Biomimetic Polymer Film with Brilliant Brightness Using a One‐Step Water Vapor–Induced Phase Separation Method

The scales of the white Cyphochilus beetles are endowed with unusual whiteness arising from the exceptional scattering efficiency of their disordered ultrastructure optimized through millions of years of evolution. Here, a simple, one‐step method based on water vapor–induced phase separation is developed to prepare thin polystyrene films with similar microstructure and comparable optical performance. A typical biomimetic 3.5 µm PS film exhibits a diffuse reflectance of 61% at 500 nm wavelength, which translates into a transport mean free path below 1 µm. A complete optical characterization through Monte Carlo simulations reveals how such a scattering performance arises from the scattering coefficient and scattering anisotropy, whose interplay provides insight into the morphological properties of the material. The potential of bright‐white coatings as smart sensors or wearable devices is highlighted using a treated 3.5 µm film as a real‐time sensor for human exhalation

Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer

Unsupervised/self-supervised representation learning in time series is critical since labeled samples are usually scarce in real-world scenarios. Existing approaches mainly leverage the contrastive learning framework, which automatically learns to understand the similar and dissimilar data pairs. Nevertheless, they are restricted to the prior knowledge of constructing pairs, cumbersome sampling policy, and unstable performances when encountering sampling bias. Also, few works have focused on effectively modeling across temporal-spectral relations to extend the capacity of representations. In this paper, we aim at learning representations for time series from a new perspective and propose Cross Reconstruction Transformer (CRT) to solve the aforementioned problems in a unified way. CRT achieves time series representation learning through a cross-domain dropping-reconstruction task. Specifically, we transform time series into the frequency domain and randomly drop certain parts in both time and frequency domains. Dropping can maximally preserve the global context compared to cropping and masking. Then a transformer architecture is utilized to adequately capture the cross-domain correlations between temporal and spectral information through reconstructing data in both domains, which is called Dropped Temporal-Spectral Modeling. To discriminate the representations in global latent space, we propose Instance Discrimination Constraint to reduce the mutual information between different time series and sharpen the decision boundaries. Additionally, we propose a specified curriculum learning strategy to optimize the CRT, which progressively increases the dropping ratio in the training process.Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems (TNNLS

Generalized bioinspired approach to a daytime radiative cooling "skin"

Energy-saving cooling materials with strong operability are desirable towards sustainable thermal management. Inspired by the cooperative thermo-optical effect in fur of polar bear, we develop a flexible and reusable cooling skin via laminating a polydimethylsiloxane film with a highly-scattering polyethylene aerogel. Owing to its high porosity of 97.9% and tailored pore size of 3.8 +- 1.4 micrometers, superior solar reflectance of 0.96 and high transparency to irradiated thermal energy of 0.8 can be achieved at a thickness of 2.7 mm. Combined with low thermal conductivity of 0.032 W/m/K of the aerogel, the cooling skin exerts midday sub-ambient temperature drops of 5-6 degrees in a metropolitan environment, with an estimated limit of 14 degrees under ideal service conditions. We envision that this generalized bilayer approach will construct a bridge from night-time to daytime radiative cooling and pave the way for economical, scalable, flexible and reusable cooling materials.Comment: 15 pages, 4 figures, of which another version has been accepted by ACS ami but not published ye

A single dose of lipopolysaccharide elicits autofluorescence in the mouse brain

One hallmark of aging is autofluorescence (AF) in the brain. However, the underlying mechanism for inducing AF remains unknown. This study aims to determine the cause(s) of this phenomenon. The endogenous expression pattern of AF in mice was examined at differing ages. Intraperitoneal injection of a single dose of lipopolysaccharide (LPS) was performed to induce AF. Copper sulfate was applied to remove AF to allow for further immunofluorescence staining. AF appeared in the mouse brain as early as 3 months of age. In the cortex, AF occurs in the lysosomes of microglia, astrocytes, endothelial cells, and oligodendrocyte lineage cells and its prevalence increases with age. Interestingly, AF never occurs in the pericytes of young or aged brains. LPS administration resulted in a rapid and marked induction of brain AF, similar to the normal aging process. Finally, age-related and induced AF can be eliminated by low concentrations of copper sulfate solution. This pre-treatment is safe for aging and lineage tracing studies. These findings depict that AF in the brain could be associated with the innate immune response against Gram-negative bacteria infection

Effects of florfenicol exposure during early life on toxicity, gut microbiota, and fecal metabolome in SD rats

Florfenicol (FLO) is a third-generation veterinary antibiotic with a high residue detection rate in food, which cause the toxicity of FLO even at low doses, receiving notable attention. The impact of FLO exposure during early life on health and gut microbiota is still unclear. Here, the effects of FLO exposure on toxicity, gut microbiota, drug resistance genes, and the fecal metabolome during early life were investigated in suckling Sprague-Dawley (SD) rats. The results showed that FLO exposure during early life significantly increased the body weight, and WBC and LY levels in the blood, induced inflammation in the liver and intestines. FLO had a dose-dependent effect on the alpha and beta diversity of the gut microbiota, increasing the ratio of Firmicutes to Bacteroides and the abundance of some pathogenic bacteria, and changing the abundance of bacteria related to energy metabolism and inflammation, also promoted the enrichment of drug resistance genes. The fecal metabolome also demonstrated the effect of FLO exposure on metabolic pathways related to energy metabolism and inflammation. In conclusion, this research shows that FLO exposure during early life can lead to excessive weight gain, an inflammatory response, gut microbiota imbalance, the enrichment of drug resistance genes, and effects on related metabolic pathways

Development and external validation of a nomogram for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage

BackgroundPostoperative pneumonia (POP) is a common complication after aneurysmal subarachnoid hemorrhage (aSAH) associated with increased mortality rates, prolonged hospitalization, and high medical costs. It is currently understood that identifying pneumonia early and implementing aggressive treatment can significantly improve patients' outcomes. The primary objective of this study was to explore risk factors and develop a logistic regression model that assesses the risks of POP.MethodsAn internal cohort of 613 inpatients with aSAH who underwent surgery at the Neurosurgical Department of First Affiliated Hospital of Wenzhou Medical University was retrospectively analyzed to develop a nomogram for predicting POP. We assessed the discriminative power, accuracy, and clinical validity of the predictions by using the area under the receiver operating characteristic curve (AUC), the calibration curve, and decision curve analysis (DCA). The final model was validated using an external validation set of 97 samples from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.ResultsAmong patients in our internal cohort, 15.66% (n = 96/613) of patients had POP. The least absolute shrinkage and selection operator (LASSO) regression analysis identified the Glasgow Coma Scale (GCS), mechanical ventilation time (MVT), albumin, C-reactive protein (CRP), smoking, and delayed cerebral ischemia (DCI) as potential predictors of POP. We then used multivariable logistic regression analysis to evaluate the effects of these predictors and create a final model. Eighty percentage of patients in the internal cohort were randomly assigned to the training set for model development, while the remaining 20% of patients were allocated to the internal validation set. The AUC values for the training, internal, and external validation sets were 0.914, 0.856, and 0.851, and the corresponding Brier scores were 0.084, 0.098, and 0.143, respectively.ConclusionWe found that GCS, MVT, albumin, CRP, smoking, and DCI are independent predictors for the development of POP in patients with aSAH. Overall, our nomogram represents a reliable and convenient approach to predict POP in the patient population

Exploiting Multiple Embeddings for Chinese Named Entity Recognition

Identifying the named entities mentioned in text would enrich many semantic applications at the downstream level. However, due to the predominant usage of colloquial language in microblogs, the named entity recognition (NER) in Chinese microblogs experience significant performance deterioration, compared with performing NER in formal Chinese corpus. In this paper, we propose a simple yet effective neural framework to derive the character-level embeddings for NER in Chinese text, named ME-CNER. A character embedding is derived with rich semantic information harnessed at multiple granularities, ranging from radical, character to word levels. The experimental results demonstrate that the proposed approach achieves a large performance improvement on Weibo dataset and comparable performance on MSRA news dataset with lower computational cost against the existing state-of-the-art alternatives.Comment: accepted at CIKM 201