5, 7-Dihalo-8-quinolinol complex inhibits growth of ovarian cancer cells via the downregulation of expression of Wip1

Purpose: To assess the cytotoxic effect of 5, 7-dihalo-8-quinolinol complex (DHQ) on ovarian cancer cells, and the mechanism of action involved.Methods: DHQ-mediated changes in cell viability were determined using MTT assay, while apoptosis was analyzed with flow cytometry. The effect of DHQ on cell migration was determined using inverted microscopy, while its effect on invasiveness was assessed with Giemsa dyeing. FACS Caliburinstrumentation was employed for analyzing the effect of DHQ on the cell cycle. The protein expressions of Wip1 and P53 were assayed by western blotting.Results: DHQ induced cytotoxicity against A2780 and OVCAR 3 cells in the concentration range of 0.25 - 12 μM (p < 0.05). In A2780 and OVCAR 3 cells, treatment with 12 μMDHQ resulted in 69.34 and 65.46 % apoptosis, respectively. The migratory potential and invasiveness of A2780 and OVCAR3 cells were significantly decreased by 12 μMDHQ, relative to untreated cells (p < 0.05). Moreover, treatment with 12 μMDHQ arrested cell cycle at G1/G0 phase in A2780 and OVCAR3 cells, but downregulated the protein expressions of Wip1 expression in A2780 and OVCAR3 cells.Conclusion: DHQ exerts cytotoxic effect on ovarian cancer cell growth via arrest of cell cycle and activation of apoptosis. Moreover, DHQ inhibits the migratory and invasive abilities of the cells. Thus, DHQ is a potential drug candidate for the management of ovarian cancer. Keywords: 5,7-Dihalo-8-quinolinol complex, Ovarian cancer, Cytotoxicity, Apoptosis, Invasiveness, Migration, Cell cycl

Evaluation of Restoration of Damaged Ecosystems on Soil Nematode Communities and Their Functions in the Desert Steppe Open-Pit Mining Area of Inner Mongolia

In view of the general lack of analysis of the ecological restoration effects of underground soil in the desert grassland ecological restoration projects in Inner Mongolia, this study selected the most representative open-pit quarries, open-pit iron mines and open-pit coal mines in the desert steppe. Using high-throughput sequencing technology, nematode community composition, diversity, and function were analyzed to determine the response of nematodes to different ecological restoration methods in damaged areas of coal mines. In open-pit coal mines, for slope rehabilitation, vegetation blanket restoration exhibited more favorable effects than those exhibited by vegetation bag restoration and natural restoration. For rehabilitation of the platform area under the slope, the diversity of soil nematodes and thesoil fauna analysis under alien soil restoration conditions were performed and exhibited similar characteristics to those of the native vegetation. After 7 years of ecological package restoration at an open-pit quarry, two management methods (annual removal of dominant plant genera and annual reseeding of missing plants) achieved the expected outcomes of the restoration project (return to the conditions of the pre-disturbed ecosystem). We also evaluated the soil health status of different ecological package restoration time periods (2 and5 years) at an abandoned desert steppe open-pit iron mine by analysis of the soil properties and nematode communities. We suggest that ecological package restoration for 5 years achieved the expected restoration outcomes in the open-pit iron mine wasteland. Our findings provide a reference for evaluating the reclamation effect of ecological package restoration inopen-pi

In situ study of the mechanical properties of airborne haze particles

Particulate pollution has raised serious concerns regarding its potential impacts on human health in developing countries. However, much less attention has been paid to the threat of haze particles to machinery and industry. By employing a state-of-the-art in situ scanning electron microscope compression testing technique, we demonstrate that iron-rich and fly ash haze particles, which account for nearly 70% of the total micron-sized spherical haze particles, are strong enough to generate abrasive damage to most engineering alloys, and therefore can generate significant scratch damage to moving contacting surfaces in high precision machineries. Our finding calls for preventive measures to protect against haze related threat.National Basic Research Program of China (973 Program) (Grant 2012CB619402)National 111 Project of China (Grant B06025)National Science Foundation (U.S.) (Grants DMR-1120901 and DMR-1410636)National Natural Science Foundation (China) (Grants 51231005, 51471128 and 51321003

One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge Distillation

Knowledge distillation~(KD) has proven to be a highly effective approach for enhancing model performance through a teacher-student training scheme. However, most existing distillation methods are designed under the assumption that the teacher and student models belong to the same model family, particularly the hint-based approaches. By using centered kernel alignment (CKA) to compare the learned features between heterogeneous teacher and student models, we observe significant feature divergence. This divergence illustrates the ineffectiveness of previous hint-based methods in cross-architecture distillation. To tackle the challenge in distilling heterogeneous models, we propose a simple yet effective one-for-all KD framework called OFA-KD, which significantly improves the distillation performance between heterogeneous architectures. Specifically, we project intermediate features into an aligned latent space such as the logits space, where architecture-specific information is discarded. Additionally, we introduce an adaptive target enhancement scheme to prevent the student from being disturbed by irrelevant information. Extensive experiments with various architectures, including CNN, Transformer, and MLP, demonstrate the superiority of our OFA-KD framework in enabling distillation between heterogeneous architectures. Specifically, when equipped with our OFA-KD, the student models achieve notable performance improvements, with a maximum gain of 8.0% on the CIFAR-100 dataset and 0.7% on the ImageNet-1K dataset. PyTorch code and checkpoints can be found at https://github.com/Hao840/OFAKD

Image Aesthetics Assessment via Learnable Queries

Image aesthetics assessment (IAA) aims to estimate the aesthetics of images. Depending on the content of an image, diverse criteria need to be selected to assess its aesthetics. Existing works utilize pre-trained vision backbones based on content knowledge to learn image aesthetics. However, training those backbones is time-consuming and suffers from attention dispersion. Inspired by learnable queries in vision-language alignment, we propose the Image Aesthetics Assessment via Learnable Queries (IAA-LQ) approach. It adapts learnable queries to extract aesthetic features from pre-trained image features obtained from a frozen image encoder. Extensive experiments on real-world data demonstrate the advantages of IAA-LQ, beating the best state-of-the-art method by 2.2% and 2.1% in terms of SRCC and PLCC, respectively.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl