The cuproptosis-related signature predicts the prognosis and immune microenvironments of primary diffuse gliomas: a comprehensive analysis.

BACKGROUND: Evidence has revealed a connection between cuproptosis and the inhibition of tumor angiogenesis. While the efficacy of a model based on cuproptosis-related genes (CRGs) in predicting the prognosis of peripheral organ tumors has been demonstrated, the impact of CRGs on the prognosis and the immunological landscape of gliomas remains unexplored. METHODS: We screened CRGs to construct a novel scoring tool and developed a prognostic model for gliomas within the various cohorts. Afterward, a comprehensive exploration of the relationship between the CRG risk signature and the immunological landscape of gliomas was undertaken from multiple perspectives. RESULTS: Five genes (NLRP3, ATP7B, SLC31A1, FDX1, and GCSH) were identified to build a CRG scoring system. The nomogram, based on CRG risk and other signatures, demonstrated a superior predictive performance (AUC of 0.89, 0.92, and 0.93 at 1, 2, and 3 years, respectively) in the training cohort. Furthermore, the CRG score was closely associated with various aspects of the immune landscape in gliomas, including immune cell infiltration, tumor mutations, tumor immune dysfunction and exclusion, immune checkpoints, cytotoxic T lymphocyte and immune exhaustion-related markers, as well as cancer signaling pathway biomarkers and cytokines. CONCLUSION: The CRG risk signature may serve as a robust biomarker for predicting the prognosis and the potential viability of immunotherapy responses. Moreover, the key candidate CRGs might be promising targets to explore the underlying biological background and novel therapeutic interventions in gliomas.

Revealing the spatiotemporal brain dynamics of covert speech compared with overt speech: A simultaneous EEG-fMRI study.

Covert speech (CS) refers to speaking internally to oneself without producing any sound or movement. CS is involved in multiple cognitive functions and disorders. Reconstructing CS content by brain-computer interface (BCI) is also an emerging technique. However, it is still controversial whether CS is a truncated neural process of overt speech (OS) or involves independent patterns. Here, we performed a word-speaking experiment with simultaneous EEG-fMRI. It involved 32 participants, who generated words both overtly and covertly. By integrating spatial constraints from fMRI into EEG source localization, we precisely estimated the spatiotemporal dynamics of neural activity. During CS, EEG source activity was localized in three regions: the left precentral gyrus, the left supplementary motor area, and the left putamen. Although OS involved more brain regions with stronger activations, CS was characterized by an earlier event-locked activation in the left putamen (peak at 262 ms versus 1170 ms). The left putamen was also identified as the only hub node within the functional connectivity (FC) networks of both OS and CS, while showing weaker FC strength towards speech-related regions in the dominant hemisphere during CS. Path analysis revealed significant multivariate associations, indicating an indirect association between the earlier activation in the left putamen and CS, which was mediated by reduced FC towards speech-related regions. These findings revealed the specific spatiotemporal dynamics of CS, offering insights into CS mechanisms that are potentially relevant for future treatment of self-regulation deficits, speech disorders, and development of BCI speech applications.

Promoter profiles in plasma CfDNA exhibits a potential utility of predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients.

BACKGROUND: Gene expression profiles in breast tissue biopsies contain information related to chemotherapy efficacy. The promoter profiles in cell-free DNA (cfDNA) carrying gene expression information of the original tissues may be used to predict the response to neoadjuvant chemotherapy in breast cancer as a non-invasive biomarker. In this study, the feasibility of the promoter profiles in plasma cfDNA was evaluated as a novel clinical model for noninvasively predicting the efficacy of neoadjuvant chemotherapy in breast cancer. METHOD: First of all, global chromatin (5 Mb windows), sub-compartments and promoter profiles in plasma cfDNA samples from 94 patients with breast cancer before neoadjuvant chemotherapy (pCR = 31 vs. non-pCR = 63) were analyzed, and then classifiers were developed for predicting the efficacy of neoadjuvant chemotherapy in breast cancer. Further, the promoter profile changes in sequential cfDNA samples from 30 patients (pCR = 8 vs. non-pCR = 22) during neoadjuvant chemotherapy were analyzed to explore the potential benefits of cfDNA promoter profile changes as a novel potential biomarker for predicting the treatment efficacy. RESULTS: The results showed significantly distinct promoter profile in plasma cfDNA of pCR patients compared with non-pCR patients before neoadjuvant chemotherapy. The classifier based on promoter profiles in a Random Forest model produced the largest area under the curve of 0.980 (95% CI: 0.978-0.983). After neoadjuvant chemotherapy, 332 genes with significantly differential promoter profile changes in sequential cfDNA samples of pCR patients was observed, compared with non-pCR patients, and their functions were closely related to treatment response. CONCLUSION: These results suggest that promoter profiles in plasma cfDNA may be a powerful, non-invasive tool for predicting the efficacy of neoadjuvant chemotherapy breast cancer patients before treatment, and the on-treatment cfDNA promoter profiles have potential benefits for predicting the treatment efficacy.

Realization of Gapped and Ungapped Photonic Topological Anderson Insulators.

It is commonly believed that topologically nontrivial one-dimensional systems support edge states rather than bulk states at zero energy. In this work, we find an unanticipated case of topological Anderson insulator (TAI) phase where two bulk modes are degenerate at zero energy, in addition to degenerate edge modes. We term this "ungapped TAI" to distinguish it from the previously known gapped TAIs. Our experimental realization of both gapped and ungapped TAIs relies on coupled photonic resonators, in which the disorder in coupling is judiciously engineered by adjusting the spacing between the resonators. By measuring the local density of states both in the bulk and at the edges, we demonstrate the existence of these two types of TAIs, together forming a TAI plateau in the phase diagram. Our experimental findings are well supported by theoretical analysis. In the ungapped TAI phase, we observe stable coexistence of topological edge states and localized bulk states at zero energy, highlighting the distinction between TAIs and traditional topological insulators.

Structural and decomposition analysis of TKX-50 with vacancy defects: insights from DFT and AIMD simulations.

Vacancy defects are commonly present in crystals of energetic materials, and significantly influence the structural stability and decomposition mechanisms. However, there is a lack of profound understanding regarding the introduction of vacancy defects in energetic ionic salt, dihydroxylammonium 5,5'-bitetrazole-1,1'-dioxide (TKX-50). Due to the 1 : 2 ratio of anions to cations, TKX-50 possesses a more complex distribution of vacancy defects compared to traditional energetic materials. Based on the density functional theory method, the relatively favorable thermodynamic formation of vacancy defect distributions was revealed. The noncovalent interactions within the system, as well as the planarity of the anions, were investigated to understand the structural stability of TKX-50. Through ab initio molecular dynamics simulations, we discovered that vacancy defects can expedite the proton transfer during the initial decomposition stage of TKX-50 and affect the pathways of proton transfer. In the subsequent decomposition process, introduction of vacancy defects in the TKX-50 crystal leads to an earlier onset of ring-opening reactions and accelerates the appearance of decomposition products. The findings have the potential to provide insights into modeling vacancy defects in energetic ionic salts and reveal the impact of such defects on the structural stability and decomposition mechanisms of these materials.

Insomnia disorder characterized by probabilistic metastable substates using blood-oxygenation-level-dependent (BOLD) phase signals.

PURPOSE: From a clinical point of view, how to force a transition from insomnia brain state to healthy brain state by external driven stimulation is of great interest. This needs to define brain state of insomnia disorder as metastable substates. The current study was to identify recurrent substates of insomnia disorder in terms of probability of occurrence, lifetime, and alternation profiles by using leading eigenvector dynamics analysis (LEiDA) method. METHODS: We enrolled 32 patients with insomnia disorder and 30 healthy subjects. We firstly obtained the BOLD phase coherence matrix from Hilbert transform of BOLD signals and then extracted all the leading eigenvectors from the BOLD phase coherence matrix for all subjects across all time points. Lastly, we clustered the leading eigenvectors using a k-means clustering algorithm to find the probabilistic metastable substates (PMS) and calculate the probability of occurrence and associated lifetime for substates. RESULTS: The resulting 3 clusters were optimal for brain state of insomnia disorder and healthy brain state, respectively. The occurred probabilities of the PMS were significantly different between the patients with insomnia disorder and healthy subjects, with 0.51 versus 0.44 for PMS-1 (p < 0.001), 0.25 versus 0.27 for PMS-2 (p = 0.051), and 0.24 versus 0.29 for PMS-3 (p < 0.001), as well as the lifetime (in TR) of 36.65 versus 33.15 for PMS-1 (p = 0.068), 14.36 versus 15.43 for PMS-2 (p = 0.117), and 14.80 versus 16.34 for PMS-3 (p = 0.042). The values of the diagonal of the transition matrix were much higher than the probabilities of switching states, indicating the metastable nature of substates. CONCLUSION: The resulted probabilistic metastable substates hint the characteristic brain dynamics of insomnia disorder. The results may lay a foundation to help determine how to force a transition from insomnia brain state to healthy brain state by external driven stimulation.

Omnidirectional defect mode in one-dimensional photonic crystal with a (chiral) hyperbolic metamaterial defect.

The wavelength of defect mode in all-dielectric photonic crystals (PCs) with a dielectric defect are blue-shifted as incident angle increases for both transverse electric and transverse magnetic (TM) polarized waves. The blue-shifted property of defect mode limits the design of some optical devices including omnidirectional optical filters and wide-angle polarization selectors. Here we introduce a hyperbolic metamaterial (HMM) layer as a defect into dielectric one-dimensional photonic crystals (1DPCs) to obtain an omnidirectional defect mode for TM polarized waves at near-infrared regimes. Since only one HMM layer is introduced, omnidirectional defect mode with transmittance as high as 71% can be realized. Because of the unusual angle-dependence of propagating phase in the HMM defect, the total phase for satisfying the resonance condition of defect mode can be unchanged in a wide-angle range at a fixed wavelength, which leads to the omnidirectional defect mode. Moreover, the manipulation of propagating phase can be generalized to the case of circularly polarized waves, and we obtain an omnidirectional defect mode for left-handed circularly polarized waves in 1DPCs with a chiral hyperbolic metamaterial defect. Nevertheless, the defect mode for right-handed circularly polarized waves is still blue-shifted. Such spin-selective omnidirectional defect mode can be utilized to greatly enhance circular dichroism in a wide-angle range up to 64.1°. Our structure facilitates the design of omnidirectional optical filters with a high transmittance and circular polarization selectors working in a wide-angle range.

Giant optical chirality in dielectric metasurfaces induced by toroidal dipole resonances.

Although toroidal dipole (TD) resonance is a highly localized mode with a high quality (Q) factor, in most chiral structures the TD resonance is much weaker than the electric or magnetic dipole resonances and contributes little to the chiral response. In this Letter, we theoretically propose a chiral all-dielectric TD metasurface that possesses giant optical chirality with a certain degree of incident-angle robustness induced by a strong TD resonance. Interestingly, the symmetry of the system can be broken simultaneously at oblique incidence to produce chiral quasi-bound states in the continuum. The nearly unchanged high-Q TD resonance within a certain range of incident angles can avoid the problem of a reduced image quality caused by the incident-angle sensitivity, as demonstrated by the polarization-multiplexed-field image displays. The giant chirality with a certain degree of incident-angle robustness induced by the TD mode would be useful in some applications, including high-quality optical imaging, high-performance asymmetric transmission, and sensing.

Multiple brain regions are involved in reaction to acute restraint stress in CYLD-knockout mice.

The lysine 63 deubiquitinase cylindromatosis (CYLD) is expressed at high levels in the brain and is considered to be involved in anxious and depressive behavior, cognitive inflexibility, and autism disorders. Previous research was limited in some brain regions, including the hippocampus, striatum, and amygdala. To better understand whether CYLD plays a role in adaptation to stress and which brain regions are involved, we analyzed the behavior of CYLD-knockout mice in the elevated plus maze (EPM) and light-dark box test (LDT) after acute restraint stress (ARS) and mapped their c-Fos immunoreactivity in brain sections. Here we report that CYLD deficiency leads to an unexpected reaction to ARS in mice, and is accompanied by significant neuronal activation of brain regions including the medial prefrontal cortex (mPFC), dorsal striatum (DS), nucleus accumbens (NAc), and basal lateral amygdala (BLA), but not ventral hippocampus (vHPC). Our findings show that CYLD participates in ARS-induced anxious behavior and that this involves multiple brain regions.

High-Frequency rTMS Could Improve Impaired Memory in Mild Cognitive Impairment Patients in China: A Randomized Controlled Study.

OBJECTIVE: The purpose of this study was to investigate the effects of repetitive transcranial magnetic stimulation (rTMS) on improving memory deficits in mild cognitive impairment (MCI), as well as to provide visualized evidence for neuronal specificity by using resting-state functional magnetic resonance imaging. MATERIALS AND METHODS: Forty MCI patients were enrolled to receive 10-session and sham-controlled 10Hz-rTMS over the left dorsolateral prefrontal cortex. The resting-state functional magnetic resonance imaging combined with memory scales assessment were performed before and after the intervention. To elucidate the therapeutic mechanism of rTMS, amplitude of low-frequency fluctuations (ALFF) and functional connectivity were calculated. The Pearson correlation was used to measure the relationship between ALFF and memory performance. RESULTS: Compared with the sham group, ALFF significantly increased in the right insula, right inferior frontal gyrus-opercular part, and decreased in the left middle occipital gyrus, left angular gyrus, and left lingual gyrus after rTMS. The change in Auditory Verbal Learning Test scores were negatively correlated with ALFF decreases in the left lingual gyrus. Functional connectivity significantly increased between the posterior cingulate cortex and right supramarginal gyrus, and decreased between the right frontoinsular cortex and right supramarginal gyrus after intervention. CONCLUSION: High-frequency rTMS over the left dorsolateral prefrontal cortex could facilitate improvement on impaired memory in patients with MCI via modulating the neuronal activity and brain network.

Coronary artery ectasia associated with IgG4-related disease: a case report and literature review.

BACKGROUND: Coronary artery ectasia is defined as a local or diffuse dilatation of the coronary artery more than 1.5 times the diameter of the adjacent normal segment. The etiology of coronary artery ectasia is diverse, and rarely complicated with immunoglobulin G4-related disease (IgG4-related disease). A limited number of cases have been reported, with insidious onset, slow progression but poor prognosis. CASE PRESENTATION: we report a patient with coronary artery ectasia combined with IgG4-related disease. He has been diagnosed with IgG4-related disease 5 years after his first percutaneous coronary intervention (PCI). Despite routine treatment with steroids, he develops a large coronary aneurysm and eventually died. CONCLUSIONS: It is suggested that a thorough evaluation should be performed when coronary artery ectasia is diagnosed. The factors such as manifestations of coronary artery thickening, typical imaging features, other aortas involvement, increased serum IgG4 level, etc. should be considered for early diagnosis of key etiologies.

An artificial neural network-based data filling approach for smart operation of digital wastewater treatment plants.

With the prevalence of digitization, smart operation has become mainstream in future wastewater treatment plants. This requires substantial and complete historical data for model construction. However, the data collected from the front-end sensor contained numerous missing dissolved oxygen (DO) values. Therefore, this study proposed a framework that adaptively adjusted the structure of embedded filling models according to the missing situation. Long short-term memory and gated recurrent units (GRU) were embedded for experiments, and some standard filling methods were selected as benchmarks. The experimental dataset indicated that the K-nearest neighbor could achieve good filling results by traversing the parameters. The effect obtained by the method proposed in this study was slightly better, and GRU was better among the three embedded models. Analysis of the filling results for each DO column revealed that the effect was highly correlated with the dispersion of DO data. The experimental results for the entire dataset demonstrated that the filling effect of the proposed method was significantly better and more stable than the others. The proposed model suffered from the problem of insufficient interpretability and long training time. This study provides an efficient and practical method to solve the intricate missing DO and lays the foundation for the smart operation of wastewater treatment plants.

Insomnia disorder with objective short sleep duration (ISS) phenotype and cognitive performance: a systematic review and meta-analysis.

OBJECTIVE: Insomnia disorder with objective short sleep duration (ISS) has been considered as a biologically severe subtype. The aim of this meta-analysis was to reveal the association of the ISS phenotype and cognitive performance. METHODS: We searched PubMed, EMBASE, and the Cochrane Library for studies that observed an association of cognitive performance and insomnia with objective short sleep duration (ISS) phenotype. The "metafor" and "MAd" packages in R software (version 4.2.0) were used to calculate the unbiased standardized mean difference (Hedge's g), which was adjusted so that a negative value indicated worse cognitive performance. RESULTS: The pooled analysis with 1339 participants revealed that the ISS phenotype was associated with overall cognitive impairments (Hedges' g = - 0.56 [- 0.89, - 0.23]), as well as specific cognitive domains including attention (Hedges' g = - 0.86 [- 1.25, - 0.47]), memory (Hedges' g = - 0.47 [- 0.82, - 0.12]), and executive function (Hedges' g = - 0.39 [- 0.76, - 0.02]). However, cognitive performance was not significantly different between insomnia disorder with objective normal sleep duration (INS) and good sleepers (p > .05). CONCLUSION: Insomnia disorder with the ISS phenotype, but not the INS phenotype, was associated with cognitive impairments, suggesting the possible utility of treating the ISS phenotype to improve cognitive performance.

Fluxes in CO2 and CH4 and influencing factors at the sediment-water interface in a eutrophic saline lake.

Although saline aquatic ecosystems are significant emitters of greenhouse gases (GHGs), dynamic changes in GHGs at the sediment-water interface remain unclear. The present investigation carried out a total of four sampling campaigns in Daihai Lake, which is a eutrophic saline lake situated in a semi-arid area of northern China. The aim of this study was to investigate the spatio-temporal dynamics of carbon dioxide (CO2) and methane (CH4) fluxes at the sediment-water interface and the influencing factors. The mean concentrations of porewater CO2 and CH4 were 44.98 ± 117.99 µmol L-1 and 124.36 ± 97.00 µmol L-1, far exceeding those in water column of 11.14 ± 2.16 µmol L-1 and 0.33 ± 0.23 µmol L-1, respectively. The CO2 and CH4 fluxes at the sediment-water interface (FS-WCO2 and FS-WCH4) exhibited significant spatial and temporal variations, with mean values of 9.24 ± 13.84 µmol m-2 d-1 and 3.53 ± 4.36 µmol m-2 d-1, respectively, indicating that sediment is the source of CO2 and CH4 in the water column. However, CO2 and CH4 fluxes were much lower than those measured at the water-air interface in a companion study (17.54 ± 14.54 mmol m-2d-1 and 0.50 ± 0.50 mmol m-2d-1, respectively), indicating that the diffusive flux of gases at the sediment-water interface was not the primary source of CO2 and CH4 emissions to the atmosphere. Regression and correlation analyses revealed that salinity (Sal) and nutrients were the most influential factors on porewater gas concentrations, and that gas fluxes increased with increasing gas concentrations and porosity. The microbial activity of sediment is greatly affected by nutrients and Sal. Additionally, Sal has the ability to regulate biogeochemical processes, thereby regulating GHG emissions. The present investigation addresses the research gap concerning GHG emissions from sediments of eutrophic saline lakes. The study suggests that controlling the eutrophication and salinization of lakes could be a viable strategy for reducing carbon emissions from lakes. However, further investigations are required to establish more conclusive results.

Integrative analyses of maternal plasma cell-free DNA nucleosome footprint differences reveal chromosomal aneuploidy fetuses gene expression profile.

BACKGROUND: Chromosomal aneuploidy is the most common birth defect. However, the developmental mechanism and gene expression profile of fetuses with chromosomal aneuploidy are relatively unknown, and the maternal immune changes induced by fetal aneuploidy remain unclear. The inability to obtain the placenta multiple times in real-time is a bottleneck in research on aneuploid pregnancies. Plasma cell-free DNA (cfDNA) carries the gene expression profile information of its source cells and may be used to evaluate the development of fetuses with aneuploidy and the immune changes induced in the mother owing to fetal aneuploidy. METHODS: Here, we carried out whole-genome sequencing of the plasma cfDNA of 101 pregnant women carrying a fetus with trisomy (trisomy 21, n = 42; trisomy 18, n = 28; trisomy 13, n = 31) based on non-invasive prenatal testing (NIPT) screening and 140 normal pregnant women to identify differential genes according to the cfDNA nucleosome profile in the region around the transcription start sites (TSSs). RESULTS: The plasma cfDNA promoter profiles were found to differ between aneuploid and euploid pregnancies. A total of 158 genes with significant differences were identified, of which 43 genes were upregulated and 98 genes were downregulated. Functional enrichment and signaling pathway analysis were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases found that these signal pathways were mainly related to the coordination of developmental signals during embryonic development, the control of cell growth and development, regulation of neuronal survival, and immune regulation, such as the MAPK, Hippo, TGF-ß, and Rap1 signaling pathways, which play important roles in the development of embryonic tissues and organs. Furthermore, based on the results of differential gene analysis, a total of 14 immune-related genes with significant differences from the ImmPort database were collected and analyzed. These significantly different immune genes were mainly associated with the maintenance of embryonic homeostasis and normal development. CONCLUSIONS: These results suggest that the distribution characteristics of cfDNA nucleosomes in maternal plasma can be used to reflect the status of fetal development and changes of the immune responses in trisomic pregnancies. Overall, our findings may provide research ideas for non-invasive detection of the physiological and pathological states of other diseases.

Noninvasive prediction of axillary lymph node status in breast cancer using promoter profiling of circulating cell-free DNA.

BACKGROUND: Lymph node metastasis (LNM) is one of the most important factors affecting the prognosis of breast cancer. The accurate evaluation of lymph node status is useful to predict the outcomes of patients and guide the choice of cancer treatment. However, there is still lack of a low-cost non-invasive method to assess the status of axillary lymph node (ALN). Gene expression signature has been used to assess lymph node metastasis status of breast cancer. In addition, nucleosome footprint of cell-free DNA (cfDNA) carries gene expression information of its original tissues, so it may be used to evaluate the axillary lymph node status in breast cancer. METHODS: In this study, we found that the cfDNA nucleosome footprints between the ALN-positive patients and ALN-negative patients showed different patterns by implementing whole-genome sequencing (WGS) to detect 15 ALN-positive and 15 ALN-negative patients. In order to further evaluate its potential for assessing ALN status, we developed a classifier with multiple machine learning models by using 330 WGS data of cfDNA from 162 ALN-positive and 168 ALN-negative samples to distinguish these two types of patients. RESULTS: We found that the promoter profiling between the ALN-positive patients and ALN-negative patients showed distinct patterns. In addition, we observed 1071 genes with differential promoter coverage and their functions were closely related to tumorigenesis. We found that the predictive classifier based on promoter profiling with a support vector machine model, named PPCNM, produced the largest area under the curve of 0.897 (95% confidence interval 0.86-0.93). CONCLUSIONS: These results indicate that promoter profiling can be used to distinguish ALN-positive patients from ALN-negative patients, which may be helpful to guide the choice of cancer treatment.

Omnidirectional nonreciprocal absorber realized by the magneto-optical hypercrystal.

Photonic bandgap design is one of the most basic ways to effectively control the interaction between light and matter. However, the traditional photonic bandgap is always dispersive (blueshift with the increase of the incident angle), which is disadvantageous to the construction of wide-angle optical devices. Hypercrystal, the photonic crystal with layered hyperbolic metamaterials (HMMs), can strongly modify the bandgap properties based on the anomalous wavevector dispersion of the HMM. Here, based on phase variation competition between HMM and isotropic dielectric layers, we propose for the first time to design nonreciprocal and flexible photonic bandgaps in one-dimensional photonic crystals containing magneto-optical HMMs. Especially the zero-shift cavity mode and the blueshift cavity mode are designed for the forward and backward propagations, respectively. Our results show maximum absorption about 0.99 (0.25) in an angle range of 20-75 degrees for the forward (backward) incident light at the wavelength of 367 nm. The nonreciprocal omnidirectional cavity mode not only facilitates the design of perfect unidirectional optical absorbers working in a wide-angle range, but also possesses significant applications for all-angle reflectors and filters.

On-chip multiple beam splitting assisted by bound states in the continuum.

Usually, on-chip beam splitting can be achieved by manipulating the in-plane iso-frequency curves (IFCs) of the structure, where the confinement of light along the out-of-plane direction is governed by total internal reflection. In this Letter, without needing a high-index dielectric background material for total internal reflection, we achieve on-chip beam splitting in a linear-crossing metamaterial (LCMM) mimicked by a two-dimensional photonic crystal (PhC) slab where the vertical confinement is enabled by a bound state in the continuum (BIC) and totally beyond the light cone. Particularly, the light propagating inside the LCMM can be flexibly controlled by the rotation angle of the rectangular silicon pillars in the PhC slab. On-chip triple beam splitting can further be designed by combining two kinds of LCMM with opposite rotation angles. Such light beam splitting beyond the light cone originates from the combined manipulation of the BIC and the spatial dispersion of LCMMs. Our work promotes the development of optical devices in integrated optics, such as on-chip focusing, switching, and (de)multiplexing.

Superstructured Nanocrystals/Dual-Doped Mesoporous Carbon Anodes for High-Performance Sodium-Ion Batteries.

Two-dimensional ordered superstructures have been attracting considerable attention due to their interesting properties and potential applications. However, designing ideal functional superstructures with excellent electrochemical properties is still a major challenge, and an in-depth understanding of the structure-activity relationship of electrodes remains to be achieved. To elucidate this critical issue, herein, we rationally designed and synthesized for the first time superstructured TiO2/dual-doped mesoporous carbon anodes using confined space and surface coassembly strategies. Our method primarily relied on the larger interlayer space few-layered MXene and its negatively charged surface, allowing hexamethylenetetramine intercalation and surface electrostatic adsorption. The superstructured TiO2/dual-doped mesoporous carbon was successfully assembled by the thermal decomposition of a confined carbon precursor. Subsequently, the comparison of Na+-storage properties of various anodes was carried out based on the results of structural characterization techniques and electrochemical analysis methods. The results showed that the optimized anode (N/O-C@TiO2-20) can deliver a reversible capacity of 165 mA h g-1 after 1000 cycles at a current density of 1 A g-1, indicating excellent electrochemical properties. The enhancement can be attributed to the synergistic effect of carbon domains, defective nanocrystals, and a covalently coupled interface between TiO2 and mesoporous carbon. Our work not only offered a new strategy for the assembly and regulation of superstructures to promote the electrochemical performance but also enlightened the rational design of advanced anodes for sodium-ion battery application.

Online learning-empowered smart management for A2O process in sewage treatment processes.

Using artificial intelligence method to describe general working process is a more meaningful and widely used idea in various practical projects. At the same time, it is also an important way to realize intelligent management. Water pollution is serious all over the world, also the intelligent management of sewage treatment has always been one of the urgent problems to be solved. For this, an intelligent management system is designed in this study to realize automatic monitoring and intelligent decision-making of sewage treatment. However, the existing technology usually trains artificial intelligence models based on historical data, and such models have some limitations in describing nonlinear and complex wastewater treatment processes. Offline machine learning lacks dynamic adaptive characteristics to scene changes. Considering this, this paper designs an online learning-empowered smart management for A2/O process in sewage treatment processes (OL-AP). Online learning is based on the new data generated by the scene transformation, so that the model can learn again and give better results. In this study, relevant simulation experiments are carried out on the sewage treatment data of a sewage treatment plant in Chongqing. Firstly, automatic data collection is realized based on the sensor network of the IoT. Then, according to the preprocessed data, the designed prediction model is trained and a set of parameters with better evaluation indexes is obtained. Finally, online learning uses the latest data samples based on the online feedback of real scenes to optimize the model by retraining and adjusting parameters.