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Recently, object detection in natural images has made a breakthrough, but it is still challenging in oriented ship detection for remote sensing imagery. Considering some limitations in this task, such as uncertain ship orientation, unspecific features for locating and classification in the complex optical environment, and multiplicative speckle interference of synthetic aperture radar (SAR), we propose an oriented ship detector based on the pairwise branch detection head and adaptive SAR feature enhancement. The details are as follows: (1) Firstly, the ships with arbitrary directions are described with a rotated ground truth, and an oriented region proposal network (ORPN) is designed to study the transformation from the horizontal region of interest to the rotated region of interest. The ORPN effectively improved the quality of the candidate area while only introducing a few parameters. (2) In view of the existing algorithms that tend to perform classification and regression prediction on the same output feature, this paper proposes a pairwise detection head (PBH) to design parallel branches to decouple classification and locating tasks, so that each branch can learn more task-specific features. (3) Inspired by the ratio-of-average detector in traditional SAR image processing, the SAR edge enhancement (SEE) module is proposed, which adaptively enhances edge pixels, and the threshold of the edge is learned by the channel-shared adaptive thresholds block. Experiments were carried out on both optical and SAR datasets. In the optical dataset, PBH combined with ORPN improved recall by 5.03%, and in the SAR dataset, the overall method achieved a maximum F1 score improvement of 6.07%; these results imply the validity of our method. [ABSTRACT FROM AUTHOR]

Recently, deep-learning methods have yielded rapid progress for object detection in synthetic aperture radar (SAR) imagery. It is still a great challenge to detect ships in SAR imagery due to ships' small size and confusable detail feature. This article proposes a novel anchor-free detection method composed of two modules to deal with these problems. First, for the lack of detailed information on small ships, we suggest an adaptive feature-encoding module (AFE), which gradually fuses deep semantic features into shallow layers and realizes the adaptive learning of the spatial fusion weights. Thus, it can effectively enhance the external semantics and improve the representation ability of small targets. Next, for the foreground–background imbalance, the Gaussian-guided detection head (GDH) is introduced according to the idea of soft sampling and exploits Gaussian prior to assigning different weights to the detected bounding boxes at different locations in the training optimization. Moreover, the proposed Gauss-ness can down-weight the predicted scores of bounding boxes far from the object center. Finally, the effect of the detector composed of the two modules is verified on the two SAR ship datasets. The results demonstrate that our method can effectively improve the detection performance of small ships in datasets. [ABSTRACT FROM AUTHOR]

[Display omitted] • High-quality Ga 2 O 3 heterostructures grown via LMBE with lattice/energy band matching. • Modulating APDs' voltage and dark current by adjusting MgO layer thickness. • The impact of the barrier layer on the nBn structure APDs performance was systematically studied. • The fabrication of Ga 2 O 3 -based solar-blind APDs with the current optimal performance has been achieved. Ga 2 O 3 -based solar-blind avalanche photodetectors (APDs) offer advantages such as compactness, low power consumption, and high stability. They could improve the integration and reliability of solar-blind photodetectors, and serve as a viable substitute for photomultiplier tubes (PMTs) in deep ultraviolet signal detection. We have previously demonstrated that the n-Barrier-n (nBn) structure, composed of Ga 2 O 3 /MgO/Nb:SrTiO 3 heterostructure, can efficiently reduce the dark current and enhance the reverse breakdown voltage due to the increased conduction band offsets. The barrier layer is a key factor in determining the device performance in the nBn heterostructure. Hence, we further optimize the study by varying the thickness of the barrier layer MgO to examine its effect on the devices. This modulation can affect the avalanche multiplication process, and thus enable the tuning of APDs' performance. In this paper, we present the optimized thin film growth process, and the systematic investigation of the responsivity and gain of the device with different barrier layer thicknesses. The optimal performance was achieved with 25 nm MgO thickness. We also explore the underlying mechanism to elucidate the role of the barrier layer. Our results provide insights into the influence of barrier layer thickness in the nBn structure. [ABSTRACT FROM AUTHOR]

Objective Methods Results Conclusions It is unclear whether less acetabular coverage is associated with the failure of core decompression (CD) for osteonecrosis of the femoral head (ONFH). This study aimed to investigate the clinical outcomes of CD for ONFH with small‐ or medium‐sized pre‐collapse lesions, and determine what factors, especially acetabular anatomical parameters, predict the failure of CD.Between January 2010 and December 2022, we retrospectively reviewed 269 consecutive CDs in 188 patients diagnosed with ONFH with small‐ or medium‐sized pre‐collapse lesions. The Kaplan–Meier method was used to evaluate the survival rate of CD for ONFH with progression of collapse or conversion to total hip arthroplasty (THA) as the endpoint. Univariate and multivariate logistic regression analyses were conducted to identify the potential risk factors for the failure of CD. Receiver operating characteristic (ROC) curve analysis was further performed with conversion to THA as the endpoint to determine the predictive value of these factors.The overall 5‐year survival rate of CD for ONFH with small‐ or medium‐sized pre‐collapse lesions was 74.3% (95% confidence interval (CI) 69.0%–81.1%) with progression of collapse as the endpoint and 83.9% (95% CI 79.3%–88.7%) with conversion to THA as the endpoint. Univariate logistic regression analysis showed that bilateral affected hips was significantly associated with progression of collapse, and center‐edge angle (CEA), sharp angle, acetabular head index (AHI), as well as acetabular depth ratio (ADR) were significantly associated with both progression of collapse and conversion to THA. Multivariate logistic regression analysis further indicated that CEA and AHI were independent risk factors for both progression of collapse and conversion to THA. ROC curve analysis with conversion to THA as the endpoint revealed that the cutoff values for CEA and AHI were 26.8° (sensitivity = 74.4%, specificity = 78.6%, area under the curve (AUC) = 0.809) and 79.8 (sensitivity = 78.4%, specificity = 73.8%, AUC = 0.818), respectively.CD showed satisfactory clinical outcomes for ONFH with small‐ or medium‐sized pre‐collapse lesions where less acetabular coverage with a CEA < 26.8° or AHI < 79.8 was identified as an independent risk factor for the failure of CD. [ABSTRACT FROM AUTHOR]

TE is a critical and difficult problem that tries to map traffic with various requirements to paths in dynamic communication networks. The emerging SDN enables centralized TE optimization with a global view. From the architectural viewpoint, ICN facilitates TE from many aspects, such as in-network caching which can reduce redundant traffic and content-awareness which can extract prior knowledge of content type directly. Thus, we leverage ICN to optimize SDN TE. However, ICN brings more complexities and dynamics to the network environment, which makes model-based TE methods inefficient. Inspired by recent advances in applying artificial intelligence techniques to solve complex online control problems, we investigate deep learning for content-awareness and DRL for TE decision. In addition, we propose a parallel online learning mechanism to safely utilize DRL which has trial-and-error nature. Results show that our proposal significantly improves network performance in terms of total network throughput, bandwidth utilization, and load balance. [ABSTRACT FROM AUTHOR]

With the continuing consumption of resources and increasingly prominent environmental issues, microalgal resource utilization has received extensive attention. In this study, based on the microalgal investigation in desert biological soil crusts (BSCs) using pyrosequencing technology, the cultivated crust microalgae were further isolated in order to obtain high quality microalgae for resource utilization. The results showed that with crust development and succession, microalgal diversity gradually decreased, including the number of operational taxonomic units (OTUs) and genus, although Microcoleus always was the dominant genera. Pyrosequencing obtained 630 OTUs of cyanobacteria, 25 OTUs of green algae and 9 OTUs of diatom; however, part of cultivated microalgae still could not yet be detected due to the DNA extraction preferences and errors caused by PCR amplification. After isolation, four strains were purified and cultivated, including two filamentous cyanobacteria Microcoleus vaginatus BSC-06 and Scytonema javanicum BSC-39, and two unicellular green algae Chlorella sp. BSC-24 and Monoraphidium dybowskii BSC-81. The two green algae grew fast (>250 mg L -1  d -1 ), and achieved high lipid productivity up to 75–85 mg L -1  d -1 , with lipid content of 28.7–39.0%, thus was considered as promising feedstock for biodiesel production. In addition, the two crust cyanobacteria could be used to construct artificial cyanobacterial soil crusts in desertification control, although their biomass accumulation was not as high as that in the green algae. Ultimately, combining biodiesel production with desertification control would not only improve desert environments, but also provide ideal places for the local microalgal resource exploitation, further promoting desert socioeconomic development. [ABSTRACT FROM AUTHOR]

In this study, based on the characteristic of lanthanide in oxygen storage capacity, a series of Pr a Ti b O x composite supports with different mole ratios of Pr to Ti and the corresponding Au catalysts were prepared - to investigate the oxidation of CO under CO 2 -rich atmosphere with lean oxygen content. A significant promotion in CO conversion rate over the catalyst with a molar ratio of Pr:Ti = 1:2 and a very high catalytic stability was also obtained. The addition of optimal Pr will make smaller gold particles, provide higher capacity in surface reactive oxygen releasing at low temperatures and weaken the deposition of carbonates in CO 2 -rich atmosphere. However, more Pr will strongly interact with gold, which leads to the majority of Au+ species in the catalysts and thus lower the catalytic activity. This study may provide a potential application of rare earth elements in sealed CO 2 -laser. [Display omitted] • The Pr a Ti b O x composites supported Au catalysts were studied for CO oxidation with CO 2 -rich feed. • The optimal catalyst was found with a molar ratio of Pr:Ti = 1:2. • Good catalytic performance correlated to the easy surface reactive oxygen releasing and weak deposition of carbonates. • The majority of Au+ species in the catalysts with higher Pr content results in worse performance. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) scholars and mediciners have reported AI systems that accurately detect medical imaging and COVID-19 in chest images. However, the robustness of these models remains unclear for the segmentation of images with nonuniform density distribution or the multiphase target. The most representative one is the Chan-Vese (CV) image segmentation model. In this paper, we demonstrate that the recent level set (LV) model has excellent performance on the detection of target characteristics from medical imaging relying on the filtering variational method based on the global medical pathology facture. We observe that the capability of the filtering variational method to obtain image feature quality is better than other LV models. This research reveals a far-reaching problem in medical-imaging AI knowledge detection. In addition, from the analysis of experimental results, the algorithm proposed in this paper has a good effect on detecting the lung region feature information of COVID-19 images and also proves that the algorithm has good adaptability in processing different images. These findings demonstrate that the proposed LV method should be seen as an effective clinically adjunctive method using machine-learning healthcare models. [ABSTRACT FROM AUTHOR]

Composite material is a new material prepared by several raw materials with different forms or properties through new processing methods. The composite not only maintains the advantages of the properties of each component but also can obtain the comprehensive properties that cannot be achieved by a single component through the complementarity and correlation of the properties of each component. The purpose of this paper is to study the effect of wet and hot conditions on the interlaminar fracture toughness and durability of composites. In this paper, the interlaminar fracture toughness and durability are studied, and the prediction and analysis method under wet and hot conditions is carried out. The effects of hot and humid conditions on interlaminar fracture toughness and durability were analyzed. The experimental results show that the longitudinal compressive strength of the unidirectional composite at 120°C and 1.00% moisture absorption is 29.53% lower than that at 20°C and 0.50% moisture absorption. Due to the high moisture absorption of aramid fiber, the longitudinal compressive strength of unidirectional aramid composite fiber will be significantly reduced in the humid and warm environment. [ABSTRACT FROM AUTHOR]

In the past two decades, traditional hand-crafted feature based methods and deep feature based methods have successively played the most important role in image classification. In some cases, hand-crafted features still provide better performance than deep features. This paper proposes an innovative network based on deep learning integrated with binary coding and Sinkhorn distance (DBSNet) for remote sensing and texture image classification. The statistical texture features of the image extracted by uniform local binary pattern (ULBP) are introduced as a supplement for deep features extracted by ResNet-50 to enhance the discriminability of features. After the feature fusion, both diversity and redundancy of the features have increased, thus we propose the Sinkhorn loss where an entropy regularization term plays a key role in removing redundant information and training the model quickly and efficiently. Image classification experiments are performed on two texture datasets and five remote sensing datasets. The results show that the statistical texture features of the image extracted by ULBP complement the deep features, and the new Sinkhorn loss performs better than the commonly used softmax loss. The performance of the proposed algorithm DBSNet ranks in the top three on the remote sensing datasets compared with other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a high-performance general-purpose no-reference (NR) image quality assessment (IQA) method based on image entropy. The image features are extracted from two domains. In the spatial domain, the mutual information between different color channels and the two-dimensional entropy are calculated. In the frequency domain, the statistical characteristics of the two-dimensional entropy and the mutual information of the filtered subband images are computed as the feature set of the input color image. Then, with all the extracted features, the support vector classifier (SVC) for distortion classification and support vector regression (SVR) are utilized for the quality prediction, to obtain the final quality assessment score. The proposed method, which we call entropy-based no-reference image quality assessment (ENIQA), can assess the quality of different categories of distorted images, and has a low complexity. The proposed ENIQA method was assessed on the LIVE and TID2013 databases and showed a superior performance. The experimental results confirmed that the proposed ENIQA method has a high consistency of objective and subjective assessment on color images, which indicates the good overall performance and generalization ability of ENIQA. The implementation is available on github https://github.com/jacob6/ENIQA. [ABSTRACT FROM AUTHOR]

Lung cancer is a leading cause of cancer-related deaths, and the most common type is lung adenocarcinoma (LUAD). LUAD is frequently diagnosed in people who never smoked, patients are always diagnosed at advanced inoperable stages, and the prognosis is ultimately poor. Thus, there is an urgent need for the development of novel targeted therapeutics to suppress LUAD progression. In this study, we demonstrated that the expression of DNA replication and sister chromatid cohesion 1 (DSCC1) was higher in LUAD samples than normal tissues, and the overexpression of DSCC1 or its coexpressed genes were highly correlated with poor outcomes of LUAD patients, highlighting DSCC1 might be involved in LUAD progression. Furthermore, the expression of DSCC1 was positively correlated with multiple genetic mutations which drive cancer development, including TP53, TTN, CSMD, and etc. More importantly, DSCC1 could promote the cell proliferation, stemness, EMT, and metastatic potential of LUAD cells. In addition, DSCC1 interacted with HSP90AB1 and promoted the progression of LUAD via regulating ER stress. Meanwhile, DSCC1 expression negatively correlated with immune cell infiltration in lung cancer, and DSCC1 positively regulated the expression of PD-L1 in LUAD cells. Collectively, this study revealed that DSCC1 is a novel therapeutic target to treat LUAD and a biomarker for predicting the efficiency of PD-1/PD-L1 blockade treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: Generally, the standard method of applying sentinel lymph node biopsy in breast cancer patients is via a technetium-labeled nanocolloid (radiolabeled tracer) with or without blue dye. However, the radioactive agents may cause challenges both to hospitals and patients. Alternatively, a safer candidate method, such as SPIO, has been often introduced and validated in comparison with standard methods. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic accuracy of SPIO and its clinical impact on the management of breast cancer. Based on our study, SPIO could be considered as an alternative standard of care for sentinel lymph node detection. Compared with the standard method, SPIO exhibited equivalent or even superior detection capacities, while safety could be guaranteed. Lower SPIO doses would also not impair detection capacity compared with the standard method. The absence of radioactivity of SPIO is one of the most important advantages for clinical applications. Superparamagnetic iron oxide (SPIO), an alternative mapping agent, can be used to identify sentinel lymph nodes in patients with clinically node-negative breast cancer. However, its performance in comparison with the standard method, using a radioisotope (technetium-99 m, Tc) alone or in combination with blue dye, remains controversial. Hence, a systematic review and meta-analysis were conducted to evaluate the diagnostic accuracy of SPIO and its clinical impact in the management of breast cancer. The PubMed, Embase, and Cochrane databases were comprehensively searched from inception to 1 May 2022. Cohort studies regarding the comparison of SPIO with standard methods for sentinel lymph node identification were included. A total of 19 prospective cohort studies, which collectively included 2298 clinically node-negative breast cancer patients undergoing sentinel lymph node identification through both the standard method and SPIO, were identified. The detection rate for sentinel lymph nodes (RR, 1.06; 95% CI, 1.05–1.08; p < 0.001) was considerably higher in the SPIO cohorts than in the standard method cohorts, although this difference was not significant in detected patients, patients with positive sentinel lymph nodes, or positive sentinel lymph nodes. Compared with the standard method, the SPIO method could be considered as an alternative standard of care for sentinel lymph node detection in patients with clinically node-negative breast cancer. [ABSTRACT FROM AUTHOR]

Maize seeding is greatly affected by improper seed placement and poor planter performance under a no-tillage mechanisation system. To overcome the issue, we explored the impact of separat-ing board and anti-blocking mechanism (0, 1/4, 1/2, 2/3, 3/4, and 1 type) on maize seeding under different forward speeds (3, 5, 7 km/h) and rotational speeds (260, 400, 530, 740 rpm), where the performance metrics included the mass of straw coiled, seeding height, emergence rate, soil mound depth, straw movement, and straw clearance. The study results show that separating board helps to increase forward and side displacements of the straw, which avoids localized accumulation of straw around the antiblocking mechanism. The straw clearance rate of the anti-blocking mechanism with a separating board is greater than that without the separating board. Therefore, the 2/3 type anti-blocking mechanism with a separating board is recommended for maize seeding at a forward speed of 5 km/h and a rotational speed of 400 rpm. [ABSTRACT FROM AUTHOR]

Background and Purpose: Abdominal aortic aneurysm (AAA) is a degenerative disease with irreversible and progressive dilation of the artery. But there are few options for efficacious treatment except for traditional surgery. Probucol has been widely applied to treat hyperlipidaemia and atherosclerosis in clinic, but whether it can protect against AAA remains unknown. In this study, the protective effects of probucol against AAA and its related mechanisms were explored.Experimental Approach: The model of AAA was induced in mice by periaortic application of elastase (40 min) to the abdominal aorta. Probucol at different doses was administered by daily gavage, starting on the same day as AAA was induced, for 14 days. In vitro, cultures of rat vascular smooth muscle cells (VSMCs) were stimulated with TNF-α. Haem oxygenase (HO)-1 siRNA and HO-1 plasmid were used to regulate the expression or activity of HO-1 in the VSMCs and to clarify the effects of HO-1.Key Results: Probucol dose-dependently prevented the development of AAA, reflected by decreased incidence of AAA, diameter of aortic dilation, elastin degradation, and infiltration of inflammatory cells. Probucol also protected VSMCs from oxidative injury and enhanced elastin biosynthesis. This anti-inflammatory effects of probucol on VSMCs were significantly decreased when HO-1 was inhibited by siRNA.Conclusion and Implications: Probucol protected against AAA through inhibiting the degradation of elastin induced by inflammation and oxidation and by facilitating the biosynthesis of elastin. HO-1 played a crucial role in the anti-inflammatory effects of probucol in VSMCs. [ABSTRACT FROM AUTHOR]

Thanks to the availability of large-scale data, deep Convolutional Neural Networks (CNNs) have witnessed success in various applications of computer vision. However, the performance of CNNs on Synthetic Aperture Radar (SAR) image classification is unsatisfactory due to the lack of well-labeled SAR data, as well as the differences in imaging mechanisms between SAR images and optical images. Therefore, this paper addresses the problem of SAR image classification by employing the Generative Adversarial Network (GAN) to produce more labeled SAR data. We propose special GANs for generating SAR images to be used in the training process. First, we incorporate the quadratic operation into the GAN, extending the convolution to make the discriminator better represent the SAR data; second, the statistical characteristics of SAR images are integrated into the GAN to make its value function more reasonable; finally, two types of parallel connected GANs are designed, one of which we call PWGAN, combining the Deep Convolutional GAN (DCGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP) together in the structure, and the other, which we call CNN-PGAN, applying a pre-trained CNN as a discriminator to the parallel GAN. Both PWGAN and CNN-PGAN consist of a number of discriminators and generators according to the number of target categories. Experimental results on the TerraSAR-X single polarization dataset demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Background/Aims: Acute myeloid leukemia (AML) of French-American-British (FAB) subtypes M0 and M1 are both poorly differentiated AML, but their mutational spectrum and molecular characteristics remain unknown. This study aimed to explore the mutational spectrum and prognostic factors of AML-M0 and M1. Methods: Sixty-five AML patients derived from The Cancer Genome Atlas (TCGA) database were enrolled in this study. Whole-genome sequencing was performed to depict the mutational spectrum of each patient. Clinical characteristics at diagnosis, including peripheral blood (PB) white blood cell counts (WBC), blast percentages in PB and bone marrow (BM), FAB subtypes and the frequencies of known recurrent genetic mutations were described. Survival was estimated using the Kaplan-Meier methods and log-rank test. Univariate and multivariate Cox proportional hazard models were constructed for event-free survival (EFS) and overall survival (OS), using a limited backward elimination procedure. Results: Forty-six patients had more than five recurrent genetic mutations. FLT3 had the highest mutation frequency (n=20, 31%), followed by NPM1 (n=18, 28%), DNMT3A (n=16, 25%), IDH1 (n=14, 22%), IDH2 (n=12, 18%), RUNX1 (n=11, 17%) and TET2 (n=7, 11%). Univariate analysis showed that age ≥60 years and TP53 mutations had adverse effect on EFS (P=0.015, P=0.036, respectively) and OS (P=0.003, P=0.004, respectively), WBC count ≥50×109/L and FLT3-ITD negatively affected EFS (P=0.003, P=0.034, respectively), whereas NPM1 mutations had favorable effect on OS (P=0.035) and allogeneic hematopoietic stem cell transplantation (allo-HSCT) on EFS and OS (all P< 0.001). Multivariate analysis suggested that allo-HSCT and NPM1 mutations were independent favorable prognostic factors for EFS and OS (all P< 0.05), WBC count ≥50×109/L was an independent risk factor for EFS (P=0.002) and TP53 mutations for OS (P=0.043). Conclusions: Our study provided new insights into the mutational spectrum and molecular signatures of AML-M0 and M1. We proposed that FLT3-ITD, NPM1 and TP53 be identified as markers for risk stratification of AML-M0 and M1. Patients with AML-M0 and M1 would likely benefit from allo-HSCT. [ABSTRACT FROM AUTHOR]

Background: Invasive fungal disease (IFD) causes morbidity and mortality in patients with hematological malignancy. Recurrence of IFD after chemotherapy or hematopoietic stem cell transplantation (HSCT) is associated with poor prognosis. The present study aimed to investigate the efficacy of different strategies of secondary antifungal prophylaxis (SAP) for IFD and choose an appropriate SAP regimen. Methods: Clinical data of patients with previous IFD who underwent chemotherapy or HSCT between Jan 2008 and Jun 2013 were retrospectively reviewed and followed up to 180 days post-chemotherapy or HSCT. The clinical characteristics and diagnosis were analyzed according to the diagnostic criteria for IFD. The efficacy of different strategies for SAP and risk factors influencing the failure of SAP were evaluated. Results: Of the 164 patients enrolled, 121 patients received SAP regimen (73.78%), and IFD recurred in 40 patients: 16.5% (20/121) in SAP group and 46.5% (20/43) in non-SAP group. In SAP group, 58 received SAP agents which were proven effective for their previous IFD, while other 63 patients received other broad-spectrum antifungal agents. There was no significant difference in the recurrence rates between these two subgroups (13.8% (8/58) vs 19.0% (12/63), P = 0.437). The IFD recurrence rates were statistically significant between patients with allogeneic HSCT and chemotherapy or autologous HSCT (25% vs 8.2%, P = 0.013). Multivariate analysis indicated that allogeneic HSCT was the independent risk factor of IFD recurrence after SAP. Conclusions: Secondary antifungal prophylaxis is necessary to prevent IFD recurrence in patients with hematological malignancy, especially for patients in the setting of allogeneic HSCT. [ABSTRACT FROM AUTHOR]