scholarly journals Implications of unreliable species identification methods for Neotropical deer conservation planning

2021 ◽  
Vol 19 (4) ◽  
pp. 435-442
Author(s):  
Pedro Henrique de Faria Peres ◽  
Francisco Grotta-Neto ◽  
Douglas Jovino Luduvério ◽  
Márcio Leite de Oliveira ◽  
José Maurício Barbanti Duarte
Keyword(s):  
Species Identification ◽  
Conservation Planning ◽  
Identification Methods ◽  
Neotropical Deer

Molecular taxonomy in pholcid spiders (Pholcidae, Araneae): evaluation of species identification methods using CO1 and 16S rRNA

2006 ◽  
Vol 35 (5) ◽  
pp. 441-457 ◽  
Author(s):  
Jonas J. Astrin ◽  
Bernhard A. Huber ◽  
Bernhard Misof ◽  
Cornelya F. C. Klutsch
Keyword(s):  
16S Rrna ◽  
Species Identification ◽  
Molecular Taxonomy ◽  
Identification Methods

Species of the genus stagonosporopsis in the eaeu quarantine list, development of species identification methods

2020 ◽  
Author(s):  
M.B. Kopina ◽  
◽  
O.V. Sinkevich ◽  
T.A. Surina
Keyword(s):  
Nucleotide Sequence ◽  
Species Identification ◽  
Morphological Characteristics ◽  
Identification Methods ◽  
Sequence Decoding

The article presents the results of the study of the phytopathological condition of plantations potatoes, cut and potted chrysanthemums. Descriptions of quarantine and non-quarantine fungi species isolated from plants are given. Data on species identification of micromycetes by morphological characteristics and nucleotide sequence decoding are specified.

scholarly journals Mitochondrial Markers for the Detection of Duck Breeds Using Polymerase Chain Reaction

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 857
Author(s):  
Małgorzata Natonek-Wiśniewska ◽  
Piotr Krzyścin ◽  
Dominika Rubiś
Keyword(s):  
Species Identification ◽  
Dna Amplification ◽  
Agarose Gel ◽  
Chain Reaction ◽  
Identification Methods ◽  
Mitochondrial Markers ◽  
Specific Pcr ◽  
Duck Species ◽  
Polymerase Chain ◽  
Species Specific

Species identification of the components of various duck breeds has revealed that the lowest identifiable number of components depends on the breed. The results (shown on the agarose gel) of a species-specific PCR reaction for Rouen ducks were less intense than the results for the same amount of components from other popular duck breeds, suggesting differences in the Rouen duck genome. Therefore, the present study aimed to identify part of the Rouen duck’s gene sequences and to develop two new primer pairs. The first pair enables breed-independent identification of duck DNA, and the second distinguishes Rouen ducks from Chinese and Indian Runner ducks. The sequencing reaction yielded sequences of 1386 bp in length, and the identified sequence differs by around 7% from the sequences of Chinese duck species. The detected sequence contributes to improving species identification methods for duck DNA. On its basis, two primers for the identification of duck DNA were designed. The first allows for DNA amplification with the same sensitivity regardless of duck breed. The second primer’s pair is breed specific, and it distinguishes Rouen ducks from Chinese and Indian Runner ducks. Both methods are very sensitive (0.05%).

An overview on Taxonomic Keys and Automated Species Identification (ASI)

2019 ◽  
Vol 20 ◽  
pp. 40-47
Author(s):  
Mitu De ◽  
◽  
Santi Ranjan Dey ◽  
Keyword(s):  
Species Identification ◽  
Digital Technology ◽  
Automated Identification ◽  
Plant Identification ◽  
Extensive Experience ◽  
Identification Methods ◽  
Computer Based ◽  
Multi Media ◽  
Automated Identification Systems ◽  
Taxonomic Keys

A core goal of taxonomy and systematics, entomology, field botany, horticulture, zoology and many agriculture courses involves learning to identify plants and animals. But current syllabi and time constraint allow students to see only a limited amount of taxonomic variability. Usually only experts such as taxonomists and trained technicians can identify taxa accurately because it requires special skills acquired through extensive experience. Taxonomic keys are essential tools for species identification, used by students and professionals. The development of computer-based, multi-media keys provides one means of addressing this critical identification and diagnostic function. Automated species identification (ASI) is a method of making the expertise of taxonomists available to ecologists, parataxonomists and others via digital technology and artificial intelligence. Today, most automated identification systems rely on images depicting the species for the identification. Although computer programs will not replace classical plant identification methods, they have the potential to make these methods more effective.

scholarly journals A biochemical viability assay is compatible with molecular methods for species identification

2013 ◽  
Vol 66 ◽  
pp. 29-33 ◽  
Author(s):  
N.K. Richards ◽  
L.M. Winder ◽  
I.I. Iline ◽  
M.A. Novoselov ◽  
M.R. McNeill ◽  
...  
Keyword(s):  
Species Identification ◽  
Curve Analysis ◽  
Cytochrome C Oxidase I ◽  
Melt Curve Analysis ◽  
Identification Methods ◽  
Weevil Species ◽  
Viability Assay ◽  
Sitona Lepidus ◽  
Molecular Identification Methods ◽  
Listronotus Bonariensis

A biochemical viability assay was recently developed to quickly and easily assess the viability of small immobile arthropods including eggs intercepted on plant products On finding a viable specimen species identification often becomes the next hurdle This paper demonstrates that amplifiable DNA is present in a used biochemical viability assay solution and can be used for making taxonomic identifications Cryptically labelled heattreated and untreated eggs of three weevil species (Listronotus bonariensis Sitona lepidus and S discoideus) were first tested for viability then a 1135 bp fragment of the cytochrome c oxidase I gene was amplified from each viability assay solution in the presence of a fluorescent dye SYBR Green Melt curve analysis of the amplicons (n36) revealed three distinct melt profiles that correctly corresponded to each of the three weevil species This shows that the biochemical viability assay is compatible with the application of subsequent molecular identification methods which will facilitate the appropriate management response

Improved Wood Species Identification Based On Multi-View Imagery of The Three Anatomical Planes

2022 ◽  
Author(s):  
Núbia Rosa Da Silva ◽  
Victor Deklerck ◽  
Jan Baetens ◽  
Jan Van den Bulcke ◽  
Maaike De Ridder ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Species Identification ◽  
Wood Species ◽  
Training Material ◽  
Vast Number ◽  
Identification Methods ◽  
Transversal Section ◽  
Depth Analysis ◽  
Candidate Species

Abstract Background: The identification of tropical African wood species based on microscopic imagery is a challenging problem due to the heterogeneous nature of the composition of wood combined with the vast number of candidate species. Image classification methods that rely on machine learning can facilitate this identification, provided that sufficient training material is available. Despite the fact that the three main anatomical sections contain information that is relevant for species identification, current methods only rely on the transversal section. Additionally, commonly used procedures for evaluating the performance of these methods neglect the fact that multiple images often originate from the same tree, leading to an overly optimistic estimate of the performance. Results: We introduce a new image dataset containing microscopic images of the three main anatomical sections of 77 Congolese wood species. A dedicated multiview image classification method is developed and obtains an accuracy (computed using the naive but common approach) of 95%, outperforming the singleview methods by a large margin. An in-depth analysis shows that naive accuracy estimates can lead to a dramatic over-prediction, of up to 60%, of the accuracy. Conclusions: Additional images from the non-transversal sections can boost the performance of machine-learning-based wood species identification methods. Additionally, care should be taken when evaluating the performance of machine-learningbased wood species identification methods to avoid an overestimation of the performance.

scholarly journals Identification of Yersinia Species by the Vitek GNI Card

1999 ◽  
Vol 37 (1) ◽  
pp. 211-214 ◽  
Author(s):  
Hans-Jörg Linde ◽  
Heinrich Neubauer ◽  
Hermann Meyer ◽  
Stojanca Aleksic ◽  
Norbert Lehn
Keyword(s):  
Species Identification ◽  
Species Level ◽  
Identification Methods ◽  
Genus Level ◽  
Yersinia Species

The Vitek GNI card was used to identify 212 isolates of 10Yersinia species. Identification was correct for 96.3% of the isolates (156 of 162) to the genus level and for 57.4% of the isolates (93 of 162) to the species level forYersinia spp. listed in the Vitek database. We recommend additional identification methods for isolates assigned to the genus Yersinia by the Vitek system.

Chemical Species Identification in an SEM by Changes in Emitted X-Ray Energies

1974 ◽  
Vol 32 ◽  
pp. 570-571
Author(s):  
R. H. Duff
Keyword(s):  
Species Identification ◽  
Valence Electron ◽  
Chemical Species ◽  
X Rays ◽  
Chemical Bonds ◽  
Small Shift ◽  
X Ray ◽  
Electron Transitions ◽  
Peak Energy ◽  
Chemical Combination

A material irradiated with electrons emits x-rays having energies characteristic of the elements present. Chemical combination between elements results in a small shift of the peak energies of these characteristic x-rays because chemical bonds between different elements have different energies. The energy differences of the characteristic x-rays resulting from valence electron transitions can be used to identify the chemical species present and to obtain information about the chemical bond itself. Although these peak-energy shifts have been well known for a number of years, their use for chemical-species identification in small volumes of material was not realized until the development of the electron microprobe.

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