Proteome analysis of biological fluids from autoimmune-rheumatological disorders

Autoimmune-rheumatological diseases are worldwide distributed disorders and represent a complex array of illnesses characterized by autoreactivity (reactivity against self-antigens) of T-B lymphocytes and by the synthesis of autoantibodies crucial for diagnosis (biomarkers). Yet, the effects of the autoimmune chronic inflammation on the infiltrated tissues and organs generally lead to profound tissue and organ damage with loss of function (i.e., lung, kidney, joints, exocrine glands). Although progresses have been made on the knowledge of these disorders, much still remains to be investigated on their pathogenesis and identification of new biomarkers useful in clinical practice. The rationale of using proteomics in autoimmune-rheumatological diseases has been the unmet need to collect, from biological fluids that are easily obtainable, a summary of the final biochemical events that represent the effects of the interplay between immune cells, mesenchymal cells and endothelial cells. Proteomic analysis of these fluids shows encouraging results and in this review, we addressed four major autoimmune-rheumatological diseases investigated through proteomic techniques and provide evidence-based data on the highlights obtained in systemic sclerosis, primary and secondary Sjogren's syndrome, systemic lupus erythematosus and rheumatoid arthritis.     

Validating a size measure for effort estimation in model-driven Web development

Web development is moving towards model-driven processes whose goal is the development of Web applications at a higher level of abstraction based on models and model transformations. This brings new opportunities to the Web project manager to make early estimates of the size and the effort required to produce Web applications based on their conceptual models. In the last few years, several studies for size and effort estimation have been performed. However, there are no studies regarding effort estimation in model-driven Web development. In this paper, we present the validation of a model-based size measure (OO-HFP) for Web effort estimation in the context of a model-driven Web development method. The validation is performed by comparing the prediction accuracy that OO-HFP provides with the accuracy provided by the standard function point analysis (FPA) method. The results of the study (using industrial data gathered from 31 Web projects) show that the effort estimates obtained for projects that are sized using OO-HFP are more accurate than the effort estimates obtained using the standard FPA method. This suggests that by following a model-driven development approach, the size measure obtained at the conceptual model of a Web application can be considered a suitable predictor of effort.     

Generating a catalog of unanticipated schemas in class hierarchies using Formal Concept Analysis

ContextInheritance is the cornerstone of object-oriented development, supporting conceptual modeling, subtype polymorphism and software reuse. But inheritance can be used in subtle ways that make complex systems hard to understand and extend, due to the presence of implicit dependencies in the inheritance hierarchy.ObjectiveAlthough these dependencies often specify well-known schemas (i.e., recurrent design or coding patterns, such as hook and template methods), new unanticipated dependency schemas arise in practice, and can consequently be hard to recognize and detect. Thus, a developer making changes or extensions to an object-oriented system needs to understand these implicit contracts defined by the dependencies between a class and its subclasses, or risk that seemingly innocuous changes break them.MethodTo tackle this problem, we have developed an approach based on Formal Concept Analysis. Our Formal Concept Analysis based-Reverse Engineering methodology (FoCARE) identifies undocumented hierarchical dependencies in a hierarchy by taking into account the existing structure and behavior of classes and subclasses.ResultsWe validate our approach by applying it to a large and non-trivial case study, yielding a catalog of hierarchy schemas, each one composed of a set of dependencies over methods and attributes in a class hierarchy. We show how the discovered dependency schemas can be used not only to identify good design practices, but also to expose bad smells in design, thereby helping developers in initial reengineering phases to develop a first mental model of a system. Although some of the identified schemas are already documented in existing literature, with our approach based on Formal Concept Analysis (FCA), we are also able to identify previously unidentified schemas.ConclusionsFCA is an effective tool because it is an ideal classification mining tool to identify commonalities between software artifacts, and usually these commonalities reveal known and unknown characteristics of the software artifacts. We also show that once a catalog of useful schemas stabilizes after several runs of FoCARE, the added cost of FCA is no longer needed.     

Plantwide Optimizing Control of a continuous bioethanol production process

In this work, the Plantwide Control (PWC) problem of a continuous bioethanol process is investigated from a Plantwide Optimizing Control (PWOC) perspective. PWOC addresses the plantwide control problem integrating real-time optimization and control for optimal operation. Two different PWOC approaches have been considered: a single-layer direct optimizing control approach (one-layer) and a multi-layer without coordination approach (two-layer). The performance of these two PWOC schemes is compared with more conventional decentralized architectures, demonstrating the benefits of using plantwide optimization-based control strategies in the bioethanol process, towards reaching maximum profitability.     

Graph-based representations of point clouds

This paper introduces a skeletal representation, called Point Cloud Graph, that generalizes the definition of the Reeb graph to arbitrary point clouds sampled from m-dimensional manifolds embedded in the d-dimensional space. The proposed algorithm is easy to implement and the graph representation yields to an effective abstraction of the data. Finally, we present experimental results on point-sampled surfaces and volumetric data that show the robustness of the Point Cloud Graph to non-uniform point distributions and its usefulness for shape comparison.     

Modeling the forensic two-trace problem with Bayesian networks

The forensic two-trace problem is a perplexing inference problem introduced by Evett (J Forensic Sci Soc 27:375–381, 1987). Different possible ways of wording the competing pair of propositions (i.e., one proposition advanced by the prosecution and one proposition advanced by the defence) led to different quantifications of the value of the evidence (Meester and Sjerps in Biometrics 59:727–732, 2003). Here, we re-examine this scenario with the aim of clarifying the interrelationships that exist between the different solutions, and in this way, produce a global vision of the problem. We propose to investigate the different expressions for evaluating the value of the evidence by using a graphical approach, i.e. Bayesian networks, to model the rationale behind each of the proposed solutions and the assumptions made on the unknown parameters in this problem.     

Analysis of recent ground subsidence in the Sibari plain (Italy) by means of satellite SAR interferometry-based methods

This work attempts to map recent land subsidence on the Sibari plain in Calabria region (Southern Italy), by exploiting vertical velocity values and time series of persistent scatterer interferometry (PSI) synthetic aperture radar (SAR) data acquired in the period 1992–2011 by different satellite systems.

After reviewing other sources and rates of subsidence recorded by previous works since the beginning of the Holocene, this paper presents the spatial distribution of the current settlement rates showing that subsidence is due to the compressible alluvial sediments of the plain, but that in recent times it is mostly due also to human activities. In fact, according to the most recent monitoring period (2009–2011), downward motion rates are recorded as being only localized within site-specific industrial areas, such as the ‘Sibari Lakes’ nautical and residential centre and Corigliano Calabro Industrial Zone. The temporal analysis of persistent scatterers (PS) time series, carried out through the PS-Time program, adds further useful information, finally highlighting that, in these areas, subsidence has positively decreased or stabilized from the second semester of the year 2010.

This work shows that PSI measurements provide support in the quantitative evaluation of known subsidence process and that the analysis of time series allows effective detection of the temporal evolution trends of the phenomenon, such as changes or deceleration in displacement rates, not otherwise visible by merely considering the average velocity.     

Analysis of teleconnections between AVHRR-based sea surface temperature and vegetation productivity in the semi-arid Sahel

Vegetation productivity across the Sahel is known to be affected by a variety of global sea surface temperature (SST) patterns. Often climate indices are used to relate Sahelian vegetation variability to large-scale ocean-atmosphere phenomena. However, previous research findings reporting on the Sahelian vegetation response to climate indices have been inconsistent and contradictory, which could partly be caused by the variations in spatial extent/definitions of climate indices and size of the region studied. The aim of this study was to analyze the linkage between climate indices, pixel-wise spatio-temporal patterns of global sea surface temperature and the Sahelian vegetation dynamics for 1982-2007. We stratified the Sahel into five subregions to account for the longitudinal variability in rainfall. We found significant correlations between climate indices and the Normalized Difference Vegetation Index (NDVI) in the Sahel, however with different magnitudes in terms of strength for the western, central and eastern Sahel. Also the correlations based on NDVI and global SST anomalies revealed the same East-West gradient, with a stronger association for the western than the eastern Sahel. Warmer than average SSTs throughout the Mediterranean basin seem to be associated with enhanced greenness over the central Sahel whereas colder than average SSTs in the Pacific and warmer than average SSTs in the eastern Atlantic were related to increased greenness in the most western Sahel. Accordingly, we achieved high correlations for SSTs of oceanic basins which are geographically associated to the climate indices yet by far not always these patterns were coherent. The detected SST-NDVI patterns could provide the basis to develop new means for improved forecasts in particular of the western Sahelian vegetation productivity.     

Modeling of oxygen mass transfer in the presence of oxygen-vectors using neural networks developed by differential evolution algorithm

The search capabilities of the Differential Evolution (DE) algorithm – a global optimization technique – make it suitable for finding both the architecture and the best internal parameters of a neural network, usually determined by the training phase. In this paper, two variants of the DE algorithm (classical DE and self-adaptive mechanism) were used to obtain the best neural networks in two distinct cases: for prediction and classification problems. Oxygen mass transfer in stirred bioreactors is modeled with neural networks developed with the DE algorithm, based on the consideration that the oxygen constitutes one of the decisive factors of cultivated microorganism growth and can play an important role in the scale-up and economy of aerobic biosynthesis systems. The coefficient of mass transfer oxygen is related to the viscosity, superficial speed of air, specific power, and oxygen-vector volumetric fraction (being predicted as function of these parameters) using stacked neural networks. On the other hand, simple neural networks are designed with DE in order to classify the values of the mass transfer coefficient oxygen into different classes. Satisfactory results are obtained in both cases, proving that the neural network based modeling is an appropriate technique and the DE algorithm is able to lead to the near-optimal neural network topology.