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The Bayesian model averaging (BMA), hydrological uncertainty processor (HUP), and HUP-BMA methods have been widely used to quantify flood forecast uncertainty. This study, for the first time, introduced a copula-based HUP in the framework of BMA and proposed the CHUP-BMA method to bypass the need for normal quantile transformation of the HUP-BMA method. The proposed ensemble forecast scheme consists of 8 members (two forecast precipitation inputs, two advanced long short-term memory (LSTM) models, and two objective functions used to calibrate parameters) and is applied to the interval basin between Xiangjiaba and Three Gorges Reservoir (TGR) dam-site. The ensemble forecast performance of the HUP-BMA and CHUP-BMA methods is explored in the 6–168h forecast horizons. The TGR inflow forecasting results show that the two methods can improve the forecast accuracy over the selected member with the best forecast accuracy, and the CHUP-BMA performs much better than the HUP-BMA. Compared with the HUP-BMA method, the forecast interval width with the 90 % confidence level and continuous ranked probability score metrics of the CHUP-BMA method are highest reduced by 28.42 % and 17.86 %, respectively. The probability forecast of the CHUP-BMA method has better reliability and sharpness and is more suitable for flood ensemble forecasts, providing reliable risk information for flood control decision-making.

Graph structure expression plays a vital role in distinguishing various graphs. In this work, we propose a Structure-Sensitive Graph Dictionary Embedding (SS-GDE) framework to transform input graphs into the embedding space of a graph dictionary for the graph classification task. Instead of a plain use of a base graph dictionary, we propose the variational graph dictionary adaptation (VGDA) to generate a personalized dictionary (named adapted graph dictionary) for catering to each input graph. In particular, for the adaptation, the Bernoulli sampling is introduced to adjust substructures of base graph keys according to each input, which increases the expression capacity of the base dictionary tremendously. To make cross-graph measurement sensitive as well as stable, multi-sensitivity Wasserstein encoding is proposed to produce the embeddings by designing multi-scale attention on optimal transport. To optimize the framework, we introduce mutual information as the objective, which further deduces to variational inference of the adapted graph dictionary. We perform our SS-GDE on multiple datasets of graph classification, and the experimental results demonstrate the effectiveness and superiority over the state-of-the-art methods.

An investigation was performed into the stability of three emulsion explosives, namely bulk, packaged and powdery emulsion explosive. The dynamic stability is crucial for bulk emulsion explosive, and the experimental results show that it will possibly achieve both good dynamic stability and fluidity when polyisobutene succinic anhydride (PIBSA) based emulsifiers, such as PIBSA-DTEA, PIBSA-TEA and PIBSA-STEA etc, used alone in preparing explosive. Opposite with that, the long-term storage stability is much more important for packaged emulsion explosive. In several PIBSA based emulsifiers, polyisobutene succinimide (PIBSI) is more suitable for improving the stability of packaged emulsion explosive. Moreover, the stability also can be improved through rising the toughness of oil film, like adding a certain amount of microcrystalline wax. The caking of ammonium nitrate (AN) crystals with time is the key reason, which decreasing the stability of powdery emulsion explosive. The caking phenomenon will be weakened with the addition of rosin because the softened rosin can allow the repair of damaged oil film and prevent AN crystals breaking the oil film. Besides that, adding potassium nitrate to AN solution or a certain amount of talcum powder also can effectively improve the stability.

Additional file 1: Supplementary Figure 1. a The resulting rarefaction curves indicate that microbial richness was near saturation at the applied sequencing depth. b The Shannon curves indicate that sequencing depth was adequate.

In cooperative multi-agent reinforcement learning (MARL) settings, the centralized training with decentralized execution (CTDE) becomes customary recently due to the physical demand. However, the most dilemma is the inconsistency of jointly-trained policies and individually-optimized actions. In this work, we propose a novel value-based multi-objective learning approach, named Tchebycheff value decomposition optimization (TVDO), to overcome the above dilemma. In particular, a nonlinear Tchebycheff aggregation method is designed to transform the MARL task into multi-objective optimal counterpart by tightly constraining the upper bound of individual action-value bias. We theoretically prove that TVDO well satisfies the necessary and sufficient condition of individual global max (IGM) with no extra limitations, which exactly guarantees the consistency between the global and individual optimal action-value function. Empirically, in the climb and penalty game, we verify that TVDO represents precisely from global to individual value factorization with a guarantee of the policy consistency. Furthermore, we also evaluate TVDO in the challenging scenarios of StarCraft II micromanagement tasks, and extensive experiments demonstrate that TVDO achieves more competitive performances than several state-of-the-art MARL methods.

Human multimodal emotion recognition (MER) aims to perceive human emotions via language, visual and acoustic modalities. Despite the impressive performance of previous MER approaches, the inherent multimodal heterogeneities still haunt and the contribution of different modalities varies significantly. In this work, we mitigate this issue by proposing a decoupled multimodal distillation (DMD) approach that facilitates flexible and adaptive crossmodal knowledge distillation, aiming to enhance the discriminative features of each modality. Specially, the representation of each modality is decoupled into two parts, i.e., modality-irrelevant/-exclusive spaces, in a self-regression manner. DMD utilizes a graph distillation unit (GD-Unit) for each decoupled part so that each GD can be performed in a more specialized and effective manner. A GD-Unit consists of a dynamic graph where each vertice represents a modality and each edge indicates a dynamic knowledge distillation. Such GD paradigm provides a flexible knowledge transfer manner where the distillation weights can be automatically learned, thus enabling diverse crossmodal knowledge transfer patterns. Experimental results show DMD consistently obtains superior performance than state-of-the-art MER methods. Visualization results show the graph edges in DMD exhibit meaningful distributional patterns w.r.t. the modality-irrelevant/-exclusive feature spaces. Codes are released at \url{https://github.com/mdswyz/DMD}., Comment: To appear at CVPR 2023, selected as a hightlight, 10% of accepted papers, 2.5% of submissions

Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the binary anomaly label is only given on the video level, but the output requires snippet-level predictions. So, Multiple Instance Learning (MIL) is prevailing in WSVAD. However, MIL is notoriously known to suffer from many false alarms because the snippet-level detector is easily biased towards the abnormal snippets with simple context, confused by the normality with the same bias, and missing the anomaly with a different pattern. To this end, we propose a new MIL framework: Unbiased MIL (UMIL), to learn unbiased anomaly features that improve WSVAD. At each MIL training iteration, we use the current detector to divide the samples into two groups with different context biases: the most confident abnormal/normal snippets and the rest ambiguous ones. Then, by seeking the invariant features across the two sample groups, we can remove the variant context biases. Extensive experiments on benchmarks UCF-Crime and TAD demonstrate the effectiveness of our UMIL. Our code is provided at https://github.com/ktr-hubrt/UMIL., Comment: 11 pages,10 figures

the activation of scientific concepts (such as association) is not only an important way for children to organize scientific knowledges, but also an important way for them to learn complex concepts (such as compound concepts composed of multiple knowledge points). Inspired by the details of a primary electrical lesson, we used the E-Prime software to study the activation of concepts inside and outside the electrical unit by students with different electrical knowledge levels (taking reaction time as the main index). The results showed that: ① the levels of basic knowledge was negatively correlated with the cross domain activation ability of concepts, that is, the worse the basic knowledge, the faster the cross domain activation speed (P < 0.05); ② The better the basic knowledges, the closer the activation behavior is to science teachers. Conclusion: ① the poorer the basic knowledges, the stronger the cross domain association ability and the more active the thinking; ② The reason for this phenomenon may be human’s thinking is patterned; ③ Therefore, there is a potential logical contradiction in the view that divergent thinking is the psychological basis of creativity.

BackgroundAt present, adherence to antipsychotic treatment is often poor, leading to the recurrence of symptoms. This increases the likelihood of the patient experiencing disability and thus increases the disease burden for the patient, their family, and society as a whole. However, to date, there is no clear evidence regarding the effect of medication adherence interventions on outcomes for patients with schizophrenia. Moreover, the traditional intervention methods are limited by manpower and resources in low- and middle-income countries. Recent studies have demonstrated that increasing a patient’s level of self-compassion may improve their treatment adherence. Online mental health care interventions have advantages in terms of feasibility and acceptability for patients with schizophrenia. In this regard, a WeChat-based self-compassion training protocol to improve patient treatment adherence was designed in this study and will be evaluated in the future to determine its impact on patients with schizophrenia.MethodsThe protocol for the randomized controlled trial (RCT) is based on the SPIRIT 2013 statement. This parallel RCT will aim to recruit 392 patients with schizophrenia who will be randomized at a 1:1 ratio into a 3-week intervention or control group. Both groups will receive routine care. The intervention group will also receive WeChat-based self-compassion training, which requires participants to complete three tasks every day, including a reading task, a meditation task, and a self-compassion journal task. The control group will receive WeChat-based psychological health education, which will only require participants to read positive articles about psychological health every day. Medication adherence, self-compassion, stigma, and social support will be measured at baseline (T0), immediately after the intervention (T1), and 3 weeks after the intervention (T2). Program feasibility will be evaluated throughout the course of the study, and acceptability will be measured immediately after the intervention (T1).Expected results:The intervention described here will address the barriers to accessing mental health care for people with schizophrenia, including patients’ desire for independent management, difficulty accessing providers, and concerns about privacy and stigma. The current study provides guidance for clinical nurses to carry out psychological intervention, with the ultimate aim of addressing the problems associated with a shortage of psychological professionals in low- and middle-income countries.

Stromal antigen 1 (STAG1), a component of cohesion, is overexpressed in various cancers, but it is unclear whether it has a role in the transcriptional regulation of hepatocellular carcinoma (HCC). To test this hypothesis, here, we screened global HCC datasets and performed multiscale embedded gene co-expression network analysis to identify the potential functional modules of differentially expressed STAG1 co-expressed genes. The putative transcriptional targets of STAG1 were identified using chromatin immunoprecipitation followed by high-throughput DNA sequencing. The cohesin-associated gene score (CAGS) was quantified using the The Cancer Genome Atlas HCC cohort and single-sample gene set enrichment analysis. Distinct cohesin-associated gene patterns were identified by calculating the euclidean distance of each patient. We assessed the potential ability of the CAGS in predicting immune checkpoint blockade (ICB) treatment response using IMvigor210 and GSE78220 cohorts. STAG1 was upregulated in 3313 HCC tissue samples compared with 2692 normal liver tissue samples (standard mean difference = 0.54). A total of three cohesin-associated gene patterns were identified, where cluster 2 had a high TP53 mutated rate and a poor survival outcome. Low CAGS predicted a significant survival advantage but presaged poor immunotherapy response. Differentially expressed STAG1 co-expression genes were enriched in the mitotic cell cycle, lymphocyte activation, and blood vessel development. PDS5A and PDGFRA were predicted as the downstream transcriptional targets of STAG1. In summary, STAG1 is significantly upregulated in global HCC tissue samples and may participate in blood vessel development and the mitotic cell cycle. A cohesin-associated gene scoring system may have potential to predict the ICB response.

Aim To determine the efficacy of Internet‐based interventions in decreasing the prevalence of postpartum depression in perinatal women. Design This review was conducted according to the standards outlined in the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses statement. Methods We performed a systematic meta‐analysis of randomized controlled trials on the efficacy of Internet‐based interventions for postpartum depression. Studies (2008–2018) were identified through a search conducted on PubMed, EMBASE and the Cochrane Library. Risk ratios or weighted mean differences with 95% confidence intervals were calculated using a fixed‐effects model or a random‐effects model. Stata software 11.0 was used to perform the meta‐analysis. Results Most of the seven eligible studies were randomized controlled trials. The random‐effects model indicated that Internet‐based interventions significantly improved postpartum depression (d = 0.642, N = 7). Attrition rates ranged from 4.5%–86.9% and from 0%–87.1% for the intervention and control groups, respectively.

Although fluoroless transseptal puncture (TSP) guided by intracardiac echocardiography (ICE) has been used for many years, there are no reports of an accurate site-specific method for TSP in detail, especially about the safety and efficiency of the method. This study aimed to compare the efficacy and safety of TSP guided by three-dimensional ICE using a fluoroless site-specific method with that of the conventional fluoroless method in patients with atrial fibrillation (AF).This prospective study included 60 patients with AF scheduled for radiofrequency ablation who were assigned to undergo modified fluoroless site-specific TSP (SS-ICE group, n = 30) or conventional fluoroless TSP (C-ICE group, n = 30). TSP was guided by three-dimensional ICE in both study groups.All fluoroless TSP were performed successfully in both groups. There were no significant differences in patient characteristics, Pre-TSP time (11.3 ± 1.7 min vs. 11.1 ± 1.6 min, P = 0.822) and TSP time (3.4 ± 0.9 min vs. 3.5 ± 1.1 min, P = 0.772) between the SS-ICE group and the C-ICE group. The distance between the actual traversing point and the presetting point in the fossa ovalis was less than 5 mm in 87% of patients (26/30, 3.1 ± 1.2 mm) in the SS-ICE group. There were no TSP-related complications in either group.SS-ICE method is a simple, safe, and effective approach for fluoroless site-specific TSP.

Immune checkpoint inhibitor (ICI)-associated myocarditis is a potentially fatal complication. Sparse published researches evaluated the prognostic value of cardiovascular magnetic resonance feature tracking (CMR-FT) for ICI-associated myocarditis.In the single-center retrospective study, 52 patients with ICI-associated myocarditis and CMR were included from August 2018 to July 2021. The ICI-associated myocarditis was diagnosed by using the clinical criteria of the European Society of Cardiology guidelines. Major adverse cardiovascular events (MACE) were comprised of cardiovascular death, cardiogenic shock, cardiac arrest, and complete heart block.During a median follow-up of 171 days, 14 (27%) patients developed MACE. For patients with MACE, the global circumferential strain (GCS), global radial strain (GRS), global longitudinal strain (GLS), and left ventricular ejection fraction (LVEF) were significantly worse and native T1 values and late gadolinium enhancement (LGE) extent were significantly increased, compared with patients without MACE (p0.05). The GLS remained the independent factor associated with a higher risk of MACE (hazard ratio (HR): 2.115; 95% confidence interval (CI): 1.379-3.246; p = 0.001) when adjusting for LVEF, LGE extent, age, sex, body mass index, steroid treatment, and prior cardiotoxic chemotherapy or radiation. After adjustment for LVEF, the GLS remained the independent risk factor associated with a higher rate of MACE among patients with a preserved LVEF (HR: 1.358; 95% CI: 1.007-1.830; p = 0.045).GLS could provide independent prognostic value over GCS, GRS, traditional CMR features, and clinical features in patients with ICI-associated myocarditis.• The global circumferential strain (GCS), global radial strain (GRS), and global longitudinal strain (GLS) by cardiovascular magnetic resonance feature tracking were significantly impaired in patients with an immune checkpoint inhibitor (ICI)-associated myocarditis. • GLS was still significantly impaired in patients with preserved left ventricular ejection fraction. • The worse GLS was an independent risk factor over GCS, GRS, traditional CMR features, and clinical features for predicting major adverse cardiovascular events in patients with ICI-associated myocarditis.

Decomposition-based evolutionary algorithms have become fairly popular for many-objective optimization in recent years. However, the existing decomposition methods still are quite sensitive to the various shapes of frontiers of many-objective optimization problems (MaOPs). On the one hand, the cone decomposition methods such as the penalty-based boundary intersection (PBI) are incapable of acquiring uniform frontiers for MaOPs with very convex frontiers. On the other hand, the parallel reference lines of the parallel decomposition methods including the normal boundary intersection (NBI) might result in poor diversity because of under-sampling near the boundaries for MaOPs with concave frontiers. In this paper, a collaborative decomposition method is first proposed to integrate the advantages of parallel decomposition and cone decomposition to overcome their respective disadvantages. This method inherits the NBI-style Tchebycheff function as a convergence measure to heighten the convergence and uniformity of distribution of the PBI method. Moreover, this method also adaptively tunes the extent of rotating an NBI reference line towards a PBI reference line for every subproblem to enhance the diversity of distribution of the NBI method. Furthermore, a collaborative decomposition-based evolutionary algorithm (CoDEA) is presented for many-objective optimization. A collaborative decomposition-based environmental selection mechanism is primarily designed in CoDEA to rank all the individuals associated with the same PBI reference line in the boundary layer and pick out the best ranks. CoDEA is compared with several popular algorithms on 85 benchmark test instances. The experimental results show that CoDEA achieves high competitiveness benefiting from the collaborative decomposition maintaining a good balance among the convergence, uniformity, and diversity of distribution.

Objective In the context of an increased focus on geriatric depression in recent years, this study examined the associations between different types of self-care disability, the number of self-care disabilities, and depressive symptoms among middle-aged and elderly Chinese people. Method The data for this study were extracted from the follow-up survey (conducted in 2018) of the China Health and Retirement Longitudinal Study (CHARLS). The sample comprised 10808 participants aged 45 years and older. The Activities of Daily Living (ADL) scale and the Center for Epidemiological Studies Depression (CESD-10) Scale were used to assess self-care disability and depressive symptoms, respectively. Result The prevalence of depressive symptoms and self-care disability among the surveyed residents was 45.1% and 23.4%, respectively. Overall, there was a significant positive association between self-care disability and depressive symptoms. Participants who reported having a self-care disability in relation dressing, bathing, transferring in and out of bed, using the toilet, and controlling urination and defecation were found to have a significantly higher risk of depressive symptoms. In addition, participants with a greater cumulative quantity of self-care disabilities had a higher risk of depressive symptoms, and higher CESD-10 scores. Conclusion Self-care disability is a risk factor for depressive symptoms among middle-aged and elderly Chinese people. A positive correlation between the number of self-care disabilities and the risk of depressive symptoms was found.

Sparse researches evaluated the quantitative cardiovascular magnetic resonance (CMR) parameters for immune checkpoint inhibitors (ICI)-associated myocarditis. We aimed to apply quantitative CMR mappings and late gadolinium enhancement (LGE) extent for detecting ICI-associated myocarditis.The retrospective study included patients with ICI-associated myocarditis and CMR examination from August 2018 to August 2021 in our hospital. ICI-associated myocarditis was clinically diagnosed based on the clinical criteria by European Society of Cardiology guidelines. The multiparametric CMR images including T2 mapping and black blood T2-weighted images were used to evaluate myocardial edema. The myocardial edema ratio (ER) ≥ 2.0 was applied for determining myocardial edema on T2-weighted images.56 patients with ICI-associated myocarditis were included. The global T2 value and native T1 value of patients with ICI-associated myocarditis were significantly higher than the reference ranges in our hospital (p 0.05). The rate of elevated global T2 value (92%) was significantly higher than those of abnormal native T1 value (73%), ER (52%) and LGE presence (68%) in patients with ICI-associated myocarditis (p 0.05). The LGE extent and left ventricular ejection fraction of patients with ICI-associated myocarditis were 10.38 ± 9.64% and 56.42 ± 8.54%, respectively. LGE extent inversely correlated with left ventricular ejection fraction (r = -0.38, p = 0.004) but positively correlated with native T1 value (r = 0.28, p 0.04) and extracellular volume (r = 0.50, p = 0.001).T2 mapping could detect higher rate of patients with ICI-associated myocarditis than native T1 mapping, ER and LGE presence. LGE extent inversely correlated with left ventricular ejection fraction but positively correlated with native T1 value and extracellular volume in patients with ICI-associated myocarditis.

Convolutional neural network (CNN) is one of the most widely-used successful architectures in the era of deep learning. However, the high-computational cost of CNN still hampers more universal uses to light devices. Fortunately, the Fourier transform on convolution gives an elegant and promising solution to dramatically reduce the computation cost. Recently, some studies devote to such a challenging problem and pursue the complete frequency computation without any switching between spatial domain and frequent domain. In this work, we revisit the Fourier transform theory to derive feed-forward and back-propagation frequency operations of typical network modules such as convolution, activation and pooling. Due to the calculation limitation of complex numbers on most computation tools, we especially extend the Fourier transform to the Laplace transform for CNN, which can run in the real domain with more relaxed constraints. This work more focus on a theoretical extension and discussion about frequency CNN, and lay some theoretical ground for real application.

Face recognition (FR) has made extraordinary progress owing to the advancement of deep convolutional neural networks. However, demographic bias among different racial cohorts still challenges the practical face recognition system. The race factor has been proven to be a dilemma for fair FR (FFR) as the subject-related specific attributes induce the classification bias whilst carrying some useful cues for FR. To mitigate racial bias and meantime preserve robust FR, we abstract face identity-related representation as a signal denoising problem and propose a progressive cross transformer (PCT) method for fair face recognition. Originating from the signal decomposition theory, we attempt to decouple face representation into i) identity-related components and ii) noisy/identity-unrelated components induced by race. As an extension of signal subspace decomposition, we formulate face decoupling as a generalized functional expression model to cross-predict face identity and race information. The face expression model is further concretized by designing dual cross-transformers to distill identity-related components and suppress racial noises. In order to refine face representation, we take a progressive face decoupling way to learn identity/race-specific transformations, so that identity-unrelated components induced by race could be better disentangled. We evaluate the proposed PCT on the public fair face recognition benchmarks (BFW, RFW) and verify that PCT is capable of mitigating bias in face recognition while achieving state-of-the-art FR performance. Besides, visualization results also show that the attention maps in PCT can well reveal the race-related/biased facial regions.

Accurate traffic prediction is crucial to the guidance and management of urban traffics. However, most of the existing traffic prediction models do not consider the computational burden and memory space when they capture spatial-temporal dependence among traffic data. In this work, we propose a factorized Spatial-Temporal Tensor Graph Convolutional Network to deal with traffic speed prediction. Traffic networks are modeled and unified into a graph that integrates spatial and temporal information simultaneously. We further extend graph convolution into tensor space and propose a tensor graph convolution network to extract more discriminating features from spatial-temporal graph data. To reduce the computational burden, we take Tucker tensor decomposition and derive factorized a tensor convolution, which performs separate filtering in small-scale space, time, and feature modes. Besides, we can benefit from noise suppression of traffic data when discarding those trivial components in the process of tensor decomposition. Extensive experiments on two real-world traffic speed datasets demonstrate our method is more effective than those traditional traffic prediction methods, and meantime achieves state-of-the-art performance.

Objectives This study aimed to explore the dignity and related factors among older adults in long-term care facilities. Methods Cross-sectional data were obtained from a sample of 253 Chinese older adults dwelling in long-term care facilities. Dignity among older adults was measured using the Dignity Scale, and its potential correlates were explored using multiple linear regressions. Results Results showed that the total score of the Dignity Scale is 151.95 ± 11.75. From high to low, the different factors of dignity among older adults in long-term care facilities were as follows: caring factors (4.83 ± 0.33), social factors (4.73 ± 0.41), psychological factors (4.66 ± 0.71), value factors (4.56 ± 0.53), autonomous factors (4.50 ± 0.57), and physical factors (4.38 ± 0.55). A higher score of the Dignity Scale was associated with higher economic status, fewer chronic diseases, less medication, better daily living ability and long-time lived in cities. Conclusion Older adults with low economic status, more chronic diseases, and poor daily living ability, taking more medications, or the previous residence in rural areas seem to be most at low-level dignity in long-term care facilities and thus require more attention than their peers.

Resource constraints, e.g. limited product inventory or financial strength, may affect consumers' choices or preferences in some recommendation tasks but are usually ignored in previous recommendation methods. In this paper, we aim to mine the cue of user preferences in resource-limited recommendation tasks, for which purpose we specifically build a large used car transaction dataset possessing resource-limitation characteristics. Accordingly, we propose an interest-behavior multiplicative network to predict the user's future interaction based on dynamic connections between users and items. To describe the user-item connection dynamically, mutually-recursive recurrent neural networks (MRRNNs) are introduced to capture interactive long-term dependencies, and meantime effective representations of users and items are obtained. To further take the resource limitation into consideration, a resource-limited branch is built to specifically explore the influence of resource variation on user preferences. Finally, mutual information is introduced to measure the similarity between the user action and fused features to predict future interaction, where the fused features come from both MRRNNs and resource-limited branches. We test the performance on the built used car transaction dataset as well as the Tmall dataset, and the experimental results verify the effectiveness of our framework.

Point clouds are unstructured and unordered in the embedded 3D space. In order to produce consistent responses under different permutation layouts, most existing methods aggregate local spatial points through maximum or summation operation. But such an aggregation essentially belongs to the isotropic filtering on all operated points therein, which tends to lose the information of geometric structures. In this paper, we propose a spatial transformer point convolution (STPC) method to achieve anisotropic convolution filtering on point clouds. To capture and represent implicit geometric structures, we specifically introduce spatial direction dictionary to learn those latent geometric components. To better encode unordered neighbor points, we design sparse deformer to transform them into the canonical ordered dictionary space by using direction dictionary learning. In the transformed space, the standard image-like convolution can be leveraged to generate anisotropic filtering, which is more robust to express those finer variances of local regions. Dictionary learning and encoding processes are encapsulated into a network module and jointly learnt in an end-to-end manner. Extensive experiments on several public datasets (including S3DIS, Semantic3D, SemanticKITTI) demonstrate the effectiveness of our proposed method in point clouds semantic segmentation task.

Movie graphs play an important role to bridge heterogenous modalities of videos and texts in human-centric retrieval. In this work, we propose Graph Wasserstein Correlation Analysis (GWCA) to deal with the core issue therein, i.e, cross heterogeneous graph comparison. Spectral graph filtering is introduced to encode graph signals, which are then embedded as probability distributions in a Wasserstein space, called graph Wasserstein metric learning. Such a seamless integration of graph signal filtering together with metric learning results in a surprise consistency on both learning processes, in which the goal of metric learning is just to optimize signal filters or vice versa. Further, we derive the solution of the graph comparison model as a classic generalized eigenvalue decomposition problem, which has an exactly closed-form solution. Finally, GWCA together with movie/text graphs generation are unified into the framework of movie retrieval to evaluate our proposed method. Extensive experiments on MovieGrpahs dataset demonstrate the effectiveness of our GWCA as well as the entire framework.

Additional file 7: Table S2. Related to Supplementary Material and Methods: List of ChIP primers used in this study.

Additional file 6: Table S1. List of human qRT-PCR primers used in this study.

In this work, we address semi-supervised classification of graph data, where the categories of those unlabeled nodes are inferred from labeled nodes as well as graph structures. Recent works often solve this problem via advanced graph convolution in a conventionally supervised manner, but the performance could degrade significantly when labeled data is scarce. To this end, we propose a Graph Inference Learning (GIL) framework to boost the performance of semi-supervised node classification by learning the inference of node labels on graph topology. To bridge the connection between two nodes, we formally define a structure relation by encapsulating node attributes, between-node paths, and local topological structures together, which can make the inference conveniently deduced from one node to another node. For learning the inference process, we further introduce meta-optimization on structure relations from training nodes to validation nodes, such that the learnt graph inference capability can be better self-adapted to testing nodes. Comprehensive evaluations on four benchmark datasets (including Cora, Citeseer, Pubmed, and NELL) demonstrate the superiority of our proposed GIL when compared against state-of-the-art methods on the semi-supervised node classification task., Comment: 11 pages

Thyroid carcinoma (TC) is the most common endocrinal malignancy worldwide. Cyclin E2 (CCNE2), a member of the cyclin family, acts as a regulatory subunit of cyclin-dependent kinases (CDKs). It controls the transition of quiescent cells into the cell cycle, regulates the G1/S transition, promotes DNA replication, and activates CDK2. This study explored the role and potential molecular mechanisms of CCNE2 expression in TC tissues.Immunohistochemistry was used to evaluate the CCNE2 protein expression levels in TC. High-throughput data on CCNE2 in TC were obtained from RNA sequencing (RNA-seq), microarray, and literature data. The CCNE2 expression levels in TC were comprehensively assessed through an integrated analysis. Analyses of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPIs) data facilitated the investigation of the relative molecular mechanisms of CCNE2 in TC.The immunohistochemical experiment showed a significant increase in the expression of CCNE2 in the TC tissues. For 505 TC and 59 non-cancerous samples from RNA-seq data, the area under the curve (AUC) was 0.8016 (95% confidence interval [CI] 0.742-0.8612; p0.001). With another 14 microarrays, the pool standard mean difference [SMD] was 1.01 (95% CI [0.82-1.19]). The pooled SMD of CCNE2 was 1.12 (95% CI [0.60-1.64]), and the AUC was 0.87 (95% CI [0.84-0.90]) for 1157 TC samples and 366 non-cancerous thyroid samples from all possible sources. Nine hub genes were upregulated in TC.A high expression of CCNE2 may lead to carcinogenesis and the development of TC.

In order to maintain the orbit accuracy of the GEO (geostationary orbit) navigation satellite, the thruster is not allowed to use during long-term orbital operation. The disturbance torque is absorbed by the reaction wheel system to maintain the three-axis nominal attitude. Because the reaction wheel absorbs the angular momentum accumulated by the disturbance torque, the rotation speed continues to increase. When the reaction wheel speed exceeds the safety threshold, it is necessary to switch to other working modes and use the thruster to perform angular momentum unloading. At this time satellite services were unavailable due to the effects of thruster on orbit. In order to maintain a certain degree of redundancy, usually 4 or more reaction wheels are installed on the satellite to form a redundant system. An angular momentum allocation strategy based on a pseudo-inverse solution is adopted. This allocation strategy is an energy minimization strategy. This paper proposes a distribution strategy based on the minimization of the infinite norm of the angular momentum vector. Using this distribution strategy, the maximum capacity of the reaction wheel system can be exerted, under the condition that the angular momentum accumulated by the reaction wheel system is the same, the maximum speed of the reaction wheel is the lowest. This extends the time for the reaction wheel to reach the safe threshold speed, and extends service time for GEO navigation satellites. In this paper, the reaction wheel system with a pyramid configuration is taken as an example for analysis. The angular momentum of the reaction wheel system is decomposed into a linear combination of pseudo-inverse solution and zero space vector, and it is solved under the criterion of infinite norm minimization. At the same time, this paper compares two allocation strategies for 4π space. Analysis and simulation show that the use of infinite norm minimization allocation strategy can significantly increase the available time of navigation satellites.

Kangfu Xiaoyan Suppository is widely used in the treatment of gynecological inflammatory diseases. Long-term clinical application and a certain amount of research evidences show that Kangfu Xiaoyan Suppository can alleviate the clinical symptoms of pelvic inflammatory diseases,reduce the recurrence rate,and relieve sequelae,with a better safety and economic characteristics. As a type of nationally protected traditional Chinese medicine and type B medicine included in medical insurance,it has been selected as a Chinese patent medicine for rectal administration. It was included in the Guidelines for diagnosis and treatment of common gynecological diseases of traditional Chinese medicine published by the Chinese Academy of Traditional Chinese Medicine in 2012,the Pelvic inflammatory diseases diagnosis and treatment guidelines issued by the Infectious Diseases Collaborative Group of the Obstetrics and Gynecology Branch of the Chinese Medical Association in 2014,and the group standard of Single use of traditional Chinese medicine/combined antibiot guidelines for clinical practice-pelvic inflammatory diseases of the Chinese Academy of Traditional Chinese Medicine in 2017. To further enhance clinicians' understanding of the drug and better guide its rational clinical use,experts from the field of gynecology of traditional Chinese and Western medicine were invited to develop and compile this expert consensus. This consensus takes full account of clinical evidences and expert clinical experience,and form recommendations for clinical problems based on evidences and consensus recommendations for clinical problems without evidence by nominal grouping method. The expert consensus is mainly formed in the consideration of six factors: quality of evidence,economy,efficacy,adverse reactions,patient acceptability and others. Based on clinical research evidences and expert experience,this consensus provides a preliminary reference for the clinical use of the drug in a concise and clear format. However,evidence-based support is still required in a large number of high-quality studies,and this consensus will be revised in the future according to new clinical problems and the update of evidence-based evidence in practical application.

As a thermal energy storage medium (TESM) at moderate-high temperature of concentrating solar power (CSP), the molten NaCl-MgCl2 has sharp corrosive action on a metal container. The influence of MgCl2 content on a metal corrosion behavior is not clear. The corrosion behavior of a Fe based alloy (GH1140) in molten NaCl-MgCl2 (at 1123 K) with different content of MgCl2 (wt%：0.0，48.9，61.0，93.6) were studied by the submergence corrosion experience. Results show that: after corrosion for 5 h, there is a negative correlation between the weight loss of samples and the content of MgCl2. In the molten salt without MgCl2, the sample was corroded according to the mechanism of “dissolution as anode- oxidizing- peeling off of oxide film”. After adding a little MgCl2 in the molten salt, an unprotected MgO shell was formed on the sample surface. The samples weight decreased and obeyed a line law. The elements of Fe and Cr deplete in the corrosion layer. Corrosion mechanism is “dissolution as anode- oxidizing- reduction- chlorination”. When the content of MgCl2 is very high in the molten salt, partial pressure of Cl2 increases. The corrosion mechanism is similar to what in the molten salt with a little MgCl2, but the chlorination reaction is more severe.

Conducting polymer of polypyrrole (PPy) was employed to functionalize the ordered silicon nanowire (SiNWs) with the aim to enhance gas-sensing response at room temperature (RT). A dual-MACE process was first developed to form loose SiNWs (LNWs) array facilitating uniform attachment of PPy on whole nanowire surface. The LNWs array functionalized with continuous PPy ultrathin film and discrete PPy nanoparticles, i.e., PPy-shell@LNWs and PPy-NPs@LNWs, were prepared respectively via vapor chemical polymerization (VCP) and liquid chemical polymerization (LCP) of pyrrole monomer (Py). Comparative investigations on the NH3-sensing properties of the pristine SiNWs before and after dual-MACE treatment, and PPy-functionalized SiNWs of PPy-shell@LNWs and PPy-NPs@LNWs were carried out at RT. It was found that PPy functionalization enhances the NH3-sensing response of SiNWs-based sensors effectively. Formation of continuous PPy shell film is further found to result in more pronounced response enhancement when compared to the modification by discrete PPy NPs. The PPy-shell@LNWs and PPy-NPs@LNWs respectively exhibit about 27 and 6 times response enhancement to 10 ppm NH3 gas compared to the pristine one. The underlying NH3-sensing mechanism for the PPy-functionalized SiNWs, especially for the remarkable response enhancement of PPy-shell@SiNWs was demonstrated.

Dense billets of GCr15 bearing steel were successfully fabricated by spray forming. Microstructure and porosity of spray-formed GCr15 steel under different spray conditions were analyzed. The results show that the microstructure of as-sprayed GCr15 steel is composed of refining pearlite, and different spraying conditions caused a different size of pearlite colonies. Besides, the chemical composition remains high homogeneity in the spray-formed billet. The morphologies and distribution of porosity are mainly determined by the thermal condition of spray forming. The increase of the GMR (gas to metal mass ratio) leads to the porosity evolution from interstitial pore to gas pore.

Gallium nitride (GaN) nanopencils and nanotowers have been synthesized by a chemical vapor deposition (CVD) method using the reaction of Ga2O3 and ammonia. The observed morphology of GaN nanopencils is divided into two parts: the bottom is a nanowire with large diameter; the top is a nanowire with small diameter. The observed morphology of GaN nanotowers is a layer structure. The formation mechanism of GaN nanopencils and nanotowers is a vapor-liquid-solid (VLS) mechanism. The turn on field of 2.6 V/μm is obtained for GaN nanopencils and the turn on field of 4.1 V/μm is obtained for GaN nanotowers, which are sufficient for field emission flat panel displays and cold electron sources in display devices. This growth of GaN nanopencils and nanotowers will facilitate flexible design of device architectures for nanoelectronics.

In this work, uniform core-shell structure of polypyrrole wrapped on tungsten oxide nanorods (PPy@W18O49 core-shell nanorods) were synthesized via a two-step process for gas-sensing applications. The core-nanorods of W18O49 were first grown by solvothermal method with tungsten hexachloride (WCl6) as precursor. The PPy-shell layer was then formed uniformly on the solvothermally synthesized W18O49 nanorods by in-situ chemical polymerization of pyrrole monomer (Py), with sodium dodecyl benzene sulfonic acid (DBSA) as dopant and ammonium persulfate (APS) as oxidant. High dispersion of Py achieving in ethanol is proved to be crucial to form the uniform PPy-shell layer, and the layer thickness of PPy-shell is highly controlled by adjusting the Py concentration in polymeric solution. The morphology and structure of the nanocomposite were characterized systematically; it shows that the composite exhibits perfect core-shell structure of one-dimensional (1D) nanorods with average diameter of around 70–90 nm. The NH3-sensing properties of the sensors based on the PPy@W18O49 core-shell nanorods were investigated at operating temperature of 15–130 °C over NH3 concentration ranging from 1 to 200 ppm. The response magnitude of the PPy@W18O49 sensor can be affected seriously by temperature fluctuation, even in room temperature range (15–30 °C), and meanwhile, a temperature-dependent p-n response characteristic reversal is observed for the heteronanorods sensor. At much low room temperature of 15 °C, the present PPy@W18O49 nanorods show quick and sensitive response to NH3 gas mainly due to the ultrathin, uniform PPy shell and the special heterojunction effect between p-type PPy and n-type W18O49. The underlying gas-sensing mechanism is analyzed.

Surface functionalization is very effective in enhancing sensing properties of a chemiresistive gas sensor. In this work, we develop a novel and cost-effective process to prepare Ag-modified silicon nanowire (SiNW) sensors and further suggest a resistance effect model to clarify the enhanced sensing mechanism of Ag-modified SiNWs. The SiNWs were formed via metal-assisted chemical etching (MACE), and the Ag nanoparticle (NP) modification was achieved in situ based on the MACE-produced Ag dendrites by involving a crucial anisotropic postetching of TMAH. The TMAH etching induces a loose array of needle-like, rough SiNWs (RNWs) with firm attachment of tiny Ag NPs. Comparative investigations for NH3-sensing properties indicate that the RNWs modified by discrete Ag NPs (Ag@RNWs) display an ∼3-fold enhancement in gas response at room temperature compared with pristine SiNWs. Meanwhile, transient response and ultrafast recovery are observed for the Ag@RNW sensor (tres ≤ 2 s and trec ≤ 9 s to 0.33–10 ppm of NH3). ...

In this paper, we consider boundary output feedback stabilization for a multidimensional wave equation with boundary control matched unknown nonlinear internal uncertainty and external disturbance. A new unknown input type extended state observer is proposed to recover both state and total disturbance which consists of internal uncertainty and external disturbance by using the hidden regularity of the wave equation. By the active disturbance rejection control (ADRC) strategy, the total disturbance is compensated (canceled) in the feedback loop, which together with a collocated stabilizing controller without uncertainty, leads to an output feedback stabilizing feedback control. It is shown that the resulting closed-loop system is well-posed and asymptotically stable under weak assumption on internal uncertainty and external disturbance. The numerical experiments are carried out to show the effectiveness of the proposed scheme.

Background This study was conducted to investigate the clinical advantages of modified double ring areola incision (MDRAI) compared to ordinary areola incision (OAI) in multicentric breast fibroadenoma in women. Methods Sixty cases of multicentric benign breast tumor were recruited from the First Affiliated Hospital of Bengbu Medical College from January to December 2016. The cases were divided into two groups according to surgical approach: MDRAI (n = 20) and OAI (n = 40). The operation duration, intraoperative blood loss, drainage time, and postoperative recurrence rate in the first six months were compared. Results The mean age and tumor locations were not statistically different between the groups (P > 0.05). However, more lesions and larger tumor diameter were found in the MDRAI group than in the OAI group, with statistical difference (P < 0.05). The operation duration and drainage time of the two groups were not statistically different for unilateral or bilateral lesions (P > 0.05). However, the intraoperative blood loss was statistically different between the two groups (P < 0.05). All 60 cases received six months of follow‐up. Eight recurrent cases were found in the OAI group, but none in the MDRAI group. The recurrence rate was significantly different (χ2 = 4.62, P < 0.05). Conclusion Compared with OAI, MDRAI offers greater advantages in the aspects of blood loss and recurrence for the treatment of breast benign tumor, especially for multicentric larger lesions.

As a solar energy storage medium at moderate-to-high temperature, molten chlorate severally corrodes metal vessels. The corrosion behavior and mechanisms of pure nickel (Ni), GH4033 and GH4169 in 793 K molten eutectic NaCl-MgCl 2 were studied. During the initial stage of corrosion, pure Ni and GH4033 gain weight from the many plate-like shells formed on the sample surface. After corrosion for 40 h, all sample surfaces were rather porous and loosened. MgO nuclei that were attached to the loosened microstructure surface fell off with the microstructures, which led to a continual decrease in the sample weight. The three sample surfaces were mainly composed of MgO and Ni or (Ni, Fe) from the sample substrate. Al and Ti were preferentially oxidized and stably excited on the GH4033 surface, which inhibits the corrosion process. The corrosion rate of GH4169 was the highest for the δ phase, which is rich in Nb, and phase boundaries were corroded preferentially. In the anode reaction, metal atoms escape from the corrosion system in the form of chlorides or aqueous chlorides.

Background: The clinical significance of miR-141-3p in hepatocellular carcinoma has not been verified. Therefore, we conducted this study to examine miR-141-3p expression and its clinical significance in hepatocellular carcinoma and to investigate the functions of its potential targets. Methods: The Cancer Genome Atlas database and the Gene Expression Omnibus database were used to explore the aberrant expression of miR-141-3p in hepatocellular carcinoma. Furthermore, we assessed the miR-141-3p levels in 95 hepatocellular carcinoma tissues with 95 matched adjacent tissues using real-time quantitative polymerase chain reaction. Moreover, a target gene prediction analysis of miR-141-3p, a natural language processing analysis for hepatocellular carcinoma using PubMed, and a gene functional enrichment analysis were conducted to search the potential function of miR-141-3p in the pathogenesis of hepatocellular carcinoma. Results: Regarding The Cancer Genome Atlas data, miR-141-3p levels were markedly downregulated in hepatocellular carcinoma tissue compared to para- or nontumor tissue (4.6112 [1.7096] vs 5.3053 [1.4254], P = .045). MiR-141-3p expression was reduced in patients with hepatocellular carcinoma with a low pathologic T stage (P = .006), a low grade (P = .01), elderly hepatocellular carcinoma patients (P = .001), and male patients with hepatocellular carcinoma (P = .01) compared with that in patients with hepatocellular carcinoma with high pathologic T stages, high grades, young patients with hepatocellular carcinoma, and female patients with hepatocellular carcinoma. However, according to the Gene Expression Omnibus database, no significant differences in the expression of miR-141-3p were observed between hepatocellular carcinoma tissue and normal liver tissue (P = .984). Real-time quantitative polymerase chain reaction confirmed a similar trend of decreased miR-141-3p in hepatocellular carcinoma tissue (1.7542 [0.8663] vs 2.5562 [1.7913], P = .001) as observed in The Cancer Genome Atlas. In addition, decreased miR-141-3p levels were detected in the multiple tumor nodes group (P = .004), the metastasis group (P < .001), and the advanced TNM stage group (P = .01), compared to the single tumor nodes group, the nonmetastasis group, and the early TNM stage group. Two hundred eighty-two genes were identified from the overlap between the predicted targets and the natural language processing analysis. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed several significant biological functions and pathways related to the pathogenesis of cancers, including hepatocellular carcinoma. Conclusion: Downregulation of miR-141-3p might be responsible for the carcinogenesis and aggressiveness of hepatocellular carcinoma. MiR-141-3p may act as an antitumor microRNA, which is essential for hepatocellular carcinoma progression through the regulation of various signaling pathways. Thus, interactions with miR-141-3p may provide a novel strategy for hepatocellular carcinoma treatment in the future.

New product development (NPD) project is with high uncertainty and known as a high failure rate in terms of technical performance or market performance. Concept selection serves as one of the crucial decisions in a NPD project, since the chosen sole concept at the early stage as in regular practice functions as cost driver as well as profitability driver. With the argument that the concept set selection, rather than a single concept selection, should be made for reasonable risk aversion, this paper, based on the previous research of the authors, proposes a detailed modeling effort to enable a robust set-based concept development. Utility theory integrated with uncertain technical levels of product attributes is introduced to express the selection decision risks, and information quality model is set up to connect the quality information with NPD budget. An illustrative case scenario of UCAV demonstrates the applicability of the proposed robust concept set selection framework for NPD risk control.