ABSTRACTWe studied the distribution of fungal endophytes of grapevine (Vitis viniferaL.) plants in a subalpine area of northern Italy, where viticulture is of high economic relevance. We adopted both cultivation-based and cultivation-independent approaches to address how various anthropic and nonanthropic factors shape microbial communities. Grapevine stems were harvested from several locations considering organic and integrated pest management (IPM) and from the cultivars Merlot and Chardonnay. Cultivable fungi were isolated and identified by internal-transcribed-spacer sequence analysis, using a novel colony-PCR method, to amplify DNA from fungal specimens. The composition of fungal communities was assessed using a cultivation-independent approach, automated ribosomal intergenic spacer analysis (ARISA). Multivariate statistical analysis of both culture-dependent and culture-independent data sets was convergent and indicated that fungal endophytic communities in grapevines from organically managed farms were different from those from farms utilizing IPM. Fungal communities in plants of cv. Merlot and cv. Chardonnay overlapped when analyzed using culture-dependent approaches but could be partially resolved using ARISA fingerprinting.

The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain has to explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use machine learning ensemble approach for automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. Experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners.

Microbial plant endophytes are receiving ever-increasing attention as a result of compelling evidence regarding functional interaction with the host plant. Microbial communities in plants were recently reported to be influenced by numerous environmental and anthropogenic factors, including soil and pest management. In this study we used automated ribosomal intergenic spacer analysis (ARISA) fingerprinting and pyrosequencing of 16S rDNA to assess the effect of organic production and integrated pest management (IPM) on bacterial endophytic communities in two widespread grapevines cultivars (Merlot and Chardonnay). High levels of the dominant Ralstonia, Burkholderia and Pseudomonas genera were detected in all the samples We found differences in the composition of endophytic communities in grapevines cultivated using organic production and IPM. Operational taxonomic units (OTUs) assigned to the Mesorhizobium, Caulobacter and Staphylococcus genera were relatively more abundant in plants from organic vineyards, while Ralstonia, Burkholderia and Stenotrophomonas were more abundant in grapevines from IPM vineyards. Minor differences in bacterial endophytic communities were also found in the grapevines of the two cultivars.