MiR-196a-5p facilitates progression of estrogen-dependent endometrial cancer by regulating FOXO1

Background and Purpose. Estrogen-dependent endometrial cancer mainly occurs in younger pre-menopausal and post-menopausal women and threatens their health. Recently, microRNAs (miRNAs) have been considered as novel targets in endometrial cancer treatment. Therefore, we aimed to explore the effect of miRNA (miR)-196a-5p in estrogen-dependent endometrial cancer. Methods. 17β-estradiol (E2; 2.5, 5, 10 and 20 nM) was used to treat RL95-2, HEC-1B and ECC-1 cells followed by cell viability assessment using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT). The level of miR-196a-5p was measured by reverse transcription-quantitative PCR (RT-qPCR). We then transfected miR-196a-5p mimic/inhibitor and Forkhead box protein O1 (FOXO1) small interfering RNA (siRNA) into E2-treated cells. Apoptotic cells were measured by flow cytometry. Wound healing and Transwell assays were implemented to assess migration and invasion. Bioinformatics and luciferase reporter assays were applied to confirm the interaction between miR-196a-5p and FOXO1. Immunoblotting determined the levels of FOXO1, Bcl-2, Bax, Caspase 3. Results. E2 promoted cell viability and miR-196a-5p expression in RL95-2 and ECC-1 cells. miR-196a-5p mimic enhanced cell viability, migration and invasion but suppressed apoptosis and FOXO1, whilst miR-196a-5p inhibitor blocked these processes. In addition, miR-196a-5p upregulated Bcl-2, but down regulated Bax and Caspase 3 expression, an effect that was reversed by miR-196a-5p inhibitor. We determined that miR-196a-5p targeted FOXO1, and that si-FOXO1 blocked the effects of miR-196a-5p inhibitor on viability, apoptosis, migration and invasion of E2-treated RL95-2 and ECC-1 cells. Conclusions. Our findings suggested potential diagnostic and therapeutic applications for miR-196a-5p and its FOXO1 target in patients suffering from estrogen-dependent endometrial cancer

Diving into Darkness: A Dual-Modulated Framework for High-Fidelity Super-Resolution in Ultra-Dark Environments

Super-resolution tasks oriented to images captured in ultra-dark environments is a practical yet challenging problem that has received little attention. Due to uneven illumination and low signal-to-noise ratio in dark environments, a multitude of problems such as lack of detail and color distortion may be magnified in the super-resolution process compared to normal-lighting environments. Consequently, conventional low-light enhancement or super-resolution methods, whether applied individually or in a cascaded manner for such problem, often encounter limitations in recovering luminance, color fidelity, and intricate details. To conquer these issues, this paper proposes a specialized dual-modulated learning framework that, for the first time, attempts to deeply dissect the nature of the low-light super-resolution task. Leveraging natural image color characteristics, we introduce a self-regularized luminance constraint as a prior for addressing uneven lighting. Expanding on this, we develop Illuminance-Semantic Dual Modulation (ISDM) components to enhance feature-level preservation of illumination and color details. Besides, instead of deploying naive up-sampling strategies, we design the Resolution-Sensitive Merging Up-sampler (RSMU) module that brings together different sampling modalities as substrates, effectively mitigating the presence of artifacts and halos. Comprehensive experiments showcases the applicability and generalizability of our approach to diverse and challenging ultra-low-light conditions, outperforming state-of-the-art methods with a notable improvement (i.e., $\uparrow$5\% in PSNR, and $\uparrow$43\% in LPIPS). Especially noteworthy is the 19-fold increase in the RMSE score, underscoring our method's exceptional generalization across different darkness levels. The code will be available online upon publication of the paper.Comment: 9 page

Learn from the Past: A Proxy based Adversarial Defense Framework to Boost Robustness

In light of the vulnerability of deep learning models to adversarial samples and the ensuing security issues, a range of methods, including Adversarial Training (AT) as a prominent representative, aimed at enhancing model robustness against various adversarial attacks, have seen rapid development. However, existing methods essentially assist the current state of target model to defend against parameter-oriented adversarial attacks with explicit or implicit computation burdens, which also suffers from unstable convergence behavior due to inconsistency of optimization trajectories. Diverging from previous work, this paper reconsiders the update rule of target model and corresponding deficiency to defend based on its current state. By introducing the historical state of the target model as a proxy, which is endowed with much prior information for defense, we formulate a two-stage update rule, resulting in a general adversarial defense framework, which we refer to as `LAST' ({\bf L}earn from the P{\bf ast}). Besides, we devise a Self Distillation (SD) based defense objective to constrain the update process of the proxy model without the introduction of larger teacher models. Experimentally, we demonstrate consistent and significant performance enhancements by refining a series of single-step and multi-step AT methods (e.g., up to $\bf 9.2\%$ and $\bf 20.5\%$ improvement of Robust Accuracy (RA) on CIFAR10 and CIFAR100 datasets, respectively) across various datasets, backbones and attack modalities, and validate its ability to enhance training stability and ameliorate catastrophic overfitting issues meanwhile.Comment: 16 Page

Prompt-NER: Zero-shot Named Entity Recognition in Astronomy Literature via Large Language Models

This study delves into the application of Large Language Models (LLMs) for Named Entity Recognition (NER) tasks in the field of astronomy literature. To enhance the zero-shot recognition capabilities of LLMs for astronomical named entities, we propose a strategy called Prompt-NER. Prompt-NER includes five prompt elements: Task Descriptions, Entity Definitions, Task Emphasis, Task Examples, and Second Conversation. To assess the effectiveness of the Prompt-NER strategy, we utilize three representative LLMs (Claude-2, GPT-3.5, and LLaMA-2-70b) to identify telescope and celestial object named entities in astronomical literature. Our experiments are conducted based on two distinct datasets. The first dataset comprises 30 original PDF documents, which we split into paragraphs in sequential order, resulting in a second dataset consisting of 30 paragraph collections. Additionally, we incorporate 30 astronomical telegrams to diversify our experiments and assess the performance of LLMs based on Prompt-NER on concise, complete texts. Our experimental results indicate that the Prompt-NER strategy enables LLMs to effectively accomplish NER tasks in the field of astronomy, even without prior astronomical knowledge during training. We carefully analyze the experimental results, including the mechanism of different prompt elements and the influence of different features of long and short texts on their respective experimental results. This research provides experience for zero-shot NER tasks in astronomical literature and suggests future work in this area

Efficient Cavity Searching for Gene Network of Influenza A Virus

High order structures (cavities and cliques) of the gene network of influenza A virus reveal tight associations among viruses during evolution and are key signals that indicate viral cross-species infection and cause pandemics. As indicators for sensing the dynamic changes of viral genes, these higher order structures have been the focus of attention in the field of virology. However, the size of the viral gene network is usually huge, and searching these structures in the networks introduces unacceptable delay. To mitigate this issue, in this paper, we propose a simple-yet-effective model named HyperSearch based on deep learning to search cavities in a computable complex network for influenza virus genetics. Extensive experiments conducted on a public influenza virus dataset demonstrate the effectiveness of HyperSearch over other advanced deep-learning methods without any elaborated model crafting. Moreover, HyperSearch can finish the search works in minutes while 0-1 programming takes days. Since the proposed method is simple and easy to be transferred to other complex networks, HyperSearch has the potential to facilitate the monitoring of dynamic changes in viral genes and help humans keep up with the pace of virus mutations.Comment: work in progres

Original Article Neuroprotective effect of functionalized multi-walled carbon nanotubes on spinal cord injury in rats

Abstract: Traumatic injuries to the brain and spinal cord affect a large percentage of the world's population. However, there are currently no effective treatments for these central nervous system (CNS) injuries. In our study, we evaluated the neuroprotective role of functionalized multi-walled carbon nanotubes (MWCNTs) carrying brain derived neurotrophic factor (BNDF), nogo-66 receptor (NgR) and Ras homolog gene family member A (RhoA) in spinal cord injury (SCI). Our results showed that transfection into rat cortical neurons with BDNF-DNA significantly elevated the expression of BDNF both in vitro and in vivo. Meanwhile, transfection with NgR-siRNA and RhoA-siRNA resulted in an obvious down-regulation of NgR and RhoA in neuron cells and in injured spinal cords. In addition, the functionalized MWCNTs carrying BDNF-DNA, NgR-siRNA and RhoA-siRNA exhibited remarkable therapeutic effects on injured spinal cord. Taken together, our study demonstrates that functionalized MWCNTs have a potential therapeutic application on repair and regeneration of the CNS