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Scientific disciplines applied to the soil-plant-atmosphere system, including forestry and pedology are often claimed to provide proper scientific answers about best suitable landscape management. In spite of these efforts, the results have not always produced significant impact; this is not acceptable considering the present day increase of landscape degradation processes. Here we claim that it is necessary to act in a different way if we aim to achieve a sustainable management of our forest and agriculture landscapes. We believe that such management requires to develop a truly integrated and interdisciplinary spatial knowledge system which is strongly focused on soil-plant-atmosphere interactions. Ideally, such knowledge system must also be based on quantifying soil and landscape multi-functionality and it must allow potential "bottom-up" contributions to landscape governance. In such a scenario recent developments in Spatial Decision Support Systems (S-DSS) developed through the web offer a great opportunity for forestry, pedology and agricultural sciences, in order to provide integrated, consistent and operational tools to support individuals, private companies and public bodies in their everyday management of the rural landscape. More specifically, here we present a contribution aiming to illustrate some interdisciplinary applications, including forestry, obtained through the development a web-based S-DSS project (Life SOILCONSWEB). The system has been developed through a "cyberinfrastructure" platform that supports the acquisition of advanced and also dynamic data (e.g., pedological, daily climatic, land use) and their storage, management and integration, data mining, data visualization and computer applications "on the fly" in order to perform simulation modelling (e.g., soil water balance and growth of plants), all freely accessible via Internet. The w-SDSS system is tested over a case study of 20,000 ha (Telesina Valley; Benevento, Italy); here the system produces accurate information, reporting, maps, scenario analysis, etc. for agriculture, forestry, environment and landscape planning themes. For the forestry module, the system can provide answers such as: (i) forest types; (ii) the average height of forest stands; (iii) the growing stock; (iv) above ground carbon stock, etc.

Etching technique to prepare ultra-thin sections for immunolectron microscopy have incorporated a variety of reagents to expose antigenic sites. In this paper involving 2 techniques for surface etching prior to immunoelectron microscopy, radio frequency glow discharge (RFGD) and solid-phase lactoperoxidase-glucose oxidase beads (Enzymobeads) are compared to conventional peroxide etching techniques. Measuring such parameters as intensity of granule disposition and titers antibody resulting in detectable staining, RFGD and Enzymobeads were both superior to the conventional peroxide methodology. Non-specific absorption by ferritin under the conditions utilized was not a problem with Enzymobeads or RFGD method. In addition, RFGD may be useful in situations where peroxide susceptible antigens are under study.

Background. Developmental Language Disorder (DLD) is frequent in childhood and may have long-term sequelae. By employing an evidence-based approach, this scoping review aims at identifying (a) early predictors of DLD; (b) the optimal age range for the use of screening and diagnostic tools; (c) effective diagnostic tools in preschool children. Methods. We considered systematic reviews, meta-analyses, and primary observational studies with control groups on predictive, sensitivity and specificity values of screening and diagnostic tools and psycholinguistic measures for the assessment of DLD in preschool children. We identified 37 studies, consisting of 10 systematic reviews and 27 primary studies. Results. Delay in gesture production, receptive and/or expressive vocabulary, syntactic comprehension, or word combination up to 30 months emerged as early predictors of DLD, a family history of DLD appeared to be a major risk factor, and low socioeconomic status and environmental input were reported as risk factors with lower predictive power. Optimal time for screening is suggested between age 2 and 3, for diagnosis around age 4. Because of the high variability of sensitivity and specificity values, joint use of standardized and psycholinguistic measures is suggested to increase diagnostic accuracy. Conclusions. Monitoring risk situations and employing caregivers’ reports, clinical assessment and multiple linguistic measures are fundamental for an early identification of DLD and timely interventions.

Olive cultivation is a very important activity which performs several important ecological functions in many inland areas. Recent progress in modelling and Decision Support Systems (DSS) applied to agriculture promises to deliver important positive changes. However, most of this progress regards agriculture systems other than olive growing, so failing to challenge the environmental dimension of the olive grove and the landscape, which is a key issue in the planning and management of olive cultivation. This paper aims to demonstrate that a new type of DSS developed upon the open-source Geospatial Cyberinfrastructure (GCI) platform (named GeOlive) can provide a very important web-based operational tool for olive growing as it better connects productivity and environmental sustainability. This GCI platform supports the acquisition, management, and processing of both static and dynamic data (e.g. pedology, daily climate), data visualization, and computer on-the-fly applications in order to perform simulation modelling (e.g. evaluation of bioclimatic indices), all potentially accessible via the Web. The DSS tool, applied to an area of 20,000 ha in southern Italy, is designed to assist olive grove planning and management and provide operational support for farmers, farmer associations and decision makers involved in olive grove landscape. DSS outputs include olive grove planning and management scenario analysis and maps, together with evaluation of potential and current plant water stress. A short selection of practical case studies is presented to show the different use cases of the proposed DSS.

We investigate the trading behavior of Finnish individual investors trading the stocks selected to compute the OMXH25 index in 2003 by tracking the individual daily investment decisions. We verify that the set of investors is a highly heterogeneous system under many aspects. We introduce a correlation based method that is able to detect a hierarchical structure of the trading profiles of heterogeneous individual investors. We verify that the detected hierarchical structure is highly overlapping with the cluster structure obtained with the approach of statistically validated networks when an appropriate threshold of the hierarchical trees is used. We also show that the combination of the correlation based method and of the statistically validated method provides a way to expand the information about the clusters of investors with similar trading profiles in a robust and reliable way., 25 pages, 8 figures

We describe two different bootstrap methods applied to the detection of a minimum spanning tree obtained from a set of multivariate variables. We show that two different bootstrap procedures provide partly distinct information that can be highly informative about the investigated complex system. Our case study, based on the investigation of daily returns of a portfolio of stocks traded in the US equity markets, shows the degree of robustness and completeness of the information extracted with popular information filtering methods such as the minimum spanning tree and the planar maximally filtered graph. The first method performs a "row bootstrap" whereas the second method performs a "pair bootstrap". We show that the parallel use of the two methods is suggested especially for complex systems presenting both a nested hierarchical organization together with the presence of global feedback channels., 17 pages, 7 figures

We detect the backbone of the weighted bipartite network of the Japanese credit market relationships. The backbone is detected by adapting a general method used in the investigation of weighted networks. With this approach we detect a backbone that is statistically validated against a null hypothesis of uniform diversification of loans for banks and firms. Our investigation is done year by year and it covers more than thirty years during the period from 1980 to 2011. We relate some of our findings with economic events that have characterized the Japanese credit market during the last years. The study of the time evolution of the backbone allows us to detect changes occurred in network size, fraction of credit explained, and attributes characterizing the banks and the firms present in the backbone., 14 pages, 8 figures

We present an analysis of the credit market of Japan. The analysis is performed by investigating the bipartite network of banks and firms which is obtained by setting a link between a bank and a firm when a credit relationship is present in a given time window. In our investigation we focus on a community detection algorithm which is identifying communities composed by both banks and firms. We show that the clusters obtained by directly working on the bipartite network carry information about the networked nature of the Japanese credit market. Our analysis is performed for each calendar year during the time period from 1980 to 2011. Specifically, we obtain communities of banks and networks for each of the 32 investigated years, and we introduce a method to track the time evolution of these communities on a statistical basis. We then characterize communities by detecting the simultaneous over-expression of attributes of firms and banks. Specifically, we consider as attributes the economic sector and the geographical location of firms and the type of banks. In our 32 year long analysis we detect a persistence of the over-expression of attributes of clusters of banks and firms together with a slow dynamics of changes from some specific attributes to new ones. Our empirical observations show that the credit market in Japan is a networked market where the type of banks, geographical location of firms and banks and economic sector of the firm play a role in shaping the credit relationships between banks and firms., 9 pages, 4 figures, 2 Tables

Interbank markets are fundamental for bank liquidity management. In this paper, we introduce a model of interbank trading with memory. Our model reproduces features of preferential trading patterns in the e-MID market recently empirically observed through the method of statistically validated networks. The memory mechanism is used to introduce a proxy of trust in the model. The key idea is that a lender, having lent many times to a borrower in the past, is more likely to lend to that borrower again in the future than to other borrowers, with which the lender has never (or has in- frequently) interacted. The core of the model depends on only one parameter representing the initial attractiveness of all the banks as borrowers. Model outcomes and real data are compared through a variety of measures that describe the structure and properties of trading networks, including number of statistically validated links, bidirectional links, and 3-motifs. Refinements of the pairing method are also proposed, in order to capture finite memory and reciprocity in the model. The model is implemented within the Mason framework in Java., Comment: 37 pages, 10 figures