Geometric Multi-Model Fitting by Deep Reinforcement Learning

This paper deals with the geometric multi-model fitting from noisy, unstructured point set data (e.g., laser scanned point clouds). We formulate multi-model fitting problem as a sequential decision making process. We then use a deep reinforcement learning algorithm to learn the optimal decisions towards the best fitting result. In this paper, we have compared our method against the state-of-the-art on simulated data. The results demonstrated that our approach significantly reduced the number of fitting iterations

Effect of scopoletin on fascia-wrapped diced cartilage grafts

Purpose: To evaluate the effect of scopoletin (SL) on fascia-wrapped diced cartilage grafts in rhinoplasty surgery.Methods: Cartilage grafts (2 × 2 cm) from the ears of New Zealand rabbits were diced into sections (1 mm3) and then wrapped in muscle fascia taken from the right rear leg. Each graft was placed on the back of the animal after measuring its weight, and then the rabbits were separated into two groups, viz, control and the SL-treated groups {10 mg/kg, per os (p.o.)}. The treatments were administered for 3 months, the rabbits were sacrificed, and the histopathological features and weight of the grafts were examined.Results: The weight of the grafts in the two groups did not significantly (p < 0.05) differ but the histopathological results suggested that there was a pronounced increase in the viability of the graft tissues in the SL-treated group compared to the control group. Treatment with SL decreased the resorption rate and enhanced basophilia relative to the control group. However, fibrosis, inflammation, and bone metaplasia- and calcification-like factors did not significantly differ (p < 0.05).Conclusion: Treatment with SL significantly enhances the viability of the grafts, and thus may have a beneficial effect on fascia-wrapped diced cartilage grafts.Keywords: Scopoletin, Rhinoplasty surgery, Fascia-wrapped diced cartilage graft, Histopathology, Basophilia, Inflammatio

Effect of histone deacetylase inhibitor, trichostatin A, on cartilage regeneration from free perichondrial grafts in rabbits

Purpose: To evaluate the effect of histone deacetylase (HDAC) inhibitor, trichostatin A (TCA), on cartilage regeneration in a rabbit perichondrial graft model.Methods: Perichondrial grafts (20 × 20 mm2) were derived from the ears of New Zealand rabbits and transplanted onto the paravertebral muscle of the face of each rabbit. The rabbits were separated into three groups: non-treated control group, vehicle-treated control group that received 0.3 mL of saline, and TCA-treated group administered 0.3 mL of TCA (500 ng/mL). Rabbits in all three groups were further divided into subgroups according to the duration of treatment after transplantation: 2, 4, 6, and 8 weeks (n = 12 rabbits each). The effect of TCA on cartilage regeneration was determined histologically by evaluating the thickness of the cartilage plate in the grafted rabbits.Results: TCA increased the amount of immature cartilage 4 and 6 weeks after perichondrial graft implantation. Mature cartilage was seen in the TCA-treated rabbits 8 weeks after transplantation. The thickness of the cartilage plate was significantly (p < 0.01) higher in TCA group (905 ± 36) than in either the non-treated (632 ± 22) or the vehicle-treated control (639 ± 22) group.Conclusion: Treatment with trichostatin A, an HDAC inhibitor, enhances cartilage regeneration in rabbit recipients of a perichondrial graft. Furthermore, the findings of this study should be helpful in exploring the clinical use of trichostatin A.Keywords: Histone deacetylase inhibitor, Perichondrial graft, TrichostatinA, Cartilage regeneration, Transplantatio

7-Piperazinethylchrysin inhibits melanoma cell proliferation by targeting Mek 1/2 kinase activity

Purpose: To investigate the growth-inhibitory effect of 7-piperazinethylchrysin (PEC) on melanoma cell lines.Methods: Cell viability was analyzed by trypan blue exclusion assays and the cell cycle by flow cytometry using ModFit LT software. Specifically, cells were stained with propidium iodide (0.5 mg/mL) supplemented with RNase A (50 mg/mL), and analyzed using flow cytometry and ModFit LT software.Results: In A375 and B16F10 cell cultures, proliferation was reduced to 79 and 72 %, respectively, on treatment with 30 μM PEC. PEC increased the proportion of A375 cells in G1/G0 phase to 71.23 %, versus 42.76 % in untreated cells. In B16F10 and A375 cells, treatment with PEC caused the inhibition of Mek 1/2 kinase activity and suppressed Erk 1/2 phosphorylation. The level of cAMP-response element binding protein was increased by PEC. The expression of microphthalmia-linked transcription factor was also increased by PEC treatment. Marked enhancement was observed in the level of tyrosinase in melanoma cells on treatment with PEC. Analysis of PBG-D expression showed a marked increase in B16F10 and A375 cells on the addition of PEC to cell cultures at 72 h. The level of PBG D expression was increased by 9- and 8.5-fold in B16F10 and A375 cells, respectively, on incubation with 30 μM PEC. The addition of a Mek 1/2 inhibitor (U0126) to the cultures promoted PEC-mediated growth inhibition.Conclusion: PEC inhibited melanoma cell proliferation, apparently by blocking the cell cycle at G0/G1 and downregulating the Ras/Raf/Mek/Erk pathway.Keywords: Tyrosinase, Kinase, Microphthalmia, Phosphorylation, 7-Piperazinethylchrysi

Learning to Denoise Unreliable Interactions for Link Prediction on Biomedical Knowledge Graph

Link prediction in biomedical knowledge graphs (KGs) aims at predicting unknown interactions between entities, including drug-target interaction (DTI) and drug-drug interaction (DDI), which is critical for drug discovery and therapeutics. Previous methods prefer to utilize the rich semantic relations and topological structure of the KG to predict missing links, yielding promising outcomes. However, all these works only focus on improving the predictive performance without considering the inevitable noise and unreliable interactions existing in the KGs, which limits the development of KG-based computational methods. To address these limitations, we propose a Denoised Link Prediction framework, called DenoisedLP. DenoisedLP obtains reliable interactions based on the local subgraph by denoising noisy links in a learnable way, providing a universal module for mining underlying task-relevant relations. To collaborate with the smoothed semantic information, DenoisedLP introduces the semantic subgraph by blurring conflict relations around the predicted link. By maximizing the mutual information between the reliable structure and smoothed semantic relations, DenoisedLP emphasizes the informative interactions for predicting relation-specific links. Experimental results on real-world datasets demonstrate that DenoisedLP outperforms state-of-the-art methods on DTI and DDI prediction tasks, and verify the effectiveness and robustness of denoising unreliable interactions on the contaminated KGs

Potential genetic therapies based on m6A methylation for skin regeneration: Wound healing and scars/keloids

Skin wound healing is a complex and multistage process, where any abnormalities at any stage can result in the accumulation of non-functional fibrotic tissue, leading to the formation of skin scars. Epigenetic modifications play a crucial role in regulating gene expression, inhibiting cell fate determination, and responding to environmental stimuli. m6A methylation is the most common post-transcriptional modification of eukaryotic mRNAs and long non-coding RNAs. However, it remains unclear how RNA methylation controls cell fate in different physiological environments. This review aims to discuss the current understanding of the regulatory pathways of RNA methylation in skin wound healing and their therapeutic implications with a focus on the specific mechanisms involved