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Objectives: The available infiltration resin has raised biosafety and treatment stability concerns because of the cytotoxicity of the main component, TEGDMA, and its susceptibility to hydrolysis in the oral environment. This study aimed to develop a TEGDMA-free infiltration resin to overcome these drawbacks.Methods: Using the synthetic bioderived monomer bis(methacrylate) isosorbide (IBM) and the zwitterionic compound 2-methacryloyloxyethyl phosphorylcholine (MPC), a novel infiltrant IBMA was developed and preferentially selected. We investigated the performance of the IBMA resin regarding cytotoxicity, antibiofilm adhesion, and hydrolysis resistance and further verified its ability to restore the demineralized enamel and stability of the infiltrated area under artificial aging conditions.Results: Compared with the commercial TEGDMA-based infiltration resin ICON, IBMA not only demonstrated similar enamel morphologic and esthetic restorative effects in chalky lesions but also exhibited favorable cell viability, durable Streptococcus mutans UA159 biofilm-repellent performance, and higher enamel microhardness (204.0 ± 5.12 HV) of the infiltrated enamel. Specifically, because of the high crosslink density [(47.77 ± 5.76) ×103 mol/mm3] and low water sorption [12.79 ± 2.56 µg/mm3] of the polymer network, the IBMA resin was more resistant to hydrolysis than ICON, which prevents the disruption of the infiltrant’s micropore-blocking effect after aging. Enamel lesions treated with IBMA demonstrated good color stability after the tea-staining challenge, which was significantly better than that in the ICON group.Conclusion: Based on these findings, the IBMA resin exhibits favorable cell viability, hydrolysis resistance, and biofilm-repellent properties, which alleviates the defects of traditional TEGDMA systems. Therefore, it is a better alternative for microinvasive treatment involving early caries and enamel whitish discoloration.

Global solar radiation (GSR) reaching the Earth’s surface can directly alter the energy balance of the climate system. The potential influence of inhomogeneity issues on Chinese GSR records on dimming and brightening in China has been argued for years. In this study, we explored the temporal evolution and spatial distribution of the long-term trend of GSR in eastern China (EC) during 1961–2018 using a homogenized GSR dataset, which removed the artificial signals. Results demonstrate that EC experienced a significant dimming period from 1961 to 1999, a brightening period from 2010 until 2018 and a leveling-off period between them (2000–2010), which resulted from opposite trends of GSR in the northern (dimming) and southern (brightening) regions of EC. The reduction of cloud coverage after the dimming period (2000) induced an early coming brightening in the southern region of China between 2000 and 2010, while the increase in aerosols and the rapidly extending cloud fraction resulted in the continuing dimming in northern China.

Although immune response enhancement has been reported after primary and booster vaccines of CoronaVac, neutralization breadth of SARS-CoV-2 variants is still unclear. In the present study, we examined the neutralization magnitude and breadth of SARS-CoV-2 variants including Beta (B.1.351), Delta (B.1.617.2) and Omicron (B.1.1.529) in 33 convalescent COVID-19 patients and a cohort of 55 medical staff receiving primary CoronaVac vaccines and an additional homologous booster dose. Results showed that, as compared with the two-dose primary vaccination, the homologous booster dose achieved 2.24-, 3.98-, 4.58- and 2.90-fold increase in neutralization titer against wild-type, Beta, Delta, and Omicron, respectively. After booster dose, neutralization titer reduction for variants was less than that after the primary vaccine or that for convalescents. The proportion of recipients able to neutralize 2 or more variants increased from 36.36% post the primary vaccination to 87.27% after the booster. Significant increase in neutralization breadth of 1.24 (95% confidence interval (CI), 0.89–1.59) variants was associated with a log10 increase in neutralization titer against the wild-type. In addition, anti-RBD IgG level was identified as an excellent surrogate for positive neutralization of SARS-CoV-2 and neutralization breadth of variants. These findings highlight the value of an additional homologous CoronaVac dose in broadening the cross-neutralization against SARS-CoV-2 variants, and are critical for informing the booster dose vaccination efforts.

Numerous floodplain lakes in the world have been shrinking and gradually transformed into semi-closed reservoirs under the control of sluices for the past century, which has led to substantial ecosystem services loss. In this study, historical topographic maps 1930s and 1970s and remote sensing images (1980s–2020s) are used to reconstruct the centennial evolution characteristics of typical lakes in the Central Yangtze Ecoregion, i.e., the Wanjiang lake group. Moreover, we focus on their driving factors from the perspective of sluice control and agricultural activities. Results showed that the lake wetland area in the Wanjiang Plain shrank remarkably in the 1930s–2020s, with an overall loss rate of 45.32 %, of which 84.19 % was mainly in the 1930s–1970s. The wetland loss was accompanied by the dispersion of large lakes, the extinction and newbirth of small lakes. The uneven distribution of the wetland loss in space led to the overall migration of the lakes to the Yangtze River. These results can be explained as follows: from the 1950s to the 1970s, sluices were built on waterways between 91.67 % of lakes and the Yangtze River for flood mitigation. The flood risk control further led to the surge of building state-owned farms and personal polders in the local area from the 1950s to the 1980s, which was the primary cause for the loss of lake wetlands and the regularization of lake shorelines. Since the 1980s, lake protection measures have promoted the restoration of wetlands and reduced the rate of lake shrinkage to a certain extent. Our findings can provide important guidance for the sustainable management and possible hydrological connectivity restoration project in the middle and lower reaches of the Yangtze River.

A novel instance-based algorithm for pattern classification is presented and evaluated in this paper. This new method is motivated by the challenge of pattern classifications where only limited and/or noisy training data are available. For every classification, the proposed system transforms the query data and the training templates based on their distributions in the feature space. One of the major novelties of the proposed method is the concept of template reconstruction enabling improved performance with limited training data. The technique is compared with similar algorithms and evaluated using both the image and time-series modalities to demonstrate its effectiveness and versatility. Two public image databases, FASHION-MNIST and CIFAR-10, were used to test its effectiveness for the classification of images using small amounts of training samples. An average classification improvement of 2~3% was observed while using a small subset of the training database, compared to the performances achieved by state-of-the-art techniques using the full datasets. To further explore its capability in solving more challenging classification problems such as non-stationary time-series electroencephalography (EEG) signals, a clinical grade 64-electrode EEG database, as well as a low-quality (high-noise level) EEG database, obtained using a low-cost system equipped with a single dry sensor, have also been used to test the algorithm. Adaptive reconstruction of the feature instances has been seen to have substantially improved class separation and matching performance for both still images and time-series signals. In particular, the method is found to be effective for the classification of noisy non-stationary data with limited training data volumes, indicating its potential suitability for a wide range of applications.

Abstract Background Accumulative evidence shows that an organoid is a more practical and reliable tool in cancer biology research. This study aimed to identify and validate crucial genes involved in non-small cell lung cancer carcinogenesis and development using the transcriptomic analysis of tumor tissues and organoids. Methods Gene set enrichment analysis (GSEA) of tumor tissues, tumor organoids, and normal tissues was performed to reveal the similar and different mechanisms involved in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) carcinogenesis and progression. Differentially expressed gene analysis, prognostic analysis, and gene co-expression network analysis were further used to identify hub genes involved in LUAD and LUSC carcinogenesis and development. Finally, LUAD cell lines and organoids were used to validate these findings. Results GSEA analysis was performed to reveal the similar mechanisms involved in LUAD and LUSC carcinogenesis and development, such as P53 signaling pathway, base mismatch repair, DNA replication, cAMP signaling pathway and PPAR pathway. However, comparing with LUSC organoids, LUAD organoids showed downregulation of immune-related pathways, inflammation-related pathways, MAPK signaling pathways, and Rap1 signaling pathways, although these pathways were downregulated in LUAD and LUSC tissues by comparing with normal lung tissues. Further gene co-expression network analysis and prognostic analysis indicated CDK1, CCNB2, and CDC25A as the hub tumor-promoting genes in LUAD but not in LUSC, which were further validated in other datasets. Using LUAD cell lines and organoid models, CDK1 and CCNB2 knockdown were found to suppress LUAD proliferation. However, CDC25A knockdown did not inhibit LUAD cell line proliferation but could effectively suppress LUAD organoid growth, indicating that an organoid could be used as an effective tool to study cancer biology in LUAD. Conclusions The results revealed CDK1, CCNB2, and CDC25A as the hub genes involved in LUAD carcinogenesis and development, which could be used as the potential biomarkers and targets for LUAD.

Abstract The purpose of this study was to directly and quantitatively measure BMD from Cone-beam CT (CBCT) images by enhancing the linearity and uniformity of the bone intensities based on a hybrid deep-learning model (QCBCT-NET) of combining the generative adversarial network (Cycle-GAN) and U-Net, and to compare the bone images enhanced by the QCBCT-NET with those by Cycle-GAN and U-Net. We used two phantoms of human skulls encased in acrylic, one for the training and validation datasets, and the other for the test dataset. We proposed the QCBCT-NET consisting of Cycle-GAN with residual blocks and a multi-channel U-Net using paired training data of quantitative CT (QCT) and CBCT images. The BMD images produced by QCBCT-NET significantly outperformed the images produced by the Cycle-GAN or the U-Net in mean absolute difference (MAD), peak signal to noise ratio (PSNR), normalized cross-correlation (NCC), structural similarity (SSIM), and linearity when compared to the original QCT image. The QCBCT-NET improved the contrast of the bone images by reflecting the original BMD distribution of the QCT image locally using the Cycle-GAN, and also spatial uniformity of the bone images by globally suppressing image artifacts and noise using the two-channel U-Net. The QCBCT-NET substantially enhanced the linearity, uniformity, and contrast as well as the anatomical and quantitative accuracy of the bone images, and demonstrated more accuracy than the Cycle-GAN and the U-Net for quantitatively measuring BMD in CBCT.

Spiking Neural Networks (SNNs) are often considered the third generation of Artificial Neural Networks (ANNs), owing to their high information processing capability and the accurate simulation of biological neural network behaviors. Though the research for SNNs has been quite active in recent years, there are still some challenges to applying SNNs to various potential applications, especially for robot control. In this study, a biologically inspired autonomous learning algorithm based on reward modulated spike-timing-dependent plasticity is proposed, where a novel rewarding generation mechanism is used to generate the reward signals for both learning and decision-making processes. The proposed learning algorithm is evaluated by a mobile robot obstacle avoidance task and experimental results show that the mobile robot with the proposed algorithm exhibits a good learning ability. The robot can successfully avoid obstacles in the environment after some learning trials. This provides an alternative method to design and apply the bio-inspired robot with autonomous learning capability in the typical robotic task scenario.

Abstract Background Despite numerous chimeric antigen receptor T‐cell (CAR‐T) trials conducted in China, no CAR‐T has been registered in the country. Furthermore, China law and regulations restrict the export of patient material for CAR‐T manufacture abroad. Relma‐cel (JWCAR029), an anti‐CD19 product produced with a commercial‐ready process in China, was evaluated in the first prospective, single‐arm, multicenter, pivotal study of CAR‐T therapy conducted under Chinese IND to support an NMPA‐accepted BLA submission in relapsed/refractory (r/r) LBCL (NCT04089215). Methods Patients were randomized to receive either 100 × 106 (low dose, n = 27) or 150 × 106 (high dose, n = 32) CAR+ T‐cells as a single infusion following lymphodepleting chemotherapy (fludarabine 25 mg/m2 and cyclophosphamide 250 mg/m2 daily × 3), and then, monitored for efficacy and safety outcomes and pharmacokinetics. The primary endpoint was ORR at 3 months, as assessed by the investigators. Secondary endpoints included DOR, PFS, OS, and adverse event frequency/severity and cell expansion kinetics. Results As of the data cutoff on 17 June 2020, 68 patients were enrolled, and 59 were treated. Among the 58 efficacy‐evaluable patients, the primary endpoint of 3 month ORR was 60.3% (95% CI, 46.6–73.0), excluding the null hypothesis rate of 20%. Any grade and severe grade CRS occurred in 47.5% and 5.1%, respectively, and any grade and severe grade neurotoxicity events occurred in 20.3% and 5.1%. Conclusions Relma‐cel met the primary endpoint analysis and demonstrated a high rate of durable responses and low rate of CAR‐T‐associated toxicities in patients with r/r LBCL in a multicenter trial supporting regulatory submission in China.

Abstract. Parkinson disease (PD) is the second-most common neurodegenerative disorder. Its main pathological mechanism is the selective degeneration and deletion of dopaminergic neurons in the dense part of the substantia nigra and the damage of dopaminergic neurons caused by the abnormal deposition of a Lewy body, leading to a decreased dopamine level. Positron emission computed tomography (PET)/single photon emission computed tomography (SPECT) is a molecular imaging technology that can directly or indirectly reflect changes in molecular levels by using a specific tracer. With the research and development on the tracers of related enzymes for labeling dopamine transporter and dopamine receptor and for being involved in dopamine formation, this imaging technology has been applied to all aspects of PD research. It not only contributes to clinical work but also provides an important theoretical basis for exploring the pathological mechanism of PD at a molecular level. Therefore, this review discusses the application value of PET/SPECT in PD in terms of early diagnosis, disease severity evaluation, clinical manifestations, differential diagnosis, and pathological mechanism.

The mechanisms by which mutant Huntington protein Htt leads to selective neurodegeneration are not fully understood. Here, using gene editing in HD140Q knock-in mice, the authors show that exon1 Htt is a critical pathological form of the protein.

Abstract Background To investigate potential effects of herbicide phytotoxic on crops, a major challenge is a lack of non-destructive and rapid methods to detect plant growth that could allow characterization of herbicide-resistant plants. In such a case, hyperspectral imaging can quickly obtain the spectrum for each pixel in the image and monitor status of plants harmlessly. Method Hyperspectral imaging covering the spectral range of 380–1030 nm was investigated to determine the herbicide toxicity in rice cultivars. Two rice cultivars, Xiushui 134 and Zhejing 88, were respectively treated with quinclorac alone and plus salicylic acid (SA) pre-treatment. After ten days of treatments, we collected hyperspectral images and physiological parameters to analyze the differences. The score images obtained were used to explore the differences among samples under diverse treatments by conducting principal component analysis on hyperspectral images. To get useful information from original data, feature extraction was also conducted by principal component analysis. In order to classify samples under diverse treatments, full-spectra-based support vector classification (SVC) models and extracted-feature-based SVC models were established. The prediction maps of samples under different treatments were constructed by applying the SVC models using extracted features on hyperspectral images, which provided direct visual information of rice growth status under herbicide stress. The physiological analysis with the changes of stress-responsive enzymes confirmed the differences of samples under different treatments. Results The physiological analysis showed that SA alleviated the quinclorac toxicity by stimulating enzymatic activity and reducing the levels of reactive oxygen species. The score images indicated there were spectral differences among the samples under different treatments. Full-spectra-based SVC models and extracted-feature-based SVC models obtained good results for the aboveground parts, with classification accuracy over 80% in training, validation and prediction set. The SVC models for Zhejing 88 presented better results than those for Xiushui 134, revealing the different herbicide tolerance between rice cultivars. Conclusion We develop a reliable and effective model using hyperspectral imaging technique which enables the evaluation and visualization of herbicide toxicity for rice. The reflectance spectra variations of rice could reveal the stress status of herbicide toxicity in rice along with the physiological parameters. The visualization of the herbicide toxicity in rice would help to provide the intuitive vision of herbicide toxicity in rice. A monitoring system for detecting herbicide toxicity and its alleviation by SA will benefit from the remarkable success of SVC models and distribution maps.

It is not yet clear how ubiquitously-expressed proteins can cause the selective degeneration of particular populations of neurons, such as in spinocerebellar ataxia type 17, SCA17, which results from a CAG trinucleotide repeat expansion in the ubiquitously expressed transcription factor TBP. Here, the authors show that mutant TBP suppresses the cerebellum-enriched transcription of Inpp5a and link altered levels of INPP5A to the selective degeneration of cerebellar neurons.

One major reason for the success of convolutional neural networks (CNNs) is the availability of large-scale labeled data. Effective training of CNNs relies on large annotated data. Unfortunately, large amounts of data with corresponding annotations are too expensive to obtain in some real-world applications. One reasonable alternative is to use data augmentation techniques to automatically generate annotated samples. In this paper, a novel data augmentation framework based on perspective transformation is proposed. This method automatically generates new annotated data without extra manual labeling, thus effectively extends the inadequate dataset. Perspective transformation can produce new images captured from any cameras viewpoints. Therefore, our method can mimic images taken at the angle that the camera cannot reach. Extensive experimental results on several datasets have demonstrated that our perspective transformation data augmentation strategy is an effective tool when using deep CNNs on small or imbalance datasets.

Aiming at the problems of eutrophication of water bodies and the shortage of phosphorus resources, the struvite method for treating high-concentration nitrogen and phosphorus-containing wastewater has been widely used because of its high efficiency and low consumption. This paper summarizes the research progress on the treatment efficiency, influencing factors and stability of high concentration nitrogen and phosphorus mixed wastewater in recent years, and on this basis, the feasibility and development prospect of MAP method in the treatment of high concentration nitrogen and phosphorus wastewater are proposed.

In the cultural heritage field, point clouds, as important raw data of geomatics, are not only three-dimensional (3D) spatial presentations of 3D objects but they also have the potential to gradually advance towards an intelligent data structure with scene understanding, autonomous cognition, and a decision-making ability. The approach of point cloud semantic segmentation as a preliminary stage can help to realize this advancement. With the demand for semantic comprehensibility of point cloud data and the widespread application of machine learning and deep learning approaches in point cloud semantic segmentation, there is a need for a comprehensive literature review covering the topics from the point cloud data acquisition to semantic segmentation algorithms with application strategies in cultural heritage. This paper first reviews the current trends of acquiring point cloud data of cultural heritage from a single platform with multiple sensors and multi-platform collaborative data fusion. Then, the point cloud semantic segmentation algorithms are discussed with their advantages, disadvantages, and specific applications in the cultural heritage field. These algorithms include region growing, model fitting, unsupervised clustering, supervised machine learning, and deep learning. In addition, we summarized the public benchmark point cloud datasets related to cultural heritage. Finally, the problems and constructive development trends of 3D point cloud semantic segmentation in the cultural heritage field are presented.

Recently, there is a rising interest in perceiving image aesthetics. The existing works deal with image aesthetics as a classification or regression problem. To extend the cognition from rating to reasoning, a deeper understanding of aesthetics should be based on revealing why a high‐ or low‐aesthetic score should be assigned to an image. From such a point of view, the authors propose a model referred to as Neural Aesthetic Image Reviewer, which can not only give an aesthetic score for an image, but also generate a textual description explaining why the image leads to a plausible rating score. Specifically, they propose three models based on shared aesthetically semantic layers and task‐specific embedding layers at a high level for performance improvement on different tasks. To facilitate researches on this problem, they collect the AVA‐Reviews dataset, which contains 52,118 images and 312,708 comments in total. Through multi‐task learning, the proposed models can rate aesthetic images as well as produce comments in an end‐to‐end manner. It is confirmed that the proposed models outperform the baselines according to the performance evaluation on the AVA‐Reviews dataset. Moreover, they demonstrate experimentally that the authors’ model can generate textual reviews related to aesthetics, which are consistent with human perception.

Matrix-related pores play a significant role in controlling hydrocarbon production in organic-rich shales. Multiple matrix-related pore types of typical marine shales in the Sichuan Basin have been visually investigated and identified by transmission electron microscopy (TEM) on ultra-thin sections and by focused ion beam-scanning electron microscopy (FIBSEM) on polished sections. OM-hosted pores seem universal and range in sizes from below 1 nm to hundreds of nanometers and they are not homogeneously developed and distributed, which is mainly determined by thermal maturity and OM composition. Mineral-hosted pores are defined by mineral frameworks and occur in open spaces related to ductile or rigid grain fabric. The four porous mineral types that occur are clay intrapores, carbonate solvopores, pyrite interpores, and quartz interpores, and they range in size from less than 1 nm to more than several microns. Aggregate-hosted pores are predominantly associated with clay-organic aggregates, pyrite-organic aggregates, clay-pyrite aggregates, and clay-organic-pyrite aggregates. The most common aggregate-hosted pore networks are defined by clay-organic aggregates, and the pores are largely developed between the clay and organic layers and may be the important adsorption spaces for methane. Fracture-related pores include microchannels and microfractures of various sizes and shapes and they could play a key role in providing hydrocarbon migration pathways. FIBSEM and TEM show direct evidence that OM-hosted pores and fracture-related pores contribute more to the effective pore network and the excellent reservoir quality, whereas poor reservoir quality may come from aggregate-hosted pores.

Allopolyploids play an essential role in plant evolution and confer apparent advantages on crop growth and breeding compared to low ploidy levels. A doubled haploid (DH) population derived from the cross between two artificially synthesized allohexaploid Brassica was created and self-crossed continuously. Morphological and yield-related traits showed considerable variation among different generations, different families and even within the same families. However, the flowering time, pollen viability and seed yield increased gradually during the selfing process. Ploidy level estimation and karyotyping analysis revealed that this population was chimeras with varied chromosome numbers within an identical plant. Chromosome translocations analysis showed that the B genome was more instable compared to the A and C genomes. The A genome was more prone to chromosome recombination than the C genome. Although some genomic regions were more likely to be duplicated, deleted, or rearranged, a consensus pattern was not shared between different progenies. This research deepened our understanding of the genetic variation of artificially synthesized allohexaploid Brassica. In addition, the allohexaploid Brassica can be used as a bridge to transfer some of the valuable traits blocked by reproductive barriers from wild Brassica species to cultivated species such as cold and drought resistance, etc.

Three-dimensional point cloud has been widely used in the cultural heritage field in the last two decades, gaining attention from both academic and industry communities. A large number of scientific papers have been published concerning this topic, which covers a wide range of journals, countries, and disciplines. There has been no comprehensive and systematic survey of recent literature performed in a scientometric way based on the complex network analysis methods. In this work, we extracted the terms (i.e., noun phrases included in the title, abstract and keywords), the documents, the countries that the research institutions are located in, and the categories that the literature belongs to from the Web of Science database to compose a term co-occurrence network, document co-citation network, collaborative country network and category co-occurrence network using CiteSpace software. Through visualizing and analyzing those networks, we identified the research hotspots, landmark literature, national collaboration, interdisciplinary patterns as well as the emerging trends through assessing the central nodes and the nodes with strong citation bursts. This work not only provides a structured view on state-of-art literature, but also reveals the future trends of employing 3D point cloud data for cultural heritage, aiding researchers carry out further research in this area.

In this paper, a novel re-engineering mechanism for the generation of word embeddings is proposed for document-level sentiment analysis. Current approaches to sentiment analysis often integrate feature engineering with classification, without optimizing the feature vectors explicitly. Engineering feature vectors to match the data between the training set and query sample as proposed in this paper could be a promising way for boosting the classification performance in machine learning applications. The proposed mechanism is designed to re-engineer the feature components from a set of embedding vectors for greatly increased between-class separation, hence better leveraging the informative content of the documents. The proposed mechanism was evaluated using four public benchmarking datasets for both two-way and five-way semantic classifications. The resulting embeddings have demonstrated substantially improved performance for a range of sentiment analysis tasks. Tests using all the four datasets achieved by far the best classification results compared with the state-of-the-art.

The tumor microenvironment (TME) plays an important role in the growth and invasion of glioma. This study aimed to analyze the composition of the immune microenvironment in glioma samples and analyze the important differentially expressed genes to identify novel immune-targeted therapy for glioma. We downloaded transcriptomic data of 669 glioma samples from The Cancer Genome Atlas database. CIBERSORT and ESTIMATE methods were used to calculate the proportion of tumor-infiltrating immune cells and ratio of immune and stromal components in the TME. The differentially expressed genes (DEGs) were screened by comparing the genes expressed by both stromal and immune cells. Annexin A1 (ANXA1) was determined to be an important prognostic indicator through the common overlap of univariate Cox regression analysis and protein–protein interaction network analysis. The proportion of tumor-infiltrating immune cells, calculated by CIBERSORT algorithm, had a significant difference in distribution among the high and low ANXA1 expression groups, indicating that ANXA1 could be an important immune marker of TME. Furthermore, ANXA1 level was positively correlated with the histopathological factors and negatively related to the survival of glioma patients based on the analysis of multiple databases. Finally, in vitro experiments verified that antagonizing ANXA1 expression promoted cell apoptosis and inhibited the invasion and migration capacities of glioma cells. Therefore, ANXA1 due to its immune-related functions, can be an important prognostic indicator and immune microenvironmental marker for gliomas. Further studies are warranted to confirm ANXA1 as a potential immunotherapeutic target for gliomas.

With the improvement for comprehensive and objective understanding of the network system,the research and application of network system security metrics (NSSM) are noticed more.The quantitative evaluation of network system security is developing towards precision and objectification.NSSM can provide the objective and scientific basis for the confrontation of attack-defense and decision of emergency response.The global metrics of network system security is a crucial point in the field of security metrics.From the perspective of global metrics,the status and role of global metrics in security evaluation were pointed out.Three development stages of metrics (perceiving,cognizing and deepening) and their characteristics were analyzed and summarized.The process of global metrics was described.The metrics models,metrics systems and metrics tools were analyzed,and their functions,interrelations,and features in security metrics were pointed out.Then the technical challenges of global metrics of network systems were explained in detail,and ten opportunities and challenges were summarized in tabular form.Finally,the next direction and development trend of network system security metrics research were forecasted.The survey shows that NSSM has a good application prospect in network security.

ABSTRACTRapid diagnosis of pulmonary tuberculosis is an effective measure to prevent the spread of tuberculosis. However, the grim fact is that the new, rapid, and safe methods for clinical diagnosis are lacking. Moreover, although auto-lysosome is critical in clearing Mycobacterium tuberculosis, the pathological significance of microRNAs, as biomarkers of tuberculosis, in autophagosome maturation is unclear. Here, these microRNAs were investigated by Solexa sequencing and qPCR validation, and a potential diagnostic model was established by logistic regression. Besides that, the mechanism of one of the microRNAs involved in the occurrence of tuberculosis was studied. The results showed that the expression of miR-423-5p, miR-17-5p, and miR-20b-5p were significantly increased in the serum of patients with tuberculosis. The combination of these three microRNAs established a model to diagnose tuberculosis with an accuracy of 78.18%, and an area under the curve value of 0.908. Bioinformatics analysis unveiled miR-423-5p as the most likely candidate in regulating autophagosome maturation. The up-regulation of miR-423-5p could inhibit autophagosome maturation through suppressing autophagosome–lysosome fusion in macrophages. Further investigations showed that VPS33A was the direct target of miR-423-5p, and the two CUGCCCCUC domains in VPS33A 3’-UTR were the direct regulatory sites for miR-423-5p. In addition, an inverse correlation between VPS33A and miR-423-5p was found in peripheral blood mononuclear cells of patients with tuberculosis. Since the inhibition of autolysosome formation plays a critical role in tuberculosis occurrence, our findings suggests that miR-423-5p could suppress autophagosome–lysosome fusion by post-transcriptional regulation of VPS33A, which might be important for the occurrence of active tuberculosis.

This paper introduces a novel feature extraction method for biometric recognition using EEG data and provides an analysis of the impact of electrode placements on performance. The feature extraction method is based on the wavelet transform of the raw EEG signal. Furthermore, the logarithms of wavelet coefficients are processed using the discrete cosine transform (DCT). The DCT coefficients from each wavelet band are used to form the feature vectors for classification. As an application in the biometrics scenario, the effectiveness of the electrode locations on person recognition is also investigated, and suggestions are made for electrode positioning to improve performance. The effectiveness of the proposed feature was investigated in both identification and verification scenarios. The identification results of 98.24% and 93.28% were obtained using the EEG Motor movement/imagery dataset (MM/I) and the UCI EEG database dataset, respectively, which compares favorably with other published reports while using a significantly smaller number of electrodes. The performance of the proposed system also showed substantial improvements in the verification scenario, when compared with some similar systems from the published literature. A multi-session analysis is simulated using with eyes open and eyes closed recordings from the MM/I database. It is found that the proposed feature is less influenced by time separation between training and testing compared with a conventional feature based on power spectral analysis.

In this paper, mass spectral substance detection methods are proposed, which employ long short-term memory (LSTM) recurrent neural networks to classify the mass spectrometry data and can accurately detect chemical substances. As the LSTM has the excellent understanding ability for the historical information and classification capability for the time series data, a high detection rate is obtained for the dataset which was collected by a time-of-flight proton-transfer mass spectrometer. In addition, the differential operation is used as the pre-processing method to determine the start time points of the detections which significantly improve the accuracy performance by 123%. The feature selection algorithm of Relief is also used in this paper to select the most significant channels for the mass spectrometer. It can reduce the computing resource cost, and the results show that the network size is reduced by 28% and the training speed is improved by 35%. By using these two pre-processing methods, the LSTM-based substance detection system can achieve the tradeoff between high detection rate and low computing resource consumption, which is beneficial to the devices with constraint computing resources such as low-cost embedded hardware systems.

Traditional neural networks usually concentrate on temporal data in system simulation, and lack of capabilities to reason inner logic relations between different dimensions of data collected from embedded sensors. This paper proposes a graph neural network-based modeling approach for IoT equipment (called GNNM-IoT), which considers both temporal and inner logic relations of data, in which vertices denote sensor data and edges denote relationships between vertices. The GNNM-IoT model's relationships between sensors with neural networks to produce nonlinear complex relationships. We have evaluated the GNNM-IoT using air-conditioner data from a world leading IoT company, which demonstrates that it is effective and outperforms ARIMA and LSTM methods.

In this paper, a novel image watermarking method is proposed which is based on discrete wave transformation (DWT), Hessenberg decomposition (HD), and singular value decomposition (SVD). First, in the embedding process, the host image is decomposed into a number of sub-bands through multi-level DWT, and the resulting coefficients of which are then used as the input for HD. The watermark is operated on the SVD at the same time. The watermark is finally embedded into the host image by the scaling factor. Fruit fly optimization algorithm, one of the natural-inspired optimization algorithms is devoted to find the scaling factor through the proposed objective evaluation function. The proposed method is compared to other research works under various spoof attacks, such as the filter, noise, JPEG compression, JPEG2000 compression, and sharpening attacks. The experimental results show that the proposed image watermarking method has a good trade-off between robustness and invisibility even for the watermarks with multiple sizes.

Piezoelectric catalysis (piezocatalysis) is a physical/chemical process that utilizes piezoelectric potential for accelerating chemical reactions, in which ubiquitous mechanical energies in nature are used for various catalysis applications, e.g., treating organic water pollutants. Despite the high efficiency achieved by piezocatalytic powders, the particles used tend to diffuse in water systems and are hard to be separated, thus causing secondary pollution. Herein, a free‐standing piezocatalytic foam is designed and fabricated, which is composed of BaTiO3 nanoparticles embedded in the PVDF scaffold. The as‐prepared PVDF–BaTiO3 composite foam demonstrates outstanding piezocatalytic efficiency in removing aqueous organics among state‐of‐the‐art integral piezocatalytic platforms, which lie in the synergy of piezoelectric materials and abundant interconnected pores within the foam. Significantly, PVDF–BaTiO3 foam is further applied for purifying natural water samples, by which the permanganate index of the water sample reduces by nearly 30% after 2 h of treatment. In addition, as a monolithic platform, PVDF–BaTiO3 foam is easy to be collected, with high reuse stability and applicability for treating various pollutants, resulting in dominant advantages over powder‐based systems for practical high‐flux wastewater treatment. Herein, a piezocatalytic platform is provided for the effective degradation of organic pollutants in water, with minimal environmental side effects.