Homology modeling and molecular dynamics simulations of MUC1-9/H-2Kb complex suggest novel binding interactions

International audienceHuman MUC1 is over-expressed in human adenocarcinomas and has been used as a target for immunotherapy studies. The 9-mer MUC1-9 peptide has been identified as one of the peptides which binds to murine MHC class I H-2K. The structure of MUC1-9 in complex with H-2K has been modeled and simulated with classical molecular dynamics, based on the x-ray structure of the SEV9 peptide/H-2K complex. Two independent trajectories with the solvated complex (10 ns in length) were produced. Approximately 12 hydrogen bonds were identified during both trajectories to contribute to peptide/MHC complex, as well as 1-2 water mediated hydrogen bonds. Stability of the complex was also confirmed by buried surface area analysis, although the corresponding values were about 20% lower than those of the original x-ray structure. Interestingly, a bulged conformation of the peptide's central region, partially characterized as a -turn, was found exposed form the binding groove. In addition, P1 and P9 residues remained bound in the A and F binding pockets, even though there was a suggestion that P9 was more flexible. The complex lacked numerous water mediated hydrogen bonds that were present in the reference peptide x-ray structure. Moreover, local displacements of residues Asp4, Thr5 and Pro9 resulted in loss of some key interactions with the MHC molecule. This might explain the reduced affinity of the MUC1-9 peptide, relatively to SEV9, for the MHC class I H-2K

Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery

Efficient forest fire management requires precise and up-to-date knowledge regarding the composition and spatial distribution of forest fuels at various spatial and temporal scales. Fuel-type maps are essential for effective fire prevention strategies planning, as well as the alleviation of the environmental impacts of potential wildfire events. The aim of this study was to assess and compare the potential of Disaster Monitoring Constellation and Landsat-8 OLI satellite images (Operational Land Imager), combined with Object-Based Image Analysis (GEOBIA), in operational mapping of the Mediterranean fuel types at a regional scale. The results showcase that although the images of both sensors can be used with GEOBIA analysis for the generation of accurate fuel-type maps, only the OLI images can be considered as applicable for regional mapping of the Mediterranean fuel types on an operational basis

An enhanced memory TSK-type recurrent fuzzy network for real-time classification

An enhanced memory TSK-type recurrent fuzzy network (EM-TRFN) is proposed in this paper, suitable for modeling complex dynamic systems. Feedback connections, formulated using finite impulse response (FIR) synaptic filters, are employed in the network architecture, serving as internal memories of multiple past firing values, used to determine the current rule firings. Thus, high-order temporal capabilities are embedded in the network, rendering it capable of modeling highly complex nonlinear temporal processes. The structure of the EM-TRFN is evolved in an on-line fashion, with concurrent structure and parameter learning. The proposed network is combined with the predictive modular fuzzy system (PREMOFS), leading to an efficient system for on-line time-series classification. Simulations on a gait identification problem indicate the efficiency of the proposed system. © 2007 EUCA

Biometric Digital Health Technology for Measuring Motor Function in Parkinson’s Disease: Results from a Feasibility and Patient Satisfaction Study

ObjectivesTo assess the feasibility, predictive value, and user satisfaction of objectively quantifying motor function in Parkinson’s disease (PD) through a tablet-based application (iMotor) using self-administered tests.MethodsPD and healthy controls (HCs) performed finger tapping, hand pronation–supination and reaction time tasks using the iMotor application.ResultsThirty-eight participants (19 with PD and 17 HCs) were recruited in the study. PD subjects were 53% male, with a mean age of 67.8 years (±8.8), mean disease duration of 6.5 years (±4.6), Movement Disorders Society version of the Unified Parkinson Disease Rating Scale III score 26.3 (±6.7), and Hoehn & Yahr stage 2. In the univariate analysis, most tapping variables were significantly different in PD compared to HC. Tap interval provided the highest predictive ability (90%). In the multivariable logistic regression model reaction time (reaction time test) (p = 0.021) and total taps (two-target test) (p = 0.026) were associated with PD. A combined model with two-target (total taps and accuracy) and reaction time produced maximum discriminatory performance between HC and PD. The overall accuracy of the combined model was 0.98 (95% confidence interval: 0.93–1). iMotor use achieved high rates of patients’ satisfaction as evaluated by a patient satisfaction survey.ConclusioniMotor differentiated PD subjects from HCs using simple alternating tasks of motor function. Results of this feasibility study should be replicated in larger, longitudinal, appropriately designed, controlled studies. The impact on patient care of at-home iMotor-assisted remote monitoring also deserves further evaluation

BARD: a global and regional validation burned area database

The FireCCI project is part of the European Space Agency's (ESA) Climate Change Initiative (CCI) programme. The project focuses on the Fire Disturbance Essential Climate Variable(ECV) and specifically on 'Burned Area' (BA) products. The main objective of the FireCCI project is the development and improvement of the burned area detection algorithms, including the product validation protocols. The FireCCI project has developed several global BA products: FireCCI31 and FireCCI41 based on MERIS data, FireCCI50 and the last version FireCCI51 based on MODIS data at 250 m spatial resolution (Chuvieco, et al., 2018; Lizundia-Loiola, et al., 2020). In addition, high resolution BA products have been produced for regional or continental scale (e.g. FireCCISFD11 (Roteta, et al., 2019) based on Sentinel-2 data, for Sub-Saharan Africa at 20 m spatial resolution, and FireCCIS1SA10 (Belenguer-Plomer, et al., 2019) derived from Sentinel-1 for 2017 for the Amazon basin in South America). Moreover, the FireCCI project has promoted the research on BA validation methodologies generating statistically rigorous protocols to implement the accuracy assessment of BA product according the CEOS LPVS stage 3 validation requeriments (Padilla, et al., 2014; 2015; 2017). As a result of this research and the BA product validation activities, several global and regional burned area reference datasets were generated and compiled to create a Burned Area Reference Database (BARD) for validation. Contributions from other international projects and researches as BAECV CONUS, BrFLAS, and NOFFI, have significantly increased the BARD database