dataesr/scientific-paragraphs-categorization huggingface dataset:
659 and 0. 291, respectively; Supplementary Data 4). These allele frequencies are by far the highest reported anywhere in the world. In 1000 Genomes data, other than in admixed Latin Americans (AMR), maximal frequencies are observed in East Asians, but these never exceed 0. 177 for D1908G or 0. 078 for and V991L and M932L (Supplementary Data 4). The high frequency of these variants in Native Americans probably relates to strong drift during the initial colonization of the American continent  27, 28. With regards to nociception, our analyses support the report by Zeberg et al. 7  of an effect of Neanderthal ancestry in SCN9A on pain sensitivity. Furthermore, we find that a particular pain modality impacted by Neanderthal ancestry is mechanical pain sensitivity, especially after mustard oil sensitization (POST_MPT).
Methods Study sample. The CANDELA cohort consists of more than 7000 individuals that were recruited in five Latin American countries (Brazil, Chile, Colombia, Mexico and Peru) for the genetic study of non-pathological variation in the population  15. Individuals for the QST cohort were recruited in Medellin (Colombia) via open lectures, public notice boards and distribution of flyers at Universidad de Antioquia Faculty of Odontology  21. This cohort consists of 1963 individuals recruited in Medellín (Colombia), with a median age of 22 (range 18-45) years, of which 56. 1% were female (Supplementary Data 2). Only individuals not reporting any history of medical history related to pain status were recruited for the study. This study was approved by the bioethics committee of the Odontology Faculty at the University of Antioquia (CONCEPTO-01-2013).
All participants provided written informed consent. Phenotyping. Thresholds for six pain modalities (HPT, MPT, POST_HPT, POST_MPT, PPT and WUR; Supplementary Data 9) were obtained following our QST protocol described in detail in Schmid et al. 18. Participants were also evaluated with the 16-Item Quick Inventory of Depressive Symptomatology  46. Individuals reporting severe depression (score>15) were excluded from QST. All pain thresholds were measured in the forearm, except for PPT, which was assessed on the hand (thenar muscles). The distribution of the QST traits was highly skewed (Supplementary Figs. 5 6 7 8 ), with the occurrence of sporadic outliers. For the genetic analyses we excluded extreme outliers (over ±2 standard deviations) and performed a log transformation of the data, resulting in a more normal distribution of the traits (Supplementary Figs.
5 6 7 8 ). In a test-retest reliability assessment, we observed high and statistically significant intra-class correlation coefficients for all traits  18. Genotyping, imputation and sample filtering. Chip genotyping of the QST cohort was achieved in two runs and on two different chips: Illumina OmniExpress (1177 individuals; 697,067 variants after genotyping quality control) and Illumina GSA (512 individuals; 683,494 variants after genotyping quality control). The GRCh37/hg19 build was used for genomic locations. Imputation was achieved independently on the two datasets, using all 2504 samples from 1000GP3 as reference. This dataset includes the 3 variants of interest (D1908G, V991L and M932L), actually at a relatively high frequency in American populations. Prior to imputation, variants monomorphic in the AMR populations of the 1000GP3 were excluded.
After imputation (performed by IMPUTE2  47  ), only variants with high reliability were retained. In brief, the conditions were (i) imputation quality scores ≥0. 40, (ii) concordance value >0. 70, (iii) gap ≤0. 10 between the INFO score (an imputation quality metric returned by IMPUTE2) and concordance value, (iv) call rate ≥0. 95 (after setting as uncalled genotype with maximum genotype probability <0. 90) and minor allelic frequency >0. 01. More details about the imputation procedure can be found in earlier studies of the CANDELA project  12  ). For quality control purposes, we merged the 1689 samples from both runs on a selection of 624,107 variants (genotyped on the OmniExpress chip and either genotyped on the GSA chip, or imputed). We ran KING v2. 2. 5  48  to estimate kinships between all individuals in the merge dataset and removed 40 of them so that no pairwise kinship would be higher than 5%.
Then, we performed PCA on the remaining individuals with PLINK v1. 9  49  and spotted 9 genetic outliers. We checked covariates consistency for the 1640 remaining samples; we removed 7 of them for a large discrepancy between age records at sampling and phenotyping (≥5 years) and 10 of them for inconsistency between recorded and genotyped sex, hence leaving 1623 individuals for this study. The dataset was further enriched with CANDELA samples not phenotyped on sensitivity (710 samples from the CANDELA Colombian set intersected with QST samples). We added these samples for two purposes: (1) improving phasing quality by extending the data, and (2) assessing the distribution of SCN9A haplotypes over admixed Latin American populations. To that end, we applied the same individual filtering rules as above, which led to add 5971 individuals (666 Brazilians, 1765 Chileans, 1067 Colombians, 1216 Mexicans and 1257 Peruvians).
Therefore, the combined dataset included 7594 individuals (1623 Colombians with sensitivity phenotypes and 5971 nonphenotyped Latin Americans). For all these individuals, we retained 13,167 variants that were: (i) located in a 4. 18 Mb window corresponding to SCN9A limits -/+2 Mb, (ii) retained after post-imputation filters of the GSA and the OmniExpress datasets, (iii) with MAF higher than 1%, and (iv) with variant call rate higher than 95%. This selection included the 3 variants from Zeberg et al. 7  (D1908G, V991L and M932L), whose INFO scores were all above 98. 6% in the two datasets. We also assessed the expected concordance between imputed and true genotypes using the concordance table summarized in the IMPUTE2 run. These concordance tables are obtained by masking and imputing known genotypes.
We obtained concordance rates (averaged over the two datasets) of 99. 3% for D1908G and 99. 2% for the two other variants. In addition, we found imputed genotypes of these 3 variants to perfectly match sequenced genotypes, for 38 samples of the QST cohort that were sequenced at high-coverage (>30×). Genome-wide ancestry estimations. An LD-pruned set of 93,328 autosomal SNPs was used to estimate genome-wide European, African and Native American ancestry proportions using supervised runs of ADMIXTURE  50. Unadmixed reference parental populations included in the ADMIXTURE  50  analyses consisted of 504 Sub-Saharan Africans (YRI, ESN, GWD, MSL, LWK) and 404 West Europeans (IBS, GBR, TSI, CEU) from 1000GP3 and 380 selected Native Americans (from North, Central, and South America). These reference individuals were selected, and their unadmixed nature was established, during a population genetics analysis conducted on the CANDELA samples with a worldwide set of diverse reference populations  25, [51] [52] [53] [54] [55]  in Chacón-Duque et al.
16. Local ancestry inference. Prior to infer local ancestry along all chromosomes, we ran analyses on the CANDELA cohort to optimize both (1) the set of reference samples of each of the 3 continental reference samples (African, European and Native American) and (2) the implementation of the inference methods. To that end, we sought to minimize two criteria: (i) the sum of differences with global ancestry estimates returned by ADMIX-TURE (see above), hence assuming global ancestry is better estimated by ADMIXTURE than by local ancestry methods, and (ii) the average number of ancestry switches per individual, assuming that spurious ancestry calls will be of shorter length than true calls hence inflating the number of ancestry switches. We compared versions 1 and 2 of RFMix  24  , varying the number of expectation-maximization (EM) iterations (0, 1 or 2) and asking the software to model admixture in reference samples in EM iterations >0 (version 1 "--use-reference-panels-in-EM", version 2 "--reanalyse-reference").
In the scope of this study, we retained the ancestry tracts located in the 4. 18 Mb defining the region of interest. These ancestry tracts were used at various places in downstream analyses, noticeably to assess the per-ancestry frequency of each allele at each of the 3 variants and to determine a length threshold for introgression tracts (see below and Supplementary Note 1). In addition, RFMix outputs enhanced phases, taking advantage of local ancestry calls to detect phasing errors; we used these enhanced phases as a scaffold for phasing the imputed data over the region of interest (see below).
Phasing of imputed data and alignment to local ancestry calls. We pre-phased the 7594 individuals (from both CANDELA and QST cohorts) of the combined dataset over the region of interest (13,167 imputed variants) using Shapeit4  56  (with default parameters and without reference phases). We have then aligned these pre-phases to phases obtained after running RFMix v1  24  on the same individuals (with a set of 242 reference individuals of each continental ancestry and 1 EM iteration). Achieved with a strategy 57 relying on Shapeit2  58  , this operation has two purposes. First, RFMix 24 ran on chip genotypes (see above), which are ~15× less dense than imputed data, hence less prone to phase switches. Furthermore, this program enhances the phases by detecting potential phase switches based on inferred local ancestry tracts.
Aligning the pre-phases to these phases is therefore expected to lower the number of phase switches. Secondly, the resulting phases are then aligned to local ancestry calls. This alignment therefore allows to estimate the frequencies of SCN9A haplotypes within each continental ancestry by focusing on the 12,136 chromosomes (over a total of 2 × 7594 = 15,188 chromosomes) with invariant local ancestry inferred over the studied region. Introgression scans. We carried out introgression scans on all phased chromosomes (N = 15,188) in the SCN9A region (gene limits ±2 Mb, 4. 18 Mb in total) with the script admixtureHMM  59. Using the Viterbi algorithm, this script models each chromosome as a succession of archaic or modern tracts, provided Neanderthal allele frequencies at each site in the region and for proxies of each ancestral population.
In each haplotype-based test, the individual dosage of the tested haplotype was obtained by counting the copies of this haplotype carried by an individual. This way, each haplotype was compared to the union of the 3 other haplotypes, i. e. AB vs. {Ab, aB, ab}, Ab vs. {AB, aB, ab}, aB vs. {AB, Ab, ab}and ab vs. {AB, aB, Ab}. In order to keep the effect in the same direction for all tests, we have reverted the focal allele in the last test: {AB, Ab, aB} (i. e. any haplotype that carry at least one Neanderthal variant) was counted instead of the ancestral haplotype (ab). We provided details about the coding in Supplementary Data 11, along with numbers for each haplotype-based genotype. Please note that no individual homozygous for the least frequent haplotype (aB; 2. 1% in CANDELA cohort and 0.
5). Narval and Neo-Narval have been described before in the literature. These descriptions are extensive for the case of Narval  (Donati et al. 2006) , with a succinct description of the changes of Neo-Narval provided by López  Ariste et al. (2022). As stressed in this latter publication, we note the continuity of data quality from the instrument through its upgrade, and handle Narval and Neo-Narval data as a unique dataset with no further reference to the instrument used. We performed relatively short exposures (about 3 min per polarimetric sequence) in order to ensure a peak signal-to-noise ratio (S/N) of about 2000 in Stokes I per velocity bin. A list of the observations of µ Cep is presented in Table  A. 1, corresponding to all those studied in this work. A least-squares deconvolution (LSD) procedure  (Donati et al.
As expected, having zero sources allows us to recover the basic model, unable to reproduce the anomalous signals on the red wing. On the other end, we find that beyond four sources we are not learning anything new from the inversion results, and the algorithm becomes unstable, and presents convergence issues. This can be safely understood as an excessive number of new parameters given the available information. Between one and four sources is therefore the right number of sources that we can safely infer. Interestingly, we also find that for any individual observation of our long dataset, the inferred value of the polar angle of all the sources is similar, even if the intensity and height of each one of them is different. This means that the solution found by the inversion algorithm proposes that, at a given polar angle, there are several sources of polarization at different heights and with different intensities.
There is also a clear tail further towards the red in the observations that cannot be captured with just two sources. Adding more sources would correct these missed fits, but the parameters of the added sources will not change significantly. In September 2015, the two sources over the limb bring signal comparable to anything else over the front disk. In October 2016, the two sources appear as small contributions that may drop to zero if just the red velocity boundary is shifted a few km s -1 towards the red. The modified model is therefore able to capture both those cases with important sources over the limb as well as those cases with negligible contributions. We used this model, in conjunction with two sources above the limb, to invert the whole available dataset of linearly polarized spectra of µ Cep presented in Sect.
We produced an inversion algorithm using this extended model and successfully fitted the observed profiles, including the unexpected new features. This model assumes the presence of a small number of discrete sources over the limb. Although about four such sources are required to correctly fit the profiles, we realize that, at any given date, all those sources appear to be combined to describe a unique but extended source on the star. This observation allowed us to reduce the number of sources over the limb to just two. While the fit with just two sources is not as good as it would with four sources, we still capture the main parameters of the sources and stabilize the convergence of the code, which can be automatically launched to handle the whole dataset available. The inversion results produce the polar angle position and the height of those sources.
Mechanical Test The mechanical properties of the flexor digitorum longus tendons were evaluated with tensile tests. One end of the tendon was connected to a length transducer and an electromagnetic motor, and the other one to an isometric force transducer. The tendon was kept immersed in phosphate-buffered saline throughout the measurements to avoid any drying which could affect the results. Length and force signals were recorded using Picoscope6 software (Pico Technology, St Neots, UK, www. picotech. com). The left tendon underwent sinusoidal stimulation at a frequency of 10 Hz and with a deformation of 10% from the initial length. The right one was evaluated until rupture as previously described  [18]. The first part of the stress-strain curve, before linear relation, is due to the collagen crimp and is called the toe region.
1% Tween and 5% Bovine Serum Albumin. The primary antibodies, decorin (Abcam, ref: ab137508, dilution 1/500) and tenomodulin (Abcam, ref: ab203676, dilution 1/500), were then incubated at 4 • C overnight. After washing, membranes were incubated with the secondary antibody for 1 h and then developed on an X-ray film (CL-X Posure ® films, ThermoFischer Scientific, Waltham, MA, USA) using Electrochemiluminescence Western Blotting Detection Reagents (GE Healthcare, Chicago, IL, USA). Protein levels were normalized to β-actin (Sigma, ref: A5441, dilution 1/50,000). Films were scanned and quantified by densitometry using ImageJ software (Version 1. 52A). In results, the image is cut off since the third column was an ultimately unexploited group, but the measurements were correctly made on the same gels.
They have primarily been used for optimisation and prediction  [38, 39]. Civil engineering optimisation tries to minimise a cost function subject to specific performance constraints  [40, 41]. For example, AI may be used to optimise the size, topology, and form of structural elements such that the structure fulfils minimal design/performance criteria  [34, 42, 43]. On the other hand, the prediction approaches were created to learn patterns from a dataset and generalise them to make accurate predictions  [44] [45] [46]. The literature has extensively addressed the prediction of many properties of concretes, such as mechanical, thermal, and durability qualities, and the prediction performance of various methods has been investigated and published  [47, 48]. Through sensitivity studies, several researchers looked at the impact of input characteristics (e.
26%, respectively; and the values of RMSE and MAPE in the testing set were 3. 68% and 1. 67%. They also look at how ANNs may be used to model the modulus of elasticity of RAC  [72]. The first ANNs, called ANNs-I, are built into their work by employing 324 datasets obtained from 21 articles in the literature and randomly separated into three groups: training, testing, and validation sets. A second ANN, called ANNs-II, is also created, with 16 more data sets from the authors' experiments added to the learning database of the first ANNs-I to see if the performance of ANNs can be enhanced further. The built ANNs model is then used to forecast further experimental data received from third-party articles (Case I) and test results produced in the authors' laboratory (Case II) and assess its applicability to RA obtained from other sources.
The projected findings are compared to those obtained through experimentation and those modelled using traditional regression analysis. The results showed that applying the created ANNs-I to Case I and Case II can provide results that are extremely near to the real values, with MAPE values of just 5. 21% and 3. 92%, respectively, and R 2 of 0. 9941 and 0. 9982. The performance of the ANNs-II is greatly enhanced when some of the data is added to the training sets of ANNs-I. Miličević et al. [52]  introduced a multi-layer back-propagation algorithm in ANNs, the most widely used ANNs methodology and mathematical model, for predicting different output parameters concerning RAC mechanical properties. A lower MAE, RMSE, and MAPE value indicates that the model is efficient and predictable  [52]. The results showed that the mathematical model fared the best in terms of RMSE regarding outputs, flexural strength, and modulus of elasticity.
Besides, Bu et al. [54]  presented how the dataset was used to develop ANNs whose optimal structure was determined using the trial and error method. The R 2 of 0. 96650 and the RMSE of 2. 42 showed that the model could predict real compressive strength and regulate all sample deviations to within 15%. Patil et al. [71]  used multiple linear regression (MLR) and ANNs models for the mechanical parameters of RAC. The R 2 of the three models based on ANNs were 0. 9295, 0. 9345 and 0. 9347, respectively, indicating that the discrepancy between actual and predicted values is slight. In contrast, the MLR model fails to forecast the mechanical properties of RAC and has limited capabilities. Previous literature review indicates that ANNs are the most often used approach for predicting the mechanical properties of RAC with higher accuracy.
Three advanced predictive models (multi-layer perceptron, sequential minimal optimisation regression-based SVM, and Gaussian processes regression), three regression tree models (M5P, REPTree, M5-Rules), and two ensemble methods (additive regression and bagging) are used as the base classifiers for each of the seven individual models. The correlation coefficient values for additive and Gaussian processes regression are the greatest of all the models examined for specific data sets, whereas SVM approaches had a medium accuracy. In terms of MAE and RMSE, these findings also show that additive regression has the best forecast accuracy for the comprehensive strength of PLC samples, but the individual Gaussian processes regression model has the best prediction accuracy for both the Portland Cement and the entire dataset.
Deep learning Deep learning is a sophisticated AI algorithm  [95]  with a multilayered ANNs structure at its core  [96]. Because of their tremendous capacity to tackle exceedingly complicated problems, these algorithms have gotten much attention in recent years  [97, 98]. However, most applications of deep learning models in civil engineering issues are confined to fracture detection or structural health monitoring since promising findings often require more extensive datasets  [11, 97, [99] [100] [101] [102] [103] [104] [105]. Deng et al. [96]  used the deep learning approach to determine the compressive strength of RAC. Fig. 8  depicts the structure of the prediction model. First, convolutional neural networks (CNNs) are used to learn the deep features of input parameters, and then the prediction model is developed using softmax regression.
However, with a "black-box" approach, ANNs have several drawbacks; for instance, the models are hard to explain and the training phase requires a significant amount of data. Other AI approaches, such as decision trees, sidestep the ambiguity of black-box models and produce easyto-understand outcomes  [87]. However, decision tree models were shown to be less accurate than most AI approaches, particularly tree-based ensembles  [73]. Using ANNs to extend an existing model to a different dataset is recommended based on the analyses performed in this review and noting that selecting an appropriate AI model depends on the study's purpose and the available dataset  [120]. Fig. 11  also shows that there are less used AI approaches that provide promising performance. While ensemble method approaches have not been widely used in RAC, they have surpassed other strategies in accuracy and speed  [73].
Conclusions This paper reviewed recent research studies about the AI algorithms' ability to predict the mechanical properties of RAC as well as those focusing on sensitivity analysis. The main conclusions are summarised as follows. • In general, all reviewed AI algorithms have been shown good accuracy to predict the mechanical properties of RAC. • The most widely used algorithm for predicting RAC's mechanical properties is ANNs, which have shown superior accuracy. However, these black-box models are hard to explain and its training phase requires lots of data. • SVM is an algorithm for dealing with a tiny dataset that requires less computational effort than the ANNs model. However, it is less accurate than ANNs models. • FL technique necessitates a vast dataset and relies on many hyperparameters, which are the main drawbacks.
Sample and research protocol This study is part of a larger trial employing a single-blind placebocontrolled cross-over design, followed by a longitudinal open-label phase. The trial was registered at ClinicalTrials. gov (NCT 03709940) and conducted at the Maudsley Hospital (London, UK). Please see Supplementary Methods for details on the protocol, power calculation, and inclusion criteria (p. 2). In this study, we specifically tested whether pre-treatment brain anatomy was associated with treatment response in adult ADHD. Sixty male adults with ADHD completed clinical and behavioral measures under placebo (baseline) and under an acute dose of MPH (acute MPH); and underwent diffusion imaging scanning before starting treatment with the same longacting formulation of MPH (Concerta XL, titrated up to 54 mg).