Interpolation linearization predistortion technology for FMCW LiDAR.

Frequency-modulated continuous-wave (FMCW) laser ranging technology is an important development direction of light detection and ranging (LiDAR) for the future. It has the advantages of high ranging accuracy, high resolution, wide range, and no ranging blind zone. A distributed feedback laser can be used as a high-quality light source in FMCW laser ranging systems because of its wide frequency modulation range, simple frequency modulation mode, and small package. Aiming at the nonlinear problem of the laser in the frequency modulation process, we present a novel, to our knowledge, predistortion algorithm based on interpolation linear fitting to enhance the linearity of the FMCW laser for LiDAR systems. The sweeping frequency curve of the laser is obtained using the Hilbert transform, and then the sweeping frequency curve is segmented and linearly fitted to calculate the interpolated driving current signals corresponding to linear frequency changes. Using this method, we achieved a nonlinearity error lower than 1e-7 for the swept-frequency signal and demonstrated that the ranging error is less than ±5c m at a distance of 100 m in the FMCW system. In addition, we also demonstrated a 3D static object point cloud with high imaging quality. Compared with the iterative predistortion algorithm based on the function fitting, this method avoids fitting errors at the inflection points of the triangular swept-frequency signal and the complexity of multiple iterative calculations. It enables rapid generation of pre-distorted swept-frequency signals, making it particularly suitable for real-time applications of automotive LiDAR systems.

HCT: Chinese Medical Machine Reading Comprehension Question-Answering via Hierarchically Collaborative Transformer.

Chinese medical machine reading comprehension question-answering (cMed-MRCQA) is a critical component of the intelligence question-answering task, focusing on the Chinese medical domain question-answering task. Its purpose enable machines to analyze and understand the given text and question and then extract the accurate answer. To enhance cMed-MRCQA performance, it is essential to possess a profound comprehension and analysis of the context, deduce concealed information from the textual content and, subsequently, precisely determine the answer's span. The answer span has predominantly been defined by language items, with sentences employed in most instances. However, it is worth noting that sentences may not be properly split to varying degrees in various languages, making it challenging for the model to predict the answer zone. To alleviate this issue, this paper presents a novel architecture called HCT based on a Hierarchically Collaborative Transformer. Specifically, we presented a hierarchical collaborative method to locate the boundaries of sentence and answer spans separately. First, we designed a hierarchical encoding module to obtain the local semantic features of the corpus; second, we proposed a sentence-level self-attention module and a fused interaction-attention module to get the global information about the text. Finally, the model is trained by combining loss functions. Extensive experiments were conducted on the public dataset CMedMRC and the reconstruction dataset eMedicine to validate the effectiveness of the proposed method. Experimental results showed that the proposed method performed better than the state-of-the-art methods. Using the F1 metric, our model scored 90.4% on the CMedMRC and 73.2% on eMedicine.

Cholesterol-lowering effects of rhubarb free anthraquinones and their mechanism of action.

Rhubarb free anthraquinones (RhA) have significant lipid-regulating activity. However, whether RhA monomers have a role in lipid-regulating and their mechanism of action remains unclear. Based on the cholesterol accumulated HepG2 cell model, the cholesterol-regulating effect of RhA monomers and their combinations was investigated. The expression of sterol-regulatory element binding protein 2 (SREBP2), 3-hydroxy-3-methyl glutaryl coenzyme A reductase (HMGCR) and squalene monooxygenase (SQLE) of the model cells was analyzed to preliminarily explore the mechanism of action. After that, the liposomes of each active RhA monomer were separately prepared with the same lipid materials and the same preparation method so that each monomer has similar or equal bioavailability after oral administration to rats. Finally, the hypercholesterolemic rat model was established, and the effect of active RhA monomers loaded liposomes as well as their combinations on cholesterol-regulating was investigated and their mechanism of action was analyzed. The results showed that aloe-emodin, rhein and emodin were the main cholesterol-regulating components of RhA, and the combination of rhein and emodin showed significant cholesterol-lowering effect, which may be related to the expression of SREBP2, HMGCR and SQLE in the rat liver.

Manganese-induced miR-125b-2-3p promotes anxiety-like behavior via TFR1-mediated ferroptosis.

The toxic effects of excessive manganese (Mn) levels in the environment have led to a severe public health concern. Ferroptosis is a newly form of cell death relying on iron, inherent to pathophysiological processes of psychiatric disorders, such as anxiety and depression-like behaviors. Excessive Mn exposure causes various neurological effects, including neuronal death and mood disorders. Whether Mn exposure causes anxiety and depression-like behaviors, and the underlying mechanisms of Mn-induced ferroptosis have yet to be determined. Here, Mn-exposed mice showed anxiety-like behavior. We also confirmed the accumulation of ferrous ion (Fe2+), lipid peroxidation, and depletion of antioxidant defense system both in vitro and in vivo Mn-exposed models, suggesting that Mn exposure can induce ferroptosis. Furthermore, Mn exposure downregulated the expression of miR-125b-2-3p. In turn, overexpression of miR-125b-2-3p alleviated the Mn-induced ferroptosis by targeting Transferrin receptor protein 1 (TFR1). In summary, this novel study established the propensity of Mn to cause anxiety-like behavior, an effect that was regulated by miR-125b-2-3p and ensuing ferroptosis secondary to the targeting of TFR1. These results offer promising targets for the prevention and treatment of Mn-induced neurotoxicity.

Green-Light GaN p-n Junction Luminescent Particles Enhance the Superconducting Properties of B(P)SCCO Smart Meta-Superconductors (SMSCs).

Superconducting materials exhibit unique physical properties and have great scientific value and vast industrial application prospects. However, due to limitations, such as the critical temperature (TC) and critical current density (JC), the large-scale application of superconducting materials remains challenging. Chemical doping has been a commonly used method to enhance the superconductivity of B(P)SCCO. However, satisfactory enhancement results have been difficult to achieve. In this study, we introduce green-light GaN p-n junction particles as inhomogeneous phases into B(P)SCCO polycrystalline particles to form a smart meta-superconductor (SMSC) structure. Based on the electroluminescence properties of the p-n junction, the Cooper pairs were stimulated and strengthened to enhance the superconductivity of B(P)SCCO. The experimental results demonstrate that the introduction of inhomogeneous phases can indeed enhance the critical temperature TC, critical current density JC, and complete diamagnetism (Meissner effect) of B(P)SCCO superconductors. Moreover, when the particle size of the raw material of B(P)SCCO is reduced from 30 to 5 µm, the grain size of the sintered samples also decreases, and the optimal doping concentration of the inhomogeneous phases increases from 0.15 wt.% to 0.2 wt.%, further improving the superconductivity.

Dormancy release of seeds of Podophyllum hexandrum Royle accompanied by changes in phytochemicals and inorganic elements.

Podophyllum hexandrum Royle is an alpine medicinal plant of considerable importance, and its seed dormancy severely inhibits population renewal. Although cold stratification can break dormancy to a certain extent, the migration and accumulation of phytochemicals and inorganic elements in the seeds during dormancy release and their functions remain unclear. Changes in phytochemicals and inorganic elements in different seed parts were analyzed during dormancy. The key differential phytochemicals and inorganic elements were screened and their association with dormancy release and their roles in dormancy release were explored. The results showed that dormancy release may have occurred following the decrease in palmitic acid and linoleic acid content in the seeds and the increase in 2,3-dihydro-3,5-dihydro-6-methyl-4 (h)-pyran-4-one content in the endosperm. Meanwhile, 6-propyltridecane and hexadecane in the seed coat may enhance the water permeability of seeds to speed up germination. Mg may migrate from the seed coat to the endosperm and seed embryos, whereas Co may migrate from the seed embryo to the seed coat. Ca, Mn, Mg, and Co are involved in various physiological metabolic processes, which may facilitate the dormancy release of P. hexandrum seeds. These findings have enhanced our understanding of the mechanisms of dormancy release in P. hexandrum seeds and can serve as a reference for the development of more effective dormancy-breaking techniques for the conservation of this endangered medicinal plant.

Underwater image enhancement via red channel maximum attenuation prior and multi-scale detail fusion.

The underwater environment poses great challenges, which have a negative impact on the capture and processing of underwater images. However, currently underwater imaging systems cannot adapt to various underwater environments to guarantee image quality. To address this problem, this paper designs an efficient underwater image enhancement approach that gradually adjusts colors, increases contrast, and enhances details. Based on the red channel maximum attenuation prior, we initially adjust the blue and green channels and correct the red channel from the blue and green channels. Subsequently, the maximum and minimum brightness blocks are estimated in multiple channels to globally stretch the image, which also includes our improved guided noise reduction filtering. Finally, in order to amplify local details without affecting the naturalness of the results, we use a pyramid fusion model to fuse local details extracted from two methods, taking into account the detail restoration effect of the optical model. The enhanced underwater image through our method has rich colors without distortion, effectively improved contrast and details. The objective and subjective evaluations indicate that our approach surpasses the state-of-the-art methods currently. Furthermore, our approach is versatile and can be applied to diverse underwater scenes, which facilitates subsequent applications.

Deep learning method of stochastic reconstruction of three-dimensional digital cores from a two-dimensional image.

Digital cores can characterize the true internal structure of rocks at the pore scale. This method has become one of the most effective ways to quantitatively analyze the pore structure and other properties of digital cores in rock physics and petroleum science. Deep learning can precisely extract features from training images for a rapid reconstruction of digital cores. Usually, the reconstruction of three-dimensional (3D) digital cores is performed by optimization using generative adversarial networks. The training data required for the 3D reconstruction are 3D training images. In practice, two-dimensional (2D) imaging devices are widely used because they can achieve faster imaging, higher resolution, and easier identification of different rock phases, so replacing 3D images with 2D ones avoids the difficulty of acquiring 3D images. In this paper, we propose a method, named EWGAN-GP, for the reconstruction of 3D structures from a 2D image. Our proposed method includes an encoder, a generator, and three discriminators. The main purpose of the encoder is to extract statistical features of a 2D image. The generator extends the extracted features into 3D data structures. Meanwhile, the three discriminators have been designed to gauge the similarity of morphological characteristics between cross sections of the reconstructed 3D structure and the real image. The porosity loss function is used to control the distribution of each phase in general. In the entire optimization process, a strategy using Wasserstein distance with gradient penalty makes the convergence of the training process faster and the reconstruction result more stable; it also avoids the problems of gradient disappearance and mode collapse. Finally, the reconstructed 3D structure and the target 3D structure are visualized to ascertain their similar morphologies. The morphological parameter indicators of the reconstructed 3D structure were consistent with those of the target 3D structure. The microstructure parameters of the 3D structure were also compared and analyzed. The proposed method can achieve accurate and stable 3D reconstruction compared with classical stochastic methods of image reconstruction.

Multi-modal cross-attention network for Alzheimer's disease diagnosis with multi-modality data.

Alzheimer's disease (AD) is a neurodegenerative disorder, the most common cause of dementia, so the accurate diagnosis of AD and its prodromal stage mild cognitive impairment (MCI) is significant. Recent studies have demonstrated that multiple neuroimaging and biological measures contain complementary information for diagnosis. Many existing multi-modal models based on deep learning simply concatenate each modality's features despite substantial differences in representation spaces. In this paper, we propose a novel multi-modal cross-attention AD diagnosis (MCAD) framework to learn the interaction between modalities for better playing their complementary roles for AD diagnosis with multi-modal data including structural magnetic resonance imaging (sMRI), fluorodeoxyglucose-positron emission tomography (FDG-PET) and cerebrospinal fluid (CSF) biomarkers. Specifically, the imaging and non-imaging representations are learned by the image encoder based on cascaded dilated convolutions and CSF encoder, respectively. Then, a multi-modal interaction module is introduced, which takes advantage of cross-modal attention to integrate imaging and non-imaging information and reinforce relationships between these modalities. Moreover, an extensive objective function is designed to reduce the discrepancy between modalities for effectively fusing the features of multi-modal data, which could further improve the diagnosis performance. We evaluate the effectiveness of our proposed method on the ADNI dataset, and the extensive experiments demonstrate that our MCAD achieves superior performance for multiple AD-related classification tasks, compared to several competing methods. Also, we investigate the importance of cross-attention and the contribution of each modality to the diagnostics performance. The experimental results demonstrate that combining multi-modality data via cross-attention is helpful for accurate AD diagnosis.

TREM2 expression on the microglia resolved lead exposure-induced neuroinflammation by promoting anti-inflammatory activities.

Neurotoxicity caused by environmental lead (Pb) pollution is a worldwide public health concern, and developing a therapeutic strategy against Pb-induced neurotoxicity is an important area in the current research. Our prior research has demonstrated the significant involvement of microglia-mediated inflammatory responses in the manifestation of Pb-induced neurotoxicity. Additionally, the suppression of proinflammatory mediator activity significantly mitigated the toxic effects associated with Pb exposure. Recent studies have highlighted the critical role of the triggering receptor expressed on myeloid cells 2 (TREM2) in the pathogenesis of neurodegenerative disorders. TREM2 exerted protective effects on inflammation, but whether TREM2 is involved in Pb-induced neuroinflammation is poorly understood. In the present study, cell culture experiments and animal models were designed to investigate the role of TREM2 in Pb's neuroinflammation. We examined the impact of pro- and anti-inflammatory cytokines involved in Pb-induced neuroinflammation. Flow cytometry and microscopy techniques were applied to detect microglia phagocytosis and migration ability. Our results showed that Pb treatment significantly downregulated TREM2 expression and altered the localization of TREM2 expression in microglia. The protein expression of TREM2 was restored, and the inflammatory responses provoked by Pb exposure were ameliorated upon the overexpression of TREM2. Furthermore, the phagocytosis and migratory capabilities of microglia, which were impaired due to Pb exposure, were alleviated by TREM2 overexpression. Our in vitro findings were corroborated in vivo, demonstrating that TREM2 regulates the anti-inflammatory functions of microglia, thereby mitigating Pb-induced neuroinflammation. Our results provide insights into the detailed mechanism by which TREM2 alleviates Pb-induced neuroinflammation and suggest that activating the anti-inflammatory functions of TREM2 may represent a potential therapeutic strategy against environmental Pb-induced neurotoxicity.

Sesamol alleviates manganese-induced neuroinflammation and cognitive impairment via regulating the microglial cGAS-STING/NF-κB pathway.

Toxic effects of excessive manganese (Mn) from occupational or environmental exposure cause harm to human health. Excessive Mn exposure is intimately associated with neurodegeneration and cognitive dysfunction. Inflammatory responses mediated by microglia are essential contributors to the pathogenesis of Mn-induced neurotoxicity. Inhibition of microglia-mediated inflammation has been shown to alleviate Mn-induced neurotoxicity. Sesamol, derived from sesame, has neuroprotective properties in various disease models, including neurological diseases. Whether sesamol protects against Mn-induced neurological injuries has not been determined. Here, both in vivo and in vitro Mn exposure models were established to address the beneficial effects of sesamol on Mn-induced neurotoxicity. We showed that administration of sesamol mitigated learning and memory deficits of mice treated by Mn. Furthermore, sesamol reduced Mn-induced microglial activation and the expression of proinflammatory mediators (TNF-α, iNOS, and Cxcl10), while exerting a marginal effect on anti-inflammation and microglial phagocytosis. Mn exposure activated the microglial cGAS-STING pathway and sesamol inhibited this pathway by reducing the phosphorylation of STING and NF-κB, concomitantly decreasing IFN-α and IFN-ß synthesis. In summary, our novel results indicated that sesamol exerted its protective effects on Mn-induced neuroinflammation and cognitive impairment via the microglial cGAS-STING/NF-κB pathway, providing evidence that sesamol may serve as an effective therapeutic for preventing and treating Mn-induced neurotoxicity.

MAGE: Multi-scale Context-aware Interaction based on Multi-granularity Embedding for Chinese Medical Question Answer Matching.

BACKGROUND AND OBJECTIVE: The Chinese medical question answer matching (cMedQAM) task is the essential branch of the medical question answering system. Its goal is to accurately choose the correct response from a pool of candidate answers. The relatively effective methods are deep neural network-based and attention-based to obtain rich question-and-answer representations. However, those methods overlook the crucial characteristics of Chinese characters: glyphs and pinyin. Furthermore, they lose the local semantic information of the phrase by generating attention information using only relevant medical keywords. To address this challenge, we propose the multi-scale context-aware interaction approach based on multi-granularity embedding (MAGE) in this paper. METHODS: We adapted ChineseBERT, which integrates Chinese characters glyphs and pinyin information into the language model and fine-tunes the medical corpus. It solves the common phenomenon of homonyms in Chinese. Moreover, we proposed a context-aware interactive module to correctly align question and answer sequences and infer semantic relationships. Finally, we utilized the multi-view fusion method to combine local semantic features and attention representation. RESULTS: We conducted validation experiments on the three publicly available datasets, namely cMedQA V1.0, cMedQA V2.0, and cEpilepsyQA. The proposed multi-scale context-aware interaction approach based on the multi-granularity embedding method is validated by top-1 accuracy. On cMedQA V1.0, cMedQA V2.0, and cEpilepsyQA, the top-1 accuracy on the test dataset was improved by 74.1%, 82.7%, and 60.9%, respectively. Experimental results on the three datasets demonstrate that our MAGE achieves superior performance over state-of-the-art methods for the Chinese medical question answer matching tasks. CONCLUSIONS: The experiment results indicate that the proposed model can improve the accuracy of the Chinese medical question answer matching task. Therefore, it may be considered a potential intelligent assistant tool for the future Chinese medical answer question system.

A new approach for social group detection based on spatio-temporal interpersonal distance measurement.

Visual-based social group detection aims to cluster pedestrians in crowd scenes according to social interactions and spatio-temporal position relations by using surveillance video data. It is a basic technique for crowd behaviour analysis and group-based activity understanding. According to the theory of proxemics study, the interpersonal relationship between individuals determines the scope of their self-space, while the spatial distance can reflect the closeness degree of their interpersonal relationship. In this paper, we proposed a new unsupervised approach to address the issues of interaction recognition and social group detection in public spaces, which remits the need to intensely label time-consuming training data. First, based on pedestrians' spatio-temporal trajectories, the interpersonal distances among individuals were measured from static and dynamic perspectives. Combined with proxemics' theory, a social interaction recognition scheme was designed to judge whether there is a social interaction between pedestrians. On this basis, the pedestrians are clustered to identify if they form a social group. Extensive experiments on our pedestrian dataset "SCU-VSD-Social" annotated with multi-group labels demonstrated that the proposed method has outstanding performance in both accuracy and complexity.

Medical visual question answering based on question-type reasoning and semantic space constraint.

Medical visual question answering (Med-VQA) aims to accurately answer clinical questions about medical images. Despite its enormous potential for application in the medical domain, the current technology is still in its infancy. Compared with general visual question answering task, Med-VQA task involve more demanding challenges. First, clinical questions about medical images are usually diverse due to different clinicians and the complexity of diseases. Consequently, noise is inevitably introduced when extracting question features. Second, Med-VQA task have always been regarded as a classification problem for predefined answers, ignoring the relationships between candidate responses. Thus, the Med-VQA model pays equal attention to all candidate answers when predicting answers. In this paper, a novel Med-VQA framework is proposed to alleviate the above-mentioned problems. Specifically, we employed a question-type reasoning module severally to closed-ended and open-ended questions, thereby extracting the important information contained in the questions through an attention mechanism and filtering the noise to extract more valuable question features. To take advantage of the relational information between answers, we designed a semantic constraint space to calculate the similarity between the answers and assign higher attention to answers with high correlation. To evaluate the effectiveness of the proposed method, extensive experiments were conducted on a public dataset, namely VQA-RAD. Experimental results showed that the proposed method achieved better performance compared to other the state-of-the-art methods. The overall accuracy, closed-ended accuracy, and open-ended accuracy reached 74.1 %, 82.7 %, and 60.9 %, respectively. It is worth noting that the absolute accuracy of the proposed method improved by 5.5 % for closed-ended questions.

An Improved Smart Meta-Superconductor MgB2.

Increasing and improving the critical transition temperature (TC), current density (JC) and the Meissner effect (HC) of conventional superconductors are the most important problems in superconductivity research, but progress has been slow for many years. In this study, by introducing the p-n junction nanostructured electroluminescent inhomogeneous phase with a red wavelength to realize energy injection, we found the improved property of smart meta-superconductors MgB2, the critical transition temperature TC increases by 0.8 K, the current density JC increases by 37%, and the diamagnetism of the Meissner effect HC also significantly improved, compared with pure MgB2. Compared with the previous yttrium oxide inhomogeneous phase, the p-n junction has a higher luminescence intensity, a longer stable life and simpler external field requirements. The coupling between superconducting electrons and surface plasmon polaritons may be explained by this phenomenon. The realization of smart meta-superconductor by the electroluminescent inhomogeneous phase provides a new way to improve the performance of superconductors.

Low-Complexity Multiple Transform Selection Combining Multi-Type Tree Partition Algorithm for Versatile Video Coding.

Despite the fact that Versatile Video Coding (VVC) achieves a superior coding performance to High-Efficiency Video Coding (HEVC), it takes a lot of time to encode video sequences due to the high computational complexity of the tools. Among these tools, Multiple Transform Selection (MTS) require the best of several transforms to be obtained using the Rate-Distortion Optimization (RDO) process, which increases the time spent video encoding, meaning that VVC is not suited to real-time sensor application networks. In this paper, a low-complexity multiple transform selection, combined with the multi-type tree partition algorithm, is proposed to address the above issue. First, to skip the MTS process, we introduce a method to estimate the Rate-Distortion (RD) cost of the last Coding Unit (CU) based on the relationship between the RD costs of transform candidates and the correlation between Sub-Coding Units' (sub-CUs') information entropy under binary splitting. When the sum of the RD costs of sub-CUs is greater than or equal to their parent CU, the RD checking of MTS will be skipped. Second, we make full use of the coding information of neighboring CUs to terminate MTS early. The experimental results show that, compared with the VVC, the proposed method achieves a 26.40% reduction in time, with a 0.13% increase in Bjøontegaard Delta Bitrate (BDBR).

Multi-scale discriminative regions analysis in FDG-PET imaging for early diagnosis of Alzheimer's disease.

Objective. Alzheimer's disease (AD) is a degenerative brain disorder, one of the main causes of death in elderly people, so early diagnosis of AD is vital to prompt access to medication and medical care. Fluorodeoxyglucose positron emission tomography (FDG-PET) proves to be effective to help understand neurological changes via measuring glucose uptake. Our aim is to explore information-rich regions of FDG-PET imaging, which enhance the accuracy and interpretability of AD-related diagnosis.Approach. We develop a novel method for early diagnosis of AD based on multi-scale discriminative regions in FDG-PET imaging, which considers the diagnosis interpretability. Specifically, a multi-scale region localization module is discussed to automatically identify disease-related discriminative regions in full-volume FDG-PET images in an unsupervised manner, upon which a confidence score is designed to evaluate the prioritization of regions according to the density distribution of anomalies. Then, the proposed multi-scale region classification module adaptively fuses multi-scale region representations and makes decision fusion, which not only reduces useless information but also offers complementary information. Most of previous methods concentrate on discriminating AD from cognitively normal (CN), while mild cognitive impairment, a transitional state, facilitates early diagnosis. Therefore, our method is further applied to multiple AD-related diagnosis tasks, not limited to AD vs. CN.Main results. Experimental results on the Alzheimer's Disease Neuroimaging Initiative dataset show that the proposed method achieves superior performance over state-of-the-art FDG-PET-based approaches. Besides, some cerebral cortices highlighted by extracted regions cohere with medical research, further demonstrating the superiority.Significance. This work offers an effective method to achieve AD diagnosis and detect disease-affected regions in FDG-PET imaging. Our results could be beneficial for providing an additional opinion on the clinical diagnosis.

Critical Current Density and Meissner Effect of Smart Meta-Superconductor MgB2 and Bi(Pb)SrCaCuO.

The smart meta-superconductor MgB2 and Bi(Pb)SrCaCuO increase the superconducting transition temperature (TC), but the changes in the transport critical current density (JC) and Meissner effect are still unknown. Here, we investigated the JC and Meissner effect of smart meta-superconductor MgB2 and Bi(Pb)SrCaCuO. The use of the standard four-probe method shows that Y2O3:Eu3+ and Y2O3:Eu3++Ag inhomogeneous phase significantly increase the JC, and JC decreases to a minimum value at a higher temperature. The Meissner effect was measured by direct current magnetization. The doping of Y2O3:Eu3+ and Y2O3:Eu3++Ag luminescent inhomogeneous phase causes a Meissner effect of MgB2 and Bi(Pb)SrCaCuO at a higher temperature, while the non-luminescent dopant reduces the temperature at which samples have Meissner effect. The introduction of luminescent inhomogeneous phase in conventional MgB2 and copper oxide high-temperature Bi(Pb)SrCaCuO superconductor increases the TC and JC, and Meissner effect is exerted at higher temperature. Therefore, smart meta-superconductivity is suitable for conventional and copper oxide high-temperature superconductors.

A Feature-Enriched Deep Convolutional Neural Network for JPEG Image Compression Artifacts Reduction and its Applications.

The amount of multimedia data, such as images and videos, has been increasing rapidly with the development of various imaging devices and the Internet, bringing more stress and challenges to information storage and transmission. The redundancy in images can be reduced to decrease data size via lossy compression, such as the most widely used standard Joint Photographic Experts Group (JPEG). However, the decompressed images generally suffer from various artifacts (e.g., blocking, banding, ringing, and blurring) due to the loss of information, especially at high compression ratios. This article presents a feature-enriched deep convolutional neural network for compression artifacts reduction (FeCarNet, for short). Taking the dense network as the backbone, FeCarNet enriches features to gain valuable information via introducing multi-scale dilated convolutions, along with the efficient 1 ×1 convolution for lowering both parameter complexity and computation cost. Meanwhile, to make full use of different levels of features in FeCarNet, a fusion block that consists of attention-based channel recalibration and dimension reduction is developed for local and global feature fusion. Furthermore, short and long residual connections both in the feature and pixel domains are combined to build a multi-level residual structure, thereby benefiting the network training and performance. In addition, aiming at reducing computation complexity further, pixel-shuffle-based image downsampling and upsampling layers are, respectively, arranged at the head and tail of the FeCarNet, which also enlarges the receptive field of the whole network. Experimental results show the superiority of FeCarNet over state-of-the-art compression artifacts reduction approaches in terms of both restoration capacity and model complexity. The applications of FeCarNet on several computer vision tasks, including image deblurring, edge detection, image segmentation, and object detection, demonstrate the effectiveness of FeCarNet further.

Schisandrin B promotes TH1 cell differentiation by targeting STAT1.

Schisandrin B (Sch B) is the major active ingredient of the traditional Chinese medicine Schisandra chinensis and has antitumor activity, anti-inflammatory activity. CD4+ Th subsets orchestrate immune responses to plenty of pathogen infections and participate in the pathogenesis of many immune-related diseases. However, little is known about the relationship between Sch B and T cell differentiation. Here, we showed that Sch B might participate in T cell receptor signaling pathway by using the TCMIO database. Importantly, Sch B promoted TH1 cell differentiation. Furthermore, Sch B did not affect TH2 cell and Treg differentiation. Mechanismly, Sch B increased the level of IFN-γ of CD4+ T cells by upregulating the phosphorylation of STAT1 protein. Then, STAT1 promoted T-bet expression in CD4+ T cells. In conclusion, Sch B modulates the differentiation of naïve CD4+ T cells into TH1 subset by STAT1/T-bet signaling, which may have the potential for the treatment of T cell-mediated-immune diseases.