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ObjectiveTo explore the underlying mechanisms the airway microbiome contributes to Acute Exacerbation of Chronic Obstructive Pulmonary Disease(AECOPD).MethodsWe enrolled 31 AECOPD patients and 26 stable COPD patients, their sputum samples were collected for metagenomic and RNA sequencing, and then subjected to bioinformatic analyses. The expression of host genes was validated by Quantitative Real-time PCR(qPCR) using the same batch of specimens.ResultsOur results indicated a higher expression of Rothia mucilaginosa(p=0.015) in the AECOPD group and Haemophilus influenzae(p=0.005) in the COPD group. The Different expressed genes(DEGs) detected were significantly enriched in "type I interferon signaling pathway"(p

Deposition of potentially toxic elements (PTEs) in soils due to different types of mining activities has been an increasingly important concern worldwide. Quantitative differences of soil PTEs contamination and related health risk among typical mines remain unclear. Herein, data from 110 coal mines and 168 metal mines across China were analyzed based on 265 published literatures to evaluate pollution characteristics, spatial distribution, and probabilistic health risks of soil PTEs. The results showed that PTE levels in soil from both mine types significantly exceeded background values. The geoaccumulation index (Igeo) revealed metal-mine soil pollution levels exceeded those of coal mines, with average Igeo values for Cd, Hg, As, Pb, Cu, and Zn being 3.02–15.60 times higher. Spearman correlation and redundancy analysis identified natural and anthropogenic factors affecting soil PTE contamination in both mine types. Mining activities posed a significant carcinogenic risk, with metal-mine soils showing a total carcinogenic risk an order of magnitude higher than in coal-mine soils. This study provides policymakers a quantitative foundation for developing differentiated strategies for sustainable remediation and risk-based management of PTEs in typical mining soils.

Luminescent Ag nanoclusters (Ag NCs) are a promising probe material for sensing and bioimaging applications. However, the intrinsic obstacle of poor water stability and photostability greatly restrict their practical application in biological systems. Herein, we report the intracellular hypochlorite (ClO−) detection with amphiphilic copolymer-modified luminescent Ag NCs with good biocompatibility and photostability. The Ag NCs were synthesized by using chemically inert hydrophobic ligands and then modified with an amphiphilic (1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(poly(ethylene glycol))-2000]) (DSPE-PEG-2000) and sodium dodecyl sulfonate (SDS) for phase transfer. It was found that the approach of the removal of organic solvents during the phase transfer has remarkable influences on the properties of the Ag NCs, including their size, luminescence property, and aqueous stability. Furthermore, the silver core of Ag NCs could be oxidatively damaged by ClO−, thereby causing photoluminescence (PL) quenching. The ClO−-induced PL quenching was specific over the other common reactive oxygen species (ROS) as well as some common interferences. Finally, they have been successfully applied as a fluorescent nanoprobe for detecting exogenous and endogenous ClO− in living cells.

In the task of classifying images of cracks in underwater dams, symmetry serves as a crucial geometric feature that aids in distinguishing cracks from other structural elements. Nevertheless, the asymmetry in the distribution of positive and negative samples within the underwater dam crack image dataset results in a long-tail problem. This asymmetry, coupled with the subtle nature of crack features, leads to inadequate feature extraction by existing convolutional neural networks, thereby reducing classification accuracy. To address these issues, this paper improves VanillaNet. First, the Seesaw Loss loss function is introduced to tackle the long-tail problem in classifying underwater dam crack images, enhancing the model’s ability to recognize tail categories. Second, the Adaptive Frequency Filtering Token Mixer (AFF Token Mixer) is implemented to improve the model’s capability to capture crack image features and enhance classification accuracy. Finally, label smoothing is applied to prevent overfitting to the training data and improve the model’s generalization performance. The experimental results demonstrate that the proposed improvements significantly enhance the model’s classification accuracy for underwater dam crack images. The optimized algorithm achieves superior average accuracy in classifying underwater dam crack images, showing improvements of 1.29% and 0.64% over the relatively more accurate models ConvNeXtV2 and RepVGG, respectively. Compared to VanillaNet, the proposed algorithm increases average accuracy by 2.66%. The improved model also achieves higher accuracy compared to the pre-improved model and other mainstream networks.

Due to the influence of meteorological conditions, shipboard photovoltaic (PV) systems have problems such as large fluctuation and inaccurate prediction of the output power. In this paper, a short-term PV power prediction method based on a novel digital twin (DT) model and BiLSTM is proposed. Firstly, a PV mechanism model and a data-driven model were established, in which the data-driven model was updated iteratively in real time using the sliding time window update method; then, these two models were converged to construct a PV DT model according to the DS evidence theory. Secondly, a BiLSTM model was built to make short-term predictions of the PV power using the augmented dataset of the DT model as an input. Finally, the method was tested and verified by experiments and further compared with main PV prediction methods. The research results indicate the following: firstly, the absolute error of the DT model was smaller than that of the mechanism model and the data-driven model, being as low as 5.62 W after the data update of the data-driven model; thus, the DT model realized data augmentation and high fidelity. Secondly, compared to several main PV prediction models, the PV DT model combined with BiLSTM had the lowest RMSE, MAE, and MAPE; the best followability; and the smallest absolute error under different weather conditions, which was especially obvious under cloudy weather conditions. In summary, the method can accurately predict the shipboard PV power, which has great theoretical significance and application value for improving the economy and reliability of solar ship operation.

At present, GPX4's role in the occurrence and development of diffuse large B lymphoma (DLBCL) is rarely reported. This study's purpose is to explore GPX4's significance in the diagnosis, treatment, and pathological mechanisms of DLBCL. The TIMER 2.0, GEPIA, and GEO databases were used to analyze GPX4's expression levels in DLBCL tissue, peripheral blood, and single cells, and evaluate its potential performance as a therapeutic and diagnostic marker. Cell experiments validate GPX4's role in DLBCL cells. And revealed the potential mechanism of GPX4's action from three aspects: immunity, pathogenic gene expression, and protein interaction. The results indicate that GPX4 can be used as a biomarker for treatment and diagnosis (FC > 1.5, P 0.8, KM-P value

Instance segmentation is a task that involves pixel-level classification and segmentation of each object instance in images. Various CNN-based methods have achieved promising results in natural image instance segmentation. However, the noise interference, low resolution, and blurred edges bring more significant challenges for sonar image instance segmentation. To solve these problems, we propose the Effective Strategy for Sonar Images Instance Segmentation (ESSIIS). We introduce ASception, a new network combining Atrous Spatial Pyramid Pooling (ASPP) and Extreme Inception (Xception). By integrating this with ResNet and transforming traditional convolutions into deformable convolutions, we further improve the ability of the network to extract features from sonar images. Additionally, we incorporate a bidirectional feature fusion module to enhance information fusion. Finally, we evaluate the detection accuracy and segmentation accuracy of the proposed method on the public sonar image dataset and the self-constructed dataset. ESSIIS attains a detection accuracy of 0.981 and a segmentation accuracy of 0.951 on SCTD, further impressively achieving 0.986 in both metrics when appraised on our dataset. The evaluation results demonstrate that the proposed method is more accurate, robust, and considerable for sonar image detection and segmentation.

Hepatosplenic T cell lymphoma (HSTCL) is a particularly difficult-to-treat form of lymphoma, with many patients exhibiting primary resistance to chemotherapy. At present, no effective strategy for treating relapsed and refractory HSTCL has been established, with treatment being hampered by questions of how best to overcome chemoresistance to allow patients to attain more durable therapeutic benefits. While there have been marked advances in immunotherapy, allogeneic hematopoietic stem cell transplantation (allo-HSCT) remains one of the primary approaches to curing HSTCL. Of patients who undergo immunochemotherapeutic treatment, many are resistant to conventional chemotherapeutic drugs yet remain sensitive to radiotherapy. We selected to employ a transplant pretreatment regimen consisting of total -body irradiation (TBI) and administered this regimen to two patients with HSTCL. Both patients achieved complete remission (CR) after transplantation, demonstrating extended periods without disease recurrence. We systematic reviewed previously published instances involving allo-HSCT in patients with HSTCL. We have found a total of 67 patients who have received allo-HSCT. In general, age

In adverse foggy weather conditions, images captured are adversely affected by natural environmental factors, resulting in reduced image contrast and diminished visibility. Traditional image dehazing methods typically rely on prior knowledge, but their efficacy diminishes in practical, complex environments. Deep learning methods have shown promise in single-image dehazing tasks, but often struggle to fully leverage depth and edge information, leading to blurred edges and incomplete dehazing effects. To address these challenges, this paper proposes a deep-guided bilateral grid feature fusion dehazing network. This network extracts depth information through a dedicated module, derives bilateral grid features via Unet, employs depth information to guide the sampling of bilateral grid features, reconstructs features using a dedicated module, and finally estimates dehazed images through two layers of convolutional layers and residual connections with the original images. The experimental results demonstrate the effectiveness of the proposed method on public datasets, successfully removing fog while preserving image details.

Tourism green production efficiency serves as the foundation for assessing the mutual coupling performance of the tourism economy and the ecological environment. In this paper, the tourism carbon sink is included in the measurement framework, and the TGPE of 41 cities in the Yangtze River Delta region from 2011 to 2019 is estimated by the Super-SBM model. Furthermore, kernel density estimate, spatial autocorrelation, Markov chain and spatial Durbin model are further integrated to explore its spatio-temporal evolution process, spatial effects and influencing factors. The results show that 1) TGPE in the Yangtze River Delta has been increasing during the study period. The high-efficiency and low-efficiency areas of the TGPE have a bipolar pattern characterized by “low–low convergence” and “high–high convergence.” 2) There is considerable spatial variation in TGPE from north to south. The number of hot spots and sub-hot spots increases in volatility, whereas the number of sub-cold spots and cold spots decreases. 3) Although cities with low levels of TGPE have a higher probability of moving to the next level, grade transformation across hierarchies is difficult to attain. When considering the factor of adjacent types and the influence of spatial lag on the transfer probability. 4) The positive spatial spillover effects of TGPE is significant. At the same time, economic development level, transport accessibility and tourism industry agglomeration have positive spillover effects on neighboring cities. Conversely, urbanization level and openness level have negative spillover effects.

Abstract Deep learning‐based methods have achieved notable performance for underwater image enhancement. However, previous studies are mostly focused on pursuing high similarity between the original image and the target, which incurs performance drop when the models are used for real‐world images. A new framework for underwater image enhancement is proposed to improve the generalization performance of enhancement. First, the coordinate attention module is integrated into the backbone network, which serves as a pre‐trained model, to strengthen the feature extraction capability of the network. Second, the backbone is finetuned by physical prior knowledge and real‐world images, in an unsupervised manner, to realize generalization from artificial images to real‐world images. Furthermore, a model protection mechanism is designed to guarantee the successful execution of the training. The experimental results indicate that the proposed method provides a powerful pre‐trained backbone network and the finetuning strategy can further solve the color distortion and improve the image sharpening, especially in the harsh real environment. Compared with relevant methods, the UCIQE and NIQE are, respectively, 0.525 and 4.149, with a 0.009–0.095 increase in UCIQE and a 0.256–1.032 decrease in NIQE compared to other methods.

Long non-coding RNAs (lncRNAs) are frequently reported to be involved in breast cancer (BC) oncogenicity. The goal of this study was to probe lncRNA LINC01140’s role and action mechanism in BC. Relative LINC01140, miR-200b-3p, and dystrophin (DMD) levels were determined using quantitative real-time polymerase chain reaction (qRT-PCR). DMD protein levels in BC cells were quantified using Western blotting, and the targeting relationships were validated by luciferase reporter assays and RNA immunoprecipitation experiments. The proliferative potential of the cells was evaluated using CCK-8 and colony formation tests, while the migratory and invasive abilities of the cells were assessed using scratch and transwell assays. Apoptosis was assessed by flow cytometry. Nude mouse models have been established to allow the examination of tumor growth in vivo . Pronounced downregulation of LINC01140 and DMD, as well as upregulation of miR-200b-3p, was observed in BC. LINC01140 binds directly to miR-200b-3p to downregulate DMD expression. Ectopic LINC01140 expression not only limited tumor growth in vivo but also diminished the proliferation, migration, and invasion abilities of BC cells in vitro , however, it induced apoptosis in BC cells. Elevated miR-200b-3p expression stimulated the tumorigenic potential of BC cells and attenuated the suppressive effect of LINC01140 or DMD overexpression on BC cell malignancy, whereas DMD overexpression restricted the tumorigenic potential of BC cells. Overall, LINC01140 prevents BC development via the miR-200b-3p–DMD axis. These findings support the latent potential and usefulness of the LINC01140-miR-200b-3p–DMD network as a target for BC therapy.

Abstract Autonomous Underwater Vehicle (AUV) carrying sonar for object detection has become one of the main ways of ocean exploration. However, object detection in sonar images always faces the problems of balancing detection accuracy and efficiency. To this end, this paper proposes an efficient underwater object detection network for sonar images named SDNet. In the model, the authors construct a new feature extraction network based on RepVGG to balance the detection accuracy and speed. By combining channel attention with RepVGG, useful information of high‐order feature maps captured can be selectively paid attention to. Then, the authors design a feature fusion network to efficiently converge the location and semantic information of multi‐scale feature maps. In the network, the authors propose a lightweight cross stage partial network for sonar (CSP_S) module suitable for sonar images, which can enhance the model's feature fusion capability and simplify the model. Finally, to reduce the conflict between classification and regression tasks, the authors leverage the Decoupled Head for the sonar object classification and localization. By testing on the self‐built Underwater Sonar Dataset underwater sonar dataset (USD) and the public sonar dataset sonar common target detection dataset (SCTD), the detection accuracy of SDNet reaches 99.52% and 95.20%, respectively. Moreover, the detection speed reaches 114 frames per second (FPS) and 138 FPS, respectively. The experimental results show that SDNet can effectively balance the sonar detection accuracy and efficiency. Code is available at https://github.com/Yukino111/sdnet‐pytorch.

Soil moisture (SM) plays a vital role in linking the global terrestrial water, energy, and carbon cycles. Land data assimilation (DA) is typically applied for acquiring more accurate SM estimates by incorporating remote sensing (RS) retrievals to constraint model parameters and system states. In addition to RS SM retrievals that are frequently used in land DA, the land surface temperature (LST), which can be directly and independently retrieved from thermal infrared measurements, is closely linked with SM, and can also be used in the land DA to improve SM estimation. However, it is hampered by the lack of a general observation operator that links LST retrievals with SM in the land surface model (LSM). Therefore, a novel LST DA scheme is developed in this article, in which RS LST is linked to SM by a linear relationship between the simulated ensembles of SM and LST generated by the Ensemble Kalman filter. Subsequently, MODIS LST is assimilated into the LSM to estimate SM and calibrate the soil parameters (soil porosity, soil texture) with the dual-cycle assimilation algorithm. The DA SM results are validated using an in situ SM network deployed in the central Tibetan Plateau. The results demonstrate that our DA scheme improved the SM accuracy—leading to a decrease in the root-mean-square error from 0.11 m3m−3 to 0.026 m3m−3. This article provides a novel scheme of LST assimilation, which is beneficial for the assimilation of high-resolution RS data.

Ta-10W alloy has great potential in the aerospace and nuclear industries due to its good formability, high melting point and excellent high-temperature strength. The purpose of this study was to experimentally research the tribological behaviors of Ta-10W at elevated temperatures and the effects of temperatures on the friction coefficient and the wear rate of Ta-10W in sliding wear. It was found that the main wear mechanism of the material at 100°C was abrasive wear, as well as adhesive wear and slight oxidative wear occurred at 200°C and 300°C. The friction coefficients increased as the temperature rose, which was attributed to the increase in metal viscosity at elevated temperatures. The wear rate gradually decreased with the temperature rising, such that there was a conversion from severe wear to light wear. At 200°C and 300°C, a large amount of stable oxide film covered the scar surface, resulting in decreased wear rate.

The JAK/STAT and NFκB signaling pathways are two major inflammatory signaling pathways that are usually activated simultaneously in the body’s inflammatory response to bacterial or viral infections. Hyperactivation of these two prominent signaling pathways is associated with various immune-related diseases and mortality, pointing to an urgent need for drug development targeting JAK/STAT and/or NFκB signaling. In this study, we screened 18,840 compounds using our well-established dual STAT-NFκB driven luciferase reporter based high-throughput screening system and identified a bioactive compound C498-0670, which inhibits both JAK/STAT and NFκB signaling. C498-0670 inhibits the activation of STATs and p-IKKα/β in both the immortalized cell lines and primary peritoneal macrophages, while suppressing the expression of LPS-induced inflammatory mediators in vitro. In addition, the overall anti-inflammatory effects of C498-0670 were investigated using transcriptome sequencing and bioinformatics approaches. C498-0670 was predicted to alleviate sepsis/septic shock by disease/function analysis using IPA software, which was further verified in the LPS-induced mouse sepsis model in vivo. C498 reduced LPS-induced liver and kidney damage, myeloid cell infiltration, and pro-inflammatory cytokine and chemokine production in vivo. Furthermore, the SPR-HPLC-MS-based target fishing approach was used to identify the putative drug targets, and the high affinities of JAK2 (JAK/STAT signaling), NFKBIA (NFκB signaling), and IL-1β, NLRP1b (inflammasome signaling) for C498-0670 were verified by molecular docking approach. These results suggest that C498-0670 can be used as a dual-target inhibitor of JAK/STAT and NFκB signaling pathways for the treatment of various inflammatory diseases, especially septic shock.

Abstract This paper focuses on the design and simulation of an integrated reconfigurable control strategy for networked multi‐agent systems. The control task is to remain the desired time‐varying formation in the presence of actuator faults, and to well address the pairing between fault estimation and reconfigurable formation control. A decentralized fault estimation method using observer based estimator is presented to estimate both additive and multiplicative fault values. The reconfigurable control is constructed by adaptive technique using a distributed strategy. Only the information from neighboring agents as well as the fault values are required. The adaptive gains and fault estimates are designed by analyzing the Lyapunov stability of the overall system. The simulation results validate that the proposed integrated fault‐tolerant formation control scheme can recover the original desired formation if there are faulty agents in system.

Abstract Oxygenase-catalyzed reduction and activation of oxygen molecules and the incorporation of oxygen atoms into organic molecules are undoubtedly necessary in the process of tumor development, and it is also one of the research hotspots in recent years. MOXD1 belongs to the copper-dependent monooxygenase family. The expression of MOXD1 is one of the characteristics of early tumor development. However, it is not understandable that the biological function and molecular mechanism of MOXD1 in Glioblastoma (GBM). In this study, high MOXD1 expression is strongly associated with poor survival of the patient with GBM. Moreover. MOXD1 knockdown can inhibit cell viability, proliferation, migration, invasion, and tumorigenesis of GBM cells. This is also proven for the first time that MOXD1 can bind to β3GnT2 and affect the glycosylation modification of some proteins. In addition, knockdown of MOXD1 induces endoplasmic reticulum (ER) stress and triggers the ER–mitochondrial apoptosis pathway. Taken together, these results reveal that MOXD1 is involved in the occurrence and development of GBM, and also provide a new strategy for targeted therapy.

Further improvements to the yield potential of Chinese milk vetch seed are essential for the planting demand of green manure. Flower and pod development directly determines the number of seeds and the seed yield of Chinese milk vetch. However, the accumulation and translocation of dry matter and nitrogen between plant organs directly affects flower and pod development and morphological formation. There are few studies that analyse the relationship between the accumulation and transport of dry matter and nitrogen and the number of flowers, pods, grains and seed yield during Chinese milk vetch's critical development period. This study aimed to determine the seed yield response to dry matter and nitrogen accumulation and translocation during the Chinese milk vetch growth period and to quantify the relationship between these factors to predict Chinese milk vetch seed yield. Experiments were performed during the 2017-2018 and 2018-2019 growing seasons at the Dayuzhuang experimental field. The first experiment involved five foliar application stages (late wintering stage, returning green stage, squaring stage, pre-flowering stage, and 5 days after flowering) and six foliar application concentrations of borate solution (0, 500, 1000, 2000, 4000, and 6000 mg L-1). Experiment 2 included five foliar application stages (late wintering stage, returning green stage, squaring stage, pre-flowering stage, and 5 days after flowering) and six foliar application concentrations of paclobutrazol (0, 200, 300, 400, 500, and 600 mg L-1). When the dry matter mass in the full flowering stage was 3500-4500 kg hm-2, the seed yield reached more than 800 kg hm-2. When the translocated assimilates were stored in the vegetative organs before flowering, the assimilate translocation rate and their contributions to seed yield were 1500-1800 kg hm-2, 30-35%, and 28-38%, respectively, and the Chinese milk vetch seed yield was predicted to reach 800-1000 kg hm-2 at maturity. When the nitrogen translocation amount in the vegetative organs before flowering, the nitrogen translocation rate, and the contribution rate to the seed yield were 68-78 kg hm-2, 65-75%, and 75-85%, respectively, the Chinese milk vetch seed yield was predicted to reach 800-1000 kg hm-2 at maturity. If the accumulation and translocation index values of dry matter and nitrogen were lower or higher than the above ranges, the seed yield was lower than 800 kg hm-2. The results of this study revealed the mechanism by which dry matter and nitrogen accumulation and translocation affect the Chinese milk vetch seed yield. These findings enrich the seed yield formation theory of Chinese milk vetch. They provide an early determination and quantitative regulation of high and stable seed yield for Chinese milk vetch in the field and aid researchers to integrate multiple production technologies for the sustainable production of Chinese milk vetch.

The main challenges in second near-infrared region molecular fluorophores are poor water solubility and unknown long-term toxicity at present. Herein, new NIR-II molecular fluorophores have been designed and employed to integrate biocompatible pillar[5]arene with 10 outer triethylene oxide groups for the synthesis of rotaxane IRCR. In addition, PEGylated pillar[5]arenes have been combined for the self-assembly of two supramolecular vesicular systems, i.e., PP5-IR1 and PP5-IR2, affording aqueous solubility and lowered cellular toxicity. In aqueous solution, all these fluorophores displayed room-temperature emission with λmax at 986–1013 nm and quantum yields of 0.54–1.45%. They also exhibited good chemical stability and reasonable self-assembled sizes, which may find potential applications in NIR-II imaging. In addition, PP5-IR1 can be used as a fluorescent chemosensor for selective recognition of glutathione through the cleavage of dinitrophenyl ether and release the fluorescent dye.

Myocyte enhancer factor 2A (MEF2A) is a member of the myocyte enhancer factor 2 family. MEF2A is widely distributed in various tissues and organs and participates in various physiological processes. This study aimed to investigate the effect of MEF2A expression on the proliferation and apoptosis of bovine myoblasts. CCK8, ELISA, cell cycle, and apoptosis analyses were conducted to assess cell status. In addition, the mRNA expression levels of genes associated with bovine myoblast proliferation and apoptosis were evaluated using RT-qPCR. The results showed that the upregulation of MEF2A mRNA promoted the proliferation rate of myoblasts, shortened the cycle process, and increased the anti-apoptotic rate. Furthermore, the RT-qPCR results showed that the upregulation of MEF2A mRNA significantly increased the cell proliferation factors MyoD1 and IGF1, cell cycle factors CDK2 and CCNA2, and the apoptotic factors Bcl2 and BAD (p < 0.01). These results show that the MEF2A gene can positively regulate myoblast proliferation and anti-apoptosis, providing a basis for the analysis of the regulatory mechanism of the MEF2A gene on bovine growth and development.

Glioblastoma multiforme (GBM) is the most common and invasive primary central nervous system tumor in humans, accounting for approximately 45–50% of all primary brain tumors. How to conduct early diagnosis, targeted intervention, and prognostic evaluation of GBM, in order to improve the survival rate of glioblastoma patients, has always been an urgent clinical problem to be solved. Therefore, a deeper understanding of the molecular mechanisms underlying the occurrence and development of GBM is also needed. Like many other cancers, NF-κB signaling plays a crucial role in tumor growth and therapeutic resistance in GBM. However, the molecular mechanism underlying the high activity of NF-κB in GBM remains to be elucidated. This review aims to identify and summarize the NF-κB signaling involved in the recent pathogenesis of GBM, as well as basic therapy for GBM via NF-κB signaling.

Autonomous localization and navigation, as an essential research area in robotics, has a broad scope of applications in various scenarios. To widen the utilization environment and augment domain expertise, simultaneous localization and mapping (SLAM) in underwater environments has recently become a popular topic for researchers. This paper examines the key SLAM technologies for underwater vehicles and provides an in-depth discussion on the research background, existing methods, challenges, application domains, and future trends of underwater SLAM. It is not only a comprehensive literature review on underwater SLAM, but also a systematic introduction to the theoretical framework of underwater SLAM. The aim of this paper is to assist researchers in gaining a better understanding of the system structure and development status of underwater SLAM, and to provide a feasible approach to tackle the underwater SLAM problem.

With continuous improvements in oil production, the environmental problems caused by oil exploitation are becoming increasingly serious. Rapid and accurate estimation of soil petroleum hydrocarbon content is of great significance to the investigation and restoration of environments in oil-producing areas. In this study, the content of petroleum hydrocarbon and the hyperspectral data of soil samples collected from an oil-producing area were measured. For the hyperspectral data, spectral transforms, including continuum removal (CR), first- and second-order differential (CR-FD, CR-SD), and Napierian logarithm (CR-LN), were applied to eliminate background noise. At present, there are some shortcomings in the method of feature band selection, such as large quantity, time of calculation, and unclear importance of each feature band obtained. Meanwhile, redundant bands easily exist in the feature set, which seriously affects the accuracy of the inversion algorithm. In order to solve the above problems, a new method (GARF) for hyperspectral characteristic band selection was proposed. It combined the advantage that the grouping search algorithm can effectively reduce the calculation time with the advantage that the point-by-point search algorithm can determine the importance of each band, which provided a clearer direction for further spectroscopic research. The 17 selected bands were used as the input data of partial least squares regression (PLSR) and K-nearest neighbor (KNN) algorithms to estimate soil petroleum hydrocarbon content, and the leave-one-out method was used for cross-validation. The root mean squared error (RMSE) and coefficient of determination (R2) of the estimation result were 3.52 and 0.90, which implemented a high accuracy with only 8.37% of the entire bands. The results showed that compared with the traditional characteristic band selection methods, GARF can effectively reduce the redundant bands and screen out the optimal characteristic bands in the hyperspectral data of soil petroleum hydrocarbon with the method of importance assessment, which retained the physical meaning. It provided a new idea for the research of other substances in soil.

AbstractEarthquake disasters are frequent, and the seismic intensity is large in Northeast China. Earthquake activity research is an important aspect of earthquake disaster management. We chose some unconventional means to study fault activity, to find updated activity evidence. The Ms 5.3 earthquake occurred near the Fuyu North Fault (FNF) of China on May 27, 2018. Using the Sentinel-1B descending orbit data from 2016 to 2019, the line-of-sight (LOS) surface deformation in the study area was calculated by using the small baseline subset (SBAS) method. After transforming to the horizontal EW deformation, the variance component estimation method was used for fusion reconstruction with the EW data of the surrounding GNSS stations. The polynomial least square method is used to fit the fault slip rate of three EW data on the surface trace of the FNF. The fitting results of the three regions show that the horizontal eastward distribution rate of the upper plate is significantly greater than that of the lower plate, which is left-lateral clockwise torsion. The vertical structural deformation caused by the growth strata of the upper and lower plates of the upper SYT2 seismic profile of the FNF is quantitatively calculated, and the thrust rate of the upper plate is 0.2 mm/y relative to that of the lower plate. Based on the Li Siguang chessboard structure model, we found that the compression stress in the north-south direction is gradually weakened, and the compression stress in the east-west direction is gradually enhanced. Through the Coulomb stress analysis, the three events of CMT only induced the historical focal location of the surrounding part. The events of 2017 did not induce the events of 2018, but the events of 2019 were related to the induced effects of 2017 and 2018.

Arbitrarily-oriented object detection is a challenging task. Since the object orientation in remote sensing images is arbitrary, using horizontal bounding boxes will lead to low detection accuracy. Existing regression-based rotation detectors can lead to the problem of boundary discontinuity. In this paper, we propose a remote sensing image object detection method based on angle classification that uses rotation detection bounding boxes with angle information to detect objects. Specifically, we incorporate the neural architecture search framework with feature pyramid network (NAS-FPN) module in a dense detector (RetinaNet) and use a binary encoding method in angle classification. This method reduces the background influence, so that there is almost no overlap between detection boxes. Based on the angles of the detection boxes, we can infer the information of the motion direction of the target and further determine the motion trajectory of the target. We conducted ablation experiments on a large publicly available for object detection in an aerial imagery (DOTA) to verify the effectiveness of each module in the method and compared the method with several other detection methods. The experimental results demonstrate the effectiveness of our method.

A realization form of the Mie resonance with adjustable characteristics based on a water-based metamaterial is proposed. A novel local electromagnetic field coupling mechanism based on a water-based metamaterial is established, which is used for transmission enhancement through the sub-wavelength aperture for the first time. The water-based metamaterial particle for transmission enhancement at a specific frequency is designed. By designing the microfluidic control system to control the water filling (i.e., height) of the metamaterial particle, the adjustable transmission enhancement characteristics are realized. The influence of bilateral symmetry and flexible deformation of the metamaterial particle on the transmission characteristics is analyzed. The influence of the design parameters on the enhanced transmission characteristics is discussed.

This paper reported an improved molecular beacon method for the rapid detection of aflatoxin B1 (AFB1), a natural mycotoxin with severe carcinogenicity. With the assistance of a complementary DNA (cDNA) chain, the molecular beacon which consists of a DNA aptamer flanked by FAM and BHQ1 displayed a larger fluorescent response to AFB1, contributing to the sensitive detection of AFB1. Upon optimization of some key experimental factors, rapid detection of AFB1 ranging from 1 nM to 3 μM, within 20 min, was realized by using this method. A limit of detection (LoD) of 1 nM was obtained, which was lower than the LoD (8 nM) obtained without cDNA assistance. This aptamer-based molecular beacon detection method showed advantages in easy operation, rapid analysis and larger signal response. Good specificity and anti-interference ability were demonstrated. This method showed potential in real-sample analysis.

This study aimed to explore the regulatory role of FOXO1 promoter methylation on its transcriptional level and unravel the effect of FOXO1 on the proliferation and differentiation of bovine myoblasts. Bisulfite sequencing polymerase chain reaction (BSP) and real-time quantitative PCR were performed to determine the methylation status and transcript levels of the FOXO1 promoter region at different growth stages. BSP results showed that the methylation level in the calf bovine (CB) group was significantly higher than that in the adult bovine (AB) group (p < 0.05). On the other hand, qRT-PCR results indicated that the mRNA expression level in the AB group was significantly higher than that in the CB group (p < 0.05), suggesting a significant decrease in gene expression at high levels of DNA methylation. CCK-8 and flow cytometry were applied to determine the effect of silencing the FOXO1 gene on the proliferation of bovine myoblasts. Furthermore, qRT-PCR and Western blot were conducted to analyze the expression of genes associated with the proliferation and differentiation of bovine myoblasts. Results from CCK-8 revealed that the short hairpin FOXO1 (shFOXO1) group significantly promoted the proliferation of myoblasts compared to the short-hairpin negative control (shNC) group (p < 0.05). Flow cytometry results showed a significant decrease in the number of the G1 phase cells (p < 0.05) and a significant increase in the number of the S phase cells (p < 0.05) in the shFOXO1 group compared to the shNC group. In addition, the expression of key genes for myoblast proliferation (CDK2, PCNA, and CCND1) and differentiation (MYOG, MYOD, and MYHC) was significantly increased at both mRNA and protein levels (p < 0.05). In summary, this study has demonstrated that FOXO1 transcription is regulated by methylation in the promoter region and that silencing FOXO1 promotes the proliferation and differentiation of bovine myoblasts. Overall, our findings lay the foundation for further studies on the regulatory role of epigenetics in the development of bovine myoblasts.

Background:Neurovascular compression syndromes including trigeminal neuralgia (TN) and hemifacial spasm (HFS) are caused by neurovascular conflicts at the root entry zone of the corresponding cranial nerves in the posterior fossa. Microvascular decompression (MVD) is the best choice for the treatment of TN and HFS. An accurate delineation of the responsible vessel could decrease the rate of possible operative complications such as nerve paresis.Methods:In this study, three-dimensional fast imaging employing steady-state acquisition (3D-FIESTA) and three-dimensional time-of-flight magnetic resonance angiography (3D-TOF MRA) were performed on 113 patients with TN or HFS. The imaging data were compared to the intraoperative findings and the accuracy of the data was calculated among the different responsible blood vessels and disease types. The accuracy of the data among different genders, disease durations, disease sides, and disease types was also calculated to identify the target patients for the preoperative diagnostic approach with 3D-FIESTA combined with 3D-TOF MRA.Results:The accuracy of detection with the imaging was above 75% in cases with single-vessel compression. Among these, the accuracy of the preoperative imaging result was the highest when the lesions were in the superior cerebellar artery (SCA; 91.1%). In cases of multiple-vessel compression, however, the coincidence between the preoperative and intraoperative results was only 30.0%. In most of the cases of TN, the responsible blood vessels were in the SCA, and the accuracy in the SCA reached 94.9%. In HFS patients, the responsible blood vessels were in the anterior inferior cerebellar artery (AICA) and posterior inferior cerebellar artery (PICA), and the accuracy was 86.8% and 90.0%, respectively. The differences in the accuracy of the data among different genders, disease durations, disease sides, and disease types were not statistically significant.Conclusion:This study verified the clinical instructional value of 3D-FIESTA combined with 3D-TOF MRA in MVD, and showed that this preoperative examination is reliable for all genders, disease durations, disease sides, and disease types.

Heterogeneous multi-robot system is one of the most important research directions in the robotic field. Real-time path planning for heterogeneous multi-robot system under unknown 3D environment is a new challenging research and a hot spot in this field. In this paper, an improved real-time path planning method is proposed for a heterogeneous multi-robot system, which is composed of many unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). In the proposed method, the 3D environment is modelled as a neuron topology map, based on the grid method combined with the bio-inspired neural network. Then a new 3D dynamic movement model for multi-robots is established based on an improved Dragonfly Algorithm (DA). Thus, the movements of the robots are optimized according to the activities of the neurons in the bio-inspired neural network to realize the real-time path planning. Furthermore, some simulations have been carried out. The results show that the proposed method can effectively guide the heterogeneous UAV/UGV system to the target, and has better performance than traditional methods in the real-time path planning tasks.

Despite the fact that companies increasingly value online star endorsements as Internet celebrity economy booms, scientific knowledge on the effect of online star endorsements on consumers’ purchase intention is limited. Based on the theories of self and construal level theory, this study investigates the impact of online star vs. celebrity endorsements on purchase intention and explores the underlying mechanism as well as boundary conditions. The results of four studies reveal the following: (1) Compared with no endorsement, both celebrity endorsements and online star endorsements lead to increases in consumers’ purchase intention, with no significant difference between the two. (2) Self-concept mediates these relationships; specifically, celebrity and online star endorsements activate the ideal and actual self respectively, and enhance consumers’ willingness to purchase. (3) The effect of endorsements on consumers’ purchase intention is moderated by advertising appeals. That is, celebrity endorsements enhance purchase intention when consumers are exposed to symbolic appeals in advertisements, and online star endorsements enhance purchase intention when it is matched with functional advertising appeals.

In this study, Xiangsu hybrid pig growth traits were evaluated via PRKAA2 and MSMB as candidate genes. Sanger sequencing revealed three mutation sites in PRKAA2, namely, g.42101G>T, g.60146A>T, and g.61455G>A, and all these sites were intronic mutations. Moreover, six mutation sites were identified in MSMB: intronic g.4374G>T, exonic g.4564T>C, exonic g.6378G>A, exonic g.6386C>T, intronic g.8643G>A, and intronic g.8857A>G. Association analysis revealed that g.42101G>T, g.60146A>T, g.61455G>A, g.4374G>T, g.4564T>C, g.6378G>A, g.6386C>T, g.8643G>A, and g.8857A>G showed different relationship patterns among body weight, body length, body height, chest circumference, abdominal circumference, tube circumference, and chest depth. Real-time polymerase chain reaction results revealed that the expression of PRKAA2 was highest in the longissimus dorsi muscle, followed by that in the heart, kidney, liver, lung, and spleen. The expression of MSMB was highest in the spleen, followed by that in the liver, kidney, lung, heart, and longissimus dorsi muscle. These results suggest that PRKAA2 and MSMB can be used in marker-assisted selection to improve growth related traits in Xiangsu hybrid pigs, providing new candidate genes for Pig molecular breeding.

Melanoma originates from the malignant transformation of melanocytes. Compared with other skin cancers, melanoma has a higher fatality rate. The 5-year survival rate of patients with early-stage primary melanoma through surgical resection can reach more than 90%. However, the 5-year survival rate of patients with metastatic melanoma is only 25%. Therefore, accurate assessment of melanoma progression is critical. Previous studies have found that Retinoic Acid Induced 14(RAI14) is critical in tumorigenesis. However, the biological function of RAI14 for the development of melanoma is unclear. In this study, RAI14 is highly expressed in melanoma and correlated with prognosis. The expression of RAI14 can affect the proliferation, migration and invasion of melanoma cells. F-Box Protein 32(FBXO32) is an E3 ubiquitin ligase of c-MYC. We found that RAI14 affects the transcriptional expression of FBXO32 and regulates the stability of c-MYC. These results suggest that RAI14 play an important role in the growth of melanoma and is expected to be a therapeutic target for melanoma.

B-cell-specific Moloney murine leukemia virus integration region 1 (Bmi-1, also known as RNF51 or PCGF4) is one of the important members of the PcG gene family, and is involved in regulating cell proliferation, differentiation and senescence, and maintaining the self-renewal of stem cells. Many studies in recent years have emphasized the role of Bmi-1 in the occurrence and development of tumors. In fact, Bmi-1 has multiple functions in cancer biology and is closely related to many classical molecules, including Akt, c-MYC, Pten, etc. This review summarizes the regulatory mechanisms of Bmi-1 in multiple pathways, and the interaction of Bmi-1 with noncoding RNAs. In particular, we focus on the pathological processes of Bmi-1 in cancer, and explore the clinical relevance of Bmi-1 in cancer biomarkers and prognosis, as well as its implications for chemoresistance and radioresistance. In conclusion, we summarize the role of Bmi-1 in tumor progression, reveal the pathophysiological process and molecular mechanism of Bmi-1 in tumors, and provide useful information for tumor diagnosis, treatment, and prognosis.

Besides the pilot symbols, the received data signals also contain some extra channel state information (CSI), which can be exploited to further improve the channel estimation accuracy. Considering a multi-cell multi-user massive multiple input multiple output (MIMO) with Ricean fading, the spectral efficiency for joint processing of the pilot and data strategy is investigated. Through the mutual information analysis of the multi-cell multi-user massive MIMO system, the intractable expression for the ergodic spectral efficiency is lower bounded and decomposed into three terms, where the explicit physical meanings of them are discussed in detail. Facilitated by the decomposing operation, an asymptotic closed-form expression of the spectral efficiency is obtained, and it is proven to be accurate. Comparing with separate processing, numerical results show that the joint processing outperforms the separate processing in spectral efficiency since the additional CSI is captured. The performance advantage for joint processing over separate processing becomes obvious as the coherence block duration of channels (T) increases. For a long enough T, joint processing eventually approaches the case with perfect CSI in terms of spectral efficiency. In addition, the inter-cell interference heavily reduces the system performance of joint processing when T is relatively small.

The outbreak of cyanobacterial blooms is a serious water environmental problem, and the harm it brings to aquatic ecosystems and water supply systems cannot be underestimated. It is very important to establish an accurate prediction model of cyanobacterial bloom concentration, which is a challenging issue. Machine learning techniques can improve the prediction accuracy, but a large amount of historical monitoring data is needed to train these models. For some waters with an inconvenient geographical location or frequent sensor failures, there are not enough historical data to train the model. To deal with this problem, a fused model based on a transfer learning method is proposed in this paper. In this study, the data of water environment with a large amount of historical monitoring data are taken as the source domain in order to learn the knowledge of cyanobacterial bloom growth characteristics and train the prediction model. The data of the water environment with a small amount of historical monitoring data are taken as the target domain in order to load the model trained in the source domain. Then, the training set of the target domain is used to participate in the inter-layer fine-tuning training of the model to obtain the transfer learning model. At last, the transfer learning model is fused with a convolutional neural network to obtain the prediction model. Various experiments are conducted for a 2 h prediction on the test set of the target domain. The results show that the proposed model can significantly improve the prediction accuracy of cyanobacterial blooms for the water environment with a low data volume.

With the improvement of the living standards of the residents, it is a very important and challenging task to continuously improve the accuracy of PM2.5 (particulate matter less than 2.5 μm in diameter) prediction. Deep learning-based networks, such as LSTM and CNN, have achieved good performance in recent years. However, these methods require sufficient data to train the model. The performance of these methods is limited for the sites where the data is lacking, such as the newly constructed monitoring sites. To deal with this problem, an improved deep learning model based on the hybrid transfer learning strategy is proposed for predicting PM2.5 concentration in this paper. In the proposed model, the maximum mean discrepancy (MMD) is used to select which station in the source domain is most suitable for migration to the target domain. An improved dual-stage two-phase (DSTP) model is used to extract the spatial–temporal features of the source domain and the target domain. Then the domain adversarial neural network (DANN) is used to find the domain invariant features between the source and target domains by domain adaptation. Thus, the model trained by source domain site data can be used to assist the prediction of the target site without degradation of the prediction performance due to domain drift. At last, some experiments are conducted. The experimental results show that the proposed model can effectively improve the accuracy of the PM2.5 prediction at the sites lacking data, and the proposed model outperforms most of the latest models.

MYCN, a member of MYC proto-oncogene family, encodes a basic helix-loop-helix transcription factor N-MYC. Abnormal expression of N-MYC is correlated with high-risk cancers and poor prognosis. Initially identified as an amplified oncogene in neuroblastoma in 1983, the oncogenic effect of N-MYC is expanded to multiple neuronal and nonneuronal tumors. Direct targeting N-MYC remains challenge due to its “undruggable” features. Therefore, alternative therapeutic approaches for targeting MYCN-driven tumors have been focused on the disruption of transcription, translation, protein stability as well as synthetic lethality of MYCN. In this review, we summarize the latest advances in understanding the molecular mechanisms of MYCN dysregulation in cancers.

Abstract Marked by large interannual variability, East Asian summer monsoon (EASM) rainfall has profound socio‐economic impacts through its dominant influence on floods and droughts. Improving predictions of the interannual variations of EASM rainfall has important implications for over 20% of the world's population. While coupled modeling systems have demonstrated some prediction skill related to the El Niño Southern Oscillation with remote influence on EASM rainfall, the impact of soil moisture has heretofore not been systematically investigated. Using a weakly coupled data assimilation (WCDA) system to constrain the soil moisture and soil temperature in a coupled climate model with a global land data assimilation product, this study demonstrates significant improvements in simulating the interannual variations of EASM rainfall, capturing the notable shift to a “wetter‐South‐drier‐North” rainfall pattern in China in the early 1990s. Hindcast simulations initialized with the well‐balanced states from a coupled simulation with WCDA also show significant multi‐year rainfall prediction skill over East China and Tibetan Plateau. Improvements in predicting the EASM rainfall are attributed to the strong land‐atmosphere coupling in large areas over China, which allows improved predictions of soil moisture to influence precipitation through soil moisture‐precipitation feedback, and the effects of land anomalies on the EASM circulation. This study highlights the significant contribution of land to the interannual predictability of EASM rainfall, with a great potential to advance skillful interannual predictions of benefit to the large populations influenced by the annual whiplash of the summer monsoon rain.

The rolling bearing is an extremely important basic mechanical device. The diagnosis of its fault play an important role in the safe and stable operation of the mechanical system. This study proposed an approach, based on the Fast Fourier Transform (FFT) with Decimation-In-Time (DIT) and XGBoost algorithm, to identify the fault type of bearing quickly and accurately. Firstly, the original vibration signal of rolling bearing was transformed by DIT-FFT and divided into the training set and test set. Next, the training set was used to train the fault diagnosis XGBoost model, and the test set was used to validate the well-trained XGBoost model. Finally, the proposed approach was compared with some common methods. It is demonstrated that the proposed approach is able to diagnose and identify the fault type of bearing quickly with almost 99% accuracy. It is more accurate than Machine Learning (89.88%), Ensemble Learning (93.25%), and Deep Learning (95%). This approach is suitable for the fault diagnosis of rolling bearing.

Objective: Gastric cancer is one of the most common malignant tumors. Bruceine D (BD) is one of the extracts of Brucea javanica. In recent years, it has been reported that BD has anti-tumor activity in some human cancers through different mechanisms. Here, this study try to explore the effect of BD on gastric cancer and its regulatory mechanism.Methods: Cell proliferation ability was detected by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assays, 5-bromo-2-deoxyuridine (BrdU) staining and soft agar colony formation assay, respectively. The tumor xenograft model was used to verify the effect of BD on the tumorigenicity of gastric cancer cells in vivo. Flow cytometry analysis and Western blot assay were performed to detect cell cycle and apoptosis. Gastric cancer cells were analyzed by transcriptome sequencing. The interaction between LINC01667, microRNA-138-5p (miR-138-5p) and Cyclin E1 was verified by dual luciferase experiment and RT-PCR assays.Results: We found that BD significantly inhibited cell proliferation and induced cell cycle arrest at S phase in gastric cancer cells. Transcriptome analysis found that the expression of a long non-coding RNA, LINC01667, were significantly down-regulated after BD treatment. Mechanically, it was discovered that LINC01667 upregulated the expression of Cyclin E1 by sponging miR-138-5p. Furthermore, BD enhanced the chemosensitivity of gastric cancer cells to doxorubicin, a clinically used anti-cancer agent.Conclusion: BD inhibit the growth of gastric cancer cells by downregulating the LINC01667/miR-138-5p/Cyclin E1 axis. In addition, BD enhances the chemosensitivity of gastric cancer cells to doxorubicin. This study indicates that BD may be used as a candidate drug for the treatment of patients with gastric cancer.

The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model has been widely used to define rainfall thresholds for triggering shallow landslides. In this study, the rainfall intensity(I)-duration(D) thresholds for multiple slope units of an area in Pu’an County, Guizhou Province, China were defined based on TRIGRS. Given that TRIGRS is used to simulate the slope stability under the conditions of a given increasing sequence of I-D data, if the slope reaches instability at I = a, D = b, it will also become unstable in the case of I = a, D > b or I > a, D = b. To explore the effect of these I-D data with the same I or D values on the definition of I-D thresholds and the best method to exclude these data, two screening methods were used to exclude the I-D data that caused instability in the TRIGTS simulation. First, I-D data with the same I values when D values are greater than a certain limit value were excluded. Second, several D values were selected to exclude I-D data with the same I values for a slope unit. Then, an I value was selected to exclude I-D data with the same D values. After screening, two different I-D thresholds were defined. The comparison with the thresholds defined without screening shows that the I-D data with the same I or D values will reduce the accuracy of thresholds. Moreover, the second screening method can entirely exclude these data.

The development of advanced and efficient methods for mapping and monitoring wetland regions is essential for wetland resources conservation, management, and sustainable development. Although remote sensing technology has been widely used for detecting wetlands information, it remains a challenge for wetlands classification due to the extremely complex spatial patterns and fuzzy boundaries. This study aims to implement a comprehensive and effective classification scheme for wetland land covers. To achieve this goal, a novel object-based multigrained cascade forest (OGCF) method with multisensor data (including Sentinel-2 and Radarsat-2 remote sensing imagery) was proposed to classify the wetlands and their adjacent land cover classes in the wetland National Natural Reserve. Moreover, a hybrid selection method (ReliefF-RF) was proposed to optimize the feature set in which the spectral and polarimetric decomposition features are contained. We obtained six spectral features from visible and shortwave infrared bands and 10 polarimetric decomposition features from the H/A/Alpha, Pauli, and Krogager decomposition methods. The experimental results showed that the OGCF method with multisource features for land cover classification in wetland regions achieved the overall accuracy and kappa coefficient of 88.20% and 0.86, respectively, which outperformed the support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN). The accuracy of the wetland classes ranged from 75.00% to 97.53%. The proposed OGCF method exhibits a good application potential for wetland land cover classification. The classification scheme in this study will make a positive contribution to wetland inventory and monitoring and be able to provide technical support for protecting and developing natural resources.

Canine distemper virus (CDV) belongs to the Morbillivirus genus of the Paramyxoviridae family, which causes a threat to the domestic dog and fur-animal industry. Hemagglutinin protein is a major membrane protein of the vital molecular factor in CDV tropism, also known to induce hosts to produce neutralizing antibodies. In the current study, we prepared two monoclonal antibodies, 1A5 and 2B8, against the H protein of the CDV-PS strain. A series of partially overlapping synthetic peptides covering the hemagglutinin protein (amino acids 50–204) were screened to define the linear epitope identified by 1A5 and 2B8 mAbs. 120QKTNFFNPNREFDFR134 (F8) and 178ARGDIFPPY186 (F14-1) are minimal linear epitopes recognized by 1A5 and 2B8 mAbs, respectively. Further investigations revealed that F8 is conserved in different CDV strains; however, F14-1 contains mutant residues 178, 179, and 180. The epitopes F8 and F14-1 localized at the surface of hemagglutinin protein in a three-dimensional (3D) structure. CDV-infected dog serum can also recognize the identified B-cell epitopes.

The increasing demand for environmental protection has given rise to burgeoning research on green consumption. The present research adds to this expanding literature by investigating a novel predictor of consumer green consumption: awe. As a self-transcendent emotion, awe arises when people encounter perceptually vast stimuli that overwhelm their existing knowledge and mental structures, and, meanwhile, this also elicits a need for accommodation. This research proposes and demonstrates that, compared with happiness and a neutral affective state, experience of awe promotes green consumption via an enhanced psychological ownership of nature. Moreover, this research identifies a condition by showing that, while awe promotes green consumption when interdependent self-construal is activated, this effect diminishes when independent self-construal is activated.

This experiment was designed to evaluate the potential for dietary alfalfa saponins (AS) to influence cholesterol (Ch) concentrations and the mRNA expressions of the ATP-binding cassette (ABC) that concludes transporters G5 (ABCG5) and G8 (ABCG8) in the livers of laying hens. Hundred and fifty laying hens (27 weeks old) were divided into five treatment groups randomly. Five replicates were assigned to each treatment group consisting of six birds per replicate. The same basic diet was used for all groups with supplementation of 0 (control), 60, 120, 240, and 480 mg/kg of AS, which was given ad libitum to hens for 60 days. The results showed that AS in the diets of laying hens could effectively reduce egg Ch concentrations, and increase the excretion of bile acid by the liver with the expression levels of ABCG5 and ABCG8 improving. Our study found that dietary AS could effect cholesterol metabolism by up-regulating the expression of G5 and G8 in hens, and that AS is a potential agent for reducing Ch concentrations and 120 mg/kg of AS in the diet was suitable and effective for laying hens.

The co-precipitation thermodynamics of the Li+–Fe2+/Fe3+–Al3+–F−–SO42−–PO43−–H2O system at 298 K is studied, aiming to understand the precipitation characteristics. Based on the principle of simultaneous equilibrium and the mass action law, the missing Ksp values of AlF3 and FeF3 were estimated. The results of thermodynamic calculation demonstrate that Al3+ and F− in the sulfuric acid leachate could be preferentially precipitated in the form of AlPO4 and FeF3 by the precise adjustment of the final pH value. Only a small amount of P and Fe was lost by the precipitation of Fe3(PO4)2·8H2O, FePO4, and Fe(OH)3 during the purification process. Controlling the oxidation of ferrous ions effectively is of critical significance for the loss reduction of P and Fe. Precipitation experiments at different pH value indicated that the concentration of Al3+ and F− in the leachate decreased as the final pH value rose from 3.05 to 3.90. When the final pH value was around 3.75, aluminum and fluoride ion impurities could be deeply purified, and the loss rate of phosphate ions and iron ions could be reduced as much as possible. Relevant research results can provide theoretical guidance for the purification of leachate in the wet recycling process of lithium-ion batteries.

Underwater organisms are an important part of the underwater ecological environment. More and more attention has been paid to the perception of underwater ecological environment by intelligent means, such as machine vision. However, many objective reasons affect the accuracy of underwater biological detection, such as the low-quality image, different sizes or shapes, and overlapping or occlusion of underwater organisms. Therefore, this paper proposes an underwater biological detection algorithm based on improved Faster-RCNN. Firstly, the ResNet is used as the backbone feature extraction network of Faster-RCNN. Then, BiFPN (Bidirectional Feature Pyramid Network) is used to build a ResNet–BiFPN structure which can improve the capability of feature extraction and multi-scale feature fusion. Additionally, EIoU (Effective IoU) is used to replace IoU to reduce the proportion of redundant bounding boxes in the training data. Moreover, K-means++ clustering is used to generate more suitable anchor boxes to improve detection accuracy. Finally, the experimental results show that the detection accuracy of underwater biological detection algorithm based on improved Faster-RCNN on URPC2018 dataset is improved to 88.94%, which is 8.26% higher than Faster-RCNN. The results fully prove the effectiveness of the proposed algorithm.