dataesr/scientific-paragraphs-categorization huggingface dataset:
Bayesian approaches are less prone to overtraining but do not give more accurate species trees We investigated whether Bayesian approaches [here MrBayes,  21, 22]  were less prone to overtraining than ML, as expected for an approach that samples over a likelihood landscape. This was indeed the case, and the proportion of runs with overtraining was approximately 5% with MrBayes, as compared to 24 to 36% for PhyML on the same datasets (Fig. S9 A ). However, this does not necessarily imply that Bayesian approaches are better at reconstructing species trees than ML approaches. Indeed, when comparing the topological distances between the MrBayes final (consensus) trees and the reference tree in the one hand and the topological distances between the final ML trees and the reference tree in the other hand, we observed that for most runs, the trees were identical and that when they differed, MrBayes or PhyML trees were similarly likely to be closer to the reference tree (Fig.
Exhaustive search of the most likely tree and visualization of tree likelihood landscapes. To ensure that overtraining was not attributable to a particular implementation of the ML heuristics, we performed an exhaustive search for the most likely tree for a fungal dataset of 9 species and compared to the ML tree obtained by RAxML after a normal run. We focused on 25 alignments of 5 genes where overtraining was present. For all the alignments, the final trees were the same in the normal and exhaustive searches and the final tree was always the most likely in the exhaustive search. This test also allowed us to visualize the entire tree likelihood landscape for each one of these 25 runs, by performing a multidimensional scaling (MDS) on the pairwise distance matrix of the best trees encountered (see Material and Methods).
Such a landscape is given in Figure  5. We observe different "islands" of higher likelihood and see that the most likely tree given the 5-gene alignment (white arrow) is at a different peak than the reference tree (black arrow). When looking at the 25 landscapes (Fig. S10 ), we observe that even though the species tree is never the one with highest likelihood (because we chose runs with overtraining), it is always at an apparent local optimum. Moreover, when looking at the mean rank of all trees for the 25 analyses (Figure  S11 ), the reference tree is the one with the smallest mean rank and the smallest standard deviation (mean = 2. 64; sd = 1. 11). The best ML trees of each run display higher mean ranks. Therefore, with this dataset, the reference tree is never the best (judged by the fit on the 5-gene alignment), but always has a high likelihood: that is, it seems to be robust to gene sampling effect.
Datasets Fungal dataset. -The fungal dataset  [14]  was obtained from the authors as a set of already aligned and trimmed multiple sequence alignments (See  [14]  for the methods employed). Species included in this dataset (Table  S2 ) span a wide range of the Fungal tree of life. Primate dataset. -This dataset was retrieved from Phylome-DB database  [15] , Phylome-DB phylome accession number: 98. It was obtained using the protocol detailed in  [23]. Only one-to-one orthologous sequences present in all 10 primate species considered (Table  S2 ) were kept, resulting in 7,018 genes. Each set of orthologous proteins was aligned using MUSCLE version 3. 7  [24]  with default parameters and trimmed using trimAl v1. 4. rev7  [25]  with the gappyout option (automatic detection of blocks to remove based on the distribution of gaps in the alignment).
Cyanobacterial dataset. -This dataset described in  [16]  was retrieved from Phylome-DB database  [15] , Phylome-DB phylome accession number: 30. It was constructed using all available cyanobacterial complete genomes (listed in Table  S2 ). The same method was used for the detection of orthologous sequences as for the Primate dataset (see  [23]  for a description of this method). Simulated dataset. -We used the program ROSE v. 1. 3 [17]  to generate multiple alignment for 21 species using a known fungal phylogeny  [18]  (Fig. S1 , left tree). Sequences, trees and mutation probability of each amino-acid in the input sequence were taken from  [18]. ROSE was run with default parameters. To obtain a realistic set of alignments, we tuned the branch lengths of the input tree so that the simulated sequences had the same distribution of similarity scores, after alignment and trimming, as the alignments present in the fungal phylome of PhylomeDB database  [15]  from which the input sequences were taken.
Tree Reconstruction and Overtraining Evaluation The principle of the method used in this study to evaluate overtraining is presented in Figure  1. All the analyses were performed using PhyML version 3. 0  [12]. For the Fungal dataset, RAxML version 7. 2. 8  [13]  was used in addition to PhyML. Construction of the reference tree from the large alignment. -The concatenated alignment of 100 genes (or 200 for the primate dataset) was used to reconstruct a reference tree using either PhyML  [12]  or RAxML  [13]. The PhyML parameters were -m LG -b 0 -f e -v e -a e -o tlr -c 4 and the RAxML parameters: -m PROTGAMMAIWAGF. Construction of the trees from the small alignments. -Exactly the same parameters were used in this case but all the trees considered during the optimisation were stored, using the parameter --print_trace (PhyML) or -j (RAxML).
Note that we call "trees considered" only the "current best trees", that are trees whose likelihood is the best at each step and that are thus used as starting trees for the next step. Estimation of overtraining of trees based on topological distance. -The topological distances between each tree considered (T search =[T 1 , T 2 , T 3 , …. , T n ]) and the reference tree (T ref , obtained from the large concatenated dataset) computed using the Robinson-Foulds (RF) distance  [19]  as implemented in the R package "ape"  [26]. We considered as "overtraining" cases where there exists at least one i in  [1,(n-1) ] so that RF(T i ,T ref ) < RF(T n , T ref ) Estimation of overtraining of trees based on likelihood. -Two topologically different trees can have the same topological distance to the reference tree and be therefore indistinguishable.
To have a finer measure of topological overtraining, we computed the Log-likelihood (-LogLK) of each considered tree (each topology) according to the large alignment after having set all branch lengths to 1. We only fixed the topology and let the branch length and model parameters be optimized by the software used. PhyML was executed with the following parameters: -m LG -b 0 -f e -v e -a e -o lr -c 4 and RAxML with -m PROTGAMMAIWAGF -f e, using the large alignment as the input and each considered tree as the user-defined tree topology. If we call T search =[T 1 , T 2 , T 3 , …. , T n ] the trees considered during the optimization and MSA ref thereference multiple alignment, then we considered as overtraining cases where there exists at least one i in [1,(n-1)] so that -LogLK (T i | MSA ref ) > -LogLK(T n | MSA ref ).
Bootstrap and aLRT Analyses To investigate whether an incorrect node caused by the overtraining could be distinguished from an incorrect node due to other causes, for each run where overtraining (in terms of RF distance) was detected, we computed the bootstrap support and the aLRT support of the nodes in the final tree -the tree normally obtained by the user. This analysis was performed for the Fungal dataset using PhyML and 100 bootstrap replicates for the Bootstrap analysis and the aLRT non-parametric branch support based on a Shimodaira-Hasegawa-like procedure for the approximate Likelihood Ratio Test analysis, as implemented in PhyML (option -b = -4). To distinguish between an incorrect node due to overtraining and an incorrect node due to alternative causes, the nodes in the best considered tree were compared to the reference tree and to the final tree.
Link Between the Proportion of Alternative Individual Gene Histories and Overtraining To test whether the proportion of genes following an alternative evolutionary story that the reference one had an impact on the amount of overtraining, we simulated sequences using ROSE with the same input parameters as for the simulated dataset described above but with two different input trees: the "reference topology" and the "alternative topology" (Fig. S1 ). The alternative topology was obtained from the reference topology by moving a clade of 4 species to another position in the tree (red dots in Fig. S1 ). We only considered the training set where 10 genes were concatenated and we varied the proportion of alternative topologies (0%, 10%, 20%, 30% and 40%) included in the training set. 300 repetitions were performed each time and the proportion of runs where overtraining occurred (detected with RF distances) was estimated.
Test for potential random effects leading to overtraining detection To ensure that the overtraining effect we detect is not random, i. e. is not due to paths from NJ tree (or MP trees in the case of RAxML) to ML trees passing by chance close to the reference tree, we evaluated the proportion of runs where overtraining was detected when replacing the reference tree by random trees. This was done 100 times, using the the R package ape  (29)  to generate the random trees. We used the paths obtained for the 5 concatenated genes from the fungal dataset for this test. The obtained null distribution was then compared to the observed value. Another source of artifact could be if the evolution of Robinson-Fouldsdistances against the reference tree (or Maximum Likelihood values against the large alignment) during optimization was stochastic.
In this case, we may detect cases of overtraining quite often just by chance. We used a Random Forest classifier on the curves describing the evolution of likelihood values on the small alignments to try to predict where the best stopping point was. In particular, for each point in the optimization, we computed 47 features: 43 that described the shape of the lk curve in the vicinity of that specific point, and 4 more features that described the curve globally (Table  S1 ). We used the randomForestSRC package in R  [27] , with ntree=200, na. action="na. random", importance="permute" and all other parameters left at default values. To find the AUC score, we used out-of-bag predictions (equivalent to leave-one-out cross-validation) stored in predicted. oob, and processed them using the ROCR package  [28].
Evaluation of overtraining with Bayesian approach. For comparison with ML methods, we used MrBayes v3. 2. 2  [21, 22]  on the fungal dataset for small alignments of size 3 and 5. For each size and each of the 300 repetitions, MrBayes parameters were chosen as follows: 2 runs, one single chain, 1. 000. 000 generations, and sampling every 500 generations. The model used was the same as for the PhyML tests: fixed LG substitution matrix (added in the source code by ourselves), gamma-distributed rate variation across sites and a proportion of invariable sites (rates=invgamma). Five runs did not reach convergence after 1. 000. 000 generations (average standard deviation of the split frequencies higher than 0. 01) and were discarded. For comparison with the ML approach, the consensus species tree was computed after keeping only the first 5%, 10%, 15%.
Exhaustive search of the most likely tree for 9 species We modified the 300 alignments for the 5 concatenated fungal genes; in order to keep only 9 species (Species in bold in Table  S2 ). RAxML was then used to detect overtraining with these reduced alignments, using the same approach as before. Twenty-five runs for which overtraining was detected were analyzed further. We generated, using Paup v4. 0 beta  [29] , all possible unrooted tree topologies 9 species (135135 trees) and computed for each one its likelihood according to each of the 25 alignments. We then compared the ML tree obtained with RAxML for each run to the real best tree in the exhaustive search. We also used these data to visualize "real" likelihood landscapes. To do so, we selected the 100 best trees for each run (leading to 376 unique topologies) and computed all against all RF distances between these trees using Paup v4.
Statistical difference between best and last ML trees in cases of overtraining We wanted to assess whether the last ML tree in cases of overtraining was significantly different from the best tree encountered during the optimization, with respect to the large alignment. For a subset of 50 overtraining runs (fungal dataset, 5-genes alignments), we selected the best ML tree and the last ML tree. We then used PhyML to compute sitewise likelihood of the large alignment according to each tree, letting the rate and branch length to be optimized, and finally input into CONSEL v1. 20  [31]. CONSEL allows comparing alternative topologies by performing the approximately unbiased (AU) test and computing the associated p-value. At a significance level 0. 05, we observed that for 60% of the runs (30 out of 50), the final ML could be excluded.
This shows that overtraining is not simply a "waste" of optimization time. It is also producing trees that are significantly worse than previously encountered ones in a large fraction of cases. Fig. 1. Possible tree likelihood landscapes and description of the method. A. Imaginary representation of the difference between the likelihood landscape of a tree reconstructed from a small number of genes (blue hill) and the real likelihood of a tree reconstructed from the whole dataset (red hill). The difference in the position of the pick of these two hills may explain the overtraining effect detected in this study. B. Schematic description of the method we used to detect overtraining Color code is the same as in A. Fig. 2. Overtraining detected using RF distances. Illustration of the overtraining effect detected in this study using the RF distance between each considered tree (x-axis) and the reference tree.
The 300 independent runs performed for each dataset are separated into 3 sub-categories: cases where overtraining is detected (red line), cases where the distance between each considered tree and the reference tree is stable along the optimization (grey lines) and cases where an improvement in topology is detected (green lines). The blue lines represent topological distances between each considered tree and the last tree obtained. All examples are for training alignments of size five (5 concatenated genes, see Fig. 1b ). The x-axis is normalized so that the first tree is assigned the value 0 and the final tree the value 1. Moreover, for each independent run, the x-axis is stretched so that the position of the best tree considered has always the same x value. This value is the mean position of the best considered tree over all runs.
The mean RF distance to the reference tree is then computed after separating the x-axis into bins of size 15 for all datasets (except B. for which the number of bins is 10). The bar plot on the bottom right corner represents the frequency of each scenario for each dataset tested. The portion with red dashes depicts the fraction of the runs where the topological distance to the reference tree is stable but where overtraining is detected if looking at the likelihood of the trees. On panels A to E, the small graduation on the y-axis shows RF distances normalized by the number of nodes in the trees.
Test Supplement PerfLoad ® , supplied by FYTEXIA (France), is principally obtained by alcohol and water extraction of grape (Vitis vinifera L. ) and pomegranate (Punica granatum L. ), and by water extraction of green tea (Camellia sinensis L. Kuntze). PerfLoad ® provides bioactive compounds, especially polyphenols, from flavonoid, ellagitannin, and stilbenoid families, and natural components of the methylxanthine family. PerfLoad ® complies with regulations on food contaminants and on banned and prohibited substances. The placebo product was 100% maltodextrin, which is polyphenol-and methylxanthine-free. Both PerfLoad ® and the placebo were supplied in 500-mg capsules of identical appearance and flavour. The supplement was analysed by means of high-peformance liquid chromatography (HPLC). An Agilent HPLC 1260 apparatus (Singapore) (Openlab CDS chemstation edition software (Santa Clara, CA, USA) was used) coupled with a diode array detector was used.
Separations were carried out by means of a Zorbax Stablebond SB-C18 column (4. 6 × 2 mm; 5 µm particle size; New Port, DE, USA). To detect different phenolic classes, two different analytical methods were adopted: one for bioflavonoids and trans-resveratrol and one for punicalagins. Data analyses were performed as described below. For bioflavnonoids and trans-resveratrol, mobile phase A consisted of 6% acetic acid, mobile phase B was 5% acetic acid and 30% acetonitrile, and mobile phase C was 100% acetonitrile. The program was as follows: For punicalagins, the mobile phase consisted of 0. 1% orthophosphoric acid and mobile phase C was 100% methanol. The program was as follows: (a) 0 to 15 min linear gradient from 5% to 10% B; (b) 15 to 30 min linear gradient from 10% to 30% B. Monitoring was performed at 260 nm at a flow rate of 0.
Immunoassays were read using a Bio-Plex MAGPIX Multiplex reader, and the results were analyzed with Bio-Plex manager 6. 1 software (Bio-Rad). Urine was collected using metabolic cages over 24 hr directly in refrigerated tubes (4 C). Upon collection, urine was centrifuged at 10,000 Â g for 10 min and supernatant was aliquoted and frozen at À80 C for further analysis. Urine creatinine was measured using the creatinine assay kit (R&D Systems) following the manufacturer's instructions. Total protein in urine samples was measured as previously described. 36 Briefly, proteins were precipitated from urine samples by adding 40 mL of dH2O and 200 mL of prechilled acetone to 10 mL of urine. Samples were then incubated at À20 C for 30 min, then centrifuged at 14,000 Â g, 4 C, for 15 min. Pellets were resuspended in 40 mL of dH2O and protein concentration was measured using the Pierce BCA assay (Thermo Scientific).
Albumin from urine samples was measured using the albumin ELISA kit (Bethyl Laboratories) following the manufacturer's instructions. Acute kidney injury (AKI) biomarker levels were analyzed by multiplex assays using Luminex technology. The multiplex kidney injury panels (MKI1MAG-94K and MKI2MAG-94K, Merck Millipore) were used according to the manufacturer's instructions to measure levels of B2M, renin, KIM1, IP10, vascular endothelial growth factor (VEGF), Cystatin C, epidermal growth factor (EGF), Lipocalin-2-NGAL, Clusterin, and OPN. The results were read using a Bio-Plex MAGPIX Multiplex reader and analyzed with the Bio-Plex manager 6. 1 software (Bio-Rad).
PCR products were analyzed on 2% agarose gels. Exon 23 skipping was also measured by Taqman qRT-PCR, as previously described,  13, 43  using Taqman assays that were designed against the exon 4-5 or exon 22-24 templates using the Custom Assay Design Tool (Life Technologies) (assay exon 4-5: forward: 5 0 -GGCACTGCGGGTCTTACA-3 0 ; reverse: 5 0 -CATCCACTATGTCAGTGCTTCCTAT 3 0 ; probe: 5 0 -TT CACTAAATCAACATTATTTTTC-3 0 ; assay exon 22-24: forward: 5 0 -CTGAATATGAAATAATGGAGGAGAGACTCG-3 0 ; reverse: 5 0 -CTTCAGCCATCCATTTCTGTAAGGT-3 0 ; probe: 5 0 -ATGTGA TTCTGTAATTTCC-3 0 ). An inventoried 18S assay was utilized as an endogenous control (Life Technologies). 50 ng of cDNA was used as input per reaction, and all assays were carried out in triplicate. Assays were performed under fast cycling conditions on a Bio-Rad CFX96 Touch Real-Time PCR Detection System, and all data were analyzed using the comparative Ct method.
Respiratory Function The respiratory function of mice was evaluated by whole-body plethysmography using an EMKA Technologies plethysmograph, as previously described,  13  and essentially as recommended by TREAT-NMD (http://www. treat-nmd. eu/downloads/file/sops/dmd/ MDX/DMD_M. 2. 2. 002. pdf). Briefly, unrestrained conscious mice were placed in calibrated animal chambers and the pressure difference between the reference and animal chambers was measured using a pressure transducer. Mice were allowed to acclimate in the chambers for 45 min at a stable temperature and humidity. Data were then collected every 5 s using the iox2 software (version 2. 8. 0. 19; EMKA Technologies). TI was defined as the start of inspiration to the end of inspiration and the expiration time (TE) was defined as the start of expiration to the end of expiration.
The relaxation time (RT) was defined as the time from the start of expiration to the time when 65% of the total expiratory pressure occurred. Pause and penh were defined and calculated by the following formulas: pause = (TE À RT)/RT and penh = (PEP/ PIP) Â pause, where PEP is peak expiratory pressure and PIP is peak inspiratory pressure. The value of each parameter was calculated from an average of 60 recordings of 5 s representing a total of 5 min. Inclusion criteria for each recording were >8 respiration events by 5 s and >80% of success rate as measured by the iox software.
Restraint-Induced Unconditioned Fear Mice were handled firmly but gently using the scruff method, as for standard examination or intraperitoneal injection in laboratory mice. The mouse was restrained by a trained experimenter grasping the scruff and back skin between the thumb and index finger while securing the tail between the third and little fingers and tilting the animal upside down so that the ventral part of its body faced the experimenter. After 15 s, the mouse was released to a new cage (16 Â 28 cm, with 12 cm high walls; illumination: $100 lx) and then video-tracked for 5 min using the ANY-maze software (Stoelting). All mice were tested between 10:00 am and 1:00 pm. Unconditioned fear responses induced by this acute stress were characterized by periods of tonic immobility (freezing) during the 5-min recording period in the novel cage.
Statistical Analysis Data were analyzed by GraphPad Prism5 software and shown as the mean ± SEM. n refers to the number of mice per group. Comparisons of statistical significance were assessed by non-parametric Mann-Whitney U tests for the comparison of two groups or Kruskal-Wallis for the comparison of three or more groups, followed by Dunn's multiple comparison test. Significant levels were set at *p < 0. 05; **p < 0. 01; and ***p < 0. 001.
ACKNOWLEDGMENTS We thank  SYNTHENA, Berne, Switzerland , for providing the tcDNA-AONs used in this study. We thank the centre d'évaluation fonctionnelle of the  University Pierre et marie Curie and Arnaud Ferry  for the force measurements on mdx mice. We thank  Stanislas Grassin Delyle  and the MasSpecLab platform for the LC-MS/MS analysis. We would also like to thank  Laurence Flette  from the  Pasteur Institute  for her expertise in histopathology. This work was supported by the  Agence nationale de la recherche  ( DYSther Project  grant  ANR-14-CE13-0037 , Chair of  Excellence HandiMedEx ), the  Institut National de la santé et la recherche médical (INSERM) , the  Association Monegasque contre les myopathies (AMM) , and the  Duchenne Parent project France (DPPF).
74, P<0. 001). Data from this study have been presented as a research communication at an international congress  [24]. To ensure that study investigators were experienced in assessing body condition using the BCS system, training was provided by the local scientific communication manager to a single veterinarian professional during enrolment of the clinic into the study. Activity and QOL were subjectively assessed by the veterinarian after a discussion with the owner (Table  3 ) to determine which activity and QOL descriptions were most accurate for their dog. The categories were designed to be mutually exclusive, and at least two characteristics from a category were required for it to be selected. Food-seeking behaviour was also determined subjectively by the investigator after a discussion with the owner.