dataesr/scientific-paragraphs-categorization huggingface dataset:
MATERIAL AND METHODS We examined all collections of the genus Atractocarpus from French Polynesia and related Oceanian species in the herbaria of Papeete (PAP) and Paris (P), as well as those digitized from BISH, L, U and US. The measurements given in the descriptions were taken in the field and from herbarium specimens. The distribution maps were produced from herbarium data (mainly PAP, P and US), and made it possible to assess the appropriate threat categories. Various comments from field naturalists in French Polynesia and information present on herbarium labels have made it possible to establish the ecological characteristics of the taxa.
Acknowledgements The authors wish to thank the  Herbaria L, PAP, P, U, US  for granting access to their collections during visits or through digitized material available online. They also would like to thank  Walter Teamotuaitau  and  Jacques Florence  for sharing their knowledge on the Tahitian Atractocarpus and flora in general. The authors wish to thank  David H. Lorence  and  Thierry Deroin  for their comments on the manuscript and the improvement of the text.
Numerical Estimation of the Magnetic Effect of a Magnetized Prism The magnetic field for a rectangular prism magnetized along the  x direction has been derived analytically by  Yang et al. (1990). Camacho and Sosa (2013)  adapted these results for a rectangular prism uniformly magnetized along the  ẑ direction (see their Section 2. 5). The MATLAB routine FieldBar. m by  Cébron (2021)  makes use of the results by  Camacho and Sosa (2013)  but corrects for an error in the equation of the magnetic field along the  ŷ direction and an error in the calculation of the magnetic field inside the magnet (see the ReadMe file of Cébron (2021) for details). Accounting for these corrections, the magnetic field along the  x ,  ŷ , and  ẑ directions of a rectangular prism uniformly magnetized along the  ẑ direction is given by the following equations: where x, y, and z are the distance of the observation point from the magnet's center along the  x ,  ŷ , and  ẑ directions, respectively, μ 0 is the permeability of free space, M is the magnetization of the prism, and a = L x /2, b = L y /2, and c = L z /2, with L x , L y , and L z the dimensions of the prism in the  x ,  ŷ , and  ẑ directions, respectively.
An Alternative Meteorite Identification Technique The use of magnetic susceptibility meters has been shown by many studies to be an accurate and nondestructive meteorite identification and classification technique  (Folco et al. , 2006; Kohout et al. , 2010; Macke, 2010). They can be used to not only distinguish between meteorites and terrestrial rocks (identification) but also to distinguish among different types of meteorites (classification). Rochette et al. (2003) ,  Rochette et al. (2005 Rochette et al. ( , 2008 Rochette et al. ( , 2009 Rochette et al. ( , 2010) )  have compiled an extensive database with magnetic susceptibility values for different meteorite types, including Martian and lunar rocks. Indicative of the simplicity and efficiency of magnetic susceptibility measurements for meteorite identification and classification, it has been suggested they are suitable for automated execution by robots, rovers, and landers, facilitating meteorite collection and sample return missions, on Earth, other planets, and asteroids  (Folco et al.
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 844252. One specimen of NWA 11220 was purchased by Martin Goff, two specimens of NWA 7475 were purchased by Luc Labenne, and two specimens of NWA 11921 were purchased by Said Yousfi, with funds from the latter programme. F. V. and B. P. W. also acknowledge the NASA Mars 2020 Returned Sample Scientist Participating Scientist Grant 80NSSC20K0238 for funding. The manuscript has greatly benefited from the reviews and recommendations by Jérôme Gattacceca, an anonymous reviewer, an anonymous associate editor of the journal, and the editor Laurent Montési.
Cette approche peut être adaptée à différents types d'applications, telles que le séquençage du génome (WGS), le séquençage de l'exome (WES), le séquençage de panels de gènes, le séquençage de l'ARN, les études de méthylation, le séquençage de longs fragments d'ADN (long-reads), etc. L'analyse du très grand nombre de variants identifiés chez chaque individu (plusieurs millions pour un séquençage de génome), par rapport à la séquence de référence, nécessite des étapes de filtration et d'annotation, grâce à des outils bio-informatiques et à l'existence de bases de données publiques contenant des informations de séquençage de nombreux individus contrôles, d'origines ethniques différentes. Les preuves d'un lien de causalité entre un variant identifié et le phénotype présenté par un patient sont parfois difficiles à apporter, en particulier dans les maladies extrêmement rares.
L'interprétation des variants génomiques identifiés est donc délicate et peut conduire à une surinterprétation, voire à un faux diagnostic. Afin d'améliorer la précision du diagnostic génétique, des recommandations pour l'interprétation des variants ont été élaborées par l'American College of Medical Genetics (ACMG)  [10]. Ces recommandations sont fondées sur plusieurs paramètres, tels que les données de la littérature, les études fonctionnelles, les bases de données, les scores de prédiction in silico, et les études de ségrégation intrafamiliale. Ces recommandations permettent de classer les variants selon cinq niveaux : pathogènes, probablement pathogènes, de signification incertaine, probablement bénins, et bénins. Les variants de signification inconnue sont ceux qui posent le plus de difficultés et ne doivent pas être utilisés en conseil génétique.
Non-metric variations, also referred to as "epigenetic variations" or asymptomatic bone variants, are bone or dental phenotypic variants of unknown origin, with no known pathological etiology. They are classically identified during osteological analysis to develop an individual profile, analyse biological affinities between individuals and outline possible genetic relatedness clusters in burial spaces. However, although numerous studies have focused on their occurrence in adult archaeological samples, data on their development, expression and frequency in skeletally immature individuals are still scarce. The aim of this study was therefore to document the expression and frequencies of a selection of non-metric variations in an archaeological sample of 116 individuals who died during the perinatal period, in order to outline a diachronic reference database for comparative analysis.
Résumé -Les variations anatomiques non-métriques, également appelées variants épigénétiques ou variations osseuses asymptomatiques, sont des variants phénotypiques osseux ou dentaires sans origine déterminée et sans étiologie pathologique. Elles sont classiquement utilisées pour développer un profil individuel, analyser les affinités biologiques entre les individus et identifier de possibles proximités génétiques au sein de l'espace funéraire. Même si de nombreuses études se sont attachées à décrire leurs fréquences dans des collections archéologiques constituées de sujets adultes, les données disponibles relatives à leur développement, leur expression et leurs fréquences chez les sujets ostéologiquement immatures restent limitées. L'objectif de notre étude était de documenter les modalités d'expression et les fréquences d'une sélection de variations anatomiques dans un corpus de 116 sujets décédés durant la période périnatale, afin d'esquisser une base de données de référence diachronique disponible pour les analyses comparatives.
Acknowledgments We would like to thank the  Sudan Antiquities Service  and the  French Section of the Sudan Antiquities Directorate , as well as the  French Ministry of Foreign Affairs  and the  Îlede-France region. This study was supported by a doctoral research grant from the  French ministry of education  and research to the  CNRS UMR 5199 PACEA (Pessac, France)  and to the first author, and by the  Young Researcher award  from the  Fondation des Treilles  awarded to the first author. The research by BM, DC and MG was conducted under the scientific framework of the  University of Bordeaux 's  IdEx "Investments for the Future" program/GPR "Human Past ". The final stage of the research was conducted by  CP  under the scientific framework of the Research Group on "Environment and  Human Impact  in Historical Societies" led by  Dr.
GRUPS-rs, un logiciel hauteperformance d'estimation des relations de parenté génétique dédié à l'ADN ancien reposant sur des simulations d'arbres généalogiques GRUPS-rs, a high-performance ancient DNA genetic relatedness estimation software relying on pedigree simulations Maël Lefeuvre, Michael D. Martin, Flora Jay, Marie-Claude Marsolier, Alexis Corrochano, Anaïs Lebrun et Céline Bon 1 L'étude des relations de parenté génétique proches (parents, enfants, cousins, etc. ) jouit aujourd'hui d'une place de plus en plus importante dans le domaine de la paléogénétique comme un moyen de caractériser l'organisation sociale des sociétés du passé. Cette approche reste néanmoins difficile à mettre en oeuvre lors d'analyses de routine, en particulier dans le cas de populations et/ou sites archéologiques peu caractérisés, vu l'importante dégradation du matériel génétique couramment observée dans les vestiges osseux humains.
Precision livestock farming (PLF) technologies help to detect animals experiencing specific conditions such as illness, stress, or oestrus. PLF systems either detect one or more conditions at a time, or several conditions together but without distinguishing them. The aim of this work was to detect and classify a wide range of conditions of health, stress and reproduction based on cow daily activity using machine learning on time series data derived from sensors. We used five datasets (120,000 cow*days) from experimental or commercial farms, with 28 to 300 Holstein cows per dataset and observations lasting from 1 to 12 months. Caretakers checked the animals every day and recorded any specific cow condition related to disease (lameness, mastitis, acidosis, accident, and so on), oestrus, calving, as well as potential stress due to mixing or handling.
Materials and methods We used five datasets containing information on the condition of cows (e. g. diseased, potentially disturbed by an external event, in oestrus) mostly recorded by caretakers (per day) and on their core activities (resting, standing, eating) monitored continuously by sensors (per hour). From the time spent in each of the three core activities by a cow, we calculated the hourly level of activity, hereafter called 'activity'. Then we characterised each 24 h time series of a cow activity by features describing the distribution of the activity, its variations during the day and its cyclicity. We finally applied the random forest algorithm to relate the features of 24 h time series of cow activity to the condition of the cow (Fig. 1 ).
Datasets Datasets 1 to 4 are available at https://doi. org/10. 15454/52J8YS; they are described in  Lardy et al. (2022a) ,  Lardy et al. (2022b). Datasets 1 and 2 come from the 'Herbipôle' experimental farm (https://doi. org/10. 15454/1. 5572318050509348E12) run by INRAE in France. The cow were milked twice a day and the food was delivered once a day and pushed back to the trough three times (Dataset 1) or twice a day (Dataset 2). Datasets 3 and 4 come from commercial farms. Dataset was made of cows from three farms where they were fed once a day and milked two or three times a day or with an automatic milking system. The cows from Dataset 4 were from a single farm, they were milked with an automatic milking system, they were fed once a day and the food was pushed back to the trough regularly by a robot.
Dataset 5 is stored in the Agrimetrics platform (accessible at https://app. agrimetrics. co. uk/cata log/data-sets/aac0d197-f677-48e8-946d-77208e904c1d/overview); it comes from an experimental farm run by IRTA in Spain, where the cows were fed once a day and milked twice a day  (Terre, 2022). A total of 120,743 cow*days was included: 28 cows observed for months in Dataset 1, 28 cows for 3 months in Dataset 2, 40 cows for month in Dataset 3, 300 cows for one year in Dataset 4, and 31 cows for 2 months in Dataset 5 (Table  1 ). All cows were Holstein. They were primiparous or multiparous.
Recording of cow condition The caretakers checked the cows every day. They noted any specific condition of a cow in the farm logbook: oestrus [ATOL_0005321], calving [ATOL_0000877], lameness [AHOL_0003029], clinical mastitis [AHOL_0003088], or another disease, accident-related health problems. They also noted events that can induce stress: mixing cows and disturbances of the farm routine such as handling to administer veterinary treatment (e. g. vaccines or anthelminthics) or relocation to another pen. Condition of the cow was also characterised by pH bolus to check 1 Ontologies available at //sicpa-web. cati. inrae. fr/ontologies/visualisation/o ntologie/. subacute ruminal acidosis [AHOL_0003154] in Datasets 2 and 5 (where subacute ruminal acidosis was frequent due to high-starch diets), body temperature to check inflammatory [AHOL_0003064] reaction to lipopolysaccharide (LPS) injection in Dataset 1, and milk progesterone to check oestrus [ATOL_0005321] in Dataset 3.
As a simplification we will use the term "cow condition" to refer to the actual state of cows  (oestrus, disease, acidosis,…)  or to an event that may stress cows (mixing, disturbance). On most days, the cows were not in any specific condition (i. e. control days, nothing noted in the farm logbook). The most frequent specific condition was a disturbance, followed by acidosis in Datasets 2 and 5 (Table  1 ).
Monitoring of cow activities In Datasets 1 to 4, cow activities were derived from their position in the barn detected every second by RTLS (CowView, GEA Farm Technology, Bönen, Germany). Each cow collar was equipped with a tag that emits a signal in the ultra-wideband area captured by several antennas fixed on the barn ceiling. The location of the cow is obtained by triangulation with a precision of 16 cm  (Meunier et al. , 2018). If the cow is found in a cubicle, she is considered to be resting; if she is found close to a trough, she is considered to be feeding; else she is standing (including walking) in alleys. In dataset 5, the cows were equipped with accelerometers (HOBO Pendant G Acceleration Data Loggers, Onset Computer Corp. , Pocasset, MA) on their right hind leg. The accelerometer detects when the cow is lying vs standing.
The system is considered very accurate with a 0. 99 correlation between duration of lying detected by the sensor and from videos by a trained observer  (Ledgerwood et al. , 2010). The access to food was controlled by electronic gates that record the time spent eating as the time spent with the head above the bin (MooSystem, Cortes, Spain). The difference between values provided by the electronic gates and an observer is 0. 4 s on average  (Ternman et al. , 2022). Combining the data from the accelerometer and the electronic gates allows to distinguish the time spent lying, standing (not eating), and eating.
Calculation of activity level All datasets contain the time spent by each cow per hour in three  (1) The weights attributed to each core activity were derived from a Factorial Analysis of Correspondences of Datasets 1 to 4. A similar analysis was performed on Dataset 5, which brought weights consistent with those calculated on Datasets 1-4 (Dataset 5: -0. 23 for lying, 0. 10 for standing, and 0. 42 for eating). To obtain similar ranges of variation across datasets, we decided to use the same weights on all datasets. We checked that applying the weights defined in Equation (1) to Dataset 5 allows to obtain a normal 24 h pattern, with low activity at night and two peaks of activity during the day around milkings. Using Equation (1), with time expressed in seconds, the theoretical maximum activity level in an hour is 1512 (if a cow spends an entire hour eating), and the theoretical minimum is -828 (if a cow spends an entire hour resting).
Features describing the time series The datasets were organised into 24 h overlapping time series shifted by 1 h. A 1 h shift was chosen to allow the rapid detection of a cow condition. For each 24 h series, we calculated 32 time-domain and frequency-domain features (full list and abbreviations given in Table  3 ). In the time domain, we calculated features to describe the overall level of activity (mean, maximum, minimum and mode of activity across the 24 h as well as various quantiles) or the variations of activity (e. g. standard deviation between data obtained at each hour of a 24 h series or root mean square of differences between successive hours). In the frequency domain, we calculated features to describe the circadian or ultradian cyclicity of variations: autocorrelations of orders 1 to 11 and the contribution of harmonics 1 to 4 of the Fourier transform (absolute value of the FFT associated with each harmonic, representing cyclic patterns over 24, 12, 8 and 6 h).
The features describing the 24 h time series are not independent, as they correspond to various arrangements of the same initial data. We calculated the correlations between all features. We distinguished very strong correlations (r ≥ 0. 9) from strong correlations (0. 8 ≤ r less than 0. 9). We applied the following rules to remove features according to their correlations and their weight in the random forest algorithm (see below): -When two features are very strongly correlated in all datasets, we keep only one; -When two features are very strongly or strongly correlated in some but not all datasets, we check if at least one of the two weights more than 2. 4 % in the random forest algorithm. If this is not the case, we reintroduce the feature with the highest weight to the random forest algorithm and we again check that it weighs more than 2.
As the occurrence of cow conditions in each dataset is highly imbalanced, we applied oversampling on train subsets. We used the Synthetic Minority Oversampling Technique (SMOTE) and cleaning using Edited Nearest Neighbor (ENN) method (SMOTEENN,  Batista et al. , 2004)  from the imbalanced-learn module (version 0. 7) of Python  (Lemaître et al. , 2017). The oversampling resampled all classes to obtain a similar occurrence across specific conditions and the control one. However, due to the complexity of the algorithm and the resulting computation time, oversampling on Dataset 4 was only performed to reach the occurrence of 'disturbance' conditions, which is the most frequent condition apart from the control one in this dataset.
Development of the random forest algorithm We applied a random forest algorithm on train subsets. Briefly, a random forest consists of building a multitude of decision trees (hence the term 'forest'). Each node of a tree represents a decision based on a given input (here a feature describing cow activity) that has a value above or below a certain threshold. The end of the branches brings the outputs, i. e. the predicted classes (here cow condition). In a random forest, the outputs are the classes predicted by a majority of trees. We ran the Scikit-learn module (0. 24. 2) of Python to apply random forest using the default parameters of this package  (Pedregosa et al. , 2011). We tested different values of trees -100, 500, 1000 -and choose to apply 1000 trees because of slight improvement of performances from 500 and 1000 trees.
We did not try to adjust other parameters because our aim was to illustrate the potential of the approach rather than getting an optimized model for each dataset. All calculations were performed under Python 3. 9. 7. Calculation for Dataset 4 were performed on the supercomputer facilities of Clermont Auvergne University Mésocentre (Clermont-Ferrand, France). We first applied the random forest algorithm on all features describing the time series. We then removed the features with a low weight on the classification, i. e. lower than 2. 4 % (the cumulated contribution of descriptors to be removed reached 20 %). We then applied the random forest algorithm on the remaining features. Only the results obtained on these remaining features will be presented.
Evaluation of the performance The performance of the algorithm was assessed on the test subsets in terms of sensitivity, specificity, and false discovery rate (all averaged across the 10 train/test splits of the datasets). For each condition, -the sensitivity corresponds to the proportion of 24 h series from the condition correctly classified (true positives) out of series from the condition (true positives + false negatives), -the specificity corresponds to the proportion of 24 h series not from the condition and not classified in the condition (true negatives) out of series not from the condition (true negatives + false positives). -the false omission rate was calculated only for control series. It corresponds to proportion of false alarms, calculated as the proportion of control series wrongly classified in a specific condition (false positives) out of series classified in any specific condition (false positives + true positives).
Selection of features On all datasets, the standard deviation of the difference between the activity of any hour and the activity of the next hour (STDSD) was very strongly correlated to root mean square of successive differences, i. e. the differences between the activity at a given hour and the activity at the next hour (RMSSD) (r greater than 0. 9999). We therefore excluded STDSD from further analyses. Other strong or very strong correlations on more than one dataset (Tables  SM1 to 5 ) were as follows: -Mean activity (Mean) correlated with quantiles Q50 or skewness index, -Standard deviation of the activity (STD) correlated with root mean square (RMS), -Q10 correlated with minimum or Q25, -RMS correlated with Q90. These correlations were not very strong on all datasets. The features were thus kept for further analyses.
Performance of the classification of 24 h series The random forest algorithm applied to the 21 remaining features correctly classified 91. 6 % of series from control days on average across datasets (from 77 % in Dataset 2 to 99 % in Datasets 3 and 4; Table  4 ). The algorithm classified 24 % of series from days labelled with a specific condition as 'not control days' (from 5. 6 % in Dataset 4 to 47. 2 % in Dataset 2). Calving was best predicted (44. 4 %); followed by oestrus, lameness and acidosis (18. 7 %, 18. 4 %, 18. 4 %); then mastitis, other diseases and disturbances (12. 9 %, 12. 2 %, and 11. 3 %), and mixing (9. 4 %). LPS and accidents were rarely classified correctly (5. 3 and 5. 2 %). On average across all datasets, the specificity of any specific conditions was above 95 %: 100 % for accidents, 99.
8 % for LPS, 99. 7 % for mastitis and for calving, 99. 5 for other diseases and for mixing, 99 % for lameness, 97 % for oestrus, 96. 7 % for disturbances and 95. 8 % for acidosis. Across all datasets, 77 % of series classified with a specific condition corresponded to control series (from 46 % in Dataset 2 to 89 % in Dataset 1). Time series from days labelled with a specific condition that were not correctly classified were most often confused with time series from control days (Table  5 ). The series labelled with a specific condition were also confused to a lesser extent with time series from days labelled 'disturbance' in Dataset 2 or 'oestrus' in Dataset 5. Accidents, which occurred only in Dataset 4, are also confused with calving.