dataesr/scientific-paragraphs-categorization huggingface dataset:
6. CALIOP uncertainties increase nonlinearly with increasing penetration through a layer, more so where particulate concentrations are higher and for high lidar ratios (such as for dust). This frequently leads to higher uncertainties in cases where a thick dust layer is found close to the surface (e. g. , Dubai year round, Niamey in DJF and MAM, Delhi in MAM, Canary Islands in DJF)  (Young et al. , 2013; Yu et al. , 2021). High uncertainties at lower altitudes can indicate either overestimates or underestimates of the corresponding extinction coefficient. However, for the most part they tend to flag large overestimates, which may explain why airports with high surface dust concentrations show notably lower dust mass concentrations for CAMS compared to the lidar datasets. Overall it seems most plausible that underestimates of CAMS dust mass concentrations due to the challenge of aerosol increments by DA, described in points 1-3 above are the cause of the differences between CAMS and the lidar datasets.
CALIOP L3 vs LIVAS 1. Both lidar datasets are dependent on a choice of lidar ratio to convert measured attenuated backscatter signal into extinction coefficient (44 Sr for CALIOP L3, regionally varying for LIVAS). In reality the lidar ratio will vary spatially with dust optical properties  (Schuster et al. , 2012; Floutsi et al. , 2022). This contributes to differences between LIVAS and CALIOP L3. For example, for Saharan dust regions where the LIVAS lidar ratio (53-56 sr) is larger than the CALIOP L3 lidar ratio it may explain the Marrakesh dust concentrations being larger in LIVAS, and possibly also Niamey. The slightly lower Middle East lidar ratio of 40 sr in LIVAS may contribute to the lower mass concentration profile in Dubai, though cannot feasibly explain the factor of 5 differences in some seasons.
During arrival, the hold pattern contributed over 50% of total dose due to the long time (10 minutes) spent in the hold pattern, which was compounded by hold altitude (1km) frequently occurring at, or near to, maximum dust concentrations. Contribution to dust dose from the different flight phases during departure (ground, take-off, climb) was more varied between airport and dust regime than arrival. For example, airports with seasonal elevated dust plumes (such as Beijing and Niamey in MAM and JJA and the Canary Islands and Marrakesh in JJA) experienced a dominant contribution from the higher altitude climb phase, while airports with seasonal near-surface dust plumes (e. g. , Dubai year-round, Delhi in JJA) had similar contributions from all three departure flight phases. Engine dust dose calculated from the CAMS reanalysis was compared to that from two datasets from the CALIPSO satellite with a spaceborne lidar: the standard CALIOP L3 dataset and the LIVAS dataset.
Mostly the seasonal cycles in dose were very similar between CAMS and the lidar datasets, except for minor differences in the seasonal cycle at Beijing and Delhi. Likewise, CAMS mostly represented the shape of the vertical dust structure well, though CAMS tended to be unable to represent strong peaks in dust concentration near the surface seen in certain seasons at Dubai, Delhi and Niamey, and CAMS failed to represent the vertical structure shape at Beijing. In terms of magnitude of dust concentration and also dust dose, values were frequently very different between all three datasets. At airports where dust was concentrated close to the surface such as Dubai, CALIOP L3 dust mass concentrations were up to 6 times greater than CAMS, and LIVAS doses up to 2. 5 times greater than CAMS. Large differences in dust concentrations at low altitudes are magnified when calculating dust dose by the large contribution to total dose from the ground and hold phases.
In other cases, dose calculated from LIVAS was greater than CAMS and CALIOP L3, such as at Beijing. In 46% of all seasons and airports, CAMS substantially underestimates dust dose compared to both CALIOP L3 and LIVAS, with these datasets larger by a factor of 1. 9 and 2. 8 respectively, resulting in a mean underestimate by CAMS of 2. 4 over both datasets. The differences in dust dose between CAMS, CALIOP L3 and LIVAS can be traced back to differences in the vertical profiles for each dataset. The comparisons are complex, firstly due to requirements for uncertain and variable properties of dust such as the mass extinction coefficient and its contribution from different dust size ranges in converting between massbased and optical quantities, secondly due different thresholds and methods for identifying dust in the lidar datasets, and thirdly due to challenges for both CAMS and lidar dust retrievals under conditions when dust is mixed with other aerosol types.
CALIOP data were not used to investigate variation in diurnal dose due to low sampling frequency. Further investigation of the diurnal variation in vertical dust profile from observations could be probed using the Cloud-Aerosol Transport System (CATS) onboard the International Space Station (ISS)  (Yorks et al. , 2016) , though less than three years of data are available  (Lu et al. , 2023). We note that we analyse data from the CAMS reanalysis here, which differs somewhat from the CAMS operational forecast, which undergoes more frequent updates and improvements. Here the use of the CAMS reanalysis was most appropriate due to its long, consistent dataset for generating a climatology of dust. Nevertheless, an evaluation of operational dust models in light of engine dust ingestion would be useful for future research, particularly since the representation of the dust life cycle in CAMS has been reviewed and improved  (Remy et al.
from CAMS is significantly different to the lidar datasets for Beijing in MAM, JJA and SON, with CAMS indicating a dust peak at around 2km, while the lidar datasets indicate increasing concentrations towards the surface (see https://doi. org/10. 5194/egusphere-2023-662 Preprint. Discussion started: 22 May 2023 c Author(s) 2023. CC BY 4. 0 License.
Methods In this observational cohort study, 64 participating hospitals located in 20 European countries, with the number of eligible hospitals per country determined by population size, included the first ten consecutive adults with culture-proven candidaemia after July 1, 2018, and entered data into the ECMM Candida Registry (FungiScope CandiReg). We assessed ECMM Quality of Clinical Candidaemia Management (EQUAL Candida) scores reflecting adherence to recommendations of the European Society of Clinical Microbiology and Infectious Diseases and the Infectious Diseases Society of America guidelines. Findings 632 patients with candidaemia were included from 64 institutions. Overall 90-day mortality was 43% (265/617), and increasing age, intensive care unit admission, point increases in the Charlson comorbidity index score, and Candida tropicalis as causative pathogen were independent baseline predictors of mortality in Cox regression analysis.
However, hospitals in seven countries (Croatia, Denmark, Estonia, Norway, Poland, Romania, and Hungary), although eligible, did not participate in the study. Hospitals were recruited by ECMM council representatives of each participating country, or via the COVID-19 in Hematological Malignancies (EPICOVIDEHA)  20  and FungiScope 21 networks and among the ECMM Global Guidelines contributor and fellow groups. 16 etween July 1, 2018, and March 31, 2022, participating centres entered data into the ECMM Candida Registry (FungiScope CandiReg; NCT01731353), which was described previously. 21, 22 The registry was accessible through an online platform provided by EFS Fall 2018 (Questback, Cologne, Germany). Data included patient demographics, risk factors, and characteristics; duration of hospital stay (maximum duration of follow-up 90 days); diagnostic procedures; causative Candida spp; treatment characteristics, including antifungal treatment and whether hospital stay was extended only for completion of parenteral antifungal treatment; and clinical outcomes.
The study was done in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. For the ECMM Candida Registry, retrospective data entry, and data analysis, a central ethical approval was obtained at the University of Cologne (Cologne, Germany; EK 17-485), indicating that, generally, neither informed consent nor institutional review board approval individual to each participating hospital would be required. However, each participating hospital was required to obtain local institutional review board confirmation or approval as deemed necessary by local regulations or authorities.
Statistical analysis We did all statistical analyses using IBM SPSS Statistics 25 and R, version 4. 3. 1. We used descriptive statistics for the analysis of most variables, including distribution of Candida spp and extended hospital stay for parenteral antifungal treatment. We assessed EQUAL Candida scores  11  reflecting adherence to recommendations of ESCMID and IDSA guidelines for patients for whom data were available, whereas we excluded patients and did not calculate the corresponding EQUAL Candida scores when case data were not given for one or more score variables. We presented data as frequencies, percentages, or median (IQR) values, as appropriate. We tested categorical data using χ², or Fisher's exact test if one of the observed frequencies in the two-by-two contingency table was less than five.
Three different genome assembly strategies, using Canu 5 , FALCON  6  , and MaSuRCA 7 , yielded comparable results (see Methods, Table  2 ), with >90% representation of the 255 Benchmarking Universal Single Copy Orthologs (BUSCO 8 ) of the eukaryota_odb10 dataset (Fig. 2 ), indicating high completeness. For downstream analyses, we opted to use the FALCON assembly because it was the most contiguous of the three, with the majority of the contigs (59 out of 78 primary contigs) bearing TTAGGG telomeric repeats at both ends. In addition, 14 of the remaining contigs had telomeric repeats at one end. While the presence of such conserved motives towards the end of the contigs suggests the complete assembly of most of the chromosomes and leads to an estimation of ~66 pairs of chromosomes in the M. sphyraenae nucleus, experimental evidence is needed to confirm the chromosome number in this species.
We inferred the phylogenetic relationships of our species within the CRuMs clade using publicly available data to reconstruct a dataset of 182 conserved protein markers and recovered the monophyly of the Mantamonas genus and the placement of M. sphyraenae as sister to a clade containing M. vickermanii and M. plastica (details in Methods, Phylogenomics analyses). To explore the gene content diversity of our new mantamonad species, we annotated the predicted proteomes genes with EggNOG mapper  9  and reconstructed the minimal core proteome for the genus Mantamonas and the CRuMs lineage (see details in Methods CRuMs orthologue analyses). Finally, as an additional way of assessing the completeness of the M. sphyraenae and M. vickermanii sequence data and capturing a sense of the complexity of the cellular systems in these organisms, we interrogated the complement of one well-studied set of proteins, the membrane-trafficking system.
This complex protein machinery underpins normal cellular function and is critical for feeding, cell growth, and interaction with the extracellular environment  10. While some proteins are highly conserved across eukaryotic lineages, others have rarely been retained during evolution but were nonetheless present in the Last Eukaryotic Common Ancestor (LECA)  10. Among them, the so-called "jotnarlogs" represent LECA proteins present in diverse extant eukaryotes but not in the major opisthokont model organisms. The identification of homologs of the majority of the protein complement associated with the membrane trafficking system as well as some jortnalogs in the proteomes of the new Mantamonas species (details in Methods, Analysis of the conservation of the membrane-trafficking system complement) corroborated the high completeness of our genomic and transcriptomic datasets, and suggests that these datasets may provide interesting insights in the evolution of anciently originated protein machineries.
Overall, our new Mantamonas nuclear genome and transcriptome sequences provide high quality data for a major, yet poorly known, eukaryotic supergroup. They will allow more comprehensive comparative studies of genetic diversity in microbial eukaryotes and a better understanding of deep eukaryotic evolution. Okinawa Prefecture, Japan (24° 23′ 36. 762″ N, 123° 45′ 22. 572″ E). It was isolated manually from the rough sample with a micropipette, and maintained in Erd-Schreiber medium 11 fortified with 2. 5% (final volume) freshwater Cerophyl medium (ATCC 802). Stock cultures were kept in 8 ml volumes in 25 ml culture flasks at 16-18 °C, and transferred at three-week intervals. Bulk cultures were grown at room temperature in 10 cm Petri plates containing ~10 ml medium. Live cells were observed on an Zeiss Axiovert 100 M inverted microscope equipped with DIC and phase contrast optics.
Images were captured with an Olympus DP73 17. 28-megapixel camera. Morphometric data were obtained at 1,000x magnification on 20 cells. Morphological description of Mantamonas sphyraenae sp. nov. Mantamonas sphyraenae cells exhibited three general morphologies: 'balloons' , which were typically ~5 µm long and ~3 µm wide, with a circularly curved anterior and a posterior end tapering to a point; 'kites' , which were roughly diamond-shaped, about 3. 5-4 µm long and wide; and 'mantas' , which were 4-5 µm wide and ~3 µm long, having a broadly curved anterior end, a more tightly rounded right side, a bluntly rounded projection on the left side, and a posterior comprising either straight edges culminating in a point, two shallowly concave curves, or one of each. All three morphologies were plastic to some extent, although 'mantas' were noteworthy in that the left-side projection appeared rigid, and the curved right side frequently very plastic.
A custom library of repetitive elements was generated for the polished and cleaned nuclear genomic sequence by combining the results of RepeatModeler2 17 and Transposon-PSI (http://transposonpsi. sourceforge. net/) pipelines. The gathered repeat sequences from both analyse s were merged and clustered to generate a single consensus and refined repeat library that was further compared against the Dfam database 18 to classify the repetitive elements using RepeatModeler  17  refiner and classifier modules. Repetitive elements identified by this procedure were then masked out of the nuclear genome using RepeatMasker 17 before the prediction of protein-coding genes. Subsequently, the RNA-seq libraries were mapped against the genome sequence with HISAT-2  19  to generate spliced alignments, and BRAKER2  20  was employed to predict the nuclear protein coding genes integrating the extrinsic evidence from the RNA-Seq data.
8. 2 SP1 suite. Videos were recorded using a Sony α9 digital camera. Morphometric data were obtained at 1,000x final magnification on 20 cells. Images were captured at multiple focal planes in order to visualise different cell parts. Measurements of flagella pertain to the visible parts, i. e. , the posterior flagellar length is measured beginning from the point at which it emerges from underneath the cell at the body's posterior end.
transcriptomes assembly and proteome prediction. The transcriptomic sequence of M vickermani and M. sphyraenae were assembled de novo using Spades v3. 13. 1  25  with the rna mode and default parameters specified. Transcripts were then screened to identify remaining contaminants using the Blobtools2 26 pipeline and homology searches against a custom database (see technical validation section). Predicted proteins were obtained from the clean transcripts using Transdecoder v2 (http:transdecoder. github. io) alowing for a single prediction by transcript (-single-bes-only option) and using the universat genetic code. Subsequently, CD-HIT  27  clustering was employed (with a threshold of > = 90% of identity) to produce a non-redundant data set of proteins for each of the trancriptomes, and to eliminate falsely duplicated proteins stemming from alternatively spliced transcripts.
phylogenomic analyses. The dataset of 351 conserved protein markers from Lax et al. 3  was updated by BLASTP searches  28  against the inferred proteomes of representatives of other eukaryotic lineages, including the proteomic data for our two new mantamonad strains. Each protein marker was aligned with MAFFT v. 7  29  and trimmed using TrimAl 30 with the -automated1 option. Alignments were manually inspected and edited with AliView 31 and Geneious v6. 06  32. Single-protein trees were reconstructed with IQ-TREE v1. 6. 11  33  under the corresponding best-fitting model as defined by ModelFinder 34 implemented in IQ-TREE  33. Each single-protein tree was manually inspected to discard contaminants and possible cases of horizontal gene transfer or hidden paralogy. At the end of this curation process, we kept a final taxon sampling of 14 species, including members of Ancyromonadida, Malawimonadida, Opisthokonta, and CRuMs (concatenated alignment and supplementary trees are available at Figshare  35  ), and 182 protein markers that were present in all mantamonad species (with at least 80% of markers identified in each taxon).
99 BI posterior probability and 95% ML bootstrap value are indicated by black dots. The tree was rooted between CRuMs and everything else. v3 database 39 using OrthoFinder v2. 5. 4  40. For this, we used DIAMOND  41  ("ultra-sensitive" mode, and query cover > = 50%), an inflation value of 1. 5, and the MCL clustering algorithm (Fig. 5a ). Then, the predicted proteomes of M. sphyraenae, M. vickermanii, M. plastica, D. rotans, and R. ramosa were functionally annotated with the EggNOG-mapper pipeline  9  , using DIAMOND ultra-sensitive mode and all domains of life as the target space. During this process, individual sequences composing the CRuMs orthogroups generated by OrthoFinder were assigned a COG functional category. This information was summarized at the orthogroup level by assigning to each orthogroup a single COG category corresponding to the most frequent annotation of its individual sequences, provided that it represented at least 50% of the sequences within the orthogroup.
Most of the proteins conserved among the CRuMs taxa (99. 6%) were found to have an ortholog in the EggNOG database and to belong to at least one Cluster of Orthologous Groups (COG)  42, 43  functional category, where the most highly represented were "Function unknown" and "Post-translational modificationand "Intracellular trafficking" (Fig. 5c ). By contrast, a substantial amount of orthogroups conserved among mantamonads (12%), but absent in other CRuMs lineages, could not be assigned to any cluster in the EggNOG database. In addition, most orthogroups conserved in mantamonads but absent in other CRuMs that could be connected to an existing EggNOG cluster were annotated as "Function unknown" (Fig. 5c ). Altogether, this large number of Mantamonas-specific genes of unknown function suggests that many genetic innovations occurred at the origin of this group.
analysis of the conservation of the membrane-trafficking system complement. To assess the complement of the membrane trafficking system encoded in our Mantamonas genome and transcriptome datasets, we performed homologous searches of a selection of protein query sequences from the genomes of Homo sapiens (GCF_000001405. 40), Dictyostelium discoideum (GCF_000004695. 1), Arabidopsis thaliana (GCF_000001735. 4) and Trypanosoma brucei (GCF_000002445. 2) available at the GenBank of the NCBI (National Center for Biotechnology Information) database. These proteins included components of the machinery for vesicle formation (HTAC-derived coats, ESCRTs, and ArfGAPs) and vesicle fusion (SNAREs and SM proteins, TBC-Rab GAPs, and Multi-subunit tethering complexes)  10. BLASTP and TBLASTN were used to search the predicted proteomes and nucleotide coding sequences, respectively, of M.
sphyraenae and M. vickermani. The HMMER3 package was used to find more divergent protein sequences using the hmmsearch tool  44. In cases in which only TBLASTN hits were retrieved, these were translated using Exonerate  45. Potential orthologs (i. e. , hits with an E-value below 0. 05) were further analyzed by the Reciprocal Best Hit (RBH) approach, using the Mantamonas candidate orthologs as queries against the H. sapiens, D. discoideum and A. thaliana proteomes. If the best hit was the protein of interest and had an E-value two orders of magnitude lower than the next non-orthologous hit, this was considered as orthology validation. Forward and reverse searches were performed using the AMOEBAE tool  46. We detected most proteins of the membrane-trafficking system in the two new Mantamonas species, making it one of the most complete known protein complements for this system.
Data Records The read data associated with the nuclear genome and transcriptomic datasets of Mantamonas sphyraenae and the transcriptome of Mantamonas vickermani have been submitted to the NCBI SRA database  49  (Table  4 ). The Transcriptome Shotgun Assemblies have been deposited at DDBJ/EMBL/GenBank under the accessions GKLA00000000 and GKKZ00000000 for M. vickermani and M. sphyraenae respectively. The final nuclear genome assembly of Mantamonas sphyraenae has been deposited at GenBank under the accession GCA_026936335. 1  50. The versions described in this paper are the first versions. The prediction of protein-coding genes from the genome and transcriptome assemblies of Mantamonas sphyraenae, as well as from the transcriptome assembly of M. vickermani are available at Figshare  35. Phylogenomic analysis alignments and trees, and membrane-trafficking predicted proteins table can be found on Figshare  35.
technical Validation Quality assessment of sequencing datasets. All Illumina paired-end raw reads used for genome polishing were quality-checked with FastQC v0. 11. 8  51  and trimmed using TRIMMOMATIC  52  to retain only reads with maximum quality scores. PacBio reads resulted in an N50 of 11,048 bp and an average coverage of 106x after filtering out the identified contaminant sequences (see below).
Identification and filtering of contaminant sequences. Mantamonads grow in non-axenic cultures with co-cultured prokaryotes on which they feed. Therefore, various methods were employed to ensure the correct identification and filtering of contaminant sequences in the genomic and transcriptomic datasets of M. sphyraenae and M. vickermani. For the genomic dataset of M. sphyraenae, we first identified the main bacterial contaminants from the initial genome assemblies  53. In addition, we established a custom database consisting of contigs assembled from Illumina sequencing data from bacteria only enrichment cultures derived from the lab's several xenic protist cultures. These were used to screen PacBio reads using BLASR v5. 1  54  The Illumina reads were screened similarly using Bowtie2 v2. 3.
5. 1  23. Only Illumina reads in which neither pair aligned to the bacterial database were retained for further assembly recovering 57% and 49% of the pair-end and mate pair reads from the total libraries respectively. After genome assembly using the filtered reads, remaining contaminant contigs were identified by using MyCC v1  55  , which bins contigs based on their tetranucleotide frequencies and coverage. Clusters were formed using the affinity propagation (AP) algorithm and visualized in a 2-dimensional Barnes-Hut-SNE plot. BLASTN searches using default parameters were conducted against the 'nt' database from the NCBI to taxonomically classify the bins. Contigs were identified as contaminants if they contained no hits other than to prokaryotes, and if they were clustered away from the main eukaryotic bin.