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Gummy stem blight caused by Stagonosporopsis cucurbitacearum (syn. Didymella bryoniae) is one of the major disease in cucurbits. The goal of the study is to identify and characterize S. cucurbitacearum causing gummy stem blight in Coccinia grandis. The infected samples collected from the C. grandis growing fields, surface sterilized and inoculated on PDA medium. The morphology of the inciting pathogen was thoroughly studied. For further confirmation, the molecular characterization was done by using PCR amplified DNA product with specific ITS1 and ITS4 - rDNA primers and sequenced for further analysis. Phylogenetic tree was constructed using nBLAST data, which confirmed that the pathogen as S. cucurbitacearum. The isolate was deposited in the GenBank and accession number was obtained (OL774656, OL774657, and OL774658). The pathogenicity test was carried out on detached fruit assay on ivy gourd. The characteristic symptoms on fruits and re-isolation also confirmed presence of S. cucurbitacearum. In vitro evaluation of fungicides and plants extract against pathogen was carried out, the results showed that, Kavach (34.66±0.33 at 1.0 mg L-1), effectively inhibit the growth followed by Indofil, Aliette, Sectin, and Nativo. Likewise, the methanol extract of Neem and Ficus exhibits significant inhibitory activity. Therefore, this study revealed S. cucurbitacearum responsible for causing gummy stem blight disease in C. grandis host and this is the first report in Mysuru of Karnataka, India. Further the effective management is required to minimize the disease incidence and yield loss of this high nutritional vegetable crop. [ABSTRACT FROM AUTHOR]

This study was aimed to explore molecular characters of fungal isolates and analyze the phylogenic relationship between isolates. Soil samples collected from different habitats of Jaipur, were analyzed for the prevalence of keratinophilic fungi using the hair baiting technique. The physiological conditions of soil samples were analyzed to study the effect of environmental factors on the growth and occurrence of fungi. Keratinophilic fungi were isolated and then identified on the basis of their macro and micro morphological characteristics. The identification of fungi was confirmed by DNA sequencing by PCR amplification using ITS4 and ITS5 gene primers. The fungal species were identified by the nBLAST using fungal DNA sequences against the data available in NCBI/GenBank. The isolated keratinophilic fungal species were identified as Chaetomium seminis-citrulli and Penicillium guttulosum. These two fungi were reported first time from Rajasthan, India using hair baiting technique. Phylogenic trees were constructed using MEGA7 software with the help of keratinophilic fungi reference data retrieved from NCBI GenBank data, to study the phylogenic relationship between the isolated fungi. [ABSTRACT FROM AUTHOR]

Necrosis on Cycas revoluta leaves was observed on trees growing in a local garden in Lahore, Punjab, Pakistan. Phoma herbarum was isolated and identified on the basis of morphological characters from the necrotic areas of leaf. Morphology-based identification was confirmed by sequencing of ITS region of rDNA. Koch’s pathogenicity postulates were verified. To our knowledge, this is the first report of P. herbarum as a pathogen of leaf necrosis of C. revoluta in Pakistan. [ABSTRACT FROM AUTHOR]

Summary Neural circuit mapping is generating datasets of tens of thousands of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches. We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and unreported topographical features. Fully automated clustering organized the validation dataset into 1,052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types, including searching neurons against transgene expression patterns. Finally, we show that NBLAST is effective with data from other invertebrates and zebrafish. Video Abstract [ABSTRACT FROM AUTHOR]