During a study on the mycobiota of brazil nuts (Bertholletia excelsa) in Brazil, a new Aspergillus species, A. bertholletius, was found, and is described here. A polyphasic approach was applied using morphological characters, extrolite data as well as partial β-tubulin, calmodulin and ITS sequences to characterize this taxon. A. bertholletius is represented by nineteen isolates from samples of brazil nuts at various stages of production and soil close to Bertholletia excelsa trees. The following extrolites were produced by this species: aflavinin, cyclopiazonic acid, kojic acid, tenuazonic acid and ustilaginoidin C. Phylogenetic analysis using partial β-tubulin and camodulin gene sequences showed that A. bertholletius represents a new phylogenetic clade in Aspergillus section Flavi. The type strain of A. bertholletius is CCT 7615 ( = ITAL 270/06 = IBT 29228).

Earlier identification of knee joint pathology helps the therapist to provide the appropriate clinical procedures to control the deteriorating process of arthritis. Beyond usual medical investigations, computational techniques have been used for the diagnosis of knee joint disorder. Among different methodologies, Vibroarthrographic technique is employed to identify knee joint disorder. Machine Learning contains number of classification methods for the given data. A novel technique called Greedy sequential backward feature selection based Radial kernelized least square support vector classification (GSBFS-RKLSSVC) is introduced for accurate detection of knee joint pathology with minimum time. The proposed GSBFS-RKLSSVC technique consists of three processes namely feature selection, feature evaluation and classification. Initially number of VAG signal images are taken from the dataset for detection of knee joint disorder. The relevant feature is selected through the Greedy mutual informative regressed sequential backward selection algorithm to reduce an initial dimensional feature space into a low dimensional feature subspace. Following this the dichotomous logit regression is applied to select the best features and discard others. Therefore, the feature selection process of the proposed GSBFS-RKLSSVC minimizes the time consumption of the knee joint pathology detection. Once the signal features are extracted, RKLSSVC is applied to detect the normal and abnormal VAG signal. Decision boundary is utilized by the classifier to categorize the samples based on the similarity between the training features and testing features. As a result, the accurate classification is obtained with a minimum error rate. The observed result indicates that GSBFS-RKLSSVC achieves higher accuracy, sensitivity, specificity and reduces time than the conventional methods.

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