5,7-Dihydr­oxy-3,6,8-trimethoxy­flavone

The title compound (systematic name: 5,7-dihydr­oxy-3,6,8-trimeth­oxy-4H-chromen-4-one), C18H16O7, is a flavone that was isolated from Ainsliaea henryi. There are two mol­ecules in the asymmetric unit, one of which has a disordered meth­oxy group [occupancy ratio 0.681 (9):0.319 (9)]. Both mol­ecules have an intra­molecular O—H⋯O hydrogen bond. In the crystal, mol­ecules are linked into O—H⋯O hydrogen-bonded chains parallel to [110]

Anti-tumor effects of ephedrine and anisodamine on SKBR3 human breast cancer cell line

Background: To investigate the effects of ephedrine and anisodamine on the proliferation of human breast cancer.Materials and Methods: SKBR3 cell was treated with or without ephedrine and / or anisodamine,  respectively. The trypan blue exclusion assay was used to determine cell numbers. Flow cytometry was used to assess cell cycle distribution and apoptosis. The concentration of cAMP and cyclin D1 was analyzed by enzyme-linked immunosorbent assay. Western blot was used to measure PKA.Results: Ephedrine and anisodamine inhibited cell proliferation and arrested SKBR3 cells at G0/G1 phases. Ephedrine and anisodamine increased the level of CD1 in SKBR3 cells. Furthermore, significant change in intracellular cAMP concentration was found in SKBR3 cells treated with ephedrine and anisodamine. The phosphorylation of PKA substrate was not activated after 48 hours of treatment with ephedrine and  anisodamine.Conclusion: Ephedrine and anisodamine inhibit the proliferation of SKBR3 cells via a significantly change of intracellular cAMP concentration.Key words: anisodamine, breast cancer, cyclic adenosine monophosphate, ephedrine, proliferation Abbreviations: cAMP, cyclic adenosine monophosphate; TCM, traditional Chinese medicine; ELISA,  enzyme-linked immunosorbent assay; CD1, cyclin D1; SNS, sympathetic nervous system

A dimeric sesquiterpene, gochnatiolide A

The title compound [systematic name: 5′a-hydroxy-1′,3,6,8′-tetrakis(methylene)-3a,4,5,5′,5′a,6,6′,6a,7,7′,7′a,8′,9a,9b,10′a,10′b-hexadecahydrospiro[azuleno[4,5-b]furan-9(2H),3′-[3H]benz[1,8]azuleno[4,5-b]furan]-2,2′,8,9′(1′H,3H,4′H)-tetrone acetone 0.92-solvate], C30H30O7·0.92C3H6O, is a dimeric sequiterpene formed by a cyclohexane system connecting two monomeric sesquiterpene lactone units of dehydro­zaluzanin C. It was isolated from Ainsliaea henryi

Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective

Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features. It serves as a pivotal approach to combat the curse of dimensionality, enhance model generalization, mitigate data sparsity, and extend the applicability of classical models. Existing research predominantly focuses on domain knowledge-based feature engineering or learning latent representations. However, these methods, while insightful, lack full automation and fail to yield a traceable and optimal representation space. An indispensable question arises: Can we concurrently address these limitations when reconstructing a feature space for a machine-learning task? Our initial work took a pioneering step towards this challenge by introducing a novel self-optimizing framework. This framework leverages the power of three cascading reinforced agents to automatically select candidate features and operations for generating improved feature transformation combinations. Despite the impressive strides made, there was room for enhancing its effectiveness and generalization capability. In this extended journal version, we advance our initial work from two distinct yet interconnected perspectives: 1) We propose a refinement of the original framework, which integrates a graph-based state representation method to capture the feature interactions more effectively and develop different Q-learning strategies to alleviate Q-value overestimation further. 2) We utilize a new optimization technique (actor-critic) to train the entire self-optimizing framework in order to accelerate the model convergence and improve the feature transformation performance. Finally, to validate the improved effectiveness and generalization capability of our framework, we perform extensive experiments and conduct comprehensive analyses.Comment: 21 pages, submitted to TKDD. arXiv admin note: text overlap with arXiv:2209.08044, arXiv:2205.1452

TwinTex: Geometry-aware Texture Generation for Abstracted 3D Architectural Models

Coarse architectural models are often generated at scales ranging from individual buildings to scenes for downstream applications such as Digital Twin City, Metaverse, LODs, etc. Such piece-wise planar models can be abstracted as twins from 3D dense reconstructions. However, these models typically lack realistic texture relative to the real building or scene, making them unsuitable for vivid display or direct reference. In this paper, we present TwinTex, the first automatic texture mapping framework to generate a photo-realistic texture for a piece-wise planar proxy. Our method addresses most challenges occurring in such twin texture generation. Specifically, for each primitive plane, we first select a small set of photos with greedy heuristics considering photometric quality, perspective quality and facade texture completeness. Then, different levels of line features (LoLs) are extracted from the set of selected photos to generate guidance for later steps. With LoLs, we employ optimization algorithms to align texture with geometry from local to global. Finally, we fine-tune a diffusion model with a multi-mask initialization component and a new dataset to inpaint the missing region. Experimental results on many buildings, indoor scenes and man-made objects of varying complexity demonstrate the generalization ability of our algorithm. Our approach surpasses state-of-the-art texture mapping methods in terms of high-fidelity quality and reaches a human-expert production level with much less effort. Project page: https://vcc.tech/research/2023/TwinTex.Comment: Accepted to SIGGRAPH ASIA 202

Self-Optimizing Feature Transformation

Feature transformation aims to extract a good representation (feature) space by mathematically transforming existing features. It is crucial to address the curse of dimensionality, enhance model generalization, overcome data sparsity, and expand the availability of classic models. Current research focuses on domain knowledge-based feature engineering or learning latent representations; nevertheless, these methods are not entirely automated and cannot produce a traceable and optimal representation space. When rebuilding a feature space for a machine learning task, can these limitations be addressed concurrently? In this extension study, we present a self-optimizing framework for feature transformation. To achieve a better performance, we improved the preliminary work by (1) obtaining an advanced state representation for enabling reinforced agents to comprehend the current feature set better; and (2) resolving Q-value overestimation in reinforced agents for learning unbiased and effective policies. Finally, to make experiments more convincing than the preliminary work, we conclude by adding the outlier detection task with five datasets, evaluating various state representation approaches, and comparing different training strategies. Extensive experiments and case studies show that our work is more effective and superior.Comment: Under review of TKDE. arXiv admin note: substantial text overlap with arXiv:2205.1452

Clinical features and surgical effect of vireoretinal diseases with contralateral blindness

AIM: To investigate the clinical characteristics and surgical results of vireoretinal diseases in 68 patients with contralateral blindness(solitary eye). <p>METHODS: A total of 68 patients(68 eyes)with contralateral blindness were enrolled in this retrospective consecutive study. The clinical characteristics, surgical procedures and temponade materials chosen, preoperative and postoperative visual acuity, complications and prognosis were analyzed. The follow-up ranged from 4 months to 5 years, with an average of(11.30±9.57)months. At the last follow-up, the surgical effects were evaluated.<p>RESULTS:After operation, visual acuity increased significantly. The number of eyes with vision of 0.05 or better increased from 22 eyes(32.4%)preoperative to 60 eyes(88.2%)postoperative, and that of 0.3 or better from 3 eyes(4.4%)to 37 eyes(54.4%). The best-corrected visual acuity before and after surgery also differed significantly(<i>t</i>=8.986, <i>P<</i>0.01). <p>CONCLUSION: With vitreoretinal surgery, visual impairment or loss due to vitreoretinal diseases can be avoided in most patients with contralateral blindness

Combining high-throughput micro-CT-RGB phenotyping and genome-wide association study to dissect the genetic architecture of tiller growth in rice

Manual phenotyping of rice tillers is time consuming and labor intensive and lags behind the rapid development of rice functional genomics. Thus, automated, non-destructive phenotyping of rice tiller traits at a high spatial resolution and high-throughput for large-scale assessment of rice accessions is urgently needed. In this study, we developed a high-throughput micro-CT-RGB (HCR) imaging system to non-destructively extract 730 traits from 234 rice accessions at 9 time points. We could explain 30% of the grain yield variance from 2 tiller traits assessed in the early growth stages. A total of 402 significantly associated loci were identified by GWAS, and dynamic and static genetic components were found across the nine time points. A major locus associated with tiller angle was detected at nine time points, which contained a major gene TAC1. Significant variants associated with tiller angle were enriched in the 3'-UTR of TAC1. Three haplotypes for the gene were found and rice accessions containing haplotype H3 displayed much smaller tiller angles. Further, we found two loci contained associations with both vigor-related HCR traits and yield. The superior alleles would be beneficial for breeding of high yield and dense planting

ANTI-TUMOR EFFECTS OF EPHEDRINE AND ANISODAMINE ON SKBR3 HUMAN BREAST CANCER CELL LINE

Background: To investigate the effects of ephedrine and anisodamine on the proliferation of human breast cancer. Materials and Methods: SKBR3 cell was treated with or without ephedrine and / or anisodamine, respectively. The trypan blue exclusion assay was used to determine cell numbers. Flow cytometry was used to assess cell cycle distribution and apoptosis. The concentration of cAMP and cyclin D1 was analyzed by enzyme-linked immunosorbent assay. Western blot was used to measure PKA. Results: Ephedrine and anisodamine inhibited cell proliferation and arrested SKBR3 cells at G0/G1 phases. Ephedrine and anisodamine increased the level of CD1 in SKBR3 cells. Furthermore, significant change in intracellular cAMP concentration was found in SKBR3 cells treated with ephedrine and anisodamine. The phosphorylation of PKA substrate was not activated after 48 hours of treatment with ephedrine and anisodamine. Conclusion: Ephedrine and anisodamine inhibit the proliferation of SKBR3 cells via a significantly change of intracellular cAMP concentration