Functions of Extracellular Pyruvate Kinase M2 in Tissue Repair and Regeneration

Pyruvate kinase M2 (PKM2) is a glycolytic enzyme expressed in highly proliferating cells. Studies of PKM2 have been focused on its function of promoting cell proliferation in cancer cells. Our laboratory previously discovered that extracellular PKM2 released from cancer cells promoted angiogenesis by activating endothelial cell proliferation and migration. PKM2 activated endothelial cells through integrin αvβ3. Angiogenesis and myofibroblast differentiation are key processes during wound healing. In this dissertation, I demonstrate that extracellular PKM2 released from activated neutrophils promotes angiogenesis and myofibroblast differentiation during wound healing. PKM2 activates dermal fibroblasts through integrin αvβ3 and PI3K signaling pathway. I also claim that extracellular PKM2 plays a role during liver fibrosis. PKM2 protects hepatic stellate cells from apoptosis by activating the survival signaling pathway

Target-Guided Composed Image Retrieval

Composed image retrieval (CIR) is a new and flexible image retrieval paradigm, which can retrieve the target image for a multimodal query, including a reference image and its corresponding modification text. Although existing efforts have achieved compelling success, they overlook the conflict relationship modeling between the reference image and the modification text for improving the multimodal query composition and the adaptive matching degree modeling for promoting the ranking of the candidate images that could present different levels of matching degrees with the given query. To address these two limitations, in this work, we propose a Target-Guided Composed Image Retrieval network (TG-CIR). In particular, TG-CIR first extracts the unified global and local attribute features for the reference/target image and the modification text with the contrastive language-image pre-training model (CLIP) as the backbone, where an orthogonal regularization is introduced to promote the independence among the attribute features. Then TG-CIR designs a target-query relationship-guided multimodal query composition module, comprising a target-free student composition branch and a target-based teacher composition branch, where the target-query relationship is injected into the teacher branch for guiding the conflict relationship modeling of the student branch. Last, apart from the conventional batch-based classification loss, TG-CIR additionally introduces a batch-based target similarity-guided matching degree regularization to promote the metric learning process. Extensive experiments on three benchmark datasets demonstrate the superiority of our proposed method

Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation

Multi-modal recommendation systems, which integrate diverse types of information, have gained widespread attention in recent years. However, compared to traditional collaborative filtering-based multi-modal recommendation systems, research on multi-modal sequential recommendation is still in its nascent stages. Unlike traditional sequential recommendation models that solely rely on item identifier (ID) information and focus on network structure design, multi-modal recommendation models need to emphasize item representation learning and the fusion of heterogeneous data sources. This paper investigates the impact of item representation learning on downstream recommendation tasks and examines the disparities in information fusion at different stages. Empirical experiments are conducted to demonstrate the need to design a framework suitable for collaborative learning and fusion of diverse information. Based on this, we propose a new model-agnostic framework for multi-modal sequential recommendation tasks, called Online Distillation-enhanced Multi-modal Transformer (ODMT), to enhance feature interaction and mutual learning among multi-source input (ID, text, and image), while avoiding conflicts among different features during training, thereby improving recommendation accuracy. To be specific, we first introduce an ID-aware Multi-modal Transformer module in the item representation learning stage to facilitate information interaction among different features. Secondly, we employ an online distillation training strategy in the prediction optimization stage to make multi-source data learn from each other and improve prediction robustness. Experimental results on a video content recommendation dataset and three e-commerce recommendation datasets demonstrate the effectiveness of the proposed two modules, which is approximately 10% improvement in performance compared to baseline models.Comment: 11 pages, 7 figure

Federated Class-Incremental Learning with Prompting

As Web technology continues to develop, it has become increasingly common to use data stored on different clients. At the same time, federated learning has received widespread attention due to its ability to protect data privacy when let models learn from data which is distributed across various clients. However, most existing works assume that the client's data are fixed. In real-world scenarios, such an assumption is most likely not true as data may be continuously generated and new classes may also appear. To this end, we focus on the practical and challenging federated class-incremental learning (FCIL) problem. For FCIL, the local and global models may suffer from catastrophic forgetting on old classes caused by the arrival of new classes and the data distributions of clients are non-independent and identically distributed (non-iid). In this paper, we propose a novel method called Federated Class-Incremental Learning with PrompTing (FCILPT). Given the privacy and limited memory, FCILPT does not use a rehearsal-based buffer to keep exemplars of old data. We choose to use prompts to ease the catastrophic forgetting of the old classes. Specifically, we encode the task-relevant and task-irrelevant knowledge into prompts, preserving the old and new knowledge of the local clients and solving the problem of catastrophic forgetting. We first sort the task information in the prompt pool in the local clients to align the task information on different clients before global aggregation. It ensures that the same task's knowledge are fully integrated, solving the problem of non-iid caused by the lack of classes among different clients in the same incremental task. Experiments on CIFAR-100, Mini-ImageNet, and Tiny-ImageNet demonstrate that FCILPT achieves significant accuracy improvements over the state-of-the-art methods

Dissociation products and structures of solid H2 S at strong compression

Hydrogen sulfides have recently received a great deal of interest due to the record high superconducting temperatures of up to 203 K observed on strong compression of dihydrogen sulfide (H2S). A joint theoretical and experimental study is presented in which decomposition products and structures of compressed H2S are characterized, and their superconducting properties are calculated. In addition to the experimentally known H2S and H3S phases, our first-principles structure searches have identified several energetically competitive stoichiometries that have not been reported previously; H2S3, H3S2, and H4S3. In particular, H4S3 is predicted to be thermodynamically stable within a large pressure range of 25-113 GPa. High-pressure room-temperature X-ray diffraction measurements confirm the presence of H3S and H4S3 through decomposition of H2S that emerge at 27 GPa and coexist with residual H2S, at least up to the highest pressure studied in our experiments of 140 GPa. Electron-phonon coupling calculations show that H4S3 has a small Tc of below 2 K, and that H2S is mainly responsible for the observed superconductivity of samples prepared at low temperature (<100K).Y. L. and J. H. acknowledge funding from the National Natural Science Foundation of China under Grant No. 11204111 and No. 11404148, the Natural Science Foundation of Jiangsu province under Grant No. BK20130223, and the PAPD of Jiangsu Higher Education Institutions. Y. Z. and Y. M. acknowledge funding from the National Natural Science Foundation of China under Grant Nos. 11274136 and 11534003, the 2012 Changjiang Scholars Program of China. R. J. N. acknowledges financial support from the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. [EP/J017639/1]. Calculations were performed on the Cambridge High Performance Computing Service facility and the HECToR and Archer facilities of the U.K.’s national highperformance computing service (for which access was obtained via the UKCP consortium [EP/K013564/1]). J. R. N. acknowledges financial support from the Cambridge Commonwealth Trust. I. E. acknowledges financial support from the Spanish Ministry of Economy and Competitiveness (FIS2013-48286-C2-2-P). M. C. acknowledges support from the Graphene Flagship and Agence nationale de la recherche (ANR), Grant No. ANR-13-IS10- 0003-01. Work at Carnegie was partially supported by EFree, an Energy Frontier Research Center funded by the DOE, Office of Science, Basic Energy Sciences under Award No. DE-SC-0001057 (salary support for H. L.). The infrastructure and facilities used at Carnegie were supported by NNSA Grant No. DE-NA-0002006, CDAC.This is the author accepted manuscript. The final version is available from the American Physical Society via http://dx.doi.org/10.1103/PhysRevB.93.02010

Quantum hydrogen-bond symmetrization in the superconducting hydrogen sulfide system.

The quantum nature of the proton can crucially affect the structural and physical properties of hydrogen compounds. For example, in the high-pressure phases of H2O, quantum proton fluctuations lead to symmetrization of the hydrogen bond and reduce the boundary between asymmetric and symmetric structures in the phase diagram by 30 gigapascals (ref. 3). Here we show that an analogous quantum symmetrization occurs in the recently discovered sulfur hydride superconductor with a superconducting transition temperature Tc of 203 kelvin at 155 gigapascals--the highest Tc reported for any superconductor so far. Superconductivity occurs via the formation of a compound with chemical formula H3S (sulfur trihydride) with sulfur atoms arranged on a body-centred cubic lattice. If the hydrogen atoms are treated as classical particles, then for pressures greater than about 175 gigapascals they are predicted to sit exactly halfway between two sulfur atoms in a structure with Im3m symmetry. At lower pressures, the hydrogen atoms move to an off-centre position, forming a short H-S covalent bond and a longer H···S hydrogen bond in a structure with R3m symmetry. X-ray diffraction experiments confirm the H3S stoichiometry and the sulfur lattice sites, but were unable to discriminate between the two phases. Ab initio density-functional-theory calculations show that quantum nuclear motion lowers the symmetrization pressure by 72 gigapascals for H3S and by 60 gigapascals for D3S. Consequently, we predict that the Im3m phase dominates the pressure range within which the high Tc was measured. The observed pressure dependence of Tc is accurately reproduced in our calculations for the phase, but not for the R3m phase. Therefore, the quantum nature of the proton fundamentally changes the superconducting phase diagram of H3S.We acknowledge financial support from the Spanish Ministry of Economy and Competitiveness (FIS2013- 48286-C2-2-P), French Agence Nationale de la Recherche (Grant No. ANR-13-IS10-0003- 392 01), EPSRC (UK) (Grant No. EP/J017639/1), Cambridge Commonwealth Trust, National Natural Science Foundation of China (Grants No. 11204111, 11404148, and 11274136), and 2012 Changjiang Scholars Program of China. Work at Carnegie was supported by EFree, an Energy Frontier Research Center funded by the DOE, Office of Science, Basic Energy Sciences under Award No. DE-SC-0001057. Computer facilities were provided by the PRACE project AESFT and the Donostia International Physics Center (DIPC).This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/nature1717

High-Dimensional Data Analytics Based on Spatial-Temporal Decomposition

In the last decade, significant progress in sensing and data storage technologies has ushered in a new era of spatiotemporal data analysis. This progress has dramatically increased the availability and scale of spatiotemporal data, presenting both exciting opportunities and complex challenges in this field. Spatiotemporal data from various sources exhibit unique patterns, but they share two common characteristics: (i) anomalies in spatiotemporal data are typically sparse, meaning that the number of anomalies is significantly less than the number of normal data, and (ii) anomalies cause deviations from the normal patterns of the data. To illustrate the practical application of these principles, consider a water distribution system (WDS). A sudden burst in the system can lead to a substantial drop in water pressure compared to normal conditions. To efficiently detect such anomalies, a penalized regression model based on basis expansion is introduced. This model effectively captures the features of hydraulic measurements through basis coefficients. It encourages sparsity in the anomalies (such as bursts) by applying an $L_1$ penalty. Furthermore, it encourages normal measurements to align closely with the sample mean through an $L_2$ regularization term. The model is solved using an optimization algorithm, and its performance is evaluated through a simulated case study. In the context of additive manufacturing, where images capture the manufacturing process, the profile of objects being produced must be extracted accurately. This is particularly challenging due to variations in pixel intensities between the cured profile and the background, which are caused by differences in optical properties. To address this, a tensor decomposition-based method is introduced. Difference matrices are employed to penalize variations in pixel intensities both vertically and horizontally, promoting smoothness in the background. Simultaneously, an $L_1$ regularization term enforces sparsity in the cured profile. The optimization model is solved to estimate the profile, with the effectiveness of this approach demonstrated through both simulated and real-world case studies. In the context of a surveillance system, the primary goal is to detect moving targets, especially when dealing with a moving camera. To achieve this, a novel optical flow-based method is proposed. Beyond considerations for background smoothness and foreground sparsity, this method introduces a total-variance regularization mechanism based on patches. This ensures that the optical flow associated with the foreground moves consistently. Real-world case studies are used to validate the proposed model's performance in detecting moving objects.Release after 01/18/202