RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths

Text-to-image generation has recently witnessed remarkable achievements. We introduce a text-conditional image diffusion model, termed RAPHAEL, to generate highly artistic images, which accurately portray the text prompts, encompassing multiple nouns, adjectives, and verbs. This is achieved by stacking tens of mixture-of-experts (MoEs) layers, i.e., space-MoE and time-MoE layers, enabling billions of diffusion paths (routes) from the network input to the output. Each path intuitively functions as a "painter" for depicting a particular textual concept onto a specified image region at a diffusion timestep. Comprehensive experiments reveal that RAPHAEL outperforms recent cutting-edge models, such as Stable Diffusion, ERNIE-ViLG 2.0, DeepFloyd, and DALL-E 2, in terms of both image quality and aesthetic appeal. Firstly, RAPHAEL exhibits superior performance in switching images across diverse styles, such as Japanese comics, realism, cyberpunk, and ink illustration. Secondly, a single model with three billion parameters, trained on 1,000 A100 GPUs for two months, achieves a state-of-the-art zero-shot FID score of 6.61 on the COCO dataset. Furthermore, RAPHAEL significantly surpasses its counterparts in human evaluation on the ViLG-300 benchmark. We believe that RAPHAEL holds the potential to propel the frontiers of image generation research in both academia and industry, paving the way for future breakthroughs in this rapidly evolving field. More details can be found on a project webpage: https://raphael-painter.github.io/.Comment: Technical Repor

Mixed Neural Voxels for Fast Multi-view Video Synthesis

Synthesizing high-fidelity videos from real-world multi-view input is challenging because of the complexities of real-world environments and highly dynamic motions. Previous works based on neural radiance fields have demonstrated high-quality reconstructions of dynamic scenes. However, training such models on real-world scenes is time-consuming, usually taking days or weeks. In this paper, we present a novel method named MixVoxels to better represent the dynamic scenes with fast training speed and competitive rendering qualities. The proposed MixVoxels represents the 4D dynamic scenes as a mixture of static and dynamic voxels and processes them with different networks. In this way, the computation of the required modalities for static voxels can be processed by a lightweight model, which essentially reduces the amount of computation, especially for many daily dynamic scenes dominated by the static background. To separate the two kinds of voxels, we propose a novel variation field to estimate the temporal variance of each voxel. For the dynamic voxels, we design an inner-product time query method to efficiently query multiple time steps, which is essential to recover the high-dynamic motions. As a result, with 15 minutes of training for dynamic scenes with inputs of 300-frame videos, MixVoxels achieves better PSNR than previous methods. Codes and trained models are available at https://github.com/fengres/mixvoxelsComment: ICCV 2023 (Oral

Menin Deficiency Leads to Depressive-like Behaviors in Mice by Modulating Astrocyte-Mediated Neuroinflammation

厦门大学医学院、神经科学研究所张杰教授团队发现了抑郁症新的致病基因MEN1，并阐明了MEN1调控星形胶质细胞炎症导致抑郁发生发展的新机制，为抑郁症的诊治提供了新靶点和方向。抑郁症是严重威胁人类健康的重大神经系统疾病，危及全球30%的人口。但对其发病机制并不清楚。张杰教授团队发现，在慢性不可预测以及LPS处理的模拟抑郁小鼠模型中，多发性内分泌肿瘤蛋白(menin)在大脑中的表达显著降低，并且在星形胶质细胞中降低最明显。为了研究menin是否参与了小鼠抑郁表型的产生，研究团队制作了多种神经系统menin条件性敲除小鼠。通过对这些小鼠行为学的检测，锁定了只有在星形胶质细胞中敲除menin后，小鼠才会表现出抑郁样表型。证实了menin可能是通过调控星形胶质细胞的功能促进了抑郁的发生。 MEN1基因的突变会导致多发性内分泌肿瘤，而内分泌的紊乱和抑郁等精神疾病有着密切的联系。下丘脑-垂体-肾上腺轴（HPA轴）的功能紊乱直接参与了抑郁的产生。基于此研究团队推测MEN1的基因突变是否也会导致抑郁的发生。通过和中国医学科学院基础所的许琪教授合作，研究团队对1000多例重度抑郁患者和800多例对照人群进行了MEN1基因的外显子测序。通过测序发现MEN1的一个SNP s375804228和抑郁的发生有着显著关联。该SNP导致menin第503位的氨基酸由G突变成D。通过功能研究进一步证实该突变可以阻断menin和p65的结合，从而过度激活NF-κB-IL-1β通路，导致神经炎症的发生。 张杰，厦门大学特聘教授、博士生导师。国家优秀青年科学基金；教育部新世纪优秀人才；福建省杰出青年科学基金；厦门市五四青年奖章等获得者。2011年8月加入厦门大学医学院神经科学研究所担任教授至今。张杰博士主要从事重大神经系统疾病（老年痴呆、帕金森、抑郁症、自闭症、术后认知障碍、胶质瘤）等的发病机制和药物开发研究。至今以第一作者或者通讯作者在国际知名期刊发表研究论文21篇。其中回国独立开展研究工作以后，作为通讯作者在 Neuron，Cell Reports, PNAS, The Journal of Neuroscience, Clinical Cancer Research，Cell Death and Disease, JBC, Chemistry，Chem. Biol. Drug Des.等杂志上发表多篇研究论文。【Abstract】Astrocyte dysfunction and inflammation are associated with the pathogenesis of major depressive disorder (MDD). However, the mechanisms underlying these effects remain largely unknown. Here, we found that multiple endocrine neoplasia type 1 (Men1; protein: menin) expression is attenuated in the brain of mice exposed to CUMS (chronic unpredictable mild stress) or lipopolysaccharide. Astrocyte-specific reduction of Men1 (GcKO) led to depressive-like behaviors in mice. We observed enhanced NF-κB activation and IL-1β production with menin deficiency in astrocytes, where depressive-like behaviors in GcKO mice were restored by NF-κB inhibitor or IL-1β receptor antagonist. Importantly, we identified a SNP, rs375804228, in human MEN1, where G503D substitution is associated with a higher risk of MDD onset. G503D substitution abolished menin-p65 interactions, thereby enhancing NF-κB activation and IL-1β production. Our results reveal a distinct astroglial role for menin in regulating neuroinflammation in depression, indicating that menin may be an attractive therapeutic target in MDD.We thank Prof. Guanghui Jin (Xiamen University) and Prof. Xianxin Hua (University of Pennsylvania) for providing the Men1-floxp mice. This work was supported by the National Natural Science Foundation of China (grants 81522016, 81271421, and 31571055 to J.Z.; 81625008 and 31430048 to Q.X.; 81630026 to Z.Y.; 81771163 and U1405222 to H.X.; U1505227 to G.B.; 81472725 to W.M.), the Natural Science Foundation of Fujian Province of China (grant 2013J01147 and 2014J06019 to J.Z.), the Fundamental Research Funds for the Central Universities (grants 20720150062 and 20720180049 to J.Z.), the National Key Research and Development Program of China (2016YFC1305903), and CAMS Innovation Fund for Medical Sciences (grant 2016I2M1004 to Q.X.).研究工作得到国家自然科学基金项目（81522016、81271421、31571055）以及厦门大学校长基金等资助

Calculation of Nonlimit Active Earth Pressure against Rigid Retaining Wall Rotating about Base

A retaining wall with sandy fill was considered as the research object in order to study the nonlimiting active earth pressure under the rotation about the base (RB mode). Rankine’s and Coulomb’s earth pressure theories are no longer applicable to the above conditions (RB mode and nonlimiting active earth pressure). In order to improve the traditional earth pressure calculation methods (Rankine and Coulomb), a calculation method using curvilinear thin layer elements is presented with overall considerations of wall displacement, soil arching effect, and friction angle exertion coefficient to deduce the nonlimit active earth pressure under RB. Additionally, the calculation results were in good agreement with model test data (from Fang and Smita). Moreover, a parametric analysis was carried out. It was revealed that the developed value of the shear strength decreased with the depth, and the active earth pressure distribution curve was linear and nonlinear in the upper and lower halves, respectively

Calculation of Nonlimit Active Earth Pressure against Rigid Retaining Wall Rotating about Base

A retaining wall with sandy fill was considered as the research object in order to study the nonlimiting active earth pressure under the rotation about the base (RB mode). Rankine’s and Coulomb’s earth pressure theories are no longer applicable to the above conditions (RB mode and nonlimiting active earth pressure). In order to improve the traditional earth pressure calculation methods (Rankine and Coulomb), a calculation method using curvilinear thin layer elements is presented with overall considerations of wall displacement, soil arching effect, and friction angle exertion coefficient to deduce the nonlimit active earth pressure under RB. Additionally, the calculation results were in good agreement with model test data (from Fang and Smita). Moreover, a parametric analysis was carried out. It was revealed that the developed value of the shear strength decreased with the depth, and the active earth pressure distribution curve was linear and nonlinear in the upper and lower halves, respectively

Facile Synthesis of Peptide-Conjugated Gold Nanoclusters with Different Lengths

The synthesis of ultra-small gold nanoclusters (Au NCs) with sizes down to 2 nm has received increasing interest due to their unique optical and electronic properties. Like many peptide-coated gold nanospheres synthesized before, modified gold nanoclusters with peptide conjugation are potentially significant in biomedical and catalytic fields. Here, we explore whether such small-sized gold nanoclusters can be conjugated with peptides also and characterize them using atomic force microscopy. Using a long and flexible elastin-like polypeptide (ELP)20 as the conjugated peptide, (ELP)20-Au NCs was successfully synthesized via a one-pot synthesis method. The unique optical and electronic properties of gold nanoclusters are still preserved, while a much larger size was obtained as expected due to the peptide conjugation. In addition, a short and rigid peptide (EAAAK)3 was conjugated to the gold nanoclusters. Their Yong’s modulus was characterized using atomic force microscopy (AFM). Moreover, the coated peptide on the nanoclusters was pulled using AFM-based single molecule-force spectroscopy (SMFS), showing expected properties as one of the first force spectroscopy experiments on peptide-coated nanoclusters. Our results pave the way for further modification of nanoclusters based on the conjugated peptides and show a new method to characterize these materials using AFM-SMFS

Deep cascade gradient RBF networks with output-relevant feature extraction and adaptation for nonlinear and nonstationary processes

The main challenge for industrial predictive models is how to effectively deal with big data from high-dimensional processes with nonstationary characteristics. Although deep networks, such as the stacked autoencoder (SAE), can learn useful features from massive data with multilevel architecture, it is difficult to adapt them online to track fast time-varying process dynamics. To integrate feature learning and online adaptation, this paper proposes a deep cascade gradient radial basis function (GRBF) network for online modeling and prediction of nonlinear and nonstationary processes. The proposed deep learning method consists of three modules. First, a preliminary prediction result is generated by a GRBF weak predictor, which is further combined with raw input data for feature extraction. By incorporating the prior weak prediction information, deep output-relevant features are extracted using a SAE. Online prediction is finally produced upon the extracted features with a GRBF predictor, whose weights and structure are updated online to capture fast time-varying process characteristics. Three real-world industrial case studies demonstrate that the proposed deep cascade GRBF network outperforms existing state-of-the-art online modeling approaches as well as deep networks, in terms of both online prediction accuracy and computational complexity

Efficacy of cell-free DNA methylation-based blood test for colorectal cancer screening in high-risk population: a prospective cohort study

Abstract Background Although colonoscopy is the standard screening test for colorectal cancer (CRC), its use is limited by a poor compliance rate, the need for extensive bowel preparation, and the risk of complications. As an alternative, an FDA-approved stool-based DNA test, Cologuard, has demonstrated satisfactory detection performance for CRC, but its compliance rate remains suboptimal, primarily attributable to individuals’ reluctance to provide stool samples. Methods We developed a noninvasive blood-based CRC test, ColonSecure, based on cell-free DNA containing cancer-specific CpG island methylation patterns. We initially screened publicly available datasets for differentially methylated CpG sites in CRC with prediction potential. Subsequently, we performed two sequential bisulfite-free methylation sequencing on blood samples obtained from CRC patients and non-cancer controls. Through rigorous evaluation of each marker and machine learning-assisted feature selection, we identified 149 hypermethylated markers from over 193,000 CpG sites. These markers were then utilized to construct the ColonSecure model, enabling accurate CRC detection. Results We validated the efficacy of our cell-free DNA methylation-based blood test for CRC screening with 3493 high-risk individuals identified from 114,136 urban residents. The ColonSecure test identified 89 out of 103 CRC patients diagnosed by the follow-up colonoscopy, outperforming CEA, CRP, and CA19-9 (with a sensitivity of 86.4% compared to 45.6%, 39.8%, and 25.2% for CEA, CRP, and CA19-9 respectively; an AUROC of 0.956 compared to an AUROC of < 0.77 for other methods). Conclusion Our observations emphasize the potential of our multiple cfDNA methylation marker-based test for CRC screening in high-risk populations

Limosilactobacillus mucosae-derived extracellular vesicles modulates macrophage phenotype and orchestrates gut homeostasis in a diarrheal piglet model

Abstract The diarrheal disease causes high mortality, especially in children and young animals. The gut microbiome is strongly associated with diarrheal disease, and some specific strains of bacteria have demonstrated antidiarrheal effects. However, the antidiarrheal mechanisms of probiotic strains have not been elucidated. Here, we used neonatal piglets as a translational model and found that gut microbiota dysbiosis observed in diarrheal piglets was mainly characterized by a deficiency of Lactobacillus, an abundance of Escherichia coli, and enriched lipopolysaccharide biosynthesis. Limosilactobacillus mucosae and Limosilactobacillus reuteri were a signature bacterium that differentiated healthy and diarrheal piglets. Germ-free (GF) mice transplanted with fecal microbiota from diarrheal piglets reproduced diarrheal disease symptoms. Administration of Limosilactobacillus mucosae but not Limosilactobacillus reuteri alleviated diarrheal disease symptoms induced by fecal microbiota of diarrheal piglets and by ETEC K88 challenge. Notably, Limosilactobacillus mucosae-derived extracellular vesicles alleviated diarrheal disease symptoms caused by ETEC K88 by regulating macrophage phenotypes. Macrophage elimination experiments demonstrated that the extracellular vesicles alleviated diarrheal disease symptoms in a macrophage-dependent manner. Our findings provide insights into the pathogenesis of diarrheal disease from the perspective of intestinal microbiota and the development of probiotic-based antidiarrheal therapeutic strategies