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StreamSAXS is a Python-based small- and wide-angle X-ray scattering (SAXS/WAXS) data analysis workflow platform with graphical user interface (GUI). It aims to provide an interactive and user-friendly tool for analysis of both batch data files and real-time data streams. Users can easily create customizable workflows through the GUI to meet their specific needs. One characteristic of StreamSAXS is its plug-in framework, which enables developers to extend the built-in workflow tasks. Another feature is the support for both already acquired and real-time data sources, allowing StreamSAXS to function as an offline analysis platform or be integrated into large-scale acquisition systems for end-to-end data management. This paper presents the core design of StreamSAXS and provides user cases demonstrating its utilization for SAXS/WAXS data analysis in offline and online scenarios.

X-ray scattering/diffraction tensor tomography techniques are promising methods to acquire the 3D texture information of heterogeneous biological tissues at micrometre resolution. However, the methods suffer from a long overall acquisition time due to multi-dimensional scanning across real and reciprocal space. Here, a new approach is introduced to obtain 3D reciprocal information of each illuminated scanning volume using mathematic modeling, which is equivalent to a physical scanning procedure for collecting the full reciprocal information required for voxel reconstruction. The virtual reciprocal scanning scheme was validated by a simulated 6D wide-angle X-ray diffraction tomography experiment. The theoretical validation of the method represents an important technological advancement for 6D diffraction tensor tomography and a crucial step towards pervasive applications in the characterization of heterogeneous materials.

Deep neural networks have recently been extensively utilized for Polarimetric synthetic aperture radar (PolSAR) image classification. However, this heavily relies on extensive labeled data which is both costly and labor-intensive. To lower the collection of labeling data and enhance the classification performance, a novel multichannel semi-supervised active learning (MSSAL) method is proposed for PolSAR image classification. First, a multichannel strategy-based committee model with cooperative representation classification is presented to explore more effective information in the limited training data. Second, a loss prediction (LP) module is designed to identify the most informative pixels, and an ensemble learning (EL) strategy is designed to select the pixels with the highest confidence. Then, the deep neural network is fine-tuned with the obtaining target pixels through LP and EL in each iteration. Finally, the trained deep model predicts labels for all unlabeled data, outputting the final classification results. The proposed method is evaluated on three real-world PolSAR datasets, demonstrating superior performance to other PolSAR image classification methods with limited labeled samples.

Reducing energy consumption is major challenge in the industrialization of chemical pretreatments for the extraction of cellulose nanofibrils (CNF). In this study, an integrated chemical pretreatment with alkaline/acid-chlorite/TEMPO-oxidant was used for the nano-fibrillation of CNF from pine sawdust (WS). The alkaline and acid-chlorite pretreatments effectively eliminated the non-cellulosic components present in WS, resulting in the delamination of individual cell layers and swelling of the internal structures within the cellulose fiber bundles and cellulose microfibrils that form these layers. The spacing between CNF within the cellulose microfibrils increased from 3.7 nm to 5.5 nm. These loosely packed hierarchical structures facilitated the penetration of the reagent, which led to an increase in the specific surface area during the TEMPO-oxidant reaction and consequently accelerated the reaction rate. The WS was pretreated in a very dilute solution (1 % NaOH and 0.5 % NaClO2) under mild conditions (70 °C for 1 h), which resulted in a significant reduction of the TEMPO reaction time (from 3 h to 30 min) and a lower consumption of the reaction reagent (one fourth of the amount consumed compared to the direct oxidation of WS to achieve the same degree of cellulose nano-fibrillation).

Abstract With the advancements in instrumentations of next-generation synchrotron light sources, methodologies for small-angle X-ray scattering (SAXS)/wide-angle X-ray diffraction (WAXD) experiments have dramatically evolved. Such experiments have developed into dynamic and multiscale in situ characterizations, leaving prolonged exposure time as well as radiation-induced damage a serious concern. However, reduction on exposure time or dose may result in noisier images with a lower signal-to-noise ratio, requiring powerful denoising mechanisms for physical information retrieval. Here, we tackle the problem from an algorithmic perspective by proposing a small yet effective machine-learning model for experimental SAXS/WAXD image denoising, allowing more redundancy for exposure time or dose reduction. Compared with classic models developed for natural image scenarios, our model provides a bespoke denoising solution, demonstrating superior performance on highly textured SAXS/WAXD images. The model is versatile and can be applied to denoising in other synchrotron imaging experiments when data volume and image complexity is concerned.

Deep neural networks have achieved remarkable results in the field of polarimetric synthetic aperture radar (PolSAR) image classification. However, PolSAR is affected by speckle imaging, resulting in PolSAR images usually containing a large amount of speckle noise, which usually leads to the poor spatial consistency of classification results and insufficient classification accuracy. Semantic segmentation methods based on deep learning can realize the task of image segmentation and classification at the same time, producing fine-grained and smooth classification maps. However, these approaches require enormous labeled data sets, which are laborious and time-consuming. Due to these issues, a new multi-modal contrastive fully convolutional network, named MCFCN, is proposed for PolSAR image classification in this paper, which combines multi-modal features of the same pixel as inputs to the model based on a fully convolutional network and accomplishes the classification task using only a small amount of labeled data through contrastive learning. In addition, to describe the PolSAR terrain targets more comprehensively and enhance the robustness of the classifier, a pixel overlapping classification strategy is proposed, which can not only improve the classification accuracy effectively but also enhance the stability of the method. The experiments demonstrate that compared with existing classification methods, the classification results of the proposed method for three real PolSAR datasets have higher classification accuracy.

Acrylic liquid-crystal resin (ALCR) was synthesized in three steps using 4-hydroxybenzoate 4-carboxylic phenyl ester as the mesocrystalline unit. The structure, heat resistance, and liquid–crystal properties of the ALCR were systematically investigated. The ALCR was used to produce new 3D printing inks, which exhibited good fluidity and rapid photopolymerization ability to meet the rheological prerequisites of 3D printing ink. Compared with commercial ink, the initial decomposition temperature increased from 304.9 to 334.3 °C, the glass transition temperature increased from 74.8 to 89.8 °C, and the storage modulus increased from 1107.6 to 2230.6 MPa for the ALCR inks. The tensile strength, elongation at break, flexural strength, impact strength, and hardness of an ink containing 20% ALCR were improved to 65.3 MPa, 17.3%, 103.9 MPa, 4.8 kJ m−2, and 77.1 HD, respectively, corresponding to 147.4%, 174.7%, 207.4%, 171.4%, and 106.6% of the values for commercial ink. These results indicate that ALCR can effectively improve the mechanical properties of 3D-printed products in-situ. This is because the liquid-crystal molecules in the product stack to form mesogenic domains. When subjected to an external force, these mesogenic domains exploit their own orientation, cross-linking, and high-strength characteristics to effectively improve the mechanical properties of the product.

Recently, convolutional neural networks (CNNs) have been successfully utilized in polarimetric synthetic aperture radar (PolSAR) image classification and obtained promising results. However, most CNN-based classification methods require a large number of labeled samples and it is difficult to obtain sufficient labeled samples. For this reason, an ensemble dual-branch CNN (EDb-CNN) is proposed for PolSAR image classification with small samples. First, to solve the problem of the small sample in PolSAR image classification, a new data enhancement method based on the superpixel algorithm is proposed to expand the number of labeled samples. Second, to obtain different scales of features from PolSAR images, a Db-CNN model is proposed. This model contains two parallel CNN structures. One CNN branch is used to extract the polarization features from the complex coherency matrix. The other branch is utilized to extract the spatial features based on weighted spatial neighborhood. On the top of these two branches, a feature fusion model is adopted to combine these two deep features, and a weighted loss function is employed to improve the learning procedure. Then, the ensemble learning algorithm is used for each CNN branch and Db-CNN network to obtain the better classification results. Finally, a postprocess algorithm based on the superpixel algorithm is proposed to improve the consistency of classification results. Experiments on two PolSAR datasets show that the proposed method achieves a much better performance than other classification methods, especially when only a few labeled samples are available.

Deep learning and convolutional neural networks (CNN) have been widely applied in polarimetric synthetic aperture radar (PolSAR) image classification, and satisfactory results have been obtained. However, there is one crucial issue that still has not been solved. These methods require abundant labeled samples and obtaining the labeled samples of PolSAR images is usually time-consuming and labor-intensive. To obtain better classification results with fewer labeled samples, a new attention-based 3D residual relation network (3D-ARRN) is proposed for PolSAR image. Firstly, a multilayer CNN with residual structure is used to extract depth polarimetric features. Secondly, to extract more important feature information and improve the classification results, a spatial weighted attention network (SWANet) is introduced to concentrate the feature information, which is more favorable for a classification task. Then, the features of training and test samples are integrated and CNN is utilized to compute the score of similarity between training and test samples. Finally, the similarity score is used to determine the category of test samples. Studies on four different PolSAR datasets illustrate that the proposed 3D-ARRN model can achieve higher classification results than other comparison methods with few labeled data.

Abstract The resource constraint has become an important factor hindering the further development of mobile devices (MDs). Mobile cloud computing (MCC) is a new approach proposed to extend MDs’ capacity and improve their performance by task offloading. In MCC, MDs send task requests to the application service operator (ASO), which provides application services to MDs and needs to determine whether to accept the task request according to the system condition. This paper studies the task admission control problem for ASOs with the consideration of three features (two-dimensional resources, uncertainty, and incomplete information). A task admission control model, which considers radio resource variations, computing, and radio resources, is established based on the semi-Markov decision process with the goal of maximizing the ASO’s profits while guaranteeing the quality of service (QoS). To develop the admission policy, a reinforcement learning-based policy algorithm, which develops the admission policy through system simulations without knowing the complete system information, is proposed. Experimental results show that the established model adaptively adjusts the admission policy to accept or reject different levels and classes of task requests based on the ASO load, available radio resources, and event type. The proposed policy algorithm outperforms the existing policy algorithms and maximizes the ASO’s profits while guaranteeing the QoS.

Terrain classification is an important topic in polarimetric synthetic aperture radar (PolSAR) image processing and interpretation. A novel PolSAR classification method based on the three-channel convolutional neural network (Tc-CNN) is proposed and this method can effectively take the advantage of unlabeled samples to improve the performance of classification with a small number of labeled samples. Several strategies are included in the proposed method. First, in order to take the advantage of unlabeled samples, a data enhancement method based on the neighborhood nearest neighbor propagation method is proposed to enlarge the number of labeled samples. Second, to increase the role of central pixel in convolutional neural network classification based on the pixel, a spatial weighted method is proposed to increase the weight of central pixel features and weak the weight of other types of pixel features. Third, a specific deep model for PolSAR image classification (named Tc-CNN) is proposed, which can obtain more scale and deep polarization information to improve the classification results. The experimental results show that the proposed method achieves a much better performance than the existing classification methods when the number of labeled samples is few.

Polarimetric synthetic aperture radar (PolSAR) image classification is a pixel-wise issue, which has become increasingly prevalent in recent years. As a variant of the Convolutional Neural Network (CNN), the Fully Convolutional Network (FCN), which is designed for pixel-to-pixel tasks, has obtained enormous success in semantic segmentation. Therefore, effectively using the FCN model combined with polarimetric characteristics for PolSAR image classification is quite promising. This paper proposes a novel FCN model by adopting complex-valued domain stacked-dilated convolution (CV-SDFCN). Firstly, a stacked-dilated convolution layer with different dilation rates is constructed to capture multi-scale features of PolSAR image; meanwhile, the sharing weight is employed to reduce the calculation burden. Unfortunately, the labeled training samples of PolSAR image are usually limited. Then, the encoder–decoder structure of the original FCN is reconstructed with a U-net model. Finally, in view of the significance of the phase information for PolSAR images, the proposed model is trained in the complex-valued domain rather than the real-valued domain. The experiment results show that the classification performance of the proposed method is better than several state-of-the-art PolSAR image classification methods.

Photosensitive resins used in three-dimensional (3D) printing are characterized by high forming precision and fast processing speed; however, they often possess poor mechanical properties and heat resistance. In this study, we report a photocurable bismaleimide ink with excellent comprehensive performance for stereolithography (SLA) 3D printing. First, the main chain of bismaleimide with an amino group (BDM) was synthesized, and then, the glycidyl methacrylate was grafted to the amino group to obtain the bismaleimide oligomer with an unsaturated double bond. The oligomers were combined with reaction diluents and photo-initiators to form photocurable inks that can be used for SLA 3D printing. The viscosity and curing behavior of the inks were studied, and the mechanical properties and heat resistance were tested. The tensile strength of 3D-printed samples based on BDM inks could reach 72.6 MPa (166% of that of commercial inks), glass transition temperature could reach 155 °C (205% of that of commercial inks), and energy storage modulus was 3625 MPa at 35 °C (327% of that of commercial inks). The maximum values of T-5%, T-50%, and Tmax of the 3D samples printed by BDM inks reached 351.5, 449.6, and 451.9 °C, respectively. These photocured BDM inks can be used to produce complex structural components and models with excellent mechanical and thermal properties, such as car parts, building models, and pipes.

Objective: Research has shown that cerebral ischemia-reperfusion injury (CIRI) involves a series of physiological and pathological mechanisms, including inflammation, oxidative stress, and cell apoptosis. The cannabinoid receptor 2 agonist AM1241 has been found to have anti-inflammatory and anti-oxidative stress effects. However, it is unclear whether AM1241 has a protective effect against brain ischemia-reperfusion injury, and its underlying mechanisms are not yet known. Methods: In this study, we investigated the anti-inflammatory, anti-oxidative stress, and anti-apoptotic effects of AM1241 and its mechanisms in BV2 cells stimulated with H2O2 and in a C57BL/6 mouse model of CIRI in vitro and in vivo, respectively. Results: In vitro, AM1241 significantly inhibited the release of pro-inflammatory cytokines TNF-α and IL-6, reactive oxygen species (ROS), and the increase in Toll-like receptor 4/myeloid differentiation protein 2 (MD2/TLR4) complex induced by H2O2. Under H2O2 stimulation, MD2 overexpression resulted in increased levels of MD2/TLR4 complex, TNF-α, IL-6, NOX2, BAX, and Cleaved-Caspase3 (C-Caspase3), as well as the activation of the MAPK pathway and NF-κB, which were reversed by AM1241. In addition, molecular docking experiments showed that AM1241 directly interacted with MD2. Surface Plasmon Resonance (SPR) experiments further confirmed the binding of AM1241 to MD2. In vivo, AM1241 significantly attenuated neurofunctional impairment, brain edema, increased infarct volume, oxidative stress levels, and neuronal apoptosis in CIRI mice overexpressing MD2. Conclusion: Our study demonstrates for the first time that AM1241 alleviates mouse CIRI by inhibiting the MD2/TLR4 complex, exerting anti-inflammatory, anti-oxidative stress and anti-apoptotic effects.

ABSTRACT Human cytomegalovirus (HCMV), a widely prevalent human beta-herpesvirus, establishes lifelong persistence in the host following primary infection. In healthy individuals, the virus is effectively controlled by HCMV-specific T cells and typically exhibits asymptomatic. The T cell immune response plays a pivotal role in combating HCMV infection, while HCMV employs various strategies to counteract it within the host. Previously, we reported that UL23, a tegument protein of HCMV, facilitates viral immune evasion from interferon-gamma (IFN-γ) responses, and it is well known that IFN-γ is mainly derived from T cells. However, the involvement of UL23 in viral immune evasion from T cell-mediated immunity remains unclear. Herein, we present compelling evidence that UL23 significantly enhances viral resistance against T cell-mediated cytotoxicity during HCMV infection from the co-culture assays of HCMV-infected cells with T cells. We found that IFN-γ plays a major role in regulating T cell cytotoxicity mediated by UL23. More interestingly, we demonstrated that UL23 not only regulates the IFN-γ downstream responses but also modulates the IFN-γ secretion by regulating T cell activities. Further experiments indicate that UL23 upregulates the expression and signaling of programmed death ligand 1 (PD-L1), which is responsible for inhibiting multiple aspects of T cell activities, including activation, apoptosis, and IFN-γ secretion, as determined through RNA-seq analysis and inhibitor-blocking experiments, ultimately facilitating viral replication and spread. Our findings highlight the potential role of UL23 as an alternative antagonist in suppressing T cell cytotoxicity and unveil a novel strategy for HCMV to evade T cell immunity.IMPORTANCET cell immunity is pivotal in controlling primary human cytomegalovirus (HCMV) infection, restricting periodic reactivation, and preventing HCMV-associated diseases. Despite inducing a robust T cell immune response, HCMV has developed sophisticated immune evasion mechanisms that specifically target T cell responses. Although numerous studies have been conducted on HCMV-specific T cells, the primary focus has been on the impact of HCMV on T cell recognition via major histocompatibility complex molecules. Our studies show for the first time that HCMV exploits the programmed death ligand 1 (PD-L1) inhibitory signaling pathway to evade T cell immunity by modulating the activities of T cells and thereby blocking the secretion of IFN-γ, which is directly mediated by HCMV-encoded tegument protein UL23. While PD-L1 has been extensively studied in the context of tumors and viruses, its involvement in HCMV infection and viral immune evasion is rarely reported. We observed an upregulation of PD-L1 in normal cells during HCMV infection and provided strong evidence supporting its critical role in UL23-induced inhibition of T cell-mediated cytotoxicity. The novel strategy employed by HCMV to manipulate the inhibitory signaling pathway of T cell immune activation for viral evasion through its encoded protein offers valuable insights for the understanding of HCMV-mediated T cell immunomodulation and developing innovative antiviral treatment strategies.

The transmission of high-frequency signals over long distances depends on the ionosphere’s reflective properties, with the selection of operating frequencies being closely tied to variations in the ionosphere. The accurate prediction of ionospheric critical frequency foF2 and other parameters in low latitudes is of great significance for understanding ionospheric changes in high-frequency communications. Currently, deep learning algorithms demonstrate significant advantages in capturing characteristics of the ionosphere. In this paper, a state-of-the-art hybrid neural network is utilized in conjunction with a temporal pattern attention mechanism for predicting variations in the foF2 parameter during high- and low-solar activity years. Convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM), which is capable of extracting spatiotemporal features of ionospheric variations, are incorporated into a hybrid neural network. The foF2 data used for training and testing come from three observatories in Brisbane (27°53′S, 152°92′E), Darwin (12°45′S, 130°95′E) and Townsville (19°63′S, 146°85′E) in 2000, 2008, 2009 and 2014 (the peak or trough years of solar activity in solar cycles 23 and 24), using the advanced Australian Digital Ionospheric Sounder. The results show that the proposed model accurately captures the changes in ionospheric foF2 characteristics and outperforms International Reference Ionosphere 2020 (IRI-2020) and BiLSTM ionospheric prediction models.