dataesr/scientific-paragraphs-categorization huggingface dataset:
Relative abundance of g6pc mRNA in lamprey was established by RT-qPCR analysis and expressed as means ± SE (n = 10) databases concerning g6pc expression in the brain of at least mammals (to our knowledge g6pca and g6pcb expression in brain has never been studied in holostei) was probably due to the faint expression of this gene in this tissue  [23]. For other tissues, our results in lamprey (especially for the brain expression) and tetrapods were in accordance with published works  [5, 22, 24]. Together with previous published data, our results suggest that after the 2R, g6pc duplicates underwent neofunctionalization in heart and intestine. After the Ts3R, our results showed that g6pca remained mainly expressed in intestine, liver and kidney as well as g6pcb1. In contrast, g6pcb2 was preferentially found expressed in the heart in species even though the expression in intestine, liver and kidney was anyway detected.
To our knowledge, g6pcb2 expression pattern has never been studied in non-salmonid teleosts, thus our results strongly suggested for the first time that g6pcb1 and g6pcb2 were subfunctionalised after Ts3R. Moreover, data collected from the PhyloFish database were in favor of the loss of the expression of g6pc genes in brain of nonsalmonid teleosts after the Ts3RTGD. As the functional role of g6pc in brain and heart has never been elucidated, further functional studies are required to investigate the possible functional schemes of such a loss as well as of the loss of g6pcb2 in percomorpha species. Finally, in salmonids g6pca was almost exclusively expressed in the liver as well as g6pcb1a whereas g6pcb1b mRNA was found mainly in intestine in addition to liver. By contrast, g6pcb2a and g6pcb2b displayed a ubiquitous expression but were mostly found in brain and heart whereas they were faintly expressed in liver.
In silico analysis Orthologous g6pc genes and related protein sequences were identified in the Genomicus software program, version 01. 01 (http://www. genomicus. biologie. ens. fr/genomicus-trout-01. 01/cgi-bin/search. pl) and collected from Ensembl (Ensembl release 86 -October 2016, http:// www. ensembl. org). G6pc genes in duck, chicken, platypus, opossum, Tasmania devil, armadillo, pig, tree shrew and orangutan were found using the BLAST tool in Ensembl (Additional file 1: Figure  S1 ). Elephant shark (Callorhinchus milii) g6pc genes were identified by blasting the g6pc spotted gar sequences in the Elephant shark genome project (http://esharkgenome. imcb. a-star. edu. sg) database. The phylogenetic analyse (Fig. 1 ) was performed using the MEGA package version 6 software  [48] , as previously described  [49].
The phylogenetic tree, based on full-length amino acid deduced sequences, were built using the Neighbor-Joining (NJ) method and confirmed by the Minimum evolution method (data not shown). The reliability of the inferred trees was estimated using the bootstrap method with 1000 replications. G6pc3 protein sequences from mammals and teleosts were used to root the tree. The Genomicus software program, version 01. 01 (http:// www. genomicus. biologie. ens. fr/genomicus-trout-01. 01/cgibin/search. pl) was used to confirm their identities by establishing synteny analysis (Fig. 2 ). The presence of g6pca and g6pcb2 gene remnants was queried against the zebrafish, medaka and the tetraodon genomes with BLAT  [50]  tool proposed by the UCSC Genome Browser (https://genome. ucsc. edu) and against the rainbow trout genome using the BLAT tool proposed by the Genoscope (http://www.
genoscope. cns. fr/trout/). Protein alignment (Additional file 2: Figure  S2 ) and the Percentage Identity Matrix established among G6pc sequences (Additional file 5: Table  S2 ) were performed using MUSCLE software (www. ebi. ac. uk/Tools/msa/muscle). Exon/intron structure was obtained from the Ensembl or the Genoscope databases or identified by alignment of the predicted mRNA of rainbow trout g6pc genes and the genomic sequence of zebrafish g6pc (data not shown). Finally, tissue expression profiles of g6pc genes (Fig. 3  and Additional file 3: Figure  S3 ) were identified in the PhyloFish database using the Blast tool  [16]. In the case of ohnologs which possess highly similar sequences (such as g6pcb1a and g6pcb1b in trout;  [6] ), we considered fragments that included SNPs specific to each gene in Phylo-Fish database in order to distinguish the expression of each of them.
Expression data in several Sarcopterygii (xenopus, chicken, rat, mouse, sheep, cow, babouin olive, rhesus macaque and human) were also collected from the BioGPS annotation portal (http://biogps. org) and the Expression Atlas (http://www. ebi. ac. uk/gxa/home). Total RNA extraction and cDNA synthesis Brain, muscle, liver, gills, heart, intestine, notochord, testis and ovary from ten lampreys and different part of brain from 18 trout were sampled and frozen in liquid nitrogen and then kept at -80 °C (more details are given in "fish and experimental design" section). Relative g6pc genes mRNA levels in lamprey and trout tissues was determined by quantitative real-time RT-PCR. Samples were homogenized using Precellys®24 (Bertin-Technologies, Montigny-le-Bretonneux, France) as previously described  [6, 19]  and total RNA was extracted in Trizol reagent (Invitrogen) according to the manufacturer's instructions.
Luciferase control RNA (Promega),10 pg per 1. 9 mg of tissue, was added to each sample to allow for data normalization as previously described  [19]. Total RNA (1 μg) was used for cDNA synthesis. The Super-Script III RNAse H-Reverse transcriptase kit (Invitrogen) was used with random primers (Promega, Charbonniéres, France) to synthesize cDNA. For real-time RT-PCR assays, the Roche Lightcycler 480 system was used (RocheDiagnostics, Neuilly-sur-Seine, France). The protocol conditions for real-time RT-PCR have been published previously  [6]. The primer sequences used in real-time RT-PCR assays for luciferase and g6pc genes in trout analysis were previously described  [6]. The forward and reverse primers for g6pc lamprey analysis were 5'-TCGTCTACTTCCCCATCTGC-3' and 5'-AGGTGATGGGGAACTGCTG-3' respectively.
For trout samples, data were subsequently normalized to the exogenous luciferase transcript abundance in samples diluted at 1:76 using the E method (Light Cycler software). For lamprey samples, data were subsequently normalized to the geometric mean of β-actin (forward : 5'-GCCAA CCGTGAAAAGATGACA-3' , reverse: 5'-GGATGGCGA CGTACATTGC-3'), 5S (forward : 5'-CCTACGACCATAT-CACCCTGA -3' , reverse: 5'-TTCCCAGGTAGTCTCC-CATC-3') and exogenous luciferase transcript abundance in samples diluted at 1:76 using the E method (Light Cycler software) as previously proposed by  Vandesompele et al. (2002)   [51].
Finally, these models can take into account the occurrence of the recalibration component of the response shift effect by choosing different scores as the reference score to qualify the deterioration  (Anota et al. 2013). The aim of this paper is to present the R (R Core Team 2017) package QoLR which is available from the Comprehensive R Archive Network at https://CRAN. R-project. org/ package=QoLR. This package makes it possible to calculate the scores of the EORTC QLQ-C30 and most of its modules and to determine the time to deterioration in a HRQOL score as a modality of longitudinal analysis.
Package structure The QoLR package contains a set of functions to calculate the scores of the EORTC QLQ-C30 and most of its modules and two other programs to determine the time to deterioration in HRQOL score as a modality of longitudinal analysis whatever the definition of deterioration used. Other programs make it possible to print all of the results or to output in a CSV file according to treatment arm and perform all sensitivity analyses according to one reference score. A last program was created to obtain the TTD curves calculated according to the Kaplan-Meier estimation method, with the option of displaying some information (number of patients at risk, cumulative number of events, hazard ratio and log-rank test if two treatment arms are compared). For the convenience of the reader, we summarize all the main functions, with arguments and descriptions of our package QoLR in Table  4.
Function scoring. QLQC30() The first application of the QoLR package is the estimation of the scores of most of the EORTC HRQOL questionnaires, such as the QLQ-C30 cancer specific questionnaire. The first argument of the function scoring. QLQC30 and other functions for scoring is the name of the dataset with the items comprising the answers to the questionnaire (X parameter). The patient's identification number can be specified in the id parameter. A variable identifying the HRQOL assessment number can also be specified in the time parameter in case of a longitudinal HRQOL assessment. The items must be named q1 to qi for the QLQ-C30 (i = 30), QLQ-C15-PAL (i = 15) and IN-PATSAT32 (i = 32) questionnaire. For all other supplementary modules, items must be named q31 to qi, because these modules have to be administered in conjunction with the QLQ-C30 core questionnaire.
For example, items must be named q1 to q30 for the QLQ-C30 and then q31 to q50 for the QLQ-BN20. Moreover, the order of items has to be respected in the dataset. The result is a data frame: each variable corresponds to a HRQOL score. The score names are the same as those used in the EORTC scoring manual  (Fayers et al. 1999). If a patient's identification number was specified in the id parameter, then this variable is replicated in the data frame obtained. Moreover, if a variable identifying the HRQOL assessment number was specified in the time parameter, then this variable is also replicated in the data frame obtained.
Function TTD() This function is used to estimate the time to deterioration of a HRQOL score. To apply this function, the dataset must respect a general structure. The dataset X must be in long format with the following variables in this order: 1. Patient's identification number; 2. Variable identifying the HRQOL assessment number; 3. Date of HRQOL measure; 4. HRQOL scores; 5. Other variables like the date of death or the treatment arm. The dataset must also be sorted by patient identification number and HRQOL measurement time. Dates must be in Julian format (i. e. , number of days since a reference time point). All definitions of TTD presented in this paper are programmed. Table  5  summarizes the arguments of this function and their possible values. According to the definition of deterioration retained, you must specify the following: • The name of your dataset (X); • The name of the HRQOL scores studied (score = " "); • The value of the MCID (MCID = 5, for example); • The reference score to qualify the deterioration which can be one of the following: - • Whether the deterioration corresponds to a decrease (order = 1) or an increase of the score (order = 2); • Whether patients with no baseline score are excluded (no_baseline = "excluded"), censored (no_baseline = "censored") or are in deterioration (no_baseline = "event") since baseline; • Whether patients with no follow-up score are censored (no_follow = "censored") or are in deterioration (no_follow = "event") one day after baseline; • If death is considered as an event, you must specify the name of the variable in your dataset X which contains the date of death for patients who died during the study and with missing values for patients still alive at the end of the study.
An option (sensitivity = TRUE) makes it possible to perform all sensitivity analyses available in one application of the TTD() function. For example, if you fixed no_baseline = "censored", no_follow = "censored" and sensitivity = TRUE then: • A first analysis is conducted censoring patients with no baseline and those with no follow-up score; = "baseline" (default)/= "best" /= "previous" order = 1 (default) /= 2 no_baseline = "censored" (default)/= "event" /= "excluded" no_follow = "censored" (default)/= "event" death = NA (default) or vector sensitivity = FALSE (default)/= TRUE Table  5 : Arguments of the TTD function. • Sensitivity analysis 1: A sensitivity analysis is conducted considering these patients in deterioration. If, in addition to these parameters, the variable death is equals to the variable corresponding to the date of death in your dataset, then four analyses are performed: • A first analysis censoring patients with no baseline, those with no follow-up and those who died without experiencing deterioration before dying; • Sensitivity analysis 1: considering patients with no baseline and those with no follow-up score in deterioration; • Sensitivity analysis 2: considering death as an event; • Sensitivity analysis 3: considering simultaneously patients with no baseline, those with no follow-up score and death as an event.
The result of this function is a data frame with: • the patient's identification number; • a dummy variable called event equal to 1 if the patient is deteriorated, 0 if the patient is censored; • a variable called time, equal to the time to censoring or the time to deterioration in months. Since both variables event and time are created for each score treated and each definition of TTD, we added the name of the corresponding score as a suffix. For example, if score = c("score1", "score2"). Then four variables are created: event. score1, time. score1, event. score2 and time. score2. Moreover, if sensitivity == TRUE, then added variables event and time are created: • event. SA1 for sensitivity analysis 1: event and event. SA1 are equals except for patients with no baseline score and those with no follow-up score, event.
Function plotTTD() The QoLR package also contains a program called plotTTD() to obtain the TTD curves calculated according to the Kaplan-Meier estimation method for all patients or by treatment arm (only two groups are allowed). The time parameter is a vector equal to the time to deterioration or the time to censoring, and the event parameter is a dummy vector equal to 1 if the patient is deteriorated and 0 if not. Other information can also be added using options, e. g. , at regular time point t for all patients or by treatment arm: Table  7 : Arguments of the plotTTD function. • number of patients at risk (nrisk = T); • cumulative number of events (nevents = T). In the case of TTD curves by treatment arm you must give the name of the group variable in the group parameter and the label of each group as you would like it to print in the group.
Applications of the QoLR package To apply one or more of these functions, you need to load the QoLR library in the R current session with the following command: R> library("QoLR") We shall demonstrate the use of five main programs of QoLR in one dataset dataqol corresponding to a randomized clinical trial with the answers to the 30 items of the QLQ-C30 questionnaire for 40 patients with longitudinal HRQOL assessment. Patients in this table were randomly allocated to one treatment group corresponding to the variable Arm (dichotomous variable equal to 0 or 1). This dataset is available in QoLR package and can be imported into R via the command data("dataqol"). This dataset contains the following information: • the patient identification number in the Id parameter; • the treatment group to which each patient was allocated, corresponding to the variable Arm (dichotomous variable equals to 0 or 1); • a variable indicating the theoretical HRQOL assessment number in the time parameter (equal to 0 for baseline measure); • the date of HRQOL assessment in the date parameter; • and the answers to the 30 items of the QLQ-C30 in the variables q1 to q30; • and the date of death for patients who died during the study in the death parameter (missing for all patients who were still alive at the end of the study).
This dataset is in long format. The variable date corresponds to the date of HRQOL measure in Julian format. The baseline date was set to 0 and all the following dates correspond to the time between the measure and the baseline date (the baseline date is the date of origin). HRQOL was evaluated about every 50 days. In the same way, death corresponds to the time between the baseline date and the date of death if the patient died during the study. Moreover, some items and/or measurement times are missing in order to illustrate how missing data are treated in the scoring and then in the Time to deterioration analysis. This dataset looks like the following: R> data("dataqol") R> head(dataqol) Id Arm time date q1 q2 q3 q4 q5 q6 q7 q8 q9 q10 q11 q12 q13 q14 q15 q16 q17 1 1 2 0 0 1 2 NA 2 1 2 1 1 2 2 3 2 1 3 3 2 2 3.
Date of HRQOL measure; 4. HRQOL scores; 5. Other variables, such as the date of death or the treatment arm. The date of HRQOL assessment as well as the treatment arm and date of death are available in the dataqol dataset. We thus merged the score_dataqol dataframe with the important variables of the dataqol dataframe as follows: R> info <-dataqol[, c("Id", "time","date", "death", "Arm")] R> dataqol_final <-merge(score_dataqol, info, by = c("Id", "time")) Since HRQOL was measured at several measurement times for each patient, we can study the time to deterioration of the HRQOL scores. Id As a reminder, for global health status (variable QL) and other functional scales (variables PF for physical functioning to SF for social functioning) a high score reflects a high level of HRQOL or functioning.
Conclusion and outlook The QoLR package is the first R package dedicated to the analysis of HRQOL. The implementation of the time to deterioration definitions in a HRQOL score allows the dissemination of these approaches in order to achieve the goal of standardization of longitudinal HRQOL analysis in oncology clinical trials. QoLR will be updated as new modules are developed by the EORTC HRQOL group. The package will be completed over time, by some simulation algorithms of longitudinal HRQOL data with intermittent or monotone missing data of type Missing Completely At Random or Missing Not At Random for example. Other programs will make it possible to print the results of univariate and multivariate Cox analyses in a CSV file. Moreover, an ongoing project is investigating the presence of competitive risk between events.
time QL PF RF EF CF SF FA NV PA DY SL AP CO DI FI date death ArmThen we reorganized the obtained dataqol_final dataset in order that the date variable appeared in third position: 1 1 0 58 83 83 42 100 67 33 67 33 0 67 0 33 33 0 0 NA 2 2 1 1 33 40 67 33 83 50 100 67 33 33 33 67 67 67 0 43 NA 2 3 1 2 50 80 67 42 83 50 44 33 33 0 33 67 33 33 33 92 NA 2 4 10 0 25 50 67 42 0 17 100 50 67 0 0 100 100 0 67 0 NA 1 5 10 1 17 33 67 25 67 17 100 67 83 0 67 100 0 100 67 56 NA 1 6 11 3 67 80 83 75 100 67 33 17 33 0 0 33 0 0 33 150 273 2 R> dataqol_final <-dataqol_final[, c(1:2, 18, 3:17, 19:20)] R> head(round(dataqol_final)) Id time date QL PF RF EF CF SF FA NV PA DY SL AP CO DI FI death Arm 1 1 0 0 58 83 83 42 100 67 33 67 33 0 67 0 33 33 0 NA 2 2 1 1 43 33 40 67 33 83 50 100 67 33 33 33 67 67 67 0 NA 2 3 1 2 92 50 80 67 42 83 50 44 33 33 0 33 67 33 33 33 NA 2 4 10 0 0 25 50 67 42 0 17 100 50 67 0 0 100 100 0 67 NA 1 5 10 1 56 17 33 67 25 67 17 100 67 83 0 67 100 0 100 67 NA 1 6 11 3 150 67 80 83 75 100 67 33 17 33 0 0 33 0 0 33 273 2 Finally, in order to apply the TTD function, the dataset has to be sorted by patient's identifi- cation number (Id) and HRQOL measures (time): R> dataqol_final <-dataqol_final[order(dataqol_final$time), ] R> dataqol_final <-dataqol_final[order(dataqol_final$Id), ] R> head(round(dataqol_final)) Id time date QL PF RF EF CF SF FA NV PA DY SL AP CO DI FI death Arm 1 1 0 0 58 83 83 42 100 67 33 67 33 0 67 0 33 33 0 NA 2 2 1 1 43 33 40 67 33 83 50 100 67 33 33 33 67 67 67 0 NA 2 3 1 2 92 50 80 67 42 83 50 44 33 33 0 33 67 33 33 33 NA 2 25 2 0 0 50 100 100 42 100 83 67 0 33 0 100 100 100 0 0 271 2 26 2 1 55 58 67 100 58 83 67 67 0 0 0 67 67 100 0 0 271 2 27 2 2 149 50 60 100 50 83 67 78 0 33 33 67 67 100 0 0 271 2.
, 2012). In addition, LUC changes the balance between litter input and heterotrophic respiration, resulting in changes in soil organic Published by Copernicus Publications on behalf of the European Geosciences Union. C. Yue et al. :  Gross land use change module in ORCHIDEE-MICT v8. 4. 2 carbon (SOC)  (Don et al. , 2011; Guo and Gifford, 2002; Poeplau et al. , 2011; Powers et al. , 2011). Globally, LUC activities have contributed significantly to historical anthropogenic carbon emissions. It is estimated that about 800 Mha (1 Mha = 10 6 ha) of forests were cleared for agricultural purpose and that 2000 Mha of forests were harvested during 1850-1999, giving rise to cumulative emissions of 124 Pg C, or 33 % of the total anthropogenic emissions  (Houghton, 1999). Houghton et al. (2012)  reviewed LUC emissions from multiple studies and estimated the annual global LUC emissions as 1.
Overview of the integrated assessment models In the following, we will briefly describe the power sector representation of the six participating IAMs as well as the power sector model REMIX. All the IAMs have full global coverage; POLES is the only model that explicitly represents 24 EU member states. All IAMs updated their wind and solar resource potential assumptions based on the country-level data in  (Eurek et al. , in this issue)  for wind and  (Pietzcker et al. , 2014b)  for solar. A cross-model comparison of resulting levelized costs of electricity can be found in the editorial of this special section  (Luderer et al. , in this issue). Sources for other technology assumptions can be found in the model-specific documentations referenced behind each model name. To provide a quick overview, we present a list summarizing the main power sector module characteristics (see Table  1 ) before discussing the models in more detail.
While global net VRE shares, averaged over the second half of the century in scenarios with a Tax30 climate policy, ranged from 18-64% (model-average: 38%) in the 2013 model versions used for EMF27, they now increased to 46-87% (model-average: 62%) with the new model versions -an increase of the model-average by 24 percentage points. Most of this increase (18%-points) comes from the update of integration challenge modeling, while 6%-points come from the update of wind and solar costs and resources. While AIM/CGE and WITCH use rather reduced-form approaches and have less technological detail, they still manage to incorporate several aspects of the effect of VRE on power sector dynamics. More explicit representations in models with higher technology detail, like those in IMAGE, MESSAGE, POLES or REMIND, come with higher computational challenges and data requirements, but represent the integration of wind and solar in a more comprehensive way.
Bioactive peptides play critical roles in regulating many biological processes. Recently, natural short peptides biomarkers are drawing significant attention and are considered as "hidden treasure" of drug candidates. High resolution and high mass accuracy provided by mass spectrometry (MS)-based untargeted metabolomics would enable the rapid detection and wide coverage of the low-molecularweight peptidome. However, translating unknown masses (<1 500 Da) into putative peptides is often limited due to the lack of automatic data processing tools and to the limit of peptide databases. The web server OligoNet responds to this challenge by attempting to decompose each individual mass into a combination of amino acids out of metabolomics datasets. It provides an additional network-based data interpretation named "Peptide degradation network" (PDN), which unravels interesting relations between annotated peptides and generates potential functional patterns.