Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray Data for Drug Discovery by Deep Learning and Drug Design Specifications

In this study, we provide a systems biology method to investigate the carcinogenic mechanism of oral squamous cell carcinoma (OSCC) in order to identify some important biomarkers as drug targets. Further, a systematic drug discovery method with a deep neural network (DNN)-based drug–target interaction (DTI) model and drug design specifications is proposed to design a potential multiple-molecule drug for the medical treatment of OSCC before clinical trials. First, we use big database mining to construct the candidate genome-wide genetic and epigenetic network (GWGEN) including a protein–protein interaction network (PPIN) and a gene regulatory network (GRN) for OSCC and non-OSCC. In the next step, real GWGENs are identified for OSCC and non-OSCC by system identification and system order detection methods based on the OSCC and non-OSCC microarray data, respectively. Then, the principal network projection (PNP) method was used to extract core GWGENs of OSCC and non-OSCC from real GWGENs of OSCC and non-OSCC, respectively. Afterward, core signaling pathways were constructed through the annotation of KEGG pathways, and then the carcinogenic mechanism of OSCC was investigated by comparing the core signal pathways and their downstream abnormal cellular functions of OSCC and non-OSCC. Consequently, HES1, TCF, NF-κB and SP1 are identified as significant biomarkers of OSCC. In order to discover multiple molecular drugs for these significant biomarkers (drug targets) of the carcinogenic mechanism of OSCC, we trained a DNN-based drug–target interaction (DTI) model by DTI databases to predict candidate drugs for these significant biomarkers. Finally, drug design specifications such as adequate drug regulation ability, low toxicity and high sensitivity are employed to filter out the appropriate molecular drugs metformin, gefitinib and gallic-acid to combine as a potential multiple-molecule drug for the therapeutic treatment of OSCC.     

分流制雨水管网滞留污染物的估算研究

为了定量研究降雨时分流制雨水管网滞留污染物占总污染物的比例,对某研究区域雨水管网中的滞留污染物定期取样检测,并根据研究区域的基础数据建立SWMM模型。同时,提出了一种雨水管网滞留污染物的计算方法,即以检测到的雨水管网污染物浓度及充满度计算滞留污染物质量。将滞留污染物数据输入SWMM模型中,分析对比有无滞留污染物的管网排放口污染物浓度的变化情况。模拟结果表明,在1年一遇的降雨条件下,雨水系统内的滞留污染物对COD、NH3-N、TP总污染负荷的贡献率分别为43. 30%、92. 75%、58. 83%,雨水管网滞留污染物占雨水管网总污染物的比例较大。     

Highly Stretchable and Sensitive Flexible Strain Sensor Based on Fe NWs/Graphene/PEDOT:PSS with a Porous Structure

With the rapid development of wearable smart electronic products, high-performance wearable flexible strain sensors are urgently needed. In this paper, a flexible strain sensor device with Fe NWs/Graphene/PEDOT:PSS material added under a porous structure was designed and prepared. The effects of adding different sensing materials and a different number of dips with PEDOT:PSS on the device performance were investigated. The experiments show that the flexible strain sensor obtained by using Fe NWs, graphene, and PEDOT:PSS composite is dipped in polyurethane foam once and vacuum dried in turn with a local linearity of 98.8%, and the device was stable up to 3500 times at 80% strain. The high linearity and good stability are based on the three-dimensional network structure of polyurethane foam, combined with the excellent electrical conductivity of Fe NWs, the bridging and passivation effects of graphene, and the stabilization effect of PEDOT:PSS, which force the graphene-coated Fe NWs to adhere to the porous skeleton under the action of PEDOT:PSS to form a stable three-dimensional conductive network. Flexible strain sensor devices can be applied to smart robots and other fields and show broad application prospects in intelligent wearable devices.     

异构型无线传感器网络的生命周期

研究了一种异构型无线传感器网络的能量效率问题。该网络是具有不同初始能量的两类传感器节点以不同的数量均匀地分布在监测区域形成的,它基于簇的方式周期性采集数据传输到网关(sink)节点。本文对簇头节点和成员节点的能量消耗进行了数学建模,并且分析了网络的生命周期与检测区域参数之间的关系。通过理论分析和仿真结果得到了最大网络生命周期的最佳分簇数目以及不同层次节点的能量分配比例,并进一步导出了网络生命周期与簇头能量配置的关系。     

钻孔机器人钻进工况智能感知与自适应控制机理研究

针对煤矿井下钻孔作业场景特点,分别选取了基于煤岩图像特征和基于钻进参数反馈的识别方法。为了提高煤岩界面识别准确性,对前述的2种识别方法采用深度学习模型实现了2种异构信息的耦合识别。耦合识别模型基于神经网络模型构建,以CNN卷积神经网络为基础,扩展得到深度残差神经网络模型,其结构主要由骨干网络、颈部网络、头部网络3个部分构成。通过数据库模型训练,得到最终识别方法。通过在煤矿井下进行现场钻孔试验,验证了识别方法的准确性,其中煤岩界面识别准确率达92%。

     

城市道路视频中小像素目标检测

视频图像中的小像素目标难以检测。针对城市道路视频中的小像素目标,本文提出了一种改进YOLOv3的卷积神经网络Road_Net检测方法。首先,基于改进的YOLOv3,设计了一种新的卷积神经网络Road_Net;其次,针对小像素目标检测更依赖于浅层特征,采用了4个尺度检测方法。最后,结合改进的M-Softer-NMS算法来进一步提高图像中目标的检测精度。为了验证所提出算法的有效性,本文收集并标注了用于城市道路小像素目标物体检测的数据集Road-garbage Dataset,实验结果表明,本文算法能有效地检测出诸如纸屑、石块等在视频中相对于路面的较小像素目标。     

华南风化花岗岩劈拉断裂行为的试验与细观模拟研究

为研究不同风化程度花岗岩在劈拉受载下的破损行为,该文对广东从化地区新鲜、微风化、中风化三种风化程度的花岗岩进行微观特征测量及巴西圆盘试验研究。分别基于考虑空间相关特征的随机模型和数字图像技术两种方法实现对岩样细观结构的表征,结合细观参数反演技术,建立反映岩石细观非均质组构特性的颗粒离散元模型,对花岗岩劈拉试验进行数值仿真试验研究。结果表明,随着风化级别的提高,花岗岩中粘结力较强的长石等矿物向粘土矿物转化,结晶强度降低,微孔隙等软弱结构增多,岩石宏观力学性能不断劣化,劈拉破坏由单一裂纹主导转为多条分叉状裂纹,岩样脆性也逐渐减弱。基于两种方法建立的细观随机力学模型仿真结果均表明岩样的劈拉强度随风化级别的提高逐渐降低,与试验结果变化趋势相符,分析得到不同风化花岗岩的劈拉破裂响应特征与试验结果一致,且从细观层面对花岗岩样受劈拉作用的渐进破裂响应提供了深入认识。     

1300万像素手机镜头设计

为满足高像素手机的要求,本文根据光学成像理论,利用code V软件设计出一种1300万像素手机镜头。为了增加自由度,减小像差,得到更好的像质,本文采用非球面表面进行光学设计。该镜头由五片非球面镜片、一片滤光镜组成。设计得到光圈值2.2,半视场角35°,有效焦距3.6 mm,镜头总长3.6 mm的轻薄型手机镜头。最终中心视场在中间频率处(即223 lp/mm)的MTF值大于0.6,在高频处大于0.2,在0.8视场中频MTF值大于0.4,可见优化完毕后成像效果可满足使用要求。

     

近水平煤层覆岩导水裂隙带高度预计与实测

为了准确预测青龙煤矿11607工作面开采后覆岩导水裂隙带的发育高度,以该矿实际地质开采条件为依据,采用灰色神经网络、经验公式计算方法,对其导水裂隙带高度分别进行了预测,并与现场实测结果进行比较,结果表明,基于灰色神经网络的预测结果比经验公式计算的结果更接近实际,并综合分析比较得出青龙煤矿11607工作面煤层的覆岩导水裂隙带高度为44.4～46.9 m。