A study of data-dependent triangulations for terrains

When simplifying and approximating surfaces with triangulations, we must determine what is a good triangulation. Thus, we need to define the criterion to measure the quality of a triangulation. There exist many useful criteria in the literature, such as Max-Min angle, Min-Max angle, least squares fit, Gaussian curvature, mean curvature criteria and so on. We introduce two further triangulations based on a minimum area criterion. This thesis compares these different criteria for terrain simplification for a variety of types of terrains

Evaluation and Research Analysis of Marine Ecological Suitability

Whether in the past, present and future, marine ecological environment is the most important part in the history of human development, we can call it the "patron saint" of mankind. It provides all kinds of resources and energy needed for social production, and plays an irreplaceable role in species diversity and ecological balance. However, the weakening of self-purification ability of marine ecosystem, the decline of pollution purification ability, the deterioration of marine ecological environment, and the decline of biological resources and biodiversity ,etc. all these bring fatal impact to coastal areas and even the whole terrestrial ecosystem, it is imminent to strengthen ecological protection. It is our bounden duty to protect the living environment of human. We have the right to enjoy the convenience brought by the natural environment, so we should fulfill the obligation to protect it.[Chinese Library Classification Number]   X31        [Document Code]  

Gut Microbial Metabolite TMAO Enhances Platelet Hyperreactivity and Thrombosis Risk

Normal platelet function is critical to blood hemostasis and maintenance of a closed circulatory system. Heightened platelet reactivity, however, is associated with cardiometabolic diseases and enhanced potential for thrombotic events. We now show gut microbes, through generation of trimethylamine N-oxide (TMAO), directly contribute to platelet hyperreactivity and enhanced thrombosis potential. Plasma TMAO levels in subjects (n \u3e 4,000) independently predicted incident (3 years) thrombosis (heart attack, stroke) risk. Direct exposure of platelets to TMAO enhanced sub-maximal stimulus-dependent platelet activation from multiple agonists through augmented Ca2+ release from intracellular stores. Animal model studies employing dietary choline or TMAO, germ-free mice, and microbial transplantation collectively confirm a role for gut microbiota and TMAO in modulating platelet hyperresponsiveness and thrombosis potential and identify microbial taxa associated with plasma TMAO and thrombosis potential. Collectively, the present results reveal a previously unrecognized mechanistic link between specific dietary nutrients, gut microbes, platelet function, and thrombosis risk

The Chinese pine genome and methylome unveil key features of conifer evolution

Conifers dominate the world's forest ecosystems and are the most widely planted tree species. Their giant and complex genomes present great challenges for assembling a complete reference genome for evolutionary and genomic studies. We present a 25.4-Gb chromosome-level assembly of Chinese pine (Pinus tabuliformis) and revealed that its genome size is mostly attributable to huge intergenic regions and long introns with high transposable element (TE) content. Large genes with long introns exhibited higher expressions levels. Despite a lack of recent whole-genome duplication, 91.2% of genes were duplicated through dispersed duplication, and expanded gene families are mainly related to stress responses, which may underpin conifers' adaptation, particularly in cold and/or arid conditions. The reproductive regulation network is distinct compared with angiosperms. Slow removal of TEs with high-level methylation may have contributed to genomic expansion. This study provides insights into conifer evolution and resources for advancing research on conifer adaptation and development

Topology-inspired Cross-domain Network for Developmental Cervical Stenosis Quantification

Developmental Canal Stenosis (DCS) quantification is crucial in cervical spondylosis screening. Compared with quantifying DCS manually, a more efficient and time-saving manner is provided by deep keypoint localization networks, which can be implemented in either the coordinate or the image domain. However, the vertebral visualization features often lead to abnormal topological structures during keypoint localization, including keypoint distortion with edges and weakly connected structures, which cannot be fully suppressed in either the coordinate or image domain alone. To overcome this limitation, a keypoint-edge and a reparameterization modules are utilized to restrict these abnormal structures in a cross-domain manner. The keypoint-edge constraint module restricts the keypoints on the edges of vertebrae, which ensures that the distribution pattern of keypoint coordinates is consistent with those for DCS quantification. And the reparameterization module constrains the weakly connected structures in image-domain heatmaps with coordinates combined. Moreover, the cross-domain network improves spatial generalization by utilizing heatmaps and incorporating coordinates for accurate localization, which avoids the trade-off between these two properties in an individual domain. Comprehensive results of distinct quantification tasks show the superiority and generability of the proposed Topology-inspired Cross-domain Network (TCN) compared with other competing localization methods.Comment: We have discovered that some authors' contributions have been overlooked. We need to spend some time confirming whether the authors adhere to the paper's authorship guidelines and whether their authorship order complies with the standards. After discussion with all co-authors, we decide to withdraw this pape

The optimal introversion angle and length of pedicle screw to avoid L1-S1 vascular damage

Abstract Background posterior pedicle screw fixation is common method, one of the most severe complications is iatrogenic vascular damage, no report investigated association of different introversion angles (INTAs) and length of pedicle screw. The aims were to investigate the optimal introversion angle and length of pedicle screw for improving the safety of the operation, and to analyze the differences of vascular damage types at L1-S1. Methods Lumbar CT imaging data from110 patients were analyzed by DICOM software, and all parameters were measured by new Cartesian coordinate system, INTAs (L1-L5:5°,10°,15°,S1: 0°, 5°,10°,15°), DO−AVC (the distance between the origin (O) with anterior vertebral cortex (AVC)), DAVC−PGVs (the distance between AVC and the prevertebral great vessels (PGVs)), DO−PGVs (the distance between the O and PGVs). At different INTAs, DAVC−PGVs were divided into four grades: Grade III: DAVC−PGVs ≤ 3 mm, Grade II: 3 mm  5 mm, and N: the not touching PGVs. Results The optimal INTA was 5° at L1-L3, the left was 5° and the right was 15° at L4, and screw length was less than 50 mm at L1-L4. At L5, the left optimal INTA was 5° and the right was 10°, and screw length was less than 45 mm. The optimal INTA was 15° at S1, and screw length was less than 50 mm. However, screw length was less than 40 mm when the INTA was 0° or 5° at S1. Conclusions At L5-S1, the risk of vascular injury is the highest. INTA and length of the pedicle screw in lumbar operation are closely related. 3 mm interval of screw length may be more preferable to reduce vascular damage

Multi-Source Deep Transfer Neural Network Algorithm

Transfer learning can enhance classification performance of a target domain with insufficient training data by utilizing knowledge relating to the target domain from source domain. Nowadays, it is common to see two or more source domains available for knowledge transfer, which can improve performance of learning tasks in the target domain. However, the classification performance of the target domain decreases due to mismatching of probability distribution. Recent studies have shown that deep learning can build deep structures by extracting more effective features to resist the mismatching. In this paper, we propose a new multi-source deep transfer neural network algorithm, MultiDTNN, based on convolutional neural network and multi-source transfer learning. In MultiDTNN, joint probability distribution adaptation (JPDA) is used for reducing the mismatching between source and target domains to enhance features transferability of the source domain in deep neural networks. Then, the convolutional neural network is trained by utilizing the datasets of each source and target domain to obtain a set of classifiers. Finally, the designed selection strategy selects classifier with the smallest classification error on the target domain from the set to assemble the MultiDTNN framework. The effectiveness of the proposed MultiDTNN is verified by comparing it with other state-of-the-art deep transfer learning on three datasets