dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation of PM Concentration Modeling CHIMERE simulation results compared to measurements for PM 10 and PM 2. 5 during 2013 and 2015 springs are displayed in Table  3. A model evaluation with surface measurements is necessary to quantify model skills and limits. As China is a large country with various pollution sources (anthropogenic or naturals), evaluation of surface concentrations is necessary to determine which cities present lower uncertainties in the available database to afterwards evaluate the impact of dust on PM load. PM content is underestimated in eastern and north-western areas, but slightly overestimated over central and southern China compared to measurements. Also, in these areas, NRMSE is smaller than over northern areas. The PM 2. 5 daily variation is better represented than PM 10 , with higher Pearson correlation coefficients.
5. Both criteria are met for PM 2. 5 (-0. 002 and 0. 14) and PM 10 (-0. 21 and 0. 24) for MFB and MFE respectively (MFE and MFB values are calculated on all stations available in the dataset see Section 2. 3). We then evaluated PM 2. 5 over 2011, 2013 and 2015 for the three selected cities, from US embassy data. CHIMERE vs US embassy PM 2. 5 comparisons results for 2011, 2013 and 2015 are shown in Table  4. Daily time series for Beijing, Chengdu and Shanghai are displayed in Figure  S2  (in supplement). 2011 measurements are available only for the Beijing station. The highest annual mean PM 2. 5 is observed in Beijing (92. 2 µg m -3 ), and the lowest in Shanghai (55. 3 µg m -3 ). Measurements show for all of the three stations a seasonal cycle, with highest pollution levels in winter and lowest pollution levels in summer.
Funding: M. Lachatre was funded by the  Sorbonne Université, l'Ecole Doctorale des Sciences de l'Environnement d'Ile de France  ( ED129 ) and PolEASIA  ANR  project under the allocation  ANR-15-CE04-0005. Acknowledgments: This work was granted access to the HPC resources of TGCC under the allocation A0030107232 made by  GENCI. We acknowledge the free use of MODIS AOD data from the Terra from (https://terra. nasa. gov/,last consulted  25/09/2018 ). Surface measurements provided by  U. S. Department of State Air Quality Monitoring Program, Mission China  (www. stateair. net,last consulted  07/10/2018 ). We also acknowledge the  Chinese Research Academy of Environmental Sciences (CRAES) in Beijing , for providing us PM 2. 5 and PM 10 surface concentrations data. We also acknowledge the  NASA's AERONET network.
Risk scores, prediction, pathways, progression, modifiers, and age at onset The IPDGC also made substantial progress beyond standard risk loci detection. Using as a basis the large IPDGC genetic datasets (currently including over 20,000 cases and 20,000 controls) multiple advanced analyses are performed to predict disease, assign more "function" or biology to GWAS loci and identifying potential disease relevant pathways. Shortly after the larger GWAS efforts a genetic risk score was generated based on the GWAS loci effect sizes. This genetic risk score has been identified to be useful in potential prediction of PD and including simple clinical tests such as a smell test high accuracy can be achieved  [14]. This genetic risk score is also negatively correlated with the age of onset of PD  [15]  and interestingly certain variants appear to have a large effect on age of onset while others do not seem to influence age of onset  [16].
Another important role the IPDGC has, is providing the research community with easy access result summaries that are usable and understandable to basic researchers, such as results from GWAS summary stats, from the MR studies  [13]  (https://pdgenetics. shinyapps. io/MRportal/) and PD progression studies  [17]  (https://pdgenetics. shinyapps. io/pdprogmetagwasbrowser/). Currently under development are the IPDGC sequencing browser and the IPDGC GWAS browser. In the IPDGC sequencing browser researchers can browse all variants identified in IPDGC derived sequencing data to allow quick lookup, scrutinize and assess potential pathogenic variants. The IPDGC GWAS browser is a community driven effort to nominate causal genes in GWAS loci based on public data to assist functional study design.
Advanced cohort building Data generation within IPDGC is ongoing and accelerating. With this massive growth planned in both depth and breadth of data, new models of shar-ing and analyses become necessary. It is our goal to make as much participant level data, code and summary statistics available in as close to real time as possible. Additionally, supporting these centralized repositories with enough compute infrastructure to facilitate federated analysis methods from the large central data pool to integrate many smaller silos of data isolated by various logistic constraints will be necessary. Our next phase of analyses will focus not only on just risk relating to case-control status but also on disease course, analyzing markers of progression on both the clinical and molecular levels. Precedence for this has been set by reports such as Iwaki et al 2019  [17] , illustrating the utility of large-scale data harmonization across over a dozen longitudinal studies of PD progression.
This type of detailed clinical data coupled with multi-modal biomarker studies and various * -omics assays on the same study participants will exponentially increase our data complexity and hopefully yield new insights. To maximize the utility of these complex datasets in our investigations of disease etiology, we will likely need to push past relying on current GWAS derived estimates of risk. While polygenic risk scoring works well for SNP based studies, integrating multiple data substrates and including various data silos in risk predictions will necessitate robust infrastructure and adoption of methods such as federated learning via neural networks to predict various aspects of disease course. Methods such as decomposition trees and other graph-based methods will help us identify subsets of disease that are more similar or more simply put, "predicting the flavors of parkinsonism.
Creating Parkinson disease resources for the research community Relative to other branches of research, the genetics of disease has become relatively straightforward. There is a clear and increasingly common path that yields predictable progress. Traveling this path and realizing the potential of the understanding you arrive at can be most efficiently supported through the creation of foundational resources for the research community. Most obviously the sharing of genetic data enables rapid progress and democratizes access to multimillion-dollar datasets. The discovery and dissemination of genetic findings is no longer the rate limiting step in our understanding of biology; however, the next step, translating maps to mechanisms to medicine (M2M2M), remains a significant challenge and one collectively recognized and prioritized by the global genetics community (https://www.
ACKNOWLEDGMENTS We would like to thank all of the subjects who donated their time and biological samples to be part of all IPDGC studies without them we could not have made such progress. We would also like to thank all members of the  International Parkinson Disease Genomics Consortium (IPDGC)  for their continuous support and collaboration. For a complete overview of members, acknowledgements and funding, please see: http://pdgenetics. org/partners. The work of the IPDGC has been supported either directly or indirectly by a number of bodies. These are at http://pdgenetics. org/partners and noted below, but it is also worth explicitly mentioning the support of the  Parkinson's Foundation  and  Illumina , who have both provided generous support for IPDGC meetings.
Although documented in many organs, the expression of ACE2 remains poorly investigated in the upper aerodigestive tract while these anatomical regions are at the forefront when the virus enters, given the spread by respiratory droplets  [3]. In fact, many otorhinolaryngological symptoms have been described in patients with moderate COVID19 infection, particularly a sudden loss of smell and gustatory dysfunctions  [16, 17]. Interestingly, Xu et al. recently demonstrated the expression of ACE2 in oral mucosa, specifically in epithelial cells, and to a lesser extent in T cells, B cells, and fibroblasts through single-cell RNA-seq of carcinomas and normal tissues listed from public databases, highlighting a new potential route of SARS-CoV-2 infection  [18]. Finally, ACE2 expression has also been reported in the salivary gland ducts of Rhesus macaques, supporting (i) a possible additional organ target for COVID19, (ii) growing interest on saliva for the diagnosis of the disease, and (iii) the concept that asymptomatic people could spread the infection through saliva  [19, 20].
Statistical Analyzes The median scores of the independent data groups were compared by using nonparametric Kruskal-Wallis test for the epithelial cell components. Association between ACE2 score of epithelial cells and clinical data of patients was evaluated by Mann-Whitney test. Correlation between immunostaining score of epithelial cells and age of patients was assessed by Spearman's Rho test. All statistical analyses were performed using the SPSS Statistics 23 software (IBM, Ehningen, Germany) and a p-value <0. 05 was considered to be significant.
The presence of ACE2 in respiratory epithelial cells was frequently established through non-staining procedures  [44, 45]. In the buccal epithelium, positive staining was reported through immunohistochemistry and single-cell RNAseq. As we have observed, Bertram et al. also reported a weakly positive ACE2 expression in tonsillar epithelium but stronger in oral epithelial cells  [42]  as well as Hamming et al. and Liu et al. who demonstrated a positive labeling in the epithelium lining of human oral epithelium and naso-, oro-and laryngopharynx of seven Chinese rhesus macaques, respectively  [14, 19]. A previous study also conducted single cell RNAseq analysis based on public data sets and validated the expression of ACE2 in oral epithelial cells, suggesting a new potential route of infection  [18].
Indeed, six targets were significantly upregulated (VAT1L, DESI1, CTBP2, ARHGAP35, JUND and ACSL3; Fig. 2c ), suggesting that miR-375 impairment might explain a small subset of altered proteins. Also in this case, we did not observe correlation between transcript and protein levels (PCC = 0. 274, p = 0. 599; Supplementary Fig. S1 online ). Finally, we interrogated our dataset generated by photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) analysis  12. Specifically, in that work we identified transcripts bound by both endogenous and FLAG-tagged transgenic FUS, finding that FUS WT preferentially binds intronic regions, while FUS P525L is mostly associated with 3′UTRs. We reasoned that altered protein levels might result: (a) from transcripts selectively bound by FUS WT in introns, as a consequence of decreased nuclear FUS in mutant cells; (b) from transcripts bound by the mutant protein in their 3′UTR, due to gain of aberrant functions of FUS P525L , presumably in the cytoplasm.
We considered targets that overlapped between the endogenous and exogenous FUS datasets to be high confidence targets. Several transcripts, which are bound in an intron preferentially by FUS WT , showed altered protein levels in any direction (60/323; 18. 6%; Fig. 2d ; Supplementary Fig. S2 online ). Notably, a large fraction of altered proteins is encoded by transcripts selectively bound by mutant FUS in the 3′UTR (216/323, 66. 9%; Fig. 2e ; Supplementary Fig. S2  online). We performed GO term enrichment analysis of this latter group of genes and noticed terms related to "RNA binding" and "Protein binding" molecular functions for both upregulated and downregulated proteins (Supplementary Table  S3  online). Together with our previous work, in which we showed aberrantly increased protein levels of some FUS P525L targets encoding for RBPs  12  , these results suggest that the most likely mechanism underlying proteome alteration in mutant FUS motoneurons is aberrant targeting of 3′UTRs by mutant FUS.
Another novel mutant FUS target is ASAP1, involved in regulation of membrane trafficking and cytoskeleton remodeling  23. Finally, VCP is a known ALS-linked gene involved in regulation of protein homeostasis and autophagy  24. These results uncover a broad impact of mutant FUS on expression other ALS-linked genes, oxidation-reduction processes and motoneuron cytoskeleton. proteins from mass-spectrometry analysis and significance level was set to a p value < 0. 05. RNA-seq data was derived from De Santis et al. 11. Pearson's correlation was calculated between paired proteomic and RNA-seq and statistical significance was computed using 'cor. test' function in R package. Intersection between PAR-CLIP and proteomics datasets was performed considering only genes that were detected both experiments (4,326); statistical significance of the overlap was calculated with Fisher's exact test.
Acknowledgements The authors wish to thank the  Imaging Facility  and the  Flow Cytometry Facility at Center for Life Nano Science ,  Istituto Italiano di Tecnologia , for support and technical advice. We thank  Maria Rosito  and  Silvia Di Angelantonio  for helpful discussion. This work was partially supported by grants from  AriSLA pilot  grant  2016  " StressFUS " and  Istituto Pasteur Italia-Fondazione Cenci Bolognetti  to A. R. ERC-2019-SyG (855923-ASTRA),  PRIN 2017  ( 2017P352Z4 ) and  AIRC  (IG 2019 Id. 23053) to I. B.
Data availability The raw LC-MS/MS data generated during this study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD019596. Previously published RNA-seq raw data  11  used in this study have been deposited at the GEO (GEO: GSE94888). Previously published PAR-CLIP raw data  12  used in this study have been deposited at the GEO (GEO: GSE118347). All other data generated or analyzed during this study are included in this published article and its Supplementary Information files online.
The NanoAcquity LC system and trapping column were purchased from Waters (Milford, MA, USA). The 5,600 + TripleTof MS system, ProteinPilot and MarkerView software were purchased from SCIEX (Ontario, Canada). For disulfide bonds reduction, 50 μg of proteins were incubated at 56 °C for 30 min with 10 μl of 100 mM DTT (dithiothreitol) in digestion buffer (50 mM NH 4 HCO 3 in MilliQ water, pH 8). Cysteine residues were then alkylated with 30 μl of 100 mM IAA (iodoacetamide) in digestion buffer for 20 min at room temperature in the dark. Protein precipitation with cold acetone was performed at -20 °C before overnight digestion with 1 μg of trypsin at 37 °C in 50 mM NH 4 HCO 3 in MilliQ water, pH 8. Tryptic peptides were dried under vacuum and then dissolved in 150 μl of 3% acetonitrile (ACN) + 0.
Direct comparisons from prospectively collected data on OAC persistence in clinical practice are limited. The Global Registry on Long-Term Antithrombotic Treatment in Patients with Atrial Fibrillation (GLORIA-AF) is a large, ongoing global registry program, that enrolled patients with newly diagnosed non-valvular AF and >1 risk factor(s) for stroke. Based on phase III data from GLORIA-AF, which allows for comparisons of patients being prescribed NOACs vs. VKAs in clinical practice, we compared therapy persistence for NOACs and VKAs. In addition, we assessed the extent to which NOAC dosing (once daily (QD) vs. twice daily (BID)) affects drug persistence.
Materials and Methods The design of GLORIA-AF (https://clinicaltrials. gov/ct2/home; trial registration numbers NCT01468701, NCT01671007, NCT01937377) has been previously published  [9]. In brief, the GLORIA-AF study collects clinical practice evidence regarding patients with recently diagnosed AF to evaluate the safety and effectiveness of OACs. GLORIA-AF is a three-phase study. Phase I was performed before NOACs were available for stroke prevention in AF. Phase II started when dabigatran etexilate (dabigatran) was approved in participating countries. In Phase II, baseline characteristics of all enrolled patients were collected, and individuals who were treated with dabigatran were followed-up for 2 years. Phase III was conducted when dabigatran became more widely adopted. During phase III, follow-up data were collected for up to 3 years regardless of treatment, to examine the efficacy and safety of NOACs compared with VKAs  [9].
The number of imputations was 20. Each imputed dataset was analyzed separately, and results were averaged across the imputed datasets. Kaplan-Meier estimates of persistence were used to evaluate time to discontinuation as well as persistence rates at the end of each time period (6 and 12 months) based on trimmed patient sets. Similarly, all the preceding analyses were performed for the comparative analysis for 1-year persistence among patients on NOACs QD vs. BID. All analyses were performed using SAS software version 9. 4 (SAS institute, Inc. , Cary, NC, USA).
Discussion The main finding of this analysis of GLORIA-AF phase III dataset of patients with new onset non-valvular AF is that the 1-year probability of NOAC therapy persistence was better than with VKAs (80. 3% and 74. 9%, respectively). Second, for NOACs there was little difference between QD or BID regimes in terms of persistence (79. 5% and 80. 6%, respectively). In our study, considering the first year after treatment initiation, NOAC persistence exceeded VKA persistence. Patients and physicians were aware of their participation in the clinical study, and this knowledge may have positively affected persistence via a Hawthorne effect  [16]. However, OAC therapy persistence in GLORIA-AF study was closer to a "real-world" clinical practice than to randomized controlled trials. Despite lower cost of treatment with VKAs, regular monitoring of the VKA treatment, and possible deterioration of chronic kidney disease in some patients medicated with NOACs, VKA persistence remained lower than NOAC persistence.
The majority of previous studies addressing drug persistence consistently revealed higher persistence rates for NOACs than for VKAs at 6 months and 1 year  [17] [18] [19] [20]. Indeed, NOAC persistence at 1 year ranged from 38% to 99. 7% and VKA persistence ranged from 24% to 93. 2%  [18, [20] [21] [22] [23] [24]. We found persistence with NOACs to be nearly uniform regardless of the NOAC dosing regimen (QD vs BID): 85. 8% and 86. 0%, respectively, at 6 months. The proportion of patients persisting on NOAC therapy for 1 year was 79. 5% using the QD regimen and 80. 6% with the BID regimen. A number of other studies have directly examined persistence with particular NOACs and compared dosing regimens. In one study  [25] , 63. 6% of patients were persistent with dabigatran therapy, while 68. 1% of patients were persistent with rivaroxaban at 6 months; however, this study was retrospective and used administrative data from Canada, with a shorter follow-up period than that in our study.
A retrospective analysis from the Danish National Patients Registry assessed persistence with OACs and found that after 1 year, 90% of rivaroxaban and 90% of apixaban patients were persistent, whereas 82% of dabigatran patients and 65% of warfarin patients were persistent  [26]. In one Swedish study  [27] , the proportion of patients persisting on treatment based on incidence of prescription claims in intervals of six months was 86% with apixaban, 74% with dabigatran, and 77% with rivaroxaban at 1 year. In an Italian study based on administrative databases of five local healthcare units  [28] , the probability of drug persistence at 1 year since drug initiation was 75% among apixaban users, followed by 69% with rivaroxaban, 65% with dabigatran, and 43% with warfarin. Many studies assessing persistence and adherence (defined as an active choice on the part of the patient to follow through with therapy) for NOACs revealed that the QD dosing regimen is associated with improved therapy persistence when compared with BID dosing regimen  [29] [30] [31] [32] [33].
Measurements are taken from outside the container over the OxyDot ® by holding the fiber-optic reader pen attached to the OxySense 5250i. Temperature and relative humidity were recorded every hour during the trial using data loggers (EL-USB-2 model, Lascar; Erie, PA, USA) placed in one bag of each treatment. Data presented in the plots are daily averages of temperature and relative humidity. At the end of the trials, liners of hermetic bags were inspected to assess insect abrasions and perforations (holes). We used Microsoft Excel (Microsoft Office, Redmond, WA, U. S; version 2013) to estimate means and standard errors of means. SPSS 24. 0 (IBM Corporation, Armonk, NY, USA) was used to conduct statistical analysis. The analysis of variance (ANOVA) followed by least significant difference (LSD) was used to compare means of oxygen content, germination, weight loss, grain with holes, grains with eggs, and liner abrasions and perforations.
Acknowledgments: We thank all the  INRAN  staff members who helped with the data collection. Conflicts of Interest:  Dieudonne Baributsa  and  Larry Murdock  are co-founders of  PICS Global Inc. , a social enterprise that commercializes post-harvest technologies (including PICS bags) to smallholder farmers across the world and hence declares a conflict of interest. The other authors declare no conflict of interest. The funder (BMGF) had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
Next, we quantified arbuscule size distribution in the syp132α-1 mutant and wild-type, by measuring the arbuscule size in images of 1000 arbuscules per root of wild-type and mutant plants (n = 4). However, no differences in arbuscule size distribution were observed (Figure  6C ). From the same images we quantified the fraction of collapsed arbuscules, which was not significantly different. To get more direct data on the lifetime of arbuscules, we developed a construct that expresses the fluorescent timer reporter protein dsRed-E5 fused to a nuclear localization tag from the PT4 promoter. DsRed-E5 slowly maturates from green to red fluorescence with a half-time of approximately 10 h  (Mirabella et al. , 2004). Since the PT4 promoter is switched on at the start of the formation of the PAM  (Pumplin et al.
Phylogenetic Analysis of SNAREs The protein sequences of all SYP1 and VAMP72 family members were retrieved from the Phytozome database 1  for the AM host species; Aquilegia coerulea, Brachypodium distachyon, Citrus clementina, Carica papaya, Cucumis sativus, Fragaria vesca, Ginkgo biloba, Musa acuminate, Manihot esculenta, Medicago truncatula, Oryza sativa, Phoenix dactylifera, Prunus persica, Populus trichocarpa, Setaria italic, Solanum lycopersicum, Selagionella moelendorfii, Theobroma cacao and Vitis vinifera, and the AM non-host species; Arabidopsis thaliana, Beta vulgaris, Dianthus caryophyllus, Marchantia polymorpha, Nelumbo nucifera, Physcomitrella patens, Pinus taeda, Striga hermonthica, Spirodela polyrhiza, and Utricularia gibba (Supplementary Datasheets S1, S2). To ensure the recovery of all family members, missing species in each orthogroup were confirmed by repeated homology searches using orthologs from that group as an input.
Microscopy and Quantification For confocal imaging, a Leica SP8 confocal microscope was used. An excitation wavelength of 488 nm was used for GFP and WGAalexa 488. An excitation wavelength of 543 nm was used for dsRed. Appropriate emission range settings were used to separate the fluorophores used in each experiment. For quantification of fluorescence levels, ImageJ software was used. The average arbuscule size was determined from 1000 arbuscules in each of the four roots per genotype.