dataesr/scientific-paragraphs-categorization huggingface dataset:
We found that, at saturating concentrations of fluorescein (2 µM), cells expressing the OPT scFv bound twice as much fluorescein as cells expressing the WT scFv. This difference was not due to variation in the total amount of displayed scFv, which one might control for by labeling the c-Myc epitope as in  [33]. Rather, this difference in binding reflects a difference in the fraction of displayed scFvs that are competent to bind antigen. Yeast display experiments performed at a single antigen concentration cannot distinguish such differences in the fraction of displayed scFv molecules that function properly from differences in scFv affinity. To further test the capability of Tite-Seq to infer dissociation constants from sequencing data over a wide range of values, as well as to validate our analysis procedures, we simulated Tite-Seq data in silico and analyzed the results using the same analysis pipeline that we used for our experiments.
Our data demonstrate that these confounding effects can strongly distort yeast display affinity measurements made at a single antigen concentration, and that the binding curves measured by Tite-Seq successfully deconvolve these effects from affinity measurements. More generally, changing a protein's amino acid sequence can be expected to change multiple biochemical properties of that protein. Our work emphasizes the importance of designing massively parallel assays that can disentangle these multiple effects so that measurements of a specific activity of interest can be obtained. Tite-Seq provides a general solution to this problem for massively parallel studies of protein-ligand binding. Indeed, the Tite-Seq procedure described here can be readily applied to any protein binding assay that is compatible with yeast display and FACS.
IV. METHODS Tite-Seq was performed as follows. Variant 3H and 1H regions were generated using microarray-synthesized oligos (LC Biosciences, Houston TX. USA). These were inserted into the 4-4-20 scFv of  [7]  using cassette-replacement restriction cloning as in  [34] ; see Appendix C. Yeast display experiments were performed as previously described  [36]  with modifications; see Appendix B. Sorted cells were regrown and bulk DNA was extracted using standard techniques, and amplicons containing the 1H and 3H variable regions were amplified using PCR and sequenced using the Illumina NextSeq platform; see Appendix D. Three replicate experiments were performed on different days. Raw sequencing data has been posted on the Sequence Read Archive under BioProject ID PR-JNA344711. Low-throughput flow cytometry measurements were performed on clones randomly picked from the Tite-Seq library.
Sequence data and flow cytometry data were analyzed using custom Python scripts, as described in Appendices E and F. Processed data and analysis scripts are available at github. com/jbkinney/ 16_titeseq. 4 µg/ml 112. 5 µl to facilitate mixing. Secondary labeling reactions with 0. 8 µg/ml BV421-conjugated donkey anti-rabbit antibody were performed in 60 µl. Appendix C: Cloning strategy Amplicons containing variable CDR1H or CDR3H regions were generated as follows. An oligonucleotide library containing mutagenized 1H and 3H variable regions (see Table  II ) was generated by LC Sciences using microarray-based synthesis. The specific oligos used are provided at github. com/jbkinney/16_ titeseq. 1H and 3H library oligos were separately amplified via PCR using primers oRAL10 and oRAR10 (for 1H) or oRAL11 and oRAR11 (for 3H).
Values for F bc were computed directly from the FACS data log. The probabilities p b|sc , by contrast, were inferred from Tite-Seq read counts. These probabilities are closely related to R bsc , the number of sequence reads for each sequence s from bin b at antigen concentration c. This relationship is complicated by additional factors that arise from variability in sequencing depth from bin to bin. Moreover, because there were often a small number of reads for any particular sequence in a given bin, it was necessary in our inference procedure to treat the relationship between p b|sc and R bsc probabilistically. We therefore inferred values for the probabilities p b|sc through the following maximum likelihood procedure. First, we assumed that the number of reads R bsc is related to an "expected" number of reads r bsc via a Poisson distribution.
The log likelihood of observing a specific set of read counts R bsc over all bins b and concentrations c for a given sequence s is therefore given by L s = log   b,c 1 R bsc ! r bsc R bsc e -r bsc  . (E3) The expected number of reads r bsc is, in turn, related to the probability p b|sc via r bsc = R bc C bc C c P s p b|sc. (E4) Here, R bc = s R bsc is the total number of reads from bin b at antigen concentration c, C bc is the number of cells sorted into bin b at antigen concentration c (obtained from the FACS data log), C c = b C bc is the total number of cells sorted at concentration c, and P s is the fraction of cells in the library with sequence s. The factor R bc C c /C bc in Eq. E4 accounts for differences in the depth with which each bin was sequenced. Note: Eq. E3 assumes that each final read arose from a different sorted cell.
This assumption is clearly violated if R bc > C bc. In cases where this inequality was found to hold, we rescaled all R bsc → hR bsc where h = C bc /R bc before undertaking further analysis. For each sequence s, we inferred K D,s , A s , P s , and all probabilities p b|sc by maximizing the likelihood L s subject to the constraint that b p b|sc F bc = ln A s c c + K D,s + B (E5) at every concentration c. Note that, in this procedure, the sorting probabilities p b|sc are not modeled explicitly as a function of the putative mean fluorescence. Instead, they are inferred from the data along with the parameters of the non-cooperative Hill function. Doing this dispenses with the need for a detailed characterization of the noise in the Tite-Seq sorting procedure. The validity of this procedure is evinced by the analysis of simulated data, shown in Fig.
S10. The maximum likelihood optimization problem described above was solved as follows. For each concentration c, we created a grid of 100 equally spaced points for ln f sc ∈ [F 0a , F 3a ]. For each possible value of f sc , we then used Nelder-Mead optimization of p b|sc to minimize L s under the constraint in Eq. E5. Akima interpolation was then used to create a function of the optimized probability pb|sc as a function of f sc. We then scanned a 321× 201 grid of values for the pair (K D,s , A s ) and selected the pair that minimized L s (K D,s , A s , pb|sc (K D,s , A s ), P s b,c ). We repeated this scan by varying P s over 95 different values. The final inferred values for K D,s , A s , and P s were those so found to maximize L s. Python code for this inference procedure is provided at github.
Acknowledgements. We would like to thank  Jacklyn Jansen ,  Amy Keating ,  Lother Reich , and  Bruce Stillman  for helpful discussions. We would also like to thank  Dane Wittrup  for sharing plasmids and yeast strains. RMA, TM and AMW were supported by  European Research Council Starting  Grant n. 306312. JBK was supported by the  Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory. The authors declare that they have no conflict of interests.
Search strategy A highly sensitive search strategy has been developed, and validated study design filters will be applied to retrieve articles pertaining to the use of AIT for allergic asthma from electronic bibliographic databases. We have conceptualized the search to incorporate the four elements shown in Fig. 1. To retrieve systematic reviews, we will use the systematic review filter developed at McMaster University Health Information Research Unit (HIRU) (http://hiru. mcmaster. ca/hiru/HIRU_Hedges_MEDLINE_Strategies. aspx#Reviews). To retrieve randomized controlled trials (RCTs), we will apply the Cochrane highly sensitive search strategy for identifying RCTs in MEDLINE  [8]. To retrieve case series, we will use the filter developed by librarians at Clinical Evidence: http://clinicalevidence.
bmj. com/x/set/static/ebm/learn/665076. html. We will search the following databases: • Cochrane Library including, The search strategy has been developed on OVID MEDLINE and then adapted for the other databases (see Additional file 1: Appendix S1). In all cases, the databases will be searched from inception to October 31, 2015. Additional references will be located through searching the references cited by the identified studies, and unpublished work and research in progress will be identified through discussion with experts in the field. We will invite experts who are active in the field from a range of disciplines and regions to add to the list of included studies by identifying additional published and unpublished papers they are aware of and research in progress. There will be no language restrictions employed; where possible, all relevant literature will be translated into English.
Study selection All references will be uploaded into the systematic review software Distiller and undergo initial deduplication. Study titles will be independently checked by two reviewers according to the above selection criteria and categorized as: included, not included or unsure. For those papers in the unsure category, we will retrieve the abstract and re-categorize as above. Any discrepancies will be resolved through discussion and, if necessary, a third reviewer will be consulted. Full text copies of potentially relevant studies will be obtained and their eligibility for inclusion independently assessed. Studies that do not fulfil all of the inclusion criteria will be excluded.
Data extraction, analysis and synthesis Data will be independently extracted onto a customized data extraction sheet in Distiller by two reviewers, and any discrepancies will be resolved by discussion or, if agreement cannot be reached, by arbitration by a third reviewer. A descriptive summary with summary data tables will be produced to summarize the literature. If clinically and statistically appropriate, meta-analysis using either fixedeffect or random-effects modeling will be undertaken  [8]. A narrative synthesis of the data will also be undertaken.
Next, the samples were transferred into fresh vials, and were briefly centrifuged at 4˚C to remove debris. 750 μl of supernatant were transferred into fresh vials, and 620 μl of ultrapure water (HPLC grade) were added. Samples were vortexed before lyophilization overnight. For measurements, 650 μL of D 2 O/buffer (1. 5 M KH 2 PO 4 , 2 mM NaN 3 , 0. 1% (v/v) TSP (= 3,3-(trimethylsilyl)-2,2',3,3'-tetradeuteropropionic acid) in D 2 O) (9:1) were added to the extracts, and 600 μl of this mixture were transferred into a 5 mm standard NMR tube. Spectra were recorded on a Bruker Avance III 600 spectrometer equipped with a 1 H, 13 C, 15 N-TCI triple resonance cryoprobe. 1D spectra were recorded with 64k data points, 90. 5 receiver gain and 32 scans for comparing the phenotypes and 64 scans for comparing the DEX treatment at 300 K using a 1D NOESY experiment with presaturation for water suppression.
A mixing time of 10 ms and a prescan delay of 10 s were used. Pulse length was determined automatically by the Bruker AU program pulsecal and presaturation was set corresponding to a 25 Hz pulse. Irradiation frequency for water suppression was optimized prior to acquisition. Spectra were processed identically with an exponential apodization function with line broadening of 0. 3 Hz. Automatic phasing, baseline correction and referencing were done by the Bruker AU program apk0. noe. Additionally, J-resolved spectra were recorded with identical pulse lengths, presaturation, irradiation frequency and receiver gain as the corresponding 1D NOESY. Spectra were recorded with 8192 data points in the direct dimension and 40 increments in the indirect dimension. One scan was acquired per increment. Spectra were processed with 16k × 256 data points and a sine window function in both dimensions.
3-15 pmol on column was used (R 2 > 0. 99). Data acquisition and processing was performed with the Empower3 software suite (Waters). Organic acids were extracted with 700 μl ultra-pure water for 20 min at 95˚C. These compounds were separated using an IonPac AS11-HC (2mm, ThermoScientific) column connected to an ICS-3000 system (Dionex) and quantified by conductivity detection after cation suppression (ASRS-300 2mm, suppressor current 95-120 mA). Prior separation, the column was heated to 30˚C and equilibrated with 5 column volumes of solvent A (ultra-pure water) at a flow rate of 0. 38 ml/min. Separation of anions and organic acids was achieved by increasing the concentration of solvent B (methanol) and solvent C (100mM NaOH) in buffer A as follows: 8 min 4% C, 11 min 10% C, 18. 2 min 20% B / 18.
1% C, 27. 5 min 20% B / 21% C, 32 min 24% C, 43 min 30% C, 47 min 40% C, 48 min 90% C for 8 min, and return to 4% C in 9 min. A linear three-point calibration curve was used for quantification of organic acids (0. 5-5nmol on column; R 2 > 0. 99) Data acquisition and processing was performed with the Chromeleon 6. 7 software (Dionex). Rhythmic properties of metabolite levels were assessed with the model selection based method described in Atger et al. [16]  (see also below, Rhythmicity assessment in different genotypic backgrounds).
Data analysis Identification and quantification of compounds by NMR spectroscopy. Individual compounds from zebrafish extracts were identified using both NMR chemical shift databases and spiking of samples. All compounds mentioned in the article were identified using the BBIOREFCODE databank from Bruker (Bruker BioSpin AG, Rheinstetten, Germany) or the program CHENOMX with inherent database (Chenomx Inc. , Edmonton, Canada). For every compound reported we additionally added small amounts of pure substances to the zebrafish extracts and re-recorded 1D 1   ). RNA-Seq data processing and analysis. Image analysis and base calling were performed using the Illumina pipeline (Real Time Analysis and Offline Base Caller 1. 8). All raw sequence data are available from the GEO database (accession number GSE76073, http://www.
ncbi. nlm. nih. gov/geo/). Reads were mapped onto the Zv9 assembly of the zebrafish genome using STAR 2. 3. 8  [35]. Uniquely mapped reads were counted for each gene locus as annotated in Ensembl v75 using the Python Package HTseq v0. 5. 3p1. Read counts were normalized using DESeq2  [36]. For downstream analysis a variance stabilizing transformation was applied to the normalized data  [37]. Rhythmicity assessment in different genotypic backgrounds. The rhythmicity in different conditions of gene expression was assessed as described in Atger et al. [16]. Briefly, this method is based on multiple linear regression and a subsequent model selection using the Bayesian information criterion (BIC). To this end, we first defined the relation y(t) = μ + αcos ((2π/24 h)t) + βsin((2π/24 h)t) + noise (y is the log2 transformed signal, μ is mean, t is Zeitgeber time, α and β are the coefficients of the cosine and sine functions, respectively).
Rhythmicity of metabolite data was assessed by the same approach with some modifications. We added a non-periodic time-dependent coefficient to the harmonic regression in order to match the overall decline observed in some metabolites. For the four conditions (WT -DEX, rx3 strong-DEX, WT + DEX, and rx3 strong + DEX) we identified 9 models (I-IX) that gave the best match to the data (described in S2 Table ). Gene ontology (GO) and metabolic term enrichment. Enrichment analyses were performed using the Bioconductor package TopGO  [39]. Biological Processes (BP_all) and the KEGG pathway annotation were included in the analysis. In addition, a manually curated set of zebrafish genes linked with important metabolic pathways assembled from KEGG and Ensembl annotations (S3 Table) complemented the approach.
Acknowledgments We thank  P. Gut  for critically reading the manuscript and helpful discussions. We are grateful to  S. Rastegar  for plasmids and discussions. We are thankful to  C. Lederer  for excellent technical assistance and  S. Bu ¨chler  for support in establishing the NMR extraction protocol. We would like to thank the  Metabolomics Core Technology Platform of the Excellence Cluster CellNetworks  for support with LC-based metabolite quantification.
We acknowledge funding by the  Studienstiftung des Deutschen Volkes  (to MW), by the  Marie Curie Intra-European Fellowships For Career Development  ( FP7-PEOPLE-2013-IEF) (PIEF-GA-2013-625827 ) (to MW), and by the  Deutsche Forschungsgemeinschaft  ( DFG grants   DI913/4-1  to TD and  ZUK 49/2  to GP and RH), by the  Excellence Initiative ("Anschubmittel " to BL and TD; and the  CellNetworks Excellence Cluster.
02. 21) provided by the National Institute for Environmental Studies (NIES-GOSAT;  Yoshida et al. , 2013). Similar to  Guerlet et al. (2013) , we explicitly use TCCON data from 22 sites throughout the world as reference values for the regression analysis. The regression variables and coefficients for correction of GOSAT data are determined separately for observations made over land and those made over the ocean, and we perform this analysis for both XCO 2 and XCH 4. Such a partitioning is sensible because in the SWIR XCO 2 and XCH 4 retrievals, the handling of the surface reflectance is different over land and ocean. In addition, the atmosphere over ocean is generally cleaner than that over land because of the absence of polluted air and aerosols from urban areas. These differences suggest that the bias characteristics of XCO 2 and XCH 4 retrieved over ocean differ from those over land.
XCO 2 and XCH 4 retrieved from GOSAT TANSO-FTS SWIR spectra To monitor the spatial distribution of atmospheric greenhouse gases from space, GOSAT was launched on 23 January 2009 into a sun-synchronous orbit with an overpass time of roughly 13:00 local time  (Kuze et al. , 2009). Over a 3day period, TANSO-FTS onboard GOSAT makes observations above several tens of thousands of ground points spread over the earth's surface. Measurements in the SWIR and thermal infrared (TIR) bands of TANSO-FTS allow the retrievals over cloud-free regions of XCO 2 and XCH 4 , and CO 2 and CH 4 profiles, respectively. (Yoshida et al. , 2011 (Yoshida et al. , , 2013;; Saitoh et al. , 2012). More recently,  Kuze et al. (2016)  reported update on the performance of GOSAT TANSO-FTS sensor and important changes to the data product which has been made available to users.
In addition, the JMA routinely obtains ground-based measurements at a height of 20 m over Minamitorishima. We used CO 2 and CH 4 profiles from around Minamitorishima derived from aircraft and ground-based data available via the World Data Centre for Greenhouse Gases (WDCGG) website (http://ds. data. jma. go. jp/gmd/wdcgg/). The typical JMA aircraft sampling altitudes were 0. 5-6. 5 km. Figure  1b  and Table  2  show a global map and basic information, respectively, on every aircraft measurement site used in this study. Aircraft-based XCO 2 and XCH 4 are calculated by applying the GOSAT SWIR column averaging kernels (CAK) by using the methods developed by  Miyamoto et al. (2013)  and  Inoue et al. (2014) , respectively. There is one difference in the aircraft XCH 4 calculation:  Inoue et al.
Correction and validation procedure of GOSAT data Our aim in this study is to correct GOSAT SWIR XCO 2 and XCH 4 (Ver. 02. 21) by multiple linear regression using TCCON data as reference values. In Sect. 3. 1, we explain the details of the empirical correction method. To evaluate the effectiveness of this correction method, we compare uncorrected/corrected GOSAT XCO 2 (XCH 4 ) to independent aircraft-based XCO 2 (XCH 4 ) based on aircraft measurements by CONTRAIL, NOAA, DOE, NIES, JMA, the HIPPO project, and the NIES-JAXA joint campaign (Sect. 3. 2). We compare GOSAT data retrieved on the same day and within ±5 • latitude/longitude boxes centered on each aircraft profile. We also investigate the spatial distribu-tions of uncorrected/corrected GOSAT data (Sect. 3. 3), and the temporal behavior of the GOSAT XCO 2 and XCH 4 biases (Sect.
After bias correction, the XCO 2 biases for many months approach zero, though the seasonality in the difference remains (Fig. 8d ). Note that the TCCON XCO 2 data at Lamont are values at a particular location, while GOSAT XCO 2 data are zonal averaged values. Figure  9a  shows the temporal behavior of zonal-mean uncorrected GOSAT XCH 4 and XCH 4 at the Tsukuba and Lamont TCCON sites. GOSAT XCH 4 is higher in September and October, and lower in February and March than the reference data. Although the GOSAT XCH 4 data retrieved over land were negatively biased except for summer during the analysis period (Fig. 9a  and c ), the GOSAT XCH 4 biases were reduced as a result of the empirical correction (Fig. 9b  and d ). Consequently, we suggest that the bias correction method was effective for GOSAT XCO 2 and XCH 4.
Summary and conclusions In this study, we correct XCO 2 and XCH 4 data (Ver. 02. 21) retrieved from the GOSAT TANSO-FTS SWIR spectra. First, we conducted correlation analyses between the GOSAT biases and the simultaneously retrieved auxiliary pa- rameters, using GOSAT data around TCCON sites. Based on the results, we selected several parameters and determined the regression coefficients for the correction of GOSAT XCO 2 and XCH 4 for land and ocean regions separately. To evaluate the effectiveness of the bias correction method, the uncorrected/corrected GOSAT XCO 2 and XCH 4 data were compared to aircraft measurements provided by CON-TRAIL, NOAA, DOE, NIES, JMA, the HIPPO project, and the NIES-JAXA joint campaign. After correction, biases of GOSAT XCO 2 were reduced by a factor of more than 20 over land and by a factor of 6 over ocean, while the biases of GOSAT XCH 4 were reduced by half over land and became by almost a quarter of their uncorrected values over ocean.
Data availability The latest data processing and the auxiliary information on the GOSAT SWIR XCO 2 and XCH 4 data are described in GOSAT User Interface Gateway and can be obtained at http://data. gosat. nies. go. jp/GosatUserInterfaceGateway/ guig/doc/documents/doc_en_docdist. html. Edited by: I. Aben Reviewed by: two anonymous referees.
Council grant DP140101552 and Nicholas Deutscher is supported by an ARC-DECRA Fellowship, DE140100178. The University of Bremen acknowledges the support of the EU projects InGOS, and ICOS-INWIRE, and the Senate of Bremen for support of TCCON measurements in Białystok, Bremen, Ny-Ålesund, and Orléans. Operation of the Ascension Island site was funded by the Max Planck Society. Research at the FMI was supported by the Academy of Finland under grant no. 140408.
Acknowledgements. The authors thank the many staff members of  Japan Airlines , the  JAL Foundation , and  JAMCO Tokyo  for supporting the  CONTRAIL  project. We are grateful to the  NOAA ESRL/GMD  tall tower network ( K. Davis ,  A. Desai ,  R. Teclaw ,  D. Baumann , and  C. Stanier ) for providing CO 2 tower data for Park Falls and  West Branch. DOE flights were supported by the  Office of Biological and Environmental Research of the US Department of Energy  under contract no. DE-AC02-05CH11231  as part of the  Atmospheric Radiation Measurement Program (ARM), ARM Aerial Facility, and Terrestrial Ecosystem Science Program. We gratefully thank many staff members of the  Japan Ministry of Defense  for supporting the JMA's ground-based and aircraft measurements. We also acknowledge the HIPPO team members for CO 2 and CH 4 profile data from HIPPO missions.