昆虫的鸣声通讯

概述了昆虫产生鸣声的５种机制，并对鸣声在种内的生物学意义作了介绍。     

褐飞虱求偶鸣声中有效声段及其特征

对褐飞虱Nilaparvata lugens (Stal) 雌、雄虫播放求偶鸣声信号的不同声段及其组合,结果表明在它们求偶识别过程中,由多个规则连续的声脉冲所组成的声段是有效的。播放不同声脉冲重复频率和主振频率组合的模拟信号,表明褐飞虱雌、雄虫求偶信号的声脉冲重复频率是它们识别异性的敏感声学特征参数,其敏感范围较窄,分别为70～90 Hz和22 Hz左右;而对主振频率的敏感范围较宽,分别为200 ～1 700 Hz和100～300 Hz。     

褐稻虱求偶鸣声和交尾行为

褐稻虱Nilaparvata lugens(stl)成虫交尾前均发出由固体介质(寄主植株)传播的声信号,用于个体间联系。信号产生于伴随腹部振动的摩擦发声过程,具有种的专一性,能联络、识别同种异性个体,刺激性兴奋和为雄虫寻觅雌虫定向,对求偶和完成交尾具有重要意义。为害水稻的与为害李氏禾属杂草Leersia hexandra(Swartz)的生物型之间,鸣声声学特征存在明显差异,构成型间杂交的某些障碍;而为害水稻的四种生物型之间无显著差异。     

回放雄虫第二种鸣声对稻褐飞虱生殖过程的抑制作用

结果表明：１回放稻褐飞虱雄虫第二种鸣声（ＳＭＶＳ）１～１２ｈ可以显著降低雌雄虫的成功交配率,而回放ＳＭＶＳ２４ｈ则对此无明显影响;２回放ＳＭＶＳ对交配率的影响主要在于对试虫相遇前的求偶过程,表现为雌虫鸣叫率显著下降,对雄虫求偶声（ＦＭＶＳ）的反应延迟;雄虫对雌虫求偶声（ＦＶＳ）的反应程度有所降低;雌雄虫的相遇前期显著延长;３回放ＳＭＶＳ不影响交配试虫的授精率和产卵量,但影响授精的质量,表现在精包明显变小,卵的发育率下降。     

大鹰鹃鸣声的日节律

鸟类的鸣唱具有吸引配偶和保卫领域的功能,多为雀形目雄性鸟类发出,在其婚配、繁殖中起重要作用。非雀形目鸟类缺乏内鸣肌,发出的声音较为单调。但杜鹃科(Cuculidae)等少数非雀形目鸟类,利用鸣叫来吸引异性和宣示领域,功能上类似雀形目鸟类的鸣唱。鸟类在繁殖期面临觅食等基本生理活动与求偶行为的时间权衡。而鸣唱是雄性鸟类在繁殖期耗能较多的求偶行为,鸣唱的活跃程度受到外界环境和鸟类自身习性的影响。通过研究鸟类鸣声的日节律,有助于了解鸟类对生活史策略的响应。本研究于2016年和2017年鸟类繁殖季在北京小龙门森林公园(40°00′N,115°26′E)进行。2016年和2017年在大鹰鹃(Hierococcyx sparverioides)活动区利用录音机(美国Wildlife Acoustics公司,型号SM4)分别录制了3 d和43 d的录音。通过Kaleidoscope Pro 4.0.3软件(美国Wildlife Acoustics公司),量化录音并提取反应大鹰鹃鸣声特征的参数,进而自动识别出录音中大鹰鹃的鸣声。在优化识别条件后,对大鹰鹃鸣声识别的正确率可以达到60.26%,探测率可以达到44.71%。发现大鹰鹃有两个鸣叫的高峰,一个在3:00时,另一个在19:00时。与同域分布的其他鸟类相比,大鹰鹃鸣声的高峰时段持续的时间更长,且具有夜间鸣叫的特点。结合大鹰鹃的生活史,我们对其鸣声日节律进行了讨论。     

褐稻虱求偶鸣声和交尾行为

褐稻虱Nilaparvata lugens(Stal)成虫交尾前均发出由固体介质(寄主植株)传播的声信号,用于个体间联系.信号产生于伴随腹部振动的摩擦发声过程,具有种的专一性,能联络、识别同种异性个体,刺激性兴奋和为雄虫寻觅雌虫定向,对求偶和完成交尾具有重要意义.为害水稻的与为害李氏禾属杂草Leersia hexandra(Swartz)的生物型之间,鸣声声学特征存在明显差异,构成型间杂交的某些障碍;而为害水稻的四种生物型之间无显著差异.     

回放雄虫第二种鸣声对稻褐飞虱生殖过程的抑制作用

结果表明：1. 回放稻褐飞虱雄虫第二种鸣声 (SMVS) 1～12 h可以显著降低雌雄虫的成功交配率,而回放SMVS 24 h则对此无明显影响;2. 回放SMVS对交配率的影响主要在于对试虫相遇前的求偶过程,表现为雌虫鸣叫率显著下降,对雄虫求偶声 (FMVS) 的反应延迟;雄虫对雌虫求偶声 (FVS) 的反应程度有所降低;雌雄虫的相遇前期显著延长;3. 回放SMVS不影响交配试虫的授精率和产卵量,但影响授精的质量, 表现在精包明显变小,卵的发育率下降。     

鸟类鸣声研究

介绍了鸟类呜声采集和分析的方法,呜声的多样性及其形成原因以及鸟类鸣声在个体识别、地理变异、系统分类等生态生物学研究中的应用。     

人工神经网络在蝙蝠回声定位叫声识别方面的应用

近年来,人工神经网络被不断应用于野生动物的声学研究中,本文概括地介绍了人工神经网络的概念以及这项新技术的研究方法,并且重点介绍了它在蝙蝠回声定位叫声识别方面的应用。     

稻褐飞虱雄虫第二种鸣声及其生殖竞争意义

本文研究了稻褐飞虱Nilaparvata lugens雄虫第二种鸣声(SMVS)及其生殖竞争意义,结果如下：1.SMVS是2头或2头以上同种雄虫共栖一处时才产生的特殊信号,不同密度, 不同虫龄雄虫SMVS的呜叫习性不同,虫龄和温度对SMVS有明显影响。2.具SMVS行为的雄虫在求偶、交配系列行为中的优势逐步明显,最终成功交配的SMVS雄虫达70.2%,而非SMVS雄虫仅“14.9%。3.回放SMVS录音可以显著降低稻褐飞虱交配率(下降41.0%)。 SMVS是一种生殖竞争信号,具种的专一性。     

Research on Plant Species Identification Based on Improved Convolutional Neural Network

Plant species recognition is an important research area in image recognition in recent years. However, the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy. Therefore, ShuffleNetV2 was improved by combining the current hot concern mechanism, convolution kernel size adjustment, convolution tailoring, and CSP technology to improve the accuracy and reduce the amount of computation in this study. Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning. The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local optimum. In this paper, a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed, containing 3000 images of 10 plant species on the campus of Tianjin Agricultural University. Finally, the improved model is compared with the baseline version of the model, which achieves better results in terms of improving accuracy and reducing the computational effort. The recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%, and the recognition precision reaches up to 93.6%, which is 5.1% better than the original model and reduces the computational effort by about 31% compared with the original model. In addition, the experimental results were evaluated using metrics such as the confusion matrix, which can meet the requirements of professionals for the accurate identification of plant species.     

人工神经网络的局域性对网络训练速度的影响

从网络结构入手,提出了网络局域性的概念。作为网络结构的一种定量描述,探讨了网络结构与训练速度、预测精度间的对应关系。结果表明网络的训练速度随局域性的增加而增加,网络的预测精神在局域性０．５５附近达到最高,任何偏离都会导致网络预测精度的下降。为在生化过程具体应用中选择合理的神经网络类型提供了理论依据。     

Ecological Risk Assessment of Regions Along the Roadside of the Qinghai-Tibet Highway and Railway Based on an Artificial Neural Network

A concept model of regional risk was constructed for the characteristics of ecosystems alongside the Qinghai-Tibet highway and railway based on the MLP (Multilayer perceptron) model. Seven indices such as snow hazard, drought hazard, and landslide were selected in order to evaluate the integrated ecological risk of the ecosystems along the study area. Results show that the Qaidam montane desert zone had the greatest average risk value (4.26), followed by the Golog-Nagqu high-cold scrub meadow zone (2.80) and the East Qinghai and Qilian montane steppe zone (2.73) among the ecosystems within the six natural zones within the study region. As far as land cover types are concerned, the top three ecological risk values appear in the needle-leaved forest (4.31), desert (4.12), and land without vegetation (3.62), which are higher than those in the other seven types in the study site. Although the risk values are influenced by natural factors and human activities, they are more strongly controlled by natural factors. According to the ecological risk characteristics, the ecosystems within the study area are subdivided into four subregions, including the Qaidam basin region (high risk), the Xidatan to Damxung region (moderate risk), and the Eastern Qinghai-Qilian (slight risk) and Southern Xizang (Tibet) region (slighter risk).     

脑电信号数据压缩及棘波识别的小波神经网络方法

本文在对小波神经网络及其算法研究的基础上,提出了一种对脑电信号压缩表达和痫样脑电棘波识别的新方法。实验结果显示,小波网络在大量压缩数据的同时,能够较好的恢复原有信号,另外,在脑电信号的时频谱等高线图上,得到了易于自动识别的棘波和棘慢复合波特征,说明此方法在电生理信号处理和时频分析方面有着光明的应用前景。     

基于人工神经网络的叶脉信息提取--植物活体机器识别研究I

叶片的识别是识别植物的重要组成部分,特别在野外识别植物活体尤其重要.叶脉的脉序是植物的内在特征,包含有重要的遗传信息.但由于叶脉本身的多样性,利用单一特征的图像处理方法难以有效地提取叶脉.为了充分利用图像的信息,本文提出了一种基于人工神经网络的叶脉提取方法.该方法利用边缘梯度、局部对比度和邻域统计特征等10个参数来描述像素的邻域特征,并将其作为神经网络的输入层.实验结果表明,与传统方法相比,经过训练的神经网络能够更准确地提取叶脉图像,为进一步的叶片识别打下了良好的基础.     

Study of a Bionic Pattern Classifier Based on Olfactory Neural System

Simulating biological olfactory neural system, KIII network, which is a high-dimensional chaotic neural network, is designed in this paper. Different from conventional artificial neural network, the KⅢ network works in its chaotic trajectory. It can simulate not only the output EEG waveform observed in electrophysiological experiments, but also the biological intelligence for pattern classification. The simulation analysis and application to the recognition of handwriting numerals are presented here. The classification performance of the KⅢ network at different noise levels was also investigated.     

Optimizing Artificial Neural Network-Based Models to Predict Rice Blast Epidemics in Korea

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory (LSTM), with diverse input datasets, and compares their performance. The Blast_Weather_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.     

Prediction of Apple Fruit Quality by Soil Nutrient Content and Artificial Neural Network

The effect of soil nutrient content on fruit yield and fruit quality is very important. To explore the effect of soil nutrients on apple quality we investigated 200 fruit samples from 40 orchards in Feng County, Jiangsu Province. Soil mineral elements and fruit quality were measured. The effect of soil nutrient content on fruit quality was analyzed by artificial neural network (ANN) model. The results showed that the prediction accuracy was highest (R²= 0.851, 0.847, 0.885, 0.678 and 0.746) in mass per fruit (MPF), hardness (HB), soluble solids concentrations (SSC), titratable acid concentration (TA) and solid-acid ratio (SSC/TA), respectively. The sensitivity analysis of the prediction model showed that soil available P, K, Ca and Mg contents had the greatest impact on the quality of apple fruit. Response surface method (RSM) was performed to determine the optimum range of the available P, K, Ca, and Mg contents in orchards In Feng County, which were 10∼20 mg⋅kg⁻¹, 170∼200 mg⋅kg⁻¹, 1000∼1500 mg⋅kg⁻¹, and 80∼200 mg⋅kg⁻¹, respectively. The research also concluded that improving the content of available P and available Ca in orchard soil was crucial to improve apple fruit quality in Feng County, Jiangsu Province.     

Artificial Neural Network Method for Predicting the Specificity of GalNAc-transferase

The specificity of GalNAc-transferase is consistent with the existence of an extended site composed of nine subsites, denoted by R₄, R₃, R₂, R₁, R₀, R₁, R₂, R₃, and R₄, where the acceptor at R₀ is either Ser or Thr to which the reducing monosaccharide is anchored. To predict whether a peptide will react with the enzyme to form a Ser- or Thr-conjugated glycopeptide, a neural network method—Kohonen's self-organization model is proposed in this paper. Three hundred five oligopeptides are chosen for the training site, with another 30 oligopeptides for the test set. Because of its high correct prediction rate (26/30=86.7%) and stronger fault-tolerant ability, it is expected that the neural network method can be used as a technique for predicting O-glycosylation and designing effective inhibitors of GalNAc-transferase. It might also be useful for targeting drugs to specific sites in the body and for enzyme replacement therapy for the treatment of genetic disorders.     

基于BP神经网络的siRNA活性预测

RNA干扰(RNAinterference,RNAi)是由双链RNA(dsRNA)引起的基因沉默现象,它通过降解具有同源序列的mRNA来起作用,特殊设计的siRNA能使靶基因发生特异性沉默,起到确定基因功能或沉默致病基因从而治疗疾病的目的。在RNAi技术的应用中,通常采用的是长度为19bp,正、反义链3'端各有2个不配对碱基的双链RNA(siRNA)。但针对靶基因不同位点设计的siRNA作用效果差别很大。影响siRNA效果的因素是多方面的,这些因素的作用又是非线性的。本文在研究影响siRNA作用效果的各种因素的基础上,对已经公开发表的实验数据进行特征提取,作为BP神经网络的训练数据,并将训练好的BP神经网络用于siRNA活性预测。