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Micro-Chimerism Detected By Highly Sensitive AlloHeme TM Solution Is Associated with Treatment Response and Relapse after Allogeneic Stem Cell Transplantation

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Transplantation and Cellular Therapy 2024年第2期30卷 S403-S404页

作者： Kothari, Ashish S, MD, MS Dwivedi, Nishant, MD, PhD Chattopadhyay, Shrikanta, MD Wang, Yun, PhD Zhou, Mingming Vaskova, Evgeniya, PhD Viard, Thierry, PhD Wong, Lily, MS Gulbahce, Natali, PhD Qu, Kunbin, PhD Grskovic, Marica, PhD Kongtim, Piyanuch, MD, PhD Ciurea, Stefan O., MD Clinical Development CareDx Brisbane CA SVP Medical TScan Therapeutics Waltham MA TScan Therapeutics Waltham MA Biostatistics and Data Science CareDx Inc Brisbane CA R&D CareDx Brisbane CA CareDx Inc Brisbane CA Bioinformatics CareDx Brisbane CA Data Science and Biostatistics CareDx Inc Brisbane CA Cell Transplant Therapy CareDx Inc Brisbane CA Division of Hematology/Oncology Department of Medicine Hematopoietic Stem Cell Transplantation and Cellular Therapy Program University of California Irvine Orange CA

Post-transplant chimerism status is associated with graft survival and potentially disease relapse after allogeneic stem cell transplantation (allo-HCT). However, detection of low-level micro-chimerism (MC) following allo-HCT using the current standard with short tandem repeats (STR) PCR method has been challenging due to STR's poor limit of detection (LOD) of 1-5%. A more sensitive method of chimerism detection could help inform early treatment decisions. We present preliminary results from two multi-center trials using a next-generation sequencing (NGS)-based MC detection method, AlloHeme TM (AH), with a higher sensitivity (LOD 0.035-0.043%) (Table 1), as a translational biomarker of treatment response and early relapse, including minimal residual disease (MRD), after allo-HCT for acute leukemias and MDS. Trial 1: NCT04635384 (ACROBAT, CareDx Inc.) is a prospective multi-center observational cohort study. AlloHeme is the study's primary biomarker. ACROBAT has fully enrolled 289 subjects, with a median follow-up of 150 days. Trial 2: NCT05473910 (TSCAN-001, TScan Inc.) is a multi-arm non-randomized controlled Phase 1 study evaluating the safety and efficacy of TScan's TSC100 and TSC101 - allogeneic T-cell receptor-engineered T cells that target haematopoietically restricted antigens HA-1 and HA-2 respectively, both presented on HLA-A*02:01, in mismatched reduced-intensity conditioning (RIC)-haplo donor HCT recipients. AlloHeme is used as a translational biomarker. Complete chimerism (CC) was defined as >99.9% recipient chimerism. Early review of the ongoing ACROBAT study data with CD33 CC (N=73; median follow-up 270 days, range 72-608 days) revealed three relapses (4%), which compares favorably with 18-20% 6-month relapse rates in the CIBMTR data, suggestive of prognostic potential. This data is corroborated by the preliminary findings of Trial 2. Increased MC (iMC) was defined as ≥1% increase in recipient chimerism between two consecutive AH results. Patients with

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Prediction of carbon nanostructure mechanical properties and role of defects using machine learning

arXiv

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arXiv 2021年

作者： Zhao, Qi Winetrout, Jordan J. Xu, Yanxun Wang, Yusu Heinz, Hendrik Halıcıoglu Data Science Institute University of California San Diego San diegoCA92093 United States Department of Chemical and Biological Engineering University of Colorado Boulder BoulderCO80309 United States Materials Science and Engineering Program University of Colorado Boulder BoulderCO80309 United States Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD21218 United States

Carbon fiber and graphene-based nanostructures such as carbon nanotubes (CNTs) and defective structures have extraordinary potential as strong and lightweight materials. A longstanding bottleneck has been lack of understanding and implementation of atomic-scale engineering to harness the theoretical limits of modulus and tensile strength, of which only a fraction is routinely reached today. Here we demonstrate accurate and fast predictions of mechanical properties for CNTs and arbitrary 3D graphitic assemblies based on a training set of over 1000 stress-strain curves from cutting-edge reactive MD simulation and machine learning (ML). Several ML methods are compared and show that our newly proposed hierarchically structured graph neural networks with spatial information (HS-GNNs) achieve predictions in modulus and strength for any 3D nanostructure with only 5-10% error across a wide range of possible values. The reliability is sufficient for practical applications and a great improvement over off-the shelf ML methods with up to 50% deviation, as well as over earlier models for specific chemistry with 20% deviation. The algorithms allow more than 100 times faster mechanical property predictions than traditional molecular dynamics simulations, the identification of the role of defects and random 3D morphology, and high-throughput screening of 3D structures for enhanced mechanical properties. The algorithms can be scaled to morphologies up to 100 nm in size, expanded for chemically similar compounds, and trained to predict a broader range of properties. © 2021, CC BY.

关键词： Defects

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Stability of stochastic gradient descent on nonsmooth convex losses 20

Stability of stochastic gradient descent on nonsmooth convex...

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Raef Bassily Vitaly Feldman Cristóbal Guzmán Kunal Talwar Department of Computer Science & Engineering The Ohio State University Apple Pontificia Universidad Católica de Chile Institute for Mathematical and Computational Engineering ANID – Millennium Science Initiative Program Millennium Nucleus Center for the Discovery of Structures in Complex Data

ISBN: (纸本)9781713829546

Uniform stability is a notion of algorithmic stability that bounds the worst case change in the model output by the algorithm when a single data point in the dataset is replaced. An influential work of Hardt et al. [20] provides strong upper bounds on the uniform stability of the stochastic gradient descent (SGD) algorithm on sufficiently smooth convex losses. These results led to important progress in understanding of the generalization properties of SGD and several applications to differentially private convex optimization for smooth *** work is the first to address uniform stability of SGD on nonsmooth convex losses. Specifically, we provide sharp upper and lower bounds for several forms of SGD and full-batch GD on arbitrary Lipschitz nonsmooth convex losses. Our lower bounds show that, in the nonsmooth case, (S)GD can be inherently less stable than in the smooth case. On the other hand, our upper bounds show that (S)GD is sufficiently stable for deriving new and useful bounds on generalization error. Most notably, we obtain the first dimension-independent generalization bounds for multi-pass SGD in the nonsmooth case. In addition, our bounds allow us to derive a new algorithm for differentially private nonsmooth stochastic convex optimization with optimal excess population risk. Our algorithm is simpler and more efficient than the best known algorithm for the nonsmooth case [16].

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A First Course in data science

arXiv

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arXiv 2019年

作者： Yan, Donghui Davis, Gary E. Department of Mathematics and Program in Data Science University of Massachusetts Dartmouth

data science is a discipline that provides principles, methodology and guidelines for the analysis of data for tools, values, or insights. Driven by a huge workforce demand, many academic institutions have started to offer degrees in data science, with many at the graduate, and a few at the undergraduate level. Curricula may differ at different institutions, because of varying levels of faculty expertise, and different disciplines (such as Math, computer science, and business etc) in developing the curriculum. The University of Massachusetts Dartmouth started offering degree programs in data science from Fall 2015, at both the undergraduate and the graduate level. Quite a few articles have been published that deal with graduate data science courses, much less so dealing with undergraduate ones. Our discussion will focus on undergraduate course structure and function, and specifically, a first course in data science. Our design of this course centers around a concept called the data science life cycle. That is, we view tasks or steps in the practice of data science as forming a process, consisting of states that indicate how it comes into life, how different tasks in data science depend on or interact with others until the birth of a data product or the reach of a conclusion. Naturally, different pieces of the data science life cycle then form individual parts of the course. Details of each piece are filled up by concepts, techniques, or skills that are popular in industry. Consequently, the design of our course is both "principled" and practical. A significant feature of our course philosophy is that, in line with activity theory, the course is based on the use of tools to transform real data in order to answer strongly motivated questions related to the data. Copyright © 2019, The Authors. All rights reserved.

关键词： data science

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Forecasting Vegetation Health in the MENA Region by Predicting Vegetation Indicators with Machine Learning Models

Forecasting Vegetation Health in the MENA Region by Predicti...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sachi Perera Wenzhao Li Erik Linstead Hesham El-Askary Computational and Data Sciences Graduate Program Schmid College of Science and Technology Chapman University Orange CA USA Schmid College of Science and Technology Chapman University Orange CA USA Fowler School of Engineering Chapman University Orange CA USA Faculty of Science Alexandria University Alexandria Egypt

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA region. These models are built by a total of 4556 and 519 normalized samples for training and testing purposes, respectively and with 51820 samples used for model evaluation. Models such as multilinear regression, penalized regression models, support vector regression (SVR), neural network, instance-based learning K-nearest neighbor (KNN) and partial least squares were implemented to predict future values of EVI. The models show effective performance in predicting EVI values (R 2 > 0.95) in the testing and (R 2 > 0.93) in the evaluation process.

关键词： Vegetation mapping MODIS Atmospheric modeling Remote sensing Predictive models Training Support vector machines

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The effect of organic acid hydrolysis on the properties of nanocellulose from edamame husk (Glycine max (L.) merill)

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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Wahyudi, Muhammad Sapto Holilah Bahruji, Hasliza Prasetyoko, Didik Asranudin Pratama, Agus Wedi Indriani, Dina Wahyu Suryanegara, Lisman Faradilla, RH Fitri Mahardika, Melbi Wardani, Ratih Kusuma Piluharto, Bambang Knight, Victor Feizal Norrrahim, Mohd Nor Faiz Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Cibinong Indonesia Research Collaboration Center for Nanocellulose BRIN-Andalas University Padang Indonesia Study Program of Chemistry Faculty of Science and Technology UIN Sunan Kalijaga Yogyakarta Indonesia Centre of Advanced Material and Energy Sciences Universiti Brunei Darussalam Jl. Tungku Link BE 1410 Brunei Darussalam Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Research Center for Applied Microbiology National Research and Innovation Agency (BRIN) Cibinong Indonesia Department of Biosystem Engineering Faculty of Agricultural Technology Universitas Brawijaya Jalan Veteran Malang Indonesia Department of Food Science and Technology Faculty of Agriculture Universitas Halu Oleo Kendari Indonesia Academy Pharmacy of Surabaya Ketintang Gayungan Surabaya 60231 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences University of Jember Jember Indonesia Research Center for Chemical Defence Defence Research Institute Universiti Pertahanan Nasional Malaysia Kem Sungai Besi Kuala Lumpur 57000 Malaysia

The increasing demand for sustainable materials has highlighted nanocellulose as a promising candidate due to its exceptional mechanical strength, thermal stability, and superior barrier properties. These attributes enable its application in diverse fields, including packaging, biomedical devices, and nanocomposites. This study investigates the valorization of edamame bean husks, a byproduct of the globally consumed legume snack, as a sustainable feedstock for nanocellulose production. Cellulose was extracted from the husks via delignification and bleaching, followed by a green and facile acid hydrolysis method employing organic acids (maleic acid, lactic acid, citric acid, and formic acid) to produce nanocellulose. The resulting nanocellulose exhibited a fibrillar morphology with particle sizes ranging from 7.59 to 17.51 nm, depending on the acid used. X-ray diffraction analysis revealed a crystallinity index ranging from 63.49 % to 67.24 %. Notably, acid hydrolysis significantly enhanced the thermal stability of the nanocellulose compared to both the raw husk and isolated cellulose. These findings demonstrate the potential of edamame agricultural waste as a valuable resource for sustainable and eco-friendly nanocellulose production, contributing to a circular bioeconomy. © 2025 The Authors

关键词： Characterization Edamame husk waste Green synthesis Nanocellulose Organic acids

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Improving the lifestyle behavior of type 2 diabetes mellitus patients using a mobile application

Improving the lifestyle behavior of type 2 diabetes mellitus...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Andreia Pinto João Viana Diogo Martinho Vítor Crista José Miguel Diniz Joana Reis João Matos David Greer Goreti Marreiros Alberto Freitas Department of Community Medicine Information and Health Decision Sciences (MEDCIDS) Faculty of Medicine Center for Health Technology and Services Research (CINTESIS) University of Porto Porto Portugal Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) Institute of Engineering Polytechnic of Porto Porto Portugal Health Data Science PhD Program Faculty of Medicine Center for Health Technology and Services Research (CINTESIS) University of Porto Lisboa Portugal CLOO - Behavioral Insights Unit Faculty of Psychology CICPSI - Research Center for Psychological Science University of Lisbon Lisboa Portugal Behavioral Insights Unit CLOO Porto Portugal Behavioral Insights Unit CLOO Lisboa Portugal

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

Type 2 diabetes is an increasingly prevalent disease and patients do not always manage the disease properly. Therefore, creating tools that help diabetics self-manage their condition over time is of the utmost importance. Technological tools that include features meeting this population's needs, with an integrated personalized feedback system, in an environment of gamified incentives, may be the way for developing a sustained app's usage. This work aims to present a still in development, novel approach to managing diabetes type 2 with a focus on data analysis from user-inputs, gamification, and personalized coaching, with an accessible user interface. Thus, we will present some functionalities, in particular the theoretical and practical concept that is behind this development, namely the Transtheoretical model of behavior change to create different profiles, to provide customized feedback according to the user's behavioral stage, and also to ascertain if it induces any improvement at the behavioral level.

关键词： Computers data analysis Computational modeling User interfaces Diabetes Behavioral sciences Mobile applications

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On-body non-invasive glucose monitoring sensor based on high figure of merit (FoM) surface plasmonic microwave resonator

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Scientific reports 2023年第1期13卷 17527页

作者： Farzad Soltanian Mehdi Nosrati Saleh Mobayen Chuan-Chun Li Telung Pan Ming-Ta Ke Paweł Skruch Department of Electrical Engineering University of Alberta Edmonton Canada. Department of Electrical Engineering Manhattan College New York USA. Department of Electrical Engineering University of Zanjan Zanjan Iran. mobayens@yuntech.edu.tw. Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliou 640301 Yunlin Taiwan. mobayens@yuntech.edu.tw. National Taiwan University Hospital Yunlin Branch Yunlin Taiwan. a***@***. Bachelor Program in Interdisciplinary Studies College of Future National Yunlin University of Science and Technology Yunlin 64002 Taiwan. Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliou 640301 Yunlin Taiwan. Department of Automatic Control and Robotics AGH University of Science and Technology 30-059 Kraków Poland.

High-figure of merit (FoM) plasmonic microwave resonator is researched as a non-invasive on-body sensor to monitor the human body's blood glucose variation rate in adults for biomedical applications, e.g., diabetic patients. The resonance frequencies of the proposed sensor are measured to be around [Formula: see text] GHz and [Formula: see text] GHz over the frequency band of DC to 6GHz which are suitable for monitoring interstitial fluid (ISF) changing rate. The [Formula: see text] sensor is experimentally wrapped on the human body arm to monitor the blood glucose changing rate via amplitude and frequency variations of the sensor. Amplitude variation and frequency shift are measured to be around 7 dB and 30 MHz, respectively. The measured results demonstrate the high precision of the proposed approach to depict a valid diagram for glucose changing rate due to good impedance matching of the designed microwave sensor and human body. The sensor is shown to enhance the sensitivity by a factor of 5 compared to the conventional ones.

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Consciousness in Artificial Intelligence: Insights from the science of Consciousness

arXiv

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arXiv 2023年

作者： Butlin, Patrick Long, Robert Elmoznino, Eric Bengio, Yoshua Birch, Jonathan Constant, Axel Deane, George Fleming, Stephen M. Frith, Chris Ji, Xu Kanai, Ryota Klein, Colin Lindsay, Grace Michel, Matthias Mudrik, Liad Peters, Megan A.K. Schwitzgebel, Eric Simon, Jonathan VanRullen, Rufin Future of Humanity Institute University of Oxford United Kingdom Center for AI Safety United States University of Montreal MILA - Quebec AI Institute Canada Centre for Philosophy of Natural and Social Science London School of Economics and Political Science United Kingdom School of Engineering and Informatics The University of Sussex Centre de Recherche en Éthique University of Montreal United Kingdom Department of Philosophy University of Montreal Canada Department of Experimental Psychology Wellcome Centre for Human Neuroimaging University College London United Kingdom Wellcome Centre for Human Neuroimaging University College London Institute of Philosophy University of London United Kingdom Araya Inc. School of Philosophy The Australian National University Australia Department of Psychology Center for Data Science New York University United States Center for Mind Brain and Consciousness New York University United States School of Psychological Sciences and Sagol School of Neuroscience Tel-Aviv University CIFAR Program in Brain Mind and Consciousness Israel Department of Cognitive Sciences University of California Irvine and CIFAR Program in Brain Mind and Consciousness United States Department of Philosophy University of California Riverside United States Centre de Recherche Cerveau et Cognition CNRS Université de Toulouse France

Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive"indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.1 © 2023, CC BY-NC-SA.

关键词： Neurophysiology

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SCALE RELIANT INFERENCE

arXiv

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arXiv 2022年

作者： Nixon, Michelle Pistner McGovern, Kyle C. Letourneau, Jeffrey David, Lawrence A. Lazar, Nicole A. Mukherjee, Sayan Silverman, Justin D. College of Information Sciences and Technology The Pennsylvania State University United States Program in Bioinformatics and Genomics The Pennsylvania State University United States Department of Molecular Genetics and Microbiology Duke University United States Department of Statistics The Pennsylvania State University United States Huck Institutes for the Life Sciences The Pennsylvania State University United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University United States Center for Scalable Data Analytics and Artificial Intelligence University of Leipzig Germany Max Planck Institute for Mathematics in the Natural Sciences Germany Department of Medicine The Pennsylvania State University United States

Many scientific fields, including human gut microbiome science, collect multivariate count data where the sum of the counts is unrelated to the scale of the underlying system being measured (e.g., total microbial load in a subject’s colon). This disconnect complicates downstream analyses such as differential analysis in case-control studies. This article is motivated by a novel study of in vitro human gut microbiome models. Popular tools for analyzing these data led to dramatically elevated rates of both false positives and false negatives. To understand those failures, we provide a formal problem statement that frames these challenges of scale in terms of the classical theory of identifiability. We call this the problem of Scale Reliant Inference (SRI). We use this formulation to prove fundamental limits on SRI in terms of criteria such as consistency and type-I error control. We show that the failures of existing methods stem from a fundamental failure to properly quantify uncertainty in the system scale. We demonstrate that a particular type of Bayesian model called a Bayesian Partially Identified Model (PIMs) can correctly quantify uncertainty in SRI. We introduce Scale Simulation Random Variables (SSRVs) as a flexible and efficient approach to specifying and inferring Bayesian PIMs. In the context of both real and simulated data, we find SSRVs drastically decrease type-I and type-II error rates. © 2022, CC BY.

关键词： Bayesian networks

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