Magnetic reconnection at the earliest stage of solar flux emergence

On 2016 September 20, the Interface Region Imaging Spectrograph observed an active region during its earliest emerging phase for almost 7 hours. The Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory observed continuous emergence of small-scale magnetic bipoles with a rate of $\sim$10$^{16}$ Mx~s$^{-1}$. The emergence of magnetic fluxes and interactions between different polarities lead to frequent occurrence of ultraviolet (UV) bursts, which exhibit as intense transient brightenings in the 1400 \AA{} images. In the meantime, discrete small patches with the same magnetic polarity tend to move together and merge, leading to enhancement of the magnetic fields and thus formation of pores (small sunspots) at some locations. The spectra of these UV bursts are characterized by the superposition of several chromospheric absorption lines on the greatly broadened profiles of some emission lines formed at typical transition region temperatures, suggesting heating of the local materials to a few tens of thousands of kelvin in the lower atmosphere by magnetic reconnection. Some bursts reveal blue and red shifts of $\sim$100~km~s$^{-1}$ at neighboring pixels, indicating the spatially resolved bidirectional reconnection outflows. Many such bursts appear to be associated with the cancellation of magnetic fluxes with a rate of the order of $\sim$10$^{15}$ Mx~s$^{-1}$. We also investigate the three-dimensional magnetic field topology through a magneto-hydrostatic model and find that a small fraction of the bursts are associated with bald patches (magnetic dips). Finally, we find that almost all bursts are located in regions of large squashing factor at the height of $\sim$1 Mm, reinforcing our conclusion that these bursts are produced through reconnection in the lower atmosphere.Comment: ApJ, 10 figure

Comparing Partial Least Square Approaches in Gene-or Region-based Association Study for Multiple Quantitative Phenotypes

On thinking quantitatively of complex diseases, there are at least three statistical strategies for association study: single SNP on single trait, gene-or region (with multiple SNPs) on single trait and on multiple traits. The third of which is the most general in dissecting the genetic mechanism underlying complex diseases underpinning multiple quantitative traits. Gene-or region association methods based on partial least square (PLS) approaches have been shown to have apparent power advantage. However, few attempts are developed for multiple quantitative phenotypes or traits underlying a condition or disease, and the performance of various PLS approaches used in association study for multiple quantitative traits had not been assessed. We, from regression perspective, exploit association between multiple SNPs and multiple phenotypes or traits through exhaustive scan statistics (sliding window) using PLS and sparse PLS (SPLS) regression. Simulations are conducted to assess the performance of the proposed scan statistics and compare them with the existed method. The proposed methods are applied to 12 regions of GWAS data from the European Prospective Investigation of Cancer (EPIC)-Norfolk study

Distinctive action sketch for human action recognition

Recent developments in the field of computer vision have led to a renewed interest in sketch correlated research. There have emerged considerable solid evidence which revealed the significance of sketch. However, there have been few profound discussions on sketch based action analysis so far. In this paper, we propose an approach to discover the most distinctive sketches for action recognition. The action sketches should satisfy two characteristics: sketchability and objectiveness. Primitive sketches are prepared according to the structured forests based fast edge detection. Meanwhile, we take advantage of Faster R-CNN to detect the persons in parallel. On completion of the two stages, the process of distinctive action sketch mining is carried out. After that, we present four kinds of sketch pooling methods to get a uniform representation for action videos. The experimental results show that the proposed method achieves impressive performance against several compared methods on two public datasets.The work was supported in part by the National Science Foundation of China (61472103, 61772158, 61702136, and 61701273) and Australian Research Council (ARC) grant (DP150104645)

Does the non-force-freeness matter for the extrapolation of solar magnetic field?

Magnetic field extrapolation is a fundamental tool to reconstruct the three-dimensional solar coronal magnetic field. However, the prevalently used force-free field model might not be applicable in the lower atmosphere, where plasma \b{eta} is greater than 1. In this work, we perform extrapolation in active region 12158, based on an updated magnetohydrostatic (MHS) method. By comparing the results with those from the force-free field method of Current-Field Iteration in Spherical Coordinates (CFITS), we find that the overall properties, which are characterized by the magnetic free energy and helicity, are roughly the same after volume integral. The major differences lie in the magnetic configuration and the twist number of magnetic flux rope (MFR). A coherent MFR with twist around 1 is reproduced from CFITS. In another manner, two sets of MFR, which are highly twisted and slightly coupled, are derived by the MHS method. The latter one is better constrained by the high-resolution observations, such as the filament fibrils, pre-eruptive braiding characteristics and the eruptive double-J shaped hot channel. Overall, our work shows the MHS method is more promising to reproduce the magnetic fine structures that can well match the observations not only in the chromosphere but also in the corona. This initiates the necessity of reconsidering the simplification of low atmosphere for currently widely used nonlinear force-free extrapolation method, since such assumption will not only omit the magnetic structures at low atmosphere but also affect those obtained in the corona, and therefore bringing in ambiguity in interpreting the solar eruption.Comment: 19 pages, 6 figures, accepted by Ap

Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task

In a realistic dialogue system, the input information from users is often subject to various types of input perturbations, which affects the slot-filling task. Although rule-based data augmentation methods have achieved satisfactory results, they fail to exhibit the desired generalization when faced with unknown noise disturbances. In this study, we address the challenges posed by input perturbations in slot filling by proposing Noise-BERT, a unified Perturbation-Robust Framework with Noise Alignment Pre-training. Our framework incorporates two Noise Alignment Pre-training tasks: Slot Masked Prediction and Sentence Noisiness Discrimination, aiming to guide the pre-trained language model in capturing accurate slot information and noise distribution. During fine-tuning, we employ a contrastive learning loss to enhance the semantic representation of entities and labels. Additionally, we introduce an adversarial attack training strategy to improve the model's robustness. Experimental results demonstrate the superiority of our proposed approach over state-of-the-art models, and further analysis confirms its effectiveness and generalization ability.Comment: Accepted by ICASSP 202

A PLSPM-Based Test Statistic for Detecting Gene-Gene Co-Association in Genome-Wide Association Study with Case-Control Design

For genome-wide association data analysis, two genes in any pathway, two SNPs in the two linked gene regions respectively or in the two linked exons respectively within one gene are often correlated with each other. We therefore proposed the concept of gene-gene co-association, which refers to the effects not only due to the traditional interaction under nearly independent condition but the correlation between two genes. Furthermore, we constructed a novel statistic for detecting gene-gene co-association based on Partial Least Squares Path Modeling (PLSPM). Through simulation, the relationship between traditional interaction and co-association was highlighted under three different types of co-association. Both simulation and real data analysis demonstrated that the proposed PLSPM-based statistic has better performance than single SNP-based logistic model, PCA-based logistic model, and other gene-based methods

Deterministic processes dominate microbial assembly mechanisms in the gut microbiota of cold-water fish between summer and winter

Exploring the effects of seasonal variation on the gut microbiota of cold-water fish plays an important role in understanding the relationship between seasonal variation and cold-water fish. Gut samples of cold-water fish and environmental samples were collected during summer and winter from the lower reaches of the Yalong River. The results of the 16S rRNA sequencing showed that significant differences were identified in the composition and diversity of gut bacteria of cold-water fish. Co-occurrence network complexity of the gut bacteria of cold-water fish was higher in summer compared to winter (Sum: nodes: 256; edges: 20,450; Win: nodes: 580; edges: 16,725). Furthermore, from summer to winter, the contribution of sediment bacteria (Sum: 5.3%; Win: 23.7%) decreased in the gut bacteria of cold-water fish, while the contribution of water bacteria (Sum: 0%; Win: 27.7%) increased. The normalized stochastic ratio (NST) and infer community assembly mechanisms by phylogenetic bin-based null model analysis (iCAMP) showed that deterministic processes played a more important role than stochastic processes in the microbial assembly mechanism of gut bacteria of cold-water fish. From summer to winter, the contribution of deterministic processes to gut bacteria community assembly mechanisms decreased, while the contribution of stochastic processes increased. Overall, these results demonstrated that seasonal variation influenced the gut bacteria of cold-water fish and served as a potential reference for future research to understand the adaptation of fish to varying environments

Sex Differences in the Association of HOMA-IR Index and BDNF in Han Chinese Patients With Chronic Schizophrenia

Background: Previous research has indicated that there are significant sex differences in serum BDNF levels and metabolic indicators in patients with schizophrenia. Studies have found that BDNF is involved in blood sugar regulation. Homeostasis model assessment of insulin resistance (HOMA-IR) is currently a sensitive indicator for measuring insulin resistance. Our study aims to explore the sex differences in the relationship between serum BDNF levels and HOMA-IR in patients with chronic schizophrenia (CS).Methods: A total of 332 patients with CS were enrolled in this study. General information of all participants was collected. Haematological indicators were collected, and the Positive and Negative Syndrome Scale (PANSS) was used to evaluate psychiatric symptoms. Sex differences in serum BDNF levels, HOMA-IR index and other metabolic indexes were investigated. Then, linear regression analysis was used to analyse the relationship between the HOMA-IR index and BDNF levels in male and female patients.Results: The HOMA-IR index of female patients was significantly higher than that of males, but there was no significant difference in serum BDNF levels between male patients and female patients. There was a positive correlation between BDNF level and HOMA-IR index, and this relationship only existed in female patients.Conclusion: The results show that there are significant sex differences in HOMA-IR in patients with CS. In addition, only in female patients was there a positive correlation between the HOMA-IR index and BDNF level, which suggests that sex factors should be taken into account in evaluating the relationship between BDNF and blood glucose in patients with CS