65 research outputs found
Widespread Presence of Glycolaldehyde and Ethylene Glycol Around Sagittarius B2
We report the detection of widespread CHOHCHO and HOCHCHOH
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
Retraction Note: Dysregulation of cell cycle related genes and MicroRNAs distinguish the low- from high-risk of prostate cancer
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
Study protocol for a single-centre non-inferior randomised controlled trial on a novel three-dimensional matrix positioning-based cognitive fusion-targeted biopsy and software-based fusion-targeted biopsy for the detection rate of clinically significant prostate cancer in men without a prior biopsy
Introduction The classical pathway for diagnosing prostate cancer is systematic 12-core biopsy under the guidance of transrectal ultrasound, which tends to underdiagnose the clinically significant tumour and overdiagnose the insignificant disease. Another pathway named targeted biopsy is using multiparametric MRI to localise the tumour precisely and then obtain the samples from the suspicious lesions. Targeted biopsy, which is mainly divided into cognitive fusion method and software-based fusion method, is getting prevalent for its good performance in detecting significant cancer. However, the preferred targeted biopsy technique in detecting clinically significant prostate cancer between cognitive fusion and software-based fusion is still beyond consensus.Methods and analysis This trial is a prospective, single-centre, randomised controlled and non-inferiority study in which all men suspicious to have clinically significant prostate cancer are included. This study aims to determine whether a novel three-dimensional matrix positioning cognitive fusion-targeted biopsy is non-inferior to software-based fusion-targeted biopsy in the detection rate of clinically significant cancer in men without a prior biopsy. The main inclusion criteria are men with elevated serum prostate-specific antigen above 4–20 ng/mL or with an abnormal digital rectal examination and have never had a biopsy before. A sample size of 602 participants allowing for a 10% loss will be recruited. All patients will undergo a multiparametric MRI examination, and those who fail to be found with a suspicious lesion, with the anticipation of half of the total number, will be dropped. The remaining participants will be randomly allocated to cognitive fusion-targeted biopsy (n=137) and software-based fusion-targeted biopsy (n=137). The primary outcome is the detection rate of clinically significant prostate cancer for cognitive fusion-targeted biopsy and software-based fusion-targeted biopsy in men without a prior biopsy. The clinically significant prostate cancer will be defined as the International Society of Urological Pathology grade group 2 or higher.Ethics and dissemination Ethical approval was obtained from the ethics committee of Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China. The results of the study will be disseminated and published in international peer-reviewed journals.Trial registration number ClinicalTrials.gov Registry (NCT04271527)
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