Comparing deep learning and statistical methods in forecasting crowd distribution from aggregated mobile phone data

Accurately forecasting how crowds of people are distributed in urban areas during daily activities is of key importance for the smart city vision and related applications. In this work we forecast the crowd density and distribution in an urban area by analyzing an aggregated mobile phone dataset. By comparing the forecasting performance of statistical and deep learning methods on the aggregated mobile data we show that each class of methods has its advantages and disadvantages depending on the forecasting scenario. However, for our time-series forecasting problem, deep learning methods are preferable when it comes to simplicity and immediacy of use, since they do not require a time-consuming model selection for each different cell. Deep learning approaches are also appropriate when aiming to reduce the maximum forecasting error. Statistical methods instead show their superiority in providing more precise forecasting results, but they require data domain knowledge and computationally expensive techniques in order to select the best parameters

Development and validation of a real-time PCR assay for detection and quantification of Tuber magnatum in soil

Background: Tuber magnatum, the Italian white truffle, is the most sought-after edible ectomycorrhizal mushroom. Previous studies report the difficulties of detecting its mycorrhizas and the widespread presence of its mycelium in natural production areas, suggesting that the soil mycelium could be a good indicator to evaluate its presence in the soil. In this study a specific real-time PCR assay using TaqMan chemistry was developed to detect and quantify T. magnatum in soil. This technique was then applied to four natural T. magnatum truffires located in different regions of Italy to validate the method under different environmental conditions. Results: The primer/probe sets for the detection and quantification of T. magnatum were selected from the ITS rDNA regions. Their specificity was tested in silico and using qualitative PCR on DNA extracted from 25 different fungal species. The T. magnatum DNA concentration was different in the four experimental truffires and higher in the productive plots. T. magnatum mycelium was however also detected in most of the non-productive plots. Ascoma production during the three years of the study was correlated with the concentration of T. magnatum DNA. Conclusions: Taken together, these results suggest that the specific real-time PCR assay perfected in this study could be an useful tool to evaluate the presence and dynamics of this precious truffle in natural and cultivated truffi\ue9re

Is Tuber brumale a threat to T. melanosporum and T. aestivum plantations?

True truffles in the genus Tuber are the most valuable ectomycorrhizal fungiand their cultivation has become widespread around the world. Competition with other ectomycorrhizal fungi and especially with undesired Tuber species, like T. brumale, can threaten the success of a truffle plantation. In this work, the competitiveness of T. brumale towards T. melanosporum and T. aestivum was assessed in a 14 year-old plantation carried out planting seedlings inoculated with these three truffle species in adjacent plots. Analyses of both truffle ectomycorrhizas and extra-radical mycelium were carried out in the transects separating the T. brumale plot from T. melanosporum and T. aestivum plots. The results confirm the competitiveness of T. brumale against T. aestivum and T. melanosporum due to its major ability to colonize the soil around its ectomycorrhizas. However, its competitiveness is limited to the transect areas and it was never found inside T. melanosporum plot. These results remark that, in presence of optimal conditions for T. melanosporum and T. aestivum, the greatest risk of contamination with T. brumale is due to wrong greenhouse activity

Exploring the canal environment in terms of water, bed sediments and vegetation in a reclaimed floodplain of Northern Italy

The Po plain (Italy) is one of the largest floodplains in Europe that needs environmental restoration. To achieve this goal, the knowledge of the 'environment' (water, bed sediments and vegetation) of the canals crossing such floodplain is necessary. The water flow of the canals was kept low for hydraulic safety purposes from October to March (NIR), and high for irrigation purposes from April to September (IR). Within this framework, this study aimed to assess in 9 sites of the east part of Po plain 1) the canals' environment quality in terms of vegetation diversity, and water and bed sediment physicochemical properties; and 2) how these features are influenced by canal managements and landscape properties. Water was monthly sampled both in NIR and IR periods, the bed sediments were sampled in summer and winter pe-riods, while the vegetation was recorded in spring and autumn. The low water flow during NIR worsened the water quality by increasing the concentrations of nutrients and salts. A higher salt and nutrient concentrations were observed both in water and bed sediments of canals crossing areas with fne texture alluvial deposits than in those flowing through medium texture alluvial deposits. Further, higher nutrient and salt concentrations were observed for the ca-nals used as collectors of the water coming from other canals. Despite the differences observed for the bed sediments and water quality, the vegetation type and biodiversity did not show differences among the study sites probably be-cause affected by the land use of the surrounding landscape. Indeed, the canals cross agricultural land which limit the developments of natural vegetation and do not promote plant biodiversity. Overall, the present study found out the key role of landscape properties and canal managements on 'canal environment' quality which need to be consid-ered to perform an appropriate reclamation of such environments

Transcriptome Metabolic Characterization of Tuber borchii SP1—A New Spanish Strain for In Vitro Studies of the Bianchetto Truffle

Truffles are ascomycete hypogeous fungi belonging to the Tuberaceae family of the Pezizales order that grow in ectomycorrhizal symbiosis with tree roots, and they are known for their peculiar aromas and flavors. The axenic culture of truffle mycelium is problematic because it is not possible in many cases, and the growth rate is meager when it is possible. This limitation has prompted searching and characterizing new strains that can be handled in laboratory conditions for basic and applied studies. In this work, a new strain of Tuber borchii (strain SP1) was isolated and cultured, and its transcriptome was analyzed under different in vitro culture conditions. The results showed that the highest growth of T. borchii SP1 was obtained using maltose-enriched cultures made with soft-agar and in static submerged cultures made at 22 °C. We analyzed the transcriptome of this strain cultured in different media to establish a framework for future comparative studies, paying particular attention to the central metabolic pathways, principal secondary metabolite gene clusters, and the genes involved in producing volatile aromatic compounds (VOCs). The results showed a transcription signal for around 80% of the annotated genes. In contrast, most of the transcription effort was concentrated on a limited number of genes (20% of genes account for 80% of the transcription), and the transcription profile of the central metabolism genes was similar in the different conditions analyzed. The gene expression profile suggests that T. borchii uses fermentative rather than respiratory metabolism in these cultures, even in aerobic conditions. Finally, there was a reduced expression of genes belonging to secondary metabolite clusters, whereas there was a significative transcription of those involved in producing volatile aromatic compounds

Occurrence and diversity of bacterial communities in Tuber magnatum during truffle maturation

none9Tuber magnatum, an ascomycetous fungus and obligate ectomycorrhizal symbiont, forms hypogeous fruit bodies, commonly called Italian white truffles. The diversity of bacterial communities associated with T. magnatum truffles was investigated using culture-independent and -dependent 16S rRNA genebased approaches. Eighteen truffles were classified in three groups, representing different degrees of ascocarp maturation, based on the percentage of asci containing mature spores. The culturable bacterial fraction was 4.17 (+/- 1.61) x 10.000.000, 2.60 (+/- 1.22) x 10.000.000 and 1.86 (+/-1.32) x 1.000.000 cfu g-1 for immature, intermediate and mature ascocarps respectively. The total of bacteria count was two orders of magnitude higher than the cfu g-1 count. Sequencing results from the clone library showed a significant presence of alpha-Proteobacteria (634 of the 771 total clones screened, c. 82%) affiliated with Sinorhizobium, Rhizobium and Bradyrhizobium spp. The bacterial culturable fraction was generally represented by gamma-Proteobacteria (210 of the 384 total strains isolated, c. 55%), which were mostly fluorescent pseudomonads. Fluorescent in situ hybridization confirmed that alpha-Proteobacteria (85.8%) were the predominant components of truffle bacterial communities with beta-Proteobacteria (1.5%), gamma-Proteobacteria (1.9%), Bacteroidetes (2.1%), Firmicutes (2.4%) and Actinobacteria (3%) only poorly represented. Molecular approaches made it possible to identify alpha-Proteobacteria as major constituents of a bacterial component associated with T. magnatum ascoma, independently from the degree of maturation.openE. BARBIERI; C. GUIDI; J. BERTAUX; P. FREY-KLETT; J. GARBAYE; P. CECCAROLI; R. SALTARELLI; A. ZAMBONELLI; V. STOCCHIBarbieri, Elena; C., Guidi; J., Bertaux; P., FREY KLETT; J., Garbaye; Ceccaroli, Paola; Saltarelli, Roberta; A., Zambonelli; Stocchi, Vilbert

Transcriptome analysis of skeletal muscle tissue to identify genes involved in pre-slaughter stress response in pigs

The knowledge of genes and molecular processes controlling stress reactions and involved in the genetic system determining resistance to stress in pigs could be important for the improvement of meat quality. This research aimed to compare the expression profiles of skeletal muscle between physically stressed and not stressed pigs of different breeds immediately before slaughter. DNA microarray analysis showed that different functional categories of genes are up-regulated in stressed compared to not stressed pigs and relevant differences among breeds were found. - Utilizzando la tecnica microarray vengono confrontate liste di geni differenzialmente espressi in suini di diverse razze sottoposti oppure no a trattamenti stressanti pre macellazione per identificare le basi genetiche della resistenza e suscettibilità allo stress nel suino nella fase premacellazione e vengono analizzati gli effetti dello stress sulla qualità della carne nei soggetti stressati e non stressati. I risultati indicano interessanti differenze nella risposta allo stress tra le razze Large White Italiana, Duroc Italiana e Pietrain. e diverse categorie di geni sovraregolati nel confronto tra soggetti stressati e non stressati

Normative Multi-Agent Programs and Their Logics

Multi-agent systems are viewed as consisting of individual agents whose behaviors are regulated by an organization artefact. This paper presents a simplified version of a programming language that is designed to implement norm-based artefacts. Such artefacts are specified in terms of norms being enforced by monitoring, regimenting and sanctioning mechanisms. The syntax and operational semantics of the programming language are introduced and discussed. A logic is presented that can be used to specify and verify properties of programs developed in this language

On the effect of human mobility to the design of metropolitan mobile opportunistic networks of sensors

This is the author accepted manuscriptWe live in a world where demand for monitoring natural and artificial phenomena is growing. The practical importance of Sensor Networks is continuously increasing in our society due to their broad applicability to tasks such as traffic and air-pollution monitoring, forest-fire detection, agriculture, and battlefield communication. Furthermore, we have seen the emergence of sensor technology being integrated in everyday objects such as cars, traffic lights, bicycles, phones, and even being attached to living beings such as dolphins, trees, and humans. The consequence of this widespread use of sensors is that new sensor network infrastructures may be built out of static (e.g., traffic lights) and mobile nodes (e.g., mobile phones, cars). The use of smart devices carried by people in sensor network infrastructures creates a new paradigm we refer to as Social Networks of Sensors (SNoS). This kind of opportunistic network may be fruitful and economically advantageous where the connectivity, the performance, of the scalability provided by cellular networks fail to provide an adequate quality of service. This paper delves into the issue of understanding the impact of human mobility patterns to the performance of sensor network infrastructures with respect to four different metrics, namely: detection time, report time, data delivery rate, and network coverage area ratio. Moreover, we evaluate the impact of several other mobility patterns (in addition to human mobility) to the performance of these sensor networks on the four metrics above. Finally, we propose possible improvements to the design of sensor network infrastructures