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The field of computer vision has shown great potential for the identification of crops at large scales based on multispectral images. However, the challenge in designing crop identification networks lies in striking a balance between accuracy and a lightweight framework. Furthermore, there is a lack of accurate recognition methods for non-large-scale crops. In this paper, we propose an improved encoder-decoder framework based on DeepLab v3+ to accurately identify crops with different planting patterns. The network employs ShuffleNet v2 as the backbone to extract features at multiple levels. The decoder module integrates a convolutional block attention mechanism that combines both channel and spatial attention mechanisms to fuse attention features across the channel and spatial dimensions. We establish two datasets, DS1 and DS2, where DS1 is obtained from areas with large-scale crop planting, and DS2 is obtained from areas with scattered crop planting. On DS1, the improved network achieves a mean intersection over union (mIoU) of 0.972, overall accuracy (OA) of 0.981, and recall of 0.980, indicating a significant improvement of 7.0%, 5.0%, and 5.7%, respectively, compared to the original DeepLab v3+. On DS2, the improved network improves the mIoU, OA, and recall by 5.4%, 3.9%, and 4.4%, respectively. Notably, the number of parameters and giga floating-point operations (GFLOPs) required by the proposed Deep-agriNet is significantly smaller than that of DeepLab v3+ and other classic networks. Our findings demonstrate that Deep-agriNet performs better in identifying crops with different planting scales, and can serve as an effective tool for crop identification in various regions and countries.

Substation equipment defects are important factors affecting the safe operation of power grids. However, many non-rigid defects have low detection accuracy and poor robustness,due to boundary ambiguity, irregular shape and tiny size. To address these problems,we propose a swin-transformer with recursive gated convolution framework for substation equipment non-rigid defect. Firstly, in order to effectively detect non-rigid defect objects to improve the discriminability of image features, we design the Swin-Transformer with Recursive Gated Convolution(Swin-RGC) framework to extract the interaction features between spaces in the deep model. Secondly, to avoid the loss of object location information, the Task-aligned One-stage Object Detection(TOOD) head is improved by fusing Coordinate Attention modules. Finally, a substation equipment defect detection dataset is established to provide a baseline for detecting non-rigid defects in substation power equipment. Experiment results on our dataset demonstrate that our proposed method achieves the performance of 69.9% Mean Average Pricision (mAP) in the substation equipment non-rigid defect detection, which outweighs the state-of-the-art approaches.

Cortical vein thrombosis (CVT) is a rare subtype of cerebral venous thrombosis. Because CVT is rare and its clinical and imaging findings are atypical, the misdiagnosis of CVT may be extremely high. We report a case of cortical venous infarction (CVI) secondary to CVT. Due to the atypical symptoms, we were perplexed about confirming the diagnosis between CVI and glioma hemorrhage. Eventually, CVT was confirmed by pathology combined with imaging.

Image segmentation is an important field of computer vision and has attracted significant research attention in the recent years. In this paper, we provide a survey of regional level set image segmentation models based on the energy functional similarity measure. Our survey begins with an introduction to region-based level set image segmentation and an overview of its general steps. Then the different segmentation models are summarized. We define and survey six categories of regional level set image segmentation models based on energy functional similarity measures. For every category, we present the mainstream approaches from the literature as examples. Experimental analyses are conducted to compare the segmentation performance of various methods, which allow us to draw meaningful conclusions about their mutual advantages and disadvantages. Finally, we conclude this survey by highlighting several promising directions which need to be further explored by the research community in the future.

Adding an attention module to the deep convolution semantic segmentation network has significantly enhanced the network performance. However, the existing channel attention module focusing on the channel dimension neglects the spatial relationship, causing location noise to transmit to the decoder. In addition, the spatial attention module exemplified by self-attention has a high training cost and challenges in execution efficiency, making it unsuitable to handle large-scale remote sensing data. We propose an efficient vector pooling attention (VPA) module for building the channel and spatial location relationship. The module can locate spatial information better by performing a unique vector average pooling in the vertical and horizontal dimensions of the feature maps. Furthermore, it can also learn the weights directly by using the adaptive local cross-channel interaction. Multiple weight learning ablation studies and comparison experiments with the classical attention modules were conducted by connecting the VPA module to a modified DeepLabV3 network using ResNet50 as the encoder. The results show that the mIoU of our network with the addition of an adaptive local cross-channel interaction VPA module increases by 3% compared to the standard network on the MO-CSSSD. The VPA-based semantic segmentation network can significantly improve precision efficiency compared with other conventional attention networks. Furthermore, the results on the WHU Building dataset present an improvement in IoU and F1-score by 1.69% and 0.97%, respectively. Our network raises the mIoU by 1.24% on the ISPRS Vaihingen dataset. The VPA module can also significantly improve the network’s performance on small target segmentation.

The observed early X-ray plateau in the afterglow lightcurves of some gamma-ray bursts (GRBs) is attributed to the dipole radiations (DRs) of a newborn magnetar. A quasi-periodic oscillation (QPO) signal in the plateau would be strong evidence of the magnetar precession motion. By making a time-frequency domain analysis for the X-ray afterglow lightcurve of GRB 180620A, we find a QPO signal of $\sim650$ seconds in its early X-ray plateau. We fit the lightcurve with a magnetar precession model by adopting the Markov chain Monte Carlo algorithm. The observed lightcurve and the QPO signal are well represented with our model. The derived magnetic field strength of the magnetar is $B_{\rm p}= (1.02^{+0.59}_{-0.61})\times10^{15}$~G. It rapidly spins down with angular velocity evolving as $\Omega_{s} \propto(1+t/\tau_{\rm sd})^{-0.96}$, where $\tau_{\rm sd}=9430$~s. Its precession velocity evolution is even faster than $\Omega_s$, i.e. $\Omega_{ p}\propto (1+t/\tau_{p})^{-2.18\pm0.11}$, where $\tau_{p}=2239\pm206$~s. The inferred braking index is $n=2.04$. We argue that the extra energy loss via the magnetospheric processes results in its rapid spin-down, a low braking index of the magnetar, and the strong precession motion., Comment: 5 pages, 3 figures, accepted for publication in MNRAS Letters

Image retrieval is becoming more and more important due to the rapid increase of the number of images on the web. To improve the efficiency of computing the similarity of images, hashing has moved into the focus of research. This paper proposes a Deep Attention-based Hash (DAH) retrieval model, which combines an attention module and a convolutional neural network to obtain hash codes with strong representability. Our DAH has the following features: The Hamming distance between the hash codes generated by similar images is small and the Hamming distance of hash codes of dissimilar images has a larger constant value. The quantitative loss from Euclidean distance to Hamming distance is minimized. DAH has a high image retrieval precision: We thoroughly compare it with ten state-of-the-art approaches on the CIFAR-10 dataset. The results show that the Mean Average Precision (MAP) of DAH reaches more than 92% in terms of 12, 24, 36 and 48 bit hash codes on CIFAR-10, which is better than what the state-of- art methods used for comparison can deliver.

Assuming that the shallow-decaying phase in the early X-ray lightcurves of gamma-ray bursts (GRBs) is attributed to the dipole radiations (DRs) of a newborn magnetar, we present a comparative analysis for the magnetars born in death of massive stars and merger of compact binaries with long and short GRB (lGRB and sGRB) data observed with the {\em Swift} mission. We show that the typical braking index ($n$) of the magnetars is $\sim 3$ in the sGRB sample, and it is $\sim 4$ for the magnetars in the lGRB sample. Selecting a sub-sample of the magnetars whose spin-down is dominated by DRs ($n\lesssim 3$) and adopting a universal radiation efficiency of $0.3$, we find that the typical magnetic field strength ($B_p$) is $10^{16}$ G {\em vs.} $10^{15}$ G and the typical initial period ($P_0$) is $\sim 20$ ms {\em vs.} $2$ ms for the magnetars in the sGRBs {\em vs.} lGRBs. They follow the same relation between $P_0$ and the isotropic GRB energy as $ P_0\propto E_{\rm jet}^{-0.4}$. We also extend our comparison analysis to superluminous supernovae (SLSNe) and stable pulsars. Our results show that a magnetar born in merger of compact stars tends to have a stronger $B_p$ and a longer $P_0$ by about one order of magnitude than that born in collapse of massive stars. Its spin-down is dominated by the magnetic DRs as old pulsars, being due to its strong magnetic field strength, whereas the early spin-down of magnetars born in massive star collapse is governed by both the DRs and gravitational wave (GW) emission. A magnetar with a faster rotation speed should power a more energetic jet, being independent of its formation approach., 10 pages, 2 tables, 7 figures. Published in MNRAS

The X-ray flares have usually been ascribed to long-lasting activities of the central engine of gamma-ray bursts (GRBs), e.g., fallback accretion. The GRB X-ray plateaus, however, favor a millisecond magnetar central engine. The fallback accretion can be significantly suppressed due to the propeller effect of a magnetar. Therefore, if the propeller regime cannot resist the mass flow onto the surface of the magnetar efficiently, the X-ray flares raise upon the magnetar plateau would be hinted. In this work, such peculiar cases are connected to the accretion process of a magnetar, and an implication for magnetar-disc structure is given. We investigate the repeating accretion process with multi-flare GRB 050730, and give a discussion for the accreting induced variation of the magnetic field in GRB 111209A. Two or more flares exhibit in the GRB 050730, GRB 060607A, and GRB 140304A; by adopting magnetar mass $M=1.4~ M_\odot$ and radius $R=12~\rm km$, the average mass flow rates of the corresponding surrounding disk are $3.53\times 10^{-4}~M_\odot~\rm s^{-1}$, $4.23\times 10^{-4}~M_\odot~\rm s^{-1}$, and $4.33\times 10^{-4}~M_\odot~\rm s^{-1}$, and the corresponding average sizes of the magnetosphere are $5.01~\rm \times10^{6} cm$, $6.45~\rm \times10^{6} cm$, and $1.09~\rm \times10^{7} cm$, respectively. A statistic analysis that contains 8 GRBs within 12 flares shows that the total mass loading in single flare is $\sim 2\times 10^{-5}~M_{\odot}$. In the lost mass of a disk, there are about 0.1% used to feed a collimated jet., 15 pages, 4 figures, 2 tables, to be published in RAA

Osthole is an antitumor compound, which effect on Gallbladder cancer (GBC) has been not elucidated. This study focused on its anti-GBC effect and mechanism both in vitro and in vivo. The antiproliferation effect on cell lines NOZ and SGC-996 were measured by cell counting kit-8 (CCK-8) and colony formation assay. The effects on cell apoptosis and cell cycle were investigated by flow cytometry assay. The migration effect was checked by transwell assay and the expressions of proteins were examined by Western Blots. Also, we did an in-vivo experiment by intraperitoneal injection of osthole in nude mice. The results showed that cell proliferation and viability were inhibited in a dose- and time-dependent manner. The similar phenomenon was also found in vivo. Flow cytometric assay confirmed that osthole inhibited cells proliferation via inducing apoptosis and G2/M arrest. Transwell assay indicated that osthole inhibited the migration in a dose-dependent manner. Expression of key proteins related with apoptosis and cell cycle were testified after osthole treatment. Also, we found the key proteins involved in the JAK/STAT3 signal way decreased after osthole treatment. This study suggested that osthole can inhibit the progression of human GBC cell lines, thus maybe a potential drug for GBC treatment.

China's photovoltaic power generation has experienced an ever-growing speed recently while fast expansion also exposed problems like insufficient power consumption and subsidy gaps in the finance of the government. Now the pilot project for promoting the distributed PV trading market was proposed to address the problem. Accordingly, this study simulates the trading market by analysing the economic behaviours of various agents in an expanded multi-agent-based model with extensions of local consumption principle and matchmaking bidding, which explores the interactions between agents, the trading market and the environment. This study documents that: (1) the trading market can be successfully implemented with the descending subsidy and the grid parity target; (2) the trading market can significantly facilitate local power consumption and release the burden of PV abandonment (e.g. the simulation implies that the abandonment rate of PV in the Gansu Province in 2018 could be reduced from 10.3% to 6%); (3) All firms gain considerable profits after the trading market introduced, and the government also achieves significant benefits from carbon emissions abatement; (4) the power grid suffers from negative margins while the downward trend would eventually end. This raises new perspectives on proposing proper incentive mechanisms like the system of permitted income.

Zhu et al. demonstrate that the VHL–HIF-1α axis plays an important role during the initiation of Tfh cells through glycolytic-epigenetic reprogramming. The downstream player, GAPDH, impairs Tfh cell development by decreasing icos expression through regulation of m6A modification., Follicular helper T (Tfh) cells are essential for germinal center formation and effective humoral immunity, which undergo different stages of development to become fully polarized. However, the detailed mechanisms of their regulation remain unsolved. Here we found that the E3 ubiquitin ligase VHL was required for Tfh cell development and function upon acute virus infection or antigen immunization. VHL acted through the hypoxia-inducible factor 1α (HIF-1α)−dependent glycolysis pathway to positively regulate early Tfh cell initiation. The enhanced glycolytic activity due to VHL deficiency was involved in the epigenetic regulation of ICOS expression, a critical molecule for Tfh development. By using an RNA interference screen, we identified the glycolytic enzyme GAPDH as the key target for the reduced ICOS expression via m6A modification. Our results thus demonstrated that the VHL–HIF-1α axis played an important role during the initiation of Tfh cell development through glycolytic-epigenetic reprogramming., Graphical Abstract

A rapid and specific reversed-phase high-performance liquid chromatographic (RP-HPLC) assay with UV detection has been developed and validated for the simultaneous determination of imipenem and meropenem in human plasma. The extraction process was performed through protein precipitation method using acetonitrile and dichloromethane, and the recoveries of quality controls (QCs) were > 91.5%. Isocratic elution followed by gradient elution of acetonitrile and water was employed over a C18 analytical column for separation. The detection was performed at 298 nm. This method was accurate and reproducible (coefficient of variation, CV < 8%), allowing quantification of carbapenem at the plasma-level ranges from 0.1 to 100 μg/ml without interference of any of the 30 frequently prescribed drugs. Stabilities of imipenem and meropenem were determined with or without stabilizer solutions at -80°C, -20°C, +4 °C and room temperature 20°C. These two drugs showed higher stability at the low temperatures. Addition of 3-(N-morpholino) propanesulfonic acid (MOPS) might also increase their stability. The results of therapeutic drug monitoring (TDM) in neonates and adults showed high inter- and intra- individual variabilities in the trough concentrations of imipenem and meropenem, thus confirming the importance and necessity of TDM. For neonatal patients, imipenem 20 mg/kg, q12h (40mg/kg/day) failed to produce significant therapeutic effects, and either the dose or the frequency was adjusted to achieve 60mg/kg/day or above to maintain the trough concentration required for the curative effect. The low operational cost and good separation efficiency would help implement this assay for the routine therapeutic drug monitoring of imipenem and meropenem in hospitals.

The level set is a classical image segmentation method, but during the evolution of the level set, it can produce evolutionary problems such as local spikes and deep valleys, or overly flat regions, making the iterative process of final segmentation unstable and segmentation results inaccurate. In order to ensure the stability and validity of the level set evolution during the evolution process, the level set function must be periodically initialized so that the level set is always kept as a signed distance function. We construct a new distance regularization potential function based on logarithmic and power function and give a specific analysis. During the evolution process, the level set function always approximates the signed distance function, which is stable and efficient for level set image segmentation. Experimental analyses are conducted to compare the segmentation performance of various distance regularization potential functions when combining with the classical Chan Vese model.

Household garbage images are highly diverse in color, texture and geometry, which poses significant challenges to garbage classification. Deep convolutional neural network (DCNN) have recently achieved remarkable progress due to their ability to learn high-level feature representations. It usually requires a large number of labelled image data for training a DCNN model. However, there are few public and mature data sets concerned on household garbage images. This severely limits the progress of research and the state of the art is not entirely clear. To address this problem, we introduce a new benchmark data set for household garbage image classification. This data set is called 30 Types of Household Garbage Images (HGI-30), which contains 6′000 images of 30 household garbage types, with complex backgrounds, different resolutions, and complicated variations in sample, pose, illumination and background. The publicly available HGI-30 data set allows researchers to develop more accurate and robust methods for both household garbage image processing and interpretation analysis of household garbage object. We further study the classification problem on this data set and propose a transfer learning based method, also provide a performance analysis, which serves as baseline result on this benchmark.

With the construction and development of civilized cities, image based garbage classification has gradually become an important concern in computer vision community. During the algorithms for image classification, the strong ability of Convolution Neural Networks (CNNs) in feature learning makes it the most successful approach at the moment. However, the parameters of CNNs model are very huge, and its training usually depends on a large amount of samples. In this article, we tackle the problem of lightweight neural network based garbage image classification, which aims to learn classifier with a small number of model parameters. Specifically, we utilize the MobileNetV2 for the backbone of feature extraction network and jointly train such two nets in a way of deep mutual learning. It realizes the information distillation between the teacher and the student. With this, we can significantly improve the learning ability of the MobileNetV2 based lightweight neural network. The experimental results on a self-assembled dataset show that our proposal effectively classifies the garbage and achieves a classification effect batter than the state of the arts in terms of testing accuracy, time and model size.

Early shallow-decaying X-ray afterglows of gamma-ray bursts (GRBs) may be attributed to the dipole radiations of newly-born magnetars. Assuming that the GRB jets powered by magnetars are quasi-universal, we find that the jet structure can be parameterized as a uniform jet with a luminosity of $\log L_{\rm j}/{\rm erg\ s^{-1}}=52.68^{+0.76}_{-0.33}$ (1$\sigma$) and an opening angle $\theta_{\rm j}=2.10_{-1.28}^{+1.90}$ (50\% confidence level), surrounding by a power-law decay component with an index of ${-4.00^{+0.27}_{-0.37}}$ (1$\sigma$). The inferred local GRB rate is $\rho=9.6$ Gpc$^{-3}$ yr$^{-1}$ by including both the typical GRBs and LL-GRBs as the same population. The typical viewing angle is $3.3^{o}$, and may be $20^{o}\sim30^{o}$ for LL-GRBs. The X-ray luminosity function of the dipole radiation wind can be empirically described by a broken power-law function with indices $\beta_1=0.78^{+0.16}_{-0.15}$ and $\beta_2>1.6$ broken at $\log L_{b, w}/{\rm erg\ s^{-1}}=48.51^{+0.53}_{-0.65}$. In case of that the wind outflow is collimated and co-axial with the GRB jet, we find that the wind structure is similar to the GRB jet, i.e., $\log L_{\rm c, w}/{\rm erg\ s^{-1}}=48.38^{+0.30}_{-0.48}$, $\theta_{\rm c, w}={2.65^{o}}_{-1.19^{o}}^{+0.1.73^{o}}$, and $k_{\rm w}=4.57^{+1.21}_{-0.75}$. The observed correlation between the prompt gamma-ray luminosity and X-ray luminosity of the wind may be resulted from the viewing angle effect in such a jet-wind system. Discussion on survey with the X-ray instruments on board the {\em Einstein\ Probe} mission in the soft X-ray band for the jet and wind emission is also presented., Comment: 22 pages, 10 figures, and 1 table. Accepted for publication in ApJ

Newton’s interpolation is a classical polynomial interpolation approach and plays a significant role in numerical analysis and image processing. The interpolation function of most classical approaches is unique to the given data. In this paper, univariate and bivariate parameterized Newton-type polynomial interpolation methods are introduced. In order to express the divided differences tables neatly, the multiplicity of the points can be adjusted by introducing new parameters. Our new polynomial interpolation can be constructed only based on divided differences with one or multiple parameters which satisfy the interpolation conditions. We discuss the interpolation algorithm, theorem, dual interpolation, and information matrix algorithm. Since the proposed novel interpolation functions are parametric, they are not unique to the interpolation data. Therefore, its value in the interpolant region can be adjusted under unaltered interpolant data through the parameter values. Our parameterized Newton-type polynomial interpolating functions have a simple and explicit mathematical representation, and the proposed algorithms are simple and easy to calculate. Various numerical examples are given to demonstrate the efficiency of our method.

The interpolation of Thiele-type continued fractions is thought of as the traditional rational interpolation and plays a significant role in numerical analysis and image interpolation. Different to the classical method, a novel type of bivariate Thiele-like rational interpolation continued fractions with parameters is proposed to efficiently address the interpolation problem. Firstly, the multiplicity of the points is adjusted strategically. Secondly, bivariate Thiele-like rational interpolation continued fractions with parameters is developed. We also discuss the interpolant algorithm, theorem, and dual interpolation of the proposed interpolation method. Many interpolation functions can be gained through adjusting the parameter, which is flexible and convenient. We also demonstrate that the novel interpolation function can deal with the interpolation problems that inverse differences do not exist or that there are unattainable points appearing in classical Thiele-type continued fractions interpolation. Through the selection of proper parameters, the value of the interpolation function can be changed at any point in the interpolant region under unaltered interpolant data. Numerical examples are given to show that the developed methods achieve state-of-the-art performance.

Smoke segmentation from the industrial images is a key concern of environmental monitoring. As the similarities between the gray value of the background and the smoke, the existing segmentation algorithms are difficult to accurately segment the target smoke. In this paper, we construct a cross-entropy based industrial smoke image segmentation by integrating the iterative convolution-thresholding. Specially, we use the iterative convolution-thresholding to implicitly represent the interface of each image domain through a characteristic function. We further perform the combination of a regularization term and a fidelity term in the cross-entropy model. In the proposed algorithm, the fidelity term is first converted into the product of the characteristic function and the cross-entropy function. Then the functional of the characteristic function is used to obtain the regularization term by the approach of thermonuclear convolution approximation. The experimental results demonstrate that our proposal has a more accurate segmentation effect and higher segmentation efficiency.

A newly-born magnetar is thought to be central engine of some long gamma-ray bursts (GRBs). We investigate the evolution of the electromagnetic (EM) emission from the magnetic dipole (MD) radiation wind injected by spin-down of a newly-born magnetar via both quadrupole gravitational-wave (GW) and MD radiations. We show that the EM luminosity evolves as $L_{\rm em}\propto (1+t/\tau_c)^{\alpha}$, and $\alpha$ is $-1$ and $-2$ in the GW and MD radiation dominated scenarios, respectively. Transition from the GW to MD radiation dominated epoch may show up as a smooth break with slope changing from $-1$ to $-2$. If the magnetar collapses to a black hole before $\tau_c$, the MD radiation should be shut down, then the EM light curve should be a plateau followed by a sharp drop. The expected generic light curve in this paradigm is consistent with the canonical X-ray light curve of {\em Swift} long GRBs. The X-ray emission of several long GRBs are identified and interpreted as magnetar spin-down via GW or MD, as well as constrain the physical parameters of magnetar. The combination of MD emission and GRB afterglows may make the diversity of the observed X-ray light curves. This may interpret the observed chromatic behaviors of the X-ray and optical afterglow light curves and the extremely low detection rate of a jet-like break in the X-ray afterglow light curves of long GRBs., Comment: 7 pages, 2 figures, Accepted for publication in MNRAS

Fanqi Meng,1 Le Zou,2 Tengyu Zhang,3 Lei Jiang,1 Yao Ding,4 Peng Yu,1 Jie Peng5 1Department of Drug Analysis, Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan Province, China; 2Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan Province, China; 3Department of Pharmacy, University of Copenhagen, København Ø, Denmark; 4Department of Analyses and Testing, Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples, Changsha 410013, Hunan Province, China; 5Department of Pharmacy, Jiangxi Provincial People’s Hospital, Nanchang 330006, Jiangxi Province, China Purpose: The overexpression of ATP-binding cassette transporters (ABC transporters), mainly including permeability glycoproteins (P-gp), multidrug resistance (MDR)-related protein 1 (MRP1), and breast cancer resistance proteins (BCRP), is one of the main reasons for the development of MDR which directly leads to chemotherapy failure. However, most of the currently used detection methods in MDR-related studies are qualitative or semiquantitative, but not quantitative. As a result, the measurement criteria of different experiments are not unified. Moreover, there are many contradictory results of the studies of the induction effect of drugs on ABC transporters. So, it is necessary to establish a quantitative assay for the quantification of P-gp, MRP1, and BCRP to study the mechanism of drug resistance.Methods: In this paper, a novel and advanced liquid chromatography/mass spectrometry (MS)/MS-based targeted proteomics method for the quantification of P-gp, MRP1, and BCRP was developed and validated. Then, the cell lines MCF-7, HepG-2, andSMMC-7721 were, respectively, induced bydifferent concentrations of doxorubicin (adriamycin [ADM]), mitoxantrone (MX),and methotrexate (MTX), toestablish resistance cell lines. The method established was used to quantify the expression of P-gp, MRP1, and BCRP.Results: The result showed that the induction effects of drugs on protein were relatively stable and selective. ADM, MX, and MTX could induce overexpression of P-gp, MRP1, and BCRP. And, the induction effect of different drugs on proteins was selective. The pattern of overexpression of ABC transporters in the three types of resistance cell lines was different.Conclusion: During the development of drug resistance, the cell type and patch, but not drug type, were the most important determinant factors of the overexpression level of ABC transporters in resistance cell lines. This studyprovides a good foundation for understanding the development of drug resistance in cell lines and can be used to explain the contradictory results in other published studies as described above. Keywords: P-gp, MRP1, BCRP, multidrug resistance, quantification

The paper summarizes results of the China Energy Modeling Forum's (CEMF) first study. Carbon emissions peaking scenarios, consistent with China's Paris commitment, have been simulated with seven national and industry-level energy models and compared. The CO2 emission trends in the considered scenarios peak from 2015 to 2030 at the level of 9–11 Gt. Sector-level analysis suggests that total emissions pathways before 2030 will be determined mainly by dynamics of emissions in the electric power industry and transportation sector. Both sectors will experience significant increase in demand, but have low-carbon alternative options for development. Based on a side-by-side comparison of modeling input and results, conclusions have been drawn regarding the sources of emissions projections differences, which include data, views on economic perspectives, or models' structure and theoretical framework. Some suggestions have been made regarding energy models' development priorities for further research. Keywords: Carbon emissions projections, Climate change, CO2 emissions peak, China's Paris commitment, Top-Down energy models, Bottom-Up energy models, Multi model comparative study, China Energy Modeling Forum (CEMF)

Carbon tax and energy tax are among the hot discussions in China. This study conducts simulation studies on them with a CGE model and analyzes their economic impacts, especially on the energy-intensive sectors. The Chinese economy is affected at an acceptable level by the two taxes in different scenarios. The import and export of energy-intensive industries are changed, leading to improved domestic competitiveness. Compared with implementing a single tax, a combined carbon-energy tax reduces more emissions with relatively smaller economic costs. For China, the sooner such taxes are launched, the smaller the economic costs and the more significant emission reductions.

Object detection from satellite images is challenging and either computationally expensive or labor intense. Satellite images often cover large areas of more than 10 k m × 10 k m . They include objects of different scales, which makes it hard to detect all of them at the same image resolution. Considering that airplanes are usually located in airports, ships are often distributed in ports and sea areas, and that oil depots are typically found close to airports or ports, we propose a new hierarchical object detection framework for very high-resolution satellite images. Our framework prescribes two stages: (1) detecting airports and ports in down-sampled satellite images and (2) mapping the detected object back to the original high-resolution satellite images for detecting the smaller objects near them. In order to improve the efficiency of object detection, we further propose a contextual information based deep feature extraction approach for both of the hierarchical detection steps, as well as an inclined bounding box based arbitrarily-oriented object location mechanism suitable especially for the smaller objects. Comprehensive experiments on a public dataset and two self-assembled datasets (which we made publicly available) show the superior performance of our method compared to standalone state-of-the-art object detectors.

The unusual multiwavelength lightcurves of GRB 101225A are revisited by assuming that it is from an off-axis GRB powered by a newborn magnetar. We show that its optical afterglow lightcurve is fitted with the forward shock model by parameterizing its jet structure as a Gaussian function with a half opening angle of the jet core as $1.67^{\rm o}$. The derived initial Lorentz factor ($\Gamma_0$) is 120, and the viewing angle to the jet axis is $\theta_v=3.7^{\rm o}$. Tentative QPO signatures of $P=488$ seconds and $P=250\sim 300$ seconds are found with a confidence level of 90\% by analysing its X-ray flares observed in the time interval of $[4900,\ 7500]$ seconds. Its global gamma-ray/X-ray lightcurve and the QPO signatures are represented with the magnetar dipole radiation (DR) model by considering the magnetar precession motion, assuming that the magnetar spindown is dominated by the GW emission. The bulk Lorentz factor of the DR ejecta is limited to 8, being much lower than $\Gamma_0$. Comparing GRB 101225A with the extremely off-axis GRB 170817A, we suspect that the nature of the two-component jet in GRB 170817A is a combination of a co-axial GRB jet and a DR ejecta. GRB 101225A would be among the brightest ones of the CDF-S XT2 like X-ray transient population driven by newborn magnetars. Discussion on detectability of its gravitational wave emission is also presented., Comment: 8 pages,4 figures. Accepted for publication in ApJL on 10/12/2021

BACKGROUND: Type 2 immunity can be modulated by regulatory T cell (Treg) activity. It has been suggested that the deubiquitinase CYLD plays a role in the development or function of Treg cells implying it could be important for normal protective immunity where type 2 responses are prevalent. OBJECTIVE: We sought to investigate the role of CYLD in Treg function and T helper type (Th2) immune responses under steady state conditions and during helminth infection. METHODS: Foxp3-restricted CYLD conditional knockout mice (cKO) were examined in mouse models of allergen-induced airway inflammation and Nippostrongylus brasiliensis infection. We performed multiplex magnetic bead assays, flow cytometry and qPCR to understand how a lack of CYLD affected cytokine production, homing and suppression in Tregs. Target genes regulated by CYLD were identified and validated by microarray analysis, co-immunoprecipitation, shRNA knockdown, and transfection assays. RESULTS: Treg-specific CYLD knockout mice showed severe spontaneous pulmonary inflammation with increased migration of Treg cells into the lung. CYLD-deficient Treg cells furthermore produced high levels of interleukin-4 (IL-4) and failed to suppress allergen-induced lung inflammation. Supporting this, the cKO mice displayed enhanced protection against N. brasiliensis infection by contributing to type 2 immunity. Treg conversion into IL-4 producing cells was due to augmented MAPK and NF-κB signaling. Moreover, Scinderin, a member of the actin-binding gelsolin family, was highly upregulated in CYLD-deficient Treg cells, and controlled IL-4 production through forming complexes with MEK/ERK. Correspondingly, both excessive IL-4 production in vivo and the protective role of CYLD-deficient Treg cells against N. Brasiliensis were reversed by Scinderin ablation. CONCLUSIONS: Our findings indicate that CYLD controls type 2 immune responses by regulating Treg conversion into Th2-like effector cells, which potentiates parasite resistance. CAPSULE SUMMARY: Deubiquitinating enzyme CYLD plays a role in maintaining Treg function on type 2 immunity, which will be beneficial for therapeutic approaches to autoimmune disease or pathogen infection.

In this paper, a new hybrid diffusion-based level set method is proposed to efficiently address the complex image segmentation problem. Different from the traditional methods, the proposed method is performed on image diffusion space rather than intensity space. Firstly, the nonlinear diffusion based on total variation flow and additive operator splitting scheme is performed on the original intensity image to obtain the diffused image. Then, the local diffusion energy term is constructed by performing homomorphic unsharp masking operation on diffused image so as to implement a local piecewise constant search. To avoid trapping into local minimum produced by local energy, the global diffusion energy term is formed by approximating diffused image in a global piecewise constant way. Besides, the regularization energy term is included to have penalization effect on evolving contour length and maintenance of level set function being signed distance function. By minimizing the overall energy functional which is a linear combination of local energy, global energy and regularization energy, the evolving contour can be driven to approach the object boundary. The experiments on different characteristics of complex images have shown that the proposed method can achieve satisfying segmentation performance accompanied with some good properties, i.e. the robustness to initial parameter and contour setting, noise insensitivity, quick and stable convergence.

Background Several studies have investigated the effect of non–vitamin K antagonist oral anticoagulants ( NOAC s) in atrial fibrillation ( AF ) patients with cancer, but the results remain controversial. Therefore, we conducted a meta‐analysis to compare the efficacy and safety of NOAC s versus warfarin in this population. Methods and Results We systematically searched the PubMed and Embase databases until February 16, 2019 for studies comparing the effect of NOAC s with warfarin in AF patients with cancer. Risk ratios ( RR s) with 95% CI s were extracted and pooled by a random‐effects model. Five studies involving 8908 NOAC s and 12 440 warfarin users were included. There were no significant associations between cancer status and risks of stroke or systemic embolism, major bleeding, or death in AF patients. Compared with warfarin, NOAC s were associated with decreased risks of stroke or systemic embolism ( RR , 0.52; 95% CI , 0.28–0.99), venous thromboembolism ( RR , 0.37, 95% CI , 0.22–0.63), and intracranial or gastrointestinal bleeding ( RR , 0.65; 95% CI , 0.42–0.98) and with borderline significant reductions in ischemic stroke ( RR , 0.63; 95% CI , 0.40–1.00) and major bleeding ( RR , 0.73; 95% CI , 0.53–1.00). In addition, risks of efficacy and safety outcomes of NOAC s versus warfarin were similar between AF patients with and without cancer. Conclusions In patients with AF and cancer, compared with warfarin, NOAC s had lower or similar rates of thromboembolic and bleeding events and posed a reduced risk of venous thromboembolism.

Thiele-type continued fractions interpolation may be the classical rational interpolation and plays critical role in image interpolation and numerical analysis. Different from the traditional method, a new Thiele type continued fractions rational interpolation method with parameters was presented to address the interpolation problem efficiently. Firstly, in order to gain neat expressions in terms of inverse differences, we chose the multiplicity of the points strategically. Secondly, we constructed a univariate Thiele type continued fractions rational interpolation with parameters, which can satisfy the interpolation condition. We also discussed the interpolation algorithm, interpolation theorem. Numerical examples were given to show that the presented method achieves state-of-the-art performance.

Aiming at the problem that the detection accuracy of effluent COD (chemical oxygen demand) in sewage treatment needs to be further improved, a combined model based on support vector machine and neural network is proposed to predict effluent COD. It can reduce the influence of local optimum on the global scope so as to improve the accuracy of prediction. Firstly, the sample data are divided into two categories by support vector machine. Then the BP neural network model and the Echo State Network (ESN) model are established on two sub-samples respectively. Compared with single neural network model, the mean absolute error and root mean square error of combined model are both reduced. Besides, the proposed model has better comprehensive prediction performance and can meet the actual demand of effluent COD prediction in sewage treatment.

Image segmentation plays a significant role in computer vision and image processing. In this paper, we proposed a novel Local Chan–Vese (LCV) image segmentation model. The new model combined classical LCV model with fractional order magnitude image. We used absolute value instead of square root operation to approximate the magnitude of fractional order gradient, and constructed the eight directions \( 5 \times 5 \) fractional differential masks. We can get a novel fractional order difference image and drive a new local image fitting term. We also presented a new distance regularized term. The new distance regularization term was defined by a potential function. We used the spectral residual method for getting the saliency map of the given image. The initial level set function was driven based on saliency map to accelerate the convergence speed. The experiments were given to show the effectiveness of the new image segmentation model.

DNA-binding proteins (DBPs) play pivotal roles in many biological functions such as alternative splicing, RNA editing, and methylation. Many traditional machine learning (ML) methods and deep learning (DL) methods have been proposed to predict DBPs. However, these methods either rely on manual feature extraction or fail to capture long-term dependencies in the DNA sequence. In this paper, we propose a method, called PDBP-Fusion, to identify DBPs based on the fusion of local features and long-term dependencies only from primary sequences. We utilize convolutional neural network (CNN) to learn local features and use bi-directional long-short term memory network (Bi-LSTM) to capture critical long-term dependencies in context. Besides, we perform feature extraction, model training, and model prediction simultaneously. The PDBP-Fusion approach can predict DBPs with 86.45% sensitivity, 79.13% specificity, 82.81% accuracy, and 0.661 MCC on the PDB14189 benchmark dataset. The MCC of our proposed methods has been increased by at least 9.1% compared to other advanced prediction models. Moreover, the PDBP-Fusion also gets superior performance and model robustness on the PDB2272 independent dataset. It demonstrates that the PDBP-Fusion can be used to predict DBPs from sequences accurately and effectively; the online server is at http://119.45.144.26:8080/PDBP-Fusion/.

The unusual multiwavelength lightcurves of GRB 101225A are revisited by assuming that it is from an off-axis GRB powered by a newborn magnetar. We show that its optical afterglow lightcurve is fitted with the forward shock model by parameterizing its jet structure as a Gaussian function with a half opening angle of the jet core as $1.67^{\rm o}$. The derived initial Lorentz factor ($\Gamma_0$) is 120, and the viewing angle to the jet axis is $\theta_v=3.7^{\rm o}$. Tentative QPO signatures of $P=488$ seconds and $P=250\sim 300$ seconds are found with a confidence level of 90\% by analysing its X-ray flares observed in the time interval of $[4900,\ 7500]$ seconds. Its global gamma-ray/X-ray lightcurve and the QPO signatures are represented with the magnetar dipole radiation (DR) model by considering the magnetar precession motion, assuming that the magnetar spindown is dominated by the GW emission. The bulk Lorentz factor of the DR ejecta is limited to 8, being much lower than $\Gamma_0$. Comparing GRB 101225A with the extremely off-axis GRB 170817A, we suspect that the nature of the two-component jet in GRB 170817A is a combination of a co-axial GRB jet and a DR ejecta. GRB 101225A would be among the brightest ones of the CDF-S XT2 like X-ray transient population driven by newborn magnetars. Discussion on detectability of its gravitational wave emission is also presented.

This paper analyzes Chinese household CO 2 emissions in 1994–2012 based on the Logarithmic Mean Divisia Index (LMDI) structure decomposition model, and discusses the relationship between household CO 2 emissions and economic growth based on a decoupling indicator. The results show that in 1994–2012, household CO 2 emissions grew in general and displayed an accelerated growth trend during the early 21st century. Economic growth leading to an increase in energy consumption is the main driving factor of CO 2 emission growth (an increase of 1.078 Gt CO 2 ) with cumulative contribution rate of 55.92%, while the decline in energy intensity is the main cause of CO 2 emission growth inhibition (0.723 Gt CO 2 emission reduction) with cumulative contribution rate of 38.27%. Meanwhile, household CO 2 emissions are in a weak state of decoupling in general. The change in CO 2 emissions caused by population and economic growth shows a weak decoupling and expansive decoupling state, respectively. The CO 2 emission change caused by energy intensity is in a state of strong decoupling, and the change caused by energy consumption structure fluctuates between a weak and a strong decoupling state.

In this paper, an efficient and robust level set method is presented to segment the images with intensity inhomogeneity. The multi-scale segmentation idea is incorporated into energy functional construction and a new Hermite differential operator is designed to numerically solve the level set evolution equation. Firstly, the circular shape window is used to define local region so as to approximate the image as well as intensity inhomogeneity. Then, multi-scale statistical analysis is performed on intensities of local circular regions centered in each pixel. So, the multi-scale local energy term can be constructed by fitting multi-scale approximation of inhomogeneity-free image in a piecewise constant way. To avoid the time-consuming re-initialization procedure, a new double-well potential function is adopted to construct the penalty energy term. Finally, the multi-scale segmentation is performed by minimizing the total energy functional. Here, a new differential operator based on Hermite polynomial interpolation is proposed to solve the minimization. The experiments and comparisons with three popular local region-based methods on images with different levels of intensity inhomogeneity have demonstrated the efficiency and robustness of the proposed method.

CD4 + T follicular helper (Tfh) cells are recognized as a distinct T-cell subset, which provides help for germinal center (GC) formation, B-cell development and affinity maturation, and immunoglobulin class switching, as an indispensable part of adaptive immunity. Tfh cell differentiation depends on various factors including cell-surface molecule interactions, extracellular cytokines and multiple transcription factors, with B-cell lymphoma 6 (Bcl-6) being the master regulator. T follicular regulatory (Tfr) cells are also located in the GC and share phenotypic characteristics with Tfh cells and regulatory T cells, but function as negative regulators of GC responses. Dysregulation of either Tfh or Tfr cells is linked to the pathogenesis of autoimmune diseases such as systemic lupus erythematosus. This review covers the basic Tfh and Tfr biology including their differentiation and function, and their close relationship with autoimmune diseases.

Gamma-ray burst (GRB) 150910A was detected by {\it Swift}/BAT, and then rapidly observed by {\it Swift}/XRT, {\it Swift}/UVOT, and ground-based telescopes. We report Lick Observatory spectroscopic and photometric observations of GRB~150910A, and we investigate the physical origins of both the optical and X-ray afterglows, incorporating data obtained with BAT and XRT. The light curves show that the jet emission episode lasts $\sim 360$~s with a sharp pulse from BAT to XRT (Episode I). In Episode II, the optical emission has a smooth onset bump followed by a normal decay ($\alpha_{\rm R,2} \approx -1.36$), as predicted in the standard external shock model, while the X-ray emission exhibits a plateau ($\alpha_{\rm X,1} \approx -0.36$) followed by a steep decay ($\alpha_{\rm X,2} \approx -2.12$). The light curves show obvious chromatic behavior with an excess in the X-ray flux. Our results suggest that GRB 150910A is an unusual GRB driven by a newly-born magnetar with its extremely energetic magnetic dipole (MD) wind in Episode II, which overwhelmingly dominates the observed early X-ray plateau. The radiative efficiency of the jet prompt emission is $\eta_{\gamma} \approx 11\%$. The MD wind emission was detected in both the BAT and XRT bands, making it the brightest among the current sample of MD winds seen by XRT. We infer the initial spin period ($P_0$) and the surface polar cap magnetic field strength ($B_p$) of the magnetar as $1.02 \times 10^{15}~{\rm G} \leq B_{p} \leq 1.80 \times 10^{15}~{\rm G}$ and 1~ms $\leq P_{0}v\leq 1.77$~ms, and the radiative efficiency of the wind is $\eta_w \geq 32\%$., Comment: Accepted for publication in ApJ; 26 pages, 8 figures, 3 tables

Aircraft type recognition from remote sensing images has many civil and military applications. In images obtained with modern technologies such as high spatial resolution remote sensing, even details of aircraft can become visible. With this, the identification of aircraft types from remote sensing images becomes possible. However, the existing methods for this purpose have mostly been evaluated on different data sets and under different experimental settings. This makes it hard to compare their results and judge the progress in the field. Moreover, the data sets used are often not publicly available, which brings difficulties to reproduce the works for fair comparison. This severely limits the progress of research and the state of the art is not entirely clear. To address this problem, we introduce a new benchmark data set for aircraft type recognition from remote sensing images. This data set is called Multi-Type Aircraft Remote Sensing Images (MTARSI), which contains 9’385 images of 20 aircraft types, with complex backgrounds, different spatial resolutions, and complicated variations in pose, spatial location, illumination, and time period. The publicly available MTARSI data set allows researchers to develop more accurate and robust methods for both remote sensing image processing and interpretation analysis of remote sensing object. We also provide a performance analysis of state-of-the-art aircraft type recognition and deep learning approaches on MTARSI, which serves as baseline result on this benchmark.

The traditional method of blockchain retrieval is to search the &ldquo, Block File&rdquo, in sequence from the "tail" to the "head" of the blockchain, which always takes a lot of time. How to reduce the retrieval time has been a hot issue in blockchain research. This paper proposes a fast retrieval method for the Multi-Transaction Mode Consortium Blockchain (MTMCB). Firstly, we create a &ldquo, User Set&rdquo, and &ldquo, Block Name Set&rdquo, cached in Redis. Then, according to the transaction participants and &ldquo, we can get the relevant "Block Name List", and quickly obtain the corresponding block files. On this basis, in order to meet the needs of rapid retrieval in large-scale systems, an improved retrieval algorithm based on a B+-tree data structure is proposed. Firstly, the block file information is put into different ordered sets according to the transaction participants, and the B+-tree index is established to quickly get the information of relevant block files by participants. Experimental results show that the improved method of Redis cache retrieval in this paper can greatly increase the efficiency of blockchain retrieval, and can settle some crucial problem in the blockchain application and popularization.