Bridge Diffusion Model: bridge non-English language-native text-to-image diffusion model with English communities

Text-to-Image generation (TTI) technologies are advancing rapidly, especially in the English language communities. However, English-native TTI models inherently carry biases from English world centric training data, which creates a dilemma for development of other language-native TTI models. One common choice is fine-tuning the English-native TTI model with translated samples from non-English communities. It falls short of fully addressing the model bias problem. Alternatively, training non-English language native models from scratch can effectively resolve the English world bias, but diverges from the English TTI communities, thus not able to utilize the strides continuously gaining in the English TTI communities any more. To build non-English language native TTI model meanwhile keep compatability with the English TTI communities, we propose a novel model structure referred as "Bridge Diffusion Model" (BDM). The proposed BDM employs a backbone-branch network structure to learn the non-English language semantics while keep the latent space compatible with the English-native TTI backbone, in an end-to-end manner. The unique advantages of the proposed BDM are that it's not only adept at generating images that precisely depict non-English language semantics, but also compatible with various English-native TTI plugins, such as different checkpoints, LoRA, ControlNet, Dreambooth, and Textual Inversion, etc. Moreover, BDM can concurrently generate content seamlessly combining both non-English native and English-native semantics within a single image, fostering cultural interaction. We verify our method by applying BDM to build a Chinese-native TTI model, whereas the method is generic and applicable to any other language

An iterative data-driven turbulence modeling framework based on Reynolds stress representation

Data-driven turbulence modeling studies have reached such a stage that the fundamental framework is basically settled, but several essential issues remain that strongly affect the performance, including accuracy, smoothness, and generalization capacity. Two problems are studied in the current research: (1) the processing of the Reynolds stress tensor and (2) the coupling method between the machine learning turbulence model and CFD solver. The first determines the form of predicting targets and the resulting physical completeness and interpretability. The second determines the training process and intrinsic relevance between the mean flow features and Reynolds stress. For the Reynolds stress processing issue, we perform the theoretical derivation to extend the relevant tensor arguments of Reynolds stress in addition to the strain rate and rotation rate. Then, the tensor representation theorem is employed to give the complete irreducible invariants and integrity basis. In addition, an adaptive regularization term is employed to enhance the representation performance. For the CFD coupling issue, an iterative coupling data-driven turbulence modeling framework with consistent convergence is proposed. The training data preparation, predicting target selection, and computation platform are illustrated. The framework is then applied to a canonical separated flow for verification. The mean flow results obtained by coupling computation of the trained machine learning model and CFD solver have high consistency with the DNS true values, which proves the validity of the current approach

Selective Inhibition of Bacterial Tryptophanyl-tRNA Synthetases by Indolmycin Is Mechanism-based

Indolmycin is a natural tryptophan analog that competes with tryptophan for binding to tryptophanyl-tRNA synthetase (TrpRS) enzymes. Bacterial and eukaryotic cytosolic TrpRSs have comparable affinities for tryptophan (Km ∼ 2 μm), and yet only bacterial TrpRSs are inhibited by indolmycin. Despite the similarity between these ligands, Bacillus stearothermophilus (Bs)TrpRS preferentially binds indolmycin ∼1500-fold more tightly than its tryptophan substrate. Kinetic characterization and crystallographic analysis of BsTrpRS allowed us to probe novel aspects of indolmycin inhibitory action. Previous work had revealed that long range coupling to residues within an allosteric region called the D1 switch of BsTrpRS positions the Mg2+ ion in a manner that allows it to assist in transition state stabilization. The Mg2+ ion in the inhibited complex forms significantly closer contacts with non-bridging oxygen atoms from each phosphate group of ATP and three water molecules than occur in the (presumably catalytically competent) pre-transition state (preTS) crystal structures. We propose that this altered coordination stabilizes a ground state Mg2+·ATP configuration, accounting for the high affinity inhibition of BsTrpRS by indolmycin. Conversely, both the ATP configuration and Mg2+ coordination in the human cytosolic (Hc)TrpRS preTS structure differ greatly from the BsTrpRS preTS structure. The effect of these differences is that catalysis occurs via a different transition state stabilization mechanism in HcTrpRS with a yet-to-be determined role for Mg2+. Modeling indolmycin into the tryptophan binding site points to steric hindrance and an inability to retain the interactions used for tryptophan substrate recognition as causes for the 1000-fold weaker indolmycin affinity to HcTrpRS

PF-DMD: Physics-fusion dynamic mode decomposition for accurate and robust forecasting of dynamical systems with imperfect data and physics

The DMD (Dynamic Mode Decomposition) method has attracted widespread attention as a representative modal-decomposition method and can build a predictive model. However, the DMD may give predicted results that deviate from physical reality in some scenarios, such as dealing with translation problems or noisy data. Therefore, this paper proposes a physics-fusion dynamic mode decomposition (PFDMD) method to address this issue. The proposed PFDMD method first obtains a data-driven model using DMD, then calculates the residual of the physical equations, and finally corrects the predicted results using Kalman filtering and gain coefficients. In this way, the PFDMD method can integrate the physics-informed equations with the data-driven model generated by DMD. Numerical experiments are conducted using the PFDMD, including the Allen-Cahn, advection-diffusion, and Burgers' equations. The results demonstrate that the proposed PFDMD method can significantly reduce the reconstruction and prediction errors by incorporating physics-informed equations, making it usable for translation and shock problems where the standard DMD method has failed

Amelioration of Hypertriglyceridemia with Hypo-Alpha-Cholesterolemia in LPL Deficient Mice by Hematopoietic Cell-Derived LPL

BACKGROUND: Macrophage-derived lipoprotein lipase (LPL) has been shown uniformly to promote atherosclerotic lesion formation while the extent to which it affects plasma lipid and lipoprotein levels varies in wild-type and hypercholesterolemic mice. It is known that high levels of LPL in the bulk of adipose tissue and skeletal muscle would certainly mask the contribution of macrophage LPL to metabolism of plasma lipoprotein. Therefore, we chose LPL deficient (LPL⁻/⁻) mice with severe hypertriglyceridemia as an alternative model to assess the role of macrophage LPL in plasma lipoprotein metabolism via bone marrow transplant, through which LPL will be produced mainly by hematopoietic cell-derived macrophages. METHODS AND RESULTS: Hypertriglyceridemic LPL⁻/⁻ mice were lethally irradiated, then transplanted with bone marrow from wild-type (LPL⁺/⁺) or LPL⁻/⁻ mice, respectively. Sixteen weeks later, LPL⁺/⁺ →LPL⁻/⁻ mice displayed significant reduction in plasma levels of triglyceride and cholesterol (408±44.9 vs. 2.7±0.5×10³ and 82.9±7.1 vs. 229.1±30.6 mg/dl, p<0.05, respectively), while a 2.7-fold increase in plasma high density lipoprotein- cholesterol (p<0.01) was observed, compared with LPL⁻/⁻→LPL⁻/⁻ control mice. The clearance rate for the oral fat load test in LPL⁺/⁺ →LPL⁻/⁻ mice was faster than that in LPL⁻/⁻→LPL⁻/⁻ mice, but slower than that in wild-type mice. Liver triglyceride content in LPL⁺/⁺→LPL⁻/⁻ mice was also significantly increased, compared with LPL⁻/⁻→LPL⁻/⁻ mice (6.8±0.7 vs. 4.6±0.5 mg/g wet tissue, p<0.05, n = 6). However, no significant change was observed in the expression levels of genes involved in hepatic lipid metabolism between the two groups. CONCLUSIONS: Hematopoietic cell-derived LPL could efficiently ameliorate severe hypertriglyceridemia and hypo-alpha-cholesterolemia at the compensation of increased triglyceride content of liver in LPL⁻/⁻ mice

Application of CRISPR-Cas9 gene editing technology in basic research, diagnosis and treatment of colon cancer

Colon cancer is the fourth leading cause of cancer death worldwide, and its progression is accompanied by a complex array of genetic variations. CRISPR/Cas9 can identify new drug-resistant or sensitive mutations in colon cancer, and can use gene editing technology to develop new therapeutic targets and provide personalized treatments, thereby significantly improving the treatment of colon cancer patients. CRISPR/Cas9 systems are driving advances in biotechnology. RNA-directed Cas enzymes have accelerated the pace of basic research and led to clinical breakthroughs. This article reviews the rapid development of CRISPR/Cas in colon cancer, from gene editing to transcription regulation, gene knockout, genome-wide CRISPR tools, therapeutic targets, stem cell genomics, immunotherapy, metabolism-related genes and inflammatory bowel disease. In addition, the limitations and future development of CRISPR/Cas9 in colon cancer studies are reviewed. In conclusion, this article reviews the application of CRISPR-Cas9 gene editing technology in basic research, diagnosis and treatment of colon cancer

ART⋅\boldsymbol{\cdot}V: Auto-Regressive Text-to-Video Generation with Diffusion Models

We present ART$\boldsymbol{\cdot}$V, an efficient framework for auto-regressive video generation with diffusion models. Unlike existing methods that generate entire videos in one-shot, ART$\boldsymbol{\cdot}$V generates a single frame at a time, conditioned on the previous ones. The framework offers three distinct advantages. First, it only learns simple continual motions between adjacent frames, therefore avoiding modeling complex long-range motions that require huge training data. Second, it preserves the high-fidelity generation ability of the pre-trained image diffusion models by making only minimal network modifications. Third, it can generate arbitrarily long videos conditioned on a variety of prompts such as text, image or their combinations, making it highly versatile and flexible. To combat the common drifting issue in AR models, we propose masked diffusion model which implicitly learns which information can be drawn from reference images rather than network predictions, in order to reduce the risk of generating inconsistent appearances that cause drifting. Moreover, we further enhance generation coherence by conditioning it on the initial frame, which typically contains minimal noise. This is particularly useful for long video generation. When trained for only two weeks on four GPUs, ART$\boldsymbol{\cdot}$V already can generate videos with natural motions, rich details and a high level of aesthetic quality. Besides, it enables various appealing applications, e.g., composing a long video from multiple text prompts.Comment: 24 pages, 21 figures. Project page at https://warranweng.github.io/art.

Case Report: Leiomyosarcoma of the right external iliac artery: a diagnostic-based study on a rare case

Leiomyosarcoma (LMS) is an uncommon and aggressive form of cancer that originates in the smooth muscles. It possesses the capacity for rapid growth and often manifests with general, nonspecific symptoms arising from the displacement of nearby structures rather than direct invasion. In this particular instance, an 81-year-old woman presented with right lower abdominal pain, leading to the discovery of a mass adjacent to the right external iliac artery. In this case, the patient was misdiagnosed initially because of her nonspecific and poorly distinguished clinical symptoms. The laboratory results were within normal ranges. A well-defined tumor was detected through laparoscopic operation and subsequently surgically excised. The histopathological analysis of the tumor revealed the presence of malignant spindle cells, nuclear pleomorphism, and tumor giant cells. Immunohistochemistry tests indicated positive results for CD34 and Desmin, while CD117 and DOG1 showed adverse effects. It is worth noting that LMS of the right external iliac artery is an infrequent occurrence, potentially resulting in delayed diagnosis and misidentification. To enhance our comprehension of this uncommon cancer, more cases with detailed information are essential