dataesr/scientific-paragraphs-categorization huggingface dataset:
BDNF protein expression in inguinal scWAT was measured for both sexes via multiplex ELISA assay (d). Gene expression analysis of Bdnf in inguinal scWAT of 15-17 week old male and female C57BL/6J mice (e). Animals were maintained at room temperature (RT) or cold exposed (5˚C) for 7 days; gene expression data were analyzed by two-tailed Student's t-test, N = 5 per group. Error bars are SEMs. Schematic of inguinal scWAT (f) divided into three anatomically distinct areas: 1. area anterior to subiliac lymph node (LN), 2. LN area, 3. area posterior to LN. Major vasculature is shown with the TEV  [65]  illustrated as blue line and the superficial caudal epigastric artery (SCEA)  [66]  and other vasculature illustrated as red lines. Branches of the subiliac transverse nerves (SiTN) are illustrated as green lines.
Acknowledgments The authors wish to thank the following individuals from  University of Maine :  Brenda Kennedy-Wade  and  Prof. James Weber  for assistance in the animal facility,  Dawna Beane  for paraffin embedding,  Callie Greco  for animal care,  Jacob Cote  and  Mary Lander  for technical assistance,  Prof. Alan Rosenwasser  for statistical advice, and  Prof. Marie Hayes  for advice. We thank  Dr. Dustin Updike  at  MDI Biological Laboratory  for microscopy assistance, and  Mike Hirschman  at  Joslin Diabetes Center  for advice on running wheel cages. We thank  Nick Cutter ,  Greg Cox , and  Robert Burgess  for assistance with protocols for staining and quantifying neuromuscular junctions. We also wish to thank  Dr. Matthew Lynes  at  Joslin Diabetes Center  for a critical reading of the manuscript.
Protein levels of PGP9. 5 were measured in omental adipose by western blotting; linear regression analysis was performed for assessment of normalized protein levels compared to age (h). Western blot data were normalized to βactin, band intensities were quantified in Image J, and analyzed by two-tailed Student's t-test. Protein expression was normalized to β-Actin and band intensities were quantified in Image J. Cell diameter was measured from images of histological cross-sections of adipose tissue from each patient, averaged (n = 3), and analyzed by linear regression. Due to limitations of available samples from the BNORC adipose tissue core at Boston Medical Center, the BMI distribution of omental adipose was clustered around 40. Error bars are SEMs. For (a,e) N = 9 BMI cohort, N = 9 Age cohort.
II. O2: THE SECOND ADVANCED-DETECTOR OBSERVING RUN Our data set ranges from  November 30, 2016  to  August 25, 2017. Prior to August 2017, only the Hanford and Livingston Advanced LIGO detectors were in observational mode. On August 1, 2017, Advanced Virgo joined the detector network. During O2, the combined Hanford-Livingston network sensitivity was slightly more sensitive than it was in the first bserving run (O1), achieving a roughly 30% increase in binary-neutron-star (BNS) range  [21]. The Advanced Virgo detector was less sensitive than the Advanced LIGO detectors, with a BNS range that was roughly a factor of 2-3 lower  [21]. As a result of this, including the Virgo data set did not improve the sensitivity to the short-duration searches presented in this paper. We thus present the analysis of only the Hanford-Livingston data.
Over the course of O2, the livetime of the data collected by the two LIGO detectors was about 158 days for Hanford, and about 154 days for Livingston. The amount of coincident data between the two detectors is approximately 118 days. Not all of this data is ultimately analyzed though, as the data can sometimes be polluted by instrumental and environmental noise artifacts. In particular, transient noise events known as "glitches" can potentially mimic GW properties thereby lowering the sensitivity of searches for short-duration GW bursts. To mitigate the effect of instrumental and environmental noise, a large number of auxiliary channels within the interferometer are monitored in order to characterize the relation between artifacts in these channels and the GW strain channel. This auxiliary channel information is used to identify periods of poor data quality, which is then excluded from the analysis  [22] [23] [24] [25].
Periods of poor data quality were removed as described in previous searches for short-duration GW events  [19, 20, 36]. There is some additional loss of livetime in analyzable data because cWB requires at least 1200 sec-onds of coincident data per analyzable segment. The final amount of data analyzed by cWB was 113. 9 days. The cWB analysis is performed by dividing the run into reduced periods of consecutive time epochs (called "chunks"). Each chunk is composed of about 5 days of livetime, resulting in 21 chunks in total. The background distribution of triggers for each individual chunk is calculated by time-shifting the data of one detector with respect to the other detector by an amount that breaks any correlation between detectors for a real signal. Each chunk was time shifted to give about 500 years of background data, which allows the search to reach the statistical significance of 1/100 years while allowing for a trial factor of 2 for each of the low and high frequency bands.
Omicron-LIB Omicron-LIB (oLIB) is a hierarchical search algorithm. oLIB first analyzes the data streams of individual detectors, referred to as an incoherent analysis. It then follows up stretches of data that are potentially correlated across the detector network, referred to as a coherent analysis. The incoherent analysis ("Omicron")  [37]  flags stretches of coincident excess power. The coherent follow-up ("LIB")  [38]  models GW signals and noise transients with a single sine-Gaussian, and then produces two different Bayes factors. Each of these Bayes factors is expressed as the natural logarithm of the evidence ratio of two hypotheses: (1) a GW signal versus Gaussian noise (BSN) and (  2 ) a coherent GW signal versus incoherent noise transients (BCI). The joint likelihood ratio of these two Bayes factors, Λ, is used as a ranking statistic to assign a significance to each event.
For this analysis, oLIB analyzes two frequency bands: a low-frequency search band covering 32 -1024 Hz, and a high-frequency search band covering 1024 -2048 Hz. Similarly to how the analysis was done in O1, lowfrequency oLIB event candidates are divided by the qual- ity factor of the signal into high-Q and low-Q search bins (see  [20] ). These bins are defined by slightly different cuts than in O1, with the exact choices being made after the background data is analyzed and prior to the analysis of real coincident data. The low-Q bin contains only events whose median quality factor Q lies within the range of 0. 2 -1. 2 and whose median frequency f 0 lies within the range of 32 -1024 Hz. The high-Q bin contains only events whose Q lies within the range 2 -108 and whose f 0 lies within the range of 120 -1024 Hz.
The first is that log BSN is used as a search statistic instead of BSN, which improves the accuracy of oLIB's kernel-density estimates of the signal and noise likelihoods. Second, event candidates are required to have non-extreme SNR balance across the detector network. Specifically, we require event candidates to satisfy max{BSN H1 /BSN L1 , BSN L1 /BSN H1 } < 9, where BSN i is the BSN Bayes factor estimated using only the data of detector i. This cut helps mitigate the contamination of coincident non-Gaussian noise transients, which tend to have much larger SNR imbalance than GW signals. After removing the periods of poor data quality, oLIB analyzed 114. 7 days of coincident detector livetime. This is slightly more than what was analyzed by cWB because oLIB does not have the same requirement of 1200 seconds of continuous data.
Using the time-slide method, oLIB collected 496 years worth of data to determine the background distribution of glitches. The significance of triggers found in the zero lag data is calculated by comparing oLIB's ranking statistic to that of the background distribution. Similar to the O1 analysis, we select single-detector events with SNR > 5. 0. The search results are shown in Fig. 2. No coincident events satisfy the cuts of the low-Q bin, and the event rate of the high-frequency search matches the expected rate of accidental noise coincidences. Two events in the high-Q bin are previously identified BBH events (GW170823 and GW170104). Again, to search for previously unidentified GW events, the previously known events are removed. The results after removing these events are shown as the dashed lines in Fig.
For O2, BayesWave followed up cWB events in the low frequency search, treating the LF 1 and LF 2 search bins as a single bin, and using a threshold of η c = 9. BayesWave used the same approach used by cWB to divide the 113. 9 days of analyzable data into chunks of approximately 5 days, and used the same background data set from time slides. There were nine cWB triggers which were above the η c threshold, five of which are known BBH signals  1. The results of the BayesWave analysis is shown in Fig. 3. The five BBH events were the most significant triggers in the BayesWave results, and after removing them as we did for the cWB and oLIB analysis, all events are consistent with accidental noise fluctuations. The LIGO and Virgo Collaboration recently released the First GW Transient Catalog (GWTC-1)  [3] , which reports all GWs detected by searches targeting compact binary signals in O1 and O2.
Injection Data Set The set of injected signals used in this analysis includes sine-Gaussian (SG), Gaussian (GA), and whitenoise burst (WNB) waveforms. These waveforms, which are not derived from any particular astrophysical model, are the standard in the testing and development of searches for unmodeled GW signals  [19, 20]. Each of these injected waveforms can be described by a few characteristic parameters: SG waveforms are parameterized by their central frequency (f 0 ) and quality factor (Q); GA waveforms are parameterized by the duration (τ ); and finally WNB waveforms are parameterized by their bandwidth (∆f ), lower frequency bound (f low ), and duration in time (τ ). Details about the specifics of these waveforms can be found in  [19]. To fully test the pipelines sensitivity to range of signals, these waveforms are injected with a range of amplitudes, which we measure as the root-mean-square strain (h rss ) of the waveform at earth.
A. The Search The cosmic string cusp waveform in the frequency domain is given by h cusp (f ) = Af -4/3 , where A and f are the signal amplitude and frequency respectively  [8] [9] [10]. The signal spectrum is limited by a high-frequency cutoff determined by the angle between the beamed emission from the cusp and the observer. This parameter is unknown such that a bank of waveform templates, with different high-frequency cutoff values, is used to perform a matched-filter analysis. We present the results of the search using the O2 data for GW bursts from cosmic string cusps. This search was conducted in the past using initial LIGO-Virgo data  [13]  and Advanced LIGO O1 data  [14] , and no signal was found. Here, we have used the same analysis methods, which we describe briefly below. GW bursts from cosmic string cusps are searched by projecting the data onto the bank of templates.
Triggered events detected at the Hanford and Livingston detectors are then set in time coincidence in order to reject singledetector noise. Then a multivariate likelihood ratio  [48]  Λ is computed for each of the surviving events, and is used to rank the coincident events. The pipeline's sensitivity to cosmic string signals was estimated by injecting simulated cusp signals of different amplitudes into the data, and checking whether they were recovered by the pipeline. The sensitivity as a function of amplitude can be used to set constraints on cosmic string parameters, if no signals with high significance are found. To maximize the sensitivity to GW signals we performed data quality studies to reject glitches that may mimic the waveform from cosmic string cusps. We have considered all data quality flags and vetoes produced for transient gravitational wave searches  [19, 22].
Acknowledgments -The authors gratefully acknowledge the support of the  United States National Science Foundation  ( NSF ) for the construction and operation of the  LIGO Laboratory  and  Advanced LIGO  as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of the EGO consortium.
Data In a surgical intensive care, unhealthy alcohol users received off-label baclofen to prevent agitation during the awakening phase, trying to ease mechanical ventilation weaning. We created an algorithm for baclofen dose adaptation according to estimated glomerular filtration rate. The starting day of baclofen is referred as "day 1": Patients received a loading dose of baclofen via an enteral feeding tube on day 1. Then daily doses were divided into 3 intakes until the weaning of mechanical ventilation. The pharmacokinetic study started on day 3: Samples were taken just before administration of baclofen and then from 30 min to 8 hours after administration (total of 8 samples). This article comprises: the method used to treat and analyze the samples (see Methods for baclofen assay), data files generated with the NONMEM software version 7.
g. Beckmann et al. 2017; Terrazas et al. 2017 ). It should be stressed that several other HDSs exist -for example, the Nyx code  (Almgren et al. 2013) , the MAGNETICUM simulations at different resolutions and sizes  (Hirschmann et al. 2014) , the ν2GC simulations  (Ishiyama et al. 2015) , the RHAPSODY-G simulations  (Wu et al. 2015a ), the Blue-Tides simulation  (Feng et al. 2016) , the BAHAMAS simulation  (McCarthy et al. 2017)  or the recent simulation runs from the Illustris team called Illustris-TNG  (Springel et al. 2018). Also noteworthy is that a significant amount of work has been done on zoomed-in hydro-dynamical simulations (e. g. Aucila comparison project -  Scannapieco et al. 2012; FIRE simulation -Hopkins et al. 2014; AGORA simulations -Kim et al. 2016; AURIGA project -Grand et al.
Horizon-AGN Simulation The Horizon-AGN  (Dubois et al. 2014 ) is a HDS covering a volume of V = (142 cMpc) 3 which makes use of the Adaptive Mesh Refinement code RAMSES  (Teyssier 2002). The cosmological parameters being used have been derived from the WMAP-7 cosmology  (Komatsu et al. 2011) , compatible with a Hubble constant of H 0 = 70. 4 kms -1 Mpc -1. By using 1024 3 DM particles the model achieves a DM mass resolution of M DM,res = 8 × 10 7 M , baryonic mass resolution of M bar,res = 2 × 10 6 M and spatial resolution of r res = 1 kpc. The initial SMBH seed is set to M •,seed = 10 5 M being placed in a galaxy when the gas and stellar density exceed the limit of star formation (0. 1 H cm -3 ) and the stellar velocity dispersion exceeds the limit of 100 km s -1. The model sets the condition that all SMBHs have been formed by redshift 1.
Illustris Simulation The Illustris simulation  (Vogelsberger et al. 2014a ) consists of three hydro-dynamical simulation runs of the same volume V = (106. 5 cMpc) 3 and varying resolutions, making use of the AREPO code  (Springel 2011). In this work we use the simulation with the best resolution, which considers 3×1820 3 DM particles with DM mass resolution of M DM,res = 6. 26 × 10 6 M , baryonic mass resolution of M bar,res = 1. 26 × 10 6 M and spatial resolution of r res = 0. 71 kpc. The cosmological parameters used have been derived from the WMAP-9 cosmology  (Hinshaw et al. 2013) , compatible with a Hubble constant of H 0 = 70. 4 kms -1 Mpc -1. The seed for SMBHs is set to M •,seed = 1. 42 × 10 5 M in haloes more massive than M halo = 7. 1 × 10 10 M. The accretion rate follows the Bondi-Hoyle-Lyttleton described in the previous model and is allowed to exceed the Eddington accretion limit.
EAGLE Simulation The EAGLE simulation  (Crain et al. 2015; Schaye et al. 2015; McAlpine et al. 2016 ) includes 6 hydro-dynamical simulations covering different combination of parameters (e. g. volume, resolution) using the code GADGET-3 (a descendent of the publicly available GADGET-2 code,  Springel 2005). In this work we have used the simulation with the largest volume V = (100 cMpc) 3 and with DM mass resolution of M DM,res = 9. 7 × 10 6 M , baryonic mass resolution of M bar,res = 1. 81 × 10 6 M and spatial resolution of r res = 0. 7 kpc (these values are valid down to redshift 2. 8 where the gravitational softening was fixed -see  Schaye et al. 2015  for a better description). These simulations are tracking the evolution of baryonic and DM particles with a flat ΛCDM cosmology as given by the Planck mission  (Planck Collaboration et al.
The increase of mass during starbursts is described by: ∆ M • = f • M cold /(1 + (V/V vir ) 2 ) , where M cold is the total interstellar medium mass available for the central starburst, V vir is the virial velocity of the DM halo and f • and V • are adjustable parameters. Feedback from AGN is provided by the radio mode in terms of relativistic jets, with the energy output from the SMBH used to directly reduce or completely quench the cooling flow. Shark, does not follow the spin development of SMBHs, and adopts a fixed radiation efficiency of 0. 1. The model can be retrieved from the website: https://github. com/ICRAR/shark and the data can be accessed by contacting the team at surfs@icrar. org.
ACKNOWLEDGEMENTS SA, JA, HM, IM and CP gratefully acknowledge support from the  Science and Technology Foundation (FCT, Portugal)  through the research grants  PTDC/FIS-AST/2194/2012 ,  PTDC/FIS-AST/29245/2017 ,  UID/FIS/04434/2013  and  UID/FIS/04434/2019. CDPL is funded by an  Australian Research Council   Discovery Early Career Researcher Award  ( DE150100618 ) and by the  Australian Research Council   Centre of Excellence for All Sky Astrophysics in 3 Dimensions  ( ASTRO 3D ), through project number  CE170100013. The Galform runs used the DiRAC Data Centric system at Durham University, operated by the  Institute for Computational Cosmology  on behalf of the  STFC DiRAC HPC Facility  (www. dirac. ac. uk). This equipment was funded by  BIS National E-infrastructure capital  grant  ST/K00042X/1 , STFC capital grants  ST/H008519/1  and  ST/K00087X/1 ,  STFC DiRAC Operations  grant  ST/K003267/1  and  Durham University.
8 multi immersion objectives, or 40X oil NA1. 3. Alternatively a Zeiss LSM 710 microscope with inverted Axio Observer. Z1 stand was used, with LD-LCI Plan Apo 25X NA0. 8 multi immersion objectives and Plan Apo 63X oil NA1. 4, equipped with LASER diode 405 nm, Argon LASER (458, 488, 514 nm), DPSS 561 nm, and HeNe 633 nm. ZEN 2011 software (Carl Zeiss) was used for image acquisition. Image analysis and statistics. Fiji or Photoshop softwares were used to assess signal superposition of distinct channels following segmentation, based on thresholds intensity. Channel specific binary masks were intersected and overlap determined as pixel numbers or area using "common pixels" or "histogram" functions. Overlapping areas for the Rab7:GFP/ p62 or GFP:Atg8a/LysoTracker or Rab5/GFP:Spri signals were calculated after their respective thresholding, using ROI(s) for individual selected cells or particles and expressed relative to the p62 or LysoTracker or GFP:Spri stained areas respectively, using wide field images.
Boxplots of Lyso-Tracker, p62, Rab7:GFP staining particles/area were traced using the GraphPad Prism software, omitting whiskers. Excel (Microsoft) or Prism (GraphPad) software were used to evaluate normality of populations, and statistics applied using Student's t-tests (parametric) or Mann Whitney test (non-parametric) to compared two populations. Parametric or nonparametric ANOVA compared more than two populations. Significant statistics used the following convention: � = p<0. 05; �� = p<0. 01; ��� = p<0. 001. Non-significant statistics: n. s. = (p> 0. 05).
Data using three different GFP:Spri transgenes were pooled. spri 6G1 -null cells were analyzed as above but the GFP:Spri transgene was omitted. In fed cells, the absence of vap causes in an elevation of ca. 2 fold of the Rab5-positive vesicle density, whereas the loss of spri results in a 6 fold reduction of vesicle density. Starvation is associated with a rise of 2. 2 fold of vesicular Rab5 in control cells and this is not significantly different in the vap or spri mutant cells. (w-fed, n = 13; vap 1 fed, n = 5; spri 6G1 fed, n = 3; w-sta, n = 13; vap 1 sta, n = 5; spri 6G1 sta, n = 3). Error bars are standard errors; significances are from ANOVA. Genotypes. (A) Control: w 1118 /Y; cg-GAL4/ UAS-Rab5:GFP/+. Assay: vap 1 /Y; cg-GAL4/ UAS-Rab5:GFP/+. (B, B') Assay: w 1118 /Y; cg-GAL4/ UASp-GFP:Spri 9M.
Acknowledgments We are grateful to  Thomas Neufeld ,  Gabor Juhàsz ,  JoseBottela ,  David Hughes ,  Tor Erik Rusten ,  Perlinne Rørth ,  Marcos Gonzalez-Gaita  ´n and  James Walker  for fly stocks or advises,  Didier Contamine  and  David Bilder  for antibodies,  R. Ueda  and the  NIG-Fly Stock Center , the  Transgenic RNAi Project at Harvard Medical School , the  Bloomington Drosophila Stock Center , and the  Vienna Drosophila RNAi Center  for fly stocks. We thank  Sophie Pagnotta  and  Sandra Lacas-Gervais  for assistance with electron microscopy that was performed in the  Centre Commun de Microscopie Appliquee (CCMA), electron microscopy core facility  ( UniversiteNice Sophia Antipolis ). Confocal microscopy was done in the Prism facility, " Plateforme PRISM-IBV-CNRS UMR 7277-INSERM U1091-UNS " assisted by  Sebastien Schaub ,  Marjolijn Mertz  and  Magali Mondin.
This work was supported by the CNRS: http://www. cnrs. fr,INSERM: https://www. inserm. fr(salary to A. D. ), the  European Research Council : https://erc. europa. eu/funding-and-grants/managing-project/open-access (Advanced grant no. 268813  to P. L. ), the  Labex Signalife program : http://www. agence-nationale-recherche. fr and http://signalife. unice. fr(grant  ANR-11-LABX-0028-01  to P. L. ) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Measurement of live body weight, variation in empty body weight, milk yield and dry matter intake All procedures for the measurement of zootechnical parameters (live body weight, variation of empty body weight, milk yield, dry matter intake and back fat thickness) were conducted as previously described  [25]. Briefly, after calving, cows were milked twice daily and weighed automatically after each milking (software RIC version RW1. 7). As live body weight is affected by digestive contents, the estimation of empty body weight was corrected for digestive tract contents. A change of 4. 5 kg of digestive contents per kg of dry matter intake was assumed  [26]. Variation of empty body weight was calculated every day: empty body weight of the previous day was taken as the reference weight  [27]. Dry matter intake was determined from the intake of fresh matter and the dry matter content of each feed of the ration.