dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulation setup We implemented our simulator in Python. It includes a generic packet network simulator (scheduler, link, packet transmission), on top of which we developed an implementation of the proposed MICN protocol, and lightweight reimplementations of NDN and NetCodCCN (capturing the main semantics of these protocols as described in  [3]  and  [18] ). We experimented protocols over two topologies: one simple illustrative butterfly topology and a second, more elaborate topology close to the PlanetLab topology from NetCodCCN  [18]. At the link level, the parameters our simulations are a propagation delay of 0. 1 time unit for each packet, a transmission time of 1 time unit for data packets, and a very small transmission delay for interest packets (1/2 14 6 × 10 -5 ). A small amount of uniformly distributed transmission jitter (between 0 and 1/2 18 3.
Results with the butterfly topology We first analyze the behavior of MICN on a simple butterfly topology with two sources S 1 and S 2 and two clients U 1 and U 2 connected through a set of intermediate caching routers as represented in Fig. 6. ets transmitted on all the links of the network, counted from time t = 0. The curves end when transmission of data packets stops. In the beginning, there is only innovative traffic in the network, i. e. , data packets which are innovative for the routers or the client node receiving them. While towards the end, even after the clients have received the entire generation, the outlying interests continue to generate data traffic that is just redundant. MICN-IC deletes the interests tagged with low priority, which are pending even if a client has access to content for those interests.
Canceling such interests reduces the redundant data traffic, at the price of some signaling overhead. Precisely, in the butterfly topology (Fig. 6 ), 10 transmissions of data packets over various links are necessary for delivering 2 data packets to the clients U 1 and U 2 , i. e. , 5 transmissions per packet. For a generation of size 100, a minimum of 500 transmissions are required for both clients to receive the entire generation. Fig. 8  shows that with MICN-IC, a slightly larger amount of transmissions are required. NetCodCCN achieves similar download performance, but interests are not canceled and several data packets are redundant, leading to increased traffic. The effect of sending consecutive interests by clients is analyzed in Fig. 9. To have a continuous flow of content in the butterfly network (in the absence of losses), the clients need to have at least two outstanding interests at any time (because there are two paths) and usually even more because of the propagation delays.
MICN and MICN-IC appear to have much better performance compared to NetCodCCN. MICN has the advantage of precisely identifying which interest (pointing to a subset A i ) has timed-out (no matching content received). In NetCodCCN, even if a downstream data packet is lost, the router will consider the interest satisfied. An interest repeated due to time-out is considered a new interest, and the router will typically forward it. In MICN, the repeated interest will be immediately satisfied by the router's cache.
Conclusion In this work, we propose a novel way of integrating NC and informationcentric networking. The proposed MICN protocol is built around the MILIC construction that allows the clients to request content that belongs to predefined subsets by adding an index in the interest, that indicates the subset. This interest naming allows the nodes to send multiple interests in parallel and ensures that linearly independent content satisfies each interest. In the considered scenarios, the clients download content close to their maximum capacity (like NetCodCCN). Nevertheless, thanks to interest cancellation, MILIC-IC limits the redundant data traffic considerably. This reduces the network load and leaves earlier free network resources to fetch contents from consecutive generations. Our future research includes investigating improved interest forwarding algorithms to use the multiple active links better and reduce the data traffic by adjusting the number of outgoing interests.
Acknowledgements. The authors thank  Bertrand Chapron  for helpful discussions. The authors appreciated the feedback from the anonymous reviewers and  John M. Huthnance  (editor), which helped to improve this work. Simulations were performed using the HPC facilities DATARMOR of " Pôle de Calcul Intensif pour la Mer " at Ifremer, Brest, France. Finally authors thank their respective PhD supervisors  Fabrice Ardhuin  and  Xavier Carton  for their thesis guidance over the past few years. Financial support. This research has been supported by the  Centre National d'Etude Spatiale , focused on the SWOT mission and the  Region Bretagne  through the  ARED program. CdM is funded by  Direction Générale de l'Armement (DGA). Review statement. This paper was edited by  John M. Huthnance  and reviewed by  Momme Hell  and two anonymous referees.
Sample descriptions Generation Scotland: Scottish Family Health Study (GS) GS is a family-based population cohort with over 24,000 participants  [19, 20]  set up to identify the causes of common complex disorders, such as depression. DNAm data and genetic data were both collected, processed and quality-checked for 8898 people (mean age = 49. 8 years, SD of age = 13. 7 years, 40. 90% were men) in two sets. Sample sizes for set 1 and set 2 were 4757 (mean age = 48. 5 years, SD of age = 14. 0 years, 38. 5% were men) and 4141 (mean age = 51. 4 years, SD of age = 13. 2 years, 43. 66% were men), respectively. Written informed consent was obtained for all participants. The study was approved by the NHS Tayside Research Ethics committee (05/s1401/89).
Lothian Birth Cohort (LBC)1921 and LBC1936 Participants from LBC1921 and LBC1936  [21, 22]  were born in 1921 and 1936. Almost all lived in the Edinburgh and surrounding Lothian area when recruited. They are a mostly healthy, community-dwelling sample of men and women. The sample used in the current analysis included 1330 participants from both cohorts combined with genetic and DNAm data (LBC1921: mean age = 79. 1 years, SD of age = 0. 6, 39. 7% were men; LBC1936: mean age = 69. 6 years, SD of age = 0. 8, 50. 6% were men; all participants were unrelated). Written informed consent was obtained from all participants. Ethics permission for LBC1921 was obtained from the Lothian Research Ethics Committee (LREC/1998/4/183). Ethics permission for LBC1936 was obtained from the Multi-Centre Research Ethics Committee for Scotland (MREC/01/0/56) and the Lothian Research Ethics Committee (LREC/2003/2/29)  [25, 26].
Avon Longitudinal Study of Parents and Children (ALSPAC) ALSPAC is an ongoing longitudinal population-based study that recruited pregnant women residing in Avon (South-West of England) with expected delivery dates between 1st April 1991 and 31st December 1992  [23, 24]. The cohort consists of 13,761 mothers and their partners, and their 14,901 children, now young adults  [27]. The study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool (http:// www. brist ol. ac. uk/ alspac/ resea rchers/ our-data/). Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. A subsample of 719 unrelated mothers with DNAm data (mean age = 47. 2 years, SD of age = 4.
DNAm data GS Genome-wide DNAm data was obtained from wholeblood samples using the Illumina Infinium Methylation EPIC array (https:// emea. suppo rt. illum ina. com/ array/ array_ kits/ infin ium-methy latio nepic-beadc hip-kit. html). Data processing was performed separately for each set. Quality control (QC) and normalisation were conducted using R packages 'ShinyMethyl' (version 1. 28. 0) [34] , 'watermelon' (version 1. 36. 0) [35]  and 'meffil' (version 1. 1. 1) [36]. Details of the protocol are described elsewhere  [37]. In summary, quality control procedures removed probes if there was an outlying log median methylated signal intensity against unmethylated signal for each array, or a bead count <3 in ≥5 % of the total probe sample, or a detection p-value >0. 05 for set 1 and p-value >0.
LBC1921 and LBC1936 Genome-wide DNAm data was obtained from blood sample using the HumanMethylation450K array (https:// emea. illum ina. com/ conte nt/ dam/ illum ina-marke ting/ docum ents/ produ cts/ datas heets/ datas heet_ human methy latio n450. pdf )  [41, 42]. Quality control and normalisation were performed using the 'minfi' R package (version 1. 38. 0) [41]. Probes with low call rate (<95%), outlying M-values (>3 SD from mean) or identified as cross-hybridising and polymorphic were removed  [43]. Participants with insufficient cell count information were excluded from analysis. All participants in LBC1921 and LBC1936 with methylation data were unrelated. M-value transformation were conducted consistently with the GS sample. Data for 409,319 CpG probes were retained for further analysis.
ALSPAC Illumina Infinium HumanMethylation450 Beadchip arrays were used for measuring genome-wide DNAm data from peripheral blood samples  [28]. The R package 'meffil' (version 1. 1. 1) was used for pre-processing, quality control and normalisation as previously reported  [36]. Further removal of probes was conducted based on background detection (p > 0. 05) and if they reached beyond the 3 times inter-quantile range from 25 to 75% or identified as cross-hybridising or polymorphic  [43]. Related (IBD >0. 1) participants were not included in the analyses  [30]. M-value transformation was conducted. In total, 481,600 and 449,595 CpG probes remained for analysis on ALSPAC adults and children, respectively.
Finally, an additional replication MWAS was conducted on ALSPAC children. All analyses were conducted using R (version 3. 5. 1) under Linux environment. Linear regression was used to test the associations between depression-PRS and for each CpG using the R package 'limma'  [45]  (version 3. 48. 0) for GS, LBC1921 and LBC1936. The 'lmFit' function was first implemented to test the association for each CpG. The inference statistics of each linear model was then adjusted using the 'eBayes' function, by which an empirical Bayes method was used to adjust for gene-wise variance using a shrinkage factor. Moderated t-statistics and p-values were produced by this step. In ALSPAC, the analyses were conducted using the 'meffil' (version 1. 1. 1) R package, using the 'sva' option  [36]. Self-reported smoking status, smoking pack years, DNAm-estimated white-blood cell proportions (CD8+T, CD4+T, natural killer cells, B cells and granulocytes)  [37] , batch, the first 20 principal component derived from the M-values, the first 10 principal components derived from the imputed genetic data, age and sex were included as covariates for the discovery methylome-wide association analysis (see Additional file 1: Supplementary Methods and Additional file 1: Table  S3  for details).
Colocalisation analysis We used Howard et al. 's depression GWAS  [4]  for depression-associated SNPs and GoDMC summary statistics for methylation quantitative trait loci (mQTL) analysis with PGC studies and GS study removed, which resulted in 32 remaining studies imputed to the 1000 Genome reference panel  [13]. We used the package 'gwasglue' (version 0. 0. 0. 9000, https:// mrcieu. github. io/ gwasg lue/)  [48]  to extract SNPs that were ± 1 Mb of each of the 102 genome-wide significant, lead SNPs identified in Howard et al. [33]  and then extracted the same SNPs within those regions from the GoDMC mQTL analysis. We used the coloc. abf function with default parameters in the 'coloc' package in R (version 5. 1. 0) [49]  to perform colocalisation analysis for each SNP association. The method tests for five mutually exclusive scenarios in a genetic region: H 0 : there exist no causal variants for either trait; H 1 : there exists a causal variant for trait one only; H 2 : there exists a causal variant for trait two only; H 3 : there exist two distinct causal variants, one for each trait; and H 4 : there exists a single causal variant common to both traits.
MR Three MR methods, inverse-variance weighted (IVW), weighted median (WM) and MR-Egger, were used to test for causal effects between DNAm and depression using the 'TwoSampleMR' R package (version 0. 5. 6) [50, 51]. GWAS summary statistics for DNAm were from GoDMC and GS. mQTL summary statistics from GoDMC included 32 cohorts with 25,561 participants from European ancestry  [13]. The summary statistics were computed using a two-phased design. First, every study performs a full analysis of all candidate mQTL associations, returning only associations at a threshold of p < 1 × 10 -5. All candidate mQTL associations at p < 1 × 10 -5 are combined to create a unique 'candidate list' of mQTL associations. The candidate list (n = 120,212,413) is then sent back to all cohorts, and the association estimates are obtained for every mQTL association on the candidate list.
Candidate mQTL associations were metaanalysed using fixed-effect inverse-variance method. Details of the database can be found elsewhere  [13]. mQTL summary statistics from GS (N = 8898) included a full set of all SNPs with no p-value thresholding. Summary mQTL statistics from GS were generated using the OmicS-data-based Complex Trait Analysis package (https:// cnsge nomics. com/ softw are/ osca/# eQTL/ mQTLA nalys is)  [52]. Covariates were consistent with the MWAS for depression-PRS discovery analysis. Further details of the mQTL analysis can be found in the Additional file 1: Supplementary methods. Summary statistics for depression GWAS by Howard et al. [33]  were used. A total of 807,553 unrelated, European participants were included in the analysis. Details for the study can be found elsewhere  [33].
GoDMC, GS and depression GWAS samples were mutually exclusive. Individual cohorts that overlapped with the Howard et al. depression GWAS and GS were removed from the GoDMC mQTL meta-analysis. Depression GWAS summary statistics from Howard et al. (2019) were calculated excluding GS participants  [33]. See Additional file 1: Supplementary methods for details. First, we used mQTL summary statistics from GoDMC to identify causal effects from DNAm to depression. Second, we used full mQTL summary statistics from GS to replicate the findings and to test the causal effect to DNAm and depression bi-directionally. Finally, in contrast to the univariable MR analyses (that is, each riskoutcome pair tested separately), a multi-variable MR analysis was conducted to test for direct causal associations from DNAm at multiple CpGs to depression, using the mQTL data from GS.
Using this method, all CpG probes where there was evidence of a potential causal effect on depression were entered into the two-sample MR analysis simultaneously, in order to prioritise SNPs that showed the strongest independent casual associations with depression. Exposures were selected from CpG probes found significantly associated with depression-PRS generated using the p-threshold = 5 × 10 -8. The probes were further removed from analyses if they did not present in the GoDMC/GS mQTL data or had <5 independent genetic instruments overlapping with the outcome summary statistics. In result, 15 probes were selected for final analyses. Scripts for all the above analyses can be found in the GitHub repository: https:// github. com/ xshen 796/ MDD_ PRS_ MWAS  [53]. A detailed summary for the directories of each individual analysis can be found in the URL: https:// github.
DNAm association with depression-PRS at a GWAS p-value association threshold of 5 × 10 -8 The most significant associations of DNAm with depression-PRS were found in the major histocompatibility complex (MHC) region (25-35 Mb on Chromosome 6, Fig. 2 ), with 45/71 (63. 4%) of significant associations within this region (p Bonferroni ranged from 0. 03 to 7. 28 × 10 -11 ). The top ten probes that showed the greatest associations are listed in Additional file 1: Table  S5  (all p Bon- ferroni < 8. 62 × 10 -8 ). After pruning (r < 0. 1 for at least two nearest probes, window = 3 Mb), the top CpG probe identified within the MHC region was cg14345882 (all p Bonferroni = 7. 28 × 10 -11 ). UCSC gene database annotation shows genes that are nearest to the significant probes in the MHC region are, for example, TRIM27, HIST1H2AI and BTN3A2.
Statistical evidence was stronger for the causal effect from DNAm to depression compared to the opposite direction, despite that more genetic variants were used for the reversed causal effect, and thus statistical power was greater (N SNP for DNAm to depression ranged from 4 to 22 and N SNP for depression to DNAm was 122). The highly consistent methylome-wide associations found across adults and adolescents may indicate that early genetic influence on DNAm result in a predominantly directional effect from DNAm to depression  [70]. The present study utilises large samples with replication analyses yielding highly consistent results. One limitation for interpreting the current findings is that DNAm data was collected from blood samples that may not reflect the most relevant cell types in depression.
This work is supported by two Wellcome Trust grants to AMM ( Investigator Award in Science: 220857/Z/20/Z and Strategic Award 104036/Z/14/Z). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. In addition, DNAm profiling was supported by funding from NARSAD (Ref 27404, DMH) and the Royal College of Physicians of Edinburgh (SIM Fellowship, HCW). Genotyping of the GS samples was funded by the MRC and Wellcome Trust [104036/Z/14/Z]. GS also receives support from the Chief.
Acknowledgements We thank the participants and team members for their ongoing contribution to the recruitment, data management and technical and legal support for GS. We are grateful to all the participants and team members of  LBC1936  and  LBC1921. We thank all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses.
Finally, we extended this method to the characterization of POLE VUS. Results: For the 23 variants that were unclassified or for which conflicting interpretations had been reported, 15 were classified as functionally normal and 6 as functionally abnormal. Another two variants were found to have intermediate consequences, both with potential impacts on splicing. We then compared these scores to the patients' responses to PARP inhibitors when possible. Finally, to prove the application of our method to the classification of variants from other tumor suppressor genes, we exemplified with three POLE VUS. Among them, two were classified with an intermediate functional impact and one was functionally abnormal. Eventually, four POLE variants previously classified in databases were also evaluated.
Background Over the last two decades, the therapeutic options available in oncology have evolved towards therapies targeted based on tumoral genetic information  [1]. These new treatments improve patient outcomes and have fewer adverse effects. However, the functional significance of many variants of targetable genes remains unknown. In the ClinVar  [2]  database, 237 934 variants of uncertain significance (VUS) are registered for the total set of genes considered (32 951 genes represented). Most are missense variants and potential splice site variants. With recent technical improvements and the development of whole-exome and whole-genome sequencing, this number of VUS is likely to rise still further in the coming years  [3] , with one in three variants classified as VUS overall, and 80% located on tumor suppressor genes  [4].
7% and 45. 7% of the total currently known BRCA1 and BRCA2 variants are VUS. However, databases, such as ClinVar (https://www. ncbi. nlm. nih. gov/ clinvar/), BRCA exchange (https://brcaexchange. org/), and UMD  [13]  (Universal Mutation Database, http:// www. umd. be/BRCA1/), mostly contain germline variants. Somatic variants, which may be detected in only one or two individuals, also exist and many such variants remain unreported. Functional testing in vitro is currently based on transcriptional activation  [14, 15] , HR activity  [16, 17] , and splicing  [18] [19] [20]. However, such tests are not necessarily compatible with clinical management in terms of the time taken to obtain results to guide treatment. Thus, CRISPR-Cas9 genome editing is a promising tool for meeting the challenge of VUS classification  [21] [22] [23].
VUS selection and gRNA design First, ten variants of BRCA1, three of BRCA2, and two of POLE were selected for study based on their status as variants of uncertain significance or unclassified variants; 10 well-known variants of BRCA1 (p. Tyr179Cys, p. Cys197=, p. Tyr422X, p. Gln1604X, p. Gln1604=, and p. Pro1812Ala) and BRCA2 (p. Asp935Asn, p. Ser1882X, p. Val2728Ile and p. Gln2829Arg) were also analyzed as controls. All these 25 variants were characterized by NGS in our laboratory during somatic mutation testing in patients. To increase the number of characterized VUS, we also selected 10 variants of BRCA1 (p. Ile31Asn, p. Glu149Ala, p. Val191Asp, p. Gln210=, p. Gly462Arg, p. Arg979Cys, p. Gly1201Ser, p. Thr1394Ile, p. Ala1752-Pro, p. Gly1770Val) and a POLE variant (p. Arg1826Trp) for which conflicting interpretations had been reported in the databases (ClinVar  [2] , OncoKB  [29] ).
Four variants of POLE (p. Ala31Ser, p. Pro286Ser, p. Leu424Val, p. Phe695Ile) were also selected in databases and used as controls (ClinVar, OncoKB). Alt-R CRISPR-Cas9 crRNA (IDT DNA) were designed with the Alt-R Custom Cas9 crRNA design tool (IDT DNA) (https://eu. idtdna. com/site/ order/designtool/index/CRISPR_CUSTOM). The PAM sequence had to be adjacent to the variant to facilitate the editing of KO LIG4 HAP1 cells  [30]. We also selected the gRNA based on the possibility of inserting a silent variant into the PAM sequence or the 3 to 5 nucleotides immediately upstream  [30]. This increases editing efficiency and will be used as a control in subsequent experiments (reference variant). For each variant, we designed two Ultramer DNA Oligos (IDT DNA) of about 84 nt. The first contained the patient's variant (testing variant) and the second contained a silent variant (control variant), already reported to be benign if possible, and therefore functionally normal.
The prepared libraries were then sequenced on an Ion Torrent S5 Sequencer with the Ion 520 and 530 Chef Kit (Thermo Fisher Scientific). Variants of interest were visualized with Integrative Genomics Viewer (IGV)  [31]. Human Genome Variation Society (HGVS)-approved guidelines (http://www. hgvs. org/ mutnomen/) were used. The variants found by NGS were researched in the UMD-BRCA1 or UMD-BRCA2 databases  [13] , the ClinVar database, the BRCA exchange database, and the BRCA Mutation Database (http://www. arup. utah. edu/database/BRCA/Home/BRCA1_landing. php) to assess their classification and functional impact where available.
Sequencing analysis and VUS functional score evaluation Following NGS sequencing, insertions or deletions located around the expected cleavage site, in the eight nucleotides centered on the PAM sequence or the seven nucleotides centered on the VUS, were also counted. Indel frequencies were then calculated by dividing the total amount of indels by the total number of reads. For the evaluation of SNV coverage, the ratio of the total numbers of reads for the VUS evaluated and the control SNV was calculated. Finally, functional scores for all the variants studied were calculated by comparing the sequence frequencies of all the inserted variants (VUS of interest, silent control SNV, and silent reference SNV) and the results contained in the available databases (UMD database, ClinVar, BRCA exchange).