Epigenetic and Genetic Factors Related to Curve Progression in Adolescent Idiopathic Scoliosis: A Systematic Scoping Review of the Current Literature

Adolescent idiopathic scoliosis (AIS) is a progressive deformity of the spine. Scoliotic curves progress until skeletal maturity leading, in rare cases, to a severe deformity. While the Cobb angle is a straightforward tool in initial curve magnitude measurement, assessing the risk of curve progression at the time of diagnosis may be more challenging. Epigenetic and genetic markers are potential prognostic tools to predict curve progression. The aim of this study is to review the available literature regarding the epigenetic and genetic factors associated with the risk of AIS curve progression. This review was carried out in accordance with Preferential Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The search was carried out in January 2022. Only peer-reviewed articles were considered for inclusion. Forty studies were included; fifteen genes were reported as having SNPs with significant association with progressive AIS, but none showed sufficient power to sustain clinical applications. In contrast, nine studies reporting epigenetic modifications showed promising results in terms of reliable markers. Prognostic testing for AIS has the potential to significantly modify disease management. Most recent evidence suggests epigenetics as a more promising field for the identification of factors associated with AIS progression, offering a rationale for further investigation in this field.     

Immobilization of Lathyrus cicera Amine Oxidase on Magnetic Microparticles for Biocatalytic Applications

Amine oxidases are enzymes belonging to the class of oxidoreductases that are widespread, from bacteria to humans. The amine oxidase from Lathyrus cicera has recently appeared in the landscape of biocatalysis, showing good potential in the green synthesis of aldehydes. This enzyme catalyzes the oxidative deamination of a wide range of primary amines into the corresponding aldehydes but its use as a biocatalyst is challenging due to the possible inactivation that might occur at high product concentrations. Here, we show that the enzyme’s performance can be greatly improved by immobilization on solid supports. The best results are achieved using amino-functionalized magnetic microparticles: the immobilized enzyme retains its activity, greatly improves its thermostability (4 h at 75 °C), and can be recycled up to 8 times with a set of aromatic ethylamines. After the last reaction cycle, the overall conversion is about 90% for all tested substrates, with an aldehyde production ranging between 100 and 270 mg depending on the substrate used. As a proof concept, one of the aldehydes thus produced was successfully used for the biomimetic synthesis of a non-natural benzylisoquinoline alkaloid.     

A highly efficient receiver for satellite‐based automatic identification system signal detection

An innovative receiver architecture for the satellite‐based automatic identification system has been recently proposed. In this paper, we describe a few modifications that can be introduced on the algorithms for synchronization and detection, which provide an impressive performance improvement. The receiver architecture has been designed for an on‐board implementation, and a prototype has been implemented by the University of Parma and CGS S.p.A. Compagnia Generale per lo Spazio under the European Space Agency project FENICE (Flexible innovative AIS receiver prototype). A few modifications are also here described that could allow a further performance improvement in case of processing moved to ground‐based stations, based on a priori information there available. Copyright © 2014 John Wiley & Sons, Ltd.     

Automatic word naming recognition for an on-line aphasia treatment system

One of the most common effects among aphasia patients is the difficulty to recall names or words. Typically, word retrieval problems can be treated through word naming therapeutic exercises. In fact, the frequency and the intensity of speech therapy are key factors in the recovery of lost communication functionalities. In this sense, speech and language technology can have a relevant contribution in the development of automatic therapy methods. In this work, we present an on-line system designed to behave as a virtual therapist incorporating automatic speech recognition technology that permits aphasia patients to perform word naming training exercises. We focus on the study of the automatic word naming detector module and on its utility for both global evaluation and treatment. For that purpose, a database consisting of word naming therapy sessions of aphasic Portuguese native speakers has been collected. In spite of the different patient characteristics and speech quality conditions of the collected data, encouraging results have been obtained thanks to a calibration method that makes use of the patients’ word naming ability to automatically adapt to the patients’ speech particularities.     

Viscosity virtual sensor to control combustion in fossil fuel power plants

Thermo-electrical power plants utilize fossil fuel oil to transform the calorific power of fuel into electric power. An optimal combustion in the boiler requires the fuel oil to be in its best conditions. One of fuel's most important properties to consider is viscosity. Viscosity has influence on the optimal combustion between fuel and air. Hardware viscosity meters for fuel oils are expensive and unreliable to operate in power plant conditions. Chemical laboratory measures viscosity accurately with special apparatus, but they cannot be used in a real time process. This paper describes the development of a virtual sensor that estimates fuel oil viscosity in the combustion process of a power plant. A virtual sensor or soft sensor is a computer program that estimates the value of a certain variable based on related measurements and a model of the process where the variable participates. In this project, a probabilistic model is constructed using automatic learning algorithms with historical data and experts' advice. The learning and validation experiments are described and discussed. The virtual sensor is installed in the Tuxpan Power Plant in Veracruz, Mexico.     

Modeling the leadership – project performance relation: radial basis function,Gaussian and Kriging methods as alternatives to linear regression

The purpose of this paper is to analyze alternative forecasting methods that produce results at least similar to or better than linear regression (MLR) that can be used in the modeling of social systems. While organizations may be considered as typically non-linear systems, the common feature of most models found in literature continues to be the use of linear regression techniques. From a case study, advanced statistical methods of Gaussian and Kriging are evaluated, as well as an artificial intelligence (AI) tool, the radial basis function (RBF). The results show the best performance of the suggested methods compared to MLR, especially RBF, because of its uniform prediction behavior throughout all ranges of evaluation. These techniques, although somewhat unconventional in social systems modeling, present a potential contribution in increasing the accuracy and precision of the predictions allowing a more accurate assessment of the impact of certain strategies on the project performance to be made before the allocation of material, human and financial resources.     

Topological and dimensional synthesis of planar linkages for multiple kinematic tasks

This paper presents the combined use of two systematic methods for the synthesis of planar linkage mechanisms satisfying multiple kinematic tasks. First, a Graph Theory-based method is used to exhaustively enumerate the topological alternatives for a given problem. Then each feasible alternative is automatically dimensioned using the Precision Position Method; this computation includes space and design constraints. The existing methods to synthesize multiple tasks solve, in sequence, a decomposition of the problem into single kinematic tasks. The task decomposition and the topology selection for each task are usually performed by hand. This process leads to topologies with a repeated pattern and could lead to ignoring potentially desirable topologies. This paper analyzes a design strategy for the simultaneous solution of multiple kinematic tasks. This strategy has two advantages: (i) it eliminates the need for task decomposition, and (ii) it allows the exhaustive exploration of all non-isomorphic topologies up to a defined number of links. An example of simultaneous synthesis for a double rigid-body guidance task with application to a flap-tab mechanism is shown to illustrate the methodology.     

Gene regulatory networks reconstruction from time series datasets using genetic programming: a comparison between tree-based and graph-based approaches

Genetic programming researchers have shown a growing interest in the study of gene regulatory networks in the last few years. Our team has also contributed to the field, by defining two systems for the automatic reverse engineering of gene regulatory networks called GRNGen and GeNet. In this paper, we revise this work by describing in detail the two approaches and empirically comparing them. The results we report, and in particular the fact that GeNet can be used on large networks while GRNGen cannot, encourage us to pursue the study of GeNet in the future. We conclude the paper by discussing the main research directions that we are planning to investigate to improve GeNet.