Revealing the Biexciton and Trion-exciton Complexes in BN Encapsulated WSe2

Strong Coulomb interactions in single-layer transition metal dichalcogenides (TMDs) result in the emergence of strongly bound excitons, trions and biexcitons. These excitonic complexes possess the valley degree of freedom, which can be exploited for quantum optoelectronics. However, in contrast to the good understanding of the exciton and trion properties, the binding energy of the biexciton remains elusive, with theoretical calculations and experimental studies reporting discrepant results. In this work, we resolve the conflict by employing low-temperature photoluminescence spectroscopy to identify the biexciton state in BN encapsulated single-layer WSe2. The biexciton state only exists in charge neutral WSe2, which is realized through the control of efficient electrostatic gating. In the lightly electron-doped WSe2, one free electron binds to a biexciton and forms the trion-exciton complex. Improved understanding of the biexciton and trion-exciton complexes paves the way for exploiting the many-body physics in TMDs for novel optoelectronics applications

Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named LFH that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with eleven state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (average 12.5% in node classification and 13.3% in link prediction) compared with recent state-of-the-art methods

Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning

Sleep stage classification is crucial for detecting patients' health conditions. Existing models, which mainly use Convolutional Neural Networks (CNN) for modelling Euclidean data and Graph Convolution Networks (GNN) for modelling non-Euclidean data, are unable to consider the heterogeneity and interactivity of multimodal data as well as the spatial-temporal correlation simultaneously, which hinders a further improvement of classification performance. In this paper, we propose a dynamic learning framework STHL, which introduces hypergraph to encode spatial-temporal data for sleep stage classification. Hypergraphs can construct multi-modal/multi-type data instead of using simple pairwise between two subjects. STHL creates spatial and temporal hyperedges separately to build node correlations, then it conducts type-specific hypergraph learning process to encode the attributes into the embedding space. Extensive experiments show that our proposed STHL outperforms the state-of-the-art models in sleep stage classification tasks

Protective Effect of Ocotillol, the Derivate of Ocotillol-Type Saponins in Panax Genus, against Acetic Acid-Induced Gastric Ulcer in Rats Based on Untargeted Metabolomics

Gastric ulcer (GU), a prevalent digestive disease, has a high incidence and is seriously harmful to human health. Finding a natural drug with a gastroprotective effect is needed. Ocotillol, the derivate of ocotillol-type saponins in the Panax genus, possesses good anti-inflammatory activity. The study aimed to investigate the gastroprotective effect of ocotillol on acetic acid-induced GU rats. The serum levels of endothelin-1 (ET-1) and nitric oxide (NO), the gastric mucosa levels of epidermal growth factor, superoxide dismutase and NO were assessed. Hematoxylin and eosin staining of gastric mucosa for pathological changes and immunohistochemical staining of ET-1, epidermal growth factor receptors and inducible nitric oxide synthase were evaluated. A UPLC-QTOF-MS-based serum metabolomics approach was applied to explore the latent mechanism. A total of 21 potential metabolites involved in 7 metabolic pathways were identified. The study helps us to understand the pathogenesis of GU and to provide a potential natural anti-ulcer agent

Excess charge-carrier induced instability of hybrid perovskites

Identifying the origin of intrinsic instability for organic–inorganic halide perovskites (OIHPs) is crucial for their application in electronic devices, including solar cells, photodetectors, radiation detectors, and light-emitting diodes, as their efficiencies or sensitivities have already been demonstrated to be competitive with commercial available devices. Here we show that free charges in OIHPs, whether generated by incident light or by current-injection from electrodes, can reduce their stability, while efficient charge extraction effectively stabilizes the perovskite materials. The excess of both holes and electrons reduce the activation energy for ion migration within OIHPs, accelerating the degradation of OIHPs, while the excess holes and electrons facilitate the migration of cations or anions, respectively. OIHP solar cells capable of efficient charge-carrier extraction show improved light stability under regular operation conditions compared to an open-circuit condition where the photo-generated charges are confined in the perovskite layers

Valley-polarized Exitonic Mott Insulator in WS2/WSe2 Moir\'e Superlattice

Strongly enhanced electron-electron interaction in semiconducting moir\'e superlattices formed by transition metal dichalcogenides (TMDCs) heterobilayers has led to a plethora of intriguing fermionic correlated states. Meanwhile, interlayer excitons in a type-II aligned TMDC heterobilayer moir\'e superlattice, with electrons and holes separated in different layers, inherit this enhanced interaction and strongly interact with each other, promising for realizing tunable correlated bosonic quasiparticles with valley degree of freedom. We employ photoluminescence spectroscopy to investigate the strong repulsion between interlayer excitons and correlated electrons in a WS2/WSe2 moir\'e superlattice and combine with theoretical calculations to reveal the spatial extent of interlayer excitons and the band hierarchy of correlated states. We further find that an excitonic Mott insulator state emerges when one interlayer exciton occupies one moir\'e cell, evidenced by emerging photoluminescence peaks under increased optical excitation power. Double occupancy of excitons in one unit cell requires overcoming the energy cost of exciton-exciton repulsion of about 30-40 meV, depending on the stacking configuration of the WS2/WSe2 heterobilayer. Further, the valley polarization of the excitonic Mott insulator state is enhanced by nearly one order of magnitude. Our study demonstrates the WS2/WSe2 moir\'e superlattice as a promising platform for engineering and exploring new correlated states of fermion, bosons, and a mixture of both

PD-L1 expression in glioblastoma, the clinical and prognostic significance: a systematic literature review and meta-analysis

Background: The clinical and prognostic value of programmed death-ligand 1, PD-L1, in glioblastoma (GBM) remains controversial. The present study aimed to identify the expression of PD-L1 for its prognostic value in glioblastoma. Methods: A comprehensive literature search was performed using the PubMed and CNKI databases. The overall survival (OS) and disease-free survival (DFS) of GBM was analyzed based on Hazard ratios (HRs) and 95% confidence intervals (CIs). Furthermore, Odds ratios (ORs) and 95% CIs were summarized for clinicopathological parameters. The statistical analysis was using RevMan 5.3 software. Results: The meta-analysis was performed by using total nine studies including 806 patients who had glioblastoma. The pooled results indicated that PD-L1 expression in tumour tissues was significantly related to a poor OS (HR=1.63, 95%CI: 1.19-2.24, P=0.003, random effects model) with heterogeneity (I2=51%). In subgroup analyses, PD-L1 positivity was significantly associated with a worse OS for patients of American and Asian regions, but not for those of European regions. Moreover, PD-L1 expression implied a trend toward the mutation status of the IDH1 gene (coding the Isocitrate Dehydrogenase (NADP(+))-1 protein) (HR=9.92, 95%CI: 1.85-53.08, P=0.007, fixed effects model). However, the prediction overall survival (OS) of the patients showed that PD-L1 expression was independent from other clinicopathological features, such as gender, age and tumour progression/recurrence. Conclusions: Our analyses indicated that high expression of PD-L1 in glioblastoma tumour tissues is associated with poor survival of patients, and PD-L1 may act as a prognostic predictor and an effective therapeutic target for glioblastoma