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Effect of TiO2concentration in PVDF-TiO2-PVP mixed matrix membrane performance using ultrafiltration process 4

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Effect of TiO2concentration in PVDF-TiO2-PVP mixed matrix me...

4th International Conference on Separation Technology, ICoST 2023

作者： Gayatri, Rianyza Yuliwati, Erna Noor Syimir Fizal, Ahmad Zulhilmi Zailani, Muhamad Jaafar, Juhana Zulkifli, Muzafar Taweepreda, Wirach Naim Ahmad Yahaya, Ahmad Universiti Kuala Lumpur Malaysian Institute of Chemical and Bioengineering Technology Alor Gajah Melaka78000 Malaysia Hat Yai Songkla90110 Thailand Program Study of Chemical Engineering Faculty of Engineering Universitas Muhammadiyah Palembang Jalan A.Yani 13 Ulu Kota Palembang30263 Indonesia Faculty of Chemical and Energy Engineering Universiti Teknologi Malaysia Universiti Malaysia Pahang Lebuh Persiaran Tun Khalil Yaakob Pahang Gambang26300 Malaysia

This study investigated how the addition of hydrophilic TiO2to a porous asymmetric polyvinylidene fluoride (PVDF) Ultrafiltration (UF) membrane altered its properties and performance. The hydrophilicity and flux of PVDF and polyvinylpyrrolidone (PVP) combinations will be lower, and TiO2will significantly impact raising those values. By mixing titanium dioxide (TiO2) nanoparticles with a pore-forming agent in a dope solution, PVDF-TiO2-PVP membranes were prepared via non-solvent-induced phase inversion method. Experiments using PVDF membranes with varying concentrations of TiO2(0, 0.25, 0.5, 1, 1.5, and 2% wt) were applied to evaluate pure water flux and Bovine Serum Albumin (BSA) flux. The functional group composition of membranes was investigated by Fourier transform infrared spectroscopy. The wettability of porous membranes was determined by measuring contact angles. Results demonstrated that membranes constructed from PVDF/PVP/TiO2with a lower loading of TiO2nanoparticles had a smaller mean pore size, more apertures inside the membrane, and better membrane hydrophilicity. The highest flux of pure water (121 L/m2h) was achieved by the PVDF-TiO2-PVP mixed-matrix membrane at a TiO2concentration of 2 wt%. © 2024 Elsevier Ltd. All rights reserved.

关键词： Titanium dioxide

FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning

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arXiv 2023年

作者： Sun, Guangyu Mendieta, Matias Luo, Jun Wu, Shandong Chen, Chen Center for Research in Computer Vision University of Central Florida United States Intelligent Systems Program University of Pittsburgh United States Department of Radiology Biomedical Informatics and Bioengineering University of Pittsburgh United States

Personalized Federated Learning (PFL) represents a promising solution for decentralized learning in heterogeneous data environments. Partial model personalization has been proposed to improve the efficiency of PFL by selectively updating local model parameters instead of aggregating all of them. However, previous work on partial model personalization has mainly focused on Convolutional Neural Networks (CNNs), leaving a gap in understanding how it can be applied to other popular models such as Vision Transformers (ViTs). In this work, we investigate where and how to partially personalize a ViT model. Specifically, we empirically evaluate the sensitivity to data distribution of each type of layer. Based on the insights that the self-attention layer and the classification head are the most sensitive parts of a ViT, we propose a novel approach called FedPerfix, which leverages plugins to transfer information from the aggregated model to the local client as a personalization. Finally, we evaluate the proposed approach on CIFAR-100, OrganAMNIST, and Office-Home datasets and demonstrate its effectiveness in improving the model’s performance compared to several advanced PFL methods. Code is available at https://***/imguangyu/FedPerfix Copyright © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

Quantitative Imaging of 55CO and 18F-Labeled Tracers in a Single “Multiplexed” Pet Imaging Session

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Quantitative Imaging of 55CO and 18F-Labeled Tracers in a Si...

IEEE International Symposium on Biomedical Imaging

作者： Sarah J. Zou Irene Lim Jackson W. Foster Garry Chinn Hailey A. Houson Suzanne E. Lapi Jianghong Rao Craig S. Levin Department of Electrical Engineering Stanford University Department of Radiology and Molecular Imaging Program at Stanford (MIPS) Stanford University Chemistry and Biochemistry Department Bates College Department of Radiology University of Alabama at Birmingham Department of Bioengineering Stanford University Department of Physics Stanford University

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

In this study, we explore the use of Cobalt-55 as a radioisotope for multiplexed PET (mPET) by utilizing its emission of a prompt gamma-ray in cascade with a positron during decay. We leverage the prompt-gamma signal to generate triple coincidences for a Co-55-labeled tracer, allowing us to distinguish it from a tracer labeled with a pure positron emitter F-18, which yields a two photon coincidence. By employing this triple versus double coincidence detection and signal processing methodology, we show the potential to separate and quantify the Co-55 signal from that of F-18. Phantom studies showed strong linear correlation $(R^{2}=0.99)$ between Co-55 double and triple coincidence counts and total Co-55 activity. Finally, we highlight the capability of this method to quantify overlapping and non-overlapping hot spots within a warm background to within a 3 to 15% error range. Moreover, we demonstrate the feasibility of simultaneously imaging two tracers in vivo during a single PET session using mice.

关键词： Multiplexing Sensitivity Translation Positrons Biomarkers Recording Labeling Positron emission tomography Isotopes Cancer

Substance Use and Outcomes of Left-Sided Valve Replacement in Patients With Infective Endocarditis

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Annals of thoracic surgery short reports

Annals of thoracic surgery short reports 2024年第4期2卷 759-764页

作者： Katie J Hogan Christopher B Sylvester Travis J Miles Matthew Wall Todd K Rosengart Marc R Moon Joseph S Coselli Subhasis Chatterjee Ravi K Ghanta Michael E. DeBakey Department of Surgery Baylor College of Medicine Houston Texas. Medical Scientist Training Program Baylor College of Medicine Houston Texas. Department of Bioengineering Rice University Houston Texas. Department of Cardiovascular Surgery The Texas Heart Institute Houston Texas.

Background:Rising rates of substance use (SU) have resulted in an increasing need for left-sided valve surgery for SU-associated infective endocarditis (SU-IE). We compared outcomes, readmissions, and costs between IE patients with and without SU-IE in a national cohort. Methods:Using the Nationwide Readmissions Database (2016-2018), we identified 10,098 patients with infective endocarditis (IE) who underwent isolated aortic or mitral valve replacement. Outcomes within the same calendar year as the index operation were compared between patients with and without SU-IE. Multivariable logistic regressions were used to identify factors associated with in-hospital mortality and 30-day and 90-day readmissions. Kaplan-Meier analysis and a Cox proportional hazards model were used to compare freedom from calendar-year readmission between the groups. Results:Of the 10,098 patients with IE, 2145 (21%) had SU-IE. Although patients with SU-IE were younger (38 years vs 60 years; < .001) and had fewer comorbidities (Elixhauser score: 12 vs 20; < .001) than patients who did not have SU-IE, patients with SU-IE had longer hospital stays (25 days vs 18 days; < .001) and costlier admissions ($84,949 vs $74,122; < .001). Patients with SU-IE had less in-hospital mortality (3.0% vs 5.8%; < .001) but more often died when readmitted (9.6% vs 4.6%; < .001). Readmissions were similar at 30 days (18.5% vs 18.9%; = .8) and 90 days (31.8% vs 29.3%; = .2), but patients with SU-IE had more calendar-year readmissions (35.1% vs 31.0%; < .018). Conclusions:Despite their younger age and fewer comorbidities, patients who undergo valve surgery for SU-IE use more resources and more often have calendar-year readmissions than patients with IE but without SU. Strategies are needed to expedite discharge and prevent readmission in patients with SU-IE.

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Adversarially Robust Feature Learning for Breast Cancer Diagnosis

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arXiv 2024年

作者： Hao, Degan Arefan, Dooman Zuley, Margarita Berg, Wendie Wu, Shandong Intelligent Systems Program University of Pittsburgh Pittsburgh United States Department of Radiology University of Pittsburgh Pittsburgh United States Department of Bioengineering University of Pittsburgh Pittsburgh United States Department of Biomedical Informatics University of Pittsburgh Pittsburgh United States

Adversarial data can lead to malfunction of deep learning applications. It is essential to develop deep learning models that are robust to adversarial data while accurate on standard, clean data. In this study, we proposed a novel adversarially robust feature learning (ARFL) method for a real-world application of breast cancer diagnosis. ARFL facilitates adversarial training using both standard data and adversarial data, where a feature correlation measure is incorporated as an objective function to encourage learning of robust features and restrain spurious features. To show the effects of ARFL in breast cancer diagnosis, we built and evaluated diagnosis models using two independent clinically collected breast imaging datasets, comprising a total of 9,548 mammogram images. We performed extensive experiments showing that our method outperformed several state-of-the-art methods and that our method can enhance safer breast cancer diagnosis against adversarial attacks in clinical settings. © 2024, CC BY.

关键词： Deep learning

Data Format Standardization and DICOM Integration for Hyperpolarized 13C MRI

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arXiv 2024年

作者： Diaz, Ernesto Sriram, Renuka Gordon, Jeremy W. Sinha, Avantika Liu, Xiaoxi Sahin, Sule Crane, Jason Olson, Marram P. Chen, Hsin-Yu Bernard, Jenna Vigneron, Daniel B. Wang, Zhen Jane Xu, Duan Larson, Peder E.Z. Department of Radiology and Biomedical Imaging University of California - San Francisco San FranciscoCA United States UC Berkeley-UCSF Graduate Program in Bioengineering University of California Berkeley United States University of California San FranciscoCA United States

Hyperpolarized (HP) 13C MRI has shown promise as a valuable modality for in vivo measurements of metabolism and is currently in human trials at 15 research sites worldwide. With this growth it is important to adopt standardized data storage practices as it will allow sites to meaningfully compare data. In this paper we (1) describe data that we believe should be stored and (2) demonstrate pipelines and methods that utilize the Digital Imaging and Communications in Medicine (DICOM) standard. This includes proposing a set of minimum set of information that is specific to HP 13C MRI studies. We then show where the majority of these can be fit into existing DICOM Attributes, primarily via the "Contrast/Bolus" module. We also demonstrate pipelines for utilizing DICOM for HP 13C MRI. DICOM is the most common standard for clinical medical image storage and provides the flexibility to accommodate the unique aspects of HP 13C MRI, including the HP agent information but also spectroscopic and metabolite dimensions. The pipelines shown include creating DICOM objects for studies on human and animal imaging systems with various pulse sequences. We also show a python-based method to efficiently modify DICOM objects to incorporate the unique HP 13C MRI information that is not captured by existing pipelines. Moreover, we propose best practices for HP 13C MRI data storage that will support future multi-site trials, research studies and technical developments of this imaging technique. © 2024, CC BY.

关键词： Magnetic resonance imaging

GranatumX:A Community-engaging, Modularized, and Flexible Webtool for Single-cell Data Analysis

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Genomics, Proteomics & Bioinformatics 2021年第3期19卷 452-460页

作者： David G.Garmire Xun Zhu Aravind Mantravadi Qianhui Huang Breck Yunits Yu Liu Thomas Wolfgruber Olivier Poirion Tianying Zhao Cédric Arisdakessian Stefan Stanojevic Lana X.Garmire Department of Electrical Engineering&Computer Science University of MichiganAnn ArborMI 48109USA Epidemiology Program University of Hawaii Cancer CenterHonoluluHI 96813USA Department of Biostatistics University of MichiganAnn ArborMI 48109USA Department of Computational Medicine and Bioinformatics University of MichiganAnn ArborMI 48105USA Molecular Biosciences and Bioengineering Graduate Program University of Hawaii at ManoaHonoluluHI 96822USA

We present GranatumX,a next-generation software environment for single-cell RNA sequencing(scRNA-seq)data *** is inspired by the interactive webtool *** enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical *** also offers software developers the opportunity to rapidly promote their own tools with others in customizable *** architecture of GranatumX allows for easy inclusion of plugin modules,named Gboxes,which wrap around bioinformatics tools written in various programming languages and on various *** can be run on the cloud or private servers and generate reproducible *** is a community-engaging,flexible,and evolving software ecosystem for scRNA-seq analysis,connecting developers with bench *** is freely accessible at http://***/granatumx/app.

关键词： Single-cell RNA sequencing Analysis Pipeline Webtool Module

COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentation

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arXiv 2025年

作者： Jo, Sanghyun Lee, Seo Jin Lee, Seungwoo Hong, Seohyung Seo, Hyungseok Kim, Kyungsu OGQ Seoul Korea Republic of Laboratory of Cell & Gene Therapy Institute of Pharmaceutical Sciences College of Pharmacy Seoul National University Korea Republic of Department of Biomedical Science and Medical Research Center College of Pharmacy Seoul National University Korea Republic of School of Transdisciplinary Innovations Interdisciplinary Program in Bioengineering Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

Cell instance segmentation (CIS) is crucial for identifying individual cell morphologies in histopathological images, providing valuable insights for biological and medical research. While unsupervised CIS (UCIS) models aim to reduce the heavy reliance on labor-intensive image annotations, they fail to accurately capture cell boundaries, causing missed detections and poor performance. Recognizing the absence of error-free instances as a key limitation, we present COIN (COnfidence score-guided INstance distillation), a novel annotation-free framework with three key steps: (1) Increasing the sensitivity for the presence of error-free instances via unsupervised semantic segmentation with optimal transport, leveraging its ability to discriminate spatially minor instances, (2) Instance-level confidence scoring to measure the consistency between model prediction and refined mask and identify highly confident instances, offering an alternative to ground truth annotations, and (3) Progressive expansion of confidence with recursive self-distillation. Extensive experiments across six datasets show COIN outperforming existing UCIS methods, even surpassing semi- and weakly-supervised approaches across all metrics on the MoNuSeg and TNBC datasets. The code is available at https://***/shjo-april/COIN. © 2025, CC BY.

关键词： Semantic Segmentation

Structure-guided Redesign of OxyR-based Optogenetic Reporters for Sensitive Monitoring of Intracellular H2O2 Real-time

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Structure-guided Redesign of OxyR-based Optogenetic Reporter...

Optics and the Brain, BRAIN 2023 - Part of Optica Biophotonics Congress: Optics in the Life Sciences 2023

作者： Lee, Justin Daho Wang, Yihan Nguyen, Amanda Berndt, Andre Molecular Engineering Ph.D. Program University of Washington SeattleWA98195 United States Department of Bioengineering University of Washington SeattleWA98195 United States Institute for Stem Cell and Regenerative Medicine University of Washington SeattleWA98109 United States Department of Pharmacology Center for Neuroscience of Addiction Pain and Emotion University of Washington SeattleWA98195 United States

OxyR is a protein exhibiting exceptional sensitivity and specificity towards H2O2. However, OxyR-based optogenetic sensors do not closely recapitulate the native sensitivity and kinetics. Here, we discuss a novel desi... 详细信息

ISBN: (纸本)9781957171210

关键词： Diffusion Molecules Proteins Sensor performance Sensors Strain