Search

To achieve the accurate recognition of biomarkers or pathological characteristics within tissues or cells, in situ detection using biosensor technology offers crucial insights into the nature, stage, and progression of diseases, paving the way for enhanced precision in diagnostic approaches and treatment strategies. The implementation of needle-shaped biosensors (N-biosensors) presents a highly promising method for conducting in situ measurements of clinical biomarkers in various organs, such as in the brain or spinal cord. Previous studies have highlighted the excellent performance of different N-biosensor designs in detecting biomarkers from clinical samples in vitro. Recent preclinical in vivo studies have also shown significant progress in the clinical translation of N-biosensor technology for in situ biomarker detection, enabling highly accurate diagnoses for cancer, diabetes, and infectious diseases. This article begins with an overview of current state-of-the-art benchtop N-biosensor designs, discusses their preclinical applications for sensitive diagnoses, and concludes by exploring the challenges and potential avenues for next-generation N-biosensor technology.

The WUSCHEL-related homeobox (WOX) gene family has a critical effect on plant development and abiotic stress. However, there have been no genome-wide studies on WOX genes within sweet cherry (Prunus avium L.). In the present work, eight PavWOX genes were discovered within sweet cherry at the genome-wide level, and they were mapped to six chromosomes. Based on phylogenetic relationships, these genes were classified into three groups, with genes in one group having similar gene structures and conserved motifs. Meanwhile, the PavWOX genes possessed cis-acting elements and functions associated with hormone responses, stress responses, and development. As revealed by expression patterns, certain PavWOX genes are specifically expressed within tissues, suggesting that they may have unique functions. Additionally, the gene family expression patterns under drought stress were analyzed. PavWOX4, PavWOX5, PavWOX13A, and PavWOX13B had increased expressions upon drought stress. In addition, the transcription factor of PavWOX4 and PavWOX13A was localized in the nucleus, confirming the estimated results. Our findings lay the foundation for determining the expression patterns and functions of the PavWOX gene family within sweet cherry and shed more light on the underlying regulatory mechanisms.

Multi-view stereo plays an important role in 3D reconstruction but suffers from low reconstruction efficiency and has difficulties reconstructing areas with low or repeated textures. To address this, we propose MVP-Stereo, a novel multi-view parallel patchmatch stereo method. MVP-Stereo employs two key techniques. First, MVP-Stereo utilizes multi-view dilated ZNCC to handle low texture and repeated texture by dynamically adjusting the matching window size based on image variance and using a portion of pixels to calculate matching costs without increasing computational complexity. Second, MVP-Stereo leverages multi-scale parallel patchmatch to reconstruct the depth map for each image in a highly efficient manner, which is implemented by CUDA with random initialization, multi-scale parallel spatial propagation, random refinement, and the coarse-to-fine strategy. Experiments on the Strecha dataset, the ETH3D benchmark, and the UAV dataset demonstrate that MVP-Stereo can achieve competitive reconstruction quality compared to state-of-the-art methods with the highest reconstruction efficiency. For example, MVP-Stereo outperforms COLMAP in reconstruction quality by around 30% of reconstruction time, and achieves around 90% of the quality of ACMMP and SD-MVS in only around 20% of the time. In summary, MVP-Stereo can efficiently reconstruct high-quality point clouds and meet the requirements of several photogrammetric applications, such as emergency relief, infrastructure inspection, and environmental monitoring.

Human herpes simplex virus (HSV), a double-stranded DNA virus belonging to the Herpesviridae family and alpha herpesvirus subfamily, is one of the most epidemic pathogens in the population. Cell-to-cell spread is a special intercellular transmission mechanism of HSV that indicates the virulence of this virus. Through numerous studies on mutant HSV strains, many viral and host proteins involved in this process have been identified; however, the mechanisms remain poorly understood. Here, we evaluated the effect of the membrane protein genes US7 and UL56 on cell-to-cell spread in vitro between two HSV-1 (HB94 and HN19) strains using a plaque assay, syncytium formation assay, and the CRISPR/Cas9 technique. US7 knockout resulted in the inhibition of viral cell-to-cell spread; additionally, glycoprotein I (US7) of the HB94 strain was found to promote cell-to-cell spread compared to that of the HN19 strain. UL56 knockout did not affect plaque size and syncytium formation; however, the gene product of UL56 from the HN19 strain inhibited plaque formation and membrane infusion. This study presents preliminary evidence of the functions of US7 and UL56 in the cell-to-cell spread of HSV-1, which will provide important clues to reveal the mechanisms of cell-to-cell spread, and contributes to the clinical drugs development.

Aiming to solve the problem that spatially distributed similar instances cannot be distinguished in 3D point cloud instance segmentation, a 3D point cloud instance segmentation network, considering the global shape contour, was proposed. This research used the global-to-local design idea and added the global shape constraint to solve this problem. A Transformer module (Global Shape Attention, GSA) that can capture the shape contour information of the instance in the scene was designed. This module encoded the shape contour information into the Transformer structure as a Key-Value and extracted the instance fused with the global shape contour features, for instance, segmentation. At the same time, the network directly predicted the instance mask in an end-to-end mode, avoiding heavy post-processing algorithms. Many experiments have been conducted on the S3DIS, ScanNet, and STPL3D datasets, and our experimental results showed that our proposed network can efficiently capture the shape contour information of scene instances and can help to alleviate the problem of the difficulty distinguishing between spatially distributed similar instances in a scene, improving the efficiency and stability of instance segmentation.

Introduction: The objective was to evaluate the poisoning severity score (PSS) as an early prognostic predictor in patients with wasp stings and identify associated clinical characteristics and risk factors for mortality. Methods: A total of 363 patients with wasp stings at Suining Central Hospital between January 2016 and December 2018 were enrolled. Within the first 24 h of admission, the poisoning severity score (PSS) and the Chinese expert consensus on standardized diagnosis and treatment of wasp stings (CECC) were utilized for severity classification, and their correlation was examined. Patients were then divided into survival and death groups based on discharge status. Logistic regression analysis was employed to analyze factors influencing patients’ outcomes. Results: The mortality of wasp sting patients was 3.9%. The PSS and CECC were found to correlate for severity classification. Additionally, female gender, age, number of stings, and PSS were identified as independent risk factors for mortality in wasp sting patients. Combining these four factors yielded an AUC of 0.962 for predicting death. Conclusions: PSS aids in early severity classification of wasp stings. Female gender, age, number of stings, and PSS were independent mortality risk factors in these patients.

Automatic reconstruction of surfaces from satellite imagery is a hot topic in computer vision and photogrammetry. State-of-the-art reconstruction methods typically produce 2.5D elevation data. In contrast, we propose a one-stage method directly generating a 3D mesh model from multi-view satellite imagery. We introduce a novel Sat-Mesh approach for satellite implicit surface reconstruction: We represent the scene as a continuous signed distance function (SDF) and leverage a volume rendering framework to learn the SDF values. To address the challenges posed by lighting variations and inconsistent appearances in satellite imagery, we incorporate a latent vector in the network architecture to encode image appearances. Furthermore, we introduce a multi-view stereo constraint to enhance surface quality. This constraint minimizes the similarity between image patches to optimize the position and orientation of the SDF surface. Experimental results demonstrate that our method achieves superior visual quality and quantitative accuracy in generating mesh models. Moreover, our approach can learn seasonal variations in satellite imagery, resulting in texture mesh models with different and consistent seasonal appearances.

A series of highly fluorinated polyimide/allomelanin nanoparticles (FPI/AMNPs) films were prepared with FPI as the matrix and AMNPs as the filler. Due to the formation of hydrogen bonds, significantly reinforced mechanical and UV-shielding properties are acquired. Stress–strain curves demonstrated a maximum tensile strength of 150.59 MPa and a fracture elongation of 1.40% (0.7 wt.% AMNPs), respectively, 1.78 and 1.56× that of pure FPI. The measurements of the UV-vis spectrum, photodegradation of curcumin and repeated running tests confirmed the splendid UV-shielding capabilities of FPI/AMNPs films. The enhancement mechanisms, such as synergistic UV absorption of the charge transfer complexes in FPI and AMNPs and photothermal conversion, were the reasons for its exceptional UV shielding. The excellent comprehensive properties above enable FPI/AMNPs nanocomposites to be potential candidates in the field of UV shielding.

Magnetotelluric (MT) inversion and forward modeling are closely linked. The optimization and iteration processes of the inverse algorithm require frequent calls to forward modeling. However, traditional numerical simulations for forward modeling are computationally expensive; here, deep learning (DL) networks can simulate forward modeling and significantly improve forward speed. Applying DL for forward modeling in inversion problems requires a high-precision network capable of responding to fine changes in the model to achieve high accuracy in inversion optimization. Most existing MT studies have used a convolutional neural network, but this method is limited by the receptive field and cannot extract global feature information. In contrast, the Mix Transformer has the ability to globally model and extract features. In this study, we used a Mix Transformer to hierarchically extract feature information, adopted a multiscale approach to restore feature information to the decoder, and eliminated the skip connection between the encoder and decoder. We designed a forward modeling network model (MT-MitNet) oriented toward inversion. A sample dataset required for DL forward was established using the forward data generated from the traditional inverse calculation iteration process. The trained network quickly and accurately calculates the forward response. The experimental results indicate a high agreement between the forward results of MT-MitNet and those obtained with traditional methods. When MT-MitNet replaces the forward computation in traditional inversion, the inversion results obtained with it are also highly in agreement with the traditional inversion results. Importantly, under the premise of ensuring high accuracy, the forward speed of MT-MitNet is hundreds of times faster than that of traditional inversion methods in the same process.

Severe fever with thrombocytopenia syndrome (SFTS) caused by a novel bunyavirus (SFTSV) is an emerging infectious disease with up to 30% case fatality. Currently, there are no specific antiviral drugs or vaccines for SFTS. Here, we constructed a reporter SFTSV in which the virulent factor nonstructural protein (NSs) was replaced by eGFP for drug screening. First, we developed a reverse genetics system based on the SFTSV HBMC5 strain. Then, the reporter virus SFTSV-delNSs-eGFP was constructed, rescued, and characterized in vitro. SFTSV-delNSs-eGFP showed similar growth kinetics with the wild-type virus in Vero cells. We further detected the antiviral efficacy of favipiravir and chloroquine against wild-type and recombinant SFTSV by the quantification of viral RNA, and compared the results with that of fluorescent assay using high-content screening. The results showed that SFTSV-delNSs-eGFP could be used as a reporter virus for antiviral drug screening in vitro. In addition, we analyzed the pathogenesis of SFTSV-delNSs-eGFP in interferon receptor-deficient (IFNAR−/−) C57BL/6J mice and found that unlike the fatal infection of the wild-type virus, no obvious pathological change or viral replication were observed in SFTSV-delNSs-eGFP-infected mice. Taken together, the green fluorescence and attenuated pathogenicity make SFTSV-delNSs-eGFP a potent tool for the future high-throughput screening of antiviral drugs.

The surface/subsurface damage of engineering ceramics after machining has a great influence on the service performance of parts. In order to obtain a high grinding surface quality of engineering ceramics, and take silicon nitride ceramic as a research object, a series of grinding experiments were carried out. The effects of grinding parameters on longitudinal crack propagation depth and the surface residual stress of silicon nitride ceramics were analyzed by grinding experiments, and the residual stress at the location of crack propagation was obtained. The variation in the grinding temperature under different grinding parameters was explored. The influences of the grinding temperature on crack propagation depth and surface residual stress were clarified, the distribution of residual stress along the depth direction was discussed, and the relationship between the residual stress and crack propagation was revealed. The results show that the residual compressive stress on the surface of silicon nitride ceramics decreases with the increase in the depth of crack propagation and the degree of surface brittle spalling. The residual stress at the location of the crack propagation was residual tensile stress. The crack propagation depth increased with the increase in the residual tensile stress. The research provides a reference for the realization of high-quality surfaces in the grinding of silicon nitride ceramics.

In 2013, the Chinese government implemented a two-child policy to increase the country’s fertility rate. However, the persistently low rates necessitated other measures to boost fertility. This study empirically investigated the association between intergenerational support and second-child fertility intention in the Chinese sandwich generation and demonstrated the mediating role of parental burnout and burnout in caring for grandparents. Survey data collected at Time 1, Time 2, and Time 3 included 2939 participants from different regions of China. Before analyzing the data, coarsened exact matching and propensity score matching was conducted to reduce sampling bias. Regression analysis results indicated that intergenerational support has a significant total positive effect on second-child fertility intention. Furthermore, mediation path analysis revealed that parental burnout and burnout in caring for grandparents play significant but opposite directional mediating roles in the association between intergenerational support and second-child fertility intention. Sensitivity analysis using different calipers yielded similar results. These results indicated that second-child fertility intention can be increased among the Chinese sandwich generation with intergenerational support, by mitigating parental burnout. However, intergenerational support did not alleviate burnout in caring for grandparents in the sandwich generation; therefore, formal older adult care policies are required to help the sandwich generation experience lower burnout, while receiving intergenerational support.

In speaker recognition tasks, convolutional neural network (CNN)-based approaches have shown significant success. Modeling the long-term contexts and efficiently aggregating the information are two challenges in speaker recognition, and they have a critical impact on system performance. Previous research has addressed these issues by introducing deeper, wider, and more complex network architectures and aggregation methods. However, it is difficult to significantly improve the performance with these approaches because they also have trouble fully utilizing global information, channel information, and time-frequency information. To address the above issues, we propose a lighter and more efficient CNN-based end-to-end speaker recognition architecture, ResSKNet-SSDP. ResSKNet-SSDP consists of a residual selective kernel network (ResSKNet) and self-attentive standard deviation pooling (SSDP). ResSKNet can capture long-term contexts, neighboring information, and global information, thus extracting a more informative frame-level. SSDP can capture short- and long-term changes in frame-level features, aggregating the variable-length frame-level features into fixed-length, more distinctive utterance-level features. Extensive comparison experiments were performed on two popular public speaker recognition datasets, Voxceleb and CN-Celeb, with current state-of-the-art speaker recognition systems and achieved the lowest EER/DCF of 2.33%/0.2298, 2.44%/0.2559, 4.10%/0.3502, and 12.28%/0.5051. Compared with the lightest x-vector, our designed ResSKNet-SSDP has 3.1 M fewer parameters and 31.6 ms less inference time, but 35.1% better performance. The results show that ResSKNet-SSDP significantly outperforms the current state-of-the-art speaker recognition architectures on all test sets and is an end-to-end architecture with fewer parameters and higher efficiency for applications in realistic situations. The ablation experiments further show that our proposed approaches also provide significant improvements over previous methods.

In image-based three-dimensional (3D) reconstruction, texture-mapping techniques can give the model realistic textures. When the geometric surface in some regions is not reconstructed, such as for moving cars, powerlines, and telegraph poles, the textures in the corresponding image are textured to other regions, resulting in errors. To solve this problem, this letter proposes an image consistency detection method based on the Binary Robust Independent Elementary Features (BRIEF) descriptor. The method is composed of two parts. First, each triangle in the mesh and its neighboring triangles are sampled uniformly to obtain sampling points. Then, these sampled points are projected into the visible image of the triangle, and the corresponding sampled points and their RGB color values are obtained on the corresponding image. Based on the sampled points on these images, a BRIEF descriptor is calculated for each image corresponding to that triangle. In the second step, the Hamming distance between these BRIEF descriptors is calculated, outliers are removed according to the method, and noisy images are also removed. In addition, we propose adding semantic information to Markov energy optimization to reduce errors further. The two methods effectively reduced errors in texture mapping caused by objects not reconstructed, improving the texture quality of 3D models.

Baculoviruses have been widely used as expression vectors. However, numerous genes in the baculoviral genome are non-essential for cellular infection and protein expression, making the optimisation of baculovirus expression vectors possible. We used a synthetic biological method to reduce the number of genes in a partial region of the autograph californica multiple nucleopolyhedrovirus (AcMNPV), the most widely used baculovirus expression vector. The C1 region of the AcMNPV is 46.4 kb and is subdivided into B1, B2, and B3 fragments. We first designed modified B1, B2, and B3 fragments by deleting the non-essential genes, and then synthesised complete viral genomes containing either individual modified B fragments or joint modified B fragments through transformation-related recombination in yeast. The synthetic genomes were then transfected into Sf9 cells to rescue the progeny viruses and test their infectivity. The design-build-test cycle was repeated until the ultimately rescued virus could produce progeny viruses efficiently. Finally, AcMNPV-Syn-mC1-1.1 by deleting approximately 17.2 kb, including 20 ORFs, in the C1 region, was obtained. This is essential to the synthesis of a minimal AcMNPV genome that can generate infectious progeny viruses and can be further used to optimise the foundation of baculovirus expression vectors.

Variational mesh refinement is a crucial step in multiview 3D reconstruction. Existing algorithms either focus on recovering mesh details or focus on suppressing noise. Approaches with consideration of both are lacking. To address this limitation, we proposed a new variational mesh refinement method named total differential mesh refinement (TDR), which mainly included two improvements. First, the traditional partial-differential photo-consistency gradient used in the variational mesh refinement method was replaced by the proposed total-differential photo-consistency gradient. With consideration of the photo-consistency correlation between adjacent pixels, our method can make photo-consistency achieve a more effective convergence than traditional approaches. Second, we introduced the bilateral normal filter with a novel self-adaptive mesh denoising strategy into the variational mesh refinement. This strategy maintains a balance between detail preservation and effective denoising via the zero-normalized cross-correlation (ZNCC) map. Various experiments demonstrated that our method is superior to traditional variational mesh refinement approaches in both accuracy and denoising effect. Moreover, compared with the mesh generated by open-source and commercial software (Context Capture), our meshes are more detailed, regular, and smooth.

Myo-inositol, a carbocyclic sugar, is believed to be relevant to renal pathobiology since the kidney is the major site for its catabolism. Its role in acute kidney injury (AKI) has not been fully investigated. Ferroptosis, a unique form of regulated cell death, is involved in various types of renal injuries. The relevance of myo-inositol with respect to the process of ferroptosis has not been explored either. Herein, our current exploratory studies revealed that supplementation of myo-inositol attenuates cisplatin-induced injury in cultured Boston University mouse proximal tubular (BUMPT) cells and renal tubules in vivo. Moreover, our studies unraveled that metabolic parameters pertaining to ferroptosis were disrupted in cisplatin-treated proximal tubular cells, which were seemingly remedied by the administration of myo-inositol. Mechanistically, we noted that cisplatin treatment led to the up-regulation of NOX4, a key enzyme relevant to ferroptosis, which was normalized by the administration of myo-inositol. Furthermore, we observed that changes in the NOX4 expression induced by cisplatin or myo-inositol were modulated by carboxy-terminus of Hsc70-interacting protein (CHIP), an E3 ubiquitin ligase. Taken together, our investigation suggests that myo-inositol promotes CHIP-mediated ubiquitination of NOX4 to decelerate the process of ferroptosis, leading to the amelioration of cisplatin-induced AKI.

The study of convolutional neural networks for 3D point clouds is becoming increasingly popular, and the difficulty lies mainly in the disorder and irregularity of point clouds. At present, it is straightforward to propose a convolution operation and perform experimental validation. Although good results are achieved, the principles behind them are not explained—i.e., why this can solve the disorder and irregularity of point clouds—and it is difficult for the researchers to design a point cloud convolution network suitable for their needs. For this phenomenon, we propose a point convolution network framework based on spatial location correspondence. Following the correspondence principle can guide us in designing convolution networks adapted to our needs. We analyzed the intrinsic mathematical nature of the convolution operation, and we argue that the convolution operation remains the same when the spatial location correspondence between the convolution kernel points and the convolution range elements remains unchanged. Guided by this principle, we formulated a general point convolution framework based on spatial location correspondence, which explains how to handle a disordered point cloud. Moreover, we discuss different kinds of correspondence based on spatial location, including M-to-M, M-to-N, and M-to-1 relationships, etc., which explain how to handle the irregularity of point clouds. Finally, we give the example of a point convolution network whose convolution kernel points are generated based on the sample’s covariance matrix distribution according to our framework. Our convolution operation can be applied to various point cloud processing networks. We demonstrated the effectiveness of our framework for point cloud classification and semantic segmentation tasks, achieving competitive results with state-of-the-art networks.

The RAPID (reconstruction algorithm for probabilistic inspection of defect) method based on Lamb wave detection is an effective method to give the position information of a defect in composite plate. In this paper, an improved RAPID imaging method based on machine learning (ML) is proposed to precisely visualize the location and features of defects in composite plate. First, the specific feature information of the defect, such as type, size and direction, can be identified by analyzing the detection signals through multiple machine learning models. Then, according to the obtained defect features, the scaling parameter β of the RAPID method which controls the size of the elliptical area is revised, and weights are set to the important detection paths which are related to defect features to realize precise defect imaging. The simulation results show that the proposed method can intuitively characterize the location and related feature information of the defect, and effectively improve the accuracy of defect imaging.

Low light stress increases the chalkiness of rice; however, this effect has not been fully characterized. In this study, we demonstrated that low light resulted in markedly decreased activity of ADP-glucose pyrophosphorylase in the grains and those of sucrose synthase and soluble starch synthase in the early period of grain filling. Furthermore, low light also resulted in decreased activities of granule-bound starch synthase and starch branching enzyme in the late period of grain filling. Therefore, the maximum and mean grain filling rates were reduced but the time to reach the maximum grain filling rates and effective grain filling period were increased by low light. Thus, it significantly decreased the grain weight at the maximum grain filling rate and grain weight and retarded the endosperm growth and development, leading to a loose arrangement of the amyloplasts and an increase in the chalkiness of the rice grains. Compared to the grains at the top panicle part, low light led to a greater decrease in the grain weight at the maximum grain filling rate and time to reach the grain weight at the maximum grain filling rate at the bottom panicle part, which contributed to an increase in chalkiness by increasing the rates of different chalky types at the bottom panicle part. In conclusion, low light disturbed starch synthesis in grains, thereby impeding the grain filling progress and increasing chalkiness, particularly for grains at the bottom panicle part.

Sowing quality is directly influenced by the seed-filling characteristics of a centralized seed-metering device, and vigorous seed-motion status deteriorates seeding performance resulting from field surface roughness, seeder vibration, etc. However, the relationship between vibration and seed-filling properties remains unclear. This study measured the vibration characteristics of a seed-metering device, examined unmeasurable seed-motion characteristics based on DEM simulation, and used bench tests to determine the seed-filling characteristics under vibration conditions. The frequency distribution of the rapeseed seeder was 0–180 Hz. The simulation results revealed that the vibration mainly changed the level of seed in the seed-filling area; moreover, it significantly increased the seed-filling angle, ranging from 56.00°–59.00° to 69.78°–91.40°, which affected the seed-filling performance. A vibration frequency of 10–40 Hz resulted in a target seed rate within 88.67–99.44%, but when there is no vibration, the target seed rate reached 100%. The bench test results demonstrated that the seed-filling angle under vibration will increase with an increase in longitudinal distance, and will change between 15.49°–102°. The seed-filling performance affected by longitudinal distance was larger than that affected by lateral distance. By adjusting the longitudinal distance to range from 0–20 mm, the target seed rate could be maintained above 91%. The findings help to improve our knowledge of seed-filling characteristics under vibration conditions, and should be conducive to improving seeding performance by adjusting the seed layer height to reduce the impact of vibration on field operations.

To improve the accuracy of centrifugal pump fault diagnosis, a novel fault diagnosis method based on improved multiple fractal detrended fluctuation analysis (MFDFA), the fusion of multi-sensing information derived from the back propagation (BP) neural network and the Dempster–Shafter (D-S) evidence theory, is accordingly proposed. Firstly, the multifractal spectral parameters of four sensor signals under four different operating conditions were extracted as centrifugal pump fault feature vectors using improved MFDFA and input to the BP neural network. Then, the basic trust assignment function was constructed by calculating trustworthiness (both local and global) as priori information, which is based on the output results of the neural networks specific to of each group of sensors. Finally, the basic trust assignment function was fused with decision processing in accordance with the D-S evidence combination rule in order to effectively achieve the multi-sensor information fusion centrifugal pump fault diagnosis. The experimental results show the multiple fractal spectrum feature parameters extracted by the improved MFDFA can accurately reflect the signal essence, and can be used as the fault feature vector. On this basis, this multi-sensor fault diagnosis reduces the uncertainty of fault classification and demonstrates improved accuracy compared to the single-sensor fault diagnosis thanks to being based on a combination of the BP neural networks and D-S evidence theory. Thereby, this method can facilitate accurate diagnosis of the centrifugal pump fault type with high confidence, subsequently providing a novel and alternative method to existing methods of diagnosis.

In animals and plants, circRNAs regulate gene expression and act as sponges that inhibit the activity of microRNAs. This study aimed to determine how specific circRNAs are expressed in rice grains at different stages of grain filling, under normal and low light conditions. We extracted total RNA from rice grains under low and sufficient light conditions. Deep sequencing was performed using circRNA libraries, and bioinformatics tools were used to identify the circRNAs. In addition, we analyzed targeted messenger RNA functions using two databases to predict the processes involved in rice grain development, and we conducted real-time PCR on 15 of the circRNAs as well as Sanger sequencing. During the grain development process, 8015 candidate circRNAs were isolated, among which the number of known circRNAs was 1661. We also found that the number of circRNAs changed with the time of development. Among them, six circRNAs acted as sponges that targeted more than two microRNAs at different stages of development, and these circRNAs showed a regulatory pattern consistent with the transcriptome sequencing results. More circRNA diversity was found under low light treatment compared to normal light. These findings reveal a possible link between circRNA regulation and the expression of the functional genes associated with photosignal-mediated rice grain development.

Rapeseed is mainly cultivated in hilly areas in China and the inclination angle is an important parameter for influencing the seeding quality of a seed-metering device. However, the relationship between seeding performance and inclination angle remains unclear. This study analyzes the influence of inclination angle (θ) on the characteristics of seed-filling and seeding performance in a centralized seed-metering system of rapeseed using DEM simulation and the bench test. The results revealed that the inclination angle (θ) had a significant effect on seed-filling performance. The missing seeds ratio and the deviation of seed layer height increased with an increase in a lateral inclination angle. The missing seeds ratio in the forward direction (−Y direction) and seed-filling angle, double seed-filling ratio, and seed layer height in the back direction (Y direction) increased with the increasing inclination angle. Under seed-cleaning treatment, the missing seeds ratio was significantly reduced in the −Y direction at a seed layer height of 25 mm, and the qualified ratio was more than 91.0% at a seed layer height of 20 mm in X direction. The phenomenon of seeds passing through the seed-metering wheel disappeared with a qualified ratio larger than 90.0% in Y direction by installing a seed-cleaning brush. The results contributed to improving the knowledge of seed-filling characteristics of the centralized seed-metering device at inclination conditions and should help to improve seeding performance by adjusting the seed layer height and installing a seed-cleaning brush.

Ultrasonic guided waves are sensitive to many different types of defects and have been studied for defect recognition in rail. However, most fault recognition algorithms need to extract features from the time domain, frequency domain, or time-frequency domain based on experience or professional knowledge. This paper proposes a new method for identifying many different types of rail defects. The segment principal components analysis (S-PCA) is developed to extract characteristics from signals collected by sensors located at different positions. Then, the Support Vector Machine (SVM) model is used to identify different defects depending on the features extracted. Combining simulations and experiments of the rails with different kinds of defects are established to verify the effectiveness of the proposed defect identification techniques, such as crack, corrosion, and transverse crack under the shelling. There are nine channels of the excitation-reception to acquire guided wave detection signals. The results show that the defect classification accuracy rates are 96.29% and 96.15% for combining multiple signals, such as the method of single-point excitation and multi-point reception, or the method of multi-point excitation and reception at a single point.

Crimean-Congo hemorrhagic fever virus (CCHFV) is a widespread, tick-borne pathogen that causes Crimean-Congo hemorrhagic fever (CCHF) with high morbidity and mortality. CCHFV is transmitted to humans through tick bites or direct contact with patients or infected animals with viremia. Currently, climate change and globalization have increased the transmission risk of this biosafety level (BSL)-4 virus. The treatment options of CCHFV infection remain limited and there is no FDA-approved vaccine or specific antivirals, which urges the identification of potential therapeutic targets and the design of CCHF therapies with greater effort. In this article, we discuss the current progress and some future directions in the development of antiviral strategies against CCHFV.

Structure from motion (SfM) has been treated as a mature technique to carry out the task of image orientation and 3D reconstruction. However, it is an ongoing challenge to obtain correct reconstruction results from image sets consisting of problematic match pairs. This paper investigated two types of problematic match pairs, stemming from repetitive structures and very short baselines. We built a weighted view-graph based on all potential match pairs and propose a progressive SfM method (PRMP-PSfM) that iteratively prioritizes and refines its match pairs (or edges). The method has two main steps: initialization and expansion. Initialization is developed for reliable seed reconstruction. Specifically, we prioritize a subset of match pairs by the union of multiple independent minimum spanning trees and refine them by the idea of cycle consistency inference (CCI), which aims to infer incorrect edges by analyzing the geometric consistency over cycles of the view-graph. The seed reconstruction is progressively expanded by iteratively adding new minimum spanning trees and refining the corresponding match pairs, and the expansion terminates when a certain completeness of the block is achieved. Results from evaluations on several public datasets demonstrate that PRMP-PSfM can successfully accomplish the image orientation task for datasets with repetitive structures and very short baselines and can obtain better or similar accuracy of reconstruction results compared to several state-of-the-art incremental and hierarchical SfM methods.

Obesity is associated with perturbations in cellular energy homeostasis and consequential renal injury leading to chronic renal disease (CKD). Myo-inositol oxygenase (MIOX), a tubular enzyme, alters redox balance and subsequent tubular injury in the settings of obesity. Mechanism(s) for such adverse changes remain enigmatic. Conceivably, MIOX accentuates renal injury via reducing expression/activity of metabolic sensors, which perturb mitochondrial dynamics and, if sustained, would ultimately contribute towards CKD. In this brief communication, we utilized MIOX-TG (Transgenic) and MIOXKO mice, and subjected them to high fat diet (HFD) administration. In addition, ob/ob and ob/MIOXKO mice of comparable age were used. Mice fed with HFD had increased MIOX expression and remarkable derangements in tubular injury biomarkers. Decreased expression of p-AMPKα (phospho AMP-activated protein kinase) in the tubules was also observed, and it was accentuated in MIOX-TG mice. Interestingly, ob/ob mice also had decreased p-AMPKα expression, which was restored in ob/MIOXKO mice. Parallel changes were observed in Sirt1/Sirt3 (silent mating type information regulation 2 homolog), and expression of other metabolic sensors, i.e., PGC-1α (Peroxisome proliferator-activated receptor gamma coactivator 1-alpha) and Yin Yang (YY-1). In vitro experiments with tubular cells subjected to palmitate-BSA and MIOX-siRNA had results in conformity with the in vivo observations. These findings link the biology of metabolic sensors to MIOX expression in impaired cellular energy homeostasis with exacerbation/amelioration of renal injury.

Artaxa digramma is a lepidopteran pest distributed throughout southern China, Myanmar, Indonesia, and India. Artaxa digramma nucleopolyhedrovirus (ArdiNPV) is a specific viral pathogen of A. digramma and deemed as a promising biocontrol agent against the pest. In this study, the complete genome sequence of ArdiNPV was determined by deep sequencing. The genome of ArdiNPV contains a double-stranded DNA (dsDNA) of 161,734 bp in length and 39.1% G+C content. Further, 149 hypothetical open reading frames (ORFs) were predicted to encode proteins >50 amino acids in length, covering 83% of the whole genome. Among these ORFs, 38 were baculovirus core genes, 22 were lepidopteran baculovirus conserved genes, and seven were unique to ArdiNPV, respectively. No typical baculoviral homologous regions (hrs) were identified in the genome. ArdiNPV had five multi-copy genes including baculovirus repeated ORFs (bros), calcium/sodium antiporter B (chaB), DNA binding protein (dbp), inhibitor of apoptosis protein (iap), and p26. Interestingly, phylogenetic analyses showed that ArdiNPV belonged to Clade II.b of Group II Alphabaculoviruses, which all contain a second copy of dbp. The genome of ArdiNPV was the closest to Euproctis pseudoconspersa nucleopolyhedrovirus, with 57.4% whole-genome similarity. Therefore, these results suggest that ArdiNPV is a novel baculovirus belonging to a newly identified cluster of Clade II.b Alphabaculoviruses.

Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained using two real HSI datasets, i.e., the PaviaU (PU) and SalinasA datasets, representing complex and simple datasets, respectively, and which are used to investigate the robustness or representation of every model or classifier. In addition, a comparable paradigm of network architecture design has been proposed for the comparison of CNN and CapsNet. Experiments demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-of-the-art CNN. For CapsNet using the PU dataset, the Kappa coefficient, overall accuracy, and average accuracy are 0.9456, 95.90%, and 96.27%, respectively, compared to the corresponding values yielded by CNN of 0.9345, 95.11%, and 95.63%. Moreover, we observed that CapsNet has much higher confidence for the predicted probabilities. Subsequently, this finding was analyzed and discussed with probability maps and uncertainty analysis. In terms of the existing literature, CapsNet provides promising results and explicit merits in comparison with CNN and two baseline classifiers, i.e., random forests (RFs) and support vector machines (SVMs).

Machine learning-based remote-sensing techniques have been widely used for the production of specific land cover maps at a fine scale. P-learning is a collection of machine learning techniques for training the class descriptors on the positive samples only. Panax notoginseng is a rare medicinal plant, which also has been a highly regarded traditional Chinese medicine resource in China for hundreds of years. Until now, Panax notoginseng has scarcely been observed and monitored from space. Remote sensing of natural resources provides us new insights into the resource inventory of Chinese materia medica resources, particularly of Panax notoginseng. Generally, land-cover mapping involves focusing on a number of landscape classes. However, sometimes a subset or one of the classes will be the only part of interest. In term of this study, the Panax notoginseng field is the right unit class. Such a situation makes single-class data descriptors (SCDDs) especially significant for specific land-cover interpretation. In this paper, we delineated the application such that a stack of SCDDs were trained for remote-sensing mapping of Panax notoginseng fields through P-learning. We employed and compared SCDDs, i.e., the simple Gaussian target distribution, the robust Gaussian target distribution, the minimum covariance determinant Gaussian, the mixture of Gaussian, the auto-encoder neural network, the k-means clustering, the self-organizing map, the minimum spanning tree, the k-nearest neighbor, the incremental support vector data description, the Parzen density estimator, and the principal component analysis; as well as three ensemble classifiers, i.e., the mean, median, and voting combiners. Experiments demonstrate that most SCDDs could achieve promising classification performance. Furthermore, this work utilized a set of the elaborate samples manually collected at a pixel-level by experts, which was intended to be a benchmark dataset for the future work. The measuring performance of SCDDs gives us challenging insights to define the selection criteria and scoring proof for choosing a fine SCDD in mapping a specific landscape class. With the increment of remotely sensed satellite data of the study area, the spatial distribution of Panax notoginseng could be continuously derived in the local area on the basis of SCDDs.

The baculovirus-insect cell expression system has been widely used for heterologous protein expression and virus-like particles (VLPs) expression. In this study, we established a new method for antiviral screening targeting to glycoprotein E of flaviviruses based on the baculovirus expression system. ZIKV is a mosquito-borne flavivirus and has posed great threat to the public health. It has been reported that ZIKV infection was associated with microcephaly and serious neurological complications. Our study showed that either ZIKV E or prME protein expressed in insect cells can form VLPs and induce membrane fusion between insect cells. Therefore, the E protein, which is responsible for receptor binding, attachment, and virus fusion during viral entry, achieved proper folding and retained its fusogenic ability in VLPs when expressed in this system. The syncytia in insect cells were significantly reduced by the anti-ZIKV-E specific polyclonal antibody in a dose-dependent manner. AMS, a thiol-conjugating reagent, was also shown to have an inhibitory effect on the E protein induced syncytia and inhibited ZIKV infection by blocking viral entry. Indeed the phenomenon of syncytial formation induced by E protein expressed VLPs in insect cells is common among flaviviruses, including Japanese encephalitis virus (JEV), Dengue virus type 2 (DENV-2), and tick-borne encephalitis virus (TBEV). This inhibition effect on syncytial formation can be developed as a novel, safe, and simple antiviral screening approach for inhibitory antibodies, peptides, or small molecules targeting to E protein of ZIKV and other flaviviruses.

The conventional rolling of magnesium alloy with a single pass and large reduction will cause severe edge cracking. The sheet without cracks can be achieved by limited width rolling. The microstructure evolution of the sheet with cracks after conventional rolling and the sheet without cracks after limited width rolling is explored, and an effective mechanism for solving edge cracks is proposed. Conventional rolling can fully develop twin evolution due to high deformation, and three stages of twinning evolution can be observed and the secondary twins easily become the nucleation points of micro cracks, resulting in a large number of cracks propagating along the twin lamellae. Cracks terminate at dislocation accumulation because the accumulation of a large number of dislocations can hinder propagation. Dislocation shearing of twins to eliminate the high localization caused by twins and induce the tensile twins to weaken the basal surface texture provides an effective plastic deformation mechanism of crack inhibition, which is useful for expanding the engineering application of magnesium alloy rolled sheets.