Ri质粒转化烟草的影响因素及豌豆凝集素基因在烟草发根中的表达

对Ri质粒转化烟草的影响因素进行研究,以期获得较为理想的实验系统,用发根农杆菌介导,将豌豆凝集素基因导入烟草,获得转基因的烟草发根,GUS组织化学染色,蛋白免疫原位杂交证明,gus报告基因和豌豆凝集素基因已在转基因烟草发根中得到了转译表达,本结构将为瘤菌能否扩大宿主范围,为研究烟草与豌豆根瘤菌相互作用,结瘤固氮的可能性奠定基础。     

Testosterone and the allocation of reproductive effort in male house finches (Carpodacus mexicanus)

Testosterone has been proposed to serve as the mediator that controls the relative effort that an individual male bird will devote to mating effort versus parental effort. Here, we demonstrate a testosterone-influenced trade-off between parental and mating efforts in male house finches. Male house finches with experimentally elevated testosterone fed nestlings at a significantly lower rate, but sang at a higher rate than males without manipulated testosterone levels. Females mated to testosterone-implanted males fed nestlings at a significantly higher rate than females mated to males without testosterone implants, resulting in similar feeding rates for both treated and untreated pairs. The effects of testosterone on male house finches, however, were not as dramatic as the effects of testosterone observed in some other socially monogamous species of birds. Because extra-pair copulations are uncommon in house finches and males provide substantial amounts of parental care, these more modest effects may be due to differences in how the allocation of reproductive effort affects the costs and benefits of different reproductive behaviors. Received: 6 June 2000 / Accepted: 17 July 2000     

基于模糊模式识别的系统安全模糊综合评价

针对模糊综合评价的不足之处,提出把权广义距离之和模糊模式识别运用到模糊综合评价当中,建立了基于模糊模式识别的模糊综合评价模型.通过对陕西某高层建筑火灾危险评价实例证明,该模型能使系统安全评价结果更准确、合理.     

BP网络在手写体数字识别中的应用

介绍了BP网络在手写体数字识别中的应用，并在神经网络中引入部分连接结构。实验证明，在神经网络中引入部分连接功能可以大大增强网络的灵活性，可以根据不同的应用来调节网络密度，以达到网络的最高性能。目前识别率为94．61％，实验结果表明，BP神经网络可成功地用于手写数字字符的识别问题。     

基于AlphaPose与REBA的手工搬运作业姿势风险评估方法

为有效预防职业性肌肉骨骼疾患(WMSDs),提出基于计算机视觉技术与快速全身评估(REBA)结合的作业姿势评估方法,以手工搬运作业为例,通过人体姿态识别AlphaPose模型检测作业姿势,利用提取的各关节点空间位置信息计算身体各部位关节角度;利用SG滤波法处理身体遮挡、光线等对数据的影响,有效保留数据变化信息的同时消除数据突变;根据身体关节角度范围与危害程度对应关系自动评判作业姿势的风险大小。研究结果表明：作业姿势评估方法可通过采集的视频数据自动评估搬运作业的姿势危险性,降低WMSDs风险,通过分析不同下蹲取物姿势风险,可论证作业姿势评估方法的可行性,为及时改善搬运作业中的不安全动作提供参考。     

Volatile Organic Compounds in the Area of Madrid: A Chemometrical Approach

Multivariate techniques have been applied to the set of dataobtained after one year sampling of volatile organic compounds(VOCs), including volatile aldehydes in the area of Madrid inorder to evaluate the possible correspondence among groups ofanalysed compounds, as far as modelling of different emissionsources in relation to location and season. The measurements were carried out at four sites in Madrid, characteristic for urban and suburban areas. Additionally, as reference, a rural area 100 km far from the city was considered. Results of correlation analysis, factor and cluster analysis are presented.Higher correlations were found between variables related withtraffic emissions. Factor analysis results showed two mainsignificant variables related to anthropogenic and biogenicemissions respectively. In relation to cluster analysis, samples were grouped according to sampling site and seasonal variations.     

Applying deep learning to right whale photo identification

Photo identification is an important tool for estimating abundance and monitoring population trends over time. However, manually matching photographs to known individuals is time-consuming. Motivated by recent developments in image recognition, we hosted a data science challenge on the crowdsourcing platform Kaggle to automate the identification of endangered North Atlantic right whales (Eubalaena glacialis). The winning solution automatically identified individual whales with 87% accuracy with a series of convolutional neural networks to identify the region of interest on an image, rotate, crop, and create standardized photographs of uniform size and orientation and then identify the correct individual whale from these passport-like photographs. Recent advances in deep learning coupled with this fully automated workflow have yielded impressive results and have the potential to revolutionize traditional methods for the collection of data on the abundance and distribution of wild populations. Presenting these results to a broad audience should further bridge the gap between the data science and conservation science communities.     

Separation of dissolved iron from the aqueous system with excess ligand

A new technique for the separation and pre-concentration of dissolved Fe(III) from the ligand-rich aqueous system is proposed. A solid phase extraction (SPE) system with an immobilized macrocyclic material, commonly known as molecular recognition technology (MRT) gel and available commercially, was used. Synthetic Fe(III) solution in aqueous matrices spiked with a 100-fold concentration of EDTA was used. Dissolved iron that was ‘captured’ by the MRT gel was eluted using hydrochloric acid and subsequently determined by graphite furnace atomic absorption spectrometry. The effect of different variables, such as pH, reagent concentration, flow rate and interfering ions, on the recovery of analyte was investigated. Quantitative maximum separation (∼100%) of the dissolved Fe(III) from synthetic aqueous solutions at a natural pH range was observed at a flow rate of 0.2 mL min^-1. The extraction efficiency of the MRT gel is largely unaltered by the coexisting ions commonly found in natural water. When compared with different SPE materials, the separation performance of MRT gel is also much higher.     

In silico prediction of Tetrahymena pyriformis toxicity for diverse industrial chemicals with substructure pattern recognition and machine learning methods

There is an increasing need for the rapid safety assessment of chemicals by both industries and regulatory agencies throughout the world. In silico techniques are practical alternatives in the environmental hazard assessment. It is especially true to address the persistence, bioaccumulative and toxicity potentials of organic chemicals. Tetrahymena pyriformis toxicity is often used as a toxic endpoint. In this study, 1571 diverse unique chemicals were collected from the literature and composed of the largest diverse data set for T. pyriformis toxicity. Classification predictive models of T. pyriformis toxicity were developed by substructure pattern recognition and different machine learning methods, including support vector machine (SVM), C4.5 decision tree, k-nearest neighbors and random forest. The results of a 5-fold cross-validation showed that the SVM method performed better than other algorithms. The overall predictive accuracies of the SVM classification model with radial basis functions kernel was 92.2% for the 5-fold cross-validation and 92.6% for the external validation set, respectively. Furthermore, several representative substructure patterns for characterizing T. pyriformis toxicity were also identified via the information gain analysis methods.     

洁净室空气洁净度评价的模糊模式识别方法

洁净室空气洁净程度的高低属于模糊概念,将各评价样本实测指标矩阵转变为相对隶属度矩阵,同时将各标准模式指标标准值矩阵转变为指标准相对隶属度矩阵,以全体样本对于各标准模式间的加权广义距离平方和最小建立目标函数,经构造拉格朗日函数,建立了洁净室空气洁净度评价的模糊模式识别模型。斗净室洁净程度评价是一个多目标评价问题,提出了确定指标权重的主、客观结合法,即先建立求解各指标权重的迭代模型并求出指标权重的初始值,然后根据评价的经验,采用重要性语气算子调整各指标的相对隶属度,对初始值进行调整,归一化后得到合理的指标权重值,经计算实例验证,本建立的模式识别方法物理意义明确,评价结果符合实际。