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Optimizing drug therapies for any disease requires a solid understanding of pharmacokinetics (the drug concentration at a given time point in different body compartments) and pharmacodynamics (the effect a drug has at a given concentration). Mathematical models are frequently used to infer drug concentrations over time based on infrequent sampling and/or in inaccessible body compartments. Models are also used to translate drug action from in vitro to in vivo conditions or from animal models to human patients. Recently, mathematical models that incorporate drug-target binding and subsequent downstream responses have been shown to advance our understanding and increase predictive power of drug efficacy predictions. We here discuss current approaches of modeling drug binding kinetics that aim at improving model-based drug development in the future. This in turn might aid in reducing the large number of failed clinical trials.

The methods based on the combination of word-level and character-level features can effectively boost performance on Chinese short text classification. A lot of works concatenate two-level features with little processing, which leads to losing feature information. In this work, we propose a novel framework called Mutual-Attention Convolutional Neural Networks, which integrates word and character-level features without losing too much feature information. We first generate two matrices with aligned information of two-level features by multiplying word and character features with a trainable matrix. Then, we stack them as a three-dimensional tensor. Finally, we generate the integrated features using a convolutional neural network. Extensive experiments on six public datasets demonstrate improved performance of our new framework over current methods.

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In Alzheimer’s disease (AD), dysregulation of intracellular Ca2+ signalling has been observed as an early event prior to the presence of clinical symptoms and is believed to be a crucial factor contributing to AD pathogenesis. Amyloid-β oligomers (AβOs) disturb the N-methyl-D-aspartate receptor (NMDAR)-mediated postsynaptic Ca2+ signalling in response to presynaptic stimulation by increasing the availability of extracellular glutamate as well as directly disturbing the NMDARs. The abnormal Ca2+ response can further lead to impairments in long-term potentiation (LTP), an important process in memory formation. In this study, we develop a mathematical model of a CA1 pyramidal dendritic spine and conduct computational experiments. We use this model to mimic alterations by AβOs under AD conditions to investigate how they are involved in the Ca2+ dysregulation in the dendritic spine. The alterations in glutamate availability, as well as NMDAR availability and activity, are studied both individually and globally. The simulation results suggest that alterations in glutamate availability mostly affect the synaptic response and have limited effects on the extrasynaptic receptors. Moreover, overactivation of extrasynaptic NMDARs in AD is unlikely to be induced by presynaptic stimulation, but by upregulation of the resting level of glutamate, possibly resulting from these alterations. Furthermore, internalisation of synaptic NR2A-NMDAR shows greater damage to the postsynaptic Ca2+ response in comparison with the internalisation of NR2B-NMDARs; thus, the suggested neuroprotective role of the latter is very limited during synaptic transmission in AD. We integrate a CaMKII state transition model with the Ca2+ model to further study the effects of alterations of NMDARs in the CaMKII state transition, an important downstream event in the early phase of LTP. The model reveals that cooperation between NR2A- and NR2B-NMDAR is required for LTP induction. Under AD conditions, internalisation of membrane NMDARs is suggested to be the cause of the loss of synapse numbers by disrupting CaMKII-NMDAR formation.

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The computation amount in blind source separation based on bioinspired intelligence optimization is high. In order to solve this problem, we propose an effective blind source separation algorithm based on the artificial bee colony algorithm. In the proposed algorithm, the covariance ratio of the signals is utilized as the objective function and the artificial bee colony algorithm is used to solve it. The source signal component which is separated out, is then wiped off from mixtures using the deflation method. All the source signals can be recovered successfully by repeating the separation process. Simulation experiments demonstrate that significant improvement of the computation amount and the quality of signal separation is achieved by the proposed algorithm when compared to previous algorithms.

The problem of sample imbalance will lead to poor generalization ability of the deep learning model algorithm, and the phenomenon of overfitting during network training, which limits the accuracy of intelligent fault diagnosis of switchgear equipment. In view of this, this paper proposes a data augmentation method for switchgear defect samples based on Wasserstein generative adversarial network with the partial discharge live detection data of the substation and the real-time switchgear partial discharge simulation experimental data. This method can improve the imbalanced distribution of data, and solve the problems such as the disappearance of gradients and model collapses in the classic generative adversarial network model, and greatly improve the stability of training. Verification through examples and comparison with traditional data augmentation methods. The results show that the data augmentation method mentioned in this paper can more effectively reduce the data imbalance, improve the performance of data-driven technology, and provide data support for subsequent fault diagnosis of switchgear equipment.