Temporal transformer-spatial graph convolutional network: an intelligent classification model for anti N-methyl-D-aspartate receptor encephalitis based on electroencephalogram signal

Encephalitis is a disease typically caused by viral infections or autoimmunity. The most common type of autoimmune encephalitis is anti-N-methyl-D-aspartate receptor (NMDAR) antibody-mediated, known as anti-NMDA receptor encephalitis, which is a rare disease. Specific EEG patterns, including “extreme delta brush” (EDB), have been reported in patients with anti-NMDA receptor encephalitis. The aim of this study was to develop an intelligent diagnostic model for encephalitis based on EEG signals. A total of 131 Participants were selected based on reasonable inclusion criteria and divided into three groups: health control (35 participants), viral encephalitis (58 participants), and anti NMDAR receptor encephalitis (55 participants). Due to the low prevalence of anti-NMDAR receptor encephalitis, it took several years to collect participants’ EEG signals while they were in an awake state. EEG signals were collected and analyzed following the international 10–20 system layout. We proposed a model called Temporal Transformer-Spatial Graph Convolutional Network (TT-SGCN), which consists of a Preprocess Module, a Temporal Transformer Module (TTM), and a Spatial Graph Convolutional Module (SGCM). The raw EEG signal was preprocessed according to traditional procedures, including filtering, averaging, and Independent Component Analysis (ICA) method. The EEG signal was then segmented and transformed using short-time Fourier transform (STFT) to produce concatenated power density (CPD) maps, which served as inputs for the proposed model. TTM extracted the time-frequency features of each channel, and SGCM fused these features using graph convolutional methods based on the location of electrodes. The model was evaluated in two experiments: classification of the three groups and pairwise classification among the three groups. The model was trained using two stages and achieved the performance, with an accuracy of 82.23%, recall of 80.75%, precision of 82.51%, and F1 score of 81.23% in the classification of the three groups. The proposed model has the potential to become an intelligent auxiliary diagnostic tool for encephalitis

Aberrant Expression of Proteins Involved in Signal Transduction and DNA Repair Pathways in Lung Cancer and Their Association with Clinical Parameters

Because cell signaling and cell metabolic pathways are executed through proteins, protein signatures in primary tumors are useful for identifying key nodes in signaling networks whose alteration is associated with malignancy and/or clinical outcomes. This study aimed to determine protein signatures in primary lung cancer tissues.We analyzed 126 proteins and/or protein phosphorylation sites in case-matched normal and tumor samples from 101 lung cancer patients with reverse-phase protein array (RPPA) assay. The results showed that 18 molecules were significantly different (p<0.05) by at least 30% between normal and tumor tissues. Most of those molecules play roles in cell proliferation, DNA repair, signal transduction and lipid metabolism, or function as cell surface/matrix proteins. We also validated RPPA results by Western blot and/or immunohistochemical analyses for some of those molecules. Statistical analyses showed that Ku80 levels were significantly higher in tumors of nonsmokers than in those of smokers. Cyclin B1 levels were significantly overexpressed in poorly differentiated tumors while Cox2 levels were significantly overexpressed in neuroendocrinal tumors. A high level of Stat5 is associated with favorable survival outcome for patients treated with surgery.Our results revealed that some molecules involved in DNA damage/repair, signal transductions, lipid metabolism, and cell proliferation were drastically aberrant in lung cancer tissues, and Stat5 may serve a molecular marker for prognosis of lung cancers

MEI Kodierung der frühesten Notation in linienlosen Neumen

Das Optical Neume Recognition Project (ONRP) hat die digitale Kodierung von musikalischen Notationszeichen aus dem Jahr um 1000 zum Ziel – ein ambitioniertes Vorhaben, das die Projektmitglieder veranlasste, verschiedenste methodische Ansätze zu evaluieren. Die Optical Music Recognition-Software soll eine linienlose Notation aus einem der ältesten erhaltenen Quellen mit Notationszeichen, dem Antiphonar Hartker aus der Benediktinerabtei St. Gallen (Schweiz), welches heute in zwei Bänden in der Stiftsbibliothek in St. Gallen aufbewahrt wird, erfassen. Aufgrund der handgeschriebenen, linienlosen Notation stellt dieser Gregorianische Gesang den Forscher vor viele Herausforderungen. Das Werk umfasst über 300 verschiedene Neumenzeichen und ihre Notation, die mit Hilfe der Music Encoding Initiative (MEI) erfasst und beschrieben werden sollen. Der folgende Artikel beschreibt den Prozess der Adaptierung, um die MEI auf die Notation von Neumen ohne Notenlinien anzuwenden. Beschrieben werden Eigenschaften der Neumennotation, um zu verdeutlichen, wo die Herausforderungen dieser Arbeit liegen sowie die Funktionsweise des Classifiers, einer Art digitalen Neumenwörterbuchs

宽视场干涉光谱成像仪技术研究

In order to meet the demand of wide field of view (FOV), wide swath width, and high throughput for the ocean spectrum, a new Fourier transform imaging spectrometer with wide FOV based on the image plane interference technologies presented. And the principle and expressions of the image plane interference are studied and educed.The optical system is designed and optimized based on the calculated detailed parameters. Dyson and double Gauss structures are used in the relay lensand fore optics respectively. Under the circumstance of 400~900 nm spectral range, 35.5&deg; FOV, 320 km swath width, F/4, and 100 mm focal length, the average signal noise ratio (SNR) is greater than 100and the modulation transfer function (MTF) of the current design is greater than 0.5 at 32 lp/mm. All the parameters are well satisfied by the present design. &copy; 2016, Chinese Laser Press. All right reserved

An improved schlieren method for measurement and automatic reconstruction of the far-field focal spot.

The schlieren method of measuring far-field focal spots offers many advantages at the Shenguang III laser facility such as low cost and automatic laser-path collimation. However, current methods of far-field focal spot measurement often suffer from low precision and efficiency when the final focal spot is merged manually, thereby reducing the accuracy of reconstruction. In this paper, we introduce an improved schlieren method to construct the high dynamic-range image of far-field focal spots and improve the reconstruction accuracy and efficiency. First, a detection method based on weak light beam sampling and magnification imaging was designed; images of the main and side lobes of the focused laser irradiance in the far field were obtained using two scientific CCD cameras. Second, using a self-correlation template matching algorithm, a circle the same size as the schlieren ball was dug from the main lobe cutting image and used to change the relative region of the main lobe cutting image within a 100×100 pixel region. The position that had the largest correlation coefficient between the side lobe cutting image and the main lobe cutting image when a circle was dug was identified as the best matching point. Finally, the least squares method was used to fit the center of the side lobe schlieren small ball, and the error was less than 1 pixel. The experimental results show that this method enables the accurate, high-dynamic-range measurement of a far-field focal spot and automatic image reconstruction. Because the best matching point is obtained through image processing rather than traditional reconstruction methods based on manual splicing, this method is less sensitive to the efficiency of focal-spot reconstruction and thus offers better experimental precision

The Correction of Recovered Spectral Images in a Hadamard Transform Spectral Imager Based on a Digital Micro-Mirror Device

The Hadamard transform spectral imager based on a digital micro-mirror device (DMD) is a novel type of spectral imager developed in recent years. This paper describes the designing scheme of the Hadamard encoding mask and analyzes the encoding process of the detector pixels. Generally the Hadamard encoding mask constructed by DMD cannot completely encode the dispersed spectrum of all the pixels according to the Hadamard matrix; therefore, the spectral images recovered by inverse Hadamard transform inevitably have errors. A correction method for the recovered spectral images is proposed. The experimental results show that this method improves the quality of the recovered spectral images

Calligraphy and Painting Identification 3D-CNN Model Based on Hyperspectral Image MNF Dimensionality Reduction

As a kind of cultural art, calligraphy and painting are not only an important part of traditional culture but also has important value of art collection and trade. The existence of forgeries has seriously affected the fair trade, protection, and inheritance of calligraphy and painting. There is an urgent need for the efficient, accurate, and intelligent technical identification method. By combining the advantages of material attribute recognition and imaging detection of hyperspectral imaging technology with the powerful feature expression ability and classification ability of the convolutional neural network, it can greatly improve the comprehension efficiency of calligraphy and painting identification; meanwhile, in order to reduce the redundancy and the amount of parameters in the method of directly using the hyperspectral image, an objective convex dimensionality reduction method should be used for compressing the original hyperspectral image before deep learning. Based on this, we propose a kind of deep learning method to classify author and authenticity based on the multichannel images obtained by minimum noise fraction (MNF) dimensionality reduction to calligraphy and painting hyperspectral data, and its core is the 2D-CNN or 3D-CNN model with the basic network of “4 convolution layers + 4 pooling layers + 2 full-link layers.” The experimental results show that the identification accuracy of the 2D-CNN calligraphy and painting identification with MNF pseudocolor image mosaic as input and the 2D-CNN calligraphy and painting identification with multichannel MNF dimensionality reduced images direct as input have high accuracy, while the 3D-CNN calligraphy and painting identification with multichannel MNF dimensionality reduced images direct as input not only maintains excellent identification accuracy but also has better learning convergence (step number) and stability compared with the 2D-CNN model. Especially, the 3D-CNN identification accuracy of calligraphy and painting’s author and authenticity on the test set can reach 93.2% and 95.2%, respectively

Recent advances of lanthanide nanomaterials in Tumor NIR fluorescence detection and treatment

Lanthanide nanomaterials have garnered significant attention from researchers among the main near-infrared (NIR) fluorescent nanomaterials due to their excellent chemical and fluorescence stability, narrow emission band, adjustable luminescence color, and long lifetime. In recent years, with the preparation, functional modification, and fluorescence improvement of lanthanide materials, great progress has been made in their application in the biomedical field. This review focuses on the latest progress of lanthanide nanomaterials in tumor diagnosis and treatment, as well as the interaction mechanism between fluorescence and biological tissues. We introduce a set of efficient strategies for improving the fluorescence properties of lanthanide nanomaterials and discuss some representative in-depth research work in detail, showcasing their superiority in early detection of ultra-small tumors, phototherapy, and real-time guidance for surgical resection. However, lanthanide nanomaterials have only realized a portion of their potential in tumor applications so far. Therefore, we discuss promising methods for further improving the performance of lanthanide nanomaterials and their future development directions