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Independent manipulation of transmitted and reflected light fields is a key technology for the realization of multifunctional optical applications, which can be implemented based on multilayered plasmonic or supercell subwavelength structures. However, the former is not suitable for the optical bands, while the latter is insufficient in generating large phase gradients. Here, an adjoint-optimization-based inverse design methodology is proposed, which utilizes the polarization-selective local interference between individual meta-atoms and enables monolayer dielectric metasurfaces to decouple the wavefront of transmitted and reflected optical fields. Moreover, this methodology serves to mitigate the aperiodic electromagnetic crosstalk inherent between adjacent meta-atoms, consequently leading to a significant enhancement in the performance of meta-devices. We analyzed the physical mechanism of adjoint optimization and proposed the concept of phase factors, highlighting their importance in the rapid inverse design of meta-devices-an aspect often overlooked in previous research. To demonstrate the feasibility and robustness of our method, we optimize monolayer metasurfaces with different initial structures. These devices efficiently focus and deflect x-linearly and y-linearly polarized incident light in transmission and reflection spaces, respectively. Overall, this methodology holds immense potential for designing multifunctional, high-performing metasurfaces that meet multiple constraints, opening up broad prospects for applications. [ABSTRACT FROM AUTHOR]

As one of the core modules of the dialogue system, intent recognition plays an important role in human–computer interaction. Most of the existing intent recognition research is limited to simple, direct, and explicit intent recognitions. However, the natural human–computer interactions are flexible and diverse, and the expressions are often the euphemistic implicit intentions. Therefore, the implicit intent recognition brings new research challenges in this field. This paper pioneers a Chinese Implicit Intent Dataset (CIID), which covers seven common intents from different fields, and the data is the text containing the user's implicit intent. Based on this corpus, it is the first-time the prompt learning is employed for implicit intent recognition and by constructing a suitable prompt template so that the model can get "relevant hints" to dig out the true intention of the user. We also evaluate a range of classification models on CIID dataset in this study. Experimental results show that our proposed model achieves the state-of-the-art recognition accuracy. Furthermore, we explore the performance of the model with few-shot settings, the results also prove that the proposed method is advanced and robust. [ABSTRACT FROM AUTHOR]

Exploiting the fundamental features of quantum mechanics, an entanglement-based quantum network offers a promising platform for many dramatic applications such as multi-user cryptography. Nevertheless, the implementation of a large-scale quantum network in real-world scenarios remains challenging owing to the multiple scattering events in complex environment, particularly those frequency-sensitive scatterings that disturb quantum correlation both spatially and temporally. Here, we demonstrate the frequency-insensitive spatiotemporal control of entangled photons in a fully connected network by leveraging a Fourier-transform setup and the genetic algorithm. Such an approach can effectively improve the entanglement distribution process through a multimode fiber while the quantum characteristic of the network can be maintained well after the spatiotemporal shaping. Our scheme can serve as a bridging technology to establish entanglement between remote nodes of spectrally interconnected quantum systems and has great potential applications in future real-world quantum networks. [ABSTRACT FROM AUTHOR]

Transcriptional heterogeneity due to plasticity of the epigenetic state of chromatin contributes to tumour evolution, metastasis and drug resistance1–3. However, the mechanisms that cause this epigenetic variation are incompletely understood. Here we identify micronuclei and chromosome bridges, aberrations in the nucleus common in cancer4,5, as sources of heritable transcriptional suppression. Using a combination of approaches, including long-term live-cell imaging and same-cell single-cell RNA sequencing (Look-Seq2), we identified reductions in gene expression in chromosomes from micronuclei. With heterogeneous penetrance, these changes in gene expression can be heritable even after the chromosome from the micronucleus has been re-incorporated into a normal daughter cell nucleus. Concomitantly, micronuclear chromosomes acquire aberrant epigenetic chromatin marks. These defects may persist as variably reduced chromatin accessibility and reduced gene expression after clonal expansion from single cells. Persistent transcriptional repression is strongly associated with, and may be explained by, markedly long-lived DNA damage. Epigenetic alterations in transcription may therefore be inherently coupled to chromosomal instability and aberrations in nuclear architecture.Micronuclei, which are common features of nuclei in cancer cells, can generate heritable sources of transcriptional suppression, a finding that establishes an inherent relationship between chromosomal instability and variation in chromatin state and gene expression. [ABSTRACT FROM AUTHOR]

Optical encryption is a promising approach to protecting secret information owing to the advantages of low-power consumption, parallel, high-speed, and multi-dimensional processing capabilities. Nevertheless, conventional strategies generally suffer from bulky system volume, relatively low security level, redundant measurement, and/or requirement of digital decryption algorithms. Here, we propose a general optical security strategy dubbed meta-optics-empowered vector visual cryptography, which fully exploits the abundant degrees of freedom of light as well as the spatial dislocation as key parameters, significantly upgrading the security level. We also demonstrate a decryption meta-camera that can implement the reversal coding procedure for real-time imaging display of hidden information, avoiding redundant measurement and digital post-processing. Our strategy features the merits of a compact footprint, high security, and rapid decryption, which may open an avenue for optical information security and anti-counterfeiting. Usual methods for optical encryption suffer from a tradeoff between the level of security and the complexity of operation given by multiple optical measurements or digital postprocessing. Here, the authors show a multi-d.o.f. metasurface-based vector optical manipulation protocol enabling secure decryption in real time. [ABSTRACT FROM AUTHOR]

Purpose: Finding the biomarkers associated with autism spectrum disorder (ASD) is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatments. In essence, we are faced with two challenges (i) how to learn a node representation and a clean graph structure from original graph data with high dimensionality and (ii) how to jointly model the procedure of node representation learning, structure learning and graph classification. Methods: We propose FSL-BrainNet, an interpretable graph convolution network (GCN) model for jointly Learning of node Features and clean Structures in brain networks for automatic brain network classification and interpretation. We formulate an end-to-end trainable and interpretable framework for graph classification and biomarkers (salient brain regions and potential subnetworks) identification. Results: The experimental results on the ABIDE dataset show that our proposed methods not only achieve improved prediction performance compared with the state-of-the-art methods, but also find a compact set of highly suggestive biomarkers including relevant brain regions and subnetworks to ASD. Conclusion: Through node feature learning and structure learning, our model can simultaneously select important brain regions and identify subnetworks. [ABSTRACT FROM AUTHOR]

Micro-expression recognition has attracted extensive attention from psychological and computer vision communities due to its multiple real-life applications. Compared with macro-expression, the change of micro-expression is subtle and difficult for detection and capture. Hence, the recognition is challenging. This paper proposes a micro-expression recognition model SQU-C3D which combines SqueezeNet and C3D methods. The features of micro-expression are mainly reflected in the apex frame, therefore a lightweight network called SqueezeNet is adopted to implement a reliable apex frame spotting method for dataset without apex frame labels. The position of the apex frame is detected by comparing the feature difference between the current and onset frames. In addition, the Convolutional 3D (C3D) network is utilized for micro-expression recognition due to its strong capability of extracting spatial–temporal features. The apex frame determined by SqueezeNet is fed into the C3D network along with the onset and offset frames, and the micro-expression is recognized by learning the features of these three key frames. Extensive experiments are conducted on three spontaneous micro-expression databases, namely, CASME II, SAMM, and SMIC-HS, where CASME II and SAMM include apex frame labels, whereas SMIC-HS does not. SQU-C3D achieves accuracy of 80.29% with 7 classes, 81.33% with 5 classes and 79.12% with 3 classes on the micro-expression benchmark dataset of CASME II, SAMM and SMIC-HS, respectively. Experimental results reveal that the proposed framework performs better than the state-of-the-art methods in the comparison. [ABSTRACT FROM AUTHOR]

The diagnosis of chest diseases is a challenging task for assessing thousands of radiology subjects. Their diagnosis decisions heavily rely on the expert radiologists' manual annotations. It is important to develop automated analysis methods for the computer-aided diagnosis of chest diseases on chest radiography. To explore the label relationship and improve the diagnosis performance, we present an end-to-end multi-label learning framework for jointly modeling the global and local label correlation, called GL-MLL that (1) explores the label correlation from a globally static view and a locally adaptive view, (2) considers the imbalanced class distribution, and (3) focuses on capturing label-specific features in image-level representation. We validate the performance of the proposed framework on the CheXpert dataset. The results demonstrate that the proposed GL-MLL outperforms state-of-the-art approaches. The code is available at https://github.com/llt1836/GL-MLL. [ABSTRACT FROM AUTHOR]

Background and aims: Ridge tillage with rice-rape rotation is an important paddy-upland rotation planting pattern in southern China. In order to improve physical environment of soil and increase yield of rice and rape, it is necessary to clarify effects of ridge tillage on root characteristic and yield, which can provide scientific basis for efficient utilization of paddy field. A field plot experiment was conducted to compare effects of conventional tillage with rice and winter fallow (CK1), conventional tillage with rice-rape rotation (CK2), wide ridge tillage with rice-rape rotation (RT1) and narrow ridge tillage with rice-rape rotation (RT2) on root characteristic and yield of rape and rice.Ridge tillage (including RT1 and RT2) had positive effects on relevant root indexes and the influence degree was different at different growth stages. For example, at maturity stage, the total root length and surface area of rape treated with RT2 were significantly higher than CK2 by 116.66% and 111.03%. Ridge tillage can significantly improve economic characteristics and yield. Strikingly, compared with CK1 and CK2, the actual yield of rice treated with RT1 was higher by 20.82% and 13.54%.Ridge tillage can promote root growth, enhance root vitality and increase yield. Narrow ridge tillage (RT2) is more beneficial to the root growth of rape but wide ridge tillage (RT1) is more beneficial to the root growth of rice. The wide ridge is more beneficial to the economic characteristics and yield of rape and rice than narrow ridge.Methods: Ridge tillage with rice-rape rotation is an important paddy-upland rotation planting pattern in southern China. In order to improve physical environment of soil and increase yield of rice and rape, it is necessary to clarify effects of ridge tillage on root characteristic and yield, which can provide scientific basis for efficient utilization of paddy field. A field plot experiment was conducted to compare effects of conventional tillage with rice and winter fallow (CK1), conventional tillage with rice-rape rotation (CK2), wide ridge tillage with rice-rape rotation (RT1) and narrow ridge tillage with rice-rape rotation (RT2) on root characteristic and yield of rape and rice.Ridge tillage (including RT1 and RT2) had positive effects on relevant root indexes and the influence degree was different at different growth stages. For example, at maturity stage, the total root length and surface area of rape treated with RT2 were significantly higher than CK2 by 116.66% and 111.03%. Ridge tillage can significantly improve economic characteristics and yield. Strikingly, compared with CK1 and CK2, the actual yield of rice treated with RT1 was higher by 20.82% and 13.54%.Ridge tillage can promote root growth, enhance root vitality and increase yield. Narrow ridge tillage (RT2) is more beneficial to the root growth of rape but wide ridge tillage (RT1) is more beneficial to the root growth of rice. The wide ridge is more beneficial to the economic characteristics and yield of rape and rice than narrow ridge.Results: Ridge tillage with rice-rape rotation is an important paddy-upland rotation planting pattern in southern China. In order to improve physical environment of soil and increase yield of rice and rape, it is necessary to clarify effects of ridge tillage on root characteristic and yield, which can provide scientific basis for efficient utilization of paddy field. A field plot experiment was conducted to compare effects of conventional tillage with rice and winter fallow (CK1), conventional tillage with rice-rape rotation (CK2), wide ridge tillage with rice-rape rotation (RT1) and narrow ridge tillage with rice-rape rotation (RT2) on root characteristic and yield of rape and rice.Ridge tillage (including RT1 and RT2) had positive effects on relevant root indexes and the influence degree was different at different growth stages. For example, at maturity stage, the total root length and surface area of rape treated with RT2 were significantly higher than CK2 by 116.66% and 111.03%. Ridge tillage can significantly improve economic characteristics and yield. Strikingly, compared with CK1 and CK2, the actual yield of rice treated with RT1 was higher by 20.82% and 13.54%.Ridge tillage can promote root growth, enhance root vitality and increase yield. Narrow ridge tillage (RT2) is more beneficial to the root growth of rape but wide ridge tillage (RT1) is more beneficial to the root growth of rice. The wide ridge is more beneficial to the economic characteristics and yield of rape and rice than narrow ridge.Conclusions: Ridge tillage with rice-rape rotation is an important paddy-upland rotation planting pattern in southern China. In order to improve physical environment of soil and increase yield of rice and rape, it is necessary to clarify effects of ridge tillage on root characteristic and yield, which can provide scientific basis for efficient utilization of paddy field. A field plot experiment was conducted to compare effects of conventional tillage with rice and winter fallow (CK1), conventional tillage with rice-rape rotation (CK2), wide ridge tillage with rice-rape rotation (RT1) and narrow ridge tillage with rice-rape rotation (RT2) on root characteristic and yield of rape and rice.Ridge tillage (including RT1 and RT2) had positive effects on relevant root indexes and the influence degree was different at different growth stages. For example, at maturity stage, the total root length and surface area of rape treated with RT2 were significantly higher than CK2 by 116.66% and 111.03%. Ridge tillage can significantly improve economic characteristics and yield. Strikingly, compared with CK1 and CK2, the actual yield of rice treated with RT1 was higher by 20.82% and 13.54%.Ridge tillage can promote root growth, enhance root vitality and increase yield. Narrow ridge tillage (RT2) is more beneficial to the root growth of rape but wide ridge tillage (RT1) is more beneficial to the root growth of rice. The wide ridge is more beneficial to the economic characteristics and yield of rape and rice than narrow ridge. [ABSTRACT FROM AUTHOR]

Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life for patients and potentially supports the development of new treatments. Graph convolutional networks (GCNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GCNs model for brain networks faces several challenges, including high dimensional and noisy correlation in the brain networks, limited labeled training data, and depth limitation of GCN learning. Generalization and interpretability are important in developing predictive models for clinical diagnosis. To address these challenges, we proposed an ensemble framework involving hierarchical GCN and transfer learning for sparse brain networks, which allows GCN to capture the intrinsic correlation among the subjects and domains, to improve the network embedding learning for disease diagnosis. Extensive experiments on two real medical clinical applications: diagnosis of Autism spectrum disorder (ASD) and diagnosis of Alzheimer's disease (AD) on both the ADNI and ABIDE databases, showing the effectiveness of the proposed framework. We achieved state-of-the-art accuracy and AUC for AD/MCI and ASD/NC (Normal control) classification in comparison with studies that used functional connectivity as features or GCN models. The proposed TE-HI-GCN model achieves the best classification performance, leading to about 27.93% (31.38%) improvement for ASD and 16.86% (44.50%) for AD in terms of accuracy and AUC compared with the traditional GCN model. Moreover, the obtained clustering results show high correspondence with the previous neuroimaging derived evidence of within and between-networks biomarkers for ASD. The discovered subnetworks are used as evidence for the proposed TE-HI-GCN model. Furthermore, this work is the first attempt of transfer learning on the two related disorder domains to uncover the correlation among the two diseases with a transfer learning scheme. [ABSTRACT FROM AUTHOR]

The insula, consisting of functionally diverse subdivisions, plays a significant role in Parkinson's disease (PD)-related cognitive disorders. However, the functional connectivity (FC) patterns of insular subdivisions in PD remain unclear. Our aim is to investigate the changes in FC patterns of insular subdivisions and their relationships with cognitive domains. Three groups of participants were recruited in this study, including PD patients with mild cognitive impairment (PD-MCI, n = 25), PD patients with normal cognition (PD-NC, n = 13), and healthy controls (HCs, n = 17). Resting-state functional magnetic resonance imaging (rs-fMRI) was used to investigate the FC in insular subdivisions of the three groups. Moreover, all participants underwent a neuropsychological battery to assess cognition so that the relationship between altered FC and cognitive performance could be elucidated. Compared with the PD-NC group, the PD-MCI group exhibited increased FC between the left dorsal anterior insular (dAI) and the right superior parietal gyrus (SPG), and altered FC was negatively correlated with memory and executive function. Compared with the HC group, the PD-MCI group showed significantly increased FC between the right dAI and the right median cingulate and paracingulate gyri (DCG), and altered FC was positively related to attention/working memory, visuospatial function, and language. Our findings highlighted the different abnormal FC patterns of insular subdivisions in PD patients with different cognitive abilities. Furthermore, dysfunction of the dAI may partly contribute to the decline in executive function and memory in early drug-naïve PD patients. [ABSTRACT FROM AUTHOR]

Action recognition based on a human skeleton is an extremely challenging research problem. The temporal information contained in the human skeleton is more difficult to extract than the spatial information. Many researchers focus on graph convolution networks and apply them to action recognition. In this study, an action recognition method based on a two-stream network called RNXt-GCN is proposed on the basis of the Spatial-Temporal Graph Convolutional Network (ST-GCN). The human skeleton is converted first into a spatial-temporal graph and a SkeleMotion image which are input into ST-GCN and ResNeXt, respectively, for performing the spatial-temporal convolution. The convolved features are then fused. The proposed method models the temporal information in action from the amplitude and direction of the action and addresses the shortcomings of isolated temporal information in the ST-GCN. The experiments are comprehensively performed on the four datasets: 1) UTD-MHAD, 2) Northwestern-UCLA, 3) NTU RGB-D 60, and 4) NTU RGB-D 120. The proposed model shows very competitive results compared with other models in our experiments. On the experiments of NTU RGB + D 120 dataset, our proposed model outperforms those of the state-of-the-art two-stream models. [ABSTRACT FROM AUTHOR]

Introduction: The objective of the present study was to evaluate the difference in net clinical benefit of clopidogrel plus aspirin compared with ticagrelor plus aspirin after 12 months in patients in mainland China with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI) with newer-generation drug-eluting stents (DESs). Methods: In this multicenter, retrospective, real-world study, the data were sourced from three databases: BRIC-ACS(I) study, COSTIC study, and 301 Hospital PCI patient database from January 2014 to October 2017. The primary endpoint of the study was net adverse clinical and cerebral events (NACCE) comprised of all-cause death, non-fatal myocardial infarction (MI), non-fatal stroke or Bleeding Academic Research Consortium (BARC) type ≥ 2 (excluding BARC type = 4) bleeding, whereas the secondary end point was evaluation of major adverse cardiovascular events (MACE) and BARC type ≥ 2 bleeding events. Results: A total of 7862 ACS patients were included in the final analysis, of whom propensity score matching (PSM) analysis yielded 2165 patients in each cohort. After PSM analysis, cumulative incidence of NACCE was significantly lower with clopidogrel and aspirin than with ticagrelor and aspirin [117 (5.4%) vs. 180 (8.3%), P < 0.001] at 12 months. Effect estimates showed reduced risk of NACCE occurrence in patients treated with clopidogrel and aspirin [adjusted hazard ratio (aHR): 0.61, 95% CI 0.48–0.77, P < 0.001]. Incidence of bleeding was significantly lower in the clopidogrel cohort than in the ticagrelor cohort (aHR: 0.48, 95% CI 0.35–0.66, P < 0.001). Clopidogrel and aspirin therapy was comparable to ticagrelor and aspirin in reducing the incidence of MACE after PSM analysis. Conclusion: In Chinese ACS patients who underwent PCI with second-generation DESs, outpatient use of clopidogrel dual antiplatelet therapy (DAPT) was associated with reduction in NACCE and bleeding. [ABSTRACT FROM AUTHOR]

In order to study the mechanism of selective catalytic reduction of activated coke to remove NO in low-temperature flue gas and provide some theoretical basis for the development of related technologies. The pore size distribution and BET specific surface area of AC were obtain by data analyzing of N2 adsorption/desorption isotherm at − 196 °C and carbon matrix and surface chemistry of virgin activated coke samples were characterized by acid–base titration and XPS. The process of selective catalytic reduction of activated coke (AC) samples with NH3 as reducing agent was studied in a fixed bed reactor at 150 °C. The result shows that pore size distribution or BET specific surface of activated cokes have not correlation with denitrification activity for SCR. The NO reduction activities of the activated cokes are apparent to increase with their surface oxygen element content and total amount of acidic sites. Obviously there is good linear relationship between the NH3 adsorption capacity and activity for SCR with linear correlation coefficient 0.943. It has been presented that adsorption of NH3 on acidic functional groups in the edge of large polycyclic aromatic ring of activated coke is key rate controlling step in the SCR heterogeneous catalytic reaction. [ABSTRACT FROM AUTHOR]

Glycosylated hemoglobin A1c (HbA1c) is a useful marker for the diagnosis of diabetes mellitus. Commercial column separation methods for HbA1c measurement were lacking throughput and sometimes interfered with hemoglobin variants. In this work, we developed a high-throughput and specific method for HbA1c by quantitative measurement of N-terminal peptides (NT method). Two thousand specimens could be measured in 8 h. The high-throughput was achieved by using a fast analysis of matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS) and an efficient proteolysis accelerated by laser irradiation. An intensity ratio of glycosylated to non-glycosylated hemoglobin N-terminal peptides was used to calculate the HbA1c level in blood. Interference from Hb variants of N-terminal peptides could be excluded by a highly accurate mass selection. The coefficient of variation (CV) of intra-assay precision was 9.8 and 9.9 %, respectively. The CVs of inter-assay precision over 20 days were 9.1 and 8.4 %, respectively. Measurement results were well correlated with the commercially available column method ( r = 0.995). The NT method is promising for large-scale screening for diabetes mellitus among people. [ABSTRACT FROM AUTHOR]

A novel method to isolate global N-termini using sulfydryl tagging and gold-nanoparticle-based depletion (STagAu method) is presented. The N-terminal and lysine amino groups were first completely dimethylated at the protein level, after which the proteins were digested. The newly generated internal peptides were tagged with sulfydryl by Traut's reagent through digested N-terminal amines in yields of 96 %. The resulting sulfydryl peptides were depleted through binding onto nano gold composite materials. The Au-S bond is stable and widely used in materials science. Nano gold composite materials showed nearly complete depletion of sulfydryl peptides. A set of the acetylated and dimethylated N-terminal peptides were analyzed by liquid chromatography-tandem mass spectrometry. This method was demonstrated to be an efficient N-terminus enrichment method because of the use of an effective derivatization reaction, in combination with robust and relative easy to implement Au-S coupling. We identified 632 N-terminal peptides from 386 proteins in a mouse liver sample. The STagAu approach presented is therefore a facile and efficient method for mass-spectrometry-based analysis of proteome N-termini or protease-generated cleavage products. [ABSTRACT FROM AUTHOR]

To give full play to the high efficiency and parallelism of multi-processor systems, the fireworks algorithm (FWA) is improved, and a multi-processor task scheduling algorithm based on improved FWA, named IMFWA, is proposed. IMFWA maps continuous space to discrete space by designing the fireworks location coding method, improves the Gaussian mutation process, and sets adaptive dimensions to accelerate the convergence speed of the algorithm. At the same time, in order to reduce the time complexity of the algorithm and shorten the time finding the optimal task scheduling sequence, the fitness-based tournament selection strategy is used instead of the rule based on Euclidean distance. Finally, IMFWA is compared with the basic fireworks algorithm and the genetic algorithms on the Matlab platform for performance analysis. The results show that the IMFWA has advantages in the convergence speed, and the negative impact of the number of tasks is also lower than the fireworks algorithm and genetic algorithm. [ABSTRACT FROM AUTHOR]