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Graph neural networks (GNNs) are widely used for analyzing graph-structural data and solving graph-related tasks due to their powerful expressiveness. However, existing off-the-shelf GNN-based models usually consist of no more than three layers. Deeper GNNs usually suffer from severe performance degradation due to several issues including the infamous "over-smoothing" issue, which restricts the further development of GNNs. In this article, we investigate the over-smoothing issue in deep GNNs. We discover that over-smoothing not only results in indistinguishable embeddings of graph nodes, but also alters and even corrupts their semantic structures, dubbed semantic over-smoothing. Existing techniques, e.g., graph normalization, aim at handling the former concern, but neglect the importance of preserving the semantic structures in the spatial domain, which hinders the further improvement of model performance. To alleviate the concern, we propose a cluster-keeping sparse aggregation strategy to preserve the semantic structure of embeddings in deep GNNs (especially for spatial GNNs). Particularly, our strategy heuristically redistributes the extent of aggregations for all the nodes from layers, instead of aggregating them equally, so that it enables aggregate concise yet meaningful information for deep layers. Without any bells and whistles, it can be easily implemented as a plug-and-play structure of GNNs via weighted residual connections. Last, we analyze the over-smoothing issue on the GNNs with weighted residual structures and conduct experiments to demonstrate the performance comparable to the state-of-the-arts.

Yang Fu,&ast; Xiao-Qin Huang,&ast; Hang-Bo Qu, Yan Zhi Ge, Xuan-Liang Ru Department of Orthopedics, Zhejiang Hospital, Hangzhou, Zhejiang Province, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xuan-Liang Ru, Department of Orthopedics, Zhejiang Hospital, No. 1229 Gudun Road, Hangzhou, Zhejiang Province, 310013, People’s Republic of China, Tel +86-133-3612-4875, Email zjyyrxl@qq.comBackground: Intervertebral disc degeneration (IVDD) is the main cause of low back pain (LBP), but the specific regulatory factors, pathways and specific molecular mechanisms remain unclear.Methods: We identified and quantitatively analyzed Pfirrmann Grade II (n=3) and Pfirrmann Grade IV (n=3) pulposus samples via MRI. The differential abundance of proteins in the samples was determined and quantitatively analyzed by relative and absolute quantitative analysis of the isotope marker levels combined with the liquid chromatography–tandem mass spectrometry (LC‒MSMS/MS).Results: A total of 70 proteins (30 significantly increased proteins (> 1.2-fold change) and 40 significantly decreased proteins (< 0.8-fold change)) showed different levels among the groups. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology (GO) enrichment analyses and Western blot analysis showed that CYCS, RAC1, and PSMD14 may play important roles in IVDD and that Epstein‒Barr virus infection, viral myocarditis, colorectal cancer, nonalcoholic fatty liver disease (NAFLD) and amyotrophic lateral sclerosis (ALS) are the main pathways involved in IVDD.Conclusion: CYCS, RAC1 and PSMD14 may play important roles in IVDD, and Epstein‒Barr virus infection, viral myocarditis, colorectal cancer, NAFLD and ALS may be the main pathways involved in IVDD.Keywords: TMT, LC‒MS/MS, proteomics, low back pain, nucleus pulposus

Knowledge graph (KG) has been fully considered in natural language generation (NLG) tasks. A KG can help models generate controllable text and achieve better performance. However, most existing related approaches still lack explainability and scalability in large-scale knowledge reasoning. In this work, we propose a novel CogNLG framework for KG-to-text generation tasks. Our CogNLG is implemented based on the dual-process theory in cognitive science. It consists of two systems: one system acts as the analytic system for knowledge extraction, and another is the perceptual system for text generation by using existing knowledge. During text generation, CogNLG provides a visible and explainable reasoning path. Our framework shows excellent performance on all datasets and achieves a BLEU score of 36.7, which increases by 6.7 compared to the best competitor.

The current study explores the interplay between central and peripheral processes in second language (L2) handwriting among bilinguals with diverse orthographic backgrounds. Our investigation delves into the cross-linguistic transfer effect in Spanish–English and Chinese–English bilinguals, emphasizing lexical frequency and phoneme-grapheme (P-O) consistency in spelling-to-dictation and immediate copying tasks. Results reveal that the interaction between central and peripheral processes in L2 handwritten production is shaped by the bilinguals' native language (L1) orthographic characteristics. Spanish–English bilinguals exhibited sensitivity to P-O consistency and the spread of this effect from central to peripheral processes throughout both tasks. Conversely, Chinese–English bilinguals showed heightened sensitivity to lexical frequency during orthographic planning and motor execution, particularly in the immediate copying task. In a broader context, these findings suggest that the parallel and cascading coordination of the L2 writing system is modulated by cross-linguistic variations. The implications of our findings hold relevance for handwriting production and bilingualism research.

The weakening Indian monsoon during recent warmer decades has caused greening and browning trends in water- and energy-limited terrestrial ecosystems of the Tibetan Plateau, respectively. Little is known, however, on the underlying mechanisms of such divergent responses of vegetation to the monsoon weakening. Herein, using the Indian monsoon index, high-resolution vegetation indices and multi-source climate data, we assessed the response of vegetation greenness to the Indian monsoon from 2000 to 2018 in two adjacent basins dominated by water- and energy-limited ecosystems and located in the southeastern Tibetan Plateau. The two basins showed opposite greenness trends due to direct and indirect effects of the weakening monsoon. The weakening monsoon increased vegetation greenness in the humid sites due to more heat exposure and solar radiation, but not in dry sites because of amplified drought stress. Thus, a weaker monsoon would not favor productivity in drought-prone ecosystems, whereas it would benefit energy-limited sites due to more radiation and warmer conditions associated to lower cloudiness. The effect of atmospheric circulation and its inter-decadal oscillation on basin-scale ecological processes should be therefore taken into account to refine earth system models and climate forecasts.

Xiaoguang Xiao,1 Jianhai Sun,2 Dongsheng Zhang,3 Linjun Li,4 Haibo Zhou,5 Yongjun Li,6 Quan Li,7 Zhongshi He,8 Yang Fu,9 Qiwen Duan,10 Guping Zheng,11 Ze Tang,12 Qian Chu,1,&ast; Yuan Chen1,&ast; 1Department of Oncology, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, People’s Republic of China; 2Department of Oncology, Hubei Zhongshan Hospital, Wuhan, Hubei, People’s Republic of China; 3Department of Oncology, Suizhou Hospital, Hubei University of Medicine, Suizhou, Hubei, People’s Republic of China; 4Department of Oncology, Hubei Provincial Hospital of Integrated Chinese and Western Medicine, Wuhan, Hubei, People’s Republic of China; 5Department of Oncology, Yichang Central People’s Hospital, Yichang, Hubei, People’s Republic of China; 6Department of Oncology, Yichang Second People’s Hospital, Yichang, Hubei, People’s Republic of China; 7Department of Oncology, Xiangyang Central Hospital, Xiangyang, Hubei, People’s Republic of China; 8Department of Oncology, Xiangyang No 1 People’s Hospital Affiliated to Hubei University of Medicine, Xiangyang, Hubei, People’s Republic of China; 9Department of Oncology, Xiangyang Hospital of Traditional Chinese Medicine, Xiangyang, Hubei, People’s Republic of China; 10Department of Oncology, Taihe Hospital, Shiyan, Hubei, People’s Republic of China; 11Department of Oncology, Xiaogan Central Hospital, Xiaogan, Hubei, People’s Republic of China; 12Department of Oncology, Huangshi Central Hospital, Huangshi, Hubei, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Qian Chu; Yuan Chen, Email qianchu@tjh.tjmu.edu.cn; chenyuantj2023@126.comBackground: Studies have shown that oral oxycontin tablets can be used for opioid titration. The European Society for Medical Oncology (ESMO) guidelines for adult cancer pain recommend opioid titration through the parenteral route, usually the intravenous or su

Purpose: Helicobacter pylori (H. pylori) is a significant risk factor for development of gastric cancer (GC), one of the deadliest malignancies in the world. However, the mechanism by which H. pylori induces gastric oncogenesis remains unclear. Here, we investigated the function of IL-6 in gastric oncogenesis and macrophage-epithelial cell interactions. Methods: We analyzed publicly available datasets to investigate the expression of IL-6 and infiltration of M2 macrophages in GC tissues, and determine the inter-cellular communication in the context of IL-6. Human gastric epithelial and macrophage cell lines (GES-1 and THP-1-derived macrophages, respectively) were used in mono- and co-culture experiments to investigate autocrine-and paracrine induction of IL-6 expression in response to H. pylori or IL-6 stimulation. Results: We found that IL-6 is highly expressed in GC and modulates survival. M2 macrophage infiltration is predominant in GC and drives an IL-6 mediated communication with gastric epithelium cells. In vitro, IL-6 triggers its own expression in GES-1 and THP-1-derived macrophages cells. In addition, these cell lines are able to upregulate each other's IL-6 levels in an autocrine fashion, which is enhanced by H. pylori stimulation. Conclusion: This study indicates that IL-6 in the tumor microenvironment is essential for intercellular communication. We show that H. pylori enhances an IL-6-driven autocrine and paracrine positive feedback loop between macrophages and gastric epithelial cells, which may contribute to gastric carcinogenesis.

3D hand-object interaction data is scarce due to the hardware constraints in scaling up the data collection process. In this paper, we propose HOIDiffusion for generating realistic and diverse 3D hand-object interaction data. Our model is a conditional diffusion model that takes both the 3D hand-object geometric structure and text description as inputs for image synthesis. This offers a more controllable and realistic synthesis as we can specify the structure and style inputs in a disentangled manner. HOIDiffusion is trained by leveraging a diffusion model pre-trained on large-scale natural images and a few 3D human demonstrations. Beyond controllable image synthesis, we adopt the generated 3D data for learning 6D object pose estimation and show its effectiveness in improving perception systems. Project page: https://mq-zhang1.github.io/HOIDiffusion, Comment: Project page: https://mq-zhang1.github.io/HOIDiffusion

An open problem in mobile manipulation is how to represent objects and scenes in a unified manner, so that robots can use it both for navigating in the environment and manipulating objects. The latter requires capturing intricate geometry while understanding fine-grained semantics, whereas the former involves capturing the complexity inherit to an expansive physical scale. In this work, we present GeFF (Generalizable Feature Fields), a scene-level generalizable neural feature field that acts as a unified representation for both navigation and manipulation that performs in real-time. To do so, we treat generative novel view synthesis as a pre-training task, and then align the resulting rich scene priors with natural language via CLIP feature distillation. We demonstrate the effectiveness of this approach by deploying GeFF on a quadrupedal robot equipped with a manipulator. We evaluate GeFF's ability to generalize to open-set objects as well as running time, when performing open-vocabulary mobile manipulation in dynamic scenes., Comment: Preprint. Project website is at: https://geff-b1.github.io

We introduce a new RGB-D object dataset captured in the wild called WildRGB-D. Unlike most existing real-world object-centric datasets which only come with RGB capturing, the direct capture of the depth channel allows better 3D annotations and broader downstream applications. WildRGB-D comprises large-scale category-level RGB-D object videos, which are taken using an iPhone to go around the objects in 360 degrees. It contains around 8500 recorded objects and nearly 20000 RGB-D videos across 46 common object categories. These videos are taken with diverse cluttered backgrounds with three setups to cover as many real-world scenarios as possible: (i) a single object in one video; (ii) multiple objects in one video; and (iii) an object with a static hand in one video. The dataset is annotated with object masks, real-world scale camera poses, and reconstructed aggregated point clouds from RGBD videos. We benchmark four tasks with WildRGB-D including novel view synthesis, camera pose estimation, object 6d pose estimation, and object surface reconstruction. Our experiments show that the large-scale capture of RGB-D objects provides a large potential to advance 3D object learning. Our project page is https://wildrgbd.github.io/., Comment: Our project page: https://wildrgbd.github.io

Vision Language Models (VLMs) have demonstrated remarkable performance in 2D vision and language tasks. However, their ability to reason about spatial arrangements remains limited. In this work, we introduce Spatial Region GPT (SpatialRGPT) to enhance VLMs' spatial perception and reasoning capabilities. SpatialRGPT advances VLMs' spatial understanding through two key innovations: (1) a data curation pipeline that enables effective learning of regional representation from 3D scene graphs, and (2) a flexible plugin module for integrating depth information into the visual encoder of existing VLMs. During inference, when provided with user-specified region proposals, SpatialRGPT can accurately perceive their relative directions and distances. Additionally, we propose SpatialRGBT-Bench, a benchmark with ground-truth 3D annotations encompassing indoor, outdoor, and simulated environments, for evaluating 3D spatial cognition in VLMs. Our results demonstrate that SpatialRGPT significantly enhances performance in spatial reasoning tasks, both with and without local region prompts. The model also exhibits strong generalization capabilities, effectively reasoning about complex spatial relations and functioning as a region-aware dense reward annotator for robotic tasks. Code, dataset, and benchmark will be released at https://www.anjiecheng.me/SpatialRGPT, Comment: Project Page: https://www.anjiecheng.me/SpatialRGPT

Novel view synthesis from a sparse set of input images is a challenging problem of great practical interest, especially when camera poses are absent or inaccurate. Direct optimization of camera poses and usage of estimated depths in neural radiance field algorithms usually do not produce good results because of the coupling between poses and depths, and inaccuracies in monocular depth estimation. In this paper, we leverage the recent 3D Gaussian splatting method to develop a novel construct-and-optimize method for sparse view synthesis without camera poses. Specifically, we construct a solution progressively by using monocular depth and projecting pixels back into the 3D world. During construction, we optimize the solution by detecting 2D correspondences between training views and the corresponding rendered images. We develop a unified differentiable pipeline for camera registration and adjustment of both camera poses and depths, followed by back-projection. We also introduce a novel notion of an expected surface in Gaussian splatting, which is critical to our optimization. These steps enable a coarse solution, which can then be low-pass filtered and refined using standard optimization methods. We demonstrate results on the Tanks and Temples and Static Hikes datasets with as few as three widely-spaced views, showing significantly better quality than competing methods, including those with approximate camera pose information. Moreover, our results improve with more views and outperform previous InstantNGP and Gaussian Splatting algorithms even when using half the dataset.

Temperature fluctuation damages the quality of starch-based foods, and the freeze-thaw cycle (FTC) can simulate this process. This study investigated how Mulberry leaf polysaccharides (MLP) affect the freeze-thaw stability and digestibility of wheat starch (WS). During FTC, MLP reduced the syneresis and retarded the water migration of WS gel. Meanwhile, MLP delayed the recrystallization of starch, which reduced the enthalpy value and crystallinity of WS gel during FTC. The addition of MLP significantly reduced the hardness of WS gel. Furthermore, MLP could prevent the deterioration of the rheological characteristic and preserve the network structure of the WS gel. In addition, MLP boosted the resistant starch and decreased the fast-digesting starch content in the freeze-thaw gel. Our findings suggested that MLP could enhance the freeze-thaw stability of WS gel, decrease the starch digestibility, and be an effective component in improving the quality of wheat starch-based frozen food.

Yajuan Zheng,1,&ast; Peipei Yuan,1,2,&ast; Zhenkai Zhang,1 Yang Fu,1,2 Saifei Li,1 Yuan Ruan,1 Panying Li,1 Yi Chen,1 Weisheng Feng,1– 3 Xiaoke Zheng1– 3 1College of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, 450008, People’s Republic of China; 2The Engineering and Technology Center for Chinese Medicine Development of Henan Province, Zhengzhou, 450046, People’s Republic of China; 3Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases Co-Constructed by Henan province & Education Ministry of P. R. China, Zhengzhou, 450008, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Weisheng Feng; Xiaoke Zheng, Email fwsh@hactcm.edu.cn; zhengxk.2006@163.comPurpose: Fatty oil of Descurainia Sophia (OIL) has poor stability and low solubility, which limits its pharmacological effects. We hypothesized that fatty oil nanoparticles (OIL-NPs) could overcome this limitation. The protective effect of OIL-NPs against monocrotaline-induced lung injury in rats was studied.Methods: We prepared OIL-NPs by wrapping fatty oil with polylactic-polyglycolide nanoparticles (PLGA-NPs) and conducted in vivo and in vitro experiments to explore its anti-pulmonary hypertension (PH) effect. In vitro, we induced malignant proliferation of pulmonary artery smooth muscle cells (RPASMC) using anoxic chambers, and studied the effects of OIL-NPs on the malignant proliferation of RPASMC cells and phospholipase C (PLC)/inositol triphosphate receptor (IP3R)/Ca2+ signal pathways. In vivo, we used small animal echocardiography, flow cytometry, immunohistochemistry, western blotting (WB), polymerase chain reaction (PCR) and metabolomics to explore the effects of OIL-NPs on the heart and lung pathological damage and PLC/IP3R/Ca2+ signal pathway of pulmonary hypertension rats.Results: We prepared fatty into OIL-NPs. In vitro, OIL-NPs could improve the mitochondrial function and inhibit the malignant proliferati

Ferroptosis is a new type of iron-dependent programmed cell death induced by lipid peroxidation. However, the underlying mechanisms and function in tumor therapy still remain undisclosed especially in post-transcription regulation. Here, we found that targeting AKT significantly induced GPX4 dependent ferroptosis and suppressed colorectal cancer growth both in vitro and in vivo. During this process, demethylase FTO was downregulated, which increased the m6A methylation level of GPX4, subsequently recognized by YTHDF2 and degraded. Prediction results showed that there are three potential methylated sites (193/647/766), and 193 site was identified as the right one, which was demethylated by FTO and read by YTHDF2. In parallel, AKT inhibition caused the accumulation of ROS which had a negative feedback on GPX4 expression. In addition, protective autophagy was initiated by MK2206 stimulation, while blocking autophagy further increased ferroptosis and markedly enhanced the anti-tumor activity of MK2206. In a word, inhibiting AKT activated ferroptosis through FTO/YTHDF2/GPX4 axis to suppress colon cancer progression, which raised FTO/GPX4 as potential biomarkers and targets in colorectal cancer therapy.

Elastocaloric cooling has no environmental effects during operation, and achieving a compact structure especially the driver is significant for its commercialization. In this article, a compact, standalone crankshaft driven single long NiTi tube compressive elastocaloric cooler is developed. A crankshaft driver was designed and fabricated to drive a compressive elastocaloric regenerator utilizing a single long polycrystalline superelastic NiTi shape memory alloy tube (outer diameter 5 mm, wall thickness 1 mm, and initial length 305 mm). A novel design of ceramic heat insulation plate was applied to the cooler to reduce the conduction heat loss from the NiTi tube to the stainless-steel loading heads. The cooling performance of the cooler was characterized using synchronized thermocouples and infrared thermography, and the specific cooling(heating) power, temperature span, and coefficient of performance of up to 65(125) W·kg−1, 9.1 K, and 5.0, respectively were measured. The progressions of the temperature span, specific cooling(heating) power, and coefficient of performance with the operation cycle and temperature lift were analyzed. An energy analysis revealed that the heat transfer fluid carried out only 14% of the latent heat generated by the NiTi tube, which demonstrated a potential to enhance the cooling performance by the improvement in the regenerator structure. © Copyright © 2023 ASHRAE.

Recent works on pose-based gait recognition have demonstrated the potential of using such simple information to achieve results comparable to silhouette-based methods. However, the generalization ability of pose-based methods on different datasets is undesirably inferior to that of silhouette-based ones, which has received little attention but hinders the application of these methods in real-world scenarios. To improve the generalization ability of pose-based methods across datasets, we propose a \textbf{G}eneralized \textbf{P}ose-based \textbf{Gait} recognition (\textbf{GPGait}) framework. First, a Human-Oriented Transformation (HOT) and a series of Human-Oriented Descriptors (HOD) are proposed to obtain a unified pose representation with discriminative multi-features. Then, given the slight variations in the unified representation after HOT and HOD, it becomes crucial for the network to extract local-global relationships between the keypoints. To this end, a Part-Aware Graph Convolutional Network (PAGCN) is proposed to enable efficient graph partition and local-global spatial feature extraction. Experiments on four public gait recognition datasets, CASIA-B, OUMVLP-Pose, Gait3D and GREW, show that our model demonstrates better and more stable cross-domain capabilities compared to existing skeleton-based methods, achieving comparable recognition results to silhouette-based ones. Code is available at https://github.com/BNU-IVC/FastPoseGait., Comment: ICCV Camera Ready

Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial task. This paper presents a visual analytics workflow for studying multivariate networks to extract associations between different structural and semantic characteristics of the networks (e.g., what are the combinations of attributes largely relating to the density of a social network?). The workflow consists of a neural-network-based learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. A key part of our design is a composite variable construction step that remodels nonlinear features obtained by neural networks into linear features that are intuitive to interpret. We demonstrate the capabilities of this workflow with multiple case studies on networks derived from social media usage and also evaluate the workflow through an expert interview., Comment: The previous version of this manuscript was accepted for publication by a journal. We decided to withdraw the version because the journal made an unacceptable requirement for us: change of the authors' affiliations

Despite Graph neural networks' significant performance gain over many classic techniques in various graph-related downstream tasks, their successes are restricted in shallow models due to over-smoothness and the difficulties of optimizations among many other issues. In this paper, to alleviate the over-smoothing issue, we propose a soft graph normalization method to preserve the diversities of node embeddings and prevent indiscrimination due to possible over-closeness. Combined with residual connections, we analyze the reason why the method can effectively capture the knowledge in both input graph structures and node features even with deep networks. Additionally, inspired by Curriculum Learning that learns easy examples before the hard ones, we propose a novel label-smoothing-based learning framework to enhance the optimization of deep GNNs, which iteratively smooths labels in an auxiliary graph and constructs many gradual non-smooth tasks for extracting increasingly complex knowledge and gradually discriminating nodes from coarse to fine. The method arguably reduces the risk of overfitting and generalizes better results. Finally, extensive experiments are carried out to demonstrate the effectiveness and potential of the proposed model and learning framework through comparison with twelve existing baselines including the state-of-the-art methods on twelve real-world node classification benchmarks., Comment: Accepted by AAAI 2024

While neural rendering has led to impressive advances in scene reconstruction and novel view synthesis, it relies heavily on accurately pre-computed camera poses. To relax this constraint, multiple efforts have been made to train Neural Radiance Fields (NeRFs) without pre-processed camera poses. However, the implicit representations of NeRFs provide extra challenges to optimize the 3D structure and camera poses at the same time. On the other hand, the recently proposed 3D Gaussian Splatting provides new opportunities given its explicit point cloud representations. This paper leverages both the explicit geometric representation and the continuity of the input video stream to perform novel view synthesis without any SfM preprocessing. We process the input frames in a sequential manner and progressively grow the 3D Gaussians set by taking one input frame at a time, without the need to pre-compute the camera poses. Our method significantly improves over previous approaches in view synthesis and camera pose estimation under large motion changes. Our project page is https://oasisyang.github.io/colmap-free-3dgs, Comment: Project Page: https://oasisyang.github.io/colmap-free-3dgs

High-fidelity 3D scene reconstruction has been substantially advanced by recent progress in neural fields. However, most existing methods train a separate network from scratch for each individual scene. This is not scalable, inefficient, and unable to yield good results given limited views. While learning-based multi-view stereo methods alleviate this issue to some extent, their multi-view setting makes it less flexible to scale up and to broad applications. Instead, we introduce training generalizable Neural Fields incorporating scene Priors (NFPs). The NFP network maps any single-view RGB-D image into signed distance and radiance values. A complete scene can be reconstructed by merging individual frames in the volumetric space WITHOUT a fusion module, which provides better flexibility. The scene priors can be trained on large-scale datasets, allowing for fast adaptation to the reconstruction of a new scene with fewer views. NFP not only demonstrates SOTA scene reconstruction performance and efficiency, but it also supports single-image novel-view synthesis, which is underexplored in neural fields. More qualitative results are available at: https://oasisyang.github.io/neural-prior, Comment: Project Page: https://oasisyang.github.io/neural-prior

We present FastPoseGait, an open-source toolbox for pose-based gait recognition based on PyTorch. Our toolbox supports a set of cutting-edge pose-based gait recognition algorithms and a variety of related benchmarks. Unlike other pose-based projects that focus on a single algorithm, FastPoseGait integrates several state-of-the-art (SOTA) algorithms under a unified framework, incorporating both the latest advancements and best practices to ease the comparison of effectiveness and efficiency. In addition, to promote future research on pose-based gait recognition, we provide numerous pre-trained models and detailed benchmark results, which offer valuable insights and serve as a reference for further investigations. By leveraging the highly modular structure and diverse methods offered by FastPoseGait, researchers can quickly delve into pose-based gait recognition and promote development in the field. In this paper, we outline various features of this toolbox, aiming that our toolbox and benchmarks can further foster collaboration, facilitate reproducibility, and encourage the development of innovative algorithms for pose-based gait recognition. FastPoseGait is available at https://github.com//BNU-IVC/FastPoseGait and is actively maintained. We will continue updating this report as we add new features., Comment: 10 pages, 4 figures

Yang Fu,1,2 Peipei Yuan,1,2 Yajuan Zheng,1 Yaxin Wei,1 Liyuan Gao,1 Yuan Ruan,1 Yi Chen,1 Panying Li,1 Weisheng Feng,1,2 Xiaoke Zheng1,2 1Department of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, 450046, People’s Republic of China; 2The Engineering and Technology Center for Chinese Medicine Development of Henan Province, Zhengzhou, 450046, People’s Republic of ChinaCorrespondence: Xiaoke Zheng; Weisheng Feng, Department of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, 450046, People’s Republic of China, Email zhengxk.2006@163.com; fwsh@hactcm.edu.cnPurpose: Pseudoephedrine (PSE) has rapid absorption and metabolism, which limits its pharmacologic actions. We postulated that pseudoephedrine nanoparticles (PSE-NPs) with high bioavailability could overcome this limitation. The defensive function of PSE-NPs nanoparticles against adriamycin-induced reproductive toxicity in mice was studied.Methods: We encapsulated PSE in polylactide-polyglycolide nanoparticles (PLGA-NPs) and verified their protective activity against testicular injury in vivo and in vitro.Results: We report a promising delivery system that loads PSE into PLGA-NPs and finally assembles it into a nanocomposite particle. In vitro, PSE-NPs reduced the adriamycin-induced apoptosis of GC-1 cells significantly, improved mitochondrial energy metabolism and promoted expression of the proteins related to the gonadotropin-releasing hormone (GnRh) receptor signaling pathway. In vivo, evaluation of sperm indices and histology showed that adriamycin could induce testicular toxicity. PSE-NPs significantly increased the sperm motility of mice, reduced the percent apoptosis and oxidative stress of testes, increased serum levels of GnRh, activated the GnRhR signaling pathway in testes and promoted expression of meiosis-related factors.Conclusion: In view of their safety and efficiency, these PSE-NPs have potential applications in alleviating adriamycin-induced reproductive toxicity.Graphic

We develop a method for lattice QCD calculation of the two-photon exchange contribution to the muonic-hydrogen Lamb shift. To demonstrate its feasibility, we present the first lattice calculation with a gauge ensemble at $m_\pi = 142$ MeV. By adopting the infinite-volume reconstruction method along with an optimized subtraction scheme, we obtain $\Delta E_{\text{TPE}} = -28.9(4.9)~\mu\text{eV} + 93.72~\mu\text{eV}/\text{fm}^2 \cdot\langle r_p^2 \rangle$, or $\Delta E_{\text{TPE}} = 37.4(4.9)~\mu$eV, which is consistent with the previous theoretical results in a range of 20-50 $\mu$eV., Comment: 7 pages + 3 pages supplemental material, 12 figures

Sorbitol has been recognized as one of the 12 most important platform chemicals obtainable from biomass. It is usually produced by glucose hydrogenation in the presence of metal-based catalysts. Noble metal catalysts such as Ru demonstrate better catalytic performance than non-noble ones like Ni, while the latter is more preferable on the industrial scale due to its low cost and availability. Optimizing non-noble catalysts systems to push their properties closer to noble metals is also crucial for gaining overall understanding on the nature and associated mechanisms of non-noble metal formulations. In this thesis, three aspects have been considered to design Ni -based metal nanoparticles catalysts – shape, size and composition. We first obtained bare Ni nanoparticles with different shapes, and analysed their morphologies, crystallinities and compositions. Amorphous structures and P-doping were found to be critical factors in enhancing catalytic performance. Secondly, carbon supports - carbon black (CB) and graphite nanoplatelets (GNPs) were introduced to disperse the NPs with different NPs sizes. Ni catalysts on CB support showed higher reactivities than with GNPs support, and Ni particles with an average size of 17 nm were demonstrated to exhibit the highest catalytic performance compared to other sizes. Thirdly, Fe was introduced into the Ni catalysts, forming Fe-Ni alloy catalysts, which achieved more than twice the glucose conversion of a monometallic Ni catalyst. At the end of the thesis, we found that sintering, surface oxidation and leaching were the main causes of deactivation, and the mitigation of leaching was achieved by changing the catalyst composition and reaction conditions and solvent., (SC - Sciences) -- UCL, 2022

Structure-activity relationships of Ni particles with different morphologies, crystallinities and compositions were investigated for glucose hydrogenation. Nanorods and nanospheres were obtained respectively by altering hydrogen pressure during synthesis, and nanobead chains were synthesized for analogy analysis following a known procedure. Glucose hydrogenation over Ni nanosphere catalysts exhibits the lowest reaction barrier (Ea = 32 kJ/mol) compared to nanorods and nanobead chains (98 kJ/mol), and a constant sorbitol selectivity of 100 % was obtained at a temperature of 140 °C. A comprehensive characterization with ATR-IR, Raman, XPS, ICP, P-XRD, HRTEM and SAED revealed that the combined factors of poly-crystallinity, amorphous structure and P- dopant make Ni nanospheres superior. This provides a valuable path in the process of designing better non-noble catalysts to definitely avoid the use of noble metals in the future in biomass valorization reactions.

Ni nanoparticles supported on carbon black (Ni/CB) with Ni particle sizes 6.9–23.5 nm were found to exhibit a size effect, with a significant impact on catalytic performance in the glucose hydrogenation reaction. A series of Ni nanoparticles with different sizes, as determined by statistical analysis of TEM images, were obtained by varying the synthesis temperature using a sol-gel method. The highest glucose conversion and sorbitol production in glucose hydrogenation was possible with Ni/CB catalysts with Ni particles size around 17 nm obtained at 550 °C. Similarly, graphene nanoplatelets (GNP) supported Ni with different particle sizes synthesized under different temperature conditions have an identical behaviour, that is, GNP-550 exhibits the highest catalytic performance. Therefore, it can be concluded that size effect appeared when the particle size is above 10 nm, which provides a reference for the design of subsequent catalysts for liquid phase hydrogenation reactions pertinent to biomass conversion.

Soluble epoxide hydrolase (sEH) is related to arachidonic acid cascade and is over-expressed in a variety of diseases, making sEH an attractive target for the treatment of pain as well as inflammatory-related diseases. A new series of memantyl urea derivatives as potent sEH inhibitors was obtained using our previous reported compound 4 as lead compound. A preferential modification of piperidinyl to 3-carbamoyl piperidinyl was identified for this series via structure-based rational drug design. Compound A20 exhibited moderate percentage plasma protein binding (88.6%) and better metabolic stability in vitro. After oral administration, the bioavailability of A20 was 28.6%. Acute toxicity test showed that A20 was well tolerated and there was no adverse event encountered at dose of 6.0 g/kg. Inhibitor A20 also displayed robust analgesic effect in vivo and dose-dependently attenuated neuropathic pain in rat model induced by spared nerve injury, which was better than gabapentin and sEH inhibitor (±)-EC-5026. In one word, the oral administration of A20 significantly alleviated pain and improved the health status of the rats, demonstrating that A20 was a promising candidate to be further evaluated for the treatment of neuropathic pain.

Yang Fu, Xiaohua Zhu, Ping Cao, Chen Shen, Xiaowen Qian, Hui Miao, Yi Yu, Hongsheng Wang, Xiaowen Zhai Department of Hematology, National Children’s Medical Center Children’s Hospital of Fudan University, Shanghai, People’s Republic of ChinaCorrespondence: Hongsheng Wang; Xiaowen Zhai, Department of Hematology, National Children’s Medical Center Children’s Hospital of Fudan University, 399 Wanyuan Road, Shanghai, 201102, People’s Republic of China, Tel +86 21 64931123, Fax +86 21 64931901, Email honswang@hotmail.com; xwzhai@fudan.edu.cnPurpose: To analyze the contribution of metagenomic next-generation sequencing (mNGS) in the guidance of clinical treatment and outcomes of infection during myelosuppression among children with hematological and neoplastic diseases.Patients and Methods: The clinical data and results of mNGS assay of febrile patients suspected of infection were retrospectively collected. The characteristics of pathogenic microorganisms and clinical course of myelosuppressed children with hematological diseases were summarized.Results: Our study included 70 patients (45 males) with a median age of 5 years (range: 0.5 to 13 y). During the study period, there were 96 events of suspected infection. According to comprehensive clinical diagnosis, 73 blood infections, 43 pneumonia and 2 urinary tract infections occurred. The positive rate of mNGS was significantly higher than that of traditional microbial detection (83.3% vs 17.7%). The main pathogens detected by mNGS were Pseudomonas aeruginosa, Acinetobacter, human herpesvirus, Candida and Aspergillus. The average duration of fever was 4.9 days and 11.6 days (P < 0.05), and the average cost of anti-infection treatment was RMB ¥ 28,077 and 39,898 (P < 0.05) among children received mNGS within 48 hours and more than 48 hours after the onset of infection symptoms.Conclusion: mNGS contributes to clinical management of children with infection during myelosuppression, especially among patients

We propose a generalizable neural radiance fields - MonoNeRF, that can be trained on large-scale monocular videos of moving in static scenes without any ground-truth annotations of depth and camera poses. MonoNeRF follows an Autoencoder-based architecture, where the encoder estimates the monocular depth and the camera pose, and the decoder constructs a Multiplane NeRF representation based on the depth encoder feature, and renders the input frames with the estimated camera. The learning is supervised by the reconstruction error. Once the model is learned, it can be applied to multiple applications including depth estimation, camera pose estimation, and single-image novel view synthesis. More qualitative results are available at: https://oasisyang.github.io/mononerf ., Comment: ICML 2023 camera ready version. Project page: https://oasisyang.github.io/mononerf

While 6D object pose estimation has wide applications across computer vision and robotics, it remains far from being solved due to the lack of annotations. The problem becomes even more challenging when moving to category-level 6D pose, which requires generalization to unseen instances. Current approaches are restricted by leveraging annotations from simulation or collected from humans. In this paper, we overcome this barrier by introducing a self-supervised learning approach trained directly on large-scale real-world object videos for category-level 6D pose estimation in the wild. Our framework reconstructs the canonical 3D shape of an object category and learns dense correspondences between input images and the canonical shape via surface embedding. For training, we propose novel geometrical cycle-consistency losses which construct cycles across 2D-3D spaces, across different instances and different time steps. The learned correspondence can be applied for 6D pose estimation and other downstream tasks such as keypoint transfer. Surprisingly, our method, without any human annotations or simulators, can achieve on-par or even better performance than previous supervised or semi-supervised methods on in-the-wild images. Our project page is: https://kywind.github.io/self-pose ., Comment: Project page: https://kywind.github.io/self-pose

6D object pose estimation is one of the fundamental problems in computer vision and robotics research. While a lot of recent efforts have been made on generalizing pose estimation to novel object instances within the same category, namely category-level 6D pose estimation, it is still restricted in constrained environments given the limited number of annotated data. In this paper, we collect Wild6D, a new unlabeled RGBD object video dataset with diverse instances and backgrounds. We utilize this data to generalize category-level 6D object pose estimation in the wild with semi-supervised learning. We propose a new model, called Rendering for Pose estimation network RePoNet, that is jointly trained using the free ground-truths with the synthetic data, and a silhouette matching objective function on the real-world data. Without using any 3D annotations on real data, our method outperforms state-of-the-art methods on the previous dataset and our Wild6D test set (with manual annotations for evaluation) by a large margin. Project page with Wild6D data: https://oasisyang.github.io/semi-pose ., Comment: Project page: https://oasisyang.github.io/semi-pose

Yang Fu, Ying-Chao Yan, Xuan-Liang Ru, Hang-Bo Qu Department of Orthopedics, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of ChinaCorrespondence: Hang-Bo Qu, Department of Orthopedics, Affiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China, Tel +86-153-5571-0890, Email quhb1222@163.comObjective: Chronic low back pain (CLBP) after percutaneous endoscopic transforaminal discectomy (PTED) surgery may be caused by preoperative lumbar microinstability (MI). However, there is a paucity of research on the relationship between lumbar microinstability and chronic low back pain. The purpose of this article is to assess the preoperative radiographic characteristics of patients and evaluate the effects of lumbar microinstability on patient-reported outcomes among single-level lumbar disc herniation (LDH) patients who underwent PTED.Methods: This study retrospectively reviewed the radiographic characteristics of a consecutive series of 127 patients with low back pain and leg pain caused by single-level LDH underwent PTED from August 2018 to March 2021. They were divided into three groups according to the radiographic parameters: the stable group (Group S), the dysfunctional group (Group D), and the microinstability group (Group M). The visual analogue scale (VAS) scores for leg and low back pain and Oswestry Disability Index (ODI) were evaluated preoperatively and postoperatively. Logistic regression analysis was used to identify independent risk factors for CLBP.Results: Compared with Group D and Group S, Group M had the highest ODI scores (P < 0.01) and VAS scores (low back pain) (P < 0.01) after 1 year, while there were no significant differences in the VAS scores for leg pain at different time points after surgery (P > 0.05). In addition, the logistic regression analysis results regarding CLBP revealed that muscle fatty degeneration on MRI (95% CI, 1.20– 8.

The antitumor effects of ionizing radiation (IR) are mediated in part through activation of innate and adaptive immunity. Here we report that gut microbiota influences tumor control following IR. Vancomycin decreased the abundance of butyrate-producing gut bacteria and enhanced antitumor responses to IR. Oral administration of Lachnospiraceae, a family of vancomycin-sensitive bacteria, was associated with increased systemic and intratumoral butyric acid levels and impaired the efficacy of IR in germ-free (GF) mice. Local butyrate inhibited STING-activated type I IFN expression in dendritic cells (DCs) through blockade of TBK1 and IRF3 phosphorylation, which abrogated IR-induced tumor-specific cytotoxic T cell immune responses without directly protecting tumor cells from radiation. Our findings demonstrate that the selective targeting of butyrate-producing microbiota may provide a novel therapeutic option to enhance tumor radiation sensitivity.

We develop a method for lattice QCD calculation of the two-photon exchange (TPE) contribution to the muonic-hydrogen Lamb shift. To demonstrate the feasibility of this method, we also present an exploratory study with a gauge ensemble at $m_\pi = 142$ MeV. By adopting the infinite-volume reconstruction (IVR) method along with an optimized subtraction scheme, we obtain a preliminary result of the TPE contribution which agrees well with previous calculation using other methods and one magnitude smaller compare to the large $\sim300~\mu$eV discrepancy for the proton radius puzzle., Comment: 11 pages, 4 figures, Proceedings of the 38th International Symposium on Lattice Field Theory, 26th-30th July 2021, Zoom/Gather@Massachusetts Institute of Technology

New solid polymer electrolytes are of particular interest for next-generation high-energy batteries since they can overcome the limited voltage window of conventional polyether-based electrolytes. Herein, a flame-retardant phosphorus-containing polymer, poly(dimethyl(methacryloyloxy)methyl phosphonate) (PMAPC1) is introduced as a promising polymer matrix. Free-standing membranes are easily obtained by mixing PMAPC1 with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) and a small amount of acetonitrile (AN). LiTFSI/AN mixed aggregates are formed that act as plasticizers and enable ionic conductivities up to 1.6 × 10–3 S cm–1 at 100 °C. The high content of LiTFSI used in our electrolytes leads to the formation of a stable LiF solid-electrolyte interphase, which can effectively suppress Li dendrites and the chemical degradation of AN in contact with Li. Accordingly the electrolyte membranes exhibit a wide electrochemical stability window above 4.7 V versus Li+/Li and fire-retardant properties due to the presence of the phosphorus-containing polymer. Atomistic molecular modeling simulations have been performed to determine the structure of the electrolytes on the microscopic scale and to rationalize the trends in ionic conductivity and the transport regime as a function of the electrolyte composition. Finally, our electrolyte membranes enable stable cycling performance for LiFePO4|PMAPC1 + LiTFSI + AN|Li batteries.

Carbon black (CB) supported Ni, Fe, or Fe-Ni alloy catalysts were synthesized by sol-gel to elucidate the reaction pathways over each catalyst, as well as synergistic effects in glucose to sorbitol hydrogenation. The bimetallic materials presented small and alloyed nanoparticles that were richer in reduced metallic sites at the surface than their monometallic counterparts. Glucose isomerization to fructose was favoured over Fe/CB, while glucose hydrogenation to sorbitol is the dominating pathway over Ni/CB catalyst. By contrast, sorbitol production was promoted and undesired isomerization was suppressed when Fe and Ni formed a nanoalloy. In addition, the alloy catalyst presented better stability than the corresponding monometallic catalyst. A comparison with a mechanical mixture of Fe/CB and Ni/CB monometallic catalysts demonstrated the synergy at the nanoscale in the alloy. By comparing different Fe:Ni ratios, the 1:1 formulation was identified as the best compromise to achieve a high activity while maintaining high sorbitol selectivity.

Integrated energy system (IES) is an effective solution for energy and environment problems. In view of the difficulty of traditional reliability assessment methods to reasonably and effectively assess the reliability of the IES, an energy supply reliability assessment method based on sequential Monte Carlo simulation is proposed in this study. The optimal operation of the system is realised by mobilising the multi-energy complementary characteristics during device failure. The reliability of the system under three different operation objectives is compared and analysed. Three new reliability indices are proposed, which take into account the supply of multiple energy loads and the differences among different energy. Finally, through the analysis of energy supply reliability under different objectives, the reasonability of the proposed indices and the flexible scheduling of energy flow under different objectives are verified; therefore, the IES can realise adaptive and targeted operation strategy during device failure. Also, the influence of different device faults on the system's energy supply reliability is ranked, which is of great significance to find the weak parts of the system and provide a reference for the system to improve energy supply reliability.

The preparation of compounds with novel atomic oxidation states and emergent properties is of fundamental interest in chemistry. As s-block elements, alkali-earth metals invariably show a +2 formal oxidation state at normal conditions, and among them, barium (Ba) presents the strongest chemical reactivity. Herein, we propose that novel valence states of Ba can be achieved in pressure-induced chalcogenides, where it also shows a feature of 5d-elements. First-principles swarm-intelligence structural search calculations identify three novel stoichiometric compounds: BaCh4 (Ch = O, S) containing Ba2+, Ba3Ch2 (Ch = S, Se, Te) with Ba+ and Ba2+, and Ba2Ch (Ch = Se, Te) with Ba+ cations. The pressure-induced drop of the Ba 5d level relative to Ba 6s is responsible for this unusual oxidation state. These compounds display captivating structural characters, such as Ba-centered polyhedra and chain-shaped Ch units. More interestingly still, the interaction between two Ba+ ions ensures their structural stability.

While significant progress has been made on understanding hand-object interactions in computer vision, it is still very challenging for robots to perform complex dexterous manipulation. In this paper, we propose a new platform and pipeline DexMV (Dexterous Manipulation from Videos) for imitation learning. We design a platform with: (i) a simulation system for complex dexterous manipulation tasks with a multi-finger robot hand and (ii) a computer vision system to record large-scale demonstrations of a human hand conducting the same tasks. In our novel pipeline, we extract 3D hand and object poses from videos, and propose a novel demonstration translation method to convert human motion to robot demonstrations. We then apply and benchmark multiple imitation learning algorithms with the demonstrations. We show that the demonstrations can indeed improve robot learning by a large margin and solve the complex tasks which reinforcement learning alone cannot solve. More details can be found in the project page: https://yzqin.github.io/dexmv, Comment: https://yzqin.github.io/dexmv

Tracking segmentation masks of multiple instances has been intensively studied, but still faces two fundamental challenges: 1) the requirement of large-scale, frame-wise annotation, and 2) the complexity of two-stage approaches. To resolve these challenges, we introduce a novel semi-supervised framework by learning instance tracking networks with only a labeled image dataset and unlabeled video sequences. With an instance contrastive objective, we learn an embedding to discriminate each instance from the others. We show that even when only trained with images, the learned feature representation is robust to instance appearance variations, and is thus able to track objects steadily across frames. We further enhance the tracking capability of the embedding by learning correspondence from unlabeled videos in a self-supervised manner. In addition, we integrate this module into single-stage instance segmentation and pose estimation frameworks, which significantly reduce the computational complexity of tracking compared to two-stage networks. We conduct experiments on the YouTube-VIS and PoseTrack datasets. Without any video annotation efforts, our proposed method can achieve comparable or even better performance than most fully-supervised methods., Comment: CVPR 2021

Ying Hou,1,* Peipei Yuan,1,* Yang Fu,1 Qi Zhang,1 Liyuan Gao,1 Yaxin Wei,1 Xiaoke Zheng,1,2 Weisheng Feng1,2 1College of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, People’s Republic of China; 2Engineering and Technology Center for Chinese Medicine Development of Henan Province, Henan Science and Technology Department, Zhengzhou, 450046, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaoke Zheng; Weisheng FengCollege of Pharmacy, Henan University of Chinese Medicine, No. 156, Jinshui East Road, Zhengzhou, 450046, People’s Republic of ChinaTel +86-371-6019-0296Email zhengxk.2006@163.com; fwsh@hactcm.edu.cnIntroduction: Hypertension is closely related to myocardial injury. Long-term hypertension can cause myocardial injury. Therefore, it is very important to find drugs to treat myocardial injury caused by hypertension. The aim of present study is to investigate the effects and mechanisms of geniposide on myocardial injuries in spontaneously hypertensive rats (SHR) and H9c2 cells induced by NaCl solution.Materials and Methods: Male Wistar–Kyoto (WKY) and SHR rats were given different doses of geniposide (25 mg/kg/d or 50 mg/kg/d) or distilled water for three consecutive weeks. Meanwhile, an H9c2 cell line-injury model was established using a solution of 150 μmol/L NaCl for 8 h. The cardiac function and related indexes of rats were detected.Results: The results showed that geniposide decreased the levels of COI and COIII, which promoted the phosphorylation of AMPK (p-AMPK) and enhanced the energy metabolism pathway. Geniposide improved myocardial apoptosis by regulating apoptotic proteins (p38, BAX and Bcl-2). Finally, heart function was regulated, and the markers of myocardial injury were decreased. Geniposide increased the viability of H9c2 cells treated with the NaCl solution and decreased the rate of apoptosis by regulating the levels of apoptotic proteins. Genip

Yang Fu,1 Xuewei Zhuang,2,3 Xiyan Xia,4 Xiaohui Li,3 Ke Xiao,3 Xiaojing Liu3 1Department of Reproductive Medicine Center, Jinan Maternity and Child Care Hospital, Jinan, 250001, People’s Republic of China; 2Department of Clinical Laboratory Medicine, Shandong Provincial Third Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250031, People’s Republic of China; 3Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China; 4Department of Microbial Immune, Jinan Vocational College of Nursing, Jinan, 250012, People’s Republic of ChinaCorrespondence: Xuewei Zhuang Tel +86-531-81656669Fax +86-531-86927544Email zhuangxuewei@sdu.edu.cnIntroduction: Syncytin-1 is a human endogenous retroviral (HERVW) envelope protein, which has been implicated in trophoblast and cancer cell fusions as well as in immunomodulatory functions. We investigated syncytin-1 expression and promoter methylation in non-small cell lung cancer (NSCLC) and the adjacent, para-carcinoma tissues. In addition, the correlation to patient survival differentiation of between 5-year survival and death group was analyzed.Methods: Survival ratio was calculated by Kaplan-Meier survival curve. Death risk assessment was executed by Cox risk regression model. The 5ʹ-LTR methylation level of HERVW promoter was detected by EpiTYPER method.Results: Syncytin-1 expression in NSCLC tissue was found to be significantly higher than in para-carcinoma tissues. Moreover, the 5-year survival group has a lower syncytin-1 expression than the death group. Clinical stage and the percentage of syncytin-1 positive cells were top risk factors according to Cox ratio risk regression model analysis. While the methylation level of the 5ʹ-LTR in HERVW gene promoter was relatively lower in NSCLC than para-carcinoma tissues, the methylation status of a CpG-2 site overlapping the Oct-1 binding site was found to be an important element potent

Yang Fu,* Yue Zheng,* Pei-Pei Wang, Yue-Yun Chen, Zhen-Yu Ding Department of Biotherapy, Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhen-Yu DingDepartment of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, People’s Republic of ChinaTel +86 028 8542 2562Fax +86 028 8516 4059Email dingzhenyu@scu.edu.cnBackground: Lymphoepithelioma-like carcinoma (LELC) is a rare malignant tumor of the lung. It is related to EB virus infection. Epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) are rarely found in this disease, while high level programmed cell death ligand 1 (PD-L1) expression is observed. Here a series of patients with advanced LELC treated with immunotherapy were summarized.Methods: This retrospective, observational study was conducted in patients who were pathologically confirmed, metastatic or recurrent LELC patients. Patients were prescribed with either chemotherapy or immunotherapy, according to treating physicians’ discretion.Results: A total of 27 patients were included in our study, 10 with immunotherapy (ICI group) and 17 with chemotherapy (Chemo group). The objective response rates (ORR) of the two groups were 80.0% and 70.5% (p=0.678), and disease control rates (DCR) were 100% and 88.2% (p=0.516). However, the response depth was better in the ICI group. Although the cohort of patients in the ICI group was in a disadvantageous state (both up-front and salvage), the progression-free survival (PFS) was much longer (15.0 and 7.9 m, p=0.005). The 1-year PFS rate in the ICI group was also much higher (40% and 5.9%, p=0.047).Conclusion: This study implicated the high efficiency of ICI therapy in this disease.Keywords: lymphoepithelioma-like carcinoma, ICIs, PD-L1, immunotherapy

Murong You, Liang Zhang, Xiaoxiang Zhang, Yang Fu, Xieping Dong Department of Orthopedics, Jiangxi Provincial People’s Hospital, Nanchang, Jiangxi Province 330006, People’s Republic of ChinaCorrespondence: Murong YouDepartment of Orthopedics, Jiangxi Provincial People’s Hospital, Nanchang, Jiangxi Province 330006, People’s Republic of ChinaEmail mr1058@163.comBackground: Studies have shown that microRNA (miRNA) regulates gene expression of osteoporosis (OS). It is known that miR-197-3p is abnormally expressed in osteoporosis. This study is to investigate the mechanism of miR-197-3p in regulating osteoblast differentiation.Methods: Rats were ovariectomized to establish an animal model of postmenopausal osteoporosis. The expression of miR-197-3p and KLF10 was detected in ovariectomized rat models. Primary osteoblasts and MC3T-E1 cells were divided into the control group, miR-197-3p inhibitor group, NC inhibitor group and miR-197-3p inhibitor + si-KLF10 group. The expression of miR-197-3p and Kruppel-like factor 10 (KLF10) was detected by qRT-PCR and Western blot. The relationship between miR-197-3p and KLF10 was analyzed by bioinformatics and luciferase reporter assay. Cell viability was evaluated by MTT assay. The ALP activity measurement and mineralization analysis were performed.Results: The expression of miR-197-3p was significantly raised in ovariectomized osteoporosis rats. During the differentiation of osteoblasts, the expression of miR-197-3p was significantly decreased, while the expression of KLF10 was significantly raised in primary osteoblasts and MC3E3T1 cells. The expression of RUNX2, ALP, OCN and OSX in miR-197-3p inhibitor group and MC3T3-E1 group was significantly raised, and the cell survival rate and mineralized nodule were raised as well. KLF10 may be the downstream target gene of miR-197-3p. After co-transfection of miR-197-3p inhibitor and si-klf10, ALP, Runx2, OCN and OSX mRNA, cell survival rate and miner

With increasing power consumption and the number of active units, such as distributed generators (DGs), energy storage systems (ESSs), and transportable ESSs (TESSs), in the distribution system, the requirements for the operability of the power system restoration service increase as well. In this study, the authors propose a new bi-level optimisation strategy for service restoration (SR) to minimise the final cost of the restoration process and to re-energise the maximum number of customers. The proposed method considers different power resupply methodologies, such as topology reconfiguration-based restoration, islanding operation of DG, and the application of TESS for SR, along with their optimal combination, to minimise the financial costs. The primary level comprises a reconfiguration approach and a genetic algorithm to optimise the islanding partition of an active distribution system; the second level consists of the fuzzy logic procedure to set up the combination of energy resources. In addition, this study considers the objective problem formulation as a combination of different subtasks, which represent the financial costs of various components during the restoration process. Case studies are conducted on a modified IEEE 39-bus distribution network, where the validity and effectiveness of the proposed strategy are demonstrated.