Prevalence of diabetic striatopathy and predictive role of glycated hemoglobin level.

BACKGROUND: Diabetic striatopathy is defined as a state of hyperglycemia associated with chorea/ballism, striatal hyperdensity at CT, or hyperintensity at T1-weighted MRI. It is considered a rare complication of uncontrolled diabetes but prevalence data are scarce. OBJECTIVES: Characterize diabetic striatopathy prevalence in the population afferent to the largest teaching hospital in Genova (Liguria, Italy) and investigate the role of glycated hemoglobin level in predicting the risk. METHODS: Data were retrospectively obtained from general population undergoing blood sampling for glycated hemoglobin and resulting with HbA1c values ≥ 8%, from January 2014 to June 2017. Brain neuroimaging of those who underwent at least a brain CT or MRI was examined in search of findings compatible with diabetic striatopathy and clinical information was collected. Logistic regression was used to predict the risk of diabetic striatopathy based on age and HbA1c values. RESULTS: Subjects with uncontrolled diabetes were 4603. Brain neuroimaging was available in 1806 subjects and three patients with diabetic striatopathy were identified, all of them reporting choreic movements. The prevalence of hemichorea due to diabetic striatopathy was therefore 3 cases out of 1806 (0.16%) in our population. Hepatic and hypoxic encephalopathies were the conditions most frequently mimicking diabetic striatopathy. Odds ratio of diabetic striatopathy and HbA1c level was significantly correlated (p = 0.0009). CONCLUSIONS: To the best of our knowledge, this study is the first to evaluate the prevalence of diabetic striatopathy in Italy. High HbA1c values may have a role in predicting diabetic striatopathy.

Prognostic approach to Class III malocclusion through case-based reasoning.

OBJECTIVE: This investigation evaluates the evidence of case-based reasoning (CBR) in providing additional information on the prediction of future Class III craniofacial growth. SETTINGS AND SAMPLE POPULATION: The craniofacial characteristics of 104 untreated Class III subjects (7-17 years of age), monitored with two lateral cephalograms obtained during the growth process, were evaluated. MATERIALS AND METHODS: Data were compared with the skeletal characteristics of subjects who showed a high degree of skeletal imbalance ('prototypes') obtained from a large data set of 1263 Class III cross-sectional subjects (7-17 years of age). RESULTS: The degree of similarity of longitudinal subjects with the most unbalanced prototypes allowed the identification of subjects who would develop a subsequent unfavourable skeletal growth (accuracy: 81%). The angle between the palatal plane and the sella-nasion line (PP-SN angle) and the Wits appraisal were two additional craniofacial features involved in the early prediction of the adverse progression of the Class III skeletal imbalance. CONCLUSIONS: Case-based reasoning methodology, which uses a personalized inference method, may bring additional information to approximate the skeletal progression of Class III malocclusion.

Exploiting the interplay between cross-sectional and longitudinal data in Class III malocclusion patients.

The aim of the study was to investigate how to improve the forecasting of craniofacial unbalance risk during growth among patients affected by Class III malocclusion. To this purpose we used computational methodologies such as Transductive Learning (TL), Boosting (B), and Feature Engineering (FE) instead of the traditional statistical analysis based on Classification trees and logistic models. Such techniques have been applied to cephalometric data from 728 cross-sectional untreated Class III subjects (6-14 years of age) and from 91 untreated Class III subjects followed longitudinally during the growth process. A cephalometric analysis comprising 11 variables has also been performed. The subjects followed longitudinally were divided into two subgroups: favourable and unfavourable growth, in comparison with normal craniofacial growth. With respect to traditional statistical predictive analytics, TL increased the accuracy in identifying subjects at risk of unfavourable growth. TL algorithm was useful in diffusion of information from longitudinal to cross-sectional subjects. The accuracy in identifying high-risk subjects to growth worsening increased from 63% to 78%. Finally, a further increase in identification accuracy, up to 83%, was produced by FE. A ranking of important variables in identifying subjects at risk of growth worsening, therefore, has been obtained.

Multiple linear regression and artificial neural networks for delta-endotoxin and protease yields modelling of Bacillus thuringiensis.

The aim of the present work was to develop a model that supplies accurate predictions of the yields of delta-endotoxins and proteases produced by B. thuringiensis var. kurstaki HD-1. Using available medium ingredients as variables, a mathematical method, based on Plackett-Burman design (PB), was employed to analyze and compare data generated by the Bootstrap method and processed by multiple linear regressions (MLR) and artificial neural networks (ANN) including multilayer perceptron (MLP) and radial basis function (RBF) models. The predictive ability of these models was evaluated by comparison of output data through the determination of coefficient (R 2) and mean square error (MSE) values. The results demonstrate that the prediction of the yields of delta-endotoxin and protease was more accurate by ANN technique (87 and 89% for delta-endotoxin and protease determination coefficients, respectively) when compared with MLR method (73.1 and 77.2% for delta-endotoxin and protease determination coefficients, respectively), suggesting that the proposed ANNs, especially MLP, is a suitable new approach for determining yields of bacterial products that allow us to make more appropriate predictions in a shorter time and with less engineering effort.

A novel application of an adaptable modeling approach to the management of toxic microalgal bloom events in coastal areas.

Harmful algal blooms have been increasing in frequency in recent years, and attention has shifted from describing to modeling and trying to predict these phenomena, since in many cases they pose a risk to human health and coastal activities. Predicting ecological phenomena is often time and resource consuming, since a large number of field collected data are required. We propose a novel approach that involves the use of modeled meteorological data as input features to predict the concentration of the toxic benthic dinoflagellate Ostreopsis cf. ovata in seawater. Ten meteorological features were used to train a Quantile Random Forests model, which was then validated using field collected concentration data over the course of a summer sampling season. The proposed model was able to accurately describe Ostreopsis abundance in the water column in response to meteorological variables. Furthermore, the predictive power of this model appears good, as indicated by the validation results, especially when the quantile for predictions is tuned to match management requirements. The Quantile Random Forests method was selected, as it allows for greater flexibility in the generated predictions, thus making this model suitable as a tool for coastal management. The application of this approach is novel, as no other models or tools that are adaptable to this degree are currently available. The model presented here was developed for a single species over a limited geographical extension, but its methodological basis appears flexible enough to be applied to the prediction of HABs in general and it could also be extended to the case of other ecological phenomena that are strongly dependent on meteorological drivers, that can be independently modeled and potentially globally available.

Looking inside the Ocean: Toward an Autonomous Imaging System for Monitoring Gelatinous Zooplankton.

Marine plankton abundance and dynamics in the open and interior ocean is still an unknown field. The knowledge of gelatinous zooplankton distribution is especially challenging, because this type of plankton has a very fragile structure and cannot be directly sampled using traditional net based techniques. To overcome this shortcoming, Computer Vision techniques can be successfully used for the automatic monitoring of this group.This paper presents the GUARD1 imaging system, a low-cost stand-alone instrument for underwater image acquisition and recognition of gelatinous zooplankton, and discusses the performance of three different methodologies, Tikhonov Regularization, Support Vector Machines and Genetic Programming, that have been compared in order to select the one to be run onboard the system for the automatic recognition of gelatinous zooplankton. The performance comparison results highlight the high accuracy of the three methods in gelatinous zooplankton identification, showing their good capability in robustly selecting relevant features. In particular, Genetic Programming technique achieves the same performances of the other two methods by using a smaller set of features, thus being the most efficient in avoiding computationally consuming preprocessing stages, that is a crucial requirement for running on an autonomous imaging system designed for long lasting deployments, like the GUARD1. The Genetic Programming algorithm has been installed onboard the system, that has been operationally tested in a two-months survey in the Ligurian Sea, providing satisfactory results in terms of monitoring and recognition performances.

Classical and Bayesian predictions applied to Bacillus toxin production.

Bacillus thuringiensis is a bacterium with unusual properties that make it useful for pest control in ecoagriculture. It can form a parasporal crystal containing polypeptides (also called delta-endotoxins). These entomopathogenic toxins are made during the stationary phase of the bacterial growth cycle and were initially characterized as an insect pathogen. Nowadays, the use of saturated two-level designs is very popular. This method is especially used in industrial applications where the cost of experiments is expensive. Standard classical approaches are not appropriate to analyze data from saturated designs. It is due to the fact that they only allow to estimate the main factor effects and cannot assure enough freedom degrees to estimate the error variance. In this paper, we propose the use of empirical Bayesian procedures to get inferences for data obtained from saturated designs, inspired from Hadamard matrices. The proposed methodology is illustrated by assuming a dataset to prove the model robustness. The comparison between the two studied mathematical techniques, based on mean square error values (MSE), revealed that Bayesian method (BM) was more accurate than least square method (LSM): for example, the results showed that 2002 and 2000.7 mg/l for experimental and predicted (BM) data were obtained against 2002 and 1991 mg/l for experimental and predicted (LSM) data. This suggested method could be generalized in several application fields in biological sciences.

Experimental design and Bayesian networks for enhancement of delta-endotoxin production by Bacillus thuringiensis.

Bacillus thuringiensis (Bt) is a Gram-positive bacterium. The entomopathogenic activity of Bt is related to the existence of the crystal consisting of protoxins, also called delta-endotoxins. In order to optimize and explain the production of delta-endotoxins of Bacillus thuringiensis kurstaki, we studied seven medium components: soybean meal, starch, KH2PO4, K2HPO4, FeSO4, MnSO4, and MgSO4and their relationships with the concentration of delta-endotoxins using an experimental design (Plackett-Burman design) and Bayesian networks modelling. The effects of the ingredients of the culture medium on delta-endotoxins production were estimated. The developed model showed that different medium components are important for the Bacillus thuringiensis fermentation. The most important factors influenced the production of delta-endotoxins are FeSO4, K2HPO4, starch and soybean meal. Indeed, it was found that soybean meal, K2HPO4, KH2PO4and starch also showed positive effect on the delta-endotoxins production. However, FeSO4 and MnSO4 expressed opposite effect. The developed model, based on Bayesian techniques, can automatically learn emerging models in data to serve in the prediction of delta-endotoxins concentrations. The constructed model in the present study implies that experimental design (Plackett-Burman design) joined with Bayesian networks method could be used for identification of effect variables on delta-endotoxins variation.