dataesr/scientific-paragraphs-categorization huggingface dataset:
The MAPT was deemed null by the investigators  [30]. We leveraged this as an opportunity to explore the NRI concept with trial arm allocation adjustment statistically as a covariate in our models. Adding further assurance, analysis was restricted to participants naive to n-3 PUFA supplementation, and the results were consistent with the possible exception of greater effects of NRI on rates of cognitive decline (e. g. , NRI-1: b 5 20. 08 vs. 20. 04 SU/y; NRI-3: b 5 20. 19 vs. 20. 11 SU/y). In an exploratory analysis using a three-way interaction term in the model (trial arm ! NRI ! time), we determined that the MAPT interventions remained null regardless of baseline NRI (data not shown). The limited sample size and the fact that a multinutrient combination (n-3 PUFA, D, and HCy-lowering B vitamins) was not included in MAPT may explain these findings and prevent any firm conclusions at this point.
The consistent results regardless of the approach to these data are remarkable and encourage support for the robustness of the primary findings. The nutrient biomarker concentrations are comparable with those seen in other studies with some exceptions. The mean plasma HCy in the MAPT population was 15. 8 mmol/L. This is somewhat higher than what has been observed in older US adults (70 years and older) in both the prefolate and postfolate fortification eras (~11-14mmol/L)  [66, 67]. There are several possible explanations, including differences in the B vitamin nutriture, renal status of the populations, and duration of time before the blood spin down. However, it is perhaps important to note that hyperhomocysteinemia in isolation was not associated with cognitive decline unless it was accompanied by suboptimum D, n-3 PUFA, or both.
e. , those at nutritional risk), monitoring adherence and describing dose responses (clinicaltrials. gov: NCT01953705 and NCT03080675). In conclusion, optimizing n-3 PUFA, D, and HCy to promote cognitive health is a biologically plausible and testable hypothesis that warrants experimental and further observational study. Expanding the application of nutrient biomarkers may enable the establishment of distinct nutritional requirements for healthy brain aging that currently do not exist. and analytical assays, composed the statistical analysis plan, interpreted the data, and drafted and revised the manuscript; H. H. D. : concept and design, composed and conducted the statistical analysis plan, interpreted the data, and revised the manuscript; N. Z. : performed statistical analysis; S. G. : literature review and revised the manuscript; J.
Acknowledgments MAPT/DSA group refers to MAPT Study Group: Principal investigator:  Bruno Vellas  (Toulouse); Coordination: Sophie Guyonnet; Project leader:  Isabelle Carri  e; Clinical research assistant: Laur eane Brigitte; Investigators:  Catherine Faisant ,  Francoise Lala ,  Julien Delrieu , and H el ene Villars; Psychologists:  Emeline Combrouze ,  Carole Badufle , and  Audrey Zueras ; Methodology, statistical analysis, and data management:  Sandrine Andrieu ,  Christelle Cantet , and  Christophe Morin ; Multidomain group:  Gabor Abellan Van Kan ,  Charlotte Dupuy ,  Yves Rolland  (physical and nutritional components),  C eline Caillaud ,  Pierre-Jean Ousset  (cognitive component), and  Francoise Lala  (preventive consultation). The cognitive component was designed in collaboration with  Sherry Willis  from the  University of Seattle , and  Sylvie Belleville ,  Brigitte Gilbert , and Francine.
National Institutes of Health  funding; is an unpaid Scientific Advisory Board member of the  PROPAG-AGEING EU Horizon 2020  project; H. H. D. reports  US National Institutes of Health  funding; N. Z. reports no disclosures; S. G. reports no disclosures; the  Nestle Institute of Health Sciences  employs J. D. ; the  Nestle Institute of Health Sciences  employs A. B. ; Nestle Research Center employs J. S. ; C. H. reports no disclosures; T. B. reports no disclosures; S. A. reports grants from  JPND program ,  EU - FP7 program ,  Beaufour Ipsen Pharma , personal fees from  Beaufour Ipsen Pharma, Pierre Fabre, Lilly ,  Nestl e, Sanofi ,  Servier ,  MSD , and nonfinancial support from  Biogen ,  Pfizer , Icon, grants from  France Alzheimer Association , AMPA Association outside the submitted work; B.
| Homozygosity mapping Taking into account the recessive trait of FA and the consanguinity of the index case, we applied homozygosity mapping strategy to look for regions of homozygosity by descent and consequently locate the responsible gene(s) with the causative mutation(s). Whole exome Single Nucleotide Polymorphism (SNP) genotype data from the index case and her parents were analyzed for the presence of runs of homozygosity  (McQuillan et al. , 2008)  through the Runs of Homozygosity (ROH) tool from PLINK v1. 06 (http:// pngu. mgh. harvard. edu/purcell/plink). We kept the default settings of the "homozygous" command. Then, we applied the FSuite software for better visualization and determination of these ROHs  (Gazal, 2014).
| Whole exome sequencing In order to define disease-relevant variants, we realized WES for the index case S156-V-2 and her parents. WES and bioinformatics' analysis were performed by Oxford Gene Technology (OGT; London, UK). Briefly, samples were prepared according to Agilent's SureSelect Protocol Version 1. 2 (Agilent Technologies; Santa Clara, CA). Obtained libraries were pooled prior to sequencing with each sample at a final concentration of 10nM. Paired-end sequencing was performed on the Illumina HiSeq2000 platform using TruSeq v3 chemistry. Read files (Fastq format) were generated from the sequencing platform via the manufacturer's proprietary software. Burrows-Wheeler Aligner's sample algorithm, for paired-end-short read alignment (BWA-short version 0. 6. 2)  (Li & Durbin. , 2009) , was used for mapping to the human genome (hg19/b37).
Sequence alignment Map (SAM) was converted to Binary Alignment Map (BAM) using Sam tools version 0. 1. 18. (Li et al. , 2009). Further BAM files processing was performed using Picard tools version 1. 89 (http:// broad insti tute. github. io/picard) and Genome Analysis Tool Kit (GATK) version 1. 6 (https ://softw are. broad insti tute. org/ gatk/docum entat ion/versi on-histo ry. php)  (McKenna et al. , 2010) , which was also used for variant calling and variant filtering. Single nucleotide polymorphism (SNP) novelty was determined against dbSNP137. Candidate variants were analyzed by a range of web-based bioinformatic tools like the EnsEMBL SNP Effect Predictor (https ://www. ensem bl. org/ info/docs/tools/ vep/index. html) (McLaren et al. , 2016)  and VarioWatch (http://grch37. genep ipe.
ncgm. sinica. edu. tw/ vario watch/ main. do) (Cheng et al. , 2012). All variants were screened against the Human Gene Mutation Database Public version (Biobase) (http://www. hgmd. cf. ac. uk/ac/index. php) (Stenson et al. , 2003). In silico analysis was done to evaluate the likely pathogenicity of variants using PolyPhen (http:// genet ics. bwh. harva rd. edu/pph2/)  (Sunyaev et al. , 2001) , SIFT (https ://sift. bii. a-star. edu. sg/) (Ng & Henikoff, 2003) , Align-GVGD(http://agvgd. iarc. fr) (Tavtigian et al. , 2006) , Pmut (http://mmb2. pcb. ub. es/PMut/)  (Ferrer-Costa et al. , 2005) , and Fruitfly (http://www. fruit fly. org/seq_tools/ splice. html)  (Reese, Eeckman, Kulp, & Haussler, 1997)  for splicesite predictions.
| RESULTS In our study, we investigated at clinical, cytogenetic, and molecular levels a patient with a suspicion of FA. The cytogenetic instability assessment revealed a high frequency of chromosomal breakages (8. 64 breaks/cell) compared to control (0. 062   2a ). In addition to clinical features, these results confirmed the diagnosis of FA. The karyotype analysis and the standard chromosomal breakage test showed 46,XY in all analyzed mitoses. The father IV-1 and the mother IV-2 had both a normal karyotype (46,XY; 46,XX, respectively). This incidental discovery is suggesting sex reversion (Figure  2b ). After excluding exon 15 deletion in FANCA gene, WES data analysis allowed identification of a nonsense mutation c. Sanger sequencing confirmed the segregation of all the variants identified above by the analysis of parents samples (Figure  4 ).
Patients were ineligible if there was planned use of a P2Y 12 receptor antagonist or anticoagulant therapy during the study period; if they had a bleeding disorder, a history of intracranial bleeding, a central nervous system tumour, or an intracranial vascular abnormality; or if they had had gastrointestinal bleeding within the previous 6 months or major surgery within the previous month. The present analysis focuses on data from 10 779 patients that were randomized < _2 years from qualifying MI or < _1year from prior stopping ADP receptor inhibitor treatment, 5388 in the ticagrelor 60 mg and 5391 in the placebo group (EU label group). Patients randomized to ticagrelor 60 mg or placebo who did not qualify per the EU label are termed the non-EU label group. Data on patients randomized to ticagrelor 90 mg who would have qualified for the EU label (n = 5374) are also presented in the Supplementary material online for the sake of completeness.
Journal of Geophysical Research: Solid Earth VAN DEN ENDE ET AL. 10,219 equations (equation (  35 )), complemented with equations (  31 ) and (  32 ), is solved iteratively using the SciPy ODE package  (Jones et al. , 2001)  to yield the evolution of D, α, strain rate, strain, and porosity with time. In this presented form, equation (  35 ) encompasses numerous kinetic, geometric, and structural parameters, each involving various degrees of uncertainty. However, in accordance with the Buckingham π theorem, the number of truly independent parameters that govern the system's dynamics can be reduced greatly by making the following nondimensional substitutions: t ′ ¼ t DCΩσ e RTd 2 (36a) S ′ ¼ S d (36b) Γ sl ¼ 2γ sl dσ e Δcos θ 2 (36c) Γ el ¼ σ e 2E (36d) G ¼ Z F (36e) which gives dϕ dt ′ ¼ -1-ϕ ð Þ4π 1-α ð ÞA ε S ′ G f ðϕÞ ! 2 (37a) dα dt ′ ¼ 8π 1-α ð Þ G f ðϕÞ ! Γ sl S ′ þ G f ðϕÞ ! 2 Γ el α 2 ! (37b) Since the parameters A ε and G are constrained by geometry, the only parameters governing the dynamics of α and ϕ that involve experimental uncertainty are S ′ , Γ sl , and Γ el.
Variables definitions Height was measured with a wall-mounted stadiometer to the nearest 0. 1 cm. Weight measured to the nearest 10 grams with electronic scales (Seca, Hamburg, Germany). Waist circumflex measured mid-way between the lateral lower rib margin, and the iliac crest and according to European data which was used for Eastern Mediterranean population, we defined normal waist <80 cm in females and < 94 cm in males  [12]. Diabetes mellitus(DM) was defined as capillary blood sugar level �126 mg/dl if the participant was fasting or � 200mg/dl if the participant was non-fasting and or self-reported as currently taking any diabetes medications  [13]. For hypertension (HTN), we considered subjects to have HTN if their average systolic blood pressure (SBP) in both arms was �140 mmHg or their average diastolic blood pressure (DBP) �90 mmHg, or if they were being treated for HTN  [14, 15].
Data analysis Data was analyzed using Statistical Package for Social Sciences version 22. Categorical variables are presented as percentage, and continuous data as the means and standard deviation. We first compared differences in socio-demographic as well as lifestyle-related factors using chi square test, Somers' D test (for ordinal qualitative variables), student's t-test, or Wilcoxon's rank sum test as appropriate. Then we used logistic regression to examine the association between CVDs and their risk factors with/without adjustment for covariates. Any covariate with a p-value � 0. 25 in the age-and gender-adjusted model was considered in multiple logistic regression model. The final model was obtained using a backward stepwise procedure. We examine confounding at each step and then we examine first-order interactions in the final model.
Discussion Our study is the first to report the prevalence of CVDs among Gazans in Palestine. Previous surveys were hospital based or on United Nation Relief and Work Agency (UNRWA) report. The prevalence of CVDs nears 10% of the population above the age of 25years in this area. Data on the CVD epidemiology in the Middle-East are limited. In the Coronary Artery Disease in Saudi study (CADISS), a national community-based study conducted in urban and rural area of Saudi Arabia; 17 232 subjects aged 30-70 years randomly selected were included. Coronary artery disease was diagnosed on positive questionnaire of angina or history of possible myocardial infarction. The prevalence of CAD was 5. 5% (6. 6% in males and 4. 4% in females)  [18]. Similar findings have been reported in a Lebanese cross-sectional study using multistage cluster sample including 1200 subjects �40 years of age.
7% with higher rate in males than females,42. 9% and 27. 5% respectively  [37]. In the BREATH study, 62 086 subjects aged � 40years in ten countries in MENA region, were interviewed regarding smoking habits, with equal number of men and women. The smoking rate was estimated to be 31. 2%, ranging from 15. 3% in Morocco to 53. 9% in Lebanon. The proportion was higher in men (48. 0%) than in women (13. 8%)  [38] , the prevalence of female smokers has traditionally been low due to the Eastern Mediterranean region's conservative cultural and social values. Less than one third of our study population have dyslipidaemia. However, our finding was counterpart with data from ACE (Cardiovascular Epidemiological) study in United Arab Emirates, the prevalence was 74%  [39] , while the high prevalence of dyslipidaemia in our study in young adults (age 25-34years) calls for an earlier screening for dyslipidaemia and other risk factors.
Strengths and limitations There are few limitations to our study. First, the cross-sectional nature of this study design limits the interference of causal relation-ships between risk factors and CAD or stroke. Also, the prevalence of high blood pressure and raised blood glucose have been over estimated because these two risk factors were evaluated once (no reevaluation during another visit, and no three consecutive measures of BP according to WHO step). Further, possible bias could have been introduced since study was conducted at home and data concerning more risk factors and history of CAD and stroke were self-reported. Also, the wide age range of participants, 25 years and more is both a strength and a weakness of this study. However, 32% of the population was aged 55 years and more, who have a greatest prevalence of CVD and risk factors.
This database of uncertainty values is based on deviations of the irradiance maps, compared to three reference pyrheliometers in Tabernas, Andalucia over two years (2016 and 2017). Increased uncertainties due to transient effects are considered by detecting transient zones close to cloud shadow edges within the DNI map. The width of the transient zones is estimated by the current average cloud height, cloud speed, lead-time, and Sun position. The final spatially resolved uncertainties are validated with three reference pyrheliometers, using a data set consisting of the entire year 2018. Furthermore, we developed a procedure based on the DNI temporal variability classes to estimate the expected average uncertainties of the nowcasting system.
The nowcasted DNI with lead times > 0 min could be used for the DNI variability classification. However, this would mean that the yet unknown uncertainties of the nowcasting system would affect the DNI classification, which, in turn, serves as the basis of the uncertainty analysis. The use of the more accurate measured DNI signal of the past 15 min might cause a poorly defined uncertainty in the case of sudden atmospheric changes of the conditions, especially for high lead times, but this is only a short-term effect. As we discussed in Section 3. 1, the DNI variability classification procedure on the basis of a 15-min time window, reacts rapidly on any changes of irradiance conditions. Furthermore, we will introduce (in Section 3. 2. 2) additional spatial adaptions within transient zones of the DNI maps, which reduce potential negative effects of the DNI variability classification with data from the recent past.
For the uncertainty specification, we calculate the average deviation Dev i between the three spatially distributed reference pyrheliometers and the corresponding nowcasting DNI from the DNI maps for each time stamp i. Dev i = 3 st=1 DNI i,st,re f -DNI i,st,nc 3 (2) The index st describes the reference station number, ref the reference DNI, and nc, which is the corresponding nowcasted DNI. The resulting deviations for each image series are discretized over the DNI variability classes and two Sun elevation ranges (above and below 30 • ). The distribution of the deviations Dev i over the entire data set is analyzed within each DNI variability class, Sun elevation range, and lead-time. between the reference sensors and the nowcasting system is the basis for the uncertainty specification. The DNI variability is classified for each time stamp.
As we discussed in section 3. 1, the DNI variability classification procedure on the basis of a 15-minute time window, reacts rapidly on any changes of irradiance conditions. Furthermore, we will introduce (in section 3. 2. 2) additional spatial adaptions within transient zones of the DNI maps, which reduce potential negative effects of the DNI variability classification with data from the recent past. For the uncertainty specification, we calculate the average deviation  between the three spatially distributed reference pyrheliometers and the corresponding nowcasting DNI from the DNI maps for each time stamp i. 𝐷𝑒𝑣 = ∑ 𝐷𝑁𝐼 , , -𝐷𝑁𝐼 , , 3 (2) The index st describes the reference station number, ref the reference DNI, and nc, which is the corresponding nowcasted DNI. The resulting deviations for each image series are discretized over the DNI variability classes and two Sun elevation ranges (above and below 30°).
Final Adjustments and Validation The basic uncertainty is defined by a coverage probability of 68. 3%. The spatial filter will lead to an increase of the coverage probability due to the partial increase of the uncertainty within the transient zones. The coverage probability after introducing the spatial filters is investigated with the three reference pyrheliometers number 1, 2, and 4 (Figure  1 ). For this, we use a new dataset, consisting of the entire year 2018. DNI maps with the upper and lower uncertainty range for 16 lead times (0 to 15 in 1-min steps) are created. Subsequently, we compare whether the detected deviation between measured and predicted DNI is within the expected uncertainty range for the three corresponding pixels of the DNI maps with uncertainty. Lastly, we analyze the resulting coverage probability of the allocated uncertainties within each DNI variability class and lead-time over the entire validation data set.
The introduction of the spatial filter increased the average coverage probability from 68. 3% to roughly 74% (Figure  9a ) over all DNI variability classes and lead times. Therefore, the basic uncertainty is adjusted (reduced) for each DNI variability class and lead-time, so that the average of all classes per lead-time is roughly 68. 3% again (Figure  9b ). Remote Sens. 2019, 11, x FOR PEER REVIEW 13 of 23 three reference pyrheliometers number 1, 2, and 4 (Figure  1 ). For this, we use a new dataset, consisting of the entire year 2018. DNI maps with the upper and lower uncertainty range for 16 lead times (0 to 15 in 1-minute steps) are created. Subsequently, we compare whether the detected deviation between measured and predicted DNI is within the expected uncertainty range for the three corresponding pixels of the DNI maps with uncertainty.
For PSA, more than 60% of the cases are clear sky conditions (class 1 and class 2). Due to the hazy conditions at the NETRA site, class 1 conditions are rare but the more variable class 5 shows the highest occurrence. Overall, the NETRA site has a higher count for the four classes 5, 6, 7, and 8 with a lower average DNI. This is also apparent when looking at the yearly DNI sum. Despite the higher latitude, PSA shows a yearly DNI sum (2017) of 2430 kWh/m 2 compared to about 1160 kWh/m 2 at the NETRA site. Each time stamp receives an uncertainty value corresponding to the present DNI variability class, Sun elevation angle, and lead-time from the look-up table illustrated in Figure  4. The average uncertainty is calculated over the entire data set and for different lead times. This overall average expected uncertainty is illustrated in Figure  15.
It can be seen that similar uncertainties are expected for a lead-time of 0 min. As already mentioned, only minor differences are present at a lead-time of 0 min for the uncertainty values between the eight DNI variability classes. However, the expected uncertainties increase more for the NETRA site with an increasing lead time due to the more frequent occurrence of highly variable conditions compared to PSA. Thus, a better overall performance of the nowcasting system is expected for the PSA. Such initial estimates can be made at any site, if sufficient DNI data are available. This site-specific uncertainty estimate can help evaluate the potential benefit of a nowcasting system for the plant and grid operation.
This overall average expected uncertainty is illustrated in Figure  15. It can be seen that similar uncertainties are expected for a lead-time of 0 min. As already mentioned, only minor differences are present at a lead-time of 0 min for the uncertainty values between the eight DNI variability classes. However, the expected uncertainties increase more for the NETRA site with an increasing lead time due to the more frequent occurrence of highly variable conditions compared to PSA. Thus, a better overall performance of the nowcasting system is expected for the PSA. Such initial estimates can be made at any site, if sufficient DNI data are available. This site-specific uncertainty estimate can help evaluate the potential benefit of a nowcasting system for the plant and grid operation. Each time stamp receives an uncertainty value corresponding to the present DNI variability class, Sun elevation angle, and lead-time from the look-up table illustrated in Figure  4.
The average uncertainty is calculated over the entire data set and for different lead times. This overall average expected uncertainty is illustrated in Figure  15. It can be seen that similar uncertainties are expected for a lead-time of 0 min. As already mentioned, only minor differences are present at a lead-time of 0 min for the uncertainty values between the eight DNI variability classes. However, the expected uncertainties increase more for the NETRA site with an increasing lead time due to the more frequent occurrence of highly variable conditions compared to PSA. Thus, a better overall performance of the nowcasting system is expected for the PSA. Such initial estimates can be made at any site, if sufficient DNI data are available. This site-specific uncertainty estimate can help evaluate the potential benefit of a nowcasting system for the plant and grid operation.
Conclusions The performance of ASI-based nowcasting systems is highly related to the prevailing weather conditions. Previous studies [18-24] evaluated a nowcasting system versus ground observations and a persistence nowcast, but the results in overall accuracy metrics as e. g. , RMSD, MAD, bias, and skill scores for various lead times. The dependency on the weather situation was only taken into account by choosing a long-term data set for the validation, but the dependency of the accuracy on the actual weather conditions has not been described yet. In this work, a real-time capable method for a weather-dependent uncertainty specification of DNI nowcasts is presented, which is generated by an ASI-based nowcasting system. In this approach, the DNI is classified in one of eight DNI variability classes, according to  [25].