EvalCrafter: Benchmarking and Evaluating Large Video Generation Models

The vision and language generative models have been overgrown in recent years. For video generation, various open-sourced models and public-available services are released for generating high-visual quality videos. However, these methods often use a few academic metrics, for example, FVD or IS, to evaluate the performance. We argue that it is hard to judge the large conditional generative models from the simple metrics since these models are often trained on very large datasets with multi-aspect abilities. Thus, we propose a new framework and pipeline to exhaustively evaluate the performance of the generated videos. To achieve this, we first conduct a new prompt list for text-to-video generation by analyzing the real-world prompt list with the help of the large language model. Then, we evaluate the state-of-the-art video generative models on our carefully designed benchmarks, in terms of visual qualities, content qualities, motion qualities, and text-caption alignment with around 18 objective metrics. To obtain the final leaderboard of the models, we also fit a series of coefficients to align the objective metrics to the users' opinions. Based on the proposed opinion alignment method, our final score shows a higher correlation than simply averaging the metrics, showing the effectiveness of the proposed evaluation method.Comment: Technical Report, Project page: https://evalcrafter.github.io

High-level expression and purification of soluble recombinant FGF21 protein by SUMO fusion in Escherichia coli

<p>Abstract</p> <p>Background</p> <p>Fibroblast growth factor 21 (FGF21) is a promising drug candidate to combat metabolic diseases. However, high-level expression and purification of recombinant FGF21 (rFGF21) in <it>Escherichia coli (E. coli) </it>is difficult because rFGF21 forms inclusion bodies in the bacteria making it difficult to purify and obtain high concentrations of bioactive rFGF21. To overcome this problem, we fused the <it>FGF21 </it>with <it>SUMO </it>(Small ubiquitin-related modifier) by polymerase chain reaction (PCR), and expressed the fused gene in <it>E. coli </it>BL21(DE3).</p> <p>Results</p> <p>By inducing with IPTG, SUMO-FGF21 was expressed at a high level. Its concentration reached 30% of total protein, and exceeded 95% of all soluble proteins. The fused protein was purified by DEAE sepharose FF and Ni-NTA affinity chromatography. Once cleaved by the SUMO protease, the purity of rFGF21 by high performance liquid chromatography (HPLC) was shown to be higher than 96% with low endotoxin level (<1.0 EU/ml). The results of <it>in vivo </it>animal experiments showed that rFGF21 produced by using this method, could decrease the concentration of plasma glucose in diabetic rats by streptozotocin (STZ) injection.</p> <p>Conclusions</p> <p>This study demonstrated that SUMO, when fused with FGF21, was able to promote its soluble expression of the latter in <it>E. coli</it>, making it more convenient to purify rFGF21 than previously. This may be a better method to produce rFGF21 for pharmaceutical research and development.</p

VideoCrafter1: Open Diffusion Models for High-Quality Video Generation

Video generation has increasingly gained interest in both academia and industry. Although commercial tools can generate plausible videos, there is a limited number of open-source models available for researchers and engineers. In this work, we introduce two diffusion models for high-quality video generation, namely text-to-video (T2V) and image-to-video (I2V) models. T2V models synthesize a video based on a given text input, while I2V models incorporate an additional image input. Our proposed T2V model can generate realistic and cinematic-quality videos with a resolution of $1024 \times 576$, outperforming other open-source T2V models in terms of quality. The I2V model is designed to produce videos that strictly adhere to the content of the provided reference image, preserving its content, structure, and style. This model is the first open-source I2V foundation model capable of transforming a given image into a video clip while maintaining content preservation constraints. We believe that these open-source video generation models will contribute significantly to the technological advancements within the community.Comment: Tech Report; Github: https://github.com/AILab-CVC/VideoCrafter Homepage: https://ailab-cvc.github.io/videocrafter

PAI-1 Exacerbates White Adipose Tissue Dysfunction and Metabolic Dysregulation in High Fat Diet-Induced Obesity

Background: Plasminogen activator inhibitor (PAI)-1 levels and activity are known to increase during metabolic syndrome and obesity. In addition, previous studies have implicated PAI-1 in adipose tissue (AT) expansion while also contributing to insulin resistance. As inflammation is also known to occur in AT during obesity, we hypothesized that in a high-fat diet (HFD)-induced obese mouse model PAI-1 contributes to macrophage-mediated inflammation and metabolic dysfunction.Methods: Four- to five-weeks-old male C57B6/6J mice were fed a HFD (45%) for 14 weeks, while age-matched control mice were fed a standard laboratory chow diet (10% fat). Additional studies were performed in PAI-1 knockout mice and wild type mice treated with an inhibitor (PAI-039) of PAI-1. Macrophage polarization were measured by real time PCR.Results: HFD mice showed increased expression of PAI-1 in visceral white AT (WAT) that also displayed increased macrophage numbers. PAI-1 deficient mice exhibited increased numbers of anti-inflammatory macrophages in WAT and were resistant to HFD-induced obesity. Similarly, pharmacological inhibition of PAI-1 using PAI-039 significantly decreased macrophage infiltration in WAT and improved metabolic status in HFD-induced wild-type mice. Importantly, the numbers of M1 macrophages appeared to be increased by the HFD and decreased by either genetic PAI-1 depletion or PAI-039 treatment.Conclusions: Collectively, our findings provide support for PAI-1 contributing to the development of inflammation in adipose tissue and explain the mechanism of inflammation modulated by PAI-1 in the disordered metabolism in HFD-induced obesity

Structural insights into Ca2+-activated long-range allosteric channel gating of RyR1

Ryanodine receptors (RyRs) are a class of giant ion channels with molecular mass over 2.2 mega-Daltons. These channels mediate calcium signaling in a variety of cells. Since more than 80% of the RyR protein is folded into the cytoplasmic assembly and the remaining residues form the transmembrane domain, it has been hypothesized that the activation and regulation of RyR channels occur through an as yet uncharacterized long-range allosteric mechanism. Here we report the characterization of a Ca2+-activated open-state RyR1 structure by cryo-electron microscopy. The structure has an overall resolution of 4.9 angstrom and a resolution of 4.2 angstrom for the core region. In comparison with the previously determined apo/closed-state structure, we observed long-range allosteric gating of the channel upon Ca2+ activation. In-depth structural analyses elucidated a novel channel-gating mechanism and a novel ion selectivity mechanism of RyR1. Our work not only provides structural insights into the molecular mechanisms of channel gating and regulation of RyRs, but also sheds light on structural basis for channel-gating and ion selectivity mechanisms for the six-transmembrane-helix cation channel family.Strategic Priority Research Program of Chinese Academy of Sciences [XDB08030202]; National Basic Research Program (973 Program); Ministry of Science &amp; Technology of China [2012CB917200, 2014CB910700]; National Natural Science Foundation of China [31270768]; Ministry of Education of China (111 Program China)SCI(E)PubMed中国科技核心期刊(ISTIC)[email protected]; [email protected]

Generación automática de tests en DomJudge

Los jueces online están cobrando cada día más importancia, especialmente en el ámbito de la enseñanza. Su funciona-miento es simple, un juez/profesor sube un problema de programación con un enunciado y unos casos de prueba (entradas y salidas esperadas) al juez online. El alumno/concursante deberá subir el código que considera como solución al problema. Si el código del alumno devuelve las mismas salidas que las que se encuentran en los casos de prueba para las correspondientes entradas en los test-cases dada las mismas entradas, el código se considera correcto. En la Facultad de Informática de la Universidad Complutense de Madrid ha aparecido un juez virtual que cada vez está siendo más usado por los docentes como complemento a la hora de evaluar. Este juez online es DomJudge. DomJudge es un juez online desarrollado en la universidad de Utrecht con el fin de ser un juez virtual de concursos de programación. Su código es libre y se puede descargar y modificar, lo que lo hace ideal si se quiere adaptar su funcionamiento. Es bien sabido que generar casos de prueba de calidad es una tarea muy compleja. Existen diversas técnicas que ayudan a la generación automática de tests. Por ejemplo, la ejecución simbólica permite generar tests garantizando que todos los caminos de ejecución del programa son ejercitados hasta una cierta profundidad. El objetivo de este proyecto es hacer uso de estas técnicas de testing para generar casos de prueba de forma automática. Para que la autoevaluación realizada por estos jueces sea efectiva, los profesores deben proporcionar casos de prueba de calidad que son ejecutados automáticamente cuando los alumnos suben sus soluciones. Escribir estos casos de prueba resulta costoso y complejo, y es en este punto, donde el uso de jPET podría resultar muy útil. Los casos de prueba generados por jPET podrían servir como punto de partida a la hora de generar un conjunto de casos de prueba de calidad. Un aspecto muy interesante en este sentido, es que no es necesario que los programas de los alumnos uti licen el lenguaje Java. Lo único que sería necesario es que el profesor proporcione una solución escrita en Java. A partir de esta se podrían generar los casos de prueba iniciales. Si se diese el caso de que las soluciones de los alumnos viniesen escritas en Java, se podrían plantear enfoques más interesantes en los cuales los tests se forman a partir de ambas soluciones. La del alumno, para generarlos datos de entrada, y la del profesor, para chequear que las salidas para esas entradas son las correctas. Por eso hemos llevado a cabo no solo una traducción automática del xml generado por JPET en ficheros in y out. Sino una total integración con el sistema DomJudge para poder generar los casos de prueba sin intermediarios. Gracias a esta integración podemos facilitar el trabajo de los profesores a la hora de crear problemas en jueces online y conseguimos una generación automática de tests en DomJudge

Synergistic effect and molecular mechanism of nicotinamide and UM171 in ex vivo expansion of long-term hematopoietic stem cells

Introduction: Several approaches to expand human hematopoietic stem cells (HSCs) have been reported, but the ability of these methods to expand long-term hematopoietic stem cells (LT-HSCs) remains to be improved, which limits the application of HSCs-based therapies. Methods: CD34+ cells were purified from umbilical cord blood using MacsCD34 beads, and then cultured for 12 d in a serum-free medium. Flow cytometry was used to detect phenotype, cell cycle distribution, and apoptosis of the cultured cells. Colony-forming cell (CFC) assays can evaluate multi-lineage differentiation potential of HSCs. Real-time polymerase chain reaction was employed to detect the expression of genes related to self-renewal programs and antioxidant activity. DCFH-DA probes were used to evaluate intracellular production of reactive oxygen species (ROS). Determination of the effect of different culture conditions on the balance of cytokine by cytometric bead array. Results: Here, we show a combination, Nicotinamide (NAM) combined with pyrimidoindole derivative UM171, can massively expand LT-HSCs ex vivo, and the expanded cells maintained the capability of self-renewal and multilineage differentiation. Additionally, our data indicated that UM171 promoted self-renewal of HSCs by inducing HSCs entry into the cell cycle and activating Notch and Wnt pathways, but the infinite occurrence of this process may lead to mitochondrial metabolism disorder and differentiation of HSCs. NAM kept HSCs in their primitive and dormant states by reducing intracellular ROS levels and upregulating the expression of stemness related genes, so we believed that NAM can act as a brake to control the above process. Conclusions: The discovery of the synergistic effect of NAM and UM171 for expanding LT-HSCs provides a new strategy in solving the clinical issue of limited numbers of HSCs

Application of Tucker Decomposition in Temperature Distribution Reconstruction

Constrained by cost, measuring conditions and excessive calculation, it is difficult to reconstruct a 3D real-time temperature field. For the purpose of solving these problems, a three-dimensional temperature distribution reconstruction algorithm based on Tucker decomposition algorithm is proposed. The Tucker decomposition algorithm is used to reduce the dimension of the measured data, and the processed core tensor is used for the temperature field reconstruction of sparse data. Theoretical analysis and simulations show that the proposed method is feasible; the overall optimization is realized by selecting the appropriate core tensor dimensions; and the reconstruction error is less than 3%. Results indicate that the proposed method can yield a reliable reconstruction solution and can be applied to real-time applications