dataesr/scientific-paragraphs-categorization huggingface dataset:
Accuracy (A) : A = 1 n ( ∑ n i=1 Δ AOD ) (1) Precision (P) : P = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ 1 (n -1) ∑ n i=1 (Δ AOD -A) 2 √ (2) Uncertainty (U) : U = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅ 1 n ∑ n i=1 Δ AOD ) 2 √ √ √ √ (3) WV was computed and validated only for Sentinel-2 MSI data, thanks to the available В09 spectral band in the WV absorption region (Central Wavelength: 945 nm) that is suitable for WV retrieval. The Landsat 8 OLI instrument does not collect data in this spectral region, so WV could not be retrieved directly. The validation analysis was similar to the one for the AOD, comparing the averaged estimated WV over the 9 km × 9 km image subset with the corresponding AERONET reference measurements, which were collected at a temporal window of ±15 min from the satellites' overpass and averaged.
In this analysis, the quality pixels were the common quality-approved pixels by the Quality Assessment (QA) band estimated by LaSRC and the analysed processor's quality mask. If the processor did not provide any quality information, only the LaSRC QA was taken into consideration. LaSRC QA layer provided a high cloud producer's accuracy when compared to other cloud masks in various datasets in CMIX  (Skakun et al. , 2022) , so it was considered adequate for accurately detecting most of the clouds and defining the quality pixels. Additional thresholds to OLI B6 and B7 pixel values, as well as to MSI B11 and B12 ensured that water pixels were excluded from the analysis. The quantitative analysis was performed estimating Accuracy (A), Precision (P) and Uncertainty (U) metrics per wavelength based on the Eq.
The photometers measure from 414 nm to 1600 nm in 12 narrow spectral bands  (Meygret et al. , 2011; Marcq et al. , 2018). The BRDF values for the Sentinel-2 and Landsat 8 sun and sensor geometries were extracted, and spectrally integrated to the corresponding sensor spectral bands of Sentinel-2 and Landsat 8  (Rouquié et al. , 2017). Both La Crau and Gobabeb stations are operated by the French National Centre for Space Studies (CNES), either entirely (La Crau) or with ESA and NPL (National Physical Laboratory) collaboration (Gobabeb). The data pre-processing was also performed by CNES to fit the exercise purposes. It should be noted that the Gobabeb ROSAS system was unfortunately out of service from June to the beginning of December 2018, meaning that there were no available measurements for four out of the 12-months ACIX study period.
The black solid lines represent the uncertainty limits specifications, specs = 0. 15*AOD 550ref + 0. 05, according to the empirical uncertainty of MODIS land AOD retrievals  (Remer et al. , 2009). The blue solid line is the least-squares regression line for the two compared datasets. The results are mostly in moderate agreement with the reference values, with the correlation R 2 to range from ~ 0. 65 to 0. 77 and RMSE from ~ 0. 115 to 0. 2. Overall, SMACG and iCOR demonstrate superior performances with relatively high correlation and low RMSE. AComp, SIAC and MAJA are also found to perform well with retrievals in good agreement with the reference AOD in terms of correlation, RMSE and bias. More than ~60% of the retrievals fall within the suggested uncertainty specifications for most of the processors, with SMACG to achieve the greatest number of points (78%), while Overland the lowest (40%).
ATCOR and FORCE use a default AOD value as a fallback solution, when they are not able to detect pixels suitable for AOD retrievals, a fact that leads to large errors and impacts their general statistical performance. However, quality flags exist in FORCE, with which these conditions can be easily filtered out. In addition, this usually happens in Fig. 2. Scatterplots of AOD estimates versus AERONET measurements. The 1:1 agreement line is indicated with a dashed line, while the black solid lines represent the uncertainty limits specifications, specs = 0. 15*AOD 550ref + 0. 05. The blue solid line represents the least-squares regression line for the estimated datasets and AERONET reference measurements. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.
Water vapour validation over AERONET sites The quality assessment of water vapour was performed by comparing the averaged retrievals over 9 km × 9 km study areas with the corresponding AERONET measurements. AComp, Planet_SR and SMACG did not provide WV, so they were excluded from this analysis. In Fig. 4  the black dashed line represents the 1:1 line, the ideal case of the perfect match between WV estimates and reference. The black solid lines indicate the uncertainty limits specifications, specs = 0. 1*WV ref + 0. 2 g/ cm 2 , according to the empirical uncertainty of Sentinel-2 WV retrievals as suggested by Sen2Cor developers  (Pflug et al. , 2020). The orange solid line represents the least-squares regression line for the two compared datasets. Overall, WV retrieval is very accurate with strong correlations with the reference data, mostly >0.
e. , Evora, Granada, Kanpur, Lumbini, Murcia. The results are associated with the 10 m Sentinel-2 bands and presented by processor. Sentinel-2 data analysis, and the sites were reduced to 62 in total. For the complete analysis of the entire dataset as submitted by each processor, the results can be found on ACIX-II Land web subpage (https: //calvalportal. ceos. org/acix-ii-results).
Surface reflectance validation with RadCalNet measurements The quality assessment of the computed SRs was performed based on the RadCalNet measurements over La Crau and Gobabeb stations. In total, 5 and 14 Landsat scenes matched the valid ground data of each site respectively. ATCOR is excluded from the analysis over Gobabeb, due to  incorrectly projected outcomes. iCOR did not provide Band 3, so it is also missing from the analysis. Although the statistical sample is not adequately large, plotting the cross-comparison results in Fig. 11  exhibits the diverse performance of the processors for the six Landsat 8 bands. The minus sign (-) over the bars refers to the negative values of the Relative bias. Overall, the processors underestimate SRs for both sites. Nevertheless, large discrepancies were observed for EMBAC and LaSRC when producing SRs in the VNIR bands.
Conclusion The Atmospheric Correction Intercomparison eXercise (ACIX) is organised by ESA and NASA in the frame of CEOS WGCV. In this second implementation over land 12 atmospheric correction processors participated for Sentinel-2 or Landsat 8 data. The AOD and WV estimates were assessed over a wide variety of AERONET sites distributed globally, providing a good indication of the retrieval capability of the processors. Most of them succeeded in providing AOD with high accuracy (U < 0. 08) for aerosol loads lower than 0. 2, and demonstrated very good performances for WV estimation with RMSE <0. 25 g/cm 2. Regarding SR quality assessment, the analysis based on AERONET-derived reflectances demonstrated lower uncertainties for SWIR and NIR with increasing values at the short wavelengths. Although the computation of AERONET-derived SR is a valuable data source for assessing AC performance, it may introduce biases in the analysis when dealing with processors based on different approaches.
Regarding the RTMs differences, a more thorough examination involving other models is suggested for similar future SR validation activities. Good performances with standard deviations of the relative differences mostly <0. 05 were witnessed in the analysis based on the RadCalNet measurements. Nevertheless, the available observations in this case were limited over stable surface and atmospheric conditions. Networks of ground-based stations distributed globally and dedicated to SR validation are expected in the near future to provide valuable data for robust quality assessment of optical missions over land.
Acknowledgment We thank all the PI(s) and Co-I(s) and their staff for establishing and maintaining the over than 120 AERONET sites used in this investigation. We acknowledge also  CNES ,  ESA  and  NPL  for maintaining the RadCal-Net stations involved in the study and special thanks to  Aimé Meygret  and his team in  CNES  for processing the corresponding measurements. In addition, we thank  Bryan Keary  ( University College Cork, Ireland ) for proof-reading the paper. F. Y. ( UCL  and  NERC ) was supported for this work by the  National Centre for Earth Observation  (Grant agreement or award number:  525861 ). K. M. (KBR) was supported by  USGS  under the Contract  140G0121D0001.
MENB: a model for host and viral origin classification Our unsupervised learning model, MENB, infers parameters associated for each k-mer up to k = 3 and defines a probability distribution on viral sequences (of a fixed length), in such a way that the expected k-mer frequencies from this distribution match with those observed in the training data. As shown in Methods Sec. 5. 1. 1, this results in the following probability distribution for a viral sequence s: p(s) ∝ exp a∈S f (1) a n a (s) + ab∈S f (2) ab n ab (s) + abc∈S f (3) abc n abc (s) , where S is the set of nucleotides, n m (s) is the number of times the motif m is present in s, and the parameters indicated by f are the "forces"  [Greenbaum et al. , 2014]  to be inferred from the training data. To train our model we collected viral sequences from the BV-BRC database  [Olson et al.
Here we show the model describes the evolution of a viral strain after a host jump. We start with the case of 1918 H1N1 influenza pandemics: we collected all PB2 segments available in our dataset associated to the H1N1 strain up to 2008. It is commonly accepted that the pandemics originated with a jump from avian to human hosts  [Taubenberger et al. , 2005]. To compare the two hosts we will use in our analysis the human and the avian model trained, for each host, on all the segments of influenza viruses excluding PB2 to avoid potential overfitting. Before assigning sequences to their host, we built a phylogenetic tree, on a random subsample of up to 20 sequences per year, using Nextstrain  [Hadfield et al. , 2018]. Quite remarkably, the log-probability score introduced here works as a sort of "molecular clock", by steadily increasing as the virus adapts to the new host.
In the above analysis we have taken into account a number of limitations and delicate points that we discuss here. First, the SARS-CoV-2 sequence data is heavily biased, both geographically (a large fraction of the sequences are collected in a small number of countries) and temporarily (the rate of sequence collection increased steadily in the first months of the pandemics). Second, as discussed above, we have used the single Wuhan sequence to infer the model for the unknown virus transmitting host. Third, the time over which the adaptation to the human host has been sampled is much smaller than that of the H1N1 strain, on such a short time scale adaption driven by non-synonymous mutations with clear fitness advantages could result in a confounding signal. To address the first issue we used a curated dataset of sequences collected by Nextstrain  [Hadfield et al.
, 2018]  to build the model of SARS-CoV-2 in the human host: in this dataset sequences are subsampled to reduce biases from different geographic regions and time periods, and most of the sequences are collected in the last 6 months. As for the second issue, although a MENB model can be trained with a single sequence, in this case the motif frequencies are less representative of the virus-host pair under analysis, so additional caution must be used in this case in interpreting the results obtained. Indeed, by construction the initial log-likelihood associated to the "Wuhan" host will be higher than the one for human Coronaviridae. Moreover the "Wuhan" host is likely not to be the human  [Andersen et al. , 2020] , but using specific viral sequences that has been collected in bats or pangolins (that have been suggested to be the reservoir of SARS-CoV-2 ancestors) due to their similarity with the Wuhan sequence would give very similar results.
Data and code availability All sequence data has been collected from the BV-BRC database  [Olson et al. , 2022]. After discarding short viral sequences (length lower than 1000 bases), we selected the pairs of host and viral family so that each viral family has at least 100 sequences annotated with each host chosen. We discarded Influenza A sequences collected after 2009 as the database is dominated by strains of the H1N1 "swine flu", whose triple-reassortment origin  [Garten et al. , 2009]  and (likely) not perfect adaptation to humans is a confounding factor during training. The resulting dataset, that is the starting point for all the results presented here, is available at https://zenodo. org/doi/10. 5281/ zenodo. 10050076. The SARS-CoV-2 data used for Fig. 5B  can be downloaded at https://nextstrain.
org. Notice, however, that this data is often updated as it is focused on the last 6 months. To allow exact reproducibility of our results we uploaded the data we used (downloaded on June 30th 2023) together with the sequence data at https://zenodo. org/doi/10. 5281/zenodo. 10050076. The code to infer models is written in Julia and publicly available in the GitHub repository at https://github. com/adigioacchino/MaxEntNucleotideBiases. jl. We trained our MENB models on 100 viral sequences randomly selected from our dataset for each pair of host and viral family. We replicated this sub-sample three times, observing quite small quantitative differences based on the sequence choice (see error bars in Fig. 1 ). For the comparison with VIDHOP presented in Fig. 1A, C.
we used exactly the same sequences. A Snakemake pipeline to train and test the MENB models on the data used here is available at https://github. com/adigioacchino/MENB_ snakemake. C T AC CA CG GC AT GT TC TT AAC ACA ACG AGC ATA ATG CAC CCA CCG CGC CTA CTG GAC GCA GCG GGC GTA GTG ACT ATT CCT CTT GCT GTT TAC TAT TCC TCT TGC TGT TTC TTT Orthomyxo forces -0. 50 -0. 25 0. 00 0. 25 avian human swine -0. 75 A G AA TTG C T AC CA CG GC AT GT TC TT AAC ACA ACG AGC ATA ATG CAC CCA CCG CGC CTA CTG GAC GCA GCG GGC GTA GTG ACT ATT CCT CTT GCT GTT TAC TAT TCC TCT TGC TGT TTC TTT 0. 25 Flavi forces -0. 25 0. 00 -0. 50 avian human swine A G AA AG CC GA GG CT TA TG AAA AAG ACC AGA AGG ATC CAA CAG CCC CGA CGG CTC GAA GAG GCC GGA GGG GTC AAT AGT CAT CGT GAT GGT TAA TAG TCA TCG TGA TGG TTA TTG C T AC CA CG GC AT GT TC TT AAC ACA ACG AGC ATA ATG CAC CCA CCG CGC CTA CTG GAC GCA GCG GGC GTA GTG ACT ATT CCT CTT GCT GTT TAC TAT TCC TCT TGC TGT TTC TTT Corona forces -0.
Data preparation The Climate Forecast System Reanalysis (CFSR) (  [Saha et al. (2010) ]) hourly wind data is considered in this study as a predictor. CFSR is a global reanalysis developed by the National Centers for Environmental Prediction (NCEP) that covers the period from 1979 to the present with an hourly time step and a spatial resolution of 0. 5°by 0. 5°. The historical H s data is extracted from the hindcast database HOMERE (  [Boudière et al. (2013) ]) at the target location with spatial coordinates (45. 2°N, 1. 6°W) located in the Bay of Biscay. The temporal resolution of both wind and H s data is up-scaled to 3-hourly data. The period from 1994 to 2016 is considered in this study, leading to a dataset with n = 67208 observations. Instead of using both zonal and meridional components as a predictor, we use the projected wind (  [Obakrim et al.
(𝐸𝐸𝐸𝐸𝐸𝐸 1) 𝑧𝑧 -𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑒𝑒 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 = �𝑥𝑥 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 -𝜇𝜇 𝑇𝑇𝑇𝑇𝑇𝑇 � 𝜎𝜎 𝑇𝑇𝑇𝑇𝑇𝑇 One MRI from each of the two patients in Family 1 passed the quality checks for initial MRI data quality and processed segmentations. For each participant, z-scores of grey matter volume, white matter volume, ventricle asymmetry (Equation  2 ), was extracted. (𝐸𝐸𝐸𝐸𝐸𝐸 2) 𝑉𝑉𝑒𝑒𝐸𝐸𝑉𝑉𝑠𝑠𝑉𝑉𝑠𝑠𝑉𝑉𝑒𝑒 𝑎𝑎𝑠𝑠𝑎𝑎𝑎𝑎𝑎𝑎𝑒𝑒𝑉𝑉𝑠𝑠𝑎𝑎 = (𝑣𝑣𝑠𝑠𝑉𝑉 𝑙𝑙𝑠𝑠𝑙𝑙𝑠𝑠 -𝑣𝑣𝑠𝑠𝑉𝑉 𝑟𝑟𝑟𝑟𝑟𝑟ℎ𝑠𝑠 ) (𝑣𝑣𝑠𝑠𝑉𝑉 𝑙𝑙𝑠𝑠𝑙𝑙𝑠𝑠 + 𝑣𝑣𝑠𝑠𝑉𝑉 𝑟𝑟𝑟𝑟𝑟𝑟ℎ𝑠𝑠 ) For mice, high-resolution Mn-enhanced mouse brain 3D images were acquired on a 9. 4-Tesla BioSpec scanner equipped with 114-cm gradient coil (maximum gradient strength 530 mT/m; Bruker Biospin Corp. , Billerica, MA). A Bruker ID 4 cm quadrature volume coil was used for RF excitation and detection. Twenty-four hours before imaging, mice were injected intraperitoneally with MnCl2 at a dose of 0.
Elevated plus maze The elevated plus maze used a cross maze with 12×2-inch arms with 50 lux of light illumination. Animals were introduced to the middle portion of the maze facing an open arm and allowed to freely explore for 10 minutes. Times spent and distance travelled in the open and closed arms were measured by an automated video-tracking system (Noldus Information Technology). Data were analyzed for distance traveled in the open arm, because it is least confounded by repeated entry. At least 5 mice per group were used in this assay.
Flow cytometry analysis Thymus and spleen were collected using standard dissection. Lymphocytes were extracted by mechanical dissociation followed by erythrocyte lysis using RBC lysis buffer (Biolegend cat. 420301). Cells were filtered using a 70-µm mesh (Fisherbrand cat. 22-363-528). Cells were blocked on ice for 30 min using mouse IgG isotype control antibodies (Invitrogen cat. 10400C) at 1:1000 dilution in 2. 5% FBS in PBS. Cells were aliquoted at 1x10 6 cells/ml. Antibodies were added at the specified concentrations (Table  S1 ) and incubated for 30 min on ice protected from light. Nuclear staining was done using DAPI. Flow cytometry analysis was performed using BD LSRFortessa cell analyzer and FCS Express 6 software.
DNA quantity was estimated by real-time PCR using sequencing adapter primers, and libraries were mixed at equimolar ratios for sequencing. Samples were sequenced using NovaSeq 6000 in a 150bp/150bp paired-end mode, with the NovaSeq 6000 SBS v1 kit and S4 flow cells (Illumina). Sequenced data alignment and processing All FASTQ files corresponding to the same sample were merged. Sequencing reads were aligned to the mouse reference genome (GRCm38/mm10) downloaded from (https://genome. ucsc. edu) using bwa-mem algorithm  (54). We used bammarkduplicates to mark the duplicated reads. For the alignment summary metrics, we used Alfred v0. 1. 16  (55).
Identification and analysis of somatically rearranged genes For the detection of genes affected by observed variants we used the BEDTOOLS package  (58)  and the annotation of NCBI genes for mouse downloaded from (https://genome. ucsc. edu). To study the effect of the different subsets of variants, we used the ENSEMBL Variant Effect Predictor (https://www. ensembl. org/Tools/VEP) (59) with default settings.
Recurrence analysis of polyclonal somatic DNA rearrangements A series of R-scripts were written to first parse and filter the VCF files for events, which are available from https://github. com/kentsisresearchgroup/P5BrainReorg. The filtered events were then checked for overlaps and clusters of mutually overlapping groups were output. For the events called by the Delly2 algorithm the overlaps were computed in two ways. Method 1 looked at the actual genomic intervals and two events were defined to overlap if either the length of both was less than 100,000 bp or the percent overlap was greater than 0. 1%, where the percent overlap was computed as the length of the overlap divided by the average size of the two events. Method 2 examined the overlap between the breakpoints of the somatic variants.
The ends were defined as region of 701 bp centered on each end of the event. It was these edge regions that were then intersected for overlaps this time using the rule that the percent overlap was greater than 1%. Once all overlapping events were identified, clusters were determined by looking for groups of events that all overlapped with each other. That is each element in the cluster had to overlap with every other element, as defined as a maximal clique in graph theory. We used the R package igraph to both construct the overlap graph and find all maximal cliques in the graph. We then filtered for sets with 3 or more events and output the table of clusters. To examine how the clusters varied on the filtering parameters this entire procedure was repeat multiple times each time adjusting one the following parameters: normalized total read counts (RC/EventSize), high-quality variant junction reads (RV), variant allele frequency (RV/[RR+RV], where RR is the high-quality reference junction reads).
Acknowledgments: We thank  Andrew Kung ,  Alejandro Gutierrez ,  Michael Kharas ,  Marc Mansour ,  Anton Henssen ,  Maria Gil Mir ,  Hao Zhu ,  Helen Mueller ,  Yves Bigot ,  Gabriella Casalena  and all our laboratory members for helpful suggestions, and  Sandeep Reddy ,  Qiangqiang Zang ,  Songhai Shi ,  Adria Pares-Palacin ,  Rodrigo Gularte Merida ,  Montserrat Puiggros ,  Jan Korbel ,  Tony Papenfuss ,  Guillaume Bourque ,  Patricia Goerner Potvin ,  Nicolas Robine ,  MSK Brain Tumor Center, Molecular Cytology, Integrated Genomics, Bioinformatics, Mouse Genetics, and Animal Imaging core  facilities for technical assistance, and  Maria Jasin  for the gift of Ku80-knockout mice. AK is a Scholar of the  Leukemia & Lymphoma Society , and acknowledges generous support of multiple funders listed below.
, 2015) , all literature on spiders is freely available, making it possible to easily inspect species descriptions to ascertain an original etymology (when reported); (3) the geographical ubiquitousness of spiders  (Piel, 2018) , the great diversity of species (currently > 50 000 valid taxa; World Spider Catalog, 2022), their diverse body forms, adaptations and ecological strategies  (Cardoso et al. , 2011; Foelix, 2011; Mammola et al. , 2017) , as well as the existence of a broad community of academics and amateurs studying them  (Platnick & Raven, 2013; Mammola et al. , 2017; Jäger et al. , 2021; Holmquist & Gillespie, 2022) , all should lead to an ample variety of etymological roots for spider names. Based on a dataset of 48 464 unique taxonomic entities (World Spider Catalog, 2020), we (1) explored general features of scientific names chosen by taxonomists (length, frequency by letter and etymology types) and (  2 ) modelled the proportional use of different etymology types over time and across continents.
BackBone taxonomy On 2 June 2020, we downloaded the backbone taxonomy of the World Spider Catalog (2020), consisting at the time of 48 464 unique taxonomic entities (47 956 species and 508 subspecies of spiders). The temporal span of the database goes back to 1757 when Carl Alexander Clerck (1709-65) named 67 spiders using binomial nomenclature in his book Svenska Spindlar  (Clerck, 1757). Interestingly, Clerck's book precedes the publication of the 10 th edition of Systema Naturae  (Linnaeus, 1758) , considered the start of zoological nomenclature, by one year. Therefore, in order to consider Clerck's spider descriptions valid under the system of zoological nomenclature, Svenska Spindlar is deemed to be published on 1 January.
g. Wikipedia, Wikispecies). When we consulted an original description, we looked for the specific section describing the etymology -typically under headings such as 'Etymology' or 'Derivatio nominis'. If such a section was missing, we checked the text of the paper for hints to the meaning of the species epithet. Thanks to our knowledge of different languages, the help of collaborators (see Acknowledgements) and through the use of Google Translate, we were able to inspect papers written in Chinese, English, French, German, Italian, Japanese, Latin, Portuguese, Russian, Spanish and Swedish. For each species, we indicated in the database whether we based our classification of the etymology on the original description ('Original'; N = 22 416), whether we consulted the original description but we had to infer the etymology because it was not reported ('Original + Inferred', N = 1018) or whether we inferred it without consulting the original description ('Inferred', N = 23 890).