Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction

The correction of exposure-related issues is a pivotal component in enhancing the quality of images, offering substantial implications for various computer vision tasks. Historically, most methodologies have predominantly utilized spatial domain recovery, offering limited consideration to the potentialities of the frequency domain. Additionally, there has been a lack of a unified perspective towards low-light enhancement, exposure correction, and multi-exposure fusion, complicating and impeding the optimization of image processing. In response to these challenges, this paper proposes a novel methodology that leverages the frequency domain to improve and unify the handling of exposure correction tasks. Our method introduces Holistic Frequency Attention and Dynamic Frequency Feed-Forward Network, which replace conventional correlation computation in the spatial-domain. They form a foundational building block that facilitates a U-shaped Holistic Dynamic Frequency Transformer as a filter to extract global information and dynamically select important frequency bands for image restoration. Complementing this, we employ a Laplacian pyramid to decompose images into distinct frequency bands, followed by multiple restorers, each tuned to recover specific frequency-band information. The pyramid fusion allows a more detailed and nuanced image restoration process. Ultimately, our structure unifies the three tasks of low-light enhancement, exposure correction, and multi-exposure fusion, enabling comprehensive treatment of all classical exposure errors. Benchmarking on mainstream datasets for these tasks, our proposed method achieves state-of-the-art results, paving the way for more sophisticated and unified solutions in exposure correction

Magnetic propelled hydrogel microrobots for actively enhancing the efficiency of lycorine hydrochloride to suppress colorectal cancer

Research and development in the field of micro/nano-robots have made significant progress in the past, especially in the field of clinical medicine, where further research may lead to many revolutionary achievements. Through the research and experiment of microrobots, a controllable drug delivery system will be realized, which will solve many problems in drug treatment. In this work, we design and study the ability of magnetic-driven hydrogel microrobots to carry Lycorine hydrochloride (LH) to inhibit colorectal cancer (CRC) cells. We have successfully designed a magnetic field driven, biocompatible drug carrying hydrogel microsphere robot with Fe3O4 particles inside, which can achieve magnetic field response, and confirmed that it can transport drug through fluorescence microscope. We have successfully demonstrated the motion mode of hydrogel microrobots driven by a rotating external magnetic field. This driving method allows the microrobots to move in a precise and controllable manner, providing tremendous potential for their use in various applications. Finally, we selected drug LH and loaded it into the hydrogel microrobot for a series of experiments. LH significantly inhibited CRC cells proliferation in a dose- and time-dependent manner. LH inhibited the proliferation, mobility of CRC cells and induced apoptosis. This delivery system can significantly improve the therapeutic effect of drugs on tumors

Geo6D: Geometric Constraints Learning for 6D Pose Estimation

Numerous 6D pose estimation methods have been proposed that employ end-to-end regression to directly estimate the target pose parameters. Since the visible features of objects are implicitly influenced by their poses, the network allows inferring the pose by analyzing the differences in features in the visible region. However, due to the unpredictable and unrestricted range of pose variations, the implicitly learned visible feature-pose constraints are insufficiently covered by the training samples, making the network vulnerable to unseen object poses. To tackle these challenges, we proposed a novel geometric constraints learning approach called Geo6D for direct regression 6D pose estimation methods. It introduces a pose transformation formula expressed in relative offset representation, which is leveraged as geometric constraints to reconstruct the input and output targets of the network. These reconstructed data enable the network to estimate the pose based on explicit geometric constraints and relative offset representation mitigates the issue of the pose distribution gap. Extensive experimental results show that when equipped with Geo6D, the direct 6D methods achieve state-of-the-art performance on multiple datasets and demonstrate significant effectiveness, even with only 10% amount of data

Demographic patterns of two related desert shrubs with overlapping distributions in response to past climate changes

Numerous studies have revealed that past geological events and climatic fluctuations had profoundly affected the genetic structure and demographic patterns of species. However, related species with overlapping ranges may have responded to such environmental changes in different ways. In this study, we compared the genetic structure and population dynamics of two typical desert shrubs with overlapping distributions in northern China, Nitraria tangutorum and Nitraria sphaerocarpa, based on chloroplast DNA (cpDNA) variations and species distribution models. We sequenced two cpDNA fragments (trnH-trnA and atpH-atpI) in 633 individuals sampled from 52 natural populations. Twenty-four chlorotypes, including eight rare chlorotypes, were identified, and a single dominant haplotype (H4) widely occurred in the entire geographical ranges of the two species. There were also a few distinctive chlorotypes fixed in different geographical regions. Population structure analyses suggested that the two species had significantly different levels of total genetic diversity and interpopulation differentiation, which was highly likely correlated with the special habitat preferences of the two species. A clear phylogeographic structure was identified to exist among populations of N. sphaerocarpa, but not exist for N. tangutorum. The neutral tests, together with the distribution of pairwise differences revealed that N. tangutorum experienced a sudden demographic expansion, and its expansion approximately occurred between 21 and 7 Kya before present, while a rapid range expansion was not identified for N. sphaerocarpa. The ecological niche modeling (ENM) analysis indicated that the potential ranges of two species apparently fluctuated during the past and present periods, with obvious contraction in the Last Glacial Maximum (LGM) and recolonization in the present, respectively, comparing to the Last Interglacial (LIG). These findings suggest that the two species extensively occurred in the Northwest of China before the Quaternary, and the current populations of them originated from a few separated glacial refugia following their habitat fragmentation in the Quarternary. Our results provide new insights on the impact of past geological and climatic fluctuations on the population dynamics of desert plants in northwestern China, and further enforce the hypothesis that there were several independent glacial refugia for these species during the Quaternary glaciations

Preliminary study of improving immune tolerance in vivo of bioprosthetic heart valves through a novel antigenic removal method

The durability of bioprosthetic heart valves is always compromised by the inherent antigenicity of biomaterials. Decellularization has been a promising approach to reducing the immunogenicity of biological valves. However, current methods are insufficient in eliminating all immunogenicity from the biomaterials, necessitating the exploration of novel techniques. In this study, we investigated using a novel detergent, fatty alcohol polyoxyethylene ether sodium sulfate (AES), to remove antigens from bovine pericardium. Our results demonstrated that AES treatment achieved a higher pericardial antigen removal rate than traditional detergent treatments while preserving the mechanical properties and biocompatibility of the biomaterials. Moreover, we observed excellent immune tolerance in the in vivo rat model. Overall, our findings suggest that AES treatment is a promising method for preparing biological valves with ideal clinical application prospects

Novel Genetic Risk and Metabolic Signatures of Insulin Signaling and Androgenesis in the Anovulation of Polycystic Ovary Syndrome

Funding Information: The authors are grateful to all staff in the PCOSAct group for their effort in the collection of blood samples and clinical dataset which used in current study. Special thanks to Prof. Attila Toth from Institute of Physiological Chemistry, Dresden, Germany for the REC114 antibody. This study was supported by the National key Research and Development Program of China (2019YFC1709500); the National Collaboration Project of Critical Illness by Integrating Chinese Medicine and Western Medicine; the Project of Heilongjiang Province Innovation Team “TouYan;” the Yi-Xun Liu and Xiao-Ke Wu Academician Workstation; the Innovation Team of Reproductive Technique with Integrative Chinese Medicine and Western Medicine in Xuzhou City, China; Heilongjiang University of Chinese Medicine from the National Clinical Trial Base; Heilongjiang Provincial Clinical Research Center for Ovary Diseases; the Research Grant Council (T13-602/21-N, C5045-20EF, and 14122021); and Food and Health Bureau in Hong Kong, China (06171026). Ben Willem J. Mol is supported by a National Health and Medical Research Council (NHMRC) Investigator grant (GNT1176437). Ben Willem J. Mol reports consultancy for ObsEva and Merck and travel support from Merck. Xiaoke Wu, Yongyong Shi, and Chi Chiu Wang developed the research question and designed the study. Xiaoke Wu, Yongyong Shi, Yijuan Cao, and Chi Chiu Wang designed the analysis. Yongyong Shi and Zhiqiang Li contributed to the design of the experiment of whole-exome plus targeted SNP sequencing and the analysis, and interpreted the results. Jingshu Gao, Hui Chang, Duojia Zhang, Jing Cong, Yu Wang, Qi Wu, Xiaoxiao Han, Pui Wah Jacqueline Chung, Yiran Li, and Lin Zeng contributed to the experiment of metabolic profile and immunofluorescent staining and the analysis, and interpreted the results. Astrid Borchert and Hartmut Kuhn provided antibody support and advice. Xu Zheng and Lingxi Chen contributed to create the predictive model with deep machine learning. Jian Li, Qi Wu, Hongli Ma, Xu Zheng, and Lingxi Chen contributed to the analysis of the clinical characteristics and interpreted the results. Jian Li, Hongli Ma, Hui Chang, Jing Cong, and Chi Chiu Wang drafted the manuscript. All authors reviewed and revised the manuscript. Xiaoke Wu is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Xiaoke Wu, Chi Chiu Wang, Yijuan Cao, Jian Li, Zhiqiang Li, Hongli Ma, Jingshu Gao, Hui Chang, Duojia Zhang, Jing Cong, Yu Wang, Qi Wu, Xiaoxiao Han, Pui Wah Jacqueline Chung, Yiran Li, Xu Zheng, Lingxi Chen, Lin Zeng, Astrid Borchert, Hartmut Kuhn, Zijiang Chen, Ernest Hung Yu Ng, Elisabet Stener-Victorin, Heping Zhang, Richard S. Legro, Ben Willem J. Mol, and Yongyong Shi declare that they have no conflict of interest or financial conflicts to disclose. Funding Information: This study was supported by the National key Research and Development Program of China ( 2019YFC1709500 ); the National Collaboration Project of Critical Illness by Integrating Chinese Medicine and Western Medicine ; the Project of Heilongjiang Province Innovation Team “TouYan;” the Yi-Xun Liu and Xiao-Ke Wu Academician Workstation; the Innovation Team of Reproductive Technique with Integrative Chinese Medicine and Western Medicine in Xuzhou City , China; Heilongjiang University of Chinese Medicine from the National Clinical Trial Base ; Heilongjiang Provincial Clinical Research Center for Ovary Diseases ; the Research Grant Council ( T13-602/21-N , C5045-20EF , and 14122021 ); and Food and Health Bureau in Hong Kong, China ( 06171026 ). Publisher Copyright: © 2023Peer reviewedPublisher PD