dataesr/scientific-paragraphs-categorization huggingface dataset:
To implement the gradient boosting decision tree algorithm efficiently, we relied on the eXtreme Gradient Boosting (XGBoost) library  86. Tuning its hyper-parameters to control the implementation of the algorithm enabled to resolve many classification problems (see https:// github. com/ dmlc/ xgboo st/ blob/ master/ demo/ README. md). Moreover, XGBoost has a built-in strategy to deal with missing values by finding the best imputation  86. In this study, we used a nested cross-validation process to tune the number of decision trees and evaluate the classifier's performance (see below). Moreover, we tuned carefully several hyperparameters to control the complexity of the model and avoid overfitting. Namely, the depth of each tree was set to a relatively low value as 5. The model weights were shrunk after each learning iteration by a factor of 0.
01. The features were subsampled in each tree to make the model robust to potential noise in the data. The rate of subsampling was inversely proportional to the number of features and was selected as 0. 7 and 0. 5 in semi-automatic and automatic feature selection strategies (FSS) respectively. We used Lasso and Ridge regularization techniques to impose a penalty on the complexity of the classifier. Lasso regularization, as explained above, also helped to detect and remove less relevant features automatically which, in turn, avoided overfitting. Imbalanced datasets are common in medical studies due to the low prevalence of diseases. This causes a classifier to learn mostly patterns in the majority class, i. e. control samples. To cope with this issue, we imposed a higher weight on the misclassification error of ASD samples than that of NT ones.
Hyperparameter tuning and classifier evaluation. To tune the hyperparameters of the XGBoost classifier and to evaluate its performance, we used a 10-times repeated nested tenfold stratified cross-validation (CV) process. In each repetition of the CV process, the whole dataset was divided randomly in 10 partitions, 9 partitions for training the classifier and 1 held-out partition to test the trained classifier and to ensure that the algorithm can be generalized in future unseen samples. The train-test process of the classifier ran in 10 rounds. In each round, the hyperparameters of the classifier were tuned on train data through an internal five-fold stratified cross-validation grid search on values given in the Table  4. Optimal hyperparameters were chosen to maximize the F 0. 5 score of the classification (see below for the definition of the F 0.
5 score). The model was trained using optimal hyperparameters on the train data and the trained model was used to predict the target variable of samples in the 1 held-out test partition. Beyond F 0. 5 , local classification scores, including True Positive Rate (TPR, aka sensitivity), True Negative Rate (TNR, aka specificity) and Positive Predictive Value (PPV, aka precision) were recorded at the end of each round. This procedure was repeated 10 times with different random partitionings of the dataset resulting in 100 rounds of train-test processes. Finally, the averages of the recorded classification scores were considered as the final cross-validated scores of the classifier. The 10-times repetition of the CV procedure reduces the effect of bias on classification scores due to a relatively small number of samples.
It ensures that the classification scores and hyperparameter tuning are not affected by any specific train-test partitioning of the dataset and the classifier is generalizable to future unseen samples. F 0. 5 score. Regarding the ethical aspects of this study, we decided to minimize false positives, in the first place, while detecting ASD samples as much as possible. This goal could be achieved by maximization of PPV but it would be at a cost of decreasing TPR. To treat this issue, we chose to maximize the F 0. 5 score which balances the PPV and the TPR while it puts higher weight on PPV, i. e. it pays more attention on minimization of false positives: Important feature identification. An important goal of ML analysis in this study was to identify features that separate effectively NT from ASD babies.
The difference in HC percentile distributions of the 3 groups i. e. NT, ASD and "Large HCs ASD", was checked by ANOVA with Tukey's post-hoc test (when the normality assumption and homogeneity of variance were plausible) or the Kruskal-Wallis test with Dunn's post-hoc test in the 2nd and 3rd trimesters and before birth. Moreover, the brain growth of this group was compared to that of other ASD and NT babies by using ANCOVA and a quadratic mixed effect model as described before. Implementation. All the programming and implementation of XGBoost was done on Python v.
EMC 2   is therefore more versatile, and does not require motion priors or heuristics to close tracking gaps. In addition, for the local tracking of detectable particles, EMC 2 uses a probabilistic method and is therefore robust to very cluttered conditions. We validate the robustness and accuracy of EMC 2 with manual tracking of neurons in two calcium imaging datasets from behaving animals. Our first dataset consists of two-photon calcium imaging of few tens of neurons in the visual cortex of awake mice. We show that our algorithm accurately tracks the limited motion of single neurons in the twophoton field-of-view, enabling the fast analysis of long recordings of individual neuron activity. We then monitor single neurons of Hydra's nervous system while the animal is behaving and deforming.
Hydra imaging datasets represent perhaps the worst possible scenario for tracking purposes, since animals can have major changes in body size with non-isometric deformations. We also quantify the performance of EMC 2  using simulations of fluorescence time-lapse sequences with different types of motion (confined diffusion, linear displacement and elastic deformation), and show that EMC 2   outperforms state-of-the art tracking algorithms. After integrating EMC 2 in an open-source and freely available platform Icy  [20]  (icy. bioimageanalysis. org), we explore the utility of the algorithm in two experimental scenarios. We first monitor the activity of single neurons in two-photon calcium imaging of layer 2/3 of mouse visual cortex over multiple days and show that there is an important turnover of active neurons and that very few neurons remain active across days.
First, the Bayesian tracking algorithm (eMHT) computes tracklets of detectable particles (block 4). Due to fluctuating particle detectability, many Bayesian tracklets are terminated prematurely and new tracklets are created when particles can be detected again. To close detection gaps in single particle tracks, block 5 applies the EMC 2 algorithm. Final output of the Icy protocol is the collection of single particle tracks over the time-lapse sequence. Tracking protocol can be found here: http://icy. bioimageanalysis. org/protocol/detection-with-cluster-un-mixing-andtracking-of-neurons-with-emc2/ and is also directly accessible through the search bar of the Icy software (see step-by-step Supplementary Icy tutorial).
4-Validation of EMC 2 a-Manual tracking of calcium dataset in behaving animals To validate the capabilities of EMC 2 , we first compared the results of our algorithm with manual tracking in calcium imaging sequences of neuron activity in behaving Hydra  [5]. We used the first 250 frames (25 seconds at 10 Hz) of a timelapse sequence previously acquired in a genetically-engineered animal  [5]  and automatically detected the active neurons (bright spots) using the multi-step detection process described in the Materials and Methods. We tracked the detected particles with the eMHT algorithm (  [30] , implemented in Spot tracking plugin in Icy) and obtained short Bayesian tracklets (n = 784 tracks) for the detected neurons (step 4 of the Icy protocol in Fig. 2 ). We then manually concatenated all the corresponding tracklets, i.
b-Tracking (EMC 2 ) After having detected the positions of fluorescent spots in each time frame, a second series of blocks computed the tracks of each single particle. First, block number 4 used the positions of spots and a robust Bayesian algorithm (eMHT  [30] ) to compute single tracks of detectable particles. Due to fluctuating detectability, many computed tracks are terminated prematurely and new tracks are created when particles are detectable again. We thus applied EMC 2 algorithm (block number 5) to close gaps and reconstruct single-particle tracks over the entire time lapse sequence. Tracking protocol can be found here: http://icy. bioimageanalysis. org/protocol/detection-with-cluster-un-mixing-andtracking-of-neurons-with-emc2/ and is also directly accessible through the search bar of the Icy software.
Acknowledgements: Supported by the  NEI  ( R01EY011787 ),  NIMH  ( R01MH115900 ),  NSF  ( CRCNS 1822550 ) and  Vannevar Bush Faculty Award  ( ONR N000142012828 ). Hydra research was supported by  Burroughs Wellcome Fund   2018 Collaborative Research Travel Grant. T. L. was partly supported by the  Fondation pour la Recherche Médicale  and the  Philippe Foundation. A. H. is supported by the  NIMH  ( T32MH018870 ). A. F. is supported by the  Weill NeuroHub  and the  Simons Foundation Collaboration for the Global Brain  ( 542975SPI ). MBL research was supported by competitive fellowship funds from the H. Keffer Hartline ,  Edward F. MacNichol ,  Jr. Fellowship Fund, The  E. E. Just Endowed Research Fellowship Fund, Lucy B. Lemann Fellowship Fund ,  Frank R. Lillie Fellowship Fund Fellowship Fun, Fries Trust Research Award, Hartline MacNichol Research Award, L.
Diploids are grouped according to their level of heterozygosity as: homozygous parents (light brown), intraspecies hybrids (brown) and interspecies hybrids (dark brown) and the same colour codes apply to b, d and e. b Level of heterozygosity across the hybrid panel with the number of markers detected in non-collinear and collinear regions. Each data point is labelled with a number/colour encoded according to a. The four homozygous parents are not reported. c URA3-loss assay used for measuring RTG-induced recombination rates. d Top: the y axis reports in logarithmic scale the percentage of cells growing in 5-FOA measured with the URA3-loss assay and quantified with the LOH difference (R T6 -R T0 ) where R T6 and R T0 are the LOH rates at T6 and T0, respectively (Supplementary notes). Error bars represent standard deviations.
The colour intensity of Spo11p hotspots is proportional to the corresponding signal intensity. The heatmap of the LOHs (i. e. , two copies of ScNA alleles) resulting from the breakpoints is also reported. The boxplot is defined as follows: the box is delimited by the first quartile (Q1) and the third quartile (Q3). The line that separates the box is the median. Whiskers are defined as: upper whisker = min(max(x), Q3 + 1. 5 × IQR); lower whisker = max(min(x), Q1-1. 5 × IQR), where: x is the data, Q1 is the first quartile, Q3 is the third quartile and IQR is the interquartile range (IQR = Q3-Q1). Fig. 4  Fitness diversification of RTG clones and QTL mapping. a Extent of fitness variation in the ScMA/ScNA data set. The y axis shows the number of environments in which the cell doubling time of the RTG clone is superior or inferior compared with the ScMA/ScNA parent hybrid.
The haploid strains were mated and diploid prototrophs (MATa/MATα, ura3::KanMX/ura3::-KanMX, LYS2/lys2::URA3, msh2::NatMX/msh2::NatMX) or (MATa/MATα, ura3::KanMX/ura3::KanMX, LYS2/lys2::URA3, ndt80::NatMX/ndt80::NatMX) were selected on minimal media lacking both lysine and uracil. The CAN1 deletion in the SpNA background was obtained using the lithium acetate/PEG transformation protocol and the diploid hybrid was obtained by mating with ScNA (MATa/MATα, ura3::KanMX/ura3::KanMX, LYS2/lys2::URA3, can1:NatMX/ CAN1). All the deletions made were verified by polymerase chain reaction (PCR). All the PCR primers used in this study are reported in Supplementary Data Set 12. URA3-loss LOH assay. Each hybrid was patched from the -80 °C glycerol stock on YPD solid media (1% yeast extract, 2% peptone, 2% dextrose and 2% agar) and incubated overnight at 30 °C.
5 in SD-based medium (0. 675 % YNB, 0. 0875% Arginine drop out, 2% Dextrose) supplemented with canavanine (2%), and grown for 10 h. Finally, cells were plated on 5-FOA plates following the protocol described in the paragraph "URA3-loss LOH assay" in the Materials and Methods section. The canavanine liquid incubation was enriched for cells bearing LOH at the CAN1 locus and the passage on 5-FOA plates selected for cells carrying LOH at the LYS2 locus. Sequencing data analysis. All the samples were sequenced with Illumina pairedend technology at the NGS platform of Institut Curie according to the manufacturer's standard protocols. Short-read sequencing data were processed by means of the MuLoYDH pipeline using default parameters and the assemblies/ annotations embedded in MuLoYDH  24. All the data sets consisted of the hybrids evolved under the RTG protocol and analysed against the corresponding parent hybrid before RTG as control samples (Supplementary Data Set 9).
e. , the last non-masked marker. We excluded two WT RTG derived from ScS288C/ScSK1 owing to low coverage that did not match the standard requested by MuLoYDH. We also removed one sample from the WT ScS288C/ScSK1 RTG data set that went through two cycles of RTG in order to have only samples that performed one RTG cycle in WT and ndt80Δ. De novo SNVs and indels were determined from both competitive and standard mappings. Competitive mapping allowed for direct variant phasing in heterozygous regions. Variant calling from competitive mapping was performed setting ploidy = 1 in heterozygous regions and ploidy = 2 in LOH blocks. Regions characterised by reads with low mapping quality (MAPQ < 5 in the competitive mapping) were assessed from standard mapping using arbitrarily the assembly from parent 1.
All the de novo variants detected were checked by visual inspection using IGV  60. Testing for association between LOH breakpoints and Spo11p-induced DNA DSB or recombination hotspots. The association between LOH breakpoints (using the start/end coordinates) and DSB/recombination hotspots was tested using the regioneR package  61. In brief, regioneR implements a permutation test framework specifically designed for testing the association between two sets of genomic regions. The association tests were performed by means of the function overlapPermTest, setting the parameters: ntimes = 10000, and alternative = "greater". The fasta suite 62 (fasta36 -b 3 -d 3 -m 8, version: 36. 3. 8d April, 2016) was used to convert the coordinates of the hotspots regions detected in the original reference genomes (SGD_R62-1-1_20090218 and SGD_R58-1-1_20080305 for the hotspots reported by Pan et al.
Acknowledgements This work was supported by  Agence Nationale de la Recherche  ( ANR-11-LABX-0028-01 ,  ANR-13-BSV6-0006-01 ,  ANR-15-IDEX-01 ,  ANR-16-CE12-0019  and  ANR-18-CE12-0004 ),  Fondation pour la Recherche Médicale  ( FRM EQU202003010413 ),  CEFIPRA ,  Cancéropôle PACA  ( AAP Equipment 2018 ), Meiogenix and the  Swedish Research Council  ( 2014-6547 ,  2014-4605  and  2018-03638 ). S. Mo. is funded by the convention CIFRE 2016/0582 between Meiogenix and  ANRT. The  Institut Curie NGS  platform is supported by  ANR-10-EQPX-03  ( Equipex ),  ANR-10-INBS-09-08  ( France Génomique Consortium ),  ITMO-CANCER  and  SiRIC INCA-DGOS  ( 4654  programme).
We developed a novel approach in the field of spatiotemporal modelling, based on the spatialisation of time: the Timescape algorithm. It is especially aimed at sparsely distributed datasets in ecological research, whose spatial and temporal variability is strongly entangled. The algorithm is based on the definition of a spatiotemporal distance that incorporates a causality constraint and that is capable of accommodating the seasonal behaviour of the modelled variable as well. The actual modelling is conducted exploiting any established spatial interpolation technique, substituting the ordinary spatial distance with our Timescape distance, thus sorting, from the same input set of observations, those causally related to each estimated value at a given site and time. The notion of causality is expressed topologically and it has to be tuned for each particular case.
In some cases we could have a dynamical model at hand  [15]  so the modelling is generally conducted via partial differential equations integration, as it is often the case with population dynamics  [16]. Other trending approaches include neural networks and deep leaning techniques  [17, 18]. Furthermore, one is often confronted with the problem of model scaling  [19] , though the computational limitations are less and less important. We chose to treat our observations as a random scalar field through standard geostatistical techniques, without introducing an explicit dynamic model or any kind of machine learning. Spatial statistics demands that the distribution of our observations obeys more or less strict constraints, depending on the kind of modelling we would like to perform  [4, 20]. On the other hand, the distribution of observations in ecology if often sparse, both in time and space, especially when merging new data, collected according to a robust spatial pattern, with old observations, typically collected just at spot sites or along transects: data non-stationarity and a spatially skewed distribution of the observations is often encountered  [21] , so the modelling should be conducted, as a matter of principle, also with sub-optimal datasets, maybe at the expense of accuracy.
Our spatiotemporal distance can be tuned by a couple of parameters, the aforementioned velocity and a causal strictness parameter, plus a form factor related to seasonality. The Timescape distance is exposed in section 2. 1 and 2. 4 and the actual implementation is discussed at length in  [26]. On practical grounds, we sought for a modelling tool that is: 1) not too choosy about the distribution of the input data, 2) capable to accomodate seasonal variability, 3) suitable for both both projected and geographical coordinates, 4) capable to run on affordable hardware in decent times,  5)  distributable as open source. The resulting software implementations are a Python module  [26]  and two Java applications, one for projected  [27]  and one for geographical coordinates  [28]. The software is exposed in section 2.
Note also that one cannot remove too many events from S since it could lead to too many empty S k sets, i. e. too many null Φ k (x). Further details on the procedure can be found in Python Timescape module manual  [26]. The overall accuracy can be assessed by two figures: a global variance σ 2 model and the ratio of null target events over the total target size η model σ 2 model = 1 | T * | ∑ x∈T * σ 2 (x) , η model = | T * | | T | (17) where T * = x ∈ T Φ(x) = null is the set of not-null elements of T. The η model index, in particular, measures the predictive power of the model. σ model is expressed in the same units of Φ while η is adimensional and 0 ≤ η ≤ 1.
Timescape Implementations Two algorithm implementations have been published: a Python and two Java versions. All the software packages are released under the GNU-GPL license. Other than going deep into the mathematical subtleties of sections 2. 1 and 2. 2, the manuals accompanying the software distributions treat in detail the practical implementation and describe the code. In this paper, we only sketch the key features of the software. Both implementations are limited to two spatial dimensions and provide some statistical analysis tools to explore the source dataset and the finished models.
Python The Timescape Python module  [26]  consists in a single module, exposing all the objects, methods and functions needed to perform source data analysis, parameters tuning and the actual model interpolation, plus a bunch of functions dedicated to data plotting and exporting. All the examples in section 3 have been computed with this software. The usage is command line based, but the functions can be code. The default spatial interpolator exploits the PyKrige module functions  [39] ; a finished model is a Python object that can be saved in binary format and can be included in a GIS workflow as a multi-layered geotiff image  [40] , one band per time sheet. The storage is RAM-based, so this is the limiting factor for the number of target cells. The Python package is designed to be easy configurable, allowing users to code their interpolators.
Java The Timescape Java implementation consists in two stand-alone applications, one for projected coordinates, the local version  [27, 41] , and one for geographical coordinates, the global version  [28, 42]. These are distributed as self-consistent jar executables. The storage of the Timescape models is based on a Hibernate midlayer  [43]  that manages the connection with any compatible relational database management system (MySql  [44]  scripts are included in the distribution). The user interaction is mediated by a Graphical User Interface based on standard swing widgets. The database storage is more flexible, in that each target event is just a record of a table, but there is the need of a dedicated database service. The Java applications allow a full customization of distance and form factor via a javascript-like scripting language but, since it requires run-time interpretation, the performance is dramatically affected.
Fungi δ 15 N This dataset comes from a study about host trees -mycorrhiza relationship conducted via geostatistical methods exploiting carbon and nitrogen stable isotopes abundances  [51]. The model's scalar field represents the hypogeous fungis' ascomata δ 15 N (Tuber aestivum Vittad. -edible black truffles). The complex tree-fungi relations include the nitrogen transport from fungi to the host tree, in particular, the δ 15 N difference, or fractionation, is a clue of tree-fungi symbiosis  [52] [53] [54] , with a significant difference in terms of δ 15 N, resulting in a fungal 15 N enrichment with respect to the host  [55, 56]. From a spatio-temporal point of view, the collection took place within 64 days of continuous fungi-tree interactions, so it was impossible to treat spatial and temporal variability on different grounds.
Lowest temperatures This case study shows the ability of the Timescape algorithm to cope with seasonal variability. The source dataset consists in the monthly lowest temperatures recording of Umbria region, in central Italy  [57, 58]. The temperature records span 20 years with monthly time resolution (1/12 yr), their spatial resolution increases over time as more and more stations have been added in the two decades considered. The interpolation has been conducted with a periodic causal cone (T = 1 yr) using universal Kriging. The great number of source events, more than 2000, added further computational load: in fact, the throughput is as low as 30 target events per second, see table 3 in section 3. 5. The source events are shown in figure  13. Note the temporal gaps in the measurements. Also, new stations started logging data over time, resulting in denser events from year 1990 on, while around 1980 the collection is sparse in both space and time.