better results for the detection of precipitation events on the basis of probability of detection (POD) and critical success index (CSI) categorical evaluations compared to TRMM 3B42 and CMORPH-CRT over China and across most of the subregions. However, all four products have room for further improvement, especially in high-latitude and dry climate regions. These findings provide valuable feedback for both IMERG algorithm developers and data set users.

In the last few years, satellite-based precipitation datasets are believed to be a potential source for forcing inputs in driving hydrological models, which are important especially in complex terrain areas or ungauged basins where ground gauges are generally sparse or nonexistent. This study aims to comprehensively evaluate the satellite precipitation products, CMORPH-CRT, PERSIANN-CDR, 3B42RT, and 3B42 against gauge-based datasets and to infer their relative potential impacts on hydrological processes simulation using the HEC-HMS model in the Ganzi River Basin (GRB) of the Tibetan Plateau. Results from a quantitative statistical comparison reveal that, at annual and seasonal scales, both CMORPH-CRT and 3B42 perform better than PERSIANN-CDR and 3B42RT. The CMORPH-CRT and 3B42 tend to underestimate values at the medium and high precipitation intensities ranges, whereas the opposite tendency is found for PERSIANN-CDR and 3B42RT. Overall, 3B42 exhibits the best performance for streamflow simulations over GRB and even outperforms simulation driven by gauge data during the validation period. PERSIANN-CDR shows the worst overall performance. After recalibrating with input-specific precipitation data, the performance of all satellite precipitation forced simulations is substantially improved, except for PERSIANN-CDR. Furthermore, 3B42 is more suitable to drive hydrological models and can be a potential alternative source of sparse data in Tibetan Plateau basins.

Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), have provided hydrologists with important precipitation data sources for hydrological applications in sparsely gauged or ungauged basins. This study proposes a framework for statistical and hydrological assessment of the TRMM- and GPM-era satellite-based precipitation products (SPPs) in both near- and post-real-time versions at sub-daily temporal scales in a poorly gauged watershed in Myanmar. It evaluates six of the latest GPM-era SPPs: Integrated Multi-satellite Retrievals for GPM (IMERG) “Early”, “Late”, and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F, respectively), and Global Satellite Mapping of Precipitation (GSMaP) near-real-time (GSMaP-NRT), standard version (GSMaP-MVK), and standard version with gauge-adjustment (GSMaP-GAUGE) SPPs, and two TRMM Multi-satellite Precipitation Analysis SPPs (3B42RT and 3B42V7). Statistical assessment at grid and basin scales shows that 3B42RT generally presents higher quality, followed by IMERG-F and 3B42V7. IMERG-E, IMERG-L, GSMaP-NRT, GSMaP-MVK, and GSMaP-GAUGE largely underestimate total precipitation, and the three GSMaP SPPs have the lowest accuracy. Given that 3B42RT demonstrates the best quality among the evaluated four near-real-time SPPs, 3B42RT obtains satisfactory hydrological performance in 3-hourly flood simulation, with a Nash–Sutcliffe model efficiency coefficient (NSE) of 0.868, and it is comparable with the rain-gauge-based precipitation data (NSE = 0.895). In terms of post-real-time SPPs, IMERG-F and 3B42V7 demonstrate acceptable hydrological utility, and IMERG-F (NSE = 0.840) slightly outperforms 3B42V7 (NSE = 0.828). This study found that IMERG-F demonstrates comparable or even slightly better accuracy in statistical and hydrological evaluations in comparison with its predecessor, 3B42V7, indicating that GPM-era IMERG-F is the reliable replacement for TRMM-era 3B42V7 in the study area. The GPM scientific community still needs to further refine precipitation retrieving algorithms and improve the accuracy of SPPs, particularly IMERG-E, IMERG-L, and GSMaP SPPs, because ungauged basins urgently require accurate and timely precipitation data for flood control and disaster mitigation.

The prevalence that people share their opinions on the products and services in their daily lives on the Internet has generated a large quantity of comment data, which contain great business value. As for comment sentences, they often contain several comment aspects and the sentiment on these aspects are different, which makes it meaningless to give an overall sentiment polarity of the sentence. In this paper, we introduce Attention-based Aspect-level Recurrent Convolutional Neural Network (AARCNN) to analyze the remarks at aspect-level. The model integrates attention mechanism and target information analysis, which enables the model to concentrate on the important parts of the sentence and to make full use of the target information. The model uses bidirectional LSTM (Bi-LSTM) to build the memory of the sentence, and then CNN is applied to extracting attention from memory to get the attentive sentence representation. The model uses aspect embedding to analyze the target information of the representation and finally the model outputs the sentiment polarity through a softmax layer. The model was tested on multi-language datasets, and demonstrated that it has better performance than conventional deep learning methods.

As the Global Precipitation Measurement (GPM) Core Observatory satellite continues its mission, the latest GPM-era satellite-based precipitation estimations, including Global Satellite Mapping of Precipitation (GSMaP) and Integrated Multi-satellitE Retrievals for the GPM (IMERG), have been released. However, few studies have systematically evaluated these products over mainland China, although this is very important for both the end users and data developers. To these ends, the final-run uncalibrated IMERG V05 (V05UC), gauge-calibrated IMERG V05 (V05C) and IMERG V04 (V04C), and latest gauge-calibrated GSMaP V7 (GSMaP) are systematically evaluated and mutually compared against a merged product obtained from the China Meteorological Data Service Center via continuous statistical indices and an error decomposition analysis technology suite over mainland China from April 2014 to December 2016 at a 3 hourly scale and 0.1° × 0.1° resolution. The results show that, irrespective of the slight overestimation in the southeast and underestimation in the northern Tibetan Plateau, all four GSPEs could generally capture the spatial patterns of precipitation over mainland China. Meanwhile, the overall quality of the GSMaP is slightly superior to the IMERG products in east and south China; however, it also suffers from an overestimation of light rain and an underestimation of heavy rain. Such overestimation and underestimation are primarily from a large false precipitation in light rain and a negative hit bias in heavy rain, respectively. The latest IMERG V05 products have not shown significant improvement over the earlier version (V04C) in east and south China, but the calibrated V05C can best reproduce the probability density function in terms of precipitation intensity. Furthermore, V04C shows remarkable underestimation over the Tibetan Plateau, while this shortcoming has been resolved significantly in V05C. Alternately, the effects of the gauge calibration algorithm (GCA) used in IMERG are examined by comparison of V05UC and V05C. The results indicate that GCA cannot reduce the missed precipitation, and even enlarges the false precipitation over some regions. This reveals that GCA cannot effectively alleviate the bias resulting from the rain areas’ delineation and raining or not-raining detection. In addition, all of the products’ performance can be improved, particularly in the dry climate and high-latitude regions. This is a systematic estimation for GSPEs, providing deep insight into the characteristics and sources of error, and it could be valuable as a reference for both algorithm developers and data users, as well as for associated global products and various applications.

A critical evaluation of the newly released precipitation data set is very important for both the end users and data developers. Meanwhile, the evaluation may provide a benchmark for the product’s continued development and future improvement. To these ends, the four precipitation estimates including IMERG (the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement) V04A, IMERG V03D, CMORPH (the Climate Prediction Center Morphing technique)-CRT and TRMM (the Tropical Rainfall Measuring Mission) 3B42 are systematically evaluated against the gauge precipitation estimates at multiple spatiotemporal scales from 1 June 2014 to 30 November 2015 over three different topographic and climatic watersheds in China. Meanwhile, the statistical methods are utilized to quantize the performance of the four satellite-based precipitation estimates. The results show that: (1) over the Tibetan Plateau cold region, among all products, IMERG V04A underestimates precipitation with the largest RB (−46.98%) during the study period and the similar results are seen at the seasonal scale. However, IMERG V03D demonstrates the best performance according to RB (7.46%), RMSE (0.44 mm/day) and RRMSE (28.37%). Except for in summer, TRMM 3B42 perform better than CMORPH according to RMSEs, RRMSEs and Rs; (2) within the semi-humid Huaihe River Basin, IMERG V04A has a slight advantage over the other three satellite-based precipitation products with the lowest RMSE (0.32 mm/day) during the evaluation period and followed by IMERG V03D, TRMM 3B42 and CMORPH orderly; (3) over the arid/semi-arid Weihe River Basin, in comparison with the other three products, TRMM 3B42 demonstrates the best performance with the lowest RMSE (0.1 mm/day), RRMSE (8.44%) and highest R (0.92) during the study period. Meanwhile, IMERG V03D perform better than IMERG V04A according all the statistical indicators; (4) in winter, IMERG V04A and IMERG V03D tend to underestimate the total precipitation with RBs (−70.62% vs. −6.47% over the Tibetan Plateau, −46.92% vs. −0.66% over the Weihe River Basin, respectively); and (5) overall, except for IMERG V04A in Tibetan Plateau, all satellite-based precipitation captured the gauge-based precipitation well over the three regions according to RRMSEs, Rs and Rbs during the study period. IMERG V03D performs better than its predecessors-TRMM 3B42 and CMORPH over the Tibetan Plateau region and the Huaihe River Basin, while IMERG V04A only does so over the latter. Between the two IMERG products, IMERG V04A does not show an advantage over IMERG V03D over the Tibetan Plateau region and the Weihe River Basin. In particular, over the former, IMERG V04A performs far worse than IMERG V03D. These findings provide valuable feedback for both IMERG algorithm developers and data users.

This study aimed to statistically and hydrologically assess the performance of the four latest and widely used satellite–gauge combined precipitation estimates (SGPEs), namely CRT (CMORPH CRT), BLD (CMORPH BLD), CDR (PERSIANN CDR), 3B42 (TMPA 3B42 version 7) over the upper yellow river basins (UYRB) in china during 2001–2012 time period. The performances of the SGPEs were compared with the Chinese Meteorological Administration (CMA) datasets using the hydrologic model called Variable Infiltration Capacity (VIC) which is known as a land surface hydrologic model. Results indicated that irrespective of the slight underestimation in the western mountains and overestimation in the southeast, the four SGPEs could generally captured the spatial distribution of precipitation well. Although 3B42 exhibited a better performance in capturing the spatial distribution of daily average precipitation, BLD agreed best with CMA in the time series of watershed average precipitation, which resulted in BLD having a comparable performance to the CMA in the long-term hydrological simulations. Moreover, the potential for disastrous heavy rain mainly occurs in southeastern corner of the basin, and CRT and BLD comparisons showed to be closer to the CMA in the distribution of extreme precipitation events while 3B42 and CDR overestimated the extreme precipitation especially over the southeast of UYRB region. Therefore, CRT and BLD were able to match the high peak discharges very well for the wet seasons, while 3B42 and CDR overrated the high peak discharges. In addition, the four SGPEs performed well for the 2005 flood event but exhibited poorly when tested for the 2012 flood event. Results indicate that the application of the four SGPEs should be used with caution in simulating massive flood events over UYRB region.

Satellite precipitation products from the Global Precipitation Measurement (GPM) mission and its predecessor the Tropical Rainfall Measuring Mission (TRMM) are a critical data source for hydrological applications in ungauged basins. This study conducted an initial and early evaluation of the performance of the Integrated Multi-satellite Retrievals for GPM (IMERG) final run and the TRMM Multi-satellite Precipitation Analysis 3B42V7 precipitation products, and their feasibility in streamflow simulations in the Chindwin River basin, Myanmar, from April 2014 to December 2015 was also assessed. Results show that, although IMERG and 3B42V7 can potentially capture the spatiotemporal patterns of historical precipitation, the two products contain considerable errors. Compared with 3B42V7, no significant improvements were found in IMERG. Moreover, 3B42V7 outperformed IMERG at daily and monthly scales and in heavy rain detections at four out of five gauges. The large errors in IMERG and 3B42V7 distinctly propagated to streamflow simulations via the Xinanjiang hydrological model, with a significant underestimation of total runoff and high flows. The bias correction of the satellite precipitation effectively improved the streamflow simulations. The 3B42V7-based streamflow simulations performed better than the gauge-based simulations. In general, IMERG and 3B42V7 are feasible for use in streamflow simulations in the study area, although 3B42V7 is better suited than IMERG.

Inhibition of Diacylglycerol O-acyltransferase 1 (DGAT1) has been a mechanism of interest for metabolic disorders. DGAT1 inhibition has been shown to be a key regulator in an array of metabolic pathways; however, based on the DGAT1 KO mouse phenotype the anticipation is that pharmacological inhibition of DGAT1 could potentially lead to skin related adverse effects. One of the aims in developing small molecule DGAT1 inhibitors that target key metabolic tissues is to avoid activity on skin-localized DGAT1 enzyme. In this report we describe a modeling-based approach to identify molecules with physical properties leading to differential exposure distribution. In addition, we demonstrate histological and RNA based biomarker approaches that can detect sebaceous gland atrophy pre-clinically that could be used as potential biomarkers in a clinical setting.

The monitoring of both cold and warm season hydrologic processes in headwater watersheds is critical for accurate water resource monitoring in many alpine regions. This work presents a new method that explores the simultaneous use of remotely sensed surface soil moisture (SM) and snow depth (SD) retrievals to improve hydrological modeling in such areas. In particular, remotely sensed SM and SD retrievals are applied to filter errors present in both solid and liquid phase precipitation accumulation products acquired from satellite remote sensing. Simultaneously, SM and SD retrievals are also used to correct antecedent SM and SD states within a hydrological model. In synthetic data assimilation experiments, results suggest that the simultaneous correction of both precipitation forcing and SM/SD antecedent conditions is more efficient at improving streamflow simulation than data assimilation techniques which focus solely on the constraint of antecedent SM or SD conditions. In a real assimilation case, results demonstrate the potential benefits of remotely sensed SM and SD retrievals for improving the representation of hydrological processes in a headwater basin. In particular, it is demonstrated that dual precipitation/state correction represents an efficient strategy for improving the simulation of cold-region hydrological processes.