回声定位蝙蝠及其声通讯

综述了回声定位蝙蝠种类及其发声方式,回声定位信号的主要类型及回声定位信号声学特征,多普勒频移对长CF／FM蝙蝠的主要作用,简介了蝙蝠求偶和母婴识别等内声通讯行为,提出了一些尚待解决的重要问题。     

蝙蝠回声定位声波的可塑性及其生态适应

蝙蝠通过调节回声定位声波特征来满足自身的感官需求,表现出回声定位声波的可塑性及其对生态环境与需求的适应。声波频率、强度、脉冲持续时间和间隔时间等特征与蝙蝠所处的生态位密切相关,声波可塑性在蝙蝠进化过程中起着至关重要的作用。本文结合马铁菊头蝠(Rhinolophus ferrumequinum)和大趾鼠耳蝠(Myotis macrodactylus)回声定位声波可塑性的研究,从回声定位声波的方向性、目标距离、环境复杂度和应对干扰4个方面总结了蝙蝠如何通过改变回声定位声波特征来满足自身在导航和捕捉猎物过程中的感官需求与生态适应,并阐述了回声定位声波可塑性的研究现状,为开展蝙蝠声学和行为学研究提供参考。     

菲菊头蝠回声定位声波频率的性二态有利于性别识别

许多动物的叫声频率呈现性二态现象。蝙蝠夜间活动,主要利用声音信号导航空间、追踪猎物、传递交流信息。本研究选择成体菲菊头蝠作为研究对象,检验回声定位声波频率性二态是否有利于性别识别。研究发现,菲菊头蝠回声定位声波频率参数具有显著性别差异。播放白噪音、雄性回声定位声波及雌性回声定位声波期间,实验个体的反应叫声数量依次递减。播放白噪音、雌性回声定位声波及雄性回声定位声波后,实验个体的反应叫声数量依次递增。白噪音诱导反应叫声强度高于回声定位声波诱导反应叫声强度。研究结果表明,菲菊头蝠回声定位声波的频率参数编码发声者性别信息,有利于种群内部的性别识别。本研究暗示,回声定位声波可能在蝙蝠配偶选择中扮演一定作用。     

马铁菊头蝠对同种咀嚼声的反应研究

咀嚼是哺乳动物食物吸收的重要组成部分,对动物的生存和繁殖极其重要。动物咀嚼时会发出低频低强度的咀嚼声,研究表明人类的咀嚼声可以增强自身或他人的食欲和愉悦度。蝙蝠作为哺乳动物中的第二大类群,其咀嚼声的特征和功能仍不清楚。本研究以吉林省集安市治安村的马铁菊头蝠Rhinolophus ferrumequinum为研究对象,通过回放蝙蝠咀嚼声和空白对照实验,同步录制蝙蝠的进食行为和回声定位声波,试图阐明蝙蝠咀嚼声对其进食行为和回声定位声波的影响。结果表明:进食次数在2种回放条件下的差异无统计学意义,但捕食尝试在2种回放条件下的差异有统计学意义。因此,咀嚼声能够显著地提高蝙蝠的进食欲望。此外,在蝙蝠咀嚼声的刺激下,其回声定位声波的频率增加,持续时间延长,脉冲速率减慢。这可能是因为蝙蝠在咀嚼声的影响下改变发声动机,从而改变了其回声定位声波的频谱时间结构。本研究第一次报道了蝙蝠咀嚼声对其进食行为的影响,为进一步阐明蝙蝠咀嚼声的功能和进化具有重要意义。     

吉林省发现绯鼠耳蝠

在吉林省集安市采到绯鼠耳蝠 (Myotisformosus)样本 5只 ,为吉林省蝙蝠科新记录。对其体型与头骨进行了测量。对回声定位声波进行了录制和分析 ,发现其回声定位声波为FM型 ,一次完整声波包括1～ 2个谐波 ,主频率 (5 4 5 4± 6 3 9)kHz,通过回声定位声波特征推测 ,绯鼠耳蝠在较简单的环境中捕食中等大小的昆虫。     

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。     

回声定位蝙蝠耳蜗毛细胞静纤毛的长度特征

描电子显微镜下观察爪哇大足鼠耳蝠(Myotis adversus)、白腹管鼻蝠(Murina leucogaster)、山蝠(Nyctalus plancyi)和马铁菊头蝠(Rhinolophus ferrumequinum)耳蜗毛细胞静纤毛, 测量其长度, 并与五种非回声定位哺乳动物耳蜗静纤毛长度进行比较. 研究发现: 回声定位蝙蝠耳蜗外毛细胞静纤毛较非回声定位哺乳动物相应位置的短; 回声定位蝙蝠耳蜗内毛细胞静纤毛长于其外毛细胞静纤毛, 而非回声定位哺乳动物内毛细胞静纤毛长度并无此规律. 我们认为, 回声定位蝙蝠耳蜗听毛细胞静纤毛长度的特点可能是对高频声波和回声定位适应的结果.     

综述与专论: 蝙蝠，听觉和回声定位研究的模型动物

科学家以蝙蝠为模式动物,从听觉、回声定位和生态适应与演化等方面开展了研究,取得了令人瞩目的成果。为适应回声定位,蝙蝠听觉系统的结构和功能产生了明显的特化。从外周到中枢形成了对声频率极为有序的表征,甚至在恒频-调频（constant frequency-frequency modulation,CF-FM）蝙蝠耳蜗形成了所谓的听觉凹,以及听皮质功能组构也模块化,成为了具有代表性的特化象征。神经元反应的潜伏期对蝙蝠不仅是基本特性,也是回声定位行为调控的一部分;研究发现,有较长潜伏期的神经元具有较尖锐的回声-延迟调谐特性,而较短潜伏期的神经元则有较宽的回声-延迟调谐特性。蝙蝠听神经元对频率调谐的精准度亦远胜于人类和其他非回声定位动物;而且,源于耳蜗听觉凹的传入在各级听中枢均显示出对回声定位信号第二谐波CF成分的过度表征,以满足对靶物回声多普勒频移探测的需要。时程是回声定位蝙蝠发声信号主动改变的参数之一,而时程调谐神经元则提供了一种编码声音时相特征的重要神经机制,匹配了对回声定位信号时相信息加工的需要。在多种回声定位蝙蝠的听中枢还发现,有回声-延迟调谐神经元,它们不仅能对靶物距离进行调谐,而且...     

吉林省新纪录东方蝙蝠Vespertilio sinensis (Peters, 1880)的回声定位声波特征与分析

通过对吉林省长春市采集到的11只蝙蝠标本的外形、头骨、牙齿和阴茎骨进行测量与对照,鉴定为东方蝙蝠(Vespertilio sinensis),是吉林省翼手目新纪录.用实时录音的超声波仪录制其正常飞行状态下的回声定位声波.结果表明,东方蝙蝠发出短的、宽带的、多谐波的陡坡调频型回声定位声波,能量主要集中在第1谐波.起始频率为83.66±2.08 kHz,峰频为34.54±0.88 kHz,终止频率为24.78±0.41 kHz,带宽为58.84±2.10 kHz,声脉冲持续时间和声脉冲间隔分别为2.63±0.27 ms和61.67±7.5 ms.     

蝙蝠与超声波、回声定位(1)

回声定位是高度演化、极为复杂的过程,使蝙蝠可利用大多数动物不能利用的生态位——漆黑的洞穴和黑夜的天空。对蝙蝠的回声定位研究已有近80年的历史,科学家已经从生物声学层面基本了解和认识了蝙蝠回声定位的特征、机制、生物学意义等,关于分子和神经生物学方面的机制也得到深入研究。重点介绍蝙蝠回声定位的研究历史,以及蝙蝠的超声波和回声定位在生物学和声学层面的基础知识。     

An optimum algorithm in pathological voice quality assessment using wavelet-packet-based features,linear discriminant analysis and support vector machine

Voice impairments, attention to increased unhealthy social behavior and voice abuse, have been increasing dramatically. Therefore, diagnosis of voice diseases has an important role in the opportune treatment of pathologic voices. This paper presents an extensive study in identification of different voice disorders which their origin is in the vocal folds. Firstly, a qualitative study is applied based on short-time Fourier transform (STFT) and continuous wavelet transform (CWT) in order to investigate their aptitude in the presentation of discriminative features to identify disordered voices from normal ones. Therefore, wavelet packet transform (WPT) for their ability to analyze scrutinizingly a signal at several levels of resolution is chosen as strong speech signal parameterization method. The ability of energy and entropy features, obtained from the coefficients in the output nodes of the optimum wavelet packet tree, is investigated. Linear discriminant analysis (LDA) and principal component analysis (PCA) are evaluated as feature dimension reduction methods in order to optimize recognition algorithm. The performance of each structure is evaluated in terms of the accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC). Eventually, entropy features in the sixth level of WPT decomposition along with feature dimension reduction by LDA and a support vector machine-based classification method is the most optimum algorithm that leads to the recognition rate of 100% and AUC of 100%. Proposed system clearly outperforms previous works in both respect of accuracy and reduction of residues; which may lead in full accuracy and high speed diagnosis procedure.     

Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.     

基因表达数据小波降噪与肿瘤识别

建立了基于小波降噪和支持向量机的结肠癌基因表达数据肿瘤识别模型.对试验数据进行小波分解,并利用交叉验证的方法计算试验样本的平均分类准确率,确定小波函数与小波分解层数;引入能量阈值方法对小波分解系数进行阈值处理,达到降噪的目的;提出了基因分类贡献率与主成分分析结合的方法,提取结肠癌样本数据特征;利用支持向量机强大的非线性映射能力,实现对结肠癌样本数据的非线性分类.为了减弱样本集的划分对分类准确率的影响,本文采取Jackknife检验方法对支持向量分类器的分类器检验,其分类准确率为96.77%.试验结果证明了该方法的有效性,该方法对结肠癌的识别具有一定的参考价值.     

Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series

In this paper, a robust algorithm for disease type determination in brain magnetic resonance image (MRI) is presented. The proposed method classifies MRI into normal or one of the seven different diseases. At first two-level two-dimensional discrete wavelet transform (2D DWT) of input image is calculated. Our analysis show that the wavelet coefficients of detail sub-bands can be modeled by generalized autoregressive conditional heteroscedasticity (GARCH) statistical model. The parameters of GARCH model are considered as the primary feature vector. After feature vector normalization, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract the proper features and remove the redundancy from the primary feature vector. Finally, the extracted features are applied to the K-nearest neighbor (KNN) and support vector machine (SVM) classifiers separately to determine the normal image or disease type. Experimental results indicate that the proposed algorithm achieves high classification rate and outperforms recently introduced methods while it needs less number of features for classification.     

Prediction of protein structural classes by Chou's pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis

A prior knowledge of protein structural classes can provide useful information about its overall structure, so it is very important for quick and accurate determination of protein structural class with computation method in protein science. One of the key for computation method is accurate protein sample representation. Here, based on the concept of Chou’s pseudo-amino acid composition (AAC, Chou, Proteins: structure, function, and genetics, 43:246–255, 2001), a novel method of feature extraction that combined continuous wavelet transform (CWT) with principal component analysis (PCA) was introduced for the prediction of protein structural classes. Firstly, the digital signal was obtained by mapping each amino acid according to various physicochemical properties. Secondly, CWT was utilized to extract new feature vector based on wavelet power spectrum (WPS), which contains more abundant information of sequence order in frequency domain and time domain, and PCA was then used to reorganize the feature vector to decrease information redundancy and computational complexity. Finally, a pseudo-amino acid composition feature vector was further formed to represent primary sequence by coupling AAC vector with a set of new feature vector of WPS in an orthogonal space by PCA. As a showcase, the rigorous jackknife cross-validation test was performed on the working datasets. The results indicated that prediction quality has been improved, and the current approach of protein representation may serve as a useful complementary vehicle in classifying other attributes of proteins, such as enzyme family class, subcellular localization, membrane protein types and protein secondary structure, etc.     

A Class-Information-Based Penalized Matrix Decomposition for Identifying Plants Core Genes Responding to Abiotic Stresses

In terms of making genes expression data more interpretable and comprehensible, there exists a significant superiority on sparse methods. Many sparse methods, such as penalized matrix decomposition (PMD) and sparse principal component analysis (SPCA), have been applied to extract plants core genes. Supervised algorithms, especially the support vector machine-recursive feature elimination (SVM-RFE) method, always have good performance in gene selection. In this paper, we draw into class information via the total scatter matrix and put forward a class-information-based penalized matrix decomposition (CIPMD) method to improve the gene identification performance of PMD-based method. Firstly, the total scatter matrix is obtained based on different samples of the gene expression data. Secondly, a new data matrix is constructed by decomposing the total scatter matrix. Thirdly, the new data matrix is decomposed by PMD to obtain the sparse eigensamples. Finally, the core genes are identified according to the nonzero entries in eigensamples. The results on simulation data show that CIPMD method can reach higher identification accuracies than the conventional gene identification methods. Moreover, the results on real gene expression data demonstrate that CIPMD method can identify more core genes closely related to the abiotic stresses than the other methods.     

The Aerodynamic Cost of Head Morphology in Bats: Maybe Not as Bad as It Seems

At first sight, echolocating bats face a difficult trade-off. As flying animals, they would benefit from a streamlined geometric shape to reduce aerodynamic drag and increase flight efficiency. However, as echolocating animals, their pinnae generate the acoustic cues necessary for navigation and foraging. Moreover, species emitting sound through their nostrils often feature elaborate noseleaves that help in focussing the emitted echolocation pulses. Both pinnae and noseleaves reduce the streamlined character of a bat’s morphology. It is generally assumed that by compromising the streamlined charactered of the geometry, the head morphology generates substantial drag, thereby reducing flight efficiency. In contrast, it has also been suggested that the pinnae of bats generate lift forces counteracting the detrimental effect of the increased drag. However, very little data exist on the aerodynamic properties of bat pinnae and noseleaves. In this work, the aerodynamic forces generated by the heads of seven species of bats, including noseleaved bats, are measured by testing detailed 3D models in a wind tunnel. Models of Myotis daubentonii, Macrophyllum macrophyllum, Micronycteris microtis, Eptesicus fuscus, Rhinolophus formosae, Rhinolophus rouxi and Phyllostomus discolor are tested. The results confirm that non-streamlined facial morphologies yield considerable drag forces but also generate substantial lift. The net effect is a slight increase in the lift-to-drag ratio. Therefore, there is no evidence of high aerodynamic costs associated with the morphology of bat heads.     

基于SVR算法的林地土壤氮含量高光谱测定

提出了一种利用高光谱技术进行杉木林土壤全氮测定的新方法。以FieldSpec®3地物光谱仪采集杉木林土壤148份, 随机分成校正集(100份)和检验集(48份)。以不同方法实现了土壤光谱的预处理, 并采用偏最小二乘回归算法(PLS)建立土壤氮含量估测模型对其进行比较分析, 发现小波除噪结合多元散射校正能最有效地消除原始光谱的噪声与背景信息, 此时PLS模型校正集与预测集R²分别为0.891与0.885。为进一步优化模型, 对经小波除噪结合多元散射校正处理后的光谱采用主成分分析法(PCA)降维, 以前4个主成份为输入变量, 采用小二乘支持向量机回归算法(LS-SVR)建立了土壤氮含量估测模型, 其校正集与预测集R²分别提高至0.921与0.917, 具有比PLS算法更高的精度。结果表明:以高光谱技术进行林地土壤氮含量快速监测是可行的, 其中小波去噪结合多元散射校正系光谱预处理的优选方法, 而LS-SVR则是建模的优选方法。     

Identification of butterfly species with a single neural network system

Growing interest in conservation and biodiversity increased the demand for accurate and consistent identification of biological objects, such as insects, at the level of individual or species. Among the identification issues, butterfly identification at the species level has been strongly addressed because it is directly connected to the crop plants for human food and animal feed products. However, so far, the widely-used reliable methods were not suggested due to the complicated butterfly shape. In the present study, we propose a novel approach based on a back-propagation neural network to identify butterfly species. The neural network system was designed as a multi-class pattern classifier to identify seven different species. We used branch length similarity (BLS) entropies calculated from the boundary pixels of a butterfly shape as the input feature to the neural network. We verified the accuracy and efficiency of our method by comparing its performance to that of another single neural network system in which the binary values (0 or 1) of all pixels on an image shape are used as a feature vector. Experimental results showed that our method outperforms the binary image network in both accuracy and efficiency.     

紫外指纹图谱结合化学计量学对黄精基原植物的鉴别

运用紫外光谱技术结合化学计量学,建立快速鉴别不同基原黄精的方法。通过单因素实验确定黄精最佳提取溶剂、时间和用量,制备测试液,采用紫外光谱技术建立3种基原黄精的紫外指纹图谱,光谱数据转化后进行主成分(PCA)和系统聚类分析(HCA)。该方法重现性、精密度、稳定性较好,结果表明不同种类黄精紫外指纹图谱具有指纹特性,3种基原植物黄精紫外光谱图在210 nm、220 nm、280 nm附近差异明显;聚类分析和主成分分析三维投影图反映出不同种类黄精的化学成分积累具有差异,能较好地区分滇黄精(Polygonatum kingianum)、黄精(P. sibiricum)与多花黄精(P. cyrtonema)。紫外光谱结合化学计量学能快速鉴别不同种类黄精,可作为黄精的鉴别和质量控制新方法,为黄精临床应用、资源开发及黄精属植物分类提供辅助方法。