CLIPood: Generalizing CLIP to Out-of-Distributions

Out-of-distribution (OOD) generalization, where the model needs to handle distribution shifts from training, is a major challenge of machine learning. Contrastive language-image pre-training (CLIP) models have shown impressive zero-shot ability, but the further adaptation of CLIP on downstream tasks undesirably degrades OOD performances. This paper aims at generalizing CLIP to out-of-distribution test data on downstream tasks. We propose CLIPood, a fine-tuning method that can adapt CLIP models to OOD situations where both domain shifts and open classes may occur on the unseen test data. To exploit the semantic relations between classes from the text modality, CLIPood introduces a new training objective, margin metric softmax (MMS), with class adaptive margins for fine-tuning. To incorporate both pre-trained zero-shot model and fine-tuned task-adaptive model, CLIPood leverages a new optimization strategy, Beta moving average (BMA), to maintain a temporal ensemble weighted by Beta distribution. Experiments on diverse datasets with different OOD scenarios show that CLIPood consistently outperforms existing generalization techniques.Comment: Accepted by ICML 202

Dapagliflozin relieves renal injury in a diabetic nephropathy model by inducing autophagy through regulation of miR-30e-5p/AKT/mTOR pathway

Purpose: To investigate the mechanism of action of dapagliflozin on diabetic nephropathy. Methods: A rat model of diabetic nephropathy was established by injection of fructose-streptozotocin. Blood glucose and urinary protein levels were measured, while histopathological changes in kidney tissues were determined by hematoxylin & eosin staining (H & E). Serum levels of creatinine (Cr), blood urea nitrogen (BUN), malondialdehyde (MDA), superoxide dismutase (SOD), reduced glutathione (GSH), and lactate dehydrogenase (LDH) were evaluated by enzyme-linked immunosorbent assay (ELISA). Cell apoptosis and autophagy were investigated by evaluating apoptotic and autophagic protein expression by western blot. Results: Administration of fructose-streptozotocin increased the blood glucose level of the rats (p < 0.001) and induced pathological changes in the kidney tissues, including glomerulosclerosis, renal tubule dilation, and inflammatory cell infiltration of rats. However, long-term treatment with dapagliflozin attenuated the fructose-streptozotocin-induced increases in Cr, BUN, and urinary protein and reversed the fructose-streptozotocin-induced decrease in Bcl-2 expression and increases in Bax and cleaved PARP expression in diabetic rats. Dapagliflozin also reversed the increases in MDA and LDH and decreases in SOD and GSH in diabetic rats. The fructose-streptozotocin-induced increase in p62 expression and decreases in LC3 and Beclin 1 expression were reversed by dapagliflozin. It upregulated miR-30e-5p expression and downregulated phosphorylated AKT and mTOR expression in diabetic rats. MicroRNA-30e-5p targeted AKT and inhibition of miR-30e-5p attenuated the dapagliflozin-induced decrease in p-AKT and p-mTOR expression in diabetic rats. Conclusion: In fructose-streptozotocin-induced diabetic rats, dapagliflozin ameliorates kidney injury, suppresses cell apoptosis and oxidative stress, and promotes cell autophagy through upregulation of miR-30e-5p and inactivation of the AKT/mTOR pathway. Therefore, dapagliflozin is a potent therapeutic agent for the management of diabetic neuropathy

MeDReaders: a database for transcription factors that bind to methylated DNA

Understanding the molecular principles governing interactions between transcription factors (TFs) and DNA targets is one of the main subjects for transcriptional regulation. Recently, emerging evidence demonstrated that some TFs could bind to DNA motifs containing highly methylated CpGs both in vitro and in vivo. Identification of such TFs and elucidation of their physiological roles now become an important stepping-stone toward understanding the mechanisms underlying the methylation-mediated biological processes, which have crucial implications for human disease and disease development. Hence, we constructed a database, named as MeDReaders, to collect information about methylated DNA binding activities. A total of 731 TFs, which could bind to methylated DNA sequences, were manually curated in human and mouse studies reported in the literature. In silico approaches were applied to predict methylated and unmethylated motifs of 292 TFs by integrating whole genome bisulfite sequencing (WGBS) and ChIP-Seq datasets in six human cell lines and one mouse cell line extracted from ENCODE and GEO database. MeDReaders database will provide a comprehensive resource for further studies and aid related experiment designs. The database implemented unified access for users to most TFs involved in such methylation-associated binding actives. The website is available at http://medreader.org/

Study on Product Innovative Design Process Driven by Ideal Solution

Abstract. Product innovative design in companies today relies heavily on individual members' experience and creative ideation as well as their skills of integrating creativity and innovation tools with design methods agilely. Creative ideation and inventive ideas generation are two crucial stages in product innovative design process. Ideal solution is the desire final ideas for given problem, and the striving reaching target for product design. In this paper, a product innovative design process driven by ideal solution is proposed. This design process encourages designers to overcome their psychological inertia, to foster creativity in a systematic way for acquiring breakthrough creative and innovative solutions in a reducing sphere of solution-seeking, and results in effective product innovative design rapidly. A case study example is also presented to illustrate the effectiveness of the proposed design process

Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information

Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images