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In order to improve the reliability of the deployment of production schemes after artificial fracturing in tight reservoirs, it is urgent to carry out research on the description of fractures after artificial fracturing. In this study, taking the Chang 61 oil formation group in the Wangyao South area of Ordos Basin as an example, three different fracture modeling schemes are used to establish the geological model of fractured reservoirs, and the fitting ratios of the respective reservoir models are calculated by using the method of reservoir numerical simulation of the initial fitting, and the optimal fractured reservoir modeling scheme is screened in the end. The research area adopts three types of fracture prediction results based on FMI fracture interpretation data, seismic fracture prediction data, and rock mechanics artificial fracturing simulation data. On this basis, geological models of fractured reservoirs are established, respectively. The initial fitting of reservoir values of each geological model are compared, and the highest initial fitting rate of reservoir values is 88.44%, which is based on rock mechanics artificial fracturing simulation data. However, the initial fitting rate of the reservoir model was the lowest at 75.76%, which was established based on the fracture random modeling results of FMl fracture interpretation data. Under the constraints of seismic geostress prediction results and microseismic monitoring data, the simulation results of rock mechanics artificial fracturing fracture are used as the basis, on which the geological model of artificially fractured reservoirs is thus established, and this scheme can more realistically characterize the characteristics of fractured reservoirs after artificial fracturing in the study area.

The current seismic prediction methods of the shale brittleness index are all based on the pre-stack seismic inversion of elastic parameters, and the elastic parameters are transformed by Rickman and other simple linear mathematical relationship formulas. In order to address the low accuracy of the seismic prediction results for the brittleness index, this study proposes a method for predicting the brittleness index of shale reservoirs based on an error backpropagation neural network (BP neural network). The continuous static rock elastic parameters were calculated by fitting the triaxial test data with well logging data, and the static elastic parameters with good correlation with the brittleness index of shale minerals were selected as the sample data of the BP neural network model. A dataset of 1970 data points, characterized by Young’s modulus, Poisson’s ratio, shear modulus, and the mineral brittleness index, was constructed. A total of 367 sets of data points from well Z4 were randomly retained as model validation data, and 1603 sets of data points from the other three wells were divided into model training data and test data at a ratio of 7:3. The calculation accuracy of the model with different numbers of nodes was analyzed and the key parameters of the BP neural network structure such as the number of input layers, the number of output layers, the number of hidden layers, and the number of neurons were determined. The gradient descent method was used to determine the weight and bias of the model parameters with the smallest error, the BP neural network model was trained, and the stability of the brittleness index prediction model of the BP neural network was verified by posterior data. After obtaining Young’s modulus, Poisson’s ratio, and shear modulus through pre-stack seismic inversion, the BP neural network model established in this study was used to predict the brittleness index distribution of the target layer in the study area. Compared with the conventional Rickman method, the prediction coincidence rate is 69.16%, and the prediction coincidence rate between the prediction results and the real value is 95.79%, which is 26.63% higher. The BP neural network method proposed in this paper provides a reliable new method for seismic prediction of the shale reservoir brittleness index, which has important practical significance for clarifying the shale gas development scheme and improving shale gas exploitation efficiency.

Wheat leaf rust fungus is an obligate parasitic fungus that can absorb nutrients from its host plant through haustoria and secrete effector proteins into host cells. The effector proteins are crucial factors for pathogenesis as well as targets for host disease resistance protein recognition. Exploring the role of effector proteins in the pathogenic process of Puccinia triticina Eriks. (Pt) is of great significance for unraveling its pathogenic mechanisms. We previously found that a cysteine-rich effector protein, Pt1641, is highly expressed during the interaction between wheat and Pt, but its specific role in pathogenesis remains unclear. Therefore, this study employed techniques such as heterologous expression, qRT-PCR analysis, and host-induced gene silencing (HIGS) to investigate the role of Pt1641 in the pathogenic process of Pt. The results indicate that Pt1641 is an effector protein with a secretory function and can inhibit BAX-induced programmed cell death in Nicotiana benthamiana. qRT-PCR analyses showed that expression levels of Pt1641 were different during the interaction between the high-virulence strain THTT and low-virulence strains FGD and Thatcher, respectively. The highest expression level in the low-virulence strain FGD was four times that of the high-virulence strain THTT. The overexpression of Pt1641 in wheat near-isogenic line TcLr1 induced callose deposition and H2O2 production on TcLr1. After silencing Pt1641 in the Pt low-virulence strain FGD on wheat near-isogenic line TcLr1, the pathogenic phenotype of Pt physiological race FGD on TcLr1 changed from “;” to “3”, indicating that Pt1641 plays a non-toxic function in the pathogenicity of FGD to TcLr1. This study helps to reveal the pathogenic mechanism of wheat leaf rust and provides important guidance for the mining and application of Pt avirulent genes.

Clouds are crucial in regulating the Earth’s energy budget. Global cloud top heights have been easily retrieved from satellite measurements, but there are few methods for determining cloud base height (CBH) from satellite measurements. The Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm was proposed to derive the height of the lower-troposphere liquid cloud base by using the Cloud-Aerosol Lidar with Orthogonal polarization cloud aerosol LiDAR (CALIOP) profiles and weather observations at airports from aviation routine and special weather report (METARs and SPECIs, called METAR) observation data in the United States. A modification to the CBASE algorithm over China (CNMETAR-CBASE) is presented in this paper. In this paper, the ability of the CBASE algorithm to calculate CBH in China is evaluated, and METAR observations over China (CNMETAR) were then used to modify the CBASE algorithm. The results including CNMETAR observation data in China can better retrieve CBH over China compared with the results using the original CBASE algorithm, and the accuracy of the global CBH results has been improved. Overestimations of CBH with the original algorithm range from 500 to 800 m in China, which have been reduced to about 300 m with an improved algorithm. The deviations calculated by the algorithm also have a significant reduction, from 480 m (CBASE) to 420 m (CNMETAR-CBASE). In conclusion, the modified CBASE algorithm not only calculates the CBH more accurately in China but also improves the results of the global CBH retrieved from satellites.

To understand how the soil microbial community structure responds to vegetation restoration in alpine mining areas, this study specifically examines the grassland ecosystem in the Qianmalong mining area of the Qilian Mountains after five years of artificial restoration. High-throughput sequencing methods were employed to analyze soil bacteria and fungi microbial characteristics in diverse grassland communities. Combined with modifications in vegetation diversity as well as soil physicochemical properties, the impact of vegetation restoration on soil microbiome diversity in this alpine mining area was investigated. The findings indicated that the dominant plants were Cyperus rotundus, Carex spp., and Elymus nutans. As the extent of the grassland’s restoration increased, the number of plant species, importance values, and plant community diversity showed an increasing trend. The plant functional groups were mainly dominated by Cyperaceae, followed by Poaceae. Plant height, density, plant cover, frequency, and aboveground biomass showed an increasing trend, and soil water content (SWC) increased. While soil pH and soil electrical conductivity (EC) exhibited a declining trend, available phosphorus (AP), total phosphorus (TP), total nitrogen (TN), nitrate nitrogen (NO3-N), soil organic carbon (SOC), and soil water content (SWC) showed an increasing trend. The dominant bacterial communities were Actinobacteriota, Proteobacteria, Acidobacteriota, Chloroflexi, Firmicutes, and Gemmatimonadota, while the dominant fungal communities were Ascomycota, Mortierellomycota, Basidiomycota, unclassified_k_Fungi, and Glomeromycota. Significant differences were detected within soil microbial community composition among different degrees of restoration grasslands, with bacteria generally dominating over fungi. SWC, TP, and TN were found to be the main soil physicochemical factors affecting the distribution of soil bacterial communities’ structure; however, SOC, TN, and NO3-N were the primary factors influencing the soil distribution of fungal communities. The results of this study indicate that different degrees of vegetation restoration in alpine mining areas can significantly affect soil bacterial and fungal communities, and the degree of restoration has varying effects on the soil bacteria and fungi community structure in alpine mining areas.

To clarify the risk posed to groundwater in oil shale in situ mining areas, we examine five leached pollutants: Fe, Mn, Cr, sulfate, and ammonia nitrogen. Potential groundwater contents of these five pollutants were evaluated using an improved Nemero comprehensive index method and a health risk assessment method. The results show that, compared with the Class III groundwater quality standard (GB/T 14848-2017) used in the People’s Republic of China, average values of Fe, Mn, and sulfate in leaching solution from Fuyu oil shale exceed the standard, while Cr and ammonia nitrogen do not exceed the standard, and the leaching solution is within Class V groundwater quality. The average values of Fe and Mn in the leaching solution from Fushun oil shale exceed the Class III standard, while Cr, sulfate, and ammonia nitrogen values from this oil shale do not exceed the standard, and the leaching solution is Class IV in terms of groundwater quality. The weighting value used in the Nemero assessment method for the heavy metal Cr is the largest as its potential to cause harm to groundwater quality is the largest. The weight value for sulfate is the smallest as the harm degree is the smallest. The chemical carcinogen Cr has the greatest potential impact on human health. The health risk caused by the chemical non-carcinogen Mn is greater than that caused by Fe and ammonia nitrogen. When high pyrolysis temperatures are used, Mn will be released into groundwater in large quantities. Therefore, supervision and control should be strengthened. The results presented here can provide a reference for the comprehensive evaluation of groundwater risks caused by in situ oil shale mining.

Wheat leaf rust, caused by the obligate biotrophic fungus Puccinia triticina Eriks. (Pt), is one of the most common wheat foliar diseases that continuously threatens global wheat production. Currently, the approaches used to mitigate pathogen infestation include the application of fungicides and the deployment of resistance genes or cultivars. However, the continuous deployment of selected resistant varieties causes host selection pressures that drive Pt evolution and promote the incessant emergence of new virulent races, resulting in the demise of wheat-resistant cultivars after several years of planting. Intriguingly, diploid wheat accessions were found to confer haustorium formation-based resistance to leaf rust, which involves prehaustorial and posthaustorial resistance mechanisms. The prehaustorial resistance in the interaction between einkorn and wheat leaf rust is not influenced by specific races of the pathogen. The induced defense mechanism, known as systemic acquired resistance, also confers durable resistance against a wide array of pathogens. This review summarizes the host range, pathogenic profile, and evolutionary basis of Pt; the molecular basis underlying wheat–Pt interactions; the cloning and characterization of wheat leaf rust resistance genes; prehaustorial and posthaustorial resistance; systemic acquired resistance; and the role of reactive oxygen species. The interplay between climatic factors, genetic features, planting dates, and disease dynamics in imparting resistance is also discussed.

Even traditional pretreatments can partially remove or degrade lignin and hemicellulose from lignocellulosic biomass for enhancing its enzymatic digestibility, the remaining lignin in pretreated biomass still restricts its enzymatic hydrolysis by limiting cellulose accessibility and lignin-enzyme nonproductive interaction. Therefore, many pretreatments that can modify lignin structure in a unique way and approaches to block the lignin’s adverse impact have been proposed to directly improve the enzymatic digestibility of pretreated biomass. In this review, recent development in sulfite pretreatment that can transform the native lignin into lignosulfonate and subsequently enhance saccharification of pretreated biomass under certain conditions was summarized. In addition, we also reviewed the approaches of the addition of reactive agents to block the lignin’s reactive sites and limit the cellulase-enzyme adsorption during hydrolysis. It is our hope that this summary can provide a guideline for workers engaged in biorefining for the goal of reaching high enzymatic digestibility of lignocellulose.

Evapotranspiration plays an inevitable role in various fields of hydrology and agriculture. Reference evapotranspiration (ET0) is mostly applied in irrigation planning and monitoring. An accurate estimation of ET0 contributes to decision and policymaking processes governing water resource management, efficiency, and productivity. Direct measurements of ET0, however, are difficult to achieve, often requiring empirical methods. The Penman–Monteith FAO56 (PM-FAO56) method, for example, is still considered to be the best way of estimating ET0 in most regions of the globe. However, it requires a large number of meteorological variables, often restricting its applicability in regions with poor or missing meteorological observations. Furthermore, the objectivity of some elements of the empirical equations often used can be highly variable from region to region. The result is a need to find an alternative, objective method that can more accurately estimate ET0 in regions of interest. This study was conducted in the Hexi corridor, Northwest China. In it we aimed to evaluate the applicability of 32 simple empirical ET0 models designed under different climatic conditions with different data inputs requirements. The models evaluated in this study are classified into three types of methods based on temperature, solar radiation, and mass transfer. The performance of 32 simple equations compared to the PM-FAO56 model is evaluated based on model evaluation techniques including root mean square error (RMSE), mean absolute error (MAE), percentage bias (PBIAS), and Nash–Sutcliffe efficiency (NSE). The results show that the World Meteorological Organization (WMO) and the Mahringer (MAHR) models perform well and are ranked as the best alternative methods to estimate daily and monthly ET0 in the Hexi corridor. The WMO and MAHR performed well with monthly mean RMSE = 0.46 mm and 0.56 mm, PBIAS = 12.1% and −11.0%, and NSE = 0.93 and 0.93, before calibration, respectively. After calibration, both models showed significant improvements with approximately equal PBIAS of −2.5%, NSE = 0.99, and RMSE of 0.24 m. Calibration also significantly reduced the PBIAS of the Romanenko (ROM) method by 82.12% and increased the NSE by 16.7%.

Parkinson’s disease (PD) is the second most common neurodegenerative disease. The pathogenesis of PD is complicated and remains obscure, but growing evidence suggests the involvement of mitochondrial and lysosomal dysfunction. Mitophagy, the process of removing damaged mitochondria, is compromised in PD patients and models, and was found to be associated with accelerated neurodegeneration. Several PD-related proteins are known to participate in the regulation of mitophagy, including PINK1 and Parkin. In addition, mutations in several PD-related genes are known to cause mitochondrial defects and neurotoxicity by disturbing mitophagy, indicating that mitophagy is a critical component of PD pathogenesis. Therefore, it is crucial to understand how these genes are involved in mitochondrial quality control or mitophagy regulation in the study of PD pathogenesis and the development of novel treatment strategies. In this review, we will discuss the critical roles of mitophagy in PD pathogenesis, highlighting the potential therapeutic implications of mitophagy regulation.

The growing demands for material longevity in marine environments necessitate the development of highly efficient, low-cost, and durable corrosion-protective coatings. Although magnesium alloys are widely used in the automotive and aerospace industries, severe corrosion issues still hinder their long-term service in naval architecture. In the present work, an epoxy composite coating containing sericite nanosheets is prepared on the AZ31B Mg alloy using a one-step electrophoretic deposition method to improve corrosion resistance. Due to the polyetherimide (PEI) modification, positively charged sericite nanosheets can be highly orientated in an epoxy coating under the influence of an electric field. The sericite-incorporated epoxy coating prepared in the emulsion with 4 wt.% sericite exhibits the highest corrosion resistance, with its corrosion current density being 6 orders of magnitude lower than that of the substrate. Electrochemical measurements and immersion tests showed that the highly orientated sericite nanosheets in the epoxy coating have an excellent barrier effect against corrosive media, thus significantly improving the long-term anti-corrosion performance of the epoxy coating. This work provides new insight into the design of lamellar filler/epoxy coatings with superior anticorrosion performance and shows promise in the corrosion protection of magnesium alloys.

Peaches/nectarines (Prunus persica L.) are widely cultivated worldwide. As with other species, the sugar content is the most important trait for fruit quality, especially for precocious cultivars. Most fruits need to improve their sugar content in order to be more profitable under fierce market competition. 5-Aminolevulinic acid (ALA), a naturally occurring δ-amino acid, has been shown to improve leaf photosynthesis and fruit quality, especially sugar content. However, the mechanisms are not clear. The objective of this study is to determine the effects of exogenous ALA on leaf photosynthesis, assimilate transport, and sugar accumulation during fruit development. We used the field-cultivated precocious nectarine ‘Zhongyoutao 4’ and potted cultivated peach ‘Zhongai 33’ as materials, whereas in the second experiment, we used 14C radiolabeling to trace 14C fixation in leaves, transport in branches, and distribution in different organs. The results showed that ALA significantly enhanced the photosynthetic gas exchange capacity, and the effects were maintained for at least one month. The results of the 14C radiolabel experiment showed that ALA enhanced 14C fixation in leaves, promoted the transport to fruits, and reduced the allocation rate of young leaves. This suggests that ALA enlarges “source” volume and strengthens “sink” competition; therefore, assimilate translocation to fruits is promoted. It was observed that sucrose contributed the main saccharide for peach fruit quality at maturity, which might not be converted from glucose or fructose but from starch degradation. ALA improved starch accumulation in the young fruits as well as degradation during maturity. The RT-qPCR showed that the expression of most genes involved in sugar metabolism did not correlate or even negatively correlate with fruit sucrose content. However, the expressions of SWEET1/6/7/8/15/16/17 were highly correlated with the sucrose content, and exogenous ALA treatment up-regulated the gene expression at fruit maturity, suggesting they might play an important role in fruit sugar accumulation. These results provide important theoretical support for ALA application in fruit quality improvement, as well as a regulatory mechanism study on sugar accumulation in fruits.

Nieuwland catalyst is a key step in the dimerization of acetylene. Various zirconium metal additives incorporating Nieuwland catalysts were prepared, and their catalytic performances were assessed in acetylene dimerization. Different characterization techniques (i.e., thermogravimetric analysis, temperature-programmed reduction, X-ray diffraction, X-ray photoelectron spectroscopy, hydrogen ion concentration measurement and transmission electron microscopy) were employed in this study. The best catalytic performance was obtained over zirconium-acetylacetonate-incorporated Nieuwland catalysts, with an acetylene conversion of 53.3% and a monovinylacetylene selectivity of 87.4%. Based on these results, the zirconium acetylacetonate additive could reduce the types of transition state complexes, and it could also change the morphology of the catalyst. In addition, the additives could significantly inhibit the occurrence of trimerization products and polymers. Hence, the conversion of acetylene, monovinylacetylene selectivity, and stability of the Nieuwland catalysts were enhanced.