dataesr/scientific-paragraphs-categorization huggingface dataset:
Description of the observational datasets To evaluate the AROME and ALADIN climate simulations over northwestern Europe, long-term high spatio-temporal gridded observations are required. The daily E-OBS gridded 0. 1° (~11 km) minimum and maximum 2-meter temperature, and precipitation datasets  (Cornes et al. 2018 ) that cover Europe are available, but the quality of this dataset over many regions is questionable due to the low density of weather stations  (Lucas-Picher et al. 2013; Prein and Gobiet, 2017). To fill the gaps of the sparsely distributed weather stations for producing a gridded product, smoothing and interpolation that affect small-scale spatial details have to be used. While E-OBS might be fine for evaluating 2-meter temperature that varies smoothly in time and space, E-OBS is questionable to evaluate precipitation because of the fine-scale details and large variability in time and space of precipitation.
However, a recent addition of about three thousand stations for precipitation, including approximately one thousand stations over France, in the latest E-OBS version 23. 1 improved substantially the quality of this product, explaining why we decided to include it in the analysis, and also because it covers the entire Europe. Thus, as done in  Fantini et al. (2018) ,  Prein and Gobiet (2017) ,  Berthou et al. (2020), and Caillaud et al. (2021) , highresolution national gridded precipitation datasets have been combined in a single map (hereafter called HROBS) to evaluate the climate simulations. Eight national gridded datasets (Table  1  and Fig. 2 ) have been interpolated toward the 2. 5-km AROME grid using the first order conservative remapping cdo climate operator in order to have the smallest impact as possible on the analysis as done in recent studies  (Ban et al.
2021; Pichelli et al. 2021; Caillaud et al. 2021). The different national high-resolution gridded datasets are summarized in Table  1  and described in more detail in the appendix. In Fig. 2  showing mean fall precipitation, HROBS and E-OBS have similar precipitation distributions, but with locally higher values over the Cevennes, northwestern Spain, and western Scotland in HROBS, likely a consequence of a higher density of weather stations and radar coverage in HROBS than in E-OBS. Furthermore, to give some explanations of the models' behaviour, different variables available via satellite datasets from the EUMETSAT CM SAF website (https://wui. cmsaf. eu/safira/action/viewProduktSearch) in netcdf format were used (see overview on Table  2 ). To begin, the 0. 25° global CLARA-A2. 1 (hereafter CLARA:  Karlsson et al.
The extent to which AROME overestimates precipitation extremes over elevated areas is difficult to assess due to the large uncertainties in the observationally-based datasets. Indeed, over elevated areas, even high-resolution observational datasets are known to underestimate precipitation extremes due to precipitation undercatch by the rain gauges and by the shielding of the mountains affecting radar estimates  (Prein and Gobiet, 2017; Piazza et al. 2019; Caillaud et al. 2021). Overall, improvements of AROME over ALADIN are limited for these two daily indicators, except over high elevated regions such as the Alps and northwestern Scotland.
Cloud cover fraction and surface radiation In trying to provide some explanations of the different biases seen above by both models, this section focuses on the evaluation of the cloud cover and the surface incoming radiation (shortwave and longwave). The simulated values by the models are compared with different satellite estimates from CM SAF (SARAH, CLARA, and COMET) summarized in Table  2. Unfortunately, only the total cloud cover variable was saved when the AROME simulation was performed. Thus, the analysis will focus only on that cloud variable and will not allow differencing the radiative behaviour of clouds at different heights. Fig. 14  shows the total cloud cover of the reference satellite dataset COMET and the biases of the AROME and ALADIN simulations, and another satellite dataset CLARA (0.
16 ), AROME and ALADIN show similar spatial patterns in their biases with an overestimation from northwestern France to Denmark in winter, and also over southern France and the Po Valley in summer. However, AROME simulates less surface LW radiation over the Alps than ALADIN. In detail for the four climatic regions of interest, the difference of the cloud cover (5%) from the two satellite datasets (COMET and CLARA) can be seen in summer over London, Leipzig and Nimes (Fig. 17 ). Most of the time, AROME overestimates the cloud cover compared to the satellite datasets for London and Leipzig, but underestimates it for the Alps. All the datasets are in good agreement all year long for Nimes, which has a strong annual cycle with lower values in summer. For the surface SW radiation (Fig. 18 ), ALADIN and AROME have larger values than the satellite datasets that are in good agreement, except that ALADIN has larger biases than AROME for London and Leipzig, while it is the opposite over the Alps.
Snow cover To evaluate the snow simulated by ALADIN and AROME, the mean snow cover fraction simulated in winter and spring (Fig. 20 ), the mean annual duration of snow cover (Fig. 21 ), and the mean annual cycle of snow cover for the Alps region (Fig. 22 ) by both models are compared to the values from two surface reanalyses (ERA5-Land and UERRA MESCAN-SURFEX). With its higher resolution, AROME (2. 5 km) can simulate smaller snow cover in the Alps valleys (Fig. 20  and 21) compared to the three other coarser-resolution datasets (ALADIN: 12km, ERA5-Land 9 km, MESCAN-SURFEX 5. 5 km). The larger snow cover (Fig. 20 ), which translates into a longer snow cover duration (Fig. 21 ), by AROME compared to ALADIN over the Massif Central (Central southeastern France), higher mountains of Spain, the Pennines (Scotland), the Ardennes (southern Belgium), and the Eifel region (western Germany) are in good agreement with the two reanalyses (MESCAN-SURFEX and ERA5-Land).
5-km resolution simulation was performed over a large northwestern European domain in the so-called evaluation or hindcast mode. This simulation was compared with its lateral boundary driving field 12-km CNRM-ALADIN simulation performed over the EURO-CORDEX domain through an evaluation study using different gridded observation datasets. In order to facilitate the identification of added value over the large CNRM-AROME domain simulation, a hourly kilometric precipitation dataset was built from the collection of seven national (France, Germany, U. K. , Italy, Switzerland, Netherlands, Denmark) datasets, four of them using radar estimates. Additional gridded observed datasets of 2-meter minimum and maximum temperature, satellite estimates of surface incoming radiation and cloud cover, snow cover from surface reanalyses and mean sea level pressure were used to perform a comprehensive evaluation of CNRM-AROME and CNRM-ALADIN.
Overall, the analysis showed that CNRM-AROME is a suitable CPRCM that provides added value over its RCM counterpart CNRM-ALADIN, especially over mountainous areas  (Lucas-Picher et al. 2017) , and for indicators using hourly values for which convection is a key process  (Berthou et al. 2020 ) such as precipitation extremes, intensity and diurnal cycle. No major degradation of the CNRM-ALADIN performances by CNRM-AROME have been identified despite the novelty of the use of CNRM-AROME in a climate mode at CNRM and the limited tuning due to its heavy computational cost. The collection of seven national sub-daily kilometric precipitation datasets covering a large part of the northwestern domain allowed to identify the added value of CNRM-AROME more easily at sub-daily time scales, rather than to use individual weather stations.
One should keep in mind that the observed gridded datasets are dependent on the distribution and quality of radars and weather stations that vary in space and time, which is even truer for sub-daily datasets that rely only on a subset of weather stations. Thus, in more remote locations such as mountainous or sparsely populated regions, the precipitation and temperature datasets are subject to uncertainties that can affect the analysis. Further, it is now recognized that the skill in modelling mountainous rain and snow can be better than the skill of a collective network of precipitation gauges  (Lundquist et al. 2019 ). However, despite the challenges of radars to measure precipitation in mountainous areas, the improvement brought using precipitation radars over station data is recognized  (Piazza et al.
Additional reduction of biases could be done tuning carefully CNRM-AROME, which however will remain challenging due to the high computational costs of such high-resolution simulations. Most CPRCMs are atmosphere and land-surface only models and are forced by coarse sea surface temperature. CNRM-AROME should improve with time by including interactive couplings with other components (regional sea, anthropogenic and natural aerosols, surface hydrology) of the regional climate system such as in CNRM-RCSM  (Sevault et al. 2014 , Nabat et al. 2020) , as well as more sophisticated land and hydrology surface processes recently developed in SURFEX (Le  Moigne et al. 2020). A. 9 E-OBSv23. 1e E-OBS (European Observation;  Cornes et al. 2018)  is the version 23 of a 0. 1° daily multi-variable dataset covering Europe based on weather stations data over the period 1950-2020.
Five patients in the intervention group did not complete regular follow-ups after 1 month of the intervention and were excluded. Finally, data of 238 patients in the intervention arm and 187 patients in the control arm were analyzed. Figure  1  illustrates the flow chart of this trial. No difference was found between included and excluded patients regarding gender (P=. 45), occupation (P=. 08), hazardous drinking (P=. 36), and medication adherence (P=. 11) at baseline. However, significant differences were found in age (P<. 001) education (P=. 003), marital status (P=. 009), smoking (P=. 02), duration of ART (P<. 001), and CD4 initiation (P=. 03).
Data Collection and Measurement The following data were obtained from participants: (1) demographic and patient characteristics, (2) risk behaviors, (3) visual analog scale (VAS) of ART Adherence, (4) HIV Treatment Adherence Self-Efficacy Scale (HIV-ASES), and (5) HIV Symptom Management Self-Efficacy Scale. Before collecting data, the questionnaire was piloted in a group of HIV patients to resolve logical issues and adjust questions to improve accuracy. A face-to-face interview of 15-20 minutes was conducted in a closed room to ensure privacy and limit outside influences. Trained staff members assisted in data collection. These data were collected at baseline and at 1-month and 3-month follow-ups during their ART medication visits with doctors. Participants were asked about three risk behaviors, including drinking, smoking, and drug use, at baseline.
We estimated the effects of mark loss and recycled individuals on parameter estimates using a multistate, Arnason-Schwartz (AS), modelling framework. We explored parameter bias through simulations of a metapopulation system with different capture and survival rates. We also illustrated how mark loss can be easily estimated and accounted for using an empirical long-term (10 years) CMR dataset of bats, individually identified using both PIT tag technology as marks that can be lost, and multi-locus genotypes as 'permanent marks'. 3. The results from our simulated scenarios demonstrated that the occurrence of bias and the parameters concerned were highly dependent on the study system, and no general rules on parameter behaviour can be established a priori. The model structure and the interdependency among parameters make it challenging to predict how bias could affect estimates.
As transition probability is conditional on survival and detection on state in our AS model, we expected errors in estimates to propagate to model parameters in different ways according to state transition rate. To illustrate our approach with an empirical example, we used our decade-long mark recapture dataset of PIT-tagged and genotyped greater mouse-eared bat (Myotis myotis), a taxonomic group particularly susceptible to PIT-tags loss  (Freeland & Fry, 1995). We used 'genotype' as an individual permanent mark to estimate bias between models accounting/not accounting for mark loss and recycling, and suggested recommendations for future studies.
Material and method To quantify the potential bias induced by mark loss on parameter estimates in the AS framework, we defined several scenarios corresponding to representative situations inspired from the compilation of data from an existing database for tetrapods, the Demographic Species Knowledge Index  (Conde et al. , 2019) , and data from the literature for fish and bat species (Fig. 1 , Supporting Information 1, Table  S1 ). We limited the compilation of these survival data to published CMR studies or data from controlled conditions, i. e. marked individuals of known fate (e. g. in zoos). Among the 700 species considered in six vertebrate classes: Actinopterygii, Chondrichthyes, Amphibia, Aves, Reptilia and Mammalia, the distribution of survival rates encompasses a large range of values (Fig.
S2-3 ). This process allowed the generation of datasets that took into account mark loss in the presence of a second permanent mark. To generate datasets in which mark loss was not accounted for (no second permanent mark), we created recycled individual with the life history corresponding to the portion of life after mark loss and replaced the original life history from mark loss with zeros. For exemple, a life history "1111" of an individual that lost its mark between occasion 2 and 3 and is newly remarked at occasion 3, became two new histories: (1) "1100", the first part of life before mark loss; (2) "0011", a second individual, in fact the same but not recognised in the absence of a permanent mark, considered as newly recruited. Using this data generation process, we simulated 2 x 50 datasets for each of the 12 combination of parameters (50 with and 50 without recycling), leading to a total of 1200 simulated datasets (Supported Information 2, Fig.
Statistical models As described, regardless of the scenario or parameter values used, two different datasets for which the permanent marking assumption is violated were simulated for each combination: one, where it is technically possible to identify individuals even after the loss of mark, thanks to a second permanent mark; the second, where recycling occurred due to absence of second permanent marking (Supported Information 2, Fig. S3 ). We developed two AS models to analyse these data. ModelA, used for the first dataset, included the estimate of mark loss, while ModelW, for the second dataset, ignored mark loss (Supplementary Information 2). To estimate the state, time and age dependant survival Φ for both model we used a Bernoulli distribution (see above eq. 1): where A i,t is the life status of individual i at time t and coefficient α is a state effect, β a time effect, δ an age effect and γ a simultaneous effect of state, age and time.
Application on a bats dataset We used the simulations both to test general hypotheses about the effect of mark loss on multi-state model parameter estimates, but also to validate a parametrisation capable of estimating these parameters without bias. On this basis, we can accurately estimate the probability of mark loss in our own dataset, but also the relevance of using in the long term a permanent mark, i. e. the appropriateness of using double-marking to avoid estimation bias  (Juillet et al. , 2011). Our digit code. These small tags (2. 12x11mm, 0. 1gr) allowed identification from passive readers. All individuals caught in roosts without PIT-tags were systematically tagged, which included both individuals who lost their tag and those which were never tagged before, and genotyped as a second and permanent marking method.
S3 ). In this study, we also tested the effect of surgical adhesive (Vetbond®) after PIT-tag injection, in order to assess tag-loss reduction in comparison with selfhealing  (Lebl & Ruf, 2010; van Harten et al. , 2020). In this model, tag retention probabilities were modelled similarly to equation 7, by accounting for time since marking (two discrete classes: first year or subsequent years), individual age class (juvenile or adult), but also use of surgical adhesive (yes or no). As per the simulated datasets, two models were run on two datasets, a first one allowing tag loss estimation thanks to the genotyping (second permanent mark), and a second model ignoring this information on a transformed dataset including recycled individuals (following the same process as simulated data).
25 or 0. 4. Again, in most case, parameters that failed to converge showed a mean R-hat value less than 1. 2, only a few exceeded 1. 5 (Supporting Information 2, Fig. S5 ). Of all the simulations, no convergence failures concerned the probability of mark loss. To avoid prohibitively long computing time, we did not attempt to increase the number of iterations to achieve the complete convergence of MCMC chains for these parameters in the simulations concerned. Our results are based on 50 simulated datasets per scenario, and it was assumed that lack of convergence for these few parameters has no substantial influence on our results. For assessing bias in parameter estimates when mark loss or recycling is not accounted for, we computed the Earth Mover Distance (EMD), using the EMD-L 1 algorithm  (Ling & Okada, 2007) , a distance function that quantifies the difference between two distributions.
Our simulation results confirmed that the inaccuracy of model estimates is positively linked to the rate of mark loss, but can also occur when tag loss rate is low (5%) and can be independent of survival and capture rates. Indeed, in datasets with few recycled individuals, i. e. with low survival and capture rates, transition and resighting probabilities can be severely biased if mark loss is high. This suggests that results from studies where survival and capture are low should also be interpreted with caution if mark loss is suspected but not taken into consideration. In particular, the probability to stay in the same state is underestimated when transition from this state is low but become overestimated for high transition probabilities. Severity of bias can also vary over time, with bias in survival and recapture decreasing with time as in our simulated datasets.
, 2020). Our study proves that this generalisation is partly wrong, and rather suggests that it is difficult to generalise such a conclusion and that the pattern of mark loss is highly species dependent, among other parameters. Mark loss should therefore be carefully considered in all CMR analyses and possibly also in other studies using similar datasets, and should be explicitly modelled when necessary for more accurate estimations of population dynamics. The possible fates of an individual between occasion t and t+1. We first consider the state transition process: if the individual dies between occasions, it can only remain dead and can no longer be detected, both with probability 1 (absorbing state). The individual can, however, survive between occasions with probability φ (depending on time and state at t) and can then change state with probability ψ (see Fig.
Acknowledgements This project was funded by an  Irish Research Council   Postdoctoral Fellowship  Grant  GOIPD/2018/256  awarded to F. T. , a  European Research Council Research  Grant  ERC-2012-StG311000  and an  Irish Research Council Laureate Award  awarded to E. C. T. The French field study was supported by the  European Regional Development Fund   EU000141  and a  Contrat Nature grant  awarded to  Bretagne Vivante  (BV). S. J. P. was supported by a  Junior chair  from the  Institut Universitaire de France. We would like to thank the volunteers of BV for assistance in the fieldwork. Sampling was carried out in accordance with the French ethical and sampling guidelines issued in 3 successive permits delivered by the  Préfet du Morbihan (Brittany)  awarded to E. J. P. , F. T. and S. J. P.
empirical dataset consisted of a 10-year study of CMR data on the greater-mouse eared bat (Myotis myotis) in Brittany(France; 2010 -2019). A total of 2,561 individuals were marked in 5 maternity roosts: La Roche Bernard (47˚31'N, 2˚18'W), Férel (47˚28'N, 2˚20'W), Noyal-Muzillac (47˚35'N, 2˚27'W), Béganne (47˚35'N, 2˚14'W) and Limerzel (47˚38'N, 2˚21'W). The bats were individually tagged using Passive Integrated Transponders (PIT) tags, ID-100C (Troven®), with a unique 10-.
Full Text Deep learning (DL) models in computational biology have been applied to an increasing number of challenges  1  , such as virus detection  2  , antibiotic resistance prediction  3  , and contamination removal  4. The development of such models often uses DL application programming interfaces (API) such as Keras, which enables researchers to stack neural layers into deep neural networks. However, these interfaces do not provide solutions for e cient data handling and network training for genomic data modalities. With deepG, we provide a software library that includes adaptations for genomic datasets on the nucleotide and amino acid level and provides an easy-to-use interface for training and applying DL networks. In deepG, the standard work ow starts from collections of FASTA les divided into two or more sets that correspond to class labels.
The deepG data generator iterates over the input les and trains a deep neural network that can be applied to new datasets to perform predictions (Fig. 1A, S1 ). A common challenge of applying DL to genomics is that the input length of genomic sequences is typically longer compared to datasets of the traditional DL domains, especially when input samples are full (meta)genomes. To address this, the deepG library also includes specialized neural networks to account for long-range dependencies that span multiple batches of samples (stateful long short-term memory) as well as architectures designed for long input sequences such as WaveNet  5  , which implements residual and parameterised skip connections with dilated convolutions to speed up convergence in this data regime. Further, we have found that processing nucleotides sequences in a naïve and purely sequential fashion often leads to a highly ineffective training, since this approach successively processes potentially varying nucleotide distributions of input samples, leading to repeated regimes of under-and over tting (Fig.
de. At the read-length level, we trained a model that is able to discriminate between bacterial and human sequences, which can be used for screening of human contamination in metagenomic sequences. After 3 hours of training with a context size of 150 nucleotides (nt) (corresponding to the typical read-level size), our model achieved a balanced accuracy of 97% when trained on a set of bacterial genomes and a human reference genome. We evaluated this model on a paired end metagenomic dataset with a processing speed of over 250,000 reads per minute on a consumer-grade graphics processing unit (GPU), demonstrating a practical processing speed. In this case, the model was trained on data from the full human genome (non-read size) but optimized for the application on read data. deepG also supports the model development on read-level data (FASTQ les) and can account for this in the encoding of the network by using a probability encoding instead of one-hot encoding -which is of a potential interest for the direct processing of long and low-quality sequencing reads (Fig.