Search

Your search keyword '"Yao, Lin"' showing total 7,131 results
Start Over Author "Yao, Lin"
7,131 results on '"Yao, Lin"'

1. Uni-SMART: Universal Science Multimodal Analysis and Research Transformer

Author

Cai, Hengxing, Cai, Xiaochen, Yang, Shuwen, Wang, Jiankun, Yao, Lin, Gao, Zhifeng, Chang, Junhan, Li, Sihang, Xu, Mingjun, Wang, Changxin, Wang, Hongshuai, Li, Yongge, Lin, Mujie, Li, Yaqi, Yin, Yuqi, Zhang, Linfeng, and Ke, Guolin

Subjects
Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition
Abstract

In scientific research and its application, scientific literature analysis is crucial as it allows researchers to build on the work of others. However, the fast growth of scientific knowledge has led to a massive increase in scholarly articles, making in-depth literature analysis increasingly challenging and time-consuming. The emergence of Large Language Models (LLMs) has offered a new way to address this challenge. Known for their strong abilities in summarizing texts, LLMs are seen as a potential tool to improve the analysis of scientific literature. However, existing LLMs have their own limits. Scientific literature often includes a wide range of multimodal elements, such as tables, charts, and molecule, which are hard for text-focused LLMs to understand and analyze. This issue points to the urgent need for new solutions that can fully understand and analyze multimodal content in scientific literature. To answer this demand, we present \textbf{Uni-SMART} (Universal Science Multimodal Analysis and Research Transformer), an innovative model designed for in-depth understanding of multimodal scientific literature. Through rigorous quantitative evaluation across several domains, Uni-SMART demonstrates superior performance over other text-focused LLMs. Furthermore, our exploration extends to practical applications, including patent infringement detection and nuanced analysis of charts. These applications not only highlight Uni-SMART's adaptability but also its potential to revolutionize how we interact with scientific literature.

Published
2024

2. SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis

Author

Cai, Hengxing, Cai, Xiaochen, Chang, Junhan, Li, Sihang, Yao, Lin, Wang, Changxin, Gao, Zhifeng, Wang, Hongshuai, Li, Yongge, Lin, Mujie, Yang, Shuwen, Wang, Jiankun, Xu, Mingjun, Huang, Jin, Xi, Fang, Zhuang, Jiaxi, Yin, Yuqi, Li, Yaqi, Chen, Changhong, Cheng, Zheng, Zhao, Zifeng, Zhang, Linfeng, and Ke, Guolin

Subjects
Computer Science - Computation and Language
Abstract

Recent breakthroughs in Large Language Models (LLMs) have revolutionized natural language understanding and generation, sparking significant interest in applying them to scientific literature analysis. However, existing benchmarks fail to adequately evaluate the proficiency of LLMs in this domain, particularly in scenarios requiring higher-level abilities beyond mere memorization and the handling of multimodal data. In response to this gap, we introduce SciAssess, a benchmark specifically designed for the comprehensive evaluation of LLMs in scientific literature analysis. SciAssess aims to thoroughly assess the efficacy of LLMs by focusing on their capabilities in Memorization (L1), Comprehension (L2), and Analysis \& Reasoning (L3). It encompasses a variety of tasks drawn from diverse scientific fields, including fundamental science, alloy materials, biomedicine, drug discovery, and organic materials. To ensure the reliability of SciAssess, rigorous quality control measures have been implemented, ensuring accuracy, anonymization, and compliance with copyright standards. SciAssess evaluates 11 LLMs, including GPT, Claude, and Gemini, highlighting their strengths and areas for improvement. This evaluation supports the ongoing development of LLM applications in the analysis of scientific literature. SciAssess and its resources are available at \url{https://sci-assess.github.io/}.

Published
2024

3. Automating the audit of electronic invoices with a soft robot

Author

Cheng, Tian Jun, Chen, Chia Jung, Ong, Yao Lin, Yang, Yi Fang, and Sheu, Guang Yih

Subjects
Computer Science - Robotics
Abstract

Taiwan's Chi Mei Medical Center has completed four challenges mentioned in published robotic process automation (RPA) studies including automating a dynamic process, designing feasible human-robot collaboration, incorporating other emerging technologies, and bringing positive business impacts. Its executives called a committee to implement the electronic invoicing. This implementation includes the creation of a software robot to download automatically cloud electronic invoice (E-invoice) data from Taiwan's E-invoice platform and detect the inconsistency between them and on-premise data. This bot operates when internal auditors are off their office. They satisfied this software robot since the remaining work is only verifying the resulting inconsistency. The Chi Mei Medical Center measured the time and costs before and after adopting software robots to audit E-invoice; consequently, it welcomed more bots automating other business processes. In conclusion, integrating a software robot with other emerging technologies mitigates the possible errors provided by this bot. A good human-robot collaboration relies on the consideration of human perspective in choosing RPA tasks. Free bot creators are sufficient to verify that automating a business process using a bot is a reasonable investment., Comment: 11 pages, 6 figures, 1 table

Published
2024

4. Node-Aligned Graph-to-Graph: Elevating Template-free Deep Learning Approaches in Single-Step Retrosynthesis.

Author

Yao, Lin, Guo, Wentao, Wang, Zhen, Xiang, Shang, Liu, Wentan, and Ke, Guolin

Abstract

Single-step retrosynthesis in organic chemistry increasingly benefits from deep learning (DL) techniques in computer-aided synthesis design. While template-free DL models are flexible and promising for retrosynthesis prediction, they often ignore vital 2D molecular information and struggle with atom alignment for node generation, resulting in lower performance compared to the template-based and semi-template-based methods. To address these issues, we introduce node-aligned graph-to-graph (NAG2G), a transformer-based template-free DL model. NAG2G combines 2D molecular graphs and 3D conformations to retain comprehensive molecular details and incorporates product-reactant atom mapping through node alignment, which determines the order of the node-by-node graph outputs process in an autoregressive manner. Through rigorous benchmarking and detailed case studies, we have demonstrated that NAG2G stands out with its remarkable predictive accuracy on the expansive data sets of USPTO-50k and USPTO-FULL. Moreover, the models practical utility is underscored by its successful prediction of synthesis pathways for multiple drug candidate molecules. This proves not only NAG2Gs robustness but also its potential to revolutionize the prediction of complex chemical synthesis processes for future synthetic route design tasks.

Published
2024

5. End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction

Author

Lai, Qingsi, Yao, Lin, Gao, Zhifeng, Liu, Siyuan, Wang, Hongshuai, Lu, Shuqi, He, Di, Wang, Liwei, Wang, Cheng, and Ke, Guolin

Subjects
Physics - Chemical Physics, Computer Science - Machine Learning
Abstract

Crystal structure prediction (CSP) has made significant progress, but most methods focus on unconditional generations of inorganic crystal with limited atoms in the unit cell. This study introduces XtalNet, the first equivariant deep generative model for end-to-end CSP from Powder X-ray Diffraction (PXRD). Unlike previous methods that rely solely on composition, XtalNet leverages PXRD as an additional condition, eliminating ambiguity and enabling the generation of complex organic structures with up to 400 atoms in the unit cell. XtalNet comprises two modules: a Contrastive PXRD-Crystal Pretraining (CPCP) module that aligns PXRD space with crystal structure space, and a Conditional Crystal Structure Generation (CCSG) module that generates candidate crystal structures conditioned on PXRD patterns. Evaluation on two MOF datasets (hMOF-100 and hMOF-400) demonstrates XtalNet's effectiveness. XtalNet achieves a top-10 Match Rate of 90.2% and 79% for hMOF-100 and hMOF-400 datasets in conditional crystal structure prediction task, respectively. XtalNet represents a significant advance in CSP, enabling the prediction of complex structures from PXRD data without the need for external databases or manual intervention. It has the potential to revolutionize PXRD analysis. It enables the direct prediction of crystal structures from experimental measurements, eliminating the need for manual intervention and external databases. This opens up new possibilities for automated crystal structure determination and the accelerated discovery of novel materials.

Published
2024

8. Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step Retrosynthesis

Author

Yao, Lin, Guo, Wentao, Wang, Zhen, Xiang, Shang, Liu, Wentan, and Ke, Guolin

Subjects
Computer Science - Machine Learning, Physics - Chemical Physics, Quantitative Biology - Quantitative Methods
Abstract

Single-step retrosynthesis (SSR) in organic chemistry is increasingly benefiting from deep learning (DL) techniques in computer-aided synthesis design. While template-free DL models are flexible and promising for retrosynthesis prediction, they often ignore vital 2D molecular information and struggle with atom alignment for node generation, resulting in lower performance compared to the template-based and semi-template-based methods. To address these issues, we introduce Node-Aligned Graph-to-Graph (NAG2G), a transformer-based template-free DL model. NAG2G combines 2D molecular graphs and 3D conformations to retain comprehensive molecular details and incorporates product-reactant atom mapping through node alignment which determines the order of the node-by-node graph outputs process in an auto-regressive manner. Through rigorous benchmarking and detailed case studies, we have demonstrated that NAG2G stands out with its remarkable predictive accuracy on the expansive datasets of USPTO-50k and USPTO-FULL. Moreover, the model's practical utility is underscored by its successful prediction of synthesis pathways for multiple drug candidate molecules. This not only proves NAG2G's robustness but also its potential to revolutionize the prediction of complex chemical synthesis processes for future synthetic route design tasks.

Published
2023
Full Text
View/download PDF

9. A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training

Author

Wang, Hanwen, Qi, Yu, Yao, Lin, Wang, Yueming, Farina, Dario, and Pan, Gang

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence
Abstract

Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. Endogenous BCIs based on electroencephalogram (EEG) signals, such as motor imagery (MI) BCIs, can provide some level of control. However, mastering spontaneous BCI control requires the users to generate discriminative and stable brain signal patterns by imagery, which is challenging and is usually achieved over a very long training time (weeks/months). Here, we propose a human-machine joint learning framework to boost the learning process in endogenous BCIs, by guiding the user to generate brain signals towards an optimal distribution estimated by the decoder, given the historical brain signals of the user. To this end, we firstly model the human-machine joint learning process in a uniform formulation. Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel ``copy/new'' feedback paradigm to help shape the signal generation of the subject toward the optimal distribution; 2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process. Specifically, the decoder reweighs the brain signals generated by the subject to focus more on ``good'' samples to cope with the learning process of the subject. Online and psuedo-online BCI experiments with 18 healthy subjects demonstrated the advantages of the proposed joint learning process over co-adaptive approaches in both learning efficiency and effectiveness.

Published
2023

10. Improved Cryo-EM Pose Estimation and 3D Classification through Latent-Space Disentanglement

Author

Chen, Weijie, Wang, Yuhang, and Yao, Lin

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Biomolecules, Quantitative Biology - Quantitative Methods, J.2, J.3, I.4.5
Abstract

Due to the extremely low signal-to-noise ratio (SNR) and unknown poses (projection angles and image shifts) in cryo-electron microscopy (cryo-EM) experiments, reconstructing 3D volumes from 2D images is very challenging. In addition to these challenges, heterogeneous cryo-EM reconstruction requires conformational classification. In popular cryo-EM reconstruction algorithms, poses and conformation classification labels must be predicted for every input cryo-EM image, which can be computationally costly for large datasets. An emerging class of methods adopted the amortized inference approach. In these methods, only a subset of the input dataset is needed to train neural networks for the estimation of poses and conformations. Once trained, these neural networks can make pose/conformation predictions and 3D reconstructions at low cost for the entire dataset during inference. Unfortunately, when facing heterogeneous reconstruction tasks, it is hard for current amortized-inference-based methods to effectively estimate the conformational distribution and poses from entangled latent variables. Here, we propose a self-supervised variational autoencoder architecture called "HetACUMN" based on amortized inference. We employed an auxiliary conditional pose prediction task by inverting the order of encoder-decoder to explicitly enforce the disentanglement of conformation and pose predictions. Results on simulated datasets show that HetACUMN generated more accurate conformational classifications than other amortized or non-amortized methods. Furthermore, we show that HetACUMN is capable of performing heterogeneous 3D reconstructions of a real experimental dataset., Comment: 21 pages

Published
2023

13. Deep Learning Applications Based on WISE Infrared Data: Classification of Stars, Galaxies and Quasars

Author

Zhao, Guiyu, Qiu, Bo, Luo, A-Li, Guo, Xiaoyu, Yao, Lin, Wang, Kun, and Liu, Yuanbo

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Computer Vision and Pattern Recognition
Abstract

The Wide-field Infrared Survey Explorer (WISE) has detected hundreds of millions of sources over the entire sky. However, classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low detection levels in its two longest-wavelength bandpasses. In this paper, the deep learning classification network, IICnet (Infrared Image Classification network), is designed to classify sources from WISE images to achieve a more accurate classification goal. IICnet shows good ability on the feature extraction of the WISE sources. Experiments demonstrates that the classification results of IICnet are superior to some other methods; it has obtained 96.2% accuracy for galaxies, 97.9% accuracy for quasars, and 96.4% accuracy for stars, and the Area Under Curve (AUC) of the IICnet classifier can reach more than 99%. In addition, the superiority of IICnet in processing infrared images has been demonstrated in the comparisons with VGG16, GoogleNet, ResNet34, MobileNet, EfficientNetV2, and RepVGG-fewer parameters and faster inference. The above proves that IICnet is an effective method to classify infrared sources.

Published
2023
Full Text
View/download PDF

16. Use of phase plate cryo-EM reveals conformation diversity of therapeutic IgG with 50 kDa Fab fragment resolved below 6 Å

Author

Hsin-Hung Lin, Chun-Hsiung Wang, Shih-Hsin Huang, Sung-Yao Lin, Takayuki Kato, Keiichi Namba, Naoki Hosogi, Chihong Song, Kazuyoshi Murata, Ching-Hsuan Yen, Tsui-Ling Hsu, Chi-Huey Wong, Yi-Min Wu, I.-Ping Tu, and Wei-Hau Chang

Subjects
Medicine, Science
Abstract

Abstract While cryogenic electron microscopy (cryo-EM) is fruitfully used for harvesting high-resolution structures of sizable macromolecules, its application to small or flexible proteins composed of small domains like immunoglobulin (IgG) remain challenging. Here, we applied single particle cryo-EM to Rituximab, a therapeutic IgG mediating anti-tumor toxicity, to explore its solution conformations. We found Rituximab molecules exhibited aggregates in cryo-EM specimens contrary to its solution behavior, and utilized a non-ionic detergent to successfully disperse them as isolated particles amenable to single particle analysis. As the detergent adversely reduced the protein-to-solvent contrast, we employed phase plate contrast to mitigate the impaired protein visibility. Assisted by phase plate imaging, we obtained a canonical three-arm IgG structure with other structures displaying variable arm densities co-existing in solution, affirming high flexibility of arm-connecting linkers. Furthermore, we showed phase plate imaging enables reliable structure determination of Fab to sub-nanometer resolution from ab initio, yielding a characteristic two-lobe structure that could be unambiguously docked with crystal structure. Our findings revealed conformation diversity of IgG and demonstrated phase plate was viable for cryo-EM analysis of small proteins without symmetry. This work helps extend cryo-EM boundaries, providing a valuable imaging and structural analysis framework for macromolecules with similar challenging features.

Published
2024
Full Text
View/download PDF

17. Associations between deep venous thrombosis and thyroid diseases: a two-sample bidirectional Mendelian randomization study

Author

Lifeng Zhang, Kaibei Li, Qifan Yang, Yao Lin, Caijuan Geng, Wei Huang, and Wei Zeng

Subjects
Deep venous thrombosis, Thyroid diseases, Mendelian randomization analysis, Database, Medicine
Abstract

Abstract Background Some previous observational studies have linked deep venous thrombosis (DVT) to thyroid diseases; however, the findings were contradictory. This study aimed to investigate whether some common thyroid diseases can cause DVT using a two-sample Mendelian randomization (MR) approach. Methods This two-sample MR study used single nucleotide polymorphisms (SNPs) identified by the FinnGen genome-wide association studies (GWAS) to be highly associated with some common thyroid diseases, including autoimmune hyperthyroidism (962 cases and 172,976 controls), subacute thyroiditis (418 cases and 187,684 controls), hypothyroidism (26,342 cases and 59,827 controls), and malignant neoplasm of the thyroid gland (989 cases and 217,803 controls. These SNPs were used as instruments. Outcome datasets for the GWAS on DVT (6,767 cases and 330,392 controls) were selected from the UK Biobank data, which was obtained from the Integrative Epidemiology Unit (IEU) open GWAS project. The inverse variance weighted (IVW), MR-Egger and weighted median methods were used to estimate the causal association between DVT and thyroid diseases. The Cochran’s Q test was used to quantify the heterogeneity of the instrumental variables (IVs). MR Pleiotropy RESidual Sum and Outlier test (MR-PRESSO) was used to detect horizontal pleiotropy. When the causal relationship was significant, bidirectional MR analysis was performed to determine any reverse causal relationships between exposures and outcomes. Results This MR study illustrated that autoimmune hyperthyroidism slightly increased the risk of DVT according to the IVW [odds ratio (OR) = 1.0009; p = 0.024] and weighted median methods [OR = 1.001; p = 0.028]. According to Cochran’s Q test, there was no evidence of heterogeneity in IVs. Additionally, MR-PRESSO did not detect horizontal pleiotropy (p = 0.972). However, no association was observed between other thyroid diseases and DVT using the IVW, weighted median, and MR-Egger regression methods. Conclusions This study revealed that autoimmune hyperthyroidism may cause DVT; however, more evidence and larger sample sizes are required to draw more precise conclusions.

Published
2024
Full Text
View/download PDF

18. Morphology and classification of the second mesiobuccal canal in maxillary first molars: a cone-beam computed tomography analysis in a Chinese population

Author

Yan Xiang, Zhaojun Wu, Lvli Yang, Wei Zhang, Na Cao, Xiaoman Xu, and Yao Lin

Subjects
Cone-beam computed tomography, Maxillary first molar, Root canal configuration, The second mesiobuccal canal, Vertucci’s classification, Dentistry, RK1-715
Abstract

Abstract Background Understanding the tooth anatomy is crucial for ensuring effective endodontic treatment. This study investigated the root canal morphology of the second mesiobuccal (MB2) canal in maxillary first molars (MFMs) in a Chinese population using cone-beam computed tomography (CBCT). Methods This study evaluated 486 MFMs with MB2 canals from 285 participants undergoing CBCT examination and determined the Vertucci’s classification and position of the MB2 canal orifice. The prevalence of the MB2 canal was correlated with the sex, age, and tooth side. The correlations between the prevalence of the MB2 canal and sex and tooth side were assessed using the Fisher's exact test. The chi-square test was used for evaluating the correlation between the prevalence of the MB2 canal and age. Results The number of type II, III, IV, V, VI, VII, and other root canals in the MFMs was 30.9%, 0.6%, 65.0%, 1.2%, 1.2%, 0.4%, and 0.6%, respectively. Among the 201 cases with bilateral inclusion, 87.6% showed consistent canal configuration. Results of the first clear apparent position (FCAP) of the MB2 canals showed that 434, 44, and 3 teeth had FCAP at the upper, middle, and bottom one-third of the root, respectively. The FCAPs of the MB2 canal in the MFMs with types II, IV, and VI, as well as types III and V canals showed significant differences (p

Published
2024
Full Text
View/download PDF

19. Assessing the role of programmed cell death signatures and related gene TOP2A in progression and prognostic prediction of clear cell renal cell carcinoma

Author

Qingshui Wang, Jiamin Liu, Ruiqiong Li, Simeng Wang, Yining Xu, Yawen Wang, Hao Zhang, Yingying Zhou, Xiuli Zhang, Xuequn Chen, Wei Zhuang, and Yao Lin

Subjects
KIRC, PCD, Immunotherapy, Immune microenvironment, TOP2A, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282, Cytology, QH573-671
Abstract

Abstract Kidney Clear Cell Carcinoma (KIRC), the predominant form of kidney cancer, exhibits a diverse therapeutic response to Immune Checkpoint Inhibitors (ICIs), highlighting the need for predictive models of ICI efficacy. Our study has constructed a prognostic model based on 13 types of Programmed Cell Death (PCD), which are intertwined with tumor progression and the immune microenvironment. Validated by analyses of comprehensive datasets, this model identifies seven key PCD genes that delineate two subtypes with distinct immune profiles and sensitivities to anti-PD-1 therapy. The high-PCD group demonstrates a more immune-suppressive environment, while the low-PCD group shows better responses to PD-1 treatment. In particular, TOP2A emerged as crucial, with its inhibition markedly reducing KIRC cell growth and mobility. These findings underscore the relevance of PCDs in predicting KIRC outcomes and immunotherapy response, with implications for enhancing clinical decision-making.

Published
2024
Full Text
View/download PDF

20. Some Symmetry and Duality Theorems on Multiple Zeta(-star) Values

Author

Chen, Kwang-Wu, Eie, Minking, and Ong, Yao Lin

Subjects
Mathematics - Number Theory, 11M32, Secondary: 05A15, 33B15
Abstract

In this paper, we provide a symmetric formula and a duality formula relating multiple zeta values and zeta-star values. Leveraging Zagier's formula for computing $\zeta^\star(\{2\}^p,3,\{2\}^q)$, we employ our theorems to establish a formula for computing $\zeta^\star(\{2\}^p,1,\{2\}^q)$ for any positive integers $p$ and $q$, along with other formulas of interest., Comment: 14 pages. I would appreciate any constructive feedback and alternative perspectives on my paper. Thank you!

Published
2023

21. Boosted ab initio Cryo-EM 3D Reconstruction with ACE-EM

Author

Yao, Lin, Xu, Ruihan, Gao, Zhifeng, Ke, Guolin, and Wang, Yuhang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Quantitative Biology - Biomolecules, Quantitative Biology - Quantitative Methods, I.4.5, I.5.1, I.5.2, I.5.4
Abstract

The central problem in cryo-electron microscopy (cryo-EM) is to recover the 3D structure from noisy 2D projection images which requires estimating the missing projection angles (poses). Recent methods attempted to solve the 3D reconstruction problem with the autoencoder architecture, which suffers from the latent vector space sampling problem and frequently produces suboptimal pose inferences and inferior 3D reconstructions. Here we present an improved autoencoder architecture called ACE (Asymmetric Complementary autoEncoder), based on which we designed the ACE-EM method for cryo-EM 3D reconstructions. Compared to previous methods, ACE-EM reached higher pose space coverage within the same training time and boosted the reconstruction performance regardless of the choice of decoders. With this method, the Nyquist resolution (highest possible resolution) was reached for 3D reconstructions of both simulated and experimental cryo-EM datasets. Furthermore, ACE-EM is the only amortized inference method that reached the Nyquist resolution.

Published
2023

22. 3D Molecular Generation via Virtual Dynamics

Author

Lu, Shuqi, Yao, Lin, Chen, Xi, Zheng, Hang, He, Di, and Ke, Guolin

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning
Abstract

Structure-based drug design, i.e., finding molecules with high affinities to the target protein pocket, is one of the most critical tasks in drug discovery. Traditional solutions, like virtual screening, require exhaustively searching on a large molecular database, which are inefficient and cannot return novel molecules beyond the database. The pocket-based 3D molecular generation model, i.e., directly generating a molecule with a 3D structure and binding position in the pocket, is a new promising way to address this issue. Herein, we propose VD-Gen, a novel pocket-based 3D molecular generation pipeline. VD-Gen consists of several carefully designed stages to generate fine-grained 3D molecules with binding positions in the pocket cavity end-to-end. Rather than directly generating or sampling atoms with 3D positions in the pocket like in early attempts, in VD-Gen, we first randomly initialize many virtual particles in the pocket; then iteratively move these virtual particles, making the distribution of virtual particles approximate the distribution of molecular atoms. After virtual particles are stabilized in 3D space, we extract a 3D molecule from them. Finally, we further refine atoms in the extracted molecule by iterative movement again, to get a high-quality 3D molecule, and predict a confidence score for it. Extensive experiment results on pocket-based molecular generation demonstrate that VD-Gen can generate novel 3D molecules to fill the target pocket cavity with high binding affinities, significantly outperforming previous baselines.

Published
2023

25. Improving the precision of multiparameter estimation in the teleportation of qutrit under amplitude damping noise

Author

Li, Yan-Ling, Zeng, Yi-Bo, Yao, Lin, and Xiao, Xing

Subjects
Quantum Physics
Abstract

Since the initial discovery of quantum teleportation, it is devoted to transferring unknown quantum states from one party to another distant partner. However, in the scenarios of remote sensing, what people truly care about is the information carried by certain parameters. The problem of multiparameter estimation in the framework of qutrit teleportation under amplitude damping (AD) noise is studied. Particularly, two schemes are proposed to battle against AD noise and enhance the precision of multiparameter estimation by utilizing weak measurement (WM) and environment-assisted measurement (EAM). For two-phase parameters encoded in a qutrit state, the analytical formulas of the quantum Fisher information matrix (QFIM) can be obtained. The results prove that the scheme of EAM outperforms the WM one in the improvements of both independent and simultaneous estimation precision. Remarkably, the EAM scheme can completely ensure the estimation precision against the contamination by AD noise. The reason should be attributed to the fact that EAM is carried out after the AD noise. Thus, it extracts information from both the system and the environment. The findings show that the techniques of WM and EAM are helpful for remote quantum sensing and can be generalized to other qutrit-based quantum information tasks under AD decoherence., Comment: 9 pages, 5 figures, accepted by Annalen der Physik

Published
2023
Full Text
View/download PDF

26. Surgical Outcomes and Predictive Factors in Patients With Detrusor Underactivity Undergoing Bladder Outlet Obstruction Surgery

Author

Ming-Syun Chuang, Yin-Chien Ou, Yu-Sheng Cheng, Kuan-Yu Wu, Chang-Te Wang, Yuan-Chi Huang, and Yao-Lin Kao

Subjects
detrusor underactivity, bladder outlet obstruction, urodynamics, prostate, overactive detrusor, Diseases of the genitourinary system. Urology, RC870-923
Abstract

Purpose This study was conducted to evaluate the efficacy of bladder outlet surgery in patients with detrusor underactivity (DU) and to identify factors associated with successful outcomes. Methods We conducted a retrospective review of men diagnosed with DU in urodynamic studies who underwent bladder outlet surgery for lower urinary tract symptoms between May 2018 and April 2023. The International Prostate Symptom Score (IPSS) questionnaire, uroflowmetry (UFM), and multichannel urodynamic studies were administered. Successful treatment outcomes were defined as either an IPSS improvement of at least 50% or the regaining of spontaneous voiding in patients urethral catheterization prior to surgery. Results The study included 93 male patients. Men diagnosed with significant or equivocal bladder outlet obstruction (BOO) experienced significant postoperative improvements in IPSS (from 20.6 to 6.0 and from 17.4 to 6.5, respectively), maximum urine flow rate (from 5.0 mL/sec to 14.4 mL/sec and from 8.8 mL/sec to 12.2 mL/sec, respectively) and voiding efficiency (from 48.8% to 86.0% and from 61.2% to 85.1%, respectively). However, in the group without obstruction, the improvements in IPSS and UFM results were not significant. The presence of detrusor overactivity (odds ratio [OR], 3.152; P=0.025) and preoperative urinary catheterization (OR, 2.756; P=0.040) were associated with favorable treatment outcomes. Conversely, an unobstructed bladder outlet was identified as a negative prognostic factor. Conclusions In men with DU accompanied by equivocal or significant BOO, surgical intervention to alleviate the obstruction may enhance the IPSS, quality of life, and UFM results. However, those with DU and an unobstructed bladder outlet face a comparatively high risk of treatment failure. Preoperative detrusor overactivity and urinary catheterization are associated with more favorable surgical outcomes. Consequently, active deobstructive surgery should be considered for patients with DU who are experiencing urinary retention.

Published
2024
Full Text
View/download PDF

27. Association between circadian physical activity trajectories and incident type 2 diabetes in the UK Biobank

Author

Pufei Bai, Xian Shao, Lianqin Chen, Saijun Zhou, Yao Lin, Hongyan Liu, and Pei Yu

Subjects
Medicine, Science
Abstract

Abstract Physical activity (PA) is linked to a decreased risk of type 2 diabetes mellitus (T2DM). However, the influence of circadian PA trajectories remains uncertain. This study aims to explore the optimal circadian PA trajectory pattern for reducing the risk of T2DM. Methods: A total of 502,400 participants were recruited from the UK Biobank between 2006 and 2010, and 102,323 participants provided valid accelerometer-captured acceleration data. After excluding individuals with prior T2DM, 99,532 participants were included in the final analysis. We initially investigated the association between PA intensity at 24 hourly time points and T2DM. Subsequently, PA trajectories were identified using K-means cluster analysis. Cox proportional hazard models were employed to estimate hazard ratios (HR). Four distinct PA trajectories were identified: consistently low, single peak, double peak, and intense trajectories. Compared to consistently low, single peak, double peak and intense PA trajectory reduced the risk of T2DM progressively. Sensitivity analyses, further excluding individuals with glycated hemoglobin (HbA1c) ≥ 6.5% or random glucose ≥ 11.1 mmol/L and adjusted for daily average acceleration, yielded consistent results. This confirms that the ideal circadian PA trajectory serves as a protective factor, independently of PA intensity. Subgroup analyses indicated that these effects were more pronounced in men and individuals with eGFR

Published
2024
Full Text
View/download PDF

28. Mass splitting and spin alignment for $$\phi $$ ϕ mesons in a magnetic field in NJL model

Author

Xin-Li Sheng, Shu-Yun Yang, Yao-Lin Zou, and Defu Hou

Subjects
Astrophysics, QB460-466, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract Based on the Nambu–Jona–Lasinio (NJL) model, we develop a framework for calculating the spin alignment of vector mesons and applied it to study $$\phi $$ ϕ mesons in a magnetic field. We calculate mass spectra for $$\phi $$ ϕ mesons and observe mass splitting between the longitudinally polarized state and transversely polarized states. The $$\phi $$ ϕ meson in a thermal equilibrium system is preferred to occupy the state with spin $$\lambda =0$$ λ = 0 than those with spin $$\lambda =\pm 1$$ λ = ± 1 , because the former state has a smaller energy. As a consequence, we conclude that the spin alignment will be larger than 1/3 if one measures along the direction of the magnetic field, which is qualitatively consistent with the recent STAR data. Around the critical temperature $$T_{C}=150~\hbox {MeV}$$ T C = 150 MeV , the positive deviation from 1/3 is proportional to the square of the magnetic field strength, which agrees with the result from the non-relativistic coalescence model. Including the anomalous magnetic moments for quarks will modify the dynamical masses of quarks and thus affect the mass spectra and spin alignment of $$\phi $$ ϕ mesons. The discussion of spin alignment in the NJL model may help us better understand the formation of hadron’s spin structure during the chiral phase transition.

Published
2024
Full Text
View/download PDF

29. Association between the trajectory of ideal cardiovascular health metrics and incident chronic kidney disease among 27,635 older adults in northern China–a prospective cohort study

Author

Pufei Bai, Xian Shao, Xiaoqun Ning, Xi Jiang, Hongyan Liu, Yao Lin, Fang Hou, Yourui Zhang, Saijun Zhou, and Pei Yu

Subjects
Cardiovascular health metrics, Chronic kidney disease, Older people, Group-based trajectory model, Prospective cohort study, Geriatrics, RC952-954.6
Abstract

Abstract Background There is a lack of relevant studies evaluating the long-term impact of cardiovascular health factor (CVH) metrics on chronic kidney disease (CKD). Objective This study investigates the long-term change in CVH metrics in older people and explores the relationship between CVH metrics trajectory and CKD. Methods In total, 27,635 older people aged over 60 from the community-based Tianjin Chronic Kidney Disease Cohort study were enrolled. The participants completed five annual physical examinations between January 01, 2014, and December 31, 2018, and a subsequent follow-up between January 01, 2019, and December 31, 2021. CVH metrics trajectories were established by the group-based trajectory model to predict CKD risk. The relationships between baseline CVH, CVH change (ΔCVH), and CKD risk were also explored by logistic regression and restricted cubic spline regression model. In addition, likelihood ratio tests were used to compare the goodness of fit of the different models. Results Six distinct CVH metrics trajectories were identified among the participants: low-stable (11.19%), low-medium-stable (30.58%), medium-stable (30.54%), medium-high-decreased (5.46%), medium-high-stable (18.93%), and high-stable (3.25%). After adjustment for potential confounders, higher CVH metrics trajectory was associated with decreased risk of CKD (P for trend

Published
2024
Full Text
View/download PDF

32. A Comprehensive Study of Bright Fermi-GBM Short Gamma-Ray Bursts: II. Very Short Burst and Its Implications

Author

Hu, Ying-Yong, Huang, Yao-Lin, Huang, Jia-Wei, Zhu, Zan, and Tang, Qing-Wen

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

A thermal component is suggested to be the physical composition of the ejecta of several bright gamma-ray bursts (GRBs). Such a thermal component is discovered in the time-integrated spectra of several short GRBs as well as long GRBs. In this work, we present a comprehensive analysis of ten very short GRBs detected by Fermi Gamma-Ray Burst Monitor to search for the thermal component. We found that both the resultant low-energy spectral index and the peak energy in each GRB imply a common hard spectral feature, which is in favor of the main classification of the short/hard versus long/soft dichotomy in the GRB duration. We also found moderate evidence for the detection of thermal component in eight GRBs. Although such a thermal component contributes a small proportion of the global prompt gamma-ray emission, the modified thermal-radiation mechanism could enhance the proportion significantly, such as in subphotospheric dissipation., Comment: 14 pages, 6 figures, published in the Universe journal

Published
2022
Full Text
View/download PDF

33. Mass splitting and spin alignment for $\phi$ mesons in a magnetic field in NJL model

Author

Sheng, Xin-Li, Yang, Shu-Yun, Zou, Yao-Lin, and Hou, Defu

Subjects
Nuclear Theory, High Energy Physics - Phenomenology
Abstract

Based on the Nambu-Jona-Lasinio (NJL) model, we develop a framework for calculating the spin alignment of vector mesons and applied it to study $\phi$ mesons in a magnetic field. We calculate mass spectra for $\phi$ mesons and observe mass splitting between the longitudinally polarized state and transversely polarized states. The $\phi$ meson in a thermal equilibrium system is preferred to occupy the state with spin $\lambda=0$ than those with spin $\lambda=\pm1$, because the former state has a smaller energy. As a consequence, we conclude that the spin alignment will be larger than 1/3 if one measures along the direction of the magnetic field, which is qualitatively consistent with the recent STAR data. Around the critical temperature $T_{C}=150$ MeV, the positive deviation from 1/3 is proportional to the square of the magnetic field strength, which agrees with the result from the non-relativistic coalescence model. Including the anomalous magnetic moments for quarks will modify the dynamical masses of quarks and thus affect the mass spectra and spin alignment of $\phi$ mesons. The discussion of spin alignment in the NJL model may help us better understand the formation of hadron's spin structure during the chiral phase transition., Comment: 12 pages, 15 figures

Published
2022

35. Association of remnant cholesterol with renal function and its progression in patients with type 2 diabetes related chronic kidney disease

Author

Qiuhong Li, Tongdan Wang, Xian Shao, Xiaoguang Fan, Yao Lin, Zhuang Cui, Hongyan Liu, Saijun Zhou, and Pei Yu

Subjects
remnant cholesterol, renal function, renal function progression, type 2 diabetes, chronic kidney disease, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract

BackgroundThe association of Remnant cholesterol (RC) with renal function and its progression in patients with Type 2 diabetes (T2DM) related chronic kidney disease (CKD) remains unclear.Methods8,678 patients with T2DM-related CKD were included in cross-sectional analysis, and 6,165 patients were enrolled in longitudinal analysis and followed up for a median of 36.0 months. The outcomes were renal composite endpoint event and rapid progression of renal function.Results24.54% developed a renal composite endpoint event, and 27.64% rapid progression of renal function. RC levels above 0.56 mmol/L independently increased the risk of both renal composite endpoint (HR, 1.17; 95% CIs, 1.03-1.33) and rapid progression of renal function (OR, 1.17; 95% CIs, 1.01- 1.37). TG levels above 1.65 mmol/L only increased the risk of renal composite endpoint (HR, 1.16; 95% CIs, 1.02 -1.32). TC levels above 5.21 mmol/L increased the risk of renal composite endpoint (HR, 1.14; 95% CIs, 1.01-1.29) only in patients with proteinuria≥0.5g/d. Conversely, HDL-C levels below 1.20 mmol/L or above 1.84 mmol/L increased the risk of rapid progression of renal function (OR, 0.88; 95% CIs, 0.70 -0.99) in patients with proteinuria

Published
2024
Full Text
View/download PDF

36. GutUDB: A comprehensive multiomics database for intestinal diseases

Author

Yi Bao, Yaxin Chen, Lizhu Lin, Jingyi Li, Xinli Liu, Gang Wang, Yueqi Li, Yao Lin, Yajing Chen, Lijuan Zhou, Yawen Qi, Yufang Xie, Zhenrui Lin, Zhe Sun, Yuwen Fan, Jinjing Jiang, Feiyu Zhang, Hubin Chen, Jiemei Chu, Jiegang Huang, Xuena Chen, Hao Liang, Shuaiyi Liang, and Sanqi An

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Published
2024
Full Text
View/download PDF

37. 20.1 NVE: A 3nm 23.2TOPS/W 12b-Digital-CIM-Based Neural Engine for High-Resolution Visual-Quality Enhancement on Smart Devices.

Author

Ming-En Shih, Shih-Wei Hsieh, Ping-Yuan Tsai, Ming-Hung Lin, Pei-Kuei Tsung, En-Jui Chang, Jenwei Liang, Shu-Hsin Chang, Chung-Lun Huang, You-Yu Nian, Zhe Wan, Sushil Kumar, Cheng-Xin Xue, Gajanan Jedhe, Hidehiro Fujiwara, Haruki Mori, Chih-Wei Chen, Po-Hua Huang, Chih-Feng Juan, Chung-Yi Chen, Tsung-Yao Lin, Ch Wang, Chih-Cheng Chen, and Kevin Jou

Published
2024
Full Text
View/download PDF

42. CCMB: A Large-scale Chinese Cross-modal Benchmark

Author

Xie, Chunyu, Cai, Heng, Li, Jincheng, Kong, Fanjing, Wu, Xiaoyu, Song, Jianfei, Morimitsu, Henrique, Yao, Lin, Wang, Dexin, Zhang, Xiangzheng, Leng, Dawei, Zhang, Baochang, Ji, Xiangyang, and Deng, Yafeng

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Vision-language pre-training (VLP) on large-scale datasets has shown premier performance on various downstream tasks. In contrast to plenty of available benchmarks with English corpus, large-scale pre-training datasets and downstream datasets with Chinese corpus remain largely unexplored. In this work, we build a large-scale high-quality Chinese Cross-Modal Benchmark named CCMB for the research community, which contains the currently largest public pre-training dataset Zero and five human-annotated fine-tuning datasets for downstream tasks. Zero contains 250 million images paired with 750 million text descriptions, plus two of the five fine-tuning datasets are also currently the largest ones for Chinese cross-modal downstream tasks. Along with the CCMB, we also develop a VLP framework named R2D2, applying a pre-Ranking + Ranking strategy to learn powerful vision-language representations and a two-way distillation method (i.e., target-guided Distillation and feature-guided Distillation) to further enhance the learning capability. With the Zero and the R2D2 VLP framework, we achieve state-of-the-art performance on twelve downstream datasets from five broad categories of tasks including image-text retrieval, image-text matching, image caption, text-to-image generation, and zero-shot image classification. The datasets, models, and codes are available at https://github.com/yuxie11/R2D2

Published
2022
Full Text
View/download PDF

46. The Effect and Satisfaction of Mobile Network in the Hypertension Management of Community-dwelling Older Adults

Author

YAO lin, SHANG Danmei, ZHAO Hui, LIU Xinyu, LIU Yongwei, JIANG Yong

Subjects
hypertension, chronic disease, community chronic disease management, aged, mobile network, patient satisfaction, treatment outcome, Medicine
Abstract

Background In the context of the deep integration of information technology with various industries, as well as the strong promotion of the development of smart healthcare by the country, the management of chronic diseases in community has also been gradually explored and transformed from the traditional mode to the informationized and intelligent management mode. In the case of deep aging gradually, it is necessary to clarify whether informationized chronic disease management is effective for the elderly population.Objective To understand the role of mobile network in the management of chronic diseases in the elderly, so as to provide reference for research on informationized management of related chronic diseases.Methods Using convenience sampling method, a total of 650 elderly hypertensive patients registered with community health service centers in four communities under the jurisdiction of Linghe District, Jinzhou City were selected as study subjects from January to July 2022. The study subjects were divided into the intervention group and control group based on the principle of prioritizing the proximity of living location under informed voluntary consent, with 325 cases in each group. The control group was treated with routine community chronic disease management and face-to-face follow-up once every two months. The intervention group was treated with network hypertension management based on the control group, with an intervention of 6 months. The effects were evaluated using the Hypertension Knowledge Level Scale (HK-LS), Therapeutic Adherence Scale for Hypertensive Patients (TASHP), and Hypertension Patients Self-Management Behavior Rating Scale (HPSMBRS), and a satisfaction survey was conducted.Results After the intervention, the scores of HK-LS, TASHP and HPSMBRS dimensions in the intervention group were higher than those in the control group (P

Published
2024
Full Text
View/download PDF

47. Associations between the artificial intelligence scoring system and live birth outcomes in preimplantation genetic testing for aneuploidy cycles

Author

Chun-I Lee, Chun-Chia Huang, Tsung-Hsien Lee, Hsiu-Hui Chen, En-Hui Cheng, Pin-Yao Lin, Tzu-Ning Yu, Chung-I Chen, Chien-Hong Chen, and Maw-Sheng Lee

Subjects
iDAScore, Artificial intelligence, Live birth, Single embryo transfer, Preimplantation genetic testing for aneuploidy, Gynecology and obstetrics, RG1-991, Reproduction, QH471-489
Abstract

Abstract Background Several studies have demonstrated that iDAScore is more accurate in predicting pregnancy outcomes in cycles without preimplantation genetic testing for aneuploidy (PGT-A) compared to KIDScore and the Gardner criteria. However, the effectiveness of iDAScore in cycles with PGT-A has not been thoroughly investigated. Therefore, this study aims to assess the association between artificial intelligence (AI)-based iDAScore (version 1.0) and pregnancy outcomes in single-embryo transfer (SET) cycles with PGT-A. Methods This retrospective study was approved by the Institutional Review Board of Chung Sun Medical University, Taichung, Taiwan. Patients undergoing SET cycles (n = 482) following PGT-A at a single reproductive center between January 2017 and June 2021. The blastocyst morphology and morphokinetics of all embryos were evaluated using a time-lapse system. The blastocysts were ranked based on the scores generated by iDAScore, which were defined as AI scores, or by KIDScore D5 (version 3.2) following the manufacturer’s protocols. A single blastocyst without aneuploidy was transferred after examining the embryonic ploidy status using a next-generation sequencing-based PGT-A platform. Logistic regression analysis with generalized estimating equations was conducted to assess whether AI scores are associated with the probability of live birth (LB) while considering confounding factors. Results Logistic regression analysis revealed that AI score was significantly associated with LB probability (adjusted odds ratio [OR] = 2.037, 95% confidence interval [CI]: 1.632–2.542) when pulsatility index (PI) level and types of chromosomal abnormalities were controlled. Blastocysts were divided into quartiles in accordance with their AI score (group 1: 3.0–7.8; group 2: 7.9–8.6; group 3: 8.7–8.9; and group 4: 9.0–9.5). Group 1 had a lower LB rate (34.6% vs. 59.8–72.3%) and a higher rate of pregnancy loss (26% vs. 4.7–8.9%) compared with the other groups (p

Published
2024
Full Text
View/download PDF

48. Temporal coherence in particulate matter in East Asian outflow regions: fingerprints of ENSO and Asian dust

Author

Mien-Tze Kueh, Chuan-Yao Lin, and Yi-Yun Chien

Subjects
Environmental sciences, GE1-350, Meteorology. Climatology, QC851-999
Abstract

Abstract Understanding temporal coherence in particulate matter (PM) pollution across East Asian outflow regions is crucial for collaborative efforts in addressing air pollution challenges. In this study, we analyze daily PM exceedance patterns using ground-based measurements over the past two decades, focusing on the influence of El Niño-Southern Oscillation (ENSO) and Asian dust activity. ENSO-related coherent PM exceedance shows lower occurrence during El Niño compared to La Niña in spring, due to weakened continental outflows and increased precipitation. Temporal distribution of dust-related coherent PM exceedance closely mirrors springtime dust activity in source regions, indicating significant dust emission impact on outflow regions. Our findings have implications for climate variability and the assessment of multi-decade model simulations.

Published
2023
Full Text
View/download PDF

49. ESRP1-driven alternative splicing of CLSTN1 inhibits the metastasis of gastric cancer

Author

Chengguo Li, Yuping Yin, Ruikang Tao, Yao Lin, Tao Wang, Qian Shen, Runze Li, Kaixiong Tao, and Weizhen Liu

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282, Cytology, QH573-671
Abstract

Abstract Tumor metastasis severely limits the prognosis of gastric cancer patients. RNA-binding proteins (RBPs) are crucial in tumor metastasis, yet there is limited research into their involvement in gastric cancer. Here, we found that ESRP1, a RBP specific in epithelial cells, is important in regulating the metastasis of gastric cancer cells. ESRP1 is negatively correlated with distant metastasis and lymph node metastasis in gastric cancer patients. And we demonstrated that ESRP1 inhibit migration and invasion of gastric cancer in vitro and in vivo. Mechanistically, ESRP1 promotes exon 11 alternative splicing of CLSTN1 pre-mRNA. The post-splicing short CLSTN1 stabilizes the Ecadherin/β-catenin binding structure, and promotes β-catenin protein ubiquitination and degradation, thereby inhibiting the migration and invasion of gastric cancer cells. Our study highlights the role of ESRP1 in regulating metastasis of gastric cancer and extends its mechanism. These results provide a possibility for ESRP1 and CLSTN1 to become therapeutic targets for metastasis of gastric cancer.

Published
2023
Full Text
View/download PDF

50. Efficacy of Multifocal Soft Contact Lenses in Reducing Myopia Progression Among Taiwanese Schoolchildren: A Randomized Paired-Eye Clinical Trial

Author

Yao-Lin Liu, Ken-Kuo Lin, Li-Sheng Cheng, Chao-Wen Lin, Jiahn-Shing Lee, Chiun-Ho Hou, and Tzu-Hsun Tsai

Subjects
Axial length, Children, Myopia control, Multifocal soft contact lens, Peripheral myopic defocus, Ophthalmology, RE1-994
Abstract

Abstract Introduction To evaluate the efficacy and safety of myopia control using a multifocal soft contact lens designed with high peripheral add power in schoolchildren. Methods This 1-year multi-center, prospective, randomized, double-blind, controlled study enrolled myopic schoolchildren aged 6–15 years with refractive errors between − 1.0 D and − 10.0 D. Each participant was randomly allocated to wear a daily disposable multifocal soft contact lens as the treatment in one eye and a single-vision soft contact lens as the control in the other eye. The primary endpoints were changes in the cycloplegic spherical equivalent (SE) and axial length at 1 year. Results Fifty-two of the 59 participants (88.1%) completed the study protocol. The mean change in SE was − 0.73 ± 0.40 D in the treatment group. and − 0.85 ± 0.51 D in the control group (mean difference: − 0.12 ± 0.34 D, p = 0.012). The mean change in axial length was 0.25 ± 0.14 mm in the treatment group, and 0.33 ± 0.17 mm in the control group (mean difference: 0.08 ± 0.10 mm, p

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results