S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning

Seismic data often undergoes severe noise due to environmental factors, which seriously affects subsequent applications. Traditional hand-crafted denoisers such as filters and regularizations utilize interpretable domain knowledge to design generalizable denoising techniques, while their representation capacities may be inferior to deep learning denoisers, which can learn complex and representative denoising mappings from abundant training pairs. However, due to the scarcity of high-quality training pairs, deep learning denoisers may sustain some generalization issues over various scenarios. In this work, we propose a self-supervised method that combines the capacities of deep denoiser and the generalization abilities of hand-crafted regularization for seismic data random noise attenuation. Specifically, we leverage the Self2Self (S2S) learning framework with a trace-wise masking strategy for seismic data denoising by solely using the observed noisy data. Parallelly, we suggest the weighted total variation (WTV) to further capture the horizontal local smooth structure of seismic data. Our method, dubbed as S2S-WTV, enjoys both high representation abilities brought from the self-supervised deep network and good generalization abilities of the hand-crafted WTV regularizer and the self-supervised nature. Therefore, our method can more effectively and stably remove the random noise and preserve the details and edges of the clean signal. To tackle the S2S-WTV optimization model, we introduce an alternating direction multiplier method (ADMM)-based algorithm. Extensive experiments on synthetic and field noisy seismic data demonstrate the effectiveness of our method as compared with state-of-the-art traditional and deep learning-based seismic data denoising methods

H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization

Recently, tensor singular value decomposition (t-SVD) has emerged as a promising tool for hyperspectral image (HSI) processing. In the t-SVD, there are two key building blocks: (i) the low-rank enhanced transform and (ii) the accompanying low-rank characterization of transformed frontal slices. Previous t-SVD methods mainly focus on the developments of (i), while neglecting the other important aspect, i.e., the exact characterization of transformed frontal slices. In this letter, we exploit the potentiality in both building blocks by leveraging the \underline{\bf H}ierarchical nonlinear transform and the \underline{\bf H}ierarchical matrix factorization to establish a new \underline{\bf T}ensor \underline{\bf F}actorization (termed as H2TF). Compared to shallow counter partners, e.g., low-rank matrix factorization or its convex surrogates, H2TF can better capture complex structures of transformed frontal slices due to its hierarchical modeling abilities. We then suggest the H2TF-based HSI denoising model and develop an alternating direction method of multipliers-based algorithm to address the resultant model. Extensive experiments validate the superiority of our method over state-of-the-art HSI denoising methods

Taxonomic analysis of asteroids with artificial neural networks

We study the surface composition of asteroids with visible and/or infrared spectroscopy. For example, asteroid taxonomy is based on the spectral features or multiple color indices in visible and near-infrared wavelengths. The composition of asteroids gives key information to understand their origin and evolution. However, we lack compositional information for faint asteroids due to limits of ground-based observational instruments. In the near future, the Chinese Space Survey telescope (CSST) will provide multiple colors and spectroscopic data for asteroids of apparent magnitude brighter than 25 mag and 23 mag, respectively. For the aim of analysis of the CSST spectroscopic data, we applied an algorithm using artificial neural networks (ANNs) to establish a preliminary classification model for asteroid taxonomy according to the design of the survey module of CSST. Using the SMASS II spectra and the Bus-Binzel taxonomy system, our ANN classification tool composed of 5 individual ANNs is constructed, and the accuracy of this classification system is higher than 92 %. As the first application of our ANN tool, 64 spectra of 42 asteroids obtained in 2006 and 2007 by us with the 2.16-m telescope in the Xinglong station (Observatory Code 327) of National Astronomical Observatory of China are analyzed. The predicted labels of these spectra using our ANN tool are found to be reasonable when compared to their known taxonomic labels. Considering the accuracy and stability, our ANN tool can be applied to analyse the CSST asteroid spectra in the future.Comment: 10 pages,8 figures,accepted by AJ for publicatio

Taxonomic Analysis of Asteroids with Artificial Neural Networks

We study the surface composition of asteroids with visible and/or infrared spectroscopy. For example, asteroid taxonomy is based on the spectral features or multiple color indices in visible and near-infrared wavelengths. The composition of asteroids gives key information to understand their origin and evolution. However, we lack compositional information for faint asteroids due to the limits of ground-based observational instruments. In the near future, the Chinese Space Survey Telescope (CSST) will provide multiple colors and spectroscopic data for asteroids of apparent magnitude brighter than 25 and 23 mag, respectively. With the aim of analyzing the CSST spectroscopic data, we applied an algorithm using artificial neural networks (ANNs) to establish a preliminary classification model for asteroid taxonomy according to the design of the survey module of CSST. Using the SMASS II spectra and the Bus-Binzel taxonomic system, our ANN classification tool composed of five individual ANNs is constructed, and the accuracy of this classification system is higher than 92%. As the first application of our ANN tool, 64 spectra of 42 asteroids obtained by us in 2006 and 2007 with the 2.16 m telescope in the Xinglong station (Observatory Code 327) of National Astronomical Observatory of China are analyzed. The predicted labels of these spectra using our ANN tool are found to be reasonable when compared to their known taxonomic labels. Considering its accuracy and stability, our ANN tool can be applied to analyze CSST asteroid spectra in the future.Peer reviewe

Enhancing Stem Cell Therapy for Cartilage Repair in Osteoarthritis—A Hydrogel Focused Approach

Stem cells hold tremendous promise for the treatment of cartilage repair in osteoarthritis. In addition to their multipotency, stem cells possess immunomodulatory effects that can alleviate inflammation and enhance cartilage repair. However, the widely clinical application of stem cell therapy to cartilage repair and osteoarthritis has proven difficult due to challenges in large-scale production, viability maintenance in pathological tissue site and limited therapeutic biological activity. This review aims to provide a perspective from hydrogel-focused approach to address few key challenges in stem cell-based therapy for cartilage repair and highlight recent progress in advanced hydrogels, particularly microgels and dynamic hydrogels systems for improving stem cell survival, retention and regulation of stem cell fate. Finally, progress in hydrogel-assisted gene delivery and genome editing approaches for the development of next generation of stem cell therapy for cartilage repair in osteoarthritis are highlighted

Analysis and Design of Fiber Microprobe Displacement Sensors Including Collimated Type and Convergent Type for Ultra-Precision Displacement Measurement

In this paper, a fiber optic microprobe displacement sensor is proposed considering characteristics of micro-Michelson interference structure and its components. The principal error of micro Fabry–Perot interferometric structure is avoided, and high-precision interferometric displacement measurement is realized. The collimated microprobe and convergent microprobe are analyzed, simulated, and designed for the purposes of measuring long-distance displacement and small spot rough surface, respectively. The core parameters of the probes’ internal components are mapped to coupling efficiency and contrast of the sensor measurements, which provides a basis for the probes’ design. Finally, simulation and experimental testing of the two probes show that the collimated probe’s working distance and converging probe’s tolerance angle can reach 40 cm and ±0.5°, respectively. The designed probes are installed in the fiber laser interferometer, and a displacement resolution of 0.4 nm is achieved

A Feedback Loop Formed by ATG7/Autophagy, FOXO3a/miR-145 and PD-L1 Regulates Stem-Like Properties and Invasion in Human Bladder Cancer

Programmed cell death protein 1 (PD-1) and its ligand PD-L1 blockade have been identified to target immune checkpoints to treat human cancers with durable clinical benefit. Several studies reveal that the response to PD-1-PD-L1 blockade might correlate with PD-L1 expression levels in tumor cells. However, the mechanistic pathways that regulate PD-L1 protein expression are not understood. Here, we reported that PD-L1 protein is regulated by ATG7-autophagy with an ATG7-initiated positive feedback loop in bladder cancer (BC). Mechanistic studies revealed that ATG7 overexpression elevates PD-L1 protein level mainly through promoting autophagy-mediated degradation of FOXO3a, thereby inhibiting its initiated miR-145 transcription. The lower expression of miR-145 increases pd-l1 mRNA stability due to the reduction of its direct binding to 3′-UTR of pd-l1 mRNA, in turn leading to increasing in pd-l1 mRNA stability and expression, and finally enhancing stem-like property and invasion of BC cells. Notably, overexpression of PD-L1 in ATG7 knockdown cells can reverse the defect of autophagy activation, FOXO3A degradation, and miR-145 transcription attenuation. Collectively, our results revealed a positive feedback loop to promoting PD-L1 expression in human BC cells. Our study uncovers a novel molecular mechanism for regulating pd-l1 mRNA stability and expression via ATG7/autophagy/FOXO3A/miR-145 axis and reveals the potential for using combination treatment with autophagy inhibitors and PD-1/PD-L1 immune checkpoint blockade to enhance therapeutic efficacy for human BCs