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Abstract The chloroplast genomes of wild loquat can help to determine their place in the history of evolution. Here, we sequenced and assembled two novel wild loquat’s chloroplast genomes, one is Eriobotrya elliptica, and the other is an unidentified wild loquat, which we named “YN-1”. Their sizes are 159,471 bp and 159,399 bp, respectively. We also assembled a cultivated loquat named ‘JFZ’, its chloroplast genome size is 159,156 bp. A comparative study was conducted with six distinct species of loquats, including five wild loquats and one cultivated loquat. The results showed that both E. elliptica and “YN-1” have 127 genes, one gene more than E. fragrans, which is psbK. Regions trnF-GAA-ndhJ, petG-trnP-UGG, and rpl32-trnL-UAG were found to exhibit high variability. It was discovered that there was a positive selection on rpl22 and rps12. RNA editing analysis found several chilling stress-specific RNA editing sites, especially in rpl2 gene. Phylogenetic analysis results showed that “YN-1” is closely related to E. elliptica, E. obovata and E. henryi.

Landslide disasters, due to their widespread distribution and clustered occurrences, pose a significant threat to human society. Rainfall is considered a primary triggering factor, and the frequent clustering of landslides underscores the importance of early warning systems for regional landslide disasters in preventing and mitigating rainfall-induced landslides. Research on early warning models is crucial for accurately predicting rainfall-induced landslides. However, traditional models face challenges such as the complexity of landslide causes, insufficient data, and limited analysis methods, resulting in low accuracy and inadequate precision. This study focuses on Fujian Province, China, proposing a four-step process for building a regional landslide early warning model based on machine learning. The process includes data integration and cleaning, sample set construction, model training and validation, and practical application. By integrating and cleaning the latest and most detailed data, a training sample set (15,589 samples) for the regional landslide disaster early warning model is established. Three machine learning algorithms—Random Forest, Multilayer Perceptron, and Convolutional Neural Network—are employed and compared, the evaluation results indicated that the RF-based warning model achieved an accuracy of 0.930–0.957 and an AUC value of 0.955. The CNN-based warning model demonstrated an accuracy of 0.945–0.948 with an AUC value of 0.940. The MLP-based warning model achieved an accuracy of 0.930–0.953 and an AUC value of 0.930. The results showed comparable accuracy metrics among the three models, with RF exhibiting a significant advantage in AUC values. Finally, the models are applied to the regional landslide disasters induced by heavy rainfall in Fujian Province on 5 August 2021. The results showed that in the binary classification warning strategy, the accuracy of the Random Forest and Convolutional Neural Network was 92.9%, while that of the Multilayer Perceptron was 85.8%, all performing well. In the multi-classification hierarchical warning strategy, the Random Forest excelled, while the performance of the Convolutional Neural Network and Multilayer Perceptron was relatively limited. The findings of this study contribute to valuable attempts in landslide disaster warning model research, with anticipated further improvements through the gradual accumulation of samples and practical application verification.

Farmland boundary data, an important basic data for the operation of agricultural automation equipment, has been widely studied by scholars from all over the world. However, the common methods of farmland boundary acquisition through sensors such as LiDAR and vision cameras combined with complex algorithms suffer from problems such as serious data drift, difficulty in eliminating noise, and inaccurate plot boundary data. In order to solve this problem, this study proposes a method for estimating the orientation dimensions of farmland based on the seeding trajectory. The method firstly calculates the curvature of the discrete data of the seeding trajectory; secondly, we innovatively use a low-pass filter and integral clustering to filter the curvature values and distinguish between straight lines and curves; and finally, the straight-line portion located at the edge of the seeding trajectory is fitted with a univariate linear fit to calculate the estimation of the farmland size orientation. As verified by the field experiments, the minimum linear error of the vertices is only 0.12m, the average error is 0.315m, and the overlapping rate of the plot estimation is 98.36% compared with the real boundary of the plot. Compared with LiDAR mapping, the average linear error of the vertices’ position is reduced by 50.2%, and the plot estimation overlap rate is increased by 2.21%. The experimental results show that this method has the advantage of high accuracy, fast calculation speed, and small calculation volume, which provides a simple and accurate method for constructing farmland maps, provides the digital data support for the operation of agricultural automation equipment, and has significance for farm digital mapping.

Grain size is one of the critical factors determining rice yield. Previous studies have found the grain-size-regulating function of Gα in rice. However, the regulatory mechanism underlying the development of rice grain mediated by Gα is still unclear. To reveal the functional mechanism of Gα in grain size regulation, a mutant of Gα (Gα-Cas9) was firstly constructed through a CRISPR/Cas9 strategy and was then grown in a greenhouse and field. The results showed that the seed length, plant height, 1000-grain weight, and spike length were significantly decreased in Gα-Cas9 compared to wild-type (WT) Pi-4b. During the grain filling stage, the increase in the grain dry weight of Pi-4b occurred earlier than that of Gα-Cas9. The total starch content and amylose content of matured grains of Pi-4b were higher than those of Gα-Cas9. Secondly, transcriptome sequencing analysis of Gα-Cas9 and Pi-4b during grain filling was performed to elucidate the functional pathways regulated by Gα. In total, 2867 and 4534 differentially expressed genes (DEGs) were discovered at 5 DAF and 10 DAF, and the starch and sucrose metabolism pathway enriched by DEGs was involved in grain size regulation mediated by Gα. Gα regulated the expression of starch-synthesis-related genes during grain filling, and the Gα protein interacted with OsNYC4 to trigger the sugar signaling pathway to promote starch accumulation in grain. Additionally, the GW2-WG1-OsbZIP47 pathway was switched off by Gα to relieve the inhibition of rice grain development. In this study, the results should provide new insights into the G protein signal transduction pathway.

Abstract The decline of endothelial autophagy is closely related to vascular senescence and disease, although the molecular mechanisms connecting these outcomes in vascular endothelial cells (VECs) remain unclear. Here, we identify a crucial role for CD44, a multifunctional adhesion molecule, in controlling autophagy and ageing in VECs. The CD44 intercellular domain (CD44ICD) negatively regulates autophagy by reducing PIK3R4 and PIK3C3 levels and disrupting STAT3-dependent PtdIns3K complexes. CD44 and its homologue clec-31 are increased in ageing vascular endothelium and Caenorhabditis elegans, respectively, suggesting that an age-dependent increase in CD44 induces autophagy decline and ageing phenotypes. Accordingly, CD44 knockdown ameliorates age-associated phenotypes in VECs. The endothelium-specific CD44ICD knock-in mouse is shorter-lived, with VECs exhibiting obvious premature ageing characteristics associated with decreased basal autophagy. Autophagy activation suppresses the premature ageing of human and mouse VECs overexpressing CD44ICD, function conserved in the CD44 homologue clec-31 in C. elegans. Our work describes a mechanism coordinated by CD44 function bridging autophagy decline and ageing.

Oil shale, as a vast potential resource, is considered an important alternative energy source, and its effective development and economic utilization are of significant importance in alleviating the contradiction between energy supply and demand. Presently, the in situ conversion technology for oil shale has gained significant global attention, with numerous extraction methods undergoing active research and development. One of these methods is the in situ conversion of oil shale based on the hydration reaction of calcium-oxide-based composite material (CaO-CM). This approach harnesses the heat produced by the reaction between CaO-CM and water as a heat source for the pyrolysis of oil shale. This paper conducted experiments to assess the feasibility of temperature associated with this method. The feasibility study mainly includes two aspects: First, it is necessary to investigate whether the temperature generated by the hydration reaction of CaO-CM can meet the temperature requirements for the pyrolysis of oil shale. Through pyrolysis experiments of Xinjiang oil shale, the minimum temperature required for oil shale pyrolysis was determined to be 330 °C. High-temperature and high-pressure reaction vessels were employed to explore the temperature generated by the hydration reaction of CaO-CM. The results show that with the increase in environment pressure, environment temperature, and reaction mass, the maximum temperature generated by the hydration reaction of CaO-CM also increases (reach 455.5 °C), meeting the temperature requirements for the pyrolysis of oil shale. Second, the study evaluates whether the hydration reaction of CaO-CM can induce pyrolysis hydrocarbons of the oil shale. Through the pyrolysis experiments of oil shale based on the hydration reaction of CaO-CM, the changes in the content of pyrolysis hydrocarbons (S2) in oil shale before and after pyrolysis are measured. The results show that under 10 MPa pressure, the content of pyrolysis hydrocarbons in the oil shale decreased from 40.96 mg/g to 0.08 mg/g after pyrolysis. This confirms the feasibility of the temperature conditions for the in situ conversion of oil shale based on the hydration reaction of CaO-CM.

ABSTRACTTo evaluate the temporal and spatial distribution of the knowledge network about tumor microenvironment and prognoses and explore new research hot spots and trends. Articles and reviews on tumor microenvironment and prognoses in the Web of Science journal from January 1999 to April 2022 were included. We used the CiteSpace and VOSviewer software to analyze the knowledge network composed of journals, institutions, countries, authors, and keywords. Frontiers in Immunology, Cancers, and Frontiers in Oncology have published more than 10% of articles in this field. China and the United States have contributed the most articles. Fudan University and Sun Yat-Sen University are the most active institutions. The authors in this field work closely; Zhang Wei and Douglas have made outstanding contributions. The three main research areas of tumor microenvironment and prognoses are microenvironment, prognosis, and immunotherapy. Until 2020, the main keywords were endothelial growth factor and adhesion. In the past three years, survival analysis, immune cell infiltration, and prediction model have been used. It can be seen that the focus in this field has shifted from tumor cell behavior and directly related molecules to prognosis prediction and non-tumor cells in the microenvironment. The future research trend may be to study the changes in the tumor microenvironment to predict the prognosis and guide the treatment. VOSviewer, CiteSpace, and Microsoft Excel 2019 were used to conduct a comprehensive visual analysis of the research on tumor environment and prognoses and provide valuable reference materials for researchers.

Landslides are a natural disaster that exists widely in the world and poses a great threat to human life and property, so it is of great importance to identify and locate landslides. Traditional manual interpretation can effectively identify landslides, but its efficiency is very low for large interpreted areas. In this sense, a landslide recognition method based on the Dual Graph Convolutional Network (DGCNet) is proposed to identify the landslide in remote sensing images quickly and accurately. The remote sensing image (regional remote sensing image) of the northern mountainous area of Tuergen Township, Xinyuan County, Xinjiang Province, was obtained by GeoEye-1 (spatial resolution: 0.5 m). Then, the DGCNet is used to train the labeled images, which finally shows good accuracy of landslide recognition. To show the difference with the traditional convolutional network model, this paper adopts a convolution neural network algorithm named GoogLeNet for image recognition to carry out a comparative analysis, the remote sensing satellite images (single terrain image) of Xinyuan County, Xinjiang Province is used as the data set, and the prediction accuracy is 81.25%. Compared with the GoogLeNet model, the DGCNet model has a larger identification range, which provides a new method for landslide recognition of large-scale regional remote sensing images, but the performance of DGCNet is highly dependent on the quality and characteristics of the input image. If the input data quality is poor or the image structure is unclear, the model’s performance may decline.

Chemical looping gasification (CLG) of biomass produces high contents of syngas, which would have inhibition effect on the gasification of its biomass char. Experiments using a rice husk char as fuel and a low-cost red mud as oxygen carrier for CLG investigation were performed, and effects of temperature, concentrations of steam and H2 on gasification rate were evaluated. Meanwhile, the mathematical models coupling with reaction and diffusion were established focusing on the H2 inhibition on syngas distributions inside and surrounding a single char particle. The results indicated that H2 in the reaction atmosphere has an inhibition effect on its char conversion, and at a high temperature the inhibition effect tends to be stronger. The shrinking core model (spherical symmetry) was found to be suitable to describe the char conversion under the present conditions with the reaction kinetic parameters of E = 128.8 kJ mol−1 and A = 451.2 s−1. In the internal diffusion of a single char particle, the concentrations of CO and H2 both decrease with the increase of dimensionless radius due to the consumption of carbon. In the external diffusion of the char particle, the concentrations of CO and H2 decrease with the increase of the dimensionless radius. The accumulation of H2 inside the char particle prevents CO production, thus inhibiting char gasification.

Nitrogen metabolism is an important physiological process that affects the survival and virulence of Mycobacterium tuberculosis. M. tuberculosis’s utilization of nitrogen in the environment and its adaptation to the harsh environment of acid and low oxygen in macrophages are closely related to nitrogen metabolism. In addition, the dormancy state and drug resistance of M. tuberculosis are closely related to nitrogen metabolism. Although nitrogen metabolism is so important, limited research was performed on nitrogen metabolism as compared with carbon metabolism. M. tuberculosis can use a variety of inorganic or organic nitrogen sources, including ammonium salts, nitrate, glutamine, asparagine, etc. In these metabolic pathways, some enzymes encoded by key genes, such as GlnA1, AnsP2, etc, play important regulatory roles in the pathogenesis of TB. Although various small molecule inhibitors and drugs have been developed for different nitrogen metabolism processes, however, long-term validation is needed before their practical application. Most importantly, with the emergence of multidrug-resistant strains, eradication, and control of M. tuberculosis will still be very challenging.

ObjectiveThis study aimed to identify author, country, institutional, and journal collaborations and their impacts, assess the knowledge base, identify existing trends, and uncover emerging topics related to the role of Metalloproteinase in osteosarcoma.Methods945 Articles and reviews associated with the role of Metalloproteinase in osteosarcoma were obtained from the WoSCC and analyzed by Citespace and Vosviewer.ResultsThe main aspects of research on the role of MMP in OS are invasion and metastasis. The latest hotspots were found to be the mechanism of MMP promoting invasion and metastasis, lung metastasis, and antitumor activity. Notably, invasion, metastasis, and antitumor activity were potentially turning points in the MMP-OS field. In the future, the primary research hotspot in the field of MMP-OS may be to study the mechanism, explore their role in the OS lung metastasis, and determine their role in the cancer therapy process.ConclusionThis study thus offers a comprehensive overview of the MMP-OS-related field using bibliometrics and visual methods, which will provide a valuable reference for researchers interested in the field of MMP-OS.

Porcine epidemic diarrhea virus (PEDV) mainly infects the intestinal epithelial cells of pigs, causing porcine epidemic diarrhea (PED). In particular, the virus causes severe diarrhea, dehydration, and death in neonatal piglets. Maternal immunity effectively protects neonatal piglets from PEDV infection; however, maternal antibodies can only prevent PEDV attachment and entry into target cells, but have no effects on intracellular viruses. Intracellular antibodies targeting virus-encoded proteins are effective in preventing viral infection. We previously identified four single chain variable fragments (scFvs), ZW1-16, ZW3-21, ZW1-41, and ZW4-16, which specifically targeted the PEDV N protein and significantly inhibited PEDV replication and up-regulated interferon-λ1 (IFN-λ1) expression in host cells. In our current study, the four scFvs were subcloned into replication-defective adenovirus vectors to generate recombinant adenoviruses rAdV-ZW1-16, rAdV-ZW3-21, rAdV-ZW1-41, and rAdV-ZW4-16. ScFvs were successfully expressed in Human Embryonic Kidney 293 (HEK293) cells and intestinal porcine epithelial cell line J2 (IPEC-J2) and were biosafe for piglets as indicated by body temperature and weight, scFv excretion in feces, IFN-γ and interleukin-4 (IL-4) expression in jejunum, and pathological changes in porcine tissue after oral administration. Western blotting, immunofluorescence, and immunohistochemical analyses showed that scFvs were expressed in porcine jejunum. The prophylactic effects of rAdV-ZW, a cocktail of the four rAdV-scFvs, on piglet diarrhea caused by PEDV was investigated. Clinical symptoms in piglets orally challenged with PEDV, following a two-time treatment with rAdV-ZW, were significantly reduced when compared with PEDV-infected piglets treated with phosphate buffered saline (PBS) or rAdV-wild-type. Also, no death and jejunal lesions were observed. ScFv co-localization with the PEDV N protein in vivo was also observed. Next, the expression of pro-inflammatory serum cytokines such as tumor necrosis factor-α (TNF-α), IL-6, IL-8, IL-12, and IFN-λ was assessed by enzyme-linked immunosorbent assay (ELISA), which showed that scFvs significantly suppressed PEDV-induced pro-inflammatory cytokine expression and restored PEDV-inhibited IFN-λ expression. Therefore, our study supported a promising role for intracellular scFvs targeting the PEDV N protein to prevent and treat diarrhea in PEDV-infected piglets.

Non-contact and non-destructive polarization 3D imaging uses a passive, single-frame array image to calculate 3D information, making it possible to obtain high-precision 3D information about tunnel cracks, and offering outstanding technical advantages. Based on the introduction of the principle of crack detection with polarization 3D imaging, a tunnel lining crack detection plan was developed and a detection equipment was designed. The method and process of polarization 3D imaging for lining crack detection are described in detail. A model of the impact of the tunnel environment on 3D detection and a method for obtaining absolute information have been established to obtain high-precision 3D information about cracks. In a real tunnel environment, tests were conducted to detect wide cracks, narrow cracks, and artificial cracks. The crack detection accuracy with respect to the crack width was 0.2–0.3 mm, and with respect to crack length was 0.2–0.3 mm. At the same time, crack depth information could be obtained. The present research results can provide technical support for the application of polarization 3D imaging in tunnel crack detection.

In situ heat injection is a suitable technique for extracting shale oil from reservoirs with high organic matter content but insufficient thermal maturation. To optimize the stimulation process and to avoid unnecessary energy consumption, understanding the thermal process and the effects of thermal parameters is crucial. This research employs a self-developed simulator to build a 2D numerical model of the in situ conversion process of kerogen with electric heaters. A benchmark model is first established to determine the effects of heat injection on crude oil production and kerogen decomposition. Subsequently, this study analyzes the evolution of shale oil within the reservoir, identifying the role of thermal and physical properties in crude oil production and kerogen decomposition during the stimulation treatment. A sensitivity analysis of the thermal properties of the reservoir is also carried out, which allows for defining the role of the thermal conductivity of the rock during the stimulation process. Finally, it is observed that, when using the injection at a constant power, the injection time to achieve a suitable large rate of decomposition is shorter than at a constant temperature—consequently, it has a higher economic advantage.

The present study focuses on summarizing the recent advancements in the field of fish swimming mode research and bionic robotic fish prototypes based on smart materials. It has been widely acknowledged that fish exhibit exceptional swimming efficiency and manoeuvrability compared to conventional underwater vehicles. In the pursuit of developing autonomous underwater vehicles (AUVs), conventional experimental methods often prove to be complex and expensive. Hence, the utilization of computer simulations for hydrodynamic modelling provides a cost-effective and efficient approach for analysing the swimming behaviour of bionic robotic fish. Additionally, computer simulations can provide data that are difficult to obtain through experimental methods. Smart materials, which integrate perception, drive, and control functions, are increasingly being applied to bionic robotic fish research. However, the utilization of smart materials in this field is still an area of ongoing research and several challenges remain unresolved. This study provides an overview of the current state of research on fish swimming modes and the development of hydrodynamic modelling. The application of four distinct types of smart materials in bionic robotic fish is then reviewed, with a focus on analysing the advantages and disadvantages of each material in driving swimming behaviour. In conclusion, the paper highlights the key technical challenges that must be addressed for the practical implementation of bionic robotic fish and provides insights into the potential future directions of this field.

Some members of the Rosaceae family, particularly pear, contain stone cells in their fruits. Although stone cells in pear fruits are well studied, relatively little attention has been given to loquat stone cells. Only a few reports have suggested a relationship between stone cell traits and storage and transport tolerance of loquat fruits. Previously, we generated the variety JT8 from the interspecific hybrid of the loquat cultivar Jiefangzhong (JFZ; Eriobotrya japonica Lindl. cv. Jiefangzhong, female parent) and wild Taiwanese loquat (TL; E. deflexa Nakai, male parent). The JT8 fruits had a granular feel, similar to that of pear fruits, due to the presence of stone cells. In this study, the shape, size, development, and distribution dynamics of stone cells of Eriobotrya plants were thoroughly investigated. The results showed that loquat stone cells are brachysclereids and often contain typical branching pits. Loquat stone cells were distributed as both single stone cells and in stone cell clusters (SCCs), and the density of the stone cells near the core was higher than that near the peel. Stone cell density first increased and then decreased during fruit development. These traits noted in Eriobotrya were very similar to those observed in pear, indicating a close relationship between loquat and pear. Moreover, the contents, density dynamics, and aggregation traits of stone cells of the interspecific hybrid JT8 were derived from the male parent (TL). Transgressive segregation was likely exhibited in the content of stone cells and the size of the SCCs. More specifically, the content of stone cells reached 1.61% (w/w). In extreme cases, SCCs of JT8 exceeded 1,000 μm in length and 500 μm in width. This demonstrated that stone cell traits could be transmitted from parent to progeny through interspecific hybridization. The density dynamics of stone cells in two loquat cultivars with different storage and transport tolerances were also investigated, which indicated that the cultivar with more stone cells was more tolerant to storage and transport. We suggest that wild loquat genetic resources containing stone cells in Eriobotrya plants can be used to gradually improve the storage and transport tolerance of loquat fruits.

China’s landslide disasters are serious, and regional landslide disaster early-warning is one of the important means of disaster prevention and mitigation. The traditional regional landslide disaster early-warning model, however, is limited by the complex landslide induction mechanism, limited data accumulation, and insufficient big data analysis methods, and has problems such as limited early-warning accuracy and insufficient refinement. In this paper, a machine learning method was introduced into the field of regional landslide disaster warning. From the model construction process of training sample-set construction, sample learning and training, model parameter optimization, model preservation, warning output, and so on, a method for constructing a regional landslide early-warning model based on machine learning was systematically proposed. In the sample learning and training, 80% of the training sample-set was used as the training set, and 20% was used as the test set for five-fold cross validation. The Bayesian Optimization algorithm was used to optimize the model parameters, and the accuracy, ROC curve, and AUC value were used to verify the model accuracy and model generalization ability. With China’s Fujian province as an example, based on nine years of geological and meteorological data (2010–2018), geological environment factors, factors of hazard-affected bodies and historical disaster situations, and rainfall-induced factors in four categories, a total of 26 indicators were used as input characteristic parameters. Six machine learning algorithms were adopted to improve model training; the results showed that the Random Forest algorithm performed the best, giving an accuracy of 92.3%, and was the model with the best generalization ability (AUC was 0.955). The second best was the Artificial Neural Network model, with an accuracy of 0.937 and an AUC of 0.935. Next were the Nearest Neighbor model, the Logistic Regression model, and the Support Vector Machine; the poorest results were from the Decision Tree model. Finally, the typical rainfall-type landslide disaster process in Fujian Province was selected as an example to verify the Random Forest algorithm model. The results showed that compared with the early-warning results of the original explicit statistical model, the hit rate of the new model was 6 times, or equal to that of the original model, and the landslide density in the early-warning area of the new model was 1.6–1.7 times that of the original model. Preliminary verification showed that the new model based on the Random Forest method has obvious advantages, a higher hit rate and a smaller warning area, and can achieve more accurate warnings. The follow-up will continue to track the new landslide disaster situation in the study area and carry out model verification and correction.

Considering the slow response and unstable velocity of agricultural machinery caused by soil resistance, actuator delay, environmental change, velocity fluctuation, and other internal and external factors under real working conditions, a kind of agricultural machinery following a control system that considers variable load and control delay was proposed. Taking distance-keeping, velocity-following, and acceleration-following as parameters, the controller model was deduced, and the influence of different values of model parameters on the driving stability of agricultural machinery was analyzed in detail. In addition, this paper describes a kind of agricultural machinery following a strategy that can realize the graded adjustment of vehicle distance with the dynamic increase in vehicle weight. Then, the following strategy, under the influence of velocity and quality, was simulated and verified using MATLAB/Simulink (MATLAB2016a, mathworks: Natick, Massachusetts, USA). When the crop harvester was at 1.5 m/s and the amplitude of velocity fluctuation was 0.3 m and 1.3 m, respectively, the grain truck could adjust its velocity to keep up with the crop harvester to complete the operation task. Simulation verification was carried out for the proposed graded adjustment of vehicle distance of agricultural machinery following strategy. The unit mass of the crops was set at 360 kg, and the vehicle distance changed at 18s to adapt to the graded adjustment of the vehicle distance following strategy. Finally, a real-vehicle validation test was carried out, and the results show that the grain truck velocity can keep up with the change of crop harvester velocity on the basis of maintaining the desired vehicle distance, the grain truck velocity can keep up with the change of crop harvester velocity on the road condition, which verifies the effectiveness and feasibility of the proposed method.

The Xingmeng Orogenic Belt evolved through a long-lived orogeny involving multiple episodes of subduction and accretion. However, there is a debate on its tectonic evolution during the Late Paleozoic. Here, we report geochemical, geochronological, and isotopic data from strongly peraluminous granites and gabbro-diorites from the Sunidzuoqi–Xilinhot region. Zircon U–Pb ages suggest that the intrusive rocks were emplaced during the Early Carboniferous (333–322 Ma). The granites exhibit geochemical characteristics similar to S-type granites, with high SiO2 (72.34–76.53 wt.%), Al2O3 (12.45–14.65 wt.%), and A/CNK (1.07–1.16), but depleted Sr, Nb, and Ta contents. They exhibit positive εNd(t) and εHf(t) values (−0.3 to 2.8 and 2.7–5.7, respectively) and young Nd and Hf model ages (TDM2(Nd)=853–1110 Ma and TDM2(Hf)=975–1184 Ma), suggesting that they may be the partial melting products of heterogeneous sources with variable proportions of pelite, psammite, and metabasaltic rocks. The meta-gabbro-diorites from the Maihantaolegai pluton have low SiO2 (47.06–53.49 wt.%) and K2O (0.04–0.99 wt.%) contents, and demonstrate slight light rare earth element (REE) depletion in the chondrite-normalized REE diagrams. They have high zircon εHf(t) values (14.41–17.34) and young Hf model ages (TDM2(Hf)= 230–418 Ma), indicating a more depleted mantle source. The variations of the Sm/Yb and La/Sm ratios can thus be used to assess the melting degree of the mantle source from 5% to 20%, suggesting a quite shallow mantle melting zone. We propose that the petrogenesis and distribution of the strongly peraluminous granites and gabbro-diorites, as well as the tectonic architecture of the region, can be explained by a ridge subduction model. Based on these results, and previous studies, we suggest a southward ridge subduction model for the Sunidzuoqi–Xilinhot region.

Purpose: To determine the efficacy and safety of corticosteroid injection for trigger finger by performing a meta-analysis of all relevant studies. Methods: PubMed, EMBASE, and Cochrane Library databases were searched for randomized controlled trials (RCTs) comparing corticosteroid injection with other treatments for trigger finger. Pooled summary estimates for outcomes, including success rate, relapse rate, visual analogue score (VAS) and complications, were calculated as standardized mean difference (SMD) or relative risk (RR) either on a fixed- or random-effect model via Stata 12.0 software. Results: Ten literatures involving 806 patients (387 in corticosteroid injection group and 419 in control group) were included. Pooled analysis showed there were no differences in the success rate, VAS and complications between patients undergoing corticosteroid injection and others. However, the relapse rate was significantly higher in patients treated with corticosteroid injection than that of other treatments (RR = 19.53, 95% CI = 6.23–61.19). Subgroup analysis indicated the efficacy of corticosteroid injection was superior to other non-surgical treatments (success rate: RR = 1.54, 95% CI = 1.01–2.35), but inferior to surgery (success rate: RR = 0.55, 95% CI = 0.48–0.63; relapse rate: RR = 21.15, 95% CI = 6.06–73.85; VAS: SMD = 3.49, 95% CI = 2.84–4.14). Conclusions: Corticosteroid injection may be an effective strategy for management of trigger finger, although surgery may be needed for some patients due to recurrence.

In view of the incremental dimensionality reduction problem of existing non‐linear dimensionality reduction methods, a novel algorithm, based on locality constrained dictionary learning (LCDL), is proposed in this study. During the dictionary learning process, the neighbourhood size of some potential landmarks on a non‐linear manifold is constrained to maintain the intrinsic local geometric feature of the datasets. Meanwhile, to improve the dictionary's discrimination ability, a structured dictionary is learnt by LCDL, whose sub‐dictionaries are class‐specific. Then sparse coding and its reconstruction errors are used for classification. The experimental results of dimensionality reduction prove that, compared with the existing methods, the proposed method can solve the out of sample extension and large‐scale datasets problems efficiently. In addition, the experimental results of face, gender, and object category classification demonstrate that the authors’ algorithm outperforms some competing dictionary learning methods.

In this work, zinc oxide (ZnO) nanoparticles were modified in a circulating fluidized bed through argon and hydrogen (Ar–H) alternating-current (AC) arc plasma, which shows the characteristics of nonequilibrium and equilibrium plasma at the same time. In addition, a circulating fluidized bed with two plasma jets was used for cyclic processing. The catalytic degradation performance on Rhodamine B (Rh B) by Ar–H plasma-modified ZnO and pure ZnO was tested in aqueous media to identify the significant role of hydrogen atoms in Rh B degradation mechanism. Meanwhile, the effects of plasma treatment time on the morphology, size and photocatalytic performance of ZnO were also investigated. The results demonstrated that ZnO after 120-min treatment by Ar–H plasma showed Rh B photocatalytic degradation rate of 20 times greater than that of pure ZnO and the reaction follows a first kinetics for the Rh B degradation process. Furthermore, the photocatalyst cycle experiment curve exhibited that the modified ZnO still displays optimum photocatalytic activity after five cycles of experiment. The improvement of photocatalytic activity and luminescence performance attributes to the significant increase in the surface area, and the introduction of hydrogen atoms on the surface also could enhance the time of carrier existence where the hydrogen atoms act as shallow donors.

Air pollution presents unprecedentedly severe challenges to humans today. Various measures have been taken to monitor pollution from gas emissions and the changing atmosphere, of which imaging is of crucial importance. By images of target scenes, intuitional judgments and in-depth data are achievable. However, due to the limitations of imaging devices, effective and efficient monitoring work is often hindered by low-resolution target images. To deal with this problem, a superresolution reconstruction method was proposed in this study for high-resolution monitoring images. It was based on the idea of sparse representation. Particularly, multiple dictionary pairs were trained according to the gradient features of samples, and one optimal pair of dictionaries was chosen to reconstruct by judging the weighting of the information in different directions. Furthermore, the K-means singular value decomposition algorithm was used to train the dictionaries and the orthogonal matching pursuit algorithm was employed to calculate the sparse coding coefficients. Finally, the experiment’s results demonstrated its advantages in both visual fidelity and numerical measures.

Clock synchronization is a very important issue for the applications of wireless sensor networks. The sensors need to keep a strict clock so that users can know exactly what happens in the monitoring area at the same time. This paper proposes a novel internal distributed clock synchronization solution using group neighborhood average. Each sensor node collects the offset and skew rate of the neighbors. Group averaging of offset and skew rate value are calculated instead of conventional point-to-point averaging method. The sensor node then returns compensated value back to the neighbors. The propagation delay is considered and compensated. The analytical analysis of offset and skew compensation is presented. Simulation results validate the effectiveness of the protocol and reveal that the protocol allows sensor networks to quickly establish a consensus clock and maintain a small deviation from the consensus clock.

Rice blast, caused by the fungus, Magnaporthe grisea (M. grisea), lead to the decrease of rice yields widely and destructively, threatening global food security. Although many resistant genes had been isolated and identified in various rice varieties, it is still not enough to clearly understand the mechanism of race-specific resistant ability in rice, especially on the protein level. In this research, proteomic methods were employed to analyze the differentially expressed proteins (DEPs) in susceptible rice variety CO39 and its two near isogenic lines (NILs), CN-4a and CN-4b, in response to the infection of two isolates with different pathogenicity, GUY11 and 81278ZB15. A total of 50 DEPs with more than 1.5-fold reproducible change were identified. At 24 and 48 hpi of GUY11, 32 and 16 proteins in CN-4b were up-regulated, among which 16 and five were paralleled with the expression of their corresponding RNAs. Moreover, 13 of 50 DEPs were reported to be induced by M. grisea in previous publications. Considering the phenotypes of the three tested rice varieties, we found that 21 and 23 up-regulated proteins were responsible for the rice resistant ability to the two different blast isolates, 81278ZB15 and GUY11, respectively. Two distinct branches corresponding to GUY11 and 81278ZB15 were observed in the expression and function of the module cluster of DEPs, illuminating that the DEPs could be responsible for race-specific resistant ability in rice. In other words, DEPs in rice are involved in different patterns and functional modules’ response to different pathogenic race infection, inducing race-specific resistant ability in rice.

Foot-and-mouth disease virus (FMDV) is an economically devastating viral disease leading to a substantial loss to the swine industry worldwide. A novel alternative strategy is to develop pigs that are genetically resistant to infection. Here, we produce transgenic (TG) pigs that constitutively expressed FMDV-specific short interfering RNA (siRNA) derived from small hairpin RNA (shRNA). In vitro challenge of TG fibroblasts showed the shRNA suppressed viral growth. TG and non-TG pigs were challenged by intramuscular injection with 100 LD50 of FMDV. High fever, severe clinical signs of foot-and-mouth disease and typical histopathological changes were observed in all of the non-TG pigs but in none of the high-siRNA pigs. Our results show that TG shRNA can provide a viable tool for producing animals with enhanced resistance to FMDV.

Single nucleotide polymorphisms (SNPs) are widely used in functional genomics and genetics research work. The high-quality sequence of rice genome has provided a genome-wide SNP and proteome resource. However, the impact of SNPs on protein phosphorylation status in rice is not fully understood. In this paper, we firstly updated rice SNP resource based on the new rice genome Ver. 7.0, then systematically analyzed the potential impact of Non-synonymous SNPs (nsSNPs) on the protein phosphorylation status. There were 3,897,312 SNPs in Ver. 7.0 rice genome, among which 9.9% was nsSNPs. Whilst, a total 2,508,261 phosphorylated sites were predicted in rice proteome. Interestingly, we observed that 150,197 (39.1%) nsSNPs could influence protein phosphorylation status, among which 52.2% might induce changes of protein kinase (PK) types for adjacent phosphorylation sites. We constructed a database, SNP_rice, to deposit the updated rice SNP resource and phosSNPs information. It was freely available to academic researchers at http://bioinformatics.fafu.edu.cn. As a case study, we detected five nsSNPs that potentially influenced heterotrimeric G proteins phosphorylation status in rice, indicating that genetic polymorphisms showed impact on the signal transduction by influencing the phosphorylation status of heterotrimeric G proteins. The results in this work could be a useful resource for future experimental identification and provide interesting information for better rice breeding.

This paper presents a reference function based 3D FLC design methodology using support vector regression (SVR) learning. The concept of reference function is introduced to 3D FLC for the generation of 3D membership functions (MF), which enhance the capability of the 3D FLC to cope with more kinds of MFs. The nonlinear mathematical expression of the reference function based 3D FLC is derived, and spatial fuzzy basis functions are defined. Via relating spatial fuzzy basis functions of a 3D FLC to kernel functions of an SVR, an equivalence relationship between a 3D FLC and an SVR is established. Therefore, a 3D FLC can be constructed using the learned results of an SVR. Furthermore, the universal approximation capability of the proposed 3D fuzzy system is proven in terms of the finite covering theorem. Finally, the proposed method is applied to a catalytic packed-bed reactor and simulation results have verified its effectiveness.

To address the problem of quantitative analysis for delamination damage in composite materials, a method of evaluating delamination area based on Lamb waves multiscale features is proposed. In this method, the Lamb wave scattering signals are collected from composite plate with delamination defects using finite element simulation, and the multiscale feature vectors of time-frequency domain are extracted by using complete ensemble empirical mode decomposition with adaptive noise algorithm. In addition, the delamination area can be evaluated and predicted through a generalized regression neural network by taking advantage of nonlinear mapping capability. The hyperparameters of the neural network are also optimized using genetic algorithm, and the feature vectors calculated at different scales are assigned to the network for training and verification. The results show that the multiscale features of delamination damage are more accurate and stable for the model. The mean value and the mean square deviation of mean absolute percentage error proposed in this study is 13.35 % and 4.35 %, respectively, indicating that the overall performance is better than using single scale features and traditional signal decomposition methods. [ABSTRACT FROM AUTHOR]