dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis First, we determined the presence or absence of homogeneity of variance using Levene's test and then analysed differences in the regional CMRGlu values between the right and left hemispheres and in the 12 brain regions for each hemisphere among the three patient groups (MCSþ, MCS-and VS) using analysis of variance (ANOVA) or Welch's test. Second, significant between-group regional CMRGlu differences identified by ANOVA were further analysed by multiple comparisons using the Tukey method and significant differences highlighted by Welch's test were further analysed by multiple comparisons using the Games-Howell method. In this study, all P values were two-sided and P < 0. 05 was considered statistically significant. Furthermore, all statistical analyses were performed using SPSS ver.
To test this hypothesis, we take advantage of 13 years of data from a long-term capturemark-recapture and census program established at one of the most important known staging areas for C. c. rufa juveniles during southbound post-breeding migration, the Mingan Archipelago National Park Reserve in Quebec, Canada. Given that there are important extrinsic factors such as climatic conditions that may influence both reproductive success and timing of migration in both males and females, we adopted a multiple inference approach that involved the comparison of several competing hypotheses in order to explain variation in the proportion of juveniles present during migration.
Migration Phenology Sex differences in migration phenology of adult C. c. rufa were assessed using resighting data as they were best distributed over time compared with capture events, which were less frequent and more sporadic. To describe migration phenology, we used the median migration date, defined as the date upon which 50% of the banded/marked population has passed through the site. Differences in the median migration date between the sexes each year were tested via a permutation test using the percentile Test function in package "rcompanion" in Program R v. 3. 5. 1 (R Core Team 2019, Mangiafico 2020). We also calculated the time lag between the migratory date of adult males and females defined as the difference between the median resighting dates of females and males; positive values indicate that males migrate later than females.
Across all years, there were not enough capture events of juvenile C. c. rufa to provide a thorough description of juvenile phenology. Therefore, we used the census data where the presence of juveniles was specified during daily counts. We considered the median migration date from daily counts where juveniles were present as an indication of juvenile migration timing. In 2015 and 2016, juvenile captures were more numerous (n = 82 and 189, respectively) and relatively spread over time (from 15 August to 17 October and 21 August to 29 October, respectively). For these 2 years, we calculated the median migration date of juvenile males and females and investigated whether timing of capture differed between sexes using permutation tests as we did for adults above.
Sex Ratio We describe the variation of sex ratio (i. e. , the proportion of males) among adult birds using both resighting and capture events. Estimating sex ratios from resighting data has the advantage of a larger sample size in both space and time; however, estimating sex ratios from captures provides a more accurate snapshot of sex ratios within distinct foraging groups. Differences in sex ratios between juveniles were examined using capture events only. Because of sparse capture events, we chose to pool all years together (2007-2017) and examined the variation in sex ratio within twelve 10-day periods for juveniles and adults. In each age Abbreviations:, AHY -after hatch year; adult, F-AHY -Female adult, F-HY -Female juvenile, M-AHY -Male adult, M-HY -Male juvenile class, we examine whether the sex ratio differs from an even sex ratio using a binomial test (binom.
Migration Phenology Adults were counted during census from 9 July to 27 October across years. Median passage date of adults based on census was 9 August. The first juveniles arrived 7 August and were counted during census until 12 November, with a median passage date of 4 September. Based on observations of banded individuals of known sex, the median migration date of female AHY was 29 July (range 17 July-2 August), whereas males were consistently later at 13 August (range 26 July-18 August; Fig. 2 ; Table  1 ). Median migration dates varied by year for both sexes; however, males exhibited a significant time lag in 11 of 13 years (Table  1 ). For the 2 years where we had sufficient data for juveniles of known sex, no time lags were detected for that age class (Table  1 ).
Acknowledgments: All methods in this study were reviewed and accepted by the  Animal Care Committee and Banding office  (Banding permit # 10700 ) of the  Canadian Wildlife Service. Funding for fieldwork was provided by the  Canadian Wildlife Service, Environment and Climate Change Canada  with a contribution from the  Royal Ontario Museum , the  Endangered Species Fund of WWF Canada , and  Parks Canada. Many thanks are due to the numerous volunteers who have made the long-term capture-mark-recapture project possible in Mingan, as well as the network of researchers across the globe who have provided resighting data via the BandedBirds website and the  Canada Bird Banding Office. Thanks to the Bandedbirds regional project coordinators. Thanks also to  Amanda D. Dey ,  Endangered and Nongame Program ,  Fish and Wildlife, New Jersey Department of Environmental Protection ,  Lawrence J.
, 2021). In a case study, we demonstrate the use of the approach to detect differences in activity patterns between those of the two forest dwelling bat species Myotis bechsteinii and N. leisleri. In the next sections, we detail our process for developing and validating a random forest model to classify VHF-signals based on activity data gathered on bats, birds and humans. We showcase the possible insights in wildlife monitoring that our approach may bring by providing an ecological case study focusing on the comparison of activity patterns between two bat species. Detailed information on data processing and analysis is provided, along with an R package, example scripts and data in a hope to promote broad application in wildlife monitoring and ecology.
| Tagging of bats and birds Every year, we caught and then tagged bats and birds with customised VHF-transmitters of different sizes and weights (V3+, Dessau Telemetrie-Service; 0. 3 g 1 g). Tag weights were always <4% of the body mass of the tagged individual (see S2 for technical details, methods and permits). For the ecological case study on bats, we captured and tagged 91 bat individuals from two focus species (66 M. bechsteinii (~12 g) and 25 N. leisleri (~18 g)). For the evaluation of our approach (see 'transferability to small diurnal flying vertebrates' section) we used data of 19 bird individuals tagged in another study conducted in parallel (one Leiopicus medius, three Cyanistes caeruleus, three Erithacus rubecula, three Garrulus glandarius, three Parus major, three Sylvia atricapilla and three Turdus merula).
wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License terrain, it guarantees overlapping radiation patterns of neighbouring antennas which was necessary for bearing calculation and subsequent triangulation as described in  Gottwald et al. (2019). However, tracking of positions of tagged individuals is not part of this study. The towers of the stations had a height of approximately 8 m, and antennas were oriented north, east, south and west. We permanently monitored a frequency range of 150. 000-150. 300 MHz. From 2018 to 2020, we used the paur 4. 3 software developed by the open-source project radiotracking. eu  (Gottwald et al. , 2019)  but switched to the tRackIT operating system (https://github. com/Natur e40/tRack IT-OS)in 2021 due to high amounts of noise and frequent software failures that often went unnoticed  (Höchst et al.
, 2021). The tRackIT-system enables live transmission of parameters to assess the health of the stations as well as transmission, processing and visualisation of VHF-signals. The former greatly reduces maintenance time and the latter enables tracking of activity, positions and body temperature in near real time. For a detailed description of the hardware and software, please see  Gottwald et al. (2019)  and  Höchst et al. (2021). The VHF data were filtered by tag frequency +/-3 kHz and signal duration +/-5 milliseconds according to settings given by the manufacturer. For the data recorded with the radiotracking. eu software, we had to visually assess the success of the filtering procedure and in some cases remove recordings below a station-and frequency-specific threshold in dBW due to high amounts of electromagnetic noise.
| Groundtruth We used the patterns in the strength of the recorded VHF-signals together with a supervised ML algorithm to classify the activity of the tagged individuals. Supervised ML requires training and test data F I G U R E 2 The Marburg Open Forest in Hesse, Germany. The map shows the locations of the tRackIT-stations  (Gottwald et al. , 2019; Höchst et al. , 2021) , the roost trees of bats (M. bechsteinii, N. leisleri) observed by BatRack multi-sensor stations  (Gottwald et al. , 2021) , the breeding site of a woodpecker (Leiopicus medius) and the GPS track (shown in blue) of the activity simulation used to test the transferability of the classification method to birds and humans. (Map data from OpenStreetMap) for implementation. We monitored 23 out of the 72 tagged bat individuals (6 N.
leisleri and 17 M. bechsteinii) using a multi-sensor tool  (Gottwald et al. , 2021)  to supply the random forest model with periods of known activity and inactivity. First, the roost trees of tagged bats were located via manual radiotelemetry between 9 June 2020 and 26 July 2020 and between 10 May 2021 and 18 August 2021. We then set up custom-made video recorder ('BatRack') units to automatically record videos of tagged individuals  (Gottwald et al. , 2021;  https://natur e40. github. io/BatRa ck/ (vid. 2)). BatRacks consist of a VHF-antenna and an infrared video unit connected to a Raspberry Pi single board computer. We installed the cameras with a focus on the roost entrance and its surrounding area (40-m radius), which allowed the motion of tagged individuals to be captured on the video tracks.
| Predictor variables We calculated 29 predictor variables thought to capture the patterns in the signal strengths over time by applying rolling windows of ±10 data entries, corresponding to an approximate time window of 20 s, to each observation of the classified VHF-data recorded by the tRackIT-stations. We chose the window size to capture the dominant signal strength pattern without smoothing out even short changes of the activity state. To prevent averaging over longer periods, the dataset was split into 5 min bins per station before applying the rolling windows. For each bin, we selected the receiver with the most data entries, that is, the best data coverage. We only evaluated bins with at least 60 observations, that is, three times the window size. This procedure ensures that only stations and receivers with relatively good reception are considered for classification.
| Comparison with a threshold-based approach We compared the results of the ML-based approach with those of a threshold-based approach by calculating the difference in the signal strength between successive signals for the test datasets of bats and humans (for methods and results on the bird data see 'transferability to small diurnal flying vertebrates' section). We applied a threshold of 2. 5 dB, which was deemed appropriate to optimally separate active and passive behaviours in previous studies  (Schofield et al. , 2018). In addition, we used the optimise-function of the R-package stats (R Core Team, 2021) to identify the value of the signal strength difference that separated the training dataset into active and passive with the highest accuracy (i. e. 1. 08 dB) and applied it to the test datasets.
| Statistical analyses All analyses were conducted with R v. 4. 1. 2 (R Core Team, 2021), using the mgcv package for additive models  (Wood, 2011). We used hierarchical generalised additive models (HGAMs) to compare differences in the overnight activity patterns of M. bechsteinii and N. leisleri. These classes of models can be applied to estimate nonlinear relations between responses while allowing for a variety of error terms and random effect specifications  (Pedersen et al. , 2019). In this study, we modelled activity over the course of the 24-h cycle as shown in Equation  1 : where the probability of activity for observation i is modelled as a binomial variable (0: inactive, 1: active) as a function of the time of day (centered around sunset to account for seasonal shifts in daylight).
Since the feeding sequence was usually shorter than three consecutive VHF-signals (~2. 5 s), we classified all recorded signals within such a sequence as active. To generate sufficient inactive sequences, 2200 random data points were sampled from signals recorded by tRackIT-stations each night between 0:00 h and 2:00 h, while the woodpecker was presumably asleep, over four consecutive nights. The dataset of the woodpecker, based on the 75 observed activity sequences, consisted of 17,541 data points (8741 active, 8800 inactive). We applied the two random forest models to all recordings of the tagged woodpecker and calculated the same performance metrics as for bats and human activity for the sequences of known activity. We used the entire woodpecker data set as well as the activity classifications of six additional bird species, each represented by three individuals, to assess the transferability of the model to birds of different size and movement habits.
Since there are no actual observations for the latter and only partial observations for the woodpecker, we visually compared the classified activity of the woodpecker to patterns expected for diurnal vertebrates. Then, we calculated activity probability in relation to the time after sunset for three individuals from each of six small to medium-sized bird species (Table  5 ) using methods comparable to those of the ecological field study for bats. Performance metrics for the sequences of known activity type of the woodpecker were in line with those for bats and human activity (F1 = 0. 97; ROC-AUC = 1, Sensitivity = 0. 95; Specificity = 1; Precision = 1, Kappa = 0. 94). Note that a threshold-based approach also behaved poorly on this dataset (2. 5 dB threshold, F1 = 0. 62, Kappa = 0. 38; 1. 08 threshold, F1 = 0.
| DISCUSS ION Using a large dataset consisting of the observed behaviour of tagged bat individuals, we trained two random forest models to classify novel data from the same species into fundamental behaviour, and with high precision and high temporal resolution (~1 s interval). Our approach outperformed previous methods based on a TA B L E 3 Model coefficients (𝛃) and standard errors (SE) and test statistics (z, p) for the linear portion of the additive models (intercept) along with smoothed parameters for nonlinear terms (edf: Estimated degrees of freedom, chi-squared and p-values) threshold-based approach even when using a value calibrated with a large groundtruth dataset. Although not inadequate, the thresholdbased approach had generally lower and more variable performance metrics compared with our ML model.
We also achieved similar precision when applying the ML models to groundtruth data from other species (woodpecker and controlled human walks). The activity probability estimates of 18 additional bird individuals out of six species also matched expected activity patterns for diurnal vertebrates. This strongly suggests that our method generalises well and could be applied to a variety of vertebrates with similar accuracy (e. g. down to a body mass of 4 g with 0. 2 g transmitters;  Naef-Daenzer et al. , 2005). In the ecological case study, we demonstrate that our approach enables the detection of even subtle differences in the timing of activity according to a species' ecological preferences (differences in activity onset of <20 min). Specifically, we were able to show distinct activity patterns for these two species, characterised by a slight shift in their timing of activity and a significantly lower activity of N.
Gottwald). Future applications may also help automatically determine the (speciesspecific) onset of the breeding season in songbird communities. the Over the 4 years of the study, we collected data with two different software designs (radiotracking. eu and tRackITOS) that show significant differences in data quality. We also covered a range of suboptimal recording conditions caused by topography and vegetation, which leads us to the assumption that the approach presented here is not exclusively applicable to data recorded with tRackIT- stations. Other open-source systems such as Motus (sensorgnome;  Taylor et al. , 2017) , but also commercial systems such as the Lotek SRX/DX series receivers  (Taylor et al. , 2017) , record comparable data that may be used with the functionalities and models presented here.
DATA AVA I L A B I L I T Y S TAT E M E N T To ensure the complete reproducibility of our research, all data and the code used are stored in a data collection at data_UMR, the research data repository of Philipps-Universität Marburg (https:// data. uni-marbu rg. de/handl e/datau mr/147). Detailed information on data processing and analysis is provided, along with an R package (https://github. com/Natur e40/tRackIT; https://doi. org/10. 5281/ zenodo. 7310599). The trained models are available at: https://doi. org/10. 17192/ fdr/79. The workflow for model tuning, evaluation and comparison with a threshold-based approach is available at:.
by calibrating a machine learning (ML) model based on millions of data points representing the behaviours of multiple tagged individuals of two temperate bat species (Myotis bechsteinii, Nyctalus leisleri). Many ML algorithms are optimised for the recognition of complex patterns in a dataset and may be more robust against factors that influence signal propagation, such as changes in temperature and humidity, physical contact with conspecifics and/or multipath signal propagation (Alade, 2013) than a rule-based approach relying on a single separation value. ML approaches may therefore provide substantial improvements in the accuracy of individual activity states classification compared to threshold-based approaches.
To give equal weight to each class and to avoid overoptimistic accuracy metrics caused by a comparably well-detected majority class, we balanced the groundtruth dataset by randomly down-sampling the activity class with the most data to the amount of data contained by the class with the least data. We then split these balanced data sets into 50% training data and 50% test data for data originating from one receiver. We used the same procedure for data derived from the signals of two receivers, resulting in two training and two test datasets. From a total of 3,243,753 VHF-signals, we assigned 249,796 signals to train the two-receiver model and 588,880 signals to train the one-receiver model (Table.
spatial scale by walking within areas of 40 m 2 , and multiple back and forth displacements and displacements of at least 200 m were used to simulate large-scale movements. We performed each movement type for 3-10 min at different positions within the north-western part of the study area, which is characterised by a diverse topography and complete forest coverage (Figure 2). We recorded the beginning and end times of each sequences and all signals simultaneously recorded by one or more of the 15 tRackIT-stations and then manually assigned the known activity type (active or passive). The human activity dataset consisted of 32,175 data points (26,133 active, 6042 inactive).
activity. The same trained models were applied to the human activity datasets. In a first step, we calculated the sensitivity (true positives/ (true positives + false negatives)) and specificity (true negatives/(true negatives + false positives)) based on a comparison of the observed data with the activity class attributed by the random forest models for both datasets. Additionally, we calculated the F1 metric as the harmonic mean of the precision (true positives/(true positives + false positives)) and sensitivity, the ROC-AUC and the Kappa index, which takes the probability distribution of each class into account. Values vary between 0 and 1 (<0 and <1 for Kappa), with values close to.
Acknowledgments AM, DM and JCG acknowledge financial support from  SCARCE  project, which is supported by the  National Research Foundation, Singapore , and  National Environment Agency, Singapore  under its Closing the  Waste Loop Funding Initiative  (Award No. USS-IF-2018-4 ). JCG acknowledges funding for the microfluidic platform from the  European Research Council  under the European Union's  7th Framework Program  (FP/2007-2013)/ ERC  Grant Agreement N • [320915] " REE-CYCLE ":  Rare Earth Element reCYCling with Low harmful Emissions. FO thanks CEA's program  " Matériaux et Procédés " for a PhD support (project  MICROPRO ). All authors thank deeply  Thomas Zemb  for fruitful discussions and manuscript editing suggestions. FO thanks  Sarah Chevrier  for discussions about experimental setup and results analysis.
Le besoin d'identifier des termes médicaux qui sont en relation d'équivalence sémantique avec un syntagme de la langue générale nous oriente également vers la paraphrase, l'exemple prototypique de ce type de relation. Le but de notre travail de recherche est de trouver le plus grand nombre possible de reformulations médicales, et pour cela nous annotons et nous analysons également les reformulations non paraphrastiques, dans la mesure où le reformulé donne une précision, explication, définition du terme médical (le formulé) ou exprime une cause. Nous cherchons toutes les reformulations qui peuvent servir à la vulgarisation des textes médicaux, dans le but ultérieur de créer un corpus de reformulations et une base de données utile pour la simplification des notions médicales  (Cardon, 2021 ; Grabar et Hamon, 2015 ; Grabar et Hamon, 2016).
Annotation automatique des termes médicaux Pour notre étude nous identifions les termes médicaux présents dans nos données avec l'annotateur SIFR-BioPortal  (Tchechmedjiev et al. , 2018)  dans sa version française, utilisé pour nos expériences à l'aide d'un script en Perl. Cet outil met à disposition 28 terminologies et ontologies médicales en français. Nous utilisons l'ontologie SNOMED-3. 5VF qui contient une grande variété des notions médicales : administratif médical et traitements, agents, anatomie, diagnostics, organismes vivants, médicaments, symptômes, anatomie, maladie, procédures, substances, etc. Cette variété de concepts médicaux et la recherche par lemme nous permet de trouver une gamme large de termes médicaux dans le corpus CLEAR, dans la partie scientifique et vulgarisée également.
Une fois ces mots supprimés, nous avons une liste de 5578 termes uniques simples et polylexicaux dans le corpus scientifique et 5100 dans le corpus grand public. Une fois les listes nettoyées, nous extrayons automatiquement avec nos propres scripts les phrases qui contiennent à la fois les termes médicaux annotés par SIFR-BioPortal et des occurrences de marqueurs ou indicateurs de reformulation. Nous adaptons notre script pour identifier toutes les formes morphologiques et pour annoter automatiquement les termes médicaux et les marqueurs / indicateurs. Nous obtenons 4687 phrases pour le corpus de textes scientifiques (CLEAR EX) et 3975 phrases pour le corpus de textes médicaux pour le grand public (CLEAR GP). Notre prochaine étape de traitement est l'analyse des marqueurs et indicateurs de reformulation afin de tester nos théories sur le lien qui peut exister entre ces unités, les relations lexicales et les fonctions sémantico-pragmatiques des reformulations.