1,720,977 research outputs found
Estimation of photovoltaic generation forecasting models using limited information
This work deals with the problem of estimating a photovoltaic generation forecasting model in scenarios where measurements of meteorological variables (i.e., solar irradiance and temperature) at the plant site are not available. A novel algorithm for the estimation of the parameters of the well-known PVUSA model of a photovoltaic plant is proposed. Such a method is characterized by a low computational complexity, and efficiently exploits only power generation measurements, a theoretical clear-sky irradiance model, and temperature forecasts provided by a meteorological service. The proposed method is validated on real data
Estimating PV forecasting models from power data
In the context of photovoltaic generation forecasting, we propose a method for the estimation of the parameters of the well-known PVUSA model of a PV plant. This problem is addressed in the common scenario where on-site measurements of meteorological variables (i.e. solar irradiance and temperature) are not available. The proposed approach efficiently exploits only power generation measurements and relies on a set of tests to detect a clear-sky condition. The devised algorithm is characterized by very low computational effort. Experimental validation is presented and forecasting performance is evaluated on real data
Investigating perturbed pathway modules from gene expression data via structural equation models
Background: It is currently accepted that the perturbation of complex intracellular networks, rather than the dysregulation of a single gene, is the basis for phenotypical diversity. High-throughput gene expression data allow to investigate changes in gene expression profiles among different conditions. Recently, many efforts have been made to individuate which biological pathways are perturbed, given a list of differentially expressed genes (DEGs). In order to understand these mechanisms, it is necessary to unveil the variation of genes in relation to each other, considering the different phenotypes. In this paper, we illustrate a pipeline, based on Structural Equation Modeling (SEM) that allowed to investigate pathway modules, considering not only deregulated genes but also the connections between the perturbed ones.
Results: The procedure was tested on microarray experiments relative to two neurological diseases: frontotemporal lobar degeneration with ubiquitinated inclusions (FTLD-U) and multiple sclerosis (MS). Starting from DEGs and dysregulated biological pathways, a model for each pathway was generated using databases information biological databases, in order to design how DEGs were connected in a causal structure. Successively, SEM analysis proved if pathways differ globally, between groups, and for specific path relationships. The results confirmed the importance of certain genes in the analyzed diseases, and unveiled which connections are modified among them.
Conclusions: We propose a framework to perform differential gene expression analysis on microarray data based on SEM, which is able to: 1) find relevant genes and perturbed biological pathways, investigating putative sub-pathway models based on the concept of disease module; 2) test and improve the generated models; 3) detect a differential expression level of one gene, and differential connection between two genes. This could shed light, not only on the mechanisms affecting variations in gene expression, but also on the causes of gene-gene relationship modifications in diseased phenotypes
Sem best shortest paths for the characterization of differentially expressed genes
In the last years, systems and computational biology focused their efforts in uncovering the causal relationships among the observable perturbations of gene regulatory networks and human diseases. This problem becomes even more challenging when network models and algorithms have to take into account slightly significant effects, caused by often peripheral or unknown genes that cooperatively cause the observed diseased phenotype. Many solutions, from community and pathway analysis to information flow simulation, have been proposed, with the aim of reproducing biological regulatory networks and cascades, directly from empirical data as gene expression microarray data. In this contribute, we propose a methodology to evaluate the most important shortest paths between differentially expressed genes in biological interaction networks, with absolutely no need of user-defined parameters or heuristic rules, enabling a free-of-bias discovery and overcoming common issues affecting the most recent network-based algorithms
Going Beyond Counting First Authors in Author Co-citation Analysis
The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Pathway composite variables: a useful tool for the interpretation of biological pathways in the analysis of gene expression data
Biological pathways represent a useful tool for the identification, in the intricate network of biomolecules, of subnetworks able to explain specific activities in an organism. The advent of high-throughput gene expression technologies allowed to analyze simultaneously the expression of thousands of genes. Pathway analysis is often used to give a meaning to the set of differentially expressed genes. However, classical analyses generate a list of pathways that are over-represented or perturbed (depending on the approach used), but they do not consider, in many cases, the role of the connections between the biomolecules (genes or proteins) in the explanation of the biological phenomena studied. In this note we propose a fine-tuned method, based on Structural Equation Modeling principles, to discover pathway modules eventually able to characterize, in a network perspective, the mechanisms of the pathogenesis of a disease. The procedure relies on the concepts of shortest path, to find the initial modules, and of pathway composite variable (PCV), to improve and facilitate the interpretation of the modules proposed. The method was tested on microarray data of frontotemporal lobar degeneration with ubiquitinated inclusions (FTLD-U)
Variations on the Author
“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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