scholarly journals Wavelet Signatures of Climate and Flowering: Identification of Species Groupings

10.5772/24780 ◽  
2011 ◽  
Author(s):  
Irene Lena Hudson ◽  
Marie R. Keatley ◽  
In Kang
Keyword(s):  
Identification Of Species

Atla alpina, Sporodictyon arcticum and S. schaererianum (Verrucariaceae) in Russia

2012 ◽  
Vol 46 ◽  
pp. 135-144
Author(s):  
I. S. Zhdanov
Keyword(s):  
Identification Of Species

Atla alpina S. Savić et Tibell, Sporodictyon arcticum S. Savić et Tibell and S. schaererianum A. Massal. (Verrucariaceae), earlier referred to Polyblastia theleodes (Sommerf.) Th. Fr., are revised in Russia. All of them are reported as new for Asia. A key for identification of species of family Verrucariaceae with muriform, brown ascospores and hymenium without photobiont cells in Russia is included.

Identification of species of the genus monilinia by PCR-analysis

2020 ◽  
Vol 60 (1) ◽  
pp. 186-191
Author(s):  
Ye. V. Mikhailova ◽  
N. N. Karpun ◽  
G. G. Pantiya
Keyword(s):  
Pcr Analysis ◽  
Identification Of Species

scholarly journals Effects of Long-Wave UV Light on Monilinia Growth and Identification of Species

Plant Disease ◽  
1999 ◽  
Vol 83 (1) ◽  
pp. 62-65 ◽  
Author(s):  
A. De Cal ◽  
P. Melgarejo
Keyword(s):  
Short Distance ◽  
Mycelial Growth ◽  
Uv Light ◽  
Dark Period ◽  
Morphological Characteristics ◽  
Potato Dextrose Agar ◽  
Long Wave ◽  
The World ◽  
Identification Of Species ◽  
Respective Species

The effect of long-wave UV/dark period on mycelial growth of 46 isolates of Monilinia sp. collected in Spain and 16 isolates collected from other parts of the world was investigated. Typical isolates of M. laxa, M. fructigena, and M. fructicola were grown in the dark and identified by morphological characteristics. Long-wave UV/dark conditions reduced the growth rates of M. laxa, M. fructigena, and M. fructicola on potato dextrose agar. All isolates of M. fructigena grew more slowly than those of M. fructicola. Typical and atypical isolates of M. fructigena and M. fructicola were placed in their respective species based on long-wave UV/dark growth rate data. M. laxa isolates were readily distinguished by the short distance from their conidium to the first germ tube branch. The involvement of different photoreceptors in photoresponses by M. fructicola and M. fructigena is discussed. Differences in mycelial growth under long-wave UV may be a useful tool to identify Monilinia spp.

scholarly journals Identification of lactobacilli by pheS and rpoA gene sequence analyses

2007 ◽  
Vol 57 (12) ◽  
pp. 2777-2789 ◽  
Author(s):  
Sabri M. Naser ◽  
Peter Dawyndt ◽  
Bart Hoste ◽  
Dirk Gevers ◽  
Katrien Vandemeulebroecke ◽  
...  
Keyword(s):  
Gene Sequence ◽  
Alpha Subunit ◽  
Identification System ◽  
Rrna Gene ◽  
Gene Sequences ◽  
Intraspecies Variation ◽  
Reliable Identification ◽  
Genomic Markers ◽  
Identification Of Species ◽  
Rpoa Gene

The aim of this study was to evaluate the use of the phenylalanyl-tRNA synthase alpha subunit (pheS) and the RNA polymerase alpha subunit (rpoA) partial gene sequences for species identification of members of the genus Lactobacillus. Two hundred and one strains representing the 98 species and 17 subspecies were examined. The pheS gene sequence analysis provided an interspecies gap, which in most cases exceeded 10 % divergence, and an intraspecies variation of up to 3 %. The rpoA gene sequences revealed a somewhat lower resolution, with an interspecies gap normally exceeding 5 % and an intraspecies variation of up to 2 %. The combined use of pheS and rpoA gene sequences offers a reliable identification system for nearly all species of the genus Lactobacillus. The pheS and rpoA gene sequences provide a powerful tool for the detection of potential novel Lactobacillus species and synonymous taxa. In conclusion, the pheS and rpoA gene sequences can be used as alternative genomic markers to 16S rRNA gene sequences and have a higher discriminatory power for reliable identification of species of the genus Lactobacillus.

scholarly journals Antifungal susceptibilities and identification of species of theSporothrix schenckiicomplex isolated in Brazil

2013 ◽  
pp. 1-9 ◽  
Author(s):  
Cheila Denise Ottonelli Stopiglia ◽  
Cibele Massotti Magagnin ◽  
Mauricio Ramírez Castrillón ◽  
Sandra Denise Camargo Mendes ◽  
Daiane Heidrich ◽  
...  
Keyword(s):  
Identification Of Species

scholarly journals Towards Amazon Forest Restoration: Automatic Detection of Species from UAV Imagery

2021 ◽  
Vol 13 (13) ◽  
pp. 2627
Author(s):  
Marks Melo Moura ◽  
Luiz Eduardo Soares de Oliveira ◽  
Carlos Roberto Sanquetta ◽  
Alexis Bastos ◽  
Midhun Mohan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Forest Restoration ◽  
Amazon Forest ◽  
Forest Species ◽  
Accuracy And Precision ◽  
Identification Of Species ◽  
Aerial Vehicle ◽  
High Resolution Images ◽  
Uav Images

Precise assessments of forest species’ composition help analyze biodiversity patterns, estimate wood stocks, and improve carbon stock estimates. Therefore, the objective of this work was to evaluate the use of high-resolution images obtained from Unmanned Aerial Vehicle (UAV) for the identification of forest species in areas of forest regeneration in the Amazon. For this purpose, convolutional neural networks (CNN) were trained using the Keras–Tensorflow package with the faster_rcnn_inception_v2_pets model. Samples of six forest species were used to train CNN. From these, attempts were made with the number of thresholds, which is the cutoff value of the function; any value below this output is considered 0, and values above are treated as an output 1; that is, values above the value stipulated in the Threshold are considered as identified species. The results showed that the reduction in the threshold decreases the accuracy of identification, as well as the overlap of the polygons of species identification. However, in comparison with the data collected in the field, it was observed that there exists a high correlation between the trees identified by the CNN and those observed in the plots. The statistical metrics used to validate the classification results showed that CNN are able to identify species with accuracy above 90%. Based on our results, which demonstrate good accuracy and precision in the identification of species, we conclude that convolutional neural networks are an effective tool in classifying objects from UAV images.

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