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.     

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.     

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。     

Using Echo Ultrasound from Schooling Fish to Detect and Classify Fish Types

Fish finders have already been widely available in the fishing market for a number of years.However,the sizes of these fishfinders are too big and their prices are expensive to suit for the research of robotic fish or mini-submarine.The goal of thisresearch is to propose a low-cost fish detector and classifier which suits for underwater robot or submarine as a proximity sensor.With some pre-condition in hardware and algorithms,the experimental results show that the proposed design has good per-formance,with a detection rate of 100 % and a classification rate of 94 %.Both the existing type of fish and the group behaviorcan be revealed by statistical interpretations such as hovering passion and sparse swimming mode.     

Novel summary metrics for insular biotic assemblages based on taxonomy and phylogeny: Biogeographical,palaeogeographical and possible conservational applications

Using the land‐bound vertebrates on the marine islands as model organisms, two metrics are presented that permit quantitative and succinct synopses of the ‘evolutionary maturity’ of the hosted faunal assemblages. In turn, these reflect the geo‐physical settings and geological developments of the substrates. The assemblage lineage‐taxonomy spectrum (ALTS) is based on the constituent lineages’ taxonomic distinctiveness and diversity. Individual lineages within assemblages can in most cases be assigned to one of six categories, LT1?LT6: LT1 is a non‐endemic taxon, whereas LT6 comprises multiple endemic genera from a family that arose elsewhere. If required, the scheme can be expanded: LT9 is an endemic order. The data can then be combined to provide an assemblage spectrum, for example, 00:08:38:30:08:15[ 13 ]. Here, the first six values denote the number of lineages assigned to each category expressed as percentages of the overall total, with the sum of the processed lineages listed as the seventh (in brackets and bold). The ALTS metric highlights efficiently the key features of a marine island's biological assemblage. Notably, the contrast between spectra for suites on geologically and geo‐physically varied island types can be striking, for instance the squamate suite on the young, proximate orogenic margin island of Taiwan is coded 78:16:05:00:00:00[ 37 ] whereas the one on the distantly located, Late Eocene composite terrane island of New Caledonia is 00:11:00:11:33:44[ 9 ]. To overcome the subjectivity that is inherent in assigning supraspecific ranks, an alternative assemblage lineage‐age spectrum (ALAS) is also introduced that makes use of the binary logarithm values of the colonization times of the island lineages (0–2, 2–4, … , 32–64, >64 Ma). It is represented using a seven‐plus‐two‐number code, for instance Madagascar's squamates are 00:06:00:00:19:62:12[ 19 ( 16 )]; most colonizations took place in the Palaeogene (66–23 Ma); there are 19 lineages, but only 16 are presently age‐dated. In addition to marine‐island biogeography studies, the ALTS–ALAS spectrum approach is potentially useful for encapsulating biotas in other sorts of insular setting (e.g. lakes, mountain tops), and for evaluating palaeogeographical models. Furthermore, it may help emphasize the conservation value of an island's faunal assemblage.     

Analysis of Profiles Based on Ordinal Classification Functions and Rank Tests

In this article a test for the comparison of K groups of independent profiles is suggested. It is based on rank tests, in which the criterion is obtained by using a suitably chosen ordinal classification function. This function represents a specific view on the profiles. The test, which is easy to use, can be regarded as a generalisation of other well known tests and can be applied within a variety of situations. The relationships to other procedures for the analysis of profiles are described. Some properties are discussed.