Application of Artificial Neural Networks in Predicting Abrasion Resistance of Solution Polymerized Styrene-Butadiene Rubber Based Composites

Abrasion resistance of solution polymerized styrene-butadiene rubber (SSBR) based composites is a typical and crucial property in practical applications. Previous studies show that the abrasion resistance can be calculated by the multiple linear regression model. In our study, considering this relationship can also be described into the non-linear conditions, a Multilayer Feed-forward Neural Networks model with 3 nodes (MLFN-3) was successfully established to describe the relationship between the abrasion resistance and other properties, using 23 groups of data, with the RMS error 0.07. Our studies have proved that Artificial Neural Networks (ANN) model can be used to predict the SSBR-based composites, which is an accurate and robust process

Decoding poxvirus genome

Citation: Yang, Z. L., & Moss, B. (2015). Decoding poxvirus genome. Oncotarget, 6(30), 28513-28514. doi:10.18632/oncotarget.5892Deciphering the information encoded in genomic sequences is a key step in modern biomedical research. Recent findings indicate that this endeavor can be far more complex than anticipated, even for relatively small viral genomes. Vaccinia virus, the prototypic member of the poxvirus family, was initially annotated to have approximately 200 open reading frames (ORFs) of 65 or more amino acids within its 200 kbp double-stranded DNA genome. This annotation has framed the molecular biological studies of vaccinia virus since then. To further decode information in the vaccinia virus genome, we carried out systematic genome-wide ribosome profiling recently published in the Journal of Virology [1]. In ribosome profiling, only the mRNA fragments bound and protected by ribosomes are analyzed by next generation sequencing, which can quantify active protein translation with superb sensitivity, resolution and clarity [2]. We confirmed that the majority mRNAs of previously annotated ORFs are actively translated, although at greatly different frequencies. In addition, even though the long transcripts made during the late stages of infection read through adjacent ORFs, only the first is translated

Feasible Policy Iteration

Safe reinforcement learning (RL) aims to solve an optimal control problem under safety constraints. Existing $\textit{direct}$ safe RL methods use the original constraint throughout the learning process. They either lack theoretical guarantees of the policy during iteration or suffer from infeasibility problems. To address this issue, we propose an $\textit{indirect}$ safe RL method called feasible policy iteration (FPI) that iteratively uses the feasible region of the last policy to constrain the current policy. The feasible region is represented by a feasibility function called constraint decay function (CDF). The core of FPI is a region-wise policy update rule called feasible policy improvement, which maximizes the return under the constraint of the CDF inside the feasible region and minimizes the CDF outside the feasible region. This update rule is always feasible and ensures that the feasible region monotonically expands and the state-value function monotonically increases inside the feasible region. Using the feasible Bellman equation, we prove that FPI converges to the maximum feasible region and the optimal state-value function. Experiments on classic control tasks and Safety Gym show that our algorithms achieve lower constraint violations and comparable or higher performance than the baselines

Anticancer Drug Camptothecin Test in 3D Hydrogel Networks with HeLa cells

Citation: Liang, J., Sun, X. S., Yang, Z. L., & Cao, S. (2017). Anticancer Drug Camptothecin Test in 3D Hydrogel Networks with HeLa cells. Scientific Reports, 7, 9. doi:10.1038/srep37626Development of a biomimetic 3D culture system for drug screening is necessary to fully understand the in vivo environment. Previously, a self-assembling peptide hydrogel has been reported; the hydrogel exhibited physiological properties superior to a 3D cell culture matrix. In this work, further research using H9e hydrogel with HeLa cells was carried out considering H9e hydrogel's interaction with camptothecin, a hydrophobic drug. According to AFM images, a PGworks solution triggered H9e hydrogel fiber aggregation and forms a 3D matrix suitable for cell culture. Dynamic rheological studies showed that camptothecin was encapsulated within the hydrogel network concurrently with peptide self-assembly without permanently destroying the hydrogel's architecture and remodeling ability. Fluorescence measurement indicated negligible interaction between the fluorophore part of camptothecin and the hydrogel, especially at concentration 0.25 and 0.5 wt%. Using a dialysis method, we found that H9e hydrogel could not significantly inhibit the diffusion of camptothecin encapsulated inside the hydrogel matrix. In the cell culture experiment, HeLa cells were simultaneously embedded in the H9e hydrogel with the initialization of hydrogelation. Most importantly, cell viability data after camptothecin treatment showed responses that were drug-dose dependent but unaffected by the H9e hydrogel concentration, indicating that the hydrogel did not inhibit the drug

Automatic Optical and Infrared Image Registration for Plant Water Stress Sensing

[No abstract available

Patch Is Not All You Need

Vision Transformers have achieved great success in computer visions, delivering exceptional performance across various tasks. However, their inherent reliance on sequential input enforces the manual partitioning of images into patch sequences, which disrupts the image's inherent structural and semantic continuity. To handle this, we propose a novel Pattern Transformer (Patternformer) to adaptively convert images to pattern sequences for Transformer input. Specifically, we employ the Convolutional Neural Network to extract various patterns from the input image, with each channel representing a unique pattern that is fed into the succeeding Transformer as a visual token. By enabling the network to optimize these patterns, each pattern concentrates on its local region of interest, thereby preserving its intrinsic structural and semantic information. Only employing the vanilla ResNet and Transformer, we have accomplished state-of-the-art performance on CIFAR-10 and CIFAR-100, and have achieved competitive results on ImageNet