Liver tissue classification of en face images by fractal dimension‐based support vector machine

Full‐field optical coherence tomography (FF‐OCT) has been reported with its label‐free subcellular imaging performance. To realize quantitive cancer detection, the support vector machine model of classifying normal and cancerous human liver tissue is proposed with en face tomographic images. Twenty samples (10 normal and 10 cancerous) were operated from humans and composed of 285 en face tomographic images. Six histogram features and one proposed fractal dimension parameter that reveal the refractive index inhomogeneities of tissue were extracted and made up the training set. The other different 16 samples (8 normal and 8 cancerous) were imaged (190 images) and employed as the test set with the same features. First, a subcellular‐resolution tomographic image library for four histopathological areas in liver tissue was established. Second, the area under the receiver operating characteristics of 0.9378, 0.9858, 0.9391, 0.9517 for prediction of the cancerous hepatic cell, central vein, fibrosis, and portal vein were measured with the test set. The results indicate that the proposed classifier from FF‐OCT images shows promise as a label‐free assessment of quantified tumor detection, suggesting the fractal dimension‐based classifier could aid clinicians in detecting tumor boundaries for resection in surgery in the future.     

Detecting Nematode Features from Digital Images

Procedures for estimating and calibrating nematode features from digitial images are described and evaluated by illustration and mathematical formulae. Technical problems, such as capturing and cleaning raw images, standardizing the grey level range of images, and the detection of characteristics of the body habitus, presence or absence of stylet knobs, and tail and lip region shape are discussed. This study is the first of a series aimed at developing a set of automated methods to permit more rapid, objective characterizations of nematode features than is achievable by cumbersome conventional methods.     

A brute‐force approach to vegetation classification

Aim: Introduction of a novel approach to the classification of vegetation data (species by plot matrices). This approach copes with a large amount of noise, groups irregularly shaped in attribute space and species turnover within groups. Method: The proposed algorithm (Isopam) is based on the classification of ordination scores from isometric feature mapping. Ordination and classification are repeated in a search for either high overall fidelity of species to groups of sites, or high quantity and quality of indicator species for groups of sites. The classification is performed either as a hierarchical, divisive method or as non‐hierarchical partitioning. In divisive clustering, resulting groups are subdivided until a stopping criterion is met. Isopam was tested on 20 real‐world data sets. The resulting classifications were compared with solutions from eight widely used clustering algorithms. Results: When looking at the significance of species fidelities to groups of sites, and at quantity and quality of indicator species, Isopam often achieved high ranks as compared with other algorithms.     

Clustering high-dimensional data via feature selection

High-dimensional clustering analysis is a challenging problem in statistics and machine learning, with broad applications such as the analysis of microarray data and RNA-seq data. In this paper, we propose a new clustering procedure called spectral clustering with feature selection (SC-FS), where we first obtain an initial estimate of labels via spectral clustering, then select a small fraction of features with the largest R-squared with these labels, that is, the proportion of variation explained by group labels, and conduct clustering again using selected features. Under mild conditions, we prove that the proposed method identifies all informative features with high probability and achieves the minimax optimal clustering error rate for the sparse Gaussian mixture model. Applications of SC-FS to four real-world datasets demonstrate its usefulness in clustering high-dimensional data.     

Odour boosts visual object approach in flies

Multisensory integration is synergistic—input from one sensory modality might modulate the behavioural response to another. Work in flies has shown that a small visual object presented in the periphery elicits innate aversive steering responses in flight, likely representing an approaching threat. Object aversion is switched to approach when paired with a plume of food odour. The ‘open-loop’ design of prior work facilitated the observation of changing valence. How does odour influence visual object responses when an animal has naturally active control over its visual experience? In this study, we use closed-loop feedback conditions, in which a fly''s steering effort is coupled to the angular velocity of the visual stimulus, to confirm that flies steer toward or ‘fixate’ a long vertical stripe on the visual midline. They tend either to steer away from or ‘antifixate’ a small object or to disengage active visual control, which manifests as uncontrolled object ‘spinning’ within this experimental paradigm. Adding a plume of apple cider vinegar decreases the probability of both antifixation and spinning, while increasing the probability of frontal fixation for objects of any size, including a normally typically aversive small object.     

面向鸟鸣声识别任务的深度学习技术

在生态系统中,鸟类是重要的组成部分,对调节生态环境和监测生物多样性至关重要,甚至可以通过监测鸟群动向与监听鸟群异常鸣声对地震、海啸等自然灾害进行辅助预测和防范,为此,鸟鸣声识别和异常鸣声监测成为热门的研究方向。然而,由于传统鸟鸣声识别方法存在特征提取不充分等问题,导致识别率不高。本文采用融合特征的方法结合深度学习技术提取鸟鸣声特征,融合特征选择改良后的对数梅尔谱差分参数同原始信号参数拼接所得的特征;深度学习方法是基于Dense Net121网络结构,并融入自注意力模块与中心损失函数进行鸟鸣声识别。自注意力模块部分提高了关键通道的特征表达能力;中心损失函数可解决类内特征不紧凑问题。我们通过消融实验对比验证,对在Xeno-Canto世界野生鸟类声音公开数据集上选取的10种鸟类声音进行识别,准确率达到96.9%。代码已开源至Github:https://github.com/Carrie X6/-Xeno-Canto-.git。     

Evaluation of local field potential signals in decoding of visual attention

In the field of brain research, attention as one of the main issues in cognitive neuroscience is an important mechanism to be studied. The complicated structure of the brain cannot process all the information it receives at any moment. Attention, in fact, is considered as a possible useful mechanism in which brain concentrates on the processing of important information which is required at any certain moment. The main goal of this study is decoding the location of visual attention from local field potential signals recorded from medial temporal (MT) area of a macaque monkey. To this end, feature extraction and feature selection are applied in both the time and the frequency domains. After applying feature extraction methods such as the short time Fourier transform, continuous wavelet transform (CWT), and wavelet energy (scalogram), feature selection methods are evaluated. Feature selection methods used here are T-test, Entropy, receiver operating characteristic, and Bhattacharyya. Subsequently, different classifiers are utilized in order to decode the location of visual attention. At last, the performances of the employed classifiers are compared. The results show that the maximum information about the visual attention in area MT exists in the low frequency features. Interestingly, low frequency features over all the time-axis and all of the frequency features at the initial time interval in the spectrogram domain contain the most valuable information related to the decoding of spatial attention. In the CWT and scalogram domains, this information exists in the low frequency features at the initial time interval. Furthermore, high performances are obtained for these features in both the time and the frequency domains. Among different employed classifiers, the best achieved performance which is about 84.5 % belongs to the K-nearest neighbor classifier combined with the T-test method for feature selection in the time domain. Additionally, the best achieved result (82.9 %) is related to the spectrogram with the least number of selected features as large as 200 features using the T-test method and SVM classifier in the time−frequency domain.     

Morphology and ontogeny of multiple lateral‐line canals in the rock prickleback,Xiphister mucosus (Cottiformes: Zoarcoidei: Stichaeidae)

The structure and ontogeny of lateral‐line canals in the Rock Prickleback, Xiphister mucosus, were studied using cleared‐and‐stained specimens, and the distribution and morphology of neuromasts within lateral‐line canals were examined using histology. X. mucosus has seven cephalic canals in a pattern that, aside from four branches of the infraorbital canals, is similar to that of most teleostean fishes. Unlike most other teleosts, however, X. mucosus features multiple trunk lateral‐line canals. These include a short median posterior extension of the supratemporal canal and three paired, branching canals located on the dorsolateral, mediolateral, and ventrolateral surfaces. The ventrolateral canal (VLC) includes a loop across the ventral surface of the abdomen. All trunk canals, as well as the branches of the infraorbitals, are supported by small, dermal, ring‐like ossifications that develop independently from scales. Trunk canals develop asynchronously with the mediodorsal and dorsolateral canals (DLC) developing earliest, followed by the VLC, and, finally, by the mediolateral canal (MLC). Only the mediodorsal and DLC connect to the cephalic sensory canals. Fractal analysis shows that the complexity of the trunk lateral‐line canals stabilizes when all trunk canals develop and begin to branch. Histological sections show that neuromasts are present in all cephalic canals and in the DLC and MLC of the trunk. However, no neuromasts were identified in the VLC or its abdominal loop. The VLC cannot, therefore, directly function as a part of the mechanosensory system in X. mucosus. The evolution and functional role of multiple lateral‐line canals are discussed. J. Morphol. 276:1218–1229, 2015. © 2015 Wiley Periodicals, Inc.     

The role of local versus biogeographical processes in influencing diversity and body‐size variation in mammal assemblages

Our objective was to estimate and analyze the body‐size distribution parameters of terrestrial mammal assemblages at different spatial scales, and to determine whether these parameters are controlled by local ecological processes or by larger‐scale ones. Based on 93 local assemblages, plus the complete mammal assemblage from three continents (Africa, North, and South America), we estimated three key distribution parameters (diversity/size slope, skewness, and modal size) and compared the values to those expected if size distributions are mainly controlled by local interactions. Mammal diversity decreased much faster as body size increased than predicted by fractal niche theory, both at continental and at local scales, with continental distributions showing steeper slopes than the localities within them. South America showed a steeper slope (after controlling for species diversity), compared to Africa and North America, at local and continental scales. We also found that skewness and modal body size can show strikingly different correlations with predictor variables, such as species richness and median size, depending on the use of untransformed versus log‐transformed data, due to changes in the distribution density generated by log‐transformation. The main differences in slope, skewness, and modal size between local and continental scales appear to arise from the same biogeographical process, where small‐sized species increase in diversity much faster (due to higher spatial turnover rates) than large‐sized species. This process, which can operate even in the absence of competitive saturation at local scales, generates continental assemblages with steeper slopes, smaller modal sizes, and higher right skewness (toward small‐sized species) compared to local communities. In addition, historical factors can also affect the size distribution slopes, which are significantly steeper, in South American mammal assemblages (probably due to stronger megafauna extinction events in South America) than those in North America and Africa.     

First record and taxonomic description of the genus Thysanoplusia (Fabricius) (Lepidoptera: Noctuidae: Plusiinae) from Pakistan

Species belonging to genus, Thysanoplusia Fabricius of the subfamily Plusiinae which are polyphagous in nature and pest of vegetables, foods, legumes, fodder, fruits, ornamental plants and cotton crops. Samples were collected from different localities of district Bahawalpur. For collection, comprehensive and comparative surveys were carried out during 2017–18 on taxonomic account of species of the genus Thysanoplusia Fabricius and resulted identified one species Thysanoplusia orichalcea (Fabricius) first time from Pakistan. Morphological characters viz., vertex, frons, labial palpi, antennae, compound eyes, ocelli, proboscis, wing venation, male and female genital characteristics were used for the identification and classification. Dichotomous keys and photographs are also provided. There is hardly any substantial research work on taxonomic studies of subfamily Plusiinae Pakistan. So to fill this gap the present proposal was designed to study the diversity of Noctuid moths from Pakistan and very fruitful results have been obtained.