dataesr/scientific-paragraphs-categorization huggingface dataset:
article. BL1 is associated with the enrichment of proliferation genes and increased Ki-67 expression, and MSL is associated with EMT and is consistent with a claudin-low subtype of breast cancer. We tested our hypothesis by applying the subtypes of Lehmann et al. subtypes to a World IBC Consortium dataset that included 39 patients with TN-IBC and 49 patients with TN-non-IBC. We also compared clinical outcomes in TN-IBC and TN-non-IBC patients by subtype to investigate whether these subtypes relate to the poor prognosis of TN-IBC.
Patient cohorts and GE data Three institutions contributed to the World IBC Consortium dataset: The University of Texas MD Anderson Cancer Center, Houston, TX (patients diagnosed with IBC in 2000 through 2005); General Hospital Sint-Augustinus, Antwerp, Wilrijk, Belgium (1996 through 2009); and Institut Paoli-Calmettes, Marseille, France (1998 through 2008). The dataset also includes 252 non-IBC patients. We obtained clinical data and GE profiles, as well as breast cancer samples, for 389 patients from these three institutions. All patients gave written informed consent for voluntary participation, and this study was approved by the institutional review boards of all three participating centers. Samples were stored according to each institution's criteria, as described in the supplemental information (see Additional file 1).
IBC was defined according to the consensus diagnostic criteria published by Dawood et al. [22]. In total, 137 IBC patients (including 39 TN-IBC patients) and 252 non-IBC patients (including 49 TN-non-IBC patients) were in the World IBC Consortium database. All patients were treated with use of a multidisciplinary approach according to the guidelines of each center. Most patients were treated with neoadjuvant chemotherapy, with the addition of hormone therapy in patients with ER expression, and with the addition of trastuzumab in patients with HER2 amplification. In TNBC patients, 21 (78%) of 27 IBC patients and 29 (66%) of 44 non-IBC patients received neoadjuvant chemotherapy. Neoadjuvant regimens based on anthracycline and taxanes, such as T-FAC (weekly paclitaxel followed by 5fluorouracil [5FU] + doxorubicin + cyclophosphamide), were given at the treating physician's discretion.
We identified TNBC status with use of GE profiling. Originally, GE data were normalized with the MAS 5 algorithm, mean centered to 600, and log 2 transformed. Because 15 cases had no information on breast cancerreceptor status by immunohistochemistry (IHC) and because we prefer effective utilization for the dataset, we identified ER, PgR, and HER2 receptor status by using, respectively, ESR1 (probe set 205225_at), PgR (208305_at), and ERBB2 (216836_s_at) mRNA GE data. We constructed receiver operating characteristic curves to measure the predictive accuracy of the logistic regression models, including ESR1, PgR, and ERBB2 mRNA GE levels. Cases with normalized ESR1 mRNA expression >10. 18 were considered ER-positive cases, those with PgR mRNA expression >2. 907 were considered PR positive, and cases with ERBB2 mRNA expression >12.
54 were considered HER2 amplified  [26]. We identified 39 TN-IBC patients. This statistical analysis was performed with BRB-ArrayTools version 3. 9. 0 α software  [27]  and R software version 2. 7. 2  [28]. The World IBC Consortium arrays were not initially quantified as in Lehmann et al. ; they used the RMA algorithm, and our group used MAS 5. 0. For consistent quantification of the arrays, we used the fRMA algorithm, which allows arrays to be analyzed individually or in small batches and then the data to be combined for analysis, to normalize and quantify all the datasets before we applied the Lehmann et al. approach. Clinicopathologic characteristics of the TNBC patient cohorts are presented in Table  1.
Identification of TNBC subtypes In our previous study  [25] , we reproduced the algorithm of Lehmann et al. and extracted the centroids of the seven TNBC subtypes based on the training data (supplementary data). By using that same method, we assigned the 88 TNBC (TN-IBC, n = 39; TN-non-IBC, n = 49) samples in the World IBC Consortium dataset to a TNBC subtype with the use of both our approximated and Lehmann's gene signatures. We used the highest Pearson correlation and lowest P value as the criteria to determine to which subtype a specific sample belonged. We constructed two 7 × 2 contingency tables based on the gene signatures we obtained and the original ones in Lehmann et al. The Fisher Exact test was applied to compare the TN-IBC and TN-non-IBC subtypes. To determine the power of detecting an extreme shift, we set the margins of the 7 × 2 table of subtypes by IBC status, assigned a 0 to the first cell, randomly allocated the rest, and computed the Fisher test P value.
GE analysis We examined differences among TNBC subtypes and between IBC (stage III, IV) and non-IBC (stage III, IV) at the GE level by using feature-by-feature linear mixture models followed by fitting a beta-uniform mixture (BUM) model to control for multiple testing. Numbers of significant genes were counted for some selected false discovery rates (FDRs). The Affymetrix U133 annotation package hgu133a. db was used to export gene symbols for each of the 22,283 probes.
We had the same results; no differences in TNBC subtype distribution between IBC and non-IBC were found (see Additional file 2: Table  S5A , S5B). Lehmann et al. used gene profiles of patients with various stages of disease. However, all IBC cases are stage III or IV. Therefore, we also analyzed stagematched populations (IBC, n = 32; non-IBC, n = 23). The results indicated no significant association between TNBC subtypes and IBC status at tumor stage III or above, no matter which set of gene signatures was applied (P = 0. 47, P = 0. 77, respectively; data not shown).
Only the efficacy of an AR antagonist relative to the expression of AR has been verified; we could not evaluate the efficacy of other drugs through GE levels because cisplatin is not a targeted drug, and the efficacy of NVP-BEZ235 depends not on expression level but on mutations. In our dataset, AR expression was significantly different between TNBC subtypes. The boxplots of GE profiles of AR based on the World IBC Consortium dataset are shown in Figure  2. We selected the probes with the lowest P values from linear mixture models to represent these genes. As shown in Figure  2 , the AR expression level appears to be higher in subtype 5 defined by our approximated gene signatures and in subtype LAR defined by the gene signatures of Lehmann et al. Our approximated TNBC subtype 5 had a high correlation with the LAR subtype.
In addition, gene-structure analysis suggested that the specific biology of IBC might be hidden in other breast cancer groups, such as ER-positive and HER2-positive groups. Van Laere et al. [33]  and Woodward et al. [34]    also investigated the specific biology of IBC by using the World IBC Consortium dataset but with a different approach. They identified potential genes specific to IBC and found that the differences also were related not to TNBC subtype but to ER-positive subtypes. However, although many studies have examined and compared gene expression between IBC and non-IBC, repeatedly finding clusters associated with receptor subtype, no consistent gene signature associated with IBC has been validated. It is possible that our dataset was too small to reveal fully differences in distribution, although our 39-case TN-IBC population is the largest so far to be published.
DMFS did not vary significantly between TNBC subtypes. In our cases, in addition to the small numbers, the median follow-up period for IBC patients (n = 27) was 3. 39 years (range, 0. 84 to 6. 48 years). We need a longer follow-up time before we can discuss whether these seven subgroups have a prognostic effect. As a result, neither we nor Lehmann et al. could verify the effect of TNBC subgroups as prognostic markers. Lehmann et al. showed that their seven TNBC subtypes displayed unique GE and ontologies. They also identified representative subtypes of TNBC cell-line models and predicted that "driver" signaling pathways were pharmacologically targeted in these cell-line models as proof of concept that the analysis of distinct GE signatures can improve therapy selection. In our dataset, we determined that the subgroups that showed the highest AR expression level belonged to TN-IBC (subtype 5 and LAR subtype), revealing the possibility that an AR antagonist can be a potentially useful drug for these patients.
Acknowledgments: this article was written while holding a Visiting Fellowship at the  School of Culture, History and Language of the Australian National University , supported by an  Endeavour Award  for Postdoctoral Research; it was subsequently revised in the course of a Postdoctoral Fellowship at the  Asia Research Institute, National University of Singapore. I wish to thank  Ben Arps ,  Roy Jordaan ,  Michele Stephen , and  McComas Taylor  for their comments on a draft of this article.
Herpesviruses comprise an abundant, widely distributed group of large DNA viruses in vertebrates and invertebrates, including mammals, birds, reptiles, fish and marine molluscs. They were classified into the families Alloherpesviridae, Herpesviridae and Malacoherpesviridae in the order Herpesvirales  [11]. The genomes of herpesvirus have been accumulating since the 1980s, and sixtyeight isolated from different species have been deposited in GenBank to date. These genomes have been interpreted to give detailed views of ubiquitous and lineage-specific functions. Herpesviruses and herpes-like viruses have also been attracted particular attention because of their ecological and economic impact on wild and cultured marine molluscs during the last 20 years, and several were reported worldwide  [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27].
Determination of the AVNV genome sequence Because no reliable cell lines are available for the propagation and isolation of AVNV, a PCR-based approach was used to obtain the complete genomic DNA sequences. Initially, OsHV-1 specific primers A3/A4  [26] , C2/C6  [28]  and Gp3/Gp4  [28]  were used to amplify AVNV DNA sequences, which were then compared to the corresponding OsHV-1 sequences. The results showed that these three AVNV fragments were 99%, 97% and 99% identical to OsHV-1, respectively (data not shown), thus indicating that the DNA sequences of these two viruses may be generally highly similar. We then extended the PCR-based method to sequence the whole genome of AVNV by designing primers based on the OsHV-1 DNA sequences. The AVNV genome was initially determined to be 210,825 bp in size.
PCR amplification and DNA sequencing The AVNV genome sequence was determined using a PCR-based approach. As the sequences of OsHV-1 were highly similar in AVNV, a total of 62 PCR primer pairs were designed based on the OsHV-1 genome sequence (GenBank accession AY509253) and used to amplify overlapping AVNV DNA fragments (from 600 to 5000 bp). The AVNV genome termini were identified using the method described by Davison et al. [25, 45]. Briefly, AVNV DNA was treated with T4 DNA polymerase in the presence of the four dNTPs to produce flush ends, and ligated into the partially double-stranded adaptor (the cDNA adaptor in the Clontech Marathon kit). PCR was carried out using an adaptor-specific primer plus a primer specific for the left or right terminal region of the genome (5'-CACGGTGGGAAGGCTGAT-3' or 5'-GATAGGAGGTTAGACACGC-3' and Ex Taq polymerase (Takara).
The products were purified using a TaKaRa gel purification kit (Takara), and inserted into pGEM-T (Promega, USA). The cloned fragments were sequenced in both directions using universal primers and an ABI PRISM 3770 (Applied Biosystems, Inc. , USA). Additional primers (236 in total) were designed for sequencing the internal regions of longer PCR products (>1200 bp). At least three individual clones were sequenced for each fragment in order to exclude potential mutations generated by PCR. Computer-assisted analysis of DNA sequence data Genomic composition and structure were analyzed using DNASTAR (Lasergene). The location and amino acid sequences of ORFs were predicted using Accelrys Gene 2. 5 (Accelrys Inc. ) and NCBI ORF finder (http://www. ncbi. nlm. nih. gov/gorf/gorf. html) according to the following criteria: (1) they were ≥120 bp in size, (2) they were not located within larger ORFs, (3) polyadenylation signal were analyzed and (4) they were compared with other sequences using NCBI BLASTP (http://www.
The       For all years the data have been averaged for every 1 dbar except for 1997 Lance where the average is over 2 dbar. The data were extrapolated to the surface (0 dbar) by taking the values from the uppermost measurements and retaining these all the way to the surface. The instruments used were Seabird 911+ CTDs except in 1984 when a Neil Brown Mark III CTD was used. The CTD data, except in 1984, have been processed using the Seabird software and the conductivity has been calibrated against water samples. The salinity error is about 0. 003 except for the 1997 Polarstern cruise, where it is 0. 005. The 1984 data had a drifting pressure sensor and the data had to be adjusted over time as well as calibrated against salinity samples. Unfortunately many of the calibration samples were destroyed as a geological sample was dropped on a salinity box.
(Fig. 9 ). The ADCP data are used for comparison with the velocities determined by geostrophy. The ADCP data for the computations are selected corresponding to the centre points of where the geostrophic velocities with constraints applied are thought to be valid, i. e. , between two neighbouring CTD stations at ¼ and ¾ of the in-between distance. ADCP velocities are averaged over an area within ±0. 1 • in longitude and ±0. 02 • in latitude to these points. Both the ADCP and geostrophic velocities are averaged over a 35-55 m layer representing the surface water and over a 155-255 m layer representing the AW layer. The differences between the depth-averaged geostrophic velocity and the ADCP velocity component normal to the line connecting the CTD stations in the surface layer and AW layer are then added to the geostrophic velocities of the corresponding water masses to obtain an ADCP referenced velocity (see water mass definitions in Fig.
Data Most of the data have not been smoothed. However, since the geostrophic computations integrate the density field, smaller disturbances tend to cancel out. Some spikes have been removed by linear interpolation, but the datasets still contain small spikes, e. g. , the 2003 Lance data for the northern box has a probable salinity spike of 0. 004 present at a couple of stations. Such a spike perturbs the velocities by about 1 × 10 -5 ms -1 , which then affects the water column above the spike since the velocities are first set to zero near the bottom. The transport estimate assuming the distance between stations to be 20 km and depth 1 km above the spike, amounts to a missed volume of 0. 2 mSv. This difference is small enough to be ignored. The data close to the surface often are of poor quality.
B9)Acknowledgements. This work has received support from the European Union 6th Framework Programme under grant agreement no. 018509, DAMOCLES and from 7th Framework Programme FP7 2007-2013 under the grant agreements no. 212643, THOR and no. 308290, NACLIM, and from the Academy of Finland (no. 210551). Roberta Pirazzini and Timo Vihma are warmly thanked for the help with the ECMWF ERA Interim data and the related computations. The editor and anonymous reviewers are thanked for their valuable comments that helped to improve the paper.
We tested several models that seemed suitable to handle overdispersed data  [28]  while accounting for the clustered structure of the dataset (with repeated measures per individual). The dynamics of the microfilarial densities during the D0-D180 period was best described using a zero-inflated negative binomial model with robust variance estimates for each parameter  [29]. This model was computed using the zinb procedure with the clustercorrelated robust variance estimates available in the Stata software  [30, 31]. In this model, the variable of interest was the post-treatment microfilarial density and the regressors comprised host age, the number of palpable onchocercal nodules, the study group (ivermectin-naı ¨ve or multiply treated), and two interaction terms: one between the study group and the number of days after ivermectin treatment (0 (i.
It has been argued that the diagnostic value of nodule palpation tends to be poor in communities with low or moderate endemicity  [45]  but the results of the present study show that the distribution of nodules may have to be considered when evaluating the impact of ivermectin treatments on the levels of endemicity of onchocerciasis in those areas that were originally hyperendemic. Still, we have to keep in mind that for this study, inclusion criteria required that participants were males presenting with at least two palpable nodules and the influence of nodules on the microfilarial density after treatment should be also assessed in other strata of the population. Further analyses of existing data including a more representative sample of the general population (e. g. community trials of ivermectin during which microfilarial density and palpable nodules would be assessed) should be encouraged.
Acknowledgments We deeply thank the populations from the  Mbam and Nkam  valleys for having kindly agreed to participate to this study. We are also grateful to all those who helped in the execution of this study, and particularly  Mr. U. Olanguena , former Minister of Public Health,  Professor L. Kaptue, Dr. P. Ongolo Zogo , as well as  Drs J. C. Akono Emane ,  R. Atangana ,  X. Crespin ,  S. Efonle ,  Fifen Alassan ,  X. Garde ,  M. Ntep  and  Messrs. I. Behalal ,  C. Evini ,  J. P. Agbor , and  J. Bopda. We also thank the four referees for suggestions that contributed to improve this article.
Experimental Crystal data  Hydrogen-bond geometry (A ˚, ). (i) x þ 1 2 ; Ày þ 1 2 ; Àz þ 1 2 ; (ii) x; y; z þ 1; (iii) x þ 1 2 ; y; Àz þ 1 2. Data collection: APEX2  (Bruker, 2011) ; cell refinement: SAINT  (Bruker, 2011) ; data reduction: SAINT; program(s) used to solve structure: SIR2002  (Burla et al. , 2005) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg & Berndt, 2001) ; software used to prepare material for publication: WinGX  (Farrugia, 2012)  and CRYSCAL (T. Roisnel, local program). Thanks are due to MESRS and DG-RSDT (Ministere de l'Enseignement Superieur et de la Recherche Scientifique et la Direction Generale de la Recherche -Algeria) for financial support. Supplementary data and figures for this paper are available from the IUCr electronic archives (Reference: HB7142).
Abstract. A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH 4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling.
(2011) , was that deficiencies in the ability of CTMs to accurately reproduce atmospheric methane concentrations hindering efforts to improve our knowledge on sources and sinks of methane. These limitations were thought to be due to both errors in the meteorological datasets used as drivers of the CTMs and in the CTM parameterizations. For the following, we define "forcing errors" as errors in the meteorological fields used by the CTMs and "model errors" as errors in the CTMs themselves. Hereafter, "transport model errors" will be used to group together forcing and model errors. For a long time, efforts to deduce regional fluxes of CH 4 have been limited by the low density, in both space and time, of atmospheric observations  (Gurney et al. , 2002). However, with the increasing spatial and temporal density of surface observing networks and the availability of satellite data, this limitation is becoming less dominant, putting more pressure on forcing and model errors.
, 2002; Baker et al. , 2006) , but, to our knowledge, no study has investigated this issue for CH 4 yet. In this paper, we estimate the impact of transport model errors on inverted CH 4 fluxes using one variational inversion scheme, one flux scenario, and 10 different synthetic observation datasets built from the model database of the TransCom-CH 4 experiment  (Patra et al. , 2011). Section 2 describes the methodology and the synthetic data used for our inversions. Section 3. 1 presents the main differences in the forward modelling of CH 4 concentrations due to the different CTMs used in  Patra et al. (2011). Such differences are useful to better understand the inversion results, which are explored in Sect. 3. 2. Sensitivity tests of the impact of CTM resolution (Sect. 3. 3) and density of the measurement network (Sect.
In the TransCom experiment it was not possible to separate these two effects as simulations testing different meteorological forcings on the same CTM were not provided. The database of the TransCom-CH 4 experiment includes outputs of hourly CH 4 mixing ratios at 166 surface stations, 6 tall towers and 12 vertical profiles, each for 6 different emission scenarios. Here we apply a 2-step methodology (Fig. 1 ) to quantify the impact of the different models on the inverted methane fluxes. In a first step, daily-mean CH 4 concentrations are extracted from the ten forward simulations of the TransCom-CH 4 database, using their IAV INV (inversion-derived emissions, herein called INV) scenario, which includes monthlyand inter-annually varying emissions provided by  Bousquet et al. (2006). In doing so, we create 10 synthetic daily-mean observation datasets at selected sites.
In a second step, the same emission scenario (INV) is combined with each synthetic observation dataset to infer optimized fluxes using the PYVAR (PYthon VARiational) variational inversion system, developed at LSCE (Laboratoire des Sciences du Climat et de l'Environnement)  (Chevallier et al. , 2005)  and based on LMDZ-SACS CTM (one of the TransCom models). As the INV scenario is also used to generate synthetic observation, it can be considered as the "true" flux, hereafter referred as the "target". Therefore, fluxes derived using the LMDZ-SACS (Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) synthetic observations in the inversion system give exactly the target fluxes, within the numerical errors. CH 4 fluxes are estimated in all the grid cells of LMDZ-SACS for eight-day periods using the PYVAR inversion algorithm (see Sect.
2. 3). By repeating the inversion process for each synthetic observation dataset, ten estimates of CH 4 fluxes are obtained. Assuming that the range of the ten CTMs used here cover the typical range of transport model errors (see Sect. 2. 2), the comparison between these estimates provides an estimate of the influence of transport model errors on the CH 4 fluxes. Indeed, the differences found in the inverted CH 4 fluxes are only due to discrepancies in the modelling of atmospheric transport by the different CTMs and in the meteorological analyses/reanalyses fields driving them.