SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud

Poles and building edges are frequently observable objects on urban roads, conveying reliable hints for various computer vision tasks. To repetitively extract them as features and perform association between discrete LiDAR frames for registration, we propose the first learning-based feature segmentation and description model for 3D lines in LiDAR point cloud. To train our model without the time consuming and tedious data labeling process, we first generate synthetic primitives for the basic appearance of target lines, and build an iterative line auto-labeling process to gradually refine line labels on real LiDAR scans. Our segmentation model can extract lines under arbitrary scale perturbations, and we use shared EdgeConv encoder layers to train the two segmentation and descriptor heads jointly. Base on the model, we can build a highly-available global registration module for point cloud registration, in conditions without initial transformation hints. Experiments have demonstrated that our line-based registration method is highly competitive to state-of-the-art point-based approaches. Our code is available at https://github.com/zxrzju/SuperLine3D.git.Comment: 17 pages, ECCV 2022 Accepte

Isoflurane-Induced Spatial Memory Impairment in Mice is Prevented by the Acetylcholinesterase Inhibitor Donepezil

Although many studies have shown that isoflurane exposure impairs spatial memory in aged animals, there are no clinical treatments available to prevent this memory deficit. The anticholinergic properties of volatile anesthetics are a biologically plausible cause of cognitive dysfunction in elderly subjects. We hypothesized that pretreatment with the acetylcholinesterase inhibitor donepezil, which has been approved by the Food and Drug Administration (FDA) for the treatment of Alzheimer's disease, prevents isoflurane-induced spatial memory impairment in aged mice. In present study, eighteen-month-old mice were administered donepezil (5 mg/kg) or an equal volume of saline by oral gavage with a feeding needle for four weeks. Then the mice were exposed to isoflurane (1.2%) for six hours. Two weeks later, mice were subjected to the Morris water maze to examine the impairment of spatial memory after exposure to isoflurane. After the behavioral test, the mice were sacrificed, and the protein expression level of acetylcholinesterase (AChE), choline acetylase (ChAT) and α7 nicotinic receptor (α7-nAChR) were measured in the brain. Each group consisted of 12 mice. We found that isoflurane exposure for six hours impaired the spatial memory of the mice. Compared with the control group, isoflurane exposure dramatically decreased the protein level of ChAT, but not AChE or α7-nAChR. Donepezil prevented isoflurane-induced spatial memory impairments and increased ChAT levels, which were downregulated by isoflurane. In conclusions, pretreatment with the AChE inhibitor donepezil prevented isoflurane-induced spatial memory impairment in aged mice. The mechanism was associated with the upregulation of ChAT, which was decreased by isoflurane

A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation

Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isolate different in situ cellular components. In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate that the autoencoder can be used for efficient and coarse characterization of features of macromolecular complexes and surfaces, such as membranes. In addition, the autoencoder can be used to detect non-cellular features related to sample preparation and data collection, such as carbon edges from the grid and tomogram boundaries. The autoencoder is also able to detect patterns that may indicate spatial interactions between cellular components. Furthermore, we demonstrate that our autoencoder can be used for weakly supervised semantic segmentation of cellular components, requiring a very small amount of manual annotation.Comment: Accepted by Journal of Structural Biolog

Identification and characterization of novel amphioxus microRNAs by Solexa sequencing

An analysis of amphioxus miRNAs suggests an expansion of miRNAs played a key role in the evolution of chordates to vertebrate

Two parasitic ciliates (Protozoa: Ciliophora: Phyllopharyngea) isolated from respiratory-mucus of an unhealthy beluga whale: characterization, phylogeny and an assessment of morphological adaptations

Abstract Ciliates occur in the blowholes of marine mammals, but our understanding of their biology is poor. Consequently, we investigated an infestation of ciliates in an unhealthy, captive beluga whale that was exhibiting accelerated breathing, leukocytosis and expulsion of unusually large amounts of viscous sputum. This sputum contained ~104 ciliates mL-1 (when healthy, numbers were ten- to 100-fold lower). One known ciliate species, Planilamina ovata, is fully characterized, and a new species, Kyaroikeus paracetarius sp. nov., is here described. The new species is established based on its larger number of left kineties over its only congener. Sequences of small-subunit rDNA, large-subunit rDNA and ITS1-5.8S-ITS2 regions of these two taxa were used in phylogenetic analyses, inferring that Kyaroikeus and Planilamina have close affinity with the free-living family Dysteriidae, contradicting their morphology-based assignment to the family Kyaroikeidae. We suggest that Kyaroikeidae be relegated to subfamily status. Finally, by comparing parasitic species with free-living taxa, we suggest how these ciliates have adapted to their unique environment and how they may have initially invaded the host. We provide essential data and concepts for the continued evaluation of ciliate-parasites in whale blowholes.</jats:p

Terpenoid Esters Are the Major Constituents From Leaf Lipid Droplets of Camellia sinensis

Lipid droplets (LDs) have been widely found from diverse species and exhibit diverse functions. It remains unexplored what potential roles they played in tea. To address this question, we analyzed the chemical composition and the dynamic changes of cytosolic LDs during leaf growth and diurnal cycle. Using TopFluor cholesterol and Nile Red staining we demonstrated that cytosolic LDs were heterogeneous in tea tree (Camellia sinensis cv. Tieguanyin); the size and number of LDs increased with leaf growth. Compositional analysis showed that terpenoid esters and diacylglycerol are the major components of cytosolic LDs. The contents of total sterol esters (SEs) and β-amyrin esters increased with leaf expansion and growth; individual SE also showed diurnal changes. Our data suggest that cytosolic LDs from tea tree leave mainly serve as storage site for free sterols and triterpenoids in the form of esters. Cytosolic LDs were not the major contributors to the aroma quality of made tea

Analysis of Metabolites Difference of the Albino Tea Tree Variety 'Ming Guan'

‘Ming guan’ is a new excellent albino tea variety bred from the descendants of Bai jiguan. In order to explore the quality difference of Ming guan multi tea processing, the fresh leaves of Ming guan were used as raw materials to make the corresponding tea types according to the processing methods of green tea, black tea and white tea, and sensory quality evaluation, aroma and taste analysis were conducted. The results showed that the aroma of Ming guan green tea was tender, floral and fruity, with a mellow taste, the aroma of Ming guan black tea was sweet, floral, with a sweet taste, the aroma of Ming guan white tea was millets, floral, with a fresh taste, and the different processes of Ming guan tea had their own unique floral characteristics. Among the aroma components of Ming guan green tea, terpene aroma components with floral aroma were relatively more abundant, followed by ester aroma components with fruit aroma, which played an important role in the formation of the aroma of Ming guan green tea. The representative aroma components of Ming guan green tea were leaf alcohol ester of foliol caproate, 3-hexenyl caproate, 2-hexenyl caproate, nerolidol, leaf alcohol ester of butyric acid, olivetol and α-farnesene, the representative components of which were mainly esters with fruity aroma and alcohols with floral and fruity aroma, creating the characteristic of Ming guan green tea floral and fruity varieties. The representative components of Ming guan black tea were dihydrolinalool, α-cephalene, β-Ionone, γ-cadinene, methyl hexadecanoic acid and benzaldehyde, which were mainly terpenes and alcohols with floral and sweet aromas, contributing to the floral and sweet aromatic characteristics of Ming guan black tea. The representative components of Ming guan white tea were geraniol, myrcene, 3-carene, linalyl acetate and linalool, and the representative components are mainly alcohols and terpenes with floral aroma. The non-volatile components of Ming guan green tea, Ming guan black tea and Ming guan white tea vary greatly overall. The content of catechins, anthocyanins, some flavonols and flavonoid glycosides (quercetin-3-O-galactoside, quercetin-3-O-glucoside, quercetin-3-O-glucoside 7-O-rhamnoside, etc.) in Ming guan green tea was generally higher than that in Ming guan black tea and Ming guan white tea. The contents of theaflavins, phenolic acids, a few flavonol and flavonoid glycoside compounds (vitexin-2-O-galactoside, vitexin-2-O-rhamnoside, apigenin-6,8-di-C-glucoside, apigenin-6-C-glucoside, etc.) and some amino acid compounds (L-phenylalanine, L-tryptophan, L-isoleucine, L-valine, L-aspartic acid) in Ming guan black tea were higher than those in Ming guan green tea and Ming guan white tea.The content of some amino acid compounds (L-arginine, L-glutamine, L-lysine, L-histidine, L-tyrosine) in Ming guan white tea was higher than that in Ming guan green tea and Ming guan black tea, which may be affected by different processing technologies. This study could provide a theoretical basis for a comprehensive understanding of the chemical basis and quality differences of Ming guan green tea, Ming guan black tea and Ming guan white tea