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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Quantification of glioblastoma progression in zebrafish xenografts: Adhesion to laminin alpha 5 promotes glioblastoma microtumor formation and inhibits cell invasion

Highlights: • Developed an embryo-larval zebrafish model to study GBM progression. • Quantitatively measured GBM BV association, invasion and microtumor formation. • Laminin alpha 5 decreases U251MG dispersal and increases microtumors. • Microenvironmental laminin alpha 5 slows U251MG cell invasion in vivo. Glioblastoma (GBM) is a deadly disease due to its ability to quickly invade and destroy brain tissue. Slowing or stopping GBM cell progression is crucial to help those inflicted with the disease. Our lab created an embryo-larval zebrafish xenograft model as a tool to study human GBM progression in an observable brain environment. The zebrafish brain is a dynamic and complex environment providing an optimal setting for studying GBM cell progression. Here we demonstrate the ability of our model to quantitate GBM proliferation, dispersal, blood vessel association, microtumor formation, and individual cell invasion by evaluating the importance of an extracellular matrix protein, laminin alpha 5 (lama5), on U251MG cell progression. Lama5 has been implicated in cancer cell survival, proliferation and invasion and is a known adhesion site for GBM cells. While lama5 is highly expressed in endothelial cells in the brain, it is unknown how lama5 affects GBM behavior. Using a lama5 morpholino, we discovered that lama5 decreased U251MG dispersal by 23% and doubles the formation of blood vessel dependent microtumors. Despite lama5 being a known attachment site for GBM, lama5 expression had no effect on U251MG association with blood vessels. Analysis of individual U251MG cells revealed lama5 significantly lowered invasion as mobile U251MG cells traveled 32.5 μm less, invaded 5.0 μm/hr slower and initiated invasion 60% few times per cell.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Long noncoding RNA SOX2OT contributes to gastric cancer progression by sponging miR-194-5p from AKT2

Highlights: • The role of SOX2/miR-194-5p/AKT2 axis in gastric cancer progression has been investigated. • SOX2OT acted as a sponge LncRNA of miR-194-5p in gastric cancer. • Suppressing SOXO2 expression can inhibit gastric cancer progression. Gastric cancer (GC) is a highly malignant cancer with poor prognosis. Long non-coding RNA (LncRNA) may play an important role in tumor progression. Our present study aimed to explore the effect of LncRNA SOX2OT on GC progression. We observed that SOX2OT was overexpressed in GC tissues and cell lines. Overexpressed SOX2OT promoted cell proliferation and metastasis of GC cells (SGC-7901, TMK-1) and the phosphorylation of AKT2 as well, while knockdown of SOX2OT reversed these effects. Besides that, miR-194-5p was predicted to be a target of SOX2OT and decreased expression of miR-194-5p was observed in GC tissues and cell lines. Overexpressed miR-194-5p counteracted the promoting role of SOX2OT on cell proliferation and invasion of GC cells. Moreover, AKT2 was predicted to be a target of miR-194-5p. The expression of AKT2 was negatively regulated by miR-194-5p while positively regulated by SOX2OT. Overexpressed AKT2 also promoted GC cell proliferation and invasion. Our in vitro experiments suggested that SOX2OT promoted cell proliferation and metastasis of GC cells via sponging miR-194-5p from AKT2. Finally, our in vivo experiments indicated that overexpressed SOX2OT promoted GC tumor growth and metastasis in nude mice. Taken together, our present study suggested that SOX2OT contributed to GC progression via sponging miR-194-5p from AKT2 both in vitro and in vivo. The SOX2OT-miR-194-5p-AKT2 axis may provide a new perspective for treatment of GC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

G protein nucleolar 3 promotes Non-Hodgkin lymphoma progression by activating the Wnt/β-catenin signaling pathway

G protein nucleolar 3 (GNL3), which acts as an oncoprotein in various carcinomas, is associated with tumor progression; however, little is known regarding GNL3 function in non-Hodgkin lymphoma (NHL). In this study, we first used in silico analysis to determine associations between GNL3 and diffuse large B-cell lymphoma (DLBCL). We then examined the effect of GNL3 on NHL progression, including cell proliferation, apoptosis, and cell cycle progression, and determined its underlying molecular mechanism using in vitro lymphoma cell lines and in vivo mouse xenograft models. We found that GNL3 mRNA levels were markedly higher in DLBCL tissues than in normal tissues, with these higher levels associated with poor prognosis. Additionally, GNL3 overexpression promoted NHL cell proliferation and cell cycle progression and reduced apoptosis in vitro, and enhanced tumorigenesis in an in vivo xenograft model. Moreover, we found that GNL3 upregulated the levels of Wnt/β-catenin signaling pathway-related factors and downstream target genes, whereas the opposite result was observed in GNL3-silenced cells. Furthermore, a rescue experiment using a Wnt/β-catenin inhibitor (XAV939) confirmed that GNL3 promotes NHL progression by activating the Wnt/β-catenin signaling pathway. These findings demonstrated that GNL3 functions as an oncogenic driver in NHL via the Wnt/β-catenin pathway.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Prediction of chronic kidney disease progression using recurrent neural network and electronic health records

Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V. The RNN model generates predictions based on time-series records of patients, including repeated lab tests and other clinical variables. Our investigation reveals that using a single variable, the recorded estimated glomerular filtration rate (eGFR) over time, the RNN model achieves an average area under the receiver operating characteristic curve (AUROC) of 0.957 for predicting future CKD progression. When additional clinical variables, such as demographics, vital information, lab test results, and health behaviors, are incorporated, the average AUROC increases to 0.967. In both scenarios, the standard deviation of the AUROC across cross-validation trials is less than 0.01, indicating a stable and high prediction accuracy. Our analysis results demonstrate the proposed RNN model outperforms existing standard approaches, including static and dynamic Cox proportional hazards models, random forest, and LightGBM. The utilization of the RNN model and the time-series data of previous eGFR measurements underscores its potential as a straightforward and effective tool for assessing the clinical risk of CKD patients concerning their disease progression.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

MPACT Verification With Magnox Reactor Neutronics Progression Problems

MPACT is a state-of-the-art core simulator designed to perform high-fidelity analysis using whole-core, three-dimensional, pin-resolved neutron transport calculations on modern parallel computing hardware. MPACT was originally developed to model light water reactors, and its capabilities are being extended to simulate gas-cooled, graphite-moderated cores such as Magnox reactors. To verify MPACT’s performance in this new application, the code is being formally benchmarked using representative problems. Progression problems are a series of example models that increase in complexity designed to test a code’s performance. The progression problems include both beginning-of-cycle and depletion calculations. Reference solutions for each progression problem have been generated using Serpent 2, a continuous-energy Monte Carlo reactor physics burnup calculation code.Using the neutron multiplication eigenvalue ke as a metric, MPACT’s performance is assessed on each of the progression problems. Initial results showed that MPACT’s multi-group cross section libraries, originally developed for pressurized water reactor problems, were not sufficient to accurately solve Magnox problems. MPACT’s improved performance on the progression problems is demonstrated using this new optimized cross section library.

Luciano, Nicholas↗
DOE OSTI2024

Error-controlled Progressive Retrieval of Scientific Data under Derivable Quantities of Interest

The unprecedented amount of scientific data has introduced heavy pressure on the current data storage and transmission systems. Progressive compression has been proposed to mitigate this problem, which offers data access with on-demand precision. However, existing approaches only consider precision control on primary data, leaving uncertainties on the quantities of interest (QoIs) derived from it. In this work, we present a progressive data retrieval framework with guaranteed error control on derivable QoIs. Our contributions are three-fold. (1) We carefully derive the theories to strictly control QoI errors during progressive retrieval. Our theory is generic and can be applied to any QoIs that can be composited by the basis of derivable QoIs proved in the paper. (2) We design and develop a generic progressive retrieval framework based on the proposed theories, and optimize it by exploring feasible progressive representations. (3) We evaluate our framework using five real-world datasets with a diverse set of QoIs. Experiments demonstrate that our framework can faithfully respect any user-specified QoI error bounds in the evaluated applications. This leads to over 2.02× performance gain in data transfer tasks compared to transferring the primary data while guaranteeing a QoI error that is less than 1E-5.

Wu, Xuan↗
DOE OSTI2021

Progressive transfer learning for low-frequency data prediction in full waveform inversion

To effectively overcome the cycle-skipping issue in full waveform inversion (FWI), we developed a deep neural network (DNN) approach to predict the absent low-frequency components by exploiting the hidden physical relation connecting the low- and the high-frequency data. To efficiently solve this challenging nonlinear regression problem, two novel strategies were proposed to design the DNN architecture and to optimize the learning process: (1) dual data feed structure; (2) progressive transfer learning. With the dual data feed structure, not only the high-frequency data, but also the corresponding beat tone data are fed into the DNN to relieve the burden of feature extraction. The second strategy, progressive transfer learning, enables us to train the DNN using a single evolving training dataset. Within the framework of the progressive transfer learning, the training dataset continuously evolves in an iterative manner by gradually retrieving the subsurface information through the physics-based inversion module, progressively enhancing the prediction accuracy of the DNN and propelling the inversion process out of the local minima. Here, the synthetic numerical experiments suggest that, without any a priori geological information, the low-frequency data predicted by the progressive transfer learning are sufficiently accurate for an FWI engine to produce reliable subsurface velocity models free of cycle-skipping artifacts.

02 PETROLEUM↗
DOE OSTI2018

Progress Update on Segmentation of Neutron Tomographic Images, 2018

The present report documents the techniques developed to segment fast-neutron tomographic images of objects consisting of assemblies of machined parts with distinct boundaries. This report satisfies the fiscal year 2018 technical deliverable for project OR16-3DTomography-PD3Jb, “3D Tomography and Image Processing Using Fast Neutrons,” to report on the extension of geometric shape-finding algorithms to three dimensions. The project has two overall goals. The first of these goals is to extend associated-particle fast-neutron transmission and, particularly, induced-reaction tomographic imaging algorithms to three dimensions. This aspect of the project is beyond the scope of this report. The second goal is to automatically segment the resultant tomographic images into constituent parts and then extract information about the parts, such as the class of shape and potentially the shape parameters. This report describes progress for the image segmentation part of the project. Imaging techniques have been developed as high-confidence methods for confirming the presence and configuration of special nuclear materials (SNM). This high confidence is achieved at the cost of revealing considerable information that may be undesirable to share with the operator of the equipment. One potential way to make use of the high-confidence of imaging methods without revealing imaging data is to employ automated analysis that can extract meaningful attributes of the SNM without showing imaging data to the operator. An essential step of this automated analysis is the segmentation of the image into its constituent parts. For example, a three-dimensional (3D) fast-neutron tomographic image of an assembly containing uranium could be reconstructed and then segmented into discrete parts. The properties of a constituent part identified as uranium would be of interest and could be extracted. For instance, the volume and density of the part would together determine its mass. Moreover, its shape might be expected to fall into a particular geometric class, such as a cube, cylinder, or sphere, and the parameters of the shape (such as side length, height, or diameter) would be expected to fall in a given range. In such an analysis, automated identification of the boundaries and properties of the uranium part is an essential step. A key goal of the present work is to infer the boundaries of objects in fast-neutron tomographic images via the general method of image segmentation. Development of this capability can be broken into more manageable steps, including 1. operator-guided segmentation of two-dimensional (2D) fast-neutron tomographic images, 2. operator-guided segmentation of 3D fast-neutron tomographic images, 3. development of automated segmentation algorithms, and 4. extraction of shape parameters from constituent volumes in 3D. In the first year, the project team reported progress on the first step. Last year, progress on the second step, the extension into 3D, was reported with application to simulated data. The present report describes additional progress on the second step, and progress toward the third step, automated segmentation algorithms. In particular, a multiphase level set approach for image segmentation in three dimensions has been applied to measured 3D data for the first time. Activities related to the fourth step will also be reported, but it will focus on estimation of 3D volumes rather than shape parameters. In this report, the steps before image segmentation will be described, and the multiphase level sets approach to image segmentation will be briefly reviewed. This will be followed by a discussion of the issues encountered when applying the level set technique to measured 3D transmission images, and then a description of the inclusion of induced-reaction data will be provided. Finally, segmentation results for various measured target assemblies will be presented and the image segmentation portion of this project will be summarized and future directions will be discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

A progressive deep transfer learning approach to cycle-skipping mitigation in FWI, In SEG Technical Program Expanded Abstracts 2019, pp. 2348-2352. Society of Exploration Geophysicists, 2019.

To effectively mitigate the cycle-skipping phenomenon in full waveform inversion (FWI), we developed an inception-based deep learning neural network to reconstruct the absent low frequency data by exploiting the subsurface low wavenumber information buried in the acquired high frequency data. Two unique features of our deep learning network for the low frequency data reconstruction are 1) Dual Data Feed; 2) Progressive Transfer Learning. With the Dual Data Feed feature, not only the high frequency data, but also the corresponding Beat Tone data are fed into the neural network, significantly reducing the network complexity and the network training cost. The Progressive Transfer Learning technique enables us to initiate the deep learning network with only one training dataset. Unlike other deep learning approaches in this category, our training dataset is not fixed. Instead, the training dataset and the network accuracy are progressively enhanced in an iterative manner within the Progressive Transfer Learning workflow. The numerical experiments validated that the low frequency data reconstructed by this progressive transfer learning network are sufficiently accurate for a successful FWI inversion without suffering from the cycle-skipping without any priori geological information.

58 GEOSCIENCES↗
DOE OSTI2020

Panel Session 89: Progress on Deep Repository Around the World

This panel focused on the progress of deep repository programs, worldwide. While efforts on a deep geological repository for SNF/UNF and HLW in the USA were terminated in 2010, several other repository programs continue to make considerable progress 10 and deserve to be recognized. Panelists from national, multi-national, and international repository programs and organizations reported on the status and future plans as well as the perceived reasons underlying the success and the failure of components of their respective repository program. This session provided a forum for interested parties to present, exchange, and take advantage of, as appropriate, repository progress, challenges, concepts/designs and other lessons learned around the world, including site characterization, site selection, research and development, licensing, construction, operation, and public acceptance and participation. Panelists with presentations: Switzerland's Geological Repository Program for Radioactive Waste (Tim Vietor); Update on NWMO's Progress in Implementing Canada's Plan (Sarah Hirschorn); Cigeo Project Progress and Next Milestones (Corinne Bauer)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Enhancing segmentation fairness through curriculum learning and progressive loss: a centralized and federated perspective on radiograph analysis

Bias in medical image segmentation can lead to unequal performance across demographic subgroups, raising concerns about fairness and reliability in clinical AI systems. While deep learning models have achieved high segmentation accuracy, ensuring equitable performance across race and gender remains a significant challenge, particularly in privacy-sensitive healthcare environments. This study investigates fairness-aware medical image segmentation for hip and knee radiographs using deep learning models evaluated in both centralized and Federated Learning (FL) settings. We introduce Curriculum Learning (CL) strategies and Progressive Loss (PL) functions to regulate sample difficulty during training. In addition, we propose two novel fairness-oriented federated learning algorithms, Federated Intersection over Union (FedIoU) and Federated Intersection over Union with Outlier Analysis (FedIoUoutlier). Experiments are conducted using multiple segmentation backbones and simulated multi-site data partitions derived from the Osteoarthritis Initiative dataset. Model performance is evaluated using Intersection over Union (IoU), IoU standard deviation, Skewed Error Ratio (SER), and Min-Max Disparity across race and gender subgroups. Statistical significance was verified using paired t-tests to compare per-sample IoU performance against baseline configurations. Across both hip and knee segmentation tasks, curriculum learning and progressive loss strategies consistently improved segmentation accuracy and reduced demographic performance disparities in centralized training. In federated settings, fairness-aware aggregation further enhanced performance. Notably, FedIoUoutlier combined with balanced curriculum learning and tiered progressive loss achieved the highest mean IoU while yielding the lowest SER and Min-Max Disparity, indicating improved fairness without sacrificing accuracy. In several configurations, federated models matched or exceeded the performance of optimized centralized models, with statistically significant improvements in per-sample IoU over baseline configurations. The results demonstrate that structured training strategies and fairness-aware federated aggregation can jointly improve accuracy, stability, and demographic fairness in medical image segmentation. By integrating curriculum learning, progressive loss, and novel FL algorithms, this work provides a practical pathway toward equitable and privacy-preserving AI systems for medical imaging.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

NCOA5 is correlated with progression and prognosis in luminal breast cancer

Nuclear receptor coactivator 5 (NCOA5) is known to modulate ERα-mediated transcription and has been found to be involved in the progression of several malignancies. However, the potential correlation between NCOA5 and clinical outcome in patients with luminal breast cancer remains unknown. In the present study, we demonstrated that NCOA5 was significantly up-regulated in luminal breast cancer tissues compared with adjacent non-cancerous tissues both in validated cohort and TCGA cohort. Moreover, Kaplan-Meier analysis indicated that patients with high NOCA5 expression had significantly lower overall survival (P = 0.021). Cox regression analysis indicated that the high NOCA5 expression was independent high risk factor as well as old age (>60) and HER-2 expression (P = 0.039; P = 0.003; P = 0.005; respectively). This study provides new insights and evidences that NOCA5 over-expression was significantly correlated with progression and prognosis in luminal breast cancer. However, the precise cellular mechanisms for NOCA5 in luminal breast cancer need to be further explored. - Highlights: • NCOA5 is significantly over-expressed in human luminal breast cancer tissues. • NOCA5 was involved in the progression of luminal breast cancer. • NCOA5 can predict the progression of luminal breast cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

ELK1-induced upregulation of lncRNA HOXA10-AS promotes lung adenocarcinoma progression by increasing Wnt/β-catenin signaling

In recent years, increasing number of lncRNAs have been studied in lung adenocarcinoma (LAD). LncRNA HOXA10-AS was found to be upregulated in LAD tissues. Based on the data of TCGA database, HOXA10-AS was a prognostic factor for lung adenocarcinoma. This study aims to reveal the biological function of HOXA10-AS in LAD. qRT-PCR was applied to test expression levels of HOXA10-AS in both LAD tissues and cell lines. Next, transcription factor ELK1 was demonstrated to upregulate HOXA10-AS in LAD cells through performing bioinformatics analysis and dual luciferase activity. Loss of function assays were performed in two different LAD cell lines. Silenced HOXA10-AS was proved to inhibit LAD progression by affecting cell proliferation, cell apoptosis and cell metastasis and EMT progress. Western blot analysis revealed that HOXA10-AS increased Wnt/β-catenin signaling in LAD cell lines. Finally, rescue assays were carried out to identify the biological function of HOXA10-AS-Wnt/β-catenin signaling in LAD progression. In conclusion, ELK1-induced upregulation of HOXA10-AS improved LAD progression through increasing Wnt/β-catenin signaling.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

MiR-326 antagomir delays the progression of age-related cataract by upregulating FGF1-mediated expression of betaB2-crystallin

Highlights: • FGF-1 upregulates the expression of an anti-cataract protein βB2. • miR-326 inhibits FGF-1 expression and thereby decreases βB2 level. • miR-326 antagomir delay cataract progression via the function of miR-326. Age-related cataract, the most common cause of blindness worldwide, has been found closely associated with β-crystallin B2 (βB2 or CRYBB2). MicroRNAs (miRNAs) are the primary epigenetic regulators important for various biological processes. However, the role of miRNAs in the progression of lens cataract remains to be elucidated. In this study, we found a novel signal cascade miR-326–fibroblast growth factor 1 (FGF1)–βB2 modulating the progression of lens cataract. In brief, miR-326 exacerbated but its antagomirs attenuated H{sub 2}O{sub 2}-induced apoptosis of HLEC-B3 human lens epithelial cells. Dual-luciferase reporter assay and Western blot showed that miR-326 inhibited FGF1 expression by directly targeting its mRNA 3′-UTR. Consistent with this result, miR-326 antagomir enhanced FGF1 protein level. In addition to FGF1, miR-326 antagomir also enhanced βB2 expression and this enhancement was abolished by transfection of HLEC-B3 cells with FGF1 shRNA. These data demonstrated that miR-326 antagomir increased βB2 expression via upregulating FGF1, which was further confirmed by the studies in a rat model of selenite-induced cataract. This work suggests that miR-326 antagomir might be a promising candidate to prevent progression of age-related cataract.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Modeling strategy for progressive failure prediction in lithium-ion batteries under mechanical abuse

The prediction for the internal failure of lithium-ion batteries (LIBs) under external mechanical abuse loading remains a challenge for safe design. This paper systematically studies the modeling approach for progressive failure simulation and short-circuit prediction. Hemispherical indentation experiments are conducted on a battery specimen containing 5 representative sandwich (RS) layers to examine the deformation and progressive failure behavior of battery components. Three different models, viz, a high-fidelity detailed model, an intermediate homogenized model and a fully homogenized model, are developed and employed to simulate the progressive failure in a multi-layer battery specimen. The simulation results obtained using the three models all correlate well with experimental phenomena, capturing the overall stress-strain response and ultimate failure of the battery structure. The numerical results can help us understand the failure behavior and the mechanism for mechanical abuse-induced short circuit in LIBs. The feasibility of the three models for use in progressive failure prediction is compared by analyzing the capability of the models in predicting the critical failure events, the computational efficiency of the models, and the feasibility to implement coupled mechanical-electrochemical-thermal simulations. The results of this work provide useful insights on practical choices for the modeling strategy and safety design of LIBs under mechanical abuse conditions.

25 ENERGY STORAGE↗
DOE OSTI2021

Effect of disease progression on the podocyte cell cycle in Alport Syndrome

Progression of glomerulosclerosis is associated with loss of podocytes with subsequent glomerular tuft instability. It is thought that a diminished number of podocytes may be able to preserve tuft stability through cell hypertrophy associated with cell cycle reentry. At the same time, reentry into the cell cycle risks podocyte detachment if podocytes cross the G1/S checkpoint and undergo abortive cytokinesis. In order to study cell cycle dynamics during chronic kidney disease (CKD) development, we used a FUCCI model (fluorescence ubiquitination-based cell cycle indicator) of mice with X-linked Alport Syndrome. This model exhibits progressive CKD and expresses fluorescent reporters of cell cycle stage exclusively in podocytes. With the development of CKD, an increasing fraction of podocytes in vivo were found to be in G1 or later cell cycle stages. Podocytes in G1 and G2 were hypertrophic. Heterozygous female mice, with milder manifestations of CKD, showed G1 fraction numbers intermediate between wild-type and male Alport mice. Proteomic analysis of podocytes in different cell cycle phases showed differences in cytoskeleton reorganization and metabolic processes between G0 and G1 in disease. Additionally, in vitro experiments confirmed that damaged podocytes reentered the cell cycle comparable to podocytes in vivo. Importantly, we confirmed the upregulation of PDlim2, a highly expressed protein in podocytes in G1, in a patient with Alport Syndrome, confirming our proteomics data in the human setting. Thus, our data showed that in the Alport model of progressive CKD, podocyte cell cycle distribution is altered, suggesting that cell cycle manipulation approaches may have a role in the treatment of various progressive glomerular diseases characterized by podocytopenia.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

MicroRNA-630 inhibits breast cancer progression by directly targeting BMI1

Highlights: • MiR-630 initiates apoptosis, blocks cell cycle progression and suppresses cell proliferation in breast cancer cells. • BMI1, a member of polycomb group family, is identified as a novel direct target of miR-630. • BMI1 mediates miR-630-suppressed breast cancer progression including apoptosis induction, cell cycle arrest, and proliferation inhibition. MicroRNAs (miRNAs) play critical roles in breast cancer cell biological processes, including proliferation and apoptosis by inhibiting the expression of their target genes. Herein, we reported that miR-630 overexpression initiates apoptosis, blocks cell cycle progression and suppresses cell proliferation in breast cancer cells. Furthermore, BMI1, a member of polycomb group family, was identified as a direct target of miR-630, and there was a negative correlation between the expression levels of BMI1 and miR-630 in human breast cancer samples. With a series of biology approaches, subsequently, we proved that BMI1 was a functional downstream target of miR-630 and mediated the property of miR-630-dependent inhibition of breast cancer progression. Taken together, these findings provide further evidence on the tumor-suppression function of miR-630 in breast cancer, and clarify BMI1 as a novel functional target gene of miR-630.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Progressive transfer learning for advancing machine learning-based reduced-order modeling

Abstract To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs. For demonstration purposes, we evaluate the p-ROM with specific Barlow Twins ROMs (p-BT-ROMs) to highlight how progress learning can apply to multiple topological and physical problems with an emphasis on a small training set regime. The proposed p-BT-ROM framework has been tested using multiple examples, including transport, flow, and solid mechanics, to illustrate the importance of progressive knowledge transfer and its impact on model accuracy with reduced training samples. In both similar and different topologies, p-BT-ROM achieves improved model accuracy with much less training data. For instance, p-BT-ROM with four-parent (i.e., pre-trained models) outperforms the no-parent counterpart trained on data nine times larger. The p-ROM framework is poised to significantly enhance the capabilities of ML-based ROM approaches for scientific and engineering applications by mitigating data scarcity through progressively transferring knowledge.

97 MATHEMATICS AND COMPUTING↗