dataesr/scientific-paragraphs-categorization huggingface dataset:
Mapping the odorant pharmacological space As a final step we investigated all biological targets potentially recognized by odorant compounds. Chemical-protein interactions for a complete biological system are usually unknown apart for some drugs, and the majority of molecules have only been studied for one or few protein targets. This is especially true in the case of odorant compounds, which have been mostly studied on ORs. We decided to identify potential novel and unexpected odorantprotein interactions. Assuming that chemicals sharing highly similar structure also share similar biological properties  [56] , we used ChemProt, a large curated chemogenomic database of more than 1 150,000 molecules with over two millions chemical-protein interactions  [41]. Using MACCs fingerprints and a strict Tanimoto coefficient (Tc) distance threshold of 0.
9, 1,091 odorants were identified with odorant-protein interactions for 821 proteins. Interestingly, for 329 odorants, links to 200 proteins were already available (i. e. compounds tested for a protein), represented by 556 unique chemical-protein interactions. For example, capsaicin, described in Flavor-Base with a ''slight herbaceous odor'' (known also for its strongly perceived ''burning hot pungent taste'') has shown agonist activity on the human vanilloid receptor 1 (TRPV1) and inhibition of PTGS1, a well-known protein inhibited by nonsteroidal anti-inflammatory drugs such as aspirin. Another example, thymol is activating a human transient receptor (TRPA1) which has a central role in the pain response to endogenous inflammatory mediators  [57]. In order to reveal common structural features between odorants and other molecules, we visualized their distribution inside the chemogenomic database by developing a chemical similarity network, excluding annotations (Fig.
4 mM). The food additive phenylethyl salicylate presented activities with the same range (IC 50 = 9. 2 mM) whereas methyl c linolenate a fatty acid compound naturally present in banana, grapefruit juice, grape, melon, strawberry, tomato and chicory  have an activity on PPARc seven fold better than naringenin (IC 50 = 1. 2 mM). To assess the biological activities of these compounds, activation of the PPARc ligand binding domain (LBD) was investigated using trans-activation assays (see Methods and SI). As a result, all three compounds were able to activate the PPARc-LBD although with low efficiency (Figure  6b ). The apparent partial agonist properties of the compounds were reflected by the ability to partly antagonize rosiglitazone-induced transactivation in the same assay (data not shown).
We collected odorant that bind to ORs from public available resources and negative control was not considered in our model, which is unidirectional. One of the limitations is, only 24 human ORs showed bioactivity, which represent only 6% of the human olfactome. Moreover we should take into consideration the so-called 'Matthew effect'  [71]  resulting in maintained research interest regarding already well-investigated odorants and ORs, and then a larger amount of available data for these odorants and ORs (OR1G1, OR52D1…). This skews the findings towards interactions involving ORs already intensely investigated (OR2W1). But this also highlights the ORs, which need further attention. Hence the lack of predicted interaction between odorants and OR5P3 for instance might be the result of less available data to create the model rather than lack of biological effect.
Nearly 2000 sequence variants of this gene have been identified so far in CF patients (Cystic Fibrosis Mutation Database:). These mutations have been grouped into six different classes and affect protein synthesis, trafficking, regulation, conductance, splicing or transcription and protein stability of the CFTR chloride channel respectively  [4]. In the most commonly occurring F508del mutation, which accounts for nearly two thirds of mutated alleles in CF patients, native NBD-CL4 and CL1 interaction is disrupted, compromising CFTR domain assembly and maturation  [2, 5, 6]. As a result, ATP-dependent relay of conformational changes of NBDs and MSDs involved in chloride channel gating are affected along with CFTR biogenesis  [7]. Besides regulating chloride transport, CFTR loss of function also hampers other physiological processes regulated by this channel, such as sodium transport, ATP transport, vesicular transport, acidification of intracellular organelles and bicarbonate-chloride exchange  [8] [9] [10].
Bioinformatics Calumenin, along with other CFTR interacting proteins with chaperone functions, calcium binding properties or those belonging to the CREC family were selected from the dataset published by  Wang et al. in 2006 [14]. We decided to incorporate two other CREC proteins RCN3 and SDF4 in the dataset for comparison as well. Five proteins previously described as being intrinsically disordered  [15] [16] [17]  along with wild-type CFTR and its mutant variants F508del-CFTR, G551DCFTR were also included. Measurements for various biophysical parameters such as hydropathy, given by GRAVY scores (Grand average of hydropathy), instability, unfoldability, charge, disorder (IUPRED scores) and aggregation propensity were performed for all the proteins using the ProtParam tool (http://web. expasy.
org/ protparam/)  [24] , FoldIndex (http://bip. weizmann. ac. uk. il/ fldbin/findex)  [25] , IUPRED server (http://iupred. enzim. hu) [26, 27]  and Aggrescan server (http://bioinf. uab. es/aggrescan/) [28]. IUPRED and aggregation scores were normalized for protein length. Table  S1  shows the raw scores. Phylogenetic tree construction was performed after multiple sequence alignment using CLUSTAL omega  [29] , version 1. 2. 0, with five iterations (for guide tree construction and HMM profiling). Aligned sequences were used for tree construction in the PHYLIP format using CLUSTALW2 program with UPGMA clustering method, distance correction and percent identity matrix parameter being true  [30].
Statistical analysis All statistical analysis was performed using R 3. 0. 1 statistical package. Data normality was assessed using the Shapiro-Wilk test and depending upon whether data was observed to be normal or not, ANOVA or Kruskal-Wallis test was performed to estimate whether the mean difference observed between different protein classes was significant enough. Post-hoc analysis was carried out using either the Tukey's test or Mann-Whitney test with Bonferroni correction. Principal component analysis and hierarchial clustering were performed to observe the correlations between different biophysical parameters and the distribution of proteins across the first two components that describe the maximum variation in the data. Principal component analysis (PCA) applies orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of uncorrelated variables called principal components.
Cloning of AavLEA1 The AavLEA1 gene was cloned as described in Goyal et al. [31]  with minor modifications at the NUI Maynooth, Ireland. Briefly, the gene was amplified from cDNA (prepared using SuperScript III RT (Invitrogen)) according to Goyal et al. and cloned into the pET15b vector (Novagen) on the NdeI/BamHI sites coding for AavLEA1 protein and a N-terminal His6 tag. The vector was then transformed into BL21 (DE3) cells. The plasmid has been deposited in Addgene plasmid reference database (reference number: 53093).
IDPs have also been previously characterized by high charge and unfoldedness in their polypeptide sequences  [18, 37]. As can be observed, a majority of proteins in our dataset demonstrate a GRAVY (grand average hydropathy) score below zero. This score is a useful predictor of protein hydropathy or hydrophobicity, where positive scores indicate that proteins are highly hydrophobic and negative scores reflect protein hydrophilicity. CFTR proteins, being bona fide membrane proteins, demonstrate the highest mean GRAVY scores, while IDPs demonstrate the lowest mean GRAVY scores. An overall significant difference was detected between all the protein classes (ANOVA, F(4,24) = 8. 82,p-value,0. 001). However, Tukey's posthoc analysis revealed that CREC proteins, including calumenin, showed significantly lower GRAVY scores compared to CFTR proteins (p-value,0.
As observed in Figure  3A , the first two principal components are represented on the x and y axes respectively and cover 67. 98% and 17. 15% of the total variance observed in our dataset. The variables factor map (Figure  3B ) gives directionality to different biophysical parameters as observed on the PCA plot. As can be observed calumenin lies in the region corresponding to high charge, along with other CREC family members RCN1, RCN2, RCN3 and SDF4. Interestingly, other calcium associated proteins such as calnexin and CAL-GRANB also cluster in this region. CFTR variants are observed in the region corresponding to high GRAVY, unfoldability and aggregation propensity. Strikingly, we observe a large variation in the physicochemical characteristics of CFTR associated chaper-ones. Most of the disordered proteins in our dataset lie in the region corresponding to high disorder, except for ArLEA1A, which was observed to cluster with other CFTR associated chaperones on the PCA plot.
Discussion Calumenin is a calcium binding CREC protein with multiple EF-hands that undergoes secretion  [22]  and has recently been found to be associated with G551DCFTR protein  [12]. It has been hypothesized to function as a putative CFTR chaperone, however so far its biophysical properties have remained uncharacterized. In this study we have drawn comparisons between calumenin, including other CREC proteins, and proteins previously known to act as CFTR associated chaperones and calcium binding proteins. The composition of different protein sequences in our dataset has been deduced using various parameters such as hydropathy (GRAVY), unfoldability, aggregation propensity, instability, charge and disorder by applying bioinformatics based prediction tools such as ProtParam  [24] , FoldIndex  [25] , IUPRED  [26, 27]  and Aggrescan  [28].
Acknowledgments We would like to thank members of  INSERM UMR1078  for their help during various stages of this study especially  Dr. Chandran Ka. We would also like to thank  Phillippe Elies  and  Gerard Sinquin  from the  Plateforme d'Imagerie et de Mesures en Microscopie, UBO. Drs. Stijn van Dongen  and  Anton Enright  from the  European Bioinformatics Institute  and  Prof. Mark Blondel  have provided useful advice and suggestions.
The authors thank the  Association Gaetan Saleu ¨n , the  French foundation Vaincre la Mucoviscidose, Conseil General de Bretagne  (http://www. conseilgeneral. com/conseil-regional/conseil-regional-bretagne. htm) and  Inserm  (http://www. inserm. fr)for their financial support. BC was funded by an  Irish Research Council   EMBARK post-graduate fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
These genes are relatively highly expressed in roots and their expression is induced by various environmental stresses  [44]. A particularly interesting candidate that V G /(V G + V E ). could be possibly linked to scopolin accumulation was detected within the QTL on chromosome 5 (SCL1 in Table  4 ). It is At5g53990 encoding a UDP-glycosyltransferase, which is relatively highly expressed in Arabidopsis roots  [44]. According to the 1001 Genomes Project and our resequencing data of Est-1, this gene contains several SNPs in the coding sequences of tested accessions including the parental lines (see Additional file 3). Interestingly, the CYP81D11 and UDP-glycosyltransferase sequences originating from Est, Est-1 (both taken from the 1001 Genomes Project database) and Est-1* that was re-sequenced in our laboratory are not identical (see Additional files 2 and 3).
5 cm diameter × 10 cm high, glass jars with magenta B caps) containing 8 ml sterile liquid medium. Plants grown in liquid cultures were incubated on rotary platform shakers at 120 rpm. After 17 days plants were harvested (28 th day of culture), leaves and roots were frozen separately in liquid nitrogen and stored at -80°C. All genotypes were grown in three biological replicates (in independent flasks). The growth conditions were monitored by a HOBO U12 data logger (Onset Computer Corporation, Bourne, MA) that recorded the parameters (temperature, light intensity and relative humidity) in an interval at every five minutes.
Mass spectra were taken at 70 eV; a scan interval of 0. 5 s and fragments from 30 to 450 Da. The solvent delay was 1 to 2 min, and the total GC/MS running time was 38 min. The mass-detector used in this analysis was Turbo-Mass Gold-Perkin-Elmer, and the MS software Turbo-Mass ver-5. 1.
QTL analyses in the AI-RIL population Statistical analysis of phenotypic data was performed by Shapiro-Wilk normality test. Phenotypic data is normally distributed at the significance level α = 0. 05. QTL mapping was performed using R software (A Core Team, 2012, www. R-project. org) with R/qtl package  [57, 58] ; http://www. rqtl. org/). QTL mapping was performed with Simple Interval Mapping (SIM) (data not shown) followed by the Multiple QTL mapping (MQM) procedure. The QTLs with the highest logarithm of odds (LOD) scores detected by SIM were subsequently used to make the QTL model by the MQM. The final QTL model was done with the backward elimination of cofactors with the window size 10 cM and maximum number of cofactors 5. Significance threshold (LOD) values (P <0. 05) for the QTL presence was estimated from 10 000 permutations and is 3.
According to the 1001 Genomes Project database (www. 1001genomes. org) and re-sequencing data of Est-1 from our laboratory (see Additional files 2 and 3, indicated as Est-1*), the CYP81D11 gene contains several SNPs and one indel in the coding sequences of the parental lines of EstC mapping population and in the other accessions tested in this study (see Additional file 2).
Acknowledgements This research was supported by the  National Science Centre  ( 6815/B/P01/ 2011/40 ), the  Foundation for Polish Science (HOMING Programme)  and the  LiSMIDoS PhD fellowship  ( UDA-POKL. 04. 01. 01-00-017/1000 ). Open access publication cost supported from the project MOBI4Health that has received funding from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement no  316094. We thank  Maarten Koornneef  from the  Max Planck Institute for Plant Breeding Research in Cologne  for providing all Arabidopsis seeds used in this study and for critical reading of the manuscript.
Quantification of MitoTracker orange CMTM Ros and DCF fluorescence intensity In each individual oocyte, MitoTracker Orange CMTM Ros and DCF fluorescence intensities were measured at the equatorial plane, as in previous studies in human  [34]  and animal oocytes  [25, 26, 29, 32] , with the aid of the EZ-C1 Gold Version 3. 70 image analysis software platform for Nikon C1 (Nikon Instruments) confocal microscope. A circle of area = 100 in diameter (arbitrary value) was drawn to measure only the cytoplasmic area (512 by 512 pixels). The fluorescence intensity encountered within the programmed scan area was recorded and plotted against the conventional pixel unit scale (0-255). Fluorescence intensity was expressed as Arbitrary Densitometric Units (ADU). Parameters related to fluorescence intensity were maintained at constant values for all measurements.
Statistics Nuclear maturation rates and mt distribution patterns were compared by chi-square analysis. For confocal quantification analysis of mt activity and intracellular ROS levels, the least-square means of the dependent variable (MitoTracker Orange CMTM Ros and DCF fluorescence intensity) were calculated in examined samples and the statistical significance of the leastsquare means between treated and control groups was calculated by one-way ANOVA followed by Multiple Comparison Dunn's method (SigmaPlot software). For mt/ROS colocalization, mean values of Pearson's correlation coefficient were compared between treated and control groups by one-way ANOVA followed by Multiple Comparison Dunn's method (SigmaPlot software). Differences with P < 0. 05 were considered as being statistically significant.
Acknowledgements This document has been produced with the financial assistance of the  European Union :  ENPI-CBC-MED "PROCAMED" Project: Promotion des systèmes camelins innovants et des filières locales pour une   gestion durable des territoires saharienne ; reference number I. B/1. 1/493. The contents of this document are the sole responsibility of "Section of Clinics and  Animal Productions, Department of Emergency and Organ Trasplantation -University of Bari Aldo Moro, Italy " and can under no circumstances be regarded as reflecting the position of the European Union. Authors are grateful to the  Direction of CURP  for the logistic support and help during the implementation of this work. Additional supports were received from  CEGBA  project,  University of Bari Aldo Moro Italy  and  ONEV MIUR   PONa3_00134  project n.
nih. gov/ij/) [25]. Transmission electron microscopy Isolated islets were fixed and analysed as previously described  [26]. Insulin secretion and content Total and secreted insulin were measured after batch incubation of islets in KHB buffer as previously described  [23]. Statistics Data are given as mean ± SE or relative frequency. For comparison of continuous variables in two independent groups, Student's two-tailed t test was used. Bonferroni correction was applied for multiple comparisons. Multiple linear regression was used to assess the effect of different factors on islet cell death. A value of p<0. 05 was considered significant. Statistical analyses were performed using SPSS 18. 0 software (SPSS, Chicago, IL, USA) and Excel 2010 software (Microsoft, Redmond, WA, USA).
Acknowledgements We thank  L. Zhao  and  E. Oliver Perez  ( Imperial College London, London, UK ) for assistance with the hypoxia experiments. We would also like to thank  P. Chabosseau  ( Imperial College London, London, UK ) for assistance with measurements of cytosolic Zn  2+  and  R. Zullig  ( University of Zurich, Zurich, Switzerland ) for assistance with islet preparation. Preliminary reports of these findings have previously been published in abstract form [51] and reported at the 49th EASD Annual Meeting in Barcelona in 2013. Funding This study was supported by:  Wellcome Trust   Senior Investigator  ( WT098424AIA ) and  Royal Society Wolfson Research Merit Awards  to GAR; grants from the  MRC  ( Programme MR/ J0003042/1 ),  European Association of the Study of Diabetes (EFSD)  and  Diabetes UK  (studentships to GAR and AS); a  Diabetes UK R.
D. Lawrence Research Fellowship  ( 12/0004431 ) to DJH; and a  University of Zurich Academic Career Development Award , a  Swiss Life Jubilee Foundation  Grant and a  Swiss National Science Foundation  Grant ( SNF IZK0Z3_150520 ) to PAG. The work leading to this publication has received support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement number  155005  (IMIDIA), resources of which comprise financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution (GAR). The isolation of human islets in Geneva was supported by a  Juvenile Diabetes Research Foundation (JDRF)  award ( 31-2008-416 ) to DB. The isolation of human islets was supported by the  Oxford NIHR Biomedical Research Centre  and a  Juvenile Diabetes Research Foundation (JDRF)  award ( 31-2008-617 ) to PRVJ).
Intervention in each group Two groups will be constituted by random allocation. In the intervention group ("parenteral nutrition" group), parenteral nutrition will be initiated, whereas in the control group ("oral feeding" group), nutrition will be exclusively by the oral route, without parenteral nutrition. At inclusion, patients in both groups will be evaluated by a dietitian with the aim of allowing all patients, regardless of the treatment group to which they are allocated, to pursue oral feeding where possible. Initial evaluation will record the following data: ➢ Anthropometric data (weight, ideal weight, height, BMI) ➢ Dietetic advice given (previously or during evaluation) regarding uncomfortable symptoms ➢ Estimation of theoretic energy requirements in kcal  [18]  adjusted by a correction factor for the cancer ➢ Estimation of theoretic protein requirements (1.
Performance status will be evaluated by using the Karnofsky score, demographic and socioeconomic data will be recorded, and a complete nutritional evaluation, including CRP, albumin, and prealbumin will be performed in all patients. Patients allocated to the parenteral nutrition group will also undergo biological tests before initiation of nutrition. A consultation with a dietitian will be held for all patients. No systematic dietetic follow-up is planned for the study; the cadence of the follow-up consultations is left to the discretion of the relevant healthcare professionals in accordance with their standard practice. Last, each patient will complete the QLQ-C15-PAL, and patients who are aware of the palliative status of their disease will also complete the QUAL-E. From day 1 to day 60: Patients will have regular followup consultations during the follow-up period; at least once per month, based on the patient's planned attendance at the hospital (for oncology consultations or scheduled admission).
Patients randomized to the intervention group may refuse to consent to the intervention, in which case, they will be included in the control group. The distribution of patients between the study arms will be performed by the investigator during the first consultation to verify eligibility criteria, by means of Internet access to a 1-to-1 randomization list, balanced by center, and generated automatically by using SAS software (version 9. 3; SAS Institute Inc. , Cary, NC, USA) and administered by the Clinical Investigation Centre of the University Hospital of Besancon, France.
Statistical analysis All analyses will be performed on an intention-to-treat basis. A formal statistical analysis plan will be prepared before the database lock. Any deviation from the statistical analysis plan will be detailed and discussed in the study report. Clinical and sociodemographic variables collected at baseline will be described as mean ± standard deviation (SD) for normally distributed continuous variables, the median (interquartile) range for nonnormally distributed variables and number (percentage) for qualitative variables. The number of health-related quality-of-life questionnaires completed at each measurement point will be reported.
Change in quality of life over time Quality-of-life scores will be generated according to algorithms described in the EORTC scoring manual. The mechanism of missing scores will be studied and classified according to the methods of Diggle  [26]  and Rubin  [27]  (that is, MCAR (Missing Completely At Random), MAR (Missing At Random), and MNAR (Missing Not At Random). The mechanism of missing data will be determined by comparing patients with no missing scores with those with at least one missing score at baseline, according to baseline characteristics of patients. If required, more detailed patterns will be defined. If missing data are MNAR, multiple imputations may be performed. Otherwise, simple imputation by the mean will be retained. The time to quality-of-life score deterioration (TTD) will be defined as a modality of longitudinal quality-of-life analysis and will be defined as the time from inclusion in the study to a first deterioration, with a minimum clinically significant difference of at least 5 points as compared with the baseline score, or death of any cause, whichever occurs first  [28, 29].