Classifying aquatic macrophytes as indicators of eutrophication in European lakes

Aquatic macrophytes are one of the biological quality elements in the Water Framework Directive (WFD) for which status assessments must be defined. We tested two methods to classify macrophyte species and their response to eutrophication pressure: one based on percentiles of occurrence along a phosphorous gradient and another based on trophic ranking of species using Canonical Correspondence Analyses in the ranking procedure. The methods were tested at Europe-wide, regional and national scale as well as by alkalinity category, using 1,147 lakes from 12 European states. The grouping of species as sensitive, tolerant or indifferent to eutrophication was evaluated for some taxa, such as the sensitive Chara spp. and the large isoetids, by analysing the (non-linear) response curve along a phosphorous gradient. These thresholds revealed in these response curves can be used to set boundaries among different ecological status classes. In total 48 taxa out of 114 taxa were classified identically regardless of dataset or classification method. These taxa can be considered the most consistent and reliable indicators of sensitivity or tolerance to eutrophication at European scale. Although the general response of well known indicator species seems to hold, there are many species that were evaluated differently according to the database selection and classification methods. This hampers a Europe-wide comparison of classified species lists as used for the status assessment within the WFD implementation process.     

Associating Seasonal Range Characteristics With Survival of Female White-Tailed Deer

ABSTRACT Delineating populations is critical for understanding population dynamics and managing habitats. Our objective was to delineate subpopulations of migratory female white-tailed deer (Odocoileus virginianus) in the central Black Hills, South Dakota and Wyoming, USA, on summer and winter ranges. We used fuzzy classification to assign radiocollared deer to subpopulations based on spatial location, characterized subpopulations by trapping sites, and explored relationships among survival of subpopulations and habitat variables. In winter, Kaplan-Meier estimates for subpopulations indicated 2 groups: high (S = 0.991 ± 0.005 [x̄ ± SE]) and low (S = 0.968 ± 0.007) weekly survivorship. Survivorship increased with basal area per hectare of trees, average diameter at breast height of trees, percent cover of slash, and total point-center quarter distance of trees. Cover of grass and forbs were less for the high survivorship than the lower survivorship group. In summer, deer were spaced apart with mixed associations among subpopulations. Habitat manipulations that promote or maintain large trees (i.e., basal area = 14.8 m²/ha and average dbh of trees = 8.3 cm) would seem to improve adult survival of deer in winter.     

Morpho-colorimetric characterization by image analysis to identify diaspores of wild plant species

Digital images of ex situ germplasm stored in the Sardinian Germplasm Bank (BG-SAR) were used for the application of image analysis techniques at the Stazione Sperimentale di Granicoltura per la Sicilia. The analysed accessions refer to 148 taxonomic units belonging to 102 genera and 47 families, typical of the Sardinian flora, and of the Mediterranean basin in general.The images of diaspores were acquired by a flatbed scanner and elaborated with a macro specially developed for the morphometric and colorimetric measurements. This method allowed carrying out a database for the characterization of autochthonous germplasm in entry to the bank and the realization of statistic classifiers for the discrimination of genera and species within the following families: Apiaceae, Boraginaceae, Caryophyllaceae, Cistaceae, Fabaceae and Scrophulariaceae. Such classifiers, based on the linear discriminant analysis (LDA) technique and checked by cross-validation, showed a performance included between 74.3% and 96.4%.In addition, for the genus Astragalus, it was possible to elaborate a classifier able to identify very similar taxa of a species complex, obtaining a performance between 83.7% and 100%. Such analysis proved the validity of the methodology also from the taxonomic point of view.Suggestions for subsequent methodological progress, which could offer applications in other research issues, such as ecological analysis, soil seed bank and archaeological botany are proposed.     

Distribution,community characteristics and classification of Stipa tianschanica var. klemenzii steppe in China

Stipa tianschanica var. klemenzii steppe is the most typical formation of desert steppe in China. Based on the primary plots data obtained from fieldworks during the growing seasons from 2010 to 2016 as well as some earlier records, we studied the Stipa tianschanica var. klemenzii steppe across China systematically, including the eco-geographical distributions, community characteristics and classifications. The results showed that S. tianschanica var. klemenzii steppe distributed mainly on the Ulan Qab Plateau and western Xilin Gol Plateau. Due to the arid biotope of S. tianschanica var. klemenzii steppe, quantitative characteristics of the assemblage including height, coverage, biomass and species richness were normally lower than that of most Stipa formations in Eurasian steppe region. Moreover, 165 seed plants belonging to 85 genera and 29 families were recorded in the 80 study sites, in which rare species (occurrence frequency <20%) made up 87% of the total plants while common species and constant species could only be found occasionally. Species with the occurrence frequency exceeding 50% included S. tianschanica var. klemenzii, Convolvulus ammannii, Cleistogenes songorica, Allium tenuissimum. In addition, Hemicryptophyte and therophyte were the two dominant life forms, covering 55% and 20% of the species, respectively. As for the ecological type of water, typical xerophytes accounted for 47% of all species which is followed by super-xerophytes and meso-xerophytes. Middle Asian areal-type and east Palaeo-North areal-type were the two major floristic elements, containing 37% and 26% of the species, respectively. Based on the life forms and dominances of the species within the community, S. tianschanica var. klemenzii steppe in China could be classified into 6 association groups, 29 associations.     

Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

The characterization of plant species using first‐derivative fluorescence spectra

Plants are one of the most important parts of the ecological system and demand a reliable method for accurate classification. In this study, the first‐derivative fluorescence spectral curves (FDFSCs) based on laser‐induced fluorescence technology were proposed for the characterization of plant species. The measurement system is mainly composed of a spectrometer, an excitation light source (the two excitation wavelengths are 460 and 556 nm, respectively), and an intensified charge‐coupled device camera. FDFSCs were calculated from the deviation between the fluorescence values at each wavelength, plus and minus one band, divided by the wavelength range. Principal component analysis was utilized to analyze the FDFSCs by extracting the main attributes and reducing the dimensionality of variables. A support vector machine was used to evaluate FDFSC performance for the identification of plant species. Plant species that are difficult to distinguished by the naked eye, can be identified effectively using the proposed FDFSCs. For the 556 nm and 460 nm excitation wavelengths, the overall identification rates of the six plant species evaluated were 93.3% and 91.7%, respectively. Experimental results demonstrated that the combination of the FDFSCs with multivariate analysis could provide a simple and reliable method for the characterization of plant species.     

免疫细胞浸润在非小细胞肺癌诊断与预后中的应用

免疫细胞浸润对癌症的诊断与预后有着重要意义。文中收集TCGA数据库已收录的非小细胞肺癌肿瘤与正常组织基因表达数据,利用CIBERSORT工具得到22种免疫细胞占比来评估免疫细胞浸润情况。以22种免疫细胞占比为特征,用机器学习方法构建了非小细胞肺癌肿瘤与正常组织的分类模型,其中随机森林方法构建的模型分类效果AUC=0.987、敏感性0.98及特异性0.84。并且用随机森林方法构建的肺腺癌和肺鳞癌肿瘤组织分类模型效果AUC=0.827、敏感性0.75及特异性0.77。用LASSO回归筛选22种免疫细胞特征,保留8种强相关特征组成的免疫细胞评分结合临床特征构建了非小细胞肺癌预后模型。经评估及验证,预后模型C-index=0.71并且3年和5年的校准曲线拟合良好,可以对预后风险度进行准确预测。本研究基于免疫细胞浸润所构建的分类模型与预后模型,旨在对非小细胞肺癌的诊断与预后研究提供新的策略。