dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis GWAS was performed on COBLAnCE cohort participants of European ancestry with available genotyping data after QC and imputation. We employed a case-only study design to estimate gene-environment interactions to test the association between SNPs and smoking phenotypes in bladder cancer patients. The analyses were conducted using logistic regression (for smoking status phenotype) and linear regression models (for age at smoking initiation and average tobacco consumption), with phenotype as the dependent variable against each of the imputed SNPs as the independent variable. Regressions were adjusted for gender, age, and the first ten genetic principal components to control for the study population's latent genetic structure. The results were presented by Manhattan plots and quantile-quantile (Q-Q) plots, and the significance threshold was set at the established GWAS level (5 × 10 -8 ).
RBFOX1 (also known as A2BP1) is highly expressed in neurons in brain regions that include the hippocampus, and there is evidence of associations between this gene, smoking cessation, and nicotine dependence  [44, 45]. Interestingly, one of the pathways of RBFOX1 is transcriptional regulation by MECP2 (reactome: R-HSA-8986944). The RBFOX1 gene was identified as associated with substance dependence or the ability to quit smoking in 13 independent datasets  [46] , with smoking frequency  [47]. In addition, RBFOX1 was presented as a strong candidate for susceptibility to aggressive behavior and to several psychiatric disorders  [48, 49]. Our results also showed a significant interaction association with average tobacco consumption for the reference allele of an intronic SNP (rs2714069) located on the GRAMD1B (GRAM Domain Containing 1B) gene, a protein-coding gene.
Acknowledgments: The authors would like to thank the following hospitals for their involvement in patient recruitment:  Hôpital Foch (Suresnes) ,  Centre Hospitalier Universitaire Henri Mondor (Créteil) ,  Centre Hospitalier Universitaire Gabriel-Montpied (Clermont-Ferrand) ,  Centre Hospitalier Régional Universitaire de Lille ,  Centre Hospitalier Universitaire de Nîmes ,  Clinique Beau Soleil (Montpellier) ,  Hopital Pasteur (Colmar) ,  Centre Hospitalier René Dubos (Pontoise) ,  Clinique du Parc Rambot (Aix en Provence) ,  Centre Hospitalier Universitaire Grenoble Alpes (Grenoble) ,  Clinique Urologique Atlantis (Nantes) ,  Centre Hospitalier Européen Georges Pompidou (Paris) , and  Centre Hospitalier de Brabois (Nancy) , and  Caroline Barau ,  Pascale Maille ,  Fannie Semprez  and  Pascale Soyeux  at the  Platform of Biological Resources and Research Team INSERM U955 of Henri Mondor Hospital ,  Elodie Guyon  and  Odette Mariani  at the  Biological Resources Centre of Curie Institute ,  Clémentine Krucker  and  Isabelle Bernard-Pierrot  at  UMR144 CNRS  for the management of biological specimens,  Marie-Claude Babron ,  Pascale Jan ,  Anthony Mangin  and  Pierre Guyader  at  Bureau de Biostatistique et d'Épidémiologie, Gustave Roussy  for data management of the COBLAnCE study,  Yves Koudou  at.
So, R affects the time that is needed to encode a memorandum. For example, with the default value R = 6 and a duration of t = 0. 5 s, the strength of the association between an item being encoded and its position is η = 0. 95 which represents 95% of the maximum value. Actually, to model some variability, it is not the value R which is used but rather the outcome of a random draw from a normal distribution centered at R, with a standard deviation of s (1 by default). Refreshing in TBRS* occurs during any free time, usually right after encoding items or processing distractors. During refreshing, previous positions are considered in turn and for each one, an item is retrieved and the association with its position markers is strengthened, using the same mechanism as during the initial encoding of an item, presented previously, except that the duration is much shorter.
The Ta parameter indicates the time used for the attentional capture of a distractor. During that time, all association weights w decay and become w new = w. e -D. Ta , where D is a decay parameter usually set to 0. 5. Recall in the model involves the retrieval and output of the most activated item associated with the markers representing a given position, following the mechanism presented previously. Once an item i is recalled, its associations with the current position p are suppressed by Hebbian anti-learning (∆w ip = -ηL) in order to minimize repetition of the same item at a subsequent position. Further details of TBRS* mechanisms and parameters are described in the seminal article  [17]  or in derived models  [26]. TBRS*C  [16]  extended TBRS* with a chunking mechanism which accounts for the fact that humans may recode known sequences of items as single units to increase recall performance.
It is a white, fatty tissue that encloses axons and increases the speed of the passing nerve impulses  [28]. Myelination is a process that persists for the first three decades of human development and is affected by experience  [29]. Specifically, piano practice in certain critical developmental periods was found to be associated with plasticity in myelinating tracts  [30]. As a consequence of musical training, myelin cells around nerve fibers have been found to increase in size, contributing to the velocity of electrical impulses  [13]. Based on these biological mechanisms, the present study sought to unravel expertise differences in WM functioning in greater detail. To this end, we collected data from a complex span task with musical notation. To ensure variation in musical expertise, the task was completed by two sub-samples, namely music students and hobby musicians.
Using a larger number of trials probably might have led to fatigue and hence invalid data. Eye tracking and Midi data were recorded during the musical performance. They were used to analyze the association of the number and duration of fixation with the accuracy of performing the melodies  [32]. These analyses, however, were unrelated to the present work and hence will not be reported in any further detail. The complex span task started after participants gave informed consent and received instructions. Each of the four experimental trials began with a preparatory phase with three steps: (1) positioning the hand on the piano keyboard, (2) performing a preparatory melody, (3) calibrating the eye tracker. The tones of the melodies that had to be performed were drawn from a set of five adjacent tones.
The former phase comprised the presentation of a fixation cross (2000 ms) and of a single quarter note symbol in treble clef (2500 ms). The latter comprised a two-bar count-in (6856 ms), the musical performance (13,714 ms), and the saving of eye tracking data. During count-in and performance, a digital metronome provided the tempo of 70 beats per minute via speakers. During the countin, a preview of the first bar of the melody was provided. The whole melody appeared on the screen when the performance started. After each musical performance, the eye tracking data which had been collected during this performance was saved. Saving times varied marginally (M = 6124 ms; SD = 390 ms). In one trial of the task, the procedure depicted in Table  2  was repeated twelve times until participants had to recall all memoranda and write them in the correct serial order on a sheet of paper with an empty staff.
Prior to the first experimental trial, participants performed a warm-up trial. It was similar to the experimental trials. However, only three instead of twelve notes had to be recalled and there were some additional instructions ("memorize the following note," "play the following melody") prior to each stimulus. The purpose of this warm-up was that participants could get used to the experimental procedure and that any misunderstandings could be clarified prior to data collection. In the end of the experiment, participants completed the global scale of the Gold-MSI  [31]  termed general musical sophistication and answered questions on demographics. The experimental procedure was in full agreement with APA's Ethical Principles of Psychologists and Code of Conduct  [33]  and with German data privacy regulations.
They were created by arranging four one-bar rhythmic phrases in a random order and assigning a pitch to each note that was randomly drawn from a set of five adjacent pitches from the C major scale. All melodies had a similar structure, which guaranteed that the amount and rate of information that had to be processed was constant across all melodies. Simultaneously, as the order of rhythmic phrases and pitches was varied randomly, it was impossible for participants to anticipate how the melody would progress. Thus, they were forced to process the notes. Note images were created with the program Forte 7 Basic (https:// www. forte notat ion. com/ en/) and then altered with the graphic-editing software Gimp (https:// www. gimp. org/). To create the images containing the.
Major triads Arbitrary trichords to-be-remembered notes, the indication of meter was removed, and a single quarter note symbol (0. 3 × 1. 2 cm) was positioned in the center of a short staff (6. 0 × 1. 3 cm) with a treble clef symbol. To-be-remembered notes as well as distractor melodies (24. 0 × 0. 6 cm) were positioned in the center of a white image (49. 92 × 28. 08 cm, 1920 × 1080 pixels, 60 cm viewing distance). The experiment was programmed with the software ePrime (https:// pstnet. com/ produ cts/e-prime/); responses for the recall task and for the questionnaires were indicated on a sheet of paper.
lower-expertise) and tonal structure (major triads vs. arbitrary trichords). This inferential analysis provided insights into the data structure that was the basis for the TBRS*C simulations. Generally, TBRS*C simulations provide serial position accuracies, i. e. , mean recall accuracies per serial position. In the present study, simulations were performed separately for expertise groups and experimental conditions. Thus, to interpret the simulations correctly, it is important to understand how recall accuracy varied with serial position, expertise group, and tonal structure. Prior to analysis, six hobby musicians were excluded as they either had a very low recall accuracy of below 5% or did not adhere to experimental instructions. Experimental materials, data sets, and analysis code of this study can be accessed via https:// doi.
Results Figure  4  shows mean values by group and condition for the proportion of correctly recalled notes, of correctly recalled melodic cells, and of correctly performed distractor notes. The left plot shows that recall was more accurate in the higher-expertise group and in the major triads condition; the advantage in the major triads condition was larger in the higher-expertise group. To test this pattern statistically, we performed a Bayesian mixed logistic regression. The model was created in Stan (http:// mc-stan. org/), accessed via the R package brms  [34]. Recall accuracy (0/1) was predicted by serial position and by an interaction of expertise group (higher-expertise vs. lower-expertise) and tonal structure (major triads vs. arbitrary trichords). We implemented a full random structure, i.
The pattern of interaction was as expected: the difference in recall accuracy between sequences of major triads and Fig. 3  Two examples of the notated melodies which participants had to perform between the presentation of to-be-remembered notes sequences of arbitrary trichords was larger in the higherexpertise group. This pattern of effects did not change using weakly informative priors. Moreover, another regression analysis revealed that the accuracy of performing distractor notes (right panel of Fig. 4 ) did not differ by group, condition, or their interaction. Using these predictors in a mixed linear model with the package lme4  [37]  did not increase the fit to the data compared to a null model without any predictors (χ 2 (3) = 4. 38; p = 0. 22).
This time interval was modeled as being used for refreshing. TBRS*C simulations were performed on higher-expertise and lower-expertise hobby musicians independently. Moreover, the simulation of the data was performed in two steps: First, only the data of the arbitrary trichords condition was simulated to obtain a baseline of the parameters Ta, R, and cSD. Second, using these initial parameter estimates, the data of the major triads condition was used to estimate PCR. This two-step procedure was based on the assumption that the memory system cannot know beforehand if it would recognize a chunk in a certain sequence of memoranda. Hence, chunking is initiated in the arbitrary trichords condition as well. This means that all parameters except for the probability of chunk retrieval (PCR) are the same in both conditions.
To account for the second stage of expertise acquisition and thereby advance TBRS*C as a model of expert memory, knowledge structures could be implemented in the simulations. Templates have meaningful structures as a fixed core and slots that allow extending this core with variable information  [4]. In the case of tonal music, two-note pairs (such as C-E) might denote a fixed core. Complemented with a third note, this two-note core can form different meaningful tonal structures such as a major triad (C-E-G) or an augmented triad (C-E-G#). In TBRS*C, this could be implemented by adding two-note pairs as well as triads to LTM and by programming the simulation to search for chunks after the second note and to extend encoded chunks to form three-note templates. For example, the simulation might first encode C-E as a chunk and then, if the following note is G, might extend this chunk to form a C-E-G major triad template.
Retrieval structures are based on a different logic than templates. They could rather be described as hierarchical structures in which multiple chunks can be encoded within supergroups  [5]. In the musical domain, multiple major triads (such as C-E-G and F-G-C) belong to the same scale (such as C major) and hence are more closely associated with each other than with major triads from other scales (such as D-F#-A). This concept of supergroups could again be implemented by adapting the LTM of TBRS*C. If the LTM would not only contain major triads but also groups of multiple major triads, the simulation could encode not only three but even six notes as a single unit. Future studies might implement these two kinds of knowledge structures in TBRS*C and then compare which simulation provides a better fit to human data.
Aerosol reanalysis datasets are model-based observationally constrained continuous 3D aerosol fields with relatively high temporal frequency that can be used to assess aerosol variations and trends, climate effects and impacts upon socioeconomic sectors, such as health. Here we compare and assess the recently published MONARCH high resolution regional desert dust reanalysis over Northern Africa, the Middle East and Europe (NAMEE) with a combination of ground-based observations and space-based dust retrievals and products. In particular, we compare the total and coarse dust optical depth (DOD) from the new reanalysis with DOD products derived from MODIS, MISR and IASI space-borne instruments. Despite the larger uncertainties, satellite-based datasets provide a better geographical coverage than ground-based observations, and the use of different retrievals and products allows for at least partially overcoming some single-product weaknesses in the comparison.
Nevertheless, limitations and uncertainties due to the type of sensor, its operating principle, its sensitivity, its temporal and spatial resolution, and the methodology for retrieving or further deriving dust products, are factors that bias the reanalysis assessment. We, therefore, also used ground-based DOD observations provided by 238 stations of the AERONET network located within the NAMEE region as a reference evaluation dataset. In particular, prior to the reanalysis assessment, the satellite datasets were evaluated against AERONET, showing moderate underestimations in the vicinities of dust sources and downwind regions, whereas small or significant overestimations, depending on the dataset, can be found in the remote regions. Taking into consideration these results, the MONARCH reanalysis assessment showed that total and coarse DOD simulations are consistent with satellite and ground-based data, capturing qualitatively the major dust sources in the area as well as the dust transport patterns.
data should be harmonized in terms of procedures and quality control within the specific dataset; lastly, iv) dust speciation is essential for the dust reanalysis assessment. The latter means that the aerosol observational products should be related not to the total AOD, but specifically to its dust component, obtained through advanced products or through consolidated dust-filtering algorithms. Finally, in the assessment, it is important to consider that the observational and modeling datasets are usually available at different spatial and temporal resolutions, which implies that they must be collocated in terms of space and time before their comparison. Details about the dust AOD characterization and the spatiotemporal collocation methodology followed for every dataset are given in the next subsections.
MONARCH dust regional reanalysis The MONARCH dust regional reanalysis represents the state of the art desert dust information over a domain covering the most prominent dust source areas in Northern Africa and the Middle East. This dataset has recently been released by the Barcelona Supercomputing Center (BSC) for a 10-year period, spanning from 2007 to 2016, over a spatial domain extending from 0°N to 70°N latitude and from -30°E to 70°E longitude. An extensive description of the reanalysis set-up and dataset can be found in Di  Tomaso et al. (2022). Here we summarize the main characteristics that are relevant for this study. The MONARCH reanalysis geographical domain includes some of the world's main dust sources like the Sahara in Northern Africa, the Arabian Desert in the Middle East and the arid regions of Western Asia (Fig.
1 ), with the former emitting 50 % of total dust burden in the atmosphere  (Ginoux et al. , 2012b). It also includes maritime regions such as the Arabian Sea, the Mediterranean Sea and the northeastern Atlantic Ocean, over which long-range dust transport takes place frequently. A list of desert and arid regions, representing the major dust sources of the NAMEE region, is denoted by capital letters in Fig. 1. Figure  1  also shows the 10 sub-regions in which the MONARCH reanalysis domain is divided for evaluation purposes. MONARCH reanalysis novelty includes its unprecedented spatial and temporal resolution, as well as the assimilation of an innovative DOD dataset covering all cloud-free and snow-free land surfaces, including areas particularly relevant for dust applications such as very bright reflective surfaces.
Reanalysis fields are available at a 3-hourly time-step (starting every day at 03:00 UTC) and at a horizontal resolution of 0. 1°latitude × 0. 1°longitude in a rotated grid (∼ 10 km × 10 km at the Equator). The reanalysis has been obtained using the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH;  Pérez et al. , 2011; Klose et al. , 2021)  and satellite coarse-mode DOD at 550 nm derived from the MODerate resolution Imaging Spectroradiometer (MODIS) instrument, operating aboard NASA's Aqua satellite. More specifically the dataset assimilated in the MONARCH reanalysis consists of gridded coarse DOD retrievals over land surfaces, including desert areas, derived from the MODIS Deep Blue aerosol products (Collection 6, Level 2;  Hsu et al. , 2004)  according to the retrieval procedure described in  Ginoux et al.
(2010 Ginoux et al. ( , 2012a) )  and  Pu and Ginoux (2016). Data assimilation was performed by means of a local ensemble transform Kalman filter data assimilation scheme with a four-dimensional extension  (Hunt et al. , 2007; Miyoshi and Yamane, 2007; Schutgens et al. , 2010; Di Tomaso et al. , 2017; Tsikerdekis et al. , 2021; Escribano et al. , 2022). The reanalysis dataset consists of upper-air variables such as dust mass concentration and extinction coefficient at 550 nm, surface fields such as accumulated dust dry and wet deposition and mass surface concentration, and total column fields like instantaneous total column dust load, DOD and coarse DOD at 550 nm. Calculation of basic ensemble statistics was performed https://doi. org/10. 5194/acp-2022-655 Preprint. Discussion started: 1 November 2022 c Author(s) 2022.
MODIS-based dust product: MIDAS The MODIS total and coarse DOD used in this study is based on the recently developed ModIs Dust AeroSol (MIDAS) dataset  (Gkikas et al. , 2020 (Gkikas et al. , , 2021)). MIDAS combines quality filtered AOD from MODIS (on board NASA's Aqua satellite) at swath level (Collection 6. 1, Level 2), along with DOD-to-AOD ratios provided by the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis  (Gelaro et al. , 2017)  to calculate the contribution of mineral dust particles to the overall AOD on the MODIS native grid. MIDAS coarse mode DOD is also derived using the MERRA-2 DOD fraction, and considers only particles with radius larger than 0. 5 µm. MIDAS provides columnar daily total and coarse DOD (at 550 nm) over all cloud-free and snow-free land and ocean surfaces, at fine spatial resolution (0.
1°× 0. 1°), and over a  15-year period (2003-2017). The uncertainty of the MIDAS DOD dataset was estimated by taking into account the uncertainties of the MODIS AOD and MERRA-2 DOD-to-AOD ratio  (Gkikas et al. , 2021) , which in turn were calculated by using AERONET AOD  (Giles et al. , 2019)  and LIVAS  (Amiridis et al. , 2015; Marinou et al. , 2017)  dust fraction, respectively, as a reference. According to the uncertainty analysis performed, MIDAS uncertainties scale with DOD value  (Gkikas et al. , 2021 , see Fig. 8 ); however, in terms of relative uncertainty the MIDAS DOD product is highly reliable over dust-rich regions and becomes more uncertain in areas where dust loading is infrequent. Although the MIDAS coarse DOD product is still under testing, it was used in this study after being evaluated against ground-based AERONET coarse DOD observations (Sect.
3). Prior to the comparison, the MONARCH and MIDAS datasets were collocated in space and in time. First, MONARCH was re-gridded through bilinear interpolation, using the MIDAS grid as a reference. Regarding the temporal collocation, thanks to the wide MODIS swath (∼ 2330 km), MIDAS provides near-global DOD retrievals every 1 to 2 days; consequently, MONARCH 3-hourly time-steps had to be averaged around Aqua's overpass time. Aqua follows a sun-synchronous, nearpolar orbit, crossing the Equator once during daytime at ∼ 13:30 local time (LT). As MONARCH outputs are given in UTC, it was necessary to convert 13:30 LT to UTC units, which depends on longitude. The MONARCH spatial domain contains eight time-zones (15 degrees of longitude constitute one time-zone) from -2 hours to +5 hours, implying that Aqua/MODIS takes measurements over the MONARCH domain between 08:30 UTC (70°E) and 15:30 UTC (-30°E).
, 2013). The spatial collocation between the two datasets was obtained by re-gridding MONARCH DOD, using the coarser MISR Level 3 product grid as a reference. For the temporal collocation we followed a similar methodology as in the case of MIDAS: MISR on board of NASA's Terra satellite is crossing the equator on the descending node at about 10:30 LT. After having converted 10:30 LT to UTC time taking into account the related longitude, the MONARCH DOD was temporally averaged around MISR overpass time using the two nearest MONARCH timeslots. MISR has only 1/4 to 1/3 the spatial sampling of MODIS due to its relatively narrow swath width (∼ 380 km), resulting in global coverage every 7-9 days at mid-tolow latitudes, compared with 1-2 days by MODIS. So, sampling must be taken into consideration when comparing datasets averaged over longer timescales.