dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulation study A simulation study is conducted to validate the general effectiveness of the methodology proposed in this work. The simulation was conducted using two approaches to assess the functional and classical PCAs. Method 1 simulates landmarks using the sim. coord function  (Watanabe 2018)  where the coordinate data is generated with a specified number of specimens and landmarks from a multivariate normal distribution with zero mean and a variance-covariance structure using the mvrnorm function in the MASS R package  (Venables and Ripley 2002). Method 2 involves calculating the covariance matrix using the squared exponential function  (Rasmussen 2004 ). This method assumes that the coordinates are correlated with one another. The first three PC scores of GM and FDGM were split into training data (70%) and test data (30%) to be applied into LDA and FLDA for both methods.
Data Description The kangaroo landmark dataset is described in detail in  Butler et al. (2021). 48 landmarks on crania of 41 extant were used in this study to observe the credibility of the FDGM approach. There are 30 fixed landmarks, placed at "homologous" points on the crania and three sets of semi landmarks, equally spaced along the left molar row (six semi landmarks), right molar row (six semi landmarks), and sagittal axis of the cranial roof (six semi landmarks). To avoid human error in landmarking, the process was repeated twice for each specimen to obtain the mean shape of the two replicates for subsequent analysis  (Butler et al. 2021). Landmarks were then placed by a single operator  (Butler et al. 2021) , thus avoiding issues with inter-operator error  (Fruciano et al. 2017)  Statistical analysis to observe four dietary groups (fungivore, browser, grazer, and mixed feeder) of the extant species was performed in R version 4.
2. 1. To use the geometric morphometric data, the raw coordinates obtained from the cranial landmarks were aligned using generalised Procrustes analysis (GPA)  (Rohlf and Slice 1990)  for optimal registration using translation, rotation, and scaling using the gpagen function in the geomorph package  (Adams and Otárola-Castillo 2013). Based on  Butler et al. (2021) , each semi landmark was allowed to slide along its respective tangent directions according to the TPS method  (Gunz et al. 2005; Kraatz et al. 2015). The resulting symmetric shape data was used to perform PCA and LDA for both GM and FDGM methods. According to  McCane (2013) , outline methods produce useful and valid results when suitably constrained by landmarks, which leads to the main idea of this work to incorporate the FDA approach (Fig.
5 ) to observe the separation among the four dietary groups of the kangaroo extant species. A total of 16 MFPCs are obtained from the converted functional data using MFPCA. The first three MFPCs explained 78. 77% of the total variation in the dietary classification among the marsupials. A distinct cluster among dietary categories when using the MFPC scores (Fig. 5(b) ) is also observed. The PCA from GM yields 40 PCs, where the first three PCs explained 62. 86 % of variation (Fig. 5(a) ). There is also no overlapping among the groups when the functional approach is applied. The first three PC scores from GM and FDGM were used in LDA. The results for both approaches reveal a good separation between the four dietary categories, which are class labels. Based on GM, the percentage of separations achieved by the first discriminant function is 94.
Discussion A comparative study of FDGM and GM was done to investigate four dietary categories based on the crania of 41 extant kangaroo species. The findings of this study revealed the existence of LD 2 FLD the four dietary clusters when the standardised 3D landmarks of the views combined are converted into functional data, rather than being discretised in point sets. The PC scores improve classification as they are projected onto orthonormal eigenfunctions to capture prominent directions. The application of MFPCA reduces dimensionality by projecting the functional landmark data onto the set of orthonormal basis functions which induces the uniqueness of the MFPCA scores for each observation to improve classification accuracy. Using classical landmarkbased approach can be difficult to standardise the selections, which can drastically differ results in classification tasks  (Srivastava & Klassen 2016).
However, volcanic plumes are affected by atmospheric wind [Bur-Figure  1 : Schematic showing how a plume lies on the assumed image plane when the plume is strong compared to when the plume is impacted by the wind and does not lie on the image plane. Image analysis of volcanic plumes  Snee et al. 2023  tool, called windyPlume and available in MATLAB and Python (see Data Availability section), allows for the user to input their camera properties, weather data, and the pixels to be calibrated to determine the heights of these points above sea level. The horizontal distances between the points of interest and the camera are also calculated. We demonstrate the application of this package to two different styles of volcanic activity, a Vulcanican explosion and a sustained tephra plume, from Sabancaya volcano, Peru, and Mount Etna, Italy, respectively.
These values can be used to define the image plane (dashed-line) as described in Section 2. The wind-oriented plane has been determined from ERA5 hourly reanalysis data  [Hersbach et al. 2018 ]. Specifically, we extract the vertical profiles of geopotential and East-and North-components of wind velocity at different pressure levels at the closest time (15:00 UTC) to the explosion and closest location (latitude = -15. 75 • , longitude = -71. 75 • ) to the vent from the database of  Hersbach et al. 2018. The two components of velocity can be used to calculate the wind direction, presented in Figure  5B  as a bearing from North and as a function of altitude. In order to define a unique wind direction to use for the calibration procedure, we find the average wind direction over the 4 km above the vent to be 110 • , with a standard deviation of 20 •.
The calibrated plume height time-series for this eruption can be seen in Figure  8. The maximum plume height was determined automatically in MATLAB by first subtracting the red channel from the blue channel, and then detecting the highest location in the frame where 10 vertically connected pixels all have a pixel intensity value less than 0. 2  [Snee 2021 ]. This plume height time-series in pixels is then calibrated with the geometrical calibration (Section 2. 1,  Bombrun et al. [2018] ) to produce a wind-uncorrected plume height time-series (Figure  8 , solid black line). The wind calibration (Section 2. 2) is then performed (Figure  8 , dashed golden line). A comparison between the wind-uncorrected and wind-corrected plume height time series shows that if wind is not considered, the maximum plume height would be overestimated for this eruption (Figure  8 ).
Sources of uncertainty A significant source of uncertainty in the wind calibration arises from knowledge of the wind field itself. In this study, we have estimated wind orientation from ERA-Interim  [Berrisford et al. 2011; Dee et al. 2011 ] and ERA5  [Hersbach et al. 2018]  reanalysis data. These have temporal resolutions of 12 and 1 hour(s), respectively, and therefore some form of interpolation is necessary to estimate the wind field at the time of eruption. Additionally, shorter-timescale perturbations to the wind field cannot be accounted for. Furthermore, the limits on horizontal spatial resolution also mean a spatial interpolation is required as well  [Berrisford et al. 2011; Dee et al. 2011; Hersbach et al. 2018]. Alternative sources of weather data could include GNSS (Global Navigation Satellite System) soundings or local weather stations but all suffer from uncertainties associated with temporal and spatial resolutions.
Whilst such metadata should be recorded, there are challenges associated with this. Accurately and precisely measuring the inclination or orientation of off-the-shelf cameras is not always easy whilst cameras installed as part of a monitoring network can be moved by external factors such as birds or earthquakes. In Appendix A, we provide methods for how the camera field of view and inclination can be estimated if they are not known accurately, relying on knowledge of the geographic location of recognisable points in the image. Additionally, the code is designed such that the user can enter both minimum and maximum estimates for the horizontal field of view of the camera to facilitate quantification of the uncertainty associated with this parameter. Whilst these are useful techniques, it is always best practice to have accurate and precise metadata.
First, since each vial contained one focal individual (LH M ) for which mating behavior could be observed, individual-level data for both sexes could be collected. For females, using LH M in a population of LH M -bw allowed for accurate identification during the mating assay based on eye-color marker (LH M exhibit wild-type red eye compared with brown [bw] eye-color marker). For males, this allowed for accurate identification during the mating assay, as well as the ability to determine the number of offspring (reproductive success). As all other females within the vial were LH M -bw, any offspring sired by the focal LH M male (which was homozygous for the dominant wild-type red eye marker) would exhibit red eye color. Second, experimental populations consisted of 16 pairs, which matched the population density and sex ratio that the base population had evolved in.
Statistical Analyses Statistical analyses included two steps. First, we estimated sexual selection metrics and tested for sex differences therein. This was done on relativized data of mating success and reproductive success (i. e. , observed values divided by the average obtained for the given sex) to allow for comparison between sexes and with other studies  (Anthes et al. , 2017; Jones, 2009). In addition to Bateman's classic sexual selection metrics (i. e. , I, I S , ß SS ; see above), we also estimated the maximum standardized sexual selection differential (s' max ), defined as the product of ß SS and the square root of I S , which provides an upper limit of the strength of precopulatory sexual selection and has been found to be particularly powerful to quantify the actual strength of sexual selection  (Henshaw et al.
, 2016). We applied bootstrapping with 10,000 bootstrap samples to estimate all sexual selection metrics with their 95% confidence intervals (CIs) using the package "boot"  (Canty & Ripley, 2017)  in R (R Development Core Team, 2021). Sex comparison was done using permutation tests with 10,000 iterations in which P-values are estimated as the proportion of permutations with a difference as large or larger than the observed difference. We arbitrarily defined the sex difference between sexual selection metrics as the estimate obtained from males minus the estimate obtained from females so that positive values indicate a male bias (Δ Sex = x Males -x Female ). For completeness, we also report Bateman gradients as fits of linear regressions obtained from the lm function in R (R Development Core Team, 2021).
e. , the proportion of offspring sired relative to the number of sperm inseminated, which is the outcome of sperm competition and cryptic female choice). Hence, comparison of variance of cMS and PS allows to contrast the potential of precopulatory versus postcopulatory sexual selection. Var(mRS) ≈ Var(cMS) +Var(PS) + Var(mFec) + Cov + Error With Cov defined as Cov = 2 * Cov(cMS, PS) + 2 * Cov(cMS, mFec) + 2 * Cov Values are given as means ± SE unless otherwise stated. All data and R script for analysis, figures, and table are deposited with Zenodo (https://zenodo. org/record/7729031#. ZA9Z5uzMJQK).
Local re-alignment was performed to refine alignment at regions with InDels and structural re-arrangements. This was followed by analysis with the "Low Frequency Variant Detection" module to call mutations. Mutations were annotated using ANNOVAR, amino acid change, and splice site effects, as well as dbSNP and ClinVar information (build 155)  [35]. Functional annotation of the variants followed common guidelines  [36, 37]. Variant classification and interpretation relied on both publicly accessible and internal databases, which considered population frequencies and information from the published literature. The functional assessment of individual variants was also aided by CNA analysis conducted on tumor tissue. In this analysis, information on LOH was used to judge the pathogenicity of tumor suppressor genes including BRCA1/2 and HRR genes.
For the latter, the identification of a mutation along with LOH in the same gene could be indicative of pathogenicity  [36]. Detecting allele-specific copy number alterations. All tools used to calculate the NOGGO GIS v1 score are publicly available and can be downloaded with the relevant manuals (see below) Briefly, UMI was extracted using the tool ExtractUmisFromBam (Fgbio tools v1. 5. 0). Mapping was performed using bwa mem (v0. 7. 17-r1188) with default settings. PCR duplicates were identified using UmiAwareMarkDuplicatesWithMateCigar (Picard tools v2. 23. 1) and overlapping reads pairs were clipped using ClipBam (Fgbio tools v1. 5. 0). For the detection of allele specific can, we included the R package PureCN (v1. 20. 0)  [38]  in our pipeline in combination with Mutect (v1. 1. 4) for variant calling.
023) for Myriad MyChoice and the NOGGO GIS v1 assay, respectively. Similarly, OS data with HR for Myriad MyChoice of 0. 5 compared with 0. 370 for the NOGGO GIS v1 assay were found. for the NOGGO GIS v1 assay were found. To compare the assays independently of BRCA1/2 mutation status, the subgroup of BRCA1/2 wild-type patients was also analyzed. HR in this subgroup was comparable with 0. 364 vs. 0. 346 for Myriad MyChoice and the NOGGO GIS v1 assay, respectively. While Myriad MyChoice was unevaluable in 44 patients the dropout rate for the NOGGO GIS v1 assay was much lower with only 16 samples that could not be evaluated. To assess whether the 44 cases unevaluable with Myriad MyChoice gave a clinically meaningful result with the NOGGO GIS v1 assay, Kaplan-Meier curves were plotted for this small subgroup and showed a similar pattern, even though statistical analysis was not possible in this small subgroup (Figure  5 ).
We feel that relying solely on bridging data to the Myriad MyChoice assay is not sufficient to demonstrate the clinical performance and utility of molecular diagnostic assays that cover complex biomarkers such as HRD. Many comparative studies between commercially available assay and Myriad MyChoice have been made, but often clinical assessment is lacking  [40]. This is particularly important given the intricate nature of HRD assays and the need for them to be thoroughly evaluated to ensure their efficacy in a clinical setting, also reviewed in  [31, 41]. The ENGOT HRD initiative that made samples of the ENGOT PAOLA-1 study available for assay validation has proven to be an invaluable resource to the scientific community, as it has facilitated the validation of several HRD assays, thereby paving the way for more precise and personalized treatment approaches.
Conclusions Here we have described the NOGGO GIS v1 assay for HRD detection that was validated clinically on ENGOT PAOLA-1 clinical trial samples with PFS and OS data comparable to the Myriad MyChoice assay used in the PAOLA-1 clinical trial. The NOGGO GIS V1 assay is a modular assay with a low drop out rate that not only determines the genome instability status but also alterations like SNVs, InDels and copy number in further relevant cancer related genes.
Acknowledgments: We thank  Agilent Technologies  for supporting this project with reagents. Elena Ioana Braicu  is a  Feodor Lynen  fellow of the  Humboldt Foundation  and a participant of the  Charité Clinical Scientist Program  funded by the  Charité Universitätsmedizin Berlin  and the  Berlin Institute of Health. We would like to acknowledge the invaluable technical support from  Ingrid Gerdes ,  Simone Gnoth ,  Sabine Weber.
Funding: This research received no external funding. Institutional Review Board Statement: Informed patient consent was obtained for each patient as part of the PAOLA1 clinical trial and included the consent to perform further translational research on tumor samples. Ethic committee approval was first obtained on 14 Jan 2015. CPP No: 15/002. Reference:  A15-02. No Eudract CT:  2014-004027-52. Informed Consent Statement: Written informed consent has been obtained for all patient material and data used in this study.
The existing sources on demo-linguistic data report large differences, especially in terms of the L2 figures; among them, Ethnologue is generally considered the most reliable source; however, this source is proprietary and not free of charge. Since March 2022, the Observatory of Linguistic and Cultural Diversity in the Internet (the Observatory hereafter) offers both these indicators, and meaningful additional indicators, for the 329 languages encompassing a population of L1 speakers exceeding one million (see results in  Pimienta, 2022) , with plans for yearly updates. This is the outcome of a long process of bias depuration of a method defined in 2017, which finally yields outputs with an acceptable threshold of reliability. The Observatory is not a newcomer in this field: it has been conducting a series of pioneering measurements of Web contents in the English, German, and Latin Languages  (French, Italian, Portuguese, Spanish, and Romanian) , between 1997 and 2007  (Pimienta et al.
A model was designed to process the whole set of sources into meaningful indicators for the 139 languages with L1 speakers numbering over 5 million. Thereafter and since 2017, the work was essentially dedicated to the struggle against the various biases proper of the method or the data sources. In 2021, this resulted in a Version 2  (Pimienta, 2021)  with the same structure but with certain important biases controlled, in particular, through the usage of the Ethnologue Global dataset 24 (March 2021) for demo-linguistic data. Subsequently, the language coverage was extended to the 329 languages with L1 speakers numbering over one million. The pursuit of the fight against biases continued and, in March 2022, led to a final redefinition of the approach and the confidence that a reasonable level of control of biases had been attained, with the capacity to produce reliable figures, within a confidence interval of -+20%, an empirical estimation not sustained through any statistic computation.
Internauts This comprises the percentage of L1+L2 speakers connected to the Internet for each language. The transformation of the source' figures, expressed by country, into the required figure, expressed per language, is performed via weighting. CS(j) represents the percentage of connected speakers for language j. CS j = i=c i=1 SP i, j xCC(i)/ i=c i=1 SP(i, j) where -c indicates the total number of countries. -SP (i, j) denotes the number of L1+L2 speakers of language j in country i. -CC (i) represents the percentage of connected persons for country i. The matrix product CS = SP +. x CC in APL notation or = SumProduct (SP;CC) in Excel notation, is a weighting operation having, as input, data per country and, as output, data per language. The validity of this computation stands on the implicit hypothesis that, within the same country, all language groups share the same figure for the percentage of connected persons.
This is a founding bias of the method analyzed in chapter 2. 4. 1. The vector CS(j) is a key element of the model which serves, in weighting operations with various sources, to compute the modulation of each indicator. The source for the SP matrix is Ethnologue; the model uses Ethnologue Global Dataset #24 of March 2021. The sources for the CC matrix are the ITU and the World Bank; the ITU, the historical source of these figures, relies on government reports or its own estimation (when the former is not available). As the ITU stopped reporting its own estimations in 2017, the source is completed by using figures from the World Bank, which fills that gap in many cases. When no recent figures are available, an extrapolation of older figures is used in the model. As the technique of weighting will be utilized as applicable in the model computations, an issue emerges: the major drawback is that it does not yield credible results if all countries are not filled because the matrix product transforms this absence by zero, which unacceptably penalizes results for languages with strong presence in countries for which the source indicates no information.
Method for L For the first time, in 2021, Ethnologue extended its demolinguistic data per country to L2 speakers. This allowed for the removal of one of the most important biases of the method (in V1), which resulted from the extrapolation of data (for example percentage of connected speakers) from L1 to L2, a process which biases positively the results of languages with high presence in developing countries, such as English and French. Indeed, this process assigned Internet connection rates of L2 speakers in developing countries that are higher than those in reality. Starting in V2, with the existence of demo-linguistic data per country for L2 as well as L1, the core method is directly applied to L1+L2 populations; this extrapolation bias disappears, but obviously not the core method bias, which apply the same for L1, L2 and L1+L2.
Tra c The available tools providing repartition of traffic per country to a large set of websites (the ones considered as the most visited) are: Alexa. com, Similarweb. com, Ahrefs. com, and Semrush. com. All tools are proprietary technology of marketing companies, and these companies are not totally transparent about their method. For instance, Alexa, the older and most famous tool, performs from a banner that users can download. This banner, associated to a Web browser, reports to Alexa those sites that are visited by the user using this browser. With the collection of all the data sent by all the banners around the world, Alexa designs its outputs, both in terms of ranking sites and traffic repartition per country. Evidently, the geographical repartition of banners could be an indication of probable biases, but unfortunately, this information remains unpublished.
The algorithm was not set to target all the countries for practical reasons but was limited to the 55 countries occupying the top positions in terms of contents for languages spoken in these countries (see Table  3 ). The rule was set to select, for each country, at least the first three most visited sites including among them, at least, the first local domain (ccTLD, such as. fr for France). This rule was set to prevent the selection from being heavily concentrated in the most visited sites globally (generally. com). Obviously, this rule could not prevent the most visited global websites (such as Google. com or Facebook. com) from appearing in many country's selection, and a weighting was performed to respect those figures. To realize the selection, four tools were used (Ahrefs, Alexa, Semrush, and Similarweb); however, on certain occasions, owing to a lack of data for countries with a small population, we had to collect the data from other sources.