CSST Large-scale Structure Analysis Pipeline: II. the CSST Emulator for Slitless Spectroscopy (CESS)

The Chinese Space Station Telescope (CSST) slitless spectroscopic survey will observe objects to a limiting magnitude of ~ 23 mag (5$\sigma$, point sources) in U, V, and I over 17500 deg$^2$. The spectroscopic observations are expected to be highly efficient and complete for mapping galaxies over 0 < z < 1 with secure redshift measurements at spectral resolutions of R ~ 200, providing unprecedented data sets for cosmological studies. To quantitatively examine the survey potential, we develop a software tool, namely the CSST Emulator for Slitless Spectroscopy (CESS), to quickly generate simulated 1D slitless spectra with limited computing resources. We introduce the architecture of CESS and the detailed process of creating simulated CSST slitless spectra. The extended light distribution of a galaxy induces the self-broadening effect on the 1D slitless spectrum. We quantify the effect using morphological parameters: S\'ersic index, effective radius, position angle, and axis ratio. Moreover, we also develop a module for CESS to estimate the overlap contamination rate for CSST grating observations of galaxies in galaxy clusters. Applying CESS to the high-resolution model spectra of a sample of ~ 140 million galaxies with m_z < 21 mag selected from the Dark Energy Spectroscopic Instrument LS DR9 catalogue, we obtain the simulated CSST slitless spectra. We examine the dependence of measurement errors on different types of galaxies due to instrumental and observational effects and quantitatively investigate the redshift completeness for different environments out to z ~ 1. Our results show that the CSST spectroscopy is able to provide secure redshifts for about one-quarter of the sample galaxies.Comment: 14 pages, 15 figures, 2 tables, accepted for publication in MNRA

Gene Expression Analysis Reveals Novel Gene Signatures Between Young and Old Adults in Human Prefrontal Cortex

Human neurons function over an entire lifetime, yet the molecular mechanisms which perform their functions and protecting against neurodegenerative disease during aging are still elusive. Here, we conducted a systematic study on the human brain aging by using the weighted gene correlation network analysis (WGCNA) method to identify meaningful modules or representative biomarkers for human brain aging. Significantly, 19 distinct gene modules were detected based on the dataset GSE53890; among them, six modules related to the feature of brain aging were highly preserved in diverse independent datasets. Interestingly, network feature analysis confirmed that the blue modules demonstrated a remarkably correlation with human brain aging progress. Besides, the top hub genes including PPP3CB, CAMSAP1, ACTR3B, and GNG3 were identified and characterized by high connectivity, module membership, or gene significance in the blue module. Furthermore, these genes were validated in mice of different ages. Mechanically, the potential regulators of blue module were investigated. These findings highlight an important role of the blue module and its affiliated genes in the control of normal brain aging, which may lead to potential therapeutic interventions for brain aging by targeting the hub genes

Memory-Augmented Transformer for Remote Sensing Image Semantic Segmentation

The semantic segmentation of remote sensing images requires distinguishing local regions of different classes and exploiting a uniform global representation of the same-class instances. Such requirements make it necessary for the segmentation methods to extract discriminative local features between different classes and to explore representative features for all instances of a given class. While common deep convolutional neural networks (DCNNs) can effectively focus on local features, they are limited by their receptive field to obtain consistent global information. In this paper, we propose a memory-augmented transformer (MAT) to effectively model both the local and global information. The feature extraction pipeline of the MAT is split into a memory-based global relationship guidance module and a local feature extraction module. The local feature extraction module mainly consists of a transformer, which is used to extract features from the input images. The global relationship guidance module maintains a memory bank for the consistent encoding of the global information. Global guidance is performed by memory interaction. Bidirectional information flow between the global and local branches is conducted by a memory-query module, as well as a memory-update module, respectively. Experiment results on the ISPRS Potsdam and ISPRS Vaihingen datasets demonstrated that our method can perform competitively with state-of-the-art methods

Memory-Augmented Transformer for Remote Sensing Image Semantic Segmentation

The semantic segmentation of remote sensing images requires distinguishing local regions of different classes and exploiting a uniform global representation of the same-class instances. Such requirements make it necessary for the segmentation methods to extract discriminative local features between different classes and to explore representative features for all instances of a given class. While common deep convolutional neural networks (DCNNs) can effectively focus on local features, they are limited by their receptive field to obtain consistent global information. In this paper, we propose a memory-augmented transformer (MAT) to effectively model both the local and global information. The feature extraction pipeline of the MAT is split into a memory-based global relationship guidance module and a local feature extraction module. The local feature extraction module mainly consists of a transformer, which is used to extract features from the input images. The global relationship guidance module maintains a memory bank for the consistent encoding of the global information. Global guidance is performed by memory interaction. Bidirectional information flow between the global and local branches is conducted by a memory-query module, as well as a memory-update module, respectively. Experiment results on the ISPRS Potsdam and ISPRS Vaihingen datasets demonstrated that our method can perform competitively with state-of-the-art methods

Escherichia coli Increases its ATP Concentration in Weakly Acidic Environments Principally through the Glycolytic Pathway

Acid resistance is an intrinsic characteristic of intestinal bacteria in order to survive passage through the stomach. Adenosine triphosphate (ATP), the ubiquitous chemical used to power metabolic reactions, activate signaling cascades, and form precursors of nucleic acids, was also found to be associated with the survival of Escherichia coli (E. coli) in acidic environments. The metabolic pathway responsible for elevating the level of ATP inside these bacteria during acid adaptation has been unclear. E. coli uses several mechanisms of ATP production, including oxidative phosphorylation, glycolysis and the oxidation of organic compounds. To uncover which is primarily used during adaptation to acidic conditions, we broadly analyzed the levels of gene transcription of multiple E. coli metabolic pathway components. Our findings confirmed that the primary producers of ATP in E. coli undergoing mild acidic stress are the glycolytic enzymes Glk, PykF and Pgk, which are also essential for survival under markedly acidic conditions. By contrast, the transcription of genes related to oxidative phosphorylation was downregulated, despite it being the major producer of ATP in neutral pH environments

Immobilizing Polyether Imidazole Ionic Liquids on ZSM-5 Zeolite for the Catalytic Synthesis of Propylene Carbonate from Carbon Dioxide

Traditional ionic liquids (ILs) catalysts suffer from the difficulty of product purification and can only be used in homogeneous catalytic systems. In this work, by reacting ILs with co-catalyst (ZnBr2), we successfully converted three polyether imidazole ionic liquids (PIILs), i.e., HO-[Poly-epichlorohydrin-methimidazole]Cl (HO-[PECH-MIM]Cl), HOOC-[Poly-epichlorohydrin-methimidazole]Cl (HOOC-[PECH-MIM]Cl), and H2N-[Poly-epichlorohydrin-methimidazole]Cl (H2N-[PECH-MIM]Cl), to three composite PIIL materials, which were further immobilized on ZSM-5 zeolite by chemical bonding to result in three immobilized catalysts, namely ZSM-5-HO-[PECH-MIM]Cl/[ZnBr2], ZSM-5-HOOC-[PECH-MIM]Cl/[ZnBr2], and ZSM-5-H2N-[PECH-MIM]Cl/[ZnBr2]. Their structures, thermal stabilities, and morphologies were fully characterized by Fourier-transform infrared spectroscopy (FT-IR), X-ray diffractometry (XRD), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM). The amount of composite PIIL immobilized on ZSM-5 was determined by elemental analysis. Catalytic performance of the immobilized catalysts was evaluated through the catalytic synthesis of propylene carbonate (PC) from CO2 and propylene oxide (PO). Influences of reaction temperature, time, and pressure on catalytic performance were investigated through the orthogonal test, and the effect of catalyst circulation was also studied. Under an optimal reaction condition (130 &deg;C, 2.5 MPa, 0.75 h), the composite catalyst, ZSM-5-HOOC- [PECH-MIM]Cl/[ZnBr2], exhibited the best catalytic activity with a conversion rate of 98.3% and selectivity of 97.4%. Significantly, the immobilized catalyst could still maintain high heterogeneous catalytic activity even after being reused for eight cycles

Few-Shot SAR-ATR Based on Instance-Aware Transformer

Few-shot synthetic aperture radar automatic target recognition (SAR-ATR) aims to recognize the targets of the images (query images) based on a few annotated images (support images). Such a task requires modeling the relationship between the query and support images. In this paper, we propose the instance-aware transformer (IAT) model. The IAT exploits the power of all instances by constructing the attention map based on the similarities between the query feature and all support features. The query feature aggregates the support features based on the attention values. To align the features of the query and support images in IAT, the shared cross-transformer keep all the projections in the module shared across all features. Instance cosine distance is used in training to minimize the distance between the query feature and the support features. In testing, to fuse the support features of the same class into the class representation, Euclidean (Cosine) Loss is used to calculate the query-class distances. Experiments on the two proposed few-shot SAR-ATR test sets based on MSTAR demonstrate the superiority of the proposed method

Study on initiation characteristics of oblique detonation induced by hydrogen jets in acetylene-air mixtures

In this paper, effects of hydrogen jets in acetylene-air mixtures on the initiation characteristics of oblique detonation wave (ODW) are investigated, the two-dimensional (2D) multi-component NS equations considering detailed chemical reactions are solved. Influences of the transverse jet on flow field characteristics of the ODW are studied, including the initiation characteristics, the wave structures, and the state parameters. The existence of the jet changes the ignited mode of the ODW from double Mach interaction to single Mach interaction. Several wave configurations and their transitions induced by the jets are observed, including deflagration-to-detonation transition (DDT), jet-induced bow shock wave (BSW) in front of the transverse jet and rapidly develops into ODW, and ODW that generated directly in front of the transverse jet. The structure of these three wave systems is mainly influenced by the flow discharge of the hydrogen jet. When the hydrogen jet is small, no combustion is induced by the BSW in front of the jet. ODW is induced by the action of the deflagration wave and OSW. Increasing the hydrogen jet, BSW induces combustion and rapidly develops into ODW. When the hydrogen jet is large enough, the BSW disappears and direct detonation combustion occurs to produce ODW. Two types of relationships are obtained between the initiated position and the position of the transverse jet: the U-shaped curve at low flow discharge and the positive correlation at high flow discharge. When the flow discharge is small and no shock wave-induced combustion occurs, the initiated position is influenced by the position of the deflagration wave interacting with the OSW, which forms a U-shaped curve with the position of hydrogen jet. When the flow discharge is high and shock wave induced combustion occurs, ODW is rapidly developed behind the jet, resulting in a positive correlation between the initiated position and the position of hydrogen jet. Furthermore, the viscous effect has significant influence on the flow field characteristics of ODW, it promotes the transition between these initiation modes and shortens the initiation distance of oblique detonation. The findings of this study can better help understand the effect of transverse hydrogen jets on ODW initiation and contribute to theoretical studies and engineering applications of oblique detonation engines