Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration

The majority of point cloud registration methods currently rely on extracting features from points. However, these methods are limited by their dependence on information obtained from a single modality of points, which can result in deficiencies such as inadequate perception of global features and a lack of texture information. Actually, humans can employ visual information learned from 2D images to comprehend the 3D world. Based on this fact, we present a novel Cross-Modal Information-Guided Network (CMIGNet), which obtains global shape perception through cross-modal information to achieve precise and robust point cloud registration. Specifically, we first incorporate the projected images from the point clouds and fuse the cross-modal features using the attention mechanism. Furthermore, we employ two contrastive learning strategies, namely overlapping contrastive learning and cross-modal contrastive learning. The former focuses on features in overlapping regions, while the latter emphasizes the correspondences between 2D and 3D features. Finally, we propose a mask prediction module to identify keypoints in the point clouds. Extensive experiments on several benchmark datasets demonstrate that our network achieves superior registration performance.Comment: 8 pages, accepted by RAL 202

Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image

Powdery mildew, caused by the fungus Blumeria graminis, is a major winter wheat disease in China. Accurate delineation of powdery mildew infestations is necessary for site-specific disease management. In this study, high-resolution multispectral imagery of a 25 km2 typical outbreak site in Shaanxi, China, taken by a newly-launched satellite, SPOT-6, was analyzed for mapping powdery mildew disease. Two regions with high representation were selected for conducting a field survey of powdery mildew. Three supervised classification methods—artificial neural network, mahalanobis distance, and maximum likelihood classifier—were implemented and compared for their performance on disease detection. The accuracy assessment showed that the ANN has the highest overall accuracy of 89%, following by MD and MLC with overall accuracies of 84% and 79%, respectively. These results indicated that the high-resolution multispectral imagery with proper classification techniques incorporated with the field investigation can be a useful tool for mapping powdery mildew in winter wheat

Early diagenesis and benthic fluxes of redox-sensitive metals in eastern China shelf sediments

Thirteen Short sediment cores (30-50 cm) were collected from Bohai Sea, Yellow Sea and Changjiang Estuary in China, and the early diagenesis of several redox sensitive metals (Fe, Mn, Mo, U and V, referring to as RSMs) in sediment were studied. The recycling process of Mo and Mn was closely correlated with each other, generating benthic fluxes diffusing upward from sediment to overlying water column, and the flux rates are related to the organic carbon oxidation rates. The recycling of U and V were more tightly coupled with Fe oxides, generating benthic fluxes going downward into the sediment in most cores. Significant authigenic accumulation of U, in contrary to little to no accumulation of Mo and V, were found in the study region, even in Changjiang Estuary where hypoxic condition was often found during summer. Benthic diffusive fluxes were compared with authigenic mass accumulate rates (MAR), which indicated that, besides the benthic diffusion process, there are other processes controlling the authigenic accumulation of the RSMs. The close relationships between authigenic accumulation of RSMs with OCburial and OCburial with Sburial, indicating the authigenic accumulation of RSMs is a consequence of redox environment in shelf sediment, which directly influencing the organic carbon degradation process. Compared with other continental margin, moderate enrichment of U was found in China continental sediment. The authigenic U accumulation in BS and NYS sediments accounted for 20 - 68% of the Yellow River input, whilst in SYS sediments accounted for ~ 64% of the Yellow River and Changjiang River input, which acting as important U sinks that cannot be ignored

Influence of macrobenthos ( Meretrix meretrix Linnaeus ) on erosion‐accretion processes in intertidal flats: A case study from a cultivation zone

The activity of benthic organisms can strongly influence sediment dynamics in anintertidal flat. However, few studies have conducted a quantitative assessment of the effect of benthic organisms on erosion-accretion processes under field conditions. The aim of this study was to quantify the effects of the benthic clam Meretrix meretrix Linnaeus on bed erodibility and sediment erosion- accretion processes in an intertidal flat. Within the cultivation zone atsite A, M. meretrix is present in large numbers (up to 137 individuals/m2). On the other hand, site B is located outside the cultivation zone. At this site, which is only 500 m away from site A alongshore, M. meretrix forms a sparse population with only 3.7 individuals/m2. The results showed that the critical shear stress for erosion, denoted by τce, was 0.22 and 0.32 N/m2 at sites B and A, respectively, and the magnitudes of bed-level change were significantly higher at site A than site B. These results reveal the large effect of M. meretrix on decreasing τce, augmenting the erosion rate when the bed shear stress due to combined currents and waves, denoted by τcw, was higher than τce, and conversely enhancing the accretion rate when τcw < τce. The changes induced in these parameters are likely to have a large impact on model predictions of bed erodibility, sedimentary processes, and morphological evolution. Thus, integrated field measurements of hydrodynamic and bed-level changes, accompanied by simultaneous biological sampling, may help to improve the parameterization of hydro-sedimentary and morphodynamic models for shallow-water environmentsFil: Shi, Benwei. Tongji University; República de China. East China Normal University; República de ChinaFil: Pratolongo, Paula Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; ArgentinaFil: Duy, Yongfen. Nanjing Normal University; República de ChinaFil: Li, Jiasheng. Nanjing Normal University; República de ChinaFil: Yang, S.L.. East China Normal University; República de ChinaFil: Wu, Jihua. Fudan Universit. Institute of Biodiversity Science; República de ChinaFil: Xu, Kehui. State University of Louisiana; Estados UnidosFil: Wang, Ya Ping. East China Normal University; República de Chin

Necroptosis throws novel insights on patient classification and treatment strategies for hepatocellular carcinoma

IntroductionNecroptosis is a novel pattern of immunogenic cell death and has triggered an emerging wave in antitumor therapy. More evidence has suggested the potential associations between necroptosis and intra-tumoral heterogeneity. Currently, the underlying role of necroptosis remains elusive in hepatocellular carcinoma (HCC) at antitumor immunity and inter-tumoral heterogeneity.MethodsThis study enrolled a total of 728 HCC patients and 139 immunotherapy patients from eight public datasets. The consensus clustering approach was employed to depict tumor heterogeneity of cancer necroptosis. Subsequently, our study further decoded the heterogeneous clinical outcomes, genomic landscape, biological behaviors, and immune characteristics in necroptosis subtypes. For each patient, providing curative clinical recommendations and developing potential therapeutic drugs were used to promote precise medicine.ResultsWith the use of the weighted gene coexpression network analysis (WGCNA) algorithm, necroptosis-associated long non-coding RNAs (lncRNAs) (NALRs) were identified in HCC. Based on the NALR expression, two heterogeneous subtypes were decoded with distinct clinical outcomes. Compared to patients in C1, patients in C2 harbored superior pathological stage and presented more unfavorable overall survival and recurrence-free survival. Then, the robustness and reproducibility of necroptosis subtypes were further validated via the nearest template prediction (NTP) approach and classical immune phenotypes. Through comprehensive explorations, C1 was characterized by enriched immune-inflammatory and abundant immune infiltration, while C2 possessed elevated proliferative and metabolic activities and highly genomic instability. Moreover, our results indicated that C1 was more prone to obtain desirable benefits from immunotherapy. For patients in C2, numerous underlying therapeutic agents were developed, which might produce significant efficacy.ConclusionThis study identified two necroptosis subtypes with distinct characteristics, decoding the tumor heterogeneity. For an individualized patient, our work tailored corresponding treatment strategies to improve clinical management

Evaluation and Analysis of Hallucination in Large Vision-Language Models

Large Vision-Language Models (LVLMs) have recently achieved remarkable success. However, LVLMs are still plagued by the hallucination problem, which limits the practicality in many scenarios. Hallucination refers to the information of LVLMs' responses that does not exist in the visual input, which poses potential risks of substantial consequences. There has been limited work studying hallucination evaluation in LVLMs. In this paper, we propose Hallucination Evaluation based on Large Language Models (HaELM), an LLM-based hallucination evaluation framework. HaELM achieves an approximate 95% performance comparable to ChatGPT and has additional advantages including low cost, reproducibility, privacy preservation and local deployment. Leveraging the HaELM, we evaluate the hallucination in current LVLMs. Furthermore, we analyze the factors contributing to hallucination in LVLMs and offer helpful suggestions to mitigate the hallucination problem. Our training data and human annotation hallucination data will be made public soon.Comment: 11 pages, 5 figure

Research on the Application of Cross-Specialty Education and Situational Simulation Teaching in Operation Nursing Practice Teaching

Objective To examine the practical effect of inter-professional education and situational simulation teaching implemented in surgical nursing practice teaching. Methods On the whole, 100 undergraduate nursing students in the operating room of the hospital of the authors from May 2019 to August 2020 were selected. These students fell to two groups with the random number table method. The control received the regular teaching, and the research group were given the interprofessional education and context. The Simulation teaching was conducted to compare the theoretical knowledge, skill level, various abilities of the two groups of students, as well as the satisfaction of the operating room doctors to the nursing cooperation of the interns. Results The research group achieved higher theoretical knowledge and a higher skill level than the control (p < 0.05); the various abilities of the research group were higher than those of the control (p < 0.05); the operating room doctors of the research group were more satisfied with the nursing cooperation of interns, as compared with those of the control (p < 0.05). Conclusion In the surgical nursing practice teaching, the inter-professional education and the situational simulation teaching have significant effects and are worth clinical applications

CD8+ T cell trajectory subtypes decode tumor heterogeneity and provide treatment recommendations for hepatocellular carcinoma

IntroductionMounting evidence has revealed that the interactions and dynamic alterations among immune cells are critical in shaping the tumor microenvironment and ultimately map onto heterogeneous clinical outcomes. Currently, the underlying clinical significance of immune cell evolutions remains largely unexplored in hepatocellular carcinoma (HCC).MethodsA total of 3,817 immune cells and 1,750 HCC patients of 15 independent public datasets were retrieved. The Seurat and Monocle algorithms were used to depict T cell evolution, and nonnegative matrix factorization (NMF) was further applied to identify the molecular classification. Subsequently, the prognosis, biological characteristics, genomic variations, and immune landscape among distinct clusters were decoded. The clinical efficacy of multiple treatment approaches was further investigated.ResultsAccording to trajectory gene expression, three heterogeneous clusters with different clinical outcomes were identified. C2, with a more advanced pathological stage, presented the most dismal prognosis relative to C1 and C3. Eight independent external cohorts validated the robustness and reproducibility of the three clusters. Further explorations elucidated C1 to be characterized as lipid metabolic HCC, and C2 was referred to as cell-proliferative HCC, whereas C3 was defined as immune inflammatory HCC. Moreover, C2 also displayed the most conspicuous genomic instability, and C3 was deemed as “immune-hot”, having abundant immune cells and an elevated expression of immune checkpoints. The assessments of therapeutic intervention suggested that patients in C1 were suitable for transcatheter arterial chemoembolization treatment, and patients in C2 were sensitive to tyrosine kinase inhibitors, while patients in C3 were more responsive to immunotherapy. We also identified numerous underlying therapeutic agents, which might be conducive to clinical transformation in the future.ConclusionsOur study developed three clusters with distinct characteristics based on immune cell evolutions. For specifically stratified patients, we proposed individualized treatment strategies to improve the clinical outcomes and facilitate the clinical management