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Background: Subjects with subjective cognitive decline (SCD) are proposed as a potential population to screen for Alzheimer’s disease (AD). Objective: Investigating brain topologies would help to mine the neuromechanisms of SCD and provide new insights into the pathogenesis of AD. Methods: Objectively cognitively unimpaired subjects from communities who underwent resting-state BOLD-fMRI and clinical assessments were included. The subjects were categorized into SCD and normal control (NC) groups according to whether they exhibited self-perceived cognitive decline and were worried about it. The minimum spanning tree (MST) of the functional brain network was calculated for each subject, based on which the efficiency and centrality of the brain network organization were explored. Hippocampal/parahippocampal volumes were also detected to reveal whether the early neurodegeneration of AD could be seen in SCD. Results: A total of 49 subjects in NC and 95 subjects in SCD group were included in this study. We found the efficiency and centrality of brain network organization, as well as the hippocampal/parahippocampal volume were preserved in SCD. Besides, SCD exhibited normal cognitions, including memory, language, and execution, but increased depressive and anxious levels. Interestingly, language and execution, instead of memory, showed a significant positive correlation with the maximum betweenness centrality of the functional brain organization and hippocampal/parahippocampal volume. Neither depressive nor anxious scales exhibited correlations with the brain functional topologies or hippocampal/parahippocampal volume. Conclusion: SCD exhibited preserved efficiency and centrality of brain organization. In clinical practice, language and execution as well as depression and anxiety should be paid attention in SCD.

Circular RNAs (circRNAs) serve as covalently closed single-stranded RNAs and have been proposed to influence plant development and stress resistance. Grapevine is one of the most economically valuable fruit crops cultivated worldwide and is threatened by various abiotic stresses. Herein, we reported that a circRNA (Vv-circPTCD1) processed from the second exon of the pentatricopeptide repeat family gene PTCD1 was preferentially expressed in leaves and responded to salt and drought but not heat stress in grapevine. Additionally, the second exon sequence of PTCD1 was highly conserved, but the biogenesis of Vv-circPTCD1 is species-dependent in plants. It was further found that the overexpressed Vv-circPTCD1 can slightly decrease the abundance of the cognate host gene, and the neighboring genes are barely affected in the grapevine callus. Furthermore, we also successfully overexpressed the Vv-circPTCD1 and found that the Vv-circPTCD1 deteriorated the growth during heat, salt, and drought stresses in Arabidopsis. However, the biological effects on grapevine callus were not always consistent with those of Arabidopsis. Interestingly, we found that the transgenic plants of linear counterpart sequence also conferred the same phenotypes as those of circRNA during the three stress conditions, no matter what species it is. Those results imply that although the sequences are conserved, the biogenesis and functions of Vv-circPTCD1 are species-dependent. Our results indicate that the plant circRNA function investigation should be conducted in homologous species, which supports a valuable reference for further plant circRNA studies.

This article studies the guaranteed performance synchronization control problem for networked bilateral teleoperation systems with system uncertainties. The communication channel connecting the master and the slave is subject to asymmetrical varying time delays with unknown upper bounds. The first result on prescribed performance synchronization control for the bilateral teleoperation system under such a weak assumption on the communication time delays is provided. Moreover, a novel composite adaptive control algorithm is proposed under a much weaker interval-excitation (IE) condition. More specifically, parameter adaptive estimation accuracy and speed are quantificationally ensured by employing a composite technique. Therefore, both steady-state performance and transient-state performance are achieved for the position synchronization and parameter estimation with the proposed control strategy. The Lyapunov function and the multidimensional small-gain framework are utilized to derive system stability criteria. It demonstrates that the allowable maximal derivatives of the transmission delays can be easily computed with the given parameters of the control algorithm and the nonlinear performance functions. Finally, both simulation and experimental results are provided to demonstrate the feasibility and superiority of the proposed composite adaptive strategy.

Chinese poetry generation has been a challenging part of natural language processing due to the unique literariness and aesthetics of poetry. In most cases, the content of poetry is topic-related. That is to say, specific thoughts or emotions are usually expressed regarding given topics. However, topic information is rarely taken into consideration in current studies about poetry generation models. In this paper, we propose a topic-enhanced Chinese poetry generation model called TPoet in which the topic model is integrated into the transformer-based auto-regressive text generation model. By feeding topic information to the input layer and heterogeneous attention mechanism, TPoet can implicitly learn the latent information of topic distribution. In addition, by setting multiple identifiers such as segment, rhyme, tone, etc., the model can explicitly learn the constraints of generated poems. Extensive experimental results show that the quality of TPoet-generated poems outperforms the current advanced models or systems, and the topic consistency and diversity in generated poems have been significantly improved as well.

A prevalent skin cancer with a dismal prognosis before it spreads, cutaneous melanoma is challenging to cure. We downloaded melanoma data from the TCGA database, and the patients there were split into two groups: one for training and one for validation. After that, tumor metastatic genes in melanoma were evaluated using univariate Cox regression, LASSO regression, and multivariate Cox regression. A predictive model linked to melanoma metastasis genes was then created. To categorize melanoma patients, the model might be employed. Melanoma patients in the training and validation cohorts could be divided into high-risk and low-risk groups; the high-risk group had a considerably worse prognosis (P

While positive emotions like happiness and life satisfaction have received great attention, how to eliminate negative affect is largely neglected. This study contributes to the literature by examining the relationship between Internet use and people’s negative affect. Unlike previous studies that consider only one indicator, we capture negative affect from different dimensions by considering loneliness, sadness, and life hardship. We employ an endogenous ordered probit model to address the selection bias of Internet use and analyze the 20,107 individual-level samples sourced from the 2020 China Family Panel Studies survey. The results show that Internet use significantly reduces people’s loneliness, sadness, and life hardship. We also find that studying online and watching short videos would increase people’s loneliness feeling and shopping online deepens people’s life hardship. In contrast, using WeChat significantly reduces sadness and life hardship. Our findings confirm that guiding people to use the Internet appropriately is necessary to reduce negative affect and improve the quality of their life.

Finite-time stability (FTS) is desirable in teleoperation control as it provides fast transient, high-precision, and better disturbance rejection properties. In most cases, a stringent condition on communication time delays or an additional time-delay estimation method is necessary to guarantee finite-time synchronization with the existing finite-time control schemes for the teleoperation system. This article proposes the first result of global finite-time synchronization control algorithm for the teleoperation system without any approximation function. Specifically, a novel proportional plus damping (P + d) like control algorithm is designed by employing a continuous nonsmooth function on the position synchronization errors and the velocity signals, dexterously. Furthermore, global FTS is proved with the Lyapunov stability theory based on linear-matrix-inequality (LMI) and geometric homogeneous techniques. The delay-dependent FTS criteria are also derived, which can be used to compute the allowable maximal transmission delay with the given control parameters. Finally, simulation results demonstrate the effectiveness of the proposed approach, and experiments on a teleoperated pair of three-degree-of-freedom (DOF) PHANToM robots are also presented to show the superior performances of the control strategy.

In this brief, robot-environment interaction control problem of n-link robotic manipulator system without force sensors is investigated. At first, a novel sliding mode (SM) based two-layer adaptive force observer is proposed to estimate the robot-environment interaction force. In comparison with the most existing force observers, the proposed force observer ensures arbitrary-time convergence of the observer errors and relaxes assumptions on the external force. Moreover, an admittance controller is developed to ensure exact trajectory tracking within arbitrary time. It is mathematically proved that both trajectory tracking errors and force estimation errors will converge to zero at any setting time. Finally, numerical simulations and comparisons show that the proposed force observer-based control scheme can provide superior interaction performance.

The main method of modern ironmaking is blast furnace ironmaking, which is a very complex nonlinear dynamic process with complex physical-chemical coupling. The hot metal is the final product of blast furnace, and its silicon content not only reflects the quality of hot metal but also characterizes the operation status of the blast furnace, so its accurate prediction is very important for the operation of the blast furnace. Given the bottleneck problem in the application of the existing prediction model of hot metal silicon content in the blast furnace, this article proposed a novel data-driven modeling method. First, a nonlinear Takagi-Sugeno (T-S) fuzzy model is constructed for the hot metal silicon content to completely capture the nonlinear dynamics of the blast furnace process. Then, considering the doubts of blast furnace operators about the predicted results of the model, the Bayesian method is used to identify the consequent parameters of the fuzzy model to obtain the probability output, to present the credibility of the predicted results. Furthermore, to improve the robustness of the fuzzy model to the initial fuzzy rules, the sparse priori is adopted to construct a compact fuzzy model with strong generalization performance, in which the key fuzzy rules were screened out. In addition, two optimization methods are derived for each of the above models. Finally, the validity of the proposed methods is verified by the test of actual blast furnace data.