1,168 research outputs found
Membranous sheath of a fan worm functions as a high-performance energy absorber and stabilizer
No full textMultilayered structure at the macroscale is a prevailing pathway for developing high-performance energy absorbers. Nowadays, most multilayer-structure-based energy absorbers are constructed with rigid materials, but research on utilizing soft materials as energy-absorbing devices is still rare. By understanding the function of membranous sheathes in the stimuli responsiveness of fan worms (Polychaeta: Sabellastarte australiensis), in this work, we report a robust biological energy absorber made of multilayer-structured soft material. Our study reveals that structural features govern the mechanical performance and the energy-absorption capacity of this soft energy absorber. Ultimately, through kinematic analysis of fan worms, we elucidate the advantage of soft-material-based energy absorbers in stabilizing assistance compared with rigid counterparts. Our work takes a significant step toward understanding the design principle of soft-material-based energy absorbers and may shed light on flexible protective devices for soft robotics.</p
From a literary man to a model Confucian : Han Yu's image in the Tang anecdotes
No full textThe early Northern Song witnesses the commencement of an elevation of Han Yu’s status in intellectual history. However, the image of Han Yu as a cultural hero established by the Northern Song intellectuals departs greatly from how Han Yu perceived himself and how he was perceived during his day. This paper examines how Han Yu was perceived by the Tang intellectuals after him by reading the anecdotes preserved in the five compilations produced during the 9th and 10th centuries: Wei Xuan’s Liu binke jiahualu, Li Zhao’s Guoshibu, Zhao Lin’s Yinhualu, Zhang Du’s Xuanshizhi and Wang Dingbao’s Zhiyan. This thesis argues that the Tang literati’s recognition of Han Yu’s commitment to some basic Confucian moral values proceeded gradually throughout the second half of the Tang dynasty. Contrary to Peter K. Bol’s assertion that the transformation of Han Yu’s image from a literary genius to a model Confucian took place after the Tang collapsed, the paper supplements Anna M. Shields’ speculation and contends that Han Yu’s understanding and practice of Confucius’ teaching, along with his literature, had increasingly drawn attention from the intellectual community by the late Tang period. Not only were Han Yu’s writings exalted, his consistent concern for public affairs, his stoic antagonism to Buddhism and Daoism, and his adherence to Confucian moral values in public and private life were also highlighted. Confronting unprecedented political depression and moral deficiency, the late Tang intellectual community portrayed Han Yu as a model Confucian, an image Han Yu could not imagine during his day.Arts, Faculty ofAsian Studies, Department ofGraduat
Potential role of methylglyoxal in inducing Parkinson’s disease
No full textAbout one and a half million people are affected by Parkinson’s disease (PD) in the United States. It is a neurodegenerative disorder on dopaminergic neurons in the substantia nigra pars compacta, with primary effects on motion disorder. The causes for the Parkinsonism are generally divide into genetic factors like mutation and environmental factors like heavy metals as well as some endogenous or exogenous agents. 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and other MPTP-like neurotoxins such as salsolinol are well known and considered to cause Parkinsonism due to their in vivo metabolic products’ specific toxicity for dopaminergic neurons. Recent studies showed that the diabetic patient is more likely to develop the Parkinson’s disease. It has been suspected that the methylglyoxal, one metabolic product of glycolysis, is associated with this phenomenon because the body concentration of methylglyoxal for diabetic patients will be three to six times higher than healthy people. The dopamine-derived tetrahydroisoquinoline (TIQ), 1-acetyl-6,7-dihydroxyl-1,2,3,4-tetrahydro- isoquinoline (ADTIQ), has been detected in frozen brain tissue of human with Parkinson’s disease. It can be produced by the reaction of dopamine and methylglyoxal in physiological condition and has been regarded a novel endogenous neurotoxins. In our study, in addition to ADTIQ, 6,7-dihydroxy-1,2,3,4-tetrahydro- isoquinoline or called norsalsolinol was also detected in the reaction system of dopamine and methylglyoxal by LC-MS/MS. And based on the Pictect-Spengler reaction, there are two regioselectivities for the products under neutral conditions. So there are two isomers for both ADTIQ and norsalsolinol. Additionally, according to the proposed reaction mechanism, the norsalsolinol was generated from ADTIQ by deacylation. On the other hand, with different ratios of dopamine and methylglyoxal, different levels of reaction under the physiological condition were also monitored by HPLC-UV. It revealed that when the ratio of dopamine and methylglyoxal is over 1:10, the reaction could almost be completed within 24 hours. In conclusion, we showed for the first time that the potential neurotoxin norsalsolinol could be generated through the reaction of dopamine and methylglyoxal. And also can be one reason why the norsalsolinol was detected in the frozen brain of humans.M.S.Includes bibliographical referencesby Siyu Li
Human action parsing in untrimmed videos and its applications for elderly people healthcare
No full textMotivated by the demands and desires to improve the living quality of elderly people, in this thesis, we investigate visual human behavior analysis, and aim at proposing effective and reliable healthcare solutions for elderly people in various scenarios. Specifically, we focus on human behavior understanding from videos, captured by cameras in indoor environments, individual persons and actions at multiple granularity levels. When human behaviors can be understood automatically and reliably by computers, the living environment will become smart, provide effective assistance, and make the interaction between elderly people and smart environments as convenient as the interaction between youngsters and conventional environments.
Human behavior understanding from videos covers a broad range of tasks. To unify all the tasks, we propose the action parsing task: Given an untrimmed video with various types of actions, action parsing is to assign each individual frame an action label/cluster ID. Consequently, several classical tasks, e.g. action detection, video retrieval, action recognition and temporal action segmentation are unified within one framework. In this thesis, we first propose an unsupervised method, assigning each frame in the input video an cluster ID, and then a deep learning-based supervised method, assigning each frame an action label in an end-to-end manner. The unsupervised method is hierarchical dynamic clustering, which incorporates several novel modules and is inspired by the conventional bag-of-visual-words method for action recognition. For the deep learning-based supervised action parsing method, we employ a spatiotemporal convolutional encoder-decoder network, and propose novel bilinear pooling methods to realize fine-grained action parsing. We have applied the proposed methods to unsupervised action segmentation, abnormality (fainting) detection from omni-directional videos, explanation of how a deep neural network understands falls from videos, fine-grained human action parsing in daily living scenarios (recordings from both the third-part view and the egocentric view), and behavior understanding for surgical robots. All experimental results show that our proposed methods are effective and yield state-ofthe- art performances
3D scene understanding on point clouds
No full textThis thesis contributes to the emerging field of 3D scene understanding. That is, given a 3D scene representation as input, we address tasks such as 3D object detection, shape reconstruction and pose estimation, as well as 3D semantic- and instance-segmentation. The recent availability of inexpensive depth sensors has made 3D data widely accessible. At the same time, current aspirations in the field of robotics, augmented reality and self-driving cars require efficient and reliable algorithms for understanding different 3D scene representations, such as polygon meshes, point clouds or volumetric structures. While 3D data overcomes inherent limitations of projected 2D views, such as occlusions, scale-ambiguity and lack of geometry, it also introduces new challenges including sparsity and non-uniform sampling. Therefore, existing methods for 2D image processing might not generalize well to 3D data structures. In this thesis, we present novel approaches specific to 3D scene understanding. The main contributions are organized into three parts: The core contribution of the first part is a probabilistic formulation which integrates 3D shape and motion priors as well as stereo depth measurements into a global optimization problem. The resulting approach can jointly estimate the 3D shape, pose and motion of multiple vehicles in urban street scenes. The second part deals with new deep learning models for processing 3D point clouds. In particular, we propose sequential and recurrent consolidation units for increasing the spatial context of point networks, and a simple yet efficient dilation mechanism for increasing the receptive field size of deep point convolutional networks. Finally, in the third part, we introduce advanced deep learning models. For semantic segmentation, we present the combination of two types of convolutions operating jointly on point clouds and mesh surfaces. For instance segmentation, we propose a new paradigm combining the advantages of previous bottom-up and top-down approaches. This thesis concludes with a discussion and directions for future research
AccidentBlip: Agent of Accident Warning Based on MA-Former
No full textIn complex transportation systems, accurately sensing the surrounding environment and predicting the risk of potential accidents is crucial. Most existing accident prediction methods are based on temporal neural networks, such as RNN and LSTM. Recent multimodal fusion approaches improve vehicle localization through 3D target detection and assess potential risks by calculating inter-vehicle distances. However, these temporal networks and multimodal fusion methods suffer from limited detection robustness and high economic costs. To address these challenges, we propose AccidentBlip, a vision-only framework that employs our self-designed Motion Accident Transformer (MA-former) to process each frame of video. Unlike conventional self-attention mechanisms, MA-former replaces Q-former's self-attention with temporal attention, allowing the query corresponding to the previous frame to generate the query input for the next frame. Additionally, we introduce a residual module connection between queries of consecutive frames to enhance the model's temporal processing capabilities. For complex V2V and V2X scenarios, AccidentBlip adapts by concatenating queries from multiple cameras, effectively capturing spatial and temporal relationships. In particular, AccidentBlip achieves SOTA performance in both accident detection and prediction tasks on the DeepAccident dataset. It also outperforms current SOTA methods in V2V and V2X scenarios, demonstrating a superior capability to understand complex real-world environments
Correction: Lu et al. An Assessment of Temporal and Spatial Dynamics of Regional Water Resources Security in the DPSIR Framework in Jiangxi Province, China. Int. J. Environ. Res. Public Health 2022, 19, 3650
No full textSiyu Wang was included as an author in the original publication [...
Hydroxycinnamic acid amides from Lycium barbarum and their anti-inflammatory mechanisms
No full textOver the past few decades, much research has identified chronic inflammation as a critical component in many human diseases. Due to the intimate relationship between chronic inflammation and human diseases and conditions, an accumulating number of studies aimed to identify and discover anti-inflammatory molecules from natural resources because these molecules are able to interact with inflammatory mediators as well as modulate inflammatory pathways. Hydroxycinnamic acid amides (HCAA) are the secondary metabolites ubiquitously exist in flowering plants, formed by condensation between hydroxycinnamates and mono or polyamines. HCAA species not only serve multiple functions in plant growth and development, but also exert significant positive effects on human health. HCAA family has been recognized as the most characteristic and abundant chemical species of Lycium barbarum. The plant has been recognized as traditional remedies for hyperglycemia and other health conditions. Wolfberry or Goji berry, the fruits of Lycium barbarum, are widely consumed in Asian cuisine due to their health-promoting properties.
In the first part of my work, we synthesized a set of HCAA compounds, including trans-caffeic acid, trans-ferulic acid, and 3,4-dihydroxyhydrocinnamic acid, with extended phenolic amine components as standards to identify and quantify the corresponding compounds from different parts of Lycium barbarum. With optimized LC–MS/MS and NMR analysis, nine amide compounds were identified from the fruits, and 10 new HCAA species were further identified in root barks in addition to the ones reported in the literature. HCAA species were reported in leaves for the first time. The quantification showed the amide compounds with a tyramine moiety were the most abundant.
Moreover, the method was fully validated with respect to specificity, linearity, intra- and inter-day precision and accuracy, limit of detection (LOD), limit of quantification (LOQ), recovery, and reproducibility.
The anti-inflammatory properties of identified HCAAs were also examined by nitric oxide (NO) inhibition assay. Seven HCAA compounds had a potent NO inhibitory effect with IC50 as low as 2.381 μM (trans-N-caffeoyl phenethylamine). Two HCAA compounds (trans-N-caffeoyl tryptamine and trans-N-caffeoyl tyramine) were chosen to investigate their anti-inflammatory molecular mechanisms by both in vitro and in vivo assays. These two HCAAs inhibited NF-κB signaling pathway in murine macrophage RAW264.7, accompanied by inhibition of PI3K/Akt/IKK pathway. In vivo mouse ear edema model indicated that with treatment of the two HCAAs, TPA-induced ear edema was significant reduced by showing as reduction of ear weight and thickness, pro-inflammatory enzyme expression as well as immune cells infiltration.
The developed analytical method largely improved analytical sensitivity of HCAAs species that potentially contributes to plant metabolomics and drug discovery studies. HCAAs demonstrated promising anti-inflammatory properties that could be used as preventive agent for inflammation and inflammation-related diseases.Ph.D.Includes bibliographical referencesby Siyu Wan
Accelerating Human Avatar Creation: Pose-dependent Hybrid Representations for Efficient Rendering of Clothed Human Avatars
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