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

Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation

Federated recommender systems (FedRecs) have gained significant attention for their potential to protect user's privacy by keeping user privacy data locally and only communicating model parameters/gradients to the server. Nevertheless, the currently existing architecture of FedRecs assumes that all users have the same 0-privacy budget, i.e., they do not upload any data to the server, thus overlooking those users who are less concerned about privacy and are willing to upload data to get a better recommendation service. To bridge this gap, this paper explores a user-governed data contribution federated recommendation architecture where users are free to take control of whether they share data and the proportion of data they share to the server. To this end, this paper presents a cloud-device collaborative graph neural network federated recommendation model, named CDCGNNFed. It trains user-centric ego graphs locally, and high-order graphs based on user-shared data in the server in a collaborative manner via contrastive learning. Furthermore, a graph mending strategy is utilized to predict missing links in the graph on the server, thus leveraging the capabilities of graph neural networks over high-order graphs. Extensive experiments were conducted on two public datasets, and the results demonstrate the effectiveness of the proposed method

On-Device Recommender Systems: A Comprehensive Survey

Recommender systems have been widely deployed in various real-world applications to help users identify content of interest from massive amounts of information. Traditional recommender systems work by collecting user-item interaction data in a cloud-based data center and training a centralized model to perform the recommendation service. However, such cloud-based recommender systems (CloudRSs) inevitably suffer from excessive resource consumption, response latency, as well as privacy and security risks concerning both data and models. Recently, driven by the advances in storage, communication, and computation capabilities of edge devices, there has been a shift of focus from CloudRSs to on-device recommender systems (DeviceRSs), which leverage the capabilities of edge devices to minimize centralized data storage requirements, reduce the response latency caused by communication overheads, and enhance user privacy and security by localizing data processing and model training. Despite the rapid rise of DeviceRSs, there is a clear absence of timely literature reviews that systematically introduce, categorize and contrast these methods. To bridge this gap, we aim to provide a comprehensive survey of DeviceRSs, covering three main aspects: (1) the deployment and inference of DeviceRSs (2) the training and update of DeviceRSs (3) the security and privacy of DeviceRSs. Furthermore, we provide a fine-grained and systematic taxonomy of the methods involved in each aspect, followed by a discussion regarding challenges and future research directions. This is the first comprehensive survey on DeviceRSs that covers a spectrum of tasks to fit various needs. We believe this survey will help readers effectively grasp the current research status in this field, equip them with relevant technical foundations, and stimulate new research ideas for developing DeviceRSs

Attenuating midline thalamus bursting to mitigate absence epilepsy

Advancing the mechanistic understanding of absence epilepsy is crucial for developing new therapeutics, especially for patients unresponsive to current treatments. Utilizing a recently developed mouse model of absence epilepsy carrying the BK gain-of-function channelopathy D434G, here we report that attenuating the burst firing of midline thalamus (MLT) neurons effectively prevents absence seizures. We found that enhanced BK channel activity in the BK-D434G MLT neurons promotes synchronized bursting during the ictal phase of absence seizures. Modulating MLT neurons through pharmacological reagents, optogenetic stimulation, or deep brain stimulation effectively attenuates burst firing, leading to reduced absence seizure frequency and increased vigilance. Additionally, enhancing vigilance by amphetamine, a stimulant medication, or physical perturbation also effectively suppresses MLT bursting and prevents absence seizures. These findings suggest that the MLT is a promising target for clinical interventions. Our diverse approaches offer valuable insights for developing next generation therapeutics to treat absence epilepsy

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