152 research outputs found
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 population risk, where 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<sub>Y225D </sub>would 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
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