Development of models for prediction of the antioxidant activity of derivatives of natural compounds

Antioxidants are important for maintaining the appropriate balance between oxidizing and reducing species in the body and thus preventing oxidative stress. Many natural compounds are being screened for their possible antioxidant activity. It was found that a mushroom pigment Norbadione A, which is a pulvinic acid derivative, shows an antioxidant activity; the same was found for other pulvinic acid derivatives and structurally related coumarines. Based on the results of in vitro studies performed on these compounds as a part of this study quantitative structure–activity relationship (QSAR) predictive models were constructed using multiple linear regression, counter-propagation artificial neural networks and support vector regression (SVR). The models have been developed in accordance with current QSAR guidelines, including the assessment of the models applicability domains. A new approach for the graphical evaluation of the applicability domain for SVR models is suggested. The developed models show sufficient predictive abilities for the screening of virtual libraries for new potential antioxidants.     

Selective Template Removal by Thermal Depolymerization to Obtain Mesostructured Molybdenum Oxycarbide

The carbon content of mesostructured organic‐inorganic hybrid material of a cylindrical block copolymer template of poly(2‐vinylpyridine)‐block‐poly(allyl methacrylate) (P2VP‐b‐PAMA) and ammonium paramolybdate (APM) could be reduced by thermal depolymerization. By calcination in vacuo at 320 °C the PAMA core can be completely removed while the remaining P2VP brush preserves the mesostructure. The P2VP‐APM composite can then be carburized in‐situ to MoO_xC_y in a second pyrolysis step without any additional carbon source but P2VP. The molybdenum oxycarbide nanotubes obtained, form hierarchically porous non‐woven structures, which were tested as catalyst in the decomposition of NH₃. They proved to be catalytically active at temperatures above 450 °C. The activation energy was estimated from an Arrhenius Plot to be 127 kJ · mol^–1.     

Network regularised Cox regression and multiplex network models to predict disease comorbidities and survival of cancer

In cancer genomics, gene expression levels provide important molecular signatures for all types of cancer, and this could be very useful for predicting the survival of cancer patients. However, the main challenge of gene expression data analysis is high dimensionality, and microarray is characterised by few number of samples with large number of genes. To overcome this problem, a variety of penalised Cox proportional hazard models have been proposed. We introduce a novel network regularised Cox proportional hazard model and a novel multiplex network model to measure the disease comorbidities and to predict survival of the cancer patient. Our methods are applied to analyse seven microarray cancer gene expression datasets: breast cancer, ovarian cancer, lung cancer, liver cancer, renal cancer and osteosarcoma. Firstly, we applied a principal component analysis to reduce the dimensionality of original gene expression data. Secondly, we applied a network regularised Cox regression model on the reduced gene expression datasets. By using normalised mutual information method and multiplex network model, we predict the comorbidities for the liver cancer based on the integration of diverse set of omics and clinical data, and we find the diseasome associations (disease–gene association) among different cancers based on the identified common significant genes. Finally, we evaluated the precision of the approach with respect to the accuracy of survival prediction using ROC curves. We report that colon cancer, liver cancer and renal cancer share the CXCL5 gene, and breast cancer, ovarian cancer and renal cancer share the CCND2 gene. Our methods are useful to predict survival of the patient and disease comorbidities more accurately and helpful for improvement of the care of patients with comorbidity. Software in Matlab and R is available on our GitHub page: https://github.com/ssnhcom/NetworkRegularisedCox.git.     

Synthesis,structure and characterization of two new metal–organic coordination polymers based on the ligand 5‐iodobenzene‐1,3‐dicarboxylate

Two new coordination polymers (CPs) formed from 5‐iodobenzene‐1,3‐dicarboxylic acid (H₂iip) in the presence of the flexible 1,4‐bis(1H‐imidazol‐1‐yl)butane (bimb) auxiliary ligand, namely poly[[μ₂‐1,4‐bis(1H‐imidazol‐1‐yl)butane‐κ²N³:N^3′](μ₃‐5‐iodobenzene‐1,3‐dicarboxylato‐κ⁴O¹,O^1′:O³:O^3′)cobalt(II)], [Co(C₈H₃IO₄)(C₁₀H₁₄N₄)]_n or [Co(iip)(bimb)]_n, (1), and poly[[[μ₂‐1,4‐bis(1H‐imidazol‐1‐yl)butane‐κ²N³:N^3′](μ₂‐5‐iodobenzene‐1,3‐dicarboxylato‐κ²O¹:O³)zinc(II)] trihydrate], {[Zn(C₈H₃IO₄)(C₁₀H₁₄N₄)]·3H₂O}_n or {[Zn(iip)(bimb)]·3H₂O}_n, (2), were synthesized and characterized by FT–IR spectroscopy, thermogravimetric analysis (TGA), solid‐state UV–Vis spectroscopy, single‐crystal X‐ray diffraction analysis and powder X‐ray diffraction analysis (PXRD). The iip²⁻ ligand in (1) adopts the (κ¹,κ¹‐μ₂)(κ¹, κ¹‐μ₁)‐μ₃ coordination mode, linking adjacent secondary building units into a ladder‐like chain. These chains are further connected by the flexible bimb ligand in a trans–trans–trans conformation. As a result, a twofold three‐dimensional interpenetrating α‐Po network is formed. Complex (2) exhibits a two‐dimensional (4,4) topological network architecture in which the iip²⁻ ligand shows the (κ¹)(κ¹)‐μ₂ coordination mode. The solid‐state UV–Vis spectra of (1) and (2) were investigated, together with the fluorescence properties of (2) in the solid state.     

One‐dimensional CuII coordination polymers containing C2h‐symmetric 1,1′:4′,1′′‐terphenyl‐3,3′‐dicarboxylate linkers

Two new one‐dimensional Cu^II coordination polymers (CPs) containing the C_2h‐symmetric terphenyl‐based dicarboxylate linker 1,1′:4′,1′′‐terphenyl‐3,3′‐dicarboxylate (3,3′‐TPDC), namely catena‐poly[[bis(dimethylamine‐κN)copper(II)]‐μ‐1,1′:4′,1′′‐terphenyl‐3,3′‐dicarboxylato‐κ⁴O,O′:O′′:O′′′] monohydrate], {[Cu(C₂₀H₁₂O₄)(C₂H₇N)₂]·H₂O}_n, (I), and catena‐poly[[aquabis(dimethylamine‐κN)copper(II)]‐μ‐1,1′:4′,1′′‐terphenyl‐3,3′‐dicarboxylato‐κ²O³:O^3′] monohydrate], {[Cu(C₂₀H₁₂O₄)(C₂H₇N)₂(H₂O)]·H₂O}_n, (II), were both obtained from two different methods of preparation: one reaction was performed in the presence of 1,4‐diazabicyclo[2.2.2]octane (DABCO) as a potential pillar ligand and the other was carried out in the absence of the DABCO pillar. Both reactions afforded crystals of different colours, i.e. violet plates for (I) and blue needles for (II), both of which were analysed by X‐ray crystallography. The 3,3′‐TPDC bridging ligands coordinate the Cu^II ions in asymmetric chelating modes in (I) and in monodenate binding modes in (II), forming one‐dimensional chains in each case. Both coordination polymers contain two coordinated dimethylamine ligands in mutually trans positions, and there is an additional aqua ligand in (II). The solvent water molecules are involved in hydrogen bonds between the one‐dimensional coordination polymer chains, forming a two‐dimensional network in (I) and a three‐dimensional network in (II).     

基于DCGANs的半片光伏组件电致发光图像增强技术

针对半片光伏组件电致发光（electroluminescence,EL）缺陷自动识别过程中训练用样本不足导致模型过拟合的问题,采用深度卷积生成对抗网络（deep convolutional generative adversarial networks,DCGANs）生成可控制属性的半片光伏组件EL图像,再采用多尺度结构相似性（multiscale structural similarity,MS-SSIM）指标对生成的EL图像与拍摄的EL图像之间的相似程度进行了评估。评估结果得到,使用DCGANs生成的所有类型半片光伏组件的EL图像与拍摄的EL图像的MS-SSIM指标都大于0.55,大部分的MS-SSIM值在0.7附近。在分类模型的训练过程中,测试集准确率随着训练集中生成图像数量的增加而升高,当生成图像数量达到6 000张时,测试集准确率达到97.92%。实验结果表明,采用DCGANs能够生成高质量且可控制属性的半片光伏组件EL图像,较好地解决因缺少训练样本而导致的模型过拟合问题。     

基于PCAU-Net的快速多通道磁共振成像方法

多通道磁共振成像方法采用多个接收线圈同时欠采样k空间以加快成像速度,并基于后处理算法重建图像,但在较高加速因子时,其图像重建质量仍然较差．本文提出了一种基于PCAU-Net的快速多通道磁共振成像方法,将单通道实数U型卷积神经网络拓展到多通道复数卷积神经网络,设计了一种结构不对称的U型网络结构,通过在解码部分减小网络规模以降低模型的复杂度．PCAU-Net网络在跳跃连接前增加了1×1卷积,以实现跨通道信息交互．输入和输出之间利用残差连接为误差的反向传播提供捷径．实验结果表明,使用规则和随机采样模板,在不同加速因子时,相比常规的GRAPPA重建算法和SPIRiT重建方法,本文提出的PCAU-Net方法可高质量重建出磁共振复数图像,并且相比于PCU-Net方法,PCAU-Net减少了模型参数、缩短了训练时间．     

Enhancing network lifespan in wireless sensor networks using deep learning based Graph Neural Network

Inadequate energy of sensors is one of the most significant challenges in the development of a reliable wireless sensor network (WSN) that can withstand the demands of growing WSN applications. Implementing a sleep-wake scheduling scheme while assigning data collection and sensing chores to a dominant group of awake sensors while all other nodes are in a sleep state seems to be a potential way for preserving the energy of these sensor nodes. When the starting energy of the nodes changes from one node to another, this issue becomes more difficult to solve. The notion of a dominant set-in graph has been used in a variety of situations. The search for the smallest dominant set in a big graph might be time-consuming. Specifically, we address two issues: first, identifying the smallest possible dominant set, and second, extending the network lifespan by saving the energy of the sensors. To overcome the first problem, we design and develop a deep learning-based Graph Neural Network (DL-GNN). The GNN training method and back-propagation approach were used to train a GNN consisting of three networks such as transition network, bias network, and output network, to determine the minimal dominant set in the created graph. As a second step, we proposed a hybrid fixed-variant search (HFVS) method that considers minimal dominant sets as input and improves overall network lifespan by swapping nodes of minimal dominating sets. We prepared simulated networks with various network configurations and modeled different WSNs as undirected graphs. To get better convergence, the different values of state vector dimensions of the input vectors are investigated. When the state vector dimension is 3 or 4, minimum dominant set is recognized with high accuracy. The paper also presents comparative analyses between the proposed HFVS algorithm and other existing algorithms for extending network lifespan and discusses the trade-offs that exist between them. Lifespan of wireless sensor network, which is based on the dominant set method, is greatly increased by the techniques we have proposed.     

Delay optimization and energy balancing algorithm for improving network lifetime in fixed wireless sensor networks

Since wireless in terms of energy-restricted processes, dispersion radii, processing power limitations, buffers, bandwidth-limited connections, active network topologies, and network stream of traffic outlines, sensor networks provide difficult design issues. The number of hops and latency are decreased if there is a relay mote because it interacts directly with relay motes that are closer to the destination mote. The tremendous intensive research in the area of Wireless Sensor Networks (WSN) has gained a lot of significance among the technical community and research. The job of WSN is to sense the data using sensor motes, pass on the data to the destination detection mote which is associated with a processing center and can be used in multiple spans of Internet of Things (IoT) applications. Wireless sensor network has a set of sensor motes. By making use of sensor mote placement strategy all the sensor motes are spread in an area with each mote having its own exceptional location. Internet of things applications are delay sensitive those applications have a challenge of forming the complete path at a lower delay constraint. The proposal is to modify the game theory energy balancing algorithm by making use of relay motes so that overall network lifetime is increased. It has been proved that modified GTEB is better with respect to existing algorithms in terms of delay, figure of hops, energy depletion, figure of alive motes, figure of dead motes, lifespan ratio, routing overhead and throughput.