Landscape as mediator, landscape as commons: an introduction

Il contributo propone una trattazione dei due temi chiavi che si intersecano nel volume, andando a costituire non solo una introduzione ai saggi ivi contenuti, ma una pi\uf9 ampia trattazione delle questioni attorno a cui questi si muovono: le potenzialit\ue0 del concetto di paesaggio considerato come intermediario/mediatore e come bene comune/commons. Il volume raccoglie i migliori contributi internazionali presentati nelle sessioni sul paesaggio del congresso Eugeo 2013 (Roma) coordinate dai curatori dell'opera, e si conclude con una approfondita postfazione redatta da Kenneth R. Olwig, che ha partecipato come discussant ai lavori congressuali

Fecal metaproteomic analysis reveals unique changes of the gut microbiome functions after consumption of sourdough Carasau bread

Sourdough-leavened bread (SB) is acknowledged for its great variety of valuable effects on consumer's metabolism and health, including a low glycemic index and a reduced content of the possible carcinogen acrylamide. Here, we aimed to investigate how these effects influence the gut microbiota composition and functions. Therefore, we subjected rats to a diet supplemented with SB, baker's yeast leavened bread (BB), or unsupplemented diet (chow), and, after 4 weeks of treatment, their gut microbiota was analyzed using a metaproteogenomic approach. As a result, diet supplementation with SB led to a reduction of specific members of the intestinal microbiota previously associated to low protein diets, namely Alistipes and Mucispirillum, or known as intestinal pathobionts, i.e., Mycoplasma. Concerning functions, asparaginases expressed by Bacteroides were observed as more abundant in SB-fed rats, leading to hypothesize that in their colonic microbiota the enzyme substrate, asparagine, was available in higher amounts than in BB- and chow-fed rats. Another group of protein families, expressed by Clostridium, was detected as more abundant in animal fed SB-supplemented diet. Of these, manganese catalase, small acid-soluble proteins (SASP), Ser/Thr kinase PrkA, and V-ATPase proteolipid subunit have been all reported to take part in Clostridium sporulation, strongly suggesting that the diet supplementation with SB might promote environmental conditions inducing metabolic dormancy of Clostridium spp. within the gut microbiota. In conclusion, our data describe the effects of SB consumption on the intestinal microbiota taxonomy and functions in rats. Moreover, our results suggest that a metaproteogenomic approach can provide evidence of the interplay between metabolites deriving from bread digestion and microbial metabolism

Towards learning travelers’ preferences in a context-aware fashion

Providing personalized offers, and services in general, for the users of a system requires perceiving the context in which the users’ preferences are rooted. Accordingly, context modeling is becoming a relevant issue and an expanding research field. Moreover, the frequent changes of context may induce a change in the current preferences; thus, appropriate learning methods should be employed for the system to adapt automatically. In this work, we introduce a methodology based on the so-called Context Dimension Tree—a model for representing the possible contexts in the very first stages of Application Design—as well as an appropriate conceptual architecture to build a recommender system for travelers

Context Awareness in the Travel Companion of the Shift2Rail Initiative

Providing personalized offers, and services in general, for the users of a system requires perceiving the context in which the users' preferences are rooted. In this work, we introduce the use of an already known model and methodology-based on the so-called Context Dimen-sion Tree- A long with a conceptual architecture to build a recommender system that offers personalized services for travelers. The research is per-formed in the frame of the Shift2Rail initiative as part of the Innovation Programme 4 of EU Horizon 2020

A Deep-Learning-Based Blocking Technique for Entity Linkage

Nowadays, data integration must often manage noisy data, also containing attribute values written in natural language such as product descriptions or book reviews. Entity Linkage has the role of identifying records that contain information referring to the same object. Modern Entity Linkage methods, in order to reduce the dimension of the problem, partition the initial search space into “Blocks” of records that can be considered similar according to some metrics, greatly reducing the overall complexity of the algorithm. We propose a Blocking strategy that, differently from the traditional methods, aims at capturing the semantic properties of data by means of recent Deep Learning frameworks. This paper is mainly inspired by a recent work on Entity Linkage whose authors were among the first to investigate the application of tuple embeddings to data integration problems. We extend their method adopting an unsupervised approach: our blocking model is trained on an external corpus and then used on new datasets, exploiting a “transfer learning” paradigm. Our choice is motivated by the fact that, in most data integration scenarios, no training data is actually available. Using a semi-automatic approach to blocking, our model, after being trained on an external corpus, can be directly applied to any data integration problem. We tested our system on six popular datasets and compared its performance against five traditional blocking algorithms. The test results demonstrated that our deep-learning-based blocking solution outperforms standard blocking algorithms, especially on textual and noisy data