Phenology of sunflower cultivars. I. Classification of responses

1982 ◽  
Vol 33 (2) ◽  
pp. 243 ◽  
Author(s):  
PJ Goyne ◽  
GL Hammer ◽  
DR Woodruff
Keyword(s):  
Genetic Variability ◽  
Pattern Analysis ◽  
Cultivar Differences ◽  
Commercial Cultivars ◽  
Maturity Groups

The phenology of commercial sunflower cultivars available to Queensland growers in 1979 was studied in monthly plantings over a 12-month period at Toowoomba in southern Queensland. By using pattern analysis procedures, the cultivars were classified into three maturity groups, viz. 'Very Quick', 'Quick' and 'Medium', based on the number of days from emergence to the head-visible stage of growth. Most of the cultivars belonged to the Quick and Medium maturity groups. Cultivar differences were most obvious for plantings in the cooler months (March to October). To classify new releases, one or two plantings during this period as well as one planting in summer is recommended. Sunfola 68-2 and Hysun 30 should be included in these plantings as cultivars representative of the two major maturity groups. The study showed that there was very little genetic variability in phenology in the present commercial cultivars and there was little difference in phenology among cultivars in the main summer planting period.

scholarly journals Electromyographic Pattern Analysis and Classification for a Robotic Prosthetic Arm

2006 ◽  
Vol 3 (2) ◽  
pp. 113-119 ◽  
Author(s):  
M. José H. Erazo Macias ◽  
S. Alejandro Vega
Keyword(s):  
Pattern Classification ◽  
Classification Scheme ◽  
Pattern Analysis ◽  
Signal Classification ◽  
Wrist Movement ◽  
Shoulder Muscles ◽  
The Subject ◽  
Electromyographic Signals ◽  
Flexion And Extension

This paper deals with the statistical analysis and pattern classification of electromyographic signals from the biceps of a person with amputation below the humerus. Such signals collected from an amputation simulator are synergistically generated to produce discrete elbow movements. The purpose of this study is to utilise these signals to control an electrically driven prosthetic or orthotic elbow with minimum extra mental effort on the part of the subject. The results show very good separability of classes of movements when a learning pattern classification scheme is used, and a superposition of any composite motion to the three basic primitive motions—humeral rotation in and out, flexion and extension, and pronation and supination. Since no synergy was detected for the wrist movement, different inputs have to be provided for a grip. In addition, the method described is not limited by the location of the electrodes. For amputees with shorter stumps, synergistic signals could be obtained from the shoulder muscles. However, the presentation in this paper is limited to biceps signal classification only.

The subtypes of human immunodeficiency virus in Australia and Asia

Sexual Health ◽  
2004 ◽  
Vol 1 (1) ◽  
pp. 1 ◽  
Author(s):  
Robert Oelrichs
Keyword(s):  
Human Immunodeficiency Virus ◽  
Genetic Variability ◽  
Distribution Patterns ◽  
Human Populations ◽  
Regional Diversity ◽  
Subtype B ◽  
Immunodeficiency Virus ◽  
Australasian Region ◽  
Hiv 1

Worldwide, the human immunodeficiency virus exhibits a great genetic variability, with multiple circulating subtypes of the virus. This variability allows study of the movement of HIV strains within and between human populations but also has implications for diagnosis, treatment and monitoring. The type of HIV causing the epidemic in Australia is changing from being homogeneous subtype B, reflecting a greater regional diversity. In this paper the classification of HIV-1 subtypes and their distribution within the Australasian region are reviewed and the implications of these distribution patterns discussed.

Echinococcus granulosusstrain differentiation based on sequence heterogeneity in mitochondrial genes of cytochromecoxidase-1 and NADH dehydrogenase-1

Parasitology ◽  
2004 ◽  
Vol 128 (5) ◽  
pp. 569-575 ◽  
Author(s):  
A. OBWALLER ◽  
R. SCHNEIDER ◽  
J. WALOCHNIK ◽  
B. GOLLACKNER ◽  
A. DEUTZ ◽  
...  
Keyword(s):  
Genetic Variability ◽  
Nadh Dehydrogenase ◽  
Genetic Variance ◽  
Significant Degree ◽  
Mitochondrial Genes ◽  
Moderate Degree ◽  
Sequence Heterogeneity ◽  
Pcr Product ◽  
Cattle Strain

Genetic analyses ofEchinococcus granulosusisolates from different intermediate host species have demonstrated substantial levels of variation for some genotype (strain) clusters. To determine the range of genetic variability within and between genotypes we amplified and cloned partialcox1andnadh1genes from 16 isolates ofE. granulosusfrom 4 continents. Furthermore, we sequenced different clones from a PCR product to analyse the intra-individual genetic variance. The findings showed a moderate degree of variance within single isolates and a significant degree of variance between the cluster of genotypes G1–G3 (sheep, Tasmanian sheep and buffalo strain), genotypes G4 (horse strain) and G5 (cattle strain) and the cluster of the genotypes G6 (camel strain) and G7 (pig strain). The variance of up to 2·2% within genotypes was relatively low compared with that of 4·3–15·7% among genotypes. The present results indicate that a re-examination of the classification of 5 genotypes ofEchinococcusis warranted. Hence, our data highly support a re-evaluation of the taxonomy of the clades G1–G3, G4, G5, G6/7 and G8 (cervid strain) within the genusEchinococcus.

scholarly journals Response of Ancestral Soybean Lines and Commercial Cultivars to Rhizoctonia Root and Hypocotyl Rot

Plant Disease ◽  
2001 ◽  
Vol 85 (10) ◽  
pp. 1091-1095 ◽  
Author(s):  
C. A. Bradley ◽  
G. L. Hartman ◽  
R. L. Nelson ◽  
D. S. Mueller ◽  
W. L. Pederson
Keyword(s):  
Rhizoctonia Solani ◽  
Disease Severity ◽  
Partial Resistance ◽  
Common Disease ◽  
Root Weight ◽  
Soybean Cultivars ◽  
Commercial Cultivars ◽  
Maturity Groups

Rhizoctonia root and hypocotyl rot is a common disease of soybean caused by Rhizoctonia solani. There are no commercial cultivars marketed as resistant to Rhizoctonia root and hypocotyl rot, and only a few sources of partial resistance to this disease have been reported. Ninety ancestral soybean lines, maturity groups (MGs) 000 to X, and 700 commercial cultivars, MGs II to IV, were evaluated for resistance to R. solani under greenhouse conditions. Most of the ancestral lines and cultivars evaluated were susceptible; however, 21 of the ancestral lines and 20 of the commercial cultivars were partially resistant. Of the 21 ancestral lines, CNS, Mandarin (Ottawa), and Jackson are in the pedigree of cultivars previously reported as being partially resistant to R. solani. In an additional study, dry root weights of 21 soybean cultivars were evaluated after inoculation with R. solani. Variation in dry root weight occurred among cultivars, but there was not a significant (P = 0.05) correlation between dry root weight and disease severity.

scholarly journals Methods for separating orchards from forest using airborne LiDAR

2021 ◽  
Vol 78 (4) ◽  
Author(s):  
Tomasz Hycza ◽  
Przemysław Kupidura
Keyword(s):  
Climate Change ◽  
International Organizations ◽  
Laser Scanning ◽  
Pattern Analysis ◽  
Data Bank ◽  
Forest Vegetation ◽  
Forest Area ◽  
Airborne Laser ◽  
Granulometric Analysis

Abstract • Key message The aim of the study was to distinguish orchards from other lands with forest vegetation based on the data from airborne laser scanning. The methods based on granulometry provided better results than the pattern analysis. The analysis based on the Forest Data Bank/Cadastre polygons provided better results than the analysis based on the segmentation polygons. Classification of orchards and other areas with forest vegetation is important in the context of reporting forest area to international organizations, forest management, and mitigating effects of climate change. • Context Agricultural lands with forest vegetation, e.g., orchards, do not constitute forests according to the forest definition formulated by the national and international definitions, but contrary to the one formulated in the Kyoto Protocol. It is a reason for the inconsistency in the forest area reported by individual countries. • Aims The aim of the study was to distinguish orchards from other lands with forest vegetation based on the data from airborne laser scanning. • Methods The study analyzed the usefulness of various laser scanning products and the various features of pattern and granulometric analysis in the Milicz forest district in Poland. • Results The methods based on granulometry provided better results than the pattern analysis. The analysis based on the Forest Data Bank/Cadastre polygons provided better results than the analysis based on the segmentation polygons. • Conclusion Granulometric analysis has proved to be a useful tool in the classification of orchards and other areas with forest vegetation. It is important in the context of reporting forest area to international organizations, forest management, and mitigating effects of climate change.

scholarly journals Machine Learning Algorithms for Urban Land Use Planning: A Review

Urban Science ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 68
Author(s):  
Vineet Chaturvedi ◽  
Walter T. de Vries
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Use Change ◽  
Urban Growth ◽  
Land Use Planning ◽  
Pattern Analysis ◽  
Urban Land ◽  
Earth Observation ◽  
Urban Land Use

Urbanization is persistent globally and has increasingly significant spatial and environmental consequences. It is especially challenging in developing countries due to the increasing pressure on the limited resources, and damage to the bio-physical environment. Traditional analytical methods of studying the urban land use dynamics associated with urbanization are static and tend to rely on top-down approaches, such as linear and mathematical modeling. These traditional approaches do not capture the nonlinear properties of land use change. New technologies, such as artificial intelligence (AI) and machine learning (ML) have made it possible to model and predict the nonlinear aspects of urban land dynamics. AI and ML are programmed to recognize patterns and carry out predictions, decision making and perform operations with speed and accuracy. Classification, analysis and modeling using earth observation-based data forms the basis for the geospatial support for land use planning. In the process of achieving higher accuracies in the classification of spatial data, ML algorithms are being developed and being improved to enhance the decision-making process. The purpose of the research is to bring out the various ML algorithms and statistical models that have been applied to study aspects of land use planning using earth observation-based data (EO). It intends to review their performance, functional requirements, interoperability requirements and for which research problems can they be applied best. The literature review revealed that random forest (RF), deep learning like convolutional neural network (CNN) and support vector machine (SVM) algorithms are best suited for classification and pattern analysis of earth observation-based data. GANs (generative adversarial networks) have been used to simulate urban patterns. Algorithms like cellular automata, spatial logistic regression and agent-based modeling have been used for studying urban growth, land use change and settlement pattern analysis. Most of the papers reviewed applied ML algorithms for classification of EO data and to study urban growth and land use change. It is observed that hybrid approaches have better performance in terms of accuracies, efficiency and computational cost.

Sign in / Sign up

Export Citation Format

Share Document