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IntroductionPriming of tumor-specific T cells is a key to antitumor immune response and inflammation, in turn, is crucial for proper T-cell activation. As antigen-presenting cells can activate T cells, dendritic cells (DCs) loaded with tumor antigens have been used as immunotherapeutics against certain cancer in humans but their efficacy is modest. Necroptosis is a form of programmed cell death that results in the release of inflammatory contents. We previously generated mice with DC deficiency in a negative regulator of necroptosis, Fas-associated death domain (FADD), and found that these mice suffer from systemic inflammation due to necroptotic DCs. We hypothesize that FADD-deficient DCs could serve as a better vaccine than wild-type (WT) DCs against tumors.Materials and methodsFADD-deficient and WT mouse DCs loaded with the relevant tumor peptide were injected onto mice before or after the syngeneic tumor challenge. DC vaccinations were repeated two more times and anti-PD-1 antibodies were coinjected in some experiments. Tumor sizes were measured by caliper, and the percentages of tumor-free mice or mice survived were examined over time. The cytometric analysis was carried out to analyze various immune populations.ResultsIn two separate tumor models, we find that mice receiving FADD-deficient DCs as vaccine rejected tumors significantly better than those receiving a WT DC vaccine. Tumor growth was severely hampered, and survival extended in these mice. More activated CD8 T cells together with elevated cytokines were observed in mice receiving the FADD-deficient DC vaccine. Furthermore, we observed these effects were potent enough to protect against tumor challenge postinjection and can work in conjunction with anti-PD-1 antibodies to reduce the tumor growth.ConclusionsNecroptotic-susceptible DCs are better antitumor vaccines than WT DCs in mice. Our findings suggest that necroptosis-driven inflammation by DCs may be a novel avenue to generating a strong adaptive antitumor response in the clinical setting.

The traditional method of constructing the prior model for stochastic inversion mainly uses well-logging data to obtain the stationary range from the variogram to characterize the complex non-stationary spatial tectonic features of the subsurface layers. Unfortunately, it is hard for the stationary range to precisely describe the complex non-stationary spatial tectonic features of the subsurface layers. The established prior model is also difficult to use to describe the real spatial tectonic features of the subsurface media; thus, it cannot provide reliable prior information for the inversion. Therefore, according to the theory of random media, we extract the non-stationary statistical characteristic parameters that change with spatial coordinates from the known information such as seismic and well-logging data, to accurately and reliably characterize the non-stationary spatial tectonic of the subsurface media. Then, combined with fast Fourier transform-moving average spectral simulation, a spectral simulation method based on non-stationary statistical characteristic parameters is built to construct the spectral simulation results that can more accurately conform to the spatial tectonic features of the subsurface media, to provide reliable and reasonable geostatistical prior information for a better inversion result. [ABSTRACT FROM AUTHOR]

The buried hills of the Archean metamorphic rocks in the Bozhong Depression of the Bohai Bay Basin are the main gas-bearing strata, with burial depths ranging from 4000 m to 5500 m. However, metamorphic rocks have internal structural characteristics, such as diverse mineral components, oriented arrangement of mineral particles, complex pore connectivity, variable crystal structures, orthogonal development of multiple sets of fractures, and uneven fluid filling. Compared with conventional reservoirs, they have obvious heterogeneity and anisotropy characteristics. Traditional rock physics modeling methods are no longer suitable for predicting the elastic and anisotropic parameters of metamorphic reservoirs. Therefore, we introduced a vector mixed random medium model to calculate the effect of the oriented arrangement of metamorphic rock minerals on the modulus of the rock matrix and introduced a metamorphic factor to describe the impact of metamorphic recrystallization and alteration metasomatism on the elastic modulus of the rock matrix. Practical applications have shown that the new, improved rock physics modeling method can better estimate the S-wave velocity and anisotropy parameters in wells compared to traditional rock physics modeling methods, providing a reliable basis for predicting fractured reservoirs in metamorphic rock at buried hills. [ABSTRACT FROM AUTHOR]

Concentrating on the problem of massive energy loss in the compressor, expansion valve, and the other components present in the high-temperature heat pump system under extensive temperature lift, the dual-flash compound circulation system is proposed and the thermodynamic model of the dual-flash compound circulation system was established. The article combines the multivariate simulated annealing algorithm, utilizes the system COP as the optimization goal, and completes the calculation of the thermodynamic parameters in the steady-state of the system that is based on satisfying the conditions of the system process. Using R245fa as the refrigerant, the condensation temperature is set within the range of 110°C–140°C for the model calculation. The results show that, compared to the traditional two-stage compression system under the same environment, the COP of the dual-flash compound circulation system can be increased up to 5.71%–12.13%, and the exergy efficiency can be increased by 5.11%–10.71%, respectively. Besides, steam production per unit refrigerant is also increased by 3.79%–5.14%. Finally, the feasibility of the theoretical model is verified by simulation, and it is concluded that the dual-flash compound circulation system has better steam production performance at the extensive temperature lift and the elevated condensation temperature.

Helicobacter suis is the second most prevalent Helicobacter species in the stomach of humans suffering from gastric disease. This bacterium mainly inhabits the stomach of domesticated pigs, in which it causes gastric disease, but it appears to be absent in wild boars. Interestingly, it also colonizes the stomach of asymptomatic rhesus and cynomolgus monkeys. The origin of modern human-, pig- or non-human primate-associated H. suis strains in these respective host populations was hitherto unknown. Here we show that H. suis in pigs possibly originates from non-human primates. Our data suggest that a host jump from macaques to pigs happened between 100 000 and 15 000 years ago and that pig domestication has had a significant impact on the spread of H. suis in the pig population, from where this pathogen occasionally infects humans. Thus, in contrast to our expectations, H. suis appears to have evolved in its main host in a completely different way than its close relative Helicobacter pylori in humans.

A comprehensive drilling of wells has been conducted in the Permian Qixia Formation in the central Sichuan Basin, revealing a significant number of dolomite reservoirs. High- and medium-porosity dolomite reservoirs are the main gas-producing reservoirs in the Qixia Formation. Seismic PP-wave data show a "bright spot" for high-porosity dolomite reservoir formations but weak responses for medium-porosity dolomite reservoir formations, which is attributed to the inability of P waves to distinguish between medium-porosity reservoirs and limestone. However, medium-porosity dolomite and limestone have different S-wave velocities. Therefore, in this study, the identification of different-porosity dolomite reservoirs using multi-component seismic data was investigated. A comprehensive analysis of the elastic waves by forward modeling shows that the PS-wave amplitude is more sensitive to medium-porosity dolomite than the PP-wave amplitude. Therefore, medium-porosity dolomite reservoirs can be predicted using the amplitude attributes of the PS-wave, and high-porosity dolomite reservoirs can be characterized using the PP-wave. Meanwhile, the elastic parameter λρ (the product of Lame constant λ and density ρ), which is highly correlated with the dolomite content, can be used as an indicator of dolomite formations. Furthermore, compared to the results of PP-wave inversion, the elastic parameters derived from the joint inversion of PP- and PS-waves exhibited a better correspondence with the well-logging results. The comprehensive use of the seismic amplitude responses of PP- and PS-waves and multi-component seismic joint inversion can effectively predict high- and medium-porosity dolomite reservoirs. The predicted results can support the exploration and development of the Qixia Formation. [ABSTRACT FROM AUTHOR]

Yield prediction is an important agriculture management for crop policy making. In recent years, unmanned aerial vehicles (UAVs) and spectral sensor technology have been widely used in crop production. This study aims to evaluate the ability of UAVs equipped with spectral sensors to predict oilseed rape yield. In an experiment, RGB and hyperspectral images were captured using a UAV at the seedling (S1), budding (S2), flowering (S3), and pod (S4) stages in oilseed rape plants. Canopy reflectance and spectral indices of oilseed rape were extracted and calculated from the hyperspectral images. After correlation analysis and principal component analysis (PCA), input spectral indices were screened to build yield prediction models using random forest regression (RF), multiple linear regression (MLR), and support vector machine regression (SVM). The results showed that UAVs equipped with spectral sensors have great potential in predicting crop yield at a large scale. Machine learning approaches such as RF can improve the accuracy of yield models in comparison with traditional methods (e.g., MLR). The RF-based training model had the highest determination coefficient (R2) (0.925) and lowest relative root mean square error (RRMSE) (5.91%). In testing, the MLR-based model had the highest R2 (0.732) and lowest RRMSE (11.26%). Moreover, we found that S2 was the best stage for predicting oilseed rape yield compared with the other growth stages. This study demonstrates a relatively accurate prediction for crop yield and provides valuable insight for field crop management. [ABSTRACT FROM AUTHOR]

Milk vetch (Astragalus sinicus L.) is a winter-growing plant that can enhance soil fertility and provide essential nutrients for subsequent season crops. The fertilizing capacity of milk vetch is closely related to its above-ground biomass. Compared to the manual measurement methods of milk vetch biomass, remote sensing-based estimation methods have the advantages of rapid, noninvasive, and large-scale measurement. However, few studies have been conducted on remote sensing-based estimation of milk vetch biomass. To address this shortcoming, this study proposes combining unmanned aerial vehicle (UAV)-based hyperspectral imagery and machine learning algorithms for accurate estimation of milk vetch biomass. Through the analysis of hyperspectral images and feature selection based on the Pearson correlation and principal component analysis, vegetation indices (VIs), including near-infrared reflectance (NIR), red-edge spectral transform index (RE), and difference vegetation index (DVI), are selected as estimation metrics of the model development process. Four machine learning methods, including random forest (RF), multiple linear regression (MLR), deep neural network (DNN), and support vector machine (SVM), are used to construct the biomass models. The results show that the RF estimation model exhibits the highest coefficient of determination (R2) of 0.950 and the lowest relative root-mean-squared error (RRMSE) of 14.86% among all the models. Notably, the DNN model demonstrates promising performance on the test set, with the R2 and RRMSE values slightly superior and inferior to those of the RF, respectively. The proposed method based on UAV imagery and machine learning can provide an accurate and reliable large-scale estimation of milk vetch biomass. [ABSTRACT FROM AUTHOR]