dataesr/scientific-paragraphs-categorization huggingface dataset:
PHOTOMETRY COMPARISON WITH OTHER SAMPLES We compared the BV r i CfA SN II star sequences and light curves with overlapping data from two other groups and find reasonable agreement overall, although a few objects have larger discrepancies. Note-This table presents the CfA standard-system SN II photometry. N host is the number of host-galaxy images subtracted from the same data image. N host =0 means no host-galaxy subtraction was performed and the SN was sufficiently removed from the host galaxy. σ is the same as the light curve uncertainty as used in  Hicken et al. (2009)  and is the same as σ pipe in  Hicken et al. (2012). Only the first five nights in each band of one SN are shown here for guidance regarding its form and content. This table is available in its entirety in a machine-readable form in the online journal.
CONCLUSION The CfA SN II light curve sample consists of 60 multiband light curves: 46 have only optical, six have only NIR and eight have both optical and NIR. We also present 192 optical spectra for 47 of the 60 SN. This work includes a total of data show reasonable agreement in the vast majority of cases. These light curves add to a growing body of SN II data from the literature. This collective body of data (from the literature, this work and future samples) will be useful for providing greater insight into SN II explosions, developing analytic methods for photometric SN classification, and opening the path for their increasing use as cosmological distance indicators. Having another method for measuring the expansion history of the Universe, independent from SN Ia, will serve the important purpose of either confirming the results derived from SN Ia or offering insights for improving the cosmological use of SN Ia.
00 kV, the capillary temperature was set at 350 °C and the capillary voltage was 19 V. A full MS (m/z 107-400) scan was acquired with a nominal mass resolution. Samples were delivered by means of flow injection. MS data was analysed with Xcalibur version 2. 0. 7 software. L-homocysteine was freshly prepared right before the MS experiment at a pH of 9. 2 and a final concentration of 63 mM, and was confirmed by following the m/z molecular ion peak at 136m/z. Dimerization to L-homocystine was induced by preparing a 63 mM L-homocysteine solution at pH 9. 2, and measured after 15 h of sample preparation. L-homocystine was confirmed by following the m/z molecular ion peak at 269m/z. Both species present in the same spectrum were obtained by bubbling the measuring solution with N 2 prior to MS determination.
Acknowledgements We thank  Ainhoa Lezamiz  and  Dr Palma Rico  for protein purification. A. E. M. B. is funded by an  EPSRC DTP  fellowship. M. M. is funded by a  Fight for Sight PhD studentship. G. S. acknowledges support from  CNRS  through a  PICS allocation  ( PICS07571 ). This work was supported by the  Marie Curie CIG  ( 293462 ) grant,  BBSRC  ( J00992X/1 ) grant,  Royal Society   Research grant  ( RG120038 ),  BHF  grant ( PG/13/50/ 30426 ) and  EPSRC  Fellowship ( K00641X/1 ), all to S. G. -M.
with milk yield. Furthermore, such approaches cannot directly exploit data from automated milking systems, which are becoming more and more common. To obtain daily yields (i. e. , for a 24-hour period) from such systems, additional calculations  71, 72  must be used, which increases response variability. Our goal was to exploit this previous research to construct a more holistic model of milk production. We therefore selected the information that was needed to properly represent per-session milk yield for a given animal over its entire lactation period. We used a maximum likelihood estimation approach and explicitly modelled the mean, variance, and covariance of the random variables that we studied. First, we present a simplified version of the model's formalisation. We then provide and discuss the results.
The milking-session and daily-yield models appear to fit the observed data nicely (r = 0. 93 and r = 0. 92, respectively). For Wood's model, the correlation was slightly weaker (r = 0. 87). We then plotted the observed values alongside the estimated values obtained from three milking-session model types (i. e. , morning milking, evening milking, and daily yield; ±95% CI). We estimated daily yield by aggregating the values for a day's successive milking sessions; we then compared the results with those obtained using Wood's model (Fig. 3 ). Using the milking-session model, we estimated changes in the mean proportions of the alveolar classes over the lactation period. We also estimated changes in β (Table  4 ) and π (Table  5 ) over successive milking sessions. The estimated proportion of total variance explained by the milking-session model and the daily-yield model was 1.
Discussion In summary, our biological model incorporates different sources of variability and provides a good fit for observed milking-session and daily yields. It uses data that are easy for livestock farmers to obtain, notably milk yields for all four quarters of the udder for each milking session and the length of time between successive sessions. The proportion of activated alveoli over time is the largest contributor to variance; this variance component includes a dispersion parameter that accounts for underdispersion because of the interdependence of the alveolar classes. To characterise quarter-specific variability, quarter-specific milk yield per milking session must be quantified. Part of this variability could also stem from inconsistency in alveolar activation and inactivation rates across the lactation period or from the rates being quarter specific.
The results were visualised by plotting the empirical cumulative distribution function (R function ecdf) and estimates of the partial autocorrelation function (R function pacf), respectively. We validated the models for session-specific means and variances by simulating different sets of milking-session observations while accounting for the different conditional elements included in the variance model. We obtained a large number of simulated datasets (n = 1,000), which allowed us to estimate session-specific means and total variances. Strong correlations between the estimates obtained from the models and the simulations for the means, total variances, and 95% confidence intervals helped validate the model structure used to arrive at the values of those statistics. Taken together, the simulated datasets obtained for the full lactation period allowed us to confirm the robustness of the milking-session and daily-yield models (see Supplementary Figs  2  and 3 ).
We determined the variance due to the parameter estimators by integrating the estimator variance-covariance matrix into the simulations. Over the lactation period, the variance associated with estimator precision represented 4% of the total estimated variance. We found that, for the first milking sessions, the greatest influence was wielded by the parameter estimating the duration δ between the beginning of lactation and the first milking session. A pilot simulation program was developed to help visualise the influence of the milk-yield model's four main parameters -λ A , λ R , PL max , and π -on the mean and variance models and on the principal criteria for model validation. It uses the R packages tcltk and tkrplot (see Supplementary Fig. 6 ).
Data Availability. The data are available in a ZIP file (4. 28 MB), "ModelPL-SR-PG-20170425", which is located in an INRA repository at the permanent web address: "http://epia. clermont. inra. fr/plsrpg". In exchange for access to the file, we simply request that the interested party provide his or her email. The following information is included in the ZIP file. (1) The lactation data that was analysed in this manuscript and lactation data for two other dairy cattle species-the Holstein and the Montbéliarde. They are located in the "Data" directory; data on different lactation ranks (two to six) are also included. This information can be used to illustrate how the model can be generalised. (2) The numerical results and graphs in pdf format are located in the "GraphPDF" directory. (3) The R scripts we used are in the "ProgR" directory.
They include the scripts for model parameter estimation, the simulation integrating random parameter variability, the automatic output of numerical results and graphs in pdf format, and the running of the pilot simulation. (4) In the latter directory, there is also a general script, "ScriptRgeneral. txt", for implementing the other scripts in a logical order. We chose to leave all the scripts in R so that they are easy to read and evaluate if one has a basic understanding of the R language. All of the functions described above will ultimately be grouped into an R package once they have been optimized and once estimation procedures have been written in C. An example library for the Windows OS is included in "ProgC" directory.
Acknowledgements This work was carried out as part of the  INRA Rumiflame Metaprogramme. The observed data used in model development were collected at  INRA 's Mirecourt Farm. The project would not have been successful without the support of  Gwenaël Vourc 'h, head of the  Animal Epidemiology Research Unit (INRA Clermont-Ferrand-Theix ), and the computer skills of  Robin Loche  and  Gaël Nomezine. We wish to thank  Joël Chadeuf  ( MIA Joint Research Unit, INRA Avignon ) for his help, constructive criticism, and constant encouragement. We also wish to thank  Jessica Pearce  for translating the manuscript into English.
9, 13, 14 However, the precise mechanism(s) of how miRNAs regulate the chemotherapeutic sensitivity of PC cells remain largely unknown and require further investigation. EMT is a common feature of various types of tumors. During this process, cancer cells gradually lose expression of epithelial markers and instead, acquire the mesenchymal cell features required for further migration and invasion. 15 Interestingly, recent evidence also suggests that the EMT process is tightly correlated with drug resistance. 16, 17 ] [19] [20]  Nevertheless, exactly how the EMT process is regulated in PC is still not fully understood and elucidating the mechanisms involved could potentially provide clues for the development of novel PC therapies. Herein, by comparing genome-wide miRNA expression profiling of GR and parental PC cell lines (combined with two previous databases), we identified common low-level expression of miR-3656 in GR PC cells.
Results Identification of reduced miR-3656 levels in GR PC cell lines. To identify candidate regulators of chemoresistance in PC, we first established three independent clones of GR PANC-1 (PANC-1-GR) cells. MiRNA expression profiling of the three PANC-1-GR cells and the parental PANC-1 (PANC-1-P) cells was then performed. MiRNAs that were simultaneously upregulated or downregulated in the three PANC-1-GR clones compared with the PANC-1-P clone were selected for further analysis (Figure  1a , and Supplementary Table  1 ). When combined with two other expression profiles from gene expression omnibus (GEO) databases (GSE80616 and GSE79234) (also performed using PANC-1-GR and PANC-1-P cell lines), we found that miR-3656 was the only miRNA commonly reduced in all PANC-1-GR cell lines (Figures  1b-d ).
Our data are the first to analyze the expression of RHOF in PC and CNP human tissue samples. A clear increase in RHOF protein expression was observed in 157 FFPE PC samples compared with the CNP tissues using an immunohistochemistry (IHC) staining assay (Figures  3i  and j ). qPCR analysis also revealed elevated RHOF expression in the 46 fresh PC samples compared with their CNP tissues (Figure  3k ). Consistent with our results, both the cancer genome atlas (TCGA) and genotype-tissue expression project (GTEx) databases also show significantly increased RHOF in PC tissue samples (Figure  3I ). miR-3656 reduces RHOF expression and results in increased PC cell gemcitabine sensitivity. In order to confirm the possible involvement of RHOF in regulating chemotherapeutic efficacy, we first modified the expression levels of RHOF in PANC-1 and BXPC-3 cell lines using an RHOF overexpression vector or siRNA, respectively.
Although patients with higher miR-3656 expression had better outcomes than those with lower miR-3656 levels (Figure  7a ), reduced RHOF expression appeared to further improve the prognosis of these PC patients (Figure  7b ). Consistently, analysis of data from the TCGA database also confirmed both reduced cumulative survival rate and disease-free survival rates among those patient samples with higher RHOF levels (Figures  7c  and d ). Finally, we also performed an analysis of the correlations between patients' clinical characteristics and miR-3656 or RHOF expression in our 157 PC patients. We identified associations between miR-3656 and TNM stage, lymph node metastasis and tumor location, whereas for RHOF, expression was associated with TNM stage, lymph node metastasis and T classification.
Discussion The highly malignant nature of PC, together with its difficult detection and inherent chemoresistance all contribute to the poor prognosis of PC patients. 21 Accordingly, a better understanding of the molecular mechanisms underlying these aspects of the disease is urgently needed to enable design of targeted therapies to improve the treatment of PC. In this study, through comparing differential miRNA expression profiles between GR PC cells and parental PC cells, we built on previous database findings and identified miR-3656 as being expressed at significantly lower levels in GR PC cells. Through further mechanistic investigations using both in vitro and in vivo models, we found that miR-3656 targeted the RHOF gene to regulate EMT progression, which in turn Finally, but importantly, we also found that the expression of miR-3656 and RHOF correlated well with PC patients' prognosis.
16, 17 It has been shown that mesenchymal-type cancer cells with increased expression of genes related to the processes of invasion and metastasis often show resistance to drug treatment. 24 Epithelial cells on the other hand, show less invasive and metastatic potential and are often more sensitive to chemotherapies. 16, 17 The regulatory role of miRNAs on the EMT process could potentially be performed via several routes, such as direct targeting of EMT transcription factors or components of cell architecture required for EMT progression. Through our miRNA profiling work, we identified the miR-3656 downstream candidate target gene, RHOF, and showed direct regulation by miR-3656. Importantly, we showed significant elevations in RHOF expression in PC tissues of our own samples and also of those within the TCGA and GTEx databases.
Data were calculated with 2 -ΔΔCT method and normalized to GAPDH mRNA land RNU6B snRNA levels. Primers for miR-3656 and RNU6B were from Thermo Fisher Scientific. The other primers were purchased from Sangon Biotech (Shanghai, China) and the sequences are listed in Supplementary Table  2. Western blotting. Total proteins were extracted from PC cells using RIPA Lysis and Extraction Buffer (Thermo Fisher Scientific). Protein concentrations were measured using a BCA Protein Assay Kit (Thermo Fisher Scientific). Standard western blotting techniques, and the Bio-Rad ChemiDoc MP imaging system (Hercules, CA, USA) were used according to the procedure described previously. 14 he primary antibodies used were as follows: RHOF (1 : 1000, ab101349, Abcam, Cambridge, UK), TWIST1 (1 : 500, ab50887, Abcam), E-cadherin (1 : 1000, 3195, CST, Danvers, MA, USA), N-cadherin (1 : 1000, 14215, CST), Vimentin (1 : 1000, 5741, CST) and β-actin (1:2000, A5316, Sigma).
The mutant reporter vector of RHOF-3′-UTR was generated using a site-directed mutagenesis kit from Fast Mutagenesis System (TransGen Biotech, Beijing, China) based on the wild-type reporter vector of RHOF-3′-UTR. The protocol used for transfection and measurement of luciferase activity has been described previously. 14 oinformatics. The target genes of miR-3656 were predicted by three computer-aided algorithms,  32, 33  namely TargetScan Release 7. 0 (http://www. targetscan. org/vert_71/), Microcosm Targets (http://www. ebi. ac. uk/enright-srv/microcosm/htdocs/targets/v5/) and miRNAMap 2. 0 (http://mirnamap. mbc. nctu. edu. tw). The target genes were selected only when they were positive in all three algorithms using miRTarBase (http://mirtarbase. mbc. nctu. edu. tw/). 34 The data sets of GSE80616 and GSE79234 were downloaded from the public source GEO data repository.
35 The RHOF expression data for PC and the corresponding prognostic data were downloaded from TCGA, which were processed and analyzed by GEPIA, a web server for cancer and normal gene expression profiling and interactive analyses. 36 imal studies. The Animal Research Committee of Renji hospital and Shanghai Jiao Tong University approved all experimental protocols and surgical procedures. Twelve BALB/c nude mice (SLARC Inc. , Shanghai, China; 4-week-old; 15-20 g) were subcutaneously inoculated in the right and left hind footpads with 2 × 10 6 PANC-1-OE-Con and PANC-1-OE-miR-3656 cells, respectively. Seven days after inoculation, mice were randomly divided into two groups (gemcitabine therapy group and control group, n = 6), and subjected to intraperitoneal injection of either gemcitabine (15 mg/kg) or saline (100 μl, negative control) weekly.
Xenograft tumors were measured every week using external calipers and their volumes were calculated based on the equation: V = (length × width 2 )/2. Five weeks later, mice were killed and tumor weight was measured, followed by IHC and TUNEL staining. Statistics. Data were presented as mean ± S. E. M. Group comparisons of normally distributed data were performed with unpaired Student's t-test. For multiple comparisons, the Tukey-Kramer honestly significant difference was applied following ANOVA. Kaplan-Meier method and log-rank tests were used to determined cumulative survival and disease-free survival. The Pearson χ 2 test was used to analyze the association of miR-3656 expression with RHOF expression. Correlations between miR-3656, RHOF and EMT marker expressions in 157 PC patients was evaluated by χ 2 test.
(g) The relationship of the expression between miR-3656 and RHOF in 46 fresh PC tissues via qPCR assay. (h) Calculated percentages of RHOF expression in either miR-3656 low or high expression groups from 157 PC FFPE samples. (i and j) Representative images and semiquantitative analyses of IHC staining for RHOF in 157 PC and CNP FFPE tissues. (k) qPCR examination of RHOF levels in 46 PC and CNP tissues. (i) TCGA and GTEx database data showing the relative RHOF expression levels in PC and CNP tissues. RNU6B snRNA was used to normalize the qPCR results. All n = 3; bar, S. E. M. , n. s, not significant, *Po0. 05; **Po0. 01; ***Po0. 001; Student's t-test or χ 2 test.
Acknowledgements. We appreciate  Dr. Gavine  for proofreading the manuscript. This work was supported by the  National Science Foundation of China  (no. 81072011 ,  81272748  and  81472240  to JW; no. 81600406  to L-HY),  National Key Technology R&D Program  (no. 2012BAI06B01  to JW),  Foundation of Science and Technology Commission of Shanghai Municipality  (no. 12XD1403400  to JW), and  Foundation of Shanghai Municipal Health Bureau  (no. XBR2011035  to JW).
Characterization High resolution synchrotron powder X-ray diffraction (S-PXRD) measurements were performed at beamline BL44B2  44  , SPring-8, Japan, using a wavelength of 0. 50037 Å. The data were analyzed with the FullProf software suite  45  using both Rietveld and Le Bail refinements. The instrumental profile broadening was estimated by refinement of a model to data collected on a CeO2 standard sample (NIST SRM 674b) and included in the refinements (IRF file). The background was described by a 12th order polynomial. Peak profiles for the phase region x < 0. 3 were modeled with the Thompson-Cox-Hastings pseudo-Voigt function; for x ≥ 0. 3 when the second phase emerges, a Le Bail model was used. Trial refinements showed that a purely Lorentzian line profile (X and Y parameters) was adequate, since the Gaussian parameters refined to values of zero (within one standard deviation) when the instrumental profile broadening was included.
The X and Y profile parameters are related to the crystallite strain and size, respectively. The crystallite size was determined using the Scherrer equation. The Rietveld refinements were also used to quantify the weight fraction of the observed phases. Transmission Electron Microscopy (TEM) was performed on a Philips CM20 instrument equipped with a LaB6 filament operated at 200 keV. High resolution (HR) TEM images and elemental mapping by energy-dispersive X-ray spectroscopy (EDX) measurements were carried out on a FEI "Talos" F200X analytical (S)TEM-microscope operated at 200 keV. The program ImageJ (FIJI) was used for statistical size-analysis of the nanoparticles and for the determination of d-spacing from HR-TEM images by Fast Fourier Transformation (FFT) of individual domains showing well-defined lattice fringes.
Acknowledgments This work was supported by  Center for Materials Crystallography  ( DNRF93 ), the project  Fuel Scale Integrate (Innovation Foundation Denmark ) and the  Danish Agency for Science, Technology and Innovation (DANSCATT). The synchrotron experiments were performed at SPring-8 with the approval of RIKEN (Proposal No. 20150018 and 20160037). J. Chevalier  is acknowledged for his kind technical assistance related to the HR-TEM imaging.
Tracking juvenile and adult eagles We tracked 12 translocated juveniles (Abels) and eight native (wild) juveniles (Cains) in 2009 as they migrated from Germany (Table  1 ). Deployment of the transmitters took place approximately 1 week before fledging, for both the native and the translocated eagles (Table  S1 ). To understand juvenile behaviour in the context of elder migration strategies, we used tracking data from 10 adult LSEs with active PTTs in 2009 (Table  1 ), seven of which had already been tracked for several years and three of which were trapped and marked in 2009. For comparison, we included satellite-tracking data of a non-translocated juvenile that fledged from an eyrie in Latvia in 1993 and an adult breeding in Latvia in 1997. For each bird, we determined the date of departure as accurately as possible based on GPS fixes, complemented by Argos/Doppler fixes (LC 3,2,1, as well as lower quality fixes with coordinates at a bird's nest site or at the hacking station) when there were gaps in the GPS data.
The same could be true also for fostered, translocated, Latvian juveniles. In 2007, two out of three tracked individuals migrated over the Bosphorus  (Meyburg et al. , 2008) , where most successful LSE outward migration appears to occur. It remains unclear, however, why synchronization of departures of hacked eagles is not the same in all years  (Graszynski et al. , 2011) , whether hacked birds depart earlier and in suboptimal directions compared with adults in all years, and how internal and external cues interact to modulate departure timing in native and translocated birds  (Shamoun-Baranes et al. , 2006). There remain many exciting opportunities for further research, using larger tracking datasets spanning multiple years and decades.
Acknowledgements Our thanks go to the responsible authorities, forestry offices, etc. , in  Germany and Latvia  for their support and the necessary permissions. The project would not have been possible without the assistance of the numerous nest wardens, foresters, hunting reserve managers, landowners and ornithologists in Brandenburg, Latvia and Mecklenburg-Western Pomerania. Many people helped the senior author with trapping the adult LSEs, especially  Joachim Matthes. Andreas Hofmann  and others climbed to the nests and helped deploy the PTTs to the native young birds. We thank  Valdis Cı̄rulis ,  Jānis Granāts  and  Rimgaudas Treinys  for climbing eyries in Latvia. We are grateful to the  Tiltakalni Bird Station  as well as the  Riga Zoo  and its director,  Rolands Greiziņš , for hosting juvenile eagles before translocation.