Species Discrimination Research Articles

Polyphasic identification of Vibrio species from aquatic sources using mass spectrometry, housekeeping gene sequencing and whole genome analysis

Accurate bacterial identification is essential for determining the causative agent of an infection, thus facilitating appropriate treatment and management strategies in both human and animal health contexts. Some species in the Vibrio genus are recognized pathogens, associated with a variety of infections. However, identification of these bacteria is oftentimes controversial. Therefore, we aimed to evaluate different identification approaches in terms of their reliability in distinguishing Vibrio species. To achieve this, we selected a set of 40 Vibrio isolates previously recovered from water and floating plastic samples in a large bay environment and identified them employing MALDI-TOF mass spectrometry, and rrs and pyrH gene sequencing. A subset of isolates was also submitted to whole genome sequencing. Overall, MALDI-TOF was found to be a fast-screening methodology for identification, notably at genus-level. However, for better species discrimination, pyrH gene sequencing stood out as a more reliable tool in contrast to rrs gene sequencing and MALDI-TOF, as corroborated by whole genome sequencing analysis.

Vaginal microbiota in term pregnant women with differences in cervical ripeness revealed by 2bRAD-M

BackgroundCervical ripening is a multifactorial outcome, and the association between cervical ripening and vaginal microbiota remains unexplored in term primiparous women. A new sequencing technology, microbiome 2bRAD sequencing (2bRAD-M) that provides a higher level of species discrimination compared to amplicon sequencing. We applied 2bRAD-M to analyze the vaginal microbiota in a population with variations in cervical ripeness and to explore potential microbiota factors influencing cervical ripening.MethodsA total of 30 full-term primigravid women participated in this study, with 15 belonging to the low scoring group of cervical ripeness and 15 to the high scoring group. Clinical information was collected from the participants, and the vaginal microbiota and community structure of both groups were analyzed using 2bRAD-M sequencing. Microbiota diversity and differential analyses were conducted to explore potential factors influencing cervical ripening.ResultsA total of 605 species were detected. There was no difference in vaginal microbiota diversity between the two groups, and the vaginal microbial composition was structurally similar. In the two groups, Lactobacillus crispatus and Lactobacillus iners were identified as the two pivotal species through random forest analysis. Concurrent, extensive and close connections between species within the two groups were observed in the correlation analysis, influencing the aforementioned two species. Pairwise comparisons showed that Sphingomonas (P = 0.0017) and three others were abundant in high scoring group, while Alloprevotella (P = 0.0014), Tannerella (P = 0.0033), Bacteroides (P = 0.0132), Malassezia (P = 0.0296), Catonella (P = 0.0353) and Pseudomonas (P = 0.0353) and so on showed higher abundance in low scoring group. Linear discriminant analysis effect size identified 29 discriminative feature taxa.ConclusionFor the first time, vaginal microbiota was sequenced using 2bRAD-M. With a relatively simple structure, a more stable vaginal microbiota is associated with higher cervical ripeness, and certain microorganisms, such as Sphingomonas, may play a beneficial role in cervical ripening.

Clustering of tropical species through multivariate analysis of the bonding properties of edge-glued panels (EGP)

Tropical woods have high added value, and the use of waste for panel production is an initiative to add more value to this material, typically consisting of various species. The use of raw materials from tropical solid wood waste in the Amazon region contributes to the reduction of carbon emissions associated with the burning of this material. In this context, this study aimed to cluster species based on the bonding properties of Amazonian woods by applying multivariate analysis. The apparent density 12% and strength of finger joint and edge-glued joints of Cedrela odorata, Enterolobium schomburgkii, Erisma uncinatum and Qualea paraensis was evaluated which were joined in homogeneous and hybrid configurations with the adhesives PVA and EPI, as spreading rate of 180 g/m2. The treatments were compared by multivariate analysis of variance and discriminant analysis. The discrimination of species and combinations based on wood density (low, medium, and high) revealed that this is the most important characteristic for grouping different species that make up tropical hardwood waste for the production of EGP panels. In practice, the separation of species into groups according to wood densities can contribute to a better definition of the indications for use of edge glued panels of each density class, and assertive targeting for applications according to the strength ranges required. In the static bending, parallel tensile and shear tests all species and their combinations met the minimum requirements normative, indicating the approval of species and combinations studied for the production of EGP panels.

Spectral Discrimination of Common Karoo Shrub and Grass Species Using Spectroscopic Data

Rangelands represent about 25% of the Earth’s land surface but are under severe pressure. Rangeland degradation is a gradually increasing global environmental problem, resulting in temporary or permanent loss of ecosystem functions. Ecological rangeland studies aim to determine the productivity of rangelands as well as the severity of their degradation. Rigorous in situ assessments comprising visual identification of plant species are required as such assessments are perceived to be the most accurate way of monitoring rangeland degradation. However, in situ assessments are expensive and time-consuming exercises, especially when carried out over large areas. In situ assessments are also limited to areas that are accessible. This study aimed to evaluate the effectiveness of multispectral (MS) and hyperspectral (HS) remotely sensed, unmanned aerial vehicle (UAV)-based data and machine learning (random forest) methods to differentiate between 15 dominant Nama Karoo plant species to aid ecological impact surveys. The results showed that MS imagery is unsuitable, as classification accuracies were generally low (37.5%). In contrast, much higher classification accuracies (>70%) were achieved when the HS imagery was used. The narrow bands between 398 and 430 nanometres (nm) were found to be vital for discriminating between shrub and grass species. Using in situ analytical spectral device (ASD) spectroscopic data, additional important wavebands between 350 and 400 nm were identified, which are not covered by either the MS or HS remotely sensed data. Using feature selection methods, 12 key wavelengths were identified for discriminating among the plant species with accuracies exceeding 90%. Reducing the dimensionality of the ASD data set to the 12 key bands increased classification accuracies from 84.8% (all bands) to 91.7% (12 bands). The methodology developed in this study can potentially be used to carry out UAV-based ecological assessments over large and inaccessible areas typical of Karoo rangelands.

Isolation of Sphingopyxis kveilinensis sp. nov., a Potential Antibiotic-Degrading Bacterium, from a Karst Wetland.

A Gram-stain-negative, aerobic, mesophilic, motile, rod-shaped bacterium, designated strain TUF1T, was isolated from a karst wetland in south-west China. It was demonstrated to be capable of growing on plates containing oxytetracycline, streptomycin, or ofloxacin as the sole carbon source. Phylogenetic analysis of the 16S rRNA gene sequence revealed that this organism belongs to the genus Sphingopyxis and is closely related to S. chilensis S37T (99.17%) and S. alaskensis RB2256T (99.12%). The orthologous average nucleotide identity values (OrthoANIu, 84.42% and 87.53%) and digital DNA-DNA hybridization values (dDDH, 41.7% and 48.9%) between strain TUF1T and its close relatives were all below the standard recommended threshold values for species discrimination. The genomic DNA G + C content was determined to be 64.7%. The predominant cellular fatty acids were identified as summed feature 8 (C18:1ω7c and/or C18:1ω6c) and summed feature 3 (C16:1ω7c and/or C16:1ω6c). The major polar lipids found to be diphosphatidylglycerol, phosphatidylglycerol, phosphatidylcholine, phosphatidylethanolamine, and sphingoglycolipid. The sole respiratory quinone present was ubiquinone Q10. Based on the phylogenetic, biochemical, physiological, and chemotaxonomic analyses, strain TUF1T represents a novel species of the genus Sphingopyxis. The designation "Sphingopyxis kveilinensis sp. nov." is proposed as the name for this novel species, and the strain TUF1T (= CGMCC1.62043T = JCM36394T) is designated as the type strain.

A molecular approach to identify parrotfish (Sparisoma) species during early ontogeny.

Sparisoma species (parrotfish) comprise an important functional group contributing to coral-reef resilience. The morphological diagnostic characteristics for species identification are clearly described for adult forms but not for the early stages. Consequently, many taxonomical listings of Sparisoma larvae are restricted to the genus level. The aims of this study are to determine whether the morphological and molecular identification techniques are useful to assign the species taxonomic level to Sparisoma larvae occurring in the Gulf of Mexico and whether there is a set of diagnostic features that could be used to discriminate between species in larvae of different developmental stages. Morphological assignment of Sparisoma was performed based on morphological and meristic features for 30 larvae collected in the Gulf of Mexico from late August to mid-September 2015. To corroborate and complement the morphological assignments, molecular identification was carried out using DNA sequences from regions of two mitochondrial genes, mitochondrial cytochrome oxidase I (mtDNA COI) and mitochondrial 16S rRNA (mtDNA 16S rRNA). COI and 16S gene trees for Sparisoma and related fish taxa were constructed using sequences available in the NCBI (National Center for Biotechnology Information) GenBank and BOLD (Barcode of Life Data) databases. Two morphotypes were identified based on morphology, but no diagnostic characteristics for species discrimination were found. Molecular identification, in contrast, successfully discriminated four early development stages of Sparisoma atomarium, three stages of Sparisoma radians, and two stages of Sparisoma chrysopterum and Sparisoma aurofrenatum, therefore demonstrating the successful and necessary application of molecular taxonomic approaches for species-level identifications of Sparisoma larvae.

A-298 A Novel DNA Array Technology to Detect and Discriminate Complex Panels of Microbial Pathogens

Abstract Background UTI (Urinary Tract Infection) is a prototype for the field of polymicrobial testing. Current testing standards suggest that UTI testing may require the Detection and Discrimination of a panel of at least (@24) bacteria and fungi (@2) along with a panel of antibiotic resistance gene (ARG) markers (>12). Today, such analysis of UTI’s at the DNA level can be obtained with predicates such as large-panel qPCR reactions, e.g. the Thermo OpenArray qPCR assembly, or by NGS based microbiomics. The necessary complexity of such polymicrobial testing pushes the content limits of qPCR and, although NGS has substantial capacity to resolve complex mixtures, NGS remains relatively low throughput and requires a level of data processing that exceeds what is practical in some laboratories. Methods We have developed a new technology for UTI testing, “D3 ArrayTM-UTI”, which obviates most of the present limitations of DNA based polymicrobial testing. The process begins with urine being isolated and purified using standard automated magnetic bead DNA sample prep, already in place in most molecular laboratories, at up to 96 samples in parallel. This is followed by a single multiplex, endpoint PCR reaction on each sample, also at up to 96 samples in parallel, then automated room temperature hybridization of the PCR product in a standard 96 well format, onto a single 21x21 D3 Array at the bottom of each well. Dedicated autonomous software analyzes the hybridization data yielding the complete list of microbial pathogens and ARG’s detected in a urine specimen. The first deployment of the D3 Array-UTI test is now available to support analysis of 96 urine samples in 6hrs. The results of such preliminary testing are presented here. Results aLOD analysis has been performed with purified DNA and found to be in the 0.1-10cp/RXN range among the full set of 24 bacterial species and 12 ARG targets. Concordance analysis for bacterial Species Detection and Discrimination has been performed on 235 clinical isolates obtained from collaborators at Mako Medical laboratories (Thermo Openarray as the comparator) which averaged = 97 +/- 5% for all but two species: A urinae (88%) and M morganii (94%). Concordance analysis for Antibiotic Resistance Gene Detection and Discrimination has been performed on 384 clinical isolates also obtained from Mako Medical laboratories (Thermo Openarray as the comparator) which averaged = 98 +/-5% and was found to be >98% for all but one ARG target: gyrA point mutation (88%). Conclusions The data provided here demonstrates that the D3 Array-UTI platform can perform complex polymicrobial/ARG testing as a high throughput 96 well plate based assay, with Sensitivity and Specificity that matches or exceeds that of qPCR, using standard automation that can be deployed in any molecular laboratory. The resulting performance is unique in that Species and ARG Discrimination are readily scalable to 3-4 dozen Species per test, performed in triplicate in a single well with completely autonomous, cloud enabled data analysis, at a sample-to-answer throughput >96 samples per shift. Beta testing has been initiated among laboratory partners.

Evaluation of 1064 nm Raman for meat and bone meal species discrimination: From raw form to chemical and physical components

The 1064 nm Raman spectroscopy technique was employed to characterize raw, bone fragments (BF), meat fragments (MF), lipid, and residue of meat and bone meal (MBM) samples. Employing three chemometric algorithms, species discrimination models were optimized for the aforementioned sample types, indicating that the presence of BF favored discriminant analysis for poultry samples, yielding a classification error of 0.000. The species discrimination model constructed using Raman spectra combined with PLS-DA algorithm for residue samples was found to be overall optimal, with classification error for four species samples all below 0.061. A detailed analysis of the BF related spectral variables contributing significantly to PLS-DA discriminant analysis was conducted. Finally, this study established the complementarity of Raman spectroscopy in species discrimination analysis from both a physical and chemical components perspective, laying the theoretical groundwork for the development of a high-precision 1064 nm Raman spectroscopic analysis method for MBM species discrimination.

DNA barcoding of southern African mammal species and construction of a reference library for forensic application.

Combating wildlife crimes in South Africa requires accurate identification of traded species and their products. Diagnostic morphological characteristics needed to identify species are often lost when specimens are processed and customs officials lack the expertise to identify species. As a potential solution, DNA barcoding can be used to identify morphologically indistinguishable specimens in forensic cases. However, barcoding is hindered by the reliance on comprehensive, validated DNA barcode reference databases, which are currently limited. To overcome this limitation, we constructed a barcode library of cytochrome c oxidase subunit 1 and cytochrome b sequences for threatened and protected mammals exploited in southern Africa. Additionally, we included closely related or morphologically similar species and assessed the database's ability to identify species accurately. Published southern African sequences were incorporated to estimate intraspecific and interspecific variation. Neighbor-joining trees successfully discriminated 94%-95% of the taxa. However, some widespread species exhibited high intraspecific distances (>2%), suggesting geographic sub-structuring or cryptic speciation. Lack of reliable published data prevented the unambiguous discrimination of certain species. This study highlights the efficacy of DNA barcoding in species identification, particularly for forensic applications. It also highlights the need for a taxonomic re-evaluation of certain widespread species and challenging genera.

Assembly-free reads accurate identification (AFRAID) approach outperforms other methods of DNA barcoding in the walnut family (Juglandaceae)

DNA barcoding has been extensively used for species identification. However, species identification of mixed samples or degraded DNA is limited by current DNA barcoding methods. In this study, we use plant species in Juglandaceae to evaluate an assembly-free accurate reads identification (AFRAID) method of species identification, a novel approach for precise species identification in plants. Specifically, we determined (1) the accuracy of DNA barcoding approaches in delimiting species in Juglandaceae, (2) the minimum size of chloroplast dataset for species discrimination, and (3) minimum amount of next generation sequencing (NGS) data required for species identification. We found that species identification rates were highest when whole chloroplast genomes were used, followed by taxon-specific DNA barcodes, and then universal DNA barcodes. Species identification of 100% was achieved when chloroplast genome sequence coverage reached 20% and the original sequencing data reached 500,000 reads. AFRAID accurately identified species for all samples tested after 500,000 clean reads, with far less computing time than common approaches. These results provide a new approach to accurately identify species, overcoming limitations of traditional DNA barcodes. Our method, which uses next generation sequencing to generate partial chloroplast genomes, reveals that DNA barcode regions are not necessarily fixed, accelerating the process of species identification.

Complete genome sequences of four representative Corynebacterium belfantii strains.

This report describes the complete genome sequence assemblies from four representative isolates of the human pathogen Corynebacterium belfantii. These data provide necessary references to aid accurate sequence-based species discrimination among closely related Corynebacterium spp. pathogens.

Chloroplast mini-barcodes combined with high resolution melting analysis to identify herbal medicine Difengpi (Illicium difengpi)

Difengpi, derived from the air-dried stem bark of Illicium difengpi and enlisted in the Chinese Pharmacopoeia for its therapeutic effect against common ailments, confronts challenges due to dwindling wild resources and intentional substitution with potentially harmful botanical relatives. The imperative need to authenticate this herbal remedy has led to the development of robust methods. Here, we integrated chloroplast mini-barcoding and high resolution melting (HRM) analysis to distinguish Difengpi from the reported adulterants. We assembled the complete chloroplast (cp) genomes of I. difengpi and substituted close relatives I. jiadifengpi and I. majus, and conducted an in-depth comparative analysis to screen divergent regions for exploiting as DNA mini-barcodes. Despite the conservativeness characterizing the whole cp genomes among these Illicium species, we identified some highly variable regions with promising potential as molecular markers for species identification. Subsequently, we designed DNA mini-barcodes and subjected them to HRM analysis to assess their efficacy in species discrimination. Melting profiles unveiled that mini-barcodes designed from four divergence regions trnL–trnF, trnF–ndhJ, ycf1–ndhF and rpl32–trnL exhibited substantial discriminatory power, distinctly differentiated I. difengpi from I. jiadifengpi, I. majus and I. verum. We tested ten commercially available Difengpi products from online stores and local traditional markets using these four mini-barcodes. All ten samples clustered closely with the reference I. difengpi with genotype confidence higher than 93 %, indicating the presence of the claimed species in these samples, sans any reported toxic adulterants. Consequently, we simulated Difengpi mimic samples utilizing I. jiadifengpi, I. majus and I. verum and subjected them to evaluate the practicability of these mini-barcodes. The outcomes confirmed the precision of the four mini-barcodes in accurately discerning the mimic samples. In conclusion, integrating taxon-specific DNA mini-barcodes with HRM analysis is an efficient strategy for the authentication of species identity within commercial herbal products.

Analysis and discrimination of adhesive species using ATR-FTIR combined with Raman, and HS-GC-IMS together with multivariate statistical analysis

Identifying the species and origin of adhesives in criminal investigations aids in narrowing inquiry scope and supporting case detection. This study introduces two advanced combined analytical techniques for distinguishing adhesive species, including attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) combined with Raman spectroscopy, and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) together with multivariate statistical analysis. ATR-FTIR categorized seven adhesives into three groups based on the base materials, with further differentiation achieved via Raman spectra. Analysis of volatile components identified 79 volatile organic compounds (VOCs), with esters being the most concentrated. The fingerprint profile clearly illustrated the characteristic fingerprint sequence and unique marker compounds of each adhesive, effectively enabling their differentiation. Multivariate statistical analysis methods, including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), heatmap, and hierarchical cluster analysis (HCA), were utilized to visually interpret the classification of adhesives. This integrated analytical approach provides a comprehensive analysis of adhesive compositions, facilitating the diversification and precision of adhesive species identification, and broadening the scope for detecting and analyzing trace evidence in forensic science.

Machine Learning Approach to Support Taxonomic Discrimination of Mayflies Species Based on Morphologic Data.

Artificial intelligence (AI) and machine learning (ML) offer objective solutions in the elaboration of taxonomic keys, such as the processing of large numbers of samples, aiding in the species identification, and optimizing the time required for this process. We utilized ML to study the morphological data of eight species of Americabaetis Kluge 1992, a diverse genus in South American freshwater environments. Decision trees were employed, examining specimens from the Museu de Entomologia da Universidade Federal de Viçosa (UFVB/Brazil) and literature data. Eleven morphological traits of taxonomic importance from the literature, including frontal keel, shape of the mouthparts, and abdominal color pattern, were analyzed. The decision tree obtained with the Gini algorithm effectively differentiates eight species (40% of the known species), using only eight morphological characters. Our analysis revealed distinct groups within Americabaetis alphus Lugo-Ortiz and McCafferty 1996a, based on variations in abdominal tracheae pigmentation. This study introduces a novel approach, integrating AI techniques, biological collections, and literature data for aid in the Americabaetis species identification. It provides a valuable tool for taxonomic research on contemporary and extinct mayflies.

Investigation of the distribution patterns in moinids (Crustacea: Cladocera: Moinidae) forming ephippia with two resting eggs

During last two decades, morphological and genetic studies of the microcrustaceans from the family Moinidae Goulden, 1968 (Crustacea: Branchiopoda: Cladocera) were intensified. However, species diversity within this family remains underestimated. It refers to both subtropical and tropical areas of different continents that have traditionally been less studied compared with Central Europe and some other Palaearctic regions. In addition to this, only a restricted set of morphological characteristics is used for the discrimination of species within the genus Moina Baird, 1850. Most common moinid taxa are considered species complexes rather than single species, making any biogeographic reconstruction difficult. Here, we present an investigation of distribution patterns in moinids that form ephippia with two resting eggs based on both a reexamination of data from the literature and from specimens in museum and personal collections. Also, we redescribed morphology of poorly known taxa from the arid regions of the Old World—Moina belli Gurney, 1904 and M. kaszabi Forró, 1988 and clarify their diagnostic morphological characters and distributional ranges. We found that M. belli and M. kaszabi are morphologically very similar. In both species, setae 1 and 2 of thoracic limb I are armed bilaterally by fine, densely located short setulae. Reliable differences between M. belli and M. kaszabi concern armature of the valve posteroventral portion. Among the moinids with two resting eggs in the ephippium, M. belli and M. kaszabi are morphologically closer to M. macrocopa (Straus, 1820) and M. americana Goulden, 1968. All these species have: (1) a dorsal head pore; (2) fine long hairs on head, valve and preanal margin of postabdomen; (3) thoracic limb I of male with a long exopodite. In general, an investigated group of moinids includes both species with relatively compact and very broad distributional ranges. We concluded that maximum diversity of the moinids forming ephippia with two resting eggs is characteristic of southern portions of Palaearctic and Nearctic zoogeographical regions and approximately corresponds to subtropical climate zone. We need to underline that M. macrocopa and M. americana have a great invasive potential. Both species were anthropogenically introduced in South America and have been successfully naturalized there. Their distribution in tropical regions of the Old World and Australia requires new precise investigations. A group of moinids forming ephippia with two resting eggs can be considered as a convenient model for biogeographical reconstructions, including studies on changes in aquatic ecosystems due to global climate warming, eutrophication and the introduction of non-native species. There is no doubt that subsequent joint application of morphological and genetic data will shed more light on the global phylogeographic structure of the entire family Moinidae.

Impact of Molecular Identification of Palm leaves on the preservation and conservation of Palm Leaves-Tala patra Manuscripts

Palm leaves manuscripts preservation is the main target of this research work. Therefore, this research study aimed at molecular identification of palm leaves by DNA Barcoding to assess the potential level on manuscript preservation. This study aims to amplify the rbcL and matK genes in palm leaves collected from different regions of Bangalore. The DNA was isolated by CTAB method and the amplification was done by PCR analysis. DNA barcoding proved to be an efficient tool for proper discrimination of the related species by generating standers that can be used universally. rbcL: A gene found in the chloroplast genome, encoding a large subunit of the enzyme ribulose-bisphosphate carboxylase. matK: Another chloroplast gene coding for maturase K, involved in splicing group II introns. The discrimination is done by accurately sequencing the standard gene region in a very short time (Hebert et al., 2003). rbcL and matK have been having most recommended and vasty studied DNA barcodes for plants, as they are proved by PWG (Pair wise group) to be potential standardized barcodes for plant DNA barcoding. These barcodes are used to properly identify and discriminate any native or foreign plant species (Maloukh et al., 2017). This work guides us the impact/role of molecular identification-DNA Barcoding of palm leaves on the preservation of Palm leaves manuscripts

Development of a novel triplex-PCR assay for the identification of feline hemoplasma species and survey of hemoplasma species in cats in Türkiye

Three hemoplasma species, Mycoplasma haemofelis, Candidatus Mycoplasma haemominutum, and Candidatus Mycoplasma turicensis, have been identified in domestic and wild felids. M. haemofelis may cause severe clinical manifestations in domestic cats, whereas others can lead to mild infections. Identification of these pathogens is done using molecular diagnostic tools like conventional-PCR or real-time PCR. However, these have disadvantages, such as the failure to differentiate species or high cost. This study aimed to develop a triplex-PCR method for the diagnosis and discrimination of feline hemoplasma species. Furthermore, it is aimed at providing molecular data on the epidemiology of feline hemoplasma species in Türkiye, where there is a paucity of information on these pathogens. Triplex-PCR primers amplifying the 16S rRNA gene regions of M. haemofelis (1022 bp), Ca. Mycoplasma haemominutum (607 bp), and Ca. Mycoplasma turicensis (456 bp) species were designed and optimized. Moreover, the detection limits of the method were also determined and it was found that the primers could detect 0.001 ng/μL amount of DNA for M. haemofelis, 0.0001 ng/μL for Ca. Mycoplasma haemominutum, and 0.0002 ng/μL for Ca. Mycoplasma turicensis in the sample. 286 cat blood samples obtained from Sivas province were researched for feline hemoplasma species. Feline hemoplasma species were detected in samples of 29 out of 286 cats (10.23 %). Five samples (1.74 %) were infected with only M. haemofelis, whereas 22 (7.69 %) were only infected with Ca. Mycoplasma haemominutum. Two samples (0.69 %) were found to be infected with both M. haemofelis and Ca. Mycoplasma haemominutum. Ca. Mycoplasma turicensis was not detected in this study. A triplex-PCR method that can be used for the identification and species differentiation of feline hemoplasma species in hosts was developed. Moreover, hemoplasma species were found to be circulating in cats in the study area, and it is recommended that veterinarians and animal owners take the necessary precautions to protect themselves and their cats from these pathogens.

Band configurations and seasonality influence the predictions of common boreal tree species using UAS image data

Key messageData acquisition of remote sensing products is an essential component of modern forest inventories. The quality and properties of optical remote sensing data are further emphasised in tree species-specific inventories, where the discrimination of different tree species is based on differences in their spectral properties. Furthermore, phenology affects the spectral properties of both evergreen and deciduous trees through seasons. These confounding factors in both sensor configuration and timing of data acquisition can result in unexpectedly complicated situations if not taken into consideration. This paper examines how the timing of data acquisition and sensor properties influence the prediction of tree species proportions and volumes in a boreal forest area dominated by Norway spruce and Scots pine, with a smaller presence of deciduous trees.ContextThe effectiveness of remote sensing for vegetation mapping depends on the properties of the survey area, mapping objectives and sensor configuration.AimsThe objective of this study was to investigate the plot-level relationship between seasonality and different optical band configurations and prediction performance of common boreal tree species. The study was conducted on a 40-ha study area with a systematically sampled circular field plots.MethodsTree species proportions (0–1) and volumes (m3 ha−1) were predicted with repeated remote sensing data collections in three stages of the growing season: prior (spring), during (summer) and end (autumn). Sensor band configurations included conventional RGB and multispectral (MS). The importance of different wavelengths (red, green, blue, near-infrared and red-edge) and predictive performance of the different band configurations were analysed using zero–one-inflated beta regression and Gaussian process regression.ResultsPrediction errors of broadleaves were most affected by band configuration, MS data resulting in lower prediction errors in all seasons. The MS data exhibited slightly lower prediction errors with summer data acquisition compared to other seasons, whereas this period was found to be less suitable for RGB data.ConclusionThe MS data was found to be much less affected by seasonality than the RGB data. Spring was found to be the least optimal season to collect MS and RGB data for tree species-specific predictions.

Evaluating the Genome-Based Average Nucleotide Identity Calculation for Identification of Twelve Yeast Species.

Classifying a yeast strain into a recognized species is not always straightforward. Currently, the taxonomic delineation of yeast strains involves multiple approaches covering phenotypic characteristics and molecular methodologies, including genome-based analysis. The aim of this study was to evaluate the suitability of the Average Nucleotide Identity (ANI) calculation through FastANI, a tool created for bacterial species identification, for the assignment of strains to some yeast species. FastANI, the alignment of in silico-extracted D1/D2 sequences of LSU rRNA, and multiple alignments of orthologous genes (MAOG) were employed to analyze 644 assemblies from 12 yeast genera, encompassing various species, and on a dataset of hybrid Saccharomyces species. Overall, the analysis showed high consistency between results obtained with FastANI and MAOG, although, FastANI proved to be more discriminating than the other two methods applied to genomic sequences. In particular, FastANI was effective in distinguishing between strains belonging to different species, defining clear boundaries between them (cutoff: 94-96%). Our results show that FastANI is a reliable method for attributing a known yeast species to a particular strain. Moreover, although hybridization events make species discrimination more complex, it was revealed to be useful in the identification of these cases. We suggest its inclusion as a key component in a comprehensive approach to species delineation. Using this approach with a larger number of yeasts would validate it as a rapid technique to identify yeasts based on whole genome sequences.

Leaf spectrum analysis of three tropical timber species: Diomate (Astronium graveolens), Choibá (Dipteryx oleifera), and Algarrobo (Hymenaea courbaril)

This study analyzed the leaf spectral response of three native timber forest species in the tropical dry forest: Diomate (Astronium graveolens Jacq.), Choibá (Dipteryx oleifera Benth.), and Algarrobo (Hymenaea courbaril L.). The study was conducted at the León Morales Soto Arboretum and Palmetum, at the Universidad Nacional de Colombia in Medellín, Antioquia, Colombia. Spectral data from the leaves were collected in situ using the portable spectroradiometer ASD FieldSpec HandHeld-2, which operates with a spectral resolution of 1 nm (resampled to 10 nm) and covers a spectral range between 325 and 1,075 nm (limited to 400-900 nm). Based on the measurements, the behavior and spectral variability of the species were evaluated. One-factor Analysis of Variance and Mann Whitney-Wilcoxon U-test were implemented in reflectance spectra to select the optimal narrow bands for species discrimination. The classification capacity of the selected narrow bands was assessed using the K-nearest neighbors’ algorithm. It was found that A. graveolens and H. courbaril exhibited spectral signatures typical of healthy vegetation, while D. oleifera showed spectral changes during the early stages of senescence. Regarding spectral separability, 23 narrow bands in the visible region and near-infrared region were identified as optimal for distinguishing the plant species. The supervised classification algorithm applied to these 23 narrow bands achieved an overall accuracy of 95.8%. In conclusion, these findings provide valuable insights into the spectral response of important tropical species and contribute to their conservation efforts by enhancing understanding of their unique spectral characteristics in diverse and heterogeneous ecosystems like tropical forests.