dataesr/scientific-paragraphs-categorization huggingface dataset:
Infrageneric relations of the genetically diverse milkcap genus Lactifluus (Russulales, Basidiomycota) are poorly known. Currently used classification systems still largely reflect the traditional, mainly morphological, characters used for infrageneric delimitations of milkcaps. Increased sampling, combined with small-scale molecular studies, show that this genus is underexplored and in need of revision. For this study, we assembled an extensive dataset of the genus Lactifluus, comprising 80 % of all known species and 30 % of the type collections. To unravel the infrageneric relationships within this genus, we combined a multi-gene molecular phylogeny, based on nuclear ITS, LSU, RPB2 and RPB1, with a morphological study, focussing on five important characteristics (fruit body type, presence of a secondary velum, colour reaction of the latex/context, pileipellis type and presence of true cystidia).
With the inclusion of more tropical taxa, phylogenetic data showed that Lactarius and Russula are not two well-defined and separate clades. Russula appears to be monophyletic only if a small group of species is excluded. This small group forms a clade where Lactarius and Russula are mixed and it was described as the new genus Multifurca  (Buyck et al. 2008). The former Russula subsect. Ochricompactae, the Asian Russula zonaria and the American Lactarius furcatus were included in this genus. Multifurca species are characterised by furcate lamellae, dark yellowish lamellae and spore-prints, a strong zonation of pileus and context and the absence or presence of latex. The remainder of Lactarius falls in two different clades  (Buyck et al. 2008). The proposal to conserve Lactarius (hereafter abbreviated as L.
Sampling We included Lactifluus collections from every continent, every subgenus and every section, as well as collections with divergent morphological features. To improve species identification, we included as many type specimens as possible in our dataset. We included one collection of each species, except when sequences of only one or two genes of the type collection were available. In those cases we added an extra collection of the same species for which all four genes were sequenced. The outgroup contains nine Russulales species: Amylostereum laevigatum, Auriscalpium vulgare, Bondarzewia montana, Echinodontium tinctorium, Gloeocystidiellum porosum, Heter obasidion annosum, Peniophora nuda, Stereum hirsutum and Vararia abortiphysa (Table  1 ).
Phylogenetic analyses Sequence data were divided into the following partitions. The ITS+LSU alignment was partitioned into partial 18S, ITS1, 5. 8S, ITS2 and partial 28S. Both RPB2-and RPB1-alignments were partitioned into the intron(s) and the first, second and third codon positions of the exon. Maximum Likelihood (ML) analyses were conducted with RAxML v. 8. 0. 24  (Stamatakis 2014) , where an ML analysis was combined with the Rapid Bootstrapping algorithm with 1 000 replicates under the GTR-CAT option  (Stamatakis et al. 2008)   (Nylander et al. 2008 ). After discarding a burn-in determined in Tracer, a majority rule consensus tree was constructed. ML and BI analyses were performed on each of the four alignments. All analyses were performed on the CIPRES Science Gateway  (Miller et al. 2010).
RESuLTS Our dataset contains 213 Russulales collections, of which 189 are from the genus Lactifluus. With approximately 150 described species in Lactifluus, 80 % of the described taxa are represented in our dataset. Of the 20 % missing, most species are only known from collections too old for sequencing. The remainder are taxa from species complexes represented by at least 15 species in our dataset, for instance from Lf. subg. Gerardii and Lf. sect. Lactifluus. These complexes have been studied before and their absence in this analysis does not affect stability of the results  (Stubbe et al. 2010 , Van de Putte et al. 2010 , 2012). Fifty-one of the described species we included have never been sequenced before and 46 of the described species are represented by their type specimen. Furthermore, we included 30 unidentified collections, of which at least 15 represent new species.
PCR and sequencing success rate differed among the four genes, with 213, 195, 177 and 151 sequences obtained for ITS, LSU, RPB2 and RPB1, respectively. A total of 493 new sequences were generated for this study, the remaining were obtained from our previous studies and GenBank. ML and BI results of the three independent datasets are similar, without any supported conflicts (support: ML > 70, BI > 0. 95). We therefore used the concatenated dataset, which is 5032 bp long (including gaps). The phylogeny of the concatenated data is shown in Fig. 3. The outgroup is fully supported (ML: 100, BI: 1), as are the genera Russula (ML: 99, BI: 1), Lactarius (ML: 100, BI: 1) and Multifurca (ML: 100, BI: 1). Lactifluus is well-supported (ML: 98, BI: 1) and can be divided in four supported clades, corresponding to four subgenera: Lf.
Ecology Species of the genus Lactifluus can be found in temperate, subtropical and tropical regions, in a wide range of vegetation types, such as tropical and subtropical rain forests, subtropical dry forests, monsoon forests, tree savannahs, Mediterranean woodlands, temperate broadleaf and coniferous forests and montane forests. Basidiocarps are commonly found on soil, but sporadically on stems or aerial roots of trees, such as Lf. bru nellus (Fig. 5e ) on stems of Dicymbe corymbosa  (Miller et al. 2002). Lactifluus species are ectomycorrhizal fungi and we hypothesize that the ectomycorrhizal hosts might have played important roles in species evolution. Present data suggest that both generalists and specialists occur, but the exact mycorrhizal connection generally remains undetermined. Ecological characteristics are not commonly recorded for every collection during field work, and it is hard to find out which tree a fungal species grows with in mixed forests.
Acknowledgements The first author is supported by the  'Special Research Fund Ghent University' (BOF , grant  B/13485/01 ). The survey in Zambia was financially supported by the  Research Foundation Flanders (FWO , grant  K202014N ) and by the  Alberta Mennega Stichting. We would like to express our gratitude to all who helped during field work, especially to  Deo Baribwegure ,  André-Ledoux Njounkouo  and  Donatha Tibuhwa. We would like to thank  Viki Vandomme ,  Felix Hampe  and  Andy Vierstraete  for conducting lab work. We thank  Umberto Pera ,  Terry Henkel ,  Michael Kuo ,  Dieter Slos ,  Andy Methven ,  Gianluigi Bogi  (www. bogiphoto. com), Ruben Walleyn  † and  Todd Elliot  for providing pictures of Lactifluus species. We thank  Shaun Pennycook  and  Scott Redhead  for their help with the nomenclatural changes and Xiang- Hua Wang  for her comments on the manuscript.
We would like to thank everyone who provided material for this study:  Adamčík S, Aime MC, Bâ A, Bandala VM, Basso MT, Bhandary HR, Buyck B, Carriconde F, Cifuentes J, Courtecuisse R, Das K, De Kesel A, Degreef J, Delivorias P, D'hooge E, Dibaluka Mpulusu S, Eyssartier G, Fiard JP, Gates G, Guo J, Hampe F, Henkel T, Horak E, Justice J, Lanquetin P, Le TH, Lecomte M, Lecuru C, Leonard P, Mata JL, Matheny PB, Montoya L, Morozova O, Noé F, Noordeloos M, Petersen R, Popov E, Rammeloo J, Ratkowsky D, Rock S, Saarimäki T, Sapnelis S, Sharma S, Singer R † , Sunar ,  Tonkin JE ,  Triantafyllou M, Vellinga EC ,  Walleyn R †  and  Wang XH. We thank the  National Science Foundation (USA)  for funding to  Halling RE  (DEB grants # 0414665  and # 1020421 ), the  National Geographic Society  for funding to  Halling RE  ( CRE grant  # 8457-08 ), the  Agence Nationale de la Recherche  ( CEBA , ref  ANR-10-LABX-25-01 ) and the  scientific station of Nouragues Reserve (CNRS, grant MYCOTIN)  for funding to  Roy M , and the  Thailand Research Fund  (TRF, grant  BRG 5580009 ) for the financial support of the study of KD Hyde.
Lactifluus subg. Gymnocarpi Lactifluus sect. Luteoli Species Voucher collection Country GenBank Accession numbers (herbarium) ITS LSU RPB2 RPB1 Lactifluus brunneoviolascens AV 13-038 (GENT) Italy KR364123 KR364246 KR364264 KR364376 Lactifluus longivelutinus Type XHW 1565 (GENT) China KR364114 - - - Lactifluus luteolus AV 05-253 (GENT) North America KR364016 KR364142 KJ210067 KR364440 Lactifluus nonpiscis AV 11-137 (GENT) Togo KR364058 KR364185 KR364317 KR364445 Lactifluus nonpiscis Type BB 3171 (GENT) Zambia KR364030 KR364157 - - Lactifluus rubrobrunnescens Type EH 7194 (GENT) Indonesia KR364115 - - - Lactifluus sp. nov. KW 392 (GENT) Thailand KR364091 KR364222 KR364305 KR364433 Lactifluus sp. nov. REH 9398 (NY) Australia KR364097 KR364229 KR364307 KR364435 Lactifluus sect. Gymnocarpi Lactifluus albocinctus Type AV 99-211 (GENT) Zimbabwe KR364117 KR364249 KR364258 - Lactifluus albomembranaceus nom.
Decompensation in the last 3 months was assessed as significant hypoglycemia (<60 mg/dl with decreased level of consciousness requiring glucagon or the help of others to be reversed), and significant hyperglycemia (>400-450 mg/dl which required action by the professionals). Diabetes ketoacidosis was also collected. The HbA1c was used as a measure of metabolic control by determination in capillary blood (DCA 2000 Bayer/Siemens®) or serum in an analytical laboratory (HPLC). Weight, height and body mass index (BMI, Z score)  [12]  was also collected. The web-version of the questionnaires was developed through a generic internet tool using Ruby on Rails applications and MySQL database (http://rubyonrails. org). The self-administered questionnaire included the internet versions of the Euroqol-5D-Y (EQ-5D-Y) and KIDSCREEN-27 and KIDSCREEN-10 index as measures of HRQOL.
51 and 0. 8 moderate, and those over 0. 8 were considered large. Multiple linear regression models were adjusted to analyze the influence of factors associated with HRQOL controlling for socio-demographic, clinical factors, and mental health. Interactions terms between clinical and sociodemographic factors were also explored. Results of regressions in terms of B coefficients can be interpreted as a modification (increase [+] or decrease  [-] ) on the dependent variable (a given dimension of HRQOL) for every unit of change in the predictor variable (e. g. age). Bonferroni correction was used to control for multiple comparisons. Program STATA. 11 software versions were used in the analysis.
Evidence review and development of clinical recommendations For each question the methodology group performed a full systematic review of the literature to identify and summarize the evidence about the effects of interventions on the outcomes of interest. We also systematically searched for the information about patients' values and preferences, and resource use (cost). We systematically searched Medline, Embase and Cochrane CENTRAL electronic databases. Titles and abstracts, and subsequently full-text articles were screened in duplicate to assess eligibility according to pre-specified criteria. Panel members were contacted to confirm completeness of the body of evidence and suggest additional articles that might have been missed in electronic searches. To obtain the estimates of effects on each outcome of interest we performed metaanalyses using the Cochrane Collaboration Review Manager Software, version 5.
Evidence review and development of clinical recommendations For each question the methodology group performed a full systematic review of the literature to identify and summarize the evidence about the effects of interventions on the outcomes of interest. We also systematically searched for the information about patients' values and preferences, and resource use (cost). We systematically searched Medline, Embase and Cochrane CENTRAL electronic databases. Titles and abstracts, and subsequently full-text articles were screened in duplicate to assess eligibility according to pre-specified criteria. Panel members were contacted to confirm completeness of the body of evidence and suggest additional articles that might have been missed in electronic searches. To obtain the estimates of effects on each outcome of interest we performed meta-analyses using the Cochrane Collaboration Review Manager Software, version 5.
Brozek J et al. Allergic Rhinitis and its Impact on Asthma (ARIA) Guidelines -2016 Revision All studies in SAR and PAR investigated new generation OAH. Older generation OAH, that are usually more sedating, will have more adverse effects than observed in these studies. E63  Decision criteria and additional considerations ARIA panel members noted that some patients beginning treatment may prefer the combination therapy because of faster onset of action of OAH compared to INCS alone. This, however, may be less important in patients with PAR than with SAR. Adherence to treatment may be lower with an increase in the number of medications. We found two studies that compared the cost of INCS alone to their combination with OAH -a retrospective database analysis using pharmacy and medical claims data from a US health plan that compared medical and pharmacy cost of different treatments for rhinitis  E64  and a Swedish population-based questionnaire study that estimated an annual cost of treatment with OAH and INCS  E65.
Decision criteria and additional considerations We found one retrospective analysis of an insurance claim database in the United States that analyzed costs of treatment with montelukast compared with oral, branded second-generation antihistamines  E104. We also found descriptions of two economic models that compared various OAH and montelukast  E105, E106. However, all analyses were based on historical prices from 2000s and panel members thought they were not applicable today. The ARIA panel members noted that the cost of LTRA will frequently be higher compared to OAH but the cost of various OAH is also highly variable across countries and health care systems. Cost may be a more important factor for patients with PAR since they might use medications for longer periods of time. In settings where generic LTRA are available, cost of treatment may be lower compared to branded LTRA.
SEM/EDX Data Collection The same sample windows previously imaged with STXM were imaged again with a computer controlled scanning electron microscope (FEI, Quanta 3D FEG, Hillsboro, AL, USA) coupled with energy dispersive X-ray spectroscopy (CCSEM/EDX, PNNL, Richland, CA, USA). The SEM utilized a field emission tip to produce an electron beam which was directed and focused onto a sample with an accelerating voltage of 20 kV which can cause core shell atomic electrons to be ejected from the sample. Higher shell electrons then relax into the newly created orbital hole, releasing an elementally characteristic photon recorded by an energy dispersive X-ray detector (EDAX PV7761/54 ME with Si(Li) detector, Mahwah, NJ, USA). As the electron beam was scanned over the sample, the transmitted electron image was used to identify the exact same FOVs from the previous STXM images.
Once a FOV previously analyzed with STXM is located, a 10,000× image (30 nm/pixel resolution) was captured. This image combines both transmitted and backscattered electron images to improve particle detection  [23]. A threshold contrast level was then set to identify which areas of the collected image counted as particles using the "Genesis" software from EDAX, Inc. A software filter was then applied which discounts particles that are too small (e. g. , noise spikes) or too large (e. g. , multiple nearby particles counted as a single large particle). The electron beam was then directed towards each identified particle in sequence and an EDX spectrum was collected. Afterwards, software was used to fit the peaks of eleven relevant elements selected for this study: Na, Mg, P, S, Cl, K, Ca, Mn, Fe, Ni, and Zn.
Quantifying Higher Z Elements Using the aforementioned methods, STXM yields quantitative, absolute mass information on a sub-particle basis. SEM/EDX is more limited in this aspect, being quantitative for elements with Z > 11 (Na) but only semi-quantitative for C, N, and O  [23]. Due to the EDAX software used for EDX data collection and analysis, there is an additional caveat to the quantitation of Z > 11 elements: the software reports only the relative mass percentages compared to the elements chosen during data processing. In order to properly quantify the mixing state, the absolute mass of each element in each particle is necessary. To determine these absolute masses, a system of equations was set up using the following equation types: OD i = ρt A ∑ a=1 f a µ a,i (3) f x f y = rel. % x rel.
% y (4) A ∑ a=1 f a = 1 (5) For each pixel, OD i is the optical density taken at energy i, ρ is the density and t is the thickness of the sample (at that pixel), f a is the mass fraction of element a, and µ a,i is the mass absorption coefficient of element a at energy i. Equation (  4 ) is a general relationship, which equates the ratio of two absolute mass fractions (f x and f y ) with the ratio of relative mass percentages (rel. % x and rel. % y ) produced by the EDAX software. Equation (3) utilizes the quantitative nature of STXM whereas the relative mass percent of elements with Z > 11 were used in Equation (4) to combine the quantitative abilities of SEM/EDX. This system was then solved for the 14 absolute mass fractions (f a ) of each element chosen in this study. Equation (  5 ) is an assumption that is valid when the 14 elements analyzed comprise close to 100% of the particle's composition.
Results As a general trend, during the dry season, the whole Amazon Basin experiences a significantly higher aerosol number concentration and CO (g) concentration compared to the wet season, largely due to in-Basin fires  [30, 40]. Furthermore, in addition to biomass burning, emissions from Manaus are often observed at the T3 site (downwind of Manaus), sporadically at the ZF2 site (upwind but near the city) and rarely at ATTO (upwind and ~150 km away). Table  2  outlines some supporting measurements made at the three sites. Table  2. Supporting data during sampling times for each sampling site. Values listed under sample dates are averages over that sampling period. Values in the adjacent (Avg. ) column are monthly averages only for the hours coinciding with the sampling times listed in Table  1  (e.
ACSM data collected for past studies at site ZF2 fits well with the trends seen in the limited data presented here  [28]. Concentrations of aerosol components can fluctuate depending on the day but September (2012) averages for ammonium, chloride, nitrate, organics, and sulfate have been reported as 0. 46, 0. 01, 0. 22, 13. 9, 0. 37 µg m -3 , respectively by Artaxo et al. , 2013  [28]. Unsurprisingly, the ATTO site shows the lowest levels of almost all presented aerosol and gas components. The sample collected on 15 October also appears to be fairly average with respect to the sample-period monthly average for this collection time. The low particle concentration suggests a sample with low pollution levels which makes this sample ideal for our purposes. The values presented for the ATTO site are also in fair agreement with previously published data  [40].
Atmosphere 2017, 8, 173 10 of 27 Figure  2  shows an example FOV and the type of data calculated for all three sites. Each particle has an OD map (which is proportional to mass, refer to Equation (2) for C, N, and O as well as a C speciation map. In Figure  2 , the STXM grayscale image shown is the average intensity map over the four C edge images. There is a correlation between the brightest spots and the identification of soot in the C speciation map. This speciated image is possible due to the sub-particle spatial resolution achievable with STXM mapping, which is highlighted in the C, N, and O maps. Potential inter-site differences can be seen in this figure: the ATTO sample shows large inorganic inclusions coated by organics along with Na, Mg, and Cl representing the bulk of the higher Z elements.
40 nm with STXM). Soot inclusions identified in the C speciation map are also seen as bright spots in the SEM grayscale image in addition to many of the inorganic inclusions  [23]. From the EDX data collection, mass fraction maps for each element (on a per-particle basis) were used to calculate individual particle diversity (Di) values for each particle. Another aspect of the maps is the varying background level between SEM images, seen especially in the high background of the ZF2 image. This is a consequence of the brightness and contrast levels being set before EDX acquisition and was performed to ensure that the maximum number of particles were detected by the CCSEM particle detection software. (a)    and O ) or per-particle (for higher Z elements) mass distribution. Also shown is a color coded carbon speciation map showing soot (red), inorganic (teal), and organic (green) carbon.
40 nm with STXM). Soot inclusions identified in the C speciation map are also seen as bright spots in the SEM grayscale image in addition to many of the inorganic inclusions  [23]. From the EDX data collection, mass fraction maps for each element (on a per-particle basis) were used to calculate individual particle diversity (D i ) values for each particle. Another aspect of the maps is the varying background level between SEM images, seen especially in the high background of the ZF2 image. This is a consequence of the brightness and contrast levels being set before EDX acquisition and was performed to ensure that the maximum number of particles were detected by the CCSEM particle detection software.
This is well below the vaporization temperature for NaCl and KCl, two major sources of Cl (and inorganics in general). Hence, Table  2  AMS data underestimates Cl mass fractions. Another requirement to allow direct comparison is to change concentration of sulfate to S by multiplying by the ratio of molar masses (~32/96), reducing the concentrations seen in Table  2  to about one third. The third circumstance is the potential for beam damage using the two sequential microscopy techniques here. Some of the inorganic inclusions/cores detected using STXM/NEXAFS spectroscopy may be particularly sensitive to electron beam damage. These sensitive inorganics (particularly ammonium sulfate) could have been volatilized during the scanning/locating phase of SEM and therefore would not be well characterized with subsequent EDX spectroscopy.
Conversely, the relative absence of soot clusters or the mixed clusters further highlights the ATTO site's remoteness from regional anthropogenic (urban) influences. However, even this site is far from being pristine, as shown by the presence of significant amounts of BC, presumably from long-range transport. While the ZF2 site contains many of the same clusters present at the ATTO site, there are some notable differences. The presence of the HDI1 cluster is diminished (~1% as compared to ATTO's 26%), and both mixed clusters are seen in substantial amounts. The largest difference between the two stages is the enhancement of the LDM cluster in stage 7 data and the minor increase in all three soot clusters in stage 8 particles. Site T3 shows the presence of many clusters, with all three soot clusters present in substantial amounts.
The presence of the HDI1 cluster is diminished (~1% as compared to ATTO's 26%), and both mixed clusters are seen in substantial amounts. The largest difference between the two stages is the enhancement of the LDM cluster in stage 7 data and the minor increase in all three soot clusters in stage 8 particles. Site T3 shows the presence of many clusters, with all three soot clusters present in substantial amounts. This is expected, as automotive exhaust or energy production through fuel oil burning will produce soot particles that travel to site T3  [76]. Both organic clusters are present with a slight enhancement in stage 8 particles. Because both organic clusters are seen in reasonable amounts at each sampling site, these particles may be part of the aerosol background inherent to sampling in a heavily forested region.