Epigenetic marks of a stable host-microbiota association in the mammalian gut

To summarize, the results shown here confirms that the host-microbiota interaction is a critical check pint for intestinal inflammation and development. Though, it is still a debate whether the interaction is a cause or consequence of the disease, the results indicate a potential role of epigenetic modification in disease manifestation of UC or postnatal development. The finding might be helpful to support the combinational epigenetic and microbiota based therapies of intestine inflammation

Observational and Experimental Gravity

We indicate the progress of experimental gravity, present an outlook in this field, and summarise the Observational/Experimental Parallel Session together with a related plenary talk on gravitational waves of the 2nd LeCosPA Symposium.Comment: 1 figure, Second LeCosPa Simposium, December 2015, Taipei Taiwa

A Novel Family of Cyst Proteins with Epidermal Growth Factor Repeats in Giardia lamblia

The biological goal of Giardia lamblia life cycle is differentiation into a cyst form (encystation) that can survive in the environment and infect a new host. Since cystic stages are key to transmission of parasites, this differentiation may be a target for interruption of the life cycle. Synthesis and assembly of the extracellular cyst wall are the major hallmarks of this important differentiation. During encystation, cyst wall structural proteins are coordinately synthesized and are mainly targeted to the cyst wall. However, only a few such proteins have been identified to date. In this study, we used a combination of bioinformatics and molecular approaches to identify new cyst structural proteins from G. lamblia and found a group of Epidermal Growth Factor (EGF)-like Repeats containing Cyst Proteins (EGFCPs). Interestingly, the levels of EGFCPs proteins increased significantly during encystation, which matches the characteristics of the Giardia cyst wall protein. Further characterization and localization studies suggest that EGFCPs may function like cyst wall proteins, involved in differentiation of G. lamblia trophozoites into cysts. Our results provide valuable information regarding the function of a new group of cyst proteins in parasite differentiation into cysts and help develop ways to interrupt the parasite life cycle

Construction and Characterization of Insect Cell-Derived Influenza VLP: Cell Binding, Fusion, and EGFP Incorporation

We have constructed virus-like particles (VLPs) harboring hemagglutinin (HA), neuraminidase (NA), matrix protein 1 (M1) ,and proton channel protein (M2) using baculovirus as a vector in the SF9 insect cell. The size of the expressed VLP was estimated to be ~100 nm by light scattering experiment and transmission electron microscopy. Recognition of HA on the VLP surface by the HA2-specific monoclonal antibody IIF4 at acidic pH, as probed by surface plasmon resonance, indicated the pH-induced structural rearrangement of HA. Uptake of the particle by A549 mediated by HA-sialylose receptor interaction was visualized by the fluorescent-labeled VLP. The HA-promoted cell-virus fusion activity was illustrated by fluorescence imaging on the Jurkat cells incubated with rhodamine-loaded VLP performed at fusogenic pH. Furthermore, the green fluorescence protein (GFP) was fused to NA to produce VLP with a pH-sensitive probe, expanding the use of VLP as an antigen carrier and a tool for viral tracking

What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7\%, 42.0\%, and 70.1\% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future.Comment: Submitted to AAAI 202

Sampling Neural Radiance Fields for Refractive Objects

Recently, differentiable volume rendering in neural radiance fields (NeRF) has gained a lot of popularity, and its variants have attained many impressive results. However, existing methods usually assume the scene is a homogeneous volume so that a ray is cast along the straight path. In this work, the scene is instead a heterogeneous volume with a piecewise-constant refractive index, where the path will be curved if it intersects the different refractive indices. For novel view synthesis of refractive objects, our NeRF-based framework aims to optimize the radiance fields of bounded volume and boundary from multi-view posed images with refractive object silhouettes. To tackle this challenging problem, the refractive index of a scene is reconstructed from silhouettes. Given the refractive index, we extend the stratified and hierarchical sampling techniques in NeRF to allow drawing samples along a curved path tracked by the Eikonal equation. The results indicate that our framework outperforms the state-of-the-art method both quantitatively and qualitatively, demonstrating better performance on the perceptual similarity metric and an apparent improvement in the rendering quality on several synthetic and real scenes.Comment: SIGGRAPH Asia 2022 Technical Communications. 4 pages, 4 figures, 1 table. Project: https://alexkeroro86.github.io/SampleNeRFRO/ Code: https://github.com/alexkeroro86/SampleNeRFR

Entanglement Structure: Entanglement Partitioning in Multipartite Systems and Its Experimental Detection Using Optimizable Witnesses

Creating large-scale entanglement lies at the heart of many quantum information processing protocols and the investigation of fundamental physics. For multipartite quantum systems, it is crucial to identify not only the presence of entanglement but also its detailed structure. This is because in a generic experimental situation with sufficiently many subsystems involved, the production of so-called genuine multipartite entanglement remains a formidable challenge. Consequently, focusing exclusively on the identification of this strongest type of entanglement may result in an all or nothing situation where some inherently quantum aspects of the resource are overlooked. On the contrary, even if the system is not genuinely multipartite entangled, there may still be many-body entanglement present in the system. An identification of the entanglement structure may thus provide us with a hint about where imperfections in the setup may occur, as well as where we can identify groups of subsystems that can still exhibit strong quantum-information-processing capabilities. However, there is no known efficient methods to identify the underlying entanglement structure. Here, we propose two complementary families of witnesses for the identification of such structures. They are based on the detection of entanglement intactness and entanglement depth, each requires only the implementation of solely two local measurements. Our method is also robust against noises and other imperfections, as reflected by our experimental implementation of these tools to verify the entanglement structure of five different eight-photon entangled states. We demonstrate how their entanglement structure can be precisely and systematically inferred from the experimental data. In achieving this goal, we also illustrate how the same set of data can be classically postprocessed to learn the most about the measured system.Comment: 21 pages, 13 figure