We discover that m6A ‘writer’ METTL3 facilitates L1 retrotransposition, whereas m6A ‘eraser’ ALKBH5 suppresses it. The essential m6A cluster that is located on L1 5′ UTR serves as a docking site for eukaryotic initiation factor 3 (eIF3), enhances translational efficiency and promotes the formation of L1 ribonucleoprotein. Furthermore, through the comparative analysis of human- and primate-specific L1 lineages, we find that the most functional m6A motif-containing L1s have been positively selected and became a distinctive feature of evolutionarily young L1s. Thus, our findings demonstrate that L1 retrotransposons hijack the RNA m6A modification system for their successful replication.L1 retrotransposons can pose a threat to genome integrity. The host has evolved to restrict L1 replication. However, mechanisms underlying L1 propagation out of the host surveillance remains unclear. Here, we propose an evolutionary survival strategy of L1, which exploits RNA m 6A modifications on L1 RNA increase translation efficiency and retrotransposition in human cells. M6A motifs are more enriched in evolutionary young L1s.L1 is a group of active retrotransposons in humans. Here the authors show that m The average human genome contains 80–100 copies of retrotransposition-competent L1s3. A retrotransposition-competent L1 is 6 kb in length and consists of a 5′ untranslated region (UTR) containing an internal promoter4, two open reading frames (ORF1 and ORF2), and a short 3′ UTR. ORF1 encodes a nucleic acid chaperon protein (ORF1p)5, while ORF2 encodes a protein with endonuclease and reverse transcriptase activity (ORF2p)7. ORF1p and ORF2p associate preferentially with their parental mRNA to form an L1 ribonucleoprotein (RNP) particle8. The L1 RNP enters the nucleus and then generate the progeny through de novo insertion of its cDNA10. The mobility of L1s contributed to a source of genetic variation, but also pose a threat to genome integrity13. Although several host factors have evolved to suppress L1 retrotransposition, the youngest L1 subfamilies are still active and replicated continuously16. To date, however, the mechanism of how L1s have propagated under host surveillance remains unknown.Long interspersed element-1 (L1) is currently an active autonomous retrotransposon, and constitutes ~17% of the human genome6A) is the most prevalent internal modification in eukaryotic mRNAs, which determines RNA function and fate17. Several enzymes dynamically process the m6A modification of mRNA. The methyltransferase-like enzyme METTL3, which is the catalytic subunit of the RNA methyltransferase complex, adds m6A at the consensus motif DRAmCH 19. Conversely, m6A is removed by the demethylases α-ketoglutarate-dependent dioxygenase AlkB homolog 5 (ALKBH5) or fat mass and obesity-associated protein (FTO)21. Emerging studies have revealed that m6A modifications in viral transcripts affect the gene expression and replication of viruses such as HIV-122. Despite the critical role of m6A in pathogenic viral transcripts, it remains unclear whether m6A participates in the regulation of the endogenous parasites, L1 retrotransposons.N6-methyladenosine  for efficient translation and promotes the formation of L1 RNP, which are essential for L1 mobility. Lastly, we traced a recent episode of human- and primate-specific L1 evolution and revealed that the most functional m6A site (A332 residue in L1 5′ UTR) first appeared ~12 million years ago. During the primate evolution, A332 m6A-positive L1s have been selected and became a distinctive feature of evolutionarily young L1s, which suggests that the acquisition of m6A motif has acted as an evolutionary driving force for L1 retrotransposons.Here, we show that L1 retrotransposon exploits m6A modification affects L1 retrotransposition, we evaluated the effects of the RNA m6A machinery on L1 retrotransposition using a cell-based engineered L1-reporter assay23. For the assay, we used the pJJ101-L1-dn6 2.2 construct (hereafter referred to as pL1Hs) that contains a blasticidin S deaminase gene (mblastI) within the 3′ UTR antisense to the SV40 promoter25  in HeLa cells and transfected pL1Hs vector. In METTL3-depleted cells, the number of blasticidin S-resistant colonies, which represent successful L1 retrotransposition, was reduced by >2-fold compared to that of control siRNA . As anticipated, ALKBH5 could successfully restrained L1 mobility to levels that were comparable to that suppressed by a reverse transcription inhibitor , whereas ALKBH5H204A overexpression did not result in the restriction of L1 mobility  using human embryonic stem cells (H9 hESCs) that express endogenous L1 at sufficient levels30. Through qRT-PCR analysis of the MeRIP eluates, we detected the enrichment of L1 RNA at a level comparable to that for known m6A-modified SON and CREBBP mRNA, but much more than negative control HPRT1 mRNA 33. SRAMP analysis revealed that the 9 peaks found in the ORF1, ORF2, and 3′ UTR regions do not contain m6A motifs, and that only the 6 peaks located at 5′ UTR have potential m6A motifs  into HeLa cells treated with m6A machinery-targeting siRNA and monitored ORF1p expression. Intriguingly, the knockdown of m6A enzymes did not affect ORF1p expression in the absence of 5′ UTR  through MeRIP-seq analysis in either hESCs or L1-reporter-expressing HeLa cells  markedly inhibited L1 mobility  induced approximately 50% decline in L1 mobility compared to that induced by the wild-type L1  and MALAT1 (abundant in the nucleus), over 80% of the L1 mRNA was present in the cytoplasmic fraction and the m6A-deficient mutation did not affect the cellular localization of L1 RNA  of eIF3 subunits a, b, d, and g38. By mapping the reads from PAR-CLIP of the eIF3 subunits along the endogenous L1Hs, we revealed that eIF3 exhibits preferential binding to the L1 5′ UTR  to gauge the reverse transcriptase activity of ORF2p41 . In L1PA2 and younger lineages, the number of A332 m6A positive L1s increased drastically (92.9%)  first appeared in L1PA3 or older lineages, which indicates that the productive potential of m6A has allowed positive selection of A332 m6A-positive L1s during the evolution from the common ancestor.Over the last 40 million years of human evolution, L1 subfamilies have frequently acquired novel 5′ UTRs9%) Fig. , and the9%) Fig. . On the 9%) Fig. . We inve600 Fig. . As in ttif Fig. . In summ6A acquisition in ancestral L1 5′ UTR, we generated a chimeric pL1 construct that contained L1PA2 5′ UTR and L1Hs ORF1/2 with the mblastI reporter  did not exert the same effect  that was obtained in the past 12 million years. This suggests the potential role of m6A as a driving force in L1 evolution , which have evolved with L1s, suppress the old L1 transcription in a sequence-specific manner16. However, L1Hs, which is the youngest L1 lineage in the human genome, escapes KRAB-ZFP restriction and is not recognized by any KRAB-ZFPs16. Instead, the host defense utilizes post-transcriptional suppression mechanisms, such as small RNA interference  or APOBECs, to restrict the replication of L1s56; however, the youngest L1s are still active. Our findings provide clues on how the youngest L1s continuously replicate under host surveillance. The emergence and the propagation of the A332 m6A-positive L1s suggest that 5′ UTR m6A modification was a countermeasure against the host post-transcriptional restriction.L1s have been continuously active since the origin of mammals2.HeLa cells were cultured in DMEM supplemented with 10% (v/v) fetal bovine serum  and 1% (v/v) GlutaMAXI (Gibco). Human embryonic carcinoma PA-1 cells were cultured in RPMI 1640 supplemented with 10% (v/v) FBS (HyClone) and 1% (v/v) GlutaMAXI (Gibco). hESCs  were cultured in defined hESC culture medium (Stem Cell Technology) on hESC-qualified extracellular matrix (Corning)-coated culture dishes (Corning) or on tissue culture wall plate . The cultures were incubated at 37 °C in 5% COXbaI and PmeI (NEB). The site-directed mutagenesis of FH-ALKBH5 to catalytically inactive mutant (H204A) construct was performed using the Phusion High-Fidelity polymerase kit (Thermo Fisher Scientific).The FLAG-HA-pcDNA3.1-derived plasmids used in this study were named using FH as a prefix with the respective protein names specified. AcGFP, ALKBH5, and FTO cDNA were cloned into FLAG-HA-pcDNA3.1  for overexpressing N-terminally FLAG-HA-tagged protein. FH-based plasmids were generated by restriction enzyme cloning using 25. For the mutagenesis of L1 5′ UTR m6A sites, the L1-dn6 5′ UTR and ORF1 region containing NotI and AgeI restriction sites was recloned into plasmid pCMV14. Using site-directed mutagenesis PCR, the following mutants of pCMV14 L1 5′ UTR ORF1 plasmids were prepared: Δ5′ UTR, A332T, A495T, A600T, A332/600T, and A332/495/600T (named as m6A mut). Next, NotI-AgeI fragments of pCMV14 L1 5′ UTR ORF1 mutant constructs were amplified and subcloned into pL1Hs. To generate pL1PA25′ UTR, we synthesized the L1PA2 5′ UTR region based on reported consensus sequences using gene synthesis (Cosmogenetech). We then replaced the L1Hs 5′ UTR of pL1Hs with L1PA2 5′ UTR, as described above.pJJ101-L1-dn6 2.2, which is referred to as pL1Hs in this study, is a pCEP4-based plasmid that contains an active human L1(L1-dn6) and was generously provided by J. L. Garica-Perez45. pAD3TE1 was gift from Aurélien J. Doucet. The generation of L1 5′ UTR m6A mutant constructs of pAD3TE1 was performed according to the method for pL1Hs mutant construct generation.pAD3TE1 is an L1.3 plasmid containing the T7 gene 10 epitope tag on the carboxyl-terminus of ORF1p, TAP tag on the carboxyl-terminus of ORF2p, and 24 copies of the MS2 loop repeat in the 3′ UTR26. pYX014 encodes L1 constructs under the L1 native 5′ UTR promoter. pYX015 carries a retrotransposition-defective mutation in L1 ORF1. pYX014 and pYX015 plasmids contain a Renilla luciferase cassette to normalize transfection efficiency levels. To generate pYX014 L1Hs and m6A mut constructs, NotI-PmlI fragments that were 2166 bp in length, including those spanning from the L1 5′ UTR to the forepart of ORF2 in pL1Hs and pL1 m6A mut, were subcloned into pYX014 via restriction enzyme cloning.L1-firefly luciferase-tagged plasmids pYX014 and pYX015 were gifts from W. AnL1-neo-TET, a codon-optimized synthetic L1 construct, was generously provided by Astrid Roy-Engel . The L1-neo-TET lacks a 5′ UTR. To generate a 5′ UTR-containing L1-neo-TET construct, the 5′ UTR of pL1Hs was amplified using PCR and the amplicon was inserted downstream of the CMV promoter of L1-neo-TET.Renilla luciferase gene encoded by pYX014. Site-directed mutagenesis was used to generate the following m6A motif-abrogating mutants of pFR-based plasmids: A332T, A495T, A569T, A600T, A679T, A758T, A839T, A332/600T, and A332/495/600T.pFR-L1Hs 5′ UTR plasmids were generated by restriction enzyme cloning. The L1 5′ UTR of pL1Hs and firefly luciferase of pGL3-Basic (Promega) were cloned into pCMV14 downstream of the CMV promoter. Thereafter, the neomycin-resistant gene located downstream of the SV40 promoter was substituted with pDEST HA-derived plasmids were named using HA as a prefix with the respective proteins, YTHDF1 and YTHDF2. YTHDF1 and YTHDF2 cDNA were cloned into pDEST HA vector using pENTR/D-TOPO vector (Invitrogen) and Gateway® LR Clonase® II Enzyme mix (Invitrogen). HA tag sequence is located in 5′ end of insert for overexpressing N-terminally HA-tagged protein.siRNAs directed against METTL3 (L-005170-02), ALKBH5 (L-004281-01), FTO (L-004159-01), or nontargeting siRNAs (D-001210-01-50) were purchased from Dharmacon. All siRNA transfections were performed using the DharmaFECCT 1 transfection reagent (Dharmacon) according to the manufacturer’s instructions.57. Uncropped blots were provided in the source data file.The cells were lysed in RIPA buffer , 0.5% sodium deoxycholate, 0.05% SDS, 1 mM EDTA, 1 mM DTT) supplemented with cOmplete protease inhibitor cocktail (Roche) for 15 min on ice. The lysates were cleared by centrifugation and mixed with Laemmli sample buffer. The mixture was then boiled at 98 °C for 10 min, separated by SDS-PAGE in 10% gels, and transferred onto nitrocellulose blotting membranes (Amersham). The membranes were blocked by incubating with 5% skim milk in Tris-Buffered Saline Tween-20 (TBST) for 30 min and incubated overnight at 4 °C with the respective primary antibodies at 1:1,000 dilution, except for anti-EIF3b antibody at 1:2000. Subsequently, the membranes were washed thrice with TBST and incubated with HRP-conjugated secondary antibodies  at 1:5000 dilution in 5% skim milk/TBST. After washing thrice with TBST, the immunocomplexes were imaged using SuperSignal West Pico PLUS Chemiluminescent Substrate (Thermo Fisher Scientific). Band intensity quantification in Supplementary Fig. TM qPCR 2X PreMIX (Enzynomics) was used for subsequent qPCR reactions. The qPCR primers used in this study are listed in Supplementary Table Total RNA was extracted using TRIzol reagent (Invitrogen) according to the manufacturer’s instructions. For the removal of genomic or plasmid DNA, total RNA was treated with recombinant DNase I (Takara) for 1 h at 37 °C, followed by purification using the NucleoSpin RNA Clean-up kit (Macherey–Nagel). cDNA synthesis was performed using the ReverTra Ace qPCR RT Kit (Toyobo) according to the recommended protocol. TOPreal2, 100 μg/ml of cycloheximide) were prepared a day before polysome fractionation. For polysome fixation, 7–10 × 106 cells were incubated for 10 min in a media containing 100 μg/ml of cycloheximide at 37 °C and were collected by scrapping with PBS containing 100 μg/ml of cycloheximide. After centrifugation at 1200 × g, 4 °C for 5 min, the cell pellets were lysed in 100 μl of polysome extraction buffer  supplemented with RNase inhibitor (Enzynomics), protease/phosphatase inhibitor cocktail , and 1 mM DTT. The cells were incubated in the buffer for 10 min at 4 °C and centrifuged at 12,000 × g, 4 °C for 10 min to remove debris and nuclei. The protein concentration was measured using the Pierce BCA Protein Assay kit (Thermo Fisher Scientific). Five hundred to 600 μg of the lysate was introduced at the top of the linear sucrose gradient and centrifuged at 222,000 × g, 4 °C for 2 h using the SW41Ti rotor of the Beckman ultracentrifuge. Fifty microgram of the lysate was saved as input RNA. After centrifugation, 1 ml fractions were collected from the top to the bottom of the gradient using the BioLogic LP system and fraction collector (BioRad) with UV absorbance at 260 nm. Next, 250 μl of each fraction was mixed with 750 μl of TRIzol LS reagent (Invitrogen) and 20 ng of spike-in RNA (synthesized firefly luciferase mRNA). RNA extraction and qPCR were performed as described above. The levels of RNA in each fraction were normalized to those of spike-in RNA and input RNA.Ten milliliter of 10–50% linear sucrose gradients in base solution  at 800 ng per well using Lipofectamine 3000 (Invitrogen) following manufacturer’s instructions. Two days later, 200 μg/ml hygromycin B (Invitrogen) was added to the media to select the transfected cells. Cell selection continued for 4 days, and the hygromycin B-resistant cells were reseeded at 2.5 × 104 per well in a 6-well plate. The next day, blasticidin S (Invitrogen) was added to a final concentration of 8 μg/ml and the cells were cultured for 7–9 days in its presence. The colonies were stained with crystal violet and counted using Colony, version 1.1 (Fujifilm). Retrotransposition assays were performed using RNA interference targeted toward METTL3, ALKBH5, and FTO with slight modifications in the process described above. For this, 6 × 104 HeLa cells were seeded into 12-well plates with 40 nM siRNA-Dharmafect1 (Dharmacon) mixture. After 24 h, the cultures were divided equally and plated into two wells in 12-well plates. The next day, the cells were transfected with pL1Hs at 500 ng per well. Four days after transfection, the cells were plated at 6 × 104 cells per well in 6-well plates and selected using 8 μg/ml of blasticidin S. Retrotransposition assays with the overexpression of AcGFP, ALKBH5, and FTO were performed as described above. Briefly, HeLa cells were transfected 500 ng FH-plasmid and 700 ng of L1 plasmid, and 4 days after transfection, 6 × 104 cells were reseeded into a well in a 6-well plate and selected after treatment with blasticidin S for 7–9 days.HeLa cells were plated at 8 × 104 cells per well in 12-well plates. The next day, the cells were transfected with 800 ng per well of the pFR vector (pCMV-L1 5′ UTR-firefly luciferase) using Lipofectamine 3000 (Invitrogen). Two days later, the transfected cells were harvested and luminescence was measured using the Dual-Luciferase Reporter Assay System (Promega) according to manufacturer’s instruction. Briefly, 250 μl of passive lysis buffer was used to lyse cells in each well in 12-well plates. Next, 20 μl of the lysate was mixed with 100 μl of the Luciferase Assay Reagent II, and the luminescence of firefly luciferase was measured using a microplate luminometer (BERTHOLD). Renilla luciferase activity was subsequently measured after administering 100 μl Stop & Glo Reagent.HeLa cells were plated at 8 × 1034 with some modifications. For each experiment, 1.2 × 106 HeLa cells were plated on two 100 mm dishes each. The next day, the cells were transfected with 6 μg of L1 plasmid per dish using Lipofectamine 3000 (Invitrogen). Two days later, the cells were washed twice with cold PBS, and allowed to form UV crosslinks on ice under 150 kJ/cm2 of UV 254 nm light . The cells were scraped and transferred to PBS and pelleted by centrifugation at 1000 × g, 4 °C for 3 min. The pellets were resuspended in 1 ml of lysis buffer . The lysate was passed through a 21 G needle ten times and shock-frozen using liquid nitrogen. The lysate was thawed on ice and centrifuged at 15,000 × g for 15 min. The supernatant was further cleared by filtering through a 0.22-μm membrane. From each lysate, 5% was retained as input. For immunoprecipitation, 10 μl of Dynabeads Protein G (Invitrogen) was washed twice with lysis buffer and incubated with 3 μg of eIF3b antibody  on a rotating wheel at room temperature for 1 h. The cell lysates were mixed with the antibody-bead complex and rotated overnight at 4 °C. The beads were washed five times in high-salt buffer . The antibody-lysate mixture and the conserved input lysates were resuspended in 100 μl of 1× Proteinase K buffer . Next, 1 mg Proteinase K (Macherey–Nagel) was added into the suspensions. Protein digestion was conducted at 50 °C for 2 h in a shaking incubator. After incubation, 100 μl of 7 M Urea (w/v)-1X Proteinase K buffer was added into the immunoprecipitation samples, and the samples were re-incubated at 50 °C for 2 h in a shaking incubator. RNA was extracted using TRIzol LS supplemented with 20 ng of spike-in RNA.eIF3-RNA CLIP-qPCR was performed as described previously58 with some modifications. HeLa cells were plated on two 100 mm dishes at 1.2 × 106 cells per dish. After 18 h, the cells were transfected with 8 μg of pL1Hs per dish. After 48 h, poly (A) + RNA was extracted using the Poly (A) purist Mag kit (Invitrogen). The poly (A) + RNA was mixed with RNA fragmentation reagents (Invitrogen) and fragmented into oligonucleotide that was 50–150 nt in length by heating to 75 °C for 5 min. Fragmented RNA was purified by ethanol precipitation. Next, 6 μg of fragmented RNA was incubated with 4 μg of anti-m6A antibody  in MeRIP buffer  on a rotating wheel for 2 h at 4 °C. After that, the immunoprecipitation mixtures were mixed with Dynabead protein A (Invitrogen) and incubated overnight on a rotating wheel at 4 °C. After washing five times with MeRIP buffer, RNA was eluted twice by incubating in elution buffer on a rotating wheel for 1 h at 4 °C . The eluted RNA was purified by ethanol precipitation. cDNA libraries were prepared as previously described59. Briefly, RNA was dephosphorylated using calf intestinal alkaline phosphatase (NEB) and labeled with γ-32P-ATP using T4 polynucleotide kinase (Takara). RNA was separated by 10% urea-PAGE and purified from the excised gel corresponding to 50–150 nt RNA fragments. The extracted RNA was ligated to a 3′ adapter using T4 RNA ligase 2, truncated KQ (NEB). The RNA was then purified from free 3′ adapters by repeated gel excision. The 3′ adapter-ligated RNA was ligated to a 5′ adapter using T4 RNA ligase 1 (NEB) and subsequently reverse transcribed using SuperScript III reverse transcriptase (Invitrogen). The cDNA library was amplified by PCR using Phusion HF polymerase (Thermo Fisher Scientific), separated by 6% acrylamide gel electrophoresis, and purified by gel excision. The libraries were sequenced to 2 × 100 base-pair reads on the Illumina HiSeq 2500. The sequence of the 3′ and 5′ adapters, reverse transcription primer, and 5′ and 3′ PCR primers are listed in Supplementary Table MeRIP was performed as described earlier60 (cutadapt -g TACAGTCCGACGATC -A TGGAATTCTCGGGTGCCAAGG). The 3′ and 5′ adapter sequences in the first and second read in a pair (owing to the short insert size) were further trimmed and the read pairs with either reads < 18 bp were discarded. The remaining reads were then aligned to the combined human genome (hg19), and reporter L1 (pL1Hs) sequence using Spliced Transcripts Alignment to a Reference (STAR)61 and peak calling was performed using MACS262. For analyzing the m6A modifications in endogenous L1, the sequence reads from human embryonic stem cells were retrieved31 (accession code: GSE52600) and were aligned against L1Hs consensus sequence using STAR. The codes are available from https://github.com/hastj7373/merip-seq.For MeRIP-seq analysis, the adapters were trimmed using Cutadapt6A mut were transfected into HeLa cells. The following day, the cells were reseeded on sterile coverslips where 200 μg/ml hygromycin B was added for selection. After 3 days, the cells were fixed with 3.7% formaldehyde in PBS for 10 min, and the fixation was quenched by adding 0.1 M glycine in PBS for 10 min. The fixed cells were permeabilized in 70% ethanol for at least 3 h to 1 week at 4 °C. The cells were then rehydrated with PBS for 30 min and incubated in a prehybridization solution  for 30 min at 37 °C. Hybridization was performed overnight at 37 °C in 50 μL of hybridization solution containing 10% formamide, 2× SSC, 10% dextran sulfate, 50 μg yeast tRNA, 0.2% BSA, 0.1 M DTT, 50 units RNase inhibitor (Enzynomics), and 10 ng of MS2-Q670 probe  at 37 °C. Next, the cells were washed twice with a prehybridization solution for 30 min. For the immunofluorescence experiment, the hybridized cells were incubated in the blocking solution  for 1 h, followed by incubation with anti-T7 primary antibody  diluted in the blocking solution (1:200) for 2 h. The cells were washed twice with the prehybridization solution for 15 min and incubated with FITC-conjugated anti-goat secondary antibody  diluted in blocking solution (1:200) for 1 h. The cells were washed twice as described above, and the coverslips were mounted on slide glasses using the Vectashield antifade medium with DAPI (Vector Laboratories). The samples were imaged using an inverted microscope Nikon Eclipse Ti2 equipped with a 1.45 numerical aperture Plan apo λ ×100 oil objective and a sCMOS camera (Photometrics prime 95B 25 mm). For each field of view, stacks of images of 6 μm were captured at every 0.3 μm in the DAPI395, GFP488, and Alexa647 channels using the NIS-Elements software.The L1-MS2-stem-loop constructs pAD3TE1 L1Hs and pAD3TE1 L1 mThe sequence of the RNA FISH probes are: MS2LK20 (5′ TTTCTAGAGTCG ACCTGCAG 3′), MS2 LK51-1 (5′ CTAGGCAATTAGGTACCTTAG 3′), and MS2 LK51-2 (5′ CTAATGAACCCGGGAATACTG 3′). Each probe was labeled with two Quasar 670 dyes at both ends. The mixture of the three probes were used for RNA FISH of L1 RNA tagged with the MS2 loops.57. Protein and mRNA particles from z-stack images were detected using the TrackNTrace software64. After the detection of particles, the protein-mRNA pairs with an intermolecular distance of 330 nm (three pixels) were considered as colocalizing pairs. The intensities of proteins colocalizing with mRNA were determined based on the amplitude of the fitted 2D Gaussian function from the TrackNTrace software.Binary masks of cells were generated using the ROI manager in ImageJ41. Briefly, HeLa cells were plated (4 × 106 cells in 60 mm dishes); the following day, the cells were transfected with 3 μg of L1 plasmid (pL1Hs) using Lipofectamine 3000 (Invitrogen). After 48 h, 200 μg/ml hygromycin B was added to the media to select the cells carrying the L1 plasmid. After 2 days of selection, the cells were lysed with CHAPS lysis buffer , 1 mM MgCl2, 1 mM EGTA, and 10% glycerol) supplemented with 1 mM DTT and the cOmplete protease inhibitor cocktail (Roche) and cleared by centrifugation . The cleared lysates were loaded on a sucrose cushion  and 6 ml of 17% sucrose (from the bottom) solutions) in 13.2 ml Ultra-Clear tubes (Beckman Coulter) and centrifuged at 178,000 × g, 4 °C for 2 h in a SW41Ti rotor of Beckman ultracentrifuge. The colorless pellets were suspended by pipetting in 100 μL of RNase-free water supplemented with 1× protease inhibitors. Pierce BCA Protein Assay (Thermo fisher Scientific) was conducted to determine the protein concentration. Three microgram of the RNP samples were retained and used later in RNA isolation and immunoblotting experiments. Seven hundred and fifty nanogram of each RNP sample was mixed with the LEAP assay reaction buffer , 40 units of RNase inhibitor (Enzynomics), total reaction volume: 50 μl) and incubated at 37 °C for 1 h. One microliter of LEAP cDNA products were subsequently amplified using 0.4 μM of L1 LEAP primer with the Phusion High-Fidelity polymerase kit (Thermo Fisher Scientific). PCR amplicons were separated and visualized in EtBr-stained 2% agarose gel.The LEAP assay was performed as described previously38. Briefly, the authors immunoprecipitated eIF3b from 4-thiouridine-and-UV-treated 293 T cells to capture the eIF3-RNA complex. After high-salt washing and RNase digestion, they separated individual eIF3-RNA complexes through denaturing gel electrophoresis. eIF3a, b, d, and g were identified from four separate bands using mass-spectrometry and the interacting RNAs were purified and sequenced. Although three replicates were generated for each protein, only the first replicate was used for each. After retrieving the raw sequence files from NCBI (accession code: GSE65004), reads with low basecall qualities were excluded using the fastq_quality_filter from FASTX Toolkit . PCR duplicates were also excluded using fastx_collapser. Moreover, we excluded the reads that were shorter than 10-nt after trimming primer IDs and 3′ adapters from further analysis . The remaining reads were mapped to the L1Hs consensus sequence, wherein upto three mismatches were allowed using bowtie265 . For mean coverage analysis of 5′ UTR, ORF1, ORF2, and 3′ UTR, the number of reads that begin and end within each region were counted and the number was divided by the length of the corresponding region. The codes used for analyzing PAR-CLIP and mapping data are available from https://github.com/schanbaek/eif3_par-clip.We utilized previously published PAR-CLIP data for eIF3a, b, d, and g47 with a computational approach. Only L1s of which insertion sites and two flanking regions are supported to be unique to the human, chimpanzee or gorilla genome by BLAT and liftOver were included in the further analyses. Then, we eliminated certain ambiguous elements containing gap sequence in the reference genome data and those that were less than 5.5 kb. The flanking sequences  of each species-specific L1 candidate were manually compared to the orthologous loci in human , chimpanzee , gorilla , and orangutan  genomes. The flanking sequences were used to identify the orthologous positions in the other genomes using BLAST-Like Alignment Tool (BLAT). We collected and retrieved the species-specific full-length L1s. We then classified the L1 subfamilies  using RepeatMasker utility66. Multiple sequence alignment of species-specific full-length L1s in each genome was performed using MUSCLE (MUltiple Sequence Comparison by Log- Expectation) under the default option67. The conserved sequence motifs at the three sites  were visualized using the program Weblogo68. Species-specific L1 loci are listed in Supplementary data To identify the species-specific full-length L1s in human, chimpanzee, and gorilla genome, we used BLAT-based and liftOver-based methodst-test was used for unpaired data. Two-sided Kolmogorov–Smirnov test was used to assess the quantification of number and intensity of colocalizing puncta in Fig. P-values <0.05 were considered significant.GraphPad Prism 7.00 was used for statistical analysis. Two-sided student’s Further information on research design is available in the Supplementary InformationPeer Review FileSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary


Staying up to date and answering clinical questions with current best evidence from health research is challenging. Evidence-based clinical texts, databases, and tools can help, but clinicians first need to translate their clinical questions into searchable queries. MacPLUS FS (McMaster Premium LiteratUre Service Federated Search) is an online search engine that allows clinicians to explore multiple resources simultaneously and retrieves one single output that includes the following: (1) evidence from summaries , (2) preappraised research , and (3) non-preappraised research , with and without validated bibliographic search filters. MacPLUS FS can also be used as a laboratory to explore clinical questions and evidence retrieval.Our primary objective was to examine how clinicians formulate their queries on a federated search engine, according to the population, intervention, comparison, and outcome (PICO) framework. Our secondary objective was to assess which resources were accessed by clinicians to answer their questions.We performed an analytical survey among 908 clinicians who used MacPLUS FS in the context of a randomized controlled trial on search retrieval. Recording account log-ins and usage, we extracted all 1085 queries performed during a 6-month period and classified each search term according to the PICO framework. We further categorized queries into background  and foreground questions . We then analyzed the type of resources that clinicians accessed.P=.51). Queries included a median of 3 search terms (IQR 2-4), most often related to the population and intervention or test, rarely related to the outcome, and never related to the comparator. About half of the resources accessed  were summaries, 24.4% (149/610) were preappraised research, and 24.1% were (147/610) non-preappraised research.There were 695 structured queries, after exclusion of meaningless queries and iterations of similar searches. We classified 56.5% (393/695) of these queries as background questions and 43.5% (302/695) as foreground questions, the majority of which were related to questions about therapy , followed by diagnosis , etiology , and prognosis . This distribution did not significantly differ between postgraduate residents and medical faculty physicians (Our results, from a large sample of real-life queries, could guide the development of educational interventions to improve clinicians’ retrieval skills, as well as inform the design of more useful evidence-based resources for clinical practice.ClinicalTrials.gov NCT02038439; https://www.clinicaltrials.gov/ct2/show/NCT02038439 Web-based searches have become the norm when looking for information and answers to most of our questions in daily life. This has also become true in the practice of medicine; online medical resources to access evidence are increasingly considered “as essential as the stethoscope” . While fPhysicians are typically familiar with only a few of these resources, likely those to which they have been exposed in training or by peers, and often ignore most of the ecosystem and architecture of published evidence. Yet, their daily practice triggers, on average, five to eight questions every 10 patients -10. ClinHow physicians translate their clinical questions into searchable queries remains poorly known. How many search terms do they use? How often do their queries fit the PICO framework ,13? Do eThe type of search engine or evidence resource may also influence the way we conduct queries. Google and Wikipedia tend to retrieve relevant answers, albeit selective, with intuitive, less-structured search strategies -16. SomeWe, therefore, explored how clinicians formulate their queries in a federated online search engine, namely MacPLUS FS (McMaster Premium LiteratUre Service Federated Search). MacPLUS FS allows clinicians to explore multiple resources simultaneously, retrieving one single output that includes the following: (1) evidence from evidence-based summaries , (2) preappraised research , and (3) non-preappraised research , with and without validated search filters see . In thisWe conducted an analytical survey of clinical search queries among 431 postgraduate medical trainees and 477 medical faculty members registered to a federated search engine, MacPLUS FS. The service was freely available to registered users from any computer with an internet browser throughout the clinical setting or elsewhere.badges and reputation scores. Participating clinicians were randomized to each of the three interventions in a factorial design (A × B × C).Participating clinicians consented to be enrolled in 6-month, MacPLUS FS, randomized controlled trials , which tFor each clinician, utilization of MacPLUS FS was recorded through accounts tracking log-ins and usage, including their detailed search queries. Registration to the service was free, and access to each evidence resource was through clinicians’ academic institutions, mostly McMaster University, Hamilton, Canada. Clinicians were categorized according to their baseline search levels and specialty types .We extracted all 1085 search queries performed by clinicians during the conduct of the MacPLUS FS trials. Two authors (AS and TA) assessed each query individually, counting the number of search terms—counting all words —and documenting all abbreviations and Boolean terms . Search queries were then classified into (1) structured searches, (2) searches for specific articles , (3) iteration of structured searches, namely a group of related structured queries with a similar PICO question within the same log-in session, and (4) undetermined searches .summaries, preappraised research, and non-preappraised research (see The same two authors (AS and TA) classified structured queries into background or foreground questions see , accordiarch see .We examined types of questions  according to the level of training as well as clinicians’ specialties and baseline frequencies of search . We then examined the number and type of search terms across each type of question. We compared distributions using chi-square parametric tests when relevant and Kruskall-Wallis tests for nonnormal distributions. Data abstraction was done using Microsoft Excel 2016, version 15.29, and data analysis was performed using SPSS Statistics for Windows, version 23.0 (IBM Corp).regular searchers (≥1 search per month), occasional searchers (<1 search per month), or alert-only users (no searches).Participants were postgraduate residents and medical faculty members who had registered in MacPLUS FS prior to the trial. Of the 678 postgraduate residents and 753 medical faculty members, 431 (63.6%) and 477 (63.3%), respectively, were deemed eligible after the exclusion of 247 postgraduate residents and 266 medical faculty members, who either never logged in to MacPLUS FS during the year prior to the study or quit the institutions served by MacPLUS FS [The 908 clinicians made 1085 search queries, of which 235 (21.66%) were subsequent iterations of the same search, 124 (11.43%) were a search for a specific article, and 31 (2.86%) could not be classified and remained undetermined. A total of 695 out of 1085 queries (64.06%) were structured queries following the PICO format, with 480 out of 695 (69.1%) single queries, whereas 215 (30.9%) included a group of related queries. This corresponds to an average of 2.1 attempts per group query.P=.51) (see .51) see .P=.009). There were no differences between specialty types (P=.67).P<.001). Indeed, there were 70.2% (276/393) of background questions with 2 or fewer terms versus 18.2% (55/302) of the foreground questions, and 81.8% (247/302) of foreground questions with 3 or more terms versus 29.8% (117/393) of the background questions.Overall, 72.5% (504/695) of structured queries see  containeP<.001). Although 35.7% (248/695) of structured queries did not result in any resource access, 39.9% (277/695) led to one resource accessed, 11.8% (82/695) led to two, and 12.7% (88/695) led to three or more resources. Across all 1085 queries, the average number of resources accessed was 0.88 (SD 1.42). When users attempted a second search on the same clinical question , 7.2% (17/235) resulted in one or more resources accessed, while 92.8% (218/235) led to an end of their search query with no additional resource accessed. When searching for a specific article, 37.9% (47/124) led to one resource accessed and 12.9% (16/124) led to two or more resources accessed.The number of evidence-based resources that clinicians accessed for each type of query  are displayed in P<.001), and family physicians looked at significantly more resources than did internists and specialized physicians (P<.001).Among 1085 queries made by 908 clinicians, 695 were structured queries. A small majority were related to background questions, and most foreground questions were questions about therapy, rather than diagnostic or prognostic questions. Structured queries included a median of 3 terms, most often related to the population and intervention or test, rarely related to the outcome, and never related to the comparator. Explicit Boolean terms were rarely used; of note, the search engine assumed by default a Boolean “AND” between search terms. About half of the resources accessed were summaries, while the rest were equally divided between preappraised and non-preappraised resources.We found no difference between searches made by postgraduate resident trainees and medical faculty members. As they are in training, one could have expected postgraduate residents to have more background questions, whereas faculty members were expected to have more foreground questions, for example, in comparing the effectiveness or risks of management strategies. Our results did not confirm this assumption, as faculty members had more than half of their searches on background questions as well. This may be due to the complexity of patient care. A given faculty member may be an expert in a given field but adopt a learning strategy to rapidly get the big picture, to understand uncommon situations. Their high level of access for summary resources, such as UpToDate or DynaMed, likely supports this explanation. Similarly, family doctors also accessed more summary resources, not only because of their need for quick clear answers to questions arising within short appointments with patients, but also, perhaps, because they provide care for patients across an entire age spectrum.Another issue relates to the frequency of searches clinicians are able to perform in daily life. In our study, 908 clinicians performed only 1085 queries in 6 months. Other studies have shown that clinicians tend not to search in order to answer the questions that arise in their daily clinical practice -20. In oAnother observation from our study is that clinicians’ queries tend to remain relatively simple: few search terms, often covering few PICO concepts, mostly population and intervention. While simple strategies work well for high-level summaries, they are much less efficient with large databases like PubMed. Our daily habits for searching on the Web may explain clinicians’ tendencies for simple queries. Strictly from a user’s perspective, we have all become very efficient in searching for information mostly through Google and Wikipedia, just by typing a few intuitive keywords in the free-text bar at the top of a webpage. Medical search engines may misguide the user in having them assume the engine will work similarly to Google .One area for improvement of search engines could be to invite users to structure their queries according to the PICO framework. Schardt et al  have fouThe main limitation of our study was that clinicians likely used other means than MacPLUS FS to answer some of their daily questions. Our design also did not assess the clinical impact of the answers retrieved. This would have required mixed methods approaches to estimate the number of patients needed to benefit from information , as described by Pluye et al .Finally, our sample of searches was recorded in the context MacPLUS FS randomized controlled trials , and it Strengths include the direct record of queries in one of the largest samples of physicians from different specialties and levels of practice. It is also the first study on a federated search engine, which allowed us to show that clinicians access all resources and not only summary-level evidence.A constant flow of new articles overwhelms clinicians who are continuously exposed to them. To keep up and to answer our clinical questions, it is essential to clarify and translate clinical questions into searchable queries. Our results could lead to the development of educational and clinical interventions on how to increase searching skills . These cOur findings also highlight the potential role of federated search engines over the use of single resources to meet clinicians’ needs . A federOther avenues of research include the improvement of search functionalities and clinical interventions to meet users’ expectations in navigating through the evidence, in order to rapidly find the most relevant and least-biased answers for better clinical practice and patient care.


Current studies on nanocomposites have focused on their multifunctional properties and their industrial production. In this work, polyetheretherketone (PEEK)/graphene nanoplate (GNP) composites were produced by a direct semi-industrial process. Different percentages of untreated GNP  were added to PEEK by employing melt-compounding followed by injection-moulding. Despite the semi-industrial approach used, the modulus, strength, and Poisson coefficient of the nanocomposites (1 and 5 wt.%) were not significantly affected by the addition of GNP. However, there was a slight decrease in the strength at 10 wt.% GNP. Our study also shows that the thermal conductivities of PEEK/GNP composites are up to 2.5 times higher than that of pure PEEK. Materials science; Nanotechnology; Aerospace engineering; Nanomaterials; PEEK; Graphene; Composites; Characterisation; Thermoplastic HConsequently, over the last decade, investigations have been conducted to improve the thermal properties of PEEK through the incorporation of carbon nanofillers such as carbon nanotubes, graphene, and graphene-based materials such as graphene nanoplates (GNPs)  or graphene oxides , 10, 11.Considering the high cost of graphene, GNPs, which are formed from several stacked graphene layers bound to each other by Van der Waals forces, are an interesting option for large-scale production  of nanocThe mechanical and thermal properties of a nanocomposite depend on the dispersion of the filler, its size distribution, and the interface between the matrix and the nano-reinforcements , 20, 21.Relative to other organic/inorganic filler nanocomposites with a PEEK matrix, there are few studies about PEEK/GNP composites available in the literature. In these studies, PEEK/GNP nanocomposites were prepared by two different methods: solution intercalation (with or without a compatibilising agent) , 5, 21 aDespite the inferior mechanical properties obtained by melt-compounding, the process is still preferred for industrial applications. The absence of solvents, easy-to-scale method, ready availability of industrial equipment among other qualities makes this technique a preferred choice for nanocomposite production , 24, espIn this work, PEEK/GNP composites were produced by a semi-industrial, two-step process of melt compounding followed by injection moulding. We studied the effects of GNPs  on the mechanical and thermal properties of the as-produced PEEK matrix. The fracture surfaces and the dispersion of graphene within the matrix of samples were analysed by scanning electron microscopy (SEM).22.13, which were dried at 150 °C prior to be used for production of nanocomposites. Pristine graphene nanoplates (avan-GRAPHENE) were provided in powder form by Avanzare Innovación Tecnológica, SL (Spain). SEM analysis of the received graphene was performed by using a Helios Nano Lab 600i scanning electron microscope.PEEK 90P grade was provided in pellets by Victrex plc, UK, with glass transition temperature = 143 °C, melting temperature = 345 °C, density at 25 °C = 1.30 g/cm2.2PEEK nanocomposites were melt-blended using an extrusion-compounding machine, Coperion ZSK 26. This machine is equipped with a 26-mm diameter co-rotating twin-screw and two Brabender gravimetric feeders. Two different temperatures were set for this process: 330 °C for the extruder and 360 °C for the nozzle. A high shear rate screw profile, which was specifically designed for this process, was used to ensure proper dispersion. The rotor speed set to process all the nanocomposites was the same, 250 rpm. Two feeders were required for this process: one for feeding PEEK at a speed of 10 kg/h and a lateral one for feeding graphene. Graphene was fed in order to achieve a final concentration of the nanocomposites of 1, 5, and 10 wt.% raw GNP. The diameter of the die used for this process is 2 mm. Once the molten material was extruded through the die, it was quenched in a water bath at room temperature, then dried and cut into small pellets. Pure PEEK was also processed under the same conditions in order to use it as reference material.2.3D790-02, were injected in a mould made of tool steel (1.2790) at a constant temperature of 180 °C. The pellets were dried at 150 °C for 3 h prior to processing. The specimens were produced by injection moulding through a JSW 85 EL II injection machine with a 35-mm diameter reciprocating screw at a screw speed of 120 rpm. The dosage used in each specimen was 20 mm with an injection time of 0.36 s, the compaction pressure was 1000 bar, and the cooling time was 3 s. Pure PEEK and the specimens that contained different loadings of GNPs were prepared by following this protocol. At least 15 samples were produced for each composition.Dog-bone shaped specimens (type V) for tensile tests, under ASTM D638-02a , and spe2.43 for PEEK-90G and 2.2 g/cm3 for GNPs [The density of the injection-moulded specimens with different percentages of graphene were measured in accordance with ASTM D792-13 standard. At least four cubic samples for each level of GNP content were produced and weighed in air and then in distilled water at room temperature (≈23 °C). The theoretical density was also calculated by applying the rule of mixtures with assumed densities of 1.3 g/cmfor GNPs , and wer2.5Differential Scanning Calorimetry  was used to obtain information about the thermal properties of the nanocomposites. The samples (5–10 mg) of PEEK with different percentages of graphene  were heated from 20 °C to 400 °C at 10 °C/min and held at 400 °C for 5 min to remove the thermal history of the samples. Then the samples were cooled to 20 °C at 10 °C/min, held for 0.5 min and heated again to 400 °C at 10 °C/min. The DSC samples were cut from the bulk of the injection specimen (dog-bone) with the help of a hammer and a knife. One test per sample was carried out.2.6D790-02 in a three-point bending configuration with a distance between supports of 60 mm . At least 5 specimens (79 mm × 10 mm × 4 mm) were measured for each composite.The characterisation of the flexural properties  was carried out in accordance with ASTM 1 and ε2) of each specimen during the test was performed by digital image correlation (DIC). One side of every ‘dog bone’ specimen was painted white and then carefully and lightly sprayed with black paint to get the random speckle pattern required for DIC analysis. The images (36 mm × 4.5 mm) were taken every 2 s during the test [Tensile tests were carried out under ambient conditions with an Instron 3384 by using a load cell of 10 kN. Tests were carried out in accordance with ASTM D638-02a by using type V specimens at 1 mm/min of crosshead speed . The axithe test , 27. Fin2.7Thermal conductivity of PEEK/GNP composites was carried out by Hot Disk TPS  in accordance with ISO 22007-2:2008 standard. Two identical samples (30 mm × 10 mm × 4 mm) were polished, cleaned, and a heater/sensor element was placed between them. Full details of this process have been reported , 29. The33.1The morphology of the as-received graphene nanoplatelets was characterised by using SEM. Agglomerates of graphene are observed in 3), 3.3% more dense than pure PEEK. For all the samples, the differences among theoretical and the experimental density measures were lower than 1%, which indicates that the injection method works well, and the samples have low porosity. This low porosity matches the results of the SEM analysis and voids.For the composite samples, the addition of GNP within the matrix slightly increased the density of the material . As expeWith regards to nanofiller dispersion, for low loading of GNP (1 wt.%), we observed GNPs composed of a few stacks of graphene layers and good distribution of the GNP within the matrix c. For hiAt the interface region between the nano reinforcement and the matrix, the PEEK completely wets the surface of most of the graphene agglomerates. However, debonded regions can be found among sheets of graphene, which can result from poor interactions between the graphene agglomerates and the PEEK or among the layers of graphene within agglomerates , 22 Figd. We did3.2c) of the composites was calculated with the following equation [The thermal properties of the injected samples were analysed by DSC. The degree of crystallinity (χequation :(1)χc=ΔHS is the heat of melting calculated by integration from 130 °C  to 375 °C, ΔH0 is the heat of fusion of 100% crystalline PEEK, taken as 130 J/g [In which ΔH 130 J/g  and ϕ isFrom the first heating and later cooling ramps, both at 10 °C/min, thermal parameters such as glass transition, melting temperature, melting enthalpy, crystallinity, crystallisation temperature, and heat of crystallisation were calculated. 3.3The effect of GNPs on the mechanical properties of the composites has been explored by three-point bending and tensile tests, the results of which are shown in The addition of graphene increases the stiffness of the nanocomposite see . The nanConversely, the tensile and flexural strength slightly decreased with the addition of GNP. PEEK with 5 wt.% GNP exhibited an approximately 7% and 10% decrease in flexural and tensile strength, respectively. This reduction of strength can be attributed to a range of factors. Strong interfacial adhesion between the reinforcement and the matrix is essential to reach high strength in composites , 37. In 1. The standard ν is defined as:We also calculated Poisson's ratio (ν), which is a measure of the transverse contraction , the Poisson's ratio was similar to pure PEEK, as expected . But, thFractography analysis of the broken surfaces of pure PEEK showed two different regions; Region I and Region II  Figure a. Figure2 nanocomposites [For the nanocomposites, two distinct regions can be seen d and g. mposites . GNP migIn addition, with increasing GNP content, the size of the dimples decreases as a result of the formation of larger number of nanoplates per volume fraction of material e, h. How3.4The thermal conductivity of PEEK/GNP composites versus weight fraction of GNP is shown in This value  is the highest thermal conductivity measured and reported for PEEK/GNP composites so far; note that 37% and 90% improvements have been reported , 45 for However, the enhancement of 10 wt.% of GNP was moderate relative to the previous results with other polymeric matrices or similar nanofillers. As an example, Hwang et al.  reported4High performance PEEK/GNP nanocomposite samples even with high GNP loadings (5 and 10 wt.%) could be manufactured by an industrial two-step process (melt-compounding and injection moulding). This study shows how current industrial machines can be used to manufacture nanocomposite parts in the industry.The processing conditions were enough to obtain good dispersion of GNP agglomerates at low concentrations (1 wt.%). However, with increased amounts of GNP, larger agglomerates of GNP were found. The size of the agglomerates, even at 10 wt.% GNP, is smaller than in pristine GNP, which shows how efficient this processing technique is at GNP dispersion. These GNP/PEEK composites crystallise faster than pure PEEK, probably because GNP has a nucleating effect that results in higher crystallinities after crystallisation at high cooling rates.The addition of GNP within the PEEK matrix enhances the elastic modulus of PEEK, but decreases the strength, which indicates that there is poor transfer between nano-reinforcement and matrix. Furthermore, graphene decreases the Poisson's ratio of pure PEEK. Different failure behaviours were observed by fractography of the nanocomposite samples, which were probably a result of a decrease in strain at break through the addition of GNP. Higher contents of GNP showed larger areas associated with brittle behaviour.The introduction of graphene to the PEEK matrix improved the thermal conductivity. The sample with 10 wt.% GNP exhibited 0.73 W/mK, which is two-fold the thermal conductivity of pure PEEK. This significant increase has never observed for GNP nanocomposites manufactured by melt-compounding.To improve the dispersion and enhance the stress transfer across the interface between PEEK and graphene, a change in the semi-industrial parameters and the use of complementary techniques, such as functionalisation of graphene, or the use of compatibilizer agents need to be explored. To conclude, we have demonstrated how a semi-industrial technique can produce PEEK nanocomposites with enhanced thermal properties with retained or even slightly improved mechanical properties.A. Alvaredo-Atienza: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.Juan P. Fernández-Blázquez: Analyzed and interpreted the data.P. Castell: Performed the experiments; Contributed reagents, materials, analysis tools or data.Guzmán de Villoria: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.10.13039/100012818Community of Madrid through the NMAT2D-CM (S2018/NMT-4511). R. Guzman de Villoria was supported by the Spanish 10.13039/501100003176Ministry of Education, Culture and Sports through the Beatriz Galindo fellowship (BEAGAL18/00091).This work was supported by the The authors declare no conflict of interest.No additional information is available for this paper.


In this paper, we numerically and theoretically study the tunable plasmonically induced transparency (PIT) effect based on the graphene metasurface structure consisting of a graphene cut wire (CW) resonator and double split-ring resonators (SRRs) in the middle infrared region (MIR). Both the theoretical calculations according to the coupled harmonic oscillator model and simulation results indicate that the realization of the PIT effect significantly depends on the coupling distance and the coupling strength between the CW resonator and SRRs. In addition, the geometrical parameters of the CW resonator and the number of the graphene layers can alter the optical response of the graphene structure. Particularly, compared with the metal-based metamaterial, the PIT effect realized in the proposed metasurface can be flexibly modulated without adding other actively controlled materials and reconstructing the structure by taking advantage of the tunable complex surface conductivity of the graphene. These results could find significant applications in ultrafast variable optical attenuators, sensors and slow light devices. Typical electromagnetically induced transparency (EIT) occurs in a coherently driven atomic system, resulting from the destructive interference of two dressed states, and a narrow transparency window is generated simultaneously in a broad absorption spectral region ,2,3. HowSo far, several optical active materials such as semiconductors ,11 and nIn this paper, we propose a tunable metasurface structure to theoretically and numerically study the PIT effect, which consists of a graphene cut wire (CW) resonator and split-ring resonators (SRRs). It is found that a narrow transparency window with 90% transmission appears at 6.08 µm in the transmission spectra due to the weak hybridization between the graphene CW resonator and SRRs. We theoretically calculate the transmission spectra with various coupling coefficients according to the coupled harmonic oscillator model, and the results are approximately consistent with the numerical simulation results from different coupling distances, achieved by utilizing the finite-difference time-domain (FDTD) solutions software. Moreover, the optical response of the proposed metasurface structure can be tuned by controlling structural geometries such as the coupling distance between the graphene SRRs and CW resonator, the number of the graphene layers and the geometries parameters of the graphene CW resonator. Importantly, compared with the metal-based metamaterials, the tunable transparency window can be realized by utilizing the electrical gating to alter the Fermi level of the graphene without adding other actively controlled materials and reconstructing the proposed structure. The results will open a new avenue in tunable sensors, ultrafast optical switches and variable optical attenuators.2O3 substrate as illustrated in 2O3 substrate whose complex refractive index can be obtained in [L = 85 nm and W = 7 nm, respectively. The distance of the SRRs is 1d = 5 nm, while the coupling distance between the CW resonator and SRRs is d = 6 nm. L1, L2 and G refer to the length in the y and x-direction and the gap of the split-ring resonator and their detail values are set as L1 = 29 nm, L2 = 25 nm, and G = 19 nm, respectively. The period of the proposed metasurface structure is xP = 125 nm in the x-direction and yP = 66 nm in the y-direction.The unit cell of the proposed metasurface structure used to investigate the PIT effect is composed of a graphene CW resonator and two identical and axisymmetric graphene SRRs on the Alained in  is set a2O3 with the thickness of 40 nm can be grown by atomic layer deposition [2O3 substrate by the dry or wet transfer method [The proposed graphene-based metasurface can be fabricated in the following sequence of steps. First of all, the substrate Alposition . Next, tposition ,23. Finar method . The proThe surface conductivity of monolayer graphene ation as :(1)σ(ω)=2O3, the external voltage on graphene and the thickness of Al2O3, respectively. We employ the Here, a τ=1/2Γ , where iεdVg/eH1 ,28. Heref=106m/s  and H1=4ulations : (4)ε=1+n vacuum . ParticuNσ (N≤6) ,32.In this paper, the FDTD solutions software is utilized to numerically study the optical response of the metasurface structure, and the periodic boundary condition is employed in the x and y directions, while the perfectly matched layer (PML) absorbing boundary condition is applied along the propagation direction of the incident light.As shown in z|2-component of the unit cell with individual graphene CW resonator at 2O3 substrate as illustrated in Then, we numerically calculated the electric field of the |EIn addition, the Ez field distribution of the graphene SRRs and CW resonator at two transmissions—dip I x¨lated as ,36:(7)x=The transmission Then, we explore the influence of the geometrical parameters of the graphene CW resonator on the PIT spectra. As mentioned above, the complex surface conductivity of the graphene can be flexibly controlled by  voltage ,38,39. T voltage ,41,42. P voltage .In the simulation, the ectively . α0=e2/, which consists of a graphene CW resonator and SRRs. After optimizing the geometry of the structure, a transmission peak of


Paradata are (timestamped) records tracking the process of (electronic) data collection. We analysed paradata from a large household survey of questions capturing pregnancy outcomes to assess performance (timing and correction processes). We examined how paradata can be used to inform and improve questionnaire design and survey implementation in nationally representative household surveys, the major source for maternal and newborn health data worldwide.The EN-INDEPTH cross-sectional population-based survey of women of reproductive age in five Health and Demographic Surveillance System sites  randomly compared two modules to capture pregnancy outcomes: full pregnancy history (FPH) and the standard DHS-7 full birth history (FBH+). We used paradata related to answers recorded on tablets using the Survey Solutions platform. We evaluated the difference in paradata entries between the two reproductive modules and assessed which question characteristics  affect answer correction rates, using regression analyses. We also proposed and tested a new classification of answer correction types.We analysed 3.6 million timestamped entries from 65,768 interviews. 83.7% of all interviews had at least one corrected answer to a question. Of 3.3 million analysed questions, 7.5% had at least one correction. Among corrected questions, the median number of corrections was one, regardless of question characteristics. We classified answer corrections into eight types , zigzag, flat zigzag, missing after correction, missing after flat (zigzag) correction, missing/incomplete). 84.6% of all corrections were judged not to be problematic with a flat (simple) mistake correction. Question characteristics were important predictors of probability to make answer corrections, even after adjusting for respondent’s characteristics and location, with interviewer clustering accounted as a fixed effect. Answer correction patterns and types were similar between FPH and FBH+, as well as the overall response duration. Avoiding corrections has the potential to reduce interview duration and reproductive module completion by 0.4 min.The use of questionnaire paradata has the potential to improve measurement and the resultant quality of electronic data. Identifying sections or specific questions with multiple corrections sheds light on typically hidden challenges in the survey’s content, process, and administration, allowing for earlier real-time intervention . Given the size and complexity of paradata, additional time, data management, and programming skills are required to realise its potential. High-quality routine health data on maternal, newborn, and child health (MNCH) can be used to monitor, identify gaps, and take action to improve quality of care, optimise health system performance, and enable informed decision-making. Routine health management information systems vary in their completeness and quality across low- and middle-income countries (LMICs), and in many cases, are not able to provide the high-quality coverage data required for assessing and guiding health programmes , 2. HousThe shift from paper-based to computer-assisted personal interviewing (CAPI)-based data collection  has enabled inclusions of inbuilt validation and consistency checks, as well as a real-time review of collected data . In addiEven though paradata are widely used in the field of survey methodology in high-income countries , the useIn this paper, we examine how paradata can be used to inform and improve questionnaire design and survey implementation in a large household survey collecting information on pregnancies and births using full pregnancy histories (FPH) and full birth histories with additional questions on pregnancy losses in the past 5 years (FBH+). This paper is one of a series of papers from the Every Newborn International Network for the Demographic Evaluation of Populations and Their Health (EN-INDEPTH) study in five Health and Demographic Surveillance System (HDSS) sites in sub-Saharan Africa and Asia.To assess the differences in paradata timestamped entries between two reproductive modules (FPH and FBH+);To determine whether question characteristics  affect the duration of section completion and answer correction rates;To propose and test classification of answer correction types and determine whether they differ by two reproductive modules.This paper has three objectives:The EN-INDEPTH study aimed to compare two approaches of collecting maternity history (FPH and FBH+) to examine whether the two methods yield different estimates of stillbirth rates and neonatal mortality rates and to determine whether there is a difference in completion time for these two approaches. The study protocol and main findings can be found elsewhere , 11. BriThe analyses in this paper focus on the reproduction section section 2) of the questionnaire only  and separate per observation (in case of parity > 1). For questions with corrections, the timestamp of the final answer was taken as the question’s timestamp. To exclude implausible values, but allow for multiple corrections during the module completion and/or switching between questionnaire sections during the interview , we restt test. Statistical significance level was defined at the 5% level.We categorised all questions by type, nature, and structure. The question types included single-select ; multi-select ; numerical computational ; date-related ; and free-text input . There were three categories based on questions’ nature: two groups of potentially sensitive questions  and regular (non-sensitive) questions. Lastly, considering question structure, there were questions with built-in error notifications , warnings appearing in capital red letters, or any other prompts for correction  and those without such notifications. Differences between FPH and FBH+ were evaluated using descriptive statistics and independent sample Differences in the duration of response time and proportion of corrections by question characteristics  were evaluated using descriptive statistics. A two-part model was used to analyse which question characteristics are associated with the likelihood of question correction  with a binomial distribution and logit link) and the number of corrections (GLM with gamma distribution and log link function). Explanatory variables included question characteristics. Models were adjusted for respondent’s characteristics and location, with interviewer clustering accounted as a fixed effect. Statistical significance level was defined at the 5% level.In paradata, the process of data collection where all answers are entered and corrected is recorded as an ordered list of answers (sequence). To understand this process of data entry and correction better, we ordered all interviews based on the total number of questions asked during the interview and the number of answers (length of a sequence). We distinguished between original answers and corrections and visually inspected the resulting sequence index plot .Whenever an answer to the same question has multiple corrections, these corrections can form a distinct pattern. For example, corrections can be single or multiple; the value of the original answer and the last correction may or may not match; correction entries may have identical or different values and may lead to missing data. As currently there is no classification of answer correction types, we developed and tested one using our survey data. We then used descriptive statistics to examine whether answer correction patterns vary by question characteristics and two reproductive modules.Results are reported in accordance with STROBE Statement checklists for cross-sectional studies  Additio.We analysed 3.6 million timestamped entries corresponding to 3.3 million answered questions and their correction for 65,768 interviews, of which 52.1% related to FPH module , which by design contained more questions than the FBH+ reproductive module  , followed by numerical computational (32.1%), date-related (1.2%), and less than 0.03% being free-text and multi-select types of questions  Table . The medThe median response time per question type was longest for free-text and multi-select questions (0.6 min), followed by date-related (0.2 min), numerical computational (0.1 min), and single-select questions (0.05 min) Fig. . The medOf all asked and answered questions, 7.5% had at least one or more corrections  and numerical computational (n = 23) types of questions. In terms of questions’ nature, the maximum number of corrections was among regular questions (n = 28), followed by death-related questions (n = 23) and TOP-related questions (n = 19). Regarding the question structure, the maximum number of corrections was highest for questions with no built-in notifications (n = 28).Among corrected questions, the median number of corrections was one, regardless of question characteristics. The maximum number of corrections was the highest for single-select  and correction pattern , we developed a classification of answer correction types. We distinguished between eight possible answer correction types after accounting for possibly missing answers after corrections Table . BuildinWe tested our proposed classification using our survey data. Out of eight proposed answer correction types Table , we obseThe flat correction type was the most frequently observed correction type 84.1%), irrespective of question type, nature, or structure , the maximum numbers of corrected questions and corrections per question were high . Not only does this add to the duration of section completion (as shown in our results), and ultimately to interview duration, it could also potentially shed light on questions that are poorly understood or misunderstood by either respondent or interviewer. For example, in our survey, the highest proportion of corrections was among the numerical computational (9.7%) and TOP-related (8.0%) questions. At the same time, even higher proportions of corrections were observed for multi-select questions (44.8%) and free-text (16.2%) questions. They also had the strongest association with the probability of making answer corrections. However, these results should be treated with caution as these questions constitute less than 0.1% among all questions asked.Moreover, given the design and related data entry process , distinguishing between answers, answer combinations, and corrections might not always be straightforward. Nonetheless, we suggest that these types of questions get additional attention during training sessions, with more time being allocated to explaining and practising asking these questions, with additional guidance and supervision provided during the fieldwork. One could also consider limiting even further these types of questions in household surveys.We also developed and tested a new classification of answer correction types. We found this classification useful and suggest it for future studies. For example, we found that almost 90% of all corrections were simple mistake corrections (which is less worrisome) or impulsive and repetitive answers (most likely due to non-responsive screen); hence, they should not be considered problematic. This suggested that the reasons for the remaining multiple zigzag corrections around 10%) lie elsewhere. We speculate that the remaining corrections were made following additional clarifications or confirmations of previous or later answers , lacking missing or incomplete answers after correction. This is primarily due to a lack of ‘missing’ (or empty in content) timestamped entries in our paradata dataset (which by default is not possible) and our decision not to treat ‘Do not know’ answers as ‘missing’ entries. However, we also much acknowledge effective training and comprehensive training manuals, diligent work of our data collectors, and their dedication to prompt interviewees and complete all relevant fields, which we believe aspired minimisation of any missing data. About a third of all questions in our questionnaire had inbuilt error notification, prompts, and warnings. Our results suggest that such notifications are effective since the proportion of corrected questions was 3.3% higher for questions with notifications compared to those without them, and having notifications was significantly associated with the probability of making corrections but negatively associated with the number of such corrections. Additionally, Survey Solutions application had a built-in colour coding indicating survey section completion (red for incomplete and unanswered questions and green for complete), which prompted data collectors to answer all questions. For example, during some training sessions at several data collection sites, interviewers were insisting on learning how to achieve completeness ‘having all sections colour-coded as green’ in all survey sections and were ‘somewhat unhappy’ to finish the exercise with one or more sections remaining incomplete (or red).Given our focus on corrections during interviewing, in our analysis, we excluded implausible and impractical values (over 180 min for section completion duration) but allowed for multiple corrections during the module completion and switching between questions and questionnaire sections. We assumed that such restrictions could provide meaningful and practical insights into face-to-face data collection process, even if it would exclude and not account for long breaks in the interviews  or other errors in timestamps . However, we recognise that this decision could be considered as one of the limitations of the study, as it potentially did not capture corrections based on office data quality and error checks (following which questionnaires were returned and/or reassigned back to the interviewer for correction in-field). Moreover, in our analyses, we specifically focused only on a subset of paradata that related to answers and corrections. We did not utilise the data with timestamped events that related to process-related activities , which could be considered another limitation of the study. As paradata were not readily available for export at the beginning of our data collection (due to software limitations), we did not evaluate individual and team productivity , or estimate the response likelihood and perform measurement error evaluation . HoweverGiven a lack of other studies that have used paradata in MNCH field, we cannot compare our findings directly to other studies, and we would like to stimulate the wider use of survey paradata to advance survey design and implementation for collecting information on pregnancies and births and for other purposes.Paradata provide a wealth of information and could augment surveys, particularly overseeing the data collection process. Not surprisingly, it has already found use in other health and medical areas. For example, similar to our study, paradata were previously used in telemedicine research to estimate time spent to complete a questionnaire  and to eFuture analyses could investigate the relationship between correction rates and correction type and being supervised by a supervisor or other colleagues, which potentially could prompt additional corrections under peer pressure. Other potential uses of paradata in MNCH research could include effort indicators, tracking individual and team productivity, estimating contact attempts without success, and response propensity. Outcome indicators and case status indicators can also include non-interviews by type and refusal patterns by respondent characteristics. Paradata in MNCH research can also be used to generate a dashboard/monitoring system or a validation system for collected data against external sources of information, hence, automatically flagging incorrect entries in the interviews.Accurate estimation of coverage indicators from household surveys is vital but contingent on data quality; hence, a better understanding of how to improve the questionnaire design and survey implementation is crucial. Paradata have the potential to enhance survey design and implementation for collecting information on pregnancies and births, leading to improved metrics of measurement in maternal and newborn health research. They can help to identify questions and sections with multiple corrections and shed light on typically hidden challenges in the survey’s content, process, and administration. Overall, our experience suggests that given the size of paradata and their complex structure, analysis is not always straightforward, and consideration should be given to the additional data management and programming skills required. Nonetheless, paradata provide a wealth of data, can improve the process of data collection using live survey monitoring, and can add value in improving survey data quality as well as efficiency.Additional file 1. Detailed overview of questions in Section 2 in FPH and FBH+.Additional file 2. Example of survey paradata structure.Additional file 3. STROBE guidelines checklist.Additional file 4. Types of questions in Section 2 in FPH and FBH+.Additional file 5. Detailed overview of questions correction by question type, content and structure and reproductive module.Additional file 6. Correction patterns, 23 groups by the number of corrections per question.Additional file 7. Ethical approval of local Institutional Review Boards.