Mollugin attenuates glucocorticoid-induced osteoporosis in rats via Akt/P13K pathway

Purpose: To investigate the protective effect of mollugin on glucocorticoid (GC)-induced osteoporosis in rats.Methods: A total of 30 female Sprague Dawley rats (weighing 180 to 200 g) were randomly assigned to five groups of six rats each: control, GC and mollugin groups (20, 40 and 80 mg/kg, respectively). Except for the control group, osteoporosis was induced in the rats by intramuscular administration of dexamethasone at a dose of 2.5 mg/kg twice weekly for nine weeks. Bone mineral density (BMD) and serum activities of tartrate-resistant acid phosphatase (TRAP) and specific alkaline phosphatase (ALP), and levels of collagen type I fragment (CTX) and osteocalcin were estimated. The effect of mollugin alone, and in the presence of PI3K/Akt inhibitor on the proliferation of bone marrow osteoblasts was investigated using 3-(4, 5-dimethylthiazol-2-yl)-2, 5-tetrazolium bromide (MTT) assay. Western blotting was used for determination of the expressions of p-Akt, Akt and cyclin D1 protein.Results: There were significant increases in body weights of rats in GC group, when compared with the control group. However, treatment with mollugin significantly reduced the body weights in a dosedependent manner (p < 0.05). The BMD was significantly reduced in GC group, relative to the control group (p < 0.05). Serum activities of TRAP and ALP were significantly higher in GC group than in control group, but were significantly reduced by mollugin treatment (p < 0.05). Serum level of CTX was significantly increased and osteocalcin reduced in the GC group, relative to control (p < 0.05). Osteoblast proliferation was significantly higher in the mollugin-treated groups. The expressions of p-Akt, Akt and cyclin D1 were significantly and dose-dependently higher in mollugin-treated groups (p < 0.05). There were more viable osteoblasts in the mollugin-treated groups than in the untreated group. However, treatment with mollugin in the presence of PI3K/Akt inhibitor significantly reduced their viability (p < 0.05).Conclusion: Mollugin has therapeutic potential for GC-induced osteoporosis via mechanism involving the PI3K/Akt pathway.Keywords: Mollugin, Osteoporosis, Bone, PI3K/Akt inhibitor, Osteoblas

Experimental Demonstration of Unconditional Entanglement Swapping for Continuous Variables

The unconditional entanglement swapping for continuous variables is experimentally demonstrated. Two initial entangled states are produced from two nondegenerate optical parametric amplifiers operating at deamplification. Through implementing the direct measurement of Bell-state between two optical beams from each amplifier the remaining two optical beams, which have never directly interacted with each other, are entangled. The quantum correlation degrees of 1.23dB and 1.12dB below the shot noise limit for the amplitude and phase quadratures resulting from the entanglement swapping are straightly measured.Comment: new versio

GRAINS: Proximity Sensing of Objects in Granular Materials

Proximity sensing detects an object's presence without contact. However, research has rarely explored proximity sensing in granular materials (GM) due to GM's lack of visual and complex properties. In this paper, we propose a granular-material-embedded autonomous proximity sensing system (GRAINS) based on three granular phenomena (fluidization, jamming, and failure wedge zone). GRAINS can automatically sense buried objects beneath GM in real-time manner (at least ~20 hertz) and perceive them 0.5 ~ 7 centimeters ahead in different granules without the use of vision or touch. We introduce a new spiral trajectory for the probe raking in GM, combining linear and circular motions, inspired by a common granular fluidization technique. Based on the observation of force-raising when granular jamming occurs in the failure wedge zone in front of the probe during its raking, we employ Gaussian process regression to constantly learn and predict the force patterns and detect the force anomaly resulting from granular jamming to identify the proximity sensing of buried objects. Finally, we apply GRAINS to a Bayesian-optimization-algorithm-guided exploration strategy to successfully localize underground objects and outline their distribution using proximity sensing without contact or digging. This work offers a simple yet reliable method with potential for safe operation in building habitation infrastructure on an alien planet without human intervention.Comment: 35 pages, 5 figures,2 tables. Videos available at https://sites.google.com/view/grains2/hom

Steganography for Neural Radiance Fields by Backdooring

The utilization of implicit representation for visual data (such as images, videos, and 3D models) has recently gained significant attention in computer vision research. In this letter, we propose a novel model steganography scheme with implicit neural representation. The message sender leverages Neural Radiance Fields (NeRF) and its viewpoint synthesis capabilities by introducing a viewpoint as a key. The NeRF model generates a secret viewpoint image, which serves as a backdoor. Subsequently, we train a message extractor using overfitting to establish a one-to-one mapping between the secret message and the secret viewpoint image. The sender delivers the trained NeRF model and the message extractor to the receiver over the open channel, and the receiver utilizes the key shared by both parties to obtain the rendered image in the secret view from the NeRF model, and then obtains the secret message through the message extractor. The inherent complexity of the viewpoint information prevents attackers from stealing the secret message accurately. Experimental results demonstrate that the message extractor trained in this letter achieves high-capacity steganography with fast performance, achieving a 100\% accuracy in message extraction. Furthermore, the extensive viewpoint key space of NeRF ensures the security of the steganography scheme.Comment: 6 pages, 7 figure

Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery

In this paper, we propose a novel iterative multi-task framework to complete the segmentation mask of an occluded vehicle and recover the appearance of its invisible parts. In particular, to improve the quality of the segmentation completion, we present two coupled discriminators and introduce an auxiliary 3D model pool for sampling authentic silhouettes as adversarial samples. In addition, we propose a two-path structure with a shared network to enhance the appearance recovery capability. By iteratively performing the segmentation completion and the appearance recovery, the results will be progressively refined. To evaluate our method, we present a dataset, the Occluded Vehicle dataset, containing synthetic and real-world occluded vehicle images. We conduct comparison experiments on this dataset and demonstrate that our model outperforms the state-of-the-art in tasks of recovering segmentation mask and appearance for occluded vehicles. Moreover, we also demonstrate that our appearance recovery approach can benefit the occluded vehicle tracking in real-world videos

Decays of a Tcc+T_{cc}^+ heavy-quark-spin molecular partner to D∗DπD^{\ast}D\pi

Starting from the hypothesis that the $T_{cc}^+$ discovered at LHCb is a $D^{\ast+} D^0/D^{\ast 0}D^+$ hadronic molecule, we consider the partial width of its heavy quark spin partner, the $T_{cc}^{\ast +}$ as a $D^{\ast +} D^{\ast 0}$ shallow bound state, decaying into the $D^{\ast}D\pi$ final states including the contributions of the $D^{\ast} D$ and $D^{\ast} \pi$ final state interaction by using a nonrelativistic effective field theory. Because of the existence of the $T_{cc}^+$ pole, the $I=0$ $D^{\ast} D$ rescattering contributes at the leading order, the same order as that of the tree diagram, while the $D^{\ast} \pi$ rescattering contribution is one magnitude smaller. The partial widths of $T_{cc}^{\ast +} \to D^{\ast +} D^0 \pi^0$, $D^{\ast 0}D^+\pi^0$, and $D^{\ast 0}D^0\pi^+$ are about 44 keV, 20 keV, and 18 keV, respectively.Comment: 19 pages, 4 figues. Comments are welcom

Large kernel spectral and spatial attention networks for hyperspectral image classification.

Currently, long-range spectral and spatial dependencies have been widely demonstrated to be essential for hyperspectral image (HSI) classification. Due to the transformer superior ability to exploit long-range representations, the transformer-based methods have exhibited enormous potential. However, existing transformer-based approaches still face two crucial issues that hinder the further performance promotion of HSI classification: 1) treating HSI as 1D sequences neglects spatial properties of HSI, 2) the dependence between spectral and spatial information is not fully considered. To tackle the above problems, a large kernel spectral-spatial attention network (LKSSAN) is proposed to capture the long-range 3D properties of HSI, which is inspired by the visual attention network (VAN). Specifically, a spectral-spatial attention module is first proposed to effectively exploit discriminative 3D spectral-spatial features while keeping the 3D structure of HSI. This module introduces the large kernel attention (LKA) and convolution feed-forward (CFF) to flexibly emphasize, model, and exploit the long-range 3D feature dependencies with lower computational pressure. Finally, the features from the spectral-spatial attention module are fed into the classification module for the optimization of 3D spectral-spatial representation. To verify the effectiveness of the proposed classification method, experiments are executed on four widely used HSI data sets. The experiments demonstrate that LKSSAN is indeed an effective way for long-range 3D feature extraction of HSI