A cockpit of multiple measures for assessing film restoration quality

In machine vision, the idea of expressing the quality of a films by a single value is very popular. Usually this value is computed by processing a set of image features with the aim of resembling as much as pos- sible a kind of human judgment of the film quality. Since human quality assessment is a complex mech- anism involving many different perceptual aspects, we believe that such approach may scarcely provide a comprehensive analysis. Especially in the field of digital movie restoration, a single score can hardly provide reliable information about the effects of the various restoring operations. For this reason we in- troduce an alternative approach, where a set of measures, describing over time basic global and local visual properties of the film frames, is computed in an unsupervised way and delivered to expert evalu- ators for checking the restoration pipeline and results. The proposed framework can be viewed as a car or airplane cockpit , whose parameters (i.e. the computed measures) are necessary to control the machine status and performance. This cockpit, which is publicly available online, would like to support the digital restoration process and its assessment

HIS for quality evaluation of vitamin C content in acerola fruit.

The aim of this work is to evaluate, by HIS, vitamin C distribution in acerola fruit as discriminant quality parameter so to deliver the product to the industry according to the fruit quality

Selection of NIR wavelengths from hyperspectral imaging data for the quality evaluation of acerola fruit.

The aim of this work is to evaluate Acerola post-harvest quality using NIR hyperspectral imaging

Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction

Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel network-based approach named Patient-Net (P-Net) in which biomolecular profiles of patients are modeled in a graph-structured space that represents gene expression relationships between patients. Then a kernel-based semi-supervised transductive algorithm is applied to the graph to explore the overall topology of the graph and to predict the phenotype/clinical outcome of patients. Experimental tests involving several publicly available datasets of patients afflicted with pancreatic, breast, colon and colorectal cancer show that our proposed method is competitive with state-of-the-art supervised and semi-supervised predictive systems. Importantly, P-Net also provides interpretable models that can be easily visualized to gain clues about the relationships between patients, and to formulate hypotheses about their stratification

Comparison between FT-NIR and Micro-NIR in the evaluation of Acerola fruit quality, using PLS and SVM regression algorithms.

The aim of this work is to estimate in a non-destructive manner, in acerola fruit, titratable acidity and ascorbic acid content

Macromolecular and Micronutrient Profiles of Sprouted Chickpeas to Be Used for Integrating Cereal-Based Food

Pulse flour may be used to improve nutritional traits of gluten and gluten-free formulations in traditional food such as bread or pasta. However, owing to some intrinsic nutritional, textural, and sensory properties, the use of pulses as ingredients for production of enriched food remains limited. In this study, we investigated the modification in macromolecules and micronutrients in industrial-scale flour from partially sprouted chickpeas to define its possible use as an ingredient in cereal-based foods. Controlled sprouting resulted in significant decrease of antinutritional compounds (e.g., phytic acid and serine protease inhibitors) and in an increase of free minerals and vitamins. Sprouting also affected the overall structural organization of proteins (such as aggregate formation) and their thiol/disulfide balance, and it promoted release of peptides. All of these had a positive effect on dough mixing properties, in particular for dough development. Formulations with enrichment in sprouted chickpea flour (wheat/chickpea ratio = 100:20) were tested also as for their dough leavening properties, which improved with respect to flour from nonsprouted chickpeas. Taking into account the modifications induced by partial sprouting on an industrial scale, we can conclude that sprouted chickpea flour represents an interesting ingredient for production of enriched cereal-based food with better nutritional and rheological characteristics

DDAG K-TIPCAC : an ensemble method for protein subcellular localization

Protein subcellular location prediction is one of the most difficult multiclass prediction problems in modern computational biology. Many methods have been proposed in the literature to solve this problem, but all the existing approaches are affected by some limitations. In this contribution we propose a novel method for protein subcellular location prediction that performs multiclass classification by combining kernel classifiers through DDAG. Each base classifier, called K-TIPCAC, projects the points on a Fisher subspace estimated on the training data by means of a novel technique. Experimental results clearly indicated that DDAG K-TIPCAC performs equally, if not better, than state-of-the-art ensemble methods for protein subcellular location