Preliminary study of the ordination and classification of macroinvertebrate communities from running waters in Victoria, Australia

1994 ◽  
Vol 45 (6) ◽  
pp. 945 ◽  
Author(s):  
R Marchant ◽  
LA Barmuta ◽  
BC Chessman
Keyword(s):  
Species Level ◽  
Data Sets ◽  
Running Waters ◽  
Macroinvertebrate Communities ◽  
The Past ◽  
Level Information ◽  
Preliminary Study ◽  
Hybrid Multidimensional Scaling ◽  
Conductivity Gradient

Data on undisturbed lotic macroinvertebrate communities were assembled from a number of studies carried out in Victoria over the past 15 years; species-level information for 40 sites on nine rivers was available. Ordination (DECORANA and semi-strong hybrid multidimensional scaling) and classification (flexible UPGMA and TWINSPAN) techniques were used to assess the similarity of community composition among the sites. Correlation of environmental variables with both ordinations indicated that factors related to altitude and substratum were the most obvious gradients; a conductivity gradient was also present. The classification analyses identified four groups of sites that matched the altitudinal trends evident in the ordinations; but these techniques did not emphasize the substratum gradient. TWINSPAN also identified six groups of taxa that were characteristic of particular altitudes or regions or were widespread across all sites. The distinctiveness of the patterns from this preliminary study indicates that it would be worthwhile extending these analyses to much larger data sets from Victorian rivers.

Predictive aspect-based sentiment classification of online tourist reviews

2018 ◽  
Vol 45 (3) ◽  
pp. 341-363 ◽  
Author(s):  
Muhammad Afzaal ◽  
Muhammad Usman ◽  
Alvis Fong
Keyword(s):  
Prediction Accuracy ◽  
Sentiment Classification ◽  
Semantic Relations ◽  
Automatic Extraction ◽  
Data Sets ◽  
Classification Methods ◽  
Real World Data ◽  
The Past ◽  
Negative Orientation

With the increase of online tourists reviews, discovering sentimental idea regarding a tourist place through the posted reviews is becoming a challenging task. The presence of various aspects discussed in user reviews makes it even harder to accurately extract and classify the sentiments. Aspect-based sentiment analysis aims to extract and classify user’s positive or negative orientation towards each aspect. Although several aspect-based sentiment classification methods have been proposed in the past, limited work has been targeted towards the automatic extraction of implicit, infrequent and co-referential aspects. Moreover, existing methods lack the ability to accurately classify the overall polarity of multi-aspect sentiments. This study aims to develop a predictive framework for aspect-based extraction and classification. The proposed framework utilises the semantic relations among review phrases to extract implicit and infrequent aspects for accurate sentiment predictions. Experiments have been performed using real-world data sets crawled from predominant tourist websites such as TripAdvisor and OpenTable. Experimental results and comparison with previously reported findings prove that the predictive framework not only extracts the aspects effectively but also improves the prediction accuracy of aspects.

Influence of sample quantification and taxonomic resolution on the ordination of macroinvertebrate communities from running waters in Victoria, Australia

1995 ◽  
Vol 46 (2) ◽  
pp. 501 ◽  
Author(s):  
R Marchant ◽  
LA Barmuta ◽  
BC Chessman
Keyword(s):  
Binary Data ◽  
Environmental Gradients ◽  
Detrended Correspondence Analysis ◽  
Original Data ◽  
Taxonomic Resolution ◽  
Data Sets ◽  
Running Waters ◽  
Macroinvertebrate Communities ◽  
Data Set ◽  
Sample Quantification

The influence of sample quantification and taxonomic resolution on the ordination of macroinvertebrate communities from nine Victorian rivers was examined by progressively reducing the degree of detail in the original data (species level, quantitative). Five additional data sets were created that consisted of binary (presence or absence) data on species, quantitative or binary data on families, and quantitative data on PET (plecopteran, ephemeropteran and trichopteran) species or families. Ordinations were performed with detrended correspondence analysis (DCA) and semi-strong hybrid multi-dimensional scaling (SSH). With both ordination techniques, the ordinations of each data set (including the original) revealed the same three underlying gradients. An altitudinal gradient consistently achieved the highest correlations with the ordinations (r = 0.71-0.93), followed by a substratum gradient (r = 0.50-0.88) and a combined pH and conductivity gradient (r = 0.47-0.76). Each of the five less-complete data sets thus provides an adequate degree of detail for ordination analysis and subsequent interpretation of environmental gradients.

Second international scientific conference «Present fundamental problems of vegetation classification» (Yalta, 15–20 September 2019)

2019 ◽  
pp. 135-142
Author(s):  
K. V. Ivanova ◽  
A. M. Lapina ◽  
V. V. Neshataev
Keyword(s):  
Plant Communities ◽  
National Archive ◽  
Botanical Garden ◽  
Vegetation Classification ◽  
Scientific Conference ◽  
Scientific Journals ◽  
Time Dynamics ◽  
The Past ◽  
Research Organizations

The 2nd international scientific conference «Fundamental problems of vegetation classification» took place at the Nikitskiy Botanical Garden (Yalta, Republic of Crimea, Russia) on 15–20 September 2019. There were 56 participants from 33 cities and 43 research organizations in Russia. The conference was mostly focused on reviewing the success in classification of the vegetation done by Russian scientists in the past three years. The reports covered various topics such as classification, description of new syntaxonomical units, geobotanical mapping for different territories and types of vegetation, studies of space-time dynamics of plant communities. The final discussion on the last day covered problems yet to be solved: establishment of the Russian Prodromus and the National archive of vegetation, complications of higher education in the profile of geobotany, and the issue of the data leakage to foreign scientific journals. In conclusion, it was announced that the 3rd conference in Nikitskiy Botanical Garden will be held in 2022.

The Poetry and Music of Science

2019 ◽  
Author(s):  
Tom McLeish
Keyword(s):  
School Leaver ◽  
Creative People ◽  
Creative Processes ◽  
Arts And Sciences ◽  
The Past ◽  
The Arts ◽  
The Two Cultures ◽  
Personal Accounts ◽  
Poetry And Music

‘I could not see any place in science for my creativity or imagination’, was the explanation, of a bright school leaver to the author, of why she had abandoned all study of science. Yet as any scientist knows, the imagination is essential to the immense task of re-creating a shared model of nature from the scale of the cosmos, through biological complexity, to the smallest subatomic structures. Encounters like that one inspired this book, which takes a journey through the creative process in the arts as well as sciences. Visiting great creative people of the past, it also draws on personal accounts of scientists, artists, mathematicians, writers, and musicians today to explore the commonalities and differences in creation. Tom McLeish finds that the ‘Two Cultures’ division between the arts and the sciences is not after all, the best classification of creative processes, for all creation calls on the power of the imagination within the constraints of form. Instead, the three modes of visual, textual, and abstract imagination have woven the stories of the arts and sciences together, but using different tools. As well as panoramic assessments of creativity, calling on ideas from the ancient world, medieval thought, and twentieth-century philosophy and theology, The Poetry and Music of Science illustrates its emerging story by specific close-up explorations of musical (Schumann), literary (James, Woolf, Goethe) mathematical (Wiles), and scientific (Humboldt, Einstein) creation. The book concludes by asking how creativity contributes to what it means to be human.

scholarly journals ANCA Status or Clinical Phenotype — What Counts More?

2021 ◽  
Vol 23 (6) ◽  
Author(s):  
Martin Windpessl ◽  
Erica L. Bettac ◽  
Philipp Gauckler ◽  
Jae Il Shin ◽  
Duvuru Geetha ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Granulomatosis With Polyangiitis ◽  
Clinical Phenotype ◽  
Antineutrophil Cytoplasmic Antibody ◽  
Cardiovascular Death ◽  
Genetic Associations ◽  
Ongoing Debate ◽  
The Past ◽  
Anca Associated Vasculitis

Abstract Purpose of Review There is ongoing debate concerning the classification of antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis. That is, whether classification should be based on the serotype (proteinase 3 (PR3)- or myeloperoxidase (MPO)-ANCA) or on the clinical phenotype (granulomatosis with polyangiitis (GPA) or microscopic polyangiitis (MPA)). To add clarity, this review focused on integration of the most recent literature. Recent Findings Large clinical trials have provided evidence that a serology-based risk assessment for relapses is more predictive than distinction based on the phenotype. Research conducted in the past decade indicated that a serology-based approach more closely resembles the genetic associations, the clinical presentation (i.e., lung involvement), biomarker biology, treatment response, and is also predicting comorbidities (such as cardiovascular death). Summary Our review highlights that a serology-based approach could replace a phenotype-based approach to classify ANCA-associated vasculitides. In future, clinical trials and observational studies will presumably focus on this distinction and, as such, translate into a “personalized medicine.”

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

scholarly journals Morphological Characteristics, Classifications and Difficulties in the Use of Diagnostic Criteria for Serrated Lesions of the Large Intestine

2021 ◽  
Author(s):  
Cesar de Souza Bastos Junior ◽  
Vera Lucia Nunes Pannain ◽  
Adriana Caroli-Bottino
Keyword(s):  
Diagnostic Criteria ◽  
Morphological Characteristics ◽  
World Health ◽  
Diagnostic Tools ◽  
Serrated Adenoma ◽  
The Past ◽  
The World ◽  
Serrated Lesions ◽  
Health Organization

Abstract Introduction Colorectal carcinoma (CRC) is the most common gastrointestinal neoplasm in the world, accounting for 15% of cancer-related deaths. This condition is related to different molecular pathways, among them the recently described serrated pathway, whose characteristic entities, serrated lesions, have undergone important changes in their names and diagnostic criteria in the past thirty years. The multiplicity of denominations and criteria over the last years may be responsible for the low interobserver concordance (IOC) described in the literature. Objectives The present study aims to describe the evolution in classification of serrated lesions, based on the last three publications of the World Health Organization (WHO) and the reproducibility of these criteria by pathologists, based on the evaluation of the IOC. Methods A search was conducted in the PubMed, ResearchGate and Portal Capes databases, with the following terms: sessile serrated lesion; serrated lesions; serrated adenoma; interobserver concordance; and reproducibility. Articles published since 1990 were researched. Results and Discussion The classification of serrated lesions in the past thirty years showed different denominations and diagnostic criteria. The reproducibility and IOC of these criteria in the literature, based on the kappa coefficient, varied in most studies, from very poor to moderate. Conclusions Interobserver concordance and the reproducibility of microscopic criteria may represent a limitation for the diagnosis and appropriate management of these lesions. It is necessary to investigate diagnostic tools to improve the performance of the pathologist's evaluation, for better concordance, and, consequently, adequate diagnosis and treatment.

Detection and Classification of Anomalies in Large Data Sets on the Basis of Information Granules

2021 ◽  
pp. 1-1
Author(s):  
Adam Kiersztyn ◽  
Pawe Karczmarek ◽  
Krystyna Kiersztyn ◽  
Witold Pedrycz
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Information Granules

scholarly journals Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

2021 ◽  
Vol 12 (2) ◽  
pp. 317-334
Author(s):  
Omar Alaqeeli ◽  
Li Xing ◽  
Xuekui Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Classification Tree ◽  
Area Under The Curve ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Tree Algorithms ◽  
R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

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