dataesr/scientific-paragraphs-categorization huggingface dataset:
It is hoped that the recent advances in three-dimensional geometric morphometrics  [21, [70] [71] [72]  will quickly broaden the quantification of sutures in three dimensions. When this becomes possible, it will be important to compare the results of two-dimensional and three-dimensional studies of suture morphology and assess whether the same methods work well with both approaches, as has occurred in recent years with studies of skull and body shape  [73] [74] [75]. Given that sutures provide the main joints of the skull, consideration of their three-dimensional complexity could be crucial in understanding their overall functioning. Despite our use of X-ray microtomography and laser surface scanning to produce the dataset, all five complexity metrics could equally be implemented on high-quality (two-dimensional) photographs.
When implementing the metrics on twodimensional datasets, it would be critical to consider the possible effects of parallax. As a result of this two-dimensional implementation, each of the compared complexity metrics can be easily applied to a wide range of datasets and systems covering an array of scientific fields. Finally, while many of the methods used here have been previously applied to quantifying suture complexity in a limited number of taxa, including caimans, primates, caviomorph rodents and ammonoids  [30, 65, 76] , this is the first study to apply and compare these methods across a wide range of extant and extinct mammal species and a breadth of suture morphologies. It is surprising that there have been so few prior quantitative studies of suture morphology in mammals, given the huge diversity of the group in terms of cranial morphology, diet, habitat and life history, all of which are known to generate variety in suture morphology  [34, 77, 78].
Acknowledgements. We would like to thank  Anne-Claire Fabre ,  Ryan Felice ,  Marcela Randau ,  Carla Bardua ,  Andrew Knapp ,  Ellen Coombs ,  João Leite  and  Eve Noirault  (members of the  Goswami Lab)  who offered invaluable discussions throughout the process of this study. We would particularly like to thank  Eve Noirault , who worked tirelessly to scan and reconstruct the scans used by this study. Finally, we would like to thank the anonymous reviewers for their thoughtful feedback and comments.
Data accessibility. The semi-landmark data analysed during the current study are available in the electronic supplementary. csv file. Computer code and final raw data used during this study are available within a public GitHub repository at https://github. com/ HeatherEWhite/suture_metrics_comparison  [58]. All code and raw data are available to download, complete with MIT licence. Please cite this paper and the Zenodo doi: https://doi. org/10. 5281/ zenodo. 4037220 when using the data or raw code.
Search strategy and selection criteria The search strategy is explained in our published protocol, available at the International Prospective Register of Systematic Reviews (Registration: CRD42018092651) and Systematic Reviews  [36]. We searched MEDLINE via PubMed, Cochrane Central, and EMBASE electronic databases to identify articles published before May 2, 2018 that measured gait by using inertial sensors in people with MS. In addition, the grey literature was searched in Google Scholar, Opengrey. eu, Greylit. org, WorldCat, World Health Organization Clinical Trials Search Portal, ClinicalTrials. gov and the European Union Clinical Trials Register. All reference lists and bibliographies of included studies were also reviewed for relevant articles. Inclusion and exclusion criteria are described in the protocol  [36].
Randomized controlled trials, non-randomized controlled trials and observational studies were eligible for inclusion. Inclusion criteria were adults with a clinical diagnosis of MS and gait measured by using inertial sensors (accelerometer and/or gyroscope). We excluded articles of studies that did not include people with MS (POPULATION criterion), quantified other activities such as standing or running or assessed general physical activities using only step count or walking bout length (GAIT criterion), or used sensors other than IMUs (IMU criterion). We excluded from the metaanalysis articles that did not provide sufficient data for pooling results even though authors were contacted for missing information (see 1. 6. 1). Data were considered sufficient with one of the 3 following situations: correlation between gait parameters and EDSS or MSSS, MSWS, MSFC, MSIS-29 or T25FW scores reported or obtained from the main author; raw values of gait parameters and EDSS, MSSS, MSWS, MSFC, MSIS-29 or T25FW scores reported or obtained from the main author for every patient; raw values for gait parameters reported or obtained from the main author for groups of patients, with groups drawn from their EDSS, MSSS, MSWS, MSFC, MSIS-29 or T25FW values (REPORTING criterion).
Pooled meta-analysis All scales and tests referred to in the secondary outcomes were reported at least once in one of the 36 included articles. However, only the EDSS and MSWS scores were reported in a sufficient number of articles to calculate a pooled correlation effect size. Risk of falls or previous falls was assessed in 4/36 articles, but methods were too heterogeneous to allow a comparison because they reported number of previous falls (1 study) or different risk assessment scores (3 studies). Data for 6 kinematic parameters were sufficient to calculate a correlation effect size with the EDSS and MSWS. V, SteL, SteT, swT, stT and dstT were all analyzed in terms of the EDSS or MSWS score in at least 3 studies. For correlation with the EDSS score, a randomeffects model was used for all kinematic parameters except V, stT and swT, for which I 2 was 49%, 0% and 4%, respectively.
Individual meta-analysis Individual participant correlation data with the EDSS score could be retrieved for 6 studies. Three datasets were sent by the authors  [42] [43] [44] , one dataset was directly accessible within the publication  [45]  and 2 other datasets were accessible only as group mean and standard deviations with the group of increasing EDSS score  [46, 47]. We reconstituted these 2 datasets by generating a normal distribution of mean and standard deviation within groups. We could not retrieve individual participant correlation data with the MSWS score. Individual participant data for correlation of V, SteL and SteT with the EDSS score are in Figure  7. The inter-individual variability of measures for SteL and SteT greatly increased with high EDSS score (> 6) versus low EDSS score.
PAGE 13 Kinematic parameters easily accessible with IMUs, such as speed, step length and step duration, can help follow up disease severity for people with MS and low to medium EDSS score (1. 0-4. 5). Indeed, an EDSS score of 1. 0 was found associated with altered gait, which argues for a systemic assessment of their gait even at these very low EDSS scores  [42]. Very little data were available for patients with EDSS score 5. 0 to 5. 5 and analyzed parameters showed high variability for EDSS score ≥ 6. 0 as shown by the test for heteroskedasticity and Figure  7. Besides, to the best of our knowledge, no automated step-detection method proved robust enough to be applied to patients with EDSS score ≥ 6. 0  [44, 59]. Therefore, complementary systems such as walkways or footswitches are added to assess such patients, or manual painstaking step detection is performed, which hinders the use of such analysis in routine clinical workup.
One can refer to the survey made by Vazquez and Federico  [7]  on the threshold model and the voter model  [12]  considering static as well as evolving network. Kozma et al. [9]  have implemented Deffuant model [4] on static and adaptive network. In their setting, nodes can break their connection and get linked to other nodes. Results show that in such adaptive networks, consensus is difficult to achieve compared to static network. Maity et al. [8]  have studied opinion dynamics on time-varying datasets in which the interval of variation is different. These works on evolving networks consider one to one interaction between nodes. Weighted opinions are not taken into consideration while sharing the opinions. Many researchers have focused on the clusters/community formation in networks based on two factors: similarity in opinion and similarity in structure.
By integrating two independent in-house experiments, we identified reproducible microbial bioindicators that characterize ammonia inhibition, phenol inhibition and no inhibition. We evaluated this model by predicting AD status on two external studies assessing the influence of ammonia on AD. By doing so, we demonstrate the feasibility of detecting robust indicators evidencing the microbial symptoms of AD process dysfunction in independent studies which suggests promising applications in various biotechnologies thanks to the expansion of data deposited in public databases.
Experimental data Four studies assessing the influence of ammonia or phenol on AD were selected to build the predictive models  (Lü et al. , 2016; Peng et al. , 2018; Poirier & Chapleur, 2018a; Poirier & Chapleur, 2018b). We selected these studies as they describe the effect of different levels of the previously mentioned inhibitors on both the performances of the digesters and the dynamics of the microbiome through 16S metabarcoding. Additionally, for each study, raw sequencing data had been deposited in publicly available databases and could be linked to the samples described in the original papers with no ambiguity. In all studies, gas productions were measured to evaluate whether AD was inhibited. As described in supplementary material , studies 1 and 2 were conducted in our laboratory with the same type of substrate (biowaste) but with two different inocula collected one year apart from an industrial mesophilic digester treating wastewater treatment sludge.
Samples were taken across time and under different inhibitory conditions. DNA was extracted and 16S rRNA gene was sequenced providing datasets of raw sequences associated to different inhibitory conditions. Study 1 aimed at assessing in parallel the effect of ten different levels of total ammonia nitrogen (TAN) (from 0 to 50 g TAN/L) and phenol (from 0 to 5 g/L) on the microbial community of batch anaerobic digester  (Poirier & Chapleur, 2018b). Study 2 assessed the influence of support media addition to mitigate AD ecosystem inhibition in presence of two inhibitory conditions (19 g/L of TAN and 1. 5 g/L of phenol, respectively)  (Poirier & Chapleur, 2018a). Studies 3 and 4 were conducted in two distinct external laboratories. Study 3 was conducted by Lü et al. using as inoculum an anaerobic granular sludge collected from a plant-scale 35°C upflow anaerobic sludge bed reactor (Shanghai, China) with liquid internal recirculation that was treating paper mill wastewater.
Data processing In these four studies, sequencing of the V3-V4 or V4-V5 region of the 16S rRNA gene was performed with three different approaches, as described in supplementary material. Data from external studies were downloaded from NCBI with fastq-dump 2. 8. Adapters from each study were specifically removed with cutadapt v1. 12  (Martin, 2011). All sequences were imported into FROGS pipeline (Find, Rapidly, Otus with Galaxy Solution)  (Escudié et al. , 2018). Samples from studies 1 and 2 were processed together while studies 3 and 4 were processed independently because of the differences in sequencing approaches. Taxonomic assignment of Operational Taxonomic Units (OTUs) was performed using Silva 132 SSU as reference database. OTUs were trimmed by keeping only those present more than 10 times in the whole dataset (resp.
Statistical analyses and predictive model OTUs abundances were scaled with total sum scaling to account for uneven sequencing depth. OTUs that exceeded 3% in at least one sample were retained for the analysis. The total relative abundance of these minor OTUs represented 17% of the total number of sequences. Data were then transformed with centered log ratio (CLR) transformation to account for compositional structure of the scaled data. All statistical analyses were implemented with mixOmics R package, as described in  (Rohart et al. , 2017b). In order to obtain a first understanding of the major sources of variation in the training data (studies 1 and 2), and to obtain a first insight into the similarities between samples, we conducted principal component analyses (PCA) on the 16S rRNA tags datasets (pca function).
org/ and https://gitlab. irstea. fr/olivier. chapleur/mint-206 bioindicators/. 207 3 Results and discussion 3. 1 Integration of independent studies to identify microbial bioindicators 3. 1. 1 Inhibition status classification of digesters according to methane production performance A total of 81 samples were selected from studies 1 and 2. These samples were collected in 35 distinct digesters  (Poirier & Chapleur, 2018a; Poirier & Chapleur, 2018b ). Prior to identifying potential bioindicators characteristic of both type of inhibition, inhibition status of each digester (non-inhibited, inhibited by phenol or inhibited by ammonia) has been characterized. For this purpose, maximum CH4 production rate (mL CH4/day) was chosen as the most informative performance indicator. These values were calculated for each digester using Grofit package of R software (version 3.
A predictive model for ammonia inhibition validated in two external studies Samples from external studies 3 and 4 were analyzed with distinct sequencing techniques. Thus, different bioinformatic treatments were applied to raw sequences of each dataset due to differences in sequencing primers and targeted regions. OTUs were subsequently aggregated at the genus level (called 'clusters') in order to merge the three datasets. Since both external studied were focused on inhibition by ammonia only, we trained a new MINT model from our in-house samples where we removed the phenol condition. A total of 55 samples collected during studies 1 and 2 were retained, including 29 samples considered as non-inhibited and 26 as inhibited by ammonia.
The microbial indicators we identified were separate from cosmopolitan OTUs that tended to cooccur in all conditions. Thus, they can be considered as bioindicators to announce early signs of process dysfunction in anaerobic digesters, or, on the contrary, indicate steady functioning. Additionally these bioindicators are specific of one type of inhibitor, phenol or ammonia. Our study also emphasized on the benefit of using multivariate methods to identify a microbial signature from a training dataset (here our two in-house studies) then predict the inhibitory status of external studies despite differences in sequencing primers and targeted regions. Future investigation will include a finer definition of the role of the identified microorganisms in AD process. As 16S reference databases are currently still incomplete, taxonomic assignment at the genus level results in a substantial lack of data interpretation.
Consequently, the use of multiple marker genes DNA gyrase subunit B (gyrB)  (Poirier et al. , 2018) , RNA polymerase subunit B (rpoB)  (Case et al. , 2007) , DNA recombinase protein (recA), protein synthesis elongation factor-G (fusA), and dinitrogenase protein subunit D (nifD)  (Holmes et al. , 2004 ) could improve the assignation and abundance estimation for crucial taxonomic groups. Moreover, the use of shotgun metagenomic and metatranscriptomic tools will also be useful to obtain different type of information such as functional bioindicators. When successful, integrative multi-studies analysis not only enables to increase sample size and statistical power, but also to share and re-use data deposited in public databases. Our proposed approach will benefit the research community interested in identifying reproducible microbial signatures.
Given the uncertainty related to inhibition thresholds in individual reactors, identifying robust microbial biomarkers can improve digester management. An increasing number of 16S sequences databases is becoming available to build predictive models of different types of inhibition, irrespective of the digesters operating conditions. Leveraging on these data will allow to optimize the prediction quality by recursively updating monitoring model. We anticipate that a single metagenetic sequencing will reduce the number and the complexity of analyses targeting different inhibitors, as bioindicators identified could be correlated to different types of inhibitions. Moreover, miniaturization and portability of sequencers will soon allow high frequency on-line measurements of microbial dynamics, involving low workload, and limited sampling issues  (Shaffer, 2019).
Conclusion A robust microbial signature of AD inhibition by ammonia and phenol was identified by integrating two independent16S metabarcoding studies with the multivariate approach MINT. The model based on the identified biomarkers was successful in predicting ammonia inhibition in independent digesters. This outcome highlights the potential of our approach to for other inhibitors and studies to discover new warning bioindicators. These data could thereafter be aggregated and used to build robust statistical models of AD inhibition to detect instabilities from various sources at early stages in industrial plants, and ultimately improve management of AD.
782 s. To enable comparisons between cells, changes in the Fluo-4 fluorescence signal (DF) were normalized by basal fluorescence (F 0 ). The reduction amplitude in baseline due to tetracaine was expressed as a percentage of the caffeine response for leak comparisons between samples. All experiments were performed at room temperature. All data were extracted using Zen (Zeiss). Diastolic calcium levels at rest and under stress were assessed using the ratiometric fluorescent Ca 2+ indicator Indo1-AM (Invitrogen). The Indo1-loaded hiPSCÀ ÀCMs (2 mM for 15 min at 37 °C) were excited at 360 nm, and the resulting whole cell fluorescence was measured simultaneously at 405 and 480 nm (Ionoptix system) on a Zeiss microscope (40X oil objective). The intracellular Ca 2+ level was measured after background subtraction using IonWizard data acquisition software.
9) (Supplemental Fig. S2a ). We evaluated the expression of the cardiac markers by immunocytochemistry by focusing on the presence of sarcomeres using a-actinin, cTnI and cTnT staining. Our data showed similar sarcomeric patterns by the expression of cardiac troponin I (cTnI) and T (cTnT) and a-actinin in RyR2-H29D and isogenic control hiPSCÀ ÀCMs (Supplemental Fig. S2b ).
5/26 Entzheim station where it is a ten-year period from 1999 to 2008. The data were obtained via a personal request addressed to Météo-France and were not available on open-access. A summary of the information of the stations is presented in Table  1 , with the time ranges of the data and the number of data available (the coordinates are given in the World Geodetic System 1984). 1999 -2008 29,199 25  All the data were monitored from wind sensors placed 10 meters from the ground and the wind frequencies are available for each wind direction with 20° steps for two distinct wind discretizations: a "basic" discretization giving wind frequencies for 4 velocity ranges (from 0 to 1. 5 m/s, 1. 5 to 3. 5 m/s, 3. 5 to 8 m/s and more than 8 m/s), illustrated in Fig. 2. (A); and a "detailed" discretization giving wind frequencies by 1 m/s steps except between 0 and 0.
Comparison between the Weibull distribution and the sigmoid function The best fitting parameters of the two functions were determined for the whole dataset using a non-linear solver and the "basic" 4-velocity-range wind data. The solver was set up to solve equation (  5 ) for the four-velocity ranges  [0, 1. 5[, [1. 5, 4. 5[, [4. 5, 8[ and [8, +∞[  for both the Weibull and the sigmoid functions. This equation reflects that the sum of the frequencies between two wind velocities (i. e. the area under the curve) must be equal to the frequency given in the "basic" 4-velocity-range wind data. Since the sigmoid function has five parameters, a fifth equation to be solved was added only for this function and corresponds to (6). With this equation, it is assumed that the wind frequency tends toward 0% when the wind speed tends toward 0 m/s, as for the Weibull distribution.
Discussion This study provides tools to assess wind velocity distributions based on "basic" data and mean annual air pollutant concentrations based on CFD results. Additional work should be done to improve the methodologies and the major issues are discussed hereafter. The capability of the Weibull and the sigmoid functions to describe wind velocity distribution was assessed based on wind data from four meteorological stations in France. All of these stations were located in peri-urban environments close to large French cities. It is necessary to take into account that the results, and especially the interpolation-related errors, might be different for other types of stations such as urban and rural stations, and for other countries with different wind characteristics. In particular, the optimization suggested for the sigmoid function may not be suitable for different countries or type of station.
The resulting uncertainties vary from 0. 1% (0. 7%) to 1. 1% (8. 9%) in different (p T , y) bins for the 13 TeV (7 TeV) sample. The uncertainty related to the kinematic binning scheme of the calibration samples is studied by changing the size and the boundaries of the p and η bins, and number of SPD hits. This leads to systematic uncertainties of 0. 1-4. 6% (0. 4-5. 4%) for the 13 TeV (7 TeV) sample. The trigger efficiency is determined from simulated samples. To estimate the systematic uncertainty, a tag-and-probe method is used to estimate the trigger efficiencies in each (p T , y) bin of a ψ(2S) data sample that is independent of the detection of ψ(2S) signals  [32]. The same procedure is applied to the simulated ψ(2S) samples, and the relative difference of efficiencies between data and simulation in each kinematic bin, 0.
Acknowledgements We thank  Kuang-Ta Chao  and  Yan-Qing Ma  for frequent and interesting discussions on the production of ψ(2S) mesons. We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  MOST  and  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG  and  MPG (Germany) ;  INFN (Italy) ;  NWO (Netherlands) ;  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MSHE (Russia) ;  MinECo (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE NP  and  NSF (USA). We acknowledge the computing resources that are provided by  CERN ,  IN2P3 (France) ,  KIT  and  DESY (Germany) ,  INFN (Italy) ,  SURF (Netherlands) ,  PIC (Spain) ,  GridPP  ( United Kingdom ),  RRCKI and Yandex LLC (Russia) ,  CSCS (Switzerland) ,  IFIN-HH (Romania) ,  CBPF (Brazil) ,  PL-GRID (Poland)  and  OSC (USA).
for both peptide derivatives. 11, 20, 22 Therefore, clinical studies with  111  In-labeled CP04 (ClinicalTrials. gov identifier: NCT03246659) and  177  Lu-labeled PP-F11N (ClinicalTrials. gov identifier: NCT02088645) have been initiated, investigating the safety as well as the diagnostic and therapeutic potential of these CCK2R-targeting radiopharmaceuticals. 23, 24  A major issue only partly addressed in former optimization approaches is the limited enzymatic stability of MG within the C-terminal receptor-specific sequence potentially affecting the targeting properties. 12, 25 7] [28]  Most interestingly, the introduction of such amino acids within the receptor-binding sequence resulted in novel MG analogues, which besides increased resistance against enzymatic degradation, also showed an enhanced receptor interaction, leading to increased cell uptake of the radiolabeled peptides in CCK2Rexpressing cell lines.