Phosphopeptides P140 cause oxidative burst responses of pulmonary macrophages in an imiquimod-induced lupus model.

Recent studies challenge the dogma that a 21-mer phosphopeptide P140 protects against direct cell damage in the phase-III clinical trial (NCT02504645) for lupus, involving reactive oxygen species (ROS)-dependent release of citrullinated histone H3 (H3cit)-linked neutrophil extracellular traps. An open question is the cellular location of ROS production and H3cit formation in lupus. In this study, we examined the effects of P140 peptides on ROS production and H3cit location in lupus with in vivo and situ fluorescence imaging with subcellular resolution. We developed a mouse model of the B6 strain harbouring a bioluminescent reporter under the control of the Lysozyme M promoter. Based on the imiquimod-induced disease model of B6 mice, we used bioluminescent imaging, flow cytometry analysis, and immunohistology staining to study the effects of P140 peptides in lupus. We found a profound accumulation of CX3CR1-positive macrophages in the lungs of lupus mice after the application of P140, accompanied by lung fibrosis formation. The defined P140-mediated macrophage responses were associated with an increase of H3cit in the cytosol, interleukin-1 receptor type 1 on the extracellular membrane, and intracellular production of ROS. Of interest, the disease of imiquimod-induced lupus was prevented with an antioxidant drug apocynin. This study shows that P140 peptides play a role in aggravated murine lupus in a manner dependent on ROS production and H3cit upregulation through pulmonary macrophages.

CSTNet: A Dual-Branch Convolutional Neural Network for Imaging of Reactive Flows Using Chemical Species Tomography.

Chemical species tomography (CST) has been widely used for in situ imaging of critical parameters, e.g., species concentration and temperature, in reactive flows. However, even with state-of-the-art computational algorithms, the method is limited due to the inherently ill-posed and rank-deficient tomographic data inversion and by high computational cost. These issues hinder its application for real-time flow diagnosis. To address them, we present here a novel convolutional neural network, namely CSTNet, for high-fidelity, rapid, and simultaneous imaging of species concentration and temperature using CST. CSTNet introduces a shared feature extractor that incorporates the CST measurements and sensor layout into the learning network. In addition, a dual-branch decoder with internal crosstalk, which automatically learns the naturally correlated distributions of species concentration and temperature, is proposed for image reconstructions. The proposed CSTNet is validated both with simulated datasets and with measured data from real flames in experiments using an industry-oriented sensor. Superior performance is found relative to previous approaches in terms of reconstruction accuracy and robustness to measurement noise. This is the first time, to the best of our knowledge, that a deep learning-based method for CST has been experimentally validated for simultaneous imaging of multiple critical parameters in reactive flows using a low-complexity optical sensor with a severely limited number of laser beams.

The K-SF-42.

The purpose of the present article is to propose an alternative short form for the 199-item Arizona Life History Battery (ALHB), which we are calling the K-SF-42, as it contains 42 items as compared with the 20 items of the Mini-K, the short form that has been in greatest use for the past decade. These 42 items were selected from the ALHB, unlike those of the Mini-K, making direct comparisons of the relative psychometric performance of the two alternative short forms a valid and instructive exercise. A series of secondary data analyses were performed upon a recently completed five-nation cross-cultural survey, which was originally designed to assess the role of life history strategy in the etiology of interpersonal aggression. Only data from the ALHB that were collected in all five cross-cultural replications were used for the present analyses. The single immediate objective of this secondary data analysis was producing the K-SF-42 such that it would perform optimally across all five cultures sampled, and perhaps even generalize well to other modern industrial societies not currently sampled as a result of the geographic breadth of those included in the present study. A novel method, based on the use of the Cross-Sample Geometric Mean as a criterion for item selection, was used for generating such a cross-culturally valid short form.