Search

Pasture-based and small-scale livestock farming systems are the main source of livelihood in the mountain primary sector, ensuring socioeconomic sustainability and biodiversity in rural communities throughout Europe and beyond. Mountain livestock farming (MLF) has attracted substantial research efforts from a wide variety of scientific communities worldwide. In this study, the use of text mining and topic modelling analysis drew a detailed picture of the main research topics dealing with MLF and their trends over the last four decades. The final data corpus used for the analysis counted 2 679 documents, of which 92% were peer-reviewed scientific publications. The number of scientific outputs in MLF doubled every 10 years since 1980. Text mining found that milk, goat and sheep were the terms with the highest weighed frequency in the data corpus. Ten meaningful topics were identified by topic analysis: T1-Livestock management and vegetation dynamics; T2-Animal health and epidemiology; T3-Methodological studies on cattle; T4-Production system and sustainability; T5-Methodological studies; T6-Wildlife and conservation studies; T7-Reproduction and performance; T8-Dairy/meat production and quality; T9-Land use and its change and T10-Genetic/genomic studies. A hierarchical clustering analysis was performed to explore the interrelationships among topics, and three main clusters were identified: the first focused on sustainability, conservation and socioeconomic aspects (T4; T6 and T9), the second was related to food production and quality (T7 and T8) and the last one considered methodological studies on mountain flora and fauna (T1; T2; T3; T5 and T10). The 10 topics identified represent a useful and a starting source of information for further and more detailed analysis (e.g. systematic review) of specific research or geographical areas. A truly holistic and interdisciplinary research approach is needed to identify drivers of change and to understand current and future challenges faced by livestock farming in mountain areas., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Psychiatric disorders can be interpreted as a general dysregulation of the interplay between brain and behaviour. This is why, since the late 1990’s, the terms biological psychiatry and behavioural neurology have been gradually replaced by the term neuropsychiatry. Neuropsychiatry, when practiced in combination with clinical neuropsychology, have given rise to a paradigm that is not based solely on the usual classification models but is directed primarily towards diagnosis and treatment that are based on a functional-dimensional approach., Aim: To discuss the daily practice and organisation in a specialised department for neuro-psychiatry located in a psychiatric teaching hospital., Method: The interdisciplinary approach is explained and analysed on the basis of 10 case studies., Results: Most of the patients referred to the specialised department already had a long history of visits to the health care facilities where they had been treated by a variety of specialists in single disciplines. Often, however, this trajectory did not involve periodical re-evaluation and updating of the original diagnosis. If this strategy had been adopted, then a clear diagnosis with simplified treatment programme might have been devised which could have resulted in a patient’s successful reintegration into society., Conclusion: It is essential that the interdisciplinary approach is adopted in specialised centres for neuropsychiatry because it can make an important contribution to individual patient care and to the spread of specialised knowledge that can benefit the entire field of psychiatry.

This paper addresses the problem of both segmenting and reconstructing a noisy signal or image. The work is motivated by large problems arising in certain scientific applications, such as medical imaging. Two objectives for a segmentation and denoising algorithm are laid out: it should be computationally efficient and capable of generating statistics for the errors in the reconstruction and estimates of the boundary locations. The starting point for the development of a suitable algorithm is a variational approach to segmentation (Shah 1992). This paper then develops a precise statistical interpretation of a one dimensional (1-D) version of this variational approach to segmentation. The 1-D algorithm that arises as a result of this analysis is computationally efficient and capable of generating error statistics. A straightforward extension of this algorithm to two dimensions would incorporate recursive procedures for computing estimates of inhomogeneous Gaussian Markov random fields. Such procedures require an unacceptably large number of operations. To meet the objective of developing a computationally efficient algorithm, the use of previously developed multiscale statistical methods is investigated. This results in the development of an algorithm for segmenting and denoising which is not only computationally efficient but also capable of generating error statistics, as desired.