dataesr/scientific-paragraphs-categorization huggingface dataset:
Details of DNA extraction and sample processing for MPS library preparations are provided in the supporting information. After targeted MPS, sequencing data from each DNA sample was used to perform eRHDO analysis and determine fetal inheritance at the locus of interest. The results were set as haplotype A, B, C or D for the training cohort, as the goal was to refine analysis thresholds and determine whether the fetal haplotype was correctly inferred, regardless of its risk for SGD. For the test cohort, we studied only the gene involved in familial SGD, and the results were set as "HapI" or "HapII" for maternal at-risk or non-at-risk haplotype; and "HapIII" or "HapIV" for paternal at-risk or non-at-risk haplotype, respectively.
Data analysis Using simulated cfDNA sequencing data and data from the training cohort, we defined adjustable data variables, namely consideration of thresholds used to discriminate a heterozygous position in gDNA and minimum sequencing depth for SNP calling in gDNA (NDP) and cfDNA (PDP). Minimum interSNP distance for eRHDO analysis, and Type I and Type II error risks associated with SPRT haplotype block delineation were defined as output settings. We then tested different combinations of input and output settings and objectively examined their impact on the quality scores in order to select the best combination of input and output settings for test cohort exploration. Finally, the diagnostic performance of our noninvasive test was determined from the test cohort analysis. Details of the bioinformatics pipeline for sequencing data analysis are provided in S1 File.
While de novo haplotyping methods have been reported in the context of NIPD  [29] [30] [31] [32] [33] [34] , experimental approaches often require specialized equipment and reagents, making them costly and inappropriate for implementation in a clinical setting. Computational approaches, while accurate and less expensive, may require specific bioinformatics skills and rely on a population database, making them unreliable for rare or private variant phasing  [35, 36]. An exception would be the Targeted Locus Amplification approach, described by Vermeulen et al. , which does not depend on any specific equipment but may still require adaptations for implementation in laboratories unfamiliar with chromosome conformation capture techniques  [37]. However, experience in preimplantation genetic testing has shown that refusal of a referral due to inability to analyze a proband rarely occurs  [38].
Characterization of Au nanoparticles TEM images were recorded on a Jem1011 microscope (JEOL Ltd. , Tokio, Japan) operating at an accelerating voltage of 100 kVp. A drop of nanoparticle solution was casted onto the carbon-coated grid and let dry before imaging. The size of the nanoparticles as measured by the TEM was estimated by ImageJ Software  (Schneider et al. , 2012). Electrophoretic characterization was carried out by running the nanoparticles through 1% agarose gel immersed in TBE buffer (pH 8. 0) for 1 h at 100 V. The hydrodynamic diameter (DLS measurements) and the surface charge (Zeta potential measurements) of the nanoparticles were measured by means of a Zetasizer Nano ZS90 (Malvern Instruments, United States) equipped with a 4. 0 mW He-Ne laser operating at 633 nm and with an avalanche photodiode detector.
No language restrictions were applied. Although there are a few studies on opium use and the risk of other cancers, including cancers of the colon and rectum, brain, liver, lip, oral cavity, and pharynx, due to the small number of available studies for each of these sites, they did not meet the inclusion criteria for this review. AMF and MCT conducted an extended search of the published literature without time restriction of start date, through  31   A detailed description of the online search strategy is available in the Health Assessment Workspace Collaborative (HAWC) software  [15]  at the following website: https:// hawcp roject. iarc. fr/ asses sment/ 675/. All titles and abstracts were screened by AMF and MCT, followed by screening of the manuscript's full text where appropriate. All studies that met the eligibility criteria were included in critical appraisal and meta-analysis.
Study identification Figure  1  presents details of the inclusion and exclusion criteria to select articles for the systematic review and metaanalysis. In total, 701 studies were identified by the comprehensive literature search after removing duplicates. A total of 106 studies were retained following the first screening of titles and abstracts and the exclusion of studies that were not relevant. Following a second screen of the full text of the manuscripts, 35 studies were included (one publication  [41]  included two studies). These 35 studies then underwent quality assessment and data abstraction.
IARC Monographs vol. 126 concluded that there was strong mechanistic evidence of genotoxicity for opium dross (sukhteh) and opium pyrolysates (solid residues of combusted opium), in both human peripheral blood mononuclear cells and Chinese hamster ovary cells  [69]. Despite scant research, one study in humans exposed to opium also suggests a genotoxic effect of frequent p53 mutations in opium users  [70]. More recently, a mutational signature was identified for opium among cases of oesophageal cancer in humans  [71]. The carcinogenicity of opium in animal models is still unclear, as no high-quality in vivo experimental carcinogenicity studies have yet been conducted. Despite the limitations inherent to systematic reviews and meta-analyses, we carefully conducted our review with transparent methods and inclusion and exclusion criteria defined before the data collection.
October 2022, by searching the following bibliographic databases, namely PubMed, EMBASE and Web of Science. The following MeSH terms were used in: PUBMED: (opium[tw] OR (8006-60-4[m]) AND (neoplasm* OR carcinogen* OR malignan* OR tumor* OR tumour* OR cancer*), and in EMBASE, Mtree-Termsexpanded included: opium:ti,ab,kw AND (neoplasm* OR carcinogen* OR malignan*OR tumor* OR tumour* OR cancer).
Acknowledgements Michelle C. Turner  is funded by a  Ramón y Cajal fellowship  ( RYC-2017-01892 ) from the  Spanish Ministry of Science, Innovation, and Universities  and is co-funded by the  European Social Fund. ISGlobal acknowledges support from the  Spanish Ministry of Science and Innovation through the "Centro de Excelencia Severo Ochoa   2019-2023 "  Program  ( CEX2018-000806-S) , and support from the  Generalitat de Catalunya  through the  CERCA Program. The  IARC Monographs  programme receives funding from the  National Cancer Institute  ( 3U01CA033193-38S1 ).
and the uniqueness of indigenous goat breeds must be a priority in managing conservation and sustainable breeding programs. The accessibility of cutting-edge genomic techniques, the large amount of genetic data available in public databases and the feasibility of fine-grained bioinformatic tools allowed researchers to move forward in the study of demography and evolution of several organisms and to dissect the complex process of domestication  3. Several studies were performed on the evolutionary history of domesticated animals  7  , starting from their wild ancestors. In addition, the study of past introgression and gene flow among different breeds can help to describe their demographic history, defining admixture with foreign genetic contributions, crossing and inbreeding. Previous studies of worldwide goat breeds described a certain degree of genetic diversity among local breeds that belong to different and distant geographic areas  [8] [9] [10].
Descriptive statistics In this study, the analyses were performed on two datasets. The first one included 10 breeds belonging to Northern and Western Europe (our target area and hereafter indicated by the acronym NW-EU): Icelandic landrace (ICL), Norwegian landrace (Norwegian-NRW, Norwegian coastal-SEL, Norwegian coastal-SKO), Swedish landrace (SWE), Danish landrace (DNK), Finnish landrace (FIN), Irish landrace (Old Irish Goat-OIG, Bilberry-BLB, Traditional Aran-ARR). In the second one, a wider dataset (indicated by the acronym WHOLE), we added breeds from several countries-The Netherlands (NLD), Normandy (Fosseé-FSS), Northern and Southern Spain (Bermeya -BEY, MLG -Malagueña) and the South of Italy (Girgentana-GGT, Ciociara Grigia -CCG, Jonica -ION), as well as Alpine (ALP), Saanen (SAA) and Toggenburg (TOG), with a total of 24 breeds (see "Material and methods" for details).
Pairwise F ST and the Reynolds' distance matrices for the WHOLE dataset are shown in Supplementary Table  S1 , whereas the heterozygosity levels (Obs-H/ Exp-H) and the genomic inbreeding coefficient (FROH) calculated for the target dataset are summarised in Supplementary Table  S2. The genetic distance among all breeds ranged from 0. 01 (Alpine breeds) to 0. 48 (ICL vs SKO), and generally the values for the pairwise comparisons directly correlated with geographical distances. In the reduced dataset (NW-EU), the Swedish landrace displays less differentiation against all the Scandinavian breeds in comparison to the other breeds. The lowest is between SWE and SEL (0. 069). A general overview of the heterozygosity (H) estimates indicates that all the NW breeds have a heterozygosity slightly below 0.
We checked for a number of migration events ranging from 1 to 15 (Supplementary Fig. S5A , for the complete dataset) and from 1 to 10 (Supplementary Fig. S5B , for NW-EU dataset). In the case of the complete dataset (Fig. 7A , Supplementary Fig. S5A ), the residuals' curve (R2) against the number of migrations does not reach the minimum value recommended by the authors of the program (in our analysis is 0. 991 instead of 0. 998, Supplementary Table  S4A )  14. Some events of gene flow are still worth closely checking (Supplementary Table  S4B ). For example, the vector with the highest weight connects BEZ (bezoar, the ancestor) with GGT (~ 0. 55 edge weight) followed by the Spanish-Italian branch to DNK (edge weight 0. 45). From the branch of the Irish breeds to the French FSS we observe an edge with a weight of 0.
38, from the Norwegian SKO to the Finnish FIN an edge weight of 0. 26 and from the Spanish BEY towards the Irish OIG another migration event with a weaker   S8 : Norwegian landraces (SKO, SEL, NRW); Irish landraces (ARR, BLB, OIG); Icelandic Landrace, ICL; Finnish Landrace, FIN; Swedish Landrace, SWE; Danish Landrace, DNK. contribution (~ 0. 07). Another interesting event comes from the Norwegian branch to FSS and from the Southern breeds to the Norwegian branch. The case of the NW-EU dataset is different (Fig. 7B , Supplementary Fig. 4B ), where starting from the second migration edge, the residuals' curve reaches the critical value of 0. 99 and from the sixth onwards it reaches 0. 998 (Supplementary Table  S5A ). The maximum value is accounted for m = 8, and vectors 9-10 bring only a small increase in R 2 value.
Test for admixture and past gene flow To evaluate the presence of admixture and gene flow, we further computed the f3 and f4 statistics. We did not observe significant results for the f3 test in either of the two datasets. Interestingly, we obtained some significant results using the f4 statistic. Following the explanations of the authors  15  , if all three permutations of f4-statistic for a certain set of four populations are (significantly) non-zero, then at least one of the populations must be admixed; this is one of the most common signals of admixture used in the literature. Thus, out of 37,950 possible tree topologies tested, we selected the most valuable results (following the criteria detailed in methods), summarising them in Supplementary Table  S6A  and B. Among the most significant results (38 triplets with -20 < Z-score > + 20, in Supplementary Table  S6A ) we can observe that some of them support our previous analysis (ADMIXTURE, NeighborNet, TreeMix).
The proportion of this introgression is about 13% (f4-ratio between SEL|SWE|ICL). We retrieved a signal of admixture from the donor SWE to the Irish OIG and another potential donor NRW towards both OIG and BLB, but not towards ARR. The reason why we did not find any sign of past introgression in ARR probably lies in the high level of isolation in comparison with the other two Irish populations  10  that can erase signatures of other old demographic events. A small population size combined with a fragmented distribution probably also favoured the maintenance of high levels of inbreeding in the Icelandic, Skorpa and Aran populations, a point that emerges from our results. This is mirrored also by the very long Runs of Homozygosity (ROHs) that characterise these isolated breeds. However, all the Irish breeds also carry a greater number of ROH included in the medium-length class (> 4-8), that is related to populations that have undergone a population bottleneck  17  , as confirmed by previous data  18.
This genetic cline increases from 10-19% in individuals along the Mediterranean coast to 28-43% in the North of Spain where Indo-European Celtiberian languages were likely spoken  23. Those discoveries again confirm a complex network between the target study area during the history and corroborate the two gene flow events from Irish and Norwegian breeds towards Icelandic goats. The migration events of TreeMix, involving the Norwegian breeds and the Southern European populations (including the FSS from the Normandy) in both directions are once again probably linked to the trading routes during the Viking Age. A significant exponential LD decay is also detected in the Icelandic goats when we use the two Norwegian NRW|SEL as proxy to reference populations, providing further evidence for admixture.
Population structure and demography We used Arlequin v. 3. 5  46  to calculate the pairwise F ST values and the Reynolds' genetic distance (Supplementary Table  S1A  and B). To perform Principal Component Analysis (PCA) we used SNPrelate package v 4. 1. 2  49  and individual ancestries proportion were inferred using the unsupervised clustering method in ADMIXTURE v. 1. 3  50  with a number of clusters K ranging from 2 to 28 for the WHOLE dataset. The most probable number of K was inferred by cross-validation method and all the results were plotted using the "membercoeff. cv" and "membercoeff. circos" functions in BITE. Historical trends in effective population size (Ne) and the LD decay curves were estimated with the SNeP software  51  with default settings and a correction to adjust the LD for small sample sizes.
The genomic inbreeding coefficient FROH, the Runs of Homozygosity distribution per chromosome and classes of length as well as the total amount of the genome covered by these segments, were used to assess the level of inbreeding of the NW-EU populations and to infer past demographic events. All the calculations were performed with the R package DetectRUNS 0. 9. 5  52  (R core v4. 1) applying the consecutive function and with the following settings: minSNP = 20, maxGap = 10^6, minLengthBps = 1 Mb, maxOppRun = 1, maxMissRun = 1. The five classes of length are the following: 0-2, 2-4, 4-8, 8-16 and > 16 Mb.
Phylogeographic inference and admixture events To assess reticulate relationships between the study breeds, the matrix of Reynolds' unweighted distances previously calculated was used to infer and visualise the Neighbor-Net via the software SplitsTree 53 v4. 14. 2. The program TreeMix v1. 13  14  was applied to infer the gene flow and admixture events that occurred in the past. By using allele frequencies of SNP, we calculated a Maximum Likelihood tree that represents the evolutionary relationships across the analysed populations. We ran the program until the residuals plot R 2 reached at least 0. 998, that is from 0 (no migration) to 11 for the NW-EU dataset and from 0 to 15 for the WHOLE dataset, using the bezoar outgroup. Finally, we plotted the results by means of the plotting_funcs. R script provided within the same program.
We also applied the function "treemix. fit" in BITE  44  to evaluate TreeMix results, reporting the first population encountered in the donor subgraph, the first population encountered in the receiving subgraph, the migration weight (i. e. the estimated fraction of ancestry) in the receiving subgraph derived from the donor portion, jackknife and standard error estimate, and the p-value associated to the weight. As the authors of the program specified  14  , a "significant" p-value simply indicates that the hypothesised migration event significantly improves the fit to the data; this does not exclude the possibility of errors or indicate that the particular migration event tested is the correct one. For this reason, we used additional methods like three-and four-population (f3 and f4) statistics  14  to test the robustness of the inference.
These statistics were inferred with ADMIXTOOLS v5. 1  54  and standard errors (SE) were computed with the block-Jackknife procedure using default options and Z-scores calculated on the SE. We tested for all the possible combinations of trees from the WHOLE dataset, producing a total of 6,900 triplets for thef3 test and 37,950 possible tree topologies for the f4 test. Afterwards, we checked all the results and retained only the most significant ones (-20 < Z-score > + 20). Thus, we focused only on the NW-EU target breeds, screening the results that had a minimum of -4 < Z-score > + 4 (p-value < 1 × 10 -6).
Proportion of introgression and past gene flow estimates To confirm the signal of gene flow found with the above mentioned methods, we applied f4-ratio estimation α, in ADMIXTOOLS v5. 1  54  , which allows the inference of the admixture proportions without access to accurate surrogates for the ancestral populations. The proportion of ancestry was computed as: in which O is the outgroup (BEZ), B a first reference population, C the other possible reference population, A a population related to B, and X the target population. We tested only the breeds that could corroborate the TreeMix migration events and f4 statistics. The time of admixture was estimated with ALDER v. 1. 05  55  using the default parameters with a minimum genetic distance pre-calculated by the program and the 'maxchrom' parameter (set to 22 by default) that enables to use more than 22 chromosomes (maxchrom = 29).
Data availability The datasets analysed during the current study is available in the Dryad Repository (https:// doi. org/ 10. 5061/ dryad. v8g21 pt), on Mendeley Data (https:// doi. org/ 10. 17632/ hnd59 x6gmg. 1; URL: https:// data. mende ley. com/ datas ets/ hnd59 x6gmg/1), from NIBIO The Norwegian Genetic Resource Centre under request and from Smarter project which database will be available at the end of the project and once the manuscript is published.
Datasets construction and quality control All the details about the origin of data, breed, breed code, sampling country and geographical coordinates are in Supplementary Table  S8   [35] [36] [37]. In this study, two datasets were built: the first one constituted by 10 breeds and 206 goats belonging to Northern and Western Europe (hereafter indicated by the acronym NW-EU) which are our target populations. For the second dataset, indicated by the acronym WHOLE, we added breeds from The Netherlands, Normandy (Northern France), Northern and Southern Spain and the South of Italy, as well as Alpine, Saanen and Toggenburg, with a total of 24 breeds and 669 goats. Sampling location is detailed in the map (Fig. 8 ). We included the transboundary breeds since in the last decades they have contributed in a small fraction to the genome of Irish and Danish landraces, as suggested by previous results  5, 8  and from the Domestic Animal Diversity Information System (DAD-IS, https:// www.
fao. org/ dad-is/ browse-by-count ry-and-speci es/ en/). Another technical reason for including cosmopolitan breeds is to deal with the ascertainment bias of the SNP chip. We also took into consideration the results from Ref. 38  , in which the Old Irish Goat (OIG) breed is closely related to the Southern Italian breeds as highlighted by the Neighbour-Joining tree and historical data about the migration routes during the Viking Age (750-1050 CE)  31. Thus, the wider dataset (WHOLE) includes goat breeds that belong to countries of the ancient Vikings' routes, from the continental Europe (the Netherlands) and   S8.
By doing so, the generated ecosystem trajectory facilitates a new and unique species assemblage. [24, 25] As it occurs in cancer, novel systems often persist surrounded by the historical community within which they have emerged and are typically resilient and resistant to removal strategies. A first layer of complexity in these cancer ecosystems comprises tumor cell populations harboring different phenotypes, hence defin- F I G U R E 1 The oncospace for human cancers. Why, and how, are cancers so different or similar to one another (A-D)? Qualitative pan-cancer oncospaces targeting these questions can be built by merging available data on mutational footprints,  [8]  stromal and immune infiltrates  [9, 10]  and histopathological or stemness grading. [11, 12] Each of the three axes, namely evolutionary, ecological, and developmental alterations, can be understood as the underlying processes facilitating the acquisition of the hallmarks of cancer.
METHODS AND BACKGROUND The choice of data type is the central issue that must be addressed in a well-thought-out manner for a machine learning approach. Basically, in the environmental magnetism approach applied to monitor air quality, we aim at quantifying Fe-bearing PM. To do this, we measure a set of magnetic parameters derived from environmental magnetism techniques in order to provide information about the magnetic concentration, nature, and size of the sample. In our study, these magnetic parameters, summarized in Table  1 , were also complemented by concentrations of chemical elements (Table  1 ). Table 1: Summary of magnetic and chemical experiments performed to obtain the parameters which are included in the machine learning algorithm.
Elemental analysis. Chemical elemental analyses were performed with a Niton XL3t-900 Goldd X-ray fluorescence (XRF) portable analyzer (Thermo Scientific ® , Waltham, MA, USA) on ex-situ samples reduced to powder after drying. Before analysis, sample holders were measured empty to discard the ones that showed a strong signal. The interpretation of XRF spectra was processed in two steps: first, we assessed the presence of an element from a qualitative visual inspection of spectrum and then calculated its concentration with the NITON Data Transfer software provided by Thermo Electron CorporationTM, Waltham, MA, USA. Results were obtained in μg/g (ppm). For the purpose of this study, we used concentrations in zinc (Zn), iron (Fe), and barium (Ba), as these elements are known to discriminate of traffic-related PM sources.
Because a model will perform better when tested with data from which it was already trained on, we performed k-fold cross validation to verify the accuracy of the model and avoid overfitting or underfitting. During this process, the dataset is randomly split into approximately equal-sized sub datasets called folds. One of the folds is put aside as a test set and the remaining data are used as the training set. Then, we iteratively pass the test set through the model with different folds until all the folds have been used once as the test set and we make a record of the relevant performance metrics.