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M dwarfs are main sequence stars and they exist in all stages of galaxy evolution. As the living fossils of cosmic evolution, the study of M dwarfs is of great significance to the understanding of stars and the stellar populations of the Milky Way. Previously, M dwarf research was limited due to insufficient spectroscopic spectra. Recently, the data volume of M dwarfs was greatly increased with the launch of large sky survey telescopes such as Sloan Digital Sky Survey and Large Sky Area Multi-Object Fiber Spectroscopy Telescope. However, the spectra of M dwarfs mainly concentrate in the subtypes of M0–M4, and the number of M5–M9 is still relatively limited. With the continuous development of machine learning, the generative model was improved and provides methods to solve the shortage of specified training samples. In this paper, the Adversarial AutoEncoder is proposed and implemented to solve this problem. Adversarial AutoEncoder is a probabilistic AutoEncoder that uses the Generative Adversarial Nets to generate data by matching the posterior of the hidden code vector of the original data extracted by the AutoEncoder with a prior distribution. Matching the posterior to the prior ensures each part of prior space generated results in meaningful data. To verify the quality of the generated spectra data, we performed qualitative and quantitative verification. The experimental results indicate the generation spectra data enhance the measured spectra data and have scientific applicability.

Highlights • This finding may provide a novelty insight to help elucidate the mechanisms of the relapses. • Relapsed tumours from stage IA LUAD patients exhibited a weakened immune phenotype. • A targeted 395-gene expression NGS assay helps assess tumour immunity., Patients with early-stage non-small cell lung cancer (NSCLC), even stage IA, are at substantial risk of relapse and death. We explored the distinct features of molecular alterations and immune-related gene expression in Formalin-fixed paraffin-embedded (FFPE) samples from 25 relapsed patients compared with 25 non-relapsed patients through using whole-exome sequencing and an immune oncology panel RNA sequencing platform. Results showed that the chemokine, cytolytic activity and tumour-associated antigen gene signatures exhibited significantly higher expression in non-relapsed tumours from stage IA lung adenocarcinoma (LUAD) than that in relapsed tumours. Besides, Kaplan–Meier survival analysis revealed that the gene signatures of chemokines and tumour-associated antigens were significantly associated with the patients’ disease-free survival (DFS), indicating their prognostic value in early-stage LUAD. Cytolytic activity displayed a similar trend but failed to reach statistical significance. These findings revealed a weakened immune phenotype in relapsed tumours and provide valuable information for improving the treatment management of these high-risk patients. Due to the overall small patient number in this study, these differences should be further validated in a larger cohort.

Recently, astronomy has witnessed great advancements in detectors and telescopes. Imaging data collected by these instruments are organized into very large datasets that form data-oriented astronomy. The imaging data contain many radio galaxies (RGs) that are interesting to astronomers. However, considering that the scale of astronomical databases in the information age is extremely large, a manual search of these galaxies is impractical given the need for manual labor. Therefore, the ability to detect specific types of galaxies largely depends on computer algorithms. Applying machine learning algorithms on large astronomical data sets can more effectively detect galaxies using photometric images. Astronomers are motivated to develop tools that can automatically analyze massive imaging data, including developing an automatic morphological detection of specified radio sources. Galaxy Zoo projects have generated great interest in visually classifying galaxy samples using CNNs. Banfield studied radio morphologies and host galaxies derived from visual inspection in the Radio Galaxy Zoo project. However, there are relatively more studies on galaxy classification, while there are fewer studies on galaxy detection. We develop a galaxy detection model, which realizes the location and classification of Fanaroff–Riley class I (FR I) and Fanaroff–Riley class II (FR II) galaxies. The field of target detection has also developed rapidly since the convolutional neural network was proposed. You Only Look Once: Unified, Real-Time Object Detection (YOLO) is a neural-network-based target detection model proposed by Redmon et al. We made several improvements to the detection effect of dense galaxies based on the original YOLOv5, mainly including the following. (1) We use Varifocal loss, whose function is to weigh positive and negative samples asymmetrically and highlight the main sample of positive samples in the training phase. (2) Our neural network model adds an attention mechanism for the convolution kernel so that the feature extraction network can adjust the size of the receptive field dynamically in deep convolutional neural networks. In this way, our model has good adaptability and effect for identifying galaxies of different sizes on the picture. (3) We use empirical practices suitable for small target detection, such as image segmentation and reducing the stride of the convolutional layers. Apart from the three major contributions and novel points of the model, the thesis also included different data sources, i.e., radio images and optical images, aiming at better classification performance and more accurate positioning. We used optical image data from SDSS, radio image data from FIRST, and label data from FR Is and FR IIs catalogs to create a data set of FR Is and FR IIs. Subsequently, we used the data set to train our improved YOLOv5 model and finally realize the automatic classification and detection of FR Is and FR IIs. Experimental results prove that our improved method achieves better performance. mAP@0.5 of our model reaches 82.3%, and the location (Ra and Dec) of the galaxies can be identified more accurately. Our model has great astronomical significance. For example, it can help astronomers find FR I and FR II galaxies to build a larger-scale galaxy catalog. Our detection method can also be extended to other types of RGs. Thus, astronomers can locate the specific type of galaxies in a considerably shorter time and with minimum human intervention, or it can be combined with other observation data (spectrum and redshift) to explore other properties of the galaxies.

6534 Background: Nasopharyngeal carcinoma (NPC) is endemic with a high prevalence in Southern China, Asia,cetuximab and North Africa. Exosomes are small vesicles containing a wide range of functional proteins, mRNA and miRNA. In the progression of NPC, the tumor cells constantly release exosomes into the surrounding environment and also into the circulating blood. The aim of this study was to explore the association between RNA expression in plasma exosomes and prognosis of NPC patients after standard treatment. Methods: In this retrospective study, a total of 25 eligible NPC patients were included: 12 patients in the recurrence (R) subgroup and 13 patients in the no recurrence (NR) subgroup. RNA was extracted from the exosomes of plasma specimens which were collected at West China Hospital, Sichuan University. Gene expression profiles were conducted by using the RNA-sequencing platform. The DESeq2 package was used to analyze the differentially expressed genes (DEGs) between R and NR subgroups. The gene set variance analysis (GSVA) was performed to explore C5 gene sets enrichment related to the recurrence after standard treatment. Results: We observed 332 DEGs between R and NR subgroups, which include 125 up-regulated and 207 down-regulated genes (R vs. NR,∣log2fold change∣>1, p

e13671 Background: A 70-gene prognosis-signature, known as MammaPrint (MP), is validated as a good predictor of recurrence in patients with ER+/HER2- early stage breast cancer in Europe and America. Previous studies on Japanese and Korean breast cancer patients showed that the proportion of MP Low-Risk tumours is significantly lower than the percentage which reported in previous studies. Here we use MammaPrint to determine the gene profiles of breast cancer tumours from Chinese patients and investigate the test’s potential clinical applications. Methods: Formalin-fixed, paraffin-embedded (FFPE) tumour samples or fresh tumour samples from 594 eligible breast cancer patients were collected from 97 hospitals in China. Tumor RNAs were isolated from samples and analyzed using RNA sequencing technology. Clinical risk was categorized based on the Adjuvant! algorithm as used in the MINDACT trial. Concordance between risk predicted by the MammaPrint and clinical characteristics were evaluated. We also analyzed the clinical-pathology features of patients and compared them to previous studies. Results: Overall, 315 patients were categorized as clinical high risk (182 were MP Low-Risk and 133 MP High-Risk), while 279 patients were categorized as clinical low risk (203 were MP Low-Risk and 76 MP High-Risk). The concordance rate between risk predicted by the MammaPrint and clinical characteristics was 56.57%. Among the clinical-pathology features, age, ER/PR/HER2 status, tumour grade and tumour size were significantly related to the genomic risk (p = 0.009, 0.003, < 0.001, 0.001, < 0.001, and 0.007 respectively). Conclusions: The proportion of MP Low-Risk tumours was 64.81%, which is similar to previous validated studies in Europe and America. Of the patients that were clinical high risk, 58% was categorized as MP Low-Risk, and this group of patients may have limited benefit from chemotherapy. Our results indicate that MammaPrint is applicable to Chinese patients and has potential value in clinical practice to avoid over-treatment.

Patients with schizophrenia have a high prevalence of developing osteoporosis and osteoporosis-related fractures. We examined the prevalence of osteoporosis and its clinical correlates in Chinese patients with schizophrenia, which is not well-studied. A total of 199 inpatients (males/females=132/67; average age: 54.5±11.1years) and 107 healthy controls (males/females=22/85; average age: 41.7±11.9years) were recruited. Bone mineral density (BMD) was measured by ultrasonography of the calcaneus. The prevalence of osteoporosis and low BMD (osteoporosis and osteopenia) was 23.1% and 65.3% for the patient group, versus 7.5% and 39.3% for the control group (both p