Cryptococcus neoformans and Cryptococcus gattii are the main pathogenic species of invasive cryptococcosis among the Cryptococcus species. Taxonomic studies have shown that these two taxa have different genotypes or molecular types with biological and ecoepidemiological peculiarities. Matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) has been proposed as an alternative method for labor-intensive methods for C. neoformans and C. gattii genotype differentiation. However, Vitek MS, one of the commercial MALDI-TOF MS instruments, has not been yet been evaluated for this purpose. Thus, we constructed an in-house database with reference strains belonging to the different C. neoformans (VNI, VNII, VNIII, and VNIV) and C. gattii (VGI, VGII, VGIII, and VGIV) major molecular types by using the software Saramis Premium (bioMérieux, Marcy-l’Etoile, France). Then, this new database was evaluated for discrimination of the different genotypes. Our in-house database provided correct identification for all C. neoformans and C. gattii genotypes; however, due to the intergenotypic mass spectral similarities, a careful postanalytic evaluation is necessary to provide correct genotype identification.
Buckwheat (Fagopyrum esculentum Moench) belongs to the Polygonaceae family and has been widely cultivated due to its high nutritional, nutraceutical, and medicinal properties. Brazil ranks seventh-largest producer, with 66,000 tons produced in 2018. Buckwheat is also valued for its adaptability as a cover crop, in grain fields of soybean (Glycine max (L.) Merr., maize (Zea mays L.), and sorghum (Sorghum bicolor (L.) Moench) (Görgen et al. 2016, Babu et al. 2018) especially in fields highly infested with plant-parasitic nematodes (PPN). PPN cause severe root damage, suppressing plant development and yield production. In October 2018, six samples of roots and soil were collected in symptomatic patches of buckwheat, in Guaíra SP (20° 19' 32"S 48° 13' 15.4"W). Samples were analyzed in the Nematology Laboratory (LabNema), UNESP, Jaboticabal, SP, BR. Plants presented symptoms of yellow leaves and galled and volume-reduced roots. Meloidogyne sp. was found, comprising 6,320 eggs and second-stage juveniles (J2s) from 10 g of root and 1,628 J2s in 100 cm³ of soil. Adult morphological characteristics, isoenzyme phenotype of esterase, and molecular analysis were performed to identify the Meloidogyne species. The perineal patterns presented high and trapezoidal dorsal arch (n=15), and the males showed a trapezoidal labial region, including a high head cap formed by a large round labial disc that is raised above the medial lips and centrally concave (n=15) (Eisenback and Hirscmann 1981). These characteristics are typical in Meloidogyne incognita (Kofoid and White, 1912) Chitwood, 1949 (Nascimento et al., 2020; Eisenback and Hirschmann 1981; Netscher and Taylor 1974). The enzymatic phenotype was performed with females (n=8), and the phenotype I1 was verified, described by Esbenshade and Triantaphyllou (1985) as typical for M. incognita. To confirm the species DNA samples were extracted from individual females (n=6) and PCR with specific primers for M. incognita (Mi-F 5′- GTGAGGATTCAGCTCCCCAG-3′ and Mi-R 5′-ACGAGGAA CATACTTCTCCGTCC-3′) and M. javanica (Treub) Chitwood 1949 (Fjav 5′-GGTGCGCGATTGAACTGAGC-3′ and Rjav 5′-CAG GCCCTTCAGTGGAACTATAC-3′) that amplify SCAR markers described by Meng et al. (2004) and Zijlstra et al. (2000), respectively, and specific primers for M. enterolobii Yang & Eisenback 1983 that amplify rDNA-IGS2 region (Me-F 5′-AACTTTTG TGAAAGTGCCGCTG-3′ and Me-R 5′-TCAGTTCAGGCAGG ATCAACC-3′) described by Long et al. (2006) were tested. A fragment of 955 pb DNA size was amplified in Mi-F/R primer, which confirmed the M. incognita identification (Meng et. al., 2004). The original population was used to execute pathogenicity test. In a greenhouse, single buckwheat seeds (cv. IPR 91 Baili) were sown in six 5L pots filled with autoclaved-soil and inoculated with 3,000 eggs and J2s per pot (n=6) and control (n=6). After 60 days, the nematodes were extracted from roots and the M. incognita was confirmed. An average of 15,738 eggs and J2s were recovered, (reproductive factor = 5.24), which confirmed buckwheat as a host to M. incognita. The inoculated plants showed symptoms as those observed in the field. No symptom or nematode was noted on the control. Meloidogyne incognita has been reported causing high damage to the F. esculentum in California (Gardner and Caswell-Chen 1994) plus several crops in Brazil (Nascimento et al., 2020). However, this is the first report of this nematode infecting buckwheat in Brazil. Given the importance of buckwheat in Brazil, with extensive use as forage, cover crop, and its nutritional properties, this report is essential to specific management measures are adopted to avoid further losses.
Identifying nematode damage in large soybean areas is not always achievable in a practical way. Multispectral reflectance sensors have not been thoroughly evaluated to detect nematode damage in soybeans (Glycine max L.). The main research aims of this study were to: (i) determine the bivariate relationship between individual spectral bands and vegetation indices (VIs) relative to soybean conditions (symptomatic versus asymptomatic), and (ii) to select the best model for identifying plant conditions using three algorithms (logistic regression—LR, random forest—RF, conditional inference tree—CIT) and three options for data input using bands, vegetation indices (VIs), and bands plus VIs. The trial was conducted in Brazil on three on-farm soybean fields presenting different species of nematode infestation. Multispectral imagery was obtained using a drone-mounted MicaSense RedEdge® sensor. At each sampling, georeferenced point nematode infestation and spectral measurements of soybean plants were retrieved for the classification of symptomatic and asymptomatic areas, according to the threshold level adopted. Bivariate analysis of variance (ANOVA), LR, RF, and CIT were used to select the multispectral bands/VIs that discriminated among symptomatic and asymptomatic plants, assessing the best model via their respective parameters for accuracy, sensitivity, and specificity. The greatest classification accuracy (>0.70) was achieved when using the CIT algorithm with the spectral bands only, with green (560 ± 20 nm) and near-infrared (840 ± 40 nm) included as the main spectral input variables in the model. These results demonstrate the potential of combining remotely sensed data and machine learning to distinguish nematode-symptomatic and asymptomatic soybean plants.
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