Distribution and Permeability of Capillaries at the Skin of the Conception Vessel and the Governor Vessel in Healthy Rabbits

ObjectiveTo investigate the distribution and permeability of blood vessels on the Conception Vessel and the Governor Vessel in the physiological state.MethodsEvans blue (EB) solution was injected into the marginal ear vein of healthy rabbits. Three hours after injection, the rabbits were sacrificed and the skin on the Conception Vessel and the Governor Vessel and the corresponding bilateral non-channels was collected. EB was extracted with 7:3 acetone: physiological saline, and the absorbance of EB at each skin tissue was measured with a spectrophotometer.ResultsThe A value of EB absorbance at the Conception Vessel on the abdominal skin was lower than that of the corresponding bilateral non-channels with a statistically significant difference (P<0.01). The A value of EB absorbance at the Governor Vessel on the back was higher than that of the corresponding bilateral non-channels (P<0.05). There was no statistical difference in the A value of EB absorbance between the bilateral non-channels of the abdomen and the back (P>0.05).ConclusionThere were differences in capillary distribution and permeability between the Conception Vessel, the Governor Vessel and the corresponding bilateral non-meridians

Accretion Disk for regular black holes with sub-Planckian curvature

We investigate the accretion disk for a sort of regular black holes which are characterized by sub-Planckian curvature and Minkowskian core. We derive null geodesics outside the horizon of such regular black holes and analyze the feature of the light rays from the accretion disk which can be classified into direct emission, lensed rings, and photon rings. We find that the observed brightness under different emission models is mainly determined by direct emission, while the contribution from the flux of the lensed and photon rings is limited. By comparing with Bardeen black hole with a dS core, it is found that the black hole with a Minkowskian core exhibits distinct astronomical optical features when surrounded by accretion disk, which potentially provides a way to distinguish these two sorts of black holes by astronomical observation.Comment: 26 pages,9 figure

Molecular mechanism of ethylene stimulation of latex yield in rubber tree (Hevea brasiliensis) revealed by de novo sequencing and transcriptome analysis

Differential expression of unigenes involved in hormone signaling in E8 and E24 compared to C samples of Hevea brasiliensis. Ethylene signalling pathway: ETR1: ETHYLENE RESPONSE 1; CTR1: CONSTITUTIVE TRIPLE RESPONSE 1; EIN2: ETHYLENE INSENSITIVE 2; EIN3: ETHYLENE INSENSITIVE 3; ERF1/2: ETHYLENE RESPONSE FACTOR 1/2; EBF1/2: EIN3 binding F-Box protein 1/2; BR signaling pathway: BRI1: Brassinosteroid-Insensitive 1; BAK1: BRI1-associated kinase 1; BKI1: BRI1 KINASE INHIBITOR 1; BSK: BR SIGNALING KINASE; BSU1: bri1 SUPPRESSOR 1; BIN2: BRASSINOSTEROID-INSENSITIVE 2; BZR1/2: BRASSINAZOLE RESISTANT 1/2; TCH: TOUCH genes; CYCD3: CYCLIN D3; GA signaling pathway: GID1: GIBBERELLIN INSENSITIVE DWARF 1; GID2: GIBBERELLIN INSENSITIVE DWARF 2; DELLAs: DELLA growth inhibitors; TF: transcriptional factor; Auxin signaling pathway: AUX1: AUXIN1; TIR1: TRANSPORT INHIBITOR RESPONSE 1; IAA: INDOLE ACETIC ACID; ARF: AUXIN RESPONSE FACTOR; SAUR: Small Auxin-Up RNA; G10H: geraniol 10-hydroxylase gene; Cytokinin signaling pathway: CRE1: CYTOKININ RESPONSE 1; AHP: histidine phosphotransfer protein; B-ARR: type-B response regulator (ARR); A-ARR: type-A response regulator (ARR); SA signalling pathway: NPR1: Non-expressor of pathogenesis-related genes 1; TGA: the bZIP transcription factors; PR1: pathogenesis related protein 1; JA signaling pathway: JAR1: JASMONATES RESISTANT 1; JA-Ile: jasmonoyl isoleucine; JAZ: Jasmonate ZIM-domain-containing protein; MYC2: a basic helix-loop-helix (bHLH) transcription factor; ORCA3: Octadecanoid-derivative Responsive Catharanthus AP2-domain gene; ABA signalling pathway: PYR1/PYLs: Pyrabactin Resistance Protein1/PYR-Like proteins; PP2Cs: protein phosphatases which fall under the category of type 2C; SnRK2: SNF1 (Sucrose-Nonfermenting Kinase1)-related protein kinase 2: ABF: ABA responsive element (ABRE) binding factors. Cells with gray border lines in the upper rows represent differentially expressed unigenes in E8 compared to C and cells with green border lines in the lower rows represent differentially expressed unigenes in E24 compared to C. Relative levels of expression are showed by a color gradient from low (blue) to high (red). (JPG 249 kb

EGC: Image Generation and Classification via a Diffusion Energy-Based Model

Learning image classification and image generation using the same set of network parameters is a challenging problem. Recent advanced approaches perform well in one task often exhibit poor performance in the other. This work introduces an energy-based classifier and generator, namely EGC, which can achieve superior performance in both tasks using a single neural network. Unlike a conventional classifier that outputs a label given an image (i.e., a conditional distribution $p(y|\mathbf{x})$), the forward pass in EGC is a classifier that outputs a joint distribution $p(\mathbf{x},y)$, enabling an image generator in its backward pass by marginalizing out the label $y$. This is done by estimating the energy and classification probability given a noisy image in the forward pass, while denoising it using the score function estimated in the backward pass. EGC achieves competitive generation results compared with state-of-the-art approaches on ImageNet-1k, CelebA-HQ and LSUN Church, while achieving superior classification accuracy and robustness against adversarial attacks on CIFAR-10. This work represents the first successful attempt to simultaneously excel in both tasks using a single set of network parameters. We believe that EGC bridges the gap between discriminative and generative learning