Inhibition effects of paeonol on mice bearing EMT6 breast cancer through inducing rumor cell apoptosis

Paeonol, a phenolic component from the root bark of Paeonia moutan, has been identified to possess antitumor effects on mice bearing EMT6 breast cancer in our previous studies. However, the underlying mechanisms remain unknown. In the present study the molecular mechanisms of paeonol were further investigated in EMT6 mice model. The results showed that treatment of mice with 175 and 350 mg/kg/day of paeonol significantly inhibited the growth of the EMT6 tumor in mice, and induced tumor cell apoptosis which were demonstrated by light microscopy after hematoxylin and eosin staining and apoptosis analysis by flow cytometry. In addition, compared with the control group, paeonol increased the number of tumor cells in G0/G1 phase but decreased the number of cells in S and G2/M phase. Paeonol treatment (350 mg/kg body weight) also resulted in a decrease of Bcl-2 and an increase in Bax and caspase-3 expressions, which were demonstrated by immunohistochemical and western blot analysis. These results indicate that the antitumor effects of paeonol might be associated with arresting tumor cells in the G0/G1 phase, inducing cell apoptosis and regulation of the expression of Bcl-2, Bax and activation of caspase-3

Antitumor effect of salidroside on mice bearing HepA hepatocellular carcinoma

Salidroside, a phenylpropanoid glycoside extracted from Rhodiola rosea L., has antiproliferative effects on tumour cells in mice. However it’s antitumor mechanism remains largely unknown. In this study, 4 groups of mice bearing hepatocarcinoma cells were given treatment with vehicle alone, cyclophosphamide (25 mg/kg, i.p.) and salidroside, either 100 or 200 mg/kg (p.o.) for 14 days. The morphology of tumour specimens was analysed by transmission electron microscopy. Apoptotic cells in sections of mouse tumour tissue were analysed using an in situ apoptosis kit. The expression of Bcl-2, Bax and caspase 3 mRNA were examined with RT-PCR. The results showed that the tumour weights in groups 100 or 200 mg/kg/day of salidroside were reduced significantly (45.34 and 52.48% respectively), compared to vehicle groups. Salidroside increased apoptotic cells index, e.g. in 200 mg/kg group, it was four times higher compared to the control group. Even more, treatment with salidroside decreased Bcl-2 mRNA expression and increased Bax and caspase 3 mRNA expressions. These indicated that the antitumor mechanism of salidroside may induce tumour cell apoptosis in mice by triggering the mitochondrial-dependent pathway and activation of caspase 3

AutoDiffusion: Training-Free Optimization of Time Steps and Architectures for Automated Diffusion Model Acceleration

Diffusion models are emerging expressive generative models, in which a large number of time steps (inference steps) are required for a single image generation. To accelerate such tedious process, reducing steps uniformly is considered as an undisputed principle of diffusion models. We consider that such a uniform assumption is not the optimal solution in practice; i.e., we can find different optimal time steps for different models. Therefore, we propose to search the optimal time steps sequence and compressed model architecture in a unified framework to achieve effective image generation for diffusion models without any further training. Specifically, we first design a unified search space that consists of all possible time steps and various architectures. Then, a two stage evolutionary algorithm is introduced to find the optimal solution in the designed search space. To further accelerate the search process, we employ FID score between generated and real samples to estimate the performance of the sampled examples. As a result, the proposed method is (i).training-free, obtaining the optimal time steps and model architecture without any training process; (ii). orthogonal to most advanced diffusion samplers and can be integrated to gain better sample quality. (iii). generalized, where the searched time steps and architectures can be directly applied on different diffusion models with the same guidance scale. Experimental results show that our method achieves excellent performance by using only a few time steps, e.g. 17.86 FID score on ImageNet 64 $\times$ 64 with only four steps, compared to 138.66 with DDIM. The code is available at https://github.com/lilijiangg/AutoDiffusion

Seek Homogeneous Critical Ice Nucleus at Moderate Supercooling

The quantitative investigation and dynamical understanding of homogeneous nucleation remain a topic of intense research in the interdisciplinary subject. From supercooled water, homogeneous ice nucleation will not happen spontaneously until a critical crystallite nucleus (Nc) pre-exists. In this work, we investigate homogeneous ice nucleation with our molecular dynamics software SPONGE. Using metadynamics and two structural-based collective variables, we successfully improve the sampling technic to grow spherical nuclei with various cubicity and sizes in a 23040-water box. First, we perform the first long-term freezing in all-atom simulation, various nuclei freeze out into Isd. Instead of a certain cluster size based on classical nucleation theory, the dynamic behaviors of ice nuclei experience a wide range of intermediate states. We provide a novel critical nucleus diagram to seek the critical nucleus: the main external factor, surface area, contributes to the freezing speed of the ice nucleus; while the key internal factor, the mean tetrahedral order, controls the melting speed instead. The ice nucleation rates of our work are in good agreement with the former simulation data. We provide a brief frame to discuss the structural details of the nucleation decision

OV-VG: A Benchmark for Open-Vocabulary Visual Grounding

Open-vocabulary learning has emerged as a cutting-edge research area, particularly in light of the widespread adoption of vision-based foundational models. Its primary objective is to comprehend novel concepts that are not encompassed within a predefined vocabulary. One key facet of this endeavor is Visual Grounding, which entails locating a specific region within an image based on a corresponding language description. While current foundational models excel at various visual language tasks, there's a noticeable absence of models specifically tailored for open-vocabulary visual grounding. This research endeavor introduces novel and challenging OV tasks, namely Open-Vocabulary Visual Grounding and Open-Vocabulary Phrase Localization. The overarching aim is to establish connections between language descriptions and the localization of novel objects. To facilitate this, we have curated a comprehensive annotated benchmark, encompassing 7,272 OV-VG images and 1,000 OV-PL images. In our pursuit of addressing these challenges, we delved into various baseline methodologies rooted in existing open-vocabulary object detection, VG, and phrase localization frameworks. Surprisingly, we discovered that state-of-the-art methods often falter in diverse scenarios. Consequently, we developed a novel framework that integrates two critical components: Text-Image Query Selection and Language-Guided Feature Attention. These modules are designed to bolster the recognition of novel categories and enhance the alignment between visual and linguistic information. Extensive experiments demonstrate the efficacy of our proposed framework, which consistently attains SOTA performance across the OV-VG task. Additionally, ablation studies provide further evidence of the effectiveness of our innovative models. Codes and datasets will be made publicly available at https://github.com/cv516Buaa/OV-VG

Meta Architecture for Point Cloud Analysis

Recent advances in 3D point cloud analysis bring a diverse set of network architectures to the field. However, the lack of a unified framework to interpret those networks makes any systematic comparison, contrast, or analysis challenging, and practically limits healthy development of the field. In this paper, we take the initiative to explore and propose a unified framework called PointMeta, to which the popular 3D point cloud analysis approaches could fit. This brings three benefits. First, it allows us to compare different approaches in a fair manner, and use quick experiments to verify any empirical observations or assumptions summarized from the comparison. Second, the big picture brought by PointMeta enables us to think across different components, and revisit common beliefs and key design decisions made by the popular approaches. Third, based on the learnings from the previous two analyses, by doing simple tweaks on the existing approaches, we are able to derive a basic building block, termed PointMetaBase. It shows very strong performance in efficiency and effectiveness through extensive experiments on challenging benchmarks, and thus verifies the necessity and benefits of high-level interpretation, contrast, and comparison like PointMeta. In particular, PointMetaBase surpasses the previous state-of-the-art method by 0.7%/1.4/%2.1% mIoU with only 2%/11%/13% of the computation cost on the S3DIS datasets

GE11-antigen-loaded hepatitis B virus core antigen virus-like particles efficiently bind to TNBC tumor

PurposeThis study aimed to explore the possibility of utilizing hepatitis B core protein (HBc) virus-like particles (VLPs) encapsulate doxorubicin (Dox) to reduce the adverse effect caused by its off-target and toxic side effect.MethodsHere, a triple-negative breast cancer (TNBC) tumor-targeting GE11-HBc VLP was constructed through genetic engineering. The GE11 peptide, a 12-amino-acid peptide targeting epidermal growth factor receptor (EGFR), was inserted into the surface protein loops of VLPs. The Dox was loaded into HBc VLPs by a thermal-triggered encapsulation strategy. The in vitro release, cytotoxicity, and cellular uptake of TNBC tumor-targeting GE11-HBc VLPs was then evaluated.ResultsThese VLPs possessed excellent stability, DOX loading efficiency, and preferentially released drug payload at high GSH levels. The insertion of GE11 targeting peptide caused improved cellular uptake and enhanced cell viability inhibitory in EGFR high-expressed TNBC cells.ConclusionTogether, these results highlight DOX-loaded, EGFR-targeted VLPs as a potentially useful therapeutic choice for EGFR-overexpressing TNBC

Unique post-translational oxime formation in the biosynthesis of the azolemycin complex of novel ribosomal peptides from Streptomyces sp. FXJ1.264

Streptomycetes are a rich source of bioactive specialized metabolites, including several examples of the rapidly growing class of ribosomally-biosynthesized and post-translationally-modified peptide (RiPP) natural products. Here we report the discovery from Streptomyces sp. FXJ1.264 of azolemycins A–D, a complex of novel linear azole-containing peptides incorporating a unique oxime functional group. Bioinformatics analysis of the Streptomyces sp. FXJ1.264 draft genome sequence identified a cluster of genes that was hypothesized to be responsible for elaboration of the azolemycins from a ribosomally-biosynthesized precursor. Inactivation of genes within this cluster abolished azolemycin production, consistent with this hypothesis. Moreover, mutants lacking the azmE and azmF genes accumulated azolemycin derivatives lacking the O-methyl groups and an amino group in place of the N-terminal oxime (as well as proteolysed derivatives), respectively. Thus AzmE, a putative S-adenosyl methionine-dependent methyl transferase, is responsible for late-stage O-methylation reactions in azolemycin biosynthesis and AzmF, a putative flavin-dependent monooxygenase, catalyzes oxidation of the N-terminal amino group in an azolemycin precursor to the corresponding oxime. To the best of our knowledge, oxime formation is a hitherto unknown posttranslational modification in RiPP biosynthesis