Three-dimensional structure of the milky way dust: modeling of LAMOST data

We present a three-dimensional modeling of the Milky Way dust distribution by fitting the value-added star catalog of LAMOST spectral survey. The global dust distribution can be described by an exponential disk with scale-length of 3,192 pc and scale height of 103 pc. In this modeling, the Sun is located above the dust disk with a vertical distance of 23 pc. Besides the global smooth structure, two substructures around the solar position are also identified. The one located at $150^{\circ}<l<200^{\circ}$ and $-5^{\circ}<b<-30^{\circ}$ is consistent with the Gould Belt model of \citet{Gontcharov2009}, and the other one located at $140^{\circ}<l<165^{\circ}$ and $0^{\circ}<b<15^{\circ}$ is associated with the Camelopardalis molecular clouds.Comment: 15 pages, 6 figure, accepted by Ap

Deep Residual Transform for Multi-scale Image Decomposition

Multi-scale image decomposition (MID) is a fundamental task in computer vision and image processing that involves the transformation of an image into a hierarchical representation comprising of different levels of visual granularity from coarse structures to fine details. A well-engineered MID disentangles the image signal into meaningful components which can be used in a variety of applications such as image denoising, image compression, and object classification. Traditional MID approaches such as wavelet transforms tackle the problem through carefully designed basis functions under rigid decomposition structure assumptions. However, as the information distribution varies from one type of image content to another, rigid decomposition assumptions lead to inefficiently representation, i.e., some scales can contain little to no information. To address this issue, we present Deep Residual Transform (DRT), a data-driven MID strategy where the input signal is transformed into a hierarchy of non-linear representations at different scales, with each representation being independently learned as the representational residual of previous scales at a user-controlled detail level. As such, the proposed DRT progressively disentangles scale information from the original signal by sequentially learning residual representations. The decomposition flexibility of this approach allows for highly tailored representations cater to specific types of image content, and results in greater representational efficiency and compactness. In this study, we realize the proposed transform by leveraging a hierarchy of sequentially trained autoencoders. To explore the efficacy of the proposed DRT, we leverage two datasets comprising of very different types of image content: 1) CelebFaces and 2) Cityscapes. Experimental results show that the proposed DRT achieved highly efficient information decomposition on both datasets amid their very different visual granularity characteristics

Finite-time Anti-synchronization of Memristive Stochastic BAM Neural Networks with Probabilistic Time-varying Delays

This paper investigates the drive-response finite-time anti-synchronization for memristive bidirectional associative memory neural networks (MBAMNNs). Firstly, a class of MBAMNNs with mixed probabilistic time-varying delays and stochastic perturbations is first formulated and analyzed in this paper. Secondly, an nonlinear control law is constructed and utilized to guarantee drive-response finite-time anti-synchronization of the neural networks. Thirdly, by employing some inequality technique and constructing an appropriate Lyapunov function, some anti-synchronization criteria are derived. Finally, a number simulation is provided to demonstrate the effectiveness of the proposed mechanism

Augmenting corn starch gel printability for architectural 3D modeling for customized food

The advent of direct-ink-writing 3D printing in food processing highlights potential for innovation but underscores challenges with food-grade inks, notably their inadequate self-supporting properties post-extrusion that impede maintaining structural integrity and crating complex 3D forms. This challenge is particularly pronounced with starch—a key food ingredient. This study aims to bolster the printability of normal corn starch (NCS) through integration with pregelatinized (PG) high-amylose starch (G50 and G70, with 55% and 68% amylose contents, respectively) and proteins (soy, wheat, pea protein isolates, and whey protein). The PG starch was prepared by disorganizing the high-amylose starches in 33% CaCl2 solution and then precipitating them with ethanol. The formulation featuring an NCS/PG-G70/soy protein isolate ratio of 5:5:3 emerged superior, yielding enhanced formability, precise line printing, and robust self-support. This adapted starch-based gel facilitated the 3D printing of sophisticated structures, such as hollow and overhanging architectural forms, without necessitating chemical modification or a support bath. In vitro enzymatic hydrolysis tests on the printed constructs manifested approximately 50% resistant starch and 15% slowly digestible starch. These results suggest that the composite biopolymer ink developed in this study showcases not only superior printability but also boasts improved digestion-resistance. Thus, the findings from this research provide a foundation for developing food-grade inks capable of crafting customizable, intricately structured food products while conferring health advantages.<br/

Augmenting corn starch gel printability for architectural 3D modeling for customized food

The advent of direct-ink-writing 3D printing in food processing highlights potential for innovation but underscores challenges with food-grade inks, notably their inadequate self-supporting properties post-extrusion that impede maintaining structural integrity and crating complex 3D forms. This challenge is particularly pronounced with starch—a key food ingredient. This study aims to bolster the printability of normal corn starch (NCS) through integration with pregelatinized (PG) high-amylose starch (G50 and G70, with 55% and 68% amylose contents, respectively) and proteins (soy, wheat, pea protein isolates, and whey protein). The PG starch was prepared by disorganizing the high-amylose starches in 33% CaCl2 solution and then precipitating them with ethanol. The formulation featuring an NCS/PG-G70/soy protein isolate ratio of 5:5:3 emerged superior, yielding enhanced formability, precise line printing, and robust self-support. This adapted starch-based gel facilitated the 3D printing of sophisticated structures, such as hollow and overhanging architectural forms, without necessitating chemical modification or a support bath. In vitro enzymatic hydrolysis tests on the printed constructs manifested approximately 50% resistant starch and 15% slowly digestible starch. These results suggest that the composite biopolymer ink developed in this study showcases not only superior printability but also boasts improved digestion-resistance. Thus, the findings from this research provide a foundation for developing food-grade inks capable of crafting customizable, intricately structured food products while conferring health advantages.<br/

Comprehensive evaluation system for vegetation ecological quality: a case study of Sichuan ecological protection redline areas

Dynamic monitoring and evaluation of vegetation ecological quality (VEQ) is indispensable for ecological environment management and sustainable development. Single-indicator methods that have been widely used may cause biased results due to neglect of the variety of vegetation ecological elements. We developed the vegetation ecological quality index (VEQI) by coupling vegetation structure (vegetation cover) and function (carbon sequestration, water conservation, soil retention, and biodiversity maintenance) indicators. The changing characteristics of VEQ and the relative contribution of driving factors in the ecological protection redline areas in Sichuan Province (EPRA), China, from 2000 to 2021 were explored using VEQI, Sen’s slope, Mann-Kendall test, Hurst index, and residual analysis based on the XGBoost (Extreme gradient boosting regressor). The results showed that the VEQ in the EPRA has improved over the 22-year study period, but this trend may be unsustainable in the future. Temperature was the most influential climate factor. And human activities were the dominant factor with a relative contribution of 78.57% to VEQ changes. This study provides ideas for assessing ecological restoration in other regions, and can provide guidance for ecosystem management and conservation

AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation

Accurate automatic segmentation of medical images typically requires large datasets with high-quality annotations, making it less applicable in clinical settings due to limited training data. One-shot segmentation based on learned transformations (OSSLT) has shown promise when labeled data is extremely limited, typically including unsupervised deformable registration, data augmentation with learned registration, and segmentation learned from augmented data. However, current one-shot segmentation methods are challenged by limited data diversity during augmentation, and potential label errors caused by imperfect registration. To address these issues, we propose a novel one-shot medical image segmentation method with adversarial training and label error rectification (AdLER), with the aim of improving the diversity of generated data and correcting label errors to enhance segmentation performance. Specifically, we implement a novel dual consistency constraint to ensure anatomy-aligned registration that lessens registration errors. Furthermore, we develop an adversarial training strategy to augment the atlas image, which ensures both generation diversity and segmentation robustness. We also propose to rectify potential label errors in the augmented atlas images by estimating segmentation uncertainty, which can compensate for the imperfect nature of deformable registration and improve segmentation authenticity. Experiments on the CANDI and ABIDE datasets demonstrate that the proposed AdLER outperforms previous state-of-the-art methods by 0.7% (CANDI), 3.6% (ABIDE "seen"), and 4.9% (ABIDE "unseen") in segmentation based on Dice scores, respectively. The source code will be available at https://github.com/hsiangyuzhao/AdLER

Molecular Characterization of the 14-3-3 Gene Family in Brachypodium distachyon L. Reveals High Evolutionary Conservation and Diverse Responses to Abiotic Stresses

The 14-3-3 gene family identified in all eukaryotic organisms is involved in a wide range of biological processes, particularly in resistance to various abiotic stresses. Here, we performed the first comprehensive study on the molecular characterisation, phylogenetics and responses to various abiotic stresses of the 14-3-3 gene family in Brachypodium distachyon L.. A total of seven 14-3-3 genes from B. distachyon and 120 from five main lineages among 12 species were identified, which were divided into five well-conserved subfamilies. The molecular structure analysis showed that the plant 14-3-3 gene family is highly evolutionarily conserved, although certain divergence had occurred in different subfamilies. The duplication event investigation revealed that segmental duplication seemed to be the predominant form by which the 14-3-3 gene family had expanded. Moreover, seven critical amino acids were detected, which may contribute to functional divergence. Expression profiling analysis showed that BdGF14 genes were abundantly expressed in the roots, but showed low expression in the meristems. All seven BdGF14 genes showed significant expression changes under various abiotic stresses, including heavy metal, phytohormone, osmotic, and temperature stresses, which might play important roles in responses to multiple abiotic stresses mainly through participating in ABA-dependent signalling and reactive oxygen species-mediated MAPK cascade signalling pathways. In particular, BdGF14 genes generally showed upregulated expression in response to multiple stresses of high temperature, heavy metal, abscisic acid (ABA), and salicylic acid (SA), but downregulated expression under H2O2, NaCl, and polyethylene glycol (PEG) stresses. Meanwhile, dynamic transcriptional expression analysis of BdGF14 genes under longer treatments with heavy metals (Cd2+, Cr3+, Cu2+, and Zn2+) and phytohormone (ABA) and recovery revealed two main expression trends in both roots and leaves: up-down and up-down-up expression from stress treatments to recovery. This study provides new insights into the structures and functions of plant 14-3-3 genes