Notch2 controls hepatocyte-derived cholangiocarcinoma formation in mice.

Liver cancer comprises a group of malignant tumors, among which hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the most common. ICC is especially pernicious and associated with poor clinical outcome. Studies have shown that a subset of human ICCs may originate from mature hepatocytes. However, the mechanisms driving the trans-differentiation of hepatocytes into malignant cholangiocytes remain poorly defined. We adopted lineage tracing techniques and an established murine hepatocyte-derived ICC model by hydrodynamic injection of activated forms of AKT (myr-AKT) and Yap (YapS127A) proto-oncogenes. Wild-type, Notch1 flox/flox , and Notch2 flox/flox mice were used to investigate the role of canonical Notch signaling and Notch receptors in AKT/Yap-driven ICC formation. Human ICC and HCC cell lines were transfected with siRNA against Notch2 to determine whether Notch2 regulates biliary marker expression in liver tumor cells. We found that AKT/Yap-induced ICC formation is hepatocyte derived and this process is strictly dependent on the canonical Notch signaling pathway in vivo. Deletion of Notch2 in AKT/Yap-induced tumors switched the phenotype from ICC to hepatocellular adenoma-like lesions, while inactivation of Notch1 in hepatocytes did not result in significant histomorphological changes. Finally, in vitro studies revealed that Notch2 silencing in ICC and HCC cell lines down-regulates the expression of Sox9 and EpCAM biliary markers. Notch2 is the major determinant of hepatocyte-derived ICC formation in mice

Gauge Invariant Autoregressive Neural Networks for Quantum Lattice Models

Gauge invariance plays a crucial role in quantum mechanics from condensed matter physics to high energy physics. We develop an approach to constructing gauge invariant autoregressive neural networks for quantum lattice models. These networks can be efficiently sampled and explicitly obey gauge symmetries. We variationally optimize our gauge invariant autoregressive neural networks for ground states as well as real-time dynamics for a variety of models. We exactly represent the ground and excited states of the 2D and 3D toric codes, and the X-cube fracton model. We simulate the dynamics and the gound states of the quantum link model of $\text{U(1)}$ lattice gauge theory, obtain the phase diagram for the 2D $\mathbb{Z}_2$ gauge theory, determine the phase transition and the central charge of the $\text{SU(2)}_3$ anyonic chain, and also compute the ground state energy of the SU(2) invariant Heisenberg spin chain. Our approach provides powerful tools for exploring condensed matter physics, high energy physics and quantum information science

Applying a co-design approach with key stakeholders to design interventions to reduce illegal wildlife consumption

1. Co-design, an approach that seeks to incorporate the experiences and perspectives of different stakeholders, is increasingly being used to develop audience-oriented behaviour change interventions. 2. The complexity of wildlife consumption behaviour makes the co-design approach an important potential tool for the design of conservation interventions that aim to reduce illegal wildlife trade. Yet, little is known about how to adapt and apply the co-design approach to the wildlife trade sector. 3. Here, we applied a co-design approach to develop interventions aimed at reducing illegal animal-based medicine consumption in China. We conducted three workshops with key stakeholders: consumers of animal-based medicines, pharmacy workers who sell them and traditional Chinese medicine (TCM) doctors who prescribe them. We then developed a theory of change to ensure the relevance of the co-designed intervention prototypes. 4. Our co-design process identified five main pathways of interventions, including two inclusive solutions that may have been previously overlooked in behaviour change work in this context. These were an intervention to promote the appropriate use of TCM and one to increase consumers' capacity to identify the legality of products. Our prototype interventions also enhanced existing views related to the role of medical practitioners in health-risk communication. 5. We used our co-design process and reflections on its application to this specific market to provide guidelines for future conservation program planning in the broader wildlife trade context. Some intervention prototypes produced during co-design may need wider stakeholder involvement to increase their feasibility for implementation. 6. We show that the co-design process can integrate multiple stakeholders' perspectives in the ideation stage, and has the potential to produce inclusive intervention designs that could drive innovation in conservation efforts to reduce illegal consumption of a range of wild species

BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models

It is crucial to automatically construct knowledge graphs (KGs) of diverse new relations to support knowledge discovery and broad applications. Previous KG construction methods, based on either crowdsourcing or text mining, are often limited to a small predefined set of relations due to manual cost or restrictions in text corpus. Recent research proposed to use pretrained language models (LMs) as implicit knowledge bases that accept knowledge queries with prompts. Yet, the implicit knowledge lacks many desirable properties of a full-scale symbolic KG, such as easy access, navigation, editing, and quality assurance. In this paper, we propose a new approach of harvesting massive KGs of arbitrary relations from pretrained LMs. With minimal input of a relation definition (a prompt and a few shot of example entity pairs), the approach efficiently searches in the vast entity pair space to extract diverse accurate knowledge of the desired relation. We develop an effective search-and-rescore mechanism for improved efficiency and accuracy. We deploy the approach to harvest KGs of over 400 new relations from different LMs. Extensive human and automatic evaluations show our approach manages to extract diverse accurate knowledge, including tuples of complex relations (e.g., "A is capable of but not good at B"). The resulting KGs as a symbolic interpretation of the source LMs also reveal new insights into the LMs' knowledge capacities.Comment: ACL 2023 (Findings); Code available at https://github.com/tanyuqian/knowledge-harvest-from-lm

Crowdsourcing Detection of Sampling Biases in Image Datasets

Despite many exciting innovations in computer vision, recent studies reveal a number of risks in existing computer vision systems, suggesting results of such systems may be unfair and untrustworthy. Many of these risks can be partly attributed to the use of a training image dataset that exhibits sampling biases and thus does not accurately reflect the real visual world. Being able to detect potential sampling biases in the visual dataset prior to model development is thus essential for mitigating the fairness and trustworthy concerns in computer vision. In this paper, we propose a three-step crowdsourcing workflow to get humans into the loop for facilitating bias discovery in image datasets. Through two sets of evaluation studies, we find that the proposed workflow can effectively organize the crowd to detect sampling biases in both datasets that are artificially created with designed biases and real-world image datasets that are widely used in computer vision research and system development

Observation of Viruses, Bacteria, and Fungi in Clinical Skin Samples under Transmission Electron Microscopy

The highlight of this chapter is the description of the clinical manifestation and its pathogen and the host tissue damage observed under the transmission electron microscopy, which helps the clinician understand the pathogen’s ultrastructure, the change of host sub-cell structure, and helps the laboratory workers understand the pathogen-induced human skin lesions’ clinical characteristics, to establish a two-way learning exchange database with vivid images