Studies of Phase Transitions of Chromium Coordination Compounds under High Pressure

In this paper, the high pressure Raman scattering spectroscopy of Cd2(HATr)4(NO3)4·H2O (Cd) was measured by diamond anvil cells (DACs) up to 10GPa. The Raman spectra of Cd at 0GPa was assigned completely. With pressure increased to 6GPa, a new Raman peak appeared and the original C-NH2 bending vibration mode and N-NH2 bending vibration mode disappeared, indicating that Cd underwent a phase transition

Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance

Multi-objective learning (MOL) problems often arise in emerging machine learning problems when there are multiple learning criteria or multiple learning tasks. Recent works have developed various dynamic weighting algorithms for MOL such as MGDA and its variants, where the central idea is to find an update direction that avoids conflicts among objectives. Albeit its appealing intuition, empirical studies show that dynamic weighting methods may not always outperform static ones. To understand this theory-practical gap, we focus on a new stochastic variant of MGDA - the Multi-objective gradient with Double sampling (MoDo) algorithm, and study the generalization performance of the dynamic weighting-based MoDo and its interplay with optimization through the lens of algorithm stability. Perhaps surprisingly, we find that the key rationale behind MGDA -- updating along conflict-avoidant direction - may hinder dynamic weighting algorithms from achieving the optimal ${\cal O}(1/\sqrt{n})$ population risk, where $n$ is the number of training samples. We further demonstrate the variability of dynamic weights on the three-way trade-off among optimization, generalization, and conflict avoidance that is unique in MOL

SiTAR: Situated Trajectory Analysis for In-the-Wild Pose Error Estimation

Virtual content instability caused by device pose tracking error remains a prevalent issue in markerless augmented reality (AR), especially on smartphones and tablets. However, when examining environments which will host AR experiences, it is challenging to determine where those instability artifacts will occur; we rarely have access to ground truth pose to measure pose error, and even if pose error is available, traditional visualizations do not connect that data with the real environment, limiting their usefulness. To address these issues we present SiTAR (Situated Trajectory Analysis for Augmented Reality), the first situated trajectory analysis system for AR that incorporates estimates of pose tracking error. We start by developing the first uncertainty-based pose error estimation method for visual-inertial simultaneous localization and mapping (VI-SLAM), which allows us to obtain pose error estimates without ground truth; we achieve an average accuracy of up to 96.1% and an average F1 score of up to 0.77 in our evaluations on four VI-SLAM datasets. Next we present our SiTAR system, implemented for ARCore devices, combining a backend that supplies uncertainty-based pose error estimates with a frontend that generates situated trajectory visualizations. Finally, we evaluate the efficacy of SiTAR in realistic conditions by testing three visualization techniques in an in-the-wild study with 15 users and 13 diverse environments; this study reveals the impact both environment scale and the properties of surfaces present can have on user experience and task performance.Comment: To appear in Proceedings of IEEE ISMAR 202

Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method

Alzheimer disease (AD) is the fourth major cause of death in the elderly following cancer, heart disease and cerebrovascular disease. Finding candidate causal genes can help in the design of Gene targeted drugs and effectively reduce the risk of the disease. Complex diseases such as AD are usually caused by multiple genes. The Genome-wide association study (GWAS), has identified the potential genetic variants for most diseases. However, because of linkage disequilibrium (LD), it is difficult to identify the causative mutations that directly cause diseases. In this study, we combined expression quantitative trait locus (eQTL) studies with the GWAS, to comprehensively define the genes that cause Alzheimer disease. The method used was the Summary Mendelian randomization (SMR), which is a novel method to integrate summarized data. Two GWAS studies and five eQTL studies were referenced in this paper. We found several candidate SNPs that have a strong relationship with AD. Most of these SNPs overlap in different data sets, providing relatively strong reliability. We also explain the function of the novel AD-related genes we have discovered

CellMix: A General Instance Relationship based Method for Data Augmentation Towards Pathology Image Classification

In pathology image analysis, obtaining and maintaining high-quality annotated samples is an extremely labor-intensive task. To overcome this challenge, mixing-based methods have emerged as effective alternatives to traditional preprocessing data augmentation techniques. Nonetheless, these methods fail to fully consider the unique features of pathology images, such as local specificity, global distribution, and inner/outer-sample instance relationships. To better comprehend these characteristics and create valuable pseudo samples, we propose the CellMix framework, which employs a novel distribution-oriented in-place shuffle approach. By dividing images into patches based on the granularity of pathology instances and shuffling them within the same batch, the absolute relationships between instances can be effectively preserved when generating new samples. Moreover, we develop a curriculum learning-inspired, loss-driven strategy to handle perturbations and distribution-related noise during training, enabling the model to adaptively fit the augmented data. Our experiments in pathology image classification tasks demonstrate state-of-the-art (SOTA) performance on 7 distinct datasets. This innovative instance relationship-centered method has the potential to inform general data augmentation approaches for pathology image classification. The associated codes are available at https://github.com/sagizty/CellMix

CPIA Dataset: A Comprehensive Pathological Image Analysis Dataset for Self-supervised Learning Pre-training

Pathological image analysis is a crucial field in computer-aided diagnosis, where deep learning is widely applied. Transfer learning using pre-trained models initialized on natural images has effectively improved the downstream pathological performance. However, the lack of sophisticated domain-specific pathological initialization hinders their potential. Self-supervised learning (SSL) enables pre-training without sample-level labels, which has great potential to overcome the challenge of expensive annotations. Thus, studies focusing on pathological SSL pre-training call for a comprehensive and standardized dataset, similar to the ImageNet in computer vision. This paper presents the comprehensive pathological image analysis (CPIA) dataset, a large-scale SSL pre-training dataset combining 103 open-source datasets with extensive standardization. The CPIA dataset contains 21,427,877 standardized images, covering over 48 organs/tissues and about 100 kinds of diseases, which includes two main data types: whole slide images (WSIs) and characteristic regions of interest (ROIs). A four-scale WSI standardization process is proposed based on the uniform resolution in microns per pixel (MPP), while the ROIs are divided into three scales artificially. This multi-scale dataset is built with the diagnosis habits under the supervision of experienced senior pathologists. The CPIA dataset facilitates a comprehensive pathological understanding and enables pattern discovery explorations. Additionally, to launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL pre-training and downstream evaluation are specially conducted. The CPIA dataset along with baselines is available at https://github.com/zhanglab2021/CPIA_Dataset

TSegFormer: 3D Tooth Segmentation in Intraoral Scans with Geometry Guided Transformer

Optical Intraoral Scanners (IOS) are widely used in digital dentistry to provide detailed 3D information of dental crowns and the gingiva. Accurate 3D tooth segmentation in IOSs is critical for various dental applications, while previous methods are error-prone at complicated boundaries and exhibit unsatisfactory results across patients. In this paper, we propose TSegFormer which captures both local and global dependencies among different teeth and the gingiva in the IOS point clouds with a multi-task 3D transformer architecture. Moreover, we design a geometry-guided loss based on a novel point curvature to refine boundaries in an end-to-end manner, avoiding time-consuming post-processing to reach clinically applicable segmentation. In addition, we create a dataset with 16,000 IOSs, the largest ever IOS dataset to the best of our knowledge. The experimental results demonstrate that our TSegFormer consistently surpasses existing state-of-the-art baselines. The superiority of TSegFormer is corroborated by extensive analysis, visualizations and real-world clinical applicability tests. Our code is available at https://github.com/huiminxiong/TSegFormer.Comment: MICCAI 2023, STAR(Student Travel) award. 11 pages, 3 figures, 5 tables. arXiv admin note: text overlap with arXiv:2210.1662

Quantum Dots-Based Immunochromatographic Strip for Rapid and Sensitive Detection of Acetamiprid in Agricultural Products

In this study, a rapid and sensitive immunochromatographic strip (ICS) assay, based on quantum dots (QDs), was developed for the qualitative and quantitative detection of acetamiprid in agricultural samples. Acetamiprid-ovalbumin conjugates (ACE-OVA) and goat anti-mouse IgG were sprayed onto a nitrocellulose membrane as a test and control line. Two kinds of anti-acetamiprid monoclonal antibodies (mAb) obtained in our lab were characterized by the ELISA and surface plasmon resonance assay. The competitive immunoassay was established using a QDs-mAb conjugate probe. The visual detection limit of acetamiprid for a qualitative threshold was set as 1 ng/mL to the naked eye. In the quantitative test, the fluorescence intensity was measured by a portable strip reader and a standard curve was obtained with a linear range from 0.098 to 25 ng/mL, and the half maximal inhibitory concentration of 1.12 ng/mL. The developed method showed no evident cross-reactivities with other neonicotinoid insecticides except for thiacloprid (36.68%). The accuracy and precision of the developed QDs-ICS were further evaluated. Results showed that the average recoveries ranged from 78.38 to 126.97% in agricultural samples. Moreover, to test blind tea samples, the QDs-ICS showed comparable reliability and a high correlation with ultra-performance liquid chromatography-tandem mass spectrometry. The whole sample detection could be accomplished within 1 h. In brief, our data clearly manifested that QDs-ICS was quite qualified for the rapid and sensitive screening of acetamiprid residues in an agricultural product analysis and paves the way to point-of-care testing for other analytes

A novel anti-virulence gene revealed by proteomic analysis in Shigella flexneri 2a

<p>Abstract</p> <p>Background</p> <p><it>Shigella flexneri </it>is a gram-negative, facultative pathogen that causes the majority of communicable bacterial dysenteries in developing countries. The virulence factors of <it>S. flexneri </it>have been shown to be produced at 37 degrees C but not at 30 degrees C. To discover potential, novel virulence-related proteins of <it>S. flexneri</it>, we performed differential in-gel electrophoresis (DIGE) analysis to measure changes in the expression profile that are induced by a temperature increase.</p> <p>Results</p> <p>The ArgT protein was dramatically down-regulated at 37 degrees C. In contrast, the ArgT from the non-pathogenic <it>E. coli </it>did not show this differential expression as in <it>S. flexneri</it>, which suggested that <it>argT </it>might be a potential anti-virulence gene. Competitive invasion assays in HeLa cells and in BALB/c mice with <it>argT </it>mutants were performed, and the results indicated that the over-expression of ArgT_Y225Dwould attenuate the virulence of <it>S. flexneri</it>. A comparative proteomic analysis was subsequently performed to investigate the effects of ArgT in <it>S. flexneri </it>at the molecular level. We show that HtrA is differentially expressed among different derivative strains.</p> <p>Conclusion</p> <p>Gene <it>argT </it>is a novel anti-virulence gene that may interfere with the virulence of <it>S. flexneri </it>via the transport of specific amino acids or by affecting the expression of the virulence factor, HtrA.</p