ArXiv Pub Date : 2024-11-19

Xiao Hu, Ziqi Chen, Bo Peng, Daniel Adu-Ampratwum, Xia Ning

{"title":"log-RRIM: Yield Prediction via Local-to-global Reaction Representation Learning and Interaction Modeling.","authors":"Xiao Hu, Ziqi Chen, Bo Peng, Daniel Adu-Ampratwum, Xia Ning","doi":"","DOIUrl":"","url":null,"abstract":"Accurate prediction of chemical reaction yields is crucial for optimizing organic synthesis, potentially reducing time and resources spent on experimentation. With the rise of artificial intelligence (AI), there is growing interest in leveraging AI-based methods to accelerate yield predictions without conducting in vitro experiments. We present log-RRIM, an innovative graph transformer-based framework designed for predicting chemical reaction yields. Our approach implements a unique local-to-global reaction representation learning strategy. This approach initially captures detailed molecule-level information and then models and aggregates intermolecular interactions, ensuring that the impact of varying-sizes molecular fragments on yield is accurately accounted for. Another key feature of log-RRIM is its integration of a cross-attention mechanism that focuses on the interplay between reagents and reaction centers. This design reflects a fundamental principle in chemical reactions: the crucial role of reagents in influencing bond-breaking and formation processes, which ultimately affect reaction yields. log-RRIM outperforms existing methods in our experiments, especially for medium to high-yielding reactions, proving its reliability as a predictor. Its advanced modeling of reactant-reagent interactions and sensitivity to small molecular fragments make it a valuable tool for reaction planning and optimization in chemical synthesis. The data and codes of log-RRIM are accessible through https://github.com/ninglab/YieldlogRRIM.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11601803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-unique water and contrast agent solutions in dual-energy CT. 双能 CT 中的非独特水和造影剂溶液。

ArXiv Pub Date : 2024-11-19

J P Phillips, Emil Y Sidky, Fatma Terzioglu, Ingrid S Reiser, Guillaume Bal, Xiaochuan Pan

引用次数: 0

Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Model. 基于三维扩散模型的头颈部[18F]F-FDG PET/CT 图像的肿瘤分割。

ArXiv Pub Date : 2024-11-19

Yafei Dong, Kuang Gong

{"title":"Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Model.","authors":"Yafei Dong, Kuang Gong","doi":"","DOIUrl":"","url":null,"abstract":"Head and neck (H&N) cancers are among the most prevalent types of cancer worldwide, and [18F]F-FDG PET/CT is widely used for H&N cancer management. Recently, the diffusion model has demonstrated remarkable performance in various image-generation tasks. In this work, we proposed a 3D diffusion model to accurately perform H&N tumor segmentation from 3D PET and CT volumes. The 3D diffusion model was developed considering the 3D nature of PET and CT images acquired. During the reverse process, the model utilized a 3D UNet structure and took the concatenation of PET, CT, and Gaussian noise volumes as the network input to generate the tumor mask. Experiments based on the HECKTOR challenge dataset were conducted to evaluate the effectiveness of the proposed diffusion model. Several state-of-the-art techniques based on U-Net and Transformer structures were adopted as the reference methods. Benefits of employing both PET and CT as the network input as well as further extending the diffusion model from 2D to 3D were investigated based on various quantitative metrics and the uncertainty maps generated. Results showed that the proposed 3D diffusion model could generate more accurate segmentation results compared with other methods. Compared to the diffusion model in 2D format, the proposed 3D model yielded superior results. Our experiments also highlighted the advantage of utilizing dual-modality PET and CT data over only single-modality data for H&N tumor segmentation.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10862928/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139731192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

bia-binder: A web-native cloud compute service for the bioimage analysis community. bia-binder：面向生物图像分析界的网络原生云计算服务。

ArXiv Pub Date : 2024-11-19

Craig T Russell, Jean-Marie Burel, Awais Athar, Simon Li, Ugis Sarkans, Jason Swedlow, Alvis Brazma, Matthew Hartley, Virginie Uhlmann

引用次数: 0

Feasibility of PET-enabled dual-energy CT imaging: First physical phantom and initial patient results. PET 双能量 CT 成像的可行性：首个物理模型和患者结果。

ArXiv Pub Date : 2024-11-19

Yansong Zhu, Siqi Li, Zhaoheng Xie, Edwin K Leung, Reimund Bayerlein, Negar Omidvari, Yasser G Abdelhafez, Simon R Cherry, Jinyi Qi, Ramsey D Badawi, Benjamin A Spencer, Guobao Wang

{"title":"Feasibility of PET-enabled dual-energy CT imaging: First physical phantom and initial patient results.","authors":"Yansong Zhu, Siqi Li, Zhaoheng Xie, Edwin K Leung, Reimund Bayerlein, Negar Omidvari, Yasser G Abdelhafez, Simon R Cherry, Jinyi Qi, Ramsey D Badawi, Benjamin A Spencer, Guobao Wang","doi":"","DOIUrl":"","url":null,"abstract":"X-ray computed tomography (CT) in PET/CT is commonly operated with a single energy, resulting in a limitation of lacking tissue composition information. Dual-energy (DE) spectral CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging, but would either require hardware upgrade or increase radiation dose due to the added second x-ray CT scan. Recently proposed PET-enabled DECT method allows dual-energy spectral imaging using a conventional PET/CT scanner without the need for a second x-ray CT scan. A gamma-ray CT (gCT) image at 511 keV can be generated from the existing time-of-flight PET data with the maximum-likelihood attenuation and activity (MLAA) approach and is then combined with the low-energy x-ray CT image to form dual-energy spectral imaging. To improve the image quality of gCT, a kernel MLAA method was further proposed by incorporating x-ray CT as a priori information. The concept of this PET-enabled DECT has been validated using simulation studies, but not yet with 3D real data. In this work, we developed a general open-source implementation for gCT reconstruction from PET data and use this implementation for the first real data validation with both a physical phantom study and a human subject study on a uEXPLORER total-body PET/CT system. These results have demonstrated the feasibility of this method for spectral imaging and material decomposition.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10862937/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139731190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating Secondary Structures Information into Triangular Spatial Relationships (TSR) for Advanced Protein Classification. 将二级结构信息整合到三角空间关系（TSR）中，实现高级蛋白质分类。

ArXiv Pub Date : 2024-11-19

Poorya Khajouie, Titli Sarkar, Krishna Rauniyar, Li Chen, Wu Xu, Vijay Raghavan

{"title":"Integrating Secondary Structures Information into Triangular Spatial Relationships (TSR) for Advanced Protein Classification.","authors":"Poorya Khajouie, Titli Sarkar, Krishna Rauniyar, Li Chen, Wu Xu, Vijay Raghavan","doi":"","DOIUrl":"","url":null,"abstract":"Protein structures represent the key to deciphering biological functions. The more detailed form of similarity among these proteins is sometimes overlooked by the conventional structural comparison methods. In contrast, further advanced methods, such as Triangular Spatial Relationship (TSR), have been demonstrated to make finer differentiations. Still, the classical implementation of TSR does not provide for the integration of secondary structure information, which is important for a more detailed understanding of the folding pattern of a protein. To overcome these limitations, we developed the SSE-TSR approach. The proposed method integrates secondary structure elements (SSEs) into TSR-based protein representations. This allows an enriched representation of protein structures by considering 18 different combinations of helix, strand, and coil arrangements. Our results show that using SSEs improves the accuracy and reliability of protein classification to varying degrees. We worked with two large protein datasets of 9.2K and 7.8K samples, respectively. We applied the SSE-TSR approach and used a neural network model for classification. Interestingly, introducing SSEs improved performance statistics for Dataset 1, with accuracy moving from 96.0% to 98.3%. For Dataset 2, where the performance statistics were already good, further small improvements were found with the introduction of SSE, giving an accuracy of 99.5% compared to 99.4%. These results show that SSE integration can dramatically improve TSR key discrimination, with significant benefits in datasets with low initial accuracies and only incremental gains in those with high baseline performance. Thus, SSE-TSR is a powerful bioinformatics tool that improves protein classification and understanding of protein function and interaction.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11601798/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Consensus Statement on Brillouin Light Scattering Microscopy of Biological Materials. 生物材料布里渊光散射显微镜共识声明。

ArXiv Pub Date : 2024-11-18

Pierre Bouvet, Carlo Bevilacqua, Yogeshwari Ambekar, Giuseppe Antonacci, Joshua Au, Silvia Caponi, Sophie Chagnon-Lessard, Juergen Czarske, Thomas Dehoux, Daniele Fioretto, Yujian Fu, Jochen Guck, Thorsten Hamann, Dag Heinemann, Torsten Jähnke, Hubert Jean-Ruel, Irina Kabakova, Kristie Koski, Nektarios Koukourakis, David Krause, Salvatore La Cavera, Timm Landes, Jinhao Li, Jeremie Margueritat, Maurizio Mattarelli, Michael Monaghan, Darryl R Overby, Fernando Perez-Cota, Emanuele Pontecorvo, Robert Prevedel, Giancarlo Ruocco, John Sandercock, Giuliano Scarcelli, Filippo Scarponi, Claudia Testi, Peter Török, Lucie Vovard, Wolfgang Weninger, Vladislav Yakovlev, Seok-Hyun Yun, Jitao Zhang, Francesca Palombo, Alberto Bilenca, Kareem Elsayad

{"title":"Consensus Statement on Brillouin Light Scattering Microscopy of Biological Materials.","authors":"Pierre Bouvet, Carlo Bevilacqua, Yogeshwari Ambekar, Giuseppe Antonacci, Joshua Au, Silvia Caponi, Sophie Chagnon-Lessard, Juergen Czarske, Thomas Dehoux, Daniele Fioretto, Yujian Fu, Jochen Guck, Thorsten Hamann, Dag Heinemann, Torsten Jähnke, Hubert Jean-Ruel, Irina Kabakova, Kristie Koski, Nektarios Koukourakis, David Krause, Salvatore La Cavera, Timm Landes, Jinhao Li, Jeremie Margueritat, Maurizio Mattarelli, Michael Monaghan, Darryl R Overby, Fernando Perez-Cota, Emanuele Pontecorvo, Robert Prevedel, Giancarlo Ruocco, John Sandercock, Giuliano Scarcelli, Filippo Scarponi, Claudia Testi, Peter Török, Lucie Vovard, Wolfgang Weninger, Vladislav Yakovlev, Seok-Hyun Yun, Jitao Zhang, Francesca Palombo, Alberto Bilenca, Kareem Elsayad","doi":"","DOIUrl":"","url":null,"abstract":"Brillouin Light Scattering (BLS) spectroscopy is a non-invasive, non-contact, label-free optical technique that can provide information on the mechanical properties of a material on the sub-micron scale. Over the last decade it has seen increased applications in the life sciences, driven by the observed significance of mechanical properties in biological processes, the realization of more sensitive BLS spectrometers and its extension to an imaging modality. As with other spectroscopic techniques, BLS measurements not only detect signals characteristic of the investigated sample, but also of the experimental apparatus, and can be significantly affected by measurement conditions. The aim of this consensus statement is to improve the comparability of BLS studies by providing reporting recommendations for the measured parameters and detailing common artifacts. Given that most BLS studies of biological matter are still at proof-of-concept stages and use different--often self-built--spectrometers, a consensus statement is particularly timely to assure unified advancement.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11601801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable AI for computational pathology identifies model limitations and tissue biomarkers. 用于计算病理学的可解释人工智能可识别模型限制和组织生物标记。

ArXiv Pub Date : 2024-11-18

Jakub R Kaczmarzyk, Joel H Saltz, Peter K Koo

{"title":"Explainable AI for computational pathology identifies model limitations and tissue biomarkers.","authors":"Jakub R Kaczmarzyk, Joel H Saltz, Peter K Koo","doi":"","DOIUrl":"","url":null,"abstract":"Introduction: Deep learning models hold great promise for digital pathology, but their opaque decision-making processes undermine trust and hinder clinical adoption. Explainable AI methods are essential to enhance model transparency and reliability.Methods: We developed HIPPO, an explainable AI framework that systematically modifies tissue regions in whole slide images to generate image counterfactuals, enabling quantitative hypothesis testing, bias detection, and model evaluation beyond traditional performance metrics. HIPPO was applied to a variety of clinically important tasks, including breast metastasis detection in axillary lymph nodes, prognostication in breast cancer and melanoma, and IDH mutation classification in gliomas. In computational experiments, HIPPO was compared against traditional metrics and attention-based approaches to assess its ability to identify key tissue elements driving model predictions.Results: In metastasis detection, HIPPO uncovered critical model limitations that were undetectable by standard performance metrics or attention-based methods. For prognostic prediction, HIPPO outperformed attention by providing more nuanced insights into tissue elements influencing outcomes. In a proof-of-concept study, HIPPO facilitated hypothesis generation for identifying melanoma patients who may benefit from immunotherapy. In IDH mutation classification, HIPPO more robustly identified the pathology regions responsible for false negatives compared to attention, suggesting its potential to outperform attention in explaining model decisions.Conclusions: HIPPO expands the explainable AI toolkit for computational pathology by enabling deeper insights into model behavior. This framework supports the trustworthy development, deployment, and regulation of weakly-supervised models in clinical and research settings, promoting their broader adoption in digital pathology.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11398542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142303247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Twin Peak Method for Estimating Tissue Viscoelasticity using Shear Wave Elastography. 利用剪切波弹性成像估算组织粘弹性的双峰法

ArXiv Pub Date : 2024-11-18

Shuvrodeb Adikary, Matthew W Urban, Murthy N Guddati

{"title":"Twin Peak Method for Estimating Tissue Viscoelasticity using Shear Wave Elastography.","authors":"Shuvrodeb Adikary, Matthew W Urban, Murthy N Guddati","doi":"","DOIUrl":"","url":null,"abstract":"Tissue viscoelasticity is becoming an increasingly useful biomarker beyond elasticity and can theoretically be estimated using shear wave elastography (SWE), by inverting the propagation and attenuation characteristics of shear waves. Estimating viscosity is often more difficult than elasticity because attenuation, the main effect of viscosity, leads to poor signal-to-noise ratio of the shear wave motion. In the present work, we provide an alternative to existing methods of viscoelasticity estimation that is robust against noise. The method minimizes the difference between simulated and measured versions of two sets of peaks (twin peaks) in the frequency-wavenumber domain, obtained first by traversing through each frequency and then by traversing through each wavenumber. The slopes and deviation of the twin peaks are sensitive to elasticity and viscosity respectively, leading to the effectiveness of the proposed inversion algorithm for characterizing mechanical properties. This expected effectiveness is confirmed through in silico verification, followed by ex vivo validation and in vivo application, indicating that the proposed approach can be effectively used in accurately estimating viscoelasticity, thus potentially contributing to the development of enhanced biomarkers.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11601804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Finding high posterior density phylogenies by systematically extending a directed acyclic graph. 通过系统扩展有向无环图寻找高后代密度系统发育。

ArXiv Pub Date : 2024-11-18

Chris Jennings-Shaffer, David H Rich, Matthew Macaulay, Michael D Karcher, Tanvi Ganapathy, Shosuke Kiami, Anna Kooperberg, Cheng Zhang, Marc A Suchard, Frederick A Matsen

{"title":"Finding high posterior density phylogenies by systematically extending a directed acyclic graph.","authors":"Chris Jennings-Shaffer, David H Rich, Matthew Macaulay, Michael D Karcher, Tanvi Ganapathy, Shosuke Kiami, Anna Kooperberg, Cheng Zhang, Marc A Suchard, Frederick A Matsen","doi":"","DOIUrl":"","url":null,"abstract":"Bayesian phylogenetics typically estimates a posterior distribution, or aspects thereof, using Markov chain Monte Carlo methods. These methods integrate over tree space by applying local rearrangements to move a tree through its space as a random walk. Previous work explored the possibility of replacing this random walk with a systematic search, but was quickly overwhelmed by the large number of probable trees in the posterior distribution. In this paper we develop methods to sidestep this problem using a recently introduced structure called the subsplit directed acyclic graph (sDAG). This structure can represent many trees at once, and local rearrangements of trees translate to methods of enlarging the sDAG. Here we propose two methods of introducing, ranking, and selecting local rearrangements on sDAGs to produce a collection of trees with high posterior density. One of these methods successfully recovers the set of high posterior density trees across a range of data sets. However, we find that a simpler strategy of aggregating trees into an sDAG in fact is computationally faster and returns a higher fraction of probable trees.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11601806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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