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

Application of machine learning techniques to derive sea water turbidity from Sentinel-2 imagery

Earth Observation (EO) from satellites has the potential to provide comprehensive, rapid and inexpensive information about water bodies, integrating in situ measurements. Traditional methods to retrieve optically active water quality parameters from satellite data are based on semiempirical models relying on few bands, which often revealed to be site and season specific. The use of machine learning (ML) for remotely sensed water quality estimation has spread in recent years thanks to the advances in algorithm development and computing power. These models allow to exploit the wealth of spectral information through more flexible relationships and are less affected by atmospheric and other background factors. The present study explores the use of Sentinel-2 MultiSpectral Instrument (MSI) Level-1C Top of Atmosphere spectral radiance to derive water turbidity, through application of machine learning techniques. A dataset of 222 combination of turbidity measurements, collected in the North Tyrrhenian Sea – Italy from 2015 to 2021, and values of the 13 spectral bands in the pixel corresponding to the sample location was used. Two regression techniques were tested and compared: a Stepwise Linear Regression (SLR) and a Polynomial Kernel Regression. The two models show accurate and similar performance (R2 = 0.736, RMSE = 2.03 NTU, MAE = 1.39 NTU for the SLR and R2 = 0.725, RMSE = 2.07 NTU, MAE = 1.40 NTU for the Kernel). A band importance analysis revealed the contribution of the different spectral bands and the main role of the red-edge range. The work shows that it is possible to reach a good accuracy in turbidity estimation from MSI TOA reflectance using ML models, fed by the whole spectrum of available bands, although the possible generation of errors related to atmospheric effect in turbidity estimates was not evaluated. Comparison between turbidity estimates obtained from the models with turbidity data from Copernicus CMEMS dataset named ‘Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation’ produced consistent results. Finally, turbidity maps from satellite imagery were produced for the study area, showing the ability of the models to catch extreme events

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 MgSO4 and 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, KH2PO4 and 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

Geoffroy de Lagasnerie, Logique de la création

Les universitaires ont raison d’être inquiets. La société ignore ce qu’ils sont et ce qu’ils font. Le journalisme véhicule les pires poncifs à leur sujet. Quant à leur institution, elle vient de traverser la pire crise de son histoire et ce n’est pas fini. Plusieurs électrochocs récents, souvent aux allures burlesques, ont secoué la vieille maison qui se voit poussée à la réflexivité : une blessure narcissique, suite à la publication en 2003 du classement dit de Shanghai qui reléguait les éta..

A traffic management system for real-time traffic optimisation in railways

The increase in traffic intensity and complexity of the railway system demands new methods for real-time traffic control. This paper introduces the architecture, the approach and the current implementation of an advanced Traffic Management System (TMS) able to optimise traffic fluency in large railway networks equipped with either fixed or moving block signalling systems. The TMS takes into account both the actual position and speed of each train in the area and the actual status of the infrastructure, and the dynamic characteristics of the train and the characteristics of the infrastructure such gradients, admissible speeds, signal positions and signal patterns. Potential conflicts can be predicted in advance and solved in real time, by managing the order of trains, or using alternative routes if possible, and by issuing proper speed recommendations to train drivers. In this way, the TMS prevents or limits the number of unplanned stops and the accompanying journey time loss.

Assessing the Image Concept Drift at the OBSEA Coastal Underwater Cabled Observatory

13 pages, 9 figures, 2 tables.-- Data Availability Statement: The time series of specimen counts per species, obtained through the visual inspection of the image dataset, is provided as a supplementary material (only the images containing at least one specimen are reported). The image datasets analysed for this study can be accessed by contacting the OBSEA observatory [https://www.obsea.es/] on reasonable requestThe marine science community is engaged in the exploration and monitoring of biodiversity dynamics, with a special interest for understanding the ecosystem functioning and for tracking the growing anthropogenic impacts. The accurate monitoring of marine ecosystems requires the development of innovative and effective technological solutions to allow a remote and continuous collection of data. Cabled fixed observatories, equipped with camera systems and multiparametric sensors, allow for a non-invasive acquisition of valuable datasets, at a high-frequency rate and for periods extended in time. When large collections of visual data are acquired, the implementation of automated intelligent services is mandatory to automatically extract the relevant biological information from the gathered data. Nevertheless, the automated detection and classification of streamed visual data suffer from the “concept drift” phenomenon, consisting of a drop of performance over the time, mainly caused by the dynamic variation of the acquisition conditions. This work quantifies the degradation of the fish detection and classification performance on an image dataset acquired at the OBSEA cabled video-observatory over a one-year period and finally discusses the methodological solutions needed to implement an effective automated classification service operating in real timeThis research activity was partially funded by the “ENDURUNS - Development and demonstration of a long-endurance sea surveying autonomous unmanned vehicle with gliding capability powered by hydrogen fuel cell project”, Horizon 2020, Grant Agreement H2020-MG-2018-2019-2020 n.824348 and by the “Joint European Research Infrastructure of Coastal Observatories: Science, Service, Sustainability - JERICO-S3’’ project, Horizon 2020, Grant Agreement no. 871153. This research was also funded within the framework of the following project activities: ARIM (Autonomous Robotic sea-floor Infrastructure for benthopelagic Monitoring; MarTERA ERA-Net Cofound); RESBIO (TEC2017-87861-R; Ministerio de Ciencia, Innovación y Universidades). We also profited from the funding from the Spanish Government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S

Prognostic approach to Class III malocclusion through case‐based reasoning

ObjectiveThis 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 populationThe 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 methodsData 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).ResultsThe 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.ConclusionsCase‐based reasoning methodology, which uses a personalized inference method, may bring additional information to approximate the skeletal progression of Class III malocclusion.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/171230/1/ocr12466.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/171230/2/ocr12466_am.pd