dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2021)  derived from remote sensing (referred to as "top-down" approaches) can be significantly higher, ranging from 2 to 4 times, compared to the "bottom-up" methodologies that are predominantly adopted by entities such as the IPCC and in many modeling studies. In particular, the findings of these top-down methodologies align more consistently with data from sun photometer (e. g. , AERONET)  (Burton et al. , 2012)  and lidar networks. This congruence underscores the potential relevance of these top-down datasets to our research outcomes. The granularity of these datasets, both in time and in space, could offer critical insights into phenomena such as extreme events or long-range transportation of aerosols. This is especially relevant when studying cases such as wildfires and events that involve the transport of mixed sources of aerosols, both anthropogenic and dust-related, as investigated in this study, where we quantitatively evaluated changes in aerosol optical and microphysical properties by combining long-term data  (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019) (2020)  in situ concentrations of PM 10 , PM 2.
(b) Sun photometer Sun photometers are powerful passive remote sensing instruments that measure the optical and microphysical properties integrated into the atmospheric column of aerosols. These instruments work by measuring the intensity of direct solar radiation at different wavelengths, allowing us to estimate atmospheric aerosol loading, optical properties, and their size distribution. In this study, we used data from the Aerosol Robotic Network (AERONET;  Holben et al. , 1998).
(c) PM sensor The hourly concentrations measured in micrograms per cubic meter (µg m -3 ) of PM 10 , PM 2. 5 , and PM 1 , corrected by gravimetry  (Charron et al. , 2004) , are taken with the commercially available GRIMM OPC (optical particle counter), which is an instrument used to measure the size and number of particles in the air. This measurement was obtained by continuously sampling the air and counting the number of particles that passed through the instrument's optical sensor. Data for this study are obtained from the observational site deployed at CID (Center d'Investigació i Desenvolupament) CSIC (Consejo Superior de Investigación Cientificas) in  Barcelona (43. 38  • N, 2. 11 • E; 77 m above sea level). All the measured variables, with specifications and references, are reported in Table  1.
Networks of instruments (a) EARLINET The European Aerosol Research Lidar Network (EAR-LINET; http://www. earlinet. org, last access: 6 October 2023), established in 2000, is the first coordinated aerosol lidar network whose main objective is to provide a comprehensive, quantitative, and statistically significant database on the spatial and temporal distribution of aerosols on a continental scale  (Bösenberg and Matthias, 2003; Pappalardo et al. , 2014). Currently, the network includes 31 active stations spread across Europe  (Pappalardo et al. , 2014). Lidar observations within the network are performed on a regular schedule of one daytime measurement per week around 12:00 solar time, when the boundary layer is usually well developed, and two nighttime measurements per week, with low background light, to perform Raman extinction measurements.
(b) AERONET The NASA Aerosol Robotic Network (AERONET) is a globally distributed network of ground-based sun photometers operated by academic institutions and government agencies. These are commercially available sun photometers manufactured by Cimel Electronique  (Holben et al. , 1998). AERONET has become a critical tool for studying aerosol properties, as the network provides long-term high-quality measurements that can be used to validate satellite observations and improve our understanding of aerosol impacts on the environment. The AERONET database (https://aeronet. gsfc. nasa. gov/, last access: 6 October 2023) is openly available to the public and is widely used by researchers, government agencies, and other stakeholders around the world to inform policy decisions and advance scientific understanding of the Earth system.
Quality assurance screening To guarantee the highest possible quality of the final products and, at the same time, to ensure the homogeneity of the data from different types of lidar systems, the EAR-LINET infrastructure has implemented a rigorous quality assurance program that all affiliated stations are expected to meet  (D'Amico et al. , 2015 (D'Amico et al. , , 2016)). The implementation of the quality assurance program has been and still is one of the main activities of the infrastructure and is a prerequisite to establish and monitor the performance of different lidar systems that are part of the infrastructure over time. The quality control of EARLINET applies to both lidar instruments and analysis algorithm levels. Without the requirements of the quality assurance program, it is not possible to submit data to the data center.
Data that do not comply with the EARLINET standards are rejected already in the submission phase. Automatic feedback is provided to the "data originator" reporting all the problems incurred for each rejected file, fostering the prompt resubmission of the data. There are two types of quality control procedures: basic quality control (BQC), which is a technical quality control on the submitted product, executed to ensure that the product is mainly compliant from a technical point of view with the defined standard, and advanced quality control (AQC), which is a series of physical checks applied to the input product, performed to assess the quality from a physical point of view of the product. If one or more basic quality controls fail, the submitted product is rejected, and detailed feedback is sent to the data source.
If the product satisfies the basic quality controls, it is sent in batches to the advanced quality controls. If the input product fails at least one advanced quality control, the product is labeled as Level 1, otherwise Level 2. Level 1 data are intended as data that users should treat with caution considering failed QC procedures. Information on the result of the QC procedure is reported in the file itself for complete traceability. Level 2 data are, instead, data that are fully compliant with what a user can expect from the ACTRIS/EARLINET infrastructure and with data documentation.
Seasonal climatological analysis on long-term lidar observations Lidar instruments have higher data collection and processing demands compared to in situ sensors for measuring PM and sun photometers. Active remote sensing optical devices require more power, calibration, and maintenance to operate correctly, which can lead to data gaps. Additionally, lidar measurements are highly sensitive to atmospheric conditions, such as cloud cover, which can affect their ability to detect aerosols on cloudy days. As a result, lidar observations may be less homogeneous, and some months/years may have more data than others. Consequently, it is very difficult to assess long-term trends with an inhomogeneous database. However, it is possible to quantitatively assess the seasonal variability of the vertically resolved optical properties, which is crucial, particularly from a climatological standpoint.
acknowledge the MERRA mission scientists and associated NASA personnel for the production of the data used in this research effort. Financial support. This research has been supported by the European Union through NextgenerationEU funds and by the following projects along the years: FP5 EARLINET project (grant no. ID EVR1-CT-1999-40003), FP6 EARLINET-ASOS (ID: 25991), FP7 ACTRIS (ID: 262254), H2020 ACTRIS-2 (ID: 654109), ACTRIS-PPP (ID: 739530), ACTRIS IMP (ID: 871115) and ATMO-ACCESS (ID: 101008004), projects of the Spanish National Research programs (grant nos. TIC 431/93, AMB96-1144-C02-01, REN2000-1907-CE, REN2000-1754-C02-02/CLI, REN2003-09753-C02-C02/CLI, REN2003-09753-C02-C CGL2008-01330-E/CLI 02/CLI, REN2002-12784-E, CGL2005-5131-E, CGL2006-27108-E/CLI, CGL2006-26149.
Methods The SBR values of visually normal SPECT scans from 3 groups were studied: (1) suspected Parkinsonism and no diagnostic follow-up (ScanOnlyDB: n = 764, NM/CT 670 CZT, GE Healthcare), (2) no degenerative dopaminergic pathology after a 5-year follow-up (NoDG5YearsDB: n = 237, Symbia T2, Siemens Medical Solutions), and 3) Hv (HvDB: n = 118, commercial GE database). A general linear model (GLM) was constructed with caudate, putamen, and striatum SBR as the dependent variables, and age and gender as the independent variables. Following post-reconstruction harmonization of the data, DB were combined in pairs, ScanOnlyDB&NoDG5yearsDG and ScanOnlyDB&HvDB before performing GLM analysis. Additionally, ScanOnlyDB GLM estimates were compared to those published from Siemens commercial DB (SiemensDB) and ENC-DAT.
Results The dispersion parameters, R 2 and the SBR coefficients of variation, did not differ between databases. For all volumes of interest and all databases, SBR decreased significantly with age (e. g. , decrease per decade for the striatum: -4. 94% for ScanOnlyDB, -4. 65% for NoDG5YearsDB, -5. 69% for HvDB). There was a significant covariance between SBR and gender for ScanOnlyDB (P < 10 -5 ) and NoDG5YearsDB (P < 10 -2 ). The age-gender interaction was significant only for ScanOnlyDB (P < 10 -2 ), and the p-value decreased to 10 -6 after combining ScanOnlyDB with NoDG5YearsDB. ScanOnlyDB GLM estimates were not significantly different from those from SiemensDB or ENC-DAT except for agegender interaction. Conclusion SBR values distribution from visually normal scans were not different from the existing reference database, enabling this method to create a reference database by expert nuclear physicians.
Introduction Iodine 123-radiolabeled 2β-carbomethoxy-3β-(4iodophenyl)-N-(3-fluoropropyl) nortropane (or 123 I-FP-CIT) scintigraphy is a biomarker of the presynaptic dopaminergic system that enables the assessment of the dopamine transporter availability. In case of suspected Parkinsonism, a 123 I-FP-CIT single-photon emission computerized tomography (SPECT) examination  [1]  may be prescribed to rule out Parkinson's disease in case of a visually normal (or grade '0') scan  [2]. Visual analysis remains the gold standard, but a semi-quantitative measurement of the specific binding ratio (SBR) can improve the inter-and intra-operator reproducibility and can help with the interpretation of equivocal scintigraphy scans  [3] [4] [5]. SBR analysis is based on a comparison with reference values corresponding to age-matched healthy volunteers (Hv) from large multicenter studies, such as the European Normal Control Database of DaTSCAN (ENC-DAT)  [6] , the Japanese multicenter database of healthy controls for 123 I-FP-CIT SPECT  [7] , and the Parkinson Progression Marker Initiative (PPMI)  [8] ; GE Healthcare (Chicago, IL, USA) commercializes a database of Hv SBR values sampled from the latter  [8]  that accompanies the semiquantitative analysis software DaTQUANT ™.
A potential limitation of the use of these databases is the additional instrumental variance in SBR values caused by the heterogeneity in equipment, despite the inter-center (intersystem) calibration, which could reduce their sensitivity  [9, 10]. Alternatively, SBR reference values are obtained locally and retrospectively from normal 123 I-FP-CIT routine examinations of a patient group presenting heterogeneous suspected diagnoses, but without dopaminergic neurodegenerative pathology, after a follow-up period lasting up to 5 years  [11, 12]. SBR values depend on the imaging method, so a direct comparison between datasets acquired using two different imaging systems is not readily achievable. However, variations according to age or gender should be independent of the imaging chain and could be used as metrics for inter-database comparisons.
Nevertheless, as attenuation correction methods have proved to influence such variations  [13] , database comparisons should be restricted to data using the same attenuation correction method. Databases have therefore been compared via estimates of the variables in general linear regression model analysis and by the proportion of the explained variance (R 2 )  [11, 12]. No significant difference was observed between databases from Hv and those from patients without dopaminergic neurodegenerative pathology. However, these results are still debated, as SBR could also depend on comorbidities; recent study found that the mean posterior putamen SBR value was 8% higher in a group of symptomatic patients without dopaminergic neurodegenerative pathology than in a group of Hv  [14]. We hypothesized that it is possible to generalize this absence of difference compared to Hv or patients without disease after follow-up to all normal routine scintigraphy scans of patients presenting suspected Parkinsonism, even without collecting follow-up information (ScanOn-lyDB).
Three hours after 123 I-FP-CIT administration (mean ± standard deviation [SD] dose: 146 ± 24 MBq; Additional file 1: Table  S1 ) acquisitions, including the whole head, were performed using an NM/CT CZT 670 (GE Healthcare, Chicago, IL, USA) camera equipped with a WEUH collimator. A total of 120 projections were acquired within 30 min. The distance to the axis of rotation was fixed at 14 cm. The photopeak imaging window was set to 159 keV (-6%, + 5%). An X-ray computed tomography (CT) was performed (120 kV, noise index 6. 5, 1. 5-mm slice thickness). The reconstruction protocol and the semi-quantitative analysis method were identical to those of the commercial HvDB database using DaTQUANT ™ (GE Healthcare, Chicago, IL, USA). Two attenuation correction (AC) methods were used: 1) linear attenuation: Chang method with µ = 0.
Description of reference databases 1) The NoDG5YearsDB is composed of scans from patients presenting an indication for a 123 I FP-CIT examination without degenerative dopaminergic pathology after a mean ± SD follow-up duration of 5 ± 1 years. It was acquired at the Hospices Civils de Lyon using a Symbia T2 SPECT-CT camera (Siemens Medical Solutions USA, Inc. ) between January 2008 and December 2015, following European recommendations  [1]. This cohort contained 237 subjects, matched for age and gender; their mean ± SD age was 62 ± 15 years, and 117 (49. 4%) were women. The diagnoses are listed in Additional file 1: Table  S1. This cohort included 25 (8. 5%) young adults with attention deficit/hyperactivity disorder. Patients with suspected atypical Parkinsonism were excluded. SBRs were computed using an in-house semi-quantitation method (Additional file 2).
Scientists from Siemens (Siemens Medical Solutions USA, Inc. ) simultaneously and independently processed these raw data, using the Flash3D reconstruction method and CT AC. It resulted in different variants of corresponding reference SBR values made commercially available  [12]. This database is hereafter referred to as SiemensDB. 2) The HvDB (DaTQUANT ™ , GE Healthcare, Chicago, IL,USA) was composed of visually normal scans from 118 Hv from the PPMI cohort  [8]. The mean ± SD Hv age was 60 ± 13 years and 73 (61%) were men. Data were acquired in several centers using different gamma cameras. A detailed description of NoDG5YearsDB and HvDB can be found in Additional file 3.
Statistical analyses GLMs (details in Additional file 4) allowed the estimation of the covariation of the striatum, the putamen, the caudate, and the putamen/caudate (P/C ratio) SBR with the tested independent variables: age, gender, and their interaction, for each database (Fig. 1, Left ). The metrics used to estimate the dispersion of SBR values of the databases were the SD, coefficient of variation (CoV) after correction for mean age, and the R 2 of the GLM. We used Cohen's formula  [16]  to compare our correlation coefficients, obtained with the GLM, to those corresponding to SiemensDB and ENC-DAT  [6, 12, 17]  (Fig. 1  As SBR values depend on the acquisition and processing protocols, a post-reconstruction harmonization was necessary before GLM analysis of combined databases. First, each database was submitted to a GLM model testing for age dependency (intercept ± Standard Error [SE], slope ± SE).
Then, as previously mentioned, due to the influence of the AC method on the parameters studied, we proceeded by pair according to the attenuation correction used. The signed intercept difference was applied to harmonize ScanOnlyDB AC CT with NoDG5yearsDB and ScanOnlyDB Chang AC with HvDB (value of the offset ~ 10% SBR). Details of database harmonization can be found in Additional file 5. Statistical analyses were performed using Matlab 7 2021a (The MathWorks, Natick, MA, USA). Continuous variables were expressed as mean ± SD and compared using the Wilcoxon test or the Student's t-test, according to their distribution. Discrete variables were expressed as count (percentage) and compared using the Chi-squared test. Significance was set at P < 0. 05.
5 years, and 94 years (Fig. 2d ). At 31 years, female SBR values were 8. 8% higher than male SBR values. This difference decreased with age (6. 4% at 62. 5 years and 0. 9% at 94 years). General linear model estimates of Specific Binding Ratios (SBR) from the database composed of visually normal scans (suspected Parkinsonism and no diagnostic follow-up, ScanOnlyDB) as a function of age, gender, and age-gender interaction (for the 'male' effect) for both attenuation correction methods: Chang and CT. Chang AC: numerical method for attenuation correction, CT AC: computed tomography-based attenuation correction, standard error: SE, * p-values < 10 -5.
Comparison with ENC-DAT and SiemensDB The R 2 of the linear regressions of SBR with age were compared between the ScanOnlyDB, the HvDB, the NoDG5YearsDB, the ENC-DAT databases using BRASS analysis of uncalibrated ACSC data  [6] and the Sie-mensDB  [12] (coefficients published for each VOI and gender in Table  4 ). No significant difference was found. Comparison of slopes (estimate ± standard error) and R 2 parameters from ScanOnlyDB and those from the ENC-DAT study using BRASS analysis of uncalibrated ACSC data  [6]  and the SiemensDB  [12]. ACSC, attenuation correction and scatter correction; ENC-DAT, European Normal Control Database of DaTscan ™ The GLM analysis (R 2 = 0. 27) showed a significant covariation with age (P < 10 -5 ) and gender (P = 6. 10 -3 ). The slope ± SE was -0. 026 ± 0.
Combination of ScanOnlyDB and NoDG5YearsDB: GLM with age and gender as independent variables and age-gender interaction Figure  4  presents the SBR values of the harmonized SBR ScanOnlyDB harmonized = SBR ScanOnlyDB + 0. 33] as a function of age. The GLM analysis (R 2 = 0. 35) showed a significant covariation with age (P < 10 -5 ) and gender (P < 10 -5 ). The slope ± SE was -0. 021 ± 0. 0009 and the intercept ± SE (male) was -0. 577 ± 0. 09. The age-gender interaction slope ± SE was 0. 0057 ± 0. 001 (P = 6. 10 -6 ). The database factor was not significant (P = 0. 9). After correction for mean age, the CoV for striatal SBR was 15% and the SD was 0. 39.
Discussion In this study, we observed that the SBR values of normal scans from the ScanOnlyDB, derived from routine clinical activity, depended on age and gender, similarly to the SBR values of Hv from academic studies. SBR dispersion was not different (similar CoV and R 2 ), thereby not providing an argument for an additional variance of the ScanOnlyDB database. These results were strengthened after combination of the databases, which constitutes an additional argument in favor of their non-difference. Furthermore, the SBR threshold (-2SD) for age used to define an examination as abnormal did not differ between databases after post-processing harmonization. The variations (decrease) in SBR values with age was comparable between the ScanOnlyDB and the SiemensDB  [12] , ENC-DAT  [6] , and data from histopathological studies  [18, 19]  Mean posterior putamen SBR values were lower than putamen SBR values and decreased at the same rate.
The mean putamen and posterior-putamen SBR values were similar to those previously published  [12]. These results are not consistent with those of a recent study  [14]  showing that the mean SBR values of Hv (n = 48) and of patients without dopaminergic degeneration (n = 70) were similar for all VOIs except for the posterior putamen VOI for which values were 8% higher for the patients. As suggested by the authors, this difference could be explained by a selection bias, thereby highlighting the importance of a large sample size. The GLM analysis of the ScanOnlyDB found a negative linear regression between SBR and gender for the three VOIs, which is consistent with results of the ENC-DAT, of the Japanese database, and of the SiemensDB study  [6, 7, 12]. We did not find any interaction between age and gender for the HvDB database: this is possibly related to the use of different cameras and protocols without calibration [ causing an instrumental variance that masked this moderate effect.
Gender discrimination was not shown to be necessary in order to differentiate a patient with Parkinson disease from a Hv  [20]  and DaTQUANT ™ (GE Healthcare, Chicago, IL, USA), which ensures a semi-quantitative analysis without considering gender, is consistent with a routine clinical use for this diagnosis. This is also true for Siemens database  [12]. However, a recent study has found the benefit of information on gender in reducing the risk of error for disease-free patients (particularly elderly men) and in distinguishing subjects with possible prodromal disease  [21]. A database containing gender information could therefore present an additional useful resource in routine clinical practice. The GLM analysis found an age with gender interaction for all VOIs investigated for the ScanOnlyDB.
The analysis did not find a significant difference between these two independent databases (built with different equipment, protocols, age distribution, and selection of diagnoses) and allowed the definition of reference values for the physiological parameters. ScanOnlyDB presented dispersion parameters (notably CoV factor after correction for age) similar to those previously published  [9, 23]. The R 2 of the GLM models for the ScanOnlyDB explained up to 33% of the overall variance, which was similar to the ENC-DAT  [23] , the Japanese database  [7] , and the Siemens database  [12]. Although part of the overall variance was related to age and gender, a large proportion of the overall variance remained unexplained (65-70%). This is probably because of the cross-sectional nature of studies, which do not take inter-individual variability into account, and which may also explain why the SD of the ScanOnlyDB and of the reference databases were similar.
In addition, the -2SD value used to define age-matched pathologic SBR values was not different after harmonization and combination of databases. It is of note that processing NoDG5yearsDB with DaTQUANT ™ (GE Healthcare, Chicago, IL, USA) instead of the in-house methodology would increase homogeneity but SBR differences due to different acquisition pipelines would still be present, requiring harmonization. As the results presented herein were consistent even with pipelines using different processing methods based on VOI semi-quantitation the results are generalizable. The ScanOnlyDB might include data from patients presenting neurodegenerative damage onset, more specifically global moderate symmetrical damage, which is difficult to demonstrate by visual analysis, but the SBR values were within the 2SD interval that defines normality.
Severe global symmetrical damage can be detect as the images have a higher non-specific binding area relative enhancement (image intensity is scaled on the highest voxel, usually in the caudate area). This is a limitation of the examination, but it did not translate into statistical difference between databases. If the diagnosis of dopaminergic degeneration was to be confirmed at follow-up, these patients would be qualified as "Scan Without Evidence of Dopaminergic Deficit" (SWEDD), but according to a recent review  [24] , their proportion was estimated only to be about 2%, which is similar to the 2. 5% alpha risk (-2SD).