N = 30 each), who differed regarding their linguistic background  were asked to rate spontaneous speech samples produced by Austrian English bilingual and Austrian German monolingual speakers. Results showed that the bilingual L1 speech was perceived to sound overall less native compared to monolingual control speech. It was further observed that the two listener groups significantly differed in their perception of nativeness: Bilingual listeners were overall less likely to judge bilingual L1 pronunciation to sound non-native compared to monolingual listeners. To date, this is the first study to show that listener experience influences their perception of nativeness of L1 pronunciation and, thus, adds a new dimension to the notion of the native speaker.The aim of this study was to explore if and to what extent Austrian-English late sequential bilinguals who have been living in a second language (L2) environment for several decades are perceived to sound native in their first language (L1) when being compared to monolingual Austrian German (AG) control speakers. Furthermore, this investigation aimed to identify if listeners differ in their judgments of nativeness of L1 pronunciation depending on their own language background. For this purpose, two groups of native Austrian German listeners ( A speaker’s accent, shaped by various segmental and prosodic characteristics, is one of the most salient features of speech production and communication. Research shows that listeners are very sensitive to accented speech e.g.,  and thatL1 attrition, is frequently observed among bilinguals who have been long-term immersed in an L2 speaking country and who acquired their L2 after adolescence, i.e., at a point when the L1 is already fully developed in healthy individuals monolingual speakers of Austrian German or German-English bilingual speakers, affects their judgment of nativeness in L1 Austrian German pronunciation. In this context, we also examined if the extent to which bilinguals are perceived to sound (non-)native in their L1 pronunciation changes at different stages after immigrating to an L2 speaking country, judged by the rater groups described above. The decision to include two rater groups differing in terms of their language background is based on recent findings concerning L2 phonetic and phonological influences on L1 perception, showing that bilinguals’ L1 perceptive abilities might be modified as a result of L2 learning experience e.g., . Hence, phonetic drift  to sound not significantly differently from German control speakers in their L1 German pronunciation. At the same time, the ratings obtained for the remaining bilinguals (N = 11 out of 40) were clearly below the native German range.The extent to which segmental and prosodic divergences from L1 norms lead to a perceived global foreign accent in L2-immersed late sequential bilinguals’ L1 pronunciation has been explored in a series of foreign accent rating studies (FARs) . OverallWhile the accent rating studies outlined above focused on bilinguals with L1 German, Another study frequently discussed in the context of perceived L1 accent was conducted by Overall, the findings outlined above confirm that L2-induced phonetic and phonological modifications in a speaker’s L1 system often lead to a non-native accent in the L1. Accent rating studies offer valuable insights into pronunciation differences between monolingual and bilingual speakers and show that listeners are sensitive to diverging L1 pronunciation patterns. Furthermore, they reveal that the extent to which L2-immersed bilinguals are perceived to have a foreign accent in their L1 varies, that is, bilingual speakers are not inevitably perceived to sound less native compared to monolingual controls . There aThe third reason for the observation that not all bilinguals are perceived to sound (non-)native to the same degree relates to listener-dependent variables which might affect their perception of accented speech, including their own language background, i.e., are they monolingual or bilingual speakers, and their familiarity with non-native or accented speech, i.e., are they exposed to non-native speech on a regular basis. The role of listener variables has predominantly been explored in the context of perceived accentedness of L2 speech e.g., , but recAs pointed out above, differences in foreign accent ratings are likely to result from both speaker-specific and listener-specific features, with the latter being predominantly investigated in L2 acquisition research. Other studies examining the perception of L2-accented speech, by contrast, do not show a correlation between listeners’ linguistic experience and accent ratings. The extent to which listeners’ linguistic background and experience play a role in the perception of accented L1 speech has not been investigated in greater detail so far. The only study—to the best of our knowledge—which addresses the impact of listener-specific variables on the perception of not only non-native L2 speech but also on bilinguals’ L1 pronunciation was conducted by As previously outlined, early linguistic research focusing on bilingualism and L2 acquisition was conducted under the premise that the L1 is protected against influences from a late-acquired L2 due to biological maturation processes . That isBased on the above considerations, the present study aimed to contribute to the emerging body of research exploring listener perceptions of potentially attrited L1 speech in order to determine if and to what extent L2-immersed bilinguals are perceived to have a non-native accent in their L1 when being compared to monolingual speakers . In addiSpeech samples from AG-English bilinguals and AG monolinguals were extracted from publicly available German TV and radio interviews see . The bilThe control group included five monolingual AG male speakers, aged 69–78, who were born and raised in Thal (Austria). Unlike the bilingual subjects, the control speakers were not well-known public figures, but locals who had been informally interviewed on different occasions in Thal. Given that the bilingual samples represented non-prompted, semi-spontaneous speech, the control samples were also selected from pre-recorded broadcast interviews, i.e., all speech samples included in the rating task were produced in a non-experimental setting. While the majority of accent rating studies rely on rehearsed e.g.,  or read From the broadcast interviews, bilingual and control samples were selected following a set of pre-defined criteria see . In addiM = 3.78, SD = 0.91; see per se different in that listeners were not asked to pay attention to the speakers’ identity, but rate their pronunciation according to perceived nativeness. After completing the rating task, listeners were asked on the rating sheet if they had noticed something about one or more of the speakers. Given that we did not want listeners to make a conscious effort to identify the speakers they were listening to, we did not specifically ask them Did you recognize one or more of the speakers?, but formulated a more open question, which still gave listeners the opportunity to mention if they had identified a speaker. In fact, N = 8 listeners, who were originally asked to complete the rating task, answered this question by stating that they had recognized speaker AS and/or speaker FS in some  of the speech samples. These listeners were excluded from analysis. In order to further ensure that listeners would not be able to detect who is speaking, the selected speech samples did not contain place and proper names which may uncover the identity of the speaker.The individual samples varied in total duration, ranging from 1.95 to 5.39 s . Monolingual (ML) raters ,1 aged between 23 and 43 , were (quasi-)monolingual2 speakers of Austrian German, who reported having learned English in school for 5–9 years , but rarely or never actively used English in private or professional contexts , were Austrian German learners of L2 English who were undergraduate students of English and American Studies at the University of Graz in their 2nd to 4th year . Subjects in this group reported using English not only at university, but making moderate to frequent use of English in different communicative contexts outside university  and exts see . Bilingusity see . Raters M = 2.6, SD = 0.89) while the BIL listeners rated their proficiency in English from 4 to 6 . Results of a Mann-Whitney U test showed that the two listener groups significantly differed in their self-assessed English proficiency . In addition, each subject had to indicate if (yes/no) and how frequently (very rarely/rarely/frequently/very frequently) they used English across the four skills, i.e., speaking , listening, reading, and writing. Their answers were converted to a five-point scale, ranging from 1 = never to 5 = very frequently. As shown in U tests showed that the between-group differences in frequency of English use were significant for Speaking (private) , Watching , Reading , Listening , and Writing . By contrast, the two groups did not differ in terms of frequency of English use in professional contexts (p = 1).3In order to obtain information concerning raters’ linguistic background and language use, each subject was invited to fill in a questionnaire. Participants were asked to self-assess their overall English competence based on the six competence levels defined by the Common European Framework of Reference for Languages , which rFurthermore, the questionnaire collected information regarding subjects’ familiarity with Austrian German varieties in order to avoid that they misperceive an Austrian German Styrian regional variety as a non-native accent e.g., . Particivia e-mail. Each participant received a Power Point file, including the 28 test samples with one sound sample per slide, and detailed instructions. The rating sheet and the language background questionnaire were presented in an Excel file on two different spread sheets. As reported by some participants, it took approximately 15–20 min to complete the rating task.Due to the Covid-19-related restrictions in Austria, it was not possible to conduct the rating experiment in person. Therefore, rating materials and task descriptions were sent to the participants yes/no), and (2) indicate how certain they are concerning their judgment (certain/relatively certain/uncertain).4 In the analysis process, listener answers were transferred to a six-point Likert scale, with 6 = certain of non-native speaker status, 5 = semi-certain of non-native speaker status, 4 = uncertain of non-native speaker status, 3 = uncertain of native speaker status, 2 = semi-certain of native speaker status, and 1 = certain of native speaker status. Resulting from this, speakers who received a low rating score were perceived to sound native or near-native in their German pronunciation while speakers with a high rating score were perceived as non-native or near non-native speakers of German. The nativeness rating resulted in 1,680 individual rating scores (60 listeners × 28 ratings), which were averaged for the two rater groups (BIL vs. ML), for both speaker groups  and for each of the bilinguals individually.The nativeness judgments were based on scalar ratings which have been used in previous accent rating studies to examine both perceived L2 e.g.,  and L1  accent. Anova.clmm function from the R package RVAideMemoire , Speaker_Group  and an interaction between the two as independent variables, and Rater, Speaker and Stimuli as random factors. The model contained Rater-, Speaker-, and Stimuli-specific intercepts and by-Rater random slopes for Speaker_Group, by-Speaker random slopes for Rater_Group, and by-Stimuli-specific random slopes for Rater_Group. The second model (Model 2) aimed to test if the two listener groups (BIL vs. ML) differed in terms of their perception of nativeness of the individual bilingual speakers to determine if some of the bilinguals were perceived to be more or less native by the two listener groups, respectively. In this model, rating score was included as the dependent variable, and Rater_Group , Speaker (nine levels: one for each of the speakers), and an interaction between Rater_Group and Speaker as independent variables. Rater and Stimuli were introduced as random factors, with random slopes for Speaker (by Rater) and Rater_Group (by Stimuli).The first model (Model 1) was built to assess (1) if the two speaker groups  differed in terms of perceived nativeness, and (2) if the two rater groups (BIL vs. ML) differed in terms of their perception of nativeness of monolingual and bilingual L1 speech. Model 1 included Interrater reliability was assessed for each of the two listener groups by calculating Cronbach’s alpha coefficient. Inter-rater reliability was high in both the BIL (α = 0.84) and the ML (α = 0.89) listener group.certain of native speaker status to 6 = certain of non-native speaker status, monolingual control speakers received a median score of 1.0 (min-max = 1.0–5.0) and bilingual speakers were rated with a median score of 3.0 (min-max = 1.0–6.0), which suggests that the bilinguals were perceived to sound overall less native in their L1 compared to monolingual AG speakers. Model 1  showed a main effect for Speaker_Group, χ2[1] = 7.38, p < 0.001, confirming that the two speaker groups significantly differed in terms of the rating scores they obtained, β = 4.77, SE = 0.866, z = 5.51, and p < 0.001.Mdn = 4.0, min-max = 1.0–6.0) compared to the BIL listeners . At the same time, both rater groups judged the bilingual speakers to sound overall less native than the monolingual controls . The statistical analysis (Model 1) revealed significant main effects for Rater_Group, χ2[1] = 5.68, p < 0.001, Speaker_Group, χ2[1] = 7.38, p < 0.001, and a significant interaction between the two, χ2[1] = 17.75, p < 0.001. Pairwise comparisons showed significant differences between the listener groups in terms of the nativeness ratings assigned to the bilingual speakers, β = −2.85, SE = 0.54, z = −5.28, and p < 0.001, that is, BIL listeners were less likely to rate bilingual speakers as non-native compared to ML listeners. By contrast, no significant differences between the rater groups were identified concerning their judgments of the control speakers (p = 0.21), i.e., both BIL and ML listeners perceived the monolingual controls to sound equally native.min-max = 1.0–6.0), and speakers FS and WP both received a median score of 4.0 (min-max = 2.0–6.0). By contrast, the three bilinguals received a considerably lower rating score from the BIL rater group . Noticeably, both the BIL and the ML listeners judged speaker WP to sound more native compared to speakers AS_late and FS, but less native than the monolingual control group. It can be further observed that the speech samples produced by AS_early were rated to sound as native as the control speaker samples by the BIL listeners  while the ML listeners judged AS_early to sound slightly less native  than the monolingual controls .Rater_Group, Speaker and an interaction between the two as fixed factors, and Rater and Stimuli as random factors, showed significant main effects for Rater_Group, χ2[1] = 23.15, p < 0.001, and Speaker, χ2[8] = 405.88, p < 0.001, as well as a significant interaction between Rater_Group and Speaker, χ2[8] = 24.82, p < 0.001. Post hoc pairwise comparisons revealed that the rating differences between the two listener groups were highly significant for AS_early, β = −3.07, SE = 0.79, z = −3.87, p < 0.001, AS_late, β = −3.81, SE = 0.68, z = −5.6, p < 0.001, FS, β = −3.45, SE = 0.69, z = −5.07, p < 0.001, and WP, β = −3.21, SE = 0.76, z = −4.22, p < 0.001, again confirming that BIL raters were less likely than ML raters to judge the bilinguals’ L1 speech as non-native. While the rating differences between AS_late and FS were not shown to be significant in both the BIL (p = 1.0) and the ML (p = 0.99) rater group, the rating differences between WP and AS_late, BIL: β = 1.83, SE = 0.46, z = 3.96, and p < 0.001, ML: β = 2.44, SE = 0.51, z = 12.13, and p < 0.001, and WP and FS, BIL: β = 1.91, SE = 0.44, z = 4.33, p < 0.001, ML: β = 2.2, SE = 0.4.7, z = 4.66, and p < 0.001, turned out to be significant for both rater groups. That is, in both rater groups, speaker WP was perceived to sound more native in his L1 than AS_late and FS, but still less native than the monolingual control speakers, BIL: β = −3.96, SE = 0.23, z = −17.2, and p < 0.001; ML: β = −5.72, SE = 0.25, z = −22.41, and p < 0.001. The observation that the two rater groups differed in terms of their judgments of AS_early . In addition, no significant differences were identified in terms of the ML and BIL listeners’ rating scores assigned to the individual monolingual control speakers.Model 2, including arly see  was alsoThe present study aimed to (1) determine if long-term L2-immersed bilinguals are perceived to sound less native in their L1 when being compared to monolingual AG controls speakers, and (2) identify if and to what extent listeners’ linguistic background  affects their perception of nativeness.With regard to the first research aim, the nativeness rating experiment showed that, overall, bilingual speakers who started acquiring their L2 English as adults and who have been long-term immersed in an English-speaking country for several decades were, in fact, perceived to sound less native in their native AG pronunciation compared to monolingual AG control speakers. The observation that long-term L2 learning experience in an immersion setting may lead to a non-native L1 accent is in line with the findings of previous L1 accent rating studies . Taken tperceive as such” monolingual AG listeners who were overall more likely to judge bilingual L1 speech to sound non-native. These findings add a further dimension to the observation that “[f]oreign accent is not only the way the learners produce the L2, but also what the native speakers of the target language as such”  and L2 native to the same degree. For instance, both ML and BIL listeners perceived the bilingual speaker WP to sound more native in his L1 compared to the other two bilinguals, but still less native than the monolingual controls. The observation that bilingual speakers may differ in the degree of perceived nativeness—to the extent that some speakers are in fact not perceived differently from monolingual speakers (see section “First Language Phonetic Attrition”)—has been made in previous L1 accent rating studies e.g., , which mInterestingly, significant differences in the perception of nativeness were also observed when comparing the ratings obtained for the early (1970s) and late (2010s) speech produced by the bilingual AS. His early L1 pronunciation was judged to sound more native than his late pronunciation, but at the same time overall less native than the monolinguals’ pronunciation—at least when considering the judgments made by the ML listeners. These findings show that the extent to which a speaker is perceived to sound native in their L1 might change over time in response to L2 learning experience and being immersed in an L2-setting. A previously conducted acoustic-phonetic investigation of AS’s L1 vowels and plosives has, in fact, revealed a similar trend, namely that—in the course of L2-immersion—some of his L1 vowel and plosive targets have become less native-like, i.e., have moved away from native production norms . A reasolistener tolerance. We argue that speaking an additional language on a regular basis and use the L2 in different contexts increases listeners’ tolerance for L1 speech which might diverge from expected L1 pronunciation norms. Conversely, listeners who lack frequent and regular exposure to an L2 seem to be more sensitive toward non-native pronunciation patterns, which makes them being less lenient in their nativeness judgments. However, an alternative explanation is that the use of an additional language has led to a perceptual restructuring of the L1. As mentioned in Section “First Language Phonetic Attrition,” cross-linguistic interactions between a speaker’s L1 and L2 linguistic system are not restricted to highly experienced and long-term immersed bilinguals, but have been observed to occur at different stages of bilingualism, that is, in both beginner L2 learners  and a comparatively lower number of monolingual samples (N = 10). Still, it should be taken into consideration that the bilingual samples were produced by three individual speakers only, which is—compared to previous accent rating studies  and monolingual speech samples represented in a rating task see . As Fleges e.g., —a relatithe native speaker has been described as a standard setter, portraying the embodiment of “true” language (see true nativeness or the native speaker. If and to what extent speakers are perceived as native speakers depends on a variety of factors on behalf of both speakers and listeners. That is, the same speaker might be perceived differently by different listeners, whose perceptions are shaped and influenced by their own linguistic backgrounds and presumably by additional variables, such as their attitudes toward specific languages and accents (e.g., Taken together, the findings of the present investigation do not only make an empirical contribution to the field of bilingual speech development in that they provide evidence for the malleability of the native language system and show that speakers might be perceived as non-native speakers of their L1, but they also add a new perspective to the broader notion of nativeness and the concept of the native speaker e.g., , 2004. Duage see , againstuage see . But whauage see —speakinguage see , sufferiuage see  to experuage see . Accent-ts e.g., . Furtherts e.g., , bilinguts e.g., . Hence, ts e.g., , p. 93 pts e.g., . The obsThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of the University of Graz. The patients/participants provided their written informed consent to participate in this study.LK designed and conducted the experiment, analyzed and visualized the data, and wrote the manuscript. IM helped with the experiment design, wrote, reviewed, and edited the manuscript. Both authors have read and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The single-layer graphene obtained was transferred to a single-crystal silicon substrate by PMMA transfer technology for the subsequent growth of nano zinc oxide. The characteristics of CVD-deposited graphene were analyzed by a Raman spectrometer, an optical microscope, a four-point probe, and an ultraviolet/visible spectrometer. The sol–gel method was applied to prepare the zinc oxide seed layer film with the spin-coating method, with methanol, zinc acetate, and sodium hydroxide as the precursors for growing ZnO nanostructures. On top of the ZnO seed layer, a one-dimensional zinc oxide nanostructure was grown by a hydrothermal method at 95 °C, using a zinc nitrate and hexamethylenetetramine mixture solution. The characteristics of the nano zinc oxide were analyzed by scanning electron microscope(SEM),x-ray diffractometer(XRD), and Raman spectrometer. The obtained graphene/zinc oxide nano-heterostructure sensor has a sensitivity of 1.06 at a sensing temperature of 205 °C and a concentration of hydrogen as low as 5 ppm, with excellent sensing repeatability. The main reason for this is that the zinc oxide nanostructure has a large specific surface area, and many oxygen vacancy defects exist on its surface. In addition, the P–N heterojunction formed between the n-type zinc oxide and the p-type graphene also contributes to hydrogen sensing.In this study, hydrogen (H However, H2 is a highly flammable gas. As long as its volume ratio in the air is between 4% and 75%, it burns due to its low ignition energy and large flame propagation speed [2 gas is quite dangerous, in order to reduce the risk of use, the development of a high-sensitivity H2 sensor is important. In the future, if H2 is to be one of the options for renewable energy, the development of low-cost and high-sensitivity H2 sensors is crucial.Due to the improvement of industrial safety awareness, the detection of flammable gases has attracted more and more attention. Among industrial gases, Hon speed . BecauseRecently, metal oxide nanostructures have been widely studied as gas sensors because of their high sensitivity to target gases, such as zinc oxide , tungste2 [Graphene has excellent characteristics, such as high mechanical strength, thermal stability, good electrical conductivity, and high carrier mobility at room temperature. When used in gas sensor applications, graphene shows poor selectivity among different gases, including H2 . This pr2 . This he2 gas sensor. Graphene was deposited on Cu foil by chemical vapor deposition and transferred to Si substrate by PMMA technology [The pioneering work of this study is reported in the paper: The role of ALD-ZnO seed layers in the growth of ZnO nanorods for hydrogen sensing, published in 2019 . This stchnology . A sol–gThe graphene was prepared by chemical vapor deposition, and it was grown on a copper foil substrate at a flow ratio of 1:2 of hydrogen to methane at 950 °C. A 5 wt.% polymethyl methacrylate solution was coated onto the copper foil as a support layer for the graphene. The copper foil was etched and removed with ferric chloride , and then the silicon substrate was immersed in the solution so that the graphene was adsorbed onto the silicon substrate. The PMMA that supported the graphene was dissolved and removed with acetone  ,9.The precursor solution of the zinc oxide seed layer comprised zinc acetate and sodium hydroxide as solutes and methanol as a solvent to prepare a 1 M mixed solution. After these precursors were coated by the spin-coating method, they were baked at 200 °C for 10 min, and the coating process was repeated 4 times to obtain the zinc oxide seed layer. The specimen coated with the ZnO seed layer was put into a mixed solution prepared by zinc nitrate and hexamethylenetetramine in a molar ratio of 1:1. The concentration of the solution was 0.07 M and the temperature was 95 °C. After holding the specimen at 95 °C for a period of time, the ZnO nanostructure was obtained.2 were introduced into the chamber and the resistance variation of the sensor was recorded. The resistance value of the sensor in the air is named Rair, and the resistance value of the H2 is named Rgas. The sensitivity was calculated by the formula S = Rair/Rgas. The measurement was repeated for ten cycles under the same conditions and we checked the reproducibility is consistent.The graphene/zinc oxide nano gas sensor was placed in a closed sensing chamber. The two probe heads touched the two silver electrodes on the surface of the sensor, and the other end of the probes were connected to an electrochemical workstation  to detect the resistance variation. After the sensor was heated to the measuring temperature, different concentrations of HGraphene was grown on copper foil by chemical vapor deposition. By adjusting the process parameters, including the reaction temperature, reaction time, methane flow rate, and hydrogen flow rate, high-quality graphene could be obtained. The optimal reaction conditions for the deposition of graphene are a reaction temperature of 1025 °C, a reaction time of 20 min, a methane gas flow rate of 20 sccm, and a hydrogen gas flow rate of 10 sccm. The graphene grown on copper foil was transferred to a silicon substrate using PMMA, and a series of material property analyses were performed.−1) represents the degree of graphene defects and the G-band (1580 cm−1) represents the degree of graphitization of the material . The 2D-band (2680 cm−1) indicates the degree of graphite stacking. It is generally believed that a ratio of I2D/IG greater than 1.0 can be regarded as a single layer of graphene. The results of this experiment show an I2D/IG ratio of 1.42, which means that the prepared graphene had a single-layer structure.In order to analyze the visible light transmittance of graphene, the graphene was transferred to a glass substrate for UV–visible light spectrometer transmittance analysis. According to reports in the literature, the absorbance of a single layer of graphene for visible light is only 2.3% . In FiguThe zinc oxide nanostructure was grown on the zinc oxide seed layer by a hydrothermal method. The precursor of the solution was a 50 mL homogeneous aqueous solution prepared in equal volume ratios of zinc nitrate (0.07 M) and HMTA. The hydrothermal growth temperature was fixed at 95 °C, and the reaction was carried out in a constant temperature water tank. Ashfold et al. proposed a chemical reaction formula for the hydrothermal growth of ZnO nanostructures, as shown below :C6H12N4, it can be observed that gas molecules bind tightly with oxygen vacancies, and oxygen vacancies act as donors and release free electrons [Among them, Olectrons . Compare2 concentrations was measured at 250 °C. The measured H2 concentrations were 5 ppm, 500 ppm, 10,000 ppm, and 150,000 ppm, and the sensing results are shown in 2 concentrations. The results show that as the concentration of H2 increased, the sensitivity of the sensor also increased. In the case of high hydrogen concentrations, the sensing sensitivity greatly increased compared to low concentrations.The sensitivity of the graphene/zinc oxide nano-heterostructure sensor to different H2 concentrations and a fixed sensing temperature of 250 °C. The sensors continuously performed ten cycles of hydrogen sensing tests and the results are shown in In order to investigate the reproducibility of the graphene/zinc oxide nano-heterogeneous gas sensor prepared in this study, the sensor was placed in environments of different H2, the oxygen molecules originally adsorbed on the surface will be reduced, and the electrons will be released back into the zinc oxide. The conductivity of the zinc oxide rises, and the Fermi level also rises. The electron depletion layer at the junction becomes narrower and the overall resistance decreases. The change due to the rectification of the P–N junction can increase the resistance variation between the sensor in an air atmosphere and in the target gas, thereby improving the overall sensing performance.According to the literature , it is k2D/IG = 1.43 was prepared by chemical vapor deposition and transferred to a silicon substrate by PMMA transfer technology. One-dimensional zinc oxide nanostructures were prepared by spin-coating and hydrothermal methods and grown on the graphene top surface. The obtained graphene/zinc oxide nano-heterostructure gas sensors had good sensitivity in the hydrogen concentration range of 5 ppm to 150,000 ppm, with good sensitivity and repeatability. This can mainly be attributed to the high specific surface area of nanostructured zinc oxide and the P–N heterojunction formed between zinc oxide and graphene.In this study, single-layer graphene with I


Tau neurofibrillary tangle (NFT) pathology in the medial temporal lobe (MTL) is closely linked to neurodegeneration, and is the early pathological change associated with Alzheimer’s disease (AD). To elucidate patterns of structural change in the MTL specifically associated with tau pathology, we compared high-resolution ex vivo MRI scans of human postmortem MTL specimens with histology-based pathological assessments of the MTL. MTL specimens were obtained from twenty-nine brain donors, including patients with AD, other dementias, and individuals with no known history of neurological disease. Ex vivo MRI scans were combined using a customized groupwise diffeomorphic registration approach to construct a 3D probabilistic atlas that captures the anatomical variability of the MTL. Using serial histology imaging in eleven specimens, we labelled the MTL subregions in the atlas based on cytoarchitecture. Leveraging the atlas and neuropathological ratings of tau and TAR DNA-binding protein 43 (TDP-43) pathology severity, morphometric analysis was performed to correlate regional MTL thickness with the severity of tau pathology, after correcting for age and TDP-43 pathology. We found significant correlations between tau pathology and thickness in the entorhinal cortex (ERC) and stratum radiatum lacunosum moleculare (SRLM). When focusing on cases with low levels of TDP-43 pathology, we found strong associations between tau pathology and thickness in the ERC, SRLM and the subiculum/cornu ammonis 1 (CA1) subfields of the hippocampus, consistent with early Braak stages.The online version contains supplementary material available at 10.1186/s40478-021-01275-7. Alzheimer’s disease (AD) pathology is characterized by phosphorylated-tau in the form of neurofibrillary tangles (NFTs) and extra-cellular deposits of amyloid-beta (Aβ) –3, whichA growing number of studies have used in vivo structural MRI to derive measurements of subtle volumetric changes in the MTL caused by neurodegeneration , 12. How3 resolution post-mortem MRI scans of intact MTL specimens from twenty-nine brain donors. To overcome challenges due to the complex geometry of the hippocampus and MTL cortex and significant anatomical variability across specimens, we leverage advanced computational anatomy techniques to co-register the individual ex vivo MRI scans and capture the “average” shape of the MTL. Anatomical regions in the atlas are derived by combining information from eleven specimens in which serial Nissl histology was used to annotate MTL and hippocampal subregions based on cytoarchitecture. Leveraging this atlas, we report regional patterns of association between MTL thickness and NFT pathology , while accounting for the confounds of TDP-43 pathology.Here we develop a methodological framework that allows detailed 3D mapping of the relationships between MTL thickness and pathological measures. We generate the first-of-its-kind high-resolution 3D computational atlas of the human MTL generated by applying groupwise image registration to 200 × 200 × 200 µm3 resolution. Details of the imaging protocol are provided in Additional file Brain hemispheres were obtained from twenty-nine donors; one specimen from the brain bank operated by the National Disease Research Interchange (NDRI), sixteen specimens from autopsies performed at the University of Pennsylvania Center for Neurodegenerative Disease Research (CNDR) and twelve specimens from the University of Castilla-La Mancha (UCLM) Human Neuroanatomy Laboratory (HNL) in Spain. Human brain specimens were obtained in accordance with the University of Pennsylvania Institutional Review Board guidelines, and the Ethical Committee of UCLM. Where possible, pre-consent during life and, in all cases, next-of-kin consent at death was given. CNDR hemispheres were fixed in 10% formalin solution for at least 30 days before extracting intact MTL blocks. HNL cases were fixed by perfusion with 4% paraformaldehyde through both carotid arteries. The blocks were then imaged on a Varian 9.4 T animal scanner at a 200 × 200 × 200 µmFollowing MRI scanning, each of the specimens underwent histological processing for neuropathological examination and neuroanatomical analysis. In twenty-seven specimens, dense serial histology was performed. The specimens were cut into 2 cm blocks using custom molds that were 3D printed to fit each MTL specimen, cryoprotected and sectioned at 50 μm intervals in a sliding microtome coupled to a freezing unit . For neuropathological diagnosis, we were interested in examining the anterior ERC at the mid-level of the amygdala, and the dentate gyrus (DG) and CA subfields at the mid-level of the body of the hippocampus. Therefore, for the remaining two specimens that did not undergo serial histology, two tissue blocks were cut at the level of the amygdala and hippocampal body, cryoprotected and sectioned into 50 μm sections. For each of the specimens, two adjacent sections were sampled at the mid-level of the amygdala and the hippocampal body for immunohistochemistry. In each case, the two sections were immunostained for tau and TDP-43 using anti-human PHF-Tau , mouse, Thermo Scientific, Product Number MN1020, 1:500) and 66318-1-Ig Anti-phospho (409/410) TDP-43  respectively. Sections were then mounted on 7.5 cm × 5 cm slides and digitally scanned at 20× resolution.Regional thickness analysis was performed using histopathology ratings of tau and TDP-43 pathologies derived from the MTL both ipsilateral and contralateral to the one that was scanned. Semi-quantitative ratings for tau and TDP-43 pathology were obtained for the contralateral hemisphere by obtaining tissue samples at the time of autopsy and are available for a subset of twenty-eight donors (CNDR and HNL specimens) in three MTL regions routinely examined in the CNDR neuropathology evaluations . More spTo generate the ipsilateral ratings, an expert neuropathologist (D.J.I) provided semi-quantitative ratings of the severity of tau and TDP-43 pathology burden in the MTL using the scanned digital immunohistochemistry images for each of the neurodegenerative pathologies. Once again, the average ipsilateral rating across the three MTL locations was used in the analysis. We note that unlike the contralateral ERC ratings which are based on tissue sampled at the level of the lateral geniculate nucleus, ipsilateral ERC ratings are obtained using anterior ERC sections at the level of the amygdala.The atlas construction pipeline used in this work relies on segmentations of the MTL and SRLM to guide the registration process. To reduce the manual effort needed to generate these 3D segmentations, we adopted semi-automatic segmentation methods to label the MTL and SRLM . In someAn ex vivo MRI atlas of the MTL was generated by leveraging the registration pipeline developed by Adler et al. to build an ex vivo atlas of the human hippocampus . ConventIn eleven of the specimens that underwent serial histological processing, every tenth section  in each tissue block was stained for Nissl using 0.25% thionin and digitally scanned at 20× resolution. For each block, the scanned sections were reconstructed into a 3D volume and aligned to MRI space using a custom deformable 3D registration pipeline. Further details of the histology protocol and the approach for 3D reconstruction and matching of histology to MRI are provided in Yushkevich et al. .https://github.com/pyushkevich/histoannot) and used line drawings and text labels to annotate boundaries between adjacent anatomical regions. Following histology reconstruction and registration to MRI, the boundary annotations were overlaid on the co-registered MRI and histology images. Additionally, the manual segmentations of the whole MTL cortex and SRLM that were used to guide groupwise registration were registered to the histology images to inform the outer MTL boundary. The MRI, histology and MTL segmentation images were displayed side-by-side in ITK-SNAP [For each of the specimens, the boundaries between MTL subregions cornu ammonis (CA) 1, CA2, CA3, DG, SUB, presubiculum, parasubiculum, hippocampal amygdala transition area (HATA), SRLM, ERC, BA35, BA36, area TE and the parahippocampal cortex (areas TF and TH) were identified on the basis of cytoarchitectural features in the Nissl stained sections following the anatomical rules presented in the Atlas of the Human Brain by Mai et al. . AnnotatITK-SNAP  to facilFor each of the eleven specimens, the completed segmentations were then mapped to the MRI atlas using the deformable transformations generated by the groupwise registration pipeline. Note that for each specimen, small gaps in the segmentation may exist between blocks. A consensus segmentation of the MRI atlas was obtained by application of voxel-wise majority voting among the eleven segmentations with slight regularization by a Markov Random Field prior. More details on this approach are provided in the supplemental information, Section S1.4.5 of Adler et al. .Regional thickness of the MTL cortex, hippocampal gray matter, and SRLM was estimated by warping the atlas to the native MRI space of each specimen and performing Voronoi skeletonization , frontotemporal lobar degeneration (FTLD) with TDP-43 inclusions, cerebrovascular disease and Lewy body disease. Figure Figure p = 0.038) with a smaller effect at a trend level in the SRLM (corrected p = 0.098). When using only age as a covariate , stronger associations between tau pathology and thickness are observed in the ERC region, extending towards the transentorhinal cortex (corrected p = 0.01) and SRLM . Additionally, a significant cluster is observed in the SUB/CA1 region (corrected p = 0.031). To evaluate the effects of tau in the absence of severe TDP-43 pathology, regional thickness analysis was repeated in the subset of twenty-two specimens with low levels of TDP-43 pathology (average MTL TDP-43 rating < 1), using only age as a covariate. Once again, significant associations are found between tau and thickness in the ERC region (corrected p = 0.035) and SRLM (corrected p = 0.032), with a weakened association in the SUB/CA1 region (corrected p = 0.092). To further understand the weakened tau effects in the presence of TDP-43 pathology, we performed a supplementary analysis looking at effects of TDP-43 pathology on regional thickness with age and tau as covariates. Our analysis reveals significant associations between TDP-43 rating and atrophy in the posterior parahippocampal cortex (corrected p = 0.027), anterior hippocampus (corrected p = 0.01) and SRLM  , and associations in the SRLM (corrected p = 0.014 and 0.017) and SUB/CA1 region (corrected p = 0.047) that are stronger in absolute terms than in the ipsilateral analysis. Similar patterns of correlation are also observed between thickness and contralateral ratings of TDP-43 pathology when compared to the results of the ipsilateral TDP-43 analysis  , 33. In In a secondary analysis, we investigated the degree to which hemispheric asymmetry impacts the findings of our regional thickness analyses using data from the twenty-eight donors for whom we had neuropathological ratings sampled from both hemispheres. Previous studies have shown some asymmetry in ratings of tau pathology, and TDP-43 in 15–43% of cases with AD , 43. In The limitations of the present study are the clinical heterogeneity of our dataset and the reliance on semi-quantitative ratings of tau and TDP-43 pathology. The semi-quantitative measures are sensitive to inter-rater and intra-rater errors and likely contribute to some uncertainty in our findings . AdditioOverall, our findings provide a more refined understanding of how tau pathology is associated with cortical thinning within the MTL and motivate further characterization of the MTL in AD using detailed ex vivo MRI analysis. The clusters identified from the tau thickness analyses indicate granular MTL regions where in vivo measures of neurodegeneration are expected to be strongly associated with tau pathology. In an exploratory analysis, we attempted to use the ex vivo derived, AD-specific hotspots Fig. B as biomConstructing a probabilistic atlas of the MTL has far-reaching applications in AD research beyond the work shown here. Perhaps future analyses leveraging this technology in a patient cohort more consistent with the one we would expect to encounter in an AD clinical trial would result in hotspots that are more sensitive to longitudinal change in the presence of NFT pathology, potentially enabling the development of neurodegeneration biomarkers which are more effective during early AD clinical trials. Furthermore, in future work, 3D quantitative maps of NFT density derived from serial histology imaging will be mapped into ex vivo atlas space to generate a comprehensive probabilistic description of the progression of NFT pathology at each Braak stage , 48, 49.Additional file 1. Supplementary methods and results.


The computing results reveal that Fe-N4@graphene is a fairly promising anchoring material, in which the formed chemical bonds of Fe-S and N-Na ensure the stable adsorption of NaPSs. Furthermore, the doped transition metal iron could not only dramatically enhance the electronic conductivity and the adsorption strength of soluble NaPSs, but also significantly lower the decomposition energies of Na2S and Na2S2 on the surface of Fe-N4@graphene, which could effectively promote the full discharge of Na-S batteries. Our research provides a deep insight into the mechanism of anchoring and electrocatalytic effect of Fe-N4@graphene in sulfur cathode, which would be beneficial for the development of high-performance Na-S batteries.Developing highly efficient anchoring materials to suppress sodium polysulfides (NaPSs) shuttling is vital for the practical applications of sodium sulfur (Na-S) batteries. Herein, we systematically investigated pristine graphene and metal-N In recent years, it has become more and more difficult for the current commercial lithium-ion batteries (LIB), due to their relatively low theoretical energy density limits 200–300 Wh/kg), to meet the ever-growing demands of society’s electrical energy storage, including portable electronic devices, electric vehicles, and smart grid storage applications ,2,3. The00 Wh/kg,2Sn, where 1 ≤ n ≤ 8) have poor electrical conductivity, which leads to a low utilization of active materials; (2) the high-order NaPSs  are prone to dissolve in the ether-based liquid electrolyte, then diffuse towards the sodium anode, thus causing the so-called “shuttling effect” and leading to low coulombic efficiency and rapid capacity fading; (3) the large volume expansion of sulfur cathode during discharge can bring about poor stability of the electrode. To address the above-mentioned issues, designing appropriate multifunctional conductive anchoring materials that can both catch and catalyze the NaPSs is of vital importance.Despite these advantages, RT Na-S batteries have not yet been practically applied, since this technology still suffers from several critical challenges ,20,21,224 and Cr-N4 co-doped graphene shows a strong adsorption and full discharge of LiPSs, resulting in a greatly improved performance of Li-S batteries [4@graphene) used in Na-S batteries as multifunctional anchoring materials have been less explored up to now.Graphene nanosheet is one of the widely used electrode host materials due to its high specific surface area, attractive electrical/thermal conductivity, and excellent flexibility property ,24,25. Hatteries . However4@graphene  [4@graphene can tightly combine with NaPSs through chemical bonds, and its electric conductivity is well retained. Hence, properly doping Fe-N4 into graphene can greatly enhance the anchoring effect for NaPSs to suppress the adverse “shuttling effect” in Na-S batteries. Additionally, the doping transition metal ions can effectively lower the decomposition energy barriers of NaPSs, resulting in a signal development of their full discharge. Our results not only facilitate a deep understanding of the mechanism of metal-N4@graphene used as a multifunctional anchoring material, but also provide worthy guidance to exploit high-performance Na-S batteries.Inspired by the aforementioned investigations, here, we systemically investigated the experimentally available transition metal-N and Mn)  to illus4 doped graphene monolayers. A 3 × 3 × 1 Monkhorst-Pack [−5 eV/cell. The van der Waals (vdW) interactions are described by using the Lee et al. vdW-DF2 functionals [4@Graphene monolayers, we constructed 2, 4, 4, and 3 linear-interpolation intermediate images between the initial and final Na positions along the pathway of Na2S/Graphene, Na2S2/Graphene, Na2S/Fe-N4@Graphene, and Na2S2/Fe-N4@Graphene to perform the climbing-image nudged elastic band (ci-NEB) algorithm [2S and Na2S2 on graphene and Fe-N4@graphene from their most stable adsorption sites, and placed them far away, then optimized their structural configurations and used them as the final states of ci-NEB, respectively. The Fermi energy level of projected density of states (PDOS) has been converted to zero.The calculations of this work have been performed within the framework of the DFT by using Vienna ab initio simulation package (VASP) ,44 code.rst-Pack  k-mesh ictionals ,49, whicctionals ,40. To elgorithm ,51, respEad of NaPSs molecule on pristine graphene and metal-N4@graphene are defined as the following formula:Etot is the total energy of the substrate with adsorbed NaPSs, Esub is the energy of substrate of pristine graphene or metal-N4@graphene, and Emole is the energy of isolated NaPSs molecule. According to this definition, a positive value indicates an exothermic  reaction, and the strength of absorbing capability of substrate for NaPSs molecules enhances as the value becomes more positive.The adsorption energies 2S, Na2S2, Na2S4, Na2S6, and Na2S8) are formed, and the final product is Na2S species, as demonstrated by experiments [2Sn  species and cyclo-S8 are shown in 2Sn species and cyclo-S8 are listed in 2Sn  species are significantly shorter than those (~2.75 Å) of high-order Na2Sn  species clusters, while the S-S bonds slightly decrease with the increase in the number of S atoms, similar to the scenario of Li2Sn species [2Sn species are more easily dissolved into sodium cations and polysulfide anions than low-order Na2Sn species in the electrolyte of Na-S batteries.During the discharge process of Na-S batteries, the sodium polysulfide species  substrate in a 3 × 5 supercell (containing 59 atoms), in which all of the atoms are in the same plane. For the metal-N4@graphene, six carbon atoms in the center of the substrate are removed and then replaced by four nitrogen atoms and one metal atom. Three kinds of transition metal  atoms are considered in this work. Our calculations show that the central transition metal atom is surrounded by four equivalent metal-N bonds, with the lengths ranging from 1.89 Å to 1.96 Å, which is well consistent with previous literature [terature ,36.4 co-doped graphene heterostructure enhances its electrical conductivity. Previous studies found that an electrode structure composed with graphene can facilitate electron and ion transport [4  co-doped graphene are calculated, as illustrated in 4@graphene exhibit spin asymmetrically polarized ground states, due to the difference between the number of electrons in spin-up and spin-down components. In addition, their magnetic moments are mainly localized on the central metal atom, as shown in d orbital electrons  as the anchoring material for high-performance Na-S batteries, the adsorption energies of Na2Sn species on pristine and co-doped graphene are calculated, respectively. The computing results show that the minimum distance between Na2Sn species and pristine graphene substrate is larger than 2.8 Å, and all adsorption energies of Na2Sn species on graphene are around 0.7 eV  as anchoring materials can maintain their structures well, and the Na2Sn species and cyclo-S8 adjust themselves to achieve the most stable adsorption.To obtain the most stable adsorption configurations, we considered different orientations of Naface see . Take Fi4@graphene , one sulfur atom binds with the metal atom, and one or two sodium atoms bind with nitrogen atoms, forming a chemical binding ring configuration. For instance, the chemical ring of the Na2S adsorbed system has two Na-N bonds, and the Na2S2 adsorbed system has two Na-N bonds and one S-metal bond. All adsorption energies of Na2Sn species on Fe-N4@graphene increase significantly and are much greater than that of pristine graphene. Thus, it is obvious that the strong chemical rings with large adsorbing energies make the adsorption configuration more stable, resulting in a high affinity for Na2Sn species. This feature of co-doped graphene can significantly hinder the shuttling effect, which mainly comes from high-order Na2Sn species. Notably, the adsorption energies of Na2Sn species on Fe-N4@graphene are obviously larger than that of Co/Mn-N4@graphene  on anchoring materials is a critical factor for energy capability, utilization of sulfur, and cycling performance of Na-S batteries. As the abovementioned analysis shows, Fe-N4@graphene is the most effective anchoring material for Na2Sn species; thus, its electrocatalytic performance deserves further study, which is essential for coulombic efficiency, rate capability, and charge/discharge performance of Na-S batteries. To gain an in-depth understanding of the electrocatalytic property of doped iron, the climbing-image nudged elastic band (ci-NEB) method is applied to calculate the energy barrier for Na2S and Na2S2 decomposing on the surface of pristine graphene and Fe-N4@graphene, respectively, to evaluate the sodium extraction reaction kinetics.It is worth noting that the decomposition of the deep discharge products . The main evolution is accompanied by one sodium ion separating and moving away from the sulfur atom in Na2S/Na2S2 molecule, including the breaking of one Na-S bond. The energy profiles for the decomposition processes of sodium ion on pristine graphene and Fe-N4@graphene are shown in 2S and Na2S2, respectively), much larger than that of Fe-N4@graphene . It is known that reducing the decomposition barrier of Na2S/Na2S2 can greatly increase the utilization of active materials, decrease the formation of dead polysulfides, and achieve a high charging rate and a longer cycling life. In brief, Fe-N4@graphene demonstrates a much lower decomposition barrier of Na2S/Na2S2, which exhibits its promising potential for catalyzing polysulfides in the charging process of Na-S batteries.Herein, we considered the decomposition process from an intact Na4@graphene can not only effectively adsorb the sodium polysulfides to block the shuttling effect, but also significantly improve the electronic conductivity and catalyze the electrochemical interactions, consequently achieving multiple targets and addressing several vital challenges. Thus, our research reveals the multi-functional anchoring effects of Fe-N4@graphene in Na-S batteries, while providing valuable guidance for designing high-performance sulfur cathode materials.In one word, the anchoring material Fe-N4@graphene  as multi-functional anchoring materials to hinder the “shuttling effect” of NaPSs in Na-S batteries. Our calculation results reveal that doping with metal-N4@graphene could not only dramatically improve the electronic conductivity, but also enhance the adsorption of Na2Sn species and cyclo-S8 compared to pristine graphene, which is mainly contributed by the strong chemical bonds of metal-S and N-Na. Among the doping transition metals considered here, Fe-N4 co-doped graphene is the best anchoring material. Consequently, the electrocatalytic property of Fe-N4@graphene was studied through ci-NEB method, and we found that the doped transition metal iron could dramatically decrease the decomposition energy barrier of Na2S (dropped 0.89 eV) and Na2S2 (dropped 0.18 eV), which has a significantly improvement on the utilization of sulfur and full discharge of batteries. Our findings enrich the fundamental understanding of the micro-level mechanism of metal-N4@graphene as multifunctional anchoring materials added to sulfur electrode, and shed light on the design of high-performance Na-S batteries.Based on first-principles theory calculations, we systematically investigated the transition metal-N


LINE-1  retrotransposons are the only autonomously active transposable elements in the human genome. The evolution of L1 retrotransposition rates and its implications for L1 dynamics are poorly understood. Retrotransposition rates are commonly measured in cell culture-based assays, but it is unclear how well these measurements provide insight into L1 population dynamics. This study applied comparative methods to estimate parameters for the evolution of retrotransposition rates, and infer L1 dynamics from these estimates.Our results show that the rates at which new L1s emerge in the human population correlate positively to cell-culture based retrotransposition activities, that there is an evolutionary trend towards lower retrotransposition activity, and that this evolutionary trend is not sufficient to counter-balance the increase in active L1s resulting from continuing retrotransposition.Together, these findings support a model of the population-level L1 retrotransposition dynamics that is consistent with prior expectations and indicate the remaining gaps in the understanding of L1 dynamics in human genomes. Transposable elements (TEs) are mobile DNA segments that comprise more than half of the human genome . They arDe novo L1 insertions can be disruptive to the host genome. Some specific insertions have been linked to diseases such as hemophilia and thalassemia . OverallAlthough L1s have profound effects on their host, the dynamics of L1s in the human population are still poorly understood. Different L1 families appear to have expanded in the human lineage at different times , but it Selection on the retrotransposon level should lead to an increase in the retrotransposition rates since more active L1s generate more insertions, which tend to be more active themselves. Without selection, one would expect that after insertion the retrotransposition activity of an L1 decreases over time because random mutations of L1 sequences are more likely to disrupt the retrotransposition machinery rather than improve it. In addition, host-level selection is likely to favor low retrotransposition activity due to the general disruptive effects of retrotransposition for the host, leading to a scenario of decreasing retrotransposition activity post insertion. While this scenario is plausible, it is also conceivable that host-level selection maintains retrotransposition, due to potentially beneficial effect of retrotransposition for the host. We are not aware of any study that compared these scenarios for the evolution of L1 retrotransposition rates with data from human genomes.The evolution of the retrotransposition rate can only be understood if the retrotransposition rate can be reliably estimated for individual L1 sequences. Retrotransposition activity of individual L1s is usually measured in cell culture –11. It iComparative methods can be used to estimate the rates at which new L1s emerge in the population. However, it is likely that these population-level rates of L1 emergence do not only depend on intrinsic retrotransposition rates but also on the relative importance of selection and drift, which in turn is influenced by the effective population size. Since the human effective population size underwent some dramatic changes, it is important to account for population-level temporal trends when estimating intrinsic L1 transposition rates through comparative methods.In this study we use comparative methods to address four interrelated questions about the evolution of retrotransposition activity, namely: (i) do L1s with a higher cell-culture measured retrotransposition rate generate more new L1 insertions detectable in the human population? (ii) is there an evolutionary trend towards a lower intrinsic retrotransposition rate after insertion? (iii) is there a population-level temporal inhomogeneity in the overall retrotransposition rate and (iv) what is the L1 growth rate in human genomes resulting from estimates obtained from addressing questions (i) - (iii)?The nucleotide sequences of 155 full-length L1s in the human genome were obtained from two studies that published L1 sequences and their corresponding retrotransposition activity values , 10. ThePhylogenetic trees of L1s were reconstructed from L1 sequence alignments using BEAST v.10.4  to fit aRP were classified, respectively, as low or high retrotranspositon L1s. According to the BiSSE model, the two character-states (high and low retrotransposition activity in our case), evolve along the tree based on a Markov process, and the speciation and extinction rates of lineages depend on the character-states, leading to six parameters (Table 0 and λ1) can change by a factor that is the same for both speciation rates (λ0 and λ1). Hence, these models add one additional parameter per change point. BiSSE measures branch lengths in proportion of nucleotide substitution whereas the population genetic events in the change point model were specified in number of generations ago. To convert between these units, a substitution rate of 2.5*10− 8 per nucleotide and generation was used [A Binary State Speciation and Extinction (BiSSE) model, which combines a speciation-extinction model with the two-state Markov model , 16, wasrs Table . The fitwas used .Table 1Px denotes the number of low and high activity L1s. The dominant eigenvalue of the matrix in the above equation gives the asymptotic rate of increase of L1 insertions and the associated eigenvector the relative number of high and low activity L1s in the stationary phase.The parameters of the BiSSE model define the following system of differential equations:The posterior distributions of evolutionary rates between the two retrotransposition activity states along the phylogenetic trees were also estimated using the Bayesian MCMC software package BayesTraits . The pos0 = μ1 = 0) and that q10, the rate at which high-activity L1s become low-activity L1s is ten times higher than q01, the rate at which low-activity L1s become a high-activity L1s (Table q01) is non-zero, and any model that constrains this transition to zero fits the data substantially worse (Table 0 / λ1) is close to the ratio of mean retrotransposition values from cell cultures among low and high-activity L1s  were 1.6 times higher in the time predating the change point than in the time after the change point. All other aspects of the one-change point model are qualitatively the same as in the best-fitting constant time model. After the change point, the estimated L1 growth rate is 1.4 *10− 6 with an L1s doubling time of 5*105 generations.The phylogenetic tree of full-length L1s suggests that L1s underwent repeated phylogenetically independent transitions between high and low retrotransposition rates  event. The 155 different L1 loci studied in our analysis require 154 retrotransposition events. However, these 154 retrotransposition events do not have to exactly coincide with the 154 internal nodes of the L1 tree, because strictly speaking, the internal nodes correspond to coalescent rather than retrotransposition events and the coalescent process within the human population might be on a comparable time scale as the time between different retrotransposition events. More accurate parameter estimation might therefore require a model that considers the coalescent and retrotransposition process simultaneously. Nevertheless, the ratio of speciation rates estimated via the BiSSE model for high and low retrotransposition L1 is very close to the ratio of retrotransposition rates for these L1 classes obtained from cell cultures, suggesting that the results obtained by ignoring the coalescent process might still be reasonably accurate.The results of the BiSSE and BayesTraits models provide also information on how retrotransposition activity evolves after insertion. Both approaches show clear statistical support for a model in which the evolutionary change from high to low retrotransposition activity is much more likely than for the reverse. This is consistent with a priori expectations since random mutations of L1 sequences are more likely to disrupt the retrotransposition machinery than improve it. Both approaches indicate that, nevertheless, L1s occasionally change from low activity to high activity. Each model has its own strength and weakness. The BiSSE model requires ultrametric trees, and hence a more restrictive phylogenetic estimation procedure, but it allows incorporating the effects of activity on branching. The BayesTraits model poses no restrictions on the tree branch lengths but does not incorporate the effects of activity on branching. The fact that both models arrive at qualitatively similar conclusions about the evolution of retrotransposition activity underscores the robustness of these results.ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/, L1s were identified using the tag ““INS:ME:LINE1“ and the tag “SVLEN” was used to select L1s over 6000 bp length). Hence, the majority of L1s included in this analysis are polymorphic and occur at a higher population frequency than the average L1, suggesting that methods to detect L1 in the two studies were biased against low-frequency L1s.Interpretation of the BiSSE parameters requires a careful consideration of the data. Both studies whose data were used in this analysis , 10 searThe speciation rates of the BiSSE model estimates the rate at which new L1 insertions occur and reach high enough population frequencies to be detected. As such, the speciation rate in the BiSSE model combines the effects of mutation, selection and drift. Similarly, the rate at which an L1 generates new L1 insertions depends on its retrotransposition activity and population frequency. The estimated transition rates between high and low retrotransposition activity are therefore the result of the combination of two processes, the evolution of retrotransposition activity and changes in allele frequency. On average, full-length L1s have a negative selection coefficient  and mostSince the majority of L1s included in this analysis are polymorphic, the estimated speciation rates are likely to be higher than the allele substitution rate, i.e. the rate at which new alleles arise and become fixed in the population, and lower than the de-novo insertion rate. Estimating the substitution rate would require restricting the model to fixed L1s. However, restricting the analysis to L1s that are fixed in the population would miss the high-activity alleles that tend to be polymorphic and contribute significantly to the overall retrotransposition .0 = μ1 = 0). There are two possible explanation for these zero extinction rates. Non-zero extinction rates lead to an uptick of the apparent speciation rate in the very recent past, since these are branches that have not yet gone extinct [The best-fitting BiSSE model restricts the extinction rates to zero (μ extinct . The zer− 6 new L1 insertions per generation. The model furthermore estimates that at a steady state, 75% of L1s are low activity, leading to an average retrotransposition activity of 27%. Ewing & Kazazian estimated the L1 retrotransposition in humans to be between 1/95 and 1/270 births [3 average retrotransposition competent full-length L1s for our estimate to be compatible with the estimate by Ewing & Kazazian. However, the published estimates of the number of L1s with intact ORFs in a human genome range from 90 to 266 [According to the BiSSE model, an average full-length L1 generates 3.2 *100 births . Our pop0 to 266 , 29. The0 to 266 . Negativ5 generations. It is not clear what mechanism would lead to a negative feedback of L1 density on average retrotransposition rate that is required for a stable equilibrium. It has been suggested that a stable equilibrium for retrotransposition is obtained when the number of available genomic positions becomes limiting and L1s repeatedly insert into pre-existing L1s [It is unknown whether L1s are growing in the human population or are at a stable equilibrium. Linear models, such as the BiSSE model, only allow for exponential growth or decline. According to our parameter estimates, L1s grow currently exponentially with a doubling time in human genomes of 5*10ting L1s , 8. Howeting L1s . A more The diversification rates estimated from L1 phylogenetic trees are largely consistent with cell culture-based retrotransposition estimates, which validates both methods. The evolutionary decline of retrotransposition rates is supported by two different models and consistent with prior expectation. Hence, applying comparative methods to analyze L1 retrotransposition produces robust and coherent results that provide insight into L1 dynamics on a larger scale. According to our results, active L1s are currently increasing in human genomes.


E-Bibliotherapy deals with adolescent psychological stress by manually or automatically recommending multiple reading articles around their stressful events, using electronic devices as a medium. To make E-Bibliotherapy really useful, generating instructive questions before their reading is an important step. Such a question shall (a) attract teens’ attention; (b) convey the essential message of the reading materials so as to improve teens’ active comprehension; and most importantly (c) highlight teens’ stress to enable them to generate emotional resonance and thus willingness to pursue the reading. Therefore in this paper, we propose to generate instructive questions from the multiple recommended articles to guide teens to read. Four solutions based on the neural encoder-decoder model are presented to tackle the task. For model training and testing, we construct a novel large-scale QA dataset named TeenQA, which is specific to adolescent stress. Due to the extensibility of question expressions, we incorporate three groups of automatic evaluation metrics as well as one group of human evaluation metrics to examine the quality of the generated questions. The experimental results show that the proposed Encoder-Decoder with Summary on Contexts with Feature-rich embeddings (ED-SoCF) solution can generate good questions for guiding reading, achieving comparable performance on some semantic similarity metrics with that of humans. TeenRead which weekly recommends 4 articles to users based on the similarity between users’ stress categories and articles’ categories, as shown in TeenRead are from the psychology practitioners and some authorized users, who are willing to share literature as a means of mutual help and personal development, and the purpose of those articles is to address certain types of psychological stress of teens. With the rapid development of economy and society, teens are facing various psychological stress coming from study, family, love, peer relation, self-cognition and so on. Bibliotherapy has been proved to be an effective treatment method to deal with psychological stress ,2,3. It TeenRead’s automatic recommendation of appropriate articles to stressful teens alleviates the manual efforts on readings selection. This made one step towards online E-bibliotherapy. Beyond that, for the sake of better reading experience and more effective bibliotherapy, devising the instructive questions, instead of just devising the key phrases, from the multiple recommended articles to guide reading is also important and desirable. Such a question helps in three aspects:(1)As psychological studies show, devising questions before reading can help attract readers’ attention .(2)The instructive question conveys the essential message of the recommended articles so as to improve teens’ active comprehension. From this point of view, the question also serves as a headline.(3)TeenRead is assisting teens to ease stress by reading, the most effective way, which we believe can be achieved, is to react to and highlight teens’ stress. If the question  shows care and concern for stressful teens, they may feel resonance in emotion, and are thus willing to pursue the reading.More importantly, considering the mission of TeenRead. For the four example articles in How can you plan your future career? Formally, given N recommended articles To this end, the study explores how to generate such instructive questions from the multiple articles recommended by In the literature, generating an instructive question is not explored to our knowledge, especially from multiple articles. This is a non-trivial task facing a number of challenges:Task novelty and complexity. Although question generation (QG) task has been studied for years in the areas of reading comprehension ,9,10,11 Question type diversity. Most of the previous work on QG focused on factoid questions  , where aInformation coverage. A question contains only a few words, but is required to cover the important information of multiple long articles. It demands effective synthesis and abstraction techniques, both of which remain a challenge for current NLP technology.How can I…?”. From this perspective, question generation based on heuristic rules and templates .  These questions are simple and fact-based, their answers are a short span of words, entity or sentence.List questions. This type of question can be decomposed into several factoid questions, e.g., what are the most popular programming languages?Hypothetical questions . Hypothetical questions ask for answers based on a hypothesis, which are hard to answer because they are highly subjective to questions and the answers are not specific.Confirmation questions . This type of question requires answers in the form of yes or no. In Quora, answerers usually provide detailed explanations on why they choose yes or no.Causal and Explanatory questions . This type of question asks for answers explaining one phenomenon.In order to explain it clearly, the answers tend to be quite long.Methodological questions . This type of questions are highly valuable in our task. When teens encounter problems, they tend to pose questions for detailed solutions or advices. However, this type of question is the hardest question to derive from multiple answers. It is because the answers are completely subjective to answerers, and the key information of the question is sparsely spreading across different answers.Based on the above classification, we analyze the distribution of question types in TeenQA in As a comparison, we also analyzed the question types of datasets SQuAD, WikiQA and MARCO, which are also used in question generation tasks ,13. We rGeneral Question Generation for Reading Comprehension.The existing QA datasets for reading comprehension (RC) in study, family, peer relation, romantic relation, self-cognition, employment and life. The attention degree of each question can be inferred from question’s follows number. This makes it possible for the quantitative study of adolescent stress. We hope that TeenQA could help psychologists and education practitioners better understand and solve teens’ stress problems.Adolescent Stress Analysis. TeenQA contains nearly 700 K topic-specific questions, related to the typical stress categories of adolescents about In addition to the instructive question generation task, TeenQA could also be applied to other scenarios:N recommended articles Given The task can be viewed as a seq2seq task, which can be addressed elegantly by a neural encoder-decoder model ,26.For an input sequence Encoder. The encoder encodes the input sequence into a context vector representation rk (RNN)  with LSTrk (RNN)  units isf indicates the function of RNN unit.Then RNN encoder calculates the hidden state g is the context function. Usually, we make After all hidden states are obtained, the context vector representation is calculated as: Decoder. To better capture the sequence information, we also use LSTM-RNN as the decoder. It generates the output based on the hidden state Attention. The use of fixed-length context vector makes it hard to cope with long input sequences. Bahdanau et al. proposed the attention mechanism which allows the decoder to automatically search for relevant parts of the source sequence as context . Consequith input word while decoding Training. The encoder-decoder model is mostly trained with minimum negative log likelihood (NLL) strategy :(4)LNLL loss as Lopyrev , a simpl Lopyrev  which ram words of each article as input based on our observation that most of the useful information tends to appear in the first small part of the articles  is around 50% Coverage summarization. This is the baseline summarization method. It successively takes the lead sentences from all articles until the summary length threshold is reached.(2)Centroid summarization . To our surprise, the combination of best single features  does not necessarily get a better result, and integrating all features obtains a performance even worse than the single feature. The reason might be two-fold: (1) adding new feature(s), the dimension of the input increases, so the model is harder to converge at the optimal point with the same training parameters and the fixed size of training data; (2) there might be much overlapping effects between new feature(s) and the existing features, thus adding more features can not bring in much performance gain.From As a result, ED-SoCF takes 4 features: POS, TF-IDF, upvotes and NER.m of article’s lead summary, and the best length s of articles’ multi-document summary. While testing m, without impact on comparison result, we train our basic encoder-decoder model ED only on the first article of each training example and vary the length of the lead summary to 50, 165 and 200. While testing s, without loss of generality, we take the Submodular method as an example to analyze the impact of different summary length  on performance of the ED-SoI model. The result is shown in This experiment aims to find the best length ED from 50 to 165 does not gain many benefits but costs much longer time for model to converge. Therefore, we still use the first 50 words as the article’s lead summary.It is observed that the information quantity and density of the input significantly influence the performance. Increasing the summary length from 50 to 165, the information quantity is improved, the model performance is also improved at the same time. If continuing increasing the summary length to 200 words, the performance dramatically drops because the information density lowers down and too much subjective information confuses the model and hurt the performance. Therefore, we fix the length of multi-document summary to be 165 words. In the meantime, we note that increasing the input length of Finally, we set This experiment tests the effect of different multi-document summaries on model performance. Based on the result of experiments in As (a)In the view of information density. Combined with (b)http://duc.nist.gov/duc2004 (accessed on 10 October 2020)) [In the view of information generality. The baseline method coverage performs best, proving our assumption that the generic information mostly spreads in the lead sentences. Oppositely, Centriod, TextRank and ILP which take into account all sentences in articles are affected by the subjective information and perform even poorer than the basic solution ED-avg. Cluster is better than the above 3 methods for reason that it collects sentences based on detected themes rather than weights of single sentences. Submodular is the best multi-document summarization method in the work of Zhang et al. in DUC 2004 (r 2020)) , but it Consequently, we choose coverage in ED-SoI.nqg on SQuAD by a large margin, demonstrating the ability of our solution on generating good instructive questions for guiding reading.In this paper, we proposed to generate an instructive question from the multiple recommended articles to guide teens to read for the sake of good reading experiences and effective E-bibliotherapy. For model training and testing, we collected and built a novel large-scale QA dataset TeenQA, and analyzed its feasibility for our task. Our extensive experimental results showed the proposed ED-SoCF model surpassed the traditional non-ED methods and the previous state-of-the-art model The encoder and decoder used in our work can be further enhanced by more advanced mechanisms (like copy mechanism and pointer generator) and more advanced encoder/decoder variants (like Transformer  and TranOn the other hand, there exists the potential overfitting problem with any specific dataset. In this case, building meta-learning models that can adapt to new datasets is also worthy of exploration. Here, meta-learning aims to learn the common parts of different meta-tasks, such as how to extract important features and compare samples’ similarity, and forget the task-specific parts, so that the model can still work effectively on a new dataset.


Suspended graphene can perfectly present the excellent material properties of graphene, which has a good application prospect in graphene sensors. The existing suspended graphene pressure sensor has several problems that need to be solved, one of which is the fabrication of a suspended sample. It is still very difficult to obtain large-size suspended graphene films with a high integrity that are defect-free. Based on the simulation and analysis of the kinetic process of the traditional suspended graphene release process, a novel setup for large-size suspended graphene release was designed based on the inverted floating method (IFM). The success rate of the single-layer suspended graphene with a diameter of 200 μm transferred on a stainless-steel substrate was close to 50%, which is greatly improved compared with the traditional impregnation method. The effects of the defects and burrs around the substrate cavity on the stress concentration of graphene transfer explain why the transfer success rate of large-size suspended graphene is not high. This research lays the foundation for providing large-size suspended graphene films in the area of graphene high-precision sensors. As an emerging 2D material, graphene has superior material properties, including a high Young’s modulus, carrier mobility, light transmittance, and good piezoresistive effect ,4,5,6. IRecently, one of the main obstacles in its scientific research and commercial application has been the fabrication of a suspended sample. The mono-or-few-layer graphene is too thin to survive the fabrication processes, especially the release process from the solvent. Typical sizes of monolayer graphene membranes on a perforated substrate are in the order of tens of micrometers ,20. In oBased on the simulation and analysis of the dynamic process of the traditional release process, this paper designed a new scheme to transfer the single-layer graphene film on a stainless-steel substrate based on the IFM method. The stress concentration caused by burrs at the edge of cavities was simulated and analyzed, which would be responsible for the low yield of the suspended graphene with a size beyond 500 μm. Our efforts on large-size suspended graphene sample fabrication will promote applications of graphene-based novel sensors and electronics.The widely used method for producing high-quality graphene is chemical vapor deposition (CVD). According to the operating environment of CVD graphene film, there are two transfer methods commonly used, namely: the dry transfer method and the wet transfer method. The dry transfer method is usually favored for small-batch sample preparation at a lab level. In this paper, we focus on the wet transfer method to help solve the graphene fracture problem.The last step of the wet transfer of suspended graphene is the release process. After removing the polymethyl methacrylate (PMMA) layer from the graphene membranes using acetone, the samples were removed from the acetone and were gradually dried under atmospheric conditions. Acetone on the top surface of the graphene evaporated faster than acetone inside the hole beneath the graphene because of the tiny volume of the hole in the perforated substrate. The acetone remaining inside the hole exerted a mechanical load on the graphene. The main reason for the rupture of the graphene film is the sudden stress during the suspension release process, which makes the film stress overload and rupture. D is the diameter of the capillary.When the bottom of the graphene film is completely covered by liquid, as shown in It is known from Equation (1) that the smaller the surface tension of the solvent, the smaller the contact angle between the film and the liquid, and the smaller the load when the diameter of the film is constant.Using COMSOL software to model the process of this stage, the graphene elastic modulus is 955 GPa, the Poisson’s ratio is 0.165, the graphene film thickness is 0.335 nm, and the water surface tension is 0.0728 N/m . When thAs the liquid evaporates, the central area of the bottom surface of the graphene is exposed to air, and a new three-phase interface composed of graphene, liquid, and air is formed, as shown in Initially, the diameter Observing the micro-defects of the film through a microscope, it is found that the micro-defects in the suspended graphene fracture are mainly elliptical holes and round holes. COMSOL has been used to model and simulate the stress concentration caused by the micro-defects of the graphene film. Considering that the film thickness is much smaller than the film diameter, it is simplified to a plane stress problem. The stress concentration model is shown in D = 70 μm and the boundary load is taken as the maximum stress during the release process, q = 8 GPa and the radius of the hole a = 1 μm. Considering that the defects are much smaller than the size of the film, the stress concentration coefficients of the small holes are similar in each position of the film, which can be simplified in the software to open the center circular hole and the elliptical hole film stress concentration modeling analysis. Based on the symmetry of structure and load, only 1/4 of the model is needed for the analysis. The film diameter For the stress concentration analysis of the elliptical hole film, the b/a value is taken as 0.2. The simulation result is shown in TF or avoiding the formation of a three-phase interface is key to improving the success rate of graphene suspension. For this reason, this paper carries out the following comparative tests: the (1) conventional dipping method, (2) low-stress solution dipping method, and (3) IFM method.Reducing CVD graphene was used in the experiment, and it was transferred to a stainless-steel substrate with the thickness of 3 mm. The stainless-steel substrate was laser-drilled with a diameter ranging from 10 µm to 1500 µm. The process of removing PMMA with acetone and then drying is the most important part of the entire transfer process. Most of the graphene films are broken in this step. During the experiment, the experimental conditions, such as the quality of CVD graphene, etching of the copper foil, and handling of the graphene during transfer , were ke4F9OCH3) and replaced the acetone with C4F9OCH3. As C4F9OCH3 cannot dissolve the PMMA layer, it was just used for reducing the surface tension of the acetone. However, in method 2, graphene needed to be transferred from the acetone solution to the C4F9OCH3 solution in the actual experiment process. Other factors such as the influence of liquid disturbance and unstable operation of the transfer process affected the transfer process. As a result, the transfer success rate of method 2 did not improve much compared with that of method 1.As shown in 4F9OCH3 solvent, was slowly injected so as to substitute the acetone. Finally, we removed the suspended graphene sample and dried it with a weak nitrogen flow. The setup was home-made by a 3D printer. The dark part in the figure is the acetone container with a channel. The silvery white part is the stainless-steel substrate for the graphene transfer. There was no need to move the sample during the experiment, which reduced the difficulty of the operation process. The IFM method prevented the acetone liquid from entering the holes above the graphene film and avoided the formation of the three-phase interface, which improved the transfer success rate of the large-size suspended graphene film.In order to avoid the formation of a three-phase interface and to reduce the stress, a novel setup for large-size suspended graphene release was designed based on the IFM method. In this way, the sample was rinsed without being immersed in liquid, but rather letting the sample float on the surface of an acetone bath. The concept schematic diagram is depicted in When analyzing the suspended graphene film obtained by the three methods by scanning electron microscopy, it was found that the success rate of conventional method 1 was extremely low. 2D/IG ratio, it can be seen that the CVD graphene used in this report mostly consisted of monolayer graphene [graphene ,29,30. Tgraphene . The anaIt can be seen from 3. We could then calculate the surface pressure of about 1Pa on the graphene surface by The optical and SEM observations show that there were many sharp burrs on the edge of the cavity. During the transfer process, the sample was inevitably immersed in the acetone solution. When the sample was immersed in the acetone solution for 1 mm, the density of acetone at room temperature was 0.000788 g/cm4F9OCH3 solvent when transferring a single-layer graphene film. The success rate of the single-layer suspended graphene with a diameter of 200 μm transferred on a stainless-steel substrate was close to 50%, which is greatly improved compared with the traditional method. From the simulation, we found that that the edge quality of the hole also had an important influence on the success rate of the suspended graphene transfer. This paper lays the foundation for the study of sensors and electronics based on the suspended graphene film.In conclusion, the cracking of graphene in the conventional wet method generally occurs during the formation of the three-phase interface. Through a simulation analysis of the stress concentration effect of CVD graphene film defects, the graphene film with elliptical defects is easier to crack than the graphene with circular defects. The number of defects increases in proportion to the film area, which explains why large-size graphene films are more likely to break. Based on the understanding of the damage mechanism, an inverted floating transfer experiment was designed to prevent the acetone from entering the through holes, and we replaced the acetone with C