Transdermal delivery of paeonol using cubic gel and microemulsion gel

Maofu Luo1,2, Qi Shen1, Jinjin Chen11School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 2Jiangxi University of Traditional Chinese Medicine, Nanchang, People’s Republic of ChinaBackground: The aim of this study was to develop new systems for transdermal delivery of paeonol, in particular microemulsion gel and cubic gel formulations.Methods: Various microemulsion vehicles were prepared using isopropyl myristate as an oil phase, polyoxyethylated castor oil (Cremophor® EL) as a surfactant, and polyethylene glycol 400 as a cosurfactant. In the optimum microemulsion gel formulation, carbomer 940 was selected as the gel matrix, and consisted of 1% paeonol, 4% isopropyl myristate, 28% Cremophor EL/polyethylene glycol 400 (1:1), and 67% water. The cubic gel was prepared containing 3% paeonol, 30% water, and 67% glyceryl monooleate.Results: A skin permeability test using excised rat skins indicated that both the cubic gel and microemulsion gel formulations had higher permeability than did the paeonol solution. An in vivo pharmacokinetic study done in rats showed that the relative bioavailability of the cubic gel and microemulsion gel was enhanced by about 1.51-fold and 1.28-fold, respectively, compared with orally administered paeonol suspension.Conclusion: Both the cubic gel and microemulsion gel formulations are promising delivery systems to enhance the skin permeability of paeonol, in particular the cubic gel.Keywords: microemulsion gel, cubic gel, transdermal delivery, paeono

Decision Making on Government Subsidy for Highway Public-Private Partnership Projects in China Using an Iteration Game Model

Government subsidy is an important responsibility of fiscal expenditure in public-private partnership (PPP) projects. However, an improper subsidy strategy may cause over-compensation or under-compensation. In this research, an iteration game model combining game theory and real option is established to describe the periodic decision-making process. The strategy game model is applied to characterize the behavioral interactions between stakeholders, and the real option theory is used to predict the project performance under the influence of their decisions. Besides, two new indicators, the efficiency of fund (SE) and the total extra cost paid by the private sector (ME), are proposed to evaluate the extra project revenue caused by each unit of the subsidy and the incentive effects of the subsidy. Consequently, the preliminary results indicate that a periodic and iterative negotiations regarding the subsidy will effectively improve the efficiency of fund compared to the traditional way. The results also show that it is important for the public sector to give incentives, encouraging the private sector to make more efforts on the project, rather than merely providing fund support. Further study will focus on more detailed and complicated behaviors of stakeholders based on the model proposed in this paper

Recursive Generalization Transformer for Image Super-Resolution

Transformer architectures have exhibited remarkable performance in image super-resolution (SR). Since the quadratic computational complexity of the self-attention (SA) in Transformer, existing methods tend to adopt SA in a local region to reduce overheads. However, the local design restricts the global context exploitation, which is crucial for accurate image reconstruction. In this work, we propose the Recursive Generalization Transformer (RGT) for image SR, which can capture global spatial information and is suitable for high-resolution images. Specifically, we propose the recursive-generalization self-attention (RG-SA). It recursively aggregates input features into representative feature maps, and then utilizes cross-attention to extract global information. Meanwhile, the channel dimensions of attention matrices (query, key, and value) are further scaled to mitigate the redundancy in the channel domain. Furthermore, we combine the RG-SA with local self-attention to enhance the exploitation of the global context, and propose the hybrid adaptive integration (HAI) for module integration. The HAI allows the direct and effective fusion between features at different levels (local or global). Extensive experiments demonstrate that our RGT outperforms recent state-of-the-art methods quantitatively and qualitatively. Code is released at https://github.com/zhengchen1999/RGT.Comment: Code is available at https://github.com/zhengchen1999/RG

Large-scale simultaneous inference under dependence

Simultaneous, post-hoc inference is desirable in large-scale hypotheses testing as it allows for exploration of data while deciding on criteria for proclaiming discoveries. It was recently proved that all admissible post-hoc inference methods for the number of true discoveries must be based on closed testing. In this paper we investigate tractable and efficient closed testing with local tests of different properties, such as monotonicty, symmetry and separability, meaning that the test thresholds a monotonic or symmetric function or a function of sums of test scores for the individual hypotheses. This class includes well-known global null tests by Fisher, Stouffer and Ruschendorf, as well as newly proposed ones based on harmonic means and Cauchy combinations. Under monotonicity, we propose a new linear time statistic ("coma") that quantifies the cost of multiplicity adjustments. If the tests are also symmetric and separable, we develop several fast (mostly linear-time) algorithms for post-hoc inference, making closed testing tractable. Paired with recent advances in global null tests based on generalized means, our work immediately instantiates a series of simultaneous inference methods that can handle many complex dependence structures and signal compositions. We provide guidance on choosing from these methods via theoretical investigation of the conservativeness and sensitivity for different local tests, as well as simulations that find analogous behavior for local tests and full closed testing. One result of independent interest is the following: if $P_1,\dots,P_d$ are $p$-values from a multivariate Gaussian with arbitrary covariance, then their arithmetic average P satisfies $Pr(P \leq t) \leq t$ for $t \leq \frac{1}{2d}$.Comment: 40 page

Cross Aggregation Transformer for Image Restoration

Recently, Transformer architecture has been introduced into image restoration to replace convolution neural network (CNN) with surprising results. Considering the high computational complexity of Transformer with global attention, some methods use the local square window to limit the scope of self-attention. However, these methods lack direct interaction among different windows, which limits the establishment of long-range dependencies. To address the above issue, we propose a new image restoration model, Cross Aggregation Transformer (CAT). The core of our CAT is the Rectangle-Window Self-Attention (Rwin-SA), which utilizes horizontal and vertical rectangle window attention in different heads parallelly to expand the attention area and aggregate the features cross different windows. We also introduce the Axial-Shift operation for different window interactions. Furthermore, we propose the Locality Complementary Module to complement the self-attention mechanism, which incorporates the inductive bias of CNN (e.g., translation invariance and locality) into Transformer, enabling global-local coupling. Extensive experiments demonstrate that our CAT outperforms recent state-of-the-art methods on several image restoration applications. The code and models are available at https://github.com/zhengchen1999/CAT.Comment: Accepted to NeurIPS 2022. Code is available at https://github.com/zhengchen1999/CA

Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution

Super-resolution (SR) techniques designed for real-world applications commonly encounter two primary challenges: generalization performance and restoration accuracy. We demonstrate that when methods are trained using complex, large-range degradations to enhance generalization, a decline in accuracy is inevitable. However, since the degradation in a certain real-world applications typically exhibits a limited variation range, it becomes feasible to strike a trade-off between generalization performance and testing accuracy within this scope. In this work, we introduce a novel approach to craft training degradation distributions using a small set of reference images. Our strategy is founded upon the binned representation of the degradation space and the Fr\'echet distance between degradation distributions. Our results indicate that the proposed technique significantly improves the performance of test images while preserving generalization capabilities in real-world applications.Comment: This paper has been accepted to ICML 202

Research on Risk Measurement in Financial Market Based on GARCH-VaR and FHS——An Example of Chinese Bond Market

Accurately measuring the risk of bond market is very important for improving the risk management level of bond market and maintaining the stability of the financial system. Taking ChinaBond New Composite Wealth (gross) Index as the research object, this paper selects the closing price from January 1, 2002 to March 30, 2018, establishes the GARCH, EGARCH and GJR-GARCH model based on normal distribution and t distribution, and finds out the volatility aggregation and the leverage effect of the bond market. Then, this paper use two methods  to measure the risk of the bond market: first, we estimate the value at risk (VaR) of the bond market by the parameter method, using conditional variance estimated by the GARCH models, and we carry out backtesting analysis and the Kupiec failure rate test on measurement accuracy of VaR. The results show that t distribution hypothesis and elimination of autocorrelation of the yield rate can improve the accuracy and robustness of the estimation of the VaR; second, we simulate the future revenue path of the bond market and compare it with the actual loss, using Filtered Historical Simulation (FHS) based on Bootstrap method. The results show that the bond market has leverage effect. The maximum possible loss under extreme conditions can be far greater than the maximum possible revenue. But the estimated VaR under 95% confidence level can predict future risks very well. Finally, according to the conclusion, this paper puts forward some suggestions for regulators and investors from the perspective of risk management

Efficient One-Step Fusion PCR Based on Dual-Asymmetric Primers and Two-Step Annealing

Gene splicing by fusion PCR is a versatile and widely used methodology, especially in synthetic biology. We here describe a rapid method for splicing two fragments by one-round fusion PCR with a dual-asymmetric primers and two-step annealing (ODT) method. During the process, the asymmetric intermediate fragments were generated in the early stage. Thereafter, they were hybridized in the subsequent cycles to serve as template for the target full-length product. The process parameters such as primer ratio, elongation temperature and cycle numbers were optimized. In addition, the fusion products produced with this method were successfully applied in seamless genome editing. The fusion of two fragments by this method takes less than 0.5 day. The method is expected to facilitate various kinds of complex genetic engineering projects with enhanced efficiency

A novel C-terminal protein degron identified in bacterial aldehyde decarbonylases using directed enzyme evolution

Metabolic engineers have successfully synthesized alkanes, the bulk component of gasoline, using microbial cell factories as a sustainable alternative to petroleum-based fuels. Aldehyde decarbonylases (AD), enzymes which transform acyl aldehydes into alkanes, have been identified as the bottleneck in these alkane producing pathways. Previous studies demonstrated degradation of AD in E. coli cells via unknown molecular mechanism. Here, we present the discovery of a degradation tag (degron) in AD from Prochlorococcus marinus. AD variants were generated by random mutation using error-prone PCR, transferred into E. coli, and grown in chemostat culture with 2g/L hexanal to select for positive mutations. A short C-terminal sequence of AD from P. marinus was proven to be an intact degron by fusing to fluorescent proteins. Statistical analysis of C-terminal sequences of 371 non-redundant ADs from bacteria revealed a conserved sequence in this region, which was proven to be an effective degron. We also showed that ATP-dependent proteases clpAP and lon are responsible for the degradation of AD degron tagged protein. Furthermore, our results indicate that the AD degron caused 91.4% of green fluorescent protein (GFP) degradation when fused to its C-terminus, whereas its elimination in AD enhanced alkane production in vivo. Thus, our work demonstrated the presence of a protein degron tag in bacterial ADs, thereby facilitating further improvements in AD-based alkane production pathways. Please click Additional Files below to see the full abstract