dataesr/scientific-paragraphs-categorization huggingface dataset:
A cognitive disorder may be diagnosed by estimating the gap between the chronological and predicted (biological) age. To make such predictions, a larger dataset is required in future studies using ML. In Table  8  methods known for their high performance in classifying numerical data are compared. Figure  8  shows the ROC curves and AUC values that represent the performance of classifiers in both datasets. The accuracy of age group prediction from cognitive test performance was higher than that of PT performance (maximal AUC for the SSCT dataset was 0. 9962 vs. 0. 9382 for the POBA dataset). The significant differences between cohorts are marked in bold.
Lifelong Trend of Cognitive Performance Tasks vary across POBA and SSCT studies depending on cognitive complexity. They range from those involving low cognitive demands (e. g. , SVMR in the POBA dataset) to those requiring more complex cognition (e. g. , cognitive tasks in the SSCT dataset). On average, the cognitive tasks comprised in the SSCT dataset are more demanding compared with the battery of PTs. This may also explain why decline starts early in life according to cognitive tests, whereas psychophysiological findings begin to worsen only from middle age.
Socio-Demographic Correlates in Age-Related Cognitive Impairment Sociodemographic correlates, such as sex and education, can influence cognitive test performance. Literacy and higher educational level correlates with superior test performance on   several cognitive domains  (Ho et al. , 2019). For our analysis of both datasets, we took into consideration the educational level of participants. For instance, we used literacy as an inclusion criterion. Moreover, we included adults who indicated that they completed a professional course after finishing general education. Another way to control for the level of intelligence is by considering the years of formal education. However, this has several limitations. Firstly, the intensity of training is not considered. Secondly, it does not reflect the level of intellectual activity after completion of formal education (e.
Sex Differences in Age-Related Cognitive Impairment Despite some variation in psychophysiological and cognitive findings across the lifespan, we found significant test performance differences related to sex. The linear trends of The significant differences between intercepts and between slopes are marked in bold. the age-related changes for choice RT and updating in the SSCT test (see Table  5 ) had significantly different slopes. However, we did not observe significant sex differences in the lifelong dynamics of major test estimates. There is currently limited agreement in the literature on sex differences. Longitudinal studies comparing changes in cognitive function and probability of Alzheimer's dementia in men and women have revealed confronting results  (Barnes et al. , 2003). The largest AUC value for each dataset is marked in bold.
Reflection of Age-Related Cognitive Changes With the Psychophysiological Tests Multilayer perceptron or traditional fully-connected three-layer NN models show significantly higher AUC values (89. 6%) compared with other methods using the POBA dataset. The NN is a model that mimics the behavior of data and finds hidden patterns within it. The sensitivity and specificity of the young class were retrieved at 89 and 81%, respectively. The model was more sensitive to the young class than to the older class, which is in line with the trend observed in our assessment of the separability of the data. Our results demonstrate that cognitive tests and PTs may serve a diagnostic purpose as a screening tool. They are rapid, standardized, fully automated, easy to administer, highly reproducible, and have sufficient sensitivity and specificity (see section 5.
STRENGTHS AND LIMITATIONS OF THE STUDY A limitation of the study is its cross-sectional design. This implies that participants of different ages were born and raised at a different time. Thus, the Flynn or antiFlynn effect may have an influence, which is the change in intelligence test scores across generations, and it may influence mean RTs  (Woodley et al. , 2013). Such effects have been reported during previous decades in various countries  (Woodley, 2012). However, there is increasing evidence that the effect is due to changes in test-taking behavior over time rather than significant variability in intelligence  (Must and Must, 2013). Another limitation of the study is that the datasets we used were not completely comparable, as they were acquired within the last 5 years in different countries.
• ML models can be designed and utilized as a computer-aided detector of neurocognitive decline. Our study showed great promise for the use of classification models as predictors of age-related changes. Our results encourage us to explore a combination of tests from the battery to derive a more reliable set of tests based on performance metrics. Moreover, further investigations of other cognitive and PTs are warranted. • Future research is required to improve the performance characteristics of the ML model by using a larger sample size and an enriched test dataset that includes patients with dementia.
The range of years corresponding to each group was as follows: Adolescents were aged [0, 20) years, Young adults were aged [20, 40) years, Midlife adults were aged [40, 60) years, and Older adults were aged ≥ 60 years. As shown in Figure1, the distribution of subjects by age group was similar. Subsequently we enlarged the groups into two major clusters. The clusters of the young (<40 years) and older (40 years and above) adults were almost balanced: 48. 5/51. 5%. While solving the last task, we excluded the demographic features from the dataset because they risked biasing the prediction.
One of the top priorities of the aquaculture industry is the genetic improvement of economically important traits in fish, such as those related to processing and quality. However, the accuracy of genetic evaluations has been hindered by a lack of data on such traits from a sufficiently large population of animals. The objectives of this study were thus threefold: (i) to estimate genetic parameters of growth-, yield-, and qualityrelated traits in rainbow trout (Oncorhynchus mykiss) using three different phenotyping technologies [invasive and non-invasive: microwave-based, digital image analysis, and magnetic resonance imaging (MRI)], (ii) to detect quantitative trait loci (QTLs) associated with these traits, and (iii) to identify candidate genes present within these QTL regions. Our study collected data from 1,379 fish on growth, yield-related traits (body weight, condition coefficient, head yield, carcass yield, headless gutted carcass yield), and quality-related traits (total fat, percentage of fat in subcutaneous adipose tissue, percentage of fat in flesh, flesh colour); genotypic data were then obtained for all fish using the 57K SNP Axiom R Trout Genotyping array.
, 2012 Haffray et al. , , 2014;; Lhorente et al. , 2019; Hu et al. , 2020) ; two such selection programmes reported realised genetic gain in the range of 0. 5-1% for fat in the muscle  (Quillet et al. , 2005)  and carcass and fillet yields, as predicted by ultrasound technology  (Vandeputte et al. , 2019). However, the genetic architecture of yield traits is still poorly understood, and almost no attention has been paid to quality traits such as flesh colour or fat distribution. One major area of progress, though, has been the recent development of the Affymetrix R Axiom R 57K Trout SNP array  (Palti et al. , 2015)  and the publication of the rainbow trout reference genome assembly (GenBank assembly Accession GCA_002163495, RefSeq assembly accession GCF_002163495), which have provided the genomic tools necessary for genomewide association studies (GWASs) aimed at the detection of quantitative trait loci (QTLs) for traits of interest.
Ethics Statement This study used fin clips collected by the breeding company "Les sources de l'Avance" as part of their commercial breeding programme. Rearing was carried out in compliance with Directive 98/58/CE on the protection of animals kept for farming purposes and Directive 2010-63-EU on the protection of animals used for scientific purposes. Data were collected from sacrificed animals and thus the experiment did not require approval from an Ethics Committee, in accordance with Article 1. 5 of Directive 2010-63-EU.
Fish Production and Trait Recording All measured traits and parameters of the rearing environment were defined according to the ATOL (Animal Trait Ontology for Livestock) (Table  1 ) and EOL (Environment Ontology for Livestock) databases, available on the ATOL website 1. The fish came from a commercially selected line from the Sources de l'Avance breeding company (Pissos, France), a subsidiary of Aqualande Group. In December 2016, 831 rainbow trout families were created using a partly factorial mating design. Through 10 factorial crosses, 84 dams were crossed with 99 neomales (sexreversed females used as sires). A piece of fin was sampled for each parent for the purpose of DNA extraction. Half-sib families from each dam were incubated in eleven troughs containing five trays divided into two compartments.
At 469 dpf, fish were individually tagged with RFID transponders and their DNA was collected through a fin sample that was preserved in 95% ethanol for parentage assignment. At the end of the growing period in April 2018, 1,510 fish were randomly sampled and divided into four sub-groups. Each sub-group was treated the same way: three days of fasting (EOL_0001739), followed by live transportation by truck to the Aqualande processing plant (Roquefort, France, 50 km from the growing farm), where they were slaughtered and processed for analysis. Because RNA was to be sampled for future expression analyses, fish were humanely killed by a blow to the head and bled by cutting the gills in ice water, in accordance with good animal slaughtering practices. Post-mortem data collection and processing were accomplished as quickly as possible to ensure data accuracy.
Data collection was performed between 503 and 506 dpf. The traits that were quantified included body weight (BW, ATOL_0000351), fork length (BL, ATOL_0001658), head weight (HeadW, ATOL_0001545), headless gutted carcass weight (HGCarcW, ATOL_0002260), and viscera weight (ViscW, ATOL_0002258). These traits were then combined to create the following synthetic traits and yields (see Table  1 ): Fulton's coefficient of condition, calculated as K = BW(g) × 100/BL 3 (cm) (ATOL_0001653); headless gutted carcass yield (HGCarc%, ATOL_0002261); head yield (Head%, ATOL_0005561); and gutted carcass yield (Carc%, ATOL_0000548). The total fat content in muscle (Fat, ATOL_0001663) was recorded using a Fish Torry Fat-meter R as described in  Douirin et al. (1998) ; the probe was placed on the left side and the minimum value was recorded.
0001, and a minor allele frequency (MAF) higher than 0. 05. In total, 29,652 SNPs were retained for the analysis. Samples in which less than 90% of SNPs were genotyped were removed from the dataset. All missing SNP genotypes of the remaining individuals were imputed using FImpute software 2. 0  (Sargolzaei et al. , 2014). The final dataset therefore contained 1,379 fish with phenotypes and genotypes (for 29,652 SNPs) which were subjected to GWAS analysis.
Genome-Wide Association Study and QTL Detection A Bayesian Stochastic Search Variable Selection approach BayesCπ  (Habier et al. , 2011)  was implemented in BESSiE software (version 1. 0)  (Boerner and Tier, 2016)  to perform GWASs. In the BayesCπ model, only a certain proportion of SNPs (π) are thought to have a non-zero effect on the phenotype. The marker effects are estimated through an MCMC algorithm that considers a mixture of markers, of which proportion π have effects that follow a normal distribution N(0, σ 2 a ) and proportion 1 -π have zero effect. The general model used is: Y i µ + bBW i + n j 1 δ jk a j g ij +ε ik with Y the phenotype observed for the ith individual, µ the overall mean in the population, b the regression coefficient of Y on BW; n the total number of SNPs in the analysis, a j the additive effect of the reference allele for the jth SNP, with genotype g ij for individual i (coded as 0, 1, or 2); and ε ik the residual effect for the ith individual in the kth iteration.
Improvement in the digital image analysis especially for the segmentation of the myosepta could be a way to quantify more precisely muscle fat. Beside the classical segmentation techniques, the development of deep learning methods could allow a better segmentation on low contrast images  (Fernandes et al. , 2020). However, these techniques require the training of a CNN (convolutional neural network) like U-Net  (Falk et al. , 2019)  with manually annotated images (ground truth). The quality of the manual annotation being a key point for the prediction by the CNN. The construction of such manually annotate image databases is a quite tedious task and the precision difficult to obtain on thin tissue such as the myosepta. Going beyond the quantity and distribution of fat in tissues, another interesting selection goal is the nutritional quality of fat (content of healthy n -3 long chain polyunsaturated fatty acids in fillets).
ACKNOWLEDGMENTS We are grateful to  Jérôme Doerflinger ,  Thierry Espirat , and  Mathilde Horat  from Aqualande;  Laurence Larroquet  from  INRAE ; and  Sophie Brard ,  Romain Morvezen ,  Pierre Patrice ,  Enora Prado , and  Maxime Reverchon  from  SYSAAF  for their invaluable help in data collection. We are grateful to  Thibault Tournadre  for his help in digital image analysis. The  PRISM core facility (Rennes-Angers, France ) hosted the MRI experiments described here.
DATA AVAILABILITY STATEMENT The datasets presented in this article are not readily available because the data that support the findings of this study are available from the breeding company "Aqualande" and restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. The data can be made available for reproduction of the results on request via a material transfer agreement and with permission of the breeding company "Aqualande. " Requests to access the datasets should be directed to mathilde. dupontnivet@inrae. fr and vincent. petit2@wanadoo. fr.
(  4 ) represents HCHO loss due to reaction with OH radicals, while the second term describes total losses due to photolysis (both the radical and molecular channel). In the boundary layer, additional loss due to dry deposition has to be considered and depends on the deposition velocity v d and the boundary layer height (BLH), i. e. its mixing volume. Further loss due to wet deposition has been neglected in this study since we did not encounter significant precipitation during the AQABA campaign. The derived production and loss rates of HCHO can be influenced by direct emissions or by advective transport. Obvious direct emissions from ship plumes or other sources were excluded from the dataset. The potential role of transport is addressed in Sect. 4. In this study we use in situ observations of HCHO, OH, R(OH) and j HCHO together with the HCHO deposition velocity v d (calculated for 34 m above the ocean;  Stickler et al.
, 2007)  and ERA-5 meteorological data of the BLH (https://www. ecmwf. int/en/forecasts/datasets/ reanalysis-datasets/era5, last access: 3 December 2020), which were obtained during a ship cruise around the Arabian Peninsula as part of the AQABA (Air Quality and Climate Change in the Arabian Basin) campaign in summer 2017. From this dataset, we calculated the loss rates of HCHO, which represent P(HCHO) during PSS conditions. Scatter plots of [OH] × R(OH) versus P(HCHO) yield the lower estimate of the formaldehyde yield α with respect to total OH chemistry (including NO x , SO 2 and other non-HCHOproducing reactions; see Fig. S2  in the Supplement), reflecting the transition between rather clean to highly polluted conditions, both with respect to NO x and VOCs. α = P(HCHO) -P add * (HCHO) [OH] • R(OH) (5) For further interpretation, the effective HCHO yield α eff was determined for each region by removal of non-HCHOyielding reactions (of NO, NO 2 , SO 2 , CO, HCHO and O 3 with OH) from the OH reactivity data (R(OH) eff , Fig.
Nitrogen oxides (NO x = NO + NO 2 ) were measured with a two-channel chemiluminescence instrument (CLD 790 SR, ECO Physics AG, Dürnten, Switzerland). A detailed description of the instrument during AQABA can be found in  Tadic et al. (2020). The measurement uncertainty of the NO data was calculated to be 6 % at 5 min integration time and a confidence level of 1σ. The LOD for the NO channel was estimated as the full width at half maximum of the frequency distribution of all zero-air measurements obtained during the campaign to be 9 pptv with a confidence level of 1σ. The total uncertainty of the NO 2 data was estimated as a conservative upper limit at 23 % as the average of the relative uncertainties of all data points obtained during the campaign. The absolute detection limit of the NO 2 instrument was estimated at 112 pptv  (Tadic et al.
, 2020). O 3 measurements were performed with a commercial absorption photometer (model 202 ozone monitor, 2B Technologies, Boulder, Colorado, USA). Water and particle interferences during the expedition were minimized by sampling through a Nafion tube and a Teflon filter; the overall uncertainty of the data was 2 %  (Tadic et al. , 2020). CO and CH 4 mixing ratios were determined with a cavity ring-down spectrometer (Picarro G2401; Santa Clara, USA) with a precision of ≤ 8 ppbv for CO and ≤ 0. 3 ppbv for CH 4. The air was not dried prior to analysis, and water vapor effects were corrected. The data were quality-controlled following ICOS (Integrated Carbon Observing System) standards; further details can be found in  Paris et al. (2021). Non-methane hydrocarbons (NMHCs) were measured with two coupled GC-FID systems (GC5000VOC and GC5000BTX; AMA Instruments GmbH, Germany).
Significant cloud cover was observed only on 20-22 July and 8-10 August. The boundary layer height varied between 250 and 1400 m (Fig. 2 ), with least diurnal variation in the Arabian Sea and the Mediterranean Sea with a mean of 566 and 780 m, respectively. The boundary layer height in other regions was more variable, with a mean value (± 1σ ) for the entire dataset (excluding the stop in Kuwait) of 750 ± 113 m. The sink of HCHO was calculated by summing the loss rate coefficients through reaction with OH, photolysis and dry deposition (Eq. 4, Fig. 3 ). Based on Gaussian error propagation of uncertainty in the HCHO and OH mixing ratios, j HCHO , and in the rate coefficient k OH for reaction of OH with HCHO, the total uncertainties of the loss rates due to OH and j HCHO are 33 % and 15 %, respectively.
Loss of HCHO via photolysis and reaction with OH were found to be of the same order of magnitude, with total noontime loss rates varying from 0. 01 up to 1 pptv s -1. The formaldehyde sinks due to OH and photolysis were on the order of 0. 1 pptv s -1 during noon, with the lowest values over the Arabian Sea, the Gulf of Aden (8-15 August) and the southern Red Sea (18-20 August). Significantly enhanced noontime values (> 0. 2 pptv s -1 ) were found for the loss through photolysis over the Arabian Gulf (28-31 August) due to the elevated HCHO mixing ratios. The removal of HCHO by dry deposition depends on turbulent transport and also on the wind speed. Since our results for the HCHO mixing ratio are just one dimensional, the dry deposition could not be calculated directly for the AQABA dataset and was thus inferred with the literature value for the HCHO deposition velocity (v d ) over sea and the boundary layer height.
g. photolysis) are involved in the HCHO production from unsaturated hydrocarbons. The dataset covers a highly polluted event in AG (30 July 2017), although the high HCHO/R(OH) ratio, as well as enhanced methanol, acetaldehyde and O 3 mixing ratios  (Wang et al. , 2020; Tadic et al. , 2020) , indicates rather processed air masses which could also include primary emissions of HCHO. Primary emission from point sources would likely shift data above the regression line due to the correlation with the HCHO mixing ratio (although this effect can be compensated for depending on the OH concentration in emission plumes). The primary emission of HCHO cannot be accurately identified, but we removed obvious pollution events (e. g. plumes from ships or oil rigs) via covariance with elevated NO x , CO and SO 2 mixing ratios.
Furthermore, rainout can affect the relationship, because it would result in data points shifted below the regression line, although the impact can be neglected in this study as only minor precipitation occurred in AS on the 12 August 2017 during the night. In our analysis, we first subdivided the data into the smaller regions according to Fig. 1  and examined the correlation between P(HCHO) and [OH] × R(OH) for each subregion (Fig. S2 ). For the Arabian Sea, the dataset has been further partitioned into the two individual legs heading towards and away from the Gulf of Oman. This is justified by the significant differences in HCHO mixing ratios and R(OH) during the two legs (AS Legs 1 and 2), reflecting different airmass origins. For the individual regions, bivariate fits were performed according to  York et al.
Even though the dataset was already filtered for stack emissions, NO x showed the highest contribution of these reactants (Fig. S1 , Table  S1  in the Supplement), especially over the Gulf of Oman and while passing Bab-el-Mandeb (16 August 2017) and the Strait of Messina (30 August 2017). Over the Arabian Gulf the correction was dominated by contributions from CO, O 3 and HCHO. The use of R(OH) eff resulted in reduced data coverage and an increased slope compared to when using non-adjusted R(OH) (Figs. 4  and S2 ). The effective HCHO yield α eff increased significantly in five subregions (Suez, Arabian Gulf, Gulf of Oman, Gulf of Aden, Arabian Sea Leg 2), and the plots show in general slightly reduced scatter. Values for the intercept b were not substantially affected by choice of R(OH) or R(OH) eff , and both methods yielded similar results within their uncertainties, except for the Arabian Sea during the second leg, where the intercept decreased to similar values of the Mediterranean Sea when R(OH) eff was used.
Data availability. The data used in this study can be accessed for all scientists agreeing to the AQABA protocol at https://doi. org/10. 5281/zenodo. 3354117 (Pfannerstill et al. , 2019)  and through the KEEPER service of the Max Planck Digital Library via the person responsible on request. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-17373-2021-supplement. Author contributions. HF and DD designed the study. DD and BH performed the HCHO measurements. IT and UP provided the NO x measurements. RR, ST, MM and HH performed the LIF OH and HO 2 measurements during AQABA. EP and NW provided the OH reactivity measurements. NW, AE and CS were responsible for the OVOC measurements. EB and LE carried out the VOC measurements during AQABA, which were used together with the OVOCs by EP to calculate speciated OH reactivity.
Figure2. Time series of HCHO, OH, R(OH) and the HCHO photolysis (j HCHO , sum of the molecular and the radical channel) in 5 min resolution. For the boundary layer height (BLH), we used the ERA-5 meteorological reanalysis data in hourly averages, which resolve the earth's atmosphere on a 30 km grid (https://www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/era5, last access: 3 December 2020).
support. The AQABA campaign was funded by the Max Planck Society. Efstratios Bourtsoukidis, Jonathan Williams and Jos Lelieveld were supported by the EMME-CARE project from the European Union's Horizon 2020 Research and Innovation Programme (grant agreement no. 856612), as well as matching co-funding by the government of the Republic of Cyprus. The article processing charges for this open-access publication were covered by the Max Planck Society.