dataesr/scientific-paragraphs-categorization huggingface dataset:
HST photometry of newly detected sources We performed a simple aperture photometric analysis by computing HST AB magnitudes in a 0. 4 diameter centered at the source location for all HST broadband images 13. The magnitudes were compared to the 5σ detection limit of the corresponding HST filter (see column AB 5σ in Table  2 ). A source is defined as HST-detected when it is brighter than the 5σ detection limit in at least one of the HST filters. Note that for the sources which fall outside the region with the deepest WFC3 IR data the corresponding shallower limiting depth was used. The location of all sources without  Rafelski et al. (2015)  catalog entries are shown in Fig. 24. Among these 160 sources, 72 where considered as HST-undetected, i. e. , with all computed magnitude larger than the detection limit.
g. , dominated by the sky noise). A 1σ surface brightness emission line sensitivity (Fig. 19 ) of 2. 8 and 5. 5 10 -20 erg s -1 cm -2 Å -1 arcsec -2 is reached in the red for an aperture of 1 ×1 and for the udf-10 and mosaic datacubes, respectively. This is a factor 1. 6 better than the sensitivity measured in the first release of the HDFS datacube, demonstrating the progress achieved in the data reduction and observational strategy. A 3σ point source line detection limit (Fig. 20 ) of 1. 5 and 3. 1 10 -19 erg s -1 cm -2 is achieved in the red (6500-8500Å) and between OH sky lines for the udf-10 and mosaic datacubes, respectively. We extracted 6288 and 854 sources from the mosaic and udf-10 datacubes, using the Rafelski et al. (  2015 ) catalog and segmentation map as input for the source locations.
The remaining 72 sources fall below the detection limit of HST broadband deep images. In the HST region with deep WFC3/IR images, we compute a mean AB magnitude of 31. 0 -31. 8 within a 0. 4 diameter aperture. We use NoiseChisel, a SExtractor alternative optimized for the detection of diffuse sources, to derive an updated segmentation map for these sources when that was possible. The redshift measurement and analysis of this unprecedented data set is presented in paper II  (Inami et al. 2017). With more than 1300 high-quality redshifts, this survey is the deepest and most comprehensive spectroscopic study of the UDF ever performed. It expands the present spectroscopy data set (173 galaxies accumulated over ten years) by almost an order of magnitude and covers a Article number, page 40 of 44 wide range of galaxies, from nearby objects to z = 6.
Acknowledgements. RB,SC,HI,JBC,MS,MA acknowledges support from the  ERC  advanced grant 339659-MUSICOS. JR, DL acknowledges support from the  ERC  starting grant CALENDS. RB,TC,BG,NB,BE,JR acknowledges support from the  FOGHAR  Project with  ANR  Grant  ANR-13-BS05-0010. JS acknowledges support from the  ERC  grant  278594 -GasAroundGalaxies. SC and RAM acknowledges support from  Swiss National Science Foundation  grant  PP00P2 163824. Part of this work was granted access to the  HPC and visualization resources of the Centre de Calcul Interactif  hosted by  University Nice Sophia Antipolis. BE  acknowledges financial support from " Programme National de Cosmologie et Galaxies" (PNCG)  of  CNRS/INSU, France. JB acknowledges support by  Fundação para a Ciência e a Tecnologia (FCT)  through national funds ( UID/FIS/04434/2013 ) and Investigador FCT contract  IF/01654/2014/CP1215/CT0003.
Lipidomic sphingolipid analysis Mass spectrometry analysis of lipid species was performed in liver and brain samples from Npc1 -/-mice with or without GSH-EE treatment. Tissue homogenates were pelleted, washed in PBS, and transferred to glass vials. Sphingolipid extracts, spiked with internal standards (N-dodecanoylsphingosine, N-dodecanoylglucosylsphingosine, N-dodecanoylsphingosylphosphorylcholine and C17-dihydrosphingosine, 0. 2 nmol each), were prepared as described  [25]. The instrument consisting of a Waters Aquity UPLC system connected to a Waters LCT Premier orthogonal accelerated time of flight mass spectrometer (Waters, Millford, MA), operated in positive electrospray ionisation mode. Full scan spectra from 50 to 1500 Da were acquired and individual spectra were summed to produce data points each 0.
Statistical analyses Statistical analyses were performed using GraphPad Prism 6 (Graphpad Software Inc). Unpaired Student's t-test (two tailed) was performed between two groups and one or two-way ANOVA followed by Tukey's Multiple Comparison test were used for statistical comparisons between three or more groups. Kaplan-Meier survival curves were plotted using the GraphPad Prism and the log-rank test was undertaken to determine the statistical significance. The corresponding number of experiments is indicated in the figure legends. Data in graphs are shown as mean ± s. e. m.
P < 0. 0002 vs. Npc1 -/-mice treated with NAC or vehicle. (B) Effect of GSH-EE or NAC therapy in body weight gain of Npc1 -/-mice. Mice were treated as in (A) and body weight was recorded weekly until demise. (C) Hanging test and (D) beam transversal test in Npc1 -/-mice treated with vehicle or GSH-EE at p7 every 12 h for 6 weeks. Data are presented as means ± SEM (n=7 to 13, P < 0. 05 vs. Npc1 +/+ mice or Npc1 -/-mice, one-way ANOVA and Tukey's Multiple Comparison Post-test). (E) Calbindin immunostaining in cerebellar paraffined sections of Npc1 +/+ mice and Npc1 -/-mice following treatment with saline (vehicle) or GSH-EE. Images are representative of 5 replicates showing similar results. (F-H) Liver sections from Npc1 +/+ mice and Npc1 -/-mice treated with saline or GSH-EE analyzed by H & E.
Data are presented as means ± SEM (n=3 to 7, P < 0. 05 vs. Npc1 +/+ or Npc1 -/-mice, one-way ANOVA and Tukey's Multiple Comparison Post-test). (B) Oxidative phosphorylation through complex II was determined using succinate (10 mM) as substrate in the presence of rotenone (0. 5 mM). Uncoupled respiration through complex II was determined in the presence of oligomycin (2. 5 µM) and the electron transport System capacity was determined in the presence of FCCP. Coupled respiration was calculated by substracting the uncoupled respiration from the respiration through complex II. Data are presented as means ± SEM (n=3 to 7, P < 0. 05 vs. Npc1 +/+ or Npc1 -/-mice, one-way ANOVA and Tukey's Multiple Comparison Post-test). (C) Blue native Page electrophoresis of liver and cerebellum Npc1 +/+ or Npc1 -/- mice treated or not with GSH-EE to detect OXPHOS complexes with Total Mitoprofile OXPHOS Rodent antibody cocktail.
Images are representative of 4 replicates per group. (D) Electron microscopy analyses of cerebellum from Npc1 +/+ or Npc1 -/-mice with or without GSH-EE that were transcardially washed with cold saline before perfusion with 2. 5% glutaraldehyde. Images were acquired with a Gatan Orius digital camera and are representative of 5 replicates per group. (E) Mitochondrial number and length of cerebellar samples were quantified from images of ultrathin sections from (D) acquired by moving at random across the EM grid (two grids per animal) and analyzed using the iTEM program and ImageJ software. Data are presented as means ± SEM (n=4 to 7, P < 0. 05 vs. Npc1 +/+ , one-way ANOVA and Tukey's Multiple Comparison Post-test). [37]. These findings along with our observations suggest that the neurological alterations rather than the liver phenotype determine the overall progression of the juvenile form of the disease.
Funding The work was supported by grants  SAF-2014-57674-R ,  SAF-2015-69944-R  from  Plan Nacional de I+D, Spain ,  Fundació Marató de TV3 ,  La Mutua Madrileña ,  PI11/0325  (META) grant from the  Instituto Salud Carlos III , and by the support of  CIBEREHD ; the center grant  P50-AA-11999  Research Center for Liver and Pancretic Diseases funded by  NIAAA/NIH ; and support from  AGAUR  of the  Generalitat de Catalunya   2014-SGR785.
LATENT GROWTH CURVE MODELS FOR BIOMARKERS OF THE STRESS RESPONSE The stress response is a complex, dynamic process. This process can be best characterized as a negative emotional experience accompanied by predictable biochemical, physiological, and behavioral changes that are relevant to adaptation  (Lazarus and Folkman, 1984; Baum, 1990; Dougall and Baum, 2012). Many methods have been developed to evaluate the stress response using self-reported (i. e. , subjective) measures and biomarkers (i. e. , objective). Self-report measures and biomarkers are collected over time, and therefore require alternative statistical methods that can handle issues of repeated measurements. However, there are many methodologies that stress researchers can choose from, depending on the properties of the data and research questions.
PURPOSE OF MANUSCRIPT The purpose of this manuscript is to expand upon the most commonly used statistical methods in stress research with biomarkers. An alternative, powerful modeling framework (i. e. , the latent growth modeling framework) will be discussed in the context of specific research questions generated from commonly used laboratory stressors. The paper includes a comparison of statistical software for estimating LVMs. Sample code for various statistical software programs will be provided in the online Appendix for each LVM discussed 1.
Furthermore, the distinction between LGCMs and other approaches, such as multilevel models and MRMs, is not well pronounced. Researchers may not be aware of when an LGCM is more appropriate to use compared to these other approaches. Within the LVM framework, we can start to incorporate extensions that allow for a more complete version of the stress response to be modeled. For example, the models we present in the current manuscript (e. g. , the LGCM) can be viewed as "base models. " In other words, each of these models can be expanded in a variety of ways to incorporate manifest or latent variables influencing various parts of the growth model. These variables can be incorporated as predictors, covariates, outcomes, or distal outcomes. In this case, the model can represent a larger, inclusive system of variables that work together to better capture the stress response and promote a deeper understanding of the biomarker fluctuations that are related to the stress response.
BASIC DETAILS SURROUNDING LATENT GROWTH CURVE MODELS LGCMs are a class of LVMs designed to capture change over time. Within the context of most biomarker research, data are skewed and need to be transformed before analysis  (Miller and Plessow, 2013). However, there have been many advances for LVM techniques, and now any type of variable (e. g. , those on different scales of metric) can be modeled without data transformation  (Muthén and Asparouhov, 2002). If the item-type (e. g. , binary, ordered categorical, or count) is properly specified in the code, then the software program selects the optimal estimator and data need not be transformed. Another important factor to consider before estimating an LGCM is the number of time-points data were collected over. In order to test a linear trend, at least three time-points are needed (i.
First, a description of path diagrams and how they are used to represent LGCMs will be provided. Next, we present a sampling of the types of research questions that can be addressed using LGCMs. This is followed by a description of four specifications of the LGCM, as well as issues surrounding model fit. Each specification of the LGCM will be discussed in the context of the TSST and how they can address specific research questions. We focus on the basic LGCM, a multi-group version, the piecewise LGCM (PLGCM), and the LGCM for two parallel processes. We cover issues tied to assessment of model fit and adequacy related to these models. Finally, two statistical software packages to estimate LGCMs will be discussed; namely, Mplus and the freely available program R.
g. , using MRMs). For instance, alternative specifications of the LGCM exist where the pattern of nonlinear change can be estimated. The latent basis model is one such model that treats the pattern of change as a latent variable  (McArdle and Epstein, 1987; Meredith and Tisak, 1990). In this type of model, the user need not implement a prespecified growth pattern (e. g. , quadratic growth). Instead, the pattern of change is estimated as latent. Another form of handling nonlinearity is to use an additive model (e. g. , the generalized additive model; Hastie and Tibshirani, 1986), which is used to identify nonlinearity without specific knowledge of where the bends in the trajectory (i. e. , changes in growth patterns) are located. Example code for these two forms of handling nonlinearity in the model is included in the online Appendix.
MULTI-GROUP (OBSERVED OR UNOBSERVED) GROWTH MODEL The basic LGCM can be extended to handle multiple groups. In this case, the researcher may be interested in examining growth or change-rate differences across different groups of individuals. These groups can be observed groups such as gender, race/ethnicity, disease status, or age. In this case, the model would be called a multi-group LGCM, which indicates that the groups are observed. However, groups can also be unobserved, or latent. In this case, the theory is that the sample data were collected for multiple unobserved subpopulations, where individuals from these populations follow different growth patterns. This type of model is often referred to as a latent growth mixture model (LGMM), where the word mixture indicates that groups are unobserved (i.
e. , the grouping label for an individual is something that is estimated in the model and is not a label that appears in the data file). Examples of latent groups can include individuals representing different levels of addiction status  (Muthén and Shedden, 1999) , individuals experiencing different outcomes of a traumatic experience  (deRoon-Cassini et al. , 2010) , and adolescents with different smoking behaviors  (Colder et al. , 2001). In the context of the current paper, we might consider a subset of subjects that have a faster recovery time from the TSST compared to another group of subjects. In this case, the LGMM can be used to identify and model these subjects. For the purposes of discussion, we will continue describing the LGMM here, but the (observed) multi-group LGCM would look much the same (code for both is provided in the online Appendix).
ABSOLUTE MODEL FIT Absolute fit statistics are used to determine how well a statistical model reflects the data. This category includes the closeness-offit measures, badness-of-fit measures, and the χ 2 goodness-of-fit test  (Bentler and Bonett, 1980; Bentler, 1990). Closeness-of-fit measures include the comparative fit index (CFI) and the Tucker-Lewis index (TLI); there are many additional measures, but these are arguably the most common because they are reported in most LVM software. CFI and TLI values close to 1. 0 indicate a statistical model that adequately reflects the data. Generally, CFI and TLI values above 0. 96 (for CFI) or 0. 95 (for TLI) reflect excellent fit and values of 0. 90 reflect mediocre fit  (Bentler, 1990; Byrne, 1994; Schumacker and Lomax, 2004). Badness-of-fit measures include the root mean square error of approximation (RMSEA) and the standardized root mean square (SRMS).
STATISTICAL PROGRAMS TO ESTIMATE LGCMS The estimation of LGCMs requires the use of statistical software programs capable of estimating LVMs. There are several statistical software packages used to estimate LGCMs that differ in modeling capabilities, complexity of the program, and price. Table  2  presents the modeling features of the most commonly used statistical software programs for estimating LGCMs (i. e. , Amos, SAS, Stata, Mplus, EQS, and Lavaan through the R programming environment). The purpose of Table  2  is to provide the reader with relevant features of each program so that they can make a decision as to which program best meets their needs. We discuss and provide code for three different software programs: Mplus  (Muthén and Muthén, 1998-2016) , Amos  (Arbuckle, 2014) , and the R package Lavaan  (Rosseel, 2012; R Core Team, 2015) , with the latter being of no cost for users.
Mplus is one of the more flexible LVM programs. Researchers can evaluate change over time for any type of variable (i. e. , continuous, binary, ordered-categorical, unordered-categorical, count, and censored). Furthermore, Mplus has multiple options for handling missing data, including full-information maximum likelihood and multiple imputation. Compared to the other programs presented in Table  2 , Mplus is decidedly the most user-friendly with a website (http://www. statmodel. com) that contains example code for many different types of models. Mplus is also capable of more advanced modeling techniques such as estimating unobserved groups (i. e. , latent mixture modeling) and Bayesian estimation. The cost of this program is $1095 for an academic license, with an annual fee of $175 to keep the license current and qualify for upgrade downloads when made available.
Example code and contrived data to estimate each of the LGCM specifications in Mplus can be found in the online Appendix. The next software package discussed is Amos  (Arbuckle, 2014). Amos is another user-friendly program that has many of the same modeling capabilities as Mplus. Rather than relying on a syntax-based coding language like Mplus (and the R package, Lavaan, detailed next), Amos uses a graphical interface where the user specifies the model by drawing a path model using point-and-click tools. One limitation of Amos is that it is a more expensive program than other programs such as Mplus, with an annual fee starting at $811. 00 per license. Another limitation of Amos is that the modeling capabilities are less flexible than Mplus, where features such as multilevel modeling can be combined easily with LGCMs.
However, data management in Amos can still be housed in SPSS file formats, which may be easier to manage than text and csv files required by Mplus. In order to estimate the specifications of the LGCM discussed, a user is able to draw the models as they appear in Figures  2 3 4 5 6 7. The final software package discussed is the R package, Lavaan. Lavaan has many of the same modeling capabilities as Mplus, but is a free program and provides the additional benefits of being part of the R programming environment (i. e. , it is an open source program that is easily linked to other packages in R). One limitation of Lavaan is that it does not feature all of the modeling capabilities of Mplus, such as multiple imputation or Bayesian estimation. However, there are R packages designed to handle these issues that can be used alongside (or instead of) Lavaan, such as the R package MI  (Su et al.
, 2011)  for multiple imputation and BLavaan  (Merkle and Rosseel, 2016)  for Bayesian estimation. While Lavaan is decidedly less user-friendly than other programs presented in Table  2 , it is free and contains most of the modeling features of its more expensive counterparts. Example code and a contrived data set to estimate each of the specifications of the LGCM in Lavaan can be can be found in the online Appendix.
DISCUSSION The aim of the current paper was to present LGCMs, in a user-friendly manner, which can answer research questions that are important and under-studied in the stress-related biomarker field. We discussed how to formulate and interpret findings from relevant LGCMs, and showed how easy they are to implement by including sample code for a variety of models. LGCM methodology provides advantages to researchers interested in studying change over time of biomarkers of the stress response. The LGCM provides insight into the rate at which a variable changes over time through the specification of latent growth factors. Latent growth factors answer questions about the rate of change of a variable, and how that rate of change relates to other variables. Different specifications of the LGCM were selected to address specific research questions developed through the context of the TSST; namely, the basic LGCM, the multi-group growth model, the PLGCM, and the LGCM for two parallel processes.
The use of a larger spectral fitting window than used in previous retrievals reduces retrieval uncertainties and the scatter in the retrieved profiles due to noisy radiances. Improvements are also demonstrated through the validation in terms of bias reduction at 15-17 km relative to the OSIRIS operational v3. 0 algorithm. The diurnal variation of NO 2 along the line of sight is included in a fully spherical multiple scattering RTM for the first time. Using this forward model with built-in photochemistry, the scatter of the differences relative to the correlative balloon NO 2 profile data is reduced.
SaskTran treats water vapour as a line-by-line absorber assuming a Voigt line shape and spectroscopic parameters from the HITRAN 2008 database  (Rothman et al. , 2009). The spectral fitting is exactly the same for observed and simulated normalized radiances. For example, the actual OSIRIS wavelengths and tangent heights are inputs into the simulation and any spectral pixels which are rejected due to radiation hits or detector saturation at the Level 1 processing stage (i. e. observed radiances) are also omitted from the fit of the SaskTran-simulated radiances. The longest wavelength in the fitting window is extended to 476. 7 nm, raising the number of spectral pixels to 107, thereby increasing the spectrally integrated signal-to-noise ratio and the penetration of the lower atmosphere relative to both existing OSIRIS NO 2 algorithms mentioned above.
Validation approach and datasets Even though the OSIRIS profiles retrieved with the 2-D and 3-D mode of SaskTran account, to varying degrees, for the diurnal gradients expected for the OSIRIS viewing geometry, photochemical modelling is also required to scale all of the OSIRIS NO 2 profiles to the local time of the balloon measurement. The PRATMO box model is also used for this purpose. Balloon correlative data are used from the following instruments: Differential Optical Absorption Spectroscopy (DOAS;  Butz et al. , 2006; Kreycy et al. , 2013, and references therein) , mini-DOAS  (Weidner et al. , 2005) , Limb Profile Monitor of the Atmosphere (LPMA;  Butz et al. , 2006) , Spectromètre Infra-Rouge d'Absorption par Lasers Embarqués (SPIRALE;  Kerzenmacher et al. , 2008; Moreau et al. , 2005) , Système d'Analyse par Observation Zénithale (SAOZ;  Pommereau and Piquard, 1994; Wetzel et al.
, 2007) , SAOZ-BrO  (Pundt et al. , 2002) , mini-SAOZ  (Vicomte and Pommereau, 2011) , MkIV  (Toon et al. , 2002) , and Spectroscopie d'Absorption Lunaire pour l'Observation des Minoritaires Ozone et NO x -Nacelle 2 (SALOMON-N2) operating in solar occultation mode  (Jégou et al. , 2013). Table  2  provides information on these correlative datasets. The five balloonborne instruments not previously used in OSIRIS NO 2 validation are LPMA, mini-DOAS, SPIRALE, SALOMON-N2, and MkIV. DOAS and SAOZ had been used previously  (Brohede et al. , 2007; Haley et al. , 2004; Sioris et al. , 2003). SPIRALE, LPMA, and SALOMON-N2 data were obtained from the CNES/CNRS-INSU Ether website (http: //www. pole-ether. fr). Coincidence criteria are within 1000 km  (Brohede et al. , 2007)  and on the same calendar day (using UT time).
Only daytime correlative measurements are considered. The closest spatial coincidence is used if located within a 1000 km range. Comparisons between OSIRIS NO 2 and balloon correlative data are performed only down to the lower altitude limit of each profile retrieved with the v3. 0 algorithm in an effort to keep the number of balloon-coincident altitudes the same between that algorithm and the algorithm debuting here. The upper limit for validation is determined in all cases by the balloon float altitude. The best opportunities for validation of a denoxified profile come on 4 and 16 March 2003 in Kiruna, Sweden, when the NO 2 number density measured by LPMA and SAOZ, respectively, did not exceed 10 9 molec cm -3 at any altitude. These peak number densities are the lowest in the validation dataset.