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With the growing popularity and application of knowledge-based artificial intelligence, the scale of knowledge graph data is dramatically increasing. As an essential type of query for RDF graphs, RegularPath Queries (RPQs) have attracted increasing research efforts, whichexplore RDF graphs in a navigational manner. Moreover, path indexeshave proven successful for semi-structured data management. However,few techniques can be used effectively in practice for processing RPQon large-scale knowledge graphs. In this paper, we propose a novel indexing solution named FPIRPQ (Frequent Path Index for Regular PathQueries) by leveraging Frequent Path Mining (FPM). Unlike the existing approaches to RPQs processing, FPIRPQ takes advantage of frequentpaths, which are statistically derived from the data to accelerate RPQs. Furthermore, since there is no explicit benchmark targeted for RPQsover RDF graph yet, we design a micro-benchmark including 12 basicqueries over synthetic and real-world datasets. The experimental resultsillustrate that FPIRPQ improves the query efficiency by up to orders ofmagnitude compared to the state-of-the-art RDF storage engine.

Reliable detection of anomalies without any prior information is a critical yet challenging task in many applications, not least military and civilian fields. An intelligent anomaly detection system would use the material-specific spectral information in hyperspectral images (HSIs), thereby avoiding the loss of visually confusing objects. However, conventional hyperspectral anomaly detection methods are mainly achieved in an unsupervised way leading to limited performance due to lack of prior knowledge. In this article, we propose a novel autoencoder and adversarial-learning based semisupervised background estimation model (SBEM) that is trained only on the background spectral samples in order to accurately learn the background distribution. In particular, an unsupervised background searching method is firstly conducted on the original HSIs to search the background spectral samples. Our proposed SBEM consists of an encoder, a decoder, and a discriminator to thoroughly capture background distribution. Furthermore, jointly minimizing the reconstruction loss, spectral loss, and adversarial loss during training aids the model to learn the background distribution as required. Experiments on four real HSIs demonstrate that compared to the current state-of-the-art, the proposed framework yields higher detection capability and lower false alarm rate, which shows that it has a significant benefit in the tradeoff between detection accuracy and false alarm rate.

Theoretically, hyperspectral images (HSIs) are capable of providing subtle spectral differences between different materials, but in fact, it is difficult to distinguish between background and anomalies because the samples of anomalous pixels in HSIs are limited and susceptible to background and noise. To explore the discriminant features, a spectral adversarial feature learning (SAFL) architecture is specially designed for hyperspectral anomaly detection in this article. In addition to reconstruction loss, SAFL also introduces spectral constraint loss and adversarial loss in the network with batch normalization to extract the intrinsic spectral features in deep latent space. To further reduce the false alarm rate, we present an iterative optimization approach by a weighted suppression function that depends on the contribution rate of each feature to the detection. In particular, the structure tensor matrix is adopted to adaptively calculate the contribution rate of each feature. Benefiting from these improvements, the proposed method is superior to the typical and state-of-the-art methods either in detection probability or false alarm rate.

Anomaly detection in hyperspectral images (HSIs) faces various levels of difficulty due to the high dimensionality, redundant information and deteriorated bands. To address these problems, we propose a novel unsupervised feature representation approach by incorporating a spectral constraint strategy into adversarial autoencoders (AAE) without any prior knowledge in this paper. Our approach, called SC_AAE (spectral constraint AAE), is based on the characteristics of HSIs to obtain better discrimination represented by hidden nodes. To be specific, we adopt a spectral angle distance into the loss function of AAE to enforce spectral consistency. Considering the different contribution rates of each hidden node to anomaly detection, we individually fuse the hidden nodes by an adaptive weighting method. A bi-layer architecture is then designed to suppress the variational background (BKG) while preserving features of anomalies. The experimental results demonstrate that our proposed method outperforms the state-of-the-art methods.

With the rapid development of distributed photovoltaic, electric vehicles, energy storage and other technologies, the group of prosumers with dual functions of power production and consumption is growing. Prosumers have high responsiveness, flexible operation characteristics, and huge development potential. However, individual resources are limited and very scattered, and cannot directly participate in the power auxiliary service market. Therefore, virtual power plants are introduced to effectively aggregate and manage the resources of prosumers. Based on the theory of economic mechanism design, a bidding mechanism for prosumers to participate in the auxiliary service market has been established. This mechanism has the characteristics of incentive compatibility, which can effectively encourage Virtual power plants to report true costs, save electricity costs, and achieve effective resource allocation. The rationality and effectiveness of the proposed mechanism are verified through the analysis of a calculation example.

We propose a scoring system for early diagnosis of sleep abnormalities in neuromyelitis optica spectrum disorders (NMOSD) with hypothalamic lesions based on magnetic resonance imaging (MRI). We evaluated MRI features of 45 patients with hypothalamic lesions identified from two cohorts. Univariate logistic regression analysis identified factors associated with sleepiness, which were subsequently used to develop a scoring system. Interrater reliability was determined using intraclass correlation coefficient (ICC). Correlations between scores and clinical features were analyzed. In total, 48.9% of 45 patients with hypothalamic lesions exhibited sleepiness. The number of involved slices, maximum width/length of hypothalamic lesions, and boundaries extending beyond the hypothalamus were associated with sleepiness (all p

With the scale of RDF graph data dramatically increasing, the demands for specifying constraints on RDF graphs and detecting violations of such constraints become urgent. Recently, the validation of RDF graphs has been attracting increasing research efforts, among which ShEx (Shape Expressions) and SHACL (Shapes Constraint Language) are widely used. However, incremental validation of RDF graphs still needs further study. To this end, we propose an incremental validation method of RDF graphs. Our method differentiates the types of constraint, adjusts the order of schema validation, and validates RDF graphs from a graph theory perspective as well as incrementally validates RDF graphs updates. The experimental results show that the incremental validation method proposed in this paper improves the efficiency of the validation of knowledge graphs updates.

With the growing popularity and application of knowledge-based artificial intelligence, the scale of knowledge graph data is dramatically increasing. The RDF, as one of the mainstream models of knowledge graphs, is widely used to describe the characteristics of Web resources due to its simplicity and flexibility. However, RDF datasets are usually incomplete (without Open image in new window information) and noisy, which hinders downstream tasks. RDF entities can be characterized by their characteristic sets that is the sets of predicates of the RDF entities. Since untyped entities can be assigned to closest types by merging characteristic sets, optimally merging characteristic sets has become a crucial issue. In this paper, aiming at the Optimal Characteristic Set Merge Problem (OCSMP), we propose an Ontology-Aware Characteristic Set Merging algorithm, called OntoCSM, which extracts an ontology hierarchy using RDF characteristic sets and guides the merging process by optimizing the objective function. Extensive experiments on various datasets show that the efficiency of OntoCSM is generally higher than that of the state-of-the-art algorithms and can be improved by orders of magnitude in the best case. The accuracy and scalability of our method have been verified, which shows that OntoCSM can reach competitive results to the existing algorithms while being ontology-aware.

With the growing popularity and application of knowledge-based artificial intelligence, the scale of knowledge graph data is dramatically increasing. A Regular Path Query (RPQ) allows for retrieving vertex pairs with the paths between them satisfying regular expressions. As an essential type of queries for RDF graphs, RPQs have been attracting increasing research efforts. Since the complexity of RPQs is in polynomial time with respect to the scale of the knowledge graphs, currently, there has been no efficient method to process RPQs on large-scale knowledge graphs. In this paper, we propose a novel indexing solution by leveraging frequent path mining. Unlike the existing RPQ processing methods, our approach makes full use of frequent paths as the basic indexing facility. The frequent paths extracted from data graphs will be indexed to accelerate RPQs. Meanwhile, since no RPQ benchmark available, we create a micro-benchmark on synthetic and real-world data sets. The experimental results show that PAIRPQ improves the query efficiency by orders of magnitude than the state-of-the-art RDF storage engines.

We propose a scoring system for early diagnosis of sleep abnormalities in neuromyelitis optica spectrum disorders (NMOSD) with hypothalamic lesions based on magnetic resonance imaging (MRI).We evaluated MRI features of 45 patients with hypothalamic lesions identified from two cohorts. Univariate logistic regression analysis identified factors associated with sleepiness, which were subsequently used to develop a scoring system. Interrater reliability was determined using intraclass correlation coefficient (ICC). Correlations between scores and clinical features were analyzed.In total, 48.9% of 45 patients with hypothalamic lesions exhibited sleepiness. The number of involved slices, maximum width/length of hypothalamic lesions, and boundaries extending beyond the hypothalamus were associated with sleepiness (all p 0.05). The sensitivity and specificity of the scoring system were 68.2% and 87.0%, respectively. The ICC values for the maximum width and length measurement of hypothalamic lesions were 0.82 and 0.81, respectively. Daily sleep time and Epworth sleepiness scale scores were positively correlated with MRI-based scores (p 0.05, 95% confidence interval (CI) 0.69-0.93 and p 0.05, 95% CI 0.55-0.88, respectively).A scoring system based on MRI features was developed to provide diagnosis of sleepiness in NMOSD with hypothalamic lesions earlier than other measures.

With the development of knowledge-based artificial intelligence, the scale of knowledge graphs has been increasing rapidly. The RDF graph and the property graph are two mainstream data models of knowledge graphs. On the one hand, with the development of the Semantic Web, there are a large number of RDF knowledge graphs. On the other hand, property graphs are widely used in the graph database community. However, different families of data management methods of RDF graphs and property graphs have been seperately developed in each community over a decade, which hinder the interoperability in managing large knowledge graph data. To address this problem, we propose a unified storage scheme for knowledge graphs which can seamlessly accommodate both RDF and property graphs. Meanwhile, the concept of ontology is introduced to meet the need for RDF graph data storage and query load. Experimental results on the benchmark datasets show that the proposed ontology-aware unified storage scheme can effectively manage large-scale knowledge graphs and significantly avoid data redundancy.

Background Anti‑N‑methyl‑ d ‑aspartate receptor (NMDAR) encephalitis is a relatively common autoimmune neurological disease of the central nervous system (CNS). Neuron-specific enolase (NSE) and S100 calcium-binding protein B (S100B) are structural proteins of the central nervous system (CNS). In patients with CNS injury accompanied by nervous tissue and cellular damage, these structural proteins are released from cells; their extracellular concentrations, including those in cerebrospinal fluid (CSF) and blood, subsequently increase. Methods Thirty-six patients with anti-NMDAR encephalitis were prospectively recruited. The CSF NSE and S100B concentrations were measured in 19 and 17 patients, respectively. The CSF NSE and S100B concentrations were measured in 21 patients with noninflammatory neurological disease as controls. All measurements were performed using enzyme-linked immunosorbent assays. Results The CSF NSE and S100B concentrations were remarkably higher in the patients with anti-NMDAR encephalitis than in the controls. The early NSE or S100B concentrations in CSF were associated with the mRS. Conclusion CSF NSE and S100B concentration in patients with anti-NMDAR encephalitis is higher than in patients with non-inflammatory neurological disease. The early NSE or S100B concentrations in CSF were associated with the mRS and we can use it to determine the prognosis of the disease.

Anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis is an autoimmune disorder of the central nervous system whose pathogenesis involves interleukin (IL)-6 and IL-17A. We examined the correlations between CSF concentrations of the acute-phase protein pentraxin 3 (PTX3), the chronic inflammatory mediator CD40L, IL-6, and IL-17A in anti-NMDAR encephalitis, and the impact on clinical outcome. PTX3, CD40L, IL-6, and IL-17A were significantly higher in the CSF of patients with anti-NMDAR encephalitis than in controls. Within the former, PTX3 levels correlated positively with IL-6 and the mRS, and CD40L levels with IL-17A and the mRS. Higher PTX3 and CD40L levels may reflect the underlying neuroinflammation.

Object: CD27 belongs to the tumor necrosis factor receptor family and is constitutively expressed on T cells. The concentration of cerebrospinal fluid (CSF) soluble (s)CD27 is elevated in patients with multiple sclerosis (MS). However, whether the level of CSF sCD27 is elevated in neuromyelitis optica spectrum disorder (NMOSD) remains unknown. The aim of this study was to measure the CSF concentration of sCD27 and to determine its relationship with NMOSD disease activity. Methods: CSF CXCL13 was measured by ELISA in neuromyelitis optica (NMO) (n = 31) and MS (n = 23) patients and in controls (CTLs) (n = 22). Results: The concentration of sCD27 was higher in the NMO group than in the MS (p = 0.082) and CTL (p = 0.002) groups, and there was a positive correlation with CSF IL-6 (p = 0.000) and a negative correlation with IL-10 (p = 0.073). In the NMO group, patients with higher sCD27 concentrations exhibited worse disease disability in their CSF (p = 0.006). Moreover, the sCD27 concentrations had a significantly positive correlation with the level of CSF total protein (p = 0.030). Furthermore, the patients positive for AQP4-IgG (n = 26) seemed to have higher levels of sCD27 in their CSF (p = 0.069) than those negative for AQP4-IgG (n = 5). Conclusions: We revealed that the level of CSF sCD27 was elevated in NMOSD and correlated with NMOSD disease activity.

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is accepted as an autoimmune disorder of the central nervous system (CNS). NLR family pyrin domain containing 3 (NLRP3) inflammasome, a potent innate inflammatory mediator, can activate IL-1β and induce the migration of T helper cell into CNS. However, the possible role of NLRP3 inflammasome in the pathogenesis of anti-NMDAR encephalitis remains unclear. This study aims to determine the levels of NLRP3 and related cytokines (IL-1β, IL-6, and IL-17) in the cerebrospinal fluid (CSF) of anti-NMDAR encephalitis patients and to assess whether NLRP3 influences the clinical outcomes of this disease. Twenty-five patients with anti-NMDAR encephalitis, 12 viral meningoencephalitis patients and 26 controls with non-inflammatory neurological diseases were recruited. CSF NLRP3 inflammasome, IL-1β, IL-6, and IL-17 were measured by enzyme-linked immunosorbent assay. Thirteen out of 25 patients were re-examed for the concentrations of NLRP3 and cytokines 6 months later. Our results showed significant increases of CSF NLRP3 inflammasome, IL-1β, IL-6, and IL-17 in anti-NMDAR encephalitis patients. There were positive correlations between CSF NLRP3 inflammasome and cytokines in anti-NMDAR encephalitis patients. There was also a positive correlation between maximum modified Rankin Scale (mRS) scores and CSF NLRP3 inflammasome in anti-NMDAR encephalitis patients. During follow-up, the decrease of mRS was positively correlated with the decrease of CSF NLRP3 inflammasomes. These results suggested that the level of CSF NLRP3 inflammasome could represent the severity of anti-NMDAR encephalitis and the reduction of CSF NLRP3 inflammasome could act as an indicator for the prognosis of this disease.

The couplings and interactions among the multi-energy resources in the integrated energy system (IES) significantly improve the utilization of renewables and reduce carbon emissions. Hydrogen is considered to be one of the most potential energy carriers due to its excellent characteristics. Therefore, the use of hydrogen has become a hot spot in the energy field. Most researches on power to gas (P2G) technology do not use the potential advantages of hydrogen but only analyze the coupling relationship between electricity and natural gas, so the efficiency is low. Moreover, the uncertainties of renewable energy and load bring challenges to the power system dispatching. To solve these problems, an electricity-hydrogen-gas-heat integrated energy system (EHGHS) stochastic optimal dispatching strategy based on improved spectral clustering method is presented in this paper. First, the structure of EHGHS and the energy conversion unit in the EHGHS is modeled. A two-stage P2G technology is proposed which exploits the hydrogen utilization process and the combined heat and power generation process. Then a scenario reduction method based on improved spectral clustering is presented to describe the uncertain characteristics of renewable energy and load. The curve distance and cosine similarity are developed to represent the similarity between scenarios. Finally, the effectiveness, economics, and sensitivity of the stochastic optimal dispatching model are verified by case studies.

The increasing numbers of prosumers, which consists of multiple DERs, brings new opportunities and challenges to the operation of the distribution network. To harvest the prosumers' flexbilities, distribution system operator needs innovative methods, since i) decision variables of the dispatching model is too large to compute efficiently; ii) individual prosumer's privacy is difficult to be protected. Therefore, we present a unified quantitative model for multiple DERs, i.e., virtual battery (VB) model, and integrate multiple DERs into the prosumer cluster in the nodal unit to participate in the day-ahead optimal scheduling for distribution system operator (DSO). In this paper, we propose a day-ahead optimal scheduling model for DSO whose objective is to minimize the electricity purchasing cost in the day-ahead market under the guidance of the price mechanism of peak-valley electricity price. The security constrains are also considered in the model. Moreover, the non-convex network constraints are relaxed in the model, so that it can be solved by the second-order cone programming (SOCP). Then we can obtain the day-ahead scheduling with the minimum electricity cost of the total users, while meeting the network constraints. Finally, case studies are provided to demonstrate the validity of the model.

The aberrant expression of lncRNAs has been inferred to be closely related with the progression of neural ischemia/reperfusion (I/R) injury. RMRP is an lncRNA associated with I/R injury. In order to determine the role of RMRP in I/R injury, the effects of RMRP knockdown on oxygen-glucose deprivation/re-oxygenation (OGD/R)-induced injury in SH-SY5Y cells were evaluated. The effect of OGD/R administration on the expression of RMRP and apoptosis in SH-SY5Y cells, and the effect of RMRP suppression by siRNA on the impairments of cells proliferation and mobility potential due to OGD/R administration were assessed in the current study. At the molecular level, the current study detected the expressions of indicators involved in autophagy and PI3K/Akt/mTOR-mediated apoptosis pathways. The OGD/R administration induced the expression of RMRP and apoptosis in SH-SY5Y cells. After RMRP knockdown, the proliferation potential of SH-SY5Y cells was restored, and apoptosis and cell cycle arrest were inhibited. Moreover, RMRP inhibition also increased the invasion and migration of SH-SY5Y cells which were treated with OGD/R. The effects of RMRP suppression on the phenotypes of SH-SY5Y were associated with the inhibition of LC3II, p-PI3K, p-Akt, and p-mTOR as well as the induction of P62 and Bcl-2. Inhibition of RMRP contributed to the improvement of OGD/R-induced neuronal injury, which might be mediated through the inhibition of autophagy and apoptosis pathways.

Introduction Delta-opioid receptor (DOR) and its endogenous ligands distribute in trigeminal system and play a very important role in modulating peripheral inflammatory pain. DOR activation can trigger p44/42 mitogen-activated protein kinase (ERK1/2) and Akt signaling pathways, which participate in anti-inflammatory and neuroprotective effects. In this study, our purpose was to determine the dynamic changes of DOR in trigeminal ganglion (TG) neurons during the process of acute dental pulp inflammation and elucidate its possible mechanism. Methods Forty rats were used to generate lipopolysaccharide-induced acute pulpitis animal models at 6, 12, and 24 hours and sham-operated groups. Acute pulpitis was confirmed by hematoxylin-eosin staining, and TG neuron activation was determined by anti-c-Fos immunohistochemistry. DOR protein and gene expression in TG was investigated by immunohistochemistry, Western blotting, and real-time polymerase chain reaction, and DOR expression in trigeminal nerves and dental pulp was also determined by immunohistochemistry. To further investigate the mechanism of DOR modulating acute inflammation, the change of pErk1/2 and pAkt in TG was examined by immunohistochemistry. Results Lipopolysaccharide could successfully induce acute pulpitis and activated TG neurons. Acute pulpitis could dynamically increase DOR protein and gene expression at 6, 12, and 24 hours in TG, and DOR dimerization was significantly increased at 12 and 24 hours. Acute pulpitis also induced the dynamic change of DOR protein in trigeminal nerve and dental pulp. Furthermore, ERK1/2 and Akt signaling pathways were inhibited in TG after acute pulpitis. Conclusions Increased DOR expression and dimerization may play important roles in peripheral acute inflammatory pain.

This paper introduces the Triple-section extraction process in the three-dimensional roadway layout of stagger arrangement method for longwall top-coal caving mining. This 3-D roadway layout of stagger arrangement method without coal pillars, which arranged the air intake roadway and air return roadway in different horizons, realizing the design theory transformation of roadway layout from 2D system to 3D system. And the paper makes systematic analysis to the geological, technical and economic factors, applies this new mining roadway layout technology for raising coal recovery ratio and solving the problems about full-seam mining in thick coal seam synthetically according to theoretical study and mining practice. Furthermore, the paper presents a physical simulation about inner staggered roadway layout of this particular longwall top-coal caving method.

The effects of high magnetic field on the crystallization behavior of the Fe78Si13B9 metallic glass ribbon were studied. The samples were isothermal annealed for 30 min under high magnetic field and no field, respectively. Microstructure transformation during crystallization was identified by X-ray diffraction and transmission electron microscopy. It was found that the crystallizations of Fe78Si13B9 metallic glass processed under different conditions were that the precipitation of dendrite α-Fe(Si) and spherulite (Fe,Si)3B phases forms amorphous matrix and then the metastable (Fe,Si)3B phase transforms into the stable Fe2B phase. The grain size of the crystals is smaller and more homogeneous for the isothermal annealed samples under high magnetic field in comparison with that under no field indicating that the crystallization behavior of Fe78Si13B9 metallic glass is suppressed by high magnetic field.

The stiff clays beneath Holocene strata in the Yangtze Delta are interpreted as palaeosols, based on pedogenic features including illuvial argillans, voids, cracks, iron–manganese concretions and nodules, and carbonate materials. Variations in clay content with depth, the occurrence of foraminifera, and the environmental magnetism characteristics of the palaeosols suggest that their parent materials are floodplain deposits. Anisotropy of magnetic susceptibility (AMS) parameters of the sediments correlate well both with sedimentary dynamics and depositional processes in different sedimentary environments, and AMS analysis may be a useful tool for inferring sedimentary environments. Based on their considerable thickness (> 3.0 m), vertical changes of palaeosol maturity, and occurrence of ageing argillans throughout the palaeosols, the palaeosols are inferred to be compound ones (pedocomplexes) resulting from alternating deposition and pedogenesis on the palaeointerfluve of the Yangtze River. Phytoliths in the palaeosols indicate that climates turned generally from warm and wet to cold and dry with marked fluctuations during development of the palaeosols. This suggests that the palaeosols developed mainly during the marine regression prior to the last glacial maximum. The vertical distribution of manganese materials (e.g., concretions, nodules, speckles and mottles) in the palaeosols suggests that the groundwater tables were about 2.0–3.0 m beneath the upper boundary of the palaeosols during their development. Yellow–brown streaks and speckles are abundant from the middle to lower parts of the palaeosols, and layers with high CaCO 3 content occur in the strata just beneath the palaeosols, but which lack caliche, suggesting that the groundwater table fluctuated markedly during their development. Voids and cracks filled with clays, and carbonate nodules (less than 0.1 mm in diameter) with thin iron–manganese rims are abundant in the palaeosols, indicating that wet and dry seasons were marked during the palaeosol development. It is suggested that the monsoonal influence was marked in the Yangtze Delta, with both winter and summer monsoons strongly developed during the development of the late Quaternary palaeosols.

Fossil rice phytoliths have been identified from a lateglacial to Holocene sequence of epicontinental sediments in the East China Sea that were probably transported by the Yangtze River from its middle and/or lower reaches. The rice phytoliths occurred first in the sequence at about 13 900 cal. yr BP and disappeared during the period of 13 000–10000 cal. yr BP, implying the earliest domesticated cereal crops of the world ever reported. Based on the records of phytoliths, pollen, diatoms and foraminifera from the sequence, the climate between 13 000 and 10 000 cal. yr BP was notably colder (Younger Dryas). The coincidence of disappearance of domesticated rice phytoliths with cold climate conditions may suggest a great climatic influence on human activities during that time. Warmer and wetter conditions during the period 13900 to 13000 cal. yr BP and after 10000 cal. yr BP have probably favoured rice domestication in the area.