87 research outputs found
Model Averaging by Cross-validation for Partially Linear Functional Additive Models
In this paper, we propose a model averaging approach for addressing model
uncertainty in the context of partial linear functional additive models. These
models are designed to describe the relation between a response and mixed-types
of predictors by incorporating both the parametric effect of scalar variables
and the additive effect of a functional variable. The proposed model averaging
scheme assigns weights to candidate models based on the minimization of a
multi-fold cross-validation criterion. Furthermore, we establish the asymptotic
optimality of the resulting estimator in terms of achieving the lowest possible
square prediction error loss under model misspecification. Extensive simulation
studies and an application to a near infrared spectra dataset are presented to
support and illustrate our method
A hierarchical semantic segmentation framework for computer vision-based bridge damage detection
Computer vision-based damage detection using remote cameras and unmanned
aerial vehicles (UAVs) enables efficient and low-cost bridge health monitoring
that reduces labor costs and the needs for sensor installation and maintenance.
By leveraging recent semantic image segmentation approaches, we are able to
find regions of critical structural components and recognize damage at the
pixel level using images as the only input. However, existing methods perform
poorly when detecting small damages (e.g., cracks and exposed rebars) and thin
objects with limited image samples, especially when the components of interest
are highly imbalanced. To this end, this paper introduces a semantic
segmentation framework that imposes the hierarchical semantic relationship
between component category and damage types. For example, certain concrete
cracks only present on bridge columns and therefore the non-column region will
be masked out when detecting such damages. In this way, the damage detection
model could focus on learning features from possible damaged regions only and
avoid the effects of other irrelevant regions. We also utilize multi-scale
augmentation that provides views with different scales that preserves
contextual information of each image without losing the ability of handling
small and thin objects. Furthermore, the proposed framework employs important
sampling that repeatedly samples images containing rare components (e.g.,
railway sleeper and exposed rebars) to provide more data samples, which
addresses the imbalanced data challenge
HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosis
Monitoring bridge health using vibrations of drive-by vehicles has various
benefits, such as no need for directly installing and maintaining sensors on
the bridge. However, many of the existing drive-by monitoring approaches are
based on supervised learning models that require labeled data from every bridge
of interest, which is expensive and time-consuming, if not impossible, to
obtain. To this end, we introduce a new framework that transfers the model
learned from one bridge to diagnose damage in another bridge without any labels
from the target bridge. Our framework trains a hierarchical neural network
model in an adversarial way to extract task-shared and task-specific features
that are informative to multiple diagnostic tasks and invariant across multiple
bridges. We evaluate our framework on experimental data collected from 2
bridges and 3 vehicles. We achieve accuracies of 95% for damage detection, 93%
for localization, and up to 72% for quantification, which are ~2 times
improvements from baseline methods
Damage-sensitive and domain-invariant feature extraction for vehicle-vibration-based bridge health monitoring
We introduce a physics-guided signal processing approach to extract a
damage-sensitive and domain-invariant (DS & DI) feature from acceleration
response data of a vehicle traveling over a bridge to assess bridge health.
Motivated by indirect sensing methods' benefits, such as low-cost and
low-maintenance, vehicle-vibration-based bridge health monitoring has been
studied to efficiently monitor bridges in real-time. Yet applying this approach
is challenging because 1) physics-based features extracted manually are
generally not damage-sensitive, and 2) features from machine learning
techniques are often not applicable to different bridges. Thus, we formulate a
vehicle bridge interaction system model and find a physics-guided DS & DI
feature, which can be extracted using the synchrosqueezed wavelet transform
representing non-stationary signals as intrinsic-mode-type components. We
validate the effectiveness of the proposed feature with simulated experiments.
Compared to conventional time- and frequency-domain features, our feature
provides the best damage quantification and localization results across
different bridges in five of six experiments.Comment: To appear in Proc. ICASSP2020, May 04-08, 2020, Barcelona, Spain.
IEE
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Notch2 controls hepatocyte-derived cholangiocarcinoma formation in mice.
Liver cancer comprises a group of malignant tumors, among which hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the most common. ICC is especially pernicious and associated with poor clinical outcome. Studies have shown that a subset of human ICCs may originate from mature hepatocytes. However, the mechanisms driving the trans-differentiation of hepatocytes into malignant cholangiocytes remain poorly defined. We adopted lineage tracing techniques and an established murine hepatocyte-derived ICC model by hydrodynamic injection of activated forms of AKT (myr-AKT) and Yap (YapS127A) proto-oncogenes. Wild-type, Notch1 flox/flox , and Notch2 flox/flox mice were used to investigate the role of canonical Notch signaling and Notch receptors in AKT/Yap-driven ICC formation. Human ICC and HCC cell lines were transfected with siRNA against Notch2 to determine whether Notch2 regulates biliary marker expression in liver tumor cells. We found that AKT/Yap-induced ICC formation is hepatocyte derived and this process is strictly dependent on the canonical Notch signaling pathway in vivo. Deletion of Notch2 in AKT/Yap-induced tumors switched the phenotype from ICC to hepatocellular adenoma-like lesions, while inactivation of Notch1 in hepatocytes did not result in significant histomorphological changes. Finally, in vitro studies revealed that Notch2 silencing in ICC and HCC cell lines down-regulates the expression of Sox9 and EpCAM biliary markers. Notch2 is the major determinant of hepatocyte-derived ICC formation in mice
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Combined Treatment with MEK and mTOR Inhibitors is Effective in In Vitro and In Vivo Models of Hepatocellular Carcinoma.
Background: Hepatocellular carcinoma (HCC) is the most common primary liver cancer histotype, characterized by high biological aggressiveness and scarce treatment options. Recently, we have established a clinically relevant murine HCC model by co-expressing activated forms of v-akt murine thymoma viral oncogene homolog (AKT) and oncogene c-mesenchymal-epithelial transition (c-Met) proto-oncogenes in the mouse liver via hydrodynamic tail vein injection (AKT/c-MET mice). Tumor cells from these mice demonstrated high activity of the AKT/ mammalian target of rapamycin (mTOR) and Ras/ Mitogen-activated protein kinase (MAPK) signaling cascades, two pathways frequently co-induced in human HCC. Methods: Here, we investigated the therapeutic efficacy of sorafenib, regorafenib, the MEK inhibitor PD901 as well as the pan-mTOR inhibitor MLN0128 in the AKT/c-Met preclinical HCC model. Results: In these mice, neither sorafenib nor regorafenib demonstrated any efficacy. In contrast, administration of PD901 inhibited cell cycle progression of HCC cells in vitro. Combined PD901 and MLN0128 administration resulted in a pronounced growth constraint of HCC cell lines. In vivo, treatment with PD901 or MLN0128 alone moderately slowed HCC growth in AKT/c-MET mice. Importantly, the simultaneous administration of the two drugs led to a stable disease with limited tumor progression in mice. Mechanistically, combined mitogen-activated extracellular signal-regulated kinase (MEK) and mTOR inhibition resulted in a stronger cell cycle inhibition and growth arrest both in vitro and in vivo. Conclusions: Our study indicates that combination of MEK and mTOR inhibitors might represent an effective therapeutic approach against human HCC
Spatial Deep Deconvolution U-Net for Traffic Analyses with Distributed Acoustic Sensing
Distributed Acoustic Sensing (DAS) that transforms city-wide fiber-optic
cables into a large-scale strain sensing array has shown the potential to
revolutionize urban traffic monitoring by providing a fine-grained, scalable,
and low-maintenance monitoring solution. However, the real-world application of
DAS is hindered by challenges such as noise contamination and interference
among closely traveling cars. In response, we introduce a self-supervised U-Net
model that can suppress background noise and compress car-induced DAS signals
into high-resolution pulses through spatial deconvolution. Our work extends
recent research by introducing three key advancements. Firstly, we perform a
comprehensive resolution analysis of DAS-recorded traffic signals, laying a
theoretical foundation for our approach. Secondly, we incorporate space-domain
vehicle wavelets into our U-Net model, enabling consistent high-resolution
outputs regardless of vehicle speed variations. Finally, we employ L-2 norm
regularization in the loss function, enhancing our model's sensitivity to
weaker signals from vehicles in remote traffic lanes. We evaluate the
effectiveness and robustness of our method through field recordings under
different traffic conditions and various driving speeds. Our results show that
our method can enhance the spatial-temporal resolution and better resolve
closely traveling cars. The spatial deconvolution U-Net model also enables the
characterization of large-size vehicles to identify axle numbers and estimate
the vehicle length. Monitoring large-size vehicles also benefits imaging deep
earth by leveraging the surface waves induced by the dynamic vehicle-road
interaction.Comment: This preprint was re-submitted as a revised version to the IEEE
Transactions on Intelligent Transportation Systems on June 27, 202
Comprehensive bulk and single-cell transcriptome profiling give useful insights into the characteristics of osteoarthritis associated synovial macrophages
BackgroundOsteoarthritis (OA) is a common chronic joint disease, but the association between molecular and cellular events and the pathogenic process of OA remains unclear.ObjectiveThe study aimed to identify key molecular and cellular events in the processes of immune infiltration of the synovium in OA and to provide potential diagnostic and therapeutic targets.MethodsTo identify the common differential expression genes and function analysis in OA, we compared the expression between normal and OA samples and analyzed the protein–protein interaction (PPI). Additionally, immune infiltration analysis was used to explore the differences in common immune cell types, and Gene Set Variation Analysis (GSVA) analysis was applied to analyze the status of pathways between OA and normal groups. Furthermore, the optimal diagnostic biomarkers for OA were identified by least absolute shrinkage and selection operator (LASSO) models. Finally, the key role of biomarkers in OA synovitis microenvironment was discussed through single cell and Scissor analysis.ResultsA total of 172 DEGs (differentially expressed genes) associated with osteoarticular synovitis were identified, and these genes mainly enriched eight functional categories. In addition, immune infiltration analysis found that four immune cell types, including Macrophage, B cell memory, B cell, and Mast cell were significantly correlated with OA, and LASSO analysis showed that Macrophage were the best diagnostic biomarkers of immune infiltration in OA. Furthermore, using scRNA-seq dataset, we also analyzed the cell communication patterns of Macrophage in the OA synovial inflammatory microenvironment and found that CCL, MIF, and TNF signaling pathways were the mainly cellular communication pathways. Finally, Scissor analysis identified a population of M2-like Macrophages with high expression of CD163 and LYVE1, which has strong anti-inflammatory ability and showed that the TNF gene may play an important role in the synovial microenvironment of OA.ConclusionOverall, Macrophage is the best diagnostic marker of immune infiltration in osteoarticular synovitis, and it can communicate with other cells mainly through CCL, TNF, and MIF signaling pathways in microenvironment. In addition, TNF gene may play an important role in the development of synovitis
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