Incremental wrapper-based gene selection from microarray data for cancer classification

Gene expression microarray is a rapidly maturing technology that provides the opportunity to assay the expression levels of thousands or tens of thousands of genes in a single experiment. We present a new heuristic to select relevant gene subsets in order to further use them for the classification task. Our method is based on the statistical significance of adding a gene from a ranked-list to the final subset. The efficiency and effectiveness of our technique is demonstrated through extensive comparisons with other representative heuristics. Our approach shows an excellent performance, not only at identifying relevant genes, but also with respect to the computational cost.     

Discrimnation Of Equilibrium Moisture Equations For Soybean Usmg Nonlinearity Measures

The main goal of this work is to find the best way to represent the equilibrium conditions between soybean and air. The experimental study was based on the static method using saturated salt solutions in order to regulate the vapour pressure of the air surrounding the grains. We developed a discrimination study among some ensting equations used for grains. To discriminate these equations we explored some non-linearity measures. The obtained results showed that the Halsey modified equation was the best model in terms of nonsignifiance for bias and non-linearity measures. The distribution for the estimated parameters. using simulation procedures, also showed good normal behavior for the Halsey equation.     

Mathematical modelling of the alcoholic fermentation of glucose by Zymomonas mobilis mobilis

The kinetics of alcoholic fermentation of a strain of Zymomonas mobilis, isolated from sugarcane juice, has been studied with the objective of determining the constansts of a non-structured mathematical model that represents the fermentation process. Assays in batch and in continuous culture have been carried out with different initial concentrations of glucose. The final concentrations of glucose, ethanol and biomass were determined. The following kinetic parameters were obtained: μ_max, 0·5 h^?1; K_s, 4·64 g dm^?3; P_max, 106 g dm^?3; Y_x/s, 0·0265 g g^?1; m, 1·4 g g^?1 h^?1; α, 17·38 g g^?1; β, 0·69 g g^?1 h^?1.     

The impact of risk and protective factors on online teaching experience in high school Italian teachers during the COVID-19 pandemic

The lockdown due to COVID-19 in Italy resulted in the sudden closure of schools, with a shift from traditional teaching to the online one. Through an online questionnaire, this survey explores teachers' experience of online teaching, the level of risk factors (e.g., stress) and protective factors (e.g., locus of control) and their impact on satisfaction levels during the social distancing. One hundred seven high school teachers from Lombardy, an Italian region very affected by the COVID-19 outbreak, participated. Results show that depression and stress are the main predictors of satisfaction levels for online teaching. In addition, coping, locus of control and self-efficacy emerge as important protective factors. Finally, although there is great satisfaction with the online teaching experience, critical elements emerged. This study is relevant because it describes the critical elements of the online teaching experience, and identifies some protective factors and the main risk factors in teachers operating in an area strongly marked by social restrictions imposed by the pandemic. High school teachers emerge as a sub-group of the general population with specific psychological reactions. Considering the results, it is possible to suggest providing high-quality educational support and crisis-psychological oriented services to teachers, and help to maintain the psychological well-being.     

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoothly reproduce different RBFs by changing a real parameter τ. In addition, the mixed use of different RBF shapes in only one RBFNN is allowed. Generalized Radial Basis Function (GRBF) is based on Generalized Gaussian Distribution (GGD), which adds a shape parameter, τ, to standard Gaussian Distribution. Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers. The feasibility and benefits of the approach are demonstrated by means of six gene microarray classification problems taken from bioinformatic and biomedical domains. Three filters were applied: Fast Correlation-Based Filter (FCBF), Best Incremental Ranked Subset (BIRS), and Best Agglomerative Ranked Subset (BARS); this was done in order to identify salient expression genes from among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as new input variables. The results confirm that the GRBFNN classifier leads to a promising improvement in accuracy.     

Silicon Nitride Derived from an Organometallic Polymeric Precursor: Preparation and Characterization

Partially crystalline Si₃N₄, with nanosized crystals and a specific surface area greater than 200 m²/g, is obtained by pyrolysis of a commercially available vinylic polysilane in a stream of anhydrous NH₃ to 1000°C. This polymer does not contain N initially. Crystallization to high-purity α-Si₃N₄ proceeds with additional heating above 1400°C under N₂. The changes in crystallinity, powder morphology, infrared spectra, and elemental compositions, for samples annealed from 1000° to 1600°C under N₂, are consistent with an amorphous-to-crystalline transformation. Although macroscopic consolidation and local densification occur at 1400°C, volatilization and accompanying weight loss limit bulk densification. The effect of temperature on specific surface area is examined and related to the sintering process. These results are applicable to pyrolysis, decomposition, and crystallization studies of ceramics synthesized by polymeric precursor routes.     

Resolution proof transformation for compression and interpolation

Verification methods based on SAT, SMT, and theorem proving often rely on proofs of unsatisfiability as a powerful tool to extract information in order to reduce the overall effort. For example a proof may be traversed to identify a minimal reason that led to unsatisfiability, for computing abstractions, or for deriving Craig interpolants. In this paper we focus on two important aspects that concern efficient handling of proofs of unsatisfiability: compression and manipulation. First of all, since the proof size can be very large in general (exponential in the size of the input problem), it is indeed beneficial to adopt techniques to compress it for further processing. Secondly, proofs can be manipulated as a flexible preprocessing step in preparation for interpolant computation. Both these techniques are implemented in a framework that makes use of local rewriting rules to transform the proofs. We show that a careful use of the rules, combined with existing algorithms, can result in an effective simplification of the original proofs. We have evaluated several heuristics on a wide range of unsatisfiable problems deriving from SAT and SMT test cases.