Role of Nanocavity Plasmons in Tunneling Electron Induced Light Emission on and near a Molecule

By using a microscopic quantum model, we study theoretically different roles of nanocavity plasmons in scanning tunneling microscope(STM) induced light emission upon selective initial excitation of molecules or plasmons. The time evolution and spectroscopic properties of the emission from the coupled plasmon-molecule system in each case are studied using time-dependent quantum mater equations. When the STM tip is placed on the molecule to ensure direct carrier injection induced molecular excitation, the major role of the plasmons is to enhance the molecular emission via increasing its radiative decay rate, resulting in sharp molecule-specific emission peaks. On the other hand, when the STM tip is located in close proximity to the edge of the molecule but without direct carrier injection into the molecule, the role of the plasmon-molecule coupling is to cause destructive interferences between the two quantum objects, leading to the occurrence of Fano dips around the energy of the molecular exciton in the plasmonic emission spectra.

Classical-field description of Bose-Einstein condensation of parallel light in a nonlinear optical cavity

We study the Bose–Einstein condensation of parallel light in a two-dimensional nonlinear optical cavity,where the massive photons are converted into photon molecules(p-molecules).We extend the classical-field method to provide a description of the two-component system,and we also derive a coupled density equation which can be used to describe the conversion relation between photons and p-molecules.Furthermore,we obtain the chemical potential of the system,and we also find that the system can transform from the mixed photon and p-molecule condensate phase into a pure p-molecule condensate phase.Additionally,we investigate the collective excitation of the system.We also discuss the problem how the spontaneous decay of an atom is influenced by both the phase transition and collective excitation of the coupling system.

Transmembrane transport of multicomponent liposome-nanoparticles into giant vesicles

With the emergence and rapid development of nanotechnology,the nanoparticles hybridized with multicomponent lipids are more and more used in gene delivery.These vectors interact with the cell membrane before entering into the cell.Therefore,the nature of this interaction is important in investigating multicomponent liposome-nanoparticle(MLP)transport across the cell membrane.In this paper the transport of MLPs across the membranes of giant vesicles(GVs)in solvents is studied by using the self-consistent field theory(SCFT).Based on the analysis of the MLP permeating the GV membranes,a simple transport model is proposed.The effects of the difference in membrane morphology and the size of the nanoparticle on the endocytosis are discussed systematically.The role of energy barriers in quasi-equilibrium is also examined.The results indicate that the interaction between MLP and GV is a spontaneous process and the energy barrier needs overcoming to form metastable intermediates.The results provide theoretical reference for better understanding the transmembrane transport process of nanoparticles,and guidance for relevant experimental studies as well.

Enhanced second harmonic generation in a two-dimensional optical micro-cavity

We introduce a two-dimensional Bose-Einstein condensation model consisting of massive photon and photon-pair. Based on the new nonlinear model, the traditional process of second harmonics generation is reinvestigated. In order to describe the process, a new quantum phase, the harmonic phase, is introduced. The order parameter of the new physical phase is also given in this paper.

An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression

The identification of important nodes in a power grid has considerable benefits for safety. Power networks vary in many aspects, such as scale and structure. An index system can hardly cover all the information in various situations. Therefore, the efficiency of traditional methods using an index system is case-dependent and not universal. To solve this problem, an artificial intelligence based method is proposed for evaluating power grid node importance. First, using a network embedding approach, a feature extraction method is designed for power grid nodes, considering their structural and electrical information. Then, for a specific power network, steady-state and node fault transient simulations under various operation modes are performed to establish the sample set. The sample set can reflect the relationship between the node features and the corresponding importance. Finally, a support vector regression model is trained based on the optimized sample set for the later online use of importance evaluation. A case study demonstrates that the proposed method can effectively evaluate node importance for a power grid based on the information learned from the samples. Compared with traditional methods using an index system, the proposed method can avoid some possible bias. In addition, a particular sample set for each specific power network can be established under this artificial intelligence based framework, meeting the demand of universality.

An error recognition method for power equipment defect records based on knowledge graph technology

To recognize errors in the power equipment defect records in real time, we propose an error recognition method based on knowledge graph technology. According to the characteristics of power equipment defect records, a method for constructing a knowledge graph of power equipment defects is presented. Then, a graph search algorithm is employed to recognize different kinds of errors in defect records, based on the knowledge graph of power equipment defects. Finally, an error recognition example in terms of transformer defect records is given, by comparing the precision, recall, F1-score, accuracy, and efficiency of the proposed method with those of machine learning methods, and the factors influencing the error recognition effects of various methods are analyzed. Results show that the proposed method performs better in error recognition of defect records than machine learning methods, and can satisfy real-time requirements.

Antibody prevalence and immunotherapy response in Chinese patients with epilepsy and encephalopathy scores for patients with different neuronal surface antibodies

Background:The scale assessment was helpful in predicting the presence of antibodies to autoimmune encephalitis.This study aimed to evaluate the application of antibody prevalence in Chinese patients with epilepsy and encephalopathy(APE2-CHN)and response to immunotherapy in Chinese patients with epilepsy and encephalopathy(RITE2-CHN)for patients with different neuronal surface antibodies.Methods:A total of 1365 patients with epileptic seizures as the prominent feature in Xuanwu Hospital,Capital Medical University,from June 2016 to June 2020 were enrolled in our study.Of these,915 patients with epilepsy of unknown etiology whose serum and/or cerebrospinal fluid samples were examined for autoimmune antibodies were selected.All patients were scored with antibody prevalence in patients with epilepsy and encephalopathy(APE2),response to immunotherapy with epilepsy and encephalopathy(RITE2),APE2-CHN,and RITE2-CHN scores.Results:Of the 915 patients,191 patients were positive for neural-surface specific antibodies(115 N-methyl-D-aspartate receptor(NMDAR)Ab,47 leucine-rich glioma-inactivated protein 1(LGI1)Ab,8 contactin-associated protein 2(CASPR2)Ab,4 AMPA2RAb,and 11 GABAR-B-Ab;3 CASPR2-Ab and LGI1-Ab,2 NMDAR-Ab and CASPR2-Ab,and 1 NMDAR-Ab and myelinoligodendrocyte glycoprotein[MOG]Ab).The sensitivity and specificity of APE2≥4 in predicting the presence of neural-surface specific antibodies in our study were 74.35%and 81.77%,respectively,and the sensitivity and specificity of APE2-CHN≥4 were 75.92%and 84.53%,respectively.Eight cases had an APE2 score<4 and APE2-CHN score≥5;all these patients had memory decline as the prominent manifestation.We divided the patients into six groups according to the different antibodies.APE2-CHN scores showed higher sensitivity for the prediction of NMDAR-Ab,but lower sensitivity for LGI1-Ab.A total of 187/191(97.91%)patients received immunotherapy and 142/191(74.35%)patients benefited from the treatments.The patients who were positive for LGI1-Ab with RITE2-CHN≥8 responded well to immunotherapy.Conclusions:APE2-CHN had the highest value for predicting the positivity of NMDAR-Ab and RITE2-CHN evaluated the response of immunotherapy for anti-LGI1 encephalitis appropriately.However,RITE2 and RITE2-CHN do not appear to be good predictors of immunotherapy outcomes for patients with specific neuronal-surface antibodies and high APE2-CHN scores are often indicative of a poor response to immunotherapy.

Oculopharyngeal Weakness, Hypophrenia, Deafness, and Impaired Vision： A Novel Autosomal Dominant Myopathy with Rimmed Vacuoles

Background： Myopathies with rimnled vacuoles are a heterogeneous group of muscle disorders with progressive muscle weakness and varied clinical manifestations but similar features in muscle biopsies. Here, we describe a novel autosomal dominant myopathy with rimmed vacuoles in a large family with 11 patients of three generations affected. Methods： A clinical study including family history, obstetric, pediatric, and development history was recorded. Clinical examinations including physical examination, electromyography （EMG）, serum creatine kinase （CK）, bone X-rays, and brain magnetic resonance imaging （MRI） were performed in this family. Open muscle biopsies were performed on the proband and his mother. To find the causative gene, the whole-exome sequencing was carried out. Results： Disease onset was from adolescence to adulthood, but the affected patients of the third generation presented an earlier onset and more severe clinical manifestations than the older generations. Clinical features were characterized as dysarthria, dysphagia, external ophthalmoplegia, limb weakness, hypophrenia, deafness, and impaired vision. However, not every patient manifested all symptoms. Serum CK was mildly elevated and EMG indicated a myopathic pattern. Brain MRI showed cerebellum and brain stem mildly atrophy. Rimmed vacuoles and inclusion bodies were observed in muscle biopsy. The whole-exome sequencing was performed, but the causative gene has not been found. Conclusions： We reported a novel autosomal dominant myopathy with rimmed vacuoles characterized by dysarthria, dysphagia, external ophthalmoplegia, limb weakness, hypophrenia, deafness, and impaired vision, but the causative gene has not been tbund and needs further study.

Human Atypical Monkeypox:Early Warning for Global Outbreak?

Monkeypox is usually considered as a zoonosis caused bymonkeypox viruswith potential threat to public health in the post-smallpox era.The recent outbreak ofmonkeypox began inMay 7,2022,and has been found in many countries out of Africa.TheWorldHealth Organization declared that this endemic is an“atypical”phenomenon with definite human-to-human transmission.To understand the situation more clearly,this review briefly summarizes the epidemiologic and clinical characteristics of the disease and highlights the clinical management and preventive strategies.