dataesr/scientific-paragraphs-categorization huggingface dataset:
The factor time series were similar when FPEAK values varied. But for the factor profiles with positive FPEAK values, the factor of monoterpene more oxidized products including organic nitrates contained fewer traces of siloxanes and showed elevated fractions of the corresponding fingerprint peaks (Fig. S12 ). As discussed in Sect. 4. 3, these siloxanes can come from cosmetics and personal care products and silicone oils used in instrument pumps. The temporal variations of these siloxanes differed significantly from those of monoterpene more oxidized products. After evaluation, we chose the solution with FPEAK = +0. 6 for the high-mass range of the SMEAR II dataset, where siloxanes feature more in one factor. Factor L1 shows irregular diurnal variations with spiky peaks in the time series (Fig.
Monoterpene oxidation The oxidation processes of monoterpenes at the SMEAR II station have been investigated by several previous studies, mostly based on the highly oxidized compounds. Utilizing non-negative matrix factorization analysis on iodide-adduct CIMS data at the SMEAR II station,  Lee et al. (2018)  found that the gas-phase organic species subgroup of C 6-10 H y O ≥ 7 showed distinct daytime diel trends. Yan et al. (2016)  conducted source apportionment of HOMs at the SMEAR II station and separated various HOM formation pathways, such as monoterpene ozonolysis and monoterpene oxidation initiated by NO 3 radical. In this study, three types of monoterpene reaction products were detected: monoterpene lightly oxidized compounds, monoterpene more oxidized compounds, and monoterpene-derived organic nitrates.
The latter two were not clearly separated into different factors at the SMEAR II station due to the similarities in their overall time trends. For example, the time series of C 10 H 15 NO 5 H + correlate well with those of C 10 H 16 O 4 H + and C 10 H 16 O 5 H + (r 2 > 0. 61). Consistent with previous observations, monoterpene more oxidized products (i. e. , C 10 H 16 O 4 and C 10 H 14 O 5 ) have a broad high distribution throughout the day due to the active photochemical processes during daytime. Monoterpenederived organic nitrates (i. e. , C 10 H 17 NO 4 , C 10 H 15 NO 5 , and C 9 H 13 NO 6 ) are mainly characterized by a distinct morning peak at around 08:00, approximately 2 h after the NO peak. But their intensities are also elevated at night. PMF analysis of a NO - 3 CIMS dataset observed similar diurnal variations of terpene organic nitrates factor at a forest site in the southeastern USA  (Massoli et al.
In addition to VOC species, Vocus PTR-TOF is able to measure large amounts of oxygenated VOCs with enhanced detection efficiency. According to the results in this work, factor analysis on Vocus PTR-TOF mass spectra separated VOC precursors and their reaction products with varying oxidation degrees into different factors. These factors showed distinct characteristics in the atmosphere. Comparatively, the conventional PTR instruments or gas chromatography-mass spectrometry (GC-MS) largely detect VOC precursors of low-mass molecules  (Dewulf et al. , 2002; de Gouw and Warneke, 2007). Previous source apportionment studies on these datasets mainly identified primary biogenic and anthropogenic emission sources  (Vlasenko et al. , 2009; Patokoski et al. , 2014; Baudic et al. , 2016; Debevec et al.
, 2017; Sarkar et al. , 2017; Wang et al. , 2020). Recently, factorization methods have been applied on NO - 3 CIMS datasets to identify various atmospheric formation pathways of HOMs  (Yan et al. , 2016; Massoli et al. , 2018; Zhang et al. , 2020). Here, for the first time, source apportionment of Vocus PTR-TOF data identified various primary emission sources and secondary formation pathways of atmospheric organic vapors, highlighting the novelty of Vocus PTR-TOF in measuring both VOCs and oxygenated VOCs. The relative abundances of organic precursors, the lightly oxidized products, and the more oxidized products can be utilized by modellers to evaluate simulation output, improve model performance, and provide new perspectives to understand gas-phase physicochemical processes. Compared with VOC species, VOC reaction products are generally present in much smaller amounts in the atmosphere.
High signal intensities of sesquiterpene lightly oxidized compounds only occur at high-RH conditions. Such high RH dependence was not observed for monoterpene lightly oxidized compounds. To summarize, this study successfully performed binPMF analysis on subranges of mass spectrometry dataset acquired with a Vocus PTR-TOF in two European forest ecosystems, the Landes forest and a southern Finnish boreal forest. Both primary emission sources and secondary oxidation processes of organic vapors were identified in the two environments, particularly for terpenes and their reaction products with varying oxidation degrees (including organic nitrates). Factors of the lightly oxidized products, more oxidized products, and organic nitrates of monoterpenes/sesquiterpenes accounted for 8 %-12 % of the measured gas-phase organic vapors in the two forests.
Further interpretations show a strong RH dependence for the behavior of sesquiterpene lightly oxidized products but not for that of monoterpene lightly oxidized products, the reasons for which need more investigations in the future. Data availability. The time series of the measured trace gases, meteorological parameters, and the concentrations of isoprene and monoterpenes in the Landes forest and at the SMEAR II station are available from https://doi. org/10. 5281/zenodo. 3946644 (Li, 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-4123-2021-supplement. Author contributions. HL and ME conceived the study. HL, MR, ST, LH, PMF, EV, and EP conducted the field measurements. HL carried out the data analysis. MRC, YZ, ME, and FB participated in the discussions on data analysis.
Acknowledgements. This work was supported by the  H2020 European Research Council  (grant nos. ATM-GTP (742206 ), COALA (638703), and CHAPAs (850614)) and the  Academy of Finland  (grant nos. 317380  and  320094 ). We thank the  SMEAR II station  staff for their help during field measurements in Hyytiälä. The authors also would like to thank the  PRIME-QUAL program  for financial support (ADEME, convention # 1662C0024 ) and the  French National Research Agency (ANR)  in the frame of the " Investments for the Future" program , within the Cluster of  Excellence COTE  ( ANR-10-LABX-45 ) of the  University of Bordeaux  for financial support. Harald Stark  and  Donna T. Sueper  from  Aerodyne Research Inc. are acknowledged for helpful discussions. Financial support. This research has been supported by the  H2020 European Research Council  (grant nos.
Identification of rare and deleterious variants in the TRICL discovery set In the discovery set, a total of 2,182,753 variants were detected. Applying a three-step filtering method based on allele frequency (MAF < 1% in non-Finnish European [NFE] population from the Genome Aggregation Database [gnomAD]), variant class (missense, protein-truncating and regulatory), and functional effects (predicted deleterious and or with clinical significance from ClinVar), we identified 67,470 rare and putatively deleterious variants: 63% missense, 16% frameshift (fs), 12% in-frame indels, 6% regulatory (untranslated region [UTR] and splice acceptor/ donor), and 3% stop-gain. Single variant association analysis identified 75 potential candidates. Given the known challenge of excessive false-positive indel detection rates caused by the high frequency of homopolymerassociated sequencing errors  [25] [26] [27] [28]  , we subjected these 75 potential candidates to additional filtering and manual inspection using Genome Browser (Supplementary Table  1 ).
Meta-analyses of the discovery and validation sets In the seven validation datasets, of the 25 candidates, 100% were covered by the gnomAD, 22 (88%) in TCGA, 16 (64%) in COPDGene, nine (36%) in GELCC, and nine (36%) were covered in one of the three case-control studies (OncoArray, Affymetrix, and UKB) with genotyping data. Table  3  summarizes the top five candidates with consistent associations from the meta-analysis. The topmost risk-conferring variant is a missense SNV, p. V2716A, in the phosphatidylinositol 3-kinase (PI3K) catalytic domain of ATM (Ataxia telangiectasia mutated; OMIM 607585, UniProt Q13315). This pathogenic variant (rs587782652) is exceedingly rare in the gnomAD, with MAF 0. 0021% and 0. 0054% in non-cancer controls and NFE population, respectively. In our combined datasets, this variant presented in 0.
05% of LCs and 0. 003% controls, with remarkably high effect sizes (OR 19. 55, 95%CI 5. 04-75. 6; P-value 1. 7e-05). LC carriers of this variant were predominately enriched in smokers (8/9 carriers), AD (7/9 carriers), and early-onset (6/9 carriers; mean 55 yrs). Further, four additional rare deleterious variants were observed in ATM (Fig. 1  and Supplementary Table  3 ). No LD is present among these variants and the candidate p. V2716A (Supplementary Table  4 ). The second risk variant is c. *28delT in the 3′ UTR of POMC (Proopiomelanocortin; OMIM 176830, UniProt # P01189). The MAF of this 2 bp del (rs756770132) were 0. 086%/0. 17% in gnomAD noncancer/NFE controls; while in our dataset presented in 0. 66% of LCs and 0. 15% of controls, conferring a 4-fold risk for carriers (OR 4. 33, 95%CI 2.
Additionally, there were some racial differences in non-white between TCGA cases (27%) and gnomAD controls (30%), that could cause biased effect sizes in the meta-analysis. Genetic ancestry analysis shows 90% TCGA-LCs were inferred as genetic European ancestry  83. However, it is possible that a small portion of European ancestry TCGA-patients has AJ origin, given that 7% of ovarian cancer  84  and 24% of endometrial cancer  85  are of AJ heritage. It is of note that in our dataset, none of the variant allele carriers of the 25 candidates were found to have Africanancestry. Therefore, we expect this potential population stratification effect to be relatively small on rare variant associations, particularly in non-Africans that have not experienced severe population bottlenecks  [86] [87] [88].
Rare variant filtering and functional annotation in the discovery set Following variant calling, rare variants were further enriched by the application of three-steps: i) Variant with MAF < 1% in the gnomAD (NFE ancestry, v2. 1); ii) Variants class, including missense, protein-truncating, and regulatory; and iii) Mutation effects, i. e. , variant results in protein truncation and predicted to be deleterious from 4/6 prediction tools (SIFT, Polyphen-2, MutationTaster, MutationAssessor, FATHMM, and FATHMM-MKL). The miRNAs putatively bound to the sequence containing UTR variants were identified by the TargetScan  35. We additionally incorporated rare variants classified as pathogenic, likely pathogenic, or VUS from the ClinVar database, which compiles clinically observed human variants.
Study populations in the validation sets and meta-analysis The candidate variants were further examined in seven validation datasets, aggregated from different centers and across several platforms (four WES data and three genome-wide genotyping datasets as shown in Table  1 ). We tabulated the variant carrier counts per candidate and performed meta-analyses using the inverse-variance-weighted fixed-effects (assume the true effect size is the same in all studies). 1. GELCC study (Genetic Epidemiology of LC Consortium, 380 LCs): This included 122 familial and 258 sporadic LC cases. i) Familial LC Study Subjects (dbGaP phs000629. v1. p1). The familial cases were selected from high-risk LC families with at least two first-degree relatives affected with LC  95. The GELCC study population and recruitment scheme have been described in detail previously  96    i Representative histograms of (g).
Gene prioritization based on functional annotations and protein interactions network To better reprioritize genes and candidates, we used three prioritization tools: 1) Gene evolutionary constraint to LoF variation, which using the o/e ratio from the gnomAD. 2) Phevor PhoRank algorithm  105  , which ranks the genes based on their phenotypic relevance as defined by diverse biomedical ontologies. 3) Protein-Protein interactions (PPI) network using the STRING database  106  , with an interaction score cut-off ≥0. 15 (low confidence).
Stained cells were then analyzed by a BD LSRFortessa flow cytometer. FCS files were analyzed by FlowJo 10. 5 software. For siRNA experiments, cells were collected 72 h post transfection and median fluorescence intensity was quantified. Also, to quantify the DNA-damage positive subpopulations, 0. 5% of the mock cells were gated as the γH2AX threshold as previously demonstrated. The percentage of γH2AX positive cells in each sample was calculated and compared to its corresponding nontargeting siRNA control. For overproduction experiments, mocktransfected cells were used to set the gates to determine the GFP and γH2AX positive cells. 0. 5% of the mock cells were gated as the γH2AX threshold. The DNA-damage ratios by protein overproduction for 72 h are calculated as described. Briefly, the damage ratio is defined as (Q2/Q3)/(Q1/Q4), where Q2 is the portion of transfected γH2AX-positive cells; Q3 is the portion of transfected, γH2AX -negative cells; Q1 is the portion of untransfected, γH2AX-positive cells; and Q4 is the portion of untransfected, γH2AX-negative cells.
ACKNOWLEDGEMENTS We would like to thank all individuals who participated in this study. This work was supported by grants from the  National Institutes of Health  ( R01CA127219 ,  R01CA141769 ,  R01CA060691 ,  R01CA87895 ,  R01CA80127 ,  R01CA84354 ,  R01CA134682 ,  R01CA134433 ,  R01CA074386 ,  R01CA092824 ,  R01CA250905 ,  R01HL113264 ,  R01HL082487 ,  R01HL110883 ,  R03CA77118 ,  P20GM103534 ,  P30CA125123 ,  P30CA023108 ,  P30CA022453 ,  P30ES006096 ,  P50CA090578 ,  U01CA243483 ,  U01HL089856 ,  U01HL089897 ,  U01CA76293 ,  U19CA148127 ,  U01CA209414 ,  K07CA181480 ,  N01-HG-65404 ,  HHSN268200782096C ,  HHSN261201300011I ,  HHSN268201100011 ,  HHSN268201 200007 C, DP1-CA174424 ,  DP1-AG072751 ,  CA125123 ,  RR024574 ,  Intramural Research Program  of the  National Human Genome Research Institute  (JEB-W), and  Herrick Foundation.
DATA AVAILABILITY The data generated and/or analyzed during the related study are described in the figshare metadata record: https://doi. org/10. 6084/m9. figshare. 13280387  109. The data that support the findings of this study are available via the dbGaP (database of genotypes and phenotypes) repository. The data are controlled-access, so interested parties will need to request accessinformation on how to do so can be found on pages linked to below. The access numbers are https://identifiers. org/dbgap: phs000878. v2. p1  110  for Transdisciplinary Research in Cancer of the Lung (TRICL) study, https://identifiers. org/dbgap:phs001273. v1. p1 111 for the OncoArray study, https://identifiers. org/dbgap:phs001681. v1. p1  112  for the Affymetrix study, https:// identifiers. org/dbgap:phs000629.
Genetic ancestry analysis of TCGA patients shows the vast majority of subjects with "unknown" race were primarily genetic European ancestry (i. e. , 90% TCGA-LCs were genetically Europeans) 82. #The validation sets include 26,803 LCs and 555,107 controls: (1) Genetic Epidemiology of LC (GELCC) WES data for 380 LCs (258 sporadic and 122 FLC were selected from high-risk LC families with at least two first-degree relatives affected with LC); (2) COPDGene WES data for 318 controls with normal lung function; (3) TCGA (The Cancer Genome Atlas) germline WES data for 1015 LCs; (4) GnomAD (genome aggregation database, v2. 1) WES and WGS data for 134,187 non-cancer controls (excluded individuals from cancer cohort studies, such as the TCGA cohort). (5) OncoArray genotyping data for 17,878 LCs vs. 13,425 controls; (6) Affymetrix exome array data for 5364 LCs vs.
Transfected volumes of WT and DI genomes were calibrated to a ratio of WT:DI = 4:1 in order to approximately obtain a ratio of 1:1 after transfection. Parameter estimation was achieved through nlminb optimization function in R software  [76]  embedded in an iterative process. Each optimization consisted in minimizing the sum of the squared errors between experimental and simulated normalized data points for all variables and conditions. The least square function is as: with r indicating the replicate number and t the sampling time (t = 3. 5 to 9 hours post transfection). Initial conditions for all variables in all infection conditions were obtained from averaging experimental observations over the 3 replicates at t 0 = 2 hours post transfection. In Eq  (11) , we define: LS ¼ X r X t ½ð � G WT ðtÞ À � g WT ðt; rÞÞ 2 þ ð � G DI � x i ¼ logðx i þ 1Þ maxðg i Þ ; � y i ¼ logðy i þ 1Þ maxðv i Þ with x, resp.
Briefly, purified polyA+ RNA from infected cells is fragmented to yield 80-100 bp fragments, circularized, and subject to rolling-circle reverse transcription. This procedure yields tandem reverse transcripts that are used to correct reverse transcription errors. Variant base-calls and allele frequencies were then determined using the CirSeq v2 package (https://andino. ucsf. edu/CirSeq). Circularized repeats are oriented to the reference genome and variants are called from raw reads based on phred33 scores of 20. These tandem variant-called reads are then aligned to each other to generate consensus sequences with a theoretical error of 10 -6. Technical replicates of passaged libraries, and individual sequencing lanes, were compared after CirSeq mapping and pooled for analysis of fitness. Raw reads are deposited at Bioproject PRJNA669406.
Model predictions Cross-validation. We cross validate the results of our optimization procedure by assessing how well the model is able to predict the relative WT virus burst size for various WT to DI initial ratios (after transfection) for which it has not been trained. We first obtain an optimal set of model parameters on our time series experimental data (g and v) featuring initial WT: DI = 1:1. We then test five additional DI-to-WT initial ratios, ranging from 0 to 3. 6. Initial conditions for model simulations were set as the average of experimental values for each of the five initial ratios. In the cross-validation experiment, evaluation of the relative WT virus burst size was based on the count of plaque-forming units (PFUs). In the time-series experiment that was used for parameter estimation, the number of WT virions (v WT ) was estimated by digital droplet PCR.
g. ,  [18] [19] [20] [21] ). To have confidence in radar altimetry-based water levels, calibration/validation (cal/val) needs to be performed to assess each mission bias, inter-mission biases (when combining data from different radar altimeters), and to determine the quality of the datasets through measures such as root-mean-square error (RMSE) and correlation coefficient (R). Even though numerous cal/val permanent sites are operating over the ocean, less cal/val facilities are available for inland water. Lake Issykkul, in Kyrgyzstan (Central Asia), is, to our knowledge, a unique site where comparisons between in situ data, Global Navigation Satellite Systems (GNSS), and radar altimetry-based water levels have been performed on regular basis since mid-2000  [22] [23] [24]. Recently, a study evaluated the performances of 11 radar altimeter (RA) missions, from GEOSAT to SENTINEL-3A, over large lakes with areas ranging from 487 to 81,935 km 2  [25].
Since radar altimetry collects the elevation measurements of Earth's surface at the nadir direction, only a small portion of the Earth surface is covered by the altimetry tracks. According to  [26] , current altimetry missions on repeat orbit, not considering Cryosat-2, are only able to monitor less than half of the total number of lakes with an area larger than 100 km 2. To increase the number of lakes where water levels can be retrieved, lidar altimetry data from ICESat missions were increasingly used because of their small footprint (~70 m for ICESat/GAS and ~17 m for ICESat-2) and long-period/drifting orbit which allows to observe a larger part of the Earth's surface at the expense of temporal frequency  [27] [28] [29]. Two new satellite lidar altimetry missions were launched in 2018: ICESat-2 and the Global Ecosystem Dynamics Investigation Lidar (GEDI) whose main goals are to monitor the elevation changes of the Greenland and Antarctic ice sheets and to provide observations of forest vertical structures, respectively.
In this study, an evaluation of the performance of most of the high-resolution radar altimetry missions (i. e. , Jason-1/2/3, ERS-2, ENVISAT, SARAL, and SENTINEL-3A and B) is performed over eight Swiss lakes, with an emphasis on the recently launched SENTINEL-3A and B missions. The study area was chosen because of i) the presence of a high quality, continuous, and leveled dataset of water levels covering the whole 1995-2020 study period, and ii) the strong topography surrounding the lakes which will allow to assess the impact of the OL tracking mode on radar altimetry-based water level measurements. A complementary cal/val study was also performed on the ICESat-2 and GEDI data available in the study area to determine their advantages and drawbacks for the monitoring of lake levels.
Radar Altimetry Data Data from the following high precision radar altimetry missions were used in this study: ERS-2, ENVISAT, Jason-1/2/3, SARAL (operating in Low Resolution Mode-LRM), SENTINEL-3A and 3B (operating in SAR mode). These missions are acquiring data on three different nominal orbits. Their major characteristics are summarized below: 1. Ten-day repeat orbit period missions: Jason-1, Jason-2, and Jason-3 Jason-1, Jason-2, and Jason-3 were placed on a 1336 km of altitude and 66 • of inclination orbit with a 10-day repeat cycle, and an equatorial ground-track spacing of about 315 km, formerly used by the Topex/Poseidon mission. Jason-1 is a joint mission between the Centre National d'Etudes Spatiales (CNES) and the National Aeronautics and Space Agency (NASA) launched on 7 December 2001.
ERS-2 was launched on 25 April 1995 by the European Space Agency (ESA) as ERS-1 follow-on mission. The satellite carried, among other sensors, a radar altimeter (RA) operating at Ku-band (13. 8 GHz)  [35]. ENVISAT mission was launched on 1 March 2002 by ESA. Its payload was composed of 10 instruments including the advanced radar altimeter (RA-2). It was based on the heritage of the sensor on-board the ERS-1 and 2 satellites. RA-2 was a nadir-looking pulselimited RA operating at two frequencies at Ku-(13. 575 GHz), as ERS-1 and 2, and S-(3. 2 GHz) bands  [36]. ENVISAT remained on its nominal orbit until October 2010 and its mission ended on 8 April 2012. RA-2 stopped providing valid data at S-band in January 2008. SARAL is a joint mission between CNES and the Indian Space Research Organization (ISRO), launched on 25 February 2013.
2 GHz) bands  [36]. ENVISAT remained on its nominal orbit until October 2010 and its mission ended on 8 April 2012. RA-2 stopped providing valid data at S-band in January 2008. SARAL is a joint mission between CNES and the Indian Space Research Organization (ISRO), launched on 25 February 2013. Its payload is composed of the AltiKa radar altimeter operating at Ka-band (35. 75 GHz), the bi-frequency radiometer, and a triple system for precise orbit determination: the real-time tracking system DIODE of the DORIS instrument, a LRA, and the Advance Research and Global Observation Satellite (ARGOS-3)  [37]. It remained on its nominal orbit until July 2016.