CatVersion: Concatenating Embeddings for Diffusion-Based Text-to-Image Personalization

We propose CatVersion, an inversion-based method that learns the personalized concept through a handful of examples. Subsequently, users can utilize text prompts to generate images that embody the personalized concept, thereby achieving text-to-image personalization. In contrast to existing approaches that emphasize word embedding learning or parameter fine-tuning for the diffusion model, which potentially causes concept dilution or overfitting, our method concatenates embeddings on the feature-dense space of the text encoder in the diffusion model to learn the gap between the personalized concept and its base class, aiming to maximize the preservation of prior knowledge in diffusion models while restoring the personalized concepts. To this end, we first dissect the text encoder's integration in the image generation process to identify the feature-dense space of the encoder. Afterward, we concatenate embeddings on the Keys and Values in this space to learn the gap between the personalized concept and its base class. In this way, the concatenated embeddings ultimately manifest as a residual on the original attention output. To more accurately and unbiasedly quantify the results of personalized image generation, we improve the CLIP image alignment score based on masks. Qualitatively and quantitatively, CatVersion helps to restore personalization concepts more faithfully and enables more robust editing.Comment: For the project page, please visit https://royzhao926.github.io/CatVersion-page

Analysis and design of transition radiation in layered uniaxial crystals using Tandem neural networks

With the flourishing development of nanophotonics, Cherenkov radiation pattern can be designed to achieve superior performance in particle detection by fine-tuning the properties of metamaterials such as photonic crystals (PCs) surrounding the swift particle. However, the radiation pattern can be sensitive to the geometry and material properties of PCs, such as periodicity, unit thickness, and dielectric fraction, making direct analysis and inverse design difficult. In this article, we propose a systematic method to analyze and design PC-based transition radiation, which is assisted by deep learning neural networks. By matching boundary conditions at the interfaces, Cherenkov-like radiation of multilayered structures can be resolved analytically using the cascading scattering matrix method, despite the optical axes not being aligned with the swift electron trajectory. Once well trained, forward deep learning neural networks can be utilized to predict the radiation pattern without further direct electromagnetic simulations; moreover, Tandem neural networks have been proposed to inversely design the geometry and/or material properties for desired Cherenkov radiation pattern. Our proposal demonstrates a promising strategy for dealing with layered-medium-based Cherenkov radiation detectors, and it can be extended for other emerging metamaterials, such as photonic time crystals

Loss of androgen signaling in mesenchymal sonic hedgehog responsive cells diminishes prostate development, growth, and regeneration.

Prostate embryonic development, pubertal and adult growth, maintenance, and regeneration are regulated through androgen signaling-mediated mesenchymal-epithelial interactions. Specifically, the essential role of mesenchymal androgen signaling in the development of prostate epithelium has been observed for over 30 years. However, the identity of the mesenchymal cells responsible for this paracrine regulation and related mechanisms are still unknown. Here, we provide the first demonstration of an indispensable role of the androgen receptor (AR) in sonic hedgehog (SHH) responsive Gli1-expressing cells, in regulating prostate development, growth, and regeneration. Selective deletion of AR expression in Gli1-expressing cells during embryogenesis disrupts prostatic budding and impairs prostate development and formation. Tissue recombination assays showed that urogenital mesenchyme (UGM) containing AR-deficient mesenchymal Gli1-expressing cells combined with wildtype urogenital epithelium (UGE) failed to develop normal prostate tissue in the presence of androgens, revealing the decisive role of AR in mesenchymal SHH responsive cells in prostate development. Prepubescent deletion of AR expression in Gli1-expressing cells resulted in severe impairment of androgen-induced prostate growth and regeneration. RNA-sequencing analysis showed significant alterations in signaling pathways related to prostate development, stem cells, and organ morphogenesis in AR-deficient Gli1-expressing cells. Among these altered pathways, the transforming growth factor β1 (TGFβ1) pathway was up-regulated in AR-deficient Gli1-expressing cells. We further demonstrated the activation of TGFβ1 signaling in AR-deleted prostatic Gli1-expressing cells, which inhibits prostate epithelium growth through paracrine regulation. These data demonstrate a novel role of the AR in the Gli1-expressing cellular niche for regulating prostatic cell fate, morphogenesis, and renewal, and elucidate the mechanism by which mesenchymal androgen-signaling through SHH-responsive cells elicits the growth and regeneration of prostate epithelium

The analysis of barriers to bim implementation for industrialized building construction: a China study

The emerging Building Information Modeling (BIM) can better promote the development of building industrialization, with data integration between information-rich building models and business processes. However, the practical implementation of BIM still faces barriers. Existing studies have discussed these barriers extensively, but the research on the barriers to the implementation of BIM amid building industrialization in China is inadequate. In this study, 23 barriers were identified through literature review. A questionnaire survey approach was used to collect data from various parties. Factor analysis methods were used to process and rank barrier factors for BIM applications in the context of industrialized building. Based on the analysis of each factor, analytic hierarchy process was adopted to identify the key barriers to the implementation of BIM for industrialized building construction. The study concluded that the main barriers for BIM implementation for industrialized building were capital-related factors and the lack of support from owners. This study proposes that in addition to governmental policy support for BIM and multi-stakeholder engagement, companies should also organize experts to effectively evaluate the risks of applying BIM. Overall, this study provides suggestions on construction organizational transformations in the roadmap of moving towards digital-driven building industrialization

Response to comment on Boosted molecular mobility during common chemical reactions

Gunther et al. report that their control experiment using randomized magnetic field gradient sequences disagreed with findings we had reported using linear gradients. However, we show that measurements in our laboratory are consistent using both methods

MALT1 regulates Th2 and Th17 differentiation via NF-κB and JNK pathways, as well as correlates with disease activity and treatment outcome in rheumatoid arthritis

ObjectiveMALT1 regulates immunity and inflammation in multiple ways, while its role in rheumatoid arthritis (RA) is obscure. This study aimed to investigate the relationship of MALT1 with disease features, treatment outcome, as well as its effect on Th1/2/17 cell differentiation and underlying molecule mechanism in RA.MethodsTotally 147 RA patients were enrolled. Then their blood Th1, Th2, and Th17 cells were detected by flow cytometry. Besides, PBMC MALT1 expression was detected before treatment (baseline), at week (W) 6, W12, and W24. PBMC MALT1 in 30 osteoarthritis patients and 30 health controls were also detected. Then, blood CD4+ T cells were isolated from RA patients, followed by MALT1 overexpression or knockdown lentivirus transfection and Th1/2/17 polarization assay. In addition, IMD 0354 (NF-κB antagonist) and SP600125 (JNK antagonist) were also added to treat CD4+ T cells.ResultsMALT1 was increased in RA patients compared to osteoarthritis patients and healthy controls. Meanwhile, MALT1 positively related to CRP, ESR, DAS28 score, Th17 cells, negatively linked with Th2 cells, but did not link with other features or Th1 cells in RA patients. Notably, MALT1 decreased longitudinally during treatment, whose decrement correlated with RA treatment outcome (treatment response, low disease activity, or disease remission). In addition, MALT1 overexpression promoted Th17 differentiation, inhibited Th2 differentiation, less affected Th1 differentiation, activated NF-κB and JNK pathways in RA CD4+ T cells; while MALT1 knockdown exhibited the opposite effect. Besides, IMD 0354 and SP600125 addition attenuated MALT1’s effect on Th2 and Th17 differentiation.ConclusionMALT1 regulates Th2 and Th17 differentiation via NF-κB and JNK pathways, as well as correlates with disease activity and treatment outcome in RA

Machine learning to improve prognosis prediction of metastatic clear-cell renal cell carcinoma treated with cytoreductive nephrectomy and systemic therapy

Cytoreductive nephrectomy (CN) combined with systemic therapy is commonly used to treat metastatic clear-cell renal cell carcinoma (mccRCC). However, prognostic models for these patients are limited. In the present study, the clinical data of 782 mccRCC patients who received both CN and systemic therapy were obtained from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2016), and patients were divided into training and internal test cohorts. A total of 144 patients who met the same criteria from our center (Peking Union Medical College Hospital) were placed in the external test cohort. The cancer-specific survival rate (CSS) at 1, 3, and 5 years was set as the research outcome. Then, four ML models, i.e., a gradient boosting machine (GBM), support vector machine (SVM), random forest (RF), and logistic regression (LR), were established. Fifteen potential independent features were included in this study.  Model performance was evaluated using the area under the receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis (DCA). Seven clinical features, namely pathological grade, T stage, N stage, number of metastatic sites, brain or liver metastases, and metastasectomy were selected for subsequent analysis via the recursive feature elimination (RFE) algorithm. In conclusion, the GBM model performed best at 1-, 3- and 5-year CSS prediction (0.836, 0.819 and 0.808, respectively in the internal test cohort and 0.819, 0.805 and 0.786, respectively in the external cohort). Furthermore, we divided the patients into three strata (high-, intermediate- and low-risk) via X-tile analysis and concluded that clinically individualized treatment can be aided by these practical prognostic models

Analysis of the dermatophyte Trichophyton rubrum expressed sequence tags

BACKGROUND: Dermatophytes are the primary causative agent of dermatophytoses, a disease that affects billions of individuals worldwide. Trichophyton rubrum is the most common of the superficial fungi. Although T. rubrum is a recognized pathogen for humans, little is known about how its transcriptional pattern is related to development of the fungus and establishment of disease. It is therefore necessary to identify genes whose expression is relevant to growth, metabolism and virulence of T. rubrum. RESULTS: We generated 10 cDNA libraries covering nearly the entire growth phase and used them to isolate 11,085 unique expressed sequence tags (ESTs), including 3,816 contigs and 7,269 singletons. Comparisons with the GenBank non-redundant (NR) protein database revealed putative functions or matched homologs from other organisms for 7,764 (70%) of the ESTs. The remaining 3,321 (30%) of ESTs were only weakly similar or not similar to known sequences, suggesting that these ESTs represent novel genes. CONCLUSION: The present data provide a comprehensive view of fungal physiological processes including metabolism, sexual and asexual growth cycles, signal transduction and pathogenic mechanisms