基于药物和疾病特征关联的药物重定位混合推荐算法

针对基于协同过滤的药物重定位算法进行了研究,考虑到数据稀疏性对协同过滤算法的巨大影响,提出一种基于药物和疾病特征关联的药物重定位混合推荐算法。该算法不仅使用了药物和疾病关系数据,还利用了药物结构、靶蛋白、副作用以及药物—疾病特征矩阵等信息计算药物之间的相似性,降低了数据稀疏性对推荐效果的影响,提高了推荐精度。经过对比实验发现,该算法具备较好的推荐效果,并能够发掘具有潜在联系的药物—疾病组合,进一步验证了该算法可以有效地应用于药物重定位。     

基于有监督的多视角图神经网络的药物组合协同预测算法

药物组合疗法在癌症治疗中具有重要应用价值。通过算法预测药物协同组合,可为生物学研究提供靶向指导,从而提高研究效率,降低实验成本。针对现有算法缺乏有效的药物互相作用建模方法、无法考虑细胞系之间的关系等问题,提出了一种基于多视角图神经网络的药物协同预测算法。首先,采用变分图自编码器来学习特定细胞系药物的向量表示;然后,通过多视角框架整合同一组织内其他细胞系的药物信息,提升药物表示向量的可靠性;最后通过引入已知的药物组合得分作为监督信号对模型进行监督训练,实现可靠的药物协同效果预测。在DrugComb数据集上的实验结果验证了本方法的有效性。     

Determination of Polynomial Degree in the Regression of Drug Combinations

Studies on drug combinations are becoming more and more popular in the past few decades, with the development of computer and algorithms. One of the most common methods in optimizing drug combinations is regression of a polynomial model based on certain number of experimental observations. In this paper, we study how to determine the degree of polynomials in different circumstances of drug combination optimization. Using cross-validation, we have found that in most cases, a high degree results in failures of accurate prediction, named overfitting. An anti-noise test has also revealed that polynomial model with high degree tends to be less resistant to random errors in the observations.      

A microcomputer-assisted analysis of drug resistance in bacteria

A software package for analysis of antimicrobial drug resistance traits has been developed. It is written in PASCAL and implemented on a microcomputer. The microbiological data to be analysed is reduced to the different patterns of drug resistance found and the associated numbers of isolates. Based upon these patterns of drug resistance, the incidence of resistance to individual drugs and combinations is calculated. Furthermore, the extent and nature of the multiple drug resistance within a group of microorganisms is examined by looking at various aspects of the statistical association of drug resistance traits.     

Influence of drug colour on perceived drug effects and efficacy

A drug’s physical characteristics, such as colour, could be factors influencing its therapeutic effects. It is not well understood whether people’s expectations on drug effects and efficacy are affected by colour, especially among Chinese population. This study was conducted to examine people’s expectations on drug effects and efficacy on the basis of drug colour, and to reveal possible gender differences in colour-related drug expectations. Participants (n = 224) were asked to classify seven single-coloured and six two-coloured capsules into one of four categories of drug effects, and to indicate the strength of drug efficacy. It is found that all the coloured capsules yielded non-chance distributions in classifications of drug effects, with six single-coloured and four two-coloured capsules associated with specific drug effects. Colour also conveyed differential strengths of drug efficacy in general and in relation to specific drug effects. There were gender differences in drug expectations for some colours and colour combinations.

Practitioner Summary: Drug colour was found to have impacts on perceived drug effects and efficacy. The findings from the present study can be used by ergonomics practitioners to design appropriate drug colours in support of drug differentiation, therapeutic effects and medication adherence.     

The rating consistency of aesthetic preferences for icon-background color combinations

This study explored the degree of rating consistency for different icon-background color combinations, and identified the color combinations with the highest rating for consistency. Rating consistency is the degree of agreement among subjective aesthetic preferences for a specific color combination. In total, 3306 color combinations were rated in this study. The standard deviation of each color combination was calculated as the performance measure for assessing rating consistency among subjects for each color combination. Analysis of variance (ANOVA) results demonstrated that the gender effect was insignificant and rating consistency was very low for both males and females. Furthermore, the experimental results showed that the color combinations affected rating consistency. Hence, cluster analysis with non-hierarchical procedures was then used to categorize color combinations via the clustering variable, the standard deviation of aesthetic preference ratings. Few color combinations in clusters had an extremely high rating consistency. The results indicated that the degree of rating consistency among subjects varied with color combinations. Therefore, using a single average of user aesthetic preference scores may not be appropriate for application to color combinations. Finally, the 30 color combinations determined experimentally can be used as default color combinations as the color scheme for an interface that can be changed because they have consistently high preference scores.     

Pharmacokinetic modelling of the anti-malarial drug artesunate and its active metabolite dihydroartemisinin

A four compartment mechanistic mathematical model is developed for the pharmacokinetics of the commonly used anti-malarial drug artesunate and its principle metabolite dihydroartemisinin following oral administration of artesunate. The model is structurally unidentifiable unless additional constraints are imposed. Combinations of mechanistically derived constraints are considered to assess their effects on structural identifiability and on model fits. Certain combinations of the constraints give rise to locally or globally identifiable model structures.     

Reprint of “Pharmacokinetic modelling of the anti-malarial drug artesunate and its active metabolite dihydroartemisinin”

A four compartment mechanistic mathematical model is developed for the pharmacokinetics of the commonly used anti-malarial drug artesunate and its principle metabolite dihydroartemisinin following oral administration of artesunate. The model is structurally unidentifiable unless additional constraints are imposed. Combinations of mechanistically derived constraints are considered to assess their effects on structural identifiability and on model fits. Certain combinations of the constraints give rise to locally or globally identifiable model structures.     

A self-tuning Smith predictor applied to the control of blood pressure: simulation studies

Significant time-delays occur in the response of blood pressure to drug therapy. The classical Smith predictor algorithm used to eliminate such delays is extended here to the principle of self-tuning under mis-match conditions. Two delay-free controller algorithms are investigated, being a self-tuning pole-placement and a self-tuning PID structure. Extensive simulation studies using the well-known Slate model have demonstrated the good transient performance obtained with this self-tuning Smith principle in various combinations, and under realistic variability of patient dynamics.     

The impact of color combinations on the legibility of text presented on LCDs

The paper investigates the impact of text and background color combinations on the legibility of text presented on LCDs. The legibility of 56 elementary color combinations was tested by 308 participants. The first task required the participants to identify 21 uppercase alphabetic characters selected and presented in conformance with a Snellen chart in various color combinations. For each color combination, the number of correctly identified characters was recorded as a visual performance measure. In the second task, participants subjectively rated the legibility of color combinations on a ten-point Likert scale. The results show that neither the Le Courier legibility table nor the CRT legibility table is appropriate for LCDs. A new legibility table is proposed for LCDs where the highest ranked are contrastive color combinations with positive polarity whereas for CRT displays, the highest ranked are contrastive color combinations with negative polarity. The findings of this study can be used to determine the best possible color combinations when developing content displayed on LCDs.     

Leave One Out Error, Stability, and Generalization of Voting Combinations of Classifiers

We study the leave-one-out and generalization errors of voting combinations of learning machines. A special case considered is a variant of bagging. We analyze in detail combinations of kernel machines, such as support vector machines, and present theoretical estimates of their leave-one-out error. We also derive novel bounds on the stability of combinations of any classifiers. These bounds can be used to formally show that, for example, bagging increases the stability of unstable learning machines. We report experiments supporting the theoretical findings.     

基于门控图卷积神经网络的有机化学反应预测

随着现代医药技术和计算机技术的发展,采用人工智能技术来加速药物的研发进度成为了研究热点,而对有机化学反应产物的高效预测是药物逆合成路线设计中的关键问题。针对样本数据集中化学反应类型分布不均匀的问题,提出了一种主动采样训练下的门控图卷积神经网络（ASGGCN）模型。首先,输入化学反应物的简化分子线性输入规范（SMILES）编码,通过门控图卷积神经网络（GGCN）以及注意力机制预测反应中心所在位置;然后,根据化学约束条件和候选反应中心枚举出可能的化学键组合来生成候选产物,再通过门控图卷积差分网络对候选产物进行筛选;最终,得到反应产物。门控图卷积神经网络拥有三个权重参数矩阵并通过门控对信息加以融合,与传统的图卷积神经网络相比,它能获取更加丰富的原子隐藏特征信息。通过主动采样的方式进行训练,使得该模型能够兼顾较差样本和普通样本的分析能力。实验结果表明,所提模型对化学反应产物的Top-1预测准确率可达87.2%,对比Weisfeiler-Lehman差分网络（WLDN）模型提高了1.6个百分点,可见模型能够更准确地预测有机化学反应产物。     

Red and photographic infrared linear combinations for monitoring vegetation

In situ collected spectrometer data were used to evaluate and quantify the relationships between various linear combinations of red and photographic infrared radiances and experimental plot biomass, leaf water content, and chlorophyll content. The radiance variables evaluated included the red and photographic infrared (IR) radiance and the linear combinations of the IR/red ratio, the square root of the IR/red ratio, the IR-red difference, the vegetation index, and the transformed vegetation index. In addition, the corresponding green and red linear combinations were evaluated for comparative purposes. Three data sets were used from June, September, and October sampling periods.Regression analysis showed the increased utility of the IR and red linear combinations vis-à-vis the same green and red linear combinations. The red and IR linear combinations had 7% and 14% greater regression significance than the green and red linear combinations for the June and September sampling periods, respectively.The vegetation index, transformed vegetation index, and square root of the IR/red ratio were the most significant, followed closely by the IR/red ratio. Less than a 6% difference separated the highest and lowest of these four ER and red linear combinations. The use of these linear combinations was shown to be sensitive primarily to the green leaf area or green leaf biomass. As such, these linear combinations of the red and photographic IR radiances can be employed to monitor the photosynthetically active biomass of plant canopies.     

Data envelopment analysis for evaluating the efficiency of genetic algorithms on solving the vehicle routing problem with soft time windows

This study proposes an alternative to the conventional empirical analysis approach for evaluating the relative efficiency of distinct combinations of algorithmic operators and/or parameter values of genetic algorithms (GAs) on solving the pickup and delivery vehicle routing problem with soft time windows (PDVRPSTW). Our approach considers each combination as a decision-making unit (DMU) and adopts data envelopment analysis (DEA) to determine the relative and cross efficiencies of each combination of GA operators and parameter values on solving the PDVRPSTW. To demonstrate the applicability and advantage of this approach, we implemented a number of combinations of GA’s three main algorithmic operators, namely selection, crossover and mutation, and employed DEA to evaluate and rank the relative efficiencies of these combinations. The numerical results show that DEA is well suited for determining the efficient combinations of GA operators. Among the combinations under consideration, the combinations using tournament selection and simple crossover are generally more efficient. The proposed approach can be adopted to evaluate the relative efficiency of other meta-heuristics, so it also contributes to the algorithm development and evaluation for solving combinatorial optimization problems from the operational research perspective.     

如何在VB中使用钩子技术拦截键盘消息

阐述了如何在VB中使用钩子技术拦截键盘消息。在VB的应用程序中大部分的键盘输入消息可以很容易地通过窗体或控件的KeyDown事件来处理。本文所阐述的这种方法让我们可以处理另外一些难以获得的组合键，例如TAB键或其他组合的快捷键。     

Molecular modeling of modified peptides, potent inhibitors of the xWNT8 and hWNT8 proteins

Signaling pathways of Wnt-proteins and Fzd-receptors play important role in processes of growth and development of stem cells and in many types of cancers. The binding of the Wnt-proteins and Fzd-receptors is a complicated process, in which 19 Wnt-proteins and 10 Fzd-receptors are involved. Such a large number of combinations of Wnt-Fzd pairs leads to many different influences of Fzd-Wnt-complexes on the development and differentiation of stem cells. The molecular models of xWnt8, hWnt8, mFzd8, hFzd8-proteins and their complexes were constructed and studied in the present work. The amino acids of the binding sites of proteins which participate in these complexes formation and the protein-protein interactions were studied. The pharmacophoric model of the binding site on the xWnt8 and hWnt8-proteins was constructed. In this work we suggested the peptidomimetic ligands, which can be used for the inhibition of the xWnt8-mFzd8 and hWnt8-hFzd8 proteins formation. The de novo design method of Allegrow software was used for the predictions of most prospective functional groups of the peptidomimetic ligands. These ligands can be used as inhibitors of xWnt8-mFzd8 and hWnt8-hFzd8 complex formation and also can be used for drug design by other methods.     

Combining and automating classical and non-classical logics in classical higher-order logics

Numerous classical and non-classical logics can be elegantly embedded in Church??s simple type theory, also known as classical higher-order logic. Examples include propositional and quantified multimodal logics, intuitionistic logics, logics for security, and logics for spatial reasoning. Furthermore, simple type theory is sufficiently expressive to model combinations of embedded logics and it has a well understood semantics. Off-the-shelf reasoning systems for simple type theory exist that can be uniformly employed for reasoning within and about embedded logics and logics combinations. In this article we focus on combinations of (quantified) epistemic and doxastic logics and study their application for modeling and automating the reasoning of rational agents. We present illustrating example problems and report on experiments with off-the-shelf higher-order automated theorem provers.     

The Impact of Search Heuristics on Heavy-Tailed Behaviour

The heavy-tailed phenomenon that characterises the runtime distributions of backtrack search procedures has received considerable attention over the past few years. Some have conjectured that heavy-tailed behaviour is largely due to the characteristics of the algorithm used. Others have conjectured that problem structure is a significant contributor. In this paper we attempt to explore the former hypothesis, namely we study how variable and value ordering heuristics impact the heavy-tailedness of runtime distributions of backtrack search procedures. We demonstrate that heavy-tailed behaviour can be eliminated from particular classes of random problems by carefully selecting the search heuristics, even when using chronological backtrack search. We also show that combinations of good search heuristics can eliminate heavy tails from quasigroups with holes of order 10 and 20, and give some insights into why this is the case. These results motivate a more detailed analysis of the effects that variable and value orderings can have on heavy-tailedness. We show how combinations of variable and value ordering heuristics can result in a runtime distribution being inherently heavy-tailed. Specifically, we show that even if we were to use an oracle to refute insoluble subtrees optimally, for some combinations of heuristics we would still observe heavy-tailed behaviour. Finally, we study the distributions of refutation sizes found using different combinations of heuristics and gain some further insights into what characteristics tend to give rise to heavy-tailed behaviour.     

Combinations of weak classifiers

To obtain classification systems with both good generalization performance and efficiency in space and time, we propose a learning method based on combinations of weak classifiers, where weak classifiers are linear classifiers (perceptrons) which can do a little better than making random guesses. A randomized algorithm is proposed to find the weak classifiers. They are then combined through a majority vote. As demonstrated through systematic experiments, the method developed is able to obtain combinations of weak classifiers with good generalization performance and a fast training time on a variety of test problems and real applications. Theoretical analysis on one of the test problems investigated in our experiments provides insights on when and why the proposed method works. In particular, when the strength of weak classifiers is properly chosen, combinations of weak classifiers can achieve a good generalization performance with polynomial space- and time-complexity.     

A superposable double-gradient droplet array chip for preparation of PEGDA microspheres containing concentration-gradient drugs

This article describes a superposable double-concentration-gradient droplet array chip, which allows a variety of concentration combinations of two components to be formed simultaneously. The concentration gradients generated from two layers of the chip could be arbitrarily superimposed by adjusting the center angle between the two bonding layers. With the aqueous phase flow rate of 1.0 μL min^?1 and the oil phase flow rate of 30.0 μL min^?1, the droplets about 58 μm in diameter were produced, and the coefficients of variation were below 6.0% for single channel and 5.7% for all the channels. Using a dual-32-channel superposable gradient droplet array chip, poly(ethylene glycol) diacrylate (PEGDA) microspheres containing concentration-gradient combinations of rhodamine B and fluorescein were fabricated to demonstrate the capability of PEGDA for encapsulating hydrophilic and hydrophobic substances, as well as the proper concentration-gradient distribution. Furthermore, PEGDA microspheres loaded with two anticancer drugs, hydrophilic doxorubicin hydrochloride and hydrophobic paclitaxel, of 17 concentration combinations were simultaneously prepared. The drug-induced apoptosis of human uterine cervix cancer cells was investigated using the dual-drug-loaded PEGDA microspheres. The optimum synergistic concentration combination of the two drugs was 12.5 μg mL^?1 for doxorubicin hydrochloride and 43.75 μg mL^?1 for paclitaxel according to the preliminary screening. The superposable double-gradient droplet array generator was demonstrated to be a promising platform for screening multiple drug combination in microcarriers.