Constitutive Activation of β-Catenin in Differentiated Osteoclasts Induces Bone Loss in Mice

Background/Aims: Activation of the Wnt/β-catenin signalling pathway has been widely investigated in bone biology and shown to promote bone formation. However, its specific effects on osteoclast differentiation have not been fully elucidated. Our study aimed to identify the role of β-catenin in osteoclastogenesis and bone homeostasis. Methods: In the present study, exon 3 in the β-catenin gene (Ctnnb1) allele encoding phosphorylation target serine/threonine residues was flanked by floxP sequences. We generated mice exhibiting conditional β-catenin activation (Ctsk-Cre;Ctnnb1flox(exon3)/+, designated CA-β-catenin) by crossing Ctnnb1flox(exon3)/flox(exon3) mice with osteoclast-specific Ctsk-Cre mice. Bone growth and bone mass were analysed by micro-computed tomography (micro-CT) and histomorphometry. To further examine osteoclast activity, osteoclasts were induced from bone marrow monocytes (BMMs) isolated from CA-β-catenin and Control mice in vitro. Osteoclast differentiation was detected by tartrate-resistant acid phosphatase (TRAP) staining, immunofluorescence staining and reverse transcription-quantitative PCR (RT–qPCR) analysis. Results: Growth retardation and low bone mass were observed in CA-β-catenin mice. Compared to controls, CA-β-catenin mice had significantly reduced trabecular bone numbers under growth plates as well as thinner cortical bones. Moreover, increased TRAP-positive osteoclasts were observed on the surfaces of trabecular bones and cortical bones in the CA-β-catenin mice; consistent results were observed in vitro. In the CA-β-catenin group, excessive numbers of osteoclasts were induced from BMMs, accompanied by the increased expression of osteoclast-associated marker genes. Conclusion: These results indicated that the constitutive activation of β-catenin in osteoclasts promotes osteoclast formation, resulting in bone loss

Online Street View-Based Approach for Sky View Factor Estimation: A Case Study of Nanjing, China

The Sky View Factor (SVF) stands as a critical metric for quantitatively assessing urban spatial morphology and its estimation method based on Street View Imagery (SVI) has gained significant attention in recent years. However, most existing Street View-based methods prove inefficient and constrained in SVI dataset collection. These approaches often fall short in capturing detailed visual areas of the sky, and do not meet the requirements for handling large areas. Therefore, an online method for the rapid estimation of a large area SVF using SVI is presented in this study. The approach has been integrated into a WebGIS tool called BMapSVF, which refines the extent of the visible sky and allows for instant estimation of the SVF at observation points. In this paper, an empirical case study is carried out in the street canyons of the Qinhuai District of Nanjing to illustrate the effectiveness of the method. To validate the accuracy of the refined SVF extraction method, we employ both the SVI method based on BMapSVF and the simulation method founded on 3D urban building models. The results demonstrate an acceptable level of refinement accuracy in the test area

The Identification of <i>Fritillaria</i> Species Using Hyperspectral Imaging with Enhanced One-Dimensional Convolutional Neural Networks via Attention Mechanism

Combining deep learning and hyperspectral imaging (HSI) has proven to be an effective approach in the quality control of medicinal and edible plants. Nonetheless, hyperspectral data contains redundant information and highly correlated characteristic bands, which can adversely impact sample identification. To address this issue, we proposed an enhanced one-dimensional convolutional neural network (1DCNN) with an attention mechanism. Given an intermediate feature map, two attention modules are constructed along two separate dimensions, channel and spectral, and then combined to enhance relevant features and to suppress irrelevant ones. Validated by Fritillaria datasets, the results demonstrate that an attention-enhanced 1DCNN model outperforms several machine learning algorithms and shows consistent improvements over a vanilla 1DCNN. Notably under VNIR and SWIR lenses, the model obtained 98.97% and 99.35% for binary classification between Fritillariae Cirrhosae Bulbus (FCB) and other non-FCB species, respectively. Additionally, it still achieved an extraordinary accuracy of 97.64% and 98.39% for eight-category classification among Fritillaria species. This study demonstrated the application of HSI with artificial intelligence can serve as a reliable, efficient, and non-destructive quality control method for authenticating Fritillaria species. Moreover, our findings also illustrated the great potential of the attention mechanism in enhancing the performance of the vanilla 1DCNN method, providing reference for other HSI-related quality controls of plants with medicinal and edible uses

Below-Ground Interspecific Competition of Apple (Malus pumila M.)–Soybean (Glycine max L. Merr.) Intercropping Systems Based on Niche Overlap on the Loess Plateau of China

To provide a scientific basis and technical support for agroforestry management practices, such as interrow configuration and soil water and fertilizer management, a stratified excavation method was performed both to explore the fine-root spatial distribution and niche differentiation and to quantify the below-ground interspecific competition status of 3-, 5-, and 7-year-old apple (Malus pumila M.)&ndash;soybean (Glycine max L. Merr.) intercropping systems and monocropping systems. The fine roots of older trees occupied a larger soil space and had both a greater fine-root biomass density (FRMD) and a greater ability to reduce the FRMD of soybean, but this ability decreased with the distance from the apple tree row. Similarly, the FRMD of apple trees was also adversely affected by soybean plants, but this effect gradually increased with a decrease in tree age or with the distance from the tree row. Compared with that of the 3- and 5-year-old monocropped apple trees, the FRMD of the 3- and 5-year-old intercropped apple trees increased in the 40&ndash;100 cm and 60&ndash;100 cm soil layers, respectively. However, compared with that of the 7-year-old apple and soybean monocropping systems, the FRMD of the 7-year-old intercropped apple trees and soybean plants decreased in each soil layer. Compared with that of the corresponding monocropped systems, the fine-root vertical barycenter (FRVB) of the intercropped apple trees displaced deeper soil and that of the intercropped soybean plants displaced shallower soil. Furthermore, the FRVB of both intercropped apple trees and intercropped soybean plants displaced shallower soil with increasing tree age. Intense below-ground interspecific competition in the 3-, 5-, and 7-year-old apple&ndash;soybean intercropping systems occurred in the 0&ndash;40 cm soil layer at distances of 0.5&ndash;0.9, 0.5&ndash;1.3, and 0.5&ndash;1.7 m from the apple tree row, respectively

Variation of Fine Roots Distribution in Apple (<i>Malus pumila</i> M.)–Crop Intercropping Systems on the Loess Plateau of China

In arid and semi-arid areas, interspecific below-ground competition is prominent in agroforestry systems. To provide theoretical and technical guidance for the scientific management of apple&#8315;crop intercropping systems, a field study was conducted in the Loess Plateau of China to examine the variation of fine roots distribution in apple&#8315;crop intercropping systems. The fine roots of apple trees and crops (soybean (Glycine max (L.) Merr) or peanuts (Arachis hypogaea Linn.)) were sampled to 100 cm depth at ten distances from the tree row using the stratified excavation method. The results showed that the vertical distribution of fine roots between intercropped apple trees and intercropped crops were skewed and overlapped. Apple&#8315;crop intercropping inhibited the fine roots of apple trees in the 0&#8315;60 cm soil depth, but promoted their growth in the 60&#8315;100 cm soil depth. However, apple&#8315;crop intercropping inhibited the fine roots of intercropped crops in the 0&#8315;100 cm soil depth. For the fine roots of each component of the apple&#8315;crop intercropping systems, variation in the vertical distribution was much greater than variation in the horizontal distribution. Compared with monocropped systems, apple&#8315;crop intercropping caused the fine roots of intercropped apple trees to move to deeper soil, and those of intercropped crops to move to shallower soil. Additionally, apple&#8315;crop intercropping slightly inhibited the horizontal extension of the fine-root horizontal barycentre (FRHB) of intercropped apple trees and caused the FRHB of intercropped crops to be slightly biased towards the north of the apple tree row. Variation of the fine roots distribution of each component of the apple&#8315;soybean intercropping system was greater than that of the apple&#8315;peanut intercropping system. Thus, the interspecific below-ground competition of the apple&#8315;peanut intercropping system was weaker than that of the apple&#8315;soybean intercropping system. Intense competition occurred in the apple&#8315;peanut intercropping system and the apple&#8315;soybean intercropping system was in sections whose distance ranged from 0.5&#8315;1.3 and 0.5&#8315;1.7 m from the tree row, respectively. The interspecific below-ground competition was fiercer on the south side of the apple tree row than on the north side

Visual Responses to Moving and Flashed Stimuli of Neurons in Domestic Pigeon (Columba livia domestica) Optic Tectum

Birds can rapidly and accurately detect moving objects for better survival in complex environments. This visual ability may be attributed to the response properties of neurons in the optic tectum. However, it is unknown how neurons in the optic tectum respond differently to moving objects compared to static ones. To address this question, neuronal activities were recorded from domestic pigeon (Columba livia domestica) optic tectum, responsible for orienting to moving objects, and the responses to moving and flashed stimuli were compared. An encoding model based on the Generalized Linear Model (GLM) framework was established to explain the difference in neuronal responses. The experimental results showed that the first spike latency to moving stimuli was smaller than that to flashed ones and firing rate was higher. The model further implied the faster and stronger response to a moving target result from spatiotemporal integration process, corresponding to the spatially sequential activation of tectal neurons and the accumulation of information in time. This study provides direct electrophysiological evidence about the different tectal neuron responses to moving objects and flashed ones. The findings of this investigation increase our understanding of the motion detection mechanism of tectal neurons