Widespread Presence of Glycolaldehyde and Ethylene Glycol Around Sagittarius B2

We report the detection of widespread CH$_2$OHCHO and HOCH$_2$CH$_2$OH emission in Galactic center giant molecular cloud Sagittarius B2 using the Shanghai Tianma 65m Radio Telescope. Our observations show for the first time that the spatial distribution of these two important prebiotic molecules extends over 15 arc-minutes, corresponding to a linear size of approximately 36 pc. These two molecules are not just distributed in or near the hot cores. The abundance of these two molecules seems to decrease from the cold outer region to the central region associated with star-formation activity. Results present here suggest that these two molecules are likely to form through a low temperature process. Recent theoretical and experimental studies demonstrated that prebiotic molecules can be efficiently formed in icy grain mantles through several pathways. However, these complex ice features cannot be directly observed, and most constraints on the ice compositions come from millimeter observations of desorbed ice chemistry products. These results, combined with laboratory studies, strongly support the existence of abundant prebiotic molecules in ices.Comment: 20 pages, 7 figures, accepted by Ap

Precessing jet nozzle connecting to a spinning black hole in M87

The nearby radio galaxy M87 offers a unique opportunity to explore the connections between the central supermassive black hole and relativistic jets. Previous studies of the inner region of M87 revealed a wide opening angle for the jet originating near the black hole. The Event Horizon Telescope resolved the central radio source and found an asymmetric ring structure consistent with expectations from General Relativity. With a baseline of 17 years of observations, there was a shift in the jet's transverse position, possibly arising from an eight to ten-year quasi-periodicity. However, the origin of this sideways shift remains unclear. Here we report an analysis of radio observations over 22 years that suggests a period of about 11 years in the position angle variation of the jet. We infer that we are seeing a spinning black hole that induces the Lense-Thirring precession of a misaligned accretion disk. Similar jet precession may commonly occur in other active galactic nuclei but has been challenging to detect owing to the small magnitude and long period of the variation.Comment: 41 pages, 7 figures, 7 table

Comparative Analysis of Sampling Methods for Imbalanced Classification

Assigning class labels to instances is a key component of the machine learning technique known as classification predictive modeling. While concentrating largely on balanced classification problems, which are thought to be the easiest type, the prevalent models and assessment metrics used in classification learning assume an equal distribution of data across class labels. Many machine learning algorithms fail when the distribution of instances among classes is unbalanced, and the assessment measures used, including classification accuracy, become dangerously misleading. Numerous real-world issues, including as fraud detection, churn prediction, medical diagnosis, and many more, frequently include imbalanced class distributions. In fact, it is frequently more frequent to find unbalanced courses than balanced ones, emphasizing how important it is to solve this problem. This thesis primarily investigates innovative strategies for managing imbalanced data. One of the approaches examined is the utilization of the Majority and Minority repositioning Technique (MaMiPot) algorithms in combination with different variations of SMOTE and the application of K-means clustering before repositioning. Another method emphasized in this research is the implementation of Generative Adversarial Networks (GAN), a neural network-based technique designed for addressing imbalanced data issues. The evaluation of these approaches was performed on 25 imbalanced datasets obtained from the KEEL repository, encompassing various levels of class imbalance ratios spanning from 5.14 to 129.44. To assess the performance of the proposed method in mitigating the class imbalance problem, several evaluation metrics were utilized. These metrics include F-score, G-mean, and AUC, which provide valuable insights into the effectiveness of the approach in improving classification results and addressing the challenges posed by imbalanced datasets

Comparative Analysis of Sampling Methods for Imbalanced Classification

Assigning class labels to instances is a key component of the machine learning technique known as classification predictive modeling. While concentrating largely on balanced classification problems, which are thought to be the easiest type, the prevalent models and assessment metrics used in classification learning assume an equal distribution of data across class labels. Many machine learning algorithms fail when the distribution of instances among classes is unbalanced, and the assessment measures used, including classification accuracy, become dangerously misleading. Numerous real-world issues, including as fraud detection, churn prediction, medical diagnosis, and many more, frequently include imbalanced class distributions. In fact, it is frequently more frequent to find unbalanced courses than balanced ones, emphasizing how important it is to solve this problem. This thesis primarily investigates innovative strategies for managing imbalanced data. One of the approaches examined is the utilization of the Majority and Minority repositioning Technique (MaMiPot) algorithms in combination with different variations of SMOTE and the application of K-means clustering before repositioning. Another method emphasized in this research is the implementation of Generative Adversarial Networks (GAN), a neural network-based technique designed for addressing imbalanced data issues. The evaluation of these approaches was performed on 25 imbalanced datasets obtained from the KEEL repository, encompassing various levels of class imbalance ratios spanning from 5.14 to 129.44. To assess the performance of the proposed method in mitigating the class imbalance problem, several evaluation metrics were utilized. These metrics include F-score, G- mean, and AUC, which provide valuable insights into the effectiveness of the approach in improving classification results and addressing the challenges posed by imbalanced datasets

The complete chloroplast genome sequence of Camellia sinensis cv. Dahongpao: a most famous variety of Wuyi tea (Synonym: Thea bohea L.)

Here, combining PacBio and Illumina sequencing data, we reported the complete chloroplast genome of the first Wuyi tea (Bohea), Camellia sinensis cv. Dahongpao (DHP) with very high economic value. The chloroplast genome was 157,077 bp in length, with a large single copy (LSC) region of 86,633 bp, a small single-copy (SSC) region of 18,282 bp, separated by two inverted repeat (IR) regions of 26,081 bp each. It contained a total of 137 genes, with an overall GC content of 37.29%. The phylogenetic analysis showed that DHP was sister to C. sinensis cv. Longjing

Adaptive Deep Supervised Autoencoder Based Image Reconstruction for Face Recognition

Based on a special type of denoising autoencoder (DAE) and image reconstruction, we present a novel supervised deep learning framework for face recognition (FR). Unlike existing deep autoencoder which is unsupervised face recognition method, the proposed method takes class label information from training samples into account in the deep learning procedure and can automatically discover the underlying nonlinear manifold structures. Specifically, we define an Adaptive Deep Supervised Network Template (ADSNT) with the supervised autoencoder which is trained to extract characteristic features from corrupted/clean facial images and reconstruct the corresponding similar facial images. The reconstruction is realized by a so-called “bottleneck” neural network that learns to map face images into a low-dimensional vector and reconstruct the respective corresponding face images from the mapping vectors. Having trained the ADSNT, a new face image can then be recognized by comparing its reconstruction image with individual gallery images, respectively. Extensive experiments on three databases including AR, PubFig, and Extended Yale B demonstrate that the proposed method can significantly improve the accuracy of face recognition under enormous illumination, pose change, and a fraction of occlusion

The Utility of 18F-FDG PET/CT for Monitoring Response and Predicting Prognosis after Glucocorticoids Therapy for Sarcoidosis

Sarcoidosis has significant heterogeneity involving multiple organs; treatment of the disease is a significant therapeutic challenge due to the difficulties in accurately monitoring disease activity and estimating prognosis. Fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) plays an important role in assessing the metabolic activity. However, there is not enough evidence about the influence of this method in the clinical management and prognosis prediction for sarcoidosis. This study aims to investigate the clinical utility of 18F-FDG PET/CT for therapeutic evaluation and prognostic prediction in sarcoidosis. We had retrospectively enrolled 23 patients with sarcoidosis assigned to receive systemic glucocorticoids. All patients underwent baseline 18F-FDG PET/CT before initiating therapy and follow-up 18F-FDG PET/CT within 3 months after the therapy. The metabolic and clinical responses were classified. The baseline 18F-FDG PET/CT showed increased uptake in all patients. Based solely on biopsy-proven sites, the sensitivity of 18F-FDG PET/CT was 91.7%, and the sensitivity improved to 100% after excluding skin involvement. In the subsequent follow-up PET scans within 3 months after glucocorticoids therapy, the SUVmax were variously decreased except one; there are significant differences in the clinical remission rates and the relapse rates between patients with a favorable response and cases with no response on follow-up PET scan, the increasing metabolic response was associated with the increase in clinical remission rates and the reduction in recurrence rates. In conclusion, the present study shows that 18F-FDG PET/CT is an effective way to monitor the early therapeutic reaction and is helpful in predicting the long-term prognosis of sarcoidosis

Investigation on the Hydrogeochemical Characteristics and Controlling Mechanisms of Groundwater in the Coastal Aquifer

Groundwater contamination in coastal areas has attracted widespread attention. However, studies on the hydrogeochemical characteristics and controlling mechanisms in coastal aquifers are still lacking. In this study, 71 sets of groundwater samples were collected during the dry and wet seasons in a coastal city, Shandong Province. Correlation and principal component analyses were used to identify pollution sources. Meanwhile, Piper diagrams, Gibbs plots, ion ratios, and saturation indices were employed to investigate the hydrogeochemical controlling mechanisms. The results revealed that pollution components included Na+, NH4+, Cl−, SO42−, NO3−, NO2−, Pb, As, Se, TDS, TH, F−, and Mn. Pollution compositions in the study area were primarily derived from natural processes and anthropogenic activities. The contamination of nitrogen resulted primarily from agricultural activities. The exceedance of SO42− was mainly due to the leaching of waste by rainfall. High Na+, Cl−, and F− were related to sea intrusion. Pb and Se might have been caused by anthropogenic activities. The exceedance of As was caused by anthropogenic inputs and natural factors. The poor seepage conditions and anoxic conditions promoted the enrichment of Mn. The concentration of most components in the dry season was larger than that in the wet season. There were no significant differences in water chemistry type during the wet season and dry season. Groundwater chemical compositions were dominated by the dissolution of halite, gypsum, and anhydrite, as well as the cation exchange reaction. The influence of seawater intrusion on groundwater was not serious