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Background: Malnutrition is a poor prognostic factor in a wide range of diseases. Nevertheless, there is a lack of data investigating the association between malnutrition and outcomes of patients with type B aortic dissection (TBAD) undergoing thoracic endovascular aortic repair (TEVAR). Therefore, the aim of the present study was to report the prevalence and clinical impact of malnutrition assessed by the controlling nutritional status (CONUT) score in TBAD patients undergoing TEVAR. Methods: The retrospective study indicated that a total of 881 patients diagnosed with TBAD and treated with TEVAR from January 2010 to December 2017 were categorized into subgroups based on their CONUT score (low ≤5 vs. high >5). To assess the correlation between malnutrition and early and follow-up outcomes of TBAD patients, logistic and Cox regression analysis were utilized, incorporating inverse probability weighting. Results: Malnutrition was present in 20.3% of patients according to the CONUT score. Multivariate logistic regression analysis revealed that pre-operative CONUT score modeled as a continuous variable was an independent risk factor for prolonged intensive care unit stay (odds ratio [OR], 1.09; 95% confidence interval [CI], 1.02–1.17; p = 0.015), 30-day death (OR, 1.43; 95% CI, 1.19–1.72; p < 0.001), delirium (OR, 1.11; 95% CI, 1.01–1.23; p = 0.035) and acute kidney injury (OR, 1.09; 95% CI, 1.01–1.16; p = 0.027). During a median follow-up of 70.8 (46.1–90.8) months, 102 (11.8%) patients died (high CONUT group: 21.8% vs. low CONUT group: 9.0%; p < 0.001). Multivariable Cox proportional-hazards models showed that malnutrition was an independent predictor for follow-up mortality (hazard ratio, 1.68; 95% CI, 1.11–2.53; p = 0.014). Results remained consistent across various sensitivity analyses. Conclusions: Malnutrition assessed by the CONUT score could profoundly affect the early and follow-up prognosis in patients undergoing TEVAR. Routine pre-intervention nutritional evaluation might provide valuable prognostic information.

Research on ecosystem services and residents’ well-being in old revolutionary base areas is an important task for China’s ecological civilization construction and rural revitalization. Taking Jinzhai County, the core area of Dabie Mountains, China, as an example, based on InVEST model, the methods of spatial autocorrelation and coupling coordinated development degree, the spatiotemporal evolution, spatial heterogeneity and coupling association patterns of ecosystem services and multidimensional well-being in the study area from 2005 to 2020 were discussed. The major results are: In the past 15 years, in the core area of the Dabie Mountains, ecosystem services such as food supply, soil retention and water yield showed an upward trend, carbon sequestration and biodiversity maintenance showed a downward trend. The comprehensive index of multidimensional well-being in the core area of Dabie Mountain increased by 27.23% and the spatial difference in multidimensional well-being is gradually narrowing. By the analysis of coupling coordination, the number of units with the type of coupling disharmony between ecosystem services and multidimensional well-being in the study area decreased significantly from 56.85% in 2005 to 26.81% in 2020, respectively. The analysis of geographical detection showed that the habitat quality factor was the dominant controlling factor of coupling coordination spatial difference. By bivariate spatial autocorrelation analysis, in the past 15 years, the number of units with the “high ecology-high well-being” synergy type increased from 5.44% to 13.31%. The results can provide a reference for accurate identification, optimal regulation and synergistic improvement between ecosystem services and relative poverty in the Dabie Mountain area.

Accurate yield estimation before the wheat harvest is very important for precision management, maintaining grain market stability, and ensuring national food security. In this study, to further improve the accuracy of winter wheat yield estimation, machine learning models, including GPR, SVR, and DT, were employed to construct yield estimation models based on the single and multiple growth periods, incorporating the color and multispectral vegetation indexes. The results showed the following: (1) Overall, the performance and accuracy of the yield estimation models based on machine learning were ranked as follows: GPR, SVR, DT. (2) The combination of color indexes and multispectral vegetation indexes effectively improved the yield estimation accuracy of winter wheat compared with the multispectral vegetation indexes and color indexes alone. The accuracy of the yield estimation models based on the multiple growth periods was also higher than that of the single growth period models. The model with multiple growth periods and multiple characteristics had the highest accuracy, with an R2 of 0.83, an RMSE of 297.70 kg/hm2, and an rRMSE of 4.69%. (3) For the single growth period, the accuracy of the yield estimation models based on the color indexes was lower than that of the yield estimation models based on the multispectral vegetation indexes. For the multiple growth periods, the accuracy of the models constructed by the two types of indexes was very close, with R2 of 0.80 and 0.80, RMSE of 330.37 kg/hm2 and 328.95 kg/hm2, and rRMSE of 5.21% and 5.19%, respectively. This indicates that the low-cost RGB camera has good potential for crop yield estimation. Multi-temporal and multi-sensor remote sensing data fusion can further improve the accuracy of winter wheat yield estimation and provide methods and references for winter wheat yield estimation.

Brassica rapa, commonly known as the rapeseed plant, is globally recognized for its nutrient-rich composition and oil-packed seeds, earning its distinction as a substantial oil-seed crop. The seed quality, particularly the germination rate, is instrumental in guaranteeing a high-yield rapeseed crop. Given this, the accurate, quantitative determination and selection of germination rates in seed batches prior to sowing is of paramount importance. However, conventional germination tests, employed to determine the average germination rate of seed batches, are marred by substantial time and cost inefficiencies. This study proposes the use of near-infrared spectral analysis as a proficient, non-invasive approach for assessing germination rates in rapeseed seed batches. The research involved artificial aging of seeds procured from a variety of rapeseed strains, resulting in 228 batches with a broad germination rate spectrum of 15.73% to 99.13%. We recorded near-infrared diffuse reflectance spectra and applied a range of strategies for spectral data preprocessing and feature variable selection. Furthermore, we leveraged support vector regression (SVR) modeling to augment the detection methodology. SVR training and detection were conducted using MATLAB, with selected feature wavelengths undergoing rigorous scrutiny and discussion. The results indicated that employing Savitzky–Golay convolution smoothing for spectral preprocessing, along with Synergy interval Partial Least Squares (SiPLS) in conjunction with Random Frog (RF) for the selection of 50 feature wavelength points, yielded optimal germination rate prediction performance within the SVR model. The coefficients of determination (R2c) for the training set and (R2p) for the testing set were observed to be 0.8559 and 0.8386, respectively, while the Root Mean Square Errors of Calibration (RMSEC) and Prediction (RMSEP) were calculated to be 13.76% and 17.04%. The mechanism of detecting seed vigor through near-infrared spectroscopy was analyzed based on joint variable screening and sensitive variable traceability. Consequently, the SG–SiPLS–RF–SVR model demonstrates its effectiveness in predicting the average germination rate of seed batches, offering a rapid, non-invasive detection method that can be universally applied to various rapeseed strains, thus significantly improving seed production efficiency.

Abstract Background Heterotrimeric G-proteins, composed of Gα, Gβ and Gγ subunits, are important signal transmitters, mediating the cellular response to multiple stimuli in animals and plants. The Gγ subunit is an essential component of the G-protein, providing appropriate functional specificity to the heterotrimer complex and has been well studied in many species. However, the evolutionary history, expression pattern and functional characteristics of Gγ subunits has not been explored in the Rosaceae, representing many important fruit crops. Results In this study, 35 Gγ subunit genes were identified from the eight species belonging to the Rosaceae family. Based on the structural gene characteristics, conserved protein motifs and phylogenetic analysis of the Gγ subunit genes, the genes were classified into three clades. Purifying selection was shown to play an important role in the evolution of Gγ subunit genes, while a recent whole-genome duplication event was the principal force determining the expansion of the Gγ subunit gene family in the subfamily Maloideae. Gγ subunit genes exhibited diverse spatiotemporal expression patterns in Chinese white pear, including fruit, root, ovary and bud, and under abiotic stress conditions, the relative expression of Gγ subunit genes were up-regulated or down-regulated. In addition, seven of the Gγ subunit proteins in pear were located on the plasma membrane, in the cytoplasm or nucleus. Conclusion Overall, this study of the Gγ subunit gene family in eight Rosaceae species provided useful information to better understand the evolution and expression of these genes and facilitated further exploration of their functions in these important crop plants.

To establish a collision model of wheat grains impacting a force plate with a piezoelectric sensor, and to investigate the influence of the elastic recovery coefficient on the sensor’s detection accuracy during the collision process, this study employed object kinematic principles to construct a wheat elastic recovery coefficient measurement device. This device ascertains the elastic properties of wheat during collisions and determines the elastic recovery coefficient of the wheat collision model. The wheat variety Jinan No. 17 was selected for testing, and the effects of the contact material, grain drop height, material thickness, and grain moisture content on the collision recovery coefficient during the collision process were analyzed through single-factor and multi-factor experiments. The experimental results demonstrate that the collision recovery coefficient of wheat grains increases with the stiffness of the collision materials for different materials. The grain recovery coefficient of wheat exhibits a downward trend with increasing falling height and moisture content, while it tends to rise as the material thickness increases. Data analysis and comparison reveal that, given the determination of the collision material, the moisture content of wheat exerts the most significant effect on the elastic recovery coefficient, followed by material thickness, while the influence of falling height is less pronounced. The findings of this study can provide data support for simulation testing and product design of wheat combine harvester cleaning screen body mechanisms and wheat seeders.

In this study, in order to achieve accurate detection of grain loss during the process of combine harvester harvesting, a piezoelectric-based grain loss sensor has been developed. The sensor utilizes a double-layer circular piezoelectric ceramic sheet as its sensitive element. When different grains come into contact with the sensitive element, the piezoelectric effect of the ceramic sheet generates corresponding charges. These charges are then converted into knock charge signals through a charge amplification signal processing circuit that has been specially designed for this purpose. The harsh operating conditions of the sensor, including the presence of significant vibration and noise interference, necessitate the incorporation of a double-layer vibration-damping structure in both the top and bottom layers of the sensor. This paper seeks to analyze the vibration-damping effect of various shock-absorbing materials and structures incorporated into the sensor. This is accomplished by creating a dynamic analysis model that accounts for vibration interference. Furthermore, an experimental bench is established for the purpose of verifying the vibration-damping test results. These tests demonstrate that the utilization of a properly selected vibration-damping structure and materials can effectively eliminate mechanical vibration and noise interference. This, in turn, leads to improved detection accuracy of charge signals after knocking and enhances the overall anti-interference ability of the sensor.

Abstract Background The members of the sucrose non-fermenting 1-related protein kinase 2 (SnRK2) family are specific serine/threonine protein kinases in plants that play important roles in stress signal transduction and adaptation. Because of their positive regulatory roles in response to adverse conditions, the genes encoding thes proteins are considered potential candidates for breeding of plants for disease resistance and genetic improvement. However, there is far less information about this kinase family, and the function of these genes has not been explored in Rosaceae. Results A genome-wide survey and analysis of the genes encoding members of the SnRK2 family were performed in pear (Pyrus bretschneideri) and seven other Rosaceae species. A total of 71 SnRK2 genes were identified from the eight Rosaceae species and classified into three subgroups based on phylogenetic analysis and structural characteristics. Purifying selection played a crucial role in the evolution of SnRK2 genes, and whole-genome duplication and dispersed duplication were the primary forces underlying the characteristics of the SnRK2 gene family in Rosaceae. Transcriptome data and qRT-PCR assay results revealed that the distribution of PbrSnRK2s was very extensive, including across the roots, leaves, pollen, styles, and flowers, although most of them were mainly expressed in leaves. In addition, under stress conditions, the transcript levels of some of the genes were upregulated in leaves in response to ABA treatment. Conclusions This study provides useful information and a theoretical introduction for the study of the evolution, expression, and functions of the SnRK2 gene family in plants.

CONSTANS-like (COL) genes play important regulatory roles in multiple growth and development processes of plants but have rarely been studied in Capsicum annuum. This study explored the evolutionary relationship and expression patterns of COL genes from C. annuum. A total of 10 COL genes were identified in the genome of the cultivated pepper Zunla-1 and were named CaCOL01–10. These genes were unequally distributed among five chromosomes and could be divided into three groups based on differences in gene structure characteristics. During evolutionary history, duplications and retentions were divergent among different groups of COL genes. Tandem duplication caused amplification of group I genes. Genetic distance among COL genes was the largest in group III, suggesting that group III genes undergo more relaxed selection pressure compared with the other groups. Expression patterns of CaCOLs in tissues were significantly different, with CaCOL08 exhibiting the highest expression in stem and leaf. Some COL orthologous genes showed markedly different expression patterns in pepper compared with tomato, such as COL_1 orthologs, which may be involved in fruit development in pepper. In addition, CaCOLs participated in the regulation of abiotic stresses to varying degrees. Five CaCOL genes were induced by cold, and CaCOL02 and CaCOL03 were specifically upregulated by cold and downregulated by heat. This study provides a theoretical basis for the in-depth understanding of the functions of COL genes in pepper and their molecular mechanisms involved in growth and development and responses to abiotic stresses.

For existing problems, such as the complex interactions between a crop and a machine, the measuring difficulty and the limited measurement precision of the feeding quantity within the corn silage harvester, a method of feeding rate measurement based on key conditions data, working data cleaning, and multiple variate regression is proposed. Non-destructive rotation speed, rotation torque, and power consumption sensors are designed for the key mechanical components. The data conditions, such as rotating speed, rotating torque, power consumption, hydraulic pressure, and hydraulic flow for the key operation of parts including cutting, feeding, shredding, and throwing are monitored and collected in real-time during field harvesting. The working data are screened and preprocessed, and the Mann-Kendall boundary extraction algorithm is applied, as is multiple component time lag correction analysis, and the Grubbs exception detection method. Based on a Pearson correlation analysis results, one-factor and multiple-factor regression models are respectively developed to achieve an accurate measurement of the corn feeding rate. The field validation tests show that the working data boundary extraction results among the load-stabilizing components such as shredding roller and throwing blower are highly reliable, with a correct rate of 100%. The power monitoring data of the shredding roller and throwing blowers are significantly correlated with the crop feeding rate, with a max correlation coefficient of 0.97. The determination coefficient of the single-factor feeding rate model based on the shredding roller reaches 0.94, and the maximum absolute error of the multi-factor feeding rate model is 0.58 kg/s. The maximum relative error is ±5.84%, providing technical and data support for the automatic measuring and intelligent tuning of the feeding quantity in a silage harvester.

Phosphorus (P) is an essential macronutrient for the growth and development of fruit trees, playing an important role in photosynthesis, nucleic acid synthesis, and enzyme activity regulation. The plasticity of plant phenotypic has been investigated in diverse species under conditions of P-deficiency or P-excess. Based on these researches, P level fluctuations in different species result in different characteristics of the response. Nevertheless, little is known about the response of pear seedling rootstock (Pyrus betulifolia Bunge) to the changing of P levels. To explore the effects of different levels of P on the growth of pear seedling rootstock, we performed the hydroponic assays to determine and analyze the biological indexes including growth parameters, photosynthetic rate, root and shoot morphological traits, and concentrations of macro- and micronutrients. The results show that either deficiency or excess of P inhibited the growth and development of pear seedling rootstock. Root growth (down 44.8%), photosynthetic rate (down 59.8%), and acid phosphatase (ACP) activity (down 44.4%) were inhibited under the P-deficiency conditions (0mM), compared with normal P conditions (1mM). On the other hand, dark green leaves, suppression of root elongation (down 18.8%), and photosynthetic rate (down 25%) were observed under regimes of excessive P, compared with normal P conditions (1mM). Furthermore, the root concentration of not only P, but also those of other mineral nutrients were affected by either P treatment. In brief, these results indicated that a careful choice of P fertilizer supply is crucial to ensuring normal growth and development of pear seedling rootstock.

The combine harvester is the main machine for fieldwork during the harvest season. When the harvester fails and cannot continue to work, this indirectly affects the harvest time and the yield in the field. The emergency maintenance service of agricultural machinery can be optimized through the dynamic planning of harvester maintenance tasks, using the operation and maintenance platform. According to the scene, a priority scheme for the operation and maintenance tasks, based on the improved Q-learning algorithm, was proposed. The continuous approximation capability of the model was improved by using the BP neural network algorithm and the Q function value, in iterations, was updated continuously. At the same time, the improved TOPSIS method, based on Mahalanobis distance, was used to calculate the closeness of each harvester maintenance task, so as to determine the priority of the equipment maintenance tasks. An operation and maintenance service platform for combine harvesters was developed based on the B/S architecture, with the goal of minimizing the operation and maintenance costs and improving the tasks’ complete efficiency. In this research process, dynamic scheduling rules were formulated. Operation and maintenance resources were optimized and rationally allocated through dynamic optimization scheduling methods, and feasible solution information was generated from the operation and maintenance service platform. Finally, the actual data from the enterprise were used for verification and analysis. The verification showed the following: through a comparison of algorithm performance, it was seen that the improved BP-Q-Learning algorithm can quickly find the operation and maintenance scheduling scheme in the maintenance scheduling; the priority rules can improve the efficiency of task execution, to a certain extent; the cost of the tasks’ execution can be significantly reduced; and the maintenance distance can be shortened. This research has reference significance for the formulation and optimization of agricultural machinery maintenance for cross-regional operations.

In view of the centralized operation, high failure rate and large number of harvesters involved in the cross-regional operation of combine harvesters, which has led to a surge in maintenance service demand and a lack of effective maintenance service systems, in order to be able to quickly solve problems arising from failures during the process of cross-regional operation, an operation and maintenance (O&M) service platform for the cross-regional operation of combine harvesters was designed in this research on the basis of data resources, supported by the computing power of a big data platform and centered on an artificial intelligence algorithm. Meeting the demand for maintenance service during cross-regional operation, we built a system platform integrating service order management, maintenance service activity management, and maintenance service resource management, and a technical algorithm for operation and maintenance service resource allocation and service path optimization was developed in order to achieve service function modularization and intelligent monitoring, while early warning and display were realized using multi-dimensional platforms such as a PC, a control screen, and a mobile App. This platform was able to solve problems arising when harvesters break down, maintenance service can be carried out quickly when traditional resource information is blocked and the demand for the service is difficult to meet. The reduction in cost and the increased efficiency for agricultural machinery enterprises was also achieved, while the problem of ensuring continued service was systematically solved during the process of cross-regional operation. Finally, the performance of the software architecture and the effect of path optimization were verified. The results showed that the platform system developed using the three-layer C/S architecture offered more stable characteristics, and the path optimization in the platform system was better able to reduce the maintenance time and distance, thus making it possible to realize the dynamic on-demand configuration and scheduling management of cross-region job service resources.

Thoracic ectopic kidney with congenital diaphragmatic hernia is a rare congenital malformation. We report a case of right thoracic ectopic kidney associated with congenital diaphragmatic hernia in a 4-year-old girl. Chest X-ray indicated the right diaphragmatic distention and contrast-enhanced CT showed thoracic ectopic kidney. We chose thoracoscopic approach for a complete anatomical repair. The bottom of the lower lobe of the right lung adhered to the hernia sac. Right thoracic ectopic kidney was pushed into abdominal cavity and congenital diaphragmatic defect was closed. Postoperative hemodynamics and renal function were normal. We review similar literatures and summarize surgical experience.

As a prerequisite for the control of the brake by wire system, the identification of braking intention has a direct impact on the accuracy of the vehicle control system, and it affects the vehicle running safety under specific working conditions, thus, in order to improve the active safety and braking performance of the vehicle, aiming at the longitudinal stability control problem in vehicle dynamics, the braking intention is taken as the breakthrough point, and the classification of braking intentions is introduced. At the same time, the vehicle dynamics control of braking intention recognition based on the research status at home and abroad is outlined. Besides, combining with the braking intention recognition feature selection problem, several typical braking intention recognition methods, including fuzzy inference system, neural network, adaptive neural fuzzy inference system, hidden Markov model and cluster analysis, are comparatively analyzed. Considering the status quo of research, the research emphasis and direction of braking intention recognition for vehicle dynamics control is pointed out, including reasonably selecting characteristic parameters, transition of export target and multistandard evaluation system.

In view of the difficulty in diagnosing and discriminating fault conditions during the operation of combine harvesters, difficulty in real-time processing of health status, and low timeliness of fault processing, a comprehensive operation and maintenance platform for combine harvesters was developed in this study which realized the functions of data monitoring and the full operation and maintenance of a combine harvester. At the same time, through the comprehensive operation and maintenance platform, the harvester information was obtained in real-time, the diagnosis results were obtained, and the maintenance service was effectively carried out through the platform. The IPSO-SVM fault diagnosis algorithm was proposed, and the performance of the fault diagnosis of the combine harvester was verified by the simulation test. The experimental verification showed that the system met the requirements of remote monitoring of combine harvesters, and the prediction accuracy of this method was 97.96%. Compared with SVM (87.51%), GA-SVM (89.44%), and PSO-SVM (92.56%), this system had better generalization ability and effectively improved the management level of the comprehensive operation and maintenance of the combine harvester. A theoretical basis and technical reference will be provided for the follow-up research for the comprehensive operation and maintenance platform of the combine harvester in this paper.

For passengers, the most common feeling during running on the bumpy road is continuous vertical discomfort, and when the vehicle is braking, especially the emergency braking, the instantaneous inertia of the vehicle can also cause a strong discomfort of the passengers, so studying the comfort of the vehicle during the braking process is of great significance for improving the performance of the vehicle. This paper presented a complete control scheme for vehicles equipped with the brake-by-wire (BBW) system aiming at ensuring braking comfort. A novel braking intention classification method was proposed based on vehicle braking comfort, which divided braking intention into mild brake, medium comfort brake, and emergency brake. Correspondingly, in order to improve the control accuracy of the vehicle brake system and to best meet the driver’s brake needs, a braking intention recognizer relying on fuzzy logic was established, which used the road condition and the brake pedal voltage and its change rate as input, output real-time driver's braking intention, and braking intensity. An optimal brake force distribution strategy for the vehicle equipped with the BBW system based on slip rate was proposed to determine the relationship between braking intensity and target slip ratio. Combined with the vehicle dynamics model, improved sliding mode controller, and brake force observer, the joint simulation was conducted in Simulink and CarSim. The cosimulation results show that the proposed braking intention classification method, braking intention recognizer, brake force distribution strategy, and sliding mode control can well ensure the braking comfort of the vehicle equipped with the BBW system under the premise of ensuring brake safety.

Understanding the extent and directionality of the impact of human activities on ecosystems is directly related to their management and protection. However, the lack of historical data limits our understanding of ecosystem changes with long-term exposure to human activities. Recently, lake sedimentary DNA (sedDNA) has become a powerful tool for revealing changes in ecosystems at the century and millennium scales. Here, we used sedDNA to reveal the dynamic of the microbial community (including bacteria and micro-eukaryotes) in Lake Chao over the past 150 years, and further explored the effects of long-term nutrient and heavy metal loads on these communities. Our data show that nutrient and heavy metal loads in Lake Chao have increased by ca. 2 to 4-fold since the 1960s. In response, the community structure, diversity, and ecological network of bacteria and micro-eukaryotes changed significantly during the 1960s, the 1980s and the 2010s. Importantly, community structure was more sensitive to human activities than diversity. We also found that the relative abundance of some taxa associated with nitrification and algal blooms (e.g., taxa in Nitrospira sp., Peridinales) has increased ca. 100-fold since the 1960s. Nutrient could better explain the variation in the bacterial community (ca. twice as much as heavy metal), while heavy metal explained micro-eukaryotes better (ca. 3 or 5-fold as much as nutrient). In particular, based on parsimonious models from distance-based linear model (distLM), we further identified that Pb is the key factor affecting the bacterial and micro-eukaryotes community in Lake Chao in addition to nutrient. Our study reveals the impacts of long-term human activities on lake ecosystems from multiple perspectives of nutrient and heavy metal loads, community structure, diversity and ecological network, these findings will contribute to the management and conservation of lakes in the future. Keywords: SedDNA, Long-term dynamics, Rare taxa, Nutrient, Heavy metal, Sediment core

Non-coding RNAs (ncRNAs) are defined as RNA molecules that do not encode proteins, but recent evidence has proven that peptides/proteins encoded by ncRNAs do indeed exist and usually contain less than 100 amino acids. These peptides/proteins play an important role in regulating tumor energy metabolism, epithelial to mesenchymal transition of cancer cells, the stability of the c-Myc oncoprotein, and the ubiquitination and degradation of proliferating cell nuclear antigen (PCNA). These peptides/proteins represent promising drug targets for fighting against tumor growth or biomarkers for predicting the prognosis of cancer patients. In this review, we summarize the characteristics of peptides/proteins that have recently been identified as putative ncRNA translation products and their outlook for small molecule peptide drugs, drug targets, and biomarkers.