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Innovative E-beam technology for validation of Salmonella inactivation kinetics in fresh shell eggs: A predictive modeling approach

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INNOVATIVE FOOD SCIENCE & EMERGING TECHNOLOGIES 2025年 99卷

作者： Tesfai, Adiam Sarker, Ayesha Shen, Cangliang Matak, Kristen E. Jaczynski, Jacek West Virginia Univ Sch Agr & Food Syst POB 6108 Morgantown WV 26506 USA West Virginia State Univ Agr & Environm Res Stn Institute WV 25112 USA

FDA and IFT defined fresh shell eggs as "potentially hazardous food". By FDA, >5-log of Salmonella Enteritidis (SE) must be inactivated in pasteurized eggs. Only about 1 % of eggs are pasteurized in the U.S. E-beam was used to reduce SE and S. Typhimurium in fresh shell eggs. Two-sided e-beam showed adequate uniformity of dose absorbed (1.43 kGy). predictive model for dose absorbed was developed and validated. D-value for SE was 0.34 f 0.006 and 0.33 f 0.018 kGy on TSA and XLD, respectively. S. typhimurium was more (P < 0.05) radio-resistant at 0.40 f 0.007 and 0.39 f 0.009 kGy, respectively. predictive model for Salmonella inactivation was developed and validated. Two-sided e-beam at 3 and 5 kGy resulted in >13.0-log and > 21.7-log reduction of SE, respectively;while S. typhimurium was reduced at >11.1-log and > 18.5-log, respectively. The predictive model indicated that lower doses such as 1.25 kGy of two-sided e-beam would result in FDA compliance for >5-log reduction of SE. Industrial relevance: This work is relevant to non-thermal food processing, specifically to non-thermal inactivation of Salmonella in fresh shell eggs that are already packed in a standard retail container. Electron beam was used in this work to reduce Salmonella without heat to comply or exceed current U.S. Food and Drug Administration (FDA) requirements targeting >5-log destruction of Salmonella Enteritidis.

关键词： electron beam processing Egg processing Egg food safety Non-thermal processing Salmonella in eggs predictive modeling Ionizing radiation

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Multiscale-multiphysics predictive modeling of chemical vapor deposition processes for carbon nanotube synthesis

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CHEMICAL ENGINEERING SCIENCE 2025年 305卷

作者： Cabral, Thiago Oliveira Amama, Placidus B. Pourkargar, Davood B. Kansas State Univ Tim Taylor Dept Chem Engn Manhattan KS 66506 USA Kansas State Univ Food Sci Inst Manhattan KS 66506 USA

The synthesis of carbon nanotubes has attracted considerable interest due to their unique physical and structural properties. Despite notable experimental advancements, particularly in chemical vapor deposition (CVD) techniques, a significant gap remains in developing comprehensive mechanistic models that correlate nanotube growth dynamics with gas-phase composition. The CVD involves a complex interplay of multiscale phenomena, including hydrocarbon transport within the reactor and surface reactions on catalyst nanoparticles, collectively contributing to nucleation and growth. This paper introduces a computational modeling framework that integrates these phenomena by leveraging density functional theory energy data, microkinetic modeling, and computational fluid dynamics. The proposed approach addresses the challenges inherent in this multiscalemultiphysics problem, providing insights into nanotube growth as a function of gas composition and transport, temperature, and catalyst properties. The simulation results show strong agreement with experimental trends, highlighting the significance of gas-phase reactions in a mixed hydrocarbon feedstock and the effects of catalyst deactivation.

关键词： Multiscale modeling predictive modeling Computational fluid dynamics Chemical vapor deposition Carbon nanotube synthesis

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Advancing idiopathic pulmonary fibrosis prognosis through integrated CNN-LSTM predictive modeling and uncertainty quantification

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 100卷

作者： Karthik, A. Patthi, Sridhar Maheswari, B. Uma Devi, J. Sirisha Pradhan, Abhaya Kumar Balaji, L. Nallasivan, G. Rajaram, A. Inst Aeronaut Engn Informat Technol Hyderabad 500043 Telangana India Marri Laxman Reddy Inst Technol & Management Hyderabad Telangana India Matrusri Engn Coll Dept Math Hyderabad Telangana India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Hyderabad 500043 Telangana India Woxsen Univ Sch Technol Hyderabad Telangana India Symbiosis Int Deemed Univ SIU Lavale Symbiosis Inst Digital & Telecom Management SIDTM Pune 412115 Maharashtra India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai 62 India EGS Pillay Engn Coll Dept Elect & Commun Engn Nagapattinam 611002 India

In the realm of pulmonary medicine, prognostic assessment of Idiopathic Pulmonary Fibrosis (IPF) poses a significant challenge, necessitating advancements in predictive analytics. This study introduces a pioneering predictive model that synergistically utilizes Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to analyze CT scans and clinical data, offering a dynamic prediction of IPF progression. The model's methodology is rooted in the integration of detailed spatial features and temporal clinical patterns to forecast disease trajectory with heightened accuracy. A novel aspect of this model is its built-in mechanism for uncertainty quantification, enhancing the interpretability of its prognostic output. Through validation, the proposed model demonstrated a mean Out-of-Fold (OOF) validation score 6.9679 and accuracy of 95% notably superior to traditional methods, alongside a conservative yet precise approach to uncertainty estimation. The findings represent a substantial improvement in predictive capabilities, emphasizing the model's potential to inform clinical decision-making and facilitate personalized treatment strategies. The implications of this research extend beyond IPF, providing a framework for future explorations into machine learning applications in complex disease prognostics.

关键词： Idiopathic pulmonary fibrosis predictive modeling Convolutional neural networks Long short-term memory networks Disease progression Uncertainty quantification Machine learning

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Application of Recurrent Neural Networks for predictive modeling of Electrical Submersible Pumps in Oil Extraction

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IFAC-PapersOnLine 2025年第1期59卷 49-54页

作者： Carine Menezes Rebello Erbet Almeida Costa Odilon Santana Luiz de Abreu Galdir Reges Vinicius Viena Tefilo Paiva Guimares Mendes Marcio Fontana Francisco Raphael Ribeiro Bacellar Marcos Pellegrini Ribeiro Bernardo Pereira Foresti Leizer Schnitman Idelfonso B.R. Nogueira Department of Chemical Engineering Norwegian University of Science and Technology Norway Department of Chemical Engineering Federal University of Bahia Polytechnic School Bahia Brazil Department of Electrical and Computer Engineering Federal University of Bahia Polytechnic School Bahia Brazil CENPES Petrobras R&D Center Brazil Av. Horcio Macedo 950 Cid. Universitria Ilha do Fundo Rio de Janeiro Brazil

This study presents a mathematical modeling framework that leverages recurrent neural networks (RNNs), specifically echo state networks (ESNs) and long short-term memory (LSTM) architectures, for the predictive modeling of electrical submersible pumps (ESPs) in offshore oil extraction operations. Utilizing real operational data from an offshore oil field, the research addresses the inherent complexity and nonlinear dynamics of ESP systems by employing these recurrent structures to capture and represent the temporal dependencies within the data. Key challenges, such as data noise, variability, and limited diversity, are systematically tackled to ensure robust dynamic modeling. A comparative analysis evaluates the performance of ESN and LSTM models under these constrained data conditions, aiming to identify the superior model in terms of predictive accuracy and resilience. The modeling approach emphasizes the formulation, parameterization, and validation processes essential for effective ESP optimization and control in real-world industrial settings. Findings reveal the distinct strengths and limitations of each RNN variant when applied to offshore operational data.

关键词： Electrical submersible pumps echo state network long short-term memory predictive modeling real data

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Enhanced non-invasive machine learning approach for early colorectal cancer detection: predictive modeling and validation in a Jordanian cohort

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Computers in Biology and Medicine 2025年 191卷 110184-110184页

作者： Kanaan, Soha Altamimi, Ahmad Qattous, Hazem Rbeihat, Haitham Amman Jordan Amman Jordan Royal Medical Services Amman Jordan

Background: Colorectal cancer (CRC) ranks as the third most prevalent cancer worldwide, posing significant public health challenges. Late-stage detection often results in poor treatment outcomes, elevating mortality rates. The economic and psychological burdens of CRC treatment underscore the need for early detection. Objective: This study aims to enhance the early detection of colorectal cancer by employing machine learning (ML) algorithms on non-invasive features. The focus is on constructing a comprehensive dataset, analyzing non-invasive features, and developing predictive models to minimize the necessity for invasive procedures such as colonoscopy. By focusing on non-invasive, easily accessible data, the study aims to develop a model that can be widely applied without the associated risks of invasive procedures. Methods: A retrospective dataset of 400 patients was sourced from the colorectal cancer unit of Royal Medical Services (2021–2022). The dataset included demographic data, imaging reports, laboratory results, and clinical evaluations. The study involved three experiments, training ML models (K-Nearest Neighbors (KNN), Super Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Naïve Bayes (NB)) on the collected dataset and a public dataset to validate generalizability. The first experiment used 35 features across the ML algorithms. The second experiment focused on the most informative features. The third experiment validated the models using a public dataset, with Phase I including all data and Phase II excluding missing values. Results: The Random Forest (RF) algorithm consistently outperformed other models, achieving an accuracy of 95.8 % in the first experiment, increasing to 96.5 % in the second experiment. For the public dataset, RF accuracy was 66.0 % in Phase I and 68.9 % in Phase II. Conversely, the KNN algorithm exhibited the lowest accuracy across all experiments. Conclusion: This study highlights the effectiveness of ML in early CR

关键词： Colorectal cancer Early detection Machine learning predictive modeling Non-invasive screening Jordanian cohort

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Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify predictive modeling Tasks 25

Tempo: Helping Data Scientists and Domain Experts Collaborat...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Venkatesh Sivaraman Anika Vaishampayan Xiaotong Li Brian R Buck Ziyong Ma Richard D Boyce Adam Perer Carnegie Mellon University Pittsburgh PA USA University of Pittsburgh Pittsburgh PA USA

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predictive modeling of Drug Repositioning from Functional Genetic Screens

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Cytotherapy 2025年第5期27卷 e26-e26页

作者： Song, B. Chao, L. Cheng, X. Gao, Y. Shan, R. Li, W. Center for Genetic Medicine Research Children's National Hospital Washington DC United States Department of Genomics and Precision Medicine The George Washington University Washington DC United States Department of Biological Science University of Maryland College Park MD United States School of Engineering and Applied Science The George Washington University Washington DC United States

Background and AimsFunctional genetic screens have emerged as powerful tools for identifying novel drug targets and repurposing existing therapeutics. However, linking genetic dependencies to actionable drug candidates remains a significant challenge. Traditional manual approaches for drug inference are labor-intensive, prone to bias, and often yield low accuracy. To address this, we developed a systematic, machine-learning-driven approach to analyze associations between genetic dependencies and drug sensitivities across cancer cell lines, revealing new opportunities for drug repositioning. MethodologyWe trained predictive models using genome-wide CRISPR-Cas9 knockout viability profiles and large-scale drug response datasets comprising 4,518 compounds from DepMap. Among various modeling approaches, decision-tree-based methods achieved the highest predictive power, successfully recapitulating known gene-drug relationships, such as increased sensitivity of TP53- or MDM2-dependent cells to MDM inhibitors and the strong correlation between EGFR/ERBB2 dependency and EGFR inhibitor response. Notably, for the 3,260 drugs with identified molecular targets, only 1.5% of the corresponding genes emerged as top predictors of drug sensitivity, highlighting the importance of considering non-canonical gene-drug interactions. ResultsWe further applied our predictive models to CRISPR-Cas9 screens targeting 1,805 druggable genes across two cancer cell lines, enabling the in-silico identification of both known and novel drug sensitivities. For each cell line, we selected eight candidate drugs and validated their sensitivity through dose-dependent cell survival assays. Additionally, we explored deep learning-based classification models using the TorchDrug framework, which demonstrated improved recall over decision-tree models but with lower precision. ConclusionOur study provides the first large-scale systematic evaluation of gene dependency and drug response relationships, offering a

关键词： functional genetic screens predictive modeling Drug repurposing

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predictive modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis

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Journal of medical Internet research 2025年 27卷 e66615页

作者： Jinxi Yang Siyao Zeng Shanpeng Cui Junbo Zheng Hongliang Wang The Second Clinical Medical College Harbin Medical University Heilongjiang Province Harbin China. Department of Critical Care Medicine The Second Affiliated Hospital of Harbin Medical University Heilongjiang Province Harbin China.

BACKGROUND:Acute respiratory distress syndrome (ARDS) is a critical condition commonly encountered in the intensive care unit (ICU), characterized by a high incidence and substantial mortality rate. Early detection and accurate prediction of ARDS can significantly improve patient outcomes. While machine learning (ML) models are increasingly being used for ARDS prediction, there is a lack of consensus on the most effective model or methodology. This study is the first to systematically evaluate the performance of ARDS prediction models based on multiple quantitative data sources. We compare the effectiveness of ML models via a meta-analysis, revealing factors affecting performance and suggesting strategies to enhance generalization and prediction accuracy. OBJECTIVE:This study aims to evaluate the performance of existing ARDS prediction models through a systematic review and meta-analysis, using metrics such as area under the receiver operating characteristic curve, sensitivity, specificity, and other relevant indicators. The findings will provide evidence-based insights to support the development of more accurate and effective ARDS prediction tools. METHODS:We performed a search across 6 electronic databases for studies developing ML predictive models for ARDS, with a cutoff date of December 29, 2024. The risk of bias in these models was evaluated using the Prediction model Risk of Bias Assessment Tool. Meta-analyses and investigations into heterogeneity were carried out using Meta-DiSc software (version 1.4), developed by the Ramón y Cajal Hospital's Clinical Biostatistics team in Madrid, Spain. Furthermore, sensitivity, subgroup, and meta-regression analyses were used to explore the sources of heterogeneity more comprehensively. RESULTS:ML models achieved a pooled area under the receiver operating characteristic curve of 0.7407 for ARDS. The additional metrics were as follows: sensitivity was 0.67 (95% CI 0.66-0.67; P<.001; I²=97.1%), specificity was 0.68 (95% CI

关键词： ICU PRISMA acute respiratory distress syndrome detection effectiveness machine learning patient outcomes prediction models predictive modeling systematic evaluation

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predictive modeling of bedrock outcrops and associated shallow soil in upland glaciated landscapes

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GEODERMA 2020年 376卷 114495-114495页

作者： Fraser, Olivia L. Bailey, Scott W. Ducey, Mark J. McGuire, Kevin J. Univ New Hampshire Dept Nat Resources & Environm 56 Coll Rd Durham NH 03824 USA USDA Forest Serv White Mt Natl Forest 71 Mt Dr Campton NH 03223 USA USDA Forest Serv Northern Res Stn 234 Mirror Lake Rd North Woodstock NH 03264 USA Virginia Tech Dept Forest Resources & Environm Conservat Blacksburg VA 24061 USA Virginia Tech Virginia Water Resources Res Ctr Blacksburg VA 24061 USA

Identifying the areal extent of bedrock outcrops and shallow soils has important implications for understanding spatial patterns in vegetation composition and productivity, stream chemistry gradients, and hydrologic and soil properties of landscapes. Manual methods of delineating bedrock outcrops and associated shallow soils are still commonly employed, but they are expensive to implement over broad areas and often limited by representation of polygon units. Few studies have automated the delineation of bedrock outcrops. These focused on delineation approaches in landscapes with rapidly eroding hillslopes and sparse vegetation. The objectives of this study were to assess the accuracy of visually interpreting high-resolution relief maps for locating bedrock outcrops and associated shallow soil (BOSS) < 50 cm deep in a heavily forested landscape, to use visually interpreted point locations to train predictive models, and to compare predictions with manually delineated polygons in upland glaciated landscapes. Visual interpretation of Lidar-derived 1 m shaded relief maps at Hubbard Brook Experimental Forest (HBEF), USA resulted in a 79% accuracy of interpreting deep soil locations and 84% accuracy in distinguishing BOSS. We explored four probabilistic classifications of BOSS using multiple Lidar-derived topographic metrics as predictive variables. All four methods identified similar predictors for BOSS, including slope and topographic position indices with a 15, 100 and 200 m circular analysis window, respectively. Although all classifiers yielded similar results with little difference in interpretation, a generalized additive model had slightly higher accuracy predicting BOSS presence, yielding 85% overall accuracy using independent validation data across the primary study area, and 86% overall accuracy in a second validation area.

关键词： Bedrock outcrops Shallow soils predictive modeling Lidar Topographic metrics Digital soil mapping

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predictive modeling of die filling of the pharmaceutical granules using the flexible neural tree

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NEURAL COMPUTING & APPLICATIONS 2018年第7期29卷 467-481页

作者： Ojha, Varun Kumar Schiano, Serena Wu, Chuan-Yu Snasel, Vaclav Abraham, Ajith VSB Tech Univ Ostrava IT4Innovat Ostrava Czech Republic Univ Surrey Dept Chem & Proc Engn Guildford GU2 7XH Surrey England Machine Intelligence Res Labs Auburn WA USA

In this work, a computational intelligence (CI) technique named flexible neural tree (FNT) was developed to predict die filling performance of pharmaceutical granules and to identify significant die filling process variables. FNT resembles feedforward neural network, which creates a tree-like structure by using genetic programming. To improve accuracy, FNT parameters were optimized by using differential evolution algorithm. The performance of the FNT-based CI model was evaluated and compared with other CI techniques: multilayer perceptron, Gaussian process regression, and reduced error pruning tree. The accuracy of the CI model was evaluated experimentally using die filling as a case study. The die filling experiments were performed using a model shoe system and three different grades of microcrystalline cellulose (MCC) powders (MCC PH 101, MCC PH 102, and MCC DG). The feed powders were roll-compacted and milled into granules. The granules were then sieved into samples of various size classes. The mass of granules deposited into the die at different shoe speeds was measured. From these experiments, a dataset consisting true density, mean diameter (d50), granule size, and shoe speed as the inputs and the deposited mass as the output was generated. Cross-validation (CV) methods such as 10FCV and 5x2FCV were applied to develop and to validate the predictive models. It was found that the FNT-based CI model (for both CV methods) performed much better than other CI models. Additionally, it was observed that process variables such as the granule size and the shoe speed had a higher impact on the predictability than that of the powder property such as d50. Furthermore, validation of model prediction with experimental data showed that the die filling behavior of coarse granules could be better predicted than that of fine granules.

关键词： predictive modeling Die filling Flowability Pharmaceutical granules Flexible neural tree Feature selection

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