The Snake - a Reconnecting Coil in a Twisted Magnetic Flux Tube

We propose that the curious Galactic Center filament known as ``The Snake'' is a twisted giant magnetic flux tube, anchored in rotating molecular clouds. The MHD kink instability generates coils in the tube and subsequent magnetic reconnection injects relativistic electrons. Electrons diffuse away from a coil at an energy-dependent rate producing a flat spectral index at large distances from it. Our fit to the data of \citet{gray95a} shows that the magnetic field $\sim 0.4 \> \rm mG$ is large compared to the ambient $\sim 7 \mu \> \rm G$ field, indicating that the flux tube is force-free. If the {\em relative} level of turbulence in the Snake and the general interstellar medium are similar, then electrons have been diffusing in the Snake for about $3 \times 10^5 \> \rm yr$, comparable to the timescale at which magnetic energy is annihilated in the major kink. Estimates of the magnetic field in the G359.19-0.05 molecular complex are similar to our estimate of the magnetic field in the Snake suggesting a strong connection between the physics of the anchoring molecular regions and the Snake. We suggest that the physical processes considered here may be relevant to many of the radio filaments near the Galactic Center. We also suggest further observations of the Snake and other filaments that would be useful for obtaining further insights into the physics of these objects.Comment: 11 pages, 1 figur

Proteomic analysis of honeybee worker (Apis mellifera) hypopharyngeal gland development

<p>Abstract</p> <p>Background</p> <p>Hypopharyngeal glands (HG) of honeybee workers play an important role in honeybee nutrition and caste differentiation. Previous research mainly focused on age-dependent morphological, physiological, biochemical and genomic characters of the HG. Here proteomics and biochemical network analysis were used to follow protein changes during the HG development.</p> <p>Results</p> <p>A total of 87, 76, 85, 74, 71, and 55 proteins were unambiguously identified on day 1, 3, 6, 12, 15 and 20, respectively. These proteins were major royal jelly proteins (MRJPs), metabolism of carbohydrates, lipids and proteins, cytoskeleton, development regulation, antioxidant, molecule transporter, regulation of transcription/translation, proteins with folding functions. The most interesting is that MRJP's that have been detected in the HG of the newly emerged worker bees. The MRJP's expression is at peak level from 6-12 days, was validated by western blot analysis of MRJP1, 2 and 3. Moreover, 35 key node proteins were found in the biochemical networks of the HG.</p> <p>Conclusions</p> <p>HG secretes RJ at peak level within 6-12 days, but the worker bee can secrete royal jelly (RJ) since birth, which is a new finding. Several key node proteins play an important role in the biochemical networks of the developing HG. This provides us some target proteins when genetically manipulating honeybees.</p

The dynamics and control of large flexible space structures - 12, supplement 11

The rapid 2-D slewing and vibrational control of the unsymmetrical flexible SCOLE (Spacecraft Control Laboratory Experiment) with multi-bounded controls is considered. Pontryagin's Maximum Principle is applied to the nonlinear equations of the system to derive the necessary conditions for the optimal control. The resulting two point boundary value problem is then solved by using the quasilinearization technique, and the near minimum time is obtained by sequentially shortening the slewing time until the controls are near the bang-bang type. The tradeoff between the minimum time and the minimum flexible amplitude requirements is discussed. The numerical results show that the responses of the nonlinear system are significantly different from those of the linearized system for rapid slewing. The SCOLE station-keeping closed loop dynamics are re-examined by employing a slightly different method for developing the equations of motion in which higher order terms in the expressions for the mast modal shape functions are now included. A preliminary study on the effect of actuator mass on the closed loop dynamics of large space systems is conducted. A numerical example based on a coupled two-mass two-spring system illustrates the effect of changes caused in the mass and stiffness matrices on the closed loop system eigenvalues. In certain cases the need for redesigning control laws previously synthesized, but not accounting for actuator masses, is indicated

DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes

Despite the recent success of Neural Radiance Field (NeRF), it is still challenging to render large-scale driving scenes with long trajectories, particularly when the rendering quality and efficiency are in high demand. Existing methods for such scenes usually involve with spatial warping, geometric supervision from zero-shot normal or depth estimation, or scene division strategies, where the synthesized views are often blurry or fail to meet the requirement of efficient rendering. To address the above challenges, this paper presents a novel framework that learns a density space from the scenes to guide the construction of a point-based renderer, dubbed as DGNR (Density-Guided Neural Rendering). In DGNR, geometric priors are no longer needed, which can be intrinsically learned from the density space through volumetric rendering. Specifically, we make use of a differentiable renderer to synthesize images from the neural density features obtained from the learned density space. A density-based fusion module and geometric regularization are proposed to optimize the density space. By conducting experiments on a widely used autonomous driving dataset, we have validated the effectiveness of DGNR in synthesizing photorealistic driving scenes and achieving real-time capable rendering

LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation

Semantic map construction under bird's-eye view (BEV) plays an essential role in autonomous driving. In contrast to camera image, LiDAR provides the accurate 3D observations to project the captured 3D features onto BEV space inherently. However, the vanilla LiDAR-based BEV feature often contains many indefinite noises, where the spatial features have little texture and semantic cues. In this paper, we propose an effective LiDAR-based method to build semantic map. Specifically, we introduce a BEV feature pyramid decoder that learns the robust multi-scale BEV features for semantic map construction, which greatly boosts the accuracy of the LiDAR-based method. To mitigate the defects caused by lacking semantic cues in LiDAR data, we present an online Camera-to-LiDAR distillation scheme to facilitate the semantic learning from image to point cloud. Our distillation scheme consists of feature-level and logit-level distillation to absorb the semantic information from camera in BEV. The experimental results on challenging nuScenes dataset demonstrate the efficacy of our proposed LiDAR2Map on semantic map construction, which significantly outperforms the previous LiDAR-based methods over 27.9% mIoU and even performs better than the state-of-the-art camera-based approaches. Source code is available at: https://github.com/songw-zju/LiDAR2Map.Comment: Accepted by CVPR202

Linkage between Accretion Disks and Blazars

The magnetic field in an accretion disk is estimated assuming that all of the angular momentum within prescribed accretion disk radii is removed by a jet. The magnetic field estimated at the base of the jet is extrapolated to the blazar emission region using a model for a relativistic axisymmetric jet combined with some simplifying assumptions based on the relativistic nature of the flow. The extrapolated magnetic field is compared with estimates based upon the synchrotron and inverse Compton emission from three blazars, MKN 501, MKN 421 and PKS 2155-304. The magnetic fields evaluated from pure synchrotron self- Compton models are inconsistent with the magnetic fields extrapolated in this way. However, in two cases inverse Compton models in which a substantial part of the soft photon field is generated locally agree well, mainly because these models imply magnetic field strengths which are closer to being consistent with Poynting flux dominated jets. This comparison is based on estimating the mass accretion rate from the jet energy flux. Further comparisons along these lines will be facilitated by independent estimates of the mass accretion rate in blazars and by more detailed models for jet propagation near the black hole.Comment: Submiteed to the Astrophysics & Space Science special issue on the 5th Stromlo Symposiu

Box-supervised Instance Segmentation with Level Set Evolution

In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of the simple box annotations, which has recently attracted a lot of research attentions. In this paper, we propose a novel single-shot box-supervised instance segmentation approach, which integrates the classical level set model with deep neural network delicately. Specifically, our proposed method iteratively learns a series of level sets through a continuous Chan-Vese energy-based function in an end-to-end fashion. A simple mask supervised SOLOv2 model is adapted to predict the instance-aware mask map as the level set for each instance. Both the input image and its deep features are employed as the input data to evolve the level set curves, where a box projection function is employed to obtain the initial boundary. By minimizing the fully differentiable energy function, the level set for each instance is iteratively optimized within its corresponding bounding box annotation. The experimental results on four challenging benchmarks demonstrate the leading performance of our proposed approach to robust instance segmentation in various scenarios. The code is available at: https://github.com/LiWentomng/boxlevelset.Comment: 17 page, 4figures, ECCV202

Protective Effect of Nervonic Acid on Oxidative Damage of PC12 Cells Induced by H2O2

This study developed a cell model of oxidative damage by treating PC12 cells for 24 h with 200 µmol/L H2O2 and determined the degree of oxidative stress by assaying the activities of superoxide dismutase (SOD), glutathione peroxidase (GSH-Px) and lactate dehydrogenase (LDH) and the level of malondialdehyde (MDA). Moreover, cell apoptosis and reactive oxygen species (ROS) levels were evaluated. Western blot and real-time polymerase chain reaction (PCR) were used to detect the protein and mRNA expression levels of B-cell lymphoma-2 (Bcl-2), Bcl-2-associated X protein (Bax), caspase-3, nuclear factor E2 related factor 2 (Nrf2), kelch-like ECH-associated protein 1 (Keap1), and heme oxygenase-1 (HO-1). The results showed that after being treated with 200 µmol/L H2O2 for 24 h, the survival rate of PC12 cells was 60.12%. Cytotoxicity experiments showed that nervonic acid could significantly reduce the contents of LDH and MDA, inhibit excessive production of ROS, and enhance the activities of SOD and GSH-Px in H2O2-injuried cells. In addition, it significantly upregulated the expression of Bcl-2, Nrf2 and HO-1, and downregulated the expression of caspase-3, Bax, and Keap1. In summary, nervonic acid has a protective effect on H2O2-induced oxidative damage in PC12 cells by a mechanism associated with the activation of the Nrf2/HO-1 signaling pathway