dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , Input genotypes in IMPUTE2 : https://mathgen. stats. ox. ac. uk/impute/impute_v2. html#concordance_tables) with the quality scores of imputation, i. e. , INFO metric scores (information) in IMPUTE2 of each variant. The HWE can be computed. Variants that deviate from HWE can be post-ltered of agged for GWAS analyses. 9| Control the MAF and alleles imputed (i) Extract the MAF of each variant imputed with their alleles. (ii) Use the control database in step 6 to compare the MAFs of the imputed variants as a control of accuracy to complete the QC criteria of imputation (Figure  2. 3). The alleles can also be checked by comparison for consistency.
Solving the problem A MAF control database of the same human genome build version is used for the imputation reference panel. In our case, we compared each imputed variant to the NCBI database. A curation was necessary after imputation to compare the MAFs between the imputed variants and the controls from NCBI when the coordinates or SNPs names differed, as the released databases were slightly different. Very few imputed SNPs were thus curated by hand in the dataset to allocate the correct rs number for either duplicates, unreferenced SNPs, i. e. , missing rs number from dbSNP, merged SNPs or rs numbers that had changed records from a polymorphism to a deletion at the same position in dbSNPs. For an optimal control when possible, use a curated control database. 11| Optional step: Comparing runs with and without stringent pre-ltration (Figure  3 ) This step is optional to compare the results of the dataset post-imputation with or without conservative QC at preltration within the MAFs bins as previously described  95.
Although to recover AOH (absence of heterozygosity), higher depth coverage (5 to 8x up to 30x) would be necessary, as the lowest depth might not be e cient 106. (v) Solving the problem with a control database As a control of accuracy, upload a reference SNP in an SQL database or query an online database with similar numbers of subjects and ethnicities to the dataset to compare the MAF and alleles at each variant (e. g. Figure  2. 3).
Time Taken The time taken is similar to that of a standard QC for GWAS (pre-ltration) with an additional time to compute the MAFs of the sample set. There is a further post-ltration step with the MAFs post-imputation, which would also slightly increase the time of QC. The procedure adds 15 minutes compared to a standard imputation QC. The choice for the MAF control database to use in step 6 will need to be adressed before the start of the project, as it depends the study. The time of preparation for the MAF control database is not included in the protocol, as some public databases are available in different formats.
The 2Mb region of the chromosome 20, a surrogate of the human genome, showed a global variant distribution similar to that of the entire genome  95. For larger sample sets, the con dence for the low MAFs will improve, as more individuals will be represented at rare MAFs in the dataset. Therefore, when genotyping results are available in GWAS databases for pre-ltration, consider using the MAF of the sample set (MAF-DS) to increase the number of variants to impute and to improve the post-imputation information metric scores. The MAF computed for post-ltration (MAF-DH) also warrants more than one heterozygous individual (1. 5) and just 1% of variants with INFO < 0. 3 to eliminate and ensures a smaller loss of variants, including rare SNPs. Imputed data from the early 1000GP panel could therefore be re-analysed using our 2-step procedure to incorporate markers with quality scores between 0.
Acknowledgements We thank  Steven McGinn  for his in English language editing, and  Stephane Cretoni  for his advice in the drawing of Figure 1. We also thank  A. Leduc  from the  Bioinformatics Department at CNRGH (formerly CNG ) for building the SQL database of NCBI build 37,  Fernando Dos-Santos  for con rming the full retrieval of data from the SQL database, and  E Larsonneur  with  N. Wiart  for guiding through the work ow management system at CNRGH. The AJE issued on Novembrer 29, 2022 an editing certi cate number  9D09-9485-F3BF-B9D6-F5F7  for proper English language after reviewing the language and overall style of the manuscript. Final editing was performed by the author.
Solving the problem Use a genotyping chip or genotype data of the study based on the same version as the MAF genome control database. 6b| MAF clusterisation As previously shown, the variants are clustered in different levels of non-overlapping MAFs, such as null (0-1E-04], very rare (1E-04-1E-03], rare (1E-03-1E-02], low (1E-02-5E-02], common (5E-02-1E-01] and high (1E-01-5E-01] frequencies. The very rare and rare classes were subdivided into 2 bins: (1E-04-5E-04] and (5E-04-1E-03] for the very rare MAFs, (1E-03-5E-03] and (5E-03-1E-02] for the rare MAFs 95. (i) Optional step: The classi cation can serve for the comparison of the post-imputation results under stringent vs. non-stringent pre-ltration conditions (.
Perform the population strati cation by PCA with EIGENSTRAT (EIGENSOFT v4. 2 : http://www. hsph. harvard. edu/alkesprice/software/ https://alkesgroup. broadinstitute. org/EIGENSOFT/) 122. 3| Compute the MAF of the sample set genotyped (MAF-DS) for variant pre-ltration Calculate the MAF of the sample set, MAF-DS (MAF-dataset) to retain the variants >= MAF of the sample studied instead of ltering the SNPs with a stringent ltration such as MAF < 0. 01.
Development of a preliminary protein-based risk prediction model We used the development dataset to identify a set of risk informative proteins and train a preliminary protein-based risk model (CPS-II, HUNT, MCCS, and SCHS). We initially evaluated the association between each protein and lung cancer risk using logistic regression models with adjustment for matching factors (cohort, age, sex, year of blood collection, smoking status, smoking intensity, quit years for former smokers). The effective number of test method was used to account for multiple tests  (19) : we considered proteins associated with lung cancer risk if their P value was less than. 05 divided by the number of principal components needed to explain 95% of the variance in proteins (ie, P <. 05/n ent ). We subsequently applied the least absolute shrinkage and selection operator (LASSO) logistic regression model based on all proteins identified as being associated with lung cancer risk.
First, we used tenfold cross-validation to confirm a suitable shrinkage parameter (k). Second, we randomly generated 500 different datasets in which 75% of case-control pairs were included as the training set. For each training set, we applied the LASSO logistic regression models adjusted for matching factors, including age, sex, year of blood collection, smoking status, smoking intensity, and quit years for former smokers, as well as the PLCOm2012 model. We defined the final set of risk-informative proteins for inclusion in the preliminary protein-based risk model as those selected in at least 400 of the 500 training sets. We subsequently developed a preliminary risk model in each training set using logistic regression, the protein-based risk model that included the selected proteins and matching factors.
Discussion We developed and externally evaluated a protein-based prediction tool derived from high-throughput proteomics data using prediagnostic samples. Our study sample captured the entire spectrum of lung cancer risk of individuals with a history of smoking. Based on lung cancer participants and cancer-free participants nested in 4 prospective cohorts, we developed a 4marker protein-based model with subsequent validation in 2 independent prospective cohorts. The protein-based model performed well in the validation sample and outperformed the EarlyCDT-Lung and PLCOm2012 model in relevant strata. The protein-based risk model demonstrated good discrimination between the lung cancer participants and smoking-matched cancer-free participants with an overall AUC of 0. 75. Importantly, because the cancer-free participants were individually matched to the lung cancer participants by smoking, age, and sex, the riskdiscriminative performance afforded by those important risk factors was accounted for by design, thus substantially attenuating our AUC estimates for PLCOm2012 model and biomarker-based models compared with that expected in a randomly selected study sample  (21).
We -P A R M K L A M P 3 T G F -a lp h a C H I 3 L 1 W F D C 2 S Y N D 1 O S M I G F B P -2 G D F -1 5 S 1 0 0 A 1 1 C X C L 9 T N F S F 1 3 B M U C -1 6 C X L 1 7 I G F B P -1 C X C L 1 3 C A S P -8 C D C P 1 I L 6 M M P 1 2 C E A C A M 5 Protein Proportion (%) of times selected Average beta 0. 0 0. 1 0. 2 0. 3 0. 4 Average beta is calculated using the betas from multivariable LASSO logisitic regression model in the 500 resampling datasets Figure  1. Proportion of proteins selected in 500 training datasets by LASSO logistic regression model. Proteins selected more than 400 times are marked as black. LASSO ¼ least absolute shrinkage and selection operator; CEACAM5 ¼ carcinoembryonic antigen-related cell adhesion molecule 5; MMP12 ¼ macrophage metalloelastase; IL6 ¼ interleukin 6; CDCP1 ¼ CUB domain-containing protein 1; CASP-8 ¼ caspase-8; CXCL13 ¼ C-X-C motif chemokine 13; IGFBP-1 ¼ insulin-like growth factor-binding protein 1; CXL17 ¼ C-X-C motif chemokine 17; MUC-16 ¼ mucin-16; TNFSF13B ¼ tumor necrosis factor ligand superfamily member 13B; CXCL9 ¼ C-X-C motif chemokine 9; S100A11 ¼ Protein S100-A11; GDF-15 ¼ growth/differentiation factor 15; IGFBP-2 ¼ insulin-like growth factor-binding protein 2; OSM ¼ oncostatin-M; SYND1 ¼ syndecan-1; WFDC2 ¼ WAP four-disulfide core domain protein 2; CHI3L1 ¼ chitinase-3-like protein 1; TGF-alpha ¼ transforming growth factor alpha; LAMP3 ¼ lysosome-associated membrane glycoprotein 3; MK ¼ midkine; U-PAR ¼ urokinase plasminogen activator surface receptor.
Acknowledgements The  Trøndelag Health Study (HUNT)  is a collaboration between  HUNT Research Centre (Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology NTNU) ,  Trøndelag County Council, Central Norway Regional Health Authority , and the  Norwegian Institute of Public Health. The authors express sincere appreciation to all Cancer Prevention Study-II participants and to each member of the study and biospecimen management group. The authors would like to acknowledge the contribution to this study from central cancer registries supported through the  Centers for Disease Control and Prevention's National Program of Cancer Registries  and cancer registries supported by the  National Cancer Institute's Surveillance Epidemiology and End Results Program. The  Singapore Chinese Health Study  was supported by the  US National Institutes of Health  Grant No.
R01CA080205 ,  R01CA144034  and  UM182876. Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by  VicHealth  and  Cancer Council Victoria. The MCCS was further augmented by  Australian National Health and Medical Research Council  grants  209057 ,  396414 , and  1074383  and by infrastructure provided by  Cancer Council Victoria. We thank the  Biobank Research Unit at Umea ˚University, V€ asterbotten Intervention Programme , the  Northern Sweden MONICA study , the  Mammography Study and Region V€ asterbotten  for providing data and samples and acknowledge the contribution from  Biobank Sweden , supported by the  Swedish Research Council  ( VR 2017-00650 ). The coordination of EPIC was financially supported by  Direction G en erale de la Sant e (French Ministry of Health)  (Grant  GR-IARC-2003-09-12-01 ), the  European Commission  ( Directorate General for Health and Consumer Affairs ),  International Agency for Research on Cancer (IARC)  and by the  Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London  with additional infrastructure support provided by the  NIHR Imperial Biomedical Research Centre (BRC).
Data acquisition is carried out based on the use of transient recorders, with both analog and digital sampling capability. In this regard, it is to pointed out that roto-vibrational Raman lidar signals are very weak and, during the daytime portion of the day, they may become substantially smaller than those produced by the solar background radiation. At night, when solar radiation is absent and environmental background is usually limited, weak lidar signals can be effectively sampled in photo-counting mode. Conversely, in daytime, especially when the sun is high in the sky (sun zenith angles (SZAs) smaller than 30°), photo-counting results to be ineffective as photons from Raman echoes add up with those produced by solar radiation, with the overall signal by far exceeding the count rate suited for photon counting exploitation.
These measurements were carried out on 29 October 2022, at night (00:00 UTC) and in the central portion of the day (12:00 UTC). The water vapour roto-vibrational Raman signal, P (z), is found to extend up to an altitude of 2-2. 5 km during the day and up to 5 km or higher at night, while the N2 roto-vibrational Raman signal, P (z), is found to extend up to an altitude of 3. 5-4 km during the day and up to 5 km or higher at night. For what concerns the signals P (z) and P ‖ (z), these are found to extend up to 5 or higher for both day and night, while P ┴ (z) extends up to 3-3. 5 km in daytime and up to 5 or higher during the night. It is to be pointed out that data above 5 km are not illustrating because the sampling of the lidar signals is only up to 5. 5 km. This results from the fact that the exploitation of a laser source with a 20 kHz repetition rate prevents from using lidar echoes from altitudes above 7.
Results The system MARCO was deployed in Port-Saint-Louis-du-Rhône (France, Lat. : 43. 39 N, Lon. : 4. 81 E, Elev. : 5 m, Figure  6 ) and started continuous operation on 19 October 2022 and is still operational (more than 10 months of continuous operation). Over this long measurement period, no more than 30 hours of data are missing as a result of brief temporary stops for system maintenance or failures. A picture of the measurement site is illustrated in Figure  7 , with the lidar on the right side and the van used to transport it on the left side. The calibration of the system was carried out in Potenza, before its transportation to France, based on the use of 3 co-located radiosondes launched from the nearby IMAA-CNR radiosonde launching facility, located 7 km W-SW of the lidar site in Potenza.
Participants Of the 23 participants that initially signed up, 21 started the online experiment. A total of 14 participants performed all adaptive runs of the online experiment, of which three participants were excluded due to producing unusable data in some runs. Data from the remaining 11 participants (self-reported gender 6 females and 5 males; age range 22-41 years, mean age 29 years) was analyzed. Additional demographics were collected to further describe the participant population. It is not clear at this point, based on literature, if any of these factors may or may not affect voice perception with or without vocoding, but for the purpose of completeness, we provide these demographics details. From the 11 participants, 2 reported MBO (secondary vocational education), 1 HBO (higher vocational education), and 8 university level education (according to the Dutch schooling system), all reported to have learned 2 or more languages in addition to their first language (3 learned 3 additional languages, 2 learned 4 additional languages), no participants reported to be raised bilingual, 5 participants played a music instrument of which 3 received formal music education before the age of 10, 1 participant reported to speak with a regional accent, and 4 participants lived 1 year or more outside the Netherlands.
The stimuli were resynthesized even when the VTL was unchanged compared to the original voice. The auditory stimuli were presented without and with vocoding. Vocoding was coded in Python (as part of the VTServer v2. 2; Gaudrain, E. , 2021), in line with a previous implementation in MATLAB  (Gaudrain & Başkent, 2015)   (Gaudrain, 2016) , and ran in realtime on a dedicated online sound processing server. In line with our previous study  (Koelewijn et al. , 2021) , we used a vocoder simulating low spread of excitation (LS-vocoder), and a vocoder simulating high spread of excitation (HS-vocoder)  (Koelewijn et al. , 2021). Both vocoding conditions used 12 analysis filters. These were 12 th order (72 dB/oct. ) zero-phase Butterworth filters with a range spanning from 150 to 7000 Hz, which were uniformly divided in terms of cochlear place of excitation  (Greenwood, 1990).
Online testing and apparatus For running the experiment online, a platform was used developed by the dB SPL research group within the University Medical Center Groningen. The platform was coded in JavaScript using the jPsych library (v6. 2; de Leeuw, 2015) and was accessible through a web browser. Participants were advised to run the experiment on a desktop or laptop computer, to use a good set of headphones, and to perform the experiment in a quiet environment. The experiment started with general project information about the hearing research conducted in our lab, followed by an informed consent form, to which participants had to agree before they could continue. Next, participants had to fill out an online questionnaire containing questions on demographics relevant for voice and speech perception research.
Statistical analysis All JNDs were log-transformed to improve homogeneity of variance across conditions. We performed a 3x3 repeated-measure analysis of variance (ANOVA) on the log-transformed VTL JNDs with lexical content (forward, reversed, non-word) and vocoding (no, LS, HS) as the within subject factors. Effect sizes are reported as generalized eta-squared  (Bakeman, 2005). For planned comparisons on the data of Experiment 1, paired samples t-tests were used. The Holm-Bonferroni correction for multiple comparisons  (Holm, 1979)  was used, and adjusted p-values are reported. Effect sizes for these t-tests are reported as Cohen's d. The statistics were computed in R v4. 3. 0 (R Core Team, 2020) with the ez package v4. 4. 0  (Lawrence, 2016)  using type III sums of squares.
Experiment 2 -Words vs. Sentences Experiment 1 shows that lexical and phonemic content in words had an influence on VTL JNDs. In Experiment 2 we look at the effect of adding additional sentence information (content, context, and coarticulation) by comparing JNDs with single words to with full sentences. Nonvocoded as well as vocoded speech was presented, as full sentence processing was previously observed to have an effect on voice gender categorization in CI listeners  (Meister et al. , 2016). Since HS-vocoding conditions in Experiment 1 were shown to be very difficult, even resulting in unusable data for some of the participants, in Experiment 2 only LS-vocoding was implemented. Both F0 and VTL voice cues were independently manipulated since CI users, in that same study, as well as others (e.
Participants Of the 13 participants that signed up and started the online experiment, a total of 12 participants performed all adaptive runs, of which one participant was excluded due to producing unusable data in some runs. For the remaining 11 participants (self-reported gender 8 females and 3 males; age range 26-52 years, mean age 32 years) the data was analyzed. Additional demographics collected are the following. From the 11 participants 1 reported HAVO (senior general secondary), 1 HBO (higher vocational education), and 9 university level education, all reported to have learned 2 or more languages in addition to their first language (7 learned 3 additional languages, 1 learned 4 additional languages), 1 participant reported to be raised bilingual (Dutch and English), 3 participants played an instrument of which 1 received formal music education before the age of 10, 1 participant reported to speak with a regional accent, and 7 participants lived 1 year or more outside the Netherlands.
Statistical analysis All JNDs were log-transformed to improve homogeneity of variance across conditions. In line with our previous research  (Koelewijn et al. , 2021)  data for each voice cue was separately analyzed, since intersubject variance tends to be different for F0 and VTL. Hence, for each of the voice cues we performed a separate 2×2 ANOVA on the log-transformed JNDs with sentence information (words, sentences), and vocoding (no, LS) as the within subject factors. For post-hoc analysis, paired samples t-tests were used. The Holm-Bonferroni correction for multiple comparisons  (Holm, 1979)  was applied. ANOVAs were performed in R v4. 3. 0 (R Core Team, 2020) with the ez package v4. 4. 0 (Lawrence, 2016) using type III sums of squares. Effect sizes are reported as generalized eta-squared  (Bakeman, 2005).
Acknowledgement The authors thank  Jennifer Breetveld  and  Tessa Peijzel  for their help with facilitating this project. The study was conducted as part of the internships of third and fourth authors at the UMCG, within the programs of the European Master's in Clinical Linguistics (EMCL+) and the  EU programme for education , training, youth and sport (ERASMUS+), respectively. This work was funded by a  VICI grant  ( 918-17-603 ) from the  Netherlands Organization for Scientific Research (NWO)  and the  Netherlands Organization for Health Research and Development (ZonMw)  to the last author, the  Heinsius Houbolt Foundation , and a  Rosalind Franklin Fellowship. The study was performed within the framework of the  Laboratoire d'Excellence Centre Lyonnais d'Acoustique  ( ANR-10-LABX-0060 ) of  Université de Lyon  within the program " Investissements d'Avenir"  ( ANR-16-DEX-0005 ) operated by the  French National Research Agency (ANR)  and is part of the  research program  of the  Department of Otorhinolaryngology, University Medical Center Groningen: Healthy Aging and Communication.
As an alternative, we adopt a data driven approach, where the two classical timing computational mechanisms emerge as opposite poles of participant behavior that range from one to the other. By taking this feature into account, we develop a more complete understanding of human behavior. Our findings show that what seems to be two different timing mechanisms (i. e. , relative, or absolute timing) can be different sides of the same coin. While participants' responses reflected different algorithms (i. e. , relative, or absolute) subserving perceptual timing depending on the temporal features of the acoustic stimulus, we show that the whole pattern of responses can be captured by a single biophysical neural model. More precisely, we propose an adaptive frequency oscillator as a reasonable candidate for temporal prediction under these more ambiguous circumstances by taking qualities typically used in other models of the literature (e.
C. Participants' psychometric functions were fitted according to the two perceptual timing frames. Analysis of one exemplar participant. Responses (coded 0 for early, 1 for late) are compared to both the relative (left) and the absolute computations (right). Logistic model is fitted to the results and the slope of the fit is used as a measure of consistency with the given computational mechanism. This example shows a participant more consistent with the absolute computation of time. Dots represent single trial answers with a small vertical jitter added for visualization purposes. The data set used to generate these two panels can be found in S1 Data. The music note icons is used from openclipart. org. https://doi. org/10. 1371/journal. pcbi. 1011669. g001 quantify the function that drives the relationship between performance and algorithm dominance, we fit the dataset across experiments with polynomial functions of 1, 2, and 3 degrees.