LoG-CAN: local-global Class-aware Network for semantic segmentation of remote sensing images

Remote sensing images are known of having complex backgrounds, high intra-class variance and large variation of scales, which bring challenge to semantic segmentation. We present LoG-CAN, a multi-scale semantic segmentation network with a global class-aware (GCA) module and local class-aware (LCA) modules to remote sensing images. Specifically, the GCA module captures the global representations of class-wise context modeling to circumvent background interference; the LCA modules generate local class representations as intermediate aware elements, indirectly associating pixels with global class representations to reduce variance within a class; and a multi-scale architecture with GCA and LCA modules yields effective segmentation of objects at different scales via cascaded refinement and fusion of features. Through the evaluation on the ISPRS Vaihingen dataset and the ISPRS Potsdam dataset, experimental results indicate that LoG-CAN outperforms the state-of-the-art methods for general semantic segmentation, while significantly reducing network parameters and computation. Code is available at~\href{https://github.com/xwmaxwma/rssegmentation}{https://github.com/xwmaxwma/rssegmentation}.Comment: Accepted at ICASSP 202

Progressive Multi-view Human Mesh Recovery with Self-Supervision

To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e.g., motion capture, sport analysis) and robustness to single-view ambiguities. Existing solutions typically suffer from poor generalization performance to new settings, largely due to the limited diversity of image-mesh pairs in multi-view training data. To address this shortcoming, people have explored the use of synthetic images. But besides the usual impact of visual gap between rendered and target data, synthetic-data-driven multi-view estimators also suffer from overfitting to the camera viewpoint distribution sampled during training which usually differs from real-world distributions. Tackling both challenges, we propose a novel simulation-based training pipeline for multi-view human mesh recovery, which (a) relies on intermediate 2D representations which are more robust to synthetic-to-real domain gap; (b) leverages learnable calibration and triangulation to adapt to more diversified camera setups; and (c) progressively aggregates multi-view information in a canonical 3D space to remove ambiguities in 2D representations. Through extensive benchmarking, we demonstrate the superiority of the proposed solution especially for unseen in-the-wild scenarios.Comment: Accepted by AAAI202

PREF: Predictability Regularized Neural Motion Fields

Knowing the 3D motions in a dynamic scene is essential to many vision applications. Recent progress is mainly focused on estimating the activity of some specific elements like humans. In this paper, we leverage a neural motion field for estimating the motion of all points in a multiview setting. Modeling the motion from a dynamic scene with multiview data is challenging due to the ambiguities in points of similar color and points with time-varying color. We propose to regularize the estimated motion to be predictable. If the motion from previous frames is known, then the motion in the near future should be predictable. Therefore, we introduce a predictability regularization by first conditioning the estimated motion on latent embeddings, then by adopting a predictor network to enforce predictability on the embeddings. The proposed framework PREF (Predictability REgularized Fields) achieves on par or better results than state-of-the-art neural motion field-based dynamic scene representation methods, while requiring no prior knowledge of the scene.Comment: Accepted at ECCV 2022 (oral). Paper + supplementary materia

Sh-MARCH8 Inhibits Tumorigenesis via PI3K Pathway in Gastric Cancer

Background/Aims: To identify new treatment strategies for gastric cancer and to elucidate the mechanism underlying its pathophysiology, we transfected sh-MARCH8 into the human gastric cancer cell lines MKN-45 and AGS to investigate the roles of MARCH8 in gastric cancer. Methods: We used genetic engineering to construct the sh-MARCH8 interference plasmid and transfected it into gastric cancer cells. Colony formation assays and cell viability measurements were performed to detect the viability and proliferation of cancer cells. Wound healing assays were performed to estimate the migration and proliferation rates of the cells. Cell invasion assays were used to estimate the invasive abilities of the cells. Cell apoptosis analysis was performed by using flowing cytometry. Western blot analysis was performed to estimate the expression levels of proteins. Statistical analysis was performed using the SPSS 18.0 software. Student’s t-test was used to determine the significance of all pairwise comparisons of interest. Results: We observed that the transfection of sh-MARCH8 inhibited the survival and proliferation of MKN-45 and AGS cells. The migration and invasion of the MKN-45 and AGS cells were significantly decreased, and apoptosis was induced in comparison with the control cells. These results were further confirmed by data showing that sh-MARCH8 increased the BAX/BCL2 ratio in MKN-45 and AGS cells. We also observed that sh-MARCH8 inactivated the PI3K and ß-catenin stat3 signaling pathways by changing protein expression levels or the phosphorylation of related proteins. Conclusion: These data suggested that sh-March8 reduced viability and induced apoptosis of the MKN-45 and AGS cells through the PI3K and ß-catenin stat3 signaling pathways. Taken together, our data revealed that transfection of sh-MARCH8 into the MKN-45 and AGS gastric cancer cell lines inhibited their growth, and this approach may be useful as a novel strategy for gastric cancer therapy

Decoding Fujian’s cervical HPV landscape: unmasking dominance of non-16/18 HR-HPV and tailoring prevention strategies at a large scale

BackgroundPersistent HR-HPV causes cervical cancer, exhibiting geographic variance. Europe/Americas have higher HPV16/18 rates, while Asia/Africa predominantly have non-16/18 HR-HPV. This study in Fujian, Asia, explores non-16/18 HR-HPV infections, assessing their epidemiology and cervical lesion association for targeted prevention.MethodsA total of 101,621 women undergoing HPV screening at a hospital in Fujian Province from 2013 to 2019 were included. HPV genotyping was performed. A subset of 11,666 HPV-positive women with available histopathology results were analyzed to characterize HPV genotype distribution across cervical diagnoses.ResultsIn 101,621 samples, 24.5% tested positive for HPV. Among these samples, 17.3% exhibited single infections, while 7.2% showed evidence of multiple infections. The predominant non-16/18 high-risk HPV types identified were HPV 52, 58, 53, 51, and 81. Single HPV infections accounted for 64.1% of all HPV-positive cases, with 71.4% of these being non-16/18 high-risk HPV infections. Age-related variations were observed in 11,666 HPV-positive patients with pathological results. Cancer patients were older. In the cancer group, HPV52 (21.8%) and HPV58 (18.6%) were the predominant types, followed by HPV33, HPV31, and HPV53. Compared to single HPV16/18 infection, non-16/18 HPV predominated in LSIL. Adjusted odds ratios (OR) for LSIL were elevated: multiple HPV16/18 (OR 2.18), multiple non-16/18 HR-HPV (OR 2.53), and multiple LR-HPV (OR 2.38). Notably, solitary HPV16/18 conferred higher odds for HSIL and cancer.ConclusionOur large-scale analysis in Fujian Province highlights HPV 52, 58, 53, 51, and 81 as predominant non-16/18 HR-HPV types. Multiple HPV poses increased LSIL risks, while solitary HPV16/18 elevates HSIL and cancer odds. These findings stress tailored cervical cancer prevention, highlighting specific HPV impacts on lesion severity and guiding region-specific strategies for optimal screening in Asia, emphasizing ongoing surveillance in the vaccination era

Multi-Level Variational Spectroscopy using a Programmable Quantum Simulator

Energy spectroscopy is a powerful tool with diverse applications across various disciplines. The advent of programmable digital quantum simulators opens new possibilities for conducting spectroscopy on various models using a single device. Variational quantum-classical algorithms have emerged as a promising approach for achieving such tasks on near-term quantum simulators, despite facing significant quantum and classical resource overheads. Here, we experimentally demonstrate multi-level variational spectroscopy for fundamental many-body Hamiltonians using a superconducting programmable digital quantum simulator. By exploiting symmetries, we effectively reduce circuit depth and optimization parameters allowing us to go beyond the ground state. Combined with the subspace search method, we achieve full spectroscopy for a 4-qubit Heisenberg spin chain, yielding an average deviation of 0.13 between experimental and theoretical energies, assuming unity coupling strength. Our method, when extended to 8-qubit Heisenberg and transverse-field Ising Hamiltonians, successfully determines the three lowest energy levels. In achieving the above, we introduce a circuit-agnostic waveform compilation method that enhances the robustness of our simulator against signal crosstalk. Our study highlights symmetry-assisted resource efficiency in variational quantum algorithms and lays the foundation for practical spectroscopy on near-term quantum simulators, with potential applications in quantum chemistry and condensed matter physics

Advantages of nanocarriers for basic research in the field of traumatic brain injury

A major challenge for the efficient treatment of traumatic brain injury is the need for therapeutic molecules to cross the blood-brain barrier to enter and accumulate in brain tissue. To overcome this problem, researchers have begun to focus on nanocarriers and other brain-targeting drug delivery systems. In this review, we summarize the epidemiology, basic pathophysiology, current clinical treatment, the establishment of models, and the evaluation indicators that are commonly used for traumatic brain injury. We also report the current status of traumatic brain injury when treated with nanocarriers such as liposomes and vesicles. Nanocarriers can overcome a variety of key biological barriers, improve drug bioavailability, increase intracellular penetration and retention time, achieve drug enrichment, control drug release, and achieve brain-targeting drug delivery. However, the application of nanocarriers remains in the basic research stage and has yet to be fully translated to the clinic