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Chronic bone loss is an under-recognized complication of malaria, the underlying mechanism of which remains incompletely understood. We have previously shown that persistent accumulation of Plasmodium products in the bone marrow leads to chronic inflammation in osteoblast (OB) and osteoclast (OC) precursors causing bone loss through MyD88, an adaptor molecule for diverse inflammatory signals. However, the specific contribution of MyD88 signaling in OB or OC precursors in malaria-induced bone loss remains elusive. To assess the direct cell-intrinsic role of MyD88 signaling in adult bone metabolism under physiological and infection conditions, we used the Lox-Cre system to specifically deplete MyD88 in the OB or OC lineages. Mice lacking MyD88 primarily in the maturing OBs showed a comparable decrease in trabecular bone density by microcomputed tomography to that of controls after Plasmodium yoelii non-lethal infection. In contrast, mice lacking MyD88 in OC precursors showed significantly less trabecular bone loss than controls, suggesting that malaria-mediated inflammatory mediators are primarily controlled by MyD88 in the OC lineage. Surprisingly, however, depletion of MyD88 in OB, but not in OC, precursors resulted in reduced bone mass with decreased bone formation rates in the trabecular areas of femurs under physiological conditions. Notably, insulin-like growth factor-1, a key molecule for OB differentiation, was significantly lower locally and systemically when MyD88 was depleted in OBs. Thus, our data demonstrate an indispensable intrinsic role for MyD88 signaling in OB differentiation and bone formation, while MyD88 signaling in OC lineages plays a partial role in controlling malaria-induced inflammatory mediators and following bone pathology. These findings may lead to the identification of novel targets for specific intervention of bone pathologies, particularly in malaria-endemic regions. [ABSTRACT FROM AUTHOR]

In order to conquer the difficulty of simultaneously recognizing the multiple defects of potatoes samples placed randomly, this paper proposed a non-destructive detection method which combined manifold learning dimension reduction algorithm based on hyperspectral information fusion and extreme learning machine (ELM) to simultaneously distinguish the multiple defects of potatoes. In this paper, 367 potatoes were picked which were made up of 111 sprouting potatoes, 90 green rind potatoes, 46 blackheart potatoes and 120 normal potatoes. The hyperspectral image acquisition system contained imaging spectroradiometer (SPECIM, V10E, Finland), data acquisition box, lighting system, electric moving stage and objective table. The reflection hyperspectral information of all those potatoes was acquired by using the hyperspectral image acquisition system, whose spectral wavelength ranged from 390 to 1040 nm. The hyperspectral information included the spectral information from 520 wave bands and the imaging information from 520 gray images. After the correction to hyperspectral data with the standard black and white board, hyperspectral data in the range of 450-990 nm, which had high signal-to-noise ratio (SNR), were selected as original spectrum for subsequent processing. To deal with the spectral information, the average spectrum was abstracted from the region of interests (ROI) on every potato sample by using the environment for visualizing images (ENVI). Comparing several data preprocessing methods, detrend was determined as the optimal spectral preprocessing method. Diffusion maps (DM), locally linear embedding (LLE) and hessian locally linear embedding (HLLE) were respectively utilized for the purpose of cutting down the dimension of spectrum data after the spectral preprocessing named detrend. To deal with the hyperspectral imaging information, every pseudo-color image of potatoes was morphologically processed before extracting 84 image texture characteristics based on gray level co-occurrence matrix (GLCM). By using successive projections algorithm (SPA), 10 texture features were properly selected, which consisted of homogeneity of 45° GLCM in R gray image, energy of 90° GLCM in R gray image, correlation of 90° GLCM in R gray image, contrast of 135° GLCM in R gray image, correlation of 0° GLCM in G gray image, contrast of 0° GLCM in G gray image, correlation of 90° GLCM in G gray image, homogeneity of 45° GLCM in B gray image, correlation of 90° GLCM in B gray image and contrast of 135° GLCM in B gray image. After the fusion of the spectral characteristics and image features, the 367 potato samples were divided into calibration set and test set. The calibration set had 250 potato samples including 74 sprouting potatoes, 59 green rind potatoes, 28 blackheart potatoes and 89 normal potatoes. The whole number of samples in test set was 117 which contained 37 sprouting potatoes, 31 green rind potatoes, 18 blackheart potatoes and 31 normal potatoes. Six models were respectively established based on support vector machine (SVM) and ELM, which were DM-SVM, LLE-SVM, HLLE-SVM, DM-ELM, LLE-ELM and HLLE-ELM. Comparing and analyzing the results of the 6 models and the spending time, we discovered that the DM was the best manifold learning dimension reduction algorithm to deduce the spectral information. We alsofound that the DM-ELM model was the best model because of the better recognition rate for recognizing the multiple defects of potatoes and spending less time. For the DM-ELM model, the single recognition rate of sprouting potatoes, green rind potatoes, blackheart potatoes and normal potatoes respectively reached 97.30%, 93.55%, 94.44% and 100%, and the mixed recognition rate reached 96.58%. It only cost 0.11 s to build the DM-ELM model. The results indicate that combining hyperspectral information fusion with manifold learning dimension reduction algorithm can simultaneously distinguish the multiple defects of potatoes. [ABSTRACT FROM AUTHOR]

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with no cure except transplantation. Abnormal alveolar epithelial regeneration is a key driver of IPF development. The function of Yes1 Associated Transcriptional Regulator (YAP) in alveolar regeneration and IPF pathogenesis remains elusive. Here, we first revealed the activation of YAP in alveolar epithelium 2 cells (AEC2s) from human IPF lungs and fibrotic mouse lungs. Notably, conditional deletion of YAP in mouse AEC2s exacerbated bleomycin-induced pulmonary fibrosis. Intriguingly, we showed in both conditional knockout mice and alveolar organoids that YAP deficiency impaired AEC2 proliferation and differentiation into alveolar epithelium 1 cells (AEC1s). Mechanistically, YAP regulated expression levels of genes associated with cell cycle progression and AEC1 differentiation. Furthermore, overexpression of YAP in vitro promoted cell proliferation. These results indicate the critical role of YAP in alveolar regeneration and IPF pathogenesis. Our findings provide new insights into the regulation of alveolar regeneration and IPF pathogenesis, paving the road for developing novel treatment strategies.