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Coal gangue, a by-product of coal mining, is accumulating rapidly and As a by-product of coal mining, the amount of coal gangue is increasing in stock, which poses a serious threat to the environment. Due to the high content of SiO2 and Al2O3 mass fraction, the use of gangue to prepare molecular sieves to capture CO2 in order to realize the efficient resource utilization of coal gangue is a promising technology. In this paper, zeolite molecular sieves are prepared by hydrothermal crystallization method with coal gangue as raw material, and the effects of KOH addition and coal semi-coke doping on the structure and properties of molecular sieves are investigated; static adsorption test of CO2 is carried out to study the CO2 capture characteristics of coal gangue-based zeolite molecular sieves and amine-modified molecular sieves. The results show that when the mass ratio of KOH to coal gangue is 2∶1, the specific surface area of the molecular sieve is 100.17 m2/g. When the doping amount of coal semi-coke is 40%, the molecular sieve has a specific surface area of 249.86 m2/g, and the total pore volume is 0.249 cm3/g. Under the optimal preparation conditions, the CO2 adsorption capacity of the molecular sieve is 1.16 mmol/g. Amine loading modification could effectively enhance the CO2 capture capacity of the molecular sieves, but excessive polyethyleneimine will accumulate in the molecular sieve pores and block the pore channels, resulting in the weakening of the physical adsorption capacity of the molecular sieves.

Abstract Continuous cultivation of tobacco could cause serious soil health problems, which could cause bacterial soil to change to fungal soil. In order to study the diversity and richness of fungal community in tobacco-growing soil under different crop rotation, three treatments were set up in this study: CK (tobacco continuous cropping); B (barley-tobacco rotation cropping) and R (oilseed rape-tobacco rotation cropping). The results of this study showed that rotation with other crops significantly decreased the soil fungal OTUs, and also decreased the community richness, evenness, diversity and coverage of fungal communities. Among them, B decreased the most. In the analysis of the composition and structure of the fungal community, it was found that the proportion of plant pathogens Nectriaceae decreased from 19.67% in CK to 5.63% in B, which greatly reduced the possibility of soil-borne diseases. In the analysis of the correlation between soil environmental factors and fungal communities, it was found that Filobasidiaceae had a strong correlation with TP and AP, and Erysiphaceae had a strong correlation with TK and AK. NO3 −-N and NH4 +-N were the two environmental factors with the strongest correlation with fungal communities. The results of this study showed that rotation with other crops slowed down the process of soil fungi in tobacco-growing soil and changed the dominant species of soil fungi community. At the same time, crop rotation changed the diversity and richness of soil fungal community by changing the physical and chemical properties of soil.

Abstract Background The crossbreeding of specialized beef cattle breeds with Chinese indigenous cattle is a common method of genetic improvement. Xia’nan cattle, a crossbreed of Charolais and Nanyang cattle, is China’s first specialized beef cattle breed with independent intellectual property rights. After more than two decades of selective breeding, Xia’nan cattle exhibit a robust physique, good environmental adaptability, good tolerance to coarse feed, and high meat production rates. This study analyzed the population genetic structure, genetic diversity, and genomic variations of Xia’nan cattle using whole-genome sequencing data from 30 Xia’nan cattle and 178 published cattle genomic data. Result The ancestry estimating composition analysis showed that the ancestry proportions for Xia’nan cattle were mainly Charolais with a small amount of Nanyang cattle. Through the genetic diversity studies (nucleotide diversity and linkage disequilibrium decay), we found that the genomic diversity of Xia’nan cattle is higher than that of specialized beef cattle breeds in Europe but lower than that of Chinese native cattle. Then, we used four methods to detect genome candidate regions influencing the excellent traits of Xia’nan cattle. Among the detected results, 42 genes (θπ and CLR) and 131 genes (F ST and XP-EHH) were detected by two different detection strategies. In addition, we found a region in BTA8 with strong selection signals. Finally, we conducted functional annotation on the detected genes and found that these genes may influence body development (NR6A1), meat quality traits (MCCC1), growth traits (WSCD1, TMEM68, MFN1, NCKAP5), and immunity (IL11RA, CNTFR, CCL27, SLAMF1, SLAMF7, NAA35, and GOLM1). Conclusion We elucidated the genomic features and population structure of Xia’nan cattle and detected some selection signals in genomic regions potentially associated with crucial economic traits in Xia’nan cattle. This research provided a basis for further breeding improvements in Xia’nan cattle and served as a reference for genetic enhancements in other crossbreed cattle.

Abstract The degradation of earthen sites due to natural and human factors has become a pressing issue, necessitating urgent protection measures. In this context, accurate assessment of the vulnerability of earthen sites is essential for the development of effective conservation strategies. In this study, a comprehensive evaluation framework that incorporates multiple indicators is proposed. In particular, the entropy weight- (Technique for Order Preference by Similarity to an Ideal Solution) TOPSIS method is employed for quantitative vulnerability assessment and combined with K-means clustering to define vulnerability levels for earthen sites. To validate the proposed approach, the vulnerability of 29 sections of the Ming Great Wall is evaluated. Eventually, the 29 earthen sites are categorized into three levels: high, medium, and low, according to their degree of vulnerability. The results of gray correlation analysis and entropy weight-TOPSIS method are compared using the ontology missing amount in the original data as the validation standard. The results show that the Pearson correlation coefficient value of the entropy weight-TOPSIS method with the ontology missing amount was 0.859, while the Pearson correlation coefficient value of the gray correlation analysis method with the ontology missing amount was 0.691, so that the results of the entropy weight-TOPSIS method can more accurately reflect the actual vulnerability of earthen sites.

Abstract Background Computer-guided implant surgery has improved the quality of implant treatment by facilitating the placement of implants in a more accurate manner. This study aimed to assess the accuracy of implant placement in a clinical setting using three techniques: dynamic navigation, static surgical guides, and freehand placement. We also investigated potential factors influencing accuracy to provide a comprehensive evaluation of each technique’s advantages and disadvantages. Materials and methods Ninety-four implants in 65 patients were included in this prospective study. Patients were randomly assigned to one of three groups: dynamic navigation, static surgical guides, or freehand placement. Implants were placed using a prosthetically oriented digital implant planning approach, and postoperative CBCT scans were superimposed on preoperative plans to measure accuracy. Seven deviation values were calculated, including angular, platform, and apical deviations. Demographic and consistency analyses were performed, along with one-way ANOVA and post-hoc tests for deviation values. Results The mean global platform, global apical, and angular deviations were 0.99 mm (SD 0.52), 1.14 mm (SD 0.56), and 3.66° (SD 1.64°) for the dynamic navigation group; 0.92 mm (SD 0.36), 1.06 mm (SD 0.47), and 2.52° (SD 1.18°) for the surgical guide group; and 1.36 mm (SD 0.62), 1.73 mm (SD 0.66), and 5.82° (SD 2.79°) for the freehand group. Both the dynamic navigation and surgical guide groups exhibited statistically significant differences in all values except depth deviations compared to the freehand group (p

Due to lithological and environmental differences, the weathering degree of stone heritage varies even within the same area. Various factors dominate the weathering process at different stages. Implementing preventive measures has reduced the impact of controllable factors such as human activities. However, salt weathering caused by atmospheric pollutants, water, and salt migration in rock formations has emerged as a major contributor to the deterioration of stone heritage. Understanding the re-weathering characteristics of stone cultural heritage at different weathering degrees is crucial for preventive protection. This study investigated the weathering status of Nanshiku Temple and designed an experimental scheme based on field monitoring data. By controlling the number of freeze-thaw cycles, stones with different weathering degrees were prepared, and sulfate weathering simulations were conducted. The results revealed severe damage to sandstone due to Na2SO4 crystallization. Higher initial weathering degrees resulted in more pronounced deterioration and faster weathering rates. Surface hardness analysis approximated the decay process using a logarithmic function within a certain range but could not fully capture the overall weathering of the stone. Uniaxial compressive strength showed a strong correlation with weathering indices like p-wave velocity. These findings provide a theoretical basis for preventive protection efforts in stone cultural heritage.

The main rice-producing areas in Southwest China are facing production problems such as seasonal drought and engineering water shortage. A green and efficient irrigation and drainage technology system is key to achieving water conservation, stable yield, and high efficiency in the region, and it is of great significance to the food security of China. This study reviews the current status of water use, irrigation, and drainage in the main rice-producing areas in Southwest China, involving Yunnan, Guizhou, Sichuan, and Chongqing. It examines the basic characteristics and specific technical types of green and efficient irrigation and drainage systems in the areas, and proposes a green and efficient irrigation and drainage technology model that is composed of accurate water demand forecast of paddy fields, precision water distribution management in irrigation areas, efficient water use management in paddy fields, and efficient drainage management in paddy fields. The study found that the main rice-producing areas in Southwest China are rich in water resources but uneven in spatial and temporal distribution of water. The water-saving irrigation and drainage technologies in this region are backward and vary in the applicability of climate conditions, water resources, and topography. Therefore, we propose the following suggestions: (1) developing new technologies for the efficient and coordinated regulation of irrigation and drainage in paddy fields, (2) promoting the green and efficient irrigation and drainage technology system, (3) optimizing the water and fertilizer operation mode, (4) developing an intelligent irrigation system for paddy fields, (5) establishing a paddy field information management system, and (6) building a multi-dimensional technology promotion system, thus to achieve water conservation, pollution control, emission reduction, yield increase, and quality improvement in the main rice-producing areas of Southwest China.

Abstract PbZrO3 has been broadly considered as a prototypical antiferroelectric material for high-power energy storage. A recent theoretical study suggests that the ground state of PbZrO3 is threefold-modulated ferrielectric, which challenges the generally accepted antiferroelectric configuration. However, such a novel ferrielectric phase was predicted only to be accessible at low temperatures. Here, we successfully achieve the room-temperature construction of the strongly competing ferrielectric and antiferroelectric state by strain-mediated phase separation in PbZrO3/SrTiO3 thin film. We demonstrate that the phase separation occurs spontaneously in quasi-periodic stripe-like patterns under a compressive misfit strain and can be tailored by varying the film thickness. The ferrielectric phase strikingly exhibitsa threefold modulation period with a nearly up-up-down configuration, which could be stabilized and manipulated by the formation and evolution of interfacial defects under applied strain. The present results construct a fertile ground for further exploring the physical properties and applications based on the novel ferrielectric phase.

Abstract Ribose-5-phosphate (R5P) is a precursor for nucleic acid biogenesis; however, the importance and homeostasis of R5P in the intracellular parasite Toxoplasma gondii remain enigmatic. Here, we show that the cytoplasmic sedoheptulose-1,7-bisphosphatase (SBPase) is dispensable. Still, its co-deletion with transaldolase (TAL) impairs the double mutant’s growth and increases 13C-glucose-derived flux into pentose sugars via the transketolase (TKT) enzyme. Deletion of the latter protein affects the parasite’s fitness but is not lethal and is correlated with an increased carbon flux via the oxidative pentose phosphate pathway. Further, loss of TKT leads to a decline in 13C incorporation into glycolysis and the TCA cycle, resulting in a decrease in ATP levels and the inability of phosphoribosyl-pyrophosphate synthetase (PRPS) to convert R5P into 5′-phosphoribosyl-pyrophosphate and thereby contribute to the production of AMP and IMP. Likewise, PRPS is essential for the lytic cycle. Not least, we show that RuPE-mediated metabolic compensation is imperative for the survival of the ΔsbpaseΔtal strain. In conclusion, we demonstrate that multiple routes can flexibly supply R5P to enable parasite growth and identify catalysis by TKT and PRPS as critical enzymatic steps. Our work provides novel biological and therapeutic insights into the network design principles of intracellular parasitism in a clinically-relevant pathogen.

IntroductionSymbiotic bacteria play key roles in a variety of important life processes of insects such as development, reproduction and environmental adaptation, and the elucidation of symbiont population structure and dynamics is crucial for revealing the underlying regulatory mechanisms. The marmalade hoverfly (Episyrphus balteatus) is not only a remarkable aphid predator, but also a worldwide pollinator second to honeybees. However, its symbiont composition and dynamics remain unclear.MethodsHerein, we investigate the symbiotic bacterial dynamics in marmalade hoverfly throughout whole life cycle, across two sexes, and in its prey Megoura crassicauda by 16S rRNA sequencing.ResultsIn general, the dominant phyla were Proteobacteria and Firmicutes, and the dominant genera were Serratia and Wolbachia. Serratia mainly existed in the larval stage of hoverfly with the highest relative abundance of 86.24% in the 1st instar larvae. Wolbachia was found in adults and eggs with the highest relative abundance of 62.80% in eggs. Significant difference in species diversity was observed between the adults feeding on pollen and larvae feeding on M. crassicauda, in which the dominant symbiotic bacteria were Asaia and Serratia, respectively. However, between two sexes, the symbionts exhibited high similarity in species composition. In addition, our results suggested that E. balteatus obtainded Serratia mainly through horizontal transmission by feeding on prey aphids, whereas it acquired Wolbachia mainly through intergeneration vertical transmission. Taken together, our study revealed the effects of development stages, diet types and genders of E. balteatus on symbionts, and explored transmission modes of dominant bacteria Serratia and Wolbachia.DiscussionOur findings lay a foundation for further studying the roles of symbiotic bacteria in E. balteatus life cycle, which will benefit for revealing the co-adaptation mechanisms of insects and symbiotic bacteria.

Abstract Introduction People living with HIV (PLWH) have higher rates of non‐infectious comorbid diseases (NCDs) than individuals without HIV. We characterized the risk of NCDs among PLWH with undetectable viral load and persistent low‐level viraemia (pLLV) in the African Cohort Study (AFRICOS). We secondarily quantified the role of immune activation in the association between LLV and NCDs. Methods AFRICOS enrols participants in 12 clinics in Uganda, Kenya, Tanzania and Nigeria. Participants on antiretroviral therapy ≥ 6 months without an NCD at enrolment were included. PLLV was defined as at least two consecutive visits with a detectable viral load

Accurate estimation of crop evapotranspiration (ET) is essential for the efficient utilization of agricultural water resources, crop production enhancement, and sustainable agricultural development. However, direct measurement of ET is highly expensive, intricate, and time-consuming, highlighting the imperative of establishing a novel model to accurately estimate ET in agricultural ecosystems. To address the above problems, this study proposed a novel model (GWA-CNN-BiLSTM), which incorporates Grey Wolf Algorithm (GWA), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory network (BiLSTM) as a hyperparameter adjuster, feature extractor, and regression component, respectively, to estimate ET built upon various input combinations comprising net solar radiation (Rn), vapor pressure deficit (VPD), average air temperature (Ta), soil water content (SWC), and leaf area index (LAI) about winter wheat-spring maize rotation system during 2012–2020 in the Loess Plateau. Besides, following a comparative assessment within GWA-CNN-BiLSTM, Convolutional Bidirectional Long Short-Term Memory network (CNN-BiLSTM), BiLSTM, Long Short-Term Memory network (LSTM), and Shuttleworth-Wallace (SW) models, the results revealed that GWA-CNN-BiLSTM under varied inputs obtained the superior performance, ranging from 0.562 to 0.957 in determination coefficient (R2), 8.4–41.5 % in relative root mean square error (RRMSE), 0.349 mm d−1 to 1.521 mm d−1 in mean absolute error (MAE), −3.26 % to 14.11 % in percent bias (PBIAS), and 0.820–1.091 in regression coefficient (b0), respectively. Moreover, while the accuracy of BiLSTM over LSTM was evident, its performance was notably improved by the incorporation of the CNN module. Additionally, LSTM-type models under complete input combination present better precision than SW by 29.7−51.4 % in R2, 44.2−76.1 % in RRMSE, and 33.6−63.4 % in MAE, respectively. Furthermore, the accuracy of all models under varied inputs exhibited excellence in winter wheat compared to spring maize, and corresponding improvements ranged 1.4−4.3 % in R2, 5.1−20.1 % in RRMSE, and 3.1−17.9 % in MAE, respectively. Besides, the meteorological factors (Rn, VPD, Ta) proved to be the most important inputs for ET estimation in winter wheat and spring maize. Wherein the importance of SWC exceeded that of LAI in winter wheat, while the opposite trend was observed in spring maize. In brief, GWA-CNN-BiLSTM is the highly recommended model to estimate ET of winter wheat-spring maize rotation system under diverse input data scenarios in the Loess Plateau, which can facilitate to offer valuable assistance in regional agriculture water management decisions.

Deep multilayered reservoirs are usually developed using multilayered fracturing techniques; however, the non-uniform placement of proppant causes uneven distribution of fracture conductivity. This study introduces a semi-analytical well test model for hydraulically fractured wells in multilayered reservoirs, accounting for varying fracture conductivity within the hydraulic fracture. The model is built upon the point source function, boundary element method, Duhamel theorem, and pressure superposition principle. Verification tests are conducted to ensure calculation accuracy. Sensitivity analysis is performed on key parameters, encompassing the transmissibility factor, storativity factor, fracture extension, and fracture conductivity. The findings indicate that 1) Increased heterogeneity among layers correlates with a more pronounced pressure drop; 2) Poorly-propped fracture conductivity influences the duration of bilinear flow, becoming negligible after linear flow; 3) The model’s applicability extends to other multilayered reservoirs (e.g., carbonate reservoirs) with minor adjustments. Lastly, a case study from Xinjiang oilfield is presented to demonstrate that the proposed method can derive reservoir and fracture properties for each layer individually. This study contributes to a deeper understanding of the potential of pressure data in characterizing multilayered reservoirs.

Abstract Salt crystallization is one of the most important factors causing weathering in grottoes. Cumulative crystallization of salts causes damage such as flaking and peeling of the rocks and accelerates the weathering processes of the grottoes. The accumulated crystalline salts cause spalling, skinning and other damage and accelerate the weathering process of the grotto rock body. It is necessary to study the existing glutenite rock grottoes. This paper took the glutenite rock of the Maijishan Grottoes as a case study, and Na2SO4 or MgSO4 solutions were applied to glutenite rock specimens subjected to different deterioration cycles. The crystallization patterns of the two different salts and their damage to the glutenite rock were analyzed and studied, the mechanism for salt crystallization damage to the glutenite rock was explored, and the crystallization pressures of the two salts in the glutenite rock were derived with theoretical calculations. The results showed that both Na2SO4 and MgSO4 crystallization damaged the glutenite rock, and the former different sulfate solutions changed at different rates, and the changes in the wave caused faster damage than the latter. The physical indices of the rock samples in the velocities and tensile strengths were consistent. Na2SO4 was mainly accumulated on the surface of the specimen and damaged the glutenite rock centripetally via pulverization and exfoliation. MgSO4 mainly crystallized inside the glutenite rock, which created internal fissures and reduced the strength of the rock. The theoretical maximum crystallization pressures of Na2SO4 and MgSO4 in the glutenite rock specimens reached 33.00 MPa and 9.94 MPa, respectively. This study provides a theoretical basis for studies of salt crystallization in glutenite rock grottoes and provides a method for protecting the stones in cultural heritage sites against weathering.

Abstract The problem of change detection in remote sensing image processing is both difficult and important. It is extensively used in a variety of sectors, including land resource planning, monitoring and forecasting of agricultural plant health, and monitoring and assessment of natural disasters. Remote sensing images provide a large amount of long-term and fully covered data for earth environmental monitoring. A lot of progress has been made thanks to deep learning's quick development. But the majority of deep learning-based change detection techniques currently in use rely on the well-known Convolutional neural network (CNN). However, considering the locality of convolutional operation, CNN unable to master the interplay between global and distant semantic information. Some researches has employ Vision Transformer as a backbone in remote sensing field. Inspired by these researches, in this paper, we propose a network named Siam-Swin-Unet, which is a Siamesed pure Transformer with U-shape construction for remote sensing image change detection. Swin Transformer is a hierarchical vision transformer with shifted windows that can extract global feature. To learn local and global semantic feature information, the dual-time image are fed into Siam-Swin-Unet which is composed of Swin Transformer, Unet Siamesenet and two feature fusion module. Considered the Unet and Siamesenet are effective for change detection, We applied it to the model. The feature fusion module is designed for fusion of dual-time image features, and is efficient and low-compute confirmed by our experiments. Our network achieved 94.67 F1 on the CDD dataset (season varying).

Abstract This paper investigates the residential satisfaction levels of tenants living in rental housing converted from non-residential stock buildings in Beijing. A stratified random sampling method was used to select 353 tenants from five apartments based on the plan form and location of the units for a structured questionnaire survey. The results of a hierarchical regression analysis indicated that subjective attributes were more influential in determining residential satisfaction than the objective physical and demographic attributes of the apartments. Within the five dimensions of subjective attributes, the "interior space" dimension had the greatest impact on predicting residential satisfaction. In addition, a one-way ANOVA analysis showed that the floor plan of the apartments also played a significant role in determining residential satisfaction, S-type and office park-type (Converted from an office park) layouts received the highest satisfaction ratings. This research provides valuable insights for revitalizing non-residential stock buildings and offers theoretical support for converting more non-residential stock buildings into rental housing in the future.

Abstract Background The prognosis of SCLC is poor and difficult to predict. The aim of this study was to explore whether a model based on radiomics and clinical features could predict the prognosis of patients with limited-stage small cell lung cancer (LS-SCLC). Methods Simulated positioning CT images and clinical features were retrospectively collected from 200 patients with histological diagnosis of LS-SCLC admitted between 2013 and 2021, which were randomly divided into the training (n = 140) and testing (n = 60) groups. Radiomics features were extracted from simulated positioning CT images, and the t-test and the least absolute shrinkage and selection operator (LASSO) were used to screen radiomics features. We then constructed radiomic score (RadScore) based on the filtered radiomics features. Clinical factors were analyzed using the Kaplan–Meier method. The Cox proportional hazards model was used for further analyses of possible prognostic features and clinical factors to build three models including a radiomic model, a clinical model, and a combined model including clinical factors and RadScore. When a model has prognostic predictive value (AUC > 0.7) in both train and test groups, a nomogram will be created. The performance of three models was evaluated using area under the receiver operating characteristic curve (AUC) and Kaplan–Meier analysis. Results A total of 1037 features were extracted from simulated positioning CT images which were contrast enhanced CT of the chest. The combined model showed the best prediction, with very poor AUC for the radiomic model and the clinical model. The combined model of OS included 4 clinical features and RadScore, with AUCs of 0.71 and 0.70 in the training and test groups. The combined model of PFS included 4 clinical features and RadScore, with AUCs of 0.72 and 0.71 in the training and test groups. T stages, ProGRP and smoke status were the independent variables for OS in the combined model, whereas T stages, ProGRP and prophylactic cranial irradiation (PCI) were the independent factors for PFS. There was a statistically significant difference between the low- and high-risk groups in the combined model of OS (training group, p