dataesr/scientific-paragraphs-categorization huggingface dataset:
Setting Weekly numbers of N&G sessions and IAM ratings will be monitored over 2 years. A session starts when someone opens a Web page and ends when the person (or a relative with the same IP address) does not use the website for more than 30 min. For each quintile of SES, data will be collected for 9 months preimplementation (IAM/N&G) and 9 months postimplementation (IAM+N&G+). To avoid bias related to the novelty effect, data collected in the 3 months immediately following the implementation will be excluded from the analysis. The chosen periods also ensure seasonal comparability of the collected outcome data (before and after intervention).
Measurement N&G and McGill already use Google Analytics, an objective and reliable automatic data collection of N&G readers' demographic characteristics and website use behavior (based on javascript codes in each Web page)  [117] [118] [119] [120]. IAM raters' demographic data are collected with a questionnaire (linked to an anonymized identifier). The validated IAM questionnaire  [53]  collects self-reported information use and subsequent expected health/well-being benefits for parents and their child. Weekly proportion of IAM ratings information used for the child of someone else N&G mediated B1 Weekly proportion of IAM ratings information used for oneself and one's child N&G used B2 Weekly proportion of IAM ratings expected health or well-being benefit for a parent and child, including at least 1 of the following self-perceived benefits (IAM choices of response): improvement of the health or well-being of a child, being less worried, prevention of a problem or the worsening of a problem, handling a problem, and being more confident to decide something with someone else Expected benefits C a N&G: Naître et grandir.
Covariates The main predictor will be participants' SES as determined by the Quebec Index of Material and Social Deprivation, a validated ecological measure of the (education, income, and employment) disadvantage of a given geographic area (postal code)  [121, 122]. For each session, an index will be automatically assigned using (1) the reported postal code and the Canadian Deprivation Index Assignment Program (CDIAP) of the Public Health Agency of Canada when the data source is IAM and (2) the postal code matched to the session geotag via the "Dissemination Area Boundary File" of Statistics Canada and the CDIAP when the data source is Google Analytics. This will allow for the identification of low-SES participants (highest quintile of material and social deprivation). Using Google Analytics, participants' demographic variables that will be considered in all analyses are age, gender, rural/urban location, and province.
Participants Participants who received the N&G+ newsletter, used IAM+ at least once, have agreed to be contacted for research, and live in a high deprivation area will be approached for recruitment. The recruitment procedure will be the same as for phase 1. In 2015, more than 24,000 families received, upon request, the weekly newsletter. We will begin by recruiting 30 parents (and continue recruitment, as needed, to achieve data saturation) who report, via IAM+, positive health and well-being outcomes and/or potential negative consequences of information (prioritizing those who live in the areas of highest deprivation). As we are looking for individual stories for about 6 types of outcomes (some less frequently reported than others), data saturation may not be reached with fewer than 50 participants  [111, [133] [134] [135].
Data Collection The research assistant having experience in qualitative interviews with low-SES persons will conduct 60-min face-to-face interviews to elicit participants' IAM+ ratings. The interview guide will be based on our theoretical model (Figure  2 ) and input from team members and the steering committee. To stimulate recall (memory), the interviewee will be given the list of recent texts they rated and their ratings. For each newsletter, the research assistant will ask open questions regarding what happened to them and/or their children that led them to report an expected outcome (positive or negative). Participants will be interviewed twice 3 months apart to increase the number of described outcomes and avoid fatigue of long interviews. Participants will receive Can $20 per interview as compensation for their time.
Data Analysis Two principal investigators (PP and CL) have experience in qualitative research. With the research assistant, a collaborator anthropologist having expertise in life histories (MB) and a research trainee will read notes and interviews and meet regularly. For each case (information used with at least 1 outcome experienced by a parent and a child), they will interpret the data in the form of a small story  [112, [136] [137] [138]. This method allows researchers to describe a person's individual experience (including all perceived influences) and helps the researcher understand the individual's attitude and behavior  [33]. In research meetings, 2 questions will be answered through recorded discussion of arguments for and against each analytical decision (rigor based on sharing reflexivity): is the case story clear? and are the causal network and chain of evidence trustworthy? Disagreements about clarity and trustworthiness of case stories will be resolved with 3 other team members having experience in qualitative research (Bouthillier, Thoër, and Smythe).
We have opted for a prospective longitudinal study as monitoring is embedded in N&G routines. In addition, although randomization would provide a higher level of evidence, it was considered unethical to randomly assign low-SES parents to N&G+ or N&G information and impractical for such a popular website (contamination bias)  [159]. Moreover, 3 typical potential sources of biases in longitudinal designs may not affect our study: the use of proportions will control for historical events, there will be no respondent fatigue as our 2-year pilot data showed that the monthly number of IAM raters is stable, and the social desirability bias will affect pre-and postimplementation periods in a similar manner. Considering the commitment, expertise, and networks of our team, we expect no major challenges.
Acknowledgments The authors wish to thank  Dr Suzanne Smythe  ( Simon Fraser University, Canada ),  Mr Sean Schurr  ( AboutKidsHealth, Canada ),  Maria de Carmen Reyes  ( Centro de Investigación en Geografía y Geomática, Mexico ), and  Ms Catherine Chouinard  ( Avenir d'enfants, Canada ) for their collaboration in developing this proposal. N&G is funded by the philanthropic organization " Fondation Lucie et André Chagnon. "PP holds an investigator salary award from the  Fonds de recherche en santé du Québec. This study has been funded by the  CIHR  (# PJT153200 ).
35 fps and a field of view of 22 mm × 22 mm. Image processing is performed using the ImageJ open source software. Measurements and reproducibility. The CTAB solution (25 ml) is poured into a cylindrical container (diameter 70 mm). Then a single drop of DCM is carefully deposited onto the surface of the solution using a gastight syringe. The drop volume is 5. 6 ± 0. 1 μl. We performed 19 experiments under similar conditions; 14 of them exhibited more than 4 pulsations. The pattern observed during the first four pulsations (P 1 -P 4 ) led to the highly ordered dewetting structure described above (during P 4 ) 18 times out of 19; this structure qualitatively survived during the following pulsations if any. Determination of the evaporation rate. DCM evaporation was measured by placing the system on a precision balance.
Acknowledgements This work was supported by the  DROPDYN  project from the  2014 Programme Transversalité of the IDEX UNITI  from  Université Fédérale Toulouse Midi-Pyrénées  (grant  CNRS 127208 ). V. P. acknowledges the financial support from the  Centre National d'Etudes Spatiales. We thank  Sébastien Cazin  for his expert technical assistance with the optical and high-speed imaging acquisition systems.
Background: Normalization is essential to ensure accurate analysis and proper interpretation of sequencing data, and chromosome conformation capture data such as Hi-C have particular challenges. Although several methods have been proposed, the most widely used type of normalization of Hi-C data usually casts estimation of unwanted effects as a matrix balancing problem, relying on the assumption that all genomic regions interact equally with each other. Results: In order to explore the effect of copy-number variations on Hi-C data normalization, we first propose a simulation model that predict the effects of large copy-number changes on a diploid Hi-C contact map. We then show that the standard approaches relying on equal visibility fail to correct for unwanted effects in the presence of copy-number variations.
We thus propose a simple extension to matrix balancing methods that model these effects. Our approach can either retain the copy-number variation effects (LOIC) or remove them (CAIC). We show that this leads to better downstream analysis of the three-dimensional organization of rearranged genomes. Conclusions: Taken together, our results highlight the importance of using dedicated methods for the analysis of Hi-C cancer data. Both CAIC and LOIC methods perform well on simulated and real Hi-C data sets, each fulfilling different needs.
To date, such copy number variants (CNVs) are usually ignored in cancer Hi-C data normalization, although they raise interesting and important questions both at the biological and methodological levels. The real impact of CNVs on contact frequencies remains difficult to assess. For instance, a tandem amplification has a very different impact on local chromatin organization compared to the gain of a complete chromosome. Similarly, a genomic duplication could lead to different changes in contact frequencies depending on whether the event occurs within a TADs or across/at a TAD boundary  [10]. Addressing the question of CNVs during normalization is therefore an important challenge in the analysis of Hi-C data and their interpretation in the context of genetic and epigenetic mis-regulation in disease.
The question of how copy number signal should be treated depends mainly on the related biological questions. One strategy is to consider the copy number effect as an unwanted effect, and to remove it during the normalization step  [29]. This strategy indeed makes sense for the detection of a genome-wide list of significant contacts, or for the direct comparison of samples with different chromosomal rearrangement profiles. On the other hand, the signal from copy number alterations can also be considered as important biological information, that can be of interest for 3D modeling, genome reconstruction of cancer cells, or to simply further characterize the genomic landscape of a tumor  [30]. Here, we propose to further explore the impact of CNVs on Hi-C data and provide tools that deal with its effects on data normalization.
First, we develop a model simulating large copy number rearrangements on a diploid Hi-C contact map. Using such simulated data, we demonstrate that the naive matrix balancing algorithm which is commonly used to normalize Hi-C data, cannot be applied to cancer Hi-C data. We then propose two methods that extend the ICE algorithm and correct the data from systematic biases, either considering the CNVs as a bias to remove or as an interesting signal to conserve in the data structure. Finally, we apply these methods to several disease associated Hi-C data sets, demonstrating their relevance.
1d ). In order to validate our simulation model, we exploited available Hi-C data from two epithelial cell lines: the MCF7 breast cancer cell line and the MCF10A neardiploid, non-tumorigenic cell line  [5]. We extracted the copy number information of the MCF7 line from Affymetrix SNP6. 0 array, filtering out any altered segments that were lower than the MCF10A's Hi-C map resolution (1 Mb) and applied our simulation model on the normal-like data, thus obtaining a simulation of MCF7's abnormal Hi-C data. We then compared our simulated results with the real MCF7 Hi-C data set. As expected, the contact counts (for both the simulated data and the real data) are correlated with the copy number (Fig. 1e ). Both our simulations and the real data show blocks of higher/lower contact frequencies in regions affected by large copy number variants.
Interestingly, for the highest copy numbers, both profiles increase concurrently, but not at the same rate. One explanation would be that these regions of very high copy number correspond to complex rearrangements such as tandem focal amplifications in cis and translocation in trans. Their linear proximity on the genome would therefore explain the massive increase of contact frequencies that we observed in real data, and which are not modeled by our simulation. We then summarized both data in one dimension (1D) by summing the contact frequencies over each row. Overall, the simulated MCF7 profile is well correlated with the profile of real MCF7 Hi-C data (Spearman cor=0. 877, Fig. 1f , Table  S2 ). We can observe that the sum of interactions for a genomic window is proportional to the copy number.
Copy number effects are characterized by blocks of high/lower signal. Overall, the simulation conserves the structure and the counts/distance properties of the Hi-C maps. e. Validation of the simulation model using Hi-C data from MCF10A cell line from which we simulated the expected copy number of MCF7 cancer cell line (MCF7 simulated). The mean O/E (Observed/Expected) counts per block of copy number of intra (cis) and inter-chromosomal (trans) maps at 1 Mb resolution is represented. Looking at the intra-chromosomal maps of chr3 and 8 demonstrates that our model efficiently simulates large copy number events. f. 1D genome-wide profiles of near-diploid MCF10A, simulated MCF7 and real MCF7 Hi-C data. MCF7 gain and losses are represented in red and green copy number, by comparing the obtained matrices to the ground-truth.
Estimation of copy number from Hi-C data Analyzing cancer samples usually requires access to CNV profiles. External sources of data (such as whole-genome sequencing or microarray data) can be used to infer DNA breakpoints along the genome, and thus to define DNA segments of equal copy number. If such data is not available, we propose to directly infer the copy number profile from the Hi-C contact maps (see "Methods" section). Although in theory, all sequencing reads are useful to estimate the copy number profile, we first validated that using the counts from the contact maps (i. e. the subset of valid interaction products) is sufficient. For this, we compared our estimated copy number profile from MCF7 and T47D Hi-C contact maps, with the results of the Control-FREEC software  [31]   reads to call the CNVs (Additional file 1: Figure  S4 ).
We observe a good correlation between both methods (MCF7 spearman cor=0. 888, T47D spearman cor=0. 855) therefore validating our approach. We then applied our segmentation strategy to our Hi-C simulated data (Fig. 3b ). In order to assess the robustness of this approach, we further simulated 100 additional data sets with distinct CNVs profiles. Results on this larger number of data sets show a 91% recall and a precision of 62. 4%. In particular, we observe that the precision of breakpoint detection can sometimes be lower in highly amplified regions. Finally, we also applied our segmentation procedure to the IMR90 diploid data set. As expected, we obtain a nearly uniform copy number profile (Additional file 1: Figure  S6c ). Interestingly, we frequently observe a decrease of contacts at telomeric regions which therefore results in a breakpoint in the segmentation.
In addition, we calculated the effective fragment length, the GC content and the mappability features for each 500 Kb bin as already proposed  [26] , and then represented the average contact frequencies among those genomic features. Despite a few local enrichments due to CNVs, we observe that LOIC normalization is as effective as ICE normalization for correction of GC content, effective fragment length and mappability (Additional file 1: Figure  S5 ). We then turned to the aneuploid simulated data set. In this case, LOIC enables conservation of the inter-chromosomal scaling factor due to CNVs. Differences in intra-chromosomal maps between ICE and LOIC remain negligible and are related to the segmentation profile (Additional file 1: Figure  S6a, b ). In conclusion, the LOIC strategy presented here can be seen as a generalization of the ICE method allowing to correct the Hi-C maps for systematic biases while keeping the copy number signal.
We applied CAIC normalization to the two simulated data sets. Looking at the 1D signal of the CAIC normalized data using the cis and trans data validates that the method tends to remove the CNV effect (Fig. 4a ). In addition, the unbalanced effect that we previously observed with the ICE normalized data disappeared. We then divided the normalized contact matrices by the expected count matrices, thus removing the structure due to genomic proximity. Taking the average per block, we observe that the expected CAIC matrices are much more uniform than the ICE normalized matrices (Additional file 1: Figure  S7  and S8 ). On the aneuploid simulated data set, it is worth noting that ICE and CAIC yield very close results in cis. We then compared the normalized contact maps to the ground-truth by computing the error matrix as well as three additional error measures (see Fig.
5a , Additional file 1: Figure  S9a ). In order to estimate the copy number signal from these cell lines, we segmented the 1D Hi-C profile as previously described. Interestingly, on both T47D and MCF7 data we observed a very good correlation between the copy number signal extracted directly from the Hi-C data and the copy number profile extracted from SNP6 Affymetrix array (Fig. 5b , Additional file 1: Figure  S9b , Spearman cor=0. 87 for both MCF7 and T47D data) We then applied the LOIC strategy presented above, so that the sum of each column/row follows the segmentation profile extracted from the data. As expected, we observe that the LOIC normalized contact maps conserves the a b Fig. 4  CNV-adjusted normalization of cancer Hi-C data. a. Hi-C contact maps of the four first chromosomes of our highly rearranged simulated data, together with the 1D signal of all, cis and trans data.
Regions in red and green correspond to simulated gains and losses. b. Block-average error matrix of simulated ICE and CAIC Hi-C data. The CAIC efficiently removed the CNV effect, whereas the ICE normalization does not allow to correct for its effect copy number properties, and that the biases introduced by the ICE normalization no longer hold true. In addition, we also applied the CAIC normalization to correct for CNVs signal. Looking at the correlation between Hi-C counts and the copy number signal validates the efficiency of the methods (Fig. 5c , Additional file 1: Figure  S10 ). In conclusion, applying the LOIC and CAIC methods on both cancer data set allows us to correct for systematic bias while conserving or removing the copy number structure.
Normalization of capture-Hi-C data with genomic duplication In addition to cancer data, we also investigated the relevance of LOIC for the normalization of Hi-C data in samples with local structural events. Recently, Franke et al. [10]  investigated the effect of local duplications on chromatin structure and, in particular, on the formation of new TADs. We processed the capture Hi-C data across the Sox9 locus and generated the raw and ICE contact maps at 10kb resolution. We focused our analysis on samples with inter-TAD (dup-S) and intra-TAD (dup-L) duplications. In the context of an inter-TAD duplication, Franke et al. [10]  described the formation of a new domain in the duplicated region by comparing the raw contact maps of wild type (WT) and dup-L samples (Fig. 6c-d ). Interestingly, when we compared the WT sample and the samples with the duplication events normalized by the ICE method, we observed that the ICE normalized maps do not allow duplication effects to be observed clearly.
Discussion Chromosome conformation techniques provide a means to investigate links between the 3D organization of the genome and biological processes such as the functional and phenotypic effects of genomic variation in disease. Unsurprisingly, structural and copy number variations have also been observed to affect the genome architecture of cancer cells, perturbating TADs as well as the cell's regulation  [18, 35, 36] , and as a result, the contact count maps of the genome. How do such genomic rearrangements impact on the performance of existing pipelines? In order to better understand how large copy number variations can affect Hi-C data, we first propose a simple simulation model. From an existing diploid data set, our model is able to predict the effects of large copy number changes on interaction patterns by estimating a subsampling coefficient that can be applied in the presence of copy number variation.
In addition to providing us with a theoretical framework to assess the enrichment and depletion of contact counts with respect to copy number variations, such a model can be used to assess how normalization methods and downstream analyses are affected by copy number variation. Our model is nevertheless simplistic and can be elaborated upon. For example, we consider that duplicated regions are non-tandemly rearranged events, which, in some cases, certainly underestimates the intra-chromosomal effect of copy number. In addition, the model does not integrate any biological knowledge and is therefore not designed to simulate changes due to the alteration of regulatory elements such as insulator regions. We also note that the subsampling strategy that we have applied here requires a high resolution diploid Hi-C data set as an input.
However, as Hi-C sequencing depth increases, this should no longer be a limitation. In our study, we go on to demonstrate that applying ICE (the most commonly used normalization method on Hi-C data) to data sets with abnormal copy number profiles leads to unbalanced corrections between amplified and lost regions and between inter and intra-chromosomal contacts. This unwanted effect can then lead to problematic results in downstream analysis (for example, in the identification of open and closed compartments). We therefore propose two new normalization methods that can remove systematic biases: LOIC, which preserves the effect of copy number variation, and CAIC, which removes the effects of copy number. In order to achieve this, both normalization methods require the identification of breakpoints.
We thus also propose a segmentation procedure to directly extract the copy number signal and the location of breakpoints from the Hi-C contact maps. Although this step is crucial for the normalization and can be challenging for noisy samples, our procedure performs well on all the data set used in this study, including the normal diploid IMR90 sample. It also confirms that the Hi-C technique could become in the near-future a powerful approach to infer CNV profile in tumors. Both CAIC and LOIC methods perform well on simulated and real Hi-C data sets, each fulfilling different needs. From a methodological point of view, we note that our assumption that the CNV effect is constant for each block delimited by a copy number change, cannot always be fulfilled, especially for very large genomic alterations.