Multivariate competing endogenous RNA network characterization for cancer microRNA biomarker discovery:a novel bioinformatics model with application to prostate cancer metastasis

Background:MicroRNAs(miRNAs)are post-transcriptional regulators with potential as biomarkers for cancer management.Datadriven competing endogenous RNA(ceRNA)network modeling is an effective way to decipher the complex interplay between miRNAs and spongers.However,there are currently no general rules for ceRNA network-based biomarker prioritization.Methods and results:In this study,a novel bioinformatics model was developed by integrating gene expression with multivariate miRNA-target data for ceRNA network-based biomarker discovery.Compared with traditional methods,the structural vulnerability in the human long non-coding RNA(lncRNA)–miRNA–messenger RNAs(mRNA)network was comprehensively analyzed,and the single-line regulatory or competing mode among miRNAs,lncRNAs,and mRNAs was characterized and quantified as statistical evidence for miRNA biomarker identification.The application of this model to prostate cancer(PCa)metastasis identified a total of 12 miRNAs as putative biomarkers from the metastatic PCa-specific lncRNA–miRNA–mRNA network and nine of them have been previously reported as biomarkers for PCa metastasis.The receiver operating characteristic curve and cell line qRT-PCR experiments demonstrated the power of miR-26b-5p,miR-130a-3p,and miR-363-3p as novel candidates for predicting PCa metastasis.Moreover,PCa-associated pathways such as prostate cancer signaling,ERK/MAPK signaling,and TGF-βsignaling were significantly enriched by targets of identified miRNAs,indicating the underlying mechanisms of miRNAs in PCa carcinogenesis.Conclusions:A novel ceRNA-based bioinformatics model was proposed and applied to screen candidate miRNA biomarkers for PCa metastasis.Functional validations using human samples and clinical data will be performed for future translational studies on the identified miRNAs.

Image segmentation algorithm based on high-dimension fuzzy character and restrained clustering network

An image segmentation algorithm of the restrained fuzzy Kohonen clustering network （RFKCN） based on high- dimension fuzzy character is proposed. The algorithm includes two steps. The first step is the fuzzification of pixels in which two redundant images are built by fuzzy mean value and fuzzy median value. The second step is to construct a three-dimensional （3-D） feature vector of redundant images and their original images and cluster the feature vector through RFKCN, to realize image seg- mentation. The proposed algorithm fully takes into account not only gray distribution information of pixels, but also relevant information and fuzzy information among neighboring pixels in constructing 3- D character space. Based on the combination of competitiveness, redundancy and complementary of the information, the proposed algorithm improves the accuracy of clustering. Theoretical anal- yses and experimental results demonstrate that the proposed algorithm has a good segmentation performance.

Application of laser scanning for rock mass characterization and discrete fracture network generation in an underground limestone mine

Terrestrial laser scanning(TLS) is a useful technology for rock mass characterization. A laser scanner produces a massive point cloud of a scanned area, such as an exposed rock surface in an underground tunnel,with millimeter precision. The density of the point cloud depends on several parameters from both the TLS operational conditions and the specifications of the project, such as the resolution and the quality of the laser scan, the section of the tunnel, the distance between scanning stations, and the purpose of the scans. One purpose of the scan can be to characterize the rock mass and statistically analyze the discontinuities that compose it for further discontinuous modeling. In these instances, additional data processing and a detailed analysis should be performed on the point cloud to extract the parameters to define a discrete fracture network(DFN) for each discontinuity set. I-site studio is a point cloud processing software that allows users to edit and process laser scans. This software contains a set of geotechnical analysis tools that assist engineers during the structural mapping process, allowing for greater and more representative data regarding the structural information of the rock mass, which may be used for generating DFNs. This paper presents the procedures used during a laser scan for characterizing discontinuities in an underground limestone mine and the results of the scan as applied to the generation of DFNs for further discontinuous modeling.

Integrated Sequential Groundwater Contaminant Source Characterization and Pareto-Optimal Monitoring Network Design Application for a Contaminated Aquifer Site

Accurate and reliable groundwater contaminant source characterization with limited contaminant concentration monitoring measurement data remains a challenging problem. This study presents an illustrative application of developed methodologies to a real-life contaminated aquifer. The source characterization and optimal monitoring network design methodologies are used sequentially for a contaminated aquifer site located in New South Wales, Australia. Performance of the integrated optimal source characterization methodology combining linked simulation-optimization, fractal singularity mapping technique (FSMT) and Pareto optimal solutions is evaluated. This study presents an integrated application of optimal source characterization with spatiotemporal concentration measurement data obtained from sequentially designed monitoring networks. The proposed sequential source characterization and monitoring network design methodology shows efficiency in identifying the unknown source characteristics. The designed monitoring network achieves comparable efficiency and accuracy utilizing much smaller number of monitoring locations as compared to a more ideal scenario where concentration measurements from a very large number of widespread monitoring wells are available. The proposed methodology is potentially useful for efficient characterization of unknown contaminant sources in a complex contaminated aquifer site, where very little initial concentration measurement data are available. The illustrative application of the methodology to a real-life contaminated aquifer site demonstrates the capability and efficiency of the proposed methodology.

SYNTHESIS AND NMR CHARACTERIZATION OF PRECURSORS OF EPOXY NETWORK AS POLYMER HOST FOR SOLID ELECTROLYTE

To raise the room temperature ionic conductivity and improve the mechanical strength of a PEO-based polymer electrolyte, a noncrystalline two-component epoxy electrolyte system has been prepared. The diglycidyl ether of polyethylene glycols as precursors of the system were synthesized by a two-step process. The presumed structure of the product was characterized, by ^(13)C, ~1H NMR and IR spectroscopy. It was found that a side-reaction occurred between the secondary hydroxyl group of PEG-chlorohydrin and epichlorohydrin in some degree, resulting in a by- product containing—CH_2Cl side group. By selecting a characteristic signal, which is undistorted by the increase in the length of CH_2 CH_2—O segment, a ~1H NMR approach of determining the equivalent epoxy weight (EEW) was proposed. The method is valid to specimens even though the EEW is as high as 2,000. The examination of the specimens by DSC showed that epoxidation greatly depressed the crystallinity of the PEG's, whereas the T_g was raised.

Performance-based fractal fracture model for complex fracture network simulation

The paper presents a novel hydraulic fracturing model for the characterization and simulation of the complex fracture network in shale gas reservoirs. We go beyond the existing method that uses planar or orthogonal conjugate fractures for representing the ＇＇complexity＇＇ of the network. Bifurcation of fractures is performed utilizing the Lindenmayer system based on fractal geometry to describe the fracture propagation pattern, density and network connectivity. Four controlling parameters are proposed to describe the details of complex fractures and stimulated reservoir volume（SRV）. The results show that due to the multilevel feature of fractal fractures, the model could provide a simple method for contributing reservoir volume calibration. The primary-and second-stage fracture networks across the overall SRV are the main contributions to the production, while the induced fracture network just contributes another 20% in the late producing period. We also conduct simulation with respect to different refracturing cases and find that increasing the complexity of the fracture network provides better performance than only enhancing the fracture conductivity.

Neonatal Transfer Situation Following Implementation of a Perinatal Network: An Analysis in Douala, Cameroon

Background: Postnatal transfer (PT) is interhospital transport of care-needing newborns. In 2016, a perinatal network was implemented to facilitate PT in the town of Douala, Cameroon. The network was supposed to improve PT-related care standards. This study aimed at determining characteristics of PT five years following the implementation of this network. Methods: A cross-sectional study was carried out from February to May 2021 at neonatology wards of six hospitals in Douala. Medical records of newborns transferred to the hospitals were scrutinized to document their characteristics. Parents were contacted to obtain information on PT route and itinerary. Data were analyzed using Epi Info software and summarized as percentages, mean and odds ratio. Results: In total, 234 of the 1159 newborns admitted were transferred, giving a PT prevalence of 20.2% (95% CI 17.9% - 22.6%). Male-to-female ratio of the transferred newborns was 1.3. Neonatal infection (26.5%), prematurity (23.5%) and respiratory distress (15.4%) were the main reasons for transfer. Only 3% of the PT was medicalized while only 2% of the newborns were transferred through perinatal network. On admission, hypothermia and respiratory distress were found in 31% and 35% of the newborns, respectively. The mortality rate among babies was 20% and these had a two-fold risk of dying (95% CI 1.58 - 3.44, p Conclusion: PT and the perinatal network are lowly organized and implemented in Douala. Sensitization of medical staff on in utero transfer, creating center for coordination of the network, and implementation of neonatal transport system could improve the quality of PT.

基于改进PSO-Elman的液晶显示器颜色特性化

液晶显示器颜色特性化可以实现同一幅图像在不同设备上的准确显示。为解决液晶显示器颜色特性化存在模型建立复杂、模型鲁棒性差导致特性化精度较低的问题,提出基于改进PSO-Elman神经网络的方法建立RGB颜色空间到CIEXYZ颜色空间的转换模型(ACOPSO-Elman)。首先根据粒子种群规模和粒子位置关系构造惯性权重与学习因子的自适应调节函数提高PSO算法的全局寻优能力和收敛速度,并在寻优过程中添加混沌优化(CO),防止粒子陷入局部最优解,将改进的粒子群算法用于Elman模型参数寻优,解决了Elman模型参数较难选取的问题。通过仿真验证并与BP、Elman神经网络模型比较表明,ACOPSO-Elman模型特性化的平均色差为1.9247ΔE^(*)_(ab),最大色差为5.1252ΔE^(*)_(ab),在特性化精度上取得了较好的效果。

基于多元线性回归模型的生物质与烟煤混燃灰熔融特征温度预测

生物质中钾、钠等碱金属含量高,极易导致生物质与煤混燃灰沾污结渣严重。灰熔融特征温度是用于表征积灰结渣倾向的重要指标。该研究在充分考虑灰中酸/碱性组分对熔融特征温度影响的基础上,以生物质与烟煤混燃灰中Al_(2)O_(3)、SiO_(2)、P_(2)O_(5)、SO_(3)、K_(2)O、CaO、Fe2O_(3)7种氧化物作为变量,利用Matlab软件建立了基于多元线性回归模型的生物质与烟煤混燃灰熔融特征温度预测模型,并以中国农村典型的玉米秸秆和神木烟煤为试样,测定其在不同掺混比例、不同温度和不同停留时间下的灰分组成及熔融特征温度。试验结果表明:随着混合燃料中玉米秸秆的质量分数由25%增至75%时,灰样中MgO、K_(2)O、CaO、Na2O等碱性氧化物的含量增加,特别是对于K_(2)O而言,其质量分数由5.89%升至14.41%,而Al_(2)O_(3)、P_(2)O_(5)和SO_(3)等酸性氧化物的含量逐渐减少,其中Al_(2)O_(3)的质量分数由12.05%降至7.78%,P_(2)O_(5)的质量分数由3.66%降至1.07%,SO_(3)的含量由7.70%降至1.48%。随着灰化温度升高和停留时间延长,Cl元素的含量明显减少。将该模型的预测结果与经验公式预测结果及试验结果对比,发现该预测模型中P_(2)O_(5)、SO_(3)对灰熔融特征温度的影响系数较大,这与试验结果基本相符。灰熔融特征温度与Al_(2)O_(3)、SiO_(2)、P_(2)O_(5)、CaO等氧化物含量呈正相关,表明这些氧化物成分有助于抑制熔融结渣。利用试验测量及经验公式等方法对该预测模型的结果进行检验,利用该模型预测的熔融特征温度与试验值的误差在5%以内,验证了该模型的准确性和可靠性。该模型研究结果可为准确预测生物质与烟煤混燃灰熔融特征温度以及防治锅炉积灰结渣提供参考。

基于孪生神经网络的楔形环连接结构预紧状态辨识

楔形环连接结构由于其连接简单可靠、同时兼具节省空间及减重的优势,常被应用于鱼雷、航天飞行器等武器装备。针对楔形环连接结构预紧状态辨识方面存在的机理模型复杂、样本量小且类别不平衡的问题,提出了一种基于孪生神经网络模型的预紧状态辨识方法。为提高模型训练效率和效果,首先利用时频处理技术进行孪生神经网络模型特征增强,基于增强特征建立了3层孪生神经网络分类模型,实现楔形环预紧状态宏观分类。同时,为指导楔形环精密装配,通过特征可视化技术,深入分析了孪生神经网络训练过程特征聚类效果,并基于二维特征建立了预紧状态定量表征模型,引入目标状态聚类中心与接受域参量,用于实现楔形环连接结构预紧状态定量评估。通过试验验证了所提方法的有效性,该方法可为楔形环连接结构定量辨识提供新的技术途径和思路,具有一定工程应用价值。

Color Reproduction on CRT Displays via BP Neural Networks Under Office Environment

A CRT characterization method based on color appearance matching is presented. A matching between Munsell color chips and CRT charts was obtained in vision perceiver in typical office environment and viewing condition by recommending. And neural networks were utilized to accomplish the color space conversion from CIE standard color space to CRT device color space. The neural networks related the color space conversion and color reproduction of soft/hard-copy directly to the influence of the illuminance and viewing condition in vision perceiver. The average color difference of training samples is 3.06 and that of testing samples is 5.17. The experiment results indicated that the neural networks can satisfy the requirements for the color appearance of hard-copy reproduction in CRT.

基于多孔卷积神经网络的图像空间结构信息细节表征

针对传统图像空间结构信息表征方法存在细节表征模糊度较高、信息训练损失较高等问题,提出一种新的基于多孔卷积神经网络的图像空间结构信息细节表征方法。该方法通过图像空间结构信息细节相似性度量,并以图像的形状、颜色和纹理特征对图像空间结构信息细节进行编码,再去除图像冗余信息,利用多孔卷积神经网络对图像空间结构的深度信息进行融合,从而完成图像空间结构信息的细节表征。实验结果表明,基于多孔卷积神经网络的图像空间结构信息细节表征方法在模糊度、训练损失、图像相似性等方面都比传统的3种方法优越,能够清晰地表征图像空间结构信息。

多情景震后城市应急医疗救援网络结构特征研究

城市应急医疗服务设施系统在震时发挥重要的医疗救援作用,是驱动城市防灾韧性的重要因素。为了保障强震后的城市应急医疗救援,强化网络关键节点、优化网络空间布局。基于建筑物、应急医疗设施和道路网络等城市系统的模拟仿真,构建不同地震灾害强度下城市应急医疗救援的供需场景。该文采用复杂网络技术构建城市应急医疗救援网络模型,分析多情景震后网络社团结构与特征参数。以某城市应急医疗服务设施系统为例进行实例分析,表征多情景地震灾害对应急医疗救援网络结构的具体影响,为优化城市应急医疗救援网络的结构鲁棒性和网络抗毁性提供决策支撑。

海水二次循环冷却设备腐蚀故障在线诊断研究

为了在短时间内完成对二次循环设备腐蚀故障的精准诊断,提出海水二次循环冷却设备腐蚀故障在线诊断方法。根据冷却设备循环机组的工作参数,构建腐蚀故障出现前的设备正常运行状态函数。结合腐蚀故障产生因素及环境条件设置约束条件,确定冷却设备腐蚀故障位置影响因子。计算每个故障位置的判定系数,以在线确定故障位置。横向对比冷却设备管道中不同位置节点力学数据,并校验分析故障位置判定系数,以获取冷却设备腐蚀故障位置判定系数指标量。创新性地最大化短期记忆网络的池化层,并依据故障位置判定系数及指标量,求解腐蚀故障实际触发值和整定值,以实现设备腐蚀故障的在线诊断。测试结果表明,所提方法的诊断准确率均在97%以上。该方法具有见效快、用时短、诊断准、效果稳的特点,能够应对当前大部分海水二次循环冷却设备腐蚀故障的在线诊断任务。

基于EfficientNet网络的水声通信信号调制识别

提出一种基于EfficientNet网络的复杂水声信道下非合作水声通信信号调制识别方法。与传统的深度学习网络相比,EfficientNet网络具有更高的效率和更小的模型大小。该方法通过对2FSK、4FSK、BPSK、QPSK、DSSS-BPSK和OFDM水声通信信号的时频特征进行分析,将二维时频图作为EfficientNet网络的训练集和测试集,使用训练集训练EfficientNet网络模型,并使用测试集确定模型的准确性和效率,完成对水声通信信号的调制识别。实验结果表明:在内场实验中,信噪比大于8 dB时,所研究6种信号的调制识别率均在85%以上;通过外场数据测试,信号调制识别率均在80%以上,验证了该方法在减少模型的大小和计算成本的同时,可保证较高的水声通信信号的调制识别准确率。

UPLC-Q-Orbitrap HRMS法结合分子网络分析吴茱萸汤的化学成分

采用UPLC-Q-Orbitrap HRMS技术结合分子网络,全面快速地分析和鉴定吴茱萸汤中的化学成分。使用Waters Acquity UPLC HSS T3型色谱柱(150 mm×2.1 mm,1.8μm)进行色谱分离,离子源为HESI,在正、负离子模式下进行数据采集。随后,将质谱数据导入GNPS平台生成分子网络对化合物进行分类,再将质谱数据导入Compound Discover 3.2软件进一步对化合物进行鉴定。最终在吴茱萸汤中共鉴定出100个化合物,其中以黄酮类、生物碱类、有机酸类、萜类、糖类化合物为主,还有少量的苯丙素类、苯酚类、苷类化合物。其分析方法和结果对中药复杂体系的成分表征具有借鉴价值。

燃料电池预测模型输出结果统计分析

基于神经网络算法建立的燃料电池寿命预测模型,输出结果都因随机性存在不确定的问题,即每次预测的输出结果都不同。针对此问题,基于长短时记忆(LSTM)神经网络算法建立燃料电池寿命预测模型,多次运行试验样本数据,利用统计学方法对输出结果的分布规律进行统计特性分析,发现基于LSTM神经网络的寿命预测模型,输出结果符合正态分布规律。根据此结论,可采用多次平均结果作为燃料电池寿命预测模型的输出结果,以提升输出结果的预测精度及稳定性。

苯酐合成的反应网络及催化反应机制研究现状与展望

邻苯二甲酸酐(简称苯酐)是合成增塑剂、涂料等高价值精细化学品的重要原料,在工业生产中以邻法苯酐合成工艺为主,萘法合成工艺为辅,其中钒系催化剂因具有高苯酐选择性而备受关注。为了提高苯酐收率、降低床层温度,催化剂在工业应用中已逐步进入多床层、高进料负荷的发展阶段。本文以邻法苯酐为主要研究对象,简要讨论了催化剂的发展历程,重点关注合成工艺路线及催化机制研究进展。工业催化剂易因生成积炭、活性组分流失、TiO_(2)晶相转变等导致其失活,因此也重点探讨了催化剂的失活机制。最后对苯酐工艺的发展提出了展望,为解决当前生产工艺存在的高耗能、高碳排放、催化剂寿命短等挑战,未来苯酐合成应致力于开发新型高效催化剂和绿色反应新工艺,以推动该技术的可持续发展。

云网融合中分布式网络入侵路径跟踪检测方法

随着云计算和网络虚拟化的快速发展,云网融合环境下的分布式网络较普通网络更加复杂和庞大,导致网络入侵路径跟踪的查全率难以得到保障,为此提出云网融合环境下分布式网络入侵路径跟踪检测方法;结合云网融合环境下分布式网络的特性,构建了分布式网络模型,将网络数据流信息转化为统一的格式后,考虑到干扰因素对于网络数据流信息带来的影响,对处理后的信息进行滤波处理,利用网络数据流信息计算云网融合环境下分布式网络的入侵信息特征因子,利用入侵信息特征因子实现对入侵路径的跟踪检测;在测试结果中,网络入侵路径跟踪检测准确性可达99.9%,F_(1)值始终大于0.85,明显优于对照组,具有较高的查全性能。

人工神经网络和电阻抗谱法压电材料快速表征

压电材料作为重要的功能材料,广泛应用于社会的各领域,但其弹性常数的偏差会导致应用过程中出现错误的设计,弹性常数的正确表征对压电器件的正确设计尤为重要。与其他测量方法相比,电阻抗谱仅需要阻抗分析仪即可实现测量,通过测量阻抗谱反演获得压电材料的弹性常数。传统电阻抗谱法通过不断修正材料参数,使得测量阻抗谱和计算阻抗谱最大程度吻合,该过程需要多次迭代,计算量大,耗时较长。该文提出采用神经网络建立阻抗谱到弹性常数的正向模型,测量得到阻抗谱后仅需一次正向计算即可得到弹性常数。使用Comsol和Matlab联合仿真建立数据集,引入丢弃法避免模型过拟合,利用Pytorch建立模型,经过训练后,最大谐振频率偏差从初始2.8%降至0.8%。该技术为压电材料弹性常数精密测量提供可靠的理论与实践途径。