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In the post-epidemic era, the teaching mode of high school English has altered from a single face-to-face teaching mode to a hybrid online and offline teaching mode. This mode has a positive significance in maintaining the education order, ensuring the teaching quality of high school English teaching. The objectives of the research are the teaching method and the corresponding multiple assessment systems for high school students, and this paper applies methods of document analyses, investigation and experience summary. Based on the blended high school English teaching, the research combines process evaluation and terminal evaluation organically and diversifies evaluation subjects, evaluation contents, and evaluation manner based on the actual learning situation, building an evaluation system with adaptability and rationality. In this way, it is helpful to inspire teachers to design their teaching process in a more efficient and scientific way and solve the present obstacle that the traditional teaching evaluation system no longer meets the evaluative needs of the new teaching model.

Hemiplegia is a common dysfunction caused by the brain stroke and leads to movement disability. Although the lateralization of movement-related potential, the event-related desynchronization, and more complicated inter-regional information coupling have been investigated, seldom studies have focused on investigating the dynamic information exchanging among multiple brain regions during motor execution for post-stroke hemiplegic patients. With high temporal-resolution electroencephalogram (EEG), the time-varying network is able to reflect the dynamical complex network modalities corresponding to the movements at a millisecond level. In our present study, the wrist extension experiment was designed, along with related EEG datasets being collected. Thereafter, the corresponding time-varying networks underlying the wrist extension were accordingly constructed by adopting the adaptive directed transfer function and then statistically explored, to further uncover the dynamic network deficits (i.e., motor dysfunction) in post-stroke hemiplegic patients. Results of this study found the effective connectivity between the stroked motor area and other areas decreased in patients when compared to healthy controls; on the contrary, the enhanced connectivity between non-stroked motor areas and other areas, especially the frontal and parietal-occipital lobes, were further identified for patients during their accomplishing the designed wrist extension, which might dynamically compensate for the deficited patients' motor behaviors. These findings not only helped deepen our knowledge of the mechanism underlying the patients' motor behaviors, but also facilitated the real-time strategies for clinical therapy of brain stroke, as well as providing a reliable biomarker to predict the future rehabilitation.The online version contains supplementary material available at 10.1007/s11571-021-09738-2.

ALT tumor cells often contain abundant DNA damage foci at telomeres and rely on the alternative lengthening of telomeres (ALT) mechanism to maintain their telomeres. How the telomere chromatin is regulated and maintained in these cells remains largely unknown. In this study, we present evidence that heterochromatin protein 1 binding protein 3 (HP1BP3) can localize to telomeres and is particularly enriched on telomeres in ALT cells. HP1BP3 inhibition led to preferential growth inhibition of ALT cells, which was accompanied by telomere chromatin decompaction, increased presence of C-circles, more pronounced ALT-associated phenotypes and elongated telomeres. Furthermore, HP1BP3 appeared to participate in regulating telomere histone H3K9me3 epigenetic marks. Taken together, our data suggest that HP1BP3 functions on telomeres to maintain telomere chromatin and represents a novel target for inhibiting ALT cancer cells.

A large-scale network provides a high hierarchical level for understanding the adaptive adjustment of the human brain during cognition processes. Since high spatial resolution is required, most of the related works are based on functional magnetic resonance imaging (fMRI); however, fMRI lacks the temporal information that is important in investigating the high cognition processes. Although combining electroencephalography (EEG) inverse solution and independent component analysis (ICA), researchers detected large-scale functional subnetworks recently, few researchers focus on the unreasonable negative activation, which is biased from the nonnegative electrical source activations in the brain. In this study, considering the favorable nonnegative property of Bayesian nonnegative matrix factorization (Bayesian NMF) and combining EEG source imaging, we developed a robust approach for EEG large-scale network construction and applied it to two independent real EEG datasets (i.e., decision-making and P300). Eight and nine best-fit networks, including such important subnetworks as the somatosensory-motor network (SMN), the default mode network (DMN), etc., were successfully identified for decision-making and P300, respectively. Compared to the networks acquired with ICA, these networks not only lacked confusing negative activations but also showed clear spatial distributions that are compatible with specific brain function. Based on the constructed large-scale network, we further probed that the self-referential network (SRN), the primary visual network (PVN), and the visual network (VN) demonstrated different interaction patterns with other networks between different responses in decision-making. Our results confirm the possibility of probing the neural mechanisms of high cognition processes at a very high temporal and spatial resolution level.

The nature of the tumor immune microenvironment (TME) is essential for the head and neck squamous cell carcinomas (HNSCC) initiation, prognosis, and response to immunotherapy. However, its gene regulatory network remains to be elucidated.To identify N6-methyladenosine (m6A) regulators that are involved in regulating the HNSCC TME, a computational screen was applied to The Cancer Genome Atlas (TCGA) HNSCC patient samples. The effects of mutation, copy number variation (CNV), and transcriptional regulation on YTHDF1 expression were analyzed. We analyzed the TME infiltration, cancer-immunity cycle activities, and YTHDF1-related Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.Among the 24 m6A regulators, 3 factors (YTHDF1, ELAVL1, and METTL3) were highly correlated with TME infiltration. As the top candidate, YTHDF1 was up-regulated and amplified in HNSCC. YTHDF1 promoter gains active histone marks and high chromatin accessibility, which might be transcriptionally activated by SOX2 and TP63. Moreover, YTHDF1 expression significantly associates with tumor malignant phenotype in HNSCC, which has a positive correlation with CD4+ T cells and a negative correlation with CD8+ T cells infiltration. Specifically, YTHDF1 expression is negatively associated with the cancer-immunity cycle and immune checkpoint inhibitors. In terms of the underlying biological mechanisms, YTHDF1 may interact with YTHDF2/3 to regulate several vital immune-related pathways.We identify YTHDF1 associated with TME and elucidate an underlying mechanism of immune escape in HNSCC, which might be used as a predictive marker in guiding immunotherapy.

Tumor mutation burden high (TMB-H) is widely used in the guidance of immune checkpoint blocking (ICB) therapy for head and neck squamous cell carcinoma (HNSCC) patients. However, a few patients still had a poor response. Therefore, it is necessary to investigate a better model to guide ICB therapy. We constructed a genomic mutation model conducive to ICB therapy using an available HNSCC dataset. Moreover, treatment procedures for patients with HNSCC from our internal cohort confirmed this model. Here, a genomic mutation signature based on a list of 25 candidate genes that are favorable for immunotherapy was established. Patients with combined mutation had a respectable clinical outcome under ICB treatment. Notably, compared with patients who obtained TMB-H (TMB ≥ 10, but did not have combined mutation), those patients with TMB-L (TMB 10) and combined mutation acquired remarkably beneficial overall survival. Moreover, the combined mutation signature predicting the survival status of patients was superior to TMB, with a Youden index of 0.55. Furthermore, higher immune cell infiltration levels, more active cancer-immunity cycle activities and immune response pathways were observed in patients with combined mutation. Finally, our internal cohort further confirmed that combined mutated patients can benefit from ICB therapy rather than any other patients.

This study used large-scale time-varying network analysis to reveal the diverse network patterns during the different decision stages and found that the responses of rejection and acceptance involved different network structures. When participants accept unfair offers, the brain recruits a more bottom-up mechanism with a much stronger information flow from the visual cortex (O2) to the frontal area, but when they reject unfair offers, it displayed a more top-down flow derived from the frontal cortex (Fz) to the parietal and occipital cortices. Furthermore, we performed 2 additional studies to validate the above network models: one was to identify the 2 responses based on the out-degree information of network hub nodes, which results in 70% accuracy, and the other utilized theta burst stimulation (TBS) of transcranial magnetic stimulation (TMS) to modulate the frontal area before the decision-making tasks. We found that the intermittent TBS group demonstrated lower acceptance rates and that the continuous TBS group showed higher acceptance rates compared with the sham group. Similar effects were not observed after TBS of a control site. These results suggest that the revealed decision-making network model can serve as a potential intervention model to alter decision responses.

To promote the rehabilitation of motor function in children with cerebral palsy (CP), we developed motor imagery (MI) based training system to assist their motor rehabilitation. Eighteen CP children, ten in short- and eight in long-term rehabilitation, participated in our study. In short-term rehabilitation, every 2 days, the MI datasets were collected; whereas the duration of two adjacency MI experiments was ten days in the long-term protocol. Meanwhile, within two adjacency experiments, CP children were requested to daily rehabilitate the motor function based on our system for 30 min. In both strategies, the promoted motor information processing was observed. In terms of the relative signal power spectra, a main effect of time was revealed, as the promoted power spectra were found for the last time of MI recording, compared to that of the first one, which first validated the effectiveness of our intervention. Moreover, as for network efficiency related to the motor information processing, compared to the first MI, the increased network properties were found for the last MI, especially in long-term rehabilitation in which CP children experienced a more obvious efficiency promotion. These findings did validate that our MI-based rehabilitation system has the potential for CP children to assist their motor rehabilitation.

Information in working memory (WM) can guide visual attention towards matched features. While recent work has suggested that cognitive control can act upon WM guidance of visual attention, little is known about how the state of memorized items retaining in WM contribute to its influence over attention. Here, we disentangle the role of inhibition and maintenance on WM-guided attention with a novel delayed match-to-sample dual-task. The results showed that active inhibition facilitated searching by diminishing sensory processing and deterring attentional guidance, indexed by an attenuated P1 amplitude and unaffected N2pc amplitude, respectively. By contrast, active maintenance impaired searching by attentional guidance while sensory processing remained unimpaired, indexed by an enhanced N2pc amplitude and unchanged P1 amplitude, respectively. Furthermore, multivariate pattern analyses could sucessfully decode maintenance and inhibition, suggesting that two states differed in modulating visual attention. We propose that remembered contents may play an anchoring role for attentional guidance, and the state of those contents retaining in WM may directly influence the shifting of attention. The maintenance could guide attention by accessing input information, while the inhibition could deter the shifting of attention by suppressing sensory processing. These findings provide a possible reinterpretation of the influence of WM on attention.

Mentally imagining rather physically executing the motor behaviors is defined as motor imagery (MI). During MI, the mu rhythmical oscillation of cortical neurons is the event-related desynchronization (ERD) subserving the physiological basis of MI-based brain-computer interface. In our work, we investigated the specific brain network reconfiguration from rest idle to MI task states, and also probed the underlying relationship between the brain network reconfiguration and MI related ERD. Findings revealed that comparing to rest state, the MI showed the enhanced motor area related linkages and the deactivated activity of default mode network. In addition, the reconfigured network index was closely related to the ERDs, i.e., the higher the reconfigured network index was, the more obvious the ERDs were. These findings consistently implied that the reconfiguration from rest to task states underlaid the reallocation of related brain resources, and the efficient brain reconfiguration corresponded to a better MI performance, which provided the new insights into understanding the mechanism of MI as well as the potential biomarker to evaluate the rehabilitation quality for those patients with deficits of motor function.

Multiple studies, which studied cortical generators and cerebral networks of P300 based on electroencephalogram (EEG) and functional magnetic resonance imaging, have investigated the possible mechanism underlying the inter-subject variability of P300 component. However, few studies have investigated the large-scale networks of P300 as well as the potential relationships between large-scale networks and P300, particularly in EEG signal. When using group independent component analysis (gICA) to investigate the large-scale EEG networks, some resting-state networks, such as the default mode network, have been observed, while the related task-state networks (TSNs) of P300 are still unknown. In this paper, we estimated the time courses of cortical voxels following EEG source localization and then adopted gICA at the group level to identify the related P300-TSNs. Thereafter, we constructed and further investigated the potential relationships between the properties of functional network connectivity (FNC) and P300 amplitude. Our findings revealed six best-fit common functional networks that participated in the generation of P300, namely, the primary visual network, visual network, central executive network, left parietal network, attention network, and sensorimotor network. Specifically, the FNC properties significantly related to the P300 amplitude, as well as some FNC connections. In addition, when using FNC properties to classify the high- and low-amplitude groups, a relatively high-classification accuracy of 87.26% for linear discriminant analysis and of 86.31% for support vector machine were achieved. These findings confirmed the validation of gICA in mining large-scale networks in cortical EEG and may help us better understand the inter-subject P300 variability.

Rho GDP-dissociation inhibitor α (RhoGDIα) is an essential regulator for Rho GTPases. Although RhoGDIα may serve as an oncogene in colorectal cancer (CRC), the underlying mechanism is still unclear. We investigated the function, mechanism, and clinical significance of RhoGDIα in CRC progression. We founded that downregulation of RhoGDIα repressed CRC cell proliferation, motility, and invasion. Overexpression of RhoGDIα increased DNA damage response signals at telomeres, and led to telomere shortening in CRC cells, also being validated in 26 pairs of CRC tissues. Mechanistic studies revealed that RhoGDIα could promote telomeric repeat factor 1 (TRF1) expression through the phosphatidylinositol 3-kinase-protein kinase B signal pathway. Moreover, RhoGDIα protein levels were strongly correlated with TRF1 in CRC tissues. A cohort of 297 CRC samples validated the positive relationship between RhoGDIα and TRF1, and revealed that RhoGDIα and TRF1 levels were negatively associated with CRC patients' survival. Taken together, our results suggest that RhoGDIα regulate TRF1 and telomere length and may be novel prognostic biomarkers in colorectal cancer.

The P300 is regarded as a psychosis endophenotype of schizophrenia and a putative biomarker of risk for schizophrenia. However, the brain activity (i.e., P300 amplitude) during tasks cannot always provide satisfying discrimination of patients with schizophrenia (SZs) from healthy controls (HCs). Spontaneous activity at rest indices the potential of the brain, such that if the task information can be efficiently processed, it provides a compensatory understanding of the cognitive deficits in SZs. In this paper, based on the resting and P300 task electroencephalogram (EEG) data sets, we constructed functional EEG networks and then extracted the inherent spatial pattern of network (SPN) features for both brain states. Finally, the combined SPN features of the rest and task networks were used to recognize SZs. The findings of this paper revealed that the combined SPN features could achieve the highest accuracy of 90.48%, with the sensitivity of 89.47%, and specificity of 91.30%. These findings consistently implied that the rest and task P300 EEGs could actually provide comprehensive information to reliably classify SZs from HCs, and the SPN is a promising tool for the clinical diagnosis of SZs.

Objective Despite increasing evidence revealing the relationship between task-related brain activity and decision-making, the association between resting-state functional connectivity and decision-making remains unknown. Approach In this study, we investigated the potential relationship between the network revealed in the resting-state electroencephalogram (EEG) and decision responses and further predicted individuals' acceptance rates during the ultimatum game (UG) based on the functional connectivity revealed in the resting-state EEG. Main results The results of this study demonstrated a significant relationship between the resting-state frontal-occipital connectivity and the UG acceptance rate in the alpha band. Increased acceptance rates were accompanied by a larger clustering coefficient and global and local efficiency as well as a shorter characteristic path length. Compared to the low-acceptance group, the high-acceptance group exhibited stronger frontal-occipital linkages. Finally, a multiple linear regression model based on the resting-state EEG network properties was adopted to predict the acceptance rates when subjects made their decision in the UG task. Significance Together, the findings of this study may deepen our knowledge of decision-making and provide a potential physiological biomarker to predict the decision-making responses of subjects.

Motor imagery (MI) requires subjects to visualize the requested motor behaviors, which involves a large-scale network that spans multiple brain areas. The corresponding cortical activity reflected on the scalp is characterized by event-related desynchronization (ERD) and then by event-related synchronization (ERS). However, the network mechanisms that account for the dynamic information processing of MI during the ERD and ERS periods remain unknown. Here, we combined ERD/ERS analysis with the dynamic networks in different MI stages (i.e. motor preparation, ERD and ERS) to probe the dynamic processing of MI information. Our results show that specific dynamic network structures correspond to the ERD/ERS evolution patterns. Specifically, ERD mainly shows the contralateral networks, while ERS has the symmetric networks. Moreover, different dynamic network patterns are also revealed between the two types of MIs, in which the left-hand MIs exhibit a relatively less sustained contralateral network, which may be the network mechanism that accounts for the bilateral ERD/ERS observed for the left-hand MIs. Similar to the network topologies, the three MI stages also appear to be characterized by different network properties. The above findings all demonstrate that different MI stages that involve specific brain networks for dynamically processing the MI information.

Activation of telomerase or alternative lengthening of telomeres (ALT) is necessary for tumours to escape from dysfunctional telomere-mediated senescence. Anti-telomerase drugs might be effective in suppressing tumour growth in approximately 85–90% of telomerase-positive cancer cells. However, there are still chances for these cells to bypass drug treatment after switching to the ALT mechanism to maintain their telomere integrity. But the mechanism underlying this switch is unknown. In this study, we used telomerase-positive cancer cells (HTC75) to discover the mechanism of the telomerase-ALT switch by inducing telomere-specific DNA damage, alpha-thalassemia X-linked syndrome protein (ATRX) knockdown and deletion of death associated protein (DAXX). Surprisingly, two important ALT hallmarks in the ALT-like HTC75 cells were observed after treatments: ALT-associated promyelocytic leukaemia bodies (APBs) and extrachromosomal circular DNA of telomeric repeats. Moreover, knocking out hTERT by utilizing the CRISPR/Cas9 technique led to telomere elongation in a telomerase-independent manner in ALT-like HTC75 cells. In summary, this is the first report to show that inducing telomeric DNA damage, disrupting the ATRX/DAXX complex and inhibiting telomerase activity in telomerase-positive cancer cells lead to the ALT switch.