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Summary: Background: Dengue virus (DENV) infection during pregnancy increases the risk of adverse fetal outcomes, which has become a new clinical challenge. However, the underlying mechanism remains unknown. Methods: The effect of DENV-2 infection on fetuses was investigated using pregnant interferon α/β receptor-deficient (Ifnar1−/−) mice. The histopathological changes in the placentas were analyzed by morphological techniques. A mouse inflammation array was used to detect the cytokine and chemokine profiles in the serum and placenta. The infiltration characteristics of inflammatory cells in the placentas were evaluated by single-cell RNA sequencing. Findings: Fetal growth restriction observed in DENV-2 infection was mainly caused by the destruction of the placental vasculature rather than direct damage from the virus in our mouse model. After infection, neutrophil infiltration into the placenta disrupts the expression profile of matrix metalloproteinases, which leads to placental dysvascularization and insufficiency. Notably, similar histopathological changes were observed in the placentas from DENV-infected puerperae. Interpretation: Neutrophils play key roles in placental histopathological damage during DENV infection, which indicates that interfering with aberrant neutrophil infiltration into the placenta may be an important therapeutic target for adverse pregnancy outcomes in DENV infection. Funding: The National Key Research and Development Plans of China (2021YFC2300200-02 to J.A., 2019YFC0121905 to Q.Z.C.), the National Natural Science Foundation of China (NSFC) (U1902210 and 81972979 to J. A., 81902048 to Z. Y. S., and 82172266 to P.G.W.), and the Support Project of High-level Teachers in Beijing Municipal Universities in the Period of 13th Five-year Plan, China (IDHT20190510 to J. A.).

Fingerprint crowdsourcing has recently been promoted as a promising solution for fingerprinting-based indoor localization systems to relieve the burden of site survey. When constructing an indoor localization map from crowd-sourced samples, the following challenges should be addressed: inaccurate sample annotation, unequal sample dimensionality, measurement device diversity, and nonuniform spatial distribution. In this paper, we propose the radio map construction from crowd-sourced sample (RMapCS) scheme to handle these challenges. The RMapCS consists of four main modules: outlier detection, source selection, fingerprint interpolation, and device calibration. For each device in each grid, we first propose an improved clustering algorithm to remove outliers and use a threshold-based approach to select only those important signal sources. For a grid without enough samples, we propose a fingerprint interpolation algorithm to construct its device-specific fingerprints. Then, we propose a device calibration algorithm to fuse samples from different devices to obtain grid fingerprints. We also propose a two-step online positioning algorithm consisting of both set comparison and similarity computation. We conduct field measurements and experiments to examine the localization performance. Results show that the proposed RMapCS can achieve significant improvements over the peer schemes and the average localization error can achieve around 1.5 m by using only the received signal strength-based fingerprints.