Efficiency and Sensitivity Analysis of Observation Networks for Atmospheric Inverse Modelling with Emissions

The controllability of advection-diffusion systems, subject to uncertain initial values and emission rates, is estimated, given sparse and error affected observations of prognostic state variables. In predictive geophysical model systems, like atmospheric chemistry simulations, different parameter families influence the temporal evolution of the system.This renders initial-value-only optimisation by traditional data assimilation methods as insufficient. In this paper, a quantitative assessment method on validation of measurement configurations to optimize initial values and emission rates, and how to balance them, is introduced. In this theoretical approach, Kalman filter and smoother and their ensemble based versions are combined with a singular value decomposition, to evaluate the potential improvement associated with specific observational network configurations. Further, with the same singular vector analysis for the efficiency of observations, their sensitivity to model control can be identified by determining the direction and strength of maximum perturbation in a finite-time interval.Comment: 30 pages, 10 figures, 5 table

One Step Learning, One Step Review

Visual fine-tuning has garnered significant attention with the rise of pre-trained vision models. The current prevailing method, full fine-tuning, suffers from the issue of knowledge forgetting as it focuses solely on fitting the downstream training set. In this paper, we propose a novel weight rollback-based fine-tuning method called OLOR (One step Learning, One step Review). OLOR combines fine-tuning with optimizers, incorporating a weight rollback term into the weight update term at each step. This ensures consistency in the weight range of upstream and downstream models, effectively mitigating knowledge forgetting and enhancing fine-tuning performance. In addition, a layer-wise penalty is presented to employ penalty decay and the diversified decay rate to adjust the weight rollback levels of layers for adapting varying downstream tasks. Through extensive experiments on various tasks such as image classification, object detection, semantic segmentation, and instance segmentation, we demonstrate the general applicability and state-of-the-art performance of our proposed OLOR. Code is available at https://github.com/rainbow-xiao/OLOR-AAAI-2024.Comment: Accepted to the 38th Annual AAAI Conference on Artificial Intelligence (AAAI 2024

PivotE : revealing and visualizing the underlying entity structures for exploration

A Web-scale knowledge graph (KG) typically contains millions of entities and thousands of entity types. Due to the lack of a pre-defined data schema such as the ER model, entities in KGs are loosely coupled based on their relationships, which brings challenges for effective accesses of the KGs in a structured manner like SPARQL. This demonstration presents an entity-oriented exploratory search prototype system that is able to support search and explore KGs in a exploratory search manner, where local structures of KGs can be dynamically discovered and utilized for guiding users. The system applies a path-based ranking method for recommending similar entities and their relevant information as exploration pointers. The interface is designed to assist users to investigate a domain (particular type) of entities, as well as to explore the knowledge graphs in various relevant domains. The queries are dynamically formulated by tracing the users' dynamic clicking (exploration) behaviors. In this demonstration, we will show how our system visualize the underlying entity structures, as well as explain the semantic correlations among them in a unified interface, which not only assist users to learn about the properties of entities in many aspects but also guide them to further explore the information space.Peer reviewe

HBV infection-induced liver cirrhosis development in dual-humanized mice with human bone mesenchymal stem cell transplantation

疾病动物模型是现代医学发展的基石，尤其是重大、突发传染病暴发时，适宜的疾病动物模型可为及时发现病原体、制定防控策略提供强大保障，原创的疾病动物模型已成为衡量一个国家生物医药科研水平的标志。我校夏宁邵教授团队和浙江大学附属第一医院李君教授团队历经5年的协同攻关，终于建立了国际上首个高度模拟人类乙肝病毒（HBV）自然感染诱发的慢乙肝肝硬化小鼠模型。厦门大学公共卫生学院袁伦志博士生、浙江大学医学院附属第一医院江静博士和厦门大学公共卫生学院刘旋博士生为该论文共同第一作者。厦门大学夏宁邵教授、浙江大学附属第一医院李君教授和厦门大学程通副教授为该论文共同通讯作者。【Abstract】Objective： Developing a small animal model that accurately delineates the natural history of hepatitis B virus (HBV) infection and immunopathophysiology is necessary to clarify the mechanisms of host-virus interactions and to identify intervention strategies for HBV-related liver diseases. This study aimed to develop an HBV-induced chronic hepatitis and cirrhosis mouse model through transplantation of human bone marrow mesenchymal stem cells (hBMSCs). Design： Transplantation of hBMSCs into Fah -/- Rag2 -/- IL-2Rγc -/- SCID (FRGS) mice with fulminant hepatic failure (FHF) induced by hamster-anti-mouse CD95 antibody JO2 generated a liver and immune cell dual-humanized (hBMSC-FRGS) mouse. The generated hBMSC-FRGS mice were subjected to assessments of sustained viremia, specific immune and inflammatory responses and liver pathophysiological injury to characterize the progression of chronic hepatitis and cirrhosis after HBV infection. Results： The implantation of hBMSCs rescued FHF mice, as demonstrated by robust proliferation and transdifferentiation of functional human hepatocytes and multiple immune cell lineages, including B cells, T cells, NK cells, dendritic cells (DCs) and immune cell lineages, including B cells, T cells, NK cells, dendritic cells (DCs) and viremia and specific immune and inflammatory responses and showed progression to chronic hepatitis and liver cirrhosis at a frequency of 55% after 54 weeks. Conclusion： This new humanized mouse model recapitulates the liver cirrhosis induced by human HBV infection, thus providing research opportunities for understanding viral immune pathophysiology and testing antiviral therapies in vivo.this work was supported by the national Science and technology Major Project (grant nos. 2017ZX10304402, 2017ZX10203201 and 2018ZX09711003-005-003), the national natural Science Foundation of china(grant nos. 81672023, 81571818 and 81771996), the Scientific research Foundation of the State Key laboratory of Molecular Vaccinology and Molecular Diagnostics (grant no 2016ZY005), Zhejiang Province and State's Key Project of the research and Development Plan of china (grant nos 2017c01026 and 2016YFc1101304/3).该研究获得了传染病防治国家科技重大专项、新药创制国家科技重大专项和国家自然科学基金的资助