dataesr/scientific-paragraphs-categorization huggingface dataset:
Except for measurement errors inherent to the GPS (n = 14; deleted from the dataset), only a single case with two defective days, and 8 cases with one defective day were identified. This is particularly important as a Gaussian filter was used afterwards  [16]  (in that case, a single outlier can affect all its neighboring values). By applying the density-based clustering algorithm (DBSCAN)  [26] , values without neighbors in the AS area were identified as outliers (Figure  1 ; red dots). This method allows to identify the values far enough from each other's. Physically, two consecutive values in time from a trajectory are close in space. Thus, a value without neighbor in the AS space could be considered as an outlier. Briefly, DBSCAN algorithm needs (i) the number of values in a neighborhood to consider a dot as a core value, and (ii) the size of the neighborhood which corresponds to the maximum distance between two values quantified as the Euclidean distance [𝐷𝑖𝑠𝑡(𝑃 𝑡 , 𝑃 𝑡+ ∆𝑡 )] between two consecutive values 𝑃 𝑡 [defined by A as 𝑎(𝑡) and S as 𝑠(𝑡)] and 𝑃 𝑡+ ∆𝑡 with ∆𝑡 the associated time difference.
Statistical analysis In order to assess the reliability of the AS profile within the collection period, data were compared over two 2-weeks phases as described elsewhere  [16]. Briefly, the inter-phase reliability for A0, S0 and ASslope was quantified through the change in the mean (systematic error), the standard error of measurement (SEM, random error), both expressed in raw units and in percentage of mean values, and the interclass correlation coefficient (ICC) between two 2-weeks phases datasets. A and S were compared between positions and session types using ANOVA and post hoc tests when the significant threshold had been reached. The level of significance was set at p = 0. 05.
g. , speed session vs. specific forwards training), inherent hardware and software characteristics among others. AS profiles can change over a professional rugby union season  [28]  and are therefore likely to influence the inter-phase systematic differences observed in the present dataset. For example, in elite youth soccer players, the number and content of sessions affect S0  [17]. High-speed exposure seems to be essential to build a reliable in-situ AS profile  [17]. Here, the proposed method makes it possible to independently identify the number of points or the degree of high-speed exposure the level of uncertainty on the A0 and S0 components. This refinement constitutes a step forward for a daily use of ecological data by considering data collection and method reliabilities.
Limitations and perspectives The determination of individual in-situ AS profiles is primarily dependent on data quality  [16]. In this study, acceleration was calculated from the derivative of speed from the GPS and not from the accelerometer embedded in the GPS sensor. The reason was to avoid any shock, impact, tackle, changes of direction, and all other sudden forces susceptible to alter the vectorial acceleration (Newton's 2 nd law) which depends on the variation of speed rather than forces, for a given time. Future development would permit to directly use data from accelerometer instead of extrapolating acceleration from GPS-based data. In this view, providing more weight for the greatest acceleration at a given speed (i. e. , the moment when the athlete provides a maximum effort) would be relevant to modify the 𝜸 value.
Perspectives As time goes by, new tools are emerging in the field of biomaterials. New methods allow faster and cheaper development of new biomaterials, but also their better safety evaluation. Among them, machine learning is an approach which uses algorithms that improve upon training on large datasets and is able to find complex patterns, make predictions and decisions. These methodologies have many applications, including biomedical, mostly in the field of genomics  (Goecks et al. , 2020). Now, it is emerging as a revolutionary tool for faster biomaterials development  (Kerner et al. , 2021; Suwardi et al. , 2022). Recently, we demonstrated, for the first time, utilization of machine learning for prediction of polymer-based coating properties  (Gribova et al. , 2021; Šušteršič et al. , 2023).
All samples were stored at 8-12°C and light protected until analysis. Temperature mapping of the laboratory shaker, collecting box, heating cabinets and refrigerators was recorded using a data logger (Testo Ò 175-T1, Testo SE & Co. KGaA, Lenzkirch, Germany). Insulin remaining in the reservoirs (non-pumped samples) were stored within the laboratory shaker in a closed cardboard box at 37°C until sampling. For each sample collection, the whole reservoir was removed and stored at 2À8°C until analysis. Reservoir samples were tested for each batch in parallel with the pumped samples. Temperature was monitored using the 175-T1 data logger.
Identification and quantification of leachables (chemical species that make their way into the product during production, application, or storage) was performed via semi-quantitative headspace gas chromatography coupled to mass spectrometry (HS-GC/MS) and quantitative liquid chromatography-mass spectrometry (LC/MS) at Solvias (Basel, Switzerland). Based on previously conducted extractable (compounds that can be extracted from a container closure system when in the presence of a solvent) and leachable studies (data not shown), a list of relevant target leachables, volatile organic compounds and elements were identified (Table  1 ). Identified organic compounds below 1. 5 mg/patient/day were considered safe. 24 tractables above this threshold were evaluated separately and underwent a toxicology assessment, including in silico research on genotoxic potential (DEREK and Leadscope) and reference to the FDA database.
Data Analysis For each pump system, analytical data was obtained from pooled samples of each insulin product collected on the day of sampling. In most cases, the results were compared with specifications published in the EU and US monographs for insulin aspart  18, 31  (Table  2 ). Since phenol and metacresol concentrations are expected to decrease under the selected stress test conditions,  34  a study was conducted to determine the minimum concentration of phenol and metacresol necessary to meet USP and European Pharmacopoeia. antimicrobial effectiveness requirements. 35, 36 Concentrations above 0. 732 phenol and 0. 497 mg/mL metacresol, respectively meet these test requirements (data on file). Descriptive statistics were used to present differences between batch samples on the sampling days versus baseline samples.
All colony phenotypes were identi ed using a MALDI-TOF mass spectrometer (Maldi Biotyper, Bruker). We assessed the activity of 13 antipseudomonal agents (listed in Supplemental Table  1 ) from three classes (β-lactams, aminoglycosides, uoroquinolones) by the agar diffusion method as recommended by the EUCAST (www. eucast. org). Morphologically different colonies of PA recovered from the same sink trap sample were retained for further analysis when they showed distinct resistance pro les. The resistance pro le of isolates susceptible to all tested antibiotics was considered to be wildtype. Acquired genes encoding β-lactamases (including carbapenemases) were sought within the genomic data against the ResFinder database (25). Data. Each isolate collected was associated with its date of isolation, its patient or sink trap of origin, and its antibiotic resistance pro le.
See Supplemental Material and Methods for details on bacteriological and sequence analysis. We sequenced the full genome of all PA isolates (Supplemental Table  1 ). Sequencing data are available in the NCBI BioProject PRJNA788732. We rst identi ed the sequence type (ST) of each isolate by MultiLocus Sequence Typing (MLST) (26). Then, isolates for which the genome contained ≤ 30 different genes were clustered into groups with Core Genome MLST (cgMLST) using 3,867 core genes  (27). We further measured the genetic relatedness of isolates within each group by the number of single nucleotide polymorphisms (SNPs) between genomes. See Supplemental Fig. 1  for the justi cation of the threshold. De nitions. (i) Isolates from a given group were de ned as clonal when their genomes clustered with a threshold of seven SNPs.
Outbreaks of multidrug-resistant bacteria speci cally linked to drains or sinks are overrepresented in the literature  (35). We found all PA isolates transmitted from and to patients to be fully susceptible to β-lactams and aminoglycosides. Clinicians and microbiologists should not neglect the potential spread of strains with unspectacular resistance pro les  (37). This monocentric study was not designed to assess the effect of infection control procedures on the transmission of PA. Hence, the absence of a comparable ICU using different hygiene practices prevented assessment of the e cacy of sink trap disinfection in preventing the transmission of PA to patients. Instead, our design focused on breadth and depth sampling to identify the contamination routes of PA to patients. We isolated PA from both sink traps and patients over 10 months, collecting the largest dataset yet used for an epidemiological study (38).
As we were aware of the complex ora of the sink traps  (20) , we sampled all sink traps weekly and analyzed all colonies with various colony phenotypes and resistance pro les in each sample to obtain a complete picture of the PA population in this environmental niche. The use of isolates collected over 10 months increased the chance of nding clonal isolates that overlapped among patients and sink traps. The genomes of all isolates were fully sequenced and compared at the nucleotide level. This contrasts with typing methods previously used in epidemiological studies  (25, 39). We circumvented the absence of a consensus threshold for clonal isolate identi cation from genome-based data by performing a twostep analysis of the genomes. First, we clustered the isolates with cgMLST and grouped all isolates using a threshold (30 alleles of difference) higher than that found in the literature (15 alleles of difference) to avoid missing any clonal isolates  (27).
Statistical analysis First, a descriptive analysis of the clinical characteristics of the patients at inclusion will be performed. Quantitative data will be expressed as mean ± standard deviation or median [range], and as counts and percentages for qualitative data. The primary endpoint is the patient's survival at hospital admission defined by the return of an efficient spontaneous cardiac activity. The percentage of successful patients will be calculated at the end of step 1 if the number of successes is not compatible with continuing the study or at the end of step 2 otherwise. The 95% confidence interval of this percentage will be estimated from an exact binomial distribution. Tolerance data will be described in terms of frequencies and percentages of occurrence. Likewise, the quantitative secondary endpoints will be described as mean ± standard deviation or median [range], and other qualitative secondary endpoints in the form of frequencies and percentages.
Background The ticks Ixodes ricinus and Dermacentor reticulatus are two of the most important vectors in Europe. Climate niche modelling has been used in many studies to attempt to explain their distribution and to predict changes under a range of climate change scenarios. The aim of this study was to assess the ability of different climate niche modelling approaches to explain the known distribution of I. ricinus and D. reticulatus in Europe. Methods A series of climate niche models, using different combinations of input data, were constructed and assessed. Species occurrence records obtained from systematic literature searches and Global Biodiversity Information Facility data were thinned to different degrees to remove sampling spatial bias. Four sources of climate data were used: bioclimatic variables, WorldClim, TerraClimate and MODIS satellite-derived data.
Occurrence data Four sets of tick occurrence data were combined to build the models: data held by the Global Biodiversity Information Facility  [21] , data extracted from a systematic review of the literature published between 1970 and 2014  [22] , a systematic review of the more recent literature between 2015 and 2021  [23]  and additional publications which filled gaps in the previous sources. The additional publications were found through targeted searches for occurrence data in regions where tick distribution was not reflected in previous sources, for example the presence of I. ricinus in Portugal  [24] [25] [26] [27]. The combined occurrence dataset was then cleaned in sequential steps in R (v. 4. 2. 1)  [28, 29]  to increase reliability and quality by removing entries which met the following criteria: (i) missing or errors in coordinates; (ii) duplicated; (iii) coordinates fell within 1000 m of country/province centroids, institutions or capital cities; (iv) coordinates outside of the area of interest (see Fig.
1  for area of interest) (Additional file 1: Dataset S1). It is important for SDMs that the entire study area has been systematically or randomly sampled. To meet this assumption, the data were randomly spatially rarefied using several distance thresholds (10 km-100 km in 10-km increments). The spatial distribution of the resultant datasets was quantified using the nearest neighbour index (NNI) and the thinning distance threshold which resulted in a dataset with the NNI closest to 1, which represents a random distribution, was chosen for the occurrence data  [30]. As true absence data for ticks are difficult to obtain, these presence data were then combined with 10,000 randomly generated pseudo-absence and background points for the use in presence-absence and presence only models, respectively  [31] [32] [33].
Explanatory variables Explanatory variables were obtained from four sources and included interpolated data and satellite imagery (Table  1 ). These variables were chosen as they had previously been used to capture the climatic niche of different tick species in Europe  [16, 34, 35]. Monthly time series data, from WorldClim, TerraClimate and MODIS, were reduced by temporal Fourier transformation using an amended version of the R script provided by Estrada-Peña et al. [35, 36]. This technique reduces the time series data into annual harmonics (sine and cosine waves) with characteristic frequencies, amplitudes and phase angles which capture the annual behaviour of individual environmental variables including the start of spring and autumn as well as the duration of the summer  [35]. This method is advantageous as it retains ecologically relevant information while being statistically sound and reducing the number of variables  [35, 36].
All the variables were resampled to the same resolution (10 km × 10 km) and clipped to the study extent (Europe). Due to the pervasive influence of the training extent on model output, and as no consensus on the delimitation of this area has been made for ticks, several model training extents were tested by creating a 100 km, 200 km, 300 km, 400 km, 500 km, 600 km and 700 km buffer around the occurrence data for each species as well as using the whole study region  [37, 38]. For the Fourier transformed datasets, the variables and numbers of coefficients used were based on previous studies defining the climatic niche of tick species  [20, 34, 39]. However, different combinations of the 19 available bioclimatic variables have been used in previous studies; hence, a new selection was used here.
Modelling methods The choice of model can influence the predicted suitability for a species  [17, 42]. Here, three SDM algorithms were assessed: generalised additive model (GAM)  [43] , random forest (RF)  [44]  and maximum entropy (Max-Ent)  [45]. Individual model fitting and tuning were implemented for each algorithm. Default settings and equally weighted presence and pseudo-absence points were used for the GAM models implemented using the mgcv R package (v. 1. 8-38)  [17, 32, 46]. Random forest models were run with 1000 trees and a down-sampling approach was employed, whereby each classification tree was made with equal presence and pseudo-absence data points, and pseudo-absences were randomly sampled with replacement from the training data  [47]. RF models were implemented using the randomForest package in R (v4.
The performance threshold for MCS was 0. 25 above or below 1 and models with CBI values exceeding 0. 9 were considered well performing  [52]. The OR performance threshold was set to below 0. 15. All performance metrics were generated using the internal test data from cross-validation and then averaged across the five folds  [51]. Binary models were made using a threshold optimised for TSS. An independent tick occurrence dataset for validation was acquired through a pan-European tick surveillance projects supported by regional MSD Animal Health divisions  [55, 56]. Veterinary practices across Europe were asked to submit ticks found on pets and record their geographic location (for details, see other papers in this volume). These surveillance projects ran for varying periods between 2015 and 2022 in 15 European countries: Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Hungary, Italy, The Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Spain and UK  [57] [58] [59].
Ticks were then morphologically or molecularly identified by the study coauthors. This independent dataset was thinned so that no points were closer than 30 km and resulted in 570 occurrence points for I. ricinus and 133 for D. reticulatus. The same performance metrics used in the internal validation dataset were employed here. An uncertainty index was generated by finding the range between the minimum and maximum value of predicted environmental suitability for each cell within the five replicates for each modelling combination. This was to show the uncertainty in the results derived from different subsets of occurrence data. The sum of the uncertainty was then normalised between 0 and 1 to allow for comparison. Although quantitative analysis can provide a good estimation of the value of the model, expert qualitative analysis of the resultant maps is also a useful guide  [60].
Occurrence data The combination of occurrence data from the literature reviews, GBIF data and additional publications provided 7668 georeferenced locations of I. ricinus and 2733 for D. reticulatus (Fig. 1 ). After cleaning, the datasets were reduced to 4908 and 858 records, respectively (Additional file 1). Finally, following the removal of spatial bias, the resultant datasets consisted of 638 locations for I. ricinus (optimal thinning; 50 km, NNI = 0. 996, Z = -0. 176) and 153 for D. reticulatus (optimal thinning; 60 km, NNI = 1. 014, Z = 0. 337) (Additional file 1).
Model performance Model performance varied widely, depending on the modelling algorithm, training extent and explanatory variables used, as well as the validation dataset. Furthermore, the different modelling combinations varied in their uncertainty index and the area of Europe predicted as suitable (Figs. 2, 3, 4 and 5) (Additional files 4, 5, 6, 7 and 8). The modelling approach also influenced the pattern of suitability across Europe. For I. ricinus, western Europe, specifically Germany, had the greatest consensus between modelling approaches and was always considered of high suitability, whereas western Russia had consistently low suitability values (Fig. 4 ). There were pockets of high variation in predicted I. ricinus suitability; for example, northern Scandinavia and southern Turkey had high uncertainty indexes.
The goodness of calibration of I. ricinus models also significantly varied depending on the modelling algorithm (MCS: X 2 = 334. 21; df = 2; P < 0. 005 | CBI: X 2 = 135. 13; df = 2; P < 0. 005) with RF performing significantly better than GAM. However, the poor classification, discrimination and uncertainty performance of RF was mirrored in the suitability maps produced, where output was oversimplified (Additional file 5: Figure  S5-12 ). Compared to I. ricinus, differences in the classification (TSS: X 2 = 29. 96; df = 2; P < 0. 005) and discrimination (AUC: X 2 = 65. 83; df = 2; P < 0. 005) performance of the modelling algorithms were less evident for D. reticulatus, although RF models were still among some of the worst performing Fig. 2  Comparison of model performance metrics for Ixodes ricinus using three modelling algorithms [generalised additive models (GAM), maximum entropy (MaxEnt) and random forests (RF)], with four explanatory variable sets (bioclimatic variables, WorldClim, TerraClimate and MODIS satellite-derived variables) and with eight training extents (100 km-700 km buffering extents around occurrence data increasing in increments of 100 km and the European extent).
The area under the receiver-operating characteristic curve (AUC), true skill statistic (TSS), omission rate (OR), Miller's calibration slope (MCS) and Continuous Boyce Index (CBI) were derived from the mean values from cross-validation folds. The uncertainty index represents the range in predictions between folds and the area suitable shows the percentage of Europe predicted as suitable for the species using binary maps made using a threshold optimised for TSS. Note that the y-axes are different in each panel and the data points are jittered around the x-axis to allow for better visualisation Fig. 3  Comparison of model performance metrics for Dermacentor reticulatus using three modelling algorithms [generalised additive models (GAM), maximum entropy (MaxEnt) and random forests (RF)], with four explanatory variable sets (bioclimatic variables, WorldClim, TerraClimate and MODIS satellite-derived variables) and with eight training extents (100 km-700 km buffering extents around occurrence data increasing in increments of 100 km and the European extent).
ricinus in Europe using a model trained with TerraClimate data from the extent of Europe and run using a MaxEnt modelling algorithm (C). The uncertainty in these results is also presented (D). This model was selected as best representing the current distribution of the species by a panel of experts; the output of all 96 models evaluated can be found in Additional file 7 Fig. 5  Mean predicted environmental suitability for Dermacentor reticulatus in Europe derived from 96 modelling approaches (A). The uncertainty in results between modelling approaches is also presented (B). The bottom graphics show the predicted environmental suitability for D. reticultus in Europe using a model trained with TerraClimate data from the extent of Europe and run using a MaxEnt modelling algorithm (C). The uncertainty in these results is also presented (D).
Explanatory variables The classification and discrimination performance of models was significantly influenced by the explanatory variables for both I. ricinus (AUC: X 2 = 250. 64; df = 3; P < 0. 005 | TSS: X 2 = 278. 39; df = 3; P < 0. 005) and D. reticulatus (AUC: X 2 = 158. 09; df = 3; P < 0. 005 | TSS: X 2 = 95. 24; df = 3; P < 0. 005). However, the goodness of calibration of I. ricinus (OR: X 2 = 4. 25; df = 3; P = 0. 24 | CBI: X 2 = 7. 53; df = 3; P = 0. 06 | MCS: X 2 = 92. 43; df = 3; P < 0. 005) and D. reticulatus (OR: X 2 = 11. 39; df = 3; P = 0. 01 | CBI: X 2 = 18. 49; df = 3; P < 0. 005| MCS: X 2 = 11. 41; df = 3; P = 0. 01) models was less influenced by explanatory variables. Overall, I. ricinus models built with interpolated WorldClim data performed best, statistically, in both internal and external validation, although qualitative interpretation of the resultant maps suggests that TerraClimate variables were able to predict the suitability best (see below: Expert Opinion).