Chirality-dependent topological edge states in photonic metacrystal.

Topological edge state, a unique mode for manipulating electromagnetic waves (EMs), has been extensively studied in both fundamental and applied physics. Up to now, the work on topological edge states has focused on manipulating linearly polarized waves. Here, we realize chirality-dependent topological edge states in one-dimensional photonic crystals (1DPCs) to manipulate circularly polarized waves. By introducing the magneto-electric coupling term (chirality), the degeneracy Dirac point (DP) is opened in PCs with symmetric unit cells. The topological properties of the upper and lower bands are different in the cases of left circularly polarized (LCP) and right circularly polarized (RCP) waves by calculating the Zak phase. Moreover, mapping explicitly 1D Maxwell's equations to the Dirac equation, we demonstrate that the introduction of chirality can lead to different topological properties of bandgaps for RCP and LCP waves. Based on this chirality-dependent topology, we can further realize chirality-dependent topological edge states in photonic heterostructures composed of two kinds of PCs. Finally, we propose a realistic structure for the chirality-dependent topological edge states by placing metallic helixes in host media. Our work provides a method for manipulating topological edge states for circularly polarized waves, which has a broad range of potential applications in designing optical devices including polarizers, filters, and sensors with robustness against disorder.

Optimizing cropland use to reduce groundwater arsenic hazards in a naturally arsenic-enriched grain-producing region.

In the Hetao Basin, a grain-producing region plagued by naturally occurring arsenic (As) pollution, understanding the role of agricultural cultivation activities in mobilizing As in groundwater is worthwhile. Here we investigated the impact of cropland use characteristics on groundwater As hazards using a model that combines Random Forest (RF) classification with SHapley Additive exPlanation (SHAP). The analysis incorporated eight cropland use characteristics and three natural factors across 1258 groundwater samples as independent variables. Additionally, an optimized cropland use strategy to mitigate groundwater As hazards was proposed. The results revealed that crop cultivation area, especially within a 2500m-radius buffer around sampling points, most significantly influenced the probability of groundwater As concentrations exceeding an irrigation safety threshold of 50 µg/L, achieving an AUC of 0.86 for this prediction. The relative importance of crop areas on As hazards were as follows: sunflower > melon > wheat > maize. Specifically, a high proportion of sunflower area (>30%), particularly in regions with longer cropland irrigation history, tended to elevate groundwater As hazards. Conversely, its negative driving force on groundwater As hazards was more pronounced with the increase in the proportion of wheat area (>5%), in contrast to other crops. Transitioning from sunflower to wheat or melon cultivation in the northeast of the Hetao Basin may contribute to lower groundwater As hazards. This study provides a scientific foundation for balancing food production with environmental safety and public health considerations.

Insights into the Mechanical-Structural-Energetic Responses of RDX Nanoparticles from Molecular Dynamics Simulation.

When the particle size of energetic materials is reduced to the nanoscale, significant changes occur in their properties and behavior. In this work, compression processes of three RDX nanoparticles (A, B, and C) were simulated using ReaxFF-lg. The mechanical, structural, and energetic responses of RDX nanoparticles during compression were revealed and characterized. Simulations reveal that the compression process of the nanoparticles can be divided into three stages: elastic stage, primary damage stage, and sustained damage stage. The temperature increase rate in the elastic phase is much lower than in the primary damage phase. In addition, we found that the smaller nanoparticle B presents a smaller elastic modulus and compressive strength, and it has a slower rate of temperature increase during the primary damage phase. Compared to cuboidal nanoparticles (A and B), the spherical nanoparticle C tends to absorb less energy during the elastic stage and exhibits slower damage rate during the primary damage stage. This is a key factor contributing to the low sensitivity of spherical nanoparticles.

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.

Multiview Deep Learning-based Efficient Medical Data Management for Survival Time Forecasting.

In recent years, data-driven remote medical management has received much attention, especially in application of survival time forecasting. By monitoring the physical characteristics indexes of patients, intelligent algorithms can be deployed to implement efficient healthcare management. However, such pure medical data-driven scenes generally lack multimedia information, which brings challenge to analysis tasks. To deal with this issue, this paper introduces the idea of ensemble deep learning to enhance feature representation ability, thus enhancing knowledge discovery in remote healthcare management. Therefore, a multiview deep learning-based efficient medical data management framework for survival time forecasting is proposed in this paper, which is named as "MDL-MDM" for short. Firstly, basic monitoring data for body indexes of patients is encoded, which serves as the data foundation for forecasting tasks. Then, three different neural network models, convolution neural network, graph attention network, and graph convolution network, are selected to build a hybrid computing framework. Their combination can bring a multiview feature learning framework to realize an efficient medical data management framework. In addition, experiments are conducted on a realistic medical dataset about cancer patients in the US. Results show that the proposal can predict survival time with 1% to 2% reduction in prediction error.

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.

Functional MRI-based biomarkers of insomnia with objective short sleep duration phenotype.

BACKGROUND: Insomnia disorder with objective short sleep duration (ISS) phenotype is a more serious biological subtype than insomnia with objective normal sleep duration (INS) phenotype, and the neuroimaging data is helpful to understand the pathophysiology of the ISS phenotype. This study was to compare the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) between the ISS phenotype and the INS phenotype. METHODS: In this cross-sectional study, 55 patients with insomnia disorder were recruited, and 22 of them were defined as the ISS phenotype by the objective cardiopulmonary coupling (CPC) technique. The blood oxygen level-dependent (BOLD) sequences of all participants were obtained using the 3.0 T magnetic resonance imaging system. We analyzed and compared the ALFF, ReHo, and FC between the ISS phenotype and the INS phenotype. We also conducted Pearson's correlation analysis between significant neuroimaging biomarkers and the CPC parameters. RESULTS: The differences were not significant in ALFF (PFWE-corr＞0.05) or ReHo (PFWE-corr＞0.05) between the ISS phenotype and the INS phenotype. For the FC analysis, the ISS phenotype had a Hub-node of the left inferior occipital gyrus (IOG.L), with significantly decreased connections (p＜0.001) in the bilateral occipital, parietal, and temporal regions. The significant FCs were closely related to sleep parameters. CONCLUSION: The left inferior occipital gyrus (IOG.L), as a Hub-node with decreased functional connections, may be a potential fMRI-based biomarker of the ISS phenotype.

Non-invasive determination of gene expression in placental tissue using maternal plasma cell-free DNA fragmentation characters.

BACKGROUND: The expression profiles of placental genes are crucial for understanding the pathogenesis of fetal development and placental-origin pregnancy syndromes. However, owing to ethical limitations and the risks of puncture sampling, it is difficult to obtain placental tissue samples repeatedly, continuously, multiple times, or in real time. Establishing a non-invasive method for predicting placental gene expression profiles through maternal plasma cell-free DNA (cfDNA) sequencing, which carries information about the source tissue and gene expression, can potentially solve this problem. METHODS: Peripheral blood and placental samples were collected simultaneously from pregnant women who underwent cesarean section. Deep sequencing was performed on the separated plasma cfDNA and single-cell sequencing was performed on peripheral blood mononuclear cells (PBMC), chorioamniotic membranes (CAM), placental villi (PV), and decidua basalis (DB). The aggregation of corresponding information for each gene was combined with the transcriptome of PBMCs and a differential resolution transcriptome of the placenta. This combined information was then utilized for the construction of gene expression prediction models. After training, all models evaluated the correlation between the predicted and actual gene expression levels using external test set data. RESULTS: From five women, more than 20 million reads were obtained using deep sequencing for plasma cfDNA; PBMCs obtained 32,401 single-cell expression profiles; and placental tissue obtained 156,546 single-cell expression profiles (59,069, 44,921, and 52,556 for CAM, PV, and DB, respectively). The cells in the PBMC and placenta were clustered and annotated into five and eight cell types, respectively. A "DEPICT" gene expression prediction model was successfully constructed using deep neural networks. The predicted correlation coefficients were 0.75 in PBMCs, 0.84 in the placenta, and 0.78, 0.80, and 0.77 in CAM, BP, and PV respectively, and greater than 0.68 in different cell lines in the placenta. CONCLUSION: The DEPICT model, which can noninvasively predict placental gene expression profiles based on maternal plasma cfDNA fragmentation characteristics, was constructed to overcome the limitation of the inability to obtain real-time placental gene expression profiles and to improve research on noninvasive prediction of placental origin pregnancy syndrome.

Effects of drug-induced liver injury on the in vivo fate of liposomes.

Liposomes represent one of the most extensively studied nano-carriers due to their potential in targeted drug delivery. However, the complex in vivo fate, particularly under pathological conditions, presents challenges for clinical translation of liposomal therapeutics. Liver serves as the most important organ for liposome accumulation and metabolism. Unfortunately, the fate of liposomes under pathological liver conditions has been significantly overlooked. This study aimed to investigate the in vivo pharmacokinetic profile and biodistribution profile of liposomes under drug-induced liver injury (DILI) conditions. Two classic DILI animal models, i.e. acetaminophen-induced acute liver injury (AILI) and triptolide-induced subacute liver injury (TILI), were established to observe the effect of pathological liver conditions on the in vivo performance of liposomes. The study revealed significant changes in the in vivo fate of liposomes following DILI, including prolonged blood circulation and enhanced hepatic accumulation of liposomes. Changes in the composition of plasma proteins and mononuclear phagocyte system (MPS)-related cell subpopulations collectively led to the altered in vivo fate of liposomes under liver injury conditions. Despite liver injury, macrophages remained the primary cells responsible for liposomes uptake in liver, with the recruited monocyte-derived macrophages exhibiting enhanced ability to phagocytose liposomes under pathological conditions. These findings indicated that high capture of liposomes by the recruited hepatic macrophages not only offered potential solutions for targeted delivery, but also warned the clinical application of patients under pathological liver conditions.

Altered EEG Theta and Alpha Band Functional Connectivity in Mild Cognitive Impairment During Working Memory Coding.

Individuals with mild cognitive impairment (MCI), the preclinical stage of Alzheimer disease (AD), suffer decline in their visual working memory (WM) functions. Using large-scale network analysis of electroencephalography (EEG), the current study intended to investigate if there are differences in functional connectivity properties extracted during visual WM coding stages between MCI patients and normal controls (NC). A total of 21 MCI patients and 20 NC performed visual memory tasks of load four, while 32-channel EEG recordings were acquired. The functional connectivity properties were extracted from the acquired EEGs by the directed transform function (DTF) via spectral Granger causal analysis. Brain network analyses revealed distinctive brain network patterns between the two groups during the WM coding stage. Compared with the NC, MCI patients exhibited a reduced visual network connectivity of the frontal-temporal in θ (4-7Hz) band. A likely compensation mechanism was observed in MCI patients, with a strong brain functional connectivity of the frontal-occipital and parietal-occipital in both θ and α (8-13Hz) band. Further analyses of the network core node properties based on the differential brain network showed that, in θ band, there was a significant difference in the out-degree of the frontal lobe and parietal lobe between the two groups, while in α band, such difference was located only in the parietal lobe. The current study found that, in MCI patients, dysconnectivity is found from the prefrontal lobe to bilateral temporal lobes, leading to increased recruitment of functional connectivity in the frontal-occipital and parietal-occipital direction. The dysconnectivity pattern of MCI is more complex and primarily driven by core nodes Pz and Fz. These results significantly expanded previous knowledge of MCI patients' EEG dynamics during WM tasks and provide new insights into the underpinning neural mechanism MCI. It further provided a potential therapeutic target for clinical interventions of the condition.

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.

Effective removal of microplastics by filamentous algae and its magnetic biochar: Performance and mechanism.

In recent years, filamentous algae blooms and microplastics (MPs) pollution have become two major ecological and environmental problems in urban water systems. In order to solve these two problems at the same time, this study explored the loading capacity of MPs on fresh filamentous algae, and successfully synthesized magnetic filamentous algae biochar loading with Fe3O4 by hydrothermal method, with the purpose of removing MPs from water. The magnetic filamentous algal biochar was characterized by scanning electron microscopy (SEM), Fourier transform infrared (FTIR), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and so on. Experiments on adsorption kinetics, adsorption isotherms and optimum pH were carried out to explore the adsorption mechanism of MPs on magnetic filamentous algal biochar. The adsorption kinetics and adsorption isotherm models were evaluated, and the selection criterion for the appropriate model was determined by using the residual sum of squares (RSS) and Bayesian information criterion (BIC). Microscope images revealed that fresh filamentous algae could interact with MPs in the form of entanglement, adhesion and encapsulation. The average load of MPs in filamentous algae samples was 14.1 ± 5 items/g dry weight. The theoretical maximum adsorption capacities of polystyrene MPs (PS-MPs) by raw biochar (A500) and magnetic biochar with Fe3O4 (M2A500) were 176.99 mg/g and 215.58 mg/g, respectively. The adsorbent materials gave better reusability because they could be reused up to five times. Overall, these findings have provided new insights into the use of filamentous algae for in situ remediation of fluvial MPs pollution, as well as feasible strategies for the recycling of algal waste.

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.

Hyperbolic metamaterial empowered controllable photonic Weyl nodal line semimetals.

Motivated by unique topological semimetals in condensed matter physics, we propose an effective Hamiltonian with four degrees of freedom to describe evolutions of photonic double Weyl nodal line semimetals in one-dimensional hyper-crystals, which supports the energy bands translating or rotating independently in the form of Weyl quasiparticles. Especially, owing to the unit cells without inversion symmetry, a pair of reflection-phase singularities carrying opposite topological charges emerge near each nodal line, and result in a unique bilateral drumhead surface state. After reducing radiation leakages and absorption losses, these two singularities gather together gradually, and form a quasi-bound state in the continuum (quasi-BIC) ring at the nodal line ultimately. Our work not only reports the first realization of controllable photonics Weyl nodal line semimetals, establishes a bridge between two independent topological concepts-BICs and Weyl semimetals, but also heralds new possibilities for unconventional device applications, such as dual-mode schemes for highly sensitive sensing and switching.

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.

mIgM-mediated splenic marginal zone B cells targeting of folic acid for immunological evasion.

Folic acid is a fully oxidized synthetic folate with high bioavailability and stability which has been extensively prescribed to prevent congenital disabilities. Here we revealed the immunosuppressive effect of folic acid by targeting splenic marginal zone B (MZB) cells. Folic acid demonstrates avid binding with the Fc domain of immunoglobulin M (IgM), targeting IgM positive MZB cells in vivo to destabilize IgM-B cell receptor (BCR) complex and block immune responses. The induced anergy of MZB cells by folic acid provides an immunological escaping window for antigens. Covalent conjugation of folic acid with therapeutic proteins and antibodies induces immunological evasion to mitigate the production of anti-drug antibodies, which is a major obstacle to the long-term treatment of biologics by reducing curative effects and/or causing adverse reactions. Folic acid acts as a safe and effective immunosuppressant via IgM-mediated MZB cells targeting to boost the clinical outcomes of biologics by inhibiting the production of anti-drug antibodies, and also holds the potential to treat other indications that adverse immune responses need to be transiently shut off.

Design of a reinforcement learning-based intelligent car transfer planning system for parking lots.

In this study, a car transfer planning system for parking lots was designed based on reinforcement learning. The car transfer planning system for parking lots is an intelligent parking management system that is designed by using reinforcement learning techniques. The system features autonomous decision-making, intelligent path planning and efficient resource utilization. And the problem is solved by constructing a Markov decision process and using a dynamic planning-based reinforcement learning algorithm. The system has the advantage of looking to the future and using reinforcement learning to maximize its expected returns. And this is in contrast to manual transfer planning which relies on traditional thinking. In the context of this paper on parking lots, the states of the two locations form a finite set. The system ultimately seeks to find a strategy that is beneficial to the long-term development of the operation. It aims to prioritize strategies that have positive impacts in the future, rather than those that are focused solely on short-term benefits. To evaluate strategies, as its basis the system relies on the expected return of a state from now to the future. This approach allows for a more comprehensive assessment of the potential outcomes and ensures the selection of strategies that align with long-term goals. Experimental results show that the system has high performance and robustness in the area of car transfer planning for parking lots. By using reinforcement learning techniques, parking lot management systems can make autonomous decisions and plan optimal paths to achieve efficient resource utilization and reduce parking time.

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.

Level pinning of anti-PT-symmetric circuits for efficient wireless power transfer.

Wireless power transfer (WPT) technology based on magnetic resonance (a basic physical phenomenon) can directly transfer energy from the source to the load without wires and other physical contacts, and has been successfully applied to implantable medical devices, electric vehicles, robotic arms and other fields. However, due to the frequency splitting of near-field coupling, the resonant WPT system has some unique limitations, such as poor transmission stability and low efficiency. Here, we propose anti-resonance with level pinning for high-performance WPT. By introducing the anti-resonance mode into the basic WPT platform, we uncover the competition between dissipative coupling and coherent coupling to achieve novel level pinning, and construct an effective anti-parity-time (anti-PT)-symmetric non-Hermitian system that is superior to previous PT-symmetric WPT schemes. On the one hand, the eigenvalue of the anti-PT-symmetric system at resonance frequency is always pure real in both strong and weak coupling regions, and can be used to overcome the transmission efficiency decrease caused by weak coupling, as brought about by, for example, a large size ratio of the transmitter to receiver, or a long transmission distance. On the other hand, due to the level pinning effect of the two kinds of coupling mechanisms, the working frequency of the system is guaranteed to be locked, so frequency tracking is not required when the position and size of the receiver change. Even if the system deviates from the matching condition, an efficient WPT can be realized, thereby demonstrating the robustness of the level pinning. The experimental results show that when the size ratio of the transmitter coil to the receiver coil is 4.29 (which is in the weak coupling region), the transfer efficiency of the anti-PT-symmetric system is nearly 4.3 (3.2) times higher than that of the PT-symmetric system when the matching conditions are satisfied (deviated). With the miniaturization and integration of devices in mind, a synthetic anti-PT-symmetric system is used to realize a robust WPT. Anti-PT-symmetric WPT technology based on the synthetic dimension not only provides a good research platform for the study of abundant non-Hermitian physics, but also provides a means of going beyond traditional near-field applications with resonance mechanisms, such as resonance imaging, wireless sensing and photonic routing.

Altered functional connectivity during visual working memory state in patients with mild cognitive impairment.

Patients with mild cognitive impairment (MCI) suffer from severe memory function impairment, especially working memory [1]. Based on Electroencephalogram (EEG), this study used power spectral density and large-scale network analysis to reveal the frequency changes of brain areas and the diverse network patterns during the visual WM coding stages between MCI and normal controls (NC). The results showed, compared to NC, the left and right prefrontal lobes and central regions has significant synchronization in the θ frequency band, while the left temporal lobe has significant desynchronization during the working memory coding state among MCI. Brain network analysis revealed the diverse network patterns during the WM coding stage between two group. Compared with the NC, the brain of MCI patients reduced the top-down visual network connection flow derived from frontal lobe to parietal lobe, and recruited a more up-down mechanism with a much stronger information flow from frontal lobe to occipital lobe during the WM coding state. This result provides a new perspective for the neural mechanism of WM deficits in MCI patients.Clinical Relevance-Abnormal EEG rhythm and connectivity of brain regions may be important indicators of WM disorders in patients with MCI.