dataesr/scientific-paragraphs-categorization huggingface dataset:
19 TL mapping and bipartite network construction For eQTL analysis, we excluded SNPs from all analyses if they had a call rate under 0. 9 or a minor allele frequency <5% in any tissue. A gene was considered expressed in a sample if its read count was greater than or equal to 6. Genes that were expressed in fewer than ten of the samples in a tissue were removed for the eQTL analysis in that tissue. To correct for varying degrees of admixture in the African-American subjects, we used the first three principal components of the genotyping data provided by the GTEx consortium and included these in our eQTL model. We used the R MatrixEQTL package  20  to calculate eQTLs with an additive linear model that included age, sex and ethnic background, as well as the first three genotype PCs as covariates: Expression $ Genotype þ Age þ Sex þ Ethnic Background þ PC1 genet þ PC2 genet þ PC3 genet þ ϵ We tested for association between gene expression levels and SNPs both in cis and trans, where we defined cis-SNPs as those within 1 MB of the transcription start site of the gene based on mapping by using Bioconductor R biomaRt package.
Cancer genes We used information from two databases, the Network of Cancer Genes  27  and the COSMIC census,  28  to create a list of genes commonly mutated in cancers, or 'cancer genes' (Supplementary Table  S2 ), including both oncogenes and tumour-suppressor genes. We mapped these cancer genes to the GTEx eQTL networks. We tested whether cancer-risk SNPs are preferentially located in the promoters of the cancer genes. We downloaded transcription start site (TSS) positions for all genes present in the GTEx data from the Ensembl database  29, 30  and defined the promoters as the -750/+250-bp region around each TSS. We used Fisher's exact test to determine whether the cancer gene promoters were enriched in cancer-risk SNPs. We used LD blocks rather than SNPs in this analysis to correct for linkage disequilibrium.
Q ih ¼ 1 m X j e A ij À k i d j m δðC i ; hÞδðC j ; hÞ (2) Gene ontology functional category enrichment We extracted the list of genes within each community in each tissue-specific network, and used the R GOstat package  31  to perform a tissue-by-tissue analysis of the overrepresentation of Gene Ontology Biological Processes terms within each community enriched for cancer-risk SNPs. Our reference set consisted of all the genes present in the corresponding tissue-specific network. Communities were considered significantly enriched in a given category if the FDR-adjusted P-value was <0:05.
Most of these genes have been shown to be dysregulated in breast cancer or during epithelial-mesenchymal transition. Indeed, LAD1 has been associated with aggressive breast tumours,  41  COL17A1 is underexpressed in breast cancer and overexpressed in head and neck squamous cell carcinoma, lung squamous cell carcinoma and lung adenocarcinoma  42  and FERMT1 is a known mediator of epithelial-mesenchymal transition in colon cancer. 43 ncer-risk SNPs preferentially target cancer genes We expected that cancer-risk SNPs might be preferentially associated with genes known to be involved in cancer development and progression. We assembled a catalogue of oncogenes and tumour-suppressor genes ('cancer genes') by using databases that included the Network Gene Cancer version 5. 0  27  and the COSMIC  44  census (see Methods and Supplementary Table  S2 ).
In particular, cancer-risk SNPs are linked to altered expression of MHC class I and II genes. MHC genes are clustered on the p-arm of chromosome 6, and play a role in recognising pathogen-infected and other types of modified cells (including cancer cells) and in triggering the innate and adaptive immune system. It is well known that the power of eQTL studies to detect associations between genotype and gene expression depends on the minor allele frequency. 46, 47 In this study, we used the data from 13 tissues for which we had available matching RNA-seq and genotyping data in 200 or more samples; the sample sizes vary between 212 (HRV-heart left ventricle) and 378 samples (SKN-skin). Even the largest sample size does not allow us to reach the maximum power of eQTL detection for alleles with low-intermediate frequencies (0.
Funding: This work was supported by grants from the  US National Institutes of Health , including grants from the  National Heart, Lung and Blood Institute  ( 5P01HL105339 ,  5P01HL114501 ; J. Q. and J. P. : 5R01HL111759; J. P. :  K25HL140186 ), the  National Cancer Institute  (J. Q. :  R35CA220523 ,  5P30CA006516 ; J. Q. and M. L. K. :  5P50CA127003 ,  1U01CA190234 ; X. L. , J. Q. and M. F. :  1R35CA197449 ), the  National Institute of Allergy and Infectious Disease  (J. Q. and J. P. :  5R01AI099204 ), the  Charles A. King Trust Postdoctoral Research Fellowship Program ,  Sara Elizabeth O'Brien Trust, Bank of America , N. A. , Co-Trustees (M. L. K. ) and  Helse Sør-Øst  and  University of Oslo  through the  Centre for Molecular Medicine Norway (MLK). Additional funding was provided through a grant from the  NVIDIA foundation  (J.
Data availability: All genotyping and gene expression data were downloaded from the dbgap repository (accession number phs000424. v6. p1). GWAS results were downloaded from the EBI/NGRHI GWAS catalog website (v1. 0, accessed 8 December 2015). eQTL networks and a browsable version of our results were made available on the following website: http://networkmedicine. org:3838/cancer_eqtl/. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Calculations using the pK a database have shown Nafion to have a pK a = -6. 18 Previous density functional theory (DFT) studies  19  had shown that the sulfonic acid groups were deprotonated at λ ≥ 3 which was the reason behind deprotonating all of the sulfonic acid groups in the classical MD simulations. However, there are other experiments,  5, 6  as discussed before, which have shown the existence of different protonation states even at very high hydration levels. Therefore, we think that it is important to provide some insights, using classical MD, for both structural and dynamical properties of Nafion for various degrees of deprotonation (DDPs) at different hydration levels. Our results show that the sulfur-sulfur radial distribution functions (RDFs) peak heights showed similar trends as the neighboring intramolecular/ intrachain sulfur-sulfur (S-S) distance, although the first peak distance and the intramolecular S-S distance are noticeably different.
The structure factor calculated using eq 1 allows computation up to a minimum wave number,  32  q min = π/r m = 0. 0785 Å -1. The cluster distribution of water molecules was computed for the different hydration levels (λ) using the OVITO software. 33 A cluster is defined as a group of atoms in which each atom is within a particular predefined cutoff distance of at The Journal of Physical Chemistry B Article least another atom within that group. The oxygen atom in a water molecule and in a hydronium ion was used for computing cluster sizes. For example, cluster of 5 oxygen atoms is assumed to represent the cluster of 5 water molecules and/or hydronium molecules. The cluster distribution plots number of clusters, averaged over a time interval of 3 ns, versus the cluster size. Cluster size is the number of water and/or hydronium molecules present in a cluster, as defined in a previous simulation study.
This is the first time that ML has been used for the characterization of ELF exposure scenarios in children. Cluster analysis was applied to personal exposure measurements recorded in the EXPERS study  [5, 6]  from children living in France to identify clusters of children with similar exposure levels and similar exposure scenarios (i. e. , similar electric network configuration in the proximity of the home or school). The aim of the paper was neither to demonstrate the influence of ELF exposure on children Leukemia, nor to present a novel measurement study of personal ELF exposure. The aim was to further analyze data coming from an already done measurement campaign-the EXPERS study-with a novel approach in order to discover similarities in the exposure scenarios. In particular, the focus was to understand (i) what were the main characteristics of the clusters of ELF exposure with respect to the type of electric networks in the vicinity of the measurement location (i.
e. , a child's home and school), (ii) what were the main differences between the clusters, and (iii) which environmental factors better discriminated the clusters. The paper is organized as follows: Section 2 "Materials and Methods" reports the details of the source of the analyzed data (Section 2. 1 Data Source) and the description of the method used to perform cluster analysis and its validation (Section 2. 2 Data Analysis); Section 3 "Results" reports some statistics on the analyzed dataset (Section 3. 1 Descriptive Results), the results of the procedure used to determine the optimal number of clusters and the evaluation of the cluster solution (Section 3. 2 Choice of the Optimal Number of Clusters and Evaluation of Clustering Performance), and the detailed description of the characteristics of the clusters we found (Section 3.
Data Source A database of personal exposure measurements to ELF MF in children was analyzed. The data came from the EXPERS study  [5, 6]  and consisted of exposure measurements recorded from children aged 0-14 years located in France. The database contained the recordings from 977 children located in rural or urban places (948 children) and in Paris (29 children) (for more details about the areas under test, see  [6] ). Measurements were performed during cold seasons (February-April 2007, October 2007-April 2008, and October 2008-January 2009). An EMDEX II (Enertech, Campbell, CA, USA (https: //www. enertech. net/html/EMDEXII. html)) personal dosimeter was used to measure broadband (40-800 Hz) and harmonic (100-800 Hz) MF amplitudes with a sensitivity of 0. 01-300 µT. Measurements were recorded with a sampling rate of 3 s over 24 h for one day.
For each child, the database contained the number of power lines and substations close to the measurement site, i. e. , the child's home or school, as identified by the French grid operators (RTE for high voltage and Enedis for mid and low voltage) from the Lambert II coordinates of home and school addresses. The following 10 types of the electric networks were considered: 400 kV overhead lines within 200 m from home or school (OVHD_ultra-high), 225 kV overhead lines within 120 m (OVHD_extra-high), 150 kV or 90 or 63 kV overhead lines within 100 m (for 150 kV lines) or 70 m (for 63 and 90 kV lines) (OVHD_high), 20 kV overhead lines within 40 m (OVHD_mid), 400 V overhead lines within 40 m (OVHD_low), 225 kV underground cables within 20 m (UND_extra-high), 63 to 150 kV underground cables within 20 m (UND_high), 20 kV underground cables within 40 m (UND_mid), 400 V underground cables within 40 m (UND_low), and 20 kV/400 V substations inside a building within 40 m (Substation).
Data Analysis Using the information available in the daily activity tables, the geometric mean (GM) of the 50 Hz component of B field was computed for exposure measurements that were recorded indoors when the child was at home and, where available, at school. As in previous reference studies on ELF MF exposure  [21] , we decided to consider GM as the metric to represent B field values to facilitate the comparison of our results with similar studies. We also decided to analyze only exposures recorded during the day and not during the night to exclude any confounding effect due to the presence of the alarm clock in night recordings, as evidenced in  [6]. As written above, in this study, we decided to analyze as variables the B field measured indoor and the type of electric networks in the vicinity of the measurement site; at the moment, we did not analyze indoor sources of exposure.
A total of 1793 measurements were available for the analysis, including 977 recordings made at home and 816 at school. Cluster analysis was performed on a matrix of 1793 × 11 real data, each representing a child exposure (row) and its characterization (column) in terms of B field and number of power lines and substations in close proximity to home or school for each of the 10 types of the electric networks described in the section above. Data were analyzed with K-means clustering (Matlab (ver. R2018a), MatWorks Inc. , Natick MA, USA)  [22]. The goal of K-means clustering is to partition the data into K clusters so that the pairwise dissimilarities between data of the same cluster are smaller than those in the other clusters. In K-means clustering, dissimilarity d(x i , x i ) between two data vectors x i and x i (i, i = 1, • • • , N) of P components is measured with the squared Euclidean distance: d(x i , x i ) = P ∑ j=1 x ij -x i j 2 , (1) where x ij and x i j are the jth components (j = 1, • • • , P) of vectors x i and x i.
As such, each cluster centroid can be regarded as the "representative" of the characteristics of the pool of data assigned to the cluster. Centroid initialization was obtained from the algorithm published by  [23]  (as implemented in Matlab), which uses heuristics to find centroid seeds and improves the running times of the K-means clustering algorithm. To be sure that the partitioning solution was stable, we repeated the clustering using new initial cluster centroid positions by choosing from 30 possibly different sets of seeds, according to the algorithm by  [23]. The average silhouette score was used to determine K (i. e. , the optimal number of clusters) and the appropriateness of the cluster solution (i. e. , it was used for the validation of the cluster solution). The silhouette score S i for a data point x i is a measure of the similarity between x i and the other data of the same cluster, when compared to data in other clusters  [24].
It is computed as: S i = b i -a i max(a i , b i ) , ( 3 ) where a i is the average distance of x i to the other points in the same cluster, b i is the minimum average distance of x i to all the other points in different clusters, minimized over the clusters, and -1 ≤ S i ≤ 1. A high silhouette value close to 1 indicates that x i is far away from neighboring clusters and well-matched to its own cluster, that is, x i has been assigned to the proper cluster. A value close to -1 indicates that x i has been assigned to the wrong cluster. The average of S i across all data points (i = 1, • • • , N) is a measure of the overall appropriateness of the clustering solution over the entire dataset. The average silhouette score can be used to determine the optimal number of clusters K: by computing the average silhouette score as a function of K, it is possible to determine the best value of K as the value for which the average silhouette reaches a maximum.
Descriptive Results The average value of B field (GM) across all 1793 recordings was 0. 02 µT (1st quartile Q1: 0. 006 µT; 3rd quartile Q3: 0. 042 µT), and the maximum was 1. 09 µT; one should note that B field was >0. 4 µT only in 5 (five) recordings out of 1793. Table  1  shows the distribution of the different types of electric networks in the analyzed dataset of indoor measurements. It is possible to see that more than two thirds of the measurements (66. 8%) in the dataset came from children living or going to schools near underground networks of low voltage, while nearly half of them were from children living or going to school near underground networks of mid voltage (45. 7%) or overhead lines of low voltage (43. 8%), and fewer than 15% (13. 7%) of the measurements were from children living or going to school near a substation.
A small number of measurements (below 4%) were from children living or going to school near underground networks of high or extra-high voltage or overhead lines of mid to ultra-high voltage. A total of 201 out of 1793 (11. 2%) measurements came from sites that apparently were far from any of the electric networks considered in the present study; in other words, none of the electric networks mentioned above was found near the measurement site of these last data. This group of data may consist of measurements from places served by local distributors of electricity rather than by RTE and Enedis, representing 5% of the distribution of electricity in France. In addition, this group included measurements made in rural areas where the precision of geolocalization might have been less than in urban areas, and thus might have influenced the localization of the electric grid near the measurement site, especially if the precision was larger than 40 m.
In particular, the minimum size of the clusters was seven for the five-partition solution and three for the six-partition solution. It is a good and consolidated practice to choose the number of partitions so as to maximize the silhouette score and, at the same time, have a reasonable cluster size. For the dataset analyzed in the study, a good compromise between silhouette and cluster size was obtained by partitioning the data with four clusters. Last but not least, it is important to note that the solution with four partitions resulted in a silhouette score of nearly 0. 65 which is indicative of good quality of the partitioning.
Results of Clustering Analysis The results obtained with the four-partition solution can be seen in Table  4  which displays the coordinates (in absolute values) of the centroids of the four clusters. Each centroid is a multidimensional vector with 11 components, one for each of the 11 analyzed measurement variables. The number of measurements assigned to each cluster can be found in Table  3 , in correspondence to the solution with four partitions. It is a good and consolidated practice to choose the number of partitions so as to maximize the silhouette score and, at the same time, have a reasonable cluster size. For the dataset analyzed in the study, a good compromise between silhouette and cluster size was obtained by partitioning the data with four clusters. Last but not least, it is important to note that the solution with four partitions resulted in a silhouette score of nearly 0.
Discussion K-means cluster analysis revealed significant and recurrent patterns in personal exposure to ELF MF. The first pattern (cluster #1) corresponded to children living or going to school near overhead lines of extra (225 kV) and ultra-high voltage (400 kV); these children were all characterized by the highest values of MF observed in the dataset (the average GM value of B within the cluster was 0. 146 µT). The second pattern (cluster #2) was found for children who live or go to school near overhead lines of high voltage (63/90/150 kV); these children were characterized by a mid-to-high value of MF (the GM value of B averaged within the cluster was 0. 0523 µT). The third pattern corresponded to children with the highest number of underground cables of low (400 V) and mid voltage (20 kV) and substations in the vicinity of their home or school; they were characterized by a mid-to-low MF (the average GM value of B within the cluster was 0.
With regard to underground networks of high and extra-high voltage, the probability of being close to these types of networks is typically low. As expected, because our sample size of 977 children was representative of the French population  [6] , the resulting dataset contained a small number (12 out of 1793) of measurements made near these types of networks. This would mean that, from a methodological point of view, measurements near underground networks of high and extra-high voltage might have a lower impact on cluster analysis than those made near other types of networks that are more frequently seen in real practice. In parallel, it is also important to note that the contribution of underground networks to total indoor exposure can be small. Although the MF at the centerline of underground cables can be higher than that generated by an equivalent overhead line, the spatial attenuation of the MF is much steeper in these networks than in overhead lines.
As a result, the MF to the sides of the cable is usually significantly lower than that of a power line  [25] [26] [27] ; for example, the field at 1 m above ground level at a distance of 20 m from the centerline of a 400 kV cable is of the order of 0. 10 ÷ 0. 20 µT  [25, 26] , whereas the MF of an overhead line of equivalent power is about 2 µT. If measurements are done at more than 1 m above ground level (as in the current study), it is expected that the field will be even lower and of less contribution to total exposure. A significant percentage of the measurements in the dataset were from children (43. 8%) living near overhead lines of low voltage which were found to be of low efficacy in differentiating the clusters. A possible explanation for this aspect could be that these networks marginally contributed to the measured exposure.
This is also in line with  [6] , where no correlation was found between the 24 h GM MF exposure and low voltage lines. Finally, with regard to mid voltage overhead lines, they were found to have a marginal contribution in differentiating the clusters; again, one possible explanation for this result could be the small number of children in the dataset that live near mid voltage overhead lines (58 out of 1793). In addition, as commented in  [6] , mid voltage lines were mainly found in rural areas characterized by lower exposure values if compared to the exposure measured in highly urbanized areas, such as in Paris. This last evidence would mean that, if present, mid voltage lines marginally contribute to the exposure. As described above, the pattern observed in cluster #3 concerned children who live or go to school near substations and underground networks of low and mid voltage.
As commented above, the MF of underground networks could be very low when measured at a distance of 20 m and even lower at greater distances. Our dataset contained information regarding the presence of low and mid voltage cables within 40 m from a child's home or school; thus it could be that the contribution of low and mid voltage cables to our exposure data might be very low. On the other hand, according to the network distribution observed in our dataset, children who live near substations usually have underground networks of low and mid voltage in close proximity. Thus, we can conclude that the true differentiating variable that characterized mainly cluster #3 was the presence of substations near home or school, whereas the presence of underground networks of low and mid voltage was only a consequence of the configuration of power grids in the analyzed dataset.
Objective To analyse trends in the number of ultrasound examinations in relation to the effectiveness of prenatal detection of birth defects using population-based data in France. Design A multiple registry-based study of time trends in resource use (number of ultrasounds) and effectiveness (proportion of cases prenatally diagnosed). setting Three registries of congenital anomalies and claims data on ultrasounds for all pregnant women in France. Participants There were two samples of pregnant women. Effectiveness was assessed using data from three French birth defect registries. Resource use for ultrasound screening was based on the French national healthcare database.
6 hen a fetal anomaly is suspected, patients are referred to specialised referral centres for further investigations. 7 There are 49 referral centres for prenatal diagnosis in France and its territories. The regional implementation of centres is determined by the number of strengths and limitations of this study ► We observed the chronological trend of the French ultrasound birth defect screening programme. ► We measured the detection rate of birth defects and the number of screening ultrasound per pregnancy. ► We used two large data sources: national registries of birth defect and the national claims database. ► We excluded birth defects detected by other methods. ► There was no linkage between records of patients in the two data sources.