RST-style document-level discourse parsing remains a difficult task and efficient deep learning models on this task have rarely been presented. In this paper, we propose an attention-based hierarchical neural network model for discourse parsing. We also incorporate tensor-based transformation function to model complicated feature interactions. Experimental results show that our approach obtains comparable performance to the contemporary state-of-the-art systems with little manual feature engineering.

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Despite intense recent research, the neural correlates of conscious visual perception remain elusive. The most established paradigm for studying brain mechanisms underlying conscious perception is to keep the physical sensory inputs constant and identify brain activities that correlate with the changing content of conscious awareness. However, such a contrast based on conscious content alone would not only reveal brain activities directly contributing to conscious perception, but also include brain activities that precede or follow it. To address this issue, we devised a paradigm whereby we collected, trial-by-trial, measures of objective performance, subjective awareness, and the confidence level of subjective awareness. Using magnetoencephalography recordings in healthy human volunteers, we dissociated brain activities underlying these different cognitive phenomena. Our results provide strong evidence that widely distributed slow cortical potentials (SCPs) correlate with subjective awareness, even after the effects of objective performance and confidence were both removed. The SCP correlate of conscious perception manifests strongly in its waveform, phase, and power. In contrast, objective performance and confidence were both contributed by relatively transient brain activity. These results shed new light on the brain mechanisms of conscious, unconscious, and metacognitive processing.

Rheumatoid arthritis (RA) is a worldwide inflammatory disease that seriously threatens human health and needs more effective treatment approaches. Near infrared (NIR) light can efficiently penetrate inflamed joints affected by RA, so phototherapy, including photothermal therapy (PTT) and photodynamic therapy (PDT), may provide new opportunities. In this work, the unique Cu S nanoparticles (NPs) are prepared for RA treatment enlightened by the fact that copper (Cu)-based nanomaterials can simultaneously serve as PTT agents and photosensitizers (for PDT). Meanwhile, Cu can promote ostogenesis and chondrogenesis. The Cu S NPs combined with NIR (808 nm, 1 W cm ) irradiation not only achieve better bone preservation, including higher bone mineral density (BMD) and bone volume/total volume, but also inhibit inflamed synovial invasion, cartilage erosion, and expression of proinflammatory cytokines in vivo. Moreover, the Cu S NPs can effectively kill clinical pathogenic Staphylococcus aureus and Escherichia coli to prevent bacterial infection during intra-articular injection. Therefore, the combined PTT and PDT using the multifunctional Cu S NPs could be a novel RA treatment modality with full potential.

The dimensional overlap (DO) model proposes distinct mechanisms for stimulus-stimulus (S-S) and stimulus-response (S-R) conflict effects. Many studies have examined the independence of S-S and S-R conflict effects in the color-word Stroop and Simon tasks. However, confounds exist between the distinction of DO (i.e., S-S dimensional overlap compared with S-R dimensional overlap) and the distinction of stimulus attributes (e.g., color compared with spatial location; semantic compared with nonsemantic information), which may hinder interpretation of the independence of S-S and S-R conflicts. A spatial Stroop (word) task and a spatial Stroop (arrow) task were combined with a Simon task in Experiments 1 and 2, respectively to eliminate these confounds of stimulus attributes. The results showed that S-S and S-R conflicts affected performance additively. There was no significant correlation across participants. These findings lend further support to independent processing of S-S and S-R conflicts as it is outlined in the taxonomy of DO.

Image retrieval has been an active research topic in recent decades. In this paper, a novel and effective algorithm is proposed for printed fabric image retrieval by combining color moments methods and gist feature description methods. Color information distribution could be declared by color moments very well and gist feature description has an advantage in representation of spatial information. Therefore, color features and spatial information features are respectively extracted by color moments and the gist feature description method, which constitutes a feature database. After that, the similarity between query image features and the feature database is computed by Euclidean distance. To demonstrate the efficacy of the framework, experiments are conducted on the FABRIC database. Experimental results indicate that the proposed algorithm is more effective and accurate than other hybrid schemes for printed fabric images, in terms of precision and recall.

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BackgroundThere is no study accessible now assessing the prognostic aspect of radiomics for anti-PD-1 therapy for patients with HCC.AimThe aim of this study was to develop and validate a radiomics nomogram by incorporating the pretreatment contrast-enhanced Computed tomography (CT) images and clinical risk factors to estimate the anti-PD-1 treatment efficacy in Hepatocellular Carcinoma (HCC) patients.MethodsA total of 58 patients with advanced HCC who were refractory to the standard first-line of therapy, and received PD-1 inhibitor treatment with Toripalimab, Camrelizumab, or Sintilimab from 1st January 2019 to 31 July 2020 were enrolled and divided into two sets randomly: training set (n = 40) and validation set (n = 18). Radiomics features were extracted from non-enhanced and contrast-enhanced CT scans and selected by using the least absolute shrinkage and selection operator (LASSO) method. Finally, a radiomics nomogram was developed based on by univariate and multivariate logistic regression analysis. The performance of the nomogram was evaluated by discrimination, calibration, and clinical utility.ResultsEight radiomics features from the whole tumor and peritumoral regions were selected and comprised of the Fusion Radiomics score. Together with two clinical factors (tumor embolus and ALBI grade), a radiomics nomogram was developed with an area under the curve (AUC) of 0.894 (95% CI, 0.797–0.991) and 0.883 (95% CI, 0.716–0.998) in the training and validation cohort, respectively. The calibration curve and decision curve analysis (DCA) confirmed that nomogram had good consistency and clinical usefulness.ConclusionsThis study has developed and validated a radiomics nomogram by incorporating the pretreatment CECT images and clinical factors to predict the anti-PD-1 treatment efficacy in patients with advanced HCC.