Smart Hospital Innovation : Technology, Service, and Policy

We would like to thank all authors and reviewers for their contributions to this Research Topic. We would also like to thank Journal Manager Aimee Lee for her continuous and timely support.Peer reviewedPublisher PD

S4Net: Single Stage Salient-Instance Segmentation

We consider an interesting problem-salient instance segmentation in this paper. Other than producing bounding boxes, our network also outputs high-quality instance-level segments. Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch. Our new branch regards not only local context inside each detection window but also its surrounding context, enabling us to distinguish the instances in the same scope even with obstruction. Our network is end-to-end trainable and runs at a fast speed (40 fps when processing an image with resolution 320x320). We evaluate our approach on a publicly available benchmark and show that it outperforms other alternative solutions. We also provide a thorough analysis of the design choices to help readers better understand the functions of each part of our network. The source code can be found at \url{https://github.com/RuochenFan/S4Net}

Global versus Localized Generative Adversarial Nets

In this paper, we present a novel localized Generative Adversarial Net (GAN) to learn on the manifold of real data. Compared with the classic GAN that {\em globally} parameterizes a manifold, the Localized GAN (LGAN) uses local coordinate charts to parameterize distinct local geometry of how data points can transform at different locations on the manifold. Specifically, around each point there exists a {\em local} generator that can produce data following diverse patterns of transformations on the manifold. The locality nature of LGAN enables local generators to adapt to and directly access the local geometry without need to invert the generator in a global GAN. Furthermore, it can prevent the manifold from being locally collapsed to a dimensionally deficient tangent subspace by imposing an orthonormality prior between tangents. This provides a geometric approach to alleviating mode collapse at least locally on the manifold by imposing independence between data transformations in different tangent directions. We will also demonstrate the LGAN can be applied to train a robust classifier that prefers locally consistent classification decisions on the manifold, and the resultant regularizer is closely related with the Laplace-Beltrami operator. Our experiments show that the proposed LGANs can not only produce diverse image transformations, but also deliver superior classification performances

Effects of Salix psammophila on Groundwater Recharge in a Semiarid Area of North China

Study region: The semiarid Mu Us Sandy Land (MUSL) was selected for this study. It is in the farming-pastoral ecotone of north China and functions as an eco-environmental barrier. Study focus: Afforestation can mitigate desertification and soil erosion by improving hydrologic condition, which is particularly true for semiarid and arid regions. However, little is known about the quantitative response of hydrologic improvement to afforestation level that can be measured by leaf area index (LAI). The objective was to setup and use a physically-based model to quantitatively assess the dynamics of water fluxes from Salix psammophila afforestation in the MUSL. New hydrological insights for the region: Across the assessment period of 28 April to 3 October 2016, the total transpiration was determined to be about 294.4 mm. As LAI increased, while the transpiration tended to increase and the evaporation to decrease, the evapotranspiration tended to increase with increase of LAI until LAI =2.0 and then plateaued for LAI \u3e2.0. On the other hand, the recharge rate tended to decrease with increase of LAI until LAI =2.0 and then plateaued for LAI \u3e2.0. Overall, the impacts of Salix psammophila afforestation on soil-water replenishment and groundwater recharge would plateau for LAI \u3e2.0, mandating a good balance between solving large-scope eco-environmental problems by Salix psammophila afforestation and sustaining water resources in the long run

NLRC5 knockdown in chicken macrophages alters response to LPS and poly (I:C) stimulation

<p>Abstract</p> <p>Background</p> <p>NLRC5 is a member of the CARD domain containing, nucleotide-binding oligomerization (NOD)-like receptor (NLR) family, which recognizes pathogen-associated molecular patterns (PAMPs) and initiates an innate immune response leading to inflammation and/or cell death. However, the specific role of <it>NLRC5 </it>as a modulator of the inflammatory immune response remains controversial. It has been reported to be a mediator of type I IFNs, NF-kB, and <it>MHC class I </it>gene. But no study on <it>NLRC5 </it>function has been reported to date in chickens. In the current study, we investigated the role of <it>NLRC5 </it>in the regulation of <it>IFNA</it>, <it>IFNB</it>, <it>IL-6</it>, and <it>MHC class I </it>in the chicken HD11 macrophage cell line, by using RNAi technology. HD11 cells were transfected with one of five siRNAs (s1, s2, s3, negative-siRNA, or a mixture of s1, s2, s3-siRNAs). After 24 hours, cells were exposed to LPS or poly (I:C) or a vehicle control. Gene expression of <it>NLRC5</it>, <it>IFNA</it>, <it>IFNB</it>, <it>IL-6</it>, and <it>MHC class I </it>at 2, 4, 6, and 8 hours post stimulation (hps) was quantified by qPCR.</p> <p>Results</p> <p>The expression of <it>NLRC5</it>, <it>IFNA</it>, <it>IFNB</it>, and <it>IL-6 </it>genes in negative irrelevant transfection controls was up-regulated at 2 hps after LPS treatment compared to the vehicle controls. S3-siRNA effectively knocked down <it>NLRC5 </it>expression at 4 hps, and the expression of <it>IFNA </it>and <it>IFNB </it>(but not <it>IL-6 </it>and <it>MHC class I</it>) was also down-regulated at 4 hps in s3-siRNA transfected cells, compared to negative irrelevant transfection controls. Stimulation by LPS appeared to relatively restore the decrease in <it>NLRC5</it>, <it>IFNA</it>, and <it>IFNB </it>expression, but the difference is not significant.</p> <p>Conclusions</p> <p>Functional characterization of chicken <it>NLRC5 </it>in an <it>in vitro </it>system demonstrated its importance in regulating intracellular molecules involved in inflammatory response. The knockdown of <it>NLRC5 </it>expression negatively mediates gene expression of <it>IFNA </it>and <it>IFNB </it>in the chicken HD11 cell line; therefore, <it>NLRC5 </it>likely has a role in positive regulation of <it>IFNA </it>and <it>IFNB </it>expression. No direct relationship was found between <it>NLRC5 </it>knockdown and <it>IL-6 </it>and <it>MHC class I </it>expression. Future studies will further clarify the roles of <it>NLRC5 </it>and other NLRs in infectious diseases of chickens and may increase the efficacy of antiviral vaccine design.</p

Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement

Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in image-based 3D reconstruction. However, their implicit volumetric representations differ significantly from the widely-adopted polygonal meshes and lack support from common 3D software and hardware, making their rendering and manipulation inefficient. To overcome this limitation, we present a novel framework that generates textured surface meshes from images. Our approach begins by efficiently initializing the geometry and view-dependency decomposed appearance with a NeRF. Subsequently, a coarse mesh is extracted, and an iterative surface refining algorithm is developed to adaptively adjust both vertex positions and face density based on re-projected rendering errors. We jointly refine the appearance with geometry and bake it into texture images for real-time rendering. Extensive experiments demonstrate that our method achieves superior mesh quality and competitive rendering quality.Comment: ICCV 2023 camera-ready, Project Page: https://me.kiui.moe/nerf2mes