假基因来源的lncRNA DUXAP8表观遗传沉默CDKN1A和KLF2促进胰腺癌细胞的生长

背景与目的最近研究表明假基因来源的长链非编码RNA(long non-coding RNAs,lncRNA)是癌症的关键调控因子。然而,在胰腺癌中却鲜有对lncRNA的鉴定和研究。我们旨在明确假基因来源的lncRNA DUXAP8与胰腺癌细胞生长的关系。方法我们通过比较分析3个来自GEO的独立数据集,筛选出与人胰腺癌相关的假基因来源的lncRNA。通过实时定量反转录PCR检测DUXAP8在胰腺癌组织和细胞中的相对表达水平。应用功能缺失的方法在体外和体内研究DUXAP8对胰腺癌细胞的增殖和凋亡的作用。在胰腺癌细胞中通过RNA免疫沉淀、染色质免疫沉淀和挽救实验分析DUXAP8与靶蛋白和基因的关系。结果 DUXAP8在胰腺癌组织中的表达水平显著高于配对的癌旁正常组织。DUXAP8的高表达与胰腺癌患者的肿瘤体积较大、病理分期较晚和总生存期较短相关。此外,在体外和体内通过siRNA或shRNA沉默DUXAP8表达可抑制胰腺癌细胞增殖并促进细胞凋亡。机制研究表明,DUXAP8部分地通过下调肿瘤抑制因子CDKN1A和KLF2表达调控胰腺癌细胞增殖。结论我们的结果表明,假基因来源的lncRNA DUXAP8的表达在胰腺癌进展中起到重要作用。DUXAP8可作为胰腺癌的候选生物标志物及新的治疗靶点

Dataset Quantization

State-of-the-art deep neural networks are trained with large amounts (millions or even billions) of data. The expensive computation and memory costs make it difficult to train them on limited hardware resources, especially for recent popular large language models (LLM) and computer vision models (CV). Recent popular dataset distillation methods are thus developed, aiming to reduce the number of training samples via synthesizing small-scale datasets via gradient matching. However, as the gradient calculation is coupled with the specific network architecture, the synthesized dataset is biased and performs poorly when used for training unseen architectures. To address these limitations, we present dataset quantization (DQ), a new framework to compress large-scale datasets into small subsets which can be used for training any neural network architectures. Extensive experiments demonstrate that DQ is able to generate condensed small datasets for training unseen network architectures with state-of-the-art compression ratios for lossless model training. To the best of our knowledge, DQ is the first method that can successfully distill large-scale datasets such as ImageNet-1k with a state-of-the-art compression ratio. Notably, with 60% data from ImageNet and 20% data from Alpaca's instruction tuning data, the models can be trained with negligible or no performance drop for both vision tasks (including classification, semantic segmentation, and object detection) as well as language tasks (including instruction tuning tasks such as BBH and DROP).Comment: 9 page

Road proximity and traffic flow perceived as potential predation risks: evidence from the Tibetan antelope

Abstract Context. The risk-disturbance hypothesis predicts that animals exhibit risk-avoidance behaviours when exposed to human disturbance because they perceive the disturbance as a predatory threat. Aims. This study aimed to examine whether Tibetan antelopes (Pantholops hodgsoni) exhibit risk-avoidance behaviour with proximity to a major highway and with increasing traffic flow consistent with the risk-disturbance hypothesis. Methods. Focal-animal sampling was used to observe the behaviour of Tibetan antelopes. The behaviours were categorised as foraging, vigilance, resting, moving, or other. The time, frequency, and duration of foraging and vigilance were calculated. Key results. As distance from the road increased, time spent foraging and foraging duration increased while foraging frequency, time spent being vigilant and vigilance frequency decreased, indicating that there is a risk perception associated with roads. Tibetan antelopes presented more risk-avoidance behaviours during high-traffic periods compared with lowtraffic periods. Conclusions. Tibetan antelopes exhibited risk-avoidance behaviour towards roads that varied with proximity and traffic levels, which is consistent with the risk-disturbance hypothesis. Implications. The consequences of risk-avoidance behaviour should be reflected in wildlife management by considering human disturbance and road design