A Method for Detecting the Randomness of Barkhausen Noise in a Material Fatigue Test Using Sensitivity and Uncertainty Analysis.

The magnetic Barkhausen noise (MBN) signal provides interesting clues about the evolution of microstructure of the magnetic material (internal stresses, level of degradation, etc.). This makes it widely used in non-destructive evaluation of ferromagnetic materials. Although researchers have made great effort to explore the intrinsic random characteristics and stable features of MBN signals, they have failed to provide a deterministic definition of the stochastic quality of the MBN signals. Because many features are not reproducible, there is no quantitative description for the stochastic nature of MBN, and no uniform standards to evaluate performance of features. We aim to make further study on the stochastic characteristics of MBN signal and transform it into the quantification of signal uncertainty and sensitivity, to solve the above problems for fatigue state prediction. In the case of parameter uncertainty in the prediction model, a prior approximation method was proposed. Thus, there are two distinct sources of uncertainty: feature(observation) uncertainty and model uncertainty were discussed. We define feature uncertainty from the perspective of a probability distribution using a confidence interval sensitivity analysis, and uniformly quantize and re-parameterize the feature matrix from the feature probability distribution space. We also incorporate informed priors into the estimation process by optimizing the Kullback-Leibler divergence between prior and posterior distribution, approximating the prior to the posterior. Thus, in an insufficient data situation, informed priors can improve prediction accuracy. Experiments prove that our proposed confidence interval sensitivity analysis to capture feature uncertainty has the potential to determine the instability in MBN signals quantitatively and reduce the dispersion of features, so that all features can produce positive additive effects. The false prediction rate can be reduced to almost 0. The proposed priors can not only measure model parameter uncertainties but also show superior performance similar to that of maximum likelihood estimation (MLE). The results also show that improvements in parameter uncertainties cannot be directly propagated to improve prediction uncertainties.

Catalyst-Free One-Step Preparation of Self-Crosslinked pH-Responsive Vesicles.

The fabrication of block copolymer (BCP) vesicles with controlled membrane permeability and promising stability remains a considerable challenge. Herein, a new type of pH-responsive and self-crosslinked vesicle based on a hydrolytically hindered urea bond is reported. This kind of vesicle is formed by the self-assembly of a pH-responsive and hydrolytically self-crosslinkable copolymer poly(ethylene glycol)-block-poly[2-(3-(tert-butyl)-3-ethylureido)ethyl methacrylate-co-2-(diethylamino)ethyl methacrylate] (PEG-b-P(TBEU-co-DEA)). The BCP can be easily synthesized by reversible addition-fragmentation chain transfer (RAFT) polymerization of 2-(3-(tert-butyl)-3-ethylureido)ethyl methacrylate (TBEU) and 2-(diethylamino)ethyl methacrylate (DEA) using PEG-based macro-chain transfer agent. The copolymer could self-assemble into stable vesicles by the hydrophobic interaction and in situ cross-linking between amines and isocyanates after the hydrolysis of the hindered urea bonds without any catalyst. Dynamic light scattering (DLS) studies show that the vesicles exhibit enhanced stability against the dilution of organic solvent, and the size can be adjusted through the change of pH values. Moreover, the alkaline phosphatase-loaded vesicles can act as nano-reactor and enable free diffusion of small molecules into the vesicles, followed by the significantly improved fluorescence intensity of phosphate-caged fluorescein. This self-crosslinking and pH-sensitive vesicles may serve as a smart platform in controlled drug delivery and molecular reactor.

Chronic social defeat stress broadly inhibits gene expression in the peripheral taste system and alters taste responses in mice.

Human studies have linked stress exposure to unhealthy eating behavior. However, the mechanisms that drive stress-associated changes in eating behavior remain incompletely understood. The sense of taste plays important roles in food preference and intake. In this study, we use a chronic social defeat stress (CSDS) model in mice to address whether chronic stress impacts taste sensation and gene expression in taste buds and the gut. Our results showed that CSDS significantly elevated circulating levels of corticosterone and acylated ghrelin while lowering levels of leptin, suggesting a change in metabolic hormones that promotes food consumption. Stressed mice substantially increased their intake of food and water 3-5 days after the stress onset and gradually gained more body weight than that of controls. Moreover, CSDS significantly decreased the expression of multiple taste receptors and signaling molecules in taste buds and reduced mRNA levels of several taste progenitor/stem cell markers and regulators. Stressed mice showed significantly reduced sensitivity and response to umami and sweet taste compounds in behavioral tests. In the small intestine, the mRNA levels of Gnat3 and Tas1r2 were elevated in CSDS mice. The increased Gnat3 was mostly localized in a type of Gnat3+ and CD45+ immune cells, suggesting changes of immune cell distribution in the gut of stressed mice. Together, our study revealed broad effects of CSDS on the peripheral taste system and the gut, which may contribute to stress-associated changes in eating behavior.

An enhancer RNA-based risk model for prediction of bladder cancer prognosis.

Background: Bladder cancer patients have a high recurrence and poor survival rates worldwide. Early diagnosis and intervention are the cornerstones for favorable prognosis. However, commonly used predictive tools cannot meet clinical needs because of their insufficient accuracy. Methods: We have developed an enhancer RNA (eRNA)-based signature to improve the prediction for bladder cancer prognosis. First, we analyzed differentially expressed eRNAs in gene expression profiles and clinical data for bladder cancer from The Cancer Genome Atlas database. Then, we constructed a risk model for prognosis of bladder cancer patients, and analyzed the correlation between this model and tumor microenvironment (TME). Finally, regulatory network of downstream genes of eRNA in the model was constructed by WGCNA and enrichment analysis, then Real-time quantitative PCR verified the differentiation of related genes between tumor and adjacent tissue. Results: We first constructed a risk model composed of eight eRNAs, and found the risk model could be an independent risk factor to predict the prognosis of bladder cancer. Then, the log-rank test and time-dependent ROC curve analysis shown the model has a favorable ability to predict prognosis. The eight risk eRNAs may participate in disease progression by regulating cell adhesion and invasion, and up-regulating immune checkpoints to suppress the immunity in TME. mRNA level change in related genes further validated regulatory roles of eRNAs in bladder cancer. In summary, we constructed an eRNA-based risk model and confirmed that the model could predict the prognosis of bladder cancer patients.

Tunable and Processable Shape-Memory Materials Based on Solvent-Free, Catalyst-Free Polycondensation between Formaldehyde and Diamine at Room Temperature.

Compared with traditional thermosets, malleable thermosets have more applications in aerospace, biotechnology, and construction. Here we report a one-step, solvent-free, catalyst-free polycondensation method between diamine and formaldehyde to prepare a series of malleable hemiaminal dynamic covalent networks (HDCNs). The materials have excellent malleability and reprocessability by hot pressing. The Young's modulus and breaking strength of HDCNs obtained by the polycondensation of formaldehyde and 4,4-diaminodiphenylmethane (MDA) are as high as 1.6 GPa and 60 MPa, respectively, which can be facilely adjusted through the introduction of polyetheramine-400 (PEDA). Moreover, the HDCNs feature the shape memory ability with a recovery ratio above 93.5% and can be recycled by the addition of different monomers. This promising HDCN, prepared from economical raw materials, may have vast applications in industries.

An Enzyme-Responsive "Turn-on" Fluorescence Polymeric Superamphiphile as a Potential Visualizable Phosphate Prodrug Delivery Vehicle.

The development of inexpensive and highly efficient enzyme-responsive polymers has significantly contributed to targeted drug delivery systems. Here, a superamphiphile with a capability of fluorescent dissociation sensing is designed. It is constructed with negatively charged adenosine 5'-triphosphate (ATP) and negatively charged fluorescein diphosphate (FDP), which are used as fluorescence detection, and a cationic diblock copolymer methoxy-poly(ethylene glycol)113 -b-poly(2-dimethyl-aminoethyl methacrylate)70 . Upon addition of calf intestinal alkaline phosphatase, the superamphiphile disintegrates, presumably due to the enzymatic hydrolysis of ATP. This process is accompanied by an increase in the fluorescence emission intensity of fluorescein owing to the hydrolysis of FDP. The in vitro application of the superamphiphile is also proven. Thus, the "turn-on" fluorescence of the superamphiphile serves as a real-time module for detection of the disintegration of superamphiphile.