De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning.

One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to be further investigated, and drugs designed based on targets may not always have the desired drug efficacy. The emergence of machine learning provides a powerful tool to overcome the challenge. Herein, a machine learning-based strategy is developed for de novo generation of novel compounds with drug efficacy termed DTLS (Deep Transfer Learning-based Strategy) by using dataset of disease-direct-related activity as input. DTLS is applied in two kinds of disease: colorectal cancer (CRC) and Alzheimer's disease (AD). In each case, novel compound is discovered and identified in in vitro and in vivo disease models. Their mechanism of actionis further explored. The experimental results reveal that DTLS can not only realize the generation and identification of novel compounds with drug efficacy but also has the advantage of identifying compounds by focusing on protein targets to facilitate the mechanism study. This work highlights the significant impact of machine learning on the design of novel compounds with drug efficacy, which provides a powerful new approach to drug discovery.

Image-based thickener mud layer height prediction with attention mechanism-based CNN.

Mud layer height of thickener is the key quality index of thickening process which is difficult to achieve real-time detection with existing methods in reality. While the need of developing a soft sensor model which can be used for real-time detection of mud layer height, we proposed an end-to-end mud layer height prediction method with attention mechanism-based convolutional neural network (CNN). The dynamic features are firstly extracted from the image samples based on CNN, and then two types of attention mechanism are embedded sequentially to contribute to more precise prediction results. Compared with the traditional spatial attention mechanism, the regional spatial attention mechanism we proposed selectively divides the spatial feature map into regions, while regions containing important features are assigned larger weights. Adding the channel and regional spatial attention mechanism in CNN not only effectively improve both the precision and calculation speed, but also affect the dimension of the output feature map, so as to avoid the loss of channel or spatial attention information of the feature map. To verify the validity of the proposed method, different attention mechanisms are embedded in the CNN, and the corresponding experiments are carried out on the dataset of the thickener mud layer. The experimental results demonstrate the feasibility and effectiveness of the mud layer height prediction method.

Inflammatory mechanism of cerebral ischemia-reperfusion injury with treatment of stepharine in rats.

BACKGROUND: Increasing evidence has shown that microglia-induced neuroinflammation is involved in the pathogenesis of ischemic stroke. Stepharine, one of the alkaloids extracted from Stephania japonica (Thunb.) Miers, exhibited strong inhibitory effect on microglial overactivation. However, it is not known whether it has the potential to prevent ischemic stroke. METHODS: The neuroprotective and anti-neuroinflammatory effects of stepharine were investigated in vivo and in vitro, using a rat model of middle cerebral artery occlusion (MCAO) and lipopolysaccharide (LPS)-stimulated BV-2 cells, respectively. RESULTS: In vivo, stepharine (500 µg/kg) suppressed neurological deficits scores, brain water content and cerebral infarct volume induced by MCAO. Moreover, stepharine (500 µg/kg) inhibited NeuN+ cells loss and Iba-1+ cells increase in the MCAO ischemic cortex. In vitro, stepharine (10, 30 µM) substantially inhibited nitric oxide release as well as the mRNA and protein expression of pro-inflammatory mediators [inducible nitric oxide synthase, interleukin (IL)-6, tumor necrosis factor (TNF)-α, IL-1ß] in LPS-activated BV-2 cells. LPS-induced increase of TLR4 expression, IκBα phosphorylation, and NF-κB p65 nuclear translocation was inhibited by stepharine (10, 30 µM). Molecular docking analysis showed that stepharine directly interacted with TLR4. SPR assay further confirmed that stepharine could bind to the TLR4/MD2 complex. Meanwhile, stepharine exhibited neuroprotective effects on SH-SY5Y cells cultured with LPS-treated conditioned medium. CONCLUSION: Our study demonstrated for the first time that stepharine improved the outcomes in MCAO rats, reduced neuronal loss, and suppressed microglial overactivation via the inhibition of TLR4/NF-κB pathway. These results suggest that stepharine might be a potential therapeutic agent for the treatment of ischemic stroke.

Multi-Information Flow CNN and Attribute-Aided Reranking for Person Reidentification.

This paper presents a multi-information flow convolutional neural network (MiF-CNN) model for person reidentification (re-id). It contains several specific multilayer convolutional structures, where the input and output of a convolutional layer are concatenated together on channel dimension. With this idea, layers of model can go deeper and feature maps can be reused by each subsequent layer. Inspired by an image caption, a person attribute recognition network is proposed based on long-short-term memory network and attention mechanism. By fusing identification results of MiF-CNN and attribute recognition, this paper introduces the attribute-aided reranking algorithm to improve the accuracy of person re-id further. Experiments on VIPeR, CUHK01, and Market1501 datasets verify the proposed MiF-CNN can be trained sufficiently with small-scale datasets and obtain outstanding accuracy of person re-id. Contrast experiments also confirm the availability of the attribute-assisted reranking algorithm.

Recursive parameter estimation for Hammerstein-Wiener systems using modified EKF algorithm.

This paper focuses on the recursive parameter estimation for the single input single output Hammerstein-Wiener system model, and the study is then extended to a rarely mentioned multiple input single output Hammerstein-Wiener system. Inspired by the extended Kalman filter algorithm, two basic recursive algorithms are derived from the first and the second order Taylor approximation. Based on the form of the first order approximation algorithm, a modified algorithm with larger parameter convergence domain is proposed to cope with the problem of small parameter convergence domain of the first order one and the application limit of the second order one. The validity of the modification on the expansion of convergence domain is shown from the convergence analysis and is demonstrated with two simulation cases.

Optimization Control of the Color-Coating Production Process for Model Uncertainty.

Optimized control of the color-coating production process (CCPP) aims at reducing production costs and improving economic efficiency while meeting quality requirements. However, because optimization control of the CCPP is hampered by model uncertainty, a strategy that considers model uncertainty is proposed. Previous work has introduced a mechanistic model of CCPP based on process analysis to simulate the actual production process and generate process data. The partial least squares method is then applied to develop predictive models of film thickness and economic efficiency. To manage the model uncertainty, the robust optimization approach is introduced to improve the feasibility of the optimized solution. Iterative learning control is then utilized to further refine the model uncertainty. The constrained film thickness is transformed into one of the tracked targets to overcome the drawback that traditional iterative learning control cannot address constraints. The goal setting of economic efficiency is updated continuously according to the film thickness setting until this reaches its desired value. Finally, fuzzy parameter adjustment is adopted to ensure that the economic efficiency and film thickness converge rapidly to their optimized values under the constraint conditions. The effectiveness of the proposed optimization control strategy is validated by simulation results.