A PCR based method to detect Russula spp. in soil samples and Limodorum abortivum roots in Mediterranean environments

Aim of study. Orchidaceae has the largest number of species of any family in the plant kingdom. This family is subject to a high risk of extinction in natural environments, such as natural parks and protected areas. Recent studies have shown the prevalence of many species of orchids to be linked to fungal soil diversity, due to their myco-heterotrophic behaviour. Plant communities determine fungal soil diversity, and both generate optimal conditions for orchid development. Area of study. The work was carried out in n the two most important natural parks in Alicante (Font Roja and Sierra Mariola), in South-eastern of Spain. Material and Methods. We designed a molecular tool to monitor the presence of Russula spp. in soil and orchids roots, combined with phytosociological methods. Main results. Using a PCR-based method, we detected the presence in the soil and Limodorum abortivum orchid roots of the mycorrhizal fungi Russula spp. The species with highest coverage was Quercus rotundifolia in areas where the orchid was present. Research highlights. We present a useful tool based on PCR to detect the presence of Russula spp. in a natural environment. These results are consistent with those obtained in different studies that linked the presence of the mycorrhizal fungi Russula spp. in roots of the species Limodorum and the interaction between these fungal species and Quercus ilex trees in Mediterranean forest environments.

Research gaps in accessibility and MOOCs: A systematic literature review 2008-2016 (dataset)

This study presents the results of a systematic literature review on the combined field of Accessibility and Massive Open Online Courses (MOOCs), covering the time period from 2008 to 2016.

Detailing for a research centre in Antarctica: An experiment to force students to be creative instead of copying standard solutions

In order to equip architecture students with ambitious detail designing ability, related courses of the architecture programs should deal with subjects which are rather rare and unusual for real life practices in order to prevent students copying standard details. In 2015 an innovative project brief has been given to architecture students of Istanbul Technical University. The scenario given in the brief is to design a research station for the first group of Turkish scientist, to be built in one of the coldest and most arid regions on earth; the Antarctica. The performance requirements given in the brief were determined to prevent the students from copying details from any kind of resources as the total number of details generated in real life for those conditions are very limited and specific. The method used has demonstrated a great success and creative detail solutions were generated by the students. In the paper, the innovative coursework brief for bettering the detail design ability of architecture students is explained and the output of the studio is presented.

Research gaps in accessibility and MOOCs: A systematic literature review 2008-2016 (dataset July 2016)

This study presents the results of a systematic literature review on the combined field of Accessibility and Massive Open Online Courses (MOOCs), covering the time period from 2008 to 2016 July. This dataset updates the previous release from 2008 to 2016 May (http://hdl.handle.net/10045/54846).

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.