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The loss of sesame capsule seed prior to harvest poses a significant economical challenge in mechanized production. The metabolites involved in capsule closure are still unclear. Using comparative metabolome and transcriptome analysis, this work investigated the molecular regulation and enrichment pathways in two sesame types of indehiscent capsule WanZhi28 (ND) and dehiscent capsule WanZhi2 (WZ2). The findings demonstrated that genes and metabolites were significantly enriched in lignin synthesis-related pathways. Furthermore, data suggests that lipid and sugar metabolism may have an impact on capsule closure. Apart from its function in cell signaling, the latter may contribute to the glycosylation of lignin monomers, while the former may provide ATP for cellular microtubule movement. This work concurrently focused on a large number of differentially expressed transcription factors linked to the sesame capsule's anti-cleft mechanism, providing new evidence for the discovery and use of functional markers and genes for capsule dehiscence. The identification of key pathways and regulatory mechanisms offers valuable information for developing strategies to mitigate seed loss during harvest, ultimately contributing to more efficient and profitable sesame production.

Atherosclerosis (AS) ultimately cause major adverse cardiovascular events (MACEs). While traditional strategies by lipid-reducing have reduced MACEs, many patients continue to face significant risks. It might attribute to the upregulation of CD47 expression in AS lesions, that mediated anti-efferocytosis of macrophages. Therefore, we propose simultaneously blocking ANGPTL3, a vital regulator of lipid metabolism, and CD47 might be a potential approach for AS therapy. Firstly, we investigate the role of a novel anti-ANGPTL3 nanobody-Fc (FD03) in AS. We found that FD03 treatment significantly decreased circulating lipids, plaque size, and lipid deposition in apoE-/- mice compared to control Ab, but there was a twofold increase in plaque formation in comparison to baseline. However, immunofluorescence indicated the upregulation of CD47 expression in the plaques even after FD03 treatment compared to normal vascular tissue. Next, a bifunctional protein containing signal regulatory protein alpha (SIRPα) and FD03 (SIRPαD1-FD03) was constructed to block CD47 and ANGPTL3 concurrently, which had high purity, robust stability, and high affinity to CD47 and ANGPTL3 with biological activity in vitro. Furthermore, SIRPαD1-FD03 fusion protein exhibited the enhanced therapeutic effect on AS compared with SIRPαD1-Fc or FD03, regressing plaque contents and the necrotic core equal to baseline. Mechanistically, SIRPαD1-FD03 reduced serum lipids, augmented the efferocytosis rate and macrophage M2 polarization, and decreased the reactive oxygen species (ROS) and lipid peroxidation level in atherosclerotic plaques. Collectively, our project suggests an effective approach for AS by simultaneously blocking ANGPTL3 and CD47 to regulate lipid metabolism, macrophage activity and lipid peroxidation.

Abstract Electrification mode is one of the important factors affecting the energy consumption and drainage effect of the electroosmosis method. In this work, a series of laboratory tests were performed to explore the impact of continuous changes in the electric field on the migration of ions and electrons, as well as the migration of electroosmotic flow. The results show that different electrification modes had a significant impact on the current and water discharge. An electrode reversed from anode to cathode, from one circuit to another, is beneficial to drainage, while an electrode reversed from cathode to anode, from one circuit to another, is unfavorable to drainage. The drainage effect of the specific cyclic and progressive electroosmosis (CPE) is not as good as the conventional electroosmosis method. However, combining the two electrification modes can further enhance the shear strength of the soil near a designated electrode. The generation of transverse cracks in soil is mainly due to the drainage difference caused by the electroosmotic flow, which is caused by the adjacent electric field at that location. When the electroosmotic flow is strong, longitudinal cracks will occur in the soil along the strongest electroosmotic flow path. In practical in situ engineering applications, the electrification mode of CPE will be more energy‐efficient than conventional electrification modes with the same processing time.

Background The main challenge against patients with cancer to derive benefits from immune checkpoint inhibitors targeting PD-1/PD-L1 appears to be the immunosuppressive tumor microenvironment (TME), in which IL-33/ST2 signal fulfills critical functions. However, whether IL-33 limits the therapeutic efficacy of anti-PD-L1 remains uncertain.Methods Molecular mechanisms of IL-33/ST2 signal on anti-PD-L1 treatment lewis lung carcinoma tumor model were assessed by RNA-seq, ELISA, WB and immunofluorescence (IF). A sST2-Fc fusion protein was constructed for targeting IL-33 and combined with anti-PD-L1 antibody for immunotherapy in colon and lung tumor models. On this basis, bifunctional fusion proteins were generated for PD-L1-targeted blocking of IL-33 in tumors. The underlying mechanisms of dual targeting of IL-33 and PD-L1 revealed by RNA-seq, scRNA-seq, FACS, IF and WB.Results After anti-PD-L1 administration, tumor-infiltrating ST2+ regulatory T cells (Tregs) were elevated. Blocking IL-33/ST2 signal with sST2-Fc fusion protein potentiated antitumor efficacy of PD-L1 antibody by enhancing T cell responses in tumor models. Bifunctional fusion protein anti-PD-L1-sST2 exhibited enhanced antitumor efficacy compared with combination therapy, not only inhibited tumor progression and extended the survival, but also provided long-term protective antitumor immunity. Mechanistically, the superior antitumor activity of targeting IL-33 and PD-L1 originated from reducing immunosuppressive factors, such as Tregs and exhausted CD8+ T cells while increasing tumor-infiltrating cytotoxic T lymphocyte cells.Conclusions In this study, we demonstrated that IL-33/ST2 was involved in the immunosuppression mechanism of PD-L1 antibody therapy, and blockade by sST2-Fc or anti-PD-L1-sST2 could remodel the inflammatory TME and induce potent antitumor effect, highlighting the potential therapeutic strategies for the tumor treatment by simultaneously targeting IL-33 and PD-L1.

Abstract Non‐alcoholic fatty liver disease (NAFLD) is a prominent cause of various chronic metabolic hepatic diseases with limited therapeutics. Rubicon, an essential regulator in lysosomal degradation, is reported to exacerbate hepatic steatosis in NAFLD mice and patients, indicating its probability of being a therapeutic target for NAFLD treatment. In this study, the therapeutic potential of Rubicon blockage is investigated. Lipid nanoparticles carrying Rubicon‐specific CRISPR‐Cas9 components exhibited liver accumulation, cell internalization, and Rubicon knockdown. A single administration of the nanoparticles results in attenuated lipid deposition and hepatic steatosis, with lower circulating lipid levels and decreased adipocyte size in NAFLD mice. Furthermore, the increase of phosphatidylcholine and phosphatidylethanolamine levels can be observed in the NAFLD mice livers after Rubicon silencing, along with regulatory effects on metabolism‐related genes such as CD36, Gpcpd1, Chka, and Lpin2. The results indicate that knockdown of Rubicon improves glycerophospholipid metabolism and thereby ameliorates the NAFLD progression, which provides a potential strategy for NAFLD therapy via the restoration of Rubicon.

The paper compared three reanalysis data by the air temperature and SST based on the observation data over the Jiangsu sea area. The results showed that the air temperature and SST of reanalysis data were highly consistent with the observation data, moreover, the consistency of SST was better. Among the three reanalysis data, the reliability of ERA5 was better than that of JRA-55 and NCEP. The consistency between the reanalysis data and the observation data was highest in Waikejiao observation station which was the farther offshore observation station. During periods of typhoon influence, the reanalysis data were closer to the observation data at Lyusi and Waikejiao observation station. At the Lianyungang observation station closer to the shore, there was a greater difference between the reanalysis data and the observation data. During the period of air temperature drop caused by cold air, the difference between reanalysis data and observation data was smaller. While during the temperature recovery period, the reanalysis data was significantly higher than the observation data at Lyusi and Waikejiao observation station. During the influence of cold air, the reduction of SST reflected by the reanalysis data was significantly lower than the observation data. The results provide baseline for the study on the applicability and reliability of reanalysis data in Jiangsu sea area.

Soft clay contamination is an increasingly global issue with significant implications for land development and human health. Electrokinetic remediation (EKR) has demonstrated significant potential for cleaning contaminated soils. It is crucial to develop efficient processes that minimize environmental impact and reduce costs. A series of citric acid (CA)-enhanced EKR tests were conducted using a novel experimental setup, with the electrolyte positioned above the soil surface, to examine the impact of four different electrode arrangements on the effectiveness of EKR. The position of the electrode end had a significant impact on the migration of ions in the anolyte and catholyte, which in turn affected the volume reduction in the anolyte, the magnitude of the current, and the migration of heavy metals. The electrode arrangement mode c (electrodes suspended in the electrolytes) can enhance the migration of the anolyte and reduce the drainage of the soil, making it an effective measure for improving the removal rate of heavy metals. After the heavy metal remediation is complete, the bearing capacity of the soil should be increased. Changing the electrode arrangement to mode d (anode suspended in the anolyte, a very small part of the cathode inserted into the soil) is an effective measure for reducing the soil water content and improving soil strength.

To address the problem that conventional neural networks trained on radar echo data cannot handle the phase of the echoes, resulting in insufficient information utilization and limited performance in detection and classification, we extend neural networks from the real-valued neural networks to the complex-valued neural networks, presenting a novel algorithm for classifying small sea surface targets. The proposed algorithm leverages an improved residual fusion network and complex time–frequency spectra. Specifically, we augment the Deep Residual Network-50 (ResNet50) with a spatial pyramid pooling (SPP) module to fuse feature maps from different receptive fields. Additionally, we enhance the feature extraction and fusion capabilities by replacing the conventional residual block layer with a multi-branch residual fusion (MBRF) module. Furthermore, we construct a complex time–frequency spectrum dataset based on radar echo data from four different types of sea surface targets. We employ a complex-valued improved residual fusion network for learning and training, ultimately yielding the result of small target classification. By incorporating both the real and imaginary parts of the echoes, the proposed complex-valued improved residual fusion network has the potential to extract more comprehensive features and enhance classification performance. Experimental results demonstrate that the proposed method achieves superior classification performance across various evaluation metrics.

This paper addresses polarimetric adaptive coherent detection of radar targets embedded in sea clutter. Initially, radar clutter data across multiple polarimetric channels is modeled using a compound Gaussian framework featuring an unspecified speckle covariance matrix and lognormal texture distribution. Subsequently, three adaptive polarimetric coherent detectors are derived, employing parameter estimation and two-step versions of the generalized likelihood ratio test (GLRT): the complex parameter Rao and Wald tests. These detectors utilize both clutter texture distribution information and radar data’s polarimetric aspects to enhance detection performance. Simulation experiments demonstrate the superiority of three proposed detectors over the competitors, and that they are sensitive to polarimetric channel parameters such as secondary data quantity, target or clutter speckle correlation, and signal-to-clutter ratio disparity. Additionally, the proposed detectors exhibit a near-constant false alarm rate relative to average clutter power and speckle covariance matrix.

BackgroundAlzheimer’s disease (AD) is characterized by neuroinflammation, which is frequently accompanied by immune system dysfunction. Although the mechanism of neurodegenerative lesions is unclear, various clinical trials have highlighted that early intervention in AD is crucial to the success of treatment. In order to explore the potential of immunotherapy in the early period of AD, the present study evaluated whether application of glatiramer acetate (GA), an immunomodulatory agent approved for remitting–relapsing multiple sclerosis (RRMS), in the early stages of AD prior to amyloid beta (Aβ) deposition altered the Aβ pathology and cognitive impairments in APPswe/PSEN1dE9 (APP/PS1) transgenic mice.MethodsWe treated two cohorts of pre-depositing and amyloid-depositing (2- and 6-month-old) APP/PS1 mice with weekly-GA subcutaneous injection over a 12-week period. We then tested spatial learning and memory using the Morris water maze (MWM) and the Y maze. Immunohistochemistry staining was utilized to analyze Aβ burden in the brain as well as activated microglia. Furthermore, the inflammatory cytokine milieu within brains was estimated by quantitative real-time polymerase chain reaction, and the peripheral CD4+CD25+Foxp3+ regulatory T cells (Tregs) in the spleen were measured by flow cytometry.ResultsWe found that early GA administration reduced Aβ burden and ameliorated cognitive decline. Meanwhile, the immune microenvironment had changed in the brain, with an increase in the production of anti-inflammatory cytokines and a decrease in microglial activation. Interestingly, early GA administration also modulated the peripheral immune system through the amplification of Tregs in the spleen.ConclusionOverall, our findings revealed that GA treatment might enhance the central and peripheral immune systems’ protective capabilities in the early stages of AD, eventually improving cognitive deficits. Our research supports the advantages of immunomodulatory treatments for AD at an early stage.

Amid radar target detection fields, the detection of small floating targets in sea clutter is a tough but crucial task. An efficient solution to address the issue is feature-based detection method based on distinguishable features extracted from sea clutter and returns with target. Nevertheless, as the number of features increases, traditional algorithms with dimension limitation are not adaptable to combine the multiple features to detect. In this article, angle variance-based anomaly detection algorithms which solve the problem of the “curse of dimensionality” are introduced into floating small target detection. An improved algorithm by using the Mahalanobis distance is proposed and for the purpose of detecting the sea-surface small target, a novel detector with controlled false alarm capability is developed. On the open and recognized databases, the detection abilities of the proposed detector are assessed. It is confirmed that the proposed multifeature angle variance-based detector is more effective than other detectors based on multifeature distance or density from experimental results. This proposed detector can implement the improvement of detection performance with less computational cost compared to other well-established detectors.

Polarization diversity has been widely used in maritime radars to improve target detection performance. Full utilization of the polarimetric characteristics of sea clutter is the key to designing effective polarimetric detectors. For high-resolution maritime radars, the HH-HV dual-polarization is an affordable and effective mode to monitor small targets, owing to the simple configuration of single-polarimetric transmit and dual-polarimetric reception and lower clutter powers at the HH and HV polarizations. Enlightened by the analytical coherent detector in compound-Gaussian clutter with inverse Gamma texture, this paper investigates dual-polarimetric coherent detection in dual-polarimetric compound-Gaussian clutter with independent inverse Gamma distributed textures. The analytical dual-polarimetric near-optimum coherent detector is derived, which is a fusion of the generalized likelihood ratio test linear threshold detectors (GLRT-LTDs) at the two polarizations. For short, it is referred to as the P-GLRT-LTD. It is proven that the P-GLRT-LTD is of constant false alarm rate with respect to the Doppler steering vector, scale parameters of textures, and speckle covariance matrices. Moreover, the thresholds of the P-GLRT-LTD are given analytically. Experiments using simulated sea clutter data with the estimated scale and shape parameters from the two measured intelligent pixel processing radar (IPIX) datasets and two measured IPIX datasets with test targets are made to compare P-GLRT-LTD with other existing dual-polarimetric coherent detectors. The results show that the P-GLRT-LTD attains the same detection performance as the existing best-performance detector. The P-GLRT-LTD has a lower computational cost than the existing best-performing one.

Plant polysaccharides, a type of important bioactive compound, are involved in multiple plant defense mechanisms, and in particular polysaccharide-alleviated abiotic stress has been well studied. Polygonatum cyrtonema Hua (P. cyrtonema Hua) is a medicinal and edible perennial plant that is used in traditional Chinese medicine and is rich in polysaccharides. Previous studies suggested that sucrose might act as a precursor for polysaccharide biosynthesis. However, the role of sucrose metabolism and transport in mediating polysaccharide biosynthesis remains largely unknown in P. cyrtonema Hua. In this study, we investigated the contents of polysaccharides, sucrose, glucose, and fructose in the rhizome, stem, leaf, and flower tissues of P. cyrtonema Hua, and systemically identified the genes associated with the sucrose metabolism and transport and polysaccharide biosynthesis pathways. Our results showed that polysaccharides were mainly accumulated in rhizomes, leaves, and flowers. Besides, there was a positive correlation between sucrose and polysaccharide content, and a negative correlation between glucose and polysaccharide content in rhizome, stem, leaf, and flower tissues. Then, the transcriptomic analyses of different tissues were performed, and differentially expressed genes related to sucrose metabolism and transport, polysaccharide biosynthesis, and transcription factors were identified. The analyses of the gene expression patterns provided novel regulatory networks for the molecular basis of high accumulation of polysaccharides, especially in the rhizome tissue. Furthermore, our findings explored that polysaccharide accumulation was highly correlated with the expression levels of SUS, INV, SWEET, and PLST, which are mediated by bHLH, bZIP, ERF, ARF, C2H2, and other genes in different tissues of P. cyrtonema Hua. Herein, this study contributes to a comprehensive understanding of the transcriptional regulation of polysaccharide accumulation and provides information regarding valuable genes involved in the tolerance to abiotic stresses in P. cyrtonema Hua.

A large number of simulation results will be generated during the calculation of the power system digital simulation. Manual analysis of massive numerical results is inefficient and error-prone. Besides, some imperceptible operating rules of power system operating mode may be ignored. In order to solve these problems, a transient stability assessment method based on deep learning is proposed in this paper. By analyzing simulation data, the relationship between power grid stability characteristics and the set operation mode is constructed. According to these research results, the calculation and analysis of power system transient stability will be effectively supported.

Abstract Background Esophageal squamous cell carcinoma (ESCC) is one of the most malignant tumors in gastrointestinal system. MicroRNAs (miRNAs) have been reported to be implicated in cancer development. However, the role of miR-137 has not been fully revealed in ESCC. Methods Quantitative real-time polymerase chain reaction (qRT-PCR) and western blot analyses were separately used to examine RNA level and protein level. 5-ethynyl-2′-deoxyuridine (EdU) assay, transwell assays and flow cytometry analyses were conducted to assess biological behaviors of ESCC cells. Additionally, the interaction between genes were analyzed via Chromatin Immunoprecipitation (ChIP) assay, RNA Binding Protein Immunoprecipitation (RIP) assay, RNA pull down assay and luciferase reporter assay. Results MiR-137 was down-regulated in ESCC cells. Upregulation of miR-137 hindered ESCC cell proliferation, migration, invasion and epithelial mesenchymal transition (EMT). Besides, miR-137 enhanced the sensitivity of ESCC cells to irradiation. Moreover, CCCTC-binding factor (CTCF) inactivated miR-137 transcription in ESCC cells. Furthermore, we revealed enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2) and paxillin (PXN) as the downstream targets of miR-137. In turn, EZH2 was recruited by CTCF and induced methylation in miR-137 promoter. Conclusion CTCF/Suz12/EZH2 complex-silenced miR-137 facilitates ESCC progression and radioresistance by targeting EZH2 and PXN.

In this article, a multifeature detector based on isolation forest (iForest) algorithm is developed to detect floating small targets in sea clutter. The conventional multifeature detector can only process three features or less. The proposed detector aims to break the limitation of feature dimensions' number of the existed feature-based detectors and to improve the detection performance. It transforms the detection of floating small target into an anomaly detection problem in a high-dimensional feature space, breaking the limitation of the number of features. First, a modified isolation forest is constructed from multiple features extracted from sea clutter. Meanwhile, the relative Doppler coefficient of variation is proposed and added into the feature library. Then, taking the average path length as detection statistic, the detection threshold is obtained by Monte-Carlo technique at the given false alarm probability. Finally, the final decision is made by comparing the path length calculated from the cell under test of radar returns with the detection threshold. Detection performances are evaluated based on twenty measured IPIX radar datasets. The experiment results show that the multifeature detector based on isolation forest can obtain a significant performance improvement and has lower computation cost compared with the existed detectors.

Frequency control over networks is done using the frequency droop control technique which has the simplicity advantage although it allows that, in certain situations, frequency control is not very efficient. Artificial intelligence techniques have been increasingly used, so it is justified to explore their viability in electrical networks. The present work analyzes the use of Artificial Intelligence in networks to improve the frequency droop control. In order to realize this, a deep reinforcement learning (DRL)-based agent is proposed to tune the controller parameters for voltage source converter (VSC) in this paper. The DRL-based agent is trained by numerous hybrid grid operation conditions to lean the optimal control policy, which make it achieve a good adaptability to variety of operation conditions. For the purpose of demonstrating this method, a time-domain simulation model of hybrid power system is built with MATLAB/Simulink to act as test system. The simulation results verify the effectiveness of the proposed method.

Owing to the complicated characteristics of high-resolution sea clutter and the diversity of sea-surface small targets, there is no precise parameter model to describe sea clutter and returns with targets. As a result, target detection faces many obstacles. To distinguish sea clutter and target returns, it is effective to extract their features to transform the detection problem into a classification problem in feature space. Feature-based detection is a binary hypothesis test in the feature space that encounters two intrinsic difficulties: one difficulty is insufficient target returns versus sufficient sea clutter; the other difficulty is an uncontrolled false alarm rate in detection. To solve the first difficulty, a generator of typical targets returns that can generate sufficient simulated targets returns is used to balance the number of samples between two classes and assist to design the detector. K Nearest Neighbors (K-NN) is the type of classification method that is simple and effective; however, it cannot be used to detect small targets directly because of the uncontrolled false alarm rate. This paper proposes a modified K-NN method with a controlled false alarm rate for detecting small targets. Experimental results on the IPIX radar database indicate that the proposed detector attains 85.1% and 89.2% rates of target detection for the observation time of 0.512 s and 1.024 s, respectively, compared with other existing feature-based detectors, the proposed detector exhibits 7% and 5% improvement, respectively. Thus, the proposed detector exhibits more stable and effective detection performance than other existing feature-based detectors.

Radar target detection in sea clutter is of significance to both civilian and military. With the miniaturization and invisibility of sea targets, floating small targets with slow speed have become the focus of radar detection. However, the detection of floating small targets in the background of sea clutter has always been a problem. Floating small targets usually have a weak Radar Cross Section (RCS) and slow speed, making it difficult to detect such targets in sea clutter. Traditional target detection methods exhibit poor performance in the detection of floating small targets. For the detection of small and weak targets on the sea surface, a high-Doppler-resolution and high-range-resolution system (double high system) is an effective way to solve this problem. In the double high system, the target echo received by the radar provides readily available and sufficient information. However, how to transform and refine this information to improve detection performance has always been a challenge to the radar industry and a subject of constant innovation. In recent years, under the double high system, as an artificial feature engineering stage for intelligent radar target detection, scholars have proposed a variety of feature-based target detection methods to alleviate the difficulty in detecting floating small targets when relying only on energy information and considerably improve detection performance. To ensure that relevant radar practitioners better understand the development and future trend of this field in recent years, this paper summarizes the difficulties of sea target detection and common target detection methods, analyzes the principle and general framework of feature detection and several typical feature-based detection methods, and explores the development trend of feature-based detection methods.

Understanding and manipulating geological pore structures is of paramount importance for geo-energy productions and underground energy storages in porous media. Nevertheless, research emphases for long time have been focused on understanding the pore configurations, while few work conducted to modify and restructure the porous media. This study deploys ultrasonic treatments on typical geological in-situ core samples, with follow-up processes of high-pressure mercury injections and nitrogen adsorptions and interpretations from nuclear magnetic resonance and x-ray diffraction. The core permeability and porosity are found to increase by 8.3 mD, from 4.1 to 12.4 mD, and by 0.95%, from 14.03% to 14.98%, respectively. Meanwhile, the number and size of the micro- and mesopore are increased with progressing of ultrasonic treatment, while those of the macropore decrease, which finally increase the permeability and porosity. The increase of micro- and mesopore number, from x-ray diffraction results, is attributed to the migration and precipitation of clay minerals caused through ultrasonic wave. The relocation of clay minerals also helps to improve the pore-throat connectivity and modify the micro-scale heterogeneity. Basically, this study reveals the characterizations of geological pore reconfigurations post-ultrasonic treatments and interprets the associated mechanisms, which provides guidance to manipulate the geological pores and be of benefit for further porous media use in science and engineering.

This paper proposes a semi-automatic green tide extraction method based on the NDVI to extract Yellow Sea green tides from 2008 to 2022 using remote sensing (RS) images from multiple satellites: GF-1, Landsat 5 TM, Landsat 8 OLI_TIRS, HJ-1A/B, HY-1C, and MODIS. The results of the accuracy assessment based on three indicators: Precision, Recall, and F1-score, showed that our extraction method can be applied to the images of most satellites and different environments. We traced the source of the Yellow Sea green tide to Jiangsu Subei shoal and the southeastern Yellow Sea and earliest advanced the tracing time to early April. The Gompertz and Logistic growth curve models were selected to predict and monitor the extent and duration of the Yellow Sea green tide, and uncertainty for the predicted growth curve was estimated. The prediction for 2022 was that its start and dissipation dates were expected to be June 1 and August 15, respectively, and the accumulative cover area was expected to be approximately 1190.90–1191.21 km2.

This work addresses the problem of detecting the changes between the two SAR images from the same region with two different phases. Because of the advantages of fast computing speed and high efficiency, the development of efficient and accurate algorithm for spatial change detection is always an important research issue in radar field. Unfortunately, the conventional spatial change detection method has low performance when the change area occupies only a small part. To solve this problem, an iterative Otsu method is proposed in this paper. First, a difference image is generated by using an improved neighborhood-based ratio approach that combines the neighborhood information. Then, we propose the iterative Otsu method to obtain the segmentation threshold of the difference image, in order to solve the segmentation problem when the proportion of the changing region is very small. The experimental results show that the detection accuracy of this algorithm is obviously improved compared with the existing methods and it has better robustness for the present of coherent speckle noise.

Compressed sensing has shown great potential and power in image representation, especially in image reconstruction by sparse representation. Due to complementary information and unavoidable noise existing in synthetic aperture radar (SAR) and other source images, joint sparse representation (JSR) is developed to separate redundancy and complementary information with different properties in source images and obtain a fused image, where image de-noising is done simultaneously owing to that noise is not sparse and cannot be represented by sparse representation. As a result, one noisy remote sensing image fusion method based on JSR is presented in this paper. After obtaining redundant and complementary sub-images by JSR, an improved fusion rule based on pulse coupled neural network (PCNN) is employed to fuse complementary sparse coefficients together. At the same time, because the types of noise in SAR and other source images are different, they can be treated as the complementary information in source images and suppressed at this step. Finally, a fused image can be reconstructed by adding the redundant and fused complementary sub-images. Quantitative and qualitative experimental results show that the proposed method outperforms most of other fusion methods and it is more robust to noise, having better visual effects and values of objective evaluation metrics.

A forest fire susceptibility map generated with the fire susceptibility model is the basis of fire prevention resource allocation. A more reliable susceptibility map helps improve the effectiveness of resource allocation. Thus, further improving the prediction accuracy is always the goal of fire susceptibility modeling. This paper developed a forest fire susceptibility model based on an ensemble learning method, namely light gradient boosting machine (LightGBM), to produce an accurate fire susceptibility map. In the modeling, a subtropical national forest park in the Jiangsu province of China was used as the case study area. We collected and selected eight variables from the fire occurrence driving factors for modeling based on correlation analysis. These variables are from topographic factors, climatic factors, human activity factors, and vegetation factors. For comparative analysis, another two popular modeling methods, namely logistic regression (LR) and random forest (RF) were also applied to construct the fire susceptibility models. The results show that temperature was the main driving factor of fire in the area. In the produced fire susceptibility map, the extremely high and high susceptibility areas that were classified by LR, RF, and LightGBM were 5.82%, 18.61%, and 19%, respectively. The F1-score of the LightGBM model is higher than the LR and RF models. The accuracy of the model of LightGBM, RF, and LR is 88.8%, 84.8%, and 82.6%, respectively. The area under the curve (AUC) of them is 0.935, 0.918, and 0.868, respectively. The introduced ensemble learning method shows better ability on performance evaluation metrics.

Objectives: The role of neuroinflammation in the pathogenesis of Alzheimer's disease (AD) has attracted much attention recently. Regulatory T-cells (Tregs) play an important role in modulating inflammation. We aimed to explore the Treg-related immunosuppression status at different stages of AD.Methods: Thirty healthy control (HC) subjects, 26 patients with mild cognitive impairment (MCI), 30 patients with mild probable AD-related dementia, and 28 patients with moderate-to-severe probable AD-related dementia underwent detailed clinical history taking, structural MRI scanning, and neuropsychological assessment. Peripheral blood samples were taken to measure the percentage of CD4+CD25+CD127low/− Tregs by flow cytometry and the levels of interleukin (IL-10), interleukin (IL-35), and transforming growth factor β (TGF-β) by ELISA.Results: The percentage of Tregs in the blood of MCI patients was the highest (9.24%); there was a significant difference between patients with MCI and patients with probable AD-related dementia. The level of TGF-β in patients with MCI (47.02 ng/ml) was significantly increased compared with patients with AD-related dementia. There were positive correlations between Treg percentage, IL-35, and Mini-mental state evaluation scores in patients with MCI and probable AD-related dementia.Conclusions: Patients with MCI have stronger Treg-related immunosuppression status compared with patients with probable AD-related dementia.

In recent years, a large number of high voltage direct current (HVDC) transmission projects have been connected to AC systems. This has started to have an impact on AC/DC hybrid power grids, particularly receiving terminal power grids. An HVDC system is a large-scale power electronic integrated nonlinear system, and it includes a primary system and a control and protection system. Hence, the precision and degree of detail of HVDC systems directly affect the actual effect of simulation. In recent years, in the case of the normal operation and failure of AC power grids, the abnormal fluctuation and even locking of HVDC systems caused by the inappropriate strategies of the control and protection system component have strongly affected power grids. This has significantly affected the safety and stability of receiving power grids and normal operation. In this study, the actual engineering HVDC control logic provided by a manufacturer is analyzed and simulated based on the user defined component library of the ADPSS electromagnetic transient calculation program, and an HVDC control model based on an actual system is established. The accuracy of the DC control custom model based on ADPSS is verified through the simulation of an actual power grid. Keywords: ADPSS, HVDC control and protection system, Detailed electromagnetic transient simulation, AC–DC large power grid system

Abstract Background Sutherlandia frutescens is one of the most promising commercialized, indigenous and medicinal plants of South Africa that is used as an immune-booster, and a traditional treatment for cancer. However, few studies report on its toxicology and dosage in vivo. There is still room to better understand its cytotoxicity effects in animal systems. Methods We prepared two extracts, one with 80% (v/v) ethanol, and the other, with water. Both were studied to determine the maximum tolerable concentration when extracts were applied at 0 to 200 μg/ml to a Tuebingen zebrafish embryo line. The development of zebrafish embryos after 24 h post fertilization (hpf) was studied. A concentration range of 5 μg/ml to 50 μg/ml was then chosen to monitor the ontological development of cultured embryos. A liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) method was used to study the differences of the two experimental extracts. Chemical variation between the extracts was illustrated using chemometrics. Results Both extracts led to bleeding and pericardial cyst formation when applied at high concentrations to the zebrafish embryo culture. Chronic teratogenic toxicities, leading to pericardial edema, yolk sac swelling, and other abnormal developmental characteristics, were detected. The aqueous extracts of S. frutescens were less toxic to the larvae than the ethanol extracts, validating preference for aqueous preparations when used in traditional medicine. Chemical differences between the water extracts and alcoholic extracts were analysed using LC-MS/MS. A supervised metabolomics approach, targeting the sutherlandiosides and sutherlandins using orthogonal partial least squares-discriminant analysis (OPLS-DA), illustrated that sutherlandiosides were the main chemical features that can be used to distinguish between the two extracts, despite the extracts being highly similar in their chemical constituents. Conclusion The water extract caused less cytotoxic and abnormal developmental effects compared to the ethanolic extract, and, this is likely due to differences in concentrations of extracted chemicals rather than the chemical profile per se. This study provides more evidence of cytotoxicity effects linked to S. frutescens using the zebrafish embryo bioassay as a study tool.

In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application.

This paper focuses on the range migration (RM) and Doppler frequency migration corrections of the long-time coherent integration, and a fast algorithm is proposed for maneuvering target detection and motion parameters estimation under a low signal-to-noise ratio (SNR). By utilizing the autocorrelation function with respect to the slow time, the newly defined extended keystone transform, scaled Fourier transform, and fast Fourier transform (FFT), the proposed algorithm coherently integrates the target energy into a peak in a three-dimensional (3-D) parameter space. Thereafter, the target’s radial velocity and acceleration are estimated by the peak’s coordinate. After compensating off the RM and DFM via the estimated motion parameters, the inverse FFT along the range frequency and azimuth FFT are used to realize the coherent integration for the target detection. The cross term characteristic is also analyzed and shows the applicability of the proposed algorithm in the scenario of multiple targets. Comparisons with representative algorithms in the computational complexity, motion parameters estimation, and target detection are presented in this paper, which leads us to conclude that the proposed algorithm can greatly reduce the computational complexity with an acceptable integration SNR gain loss. Finally, the experiments with the simulated and real measured radar data are conducted to verify the proposed algorithm.

Fire risk prediction is significant for fire prevention and fire resource allocation. Fire risk maps are effective methods for quantifying regional fire risk. Laoshan National Forest Park has many precious natural resources and tourist attractions, but there is no fire risk assessment model. This paper aims to construct the forest fire risk map for Nanjing Laoshan National Forest Park. The forest fire risk model is constructed by factors (altitude, aspect, topographic wetness index, slope, distance to roads and populated areas, normalized difference vegetation index, and temperature) which have a great influence on the probability of inducing fire in Laoshan. Since the importance of factors in different study areas is inconsistent, it is necessary to calculate the significance of each factor of Laoshan. After the significance calculation is completed, the fire risk model of Laoshan can be obtained. Then, the fire risk map can be plotted based on the model. This fire risk map can clarify the fire risk level of each part of the study area, with 16.97% extremely low risk, 48.32% low risk, 17.35% moderate risk, 12.74% high risk and 4.62% extremely high risk, and it is compared with the data of MODIS fire anomaly point. The result shows that the accuracy of the risk map is 76.65%.

HER2 overexpression leads to downregulation of MHC class-I. CD4+ Th1 cytokines, IFNγ and TNFα, and monoclonal antibodies, trastuzumab and pertuzumab, restore MHC class-I expression, and enable CD8+ recognition and cytolysis. Restoration of the anti-HER2 CD4+ Th1 immune response in combination with HER2 targeted therapy appear to be critical to successful anti-HER2 CD8+ immunotherapy.

Classification of clutter, especially in the context of shore based radars, plays a crucial role in several applications. However, the task of distinguishing and classifying the sea clutter from land clutter has been historically performed using clutter models and/or coastal maps. In this paper, we propose two machine learning, particularly neural network, based approaches for sea-land clutter separation, namely the regularized randomized neural network (RRNN) and the kernel ridge regression neural network (KRR). We use a number of features, such as energy variation, discrete signal amplitude change frequency, autocorrelation performance, and other statistical characteristics of the respective clutter distributions, to improve the performance of the classification. Our evaluation based on a unique mixed dataset, which is comprised of partially synthetic clutter data for land and real clutter data from sea, offers improved classification accuracy. More specifically, the RRNN and KRR methods offer 98.50% and 98.75% accuracy, outperforming the conventional support vector machine and extreme learning based solutions.

The problem of target detection in impulsive non-Gaussian sea clutter has attracted a lot of attention in recent years. The positive alpha-stable (PαS) distribution has been validated as a suitable model for the impulsive non-Gaussian sea clutter. Since the probability density function (PDF) of the PαS variable cannot be expressed as a closed-form expression, the research into constant false alarm rate (CFAR) detectors in PαS distributed sea clutter is limited. This paper formulates and evaluates some CFAR detectors, such as Greatest Of-CFAR (GO-CFAR), Smallest Of-CFAR (SO-CFAR), Order Statistic-CFAR (OS-CFAR) and censored mean level (CML) detectors, in PαS distributed sea clutter. Firstly, the Fox’s H-function is adopted to express the PDF of the PαS variable, and the cumulative density function based on Fox’s H-function is derived in this paper. Then, by use of the properties of the H-function and PαS distribution, exact expressions of the probabilities of false alarm and detection for CFAR detectors in the PαS background are derived. Some CFAR properties of these detectors in the PαS background are also explored. Numerical results based on derived expressions are given and verified by Monte Carlo simulation. Some analyses of detection performance from a practical perspective are also given.

HER2-directed therapies are less effective in patients with ERpos compared to ERneg breast cancer, possibly reflecting bidirectional activation between HER2 and estrogen signaling pathways. We investigated dual blockade using anti-HER2 vaccination and anti-estrogen therapy in HER2pos/ERpos early breast cancer patients. In pre-clinical studies of HER2pos breast cancer cell lines, ERpos cells were partially resistant to CD4+ Th1 cytokine-induced metabolic suppression compared with ERneg cells. The addition of anti-estrogen treatment significantly enhanced cytokine sensitivity in ERpos, but not ERneg, cell lines. In two pooled phase-I clinical trials, patients with HER2pos early breast cancer were treated with neoadjuvant anti-HER2 dendritic cell vaccination; HER2pos/ERpos patients were treated with or without concurrent anti-estrogen therapy. The anti-HER2 Th1 immune response measured in the peripheral blood significantly increased following vaccination, but was similar across the three treatment groups (ERneg vaccination alone, ERpos vaccination alone, ERpos vaccination + anti-estrogen therapy). In the sentinel lymph nodes, however, the anti-HER2 Th1 immune response was significantly higher in ERpos patients treated with combination anti-HER2 vaccination plus anti-estrogen therapy compared to those treated with anti-HER2 vaccination alone. Similar rates of pathologic complete response (pCR) were observed in vaccinated ERneg patients and vaccinated ERpos patients treated with concurrent anti-estrogen therapy (31.4% vs. 28.6%); both were significantly higher than the pCR rate in vaccinated ERpos patients who did not receive anti-estrogen therapy (4.0%, p = 0.03). Since pCR portends long-term favorable outcomes, these results support additional clinical investigations using HER2-directed vaccines in combination with anti-estrogen treatments for ERpos/HER2pos DCIS and invasive breast cancer.

Despite the success of vaccines against some microbial pathogens, their utility in the prevention and treatment of cancer has thus far been limited. We have previously demonstrated that vaccination with dendritic cells activated with the TLR-4 ligand LPS and IFN-γ promotes an antigen-specific anti-tumor response that prevents tumor recurrence. To evaluate this mechanistically, we here studied the effects of this TLR-activated DC on regulatory T cell activity. Dendritic cells activated with LPS and IFN- γ negated the effects of regulatory T cells on responder cell proliferation. Restoration of responder cell proliferation was noted when TLR-activated dendritic cells were separated from both regulators and responders by a semi-permeable membrane. The effect is therefore mediated by a soluble factor but was independent of both IL-6 and IL-12. Furthermore, the soluble mediator appeared to act at least in part on the regulators themselves rather than responder cells exclusively. Because recent studies have demonstrated conversion of T regulatory cells into IL-17-producing effectors, we further questioned whether the TLR-activated dendritic cell would induce cytokine production and effector function in our system. We found that regulators produced a substantial amount of IFN- γ in the presence of TLR-activated dendritic cells but not immature dendritic cells. IFN-γ production was associated with upregulation of the Th1 transcriptional regulator T-bet, and a significant fraction of IFN-γ-producing regulators coexpressed T-bet and FoxP3. While the effects of the LPS-activated dendritic cell on responder cell proliferation were IL-12 independent, upregulation of T-bet was inhibited by a neutralizing anti-IL-12 antibody. Collectively, these and prior data suggest that varying innate immune signals may direct the phenotype of the immune response in part by inhibiting suppressor T cells and promoting differentiation of these regulators into particular subsets of effectors.