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Crop recognition with high accuracy at a large scale is hampered by the spatial heterogeneity of crop growth characteristics under the complex influence of environmental conditions. With the aim to automatically realize large-scale crop classification with high accuracy, this study proposes an automatic crop classification strategy considering spatial heterogeneity (ACCSH) by combining the geographic detector technique, random forest average accuracy model, and random forest classification model. In ACCSH, spectral and textural indexes that can quantify crop growth characteristics and environmental variables with potential driving effects are first calculated. Next, an adaptive spatial heterogeneity mining method based on the geographic detector technique is proposed to mine spatial homogeneous zones adaptively with significant differentiation of crop growth characteristics. Subsequently, in view of the differences in crop growth characteristics and key classification indexes between spatial homogeneous zones, correlation analysis, and random forest average accuracy are combined to optimize classification indexes independently within each zone. Finally, random forest is used to classify the target crop in each spatial homogeneous zone separately. The proposed ACCSH is applied to automatically recognize crop types, specifically wheat and corn, in northern France. Results show that kappa coefficients of wheat and corn using ACCSH are 15% and 26% higher than those of classifications at the global scale, respectively. In addition, the index optimization strategy in ACCSH shows apparent superiority. Kappa coefficients of wheat and corn are 5–18% and 9–42% higher than those of classifications based on non-optimized indexes, respectively. In general, ACCSH can automatically realize crop classification with a high precision that suggests its reliability. [ABSTRACT FROM AUTHOR]

Background: Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is therefore essential to protect personal data in EHRs and comply with government regulations. Named entity recognition (NER) methods have been proposed to remove personal identifiers, with deep learning-based models achieving better performance. However, manual annotation of training data is time-consuming and expensive. The aim of this study was to develop an automatic de-identification pipeline for all kinds of clinical documents based on a distant supervised method to significantly reduce the cost of manual annotations and to facilitate the transfer of the de-identification pipeline to other clinical centers. Methods: We proposed an automated annotation process for French clinical de-identification, exploiting data from the eHOP clinical data warehouse (CDW) of the CHU de Rennes and national knowledge bases, as well as other features. In addition, this paper proposes an assisted data annotation solution using the Prodigy annotation tool. This approach aims to reduce the cost required to create a reference corpus for the evaluation of state-of-the-art NER models. Finally, we evaluated and compared the effectiveness of different NER methods. Results: A French de-identification dataset was developed in this work, based on EHRs provided by the eHOP CDW at Rennes University Hospital, France. The dataset was rich in terms of personal information, and the distribution of entities was quite similar in the training and test datasets. We evaluated a Bi-LSTM + CRF sequence labeling architecture, combined with Flair + FastText word embeddings, on a test set of manually annotated clinical reports. The model outperformed the other tested models with a significant F1 score of 96,96%, demonstrating the effectiveness of our automatic approach for deidentifying sensitive information. Conclusions: This study provides an automatic de-identification pipeline for clinical notes, which can facilitate the reuse of EHRs for secondary purposes such as clinical research. Our study highlights the importance of using advanced NLP techniques for effective de-identification, as well as the need for innovative solutions such as distant supervision to overcome the challenge of limited annotated data in the medical domain. [ABSTRACT FROM AUTHOR]

Camera traps have revolutionized how ecologists monitor wildlife, but their full potential is realized only when the hundreds of thousands of collected images can be readily classified with minimal human intervention. Deep learning classification models have allowed extraordinary progress towards this end, but trained models remain rare and are only now emerging for European fauna. We report on the first milestone of the DeepFaune initiative (https://www.deepfaune.cnrs.fr), a large-scale collaboration between more than 50 partners involved in wildlife research, conservation and management in France. We developed a classification model trained to recognize 26 species or higher-level taxa that are common in Europe, with an emphasis on mammals. The classification model achieved 0.97 validation accuracy and often > 0.95 precision and recall for many classes. These performances were generally higher than 0.90 when tested on independent out-of-sample datasets for which we used image redundancy contained in sequences of images. We implemented our model in a software to classify images stored locally on a personal computer, so as to provide a free, user-friendly, and high-performance tool for wildlife practitioners to automatically classify camera trap images. The DeepFaune initiative is an ongoing project, with new partners joining regularly, which allows us to continuously add new species to the classification model. [ABSTRACT FROM AUTHOR]

Several manufacturers have developed systems that automatically measure the amount of blood in culture bottles. We compared the volumes measured automatically by the Virtuo instrument (bioMérieux, France) (height-based volumes) and those calculated by weighing the bottles. In all, 150 pairs of aerobic and anaerobic blood culture bottles (BacT/ALERT FA/FN Plus, bioMérieux) were randomly selected over two periods to compare the height- and weight-based volumes and analyze the effect of foam. We also estimated the limit of detection (LOD) and the cut-off value for 5 mL equine blood. The mean height-based volume was approximately 0.67 mL greater than the weight-based volume, particularly in anaerobic culture bottles. Foam did not have a significant effect. The LOD for the automatic height-based volume of equine blood was 0.2–0.4 mL. The 5 mL cut-off was 4–4.2 mL. Therefore, when reporting or monitoring blood volume within culture bottles in the laboratory, these performance characteristics should be adequately considered. [ABSTRACT FROM AUTHOR]

Acquiring a consistent accent and targeting a native standard like Received Pronunciation (RP) or General American (GA) are prerequisites for French learners who plan to become English teachers in France. Reliable methods to assess learners' productions are therefore extremely valuable. We recorded a little over 300 students from our English Studies department and performed auditory analysis to investigate their accents and determine how close to native models their productions were. Inter-rater comparisons were carried out; they revealed overall good agreement scores which, however, varied across phonetic cues. Then, automatic speech recognition (ASR) and automatic accent identification (AID) were applied to the data. We provide exploratory interpretations of the ASR outputs, and show to what extent they agree with and complement our auditory ratings. AID turns out to be very consistent with our perception, and both types of measurements show that two thirds of our students favour an American, and the remaining third, a British pronunciation, although most of them have mixed features from the two accents. [ABSTRACT FROM AUTHOR]

Learning how to write involves the automation of grapho-motor skills. One of the factors that determine automaticity is "motor anticipation." This is the ability to write a letter while processing information on how to produce following letters. It is essential for writing fast and smoothly. We investigated how motor anticipation processes build up during the period of handwriting automation. Children aged 8, 9 and 10 years had to write two letters ("ll," "le," "ln") in cursive writing on a digitizer. Motor anticipation referred to processing changes in size ("ll" vs. "le") and rotation direction ("le" vs." ln") of the second letter while writing the "l." We recorded three measures on the "l" upstroke and downstroke. The movement time data indicate that the "l" upstroke was very variable. The "l's" downstroke duration was shorter for "ll" than "le" and the latter was in turn shorter than "ln." This pattern was already observed at age 8. Trajectory length data revealed that the anticipation of a single parameter such as size change is enough to produce a trajectory increase but the addition of parameters is not cumulative, as we observed for stroke duration. The dysfluency data indicated that at age 8, dysfluency values were equivalent for upstrokes and downstrokes. At ages 9 and 10, the children produced more dysfluency on downstrokes than upstrokes. Previous studies on writing with adults have shown that the anticipation of the following letter affects the production of the "l's" downstroke. The production of the upstroke did not vary. This experiment suggests that learning to anticipate in handwriting production requires: a) rendering the movements to produce the upstroke constant; and b) modulating the downstroke as a function of the spatial characteristics of the following letter. The pattern of movement time data suggest that motor anticipation would start to be adult-like at around age 9. Dysfluency and latency measures do not seem to be very informative about the development of motor anticipation in handwriting.

Background: In high-dimensional data analysis, the complexity of predictive models can be reduced by selecting the most relevant features, which is crucial to reduce data noise and increase model accuracy and interpretability. Thus, in the field of clinical decision making, only the most relevant features from a set of medical descriptors should be considered when determining whether a patient is healthy or not. This statistical approach known as feature selection can be performed through regression or classification, in a supervised or unsupervised manner. Several feature selection approaches using different mathematical concepts have been described in the literature. In the field of classification, a new approach has recently been proposed that uses the [Formula: see text]-metric, an index measuring separability between different classes in heart rhythm characterization. The present study proposes a filter approach for feature selection in classification using this [Formula: see text]-metric, and evaluates its application to automatic atrial fibrillation detection.Methods: The stability and prediction performance of the [Formula: see text]-metric feature selection approach was evaluated using the support vector machine model on two heart rhythm datasets, one extracted from the PhysioNet database and the other from the database of Marseille University Hospital Center, France (Timone Hospital). Both datasets contained electrocardiogram recordings grouped into two classes: normal sinus rhythm and atrial fibrillation. The performance of this feature selection approach was compared to that of three other approaches, with the first two based on the Random Forest technique and the other on receiver operating characteristic curve analysis.Results: The [Formula: see text]-metric approach showed satisfactory results, especially for models with a smaller number of features. For the training dataset, all prediction indicators were higher for our approach (accuracy greater than 99% for models with 5 to 17 features), as was stability (greater than 0.925 regardless of the number of features included in the model). For the validation dataset, the features selected with the [Formula: see text]-metric approach differed from those selected with the other approaches; sensitivity was higher for our approach, but other indicators were similar.Conclusion: This filter approach for feature selection in classification opens up new methodological avenues for atrial fibrillation detection using short electrocardiogram recordings. [ABSTRACT FROM AUTHOR]

This work deals with the automatic dispatching of the charging currents in a charging station for power-assisted bikes (ebike). The decision variables such as arduousness index and urgency are determined. The arduousness index is carried out from the GPS ride data. Urgency is calculated using the parking time and ebike batteries state of charge. They are used to determine ebike's charging priorities at the charging station using continue fuzzy logic. Photovoltaic power forecasting is determined over the control horizon using the artificial neural network. On the one hand, the values of the priority, the photovoltaic power forecasting and the storage battery's state of charge are calculated. They allow to control the states of the switches associated with each charging spot and the operating mode of the storage battery (source or load) using discrete fuzzy logic. On the other hand, the interest of the ride's arduousness for a charging station is presented. A comparative study between the charging method integrating the ride arduousness and not is carried out. A case study of the polytech Annecy campus at the University of Savoie Mont Blanc in France is proposed. Results show that: the arduousness index is essential for controlling the charging priority of ebikes at the charging station; Fuzzy logic allows to manage the current dispatching on a charging station; taking into account the ride's arduousness allows to save up to 413.03 (Wh) of profit and 97.90% energy flexibility on the charging station. • The design and the implementation of the arduousness index using the ebike ride data (longitude, latitude and altitude). • The priority management of an ebike charging station based on the arduousness index. • The integration of the rides' arduousness in the automatic control of the ebikes charging station. • The energy management of a charging station using the ride arduousness. [ABSTRACT FROM AUTHOR]

Copyright of IRBM is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the context of climate change and rising frequency of extreme hydro-meteorological events around the world, flood risk management and mapping of heavy rainfall-related damages represent an ongoing critical challenge. For decades now, remote sensing has been largely used to investigate spatial and temporal changes in land use and water resources. Today, different satellite products provide fast and crucial knowledge for the study of hydrological disasters over large areas, possibly in remote regions, with high spatial resolution and high revisit frequency. Yet, until now, few works have sought to detect the full range of extreme rainfall-related damages with optical imagery, especially those caused by intense rainwater runoff beyond the direct vicinity of major waterways. The work presented in this paper focuses on the Aude severe weather event of October 15th, 2018, in the South of France, for which more than a thousand claims for agricultural disaster were registered, both related to river overflowing and rainwater runoff.The full resources of ground truths, contextual information, land use as well as digital elevation model (DEM) combined to high resolution and high frequency optical imagery (Sentinel-2, Pléiades) are used to develop an automatic damage detection method based on supervised classification algorithms. Through the combination of several indicators characterizing heterogeneous spectral variations among agricultural plots following the event, a Gaussian process classifier achieved various classification accuracies up to 90% on a large comparable and independent photo-interpreted validation sample. This work builds great expectations for applications in other areas with contrasted climate, topography and land cover. [ABSTRACT FROM AUTHOR]

The objective of this study was to test the feasibility of automatically extracting and exploiting data from the YouTube platform, with a focus on the videos produced by the French YouTuber HugoDécrypte during COVID-19 quarantine in France. For this, we used the YouTube API, which allows the automatic collection of data and meta-data of videos. We have identified the main topics addressed in the comments of the videos and assessed their polarity. Our results provide insights on topics trends over the course of the quarantine and highlight users sentiment towards on-going events. The method can be expanded to large video sets to automatically analyse high amount of user-produced data.

Cancer registries are collections of curated data about malignant tumor diseases. The amount of data processed by cancer registries increases every year, making manual registration more and more tedious., Objective: We sought to develop an automatic analysis pipeline that would be able to identify and preprocess registry input for incident prostate adenocarcinomas in a French regional cancer registry., Methods: Notifications from different sources submitted to the Bas-Rhin cancer registry were used here: pathology data and, ICD 10 diagnosis codes from hospital discharge data and healthcare insurance data. We trained a Support Vector Machine model (machine learning) to predict whether patient's data must be considered or not as a prostate adenocarcinoma incident case that should therefore be registered. The final registration of all identified cases was manually confirmed by a specialized technician. Text mining tools (regular expressions) were used to extract clinical and biological data from non-structured pathology reports., Results: We performed two successive analyses. First, we used 982 cases manually labeled by registrars from the 2014 dataset to predict the registration of 785 cases submitted in 2015. Then, we repeated the procedure using the 2089 cases labeled by registrars from the 2014 and 2015 datasets to predict the registration of 926 cases submitted in the 2016 data. The algorithm identified 663 cases of prostate adenocarcinoma in 2015, and 610 in 2016. From these findings, 663 and 531 cases were respectively added to the registry; and 641 and 512 cases were confirmed by the specialized technician. This registration process has achieved a precision level above 96 %. The algorithm obtained an overall precision of 99 % (99.5 % in 2015 and 98.5 % in 2016) and a recall of 97 % (97.8 % in 2015 and 96.9 % in 2016). When the information was found in pathology report, text mining was more than 90 % accuracy for major indicators: PSA test, Gleason score, and incidence date). For both PSA and tumor side, information was not detected in the majority of cases.", Conclusion: Machine learning was able to identify new cases of prostate cancer, and text mining was able to prefill the data about incident cases. Machine-learning-based automation of the registration process could reduce delays in data production and allow investigators to devote more time to complex tasks and analysis., Competing Interests: Declaration of Competing Interest The authors have no conflict of interests to declare., (Copyright © 2020 Elsevier B.V. All rights reserved.)

We propose a method for recruiting asymptomatic Amyloid positive individuals in clinical trials, using a two-step process. We first select during a pre-screening phase a subset of individuals which are more likely to be amyloid positive based on the automatic analysis of data acquired during routine clinical practice, before doing a confirmatory PET-scan to these selected individuals only. This method leads to an increased number of recruitments and to a reduced number of PET-scans, resulting in a decrease in overall recruitment costs. We validate our method on three different cohorts, and consider five different classification algorithms for the pre-screening phase. We show that the best results are obtained using solely cognitive, genetic and socio-demographic features, as the slight increased performance when using MRI or longitudinal data is balanced by the cost increase they induce. We show that the proposed method generalizes well when tested on an independent cohort, and that the characteristics of the selected set of individuals are identical to the characteristics of a population selected in a standard way. The proposed approach shows how Machine Learning can be used effectively in practice to optimize recruitment costs in clinical trials.

Background: Mortality surveillance is of fundamental importance to public health surveillance. The real-time recording of death certificates, thanks to Electronic Death Registration System (EDRS), provides valuable data for reactive mortality surveillance based on medical causes of death in free-text format. Reactive mortality surveillance is based on the monitoring of mortality syndromic groups (MSGs). An MSG is a cluster of medical causes of death (pathologies, syndromes or symptoms) that meets the objectives of early detection and impact assessment of public health events. The aim of this study is to implement and measure the performance of a rule-based method and two supervised models for automatic free-text cause of death classification from death certificates in order to implement them for routine surveillance., Method: A rule-based method was implemented using four processing steps: standardization rules, splitting causes of death using delimiters, spelling corrections and dictionary projection. A supervised machine learning method using a linear Support Vector Machine (SVM) classifier was also implemented. Two models were produced using different features (SVM1 based solely on surface features and SVM2 combining surface features and MSGs classified by the rule-based method as feature vectors). The evaluation was conducted using an annotated subset of electronic death certificates received between 2012 and 2016. Classification performance was evaluated on seven MSGs (Influenza, Low respiratory diseases, Asphyxia/abnormal respiration, Acute respiratory disease, Sepsis, Chronic digestive diseases, and Chronic endocrine diseases)., Results: The rule-based method and the SVM2 model displayed a high performance with F-measures over 0.94 for all MSGs. Precision and recall were slightly higher for the rule-based method and the SVM2 model. An error-analysis shows that errors were not specific to an MSG., Conclusion: The high performance of the rule-based method and SVM2 model will allow us to set-up a reactive mortality surveillance system based on free-text death certificates. This surveillance will be an added-value for public health decision making., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Copyright of European Review of Applied Psychology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Automatic georeferencing for historical-to-nowadays aerial images represents the main ingredient for supplying territory evolution analysis and environmental monitoring. Existing georeferencing methods based on feature extraction and matching reported successful results for multi-epoch aerial images acquired in structured and man-made environments. While improving the state-of- the-art of the multi-epoch georeferencing problem, such frameworks present several limitations when applied to unstructured scenes, such as natural feature-less environments, characterized by homogenous or texture-less areas. This is mainly due to the lack of structured areas which often results in sparse and ambiguous feature matches, introducing inconsistencies during the pose estimation process. This paper addresses the automatic georeferencing problem for historical aerial images acquired in unstructured natural environments. The research work presented in this paper introduces a feature-less algorithm designed to perform historical-to- nowadays image matching for pose estimation in a fully automatic fashion. The proposed algorithm operates within two stages: (i) 2D patch extraction and matching and (ii) 3D patch-based local alignment. The final output is a set of 3D patch matches and the 3D rigid transformation relating each homologous patches. The obtained 3D point matches are designed to be injected into traditional multi-views pose optimisation engines. Experimental results on real datasets acquired over Fabas area situated in France demonstrate the effectiveness of the proposed method. Our findings illustrate that the proposed georeferencing technique provides accurate results in presence of large periods of time separating historical from nowadays aerial images (up to 48 years time span). [ABSTRACT FROM AUTHOR]

Digestive endoscopies, along with all medical procedures in France are coded with the CCAM. This task is done by the physicians, is time-consuming and requires a good knowledge of the terminology besides a medical knowledge. This method offers an automatic coding of endoscopic procedures from free-text reports. Thanks to a supervised learning method, the reports are coded with an average precision and recall of 0.92 on a 1639 texts corpus.

Aims: Assigning vegetation plots to vegetation units is a key step in biodiversity management projects. Nevertheless, the process of plot assignment to types is usually non‐standardized, and assignment consistency remains poorly explored. To date, the efficiency of automatic classification programs has been assessed by comparing them with a unique expert judgment. Therefore, we investigated the consistency of five phytosociology expert judgments, and the consistency of these judgements with those of automatic classification programs. Location: Mainland France. Methods: We used 273 vegetation plots distributed across France and covering the diversity of the temperate and mountainous forest ecosystems of Western Europe. We asked a representative panel of five French organizations with recognized expertise in phytosociology to assign each plot to vegetation units. We provided a phytosociological classification including 228 associations, 43 alliances and eight classes. The assignments were compared among experts using an agreement ratio. We then compared the assignments suggested by three automatic classification programs with the expert judgments. Results: We observed small differences among the agreement ratios of the expert organizations; a given expert organization agreed with another one on association assignment one time in four on average, and one time in two on alliance assignment. The agreement ratios of the automatic classification programs were globally lower, but close to expert judgments. Conclusions: The results support the current trend toward unifying the existing classifications and specifying the assignment rules by creating guiding tools, which will decrease inter‐observer variation. As compared to a pool of phytosociology experts, programs perform similarly to individual experts in vegetation unit assignment, especially at the alliance level. Although programs still need to be improved, these results pave the way for the creation of habitat time series crucial for the monitoring and conservation of biodiversity. [ABSTRACT FROM AUTHOR]

In post-mining regions with seismic hazard, timely decision making for risk management faces the challenge of quick and reliable detection and location of seismic events. As a response to the increasing density of monitoring stations, generating large volumes of seismic data, automatic, full waveform-based methods have been developed in recent years in global seismology. Such methods often cannot be directly applied to post-mining monitoring with a limited station coverage, as it is the case when temporarily networks are installed as an emergency response. In this paper we propose a new methodology that bridges this gap and enables the application of a full waveform, backprojection based method (BackTrackBB) to data of sparse network. The methodology was successfully tested on an abandoned and flooded underground coalmine in South-eastern France. Steps preceding BackTrackBB application were implemented in order to remove the coherent noise that otherwise results in numerous false detections. First results indicate that seismic activity in the study area is controlled by water level variation within former room-and-pillar mine works and fault segments (re)activation below them. [ABSTRACT FROM AUTHOR]

This paper presents a method to automatically tune decentralized proportional integral (PI) controllers for an irrigation canal pool. The auto tune variation (ATV) method is based on a relay experiment, which generates small amplitude oscillations of the canal pool. The ATV procedure can be used to get the integrator delay model parameters of a canal pool, which in turn can be used to tune a PI controller using classic rules, or other rules such as the ones proposed by Litrico and Fromion. This method does not require advanced automatic control knowledge and is implemented in the SIC software developed by Cemagref, which also incorporates a supervisory control and data acquisition module for real-time control. The ATV method is evaluated by simulations and experiments on a real irrigation canal located in the South of France, for local upstream, local downstream, and distant downstream controller tuning. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Bulletin of Hygiene & Experimental Biology / Türk Hijyen ve Deneysel Biyoloji is the property of Refik Saydam National Public Health Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Decision strategies during a lineup identification give important cues to accuracy. • Retrospective self-awareness interview gives important access to decision strategies. • Identifications explained by an automatic recognition are more likely to be accurate. • Feeling or lack of familiarity is interestingly related to identification accuracy. Identifying a suspect is critical for successful criminal investigations. Research focused on two decision processes during lineup identification, namely 'automatic recognition' and 'elimination' strategy, and their relation to identification accuracy. In this article, we report two experiments conducted in France and South Africa, which further examine strategies in eyewitness decision-making. We first used a modified-RSA (Retrospective Self Awareness ; Kassin, 1985) interview method to construct questionnaires to have finer-grained measures of cognitive processes involved in lineup identification. Studies 1 and 2 tested the relevance of the questionnaire in each of the countries, and factor analysis yielded three common factors among the countries, namely the expected 'automatic recognition', and 'elimination' strategies, as well as an additional familiarity related factor. Logistic regressions showed that witnesses who reported using a familiarity feeling and/or an 'elimination strategy' to conduct their decision, were less likely to make correct decisions than witnesses who reported using 'automatic recognition'. [ABSTRACT FROM AUTHOR]

A meteorological and blowing snow data set from the high-elevation experimental site of Col du Lac Blanc (2720 m a.s.l., Grandes Rousses mountain range, French Alps) is presented and detailed in this paper. Emphasis is placed on data relevant to the observations and modelling of wind-induced snow transport in alpine terrain. This process strongly influences the spatial distribution of snow cover in mountainous terrain with consequences for snowpack, hydrological and avalanche hazard forecasting. In situ data consist of wind (speed and direction), snow depth and air temperature measurements (recorded at four automatic weather stations), a database of blowing snow occurrence and measurements of blowing snow fluxes obtained from a vertical profile of snow particle counters (2010–2016). Observations span the period from 1 December to 31 March for each winter season from 2000–2001 to 2015–2016. The time resolution has varied from 15 min until 2014 to 10 min for the last years. Atmospheric data from the meteorological reanalysis are also provided from 1 August 2000 to 1 August 2016. A digital elevation model (DEM) of the study area (1.5 km 2) at 1 m resolution is also provided in RGF 93 Lambert 93 coordinates. This data set has been used in the past to develop and evaluate physical parameterizations and numerical models of blowing and drifting snow in alpine terrain. Col du Lac Blanc is also a target site to evaluate meteorological and climate models in alpine terrain. It belongs to the CRYOBS-CLIM observatory (the CRYosphere, an OBServatory of the CLIMate), which is a part of the national research infrastructure OZCAR (Critical Zone Observatories – Application and Research) and have been a Global Cryospheric Watch Cryonet site since 2017. The data are available from the repository of the OSUG data centre 10.17178/CRYOBSCLIM.CLB.all. [ABSTRACT FROM AUTHOR]

Introduction: Half of fatal injuries among bicyclists are head injuries. While helmet use is likely to provide protection, their use often remains rare. We assessed the influence of strategies for promotion of helmet use with direct observation of behaviour by a semi-automatic video system. Methods: We performed a single-centre randomised controlled study, with 4 balanced randomisation groups. Participants were non-helmet users, aged 18-75 years, recruited at a loan facility in the city of Bordeaux, France. After completing a questionnaire investigating their attitudes towards road safety and helmet use, participants were randomly assigned to three groups with the provision of "helmet only", "helmet and information" or "information only", and to a fourth control group. Bikes were labelled with a colour code designed to enable observation of helmet use by participants while cycling, using a 7-spot semi-automatic video system located in the city. A total of 1557 participants were included in the study. Results: Between October 15th 2009 and September 28th 2010, 2621 cyclists' movements, made by 587 participants, were captured by the video system. Participants seen at least once with a helmet amounted to 6.6% of all observed participants, with higher rates in the two groups that received a helmet at baseline. The likelihood of observed helmet use was significantly increased among participants of the "helmet only" group (OR = 7.73 [2.09-28.5]) and this impact faded within six months following the intervention. No effect of information delivery was found. Conclusion: Providing a helmet may be of value, but will not be sufficient to achieve high rates of helmet wearing among adult cyclists. Integrated and repeated prevention programmes will be needed, including free provision of helmets, but also information on the protective effect of helmets and strategies to increase peer and parental pressure. [ABSTRACT FROM AUTHOR]

A map may be a useful tool for locating and analysing hazards (flood, landslide) and elements threatened by these hazards (buildings, roads). However, on such a map, the reading and understanding of information can be difficult, in particular because the graphic signs, numerous and in superimposition, can be badly contrasted. More than other parameters, colour seems important for the improvement of map legibility. In the framework of a PhD at COGIT laboratory of Institut Geographique National (IGN) France (Chesneau 2006a), a model for the automatic improvement of colour contrasts in maps was designed. A specific schema of data and rules to allow an automatic improvement in colour contrasts are defined. The solution is iterative: through successive cycles, the worst contrasts on the initial map are solved. A prototype for maps used in emergency response efforts, 'ARiCo', has been developed and experimental tests carried out with this model show an actual improvement of maps' legibility. [ABSTRACT FROM AUTHOR]

The present study examined human-computer interactions in which the operator has to detect errors made by software designed to automatically recognize technical documents. The goal was to assess the effect of user-initiated interruptions of the recognition process to correct these errors. Participants were asked to check the interpretations, either with or without the possibility of interrupting the process. Results showed that interruptions can improve efficiency by decreasing task duration, especially in the post-recognition verification phase. Interruptions provide an opportunity to correct errors during rather than after the recognition process, which is easier because it requires fewer cognitive resources. [ABSTRACT FROM AUTHOR]

Abstract: An adaptable automatic trace metal monitoring system (ATMS) was assembled and embedded in a mobile monitoring station belonging to the French Water Agency Artois-Picardie (AEAP) and deployed in the field to measure the concentration of trace metals (electroactive and acid leachable fractions) in natural waters by anodic stripping voltammetry with a hanging mercury drop electrode. Cathodic stripping voltammetry procedures were included to estimate the concentration of dissolved oxygen and reduced sulphur species. The concept of the measuring system enables easy adaptation of methods and procedures to analytes of concern and gives the opportunity to undertake in real-time a routine analysis of the dynamic behaviour of trace metals in river, pond and seawater. The system was tested in two aquatic bodies: in a pond where eutrophication processes occur recurrently and in the Deûle River, where sediments are highly contaminated by several metals such as Pb and Zn and frequently resuspended because of the river traffic. Preliminary field studies demonstrated that trace metal concentrations can evolve quickly as a function of time, depending on the turbidity and luminescence, i.e. day–night cycles. The obtained results were compared with an Environmental Quality Standard (EQS), the Criteria Maximum Concentration (CMC) and the Criterion Continuous Concentration (CCC). The whole system is also prepared for the task of “early warning”. [Copyright &y& Elsevier]

Background: The aim of this study was to assess the evolution of food supply (apart from school catering) between school years 2004/2005 and 2009/2010, in middle- and high-schools from the Aquitaine region (southwest France), in order to evaluate the impact of actions conducted within the framework and the program Nutrition, Prevention and Health of children and adolescents in Aquitaine (southwest France)., Methods: Two surveys were carried out among all middle- and high-schools of the Aquitaine region in 2004/2005 (n=536) and 2009/2010 (n=539) within the framework of a regional multidisciplinary public health program "Nutrition, prevention and health of children and teenagers in Aquitaine". For both 2004/2005 and 2009/2010, data were collected using the same questionnaire and dealt with school characteristics and modalities of food supply (apart from school catering)., Results: Response rate was 84.1% in 2004/2005 and 79.6% in 2009/2010. The proportion of schools offering food to pupils (apart from school catering) significantly decreased in 5 years (from 80.1% to 50.1%, P<0.001). Between 2004/2005 and 2009/2010, we observed a stabilization in the proportion of schools offering free food (from 19.7 to 17%, P=0.3), a significant decrease of those selling food (from 62.8 to 37.1, P<0.001), offering vending machines (from 43.5 to 7.2, P<0.001) and a significant increase of those offering water supply (from 8.2 to 44%, P<0.001). The composition of each modality of food supply (apart from school catering) has also been improved: less sweet and fat food, more bread and fruits., Conclusion: This study shows an overall improvement of food supply apart from school catering (food sale, free food and vending machines) in middle- and high-schools from the Aquitaine region (southwest France) between 2004/2005 and 2009/2010. This improvement is related to the proportion of schools offering food (quantitative improvement), as well as to the composition of food supply (qualitative improvement). These results show an improvement of food supply (apart from school catering), suggesting that actions implemented in the framework of the program "Nutrition, prevention and health of children and adolescents in Aquitaine" may have led to these improvements., (Copyright © 2012 Elsevier Masson SAS. All rights reserved.)

In this paper, we present a comparison of two approaches to automatically de-identify medical records written in French: a rule-based system and a machine-learning based system using a conditional random fields (CRF) formalism. Both systems have been designed to process nine identifiers in a corpus of medical records in cardiology. We performed two evaluations: first, on 62 documents in cardiology, and on 10 documents in foetopathology - produced by optical character recognition (OCR) - to evaluate the robustness of our systems. We achieved a 0.843 (rule-based) and 0.883 (machine-learning) exact match overall F-measure in cardiology. While the rule-based system allowed us to achieve good results on nominative (first and last names) and numerical data (dates, phone numbers, and zip codes), the machine-learning approach performed best on more complex categories (postal addresses, hospital names, medical devices, and towns). On the foetopathology corpus, although our systems have not been designed for this corpus and despite OCR character recognition errors, we obtained promising results: a 0.681 (rule-based) and 0.638 (machine-learning) exact-match overall F-measure. This demonstrates that existing tools can be applied to process new documents of lower quality.

Aims: The aim of this study was to validate the ambulatory automatic atrial threshold monitoring algorithm by comparing the measurements assessed by the automatic system and those evaluated manually by the physician at discharge, 2- and 8-month follow-up sessions., Methods and Results: This is an observational multicentric prospective study of 352 patients implanted with EnPulse(®) DR pacemakers. Mean age was 76.3 ± 9.4 years. Indications of pacing were atrio-ventricular block (AVB) (64%) and sinus dysfunction (SD) or brady-tachy syndrome (36%). The automatic atrial threshold monitoring function was maintained at nominal programming state with daily measurement scheduled at 1:00 am. Ambulatory automatic atrial threshold assessment was possible for 91.5% of patients at discharge, 97.3% at 2 months, and 95.7% at 8 months. Causes of the unsuccessful attempts to perform automatic atrial threshold were atrial arrhythmias or permanent atrial and ventricular pacing. Feasibility is significantly better for AVB indication than SD indication due to more frequent occurrence of atrial fibrillation (AF). At each stage, there is a strict correlation between the automatic measurements and those conducted manually by the physician with a P < 0.001., Conclusion: Feasibility of ambulatory automatic atrial threshold is good. Results of the study show excellent correlation between the two methods for atrial threshold: there is no statistical difference between manual and automatic measurements during follow-up.

We present a simple algorithm that calculates the cloud occurrence frequency at an altitude site using automatic camera image analysis. This algorithm was applied at the puy de Dôme station (PUY, 1465 m. a.s.l., France) over 2013–2018. Cloud detection thresholds were determined by direct comparison with simultaneous in situ cloud probe measurements (particulate volume monitor (PVM) Gerber). The cloud occurrence frequency has a seasonal cycle, with higher values in winter (60%) compared to summer (24%). A cloud diurnal cycle is observed only in summer. Comparisons with the larger scale products from satellites and global model reanalysis are also presented. The NASA cloud-aerosol transport system (CATS) cloud fraction shows the same seasonal and diurnal variations and is, on average, 11% higher. Monthly variations of the ECMWF ERA-5 fraction of cloud cover are also highly correlated with the camera cloud occurrence frequency, but the values are 19% lower and up to 40% for some winter months. The METEOSAT-SEVIRI cloud occurrence frequency also follows the same seasonal cycle but with a much smaller decrease in summer. The all-sky imager cloud fraction (CF) presents larger variability than the camera cloud occurrence but also follows similar seasonal variations (67% in winter and 44% in summer). This automatic low-cost detection of cloud occurrence is of interest in characterizing altitude observation sites, especially those that are not yet equipped with microphysical instruments and can be deployed to other high-altitude sites equipped with cameras. [ABSTRACT FROM AUTHOR]

Aim: To reduce the delay in defibrillation of out-of-hospital cardiac arrest (OHCA) patients, recent publications have shown that drones equipped with an automatic external defibrillator (AED) appear to be effective in sparsely populated areas. To study the effectiveness of AED-drones in high-density urban areas, we developed an algorithm based on emergency dispatch parameters for the rate and detection speed of cardiac arrests and technical and meteorological parameters.Methods: We ran a numerical simulation to compare the actual time required by the Basic Life Support team (BLSt) for OHCA patients in Greater Paris in 2017 to the time required by an AED-drone. Endpoints were the proportion of patients with "AED-drone first" and the defibrillation time gained. We built an open-source website (https://airborne-aed.org/) to allow modelling by modifying one or more parameters and to help other teams model their own OHCA data.Results: Of 3014 OHCA patients, 72.2 ± 0.7% were in the "no drone flight" group, 25.8 ± 0.2% in the "AED-drone first" group, and 2.1 ± 0.2% in the "BLSt-drone first" group. When a drone flight was authorized, it arrived an average 190 s before BLSt in 93% of cases. The possibility of flying the drone during the aeronautical night improved the results of the "AED-drone first" group the most (+60%).Conclusions: In our very high-density urban model, at most 26% of OHCA patients received an AED from an AED-drone before BLSt. The flexible parameters of our website model allows evaluation of the impact of each choice and concrete implementation of the AED-drone. [ABSTRACT FROM AUTHOR]

Numerous studies have shown that alcohol increases aggression. In this article it is proposed that the link between alcohol and aggression is so strong that mere exposure to alcohol-related cues will automatically activate aggressive thoughts and behaviors. Two experiments tested this automaticity theory of alcohol-related aggression. In Experiment 1, participants exposed to alcohol- or weapon-related primes made faster lexical decisions about aggression-related words than did participants exposed to neutral primes. In Experiment 2, participants exposed to alcohol- or aggression-related subliminal primes were more aggressive toward the experimenter than were participants exposed to neutral subliminal primes. In both experiments, the effects of alcohol-related cues were as strong as the effect of aggression-related cues on aggressive thoughts and behaviors. People do not need to drink a drop of alcohol to become aggressive; exposure to alcohol cues is enough to automatically increase aggression.

Benedikt Huttner and Stephan Harbarth discuss the implications of a new study that examined the impact of a national campaign in France to reduce antibiotic overuse.

Objective: The objective of this work is to create a bilingual (French/English) Drug Information Portal (DIP), in a multi-terminological context and to emphasize its exploitation by an ATC automatic indexing allowing having more pertinent information about substances, organs or systems on which drugs act and their therapeutic and chemical characteristics., Methods: The development of the DIP was based on the CISMeF portal, which catalogues and indexes the most important and quality-controlled sources of institutional health information in French. DIP has created specific functionalities and uses specific drugs terminologies such as the ATC classification which used to automatic index the DIP resources., Results: DIP is the result of collaboration between the CISMeF team and the VIDAL Company, specialized in drug information. DIP is conceived to facilitate the user information retrieval. The ATC automatic indexing provided relevant results in 76% of cases., Conclusion: Using multi-terminological context and in the framework of the drug field, indexing drugs with the appropriate codes or/and terms revealed to be very important to have the appropriate information storage and retrieval. The main challenge in the coming year is to increase the accuracy of the approach.

Objective: Currently, the use of natural language processing (NLP) approaches in order to improve search and exploration of electronic health records (EHRs) within healthcare information systems is not a common practice. One reason for this is the lack of suitable lexical resources. Indeed, in order to support such tasks, various types of such resources need to be collected or acquired (i.e., morphological, orthographic, synonymous)., Methods: We propose a novel method for the acquisition of synonymy resources. This method is language-independent and relies on existence of structured terminologies. It enables to decipher hidden synonymy relations between simple words and terms on the basis of their syntactic analysis and exploitation of their compositionality., Results: Applied to series of synonym terms from the French subset of the UMLS , the method shows 99% precision. The overlap between thus inferred terms and the existing sparse resources of synonyms is very low. In order to better integrate these resources in an EHR search system, we analyzed a sample of clinical queries submitted by healthcare professionals., Conclusions: Observation of clinical queries shows that they make a very little use of the query expansion function, and, whenever they do, synonymy relations are rarely involved.

Summary of Product Characteristics (SPC) indexing enables to extract all the information needed to analyze a prescription and find some inappropriate medications. We evaluate a French Multi-Terminology Indexer tool (F-MTI) for SPC automatic indexing. This tool uses a dictionary containing the textual forms that are likely to appear in natural language text for the drug clinical particular terms contained in the Vidal thesaurus (TUV). We developed a method to automatically generate this dictionary. The evaluation showed a precision of 52.9% and a recall of 46.2%. F-MTI will be integrated in a semi-automatic indexing tool.

Adverse Drug Events (ADE) due to medication errors and human factors are a major public health issue. They endanger patient safety and cause considerable extra healthcare costs. The European project PSIP (Patient Safety through Intelligent Procedures in medication) aims to identify and prevent ADE. Data mining of the structured hospital data bases will give a list of observed ADE with frequencies and probabilities, thereby giving a better understanding of potential risks. The main objective of the project is to develop innovative knowledge based on the mining results and to deliver to professionals and patients, in the form of alerts and decision support functions, a contextualized knowledge fitting the local risk parameters.

Objective: In a cohort of mechanically ventilated patients to compare the automatic tidal volume (VT)-respiratory rate (RR) combination generated by adaptive support ventilation (ASV) for various lung conditions., Design and Setting: Prospective observational cohort study in the 11-bed medicosurgical ICU of a general hospital., Patients: 243 patients receiving 1327 days of invasive ventilation on ASV., Measurements: Daily collection of ventilator settings, breathing pattern, arterial blood gases, and underlying clinical respiratory conditions categorized as: normal lungs, ALI/ARDS, COPD, chest wall stiffness, or acute respiratory failure., Results: Overall the respiratory mechanics differed significantly with the underlying conditions. In passive patients ASV delivered different VT-RR combinations based on the underlying condition, providing higher VT and lower RR in COPD than in ALI/ARDS: 9.3ml/kg (8.2-10.8) predicted body weight (PBW) and 13 breaths/min (11-16) vs. 7.6ml/kg (6.7-8.8) PBW and 18 breaths/min (16-22). In patients actively triggering the ventilator the VT-RR combinations did not differ between COPD, ALI/ARDS, and normal lungs., Conclusions: ASV selects different VT-RR combinations based on respiratory mechanics in passive, mechanically ventilated patients.