An integrated method for taxonomic identif ication of microorganisms

For accurate species-level identification of microorganisms, researchers today increasingly use a combination of standard microbiological cultivation and visual observation methods with molecular biological and genetic techniques that help distinguish between species and strains of microorganisms at the level of DNA or RNA molecules. The aim of this work was to identify microorganisms from the ICG SB RAS Collection using an integrated approach that involves a combination of various phenotypic and genotypic characteristics. Key molecular-genetic and phenotypic characteristics were determined for 93 microbial strains from the ICG SB RAS Collection. The strains were characterized by means of morphological, physiological, moleculargenetic, and mass-spectrometric parameters. Specific features of the growth of the strains on different media were determined, and cell morphology was evaluated. The strains were tested for the ability to utilize various substrates. The strains studied were found to significantly differ in their biochemical characteristics. Physiological characteristics of the strains from the collection were identified too, e. g., the relationship with oxygen, type of nutrition, suitable temperature and pH ranges, and NaCl tolerance. In this work, the microorganisms analyzed were combined into separate groups based on the similarities of their phenotypic characteristics. This categorization, after further refinement and expansion of the spectrum of taxa and their metabolic maps, may serve as the basis for the creation of an “artificial” classification that can be used as a key for simplified and quicker identification and recognition of microorganisms within both the ICG SB RAS Collection and other collections.

Thinking in the Future of Plant Taxonomy

This review is a trial to summarize the history of plant taxonomy to understand the situation of the taxonomical works and their progression. Taxonomy starts as an artificial classification and gradually with the increase of knowledge, civilization and facilities, plant taxonomy developed. Here, the most affected steps in the progression of plant taxonomy have been mentioned. Starting from the oldest period of using vegetative, floral and anatomical characters to the most recent works on palynology, chemotaxonomy and molecular biological data. Thinking of the modern plant taxonomy has been mentioned in response to the environmental changes and peoples thinking. Experimental biology and breeding experiments must be done to understand the way of speciation and to protect the wild species from extinction. Taxonomy must be cooperating with ecology for better understanding of the changing in the taxonomic characters and to precise identifications. Taxonomists have to survey the vegetation and try to find ways to protect the plants. We have to understand the relationships between the taxa in the populations. We have to modulate our thinking according to the new situation. Meanwhile the environmental conditions and their effect on plant characters must be kept in mind, as new species may arise and other extinct

Application Research of KNN Algorithm Based on Clustering in Big Data Talent Demand Information Classification

With the growth of massive data in the current mobile Internet, network recruitment is gradually growing into a new recruitment channel. How to effectively mine available information in the massive network recruitment data has become the technical bottleneck of current education and social supply and demand development. The renewal of talent demand information is carried out every day, which produces a large amount of text data. How to manage these talents’ demand information reasonably becomes more and more important. Artificial classification is time-consuming and laborious, which is unrealistic naturally. Therefore, using automatic text categorization technology to classify and manage this information becomes particularly important. To break through the bottleneck of this technology, a heuristic KNN text categorization algorithm based on ABC (artificial bee colony) is proposed to adjust the weight of features, and the similarity between test observation and training observation is measured by using the method of fuzzy distance measurement. Firstly, the recruitment information is segmented and feature selection and noise data elimination are carried out by using term frequency-inverse document frequency (TF-IDF) algorithm and AP (affinity propagation) clustering algorithm. Finally, the text information is classified by using KNN algorithm combined with heuristic search and fuzzy distance measurement. The experimental results show that this method effectively solves the problem of poor stability and low classification accuracy of traditional KNN algorithm in text categorization method for talent demand.

Linnaeus, Carl von (1707–78)

Linnaeus was educated in Sweden, and became a doctor of medicine in Harderwijk, Holland, in 1735. He visited other European countries then, but he never left Sweden after his return in 1738. After practising as a physician in Stockholm, he moved to Uppsala University as professor of medicine and botany in 1741. He articulated four different but complementary ways of understanding nature – through two kinds of classification, and through what can be called developmental and functional/ecological interactions. Linnaeus is best known for his classificatory work, for which he received material from all over the world. His classificatory precepts are elaborated in the Philosophia botanica of 1751, an enlarged version of the 365 aphorisms of his Fundamenta botanica of 1735; the other aspects of his work are diffused through his writings. His artificial classification system, initially very popular, was replaced by the ’natural’ system, more slowly in botany than in zoology, and more slowly in England than in some other countries. Current biological nomenclature is based on his Species plantarum, edition 1 (for plants), and Systema naturae, edition 10 (for animals). His codification of botanical terms remains influential. Almost 200 dissertations, most written by Linnaeus, were defended by his students. In these and other less well-known works, including the unpublished Nemesis divina (Stories of Divine Retribution), he covered a wide range of subjects. Quinarian thinking is noticeable in Linnaeus’ work – there are five ranks in systems, five years’ growth in flowers – and in some of the occult works that he knew. He also shows a strong combinatorial bent and a tendency to draw close analogies between the parts of animals and plants.

Research of Navel Orange Defect and Color Detection Based on Machine Vision

For the problem of high intensity, low efficiency and poor accuracy in the artificial classification of navel oranges, a detection method is proposed based on machine vision technology. In defect detection, we analyze the color information of navel oranges surface, and obtain surface defect with a proper ratio of R/B and G/B. In the detection of color and luster, we calculate the texture information of the grayscale image, and propose three characteristics such as smoothness R, "consistency" measure U and entropy descriptor e. Finally, a hierarchical model is established based on BP neural network. The test results show that this method can be used for detecting the color and luster of navel orange with a high recognition rate.