dataesr/scientific-paragraphs-categorization huggingface dataset:
This dimensionality reduction was useful for visualization and necessary for regularization (i. e. to prevent overfitting) in many of our analyses. This resulted in six principal components (PCs) with eigenvalues >1 which together explained 75% of the variance (Supplementary Table  11 ). We used these variables to evaluate the similarity between species-specific drums, by performing a hierarchical cluster analysis (HCA)  75, 76  based on Euclidean distances and the 'Ward. D2' method ('NbClust' R package  77  ). NbClust provides a clustering output resulting from the use of multiple indices (in the case of our analysis, 26 indices were used). The best number of clusters is chosen according to the majority rule, i. e. it is the one supported by the highest number of indices used. This entails creating a vector of acoustic features (22 raw acoustic measures or 22 PCs) for each of the 92 species in our dataset and calculating the Euclidean distance between these vectors to evaluate how close acoustically species were.
Therefore, the 'sympatry level' used in our PGLS regressions corresponds to the number of species sympatric to a given species (e. g. Veniliornins callonotus' (V. cal) sympatry level is '4', i. e. 4 other species in our remaining sample (n = 91 other species) have distribution areas overlapping with that of V. cal (see Supplementary Data 2). To assess whether morphological features determine drumming acoustic structure, we collected anatomical measurements on specimens from the Muséum national d'Histoire naturelle (MNHN, Paris, France) and the Natural History Museum (NHM, Tring, UK). We measured beak length, width and height (standardized, as measured at the most posterior point of the beak opening), the wing chord (from the most prominent point of the wrist joint to the most prominent point of the longest primary feather), and tarsus length on its inferior side.
Note that only one specimen was available for Celeus spectabilis (the type specimen) and Picumnus nebulosus. Body size and body mass data were collected using literature data  44, 79  and the following websites/archives for species with missing weight data: National Geographic, May 2015 (Dendrocopos noguchii); http://portal. vertnet. org/search? q=Campephilus+pollens (Campephilus pollens). Since we already had a measured proxy for body size with wing chord, we retained only body mass for later PGLS analyses. Calculation of information. We quantified the species-specific information encoded within each specific drum. To this end, we used two classification algorithms to evaluate the actual discriminative power of drumming signals (and not only that of the classification algorithms), namely a Random Forest classification (RFC) and a DFA.
09% (pDFA: p < 0. 001; Supplementary Fig. 3 ). From this matrix, we calculated the local mutual information value (measured in bits) based on Shannon's Entropy  42  and following the Eq. (  1 ): MI L ðX A Þ ¼ X M p X M X A j ð Þ * log 2 p X M jX A ð Þ p X M ð Þ ! ð1Þ where MI L is the local mutual information for a given species (X A ); p X M X A j ð Þis the conditional probability of classifying a drum as belonging to the species X M (M for the model, here the DFA) given that the actual species is X A ; and p(X M ) is the unconditional probability distribution of predicted species. MI L quantifies the discriminability of one particular species, X A , by taking into account not only the probability of correct classification but also the distribution of classifications both correct and incorrect for that species, p X M jX A ð Þ ; in comparison to that obtained for the entire dataset, p(X M ).
We carried out two types of ancestral state reconstructions: discrete reconstruction of drumming status in Fig. 1b  and of drumming types in Fig. 3a  (using 'ace' from the R 'ape' package  81  ), or continuous character reconstruction of drumming acoustic structure based on Brownian motion models (using 'fastanc' from the R 'phytools' package  82  in Supplementary Figs. 4  and 5 ) and using relaxed Brownian motion model (using 'rjmcmc. bm' from the R 'geiger' 83 package in Fig. 4a  and Supplementary Figs. 8  and 9 ). While evaluating the likelihood that drumming was already present at an early stage of woodpecker's phylogeny, we tried to represent the most complete tree of the family, based on very recent molecular data  50. Note that strictly speaking, we evaluate the state at the root but at the next internal node, i.
In this manner, we obtained information-through-time plots. These plots quantify a putative diversity of drumming signals in the clade at a particular point in time. They are similar in spirit to the disparity-through-time plots that have been used to measure specific morphological diversity in a clade through time using phylogenetic trees based on molecular data in combination with morphological measures in extant species  84. Continuous ancestral character trait reconstruction of drumming acoustic structure was carried out using either the six PCs that explain variation among the 22 drumming acoustic variables, or the six LDs that explain the variation in discriminating potential among the same variables (see above, 'Acoustic data and analysis' and 'Calculation of information' sections; Supplementary Figs.
In Exp. 1, we used a paired and randomized order design, presenting each focal individual with one D. major drum and one drum from one out of 4 different species: 2 of which have very different drumming patterns (Picus canus and Dryobates minor, both producing 'steady fast' drums), and 2 others which have similar (accelerating) drumming patterns (Dendrocopos syriacus and Dendrocopos hyperythrus). A potentially confounding factor (which is nevertheless in line with our phylogenetic analyses) lies in that the allopatric species producing a drum similar to that of D. major also happened to be closely related to our model species. We carried out 48 playback experiments (testing 24 individuals with one of 4 categories of paired signals). In Exp. 2, we altered one of the 3 acoustic features described above or all of them together (thus having 4 categories of modified signals), using Praat sound analysis software  86.
Following the work from Schuppe and colleagues  89  , playback intensity was calibrated and kept at about 80 dB measured 1 m away from the speaker. Behavioural data collection started when the first drum of the stimuli track was broadcasted and lasted 10 min from that moment on. To document focal individuals' responses, notes were taken manually and continuously, while audio was recorded with a Sennheiser ME67 microphone mounted on a tripod and connected to a digital recorder (Zoom H4N, 44. 1 kHz, 16 bit). If a response was elicited from multiple individuals in the area, only the one from a particular individual (ideally the one seen or heard before setting up the experiment) was monitored and used in further analyses. Six behavioural variables were reported, namely the number of screams, the number of drums, the approach (which was divided into three categories: 'within 25 m', '25-50 m' and 'further than 50 m') as well as the latencies to first scream and drum and the latency to closest approach.
1 was applied to the behavioural data of Exp. 2 for computation of Playback-PC2 scores. Finally, distances were approximated during continuous note-taking and confirmed post-experimentally using a National Geographic 4*21 rangefinder (measurement accuracy: ±1 m up to 200 m). Sex was not documented as sometimes birds were not seen (but just heard drumming or calling back at our playback), which we nevertheless believe to be negligible since both sexes drum and are territorial in this monogamous species  44, 90. Statistical analyses tested for differential responses of focal birds to drums of their own species versus either another species or a modified resynthesized condition. A paired comparison design was used by means of LMMs and contrasts using R software ('lme4' and 'lsmeans' packages)  91, 92.
Acknowledgements We are grateful to  M. Greenfield ,  M. Hauber ,  F. Pellegrino ,  D. Reby , the  LPO Loire France ,  E. Vericel ,  A. Villain ,  Lynx Edicions , and  Xeno-Canto  and Macaulay libraries. This research was supported by a  Fyssen Foundation post-doctoral  grant (M. G. ), the  Institut universitaire de France  (N. M. ), the  Labex CeLyA  and the  University of Lyon/ Saint-Etienne.
Confocal Microscopy and Data Analysis The sections were analyzed with a laser-scanning confocal microscope (Zeiss LSM5 Pascal, Germany) using the multichannel acquisition mode to avoid fluorescence crosstalk. Quantitative analyses were performed on a minimum of three mice per group. Synaptic structures were analyzed on images acquired with a × 100 oil-immersion objective (1. 4 numerical aperture) at a magnification of 8. 1 × 10 -3 µm 2 /pixel, and the pinhole set at 1 Airy unit. The images were processed with the image-analysis program Imaris (release 4. 2; Bitplane, Zurich, Switzerland). After segmentation, synapse density was quantified with NIH Fiji:Image J software 1. In PC-DG mice, quantitative analysis of the number of PCs expressing DG was done with α-DG immunostaining. The number of perisomatic and axodendritic synapses was determined by counting manually synaptic clusters at the surface of PCs, some of which were labeled for Car8 or calbindin.
Patch-Clamp Recording Neurons were visualized at × 640 with Nomarski optics with an upright Zeiss Axioscope microscope. Patch-clamp recordings were obtained using glass electrodes (3-5 M ) filled with the following (in mM): 140 Cs-methanesulfonate, 2 MgCl 2 , 10 HEPES, 2 MgATP, 0. 5 EGTA; pH 7. 3, with CsOH. Neurons were clamped at -70 or 0 mV. Membrane currents, recorded with a patchclamp amplifier (Axopatch 200A; Molecular Devices), were filtered at 2 kHz, digitized (10 kHz), and acquired with Clampex 10 software (Molecular Devices). The stability of the patch was checked by repetitively monitoring the input and series resistance during the experiment, and recordings were discarded when any of these parameters changed by 10%. Data were analyzed offline with Clampfit 10 (Molecular Devices).
Statistical Analysis All data are presented as mean ± standard error. Behavioral differences between groups were verified using two-way ANOVAs with repeated measures (training days, trials). Patch-clamp recordings, immunohistochemistry and electron microscopy quantifications were compared using unpaired t-test. p < 0. 05 was used to define statistical significance. Statistics were performed using the Statview 5. 0 (SPSS, United States) or GraphPad version 5. 0 (Prism) softwares.
FUNDING MS-P was supported by  Ministero dell'Istruzione, dell'Università e della Ricerca -MIUR  project " Dipartimenti di Eccellenza 2018-2022" to Department of Neuroscience  " Rita Levi Montalcini , "  Compagnia di San Paolo  (grant number  2007  and progetti di Ricerca di Ateneo 2011), the  Italian MIUR  (Prin 2008KN7J7J); CV and MS-P were supported by  Association Française contre les Myopathies  (AFM reference no. 15299 ); SD was supported by  CrestOptics-IIT  jointLab for advanced Microscopy, the MARBEL Life2020 grant, the  SynaNet H2020 program  and, Sapienza intramural grant; MD was supported by  Ricerche Universitarie (Sapienza Università di Roma , grant number  RM116154c9E68DE1 ) and  Istituto Pasteur Italia-Fondazione cenci Bolognetti ; GP was supported by  Fondazione CRT  ( Progetto Lagrange ); AP was supported by  Chica and Heinz Schaller Foundation.
Ampullary lesions (ALs) can be treated by endoscopic (EA) or surgical ampullectomy (SA) or pancreaticoduodenectomy (PD). However, EA carries significant risk of incomplete resection while surgical interventions can lead to substantial morbidity. We performed a systematic review and meta-analysis for R0, adverse-events (AEs) and recurrence between EA, SA and PD. Electronic databases were searched from 1990 to 2018. Outcomes were calculated as pooled means using fixed and random-effects models and the Freeman-Tukey-Double-Arcsine-Proportion-model. We identified 59 independent studies. The pooled R0 rate was 76. 6% (71. 8-81. 4%, I 2 = 91. 38%) for EA, 96. 4% (93. 6-99. 2%, I 2 = 37. 8%) for SA and 98. 9% (98. 0-99. 7%, I 2 = 0%) for PD. AEs were 24. 7% (19. 8-29. 6%, I 2 = 86. 4%), 28. 3% (19.
Data Sources Our search strategy and inclusion criteria were based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations  [18]. The analysis was registered in the PROSPERO-Database (on 19 August 2019, registration number 147795). We performed a systematic literature reveiw in Medline, EMBASE and SCOPUS on 27 September 2018 to identify studies that analyzed endoscopic or surgical interventions for ALs. Additionally we performed a manual research of references from representative articles and reviews.
Quality Assessment The data quality of the included studies was evaluated by the modified Newcastle-Ottawa Scale (NOS) (Table  1 ) for non-randomized studies, ranging from 0 (low-quality) to 9 (high-quality)  [19]. The overall NOS-Score was calculated by 7 items (4 points for adequate selection, 2 for comparability and 3 for outcome). A study was scored as good quality if at least 3 points for selection, 1 for comparability and 2 for outcome were achieved. Fair quality was defined as 2 points for selection, 1 point for comparability or 1 point for outcome. Studies of poor quality reached 1 point for selection, no points for comparability or 1 point in outcome. All reviewers (CH, EAA, FA, AG, MH) assessed the specific quality indicators of the included publications.
Statistical Analysis Statistical analyses were performed by means of openMeta[Analyst] (v. 12. 11. 14, http://www. cebm. brown. edu/openmeta/), "R" (version 3. 5. 3 distributed by www. r-project. org) and SPSS (version 25, IBM). We calculated pooled values for R0, complications and recurrence and presented the proportion with a 95% confidence interval (CI). The binary random effects model (DerSimonian-Laird) was used for analysis of EA and SA, as these studies showed high heterogeneity. In contrast, meta-analyses for PD were calculated by a fixed effect model with inverse variance weighting as data were of low heterogeneity. Statistical heterogeneity between the included studies was determined by forest plots and by calculating the I 2 index. In the case of high heterogeneity, a sensitivity analysis was performed.
Complete Resection in Endoscopic and Surgical Interventions We were able to include 39 datasets of EA, 10 datasets of SA and 4 of PD in the meta-analysis for complete resection. Patients were asymptomatic in 54. 4% (EA), 35. 3% (SA) and 21. 6% (PD). The mean lesion size of the different study cohorts was 19. 7 mm (EA), 17. 5 mm (SA) and 17. 0 mm (PD). T1-adenocarcinoma and carcinoma-in-situ were found in 8. 7% of patients in EA, in 30. 8% in SA and in 100% of patients in PD (all Table  3 ). The remaining patients had non-invasive ALs. Thereby, a strict comparison, matching or propensity scoring was impossible to perform. The pooled R0 rate was 76. 6% (CI 71. 8-81. 4%) for EA (Figure  2A ), 96. 4% (CI 93. 6-99. 2%) for SA (Figure  2B ) and 98. 9% (CI 98. 0-99. 7%) for PD (Figure  2C ). Nevertheless, published data of EA (I 2 = 91.
Complications Complications were assessed in 32 EA-datasets, 13 SA-datasets and 3 PD-datasets. As different types of complications were reported (Table  3 ), we conducted a meta-analysis to obtain a pooled estimate rate only for overall complications. Pooled overall complication rates were as follows: 24. 7% (CI 19. 8-29. 6%) for EA (Figure  3A ), 28. 3% (CI 19. 0-37. 7%) for SA (Figure  3B ) and 44. 7% (CI 37. 9-51. 4%) for PD (Figure  3C ). Heterogeneity was high in EA (I 2 = 86. 4%, p < 0. 001) and SA (I 2 = 76. 8%, p < 0. 001) but not PD (I 2 = 0%, p = 0. 653). The proportion-meta-analysis showed a pooled and transformed rate of complications of 24. 5% (CI 19. 8-29. 4%, SE 0. 027, I 2 = 80. 5, p < 0. 001) for EA, 28. 7% (CI 20. 0-38. 2%, SE 0. 049, I 2 = 66. 5%, p < 0. 001) for SA and 44.
7% (CI 37. 8-51. 6%, SE 0. 035, I 2 = 0%, p = 0. 662) for PD. The calculated difference was significant for EA compared to PD (p = 0. 049) but not to SA (p = 0. 524). Sensitivity analysis failed to reduce heterogeneity in the EA or SA group. We did not evaluate mortality or length of hospital stay as these data were only rarely reported. Sensitivity analysis failed to identify outliers that significantly influenced heterogeneity. In addition, we evaluated the endoscopic publications for biliary/pancreatic duct stent implantation, submucosal injection and additional therapy that could influence the development of post-interventional complications. Thereby, papers also showed a high heterogeneity in this regard (Table  4 ). Sensitivity analysis failed to reduce heterogeneity of the calculated proportions as no single or systematic outliers could be identified.
Recurrence In order to assess recurrence, we included data from 40 EA publications, 12 SA-datasets and 3 PD-datasets. The mean follow-up time was 44. 7 months (EA), 49. 6 months (SA) and 37. 9 months (PD, all Table  3 ). The pooled means for recurrences were 13. 0% (CI 10. 2-15. 6%) in EA, 9. 4% (CI 4. 8-14%) in SA and 14. 2% (CI 9. 5-18. 9%) in PD. Heterogeneity was high in EA (I 2 = 91. 3%, p < 0. 001) and SA (I 2 = 57. 3%, p = 0. 007) but low in PD (I 2 = 0%, p = 0. 330). Proportion-meta-analysis showed pooled estimates of recurrence of 13. 2% (CI 9. 1-17. 9%, SE 0. 031, I 2 = 88. 4%, p < 0. 001) in EA, 11. 0% (CI 6. 0-17. 0%, SE 0. 041, I 2 = 48. 9%, p = 0. 028) in SA and 14. 4% (9. 8-19. 7%, SE 0. 035, I 2 = 0%, p = 0. 277) in PD (see Figure  4 ). Calculations revealed no statistical significance for recurrence for EA compared to SA (p = 0.
Acknowledgments: We acknowledge support from the  German Research Foundation (DFG)  and  Universität Leipzig within the program of Open Access Publishing  (to MH). This study was conducted as a project of the 9th  Pancreas2000 education and research program  funded by the  Karolinska Institute , the  European Pancreatic Club/EPC  and the  Acibadem Mehmet Ali Aydinlar University. CH, MH, AG, EAA and FA are mentees, SR and SG are mentors of this program. Pancreas2000 is a non-commercial post-graduation educational program of the  European Pancreas Club  (www. pancreas2000. org). For all participants catering and housing were covered by Pancreas2000 through an  unrestricted educational grant. Pancreas2000  is a not-for-profit organization registered in Vienna, Austria (ZVR No. 1086784266 ). We would like to thank the  Pancreas2000 program  and specially  Dawn M.
Therefore, we defined biomarker change as the age at which the groups appear to start diverging visually. Nonetheless, we also provide the lower age at which the 95% CIs between groups did not overlap. When neurodevelopmental differences were present (different offsets such as hippocampal volumes in the youngest individuals) or when no data were available for the controls (eg, CAMCOG-DS scores), we visually described only the trajectory in adults with Down syndrome. To compare the timing of changes in Down syndrome and autosomal dominant Alzheimer's disease, we presented the biomarker changes both according to the chronological age and with respect to the median age of diagnosis of prodromal Alzheimer's disease (referred in this study as expected symptom onset). All statistical analyses were done with R statistical software.
Acknowledgments This study was supported by the  Fondo de Investigaciones Sanitario, Instituto de Salud Carlos III , and the  CIBERNED  programme, partly jointly funded by the  EU European Regional Development Fund. This work was also supported by the  National Institutes of Health, Departament de Salut de la Generalitat de Catalunya, Pla Estratègic de Recerca i Innovacio en Salut , and  Fundació La Marató  de  TV3. I. II-G is supported by the  Rio Hortega  grant from  Acción Estratégica en Salud   2013-2016 , and the  Global Brain Health Institute. Fundació.
Column was washed at 100% B for 5 min and re-equilibrated at 5% B for 5 min. The injection duty cycle was 48 min, considering the column equilibration time. Instrument control, data acquisition, and processing were performed using a modified Analyst 1. 6. 2 software®. For academic research, Scout-MRM provisional software patch is available on request from Sciex company (contact: yves. leblanc@sciex. com). MS analysis was performed in positive ionization mode using an ion spray voltage of 5500 V. The curtain gas flows and the nebulizer were set at 50 psi using nitrogen. The TurboV™ ion source was set at 550 °C with the auxiliary gas flow (nitrogen) set at 40 psi. The software Skyline v4. 1 (MacCoss Lab Software, USA) was used to product a list of suitable SRM transitions. The mass in Q1 and Q3 as well as the collision energy (CE) and the declustering potential (DP) values were predicted using Skyline for endogenous peptides when the associated heavy peptides were not available.
MS/MS spectra interpretation from GFOSS database MS/MS spectra were assigned to peptide sequences by searching against a customized RNAseq-derived database GFOSS, which was previously published  [27]. This database contains 1,311,444 putative protein sequences and 289,084,257 residues. The algorithm from the MASCOT Daemon v2. 3. 2 search engine (Matrix Science) was used for database search and spectral matching. The following parameters were used: 5 ppm peptide tolerance and 0. 02 Da MS/MS fragment tolerance, +2 or +3 peptide charge, a maximum of two missed cleavages, carbamidomethylation of cysteine as fixed modification, oxidation of methionine as variable modification, and trypsin as proteolytic enzyme. Mascot results were parsed with IRMa 1. 31. 1c software  [28]. Peptide-spectrum matches presenting a MASCOT peptide score with a p-value of <0.
Data analysis The integration of chromatographic peaks and data reprocessing was performed with MultiQuant™ software (version 2. 1. 1, Sciex). Peak areas for each peptide were log2 transformed to perform multivariate analyses. In order to identify possible outliers, sample clustering was performed using the hierarchical clustering function implemented in the lumi R package  [30]. Principal Component Analysis (PCA) was used to analyse and identify the variables explaining the maximum variance associated to the proteomic data in male gammarids caged in the contaminated or the reference sites. Differential protein abundance analysis was performed using linear models and empirical Bayes methods implemented in the limma R package  [31, 32]. Peptides showing differences with a BH adjusted p-value (FDR) <0.
1 are considered significantly different. Targeted proteomics data have been uploaded to the PeptideAtlas SRM Experiment Library (PASSEL) under dataset identifier PASS01501. 2. 12 Application of the assay 2. 12. 1 Selection and exposure of organisms For each experiment, organisms were collected from the water tanks at specific reproductive stages. For method validation, mature male and female organisms were sampled based on visual observation of couples having a female in an advanced stage of the reproductive cycle (well-developed embryos in the brood pouch as described in  [20] ). For the field studies, couples of G. fossarum were sorted in with female in the final molting stages D2 and placed by 7 in punctured polypropylene cylinders with alder leaves as food supply. The day after, four cages of 7 couples were deployed in each study site during three weeks following the reproductive bioassay protocol described in  [33].