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Background: Poststroke cognitive impairment (PSCI) is highly prevalent in stroke survivors and correlated with unfavorable clinical outcomes. This study aimed to identify the neural substrate of PSCI using atlas-based disconnectome analysis and assess the value of disconnection score, a baseline measure for stroke-induced structural disconnection, in PSCI prediction. Methods: A multicenter prospective cohort of 676 first-ever patients with acute ischemic stroke was enrolled from 3 independent hospitals in China. Sociodemographic, clinical, and neuroimaging data were collected at acute stage of stroke. Cognitive assessment was performed at 3 months after stroke. Voxel-wise and tract-wise disconnectome analysis were performed to uncover the strategic structural disconnection pattern for global PSCI. Disconnection score was calculated for each participant in leave-one-dataset-out cross-validation. Multivariable logistic regression was performed for the association between disconnection score and PSCI. Prediction models with and without disconnection score were developed, cross-validated, and compared in terms of discrimination and goodness-of-fit. Results: Compared with lesions of non-PSCI, those of PSCI were more likely to have fiber connections with left prefrontal cortex and left deep structures (thalamus and basal ganglia). Disconnection score could predict the risk and severity of PSCI during cross-validation, and was independently associated with PSCI after controlling for all baseline covariates (odds ratio, 1.38 [95% CI, 1.17–1.64]; P Conclusions: A strategic structural disconnection pattern centered on left prefrontal cortex, thalamus, and basal ganglia is identified for global PSCI using indirect disconnectome analysis. The baseline disconnection score is independently predictive of PSCI and has significant incremental value to preexisting sociodemographic, clinical, and neuroimaging predictors. Registration: URL: http://www.chictr.org.cn/enIndex.aspx ; Unique identifier: ChiCTR-ROC-17013993.

As a mechanism of genetic transmission, horizontal gene transfer (HGT) greatly contributes to the diversification and long-term evolution of green plants. Recent studies suggest that HGT events drive the evolution and adaptation of charophyte green algae and land plants. This article is protected by copyright. All rights reserved.

The prepared TCPP/CuNiCo2O4 noble metal-free nanocatalysts improved the catalytic performance of methanol oxidation reaction under light.

Curcumin, a polyphenol derived from turmeric, has multiple biological functions, such as anti-inflammatory, antioxidant, antibacterial and, above all, antitumor activity. Colorectal cancer is a common malignancy of the gastrointestinal tract with an extremely high mortality rate. However, the low bioavailability and poor targeting properties of curcumin generally limit its clinical application. In the present study, we designed a fusion protein GE11-HGFI as a nanodrug delivery system. The protein was connected by flexible linkers, inheriting the self-assembly properties of hydrophobin HGFI and the targeting ability of GE11. The data show that the encapsulation of curcumin by fusion protein GE11-HGFI can form uniform and stable nanoparticles with a size of only 80 nm. In addition, the nanocarrier had high encapsulation efficiency for curcumin and made it to release sustainably. Notably, the drug-loaded nanosystem selectively targeted colorectal cancer cells with high epidermal growth factor receptor expression, resulting in high aggregated concentrations of curcumin at tumor sites, thus showing a significant anticancer effect. These results suggest that the nanocarrier fusion protein has the potential to be a novel strategy for enhancing molecular bioactivity and drug targeting in cancer therapy.

Beyond causing local injury, stroke disrupts structural and functional organization of the brain networks, exposing patients to a high risk of cognitive impairment by affecting the neural network activity. However, the impact of these pathological changes on cognition-related neural circuits is not well understood. In this study, we mainly focused on structures and directed functional connectivity within the Papez circuit in subacute stroke patients. Forty-five stroke patients and thirty-four age-, sex-matched healthy controls were included in our study. The Papez circuit gray matter were measured to explore ischemia-induced structural alterations. And Granger causality analysis with the hippocampus as seed regions was performed to identify alterations of directional functional connectivity within the neural circuit. We also explored the associations between cerebral changes with cognitive status. Compared with healthy controls, stroke patients revealed marked atrophy in gray matter of the Papez circuit, including ipsilateral hippocampus, amygdala, thalamus, and caudal anterior cingulate gyrus. Additionally, there are alterations in the directed functional connections between the bilateral hippocampus and cingulate gyrus within the Papez circuit. These altered effective connectivities were correlated with cognitive function after cerebrovascular event. Taken together, in the early post-stroke period, disruptions of the Papez circuit in both architecture and directed functional connectivity have already occurred and might affect the cognitive function. These findings have prompted researchers to better understand the potential mechanisms underlying vascular cognitive impairment and to investigate new therapeutic targets that could reduce cognitive burden.

Photoluminescent materials play an essential part in the application of polymer systems. However, intrinsic polymer systems have rarely been intuitively interpreted based on photoluminescent regula...

In this paper, a fault identification algorithm combining a signal processing algorithm and machine learning algorithm is proposed, using a four-mass vibration MEMS gyroscope (FMVMG) for signal acquisition work, constructing a gyroscope fault dataset, and performing the model training task based on this dataset. Combining the improved EWT algorithm with SEResNeXt-50 reduces the impact of white noise in the signal on the identification task and significantly improves the accuracy of fault identification. The EWT algorithm is a wavelet analysis algorithm with adaptive wavelet analysis, which can significantly reduce the impact of boundary effects, and has a good effect on decomposition of signal segments with short length, but a reconstruction method is needed to effectively separate the noise signal and effective signal, and so this paper uses multiscale permutation entropy for calculation. For the reason that the neural network has a better ability to characterize high-dimensional signals, the one-dimensional signal is reconstructed into a two-dimensional image signal and the signal features are extracted. Then, the constructed image signals are fed into the SEResNeXt-50 network, and the characterization ability of the model is further improved in the network with the addition of the Squeeze-and-Excitation module. Finally, the proposed model is applied to the FMVMG fault dataset and compared with other models. In terms of recognition accuracy, the proposed method improves about 30.25% over the BP neural network and about 1.85% over ResNeXt-50, proving the effectiveness of the proposed method.

Electroencephalogram (EEG) channel optimization can reduce redundant information and improve EEG decoding accuracy by selecting the most informative channels. This article aims to investigate the universality regarding EEG channel optimization in terms of how well the selected EEG channels can be generalized to different participants. In particular, this study proposes a sparse logistic regression (SLR)-based EEG channel optimization algorithm using a non-zero model parameter ranking method. The proposed channel optimization algorithm was evaluated in both individual analysis and group analysis using the raw EEG data, compared with the conventional channel selection method based on the correlation coefficients (CCS). The experimental results demonstrate that the SLR-based EEG channel optimization algorithm not only filters out most redundant channels (filters 75–96.9% of channels) with a 1.65–5.1% increase in decoding accuracy, but it can also achieve a satisfactory level of decoding accuracy in the group analysis by employing only a few (2–15) common EEG electrodes, even for different participants. The proposed channel optimization algorithm can realize better universality for EEG decoding, which can reduce the burden of EEG data acquisition and enhance the real-world application of EEG-based brain–computer interface (BCI).

Background: Developing the high-efficiency and low-risk small-molecule greennematocide is the key of effective control of the nematodes. Paeonol, is a naturally occurring phenolic compound, isolated from the root bark of Paeonia suffruticosa and the whole plant of Cynanchum paniculatum. Due to its crucial phenolic ketone skeleton, modern biological science research has indicated that paeonol has a wide range of biological activities. The structural modification of paeonol into paeonol carbonyl hydrazone derivatives is a potential approach for the development of novel nematodes, which showed more toxicity than paeonol. However, there are no reports on the nematicidal activity of paeonol carbonyl hydrazone derivatives to control Heterodera glycines. Results: We always endeavor to discover and develop biorational natural products-based pesticidal agents, 4 significant intermediates and 21 novel 3/5(3,5)-(di)nitro/chloropaeonol carbonyl hydrazone derivatives were prepared, and their structures well characterized by 1H NMR, HRMS, MS, and mp. Due to the steric hindrance, the substituents on the C=N double bond of all hydrazine compounds adopted E configuration. Results of nematicidal activity revealed that, among all compounds, especially 5-nitropaeonol (5) and 3,5-dinitropaeonol (7) displayed the most potent nematicidal activity H. glycines in vivo with LC50 values of 0.0323 and 0.0367 mg/mL, respectively. Conclusion: It suggested that for the 3/5(3,5)-(di)nitro/chloropaeonol carbonyl hydrazone derivatives, a nitro group introduced at C5 position of 1 was necessary for obtaining the potent compound as nematicidal agents. These preliminary results will pave the way for further modification of paeonol in the development of potential new nematicides.

Fast observations of rapid surface large-changes are demanded in disaster evaluations and scientific studies. Digital elevation model (DEM) differencing before and after the events is an effective way to retrieve the changes. Owing to a short repeat cycle, geosynchronous synthetic aperture radar (GEO SAR) systems can quickly obtain repeat-pass data and generate postevent DEMs by interferometry. However, interferometric baselines under its quick revisit cases are short, resulting in generating low-accuracy postevent DEMs. Moreover, surface large-changes can bring height ambiguity problems under the single-baseline interferometric processing. In this letter, we address the problem through a multibaseline (MB) processing. Since GEO SAR MB data can derive from the repeat-pass interferometric data of different subapertures and revisits, a subaperture-decomposition-based temporal and spatial MB method is proposed. The simulation results verify the effectiveness of the proposed method, where the quickly generated postevent DEM can help to realize the rapid large-elevation change observations.

NbTe2 has been proven to possess excellent nonlinear optical properties in this work. It performs well as a novel broadband mode-locker to generate stable laser pulses in both Er- and Yb-doped fiber lasers.

A series of bis-BN dipyrrolyl[a,j]anthracenes and a representative carbonaceous analogue have been synthesized. We studied the optical properties and OLED applications of these BN-PAHs and compared them with the carbonaceous counterpart.

Multiple CubeSat altimeters can work independently or corporately to form altimeter constellations. Different configurations of the constellations can acquire distinguished advantages: improved spatial/temporal sampling and high cross-track resolution, which will be helpful for observations of oceanic small-scale structures and weather forecasting. Compared to single conventional altimeters, CubeSat altimeter constellations may achieve better performances with lower costs. To fully understand these systems, this article focuses on the performance analysis and methodology for CubeSat altimeter constellations. Besides the typical analyses of the resolution, revisit, and absolute sea surface height (SSH) accuracy, the performance analysis was conducted by considering the characteristics of multiple measurements provided by CubeSat altimeter constellations. Local and global spatial sampling performances are investigated for various constellations and compared by sampling density and swath size. Moreover, relative SSH accuracy is introduced and evaluated based on the spatial structure functions of errors to effectively evaluate the measurement performance. Related system requirements on power, delta-v, etc., to achieve the performance are also discussed, which ensures that the analysis fits the boundary conditions of implementation. Finally, different concepts of the CubeSat altimeter constellations are compared, where their limitations and possible solutions are also discussed.

Tensile behaviour and failure mechanism of 3D needle-punched Cf/SiC–Al composites were studied in detail. Cf/SiC–Al composites were prepared by precursor infiltration pyrolysis (PIP) and vacuum pressure infiltration processes. The bulk density and open porosity of the as-fabricated Cf/SiC–Al composites were tested by drainage method. The as-cut samples were subjected to tensile tests in air at room temperature and the stress-displacement response was analyzed. The cross-section and fracture regions of tested samples were analyzed by SEM in order to understand microstructure-property relationships. It was observed that Al alloy was successfully prepared between carbon fiber bundles which resulted in the low porosity (5.5%). Cf/SiC–Al composites showed higher values of tensile strength (159 ± 10 MPa) and modulus as compared to Cf/SiC composites. Failure mechanism of Cf/SiC–Al composites was analyzed combining the stress-displacement response and fracture morphology. Fracture mode of the 0° fiber bundle, the 90° fiber bundle and the needle fiber bundle were significantly different which had an important influence on the failure mechanism of Cf/SiC–Al composites. Fracture surface of the 0° fiber bundle was relatively flat, and there was no sign of fiber pulled-out which showed the characteristics of brittle fracture. Toughening mechanism such as interface debonding and fiber pulled-out exhibited in the 90° fiber bundle.

Grasping unseen objects has become one of the most critical abilities of robots, where identifying the feasible grasp locations is the key technique. Previously proposed methods generally rely on grid search strategy, which evaluates grasp candidates at regular interval, or directly adopt the deep convolutional network, which requires large dataset and powerful computational ability. In this paper, we propose an edge-based grasp detection strategy, which combines low-level features and a lightweight convolutional neural network (CNN). Specifically, two grip criteria are first introduced to select feasible point pairs and determine the corresponding grasp candidates. Then, a lightweight CNN model is rapidly trained with limited number of samples in order to recognize feasible grasps. Our method does not require additional sensor information and can work well with only RGB image. Comparative experiments on public dataset verify the effectiveness of our efficient grasp search, which is superior to existing grasp search strategies in accuracy and efficiency. Meanwhile, it achieves competitive results with traditional CNN-based methods using less training time. In addition, we also test the proposed approach under real-world robotic grasping scenario.