dataesr/scientific-paragraphs-categorization huggingface dataset:
Prediction and synteny analysis of aspergilli SM clusters Protein and genomic sequences of aspergilli were downloaded from AspGD (http://www. aspgd. org/). Detection of PKS, NRPS, and DMATs and further prediction of respective SM gene clusters were made with SMIPS and CASSIS tools, correspondingly (https://sbi. hki-jena. de/cassis/) Synteny analysis was performed with the help of an Aspergillus 21-way comparative database asp2_v9 powered by Sybil. Conservation of clusters was calculated as the percentage of genes in the query cluster conserved in the syntenic counterpart. Confirmation of the identity of different secondary metabolites from the aspergili were checked by ultra high performance liquid chromatography-diode array detection-high resolution mass spectrometric detection (UHPLC-DAD-HRMS)  [278].
Analysis of CYP proteins Evolutionary analyses were conducted in MEGA5  [279]. The analysis involved 19 amino acid sequences. If protein size is marked by a star, the sequence was manually curated or sequences are incomplete. Following MUSCLE alignment, the evolutionary history was inferred by using the maximum likelihood method based on the JTT matrix-based model  [280]. There was a total of 496 (CYP51F1) or 510 (CYP53) positions in the final dataset. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. All positions containing gaps and missing data were eliminated. Additional copies of CYP51F2 and CYP51F4 are excluded from the tree.
Aspergillus sugar transportome The assignment of a protein to the "Sugar and other transporters" PFAM family PF00083 is based on its level of similarity to a Hidden Markov Model (HMM). The HMM for PF00083 is derived from a seed alignment of 33 proteins chosen to be the core representatives of that family  [281, 282]. The latter includes proteins for which either transport of a specific sugar or related substrate has been experimentally demonstrated, or there is very strong indirect evidence that such a transport function exists, e. g. genetic evidence. Despite their considerable diversity in primary structure, PF00083 members exhibit a common topology of 12 membrane-spanning domains (TMSs) divided into two groups of six. Transmembrane (TM) domain predictions were obtained for all the PF00083-annotated proteins studied using TMHMM v2  [283].
Amino acid and urea transporters The high-affinity methionine transporter of S. cerevisiae (MUP1) was used to identify several close homologs in all Aspergillus species. The methionine transporters of S. cerevisiae belong to the L-type amino acid transporter family (LAT; TCDB 2. A. 3. 8) [140]  and exhibit low similarity to YATs  [286]. A. nidulans UreA [  [183, 184]  was used to search the fungal genomes analyzed in this study for the presence of urea-like transporters (Table  5 ). The S. cerevisiae proteins Tat1, Tat2, Tat3, Gap1, Hip1, Gnp, Agp1, Agp2, Agp3, Bap2, Bap3, Sam3, Mmp1, Lyp1, Alp1, Can1, Dip5, Put4, and Ssy1 obtained from the Saccharomyces Genome Database  [287]  were used as in silico probes for BlastP and in several cases, TBlastN searches in the available Aspergillus genomes at either the AspGD  [14]  or the JGI  [15]  databases.
Identification of protein class members involved in signal transduction We employed a domain-based approach for the identification of proteins for each class, i. e. G-proteins, GPCRs, kinases, and phosphatases, following the general approach described by Perez-Rodriguez et al. [293]  and using the pipeline available at  [294]. The most important step is the identification of a protein domain or block that is characteristic of the protein class/family of interest and that is represented as a HMM, either available in the public database PFAM or created in house. Then specific rules are built accounting for the domain architecture of the different families/classes, in an exclusive way, i. e. a protein can belong to one and only one family/class, which requires the introduction of forbidden domains to resolve ties (Additional file 36).
G-proteins We studied the three sub-units of G-protein complexes: alpha, beta, and gamma. G-alpha and Ggamma had already well-described, characteristic, and specific domains in the PFAM database  [282]  with accession numbers PF00503 and PF00631, respectively. The G-beta subunit has several copies of the WD40 repeat (PF00400); however, this domain is not specific for G-beta proteins. We assesed the accuracy of our classification comparing our results against those presented in Li et al. [227]. The G-gamma famly is always found as a single member in each species. However, following our domain-based apporach we could not find the corresponding protein in A. niger CBS 513. 88, A. oryzae, and S. pombe. Further inspection revealed that the protein is not annotated in the Aspergillus genomes, but we found a genomic region with the information for this gene, using the A.
We built an HMM specific for TOR kinases as described in Additional files 37 and 38. Classical Ser/Thr phosphatases (PPP and PTP) All PPPs in A. nidulans have the PFAM domain Metallophos (PF00149), but this domain also appears in other proteins. We retrieved all A. nidulans proteins that had a hit to the Metallophos domain and inferred the phylogeny, reveling that PPPs form a distinct clade in this evolutionary tree (Additional file 39), thus allowing us to build a PPP-specific HMM, named PP2Ac. In the case of the PPM family there were already specific PFAM domains that could be used to develop classification rules (Additional file 36). PTPs For most PTP superfamily members in  [236]  there were already specific domains in PFAM, i. e. Y_phosphatase, Y_phosphatase2, Y_phosphatase3, DSPc, and LMWPc.
AN3941, belonging to the CDC25 subclass, was the unique PTP with no specific Pfam domain. Thus, we built a specific HMM (CTBECDC25) for this protein phosphatase. Although SSU72 proteins share sequence similarity with the PTP superfamily, they are classified as a distinct type of protein phosphatases. In that case, we used this PFAM domain Ssu72 Pfam for their identification. Asp-based protein phosphatases Aspartate-based catalysis phosphatases, which share the DXDXT/V catalytic signature (CPDs), had a specific domain found in the PFAM database, i. e. NIF (PF03031).
Histones of the aspergilli Initial searches for histone H1 by BlastP failed to locate matches in some Aspergillus databases, while in other species the gene model was incorrect. Therefore, manual curation was required to establish its presence in A. oryzae and correction of the amino acid sequences was required for A. brasiliensis, A. kawachii, A. sydowii, A. terreus, A. versicolor, and A. niger CBS513. 88. The NCBI database for Aspergillus sojae was interrogated to search for histone proteins in this species. Manual corrections of the H3 histone gene models were required for A. niger CBS 513. 88, A. wentii, and A. aculeatus.
Data access Genome assembly and annotations are available at at the JGI fungal genome portal MycoCosm  [15]  and have been deposited at DDBJ/EMBL/GenBank under the following accessions: A. aculeatus ATCC16872 -MRCK00000000, A. brasiliensis CBS101740 -LJXV00000000, A. carbonarius I TEM5010 -AHIG00000000, A. foetidus CBS106. 47 -MRB P00000000, A. glaucus CBS516. 65 -LSTL00000000, A. sydo wii CBS593. 65 -MRCH00000000, A. tubingensis CBS134. 48 -LJXU00000000, A. versicolor CBS583. 65 -MRBN0000 0000, A. wentii CBS141173 (DTO134E9) -LJSE00000000, and A. zonatus CBS506. 65 -MRBM00000000. Links to the raw genome and transcriptome data of these species are available in Additional file 1D. This also lists the genbank (if available) and JGI accession numbers of the genomes used for comparison as well as the papers in which they have been published.
Stress sensitivities were quantified by percentage decreases in the colony diameters in comparison to controls or by decreases in the growth scores. Based on the growth inhibition values, all species tested were ranked according to their stress tolerances (Additional file 4). Physiological data have been stored in the Fungal Stress Database (FSD; http://www. fung-stress. org/) at CBS-KNAW. Stress response proteins were identified and annotated in the newly sequenced Aspergillus genomes following the procedure described previously  [298]  and newly identified orthologs were inserted into the Fungal Stress Response Database (FSRD)version 2  [298]  (FSRDv_2; http://internal. med. unideb. hu/fsrd2/?p=consortium). The phylogeny of selected well-characterized stress response proteins and their orthologs was studied following the procedure described in Additional file 3.
Acknowledgements Members of the  University of Debrecen  were supported financially by the  Hungarian Scientific Research Fund  (grant  K100464  to IP) as well as by the  TÁMOP 4. 2. 1. /B-09/1/KONV-2010-0007and SROP-4. 2. 2. B-15/1/KONV-2015-0001  projects, which were supported by the  European Union , co-financed by the  European Social Fund. EO was a recipient of a  Campus Hungary Programme scholarship  to the  CBS-KNAW Fungal Biodiversity Centre. The authors aknowledge support from the  Federal Ministery of Education and Research (BMBF) BioFung  and the  Deutsche Forschungsgemeinschaft (DFG)  to GB, ES, and  AAB. The work conducted by the  U. S. Department of Energy Joint Genome Institute  is supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231.
FC was supported by the  Major Program of National Natural Science Foundation of China  (No. 31330059 ). WC was supported by the  National Natural Science Foundation of China  (No. 31601446 ) and the  Fundamental Research Funds for the Central Universities  (Nos. 2662014BQ051  and  2662015QC003 ). TB and IB were supported by grants of the  Dutch Technology Foundation STW, Applied Science division of NWO , and the  Technology Program of the Ministry of Economic Affairs   UGC 14270  to  RPdV. PAvK ,  MaA , and  DvRU  were supported by the  Netherlands Technology Foundation STW  ( LGC 7393 ). We acknowledge financial support from the  FWF  ( M01693-B22 ) to DCa. This work was in part supported by the  Intelligent Synthetic Biology Center of Global Frontier Project  ( 2011-0031955 ) funded by the  Ministry of Education, Science and Technology  grants to JHY and  Science Foundation Ireland   13/CDA/2142  to OB.
NS was supported by a scholarship from the  Kurdistan Regional Government of Iraq. HSP was supported by the  National Research Foundation of Korea (NRF)  grant founded by the  Korean Government  (no. 2016010945 ). The work at  UW-Madison (JHY)  was supported by the  Intelligent Synthetic Biology Center of Global Frontier Project  ( 2011-0031955 ) funded by the  Ministry of Science, ICT and Future Planning. PSD, RH, and RH were supported by the  British Mycological Society  and the  Biotechnology and Biological Sciences Research Council (UK). ES and AAB were supported by  CRC 1127 ChemBioSys  and  CRC-Transregio FungiNet by Deutsche Forschungsgemeinschaft (DFG). JCF was supported by  Agilent Technologies  ( Agilent Thought Leader Award # 2871 ) and the  Novo Nordisk Foundation  (Grant  NNF 13OC0005201 ).
LK was supported by the  Hungarian Scientific Research Fund  ( NN116519 ) and a  Bólyai János Research Fellowship. The sugar transporter analysis was supported by grants  AGL2011-29925  and  AGL2015-66131-AGL2015-66131-C2-2-R (MINECO/FEDER ). BRO was supported by the  Irving S. Johnson fund of the Kansas University endowment. We are grateful to  FAPESP (The State of São Paulo Research Foundation)  for the financial support ( 2014/06923-6  to FMS;  2012/20549-4  to ARLD;  2011/08945-9  and  2014/11766-7  to JVCO;  2012/19040-0  and  2014/10351-8  to CAU). We are grateful to the  Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)  and  Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)  for the financial support ( 310186/2014-5  and  442333/2014-5  to FMS;  441912/2014-1  to ARLD).
We acknowledge the financial support of the  Dirección General de Apoyo al Personal Académico ,  UNAM , projects IN 225710 and  IN219813 , and The  Slovenian Research Agency  ( P1-0207 ,  P1-0391 , and  J4-7162 ). KS was supported by the  Slovenian Research Agency  ( P1-0207 ) and KS and NK were supported by the  Slovenian Research Agency  ( J4-7162 ). TCV and MRA acknowledge support from the  Villium Foundation , Project  VKR023437. GB was supported by a grant from the  Deutsche Forschungsgemeinschaft  ( BR1502/11-2 ). GB and RF were supported by a grant from the  Deutsche Forschungsgemeinschaft  ( FOR1334/2 ). AL was supported by  St. Petersburg State University, St. Petersburg, Russia  (grant no. 15. 61. 951. 2015 ). The Authors thank  A Gazdag ,  R Mohácsi , and  LG Tóth  ( University of Debrecen ) for contributing to the experimental work; and  V Szabó  and  T Koszó  ( University of Debrecen ) for the literature search during the construction of FSD and FSRD, respectively.
Availability of data and materials The datasets supporting the conclusions of this article are included within the article and its Additional File(s) or available through DDBJ/EMBL/GenBank under the following accessions: A. aculeatus ATCC16872 -MRCK00000000, A. brasiliensis CBS101740 -LJXV00000000, A. carbonarius ITEM5010 -AHIG00000000, A. foetidus CBS106. 47 -MRBP00000000, A. glaucus CBS516. 65 -LSTL00000000, A. sydowii CBS593. 65 -MRCH00000000, A. tubingensis CBS134. 48 -LJXU00000000, A. versicolor CBS583. 65 -MRBN00000000, A. wentii CBS141173 (DTO134E9) -LJSE00000000, and A. zonatus CBS506. 65 -MRBM00000000. Links to the raw genome and transcriptome data of these species are available in Additional file 1D. This also lists the genbank (if available) and JGI accession numbers of the genomes used for comparison as well as the papers in which they have been published.
Recently 2 randomised studies investigated a pool of mixed non-clear cell histologies and few prospective trials focused on papillary RCC (pRCC); nevertheless evidence based recommendations about systemic therapy for metastatic ChRCC (mChRCC) are limited. First described by Thoenes in 1985, ChRCC probably derives from the collecting duct system from the intercalated cells  5  , it has been associated with better prognosis than other subtypes. Large surgical cohorts suggested that localised ChRCC display a more favourable prognosis with fewer metastatic spreading. Patard et al showed that ChRCC patients had a better outcome compared to papillary or clear cell RCC  6. In this series, only 6 patients out of 396 (1. 5%) with ChRCC had distant disease. In another large retrospective database of 291 ChRCC, only 25 patients with ChRCC (8.
6%) developed recurrence and 18 (6. 2%) died from disease  7. Similarly, other small cohorts reported a low rate of specific mortality and recurrence of 2% and 6%, respectively in a serie of 50 patients with ChRCC with a mean follow up of 6 years 8 or no recurrence in a 61 patient's cohort with a follow of 4. 1 years 9. Most of the available data in mChRCC comes from retrospective small series or rare phase 2 enrolling heterogeneous population of non ccRCC. No standard of care is currently proposed for mChRCC patients in both NCCN or ESMO guidelines[A1]  10, 11. Extrapolating the benefit of VEGF and mTOR inhibitors to the others subtypes of kidney cancers, patients are routinely treated similarly to clear cell RCC. In our study, we focused on metastatic ChRCC patients to assess the clinical outcome associated with the use of targeted agents.
All patients Patient's characteristics and overall survival We collected data from 91 mChRCC patients from 26 centers in 4 countries (France, UK, Italy and Australia). One patient was excluded because pathological diagnosis of ChRCC was uncertain (Figure  1 ). Patients had been diagnosed from July 1997 to April 2013. Median followup from date of first metastasis was 6. 1 years (range: 0-13. 9). Patient and tumor characteristics are described in Table  1. Median age at diagnosis was 58 years (IQR: 49. 0-66. 6) with a majority of men (64. 4%, n= 58). Most patients had nephrectomy (92%, n=83). Median time from diagnosis to metastasis was 9. 4 months (IQR: 0. 7-37. 7). Median time from metastasis to first-line treatment was 3. 5 months (IQR: 1. 1-13. 4). In our cohort, 24. 4% (n=22) had metachronous disease while 75.
6% (n=68) were synchronous. Abdominal lymph nodes were the most common site of metastasis (41. 6%, n=37) while lung (33. 7%, n = 30) and liver metastases (19. 1%, n =17) appeared to be less common. International Metastatic RCC Database Consortium (IMDC risk model) prognosis groups were favorable for 10. 3% (n=6), intermediate for 69. 0% (n=40) and poor for 20. 7% (n=12) patients. The score was not available for 32 patients (35. 6 %). The median OS from date of first metastasis was 37. 9 months (95%CI 21. 4 to 46. 8).
0) 2 11 (16. 2) 9 (18. 0) 3 32 (47. 1) 23 (46. 0) 4 22 (32. 4) 17 (34. 0) Missing 22 11 *Beyond the 64 patients treated by systemic therapy 3 patients were excluded for missing data IQR: Interquartile range, ‡ IMDC = International Metastatic Renal Cell Carcinoma Database Consortium, £ MSKCC = Memorial Sloan Kettering Cancer Center.
During the past decade, sea ice freeboard height has been monitored with various satellite altimetric missions with the aim of producing long-term time series of ice thickness. To achieve this goal, it is essential to analyze potential intermission biases and to produce freeboard height datasets as free of instrumental error as possible. In the present study, we compare Envisat and CryoSat-2 freeboard height during the common flight period (2010)(2011)(2012). Our results show that Envisat freeboard height is always thinner (-14 cm in average) when compared to CryoSat-2 (3 cm in average). In addition, Envisat freeboard height displays an unrealistive negative growth from November to April (-2. 4 to -3. 7 cm) while CryoSat-2 dispalys a positive and coherent winter growth (2. 4 to 2. 7 cm). The discrepancy between the two datasets is found to be related to a dissimilar impact of ice roughness and snow volume scattering on SAR (CryoSat-2) and pulse-limited (Envisat) altimetry.
Following this result, we show that the freeboard height difference between the two datasets can be expressed as a function of the waveform pulse-peakiness. Based on the relation between the Envisat pulse-peakiness and the freeboard height difference, we produce a monthly CryoSat-2-like version of Envisat freeboard height that reduces the average RMSD with CryoSat-2 from ∼16 cm to ∼2 cm and improves the freeboard height growth cycle (2 -3 cm). The comparison of the altimetric datasets with in situ ice draft measurements during the common flight period shows that the corrected Envisat dataset (RMSE = 16 -29 cm) is as accurate as CryoSat-2 (RMSE = 13 -25 cm) and highly more accurate than the uncorrected Envisat dataset (RMSE = 108 -132 cm). The comparison of the improved Envisat freeboard height dataset is then extended to the rest of the Envisat mission to demonstrate the validity of the improved Envisat dataset out of the calibration period.
As a result, we find a good agreement between the Envisat and the in situ ice draft datasets (RMSE = 14 -30 cm), which demonstrates the potential of the pulse-peakiness-correction to produce accurate freeboard height estimates over the entire Envisat mission. Finally, we show the averaged-circumpolar ice thickness variations from 2002 to 2015 by combining CryoSat-2 and Envisat datasets.
In  Schwegmann et al. (2015) , Envisat and CryoSat-2 freeboard height datasets are compared over Antarctic sea ice. While the spatial and temporal distributions are consistent, it is shown that CryoSat-2 freeboard height is thicker (thinner) for thick (thin) freeboard values. The authors conclude that the discrepancy between the two datasets is likely related with the difference of characteristics between LRM and SAR altimetry: Over sea ice, conventional LRM altimeters such as Envisat have a typical footprint of 2-10 km  (Connor et al. , 2009). As the LRM technology does not allow to localize the origin of each scatterers within this large ground footprint, bright off-nadir reflexions can drive important biases on the monitoring of surface level position. Unlike LRM altimetry, the SAR technology and the Doppler post processing allow to identify the along track position of each scatters and offer SIRAL an along track footprint of ∼ 300 m (the across track being unchanged), which nearly eliminate front and back off-nadir reflections and also significantly reduced the impact of lateral off-nadir reflection.
This new footprint geometry leads to sharpen the response function of the radar signal relatively to a LRM radar altimeter  (Raney, 1998). This improvement of the SAR technology limits the impact of ice surface diffusion (ice roughness and snow volume scattering) on the radar waveform echoes, which should allow to improve the surface level estimates accuracy  (Raney, 1995). Hence, the more accurate freeboard estimates obtained with CryoSat-2 are likely related to the reduced impact of ice surface diffusion on SAR altimetry. Following this theoretical basis, we seek to analyze the discrepancy of freeboard height between CryoSat-2 and Envisat and its link with the ice surface diffusion variability. In section 2, we introduce the datasets used in the present study as well as the freeboard height retrieval algorithms.
Then, we compare the spatial and seasonal variability of Envisat and CryoSat-2 freeboard height fields (section 3. 1). In section 3. 2 we show the link between the freeboard height discrepancy and the ice surface diffusion. In section 3. 3, we build a Cryosat-2like version of Envisat freeboard height based on the relation between the freeboard height discrepancy between Envisat and CryoSat-2 and the Envisat pulse-peakiness. In section 3. 4, we convert CryoSat-2, Envisat and the corrected Envisat freeboard height datasets to ice draft fields and compare each dataset to in situ measurements. Finally, we estimate pan-Arctic seasonal ice thickness over the 2002-2015 period by combining Envisat and CryoSat-2 datasets (section 3. 5).
Envisat Envisat was launched in 2002 by ESA and was set on the same orbit than the ERS-1/2 satellites, providing coverage of the Arctic Ocean up to 81. 5 • N. The RA-2 altimeter onboard Envisat includes a Ku-band pulse-limited radar altimeter with a bandwidth of 320 MHz. To derive the Envisat freeboard height fields, we use the Sensor Geophysical Data Record (SGDR) product from ESA (http://www. aviso. altimetry. fr/) that are converted to NetCDF files by the Center of Topography of Oceans and Hydrosphere (CTOH) with recent corrections of mean sea surface height DTU15,  (Andersen and Knudsen, 2015)  and tide corrections FES14,  (Carrere et al. , 2015). Envisat freeboard height is estimated from November 2010 to April 2011 for the first common winter and from November 2012 to March 2012 for the second common winter season.
Freeboard-to-thickness conversion To convert the freeboard (f b) height to sea ice thickness (IT), we assume the hydrostatic equilibrium between snow-covered sea ice and the ocean and we use the following expression: IT = h s ρ s + f bρ i ρ w -ρ i (2) where, h s represents snow depth, ρ i is ice density, ρ s is snow density and ρ w is sea water density. To operate the freeboard-to-thickness conversion, several snow depth dataset have been employed. For instance,  Laxon et al. (2003)  and  Giles et al. (2008)  use the time and space-varying snow depth climatology (hereafter W99) from the study by  Warren et al. (1999). However, recent in situ observations have demonstrated that snow depth has thinned by 50% over First Year Ice (FYI) relatively to the W99 climatology  (Kurtz et al. , 2013; Guerreiro et al.