Soulstyler: Using Large Language Model to Guide Image Style Transfer for Target Object

Image style transfer occupies an important place in both computer graphics and computer vision. However, most current methods require reference to stylized images and cannot individually stylize specific objects. To overcome this limitation, we propose the "Soulstyler" framework, which allows users to guide the stylization of specific objects in an image through simple textual descriptions. We introduce a large language model to parse the text and identify stylization goals and specific styles. Combined with a CLIP-based semantic visual embedding encoder, the model understands and matches text and image content. We also introduce a novel localized text-image block matching loss that ensures that style transfer is performed only on specified target objects, while non-target regions remain in their original style. Experimental results demonstrate that our model is able to accurately perform style transfer on target objects according to textual descriptions without affecting the style of background regions. Our code will be available at https://github.com/yisuanwang/Soulstyler.Comment: 5 pages,3 figures,ICASSP202

Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off

Over-parameterization of deep neural networks (DNNs) has shown high prediction accuracy for many applications. Although effective, the large number of parameters hinders its popularity on resource-limited devices and has an outsize environmental impact. Sparse training (using a fixed number of nonzero weights in each iteration) could significantly mitigate the training costs by reducing the model size. However, existing sparse training methods mainly use either random-based or greedy-based drop-and-grow strategies, resulting in local minimal and low accuracy. In this work, we consider the dynamic sparse training as a sparse connectivity search problem and design an exploitation and exploration acquisition function to escape from local optima and saddle points. We further design an acquisition function and provide the theoretical guarantees for the proposed method and clarify its convergence property. Experimental results show that sparse models (up to 98\% sparsity) obtained by our proposed method outperform the SOTA sparse training methods on a wide variety of deep learning tasks. On VGG-19 / CIFAR-100, ResNet-50 / CIFAR-10, ResNet-50 / CIFAR-100, our method has even higher accuracy than dense models. On ResNet-50 / ImageNet, the proposed method has up to 8.2\% accuracy improvement compared to SOTA sparse training methods

Determination of key enzymes for threonine synthesis through in vitro metabolic pathway analysis

Figure S1. The pathway flux (J) in the in vitro system when one enzyme concentration was increased. (A) The pathway flux when purified ThrA was added to the crude enzyme extract. (B) The pathway flux when purified Asd was added to the crude enzyme extract. (C) The pathway flux when purified ThrB was added to the crude enzyme extract. (D) The pathway flux when purified ThrC was added to the crude enzyme extract

Poly[hemi(ethyl­enediammonium) [di-μ-oxalato-indium(III)] dihydrate]

In title compound, {(C2H10N2)0.5[In(C2O4)2]·2H2O}n, the unique InIII ion is coordinated by eight O atoms from four oxalate ligands in a distorted square-anti­prismatic environment. The doubly bis-chelating oxalate ligands act as bridging ligands connecting symmetry-related InIII ions and forming a three-dimensional open framework structure. Ethyl­enediammonium cations and water mol­ecules occupy the voids within the structure. The unique ethyl­enediammonium cation and one water mol­ecule both lie on a twofold rotation axis. One of the other two water mol­ecules residing on general crystallographic sites was refined as disordered with half occupancy. In the crystal structure, cations and water mol­ecules are linked to the anionic framework via inter­molecular O—H⋯O and N—H⋯O hydrogen bonds

Tubeimuside I improves the efficacy of a therapeutic Fusobacterium nucleatum dendritic cell-based vaccine against colorectal cancer

IntroductionFusobacterium nucleatum (F. nucleatum) infection has been confirmed to be associated with the development, chemoresistance, and immune evasion of colorectal cancer (CRC). The complex relationship between the microorganism, host cells, and the immune system throughout all stages of CRC progression, which makes the development of new therapeutic methods difficult.MethodsWe developed a new dendritic cell (DC) vaccine to investigate the antitumor efficacy of CRC immunotherapy strategies. By mediating a specific mode of interaction between the bacteria, tumor, and host, we found a new plant-derived adjuvant, tubeimuside I (TBI), which simultaneously improved the DC vaccine efficacy and inhibited the F. nucleatum infection. Encapsulating TBI in a nanoemulsion greatly improved the drug efficacy and reduced the drug dosage and administration times.ResultsThe nanoemulsion encapsulated TBI DC vaccine exhibited an excellent antibacterial and antitumor effect and improved the survival rate of CRC mice by inhibiting tumor development and progression.DiscussionIn this study, we provide a effective strategy for developing a DC-based vaccine against CRC and underlies the importance of further understanding the mechanism of CRC processes caused by F. nucleatum

Integrated 16S rRNA sequencing and nontargeted metabolomics analysis to reveal the mechanisms of Yu-Ye Tang on type 2 diabetes mellitus rats

IntroductionYu–Ye Tang (YYT) is a classical formula widely used in treatment of type 2 diabetes mellitus (T2DM). However, the specific mechanism of YYT in treating T2DM is not clear.MethodsThe aim of this study was to investigate the therapeutic effect of YYT on T2DM by establishing a rat model of T2DM. The mechanism of action of YYT was also explored through investigating gut microbiota and serum metabolites.ResultsThe results indicated YYT had significant therapeutic effects on T2DM. Moreover, YYT could increase the abundance of Lactobacillus, Candidatus_Saccharimonas, UCG-005, Bacteroides and Blautia while decrease the abundance of and Allobaculum and Desulfovibrio in gut microbiota of T2DM rats. Nontargeted metabolomics analysis showed YYT treatment could regulate arachidonic acid metabolism, alanine, aspartate and glutamate metabolism, arginine and proline metabolism, glycerophospholipid metabolism, pentose and glucuronate interconversions, phenylalanine metabolism, steroid hormone biosynthesis, terpenoid backbone biosynthesis, tryptophan metabolism, and tyrosine metabolism in T2DM rats.DiscussionIn conclusion, our research showed that YYT has a wide range of therapeutic effects on T2DM rats, including antioxidative and anti-inflammatory effects. Furthermore, YYT corrected the altered gut microbiota and serum metabolites in T2DM rats. This study suggests that YYT may have a therapeutic impact on T2DM by regulating gut microbiota and modulating tryptophan and glycerophospholipid metabolism, which are potential key pathways in treating T2DM