161 research outputs found
Prolyl-4-Hydroxylase α Subunit 2 Promotes Breast Cancer Progression and Metastasis by Regulating Collagen Deposition
BACKGROUND: Increased collagen deposition provides physical and biochemical signals to support tumor growth and invasion during breast cancer development. Therefore, inhibition of collagen synthesis and deposition has been considered a strategy to suppress breast cancer progression. Collagen prolyl-4-hydroxylase α subunit 2 (P4HA2), an enzyme hydroxylating proline residues in -X-Pro-Gly- sequences, is a potential therapeutic target for the disorders associated with increased collagen deposition. However, expression and function of P4HA2 in breast cancer progression are not well investigated.
METHODS: Gene co-expression analysis was performed in the published microarray datasets to identify potential regulators of collagen I, III, and IV in human breast cancer tissue. Expression of P4HA2 was silenced by shRNAs, and its activity was inhibited by 1, 4-DPCA, a prolyl-4-hydroxylase inhibitor. Three-dimensional culture assay was used to analyze roles of P4HA2 in regulating malignant phenotypes of breast cancer cells. Reduced deposition of collagen I and IV was detected by Western blotting and immunofluorescence. Control and P4HA2-silenced breast cancer cells were injected into fat pad and tail vein of SCID mice to examine effect of P4HA2 on tumor growth and lung metastasis.
RESULTS: Using gene co-expression analysis, we showed that P4HA2 was associated with expression of Col1A1, Col3A1, and Col4A1 during breast cancer development and progression. P4HA2 mRNA levels were significantly upregulated in breast cancer compared to normal mammary tissue. Increased mRNA levels of P4HA2 correlated with poor clinical outcome in breast cancer patients, which is independent of estrogen receptor status. Silencing P4HA2 expression or treatment with the P4HA inhibitor significantly inhibited cell proliferation and suppressed aggressive phenotypes of breast cancer cells in 3D culture, accompanied by reduced deposition of collagen I and IV. We also found that knockdown of P4HA2 inhibited mammary tumor growth and metastasis to lungs in xenograft models.
CONCLUSION: These results suggest the critical role of P4HA2 in breast cancer progression and identify P4HA2 as a potential therapeutic target and biomarker for breast cancer progression
Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition
When labeled data is insufficient, semi-supervised learning with the
pseudo-labeling technique can significantly improve the performance of
automatic speech recognition. However, pseudo-labels are often noisy,
containing numerous incorrect tokens. Taking noisy labels as ground-truth in
the loss function results in suboptimal performance. Previous works attempted
to mitigate this issue by either filtering out the nosiest pseudo-labels or
improving the overall quality of pseudo-labels. While these methods are
effective to some extent, it is unrealistic to entirely eliminate incorrect
tokens in pseudo-labels. In this work, we propose a novel framework named
alternative pseudo-labeling to tackle the issue of noisy pseudo-labels from the
perspective of the training objective. The framework comprises several
components. Firstly, a generalized CTC loss function is introduced to handle
noisy pseudo-labels by accepting alternative tokens in the positions of
incorrect tokens. Applying this loss function in pseudo-labeling requires
detecting incorrect tokens in the predicted pseudo-labels. In this work, we
adopt a confidence-based error detection method that identifies the incorrect
tokens by comparing their confidence scores with a given threshold, thus
necessitating the confidence score to be discriminative. Hence, the second
proposed technique is the contrastive CTC loss function that widens the
confidence gap between the correctly and incorrectly predicted tokens, thereby
improving the error detection ability. Additionally, obtaining satisfactory
performance with confidence-based error detection typically requires extensive
threshold tuning. Instead, we propose an automatic thresholding method that
uses labeled data as a proxy for determining the threshold, thus saving the
pain of manual tuning.Comment: Accepted by IEEE/ACM Transactions on Audio, Speech and Language
Processing (TASLP), 202
Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions
Though deep learning-based object detection methods have achieved promising
results on the conventional datasets, it is still challenging to locate objects
from the low-quality images captured in adverse weather conditions. The
existing methods either have difficulties in balancing the tasks of image
enhancement and object detection, or often ignore the latent information
beneficial for detection. To alleviate this problem, we propose a novel
Image-Adaptive YOLO (IA-YOLO) framework, where each image can be adaptively
enhanced for better detection performance. Specifically, a differentiable image
processing (DIP) module is presented to take into account the adverse weather
conditions for YOLO detector, whose parameters are predicted by a small
convolutional neural net-work (CNN-PP). We learn CNN-PP and YOLOv3 jointly in
an end-to-end fashion, which ensures that CNN-PP can learn an appropriate DIP
to enhance the image for detection in a weakly supervised manner. Our proposed
IA-YOLO approach can adaptively process images in both normal and adverse
weather conditions. The experimental results are very encouraging,
demonstrating the effectiveness of our proposed IA-YOLO method in both foggy
and low-light scenarios.Comment: AAAI 2022, Preprint version with Appendi
Boosting Cross-Domain Speech Recognition with Self-Supervision
The cross-domain performance of automatic speech recognition (ASR) could be
severely hampered due to the mismatch between training and testing
distributions. Since the target domain usually lacks labeled data, and domain
shifts exist at acoustic and linguistic levels, it is challenging to perform
unsupervised domain adaptation (UDA) for ASR. Previous work has shown that
self-supervised learning (SSL) or pseudo-labeling (PL) is effective in UDA by
exploiting the self-supervisions of unlabeled data. However, these
self-supervisions also face performance degradation in mismatched domain
distributions, which previous work fails to address. This work presents a
systematic UDA framework to fully utilize the unlabeled data with
self-supervision in the pre-training and fine-tuning paradigm. On the one hand,
we apply continued pre-training and data replay techniques to mitigate the
domain mismatch of the SSL pre-trained model. On the other hand, we propose a
domain-adaptive fine-tuning approach based on the PL technique with three
unique modifications: Firstly, we design a dual-branch PL method to decrease
the sensitivity to the erroneous pseudo-labels; Secondly, we devise an
uncertainty-aware confidence filtering strategy to improve pseudo-label
correctness; Thirdly, we introduce a two-step PL approach to incorporate target
domain linguistic knowledge, thus generating more accurate target domain
pseudo-labels. Experimental results on various cross-domain scenarios
demonstrate that the proposed approach effectively boosts the cross-domain
performance and significantly outperforms previous approaches.Comment: Accepted by IEEE/ACM Transactions on Audio, Speech and Language
Processing (TASLP), 202
Wav2vec-S: Semi-Supervised Pre-Training for Low-Resource ASR
Self-supervised pre-training could effectively improve the performance of
low-resource automatic speech recognition (ASR). However, existing
self-supervised pre-training are task-agnostic, i.e., could be applied to
various downstream tasks. Although it enlarges the scope of its application,
the capacity of the pre-trained model is not fully utilized for the ASR task,
and the learned representations may not be optimal for ASR. In this work, in
order to build a better pre-trained model for low-resource ASR, we propose a
pre-training approach called wav2vec-S, where we use task-specific
semi-supervised pre-training to refine the self-supervised pre-trained model
for the ASR task thus more effectively utilize the capacity of the pre-trained
model to generate task-specific representations for ASR. Experiments show that
compared to wav2vec 2.0, wav2vec-S only requires a marginal increment of
pre-training time but could significantly improve ASR performance on in-domain,
cross-domain and cross-lingual datasets. Average relative WER reductions are
24.5% and 6.6% for 1h and 10h fine-tuning, respectively. Furthermore, we show
that semi-supervised pre-training could close the representation gap between
the self-supervised pre-trained model and the corresponding fine-tuned model
through canonical correlation analysis.Comment: Accepted by Interspeech 202
Spatial representativeness and uncertainty of eddy covariance carbon flux measurements for upscaling net ecosystem productivity to the grid scale
Eddy covariance (EC) measurements are often used to validate net ecosystem productivity (NEP) estimated from satellite remote sensing data and biogeochemical models. However, EC measurements represent an integrated flux over their footprint area, which usually differs from respective model grids or remote sensing pixels. Quantifying the uncertainties of scale mismatch associated with gridded flux estimates by upscaling single EC tower NEP measurements to the grid scale is an important but not yet fully investigated issue due to limited data availability as well as knowledge of flux variability at the grid scale. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) built a flux observation matrix that includes 17 EC towers within a 5 km × 5 km area in a heterogeneous agricultural landscape in northwestern China, providing an unprecedented opportunity to evaluate the uncertainty of upscaling due to spatial representative differences at the grid scale. Based on the HiWATER-MUSOEXE data, this study evaluated the spatial representativeness and uncertainty of EC CO2 flux measurements for upscaling to the grid scale using a scheme that combines a footprint model and a model-data fusion method. The results revealed the large spatial variability of gross primary productivity (GPP), ecosystem respiration (Re), and NEP within the study site during the growing season from 10 June to 14 September 2012. The variability of fluxes led to high variability in the representativeness of single EC towers for grid-scale NEP. The systematic underestimations of a single EC tower may reach 92(±11)%, 30(±11)%, and 165(±150)% and the overestimations may reach 25(±14)%, 20(±13)%, and 40(±33)% for GPP, Re, and NEP, respectively. This finding suggests that remotely sensed NEP at the global scale (e.g., MODIS products) should not be validated against single EC tower data in the case of heterogeneous surfaces. Any systematic bias should be addressed before upscaling EC data to grid scale. Otherwise, most of the systematic bias may be propagated to grid scale due to the scale dependence of model parameters. A systematic bias greater than 20% of the EC measurements can be corrected effectively using four indicators proposed in this study. These results will contribute to the understanding of spatial representativeness of EC towers within a heterogeneous landscape, to upscaling carbon fluxes from the footprint to the grid scale, to the selection of the location of EC towers, and to the reduction in the bias of NEP products by using an improved parameterization scheme of remote-sensing driven models, such as VPRM
Chaperone Hsp47 Drives Malignant Growth and Invasion by Modulating an ECM Gene Network
The extracellular matrix (ECM) is a determining factor in the tumor microenvironment that restrains or promotes malignant growth. In this report, we show how the molecular chaperone protein Hsp47 functions as a nodal hub in regulating an ECM gene transcription network. A transcription network analysis showed that Hsp47 expression was activated during breast cancer development and progression. Hsp47 silencing reprogrammed human breast cancer cells to form growth-arrested and/or noninvasive structures in 3D cultures, and to limit tumor growth in xenograft assays by reducing deposition of collagen and fibronectin. Coexpression network analysis also showed that levels of microRNA(miR)-29b and -29c were inversely correlated with expression of Hsp47 and ECM network genes in human breast cancer tissues. We found that miR-29 repressed expression of Hsp47 along with multiple ECM network genes. Ectopic expression of miR-29b suppressed malignant phenotypes of breast cancer cells in 3D culture. Clinically, increased expression of Hsp47 and reduced levels of miR-29b and -29c were associated with poor survival outcomes in breast cancer patients. Our results show that Hsp47 is regulated by miR-29 during breast cancer development and progression, and that increased Hsp47 expression promotes cancer progression in part by enhancing deposition of ECM proteins
A Novel Macroblock Level Rate Control Method for Stereo Video Coding
To compress stereo video effectively, this paper proposes a novel macroblock (MB) level rate control method based on binocular perception. A binocular just-notification difference (BJND) model based on the parallax matching is first used to describe binocular perception. Then, the proposed rate control method is performed in stereo video coding with four levels, namely, view level, group-of-pictures (GOP) level, frame level, and MB level. In the view level, different proportions of bitrates are allocated for the left and right views of stereo video according to the prestatistical rate allocation proportion. In the GOP level, the total number of bitrates allocated to each GOP is computed and the initial quantization parameter of each GOP is set. In the frame level, the target bits allocated to each frame are computed. In the MB level, visual perception factor, which is measured by the BJND value of MB, is used to adjust the MB level bit allocation, so that the rate control results in line with the human visual characteristics. Experimental results show that the proposed method can control the bitrate more accurately and get better subjective quality of stereo video, compared with other methods
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