dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequencing control of amplification specificity To confirm the specificity of the primer sets, especially between-cluster discrimination, a subset of 35 amplicons was sequenced (314 bp). This subset was chosen to represent sampling diversity, i. e. either from blood (n =  19)  or spleen (n = 16), from different roe deer (n = 30) from the three different locations and collected in two different years. Sequences were compared to the GenBank database by applying the Blastn algorithm. Sequence identities within ankA cluster were defined previously as being always higher than 95%, while identities between Cluster numbers as in  [6]  clusters droped below 85%  [6, 7]. Cluster assignation of the sequences blasting with identities higher than 95% was controlled in each corresponding papers. To control the nPCR specificity, amplicons were sequenced either from roe deer with apparent monoinfection (only one cluster-specific nPCR was positive) or apparent mixed infections (two or three clusterspecific nPCRs were positive).
Acknowledgements We thank the local hunting associations for access to hunted animals, as well as the  Fédération Départementale des Chasseurs de Haute-Garonne , for permission to catch roe deer in the live-trap study. We thank numerous coworkers and volunteers for their assistance in field data collection, in particular  N. Cèbe ,  B. Cargnelutti ,  J-L. Rames ,  D. Picot ,  J. Merlet ,  Y. Chaval ,  M Hewison ,  N. Morellet ,  J. Joachim ,  E. Bideau ,  M-L Maublanc ,  O. Couriot ,  N Bonnot ,  B. Lourtet ,  C. Monestier  and  G. Perez. We are grateful to  Diana Warwick  for comments and suggested modifications of English to previous versions of the manuscript.
Z9-14:OAc tuned ORNs were found only at branch tips  (Renou et al. , 1996, Munoz, unpublished data) ; only two out of 100 neurons were found to respond to Z11-O16:Ac  (Jarriault et al. , 2010). Stimuli were presented at random and we retained only the firing of ORNs having responded to the 3-component blend over mineral oil background for further analysis. Due to their very similar response profiles, we analyzed responses of all active Phe-ORNs together. Recording and reference electrodes were connected to a Neurolog preamplifier (Digitimer, Hertfordshire, UK). The signal was filtered (0. 2-10 kHz) and amplified 1000 times. The electrophysiological activity was sampled at 10 kHz and 12 bit resolution with a Data Translation DT3001 analog to digital card. Control of the acquisition board, spike sorting, and extraction of spike occurrence times were done using Awave software  (Marion-Poll, 1995).
The electrode was inserted randomly in the MGC area of the AL until intracellular contact with a neuron was established. Penetrating neurons within the MGC area results in a vast majority of neurons showing an excitatory response to the pheromone followed by an inhibition phase in A. ipsilon. Neurons with this type of response pattern have previously been identified as projection neurons through intracellular staining  (Jarriault et al. , 2009; Barrozo et al. , 2011; Chaffiol et al. , 2012). Electrical signals were amplified with an AxoClamp-2B amplifier (Molecular Devices, Sunnyvale, California, USA). Neural activity was recorded, digitized, and spike occurrence times extracted using P-clamp software (Molecular Devices, Sunnyvale, California, USA).
Each animal was subjected to up to three series of 15 olfactory stimuli with inter-stimulus intervals (ISIs) of 100 s to adapt to the slow dynamics of the fluorescence signal, and one AL was recorded in each insect. The stimulation protocol was the same as in single sensillum and intracellular recordings, however, α-pinene was not tested as background odor due to a limited number of possible stimulations to be administered. Each recording lasted for 20 s, with the background odor stimulation starting 3 s after recording onset and lasting for 3 s. Short pheromone pulses (200 ms pulses) were given 4 s after recording onset. Raw data were treated for reduction of photon (shot) noise using custom-made software written in IDL (Research Systems Inc. , Colorado, USA) and signal time courses were illustrated with Visual Basic (Microsoft Excel).
Statistical Analyses For electrophysiological experiments, spike occurrence times were analyzed using custom-written R scripts (R Core Team, 2013). Firing rates were calculated using the local slope of the cumulative function of spike times  (Blejec, 2005)  calculated over a moving spike window between the n-2 and n+2 spikes (5 spikes). Thus, each spike was attributed a firing rate and its occurrence time. To quantify and compare the response intensity of receptor and central neurons with very different firing patterns, we measured the maximum firing rate. It reflects very well fast changes in neuron activity. It occurs early in the response course, and can be measured whatever the response shape. Its intensity can be determined independently from latency and its level can be used to evaluate signal salience.
ACKNOWLEDGMENTS The authors thank  Christophe Hanot  for technical assistance and  Corinne Chauvet  and  Cyril Le Corre  for insect rearing and three reviewers for helpful comments on an earlier version of the manuscript. The research was funded by grants from the  French national research funding agency  ( ANR-11-BSV7-0026 ) and the  Plant Health and Environment Department (SPE)  of the  French National Agricultural Research Institute (INRA). AR was supported by a PhD fellowship from the  French Ministry of Science and Education.
Selective Tead1 and Tead4 genomic occupancy To understand how Tead1 and Tead4 regulate gene expression in C2C12 cells, we used ChIPseq to profile their genomic occupancy. Chromatin was prepared before differentiation and after 6 days of differentiation and ChIP was performed with antibodies selective for either Tead4 or Tead1. In undifferentiated cells, Tead4 occupied 2940 sites located mainly distant from the transcription start sites (TSS)  (Fig 4A and S1 Dataset). In differentiated cells, more than 8100 sites were occupied, the majority of which were again located distant from the TSS  (Fig 4B and S1 Dataset). Occupied sites in both undifferentiated and differentiated cells showed strong enrichment of the MCAT motif (Fig 4C  and 4D ). Other motifs co-occurred with the MCAT motif at higher than expected frequency.
Further analyses showed that myogenic genes were amongst the most significantly up-regulated at days 3 and 6, whereas cell cycle progression genes were strongly repressed (S9B Fig). In addition, adipogenesis genes were also induced along with a metabolic switch involving increased expression of genes involved in oxidative phosphorylation. Following siTead1/4 silencing, up and down-regulated genes were seen at day 0. The number of de-regulated genes increased at  Following siTead1/4 silencing, deregulated genes were observed at day 0, increased at day 3 and then diminished at day 6 (Fig  7A ). In total, 563 genes were up-regulated between day 0-6, while 377 were repressed (Fig 7A and 7B and S4 Dataset). Down-regulated genes were associated with myogenesis, and oxidative phosphorylation, the hallmarks of differentiation, whereas up-regulated genes were enriched in angiogenesis and as seen in C2C12 cells in Wnt signalling (Fig  7C ).
In PMs, up-regulation of cell cycle genes was observed, but the values were less significant reflecting the reduced proliferative capacity of PMs compared to C2C12 cells. Thus, Tead factors were essential to activate genes involved in PM differentiation, but also to repress Wnt signalling and signalling pathways like Tgfβ inhibiting PM differentiation. We compared genes de-regulated by siTead1/4 silencing in PMs and C2C12 cells. As the kinetics of their activation of repression may differ, we compared non-redundant lists of all genes deregulated between days 0-6 in each cell type. A set of 119 genes strongly enriched in muscle differentiation functions were commonly down-regulated (S11A Fig). Strikingly however, a large set of 430 genes, again strongly enriched in muscle differentiation functions, was specifically down-regulated in C2C12 cells (S5 Dataset).
A smaller set of 258 genes was specifically down-regulated in PMs, but showed low enrichment in more diverse functions. Only 65 genes involved in signalling and proliferation were commonly up-regulated. However, a large set of almost 500 genes was specifically up-regulated in PMs (S11B Fig). Remarkably, these genes showed enrichment in nervous system development and other neurogenesis functions (S5 Dataset). Tead1/4 knockdown appeared to modify PM cell identity leading to the expression of neurogenesis genes, not normally expressed during PM differentiation. These results showed that Tead1/4 silencing had distinct effects on gene expression in PMs and C2C12 cells.
The tibialis anterior muscle was then dissected and its mass was measured. The tibialis anterior mass is represented as % of body weight. For fibre cross-section area measurements, transverse cryosections (8μm) of mouse tibialis anterior muscle were stained with hematoxylin and eosin. Slides were scanned using NanoZoomer-XR Digital slide scanner (Hamamatsu Photonics K. K. ). Cross-section area was analyzed using the RoiManager plugin of Fiji image analysis software.
RNA extraction, RT-qPCR and RNA-sequencing Total RNA was extracted using the GenElute Mammalian Total RNA Miniprep Kit from Sigma. cDNA was prepared with using SuperScript II Reverse Transcriptase (RT) using the kit protocol and quantitative PCR was carried out with the SYBR Green I (Qiagen) and monitored using the Roche Lightcycler 480. Primer sequences were designed using Primer3plus software and beta-actin was used as normalization control. Messenger-RNA-seq was performed essentially as described  [45, 46]. Sequence reads mapped to reference genome mm9/NCBI37 using Tophat  [47]. Data normalization and quantification of gene expression was performed using the DESeq 2 Bioconductor package  [48]. Significantly deregulated genes were selected using a log2 fold change >1 and <1 and adjusted p-value <0,05.
Accession numbers The public data for H3K27ac and H3K4me3 data were taken from the GEO accession GSE25308  [51] , Jun GSE37525, Srf, GSM915168, Runx, GSM1354734. Myod1 and Myog ChIP-seq raw data were from GSE44824  [27]  and re-analyzed in parallel to the Tead4 and Tead1 ChIP-seq data. The data in this paper have been assigned the accession number GSE82193 in the GEO database.
In addition, several genes of the Hippo pathway such as Amotl1, Amotl2 and Lats2 were also associated with Tead1/4 occupied sites. In differentiated cells, more than 2000 genes enriched in terms associated with developmental processes, muscle differentiation and contraction were annotated including the important regulatory genes Myod1, Myog and Mef2a as well as numerous structural genes of the muscle fibre. At many sites, Tead4 binding and H3K27ac was either enriched or acquired de novo at these genes during differentiation (S6D Fig). We analysed global co-localisation of Tead4 with Myod1-and Myog-occupied sites. In differentiated cells, more than 2000 Tead4 sites were co-occupied by all three factors (S7A Fig and S2 Dataset). As Tead1 occupied sites essentially only in non-differentiated cells, a comparison with Myod1 and Myog-occupied sites in differentiated cells revealed only a limited overlap of around 50 sites (S7B Fig).
day 3 and diminished by day 6 (Fig 6A). In total, 249 genes were up-regulated by siTead1/4 silencing between day 0-6, while 549 were repressed (Fig 6A and 6B and S3 Dataset). Analysis up-regulated genes at days 3 and 6 indicated strong enrichment in cell cycle, Notch, Wnt and Tgfβ signalling and epithelial to mesenchymal transition (Fig 6C). In contrast, genes involved in myogenesis and oxidative phosphorylation were repressed. Hence, Tead1/4 contribute to activation of the myogenic differentiation program, but they also directly or indirectly repress growth promoting pathways leading to defective cell cycle arrest. To identify genes directly regulated by Tead4, we determined those within 50 kb of Tead4 binding sites enriched specifically in differentiated cells. Around 4300 Tead4 occupied sites were associated with 4100 potential target genes showing expression in the RNA-seq data (Fig 6D).
Acknowledgments We thank;  D. Metzger  for the Hsa::Cre-ER T2 mice,  D Metzger  and  V. Uberschlag  for discussions pertaining to development of the muscle ChIP technique,  I. Michel  for excellent technical assistance,  C. Keime ,  B. Jost , and all the staff of the IGBMC high throughput sequencing facility, a member of " France Genomique " consortium ( ANR10-INBS-09-08 ), all IGBMC general services. ID is an 'equipe labellisee' of the  Ligue Nationale contre le Cancer.
This work was supported by grants from 1) The Association Francaise contre les Myopathies (AFM, http://www. afm-telethon. fr/association); 2) the  Ligue Nationale contre le Cancer  (https://www. ligue-cancer. net/);3) the  Agence National pour la Recherche (ANR , http://www. agence-nationalerecherche. fr/), Investissements d'Avenir program LABX-0030-INRT  and  10-IDEX-0002-02 ; the.
women, 29% non-White), who were instructed to wear wrist actigraphs for seven consecutive 24-hour periods and underwent 3T magnetic resonance imaging scans, with total gray and white matter, ventricular volume, and hippocampal volume quantified using a multi-atlas segmentation approach (MUSE software). We used function-on-scalar regression models, a data-driven approach, to quantify links between timing of actigraphic activity counts and brain volumes, adjusting for age, sex, education, CES-D score, MMSE score, and intracranial volume. Gray matter, white matter, ventricular, and hippocampal volumes were considered in separate models and time intervals rounded to the nearest 30 minutes. On average, lower gray matter volume was associated with higher activity levels between 2:30 AM and 12:30 PM and between 4:30 PM and 11:30 PM, lower white matter volume was associated with greater activity between 12:00 AM and 11:00 AM, and larger ventricles were associated with lower activity between 3:00 AM and 6:00 AM and higher activity between 6:30 PM and 12:00 AM (all p<.
Nutrient analyses are reported as averages across the study for the PMR in Table  1  and the pelleted feeds in Table  2. Milk samples were collected on days 1, 3, and once during days 5-12 of lactation, followed by weekly sampling for the remainder of the 120-day collection period. Milk samples collected during the first 2 weeks of lactation were used for both biomarker analysis (haptoglobin and β-hydroxybutyrate [BHBA]) and component analysis; subsequent samples were used only for composition analysis. Milk composition was analyzed by MQT Lab Services (Kansas City, MO). Cows were scored every 2 weeks for body condition score (BCS) on a 5-point scale (1 = extremely thin to 5 = extremely obese) from week -3 to week 17 relative to calving. Daily milk production, DIM, number of milkings per day, programmed feed daily allocated and feed provided for both pelleted feeds, and rumination data were recorded on an individual cow basis and collected using the management software, Time for Cows (T4C, Lely Ltd.
Conclusions Supplementation of dairy cows with Scutellaria baicalensis for 60 days increased wholelactation milk yield compared to control cows. Milk fat, protein, and lactose yields increased through 120 DIM and SCC was decreased during the treatment period for the 60-d treatment compared to control cows. Milk production parameters were not different for short-term administration (5-day) compared to control cows. Other than milk SCC and reduced incidence of mastitis, there were no suggestions of impacts on health outcomes. Time to pregnancy was unaffected, but retention in the herd was increased. Overall, long-term administration of S. baicalensis effectively increased milk production, although the mechanism by which this was achieved is unknown. Data were analyzed by treatment period (weeks 1-9) and carryover period (weeks 10-36).
Total pellet offered was increased for SBE60 compared to control during the weeks 1-9 (P < 0. 01) and from weeks 10-43 (P = 0. 02). Total pellet offered was not different between SBE5 and control during either weeks 1-9 (P = 0. 77) or weeks 10-43 (P = 0. 13). A treatment × week interaction was detected (P < 0. 001), and differences between SBE60 and control are indicated by * (P < 0. 05) and † (P < 0. 10). Data were analyzed by treatment period (weeks 1-9) and carryover period (weeks 10-43). Milk yield tended to be increased for SBE60 compared to control from weeks 1-9 (P = 0. 07) and was significantly increased from weeks 10-43 (P = 0. 04). Milk yield was not different between SBE5 and control during weeks 1-9 (P = 0. 35) or weeks 10-43 (P = 0. 73). A treatment × week interaction was detected (P < 0.
03), and differences between SBE60 and control are indicated by * (P < 0. 05) and † (P < 0. 10). Data were analyzed for the treatment period (weeks 1-9) and carryover period (weeks 10-17). Somatic cell count was not different between SBE5 and control during weeks 1-9 (P = 0. 37) or weeks 10-17 (P = 0. 29). Somatic cell count was decreased for SBE60 compared to control during weeks 1-9 (P = 0. 02), but not during weeks 10-17 (P = 0. 13). No treatment × week interaction was detected (P = 0. 16). Differences between SBE60 and control are indicated by * (P < 0. 05) and † (P < 0. 10).
, 2012). Ecosystems may respond linearly to a gradual change of an environmental variable but mathematical models  (May, 1977)  and empirical observations  (Scheffer and Carpenter, 2003)  suggest that the response may be rather discontinuous with sharp transitions between multiple stable states. The theory of ecological thresholds provides an interesting framework to derive ecologically relevant class boundaries  (Groffman et al. , 2006). An ecological threshold is a zone in a gradient where small changes in a control variable produce large responses of an ecosystem. Ecological thresholds can be detected from time-series in a single site or from the analysis of environmental gradients across many sites  (Andersen et al. , 2009). Although the approach in time is more intuitive to address the question of thresholds, the recent development of great observational networks in the context of the WFD offers the opportunity to analyze gradients in the large databases generated, operating in this way a time-for-space substitution  (Fukami and Wardle, 2005; Mac Nally et al.
To achieve this, a national database of fish biomass in French lakes was related to physico-chemical data. The exploratory method of gradient forest  (Ellis et al. , 2012)  was chosen here owing to its ability to perform analyses on many species at the same time, and to take into account a lot of environmental factors and interactions among them. Combining the responses of each single species to any environmental gradient, it is particularly well suited for the identification of ecological community thresholds  (Roubeix et al. , 2016; Wagenhoff et al. , 2017). 2 Material and methods 2. 1 Database 2. 1. 1 Fish data Fishes were sampled in French natural lakes and reservoirs between 2005 and 2014 using benthic multi-mesh gillnets according to Norden gillnet standardized protocol  (CEN, 2005).
Environmental data Environmental conditions were described by 36 variables (Tab. 1). Latitude, longitude, altitude, lake surface area and maximal depth were included in the analysis to account for species biogeography and lake diversity. The influence of the type of lake was also taken into account with a categorical variable (natural or reservoir). Water physico-chemical parameters were measured at the point of highest depth in each lake, following national and international standards  (MEDDE, 2012; AFNOR, 2015). Data were collected at least during one year between 2004 and 2013, on the occasions of four seasonal campaigns. For each survey, mean values of temperature, pH, conductivity and oxygen saturation were obtained from the integration of vertical profiles over the euphotic zone, whose lower limit was considered at 2.
5 times Secchi depth  (Koenings and Edmundson, 1991; Pourriot and Meybeck, 1995). In addition, temperature and oxygen saturation levels were considered at 1 m above the bottom of the lakes. Nutrients, dissolved organic carbon and alkalinity analyses were performed on an integrated water sample collected in the euphotic zone. Nutrient concentrations below the quantification limit (LQ) were given an arbitrary value of LQ/2 for data analysis. To characterize the environmental conditions of each lake, the median of the values from seasonal campaigns was retained, and the minimal and maximal values as well, for the parameters having high annual variability.
Data analysis The analysis was performed on 196 lakes, including 44 natural lakes and 156 reservoirs. Aquaculture ponds and gravel pits were excluded because their fish populations were considered too manipulated. The lake sample is well distributed within the French territory with a good geographic coverage (Fig. 1 ). Only the species occurring in more than 3. 5% of sampled lakes were kept for analysis because a minimal number of occurrences is required to elaborate specific models. This selection resulted in a final number of 25 fish species (Tab. 2), whose BPUE were used as response variables. Gradient forest  (Ellis et al. , 2012)  is an extension of random forest  (Breiman, 2001)  to address the responses of many species to environmental gradients. Gradient forest implies the construction of a random forest for each species of a survey.
Local regression was applied to strictly positive raw BPUE data using R "lowess" function (smoother span = 2/3). In order to calculate a probability of presence, the numbers of measurements and occurrences in 201 bins of each gradient were first determined. Then, the variations of these numbers were smoothed using R density function with a Gaussian kernel and the bandwidth determined by the gradient forest package for the density of data. Finally, the density of occurrences was divided by the density of measurements to give a probability of presence along the gradient.
Methodological considerations Gradient forest is a well suited exploratory method for the detection of ecological thresholds from large databases involving numerous species and environmental variables  (Ellis et al. , 2012; Pitcher et al. , 2012; Roubeix et al. , 2016). Among the numerous thresholds that have been identified herein, three criteria may be used to select the ecologically most significant ones: (1) the variable concerned is generally important for all considered species (Fig. 4 ), (2) the threshold is clearly defined in the gradient, i. e. high step in the specific cumulative importance curve, and (3) several species are sensitive to the same threshold. In this study, the gradient forest analysis highlighted common thresholds for several species in response to Tempmax (Fig. 5 ) and Secchi (Fig.
6 ) variables. The performance of gradient forest may be lower in case of linear relationships between the biota and its environment. The method of data splitting in gradient forest is based on variance reduction and may produce spurious thresholds in case of linearity  (Daily et al. , 2012). Although species-environment relationships are often nonlinear  (Terbraak and Verdonschot, 1995) , it is useful in addition to the multivariate analysis, to examine graphically the plots of species biomass in gradients, as was done in this study. However, thresholds may not appear clearly on such single plots since in gradient forest, splits are generally done on partitions of the data and not on the whole dataset. Another limitation of the analysis made in this study which is related to its large scale application, is the impossibility to take into account the rarest species in the dataset.