This article was migrated. The article was marked as recommended.Background:Within assessment of physical examination skills, two approaches are common: “Describing Findings” (students comment throughout); and examining as “Usual Practice” (students only report findings at the end). Despite numerous potential influences on both students’ performances and assessors’ judgements, no prior studies have investigated the influence of either approach on assessments.Methods:Two group, randomised, crossover design. Within a 2-station simulated physical examination OSCE, we manipulated whether students “described findings” or examined as “usual practice”, collecting 1/. performance scores; 2/. Students’/examiners’ cognitive load ratings; ratings of the 3/. fluency and 4/. completeness of students’ presentations and 5/. Students’ task-finishing, comparing all 5 end-points across conditions.Results:Neither students’ performance scores nor examiners’ cognitive load were influenced by experimental condition. Students reported higher cognitive load (7/9) when “describing findings” than “usual practice” , and were less likely to finish . Presentation completeness was higher for “describing findings” ) than “usual practice” (mean=1.92 (1.65-2.18),p=0.016), whilst fluency ratings showed a similar trend.Conclusions:The decision to “Describe Findings” or examine as “Usual Practice” does not appear neutral, potentially influencing students’ efficiency, recall and (by inference) learning. Institutions should explicitly select one option based on assessment goals. Assessment of clinical skills in medical education relies on direct observation of students and trainees as they perform clinical tasks in either simulated or clinical environments  and workWithin assessment of physical examination and procedural skills, two approaches are commonly employed. In the first, students proceed “as in usual clinical practice”, that is, they speak to the patient in order to conduct the task, but they don’t converse with the assessor until the task is complete. This requires that assessors make inferences about the meaning of students’ actions whilst the task proceeds, whilst if students forget to report a finding at the end, the examiner may be unaware of something that the student observed, felt or heard. Assessors making inferences about performance has been implicated as a contributor to unwanted variability in assessors’ judgements . AdditioIn the second approach, students “describe their findings” concurrently whilst they examine, verbalising the things they feel, hear or observe for the examiners benefit. Whilst this prevents the need for examiners to make inferences, and commits students to the interpretation of their findings, it is highly clinically inauthentic, which could reduce the validity of the assessment . MoreoveGiven the complexity and potential implications of this issue, it is surprising that it is rarely considered. Indeed, whilst some published advice briefly considers this issue , we haveDespite this paucity of directly related prior research, some useful parallels may potentially be drawn from research in psychology and related fields on the use of “think-aloud” methods . While uGiven the potential importance of this issue to assessment, and limits to the applicability of findings from think aloud research to an assessment context, we aimed to investigate the influence of students “describing” their positive and negative findings versus proceeding as in “usual practice” on assessment processes and outcomes, by addressing four specific hypotheses:H1: Describing will reduce students’ performance scores compared with usual practice.H2: Describing will increase examiners’ and students’ cognitive load scores compared with usual practice.H3: A greater proportion of students will fail to finish physical examinations when describing as compared with usual practice.H4: Describing will influence ratings of the fluency and completeness of students’ presentations of their findings.We used a two-group, randomised, crossover experimental design, collecting data from a simulated two station physical examination skills OSCE. Stations tested respiratory and abdominal examination. Counter-balancing meant that students performed one station under each experimental condition, whilst examiners examined the same station under both conditions for different students.The study population was Year 3 undergraduate medical students at Keele University School of Medicine. Sampling was from two successive year groups, with recruitment via email invitation sent to the whole cohort. Data were collected in two separate iterations using the same protocol. The first iteration was in June 2017, when students had evolving clinical experience and had undertaken the Year 3 OSCE which requires they examine as in “usual practice”. The second iteration took place in February 2018, when the following cohort of students had not yet performed their year 3 OSCE, so were more familiar with the “describing findings” format which is required in Years 1 & 2 of the course. As a result, students’ familiarity with the two study conditions was balanced across iterations of data collection. Students in the two cohorts had followed the same course since entry to medical school, with an introduction to clinical examination and limited practical placements in year 2 followed by transition to ward based learning in year 3, with attendant greater exposure to patients and opportunities for “real life” practice.All participants provided informed written consent. Ethical approval for the study was granted by Keele University Research Ethics Committee (ref ERP1327)Within iteration 1, participants were randomised into 2 groups. All undertook both OSCE stations in the same order , but group 1 performed the respiratory exam whilst “describing findings” and the abdominal exam as in “usual practice”, whereas group 2 performed the respiratory exam as in “usual practice” and the abdominal exam whilst “describing findings”. There were two examiners per station, who remained constant for all students and resultantly examined their station under both study conditions.st parallel circuit, there were 2 examiners for the respiratory station and 1 for the abdominal station, whilst for the 2nd parallel circuit there was 1 examiner for the respiratory station and 2 examiners for the abdominal station. As for iteration 1, examiners were constant for all students in their circuit, and as a result all examiners examined their station under both conditions. Details of the iterations and experimental conditions are illustrated inFor iteration 2, students were randomised to 2 parallel circuits of the simulated OSCE before further randomisation to the same two groups as in iteration 1. Participants performed both stations in the same order, and the study condition  was manipulated in the same manner as in iteration 1. Owing to examiner availability, in the 1Resultantly, the main study manipulation  was counterbalanced across iterations, stations, students and examiners. Station information clearly stated whether students were to describe their findings or perform as in usual practice. Researchers re-iterated these instructions to students immediately before entering stations and alerted examiners when the condition changed.Standard station equipment was used. Simulated patients were recruited from Keele School of Medicine’s “Patients as Educators” scheme and all had demonstrable physical signs. Stations were electronically timed. Students spent 8 minutes 30 seconds in each station with a further 90 seconds to move stations and read instructions. Regardless of study condition, all students were prompted to report their findings to the examiner(s) during the final two minutes of the station. Stations were video recorded. Different patients were used for the first and second iterations.All examiners were experienced medically qualified undergraduate OSCE examiners who had previously undergone examiner training and benchmarking. Examiners were provided with station-specific descriptors of good performance, and scored performances using Keele’s Generic Consultation Skills (GeCoS) domain-based assessment instrument  and student completed a 9 point cognitive load scale  students’ presentations of their finding. Two researchers , blinded to study condition, independently reviewed each presentations, rating them for fluency and completeness. Completeness was rated relative to the “gold standard” findings on a 4 point scale: 1, few key findings, some false findings; 2, some key findings and some false findings; 3, most key findings and no false findings or all key findings and some false findings; 4, all key findings no false findings. Fluency was rated based on CS and JL’s professional judgement on a 4 point scale: 1 being “must improve”, 2 “borderline”, 3 “proficient” and 4 “very good”.As task completion required a less subjective judgement, one researcher (CS) subsequently watched videos unblinded to determine whether students a/. finished the examination during the allotted time, or b/. did not finish the examination in the allotted time.Six variables were calculated to address our hypotheses. Firstly, we averaged each student’s scores across the 4 domains in each station to give a single performance score from each examiner. Where two examiners had scored the same performance, we used the mean of their individual scores for that performance. This gave us the first dependent variable: “performance scores”. As prior research has shown that collapsed Likert scales are adequate for parametric analyses , we treaNext we averaged across the independent fluency and completeness ratings provided by the two blinded analysts for each student performance to give the interval variables of “fluency” and “completeness” for students’ presentations for each performance.Finally we recorded the categorical judgement (finished task vs did not finish task) from the assessment of whether students finished the examination task during the allotted time.2)which is analogous to theF statistic in analysis of variance. We have reported this along and the p-value at the relevant points in the text. Where multiple comparisons were performed within the same hypothesis, we adjusted our significance threshold accordingly using the Bonferroni correction for multiple comparisons. The GLM GEE model was not able to accommodate the binary outcome of “Task Finished” vs “Task Not Finished”, so instead we compared the frequency of “finishing” or “not finishing” outcomes for conditions of “describing” or “usual practice” using a Fisher’s exact test. Whilst this ignored the data structure, other factors (iteration and station) were balanced across both conditions. We calculated statistical power of our primary end-point (performance scores) based on a simple 2-mean, 2-sided Student’s t-test, with β=0.80, α=0.05, and a minimally important difference of 0.5 out of 4.0. Main analyses were conducted in IBM SPSS version 21; power was calculated using an on-line power calculator .To accommodate the study’s crossover design we used Generalized Linear Modelling with Generalized Estimating Equations (GLM GEE) to test hypotheses. We used a subject variable of “students”, with within-subjects variables of “station”  and “describing” . We included a between-subjects variable of “iteration” (June 2017 or February 2018 data collection). As examiners were fully nested within iteration and station, and counterbalanced across conditions, we didn’t model examiners as a factor. We examined for main effects of station and iteration during each analysis, and where these were statistically significant we repeated the analysis examining the interaction of “describing” and the significant variable, and any subsequent influence on the comparisons of interest. We sequentially substituted each of the 5 dependent variables into this model, switching between linear and logistic models depending on the variable. GLM GEE analyses produce a “Wald Chi-squared value” , 133 (2018)). Twenty five expressed an interest of whom 18 participated. All were randomised and all completed the protocol, giving 36 OSCE performance scores for analysis (18 under each study condition). One student withdrew consent for inclusion of their videos so only seventeen students’ presentations of findings  were rated for fluency and completeness. No other data were missing.F(df)=0.59(1), p=0.45. Performance scores varied by iteration, with students in iteration 1 (mean 2.46 (95% CI 2.22 - 2.70) showing lower scores than students in iteration 2 =12.6(1) p=0.001). Students’ cognitive load scores had a median of 6 (IQR 1) out of 9 whilst examiners’ cognitive load scores had a median of 5.5 (IQR 0.5) out of 9. Collapsed ratings (i.e. the mean of the two independent ratings) of the fluency and completeness of students’ presentations were both normally distributed with means (and standard deviations) of 2.53 (0.83) out of 4.0 for fluency and 2.16 (0.85) out of 4.0 for completeness.2=0.70, p=0.40), whereas the influence of iteration of the study was significant (iteration 1 mean score = 2.46 (2.27-2.64), iteration 2 mean score = 3.02 (2.85-3.19) Wald χ2=18.6, p<0.001). There was no significant difference in performance scores between the “describing” and the “usual practice” condition (describing condition mean score = 2.68 (2.47-2.90), usual practice mean score = 2.79 (2.60-2.99), Wald χ2=0.45, p=0.50). Given the SD=0.54, and n=18 we had 79.4% power to detect a difference of 0.5 out of 4.0 in performance scores between conditions. Repeating the model including the interaction of “iteration” x “describing condition” showed that this interaction was non-significant  and changed the model parameters very little. As a result there was no evidence to support hypothesis 1. Students’ performance scores were not influenced by whether they described their findings or performed as they would in clinical practice.Our first hypothesis posited that the “describing findings” condition would reduce students’ performance scores compared with the “usual practice” condition. Consistent with our descriptive results, the influence of station was not significant (respiratory examination mean = 2.80 (95% C.I.s 2.59 - 3.03), abdominal examination mean = 2.67 (2.48-2.86) Wald χ2= 8.46, p=0.004), whereas there was no influence of iteration on examiners’ cognitive load . There was a trend towards significance for the “describing findings” condition (describing median examiner cognitive load = 5.25 (IQR=0.6), usual practice median examiner cognitive load = 5.50 (IQR=0.6) Wald χ2= 2.86, p=0.091), although following Bonferroni correction of the significance level to p=0.025 this parameter was non-significant. The interaction of “describing” x station was not significant  and its inclusion in the model only minimally altered the resulting parameters.Our second hypothesis posited that the “describing” condition would increase examiners’ and students’ cognitive load scores compared to the “usual practice” condition. Examiners’ cognitive load differed by station (respiratory examination median = 5.5 (IQR=0.5), abdominal examination median = 5.0 (0.6) Wald χ2=2.25,p=0.13) or iteration of the study , but students’ cognitive load scores were significantly higher for the describing condition (median 7 IQR 2) than for the usual practice condition  which remained strongly significant after within-hypothesis Bonferroni correction of the significance level to 0.025, to account for multiple comparisons. As a result Hypothesis 2 was partly supported in that students’ cognitive load scores were increased by the describing condition, but examiners’ cognitive load scores were not.Students’ cognitive load was not influenced by station =7.56 (1),p=0.007.Our third hypothesis posited that the describing condition would increase the number of students who did not finish the physical examination task, compared with the usual practice condition. In the describing condition, across both stations, 4 students finished their physical examination, while 13 students did not finish; in the usual practice condition, across both stations, 12 students finished the physical examination task, while 5 did not finish. This difference was statistically significant χ2=0.013,p=0.90) nor the iteration of the study  influenced ratings of the fluency of presentations, although as the latter showed a trend towards significance we included its interaction with the “describing” condition in the model which was non-significant  and changed the findings only minimally. There was a trend towards significance for the influence of the “describing” condition (mean rating = 2.66 (95% CIs 2.25-3.07) compared to the “usual practice” condition (mean rating = 2.37 (2.08-2.65), Wald χ2=3.81,p=0.051), although this is further from the within-hypothesis Bonferroni corrected significance level of 0.025.Our fourth hypothesis posited that blinded ratings of the fluency and completeness of students’ presentations would be influenced by the describing condition compared with the usual practice condition. Neither the station  and by iteration of the study (iteration 1 mean completeness rating = 1.88 (1.56-2.19), iteration 2 mean completeness rating = 2.42 (2.02-2.82), Wald χ2=4.38, p=0.036). Consequently the interactions of both terms with the “describe” condition were included in the model, but neither was significant  compared with when students performed as they would in usual practice (mean completeness rating = 1.92 (1.65-2.18), Wald χ2=5.80,p=0.016). This p value was below the within-hypothesis Bonferroni-corrected significance level of 0.025. As a result there was partial support for hypothesis 3: ratings of the completeness of students’ presentations were higher under the describing condition than under the usual practice condition, whilst there was a trend towards the rating of the fluency of students’ presentations also being higher under the describing condition than under usual practice conditions.Ratings of the completeness of students’ presentations were influenced by station (respiratory examination mean completeness rating = 1.85 (95% CIs 1.49-2.21), abdominal examination mean completeness rating = 2.44 (2.15-2.73), Wald χThis study examined the influence of asking students to “describe their findings” or examine as in “usual practice” whilst performing physical examination in a simulated OSCE. Contrary to expectations, we found no influence of the instruction to describe findings on either students’ performance scores or on examiners’ cognitive load scores, although students were more likely to fail to finish the physical examination task when describing. Conversely (and as predicted), we found that “describing findings” increased students’ cognitive load scores compared with usual practice, whilst also increasing ratings of the completeness of their presentations, suggesting a possible increase in the fluency of their presentations.Students’ performance scores were influenced by the iteration of the study, with students in iteration 1 (who were at the end of year 3) scoring lower than students in iteration 2 (who were at the beginning of year 3 in the next cohort). Whilst counter intuitive this may be explained by the timing of data collection: iteration 1 was just before resit OSCEs, and may therefore have recruited less able students who were resitting whereas iteration 2, at the start of a new semester, may have recruited highly motivated students.Several other theoretical considerations emanate from these findings. Most importantly there was no influence of study condition  on students’ performance scores. At a theoretical level this may be surprising. Consistent with our findings, verbalising thoughts is expected to both increase mental workload and slow task performance . The obsIt is generally accepted that cognitive resources are fixed , and wheThe increased cognitive effort which students exerted is particularly interesting if considered through the lens of “Cognitive Load Theory” . This thFurther work is needed to replicate and extend these findings before any firm suggestions can be made for practice. Nonetheless a few potential practical implications can be considered. Pertinently, within performance assessments in medical education, instructions to either “describe findings” or examine as in “usual practice” are not neutral and as a result it seems inadvisable to simply leave this instruction to examiners’ whim . ConsequIf these findings can be confirmed, we suggest that “describing findings” and performing as in “usual practice” may have different applications at different stages in learning. The added germane load of “describing findings” may produce a useful benefit for students’ learning during initial learning or observed practice of procedures or physical examination skills, and as a result it may be more useful in a coaching scenario . ConversThere are several strengths to this study. To enable unbiased measurement of the effect of “describing findings” on students and examiners we used a randomised, controlled, experimental design, addressing carefully theorised pre-specified hypotheses. Despite these strengths it has some weaknesses. The generalisability of the findings are limited by the study’s sampling, from students at a particular stage in the course at one medical school; students who are familiar with different assessment systems could have produced different results. This is partly mitigated by the fact that we included groups of students who were both 1/. accustomed and 2/. relatively naive to the “describing findings” condition. Our study was powered to find a difference in performance scores of 0.5 out 4.0 between conditions; we can’t exclude the possibility that smaller differences could have occurred. We only sampled students at a single level of training; other effects may exist for more sophisticated clinical learners. Whilst we sampled across two very common physical examination tasks, we can’t exclude that other effects might have emerged for more complex scenarios or for procedural skills. Lastly we used a self-reported rather than an objective measure of participants’ cognitive load. Whilst objective measurement may have benefitted the internal validity of the study, procedures required for objective cognitive load measurement  would haFuture research should seek to replicate these findings across contexts and for other scenarios and groups of learners, in order to determine their universality within assessment. Further work might compare effects of “describing findings” and “usual practice” on the accuracy of examiners’ observations as compared to an objective measure (i.e. video-based checking of performance or via simulation manikins where findings can be controlled), in order to determine whether “describing findings” does or does not lead to better observation of physical sign recognition by assessors.Although the performance scores did not show a difference between “describing” and “usual practice” the assessment decision to ask students to either “describe their findings” or examine as in “usual practice” is not neutral, influencing both cognitive intermediary and some outcome level processes. Institutions should reflect on this choice, making it explicit through guidance and assessor training, and allowing more time for task completion if “describing” is the preferred format. Whilst further work is required, we can recommend “describing findings” if the aim is learning but asking students to examine as in “usual practice” in summative assessments.•Describing findings to the assessor while examining the patient in an OSCE station did not significantly affect the students’ scores.•Student cognitive load was increased and task completion reduced by describing, but subsequent presentation of findings was more complete when students had described while examining.•When designing assessments, institutions should be explicit as to whether candidates are to “describe findings” or examine “as in usual practice” dependent upon the purpose of the assessment.Catherine Stephenson is a teaching fellow, lead for systems examination teaching at Keele University School of Medicine and a GP.Peter Yeates is a lecturer in medical education research and a consultant in acute and respiratory medicine. His interests include assessor cognition research, psychometrics and innovative ways to increase OSCE standardisation.Janet Lefroy is a senior lecturer in medical education, lead for consultation skills teaching at Keele University School of Medicine and a GP. Her interests include assessment of consultation skills, feedback to clinical trainees on their skills, and the transition between medical school and the clinical workplace. This review has been migrated. The reviewer awarded 5 stars out of 5 This is a well researched paper of practical significance to educators and assessors. In line with the authors' recommendations this study needs to be replicated in other settings. Personally I would like to see the effect of years of training on the outcome.WelldoneReviewer Expertise:NANo decision status is available This review has been migrated. The reviewer awarded 4 stars out of 5 Great study. Well written and clear. Great guide for future replication.Reviewer Expertise:NANo decision status is available This review has been migrated. The reviewer awarded 4 stars out of 5 Thank you for an article that speaks to an important part of the curriculum.i like the straight forward replicable methodsReviewer Expertise:NANo decision status is available This review has been migrated. The reviewer awarded 5 stars out of 5 this is a very good study. it has provided some evidence on OSCE process. it should be replicated in other medical schools.Reviewer Expertise:NANo decision status is available This review has been migrated. The reviewer awarded 4 stars out of 5 Thank you, good to have some evidence in a disputed area. My own policy in summative assessements is to instruct examiners to use 'usual practice' and the students know and expect that.They have enough to do without talking about it and they will not generally have been trained and practiced to do bothReviewer Expertise:NANo decision status is available


Effect of polydimethylsiloxane oil lubrication on the friction of cross-country UHMWPE ski bases on snow Buene AF, Auganæs SB and Klein-Paste A. (2022) Front. Sports Act. Living 4:894250. doi: 10.3389/fspor.2022.894250A corrigendum on Text CorrectionIn the published article, there was an error. The average amount of applied silicone oil to each ski was reported to be 5 mg, while the correct amount was 50 mg.Materials and Methods, Characterization and Measurements, paragraph 5. The sentence previously stated:A correction has been made to 2 or a 60 nm thick silicone oil layer assuming a uniform distribution. […]”“[…] Then the sole was wiped with Fiberlene  paper until no more oil was visually removed, leaving an average of 5 mg of silicone oil, regardless of viscosity. Over the area of one ski, this equals 6 µg/cmThe corrected sentence appears below:2 or a 600 nm thick silicone oil layer assuming a uniform distribution. […]”“[…] Then the sole was wiped with Fiberlene  paper until no more oil was visually removed, leaving an average of 50 mg of silicone oil, regardless of viscosity. Over the area of one ski, this equals 60 µg/cmDiscussion, Friction measurements, paragraph 4. The sentence previously stated:A correction has been made to “[…] the thickness of the oil layer is calculated from the amount of oil applied on the ski and is 60 nm. Under no-slip conditions, which is highly unlikely considering the icephobic nature of silicone oils, the calculated shear force is 1.90 kN. This corresponds to a very high friction coefficient of 4.75, meaning this system must have significant wall-slip. Starting with a realistic friction coefficient for this system of 0.08, the shear force would equate to 32 N assuming shear is the sole resistance mechanism, and this would require a wall-slip factor of 0.98.”The corrected sentence appears below:“[…] the thickness of the oil layer is calculated from the amount of oil applied on the ski and is 600 nm. Under no-slip conditions, which is highly unlikely considering the icephobic nature of silicone oils, the calculated shear force is 190 N. This corresponds to a very high friction coefficient of 0.475, meaning this system must have significant wall-slip. Starting with a realistic friction coefficient for this system of 0.08, the shear force would equate to 32 N assuming shear is the sole resistance mechanism, and this would require a wall-slip factor of 0.83.”The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.


This study aimed to investigate the relationship between health-related behavior and psychosocial characteristics among adolescent female smokers in South Korea using data from the 17th Korea Youth Risk Behavior Web-based Survey (KYRBWS) conducted in 2021. The analysis participants comprised 2407 adolescent smokers who were currently smoking, out of a sample of 54,835 participants. The characteristics of adolescent female smokers were examined by comparing them with those of adolescent male smokers. The results showed that male and female adolescent smokers accounted for 69.2% and 30.8% of the sample, respectively. Multiple logistic regression analysis identified school type, subjective socioeconomic status, physical activity, breakfast consumption, alcohol consumption, sexual experience, stress, generalized anxiety, and suicidal ideation as significant factors associated with adolescent female smokers. These findings provide important foundational data for the development of smoking-cessation programs and policies tailored specifically to adolescent female smokers. Smoking is a leading cause of preventable deaths and diseases worldwide . AccordiThe smoking rate among adolescents is 4.5%, with males and females at 6.0% and 2.9%, respectively, indicating that the smoking rate among females is approximately half of that among males . ParticuSmoking during adolescence has a more pronounced impact on cellular and tissue damage, compared with that during adulthood, because of the developmental characteristics of growing adolescents . ExposurAdolescent smoking also affects their mental health. The concentration of cotinine, a nicotine metabolite, has been associated with symptoms of major depressive disorder, generalized anxiety disorder (GAD) , stress,Previous research has extensively investigated various smoking-related characteristics among adolescents, including smoking experience and frequency, exposure to secondhand smoke , dual-smTo assess the health-related behaviors and psychosocial characteristics of adolescent female smokers in South Korea.To analyze the factors associated with the health-related behaviors and psychosocial characteristics of adolescent female smokers, using male smoking adolescents as a reference group.Therefore, this study aimed to examine the characteristics of adolescent female smokers in South Korea using the raw data from the 17th Korea Youth Risk Behavior Web-based Survey (KYRBWS) conducted in 2021. Using adolescent male smokers as a reference group, this study sought to analyze the health behaviors and psychosocial factors of adolescent female smokers. The specific objectives of this study were as follows:This secondary data analysis utilized data from the 17th KYRBWS in 2021, in order to compare gender-related factors based on the health-related characteristics and psychosocial characteristics of adolescent smokers . Among tThe KYRBWS is an annual survey conducted by the Korea Centers for Disease Control and Prevention since 2005 to assess health behaviors and trends among Korean adolescents. The survey is an anonymous, self-administered online questionnaire, with participation from students in grades 1 to 3 of middle school and those in high school. The survey was conducted in April 2021, and the target population included students enrolled in middle and high schools nationwide. The sampling process involved population stratification, sample distribution, and sample extraction.In the stratification phase, the national population was divided into 117 strata based on 39 regional districts and school levels . The 39 regional districts were classified as metropolitan cities, medium-sized cities, and rural areas within 17 cities and provinces, considering factors such as geographic accessibility, number of schools and population, living environment, smoking rate, alcohol consumption rate, and other relevant factors.In the sample distribution phase, 400 middle schools and 400 high schools were selected, and the sampled schools were allocated proportionally to match the composition ratio of the population based on the stratification variables. Sample extraction followed a two-stage cluster sampling method, in which schools were sampled as primary units, and one class per grade was randomly selected from the sampled schools. The survey included 796 participating schools (399 middle schools and 397 high schools). Among these schools, a sample class was selected, and all students in the selected sample comprised a total of 50,066 participants. However, in the final analysis, 54,848 students participated in the survey, resulting in a participation rate of 92.9%. Students who were chronically absent, those with disabilities preventing their participation, and those with dyslexia were excluded [The 2021 KYRBWS comprised 113 items distributed across 16 domains. Among these items, 19 were selected for analysis: seven items related to demographic characteristics, four assessing smoking-related characteristics, five exploring health-related behaviors, and three examining psychosocial characteristics. In instances where the original dataset contained a large number of categories with uneven distributions, posing challenges in terms of comparability with previous studies, we mitigated this limitation by referring to relevant research conducted on a similar cohort  and recoDemographic and sociological characteristics included gender, school type, academic performance, subjective economic status, change in economic status after COVID-19, residential area, and living arrangements with parents.Gender was categorized as “male” and “female”. The school type was divided into three categories: middle school, high school, and special high school. Academic performance and subjective economic status were reclassified into three categories: “high” for the highest category, “medium” for the middle categories, and “low” for the lowest categories. Economic status changes due to COVID-19 were categorized as “affected” (significantly worsened due to COVID-19) and “not affected” (no significant change or improvement). Residence area was categorized as “large cities” , “medium and small-sized cities” , and “province cities” with a rural area. Living arrangements with parents were categorized as “living with family” and “not living with family”, including cases of living with relatives, boarding, living alone, living in dormitories, and living in childcare facilities.The smoking-related characteristics included age at smoking initiation, e-cigarette use, smoking-cessation attempts, and secondhand smoke experience at school. Age at smoking initiation was classified into three categories: “before middle school”, “during middle school”, and “during high school”. E-cigarette use was determined by the question, “In the past 30 days, how many days did you use nicotine-containing e-cigarettes?” Those who responded with “1 day or more” were classified as “yes”. Smoking-cessation attempts were assessed using the question, “Have you attempted to quit smoking in the past 12 months?” Secondhand smoking experience at school was determined by the question “In the past 7 days, how many days did you inhale tobacco smoke from other people smoking indoors at school?” Those who responded with “1 day or more per week” were classified as “yes”.Health-related behavioral characteristics included subjective health status, physical activity, breakfast consumption frequency, current alcohol consumption, and sexual experiences. For subjective health status, participants were asked, “How do you rate your health?” Responses such as “very healthy”, “healthy”, and “normally healthy” were classified as “healthy”, while “unhealthy” and “very unhealthy” were classified as “unhealthy”. Physical activity was assessed using the question, “In the last 7 days, how many days did you spend more than 60 min a day on physical activities that increased your heart rate or made you go out of breath?” Responses were categorized as “none in the past 7 days”, “1–4 days per week”, and “5 or more days per week”. Breakfast consumption frequency was determined by the question, “How many days in the past 7 days did you eat breakfast?” Responses were categorized as “did not eat in the past 7 days”, “1–4 days per week”, and “5 or more days per week”. Current alcohol consumption was assessed using the question, “How many days in the past 30 days did you drink one or more drinks of alcohol?” Those who responded with “once or more per month” were classified as “yes”. Sexual experience was determined by the question, “Have you ever had sexual intercourse?” Responses such as “yes” and “no” were classified accordingly.Psychosocial characteristics examined in this study encompassed GAD, stress, and suicidal ideation. GAD was evaluated using the seven-item generalized anxiety disorder (GAD-7) screening tool. The GAD-7 is a self-report questionnaire specifically designed to identify individuals with GAD and measure the severity of their symptoms. Comprising seven items related to anxiety or worry, respondents rated each item on a scale ranging from 0 to 3. The total GAD-7 score ranges from 0 to 21, with scores falling within different ranges, indicating varying levels of anxiety severity: 0–4 , 5–9 (mild), 10–14 (moderate), and 15–21 (severe) . A scoreThe perceived level of stress was assessed through a single question: “How much stress do you usually feel?” Participants were given response options ranging from “not at all” to “a little”, which were classified as “no”, while “a lot” and “very much” were classified as “yes”. The stress perception item, consisting of a single question, has been utilized in previous studies as a tool to examine the factors influencing adolescent smoking .Suicidal ideation was assessed using the question, “Have you seriously thought about committing suicide in the past 12 months?” Participants’ responses were categorized either as “no” or “yes” based on their answer to this question.The Youth Health Behavior Survey (KYRBWS) is an annual publication of data that has been conducted since 2005 to assess the health behaviors and trends among Korean adolescents. During data collection, unique numbers were used to ensure the anonymity and confidentiality of participants’ personal information. Consent to participate in the study was obtained from all participants. This study utilized the 17th KYRBWS statistical data from 2021, which were downloaded and used in accordance with the regulations for the public release and management of raw data.The 17th KYRBWS survey was conducted through an anonymous self-administered online questionnaire among middle and high school students nationwide as of April 2021. The sampling process was conducted in three stages: population stratification, sample distribution, and sample extraction, as described in the research subjects section.Prior to data collection, sample schools and sample classes were selected, and student demographics were registered. Survey support teachers from the sample schools were then selected and trained. The survey support teachers explained the importance of the study and the participation process to the participants and conducted the survey with students who submitted signed consent forms. The survey was conducted using an online survey program (accessed through the “Nuri House”), and to facilitate the survey, it was administered using school computers or mobile devices (such as tablet PCs or smartphones). The entire survey process took approximately 45 to 50 min.p < 0.05 applied for hypothesis testing.This study utilized the raw data from the 17th Youth Health Behavior Survey (KYRBWS), which employed a complex sampling design. To ensure accurate statistical analysis, it is recommended to use statistics that incorporate the complex sampling design. Data analysis was conducted using complex sampling with strata, cluster weights, and finite population correction factors (FPC) to ensure population representativeness. The raw data from the 17th KYRBWS provided integrated strata, cluster variables, weights, and FPC, which were considered. The IBM SPSS Statistics 27.0 program  was used for the analysis, with a significance level of 2 =385.259, p < 0.001), and Hosmer–Lemeshow test  were examined, indicating that the model was a good fit.Before conducting the analysis, the basic assumptions for logistic regression analysis, which was used to achieve the objectives of this study, were checked. Logistic regression analysis is a non-linear regression analysis where the independent variables are continuous and the dependent variable has a binary outcome. Unlike discriminant analysis, logistic regression does not require the normality assumption of the sample for the dependent variable with a binary outcome . DiscretThe demographic characteristics, smoking-related characteristics, health-related behaviors, and psychosocial characteristics of the participants were analyzed using frequency (sample size) and percentages (applying weights).2-test.The demographic characteristics, smoking-related characteristics, health-related behaviors, and psychosocial characteristics of smoking adolescents were analyzed based on gender using the Rao-Scott χMultiple logistic regression analysis was conducted to analyze health-related behaviors and psychosocial characteristics of adolescent female smokers, with adolescent male smokers as the reference group.The specific analysis methods are as follows:Regarding smoking-related characteristics, the most common age of smoking initiation was that during middle school (69.7%), and 67.9% of the participants reported using liquid electronic cigarettes. Many adolescents used both liquid electronic and conventional cigarettes. Smoking-cessation attempts were reported by 67.9% of the participants, and 20.5% had experienced secondhand smoke at school.Among the psychosocial characteristics of smoking adolescents, the average GAD score was 5.25 ± 5.44. Most participants (39.9%) fell within the normal range, but 25.0% had moderate or higher levels of anxiety. A significant number of participants (50.3%) reported experiencing stress, and 25.0% reported having suicidal ideation.2 = 48.588, p < 0.001), subjective economic status , changes in economic status due to COVID-19 , and co-residence with parents . However, no significant differences were found in the smoking-related characteristics.2 = 20.492, p < 0.001), physical activity , breakfast consumption frequency , current alcohol consumption , and sexual experience . Psychosocial characteristics also exhibited differences in GAD , perceived stress , and suicidal ideation   .To examine the characteristics of adolescent female smokers among Korean smoking adolescents, a multiple logistic regression analysis was conducted, with adolescent male smokers serving as the reference group . The anaThe analysis revealed statistically significant variables for demographic characteristics, health-related behaviors, and psychosocial characteristics. Among the demographic characteristics, compared with adolescent male smokers, females in high school had a lower odds ratio (OR) of 0.67 (95% CI = 0.43–1.04), and females in middle school had a higher odds ratio of 1.54 (95% CI = 0.96–2.48). Regarding subjective economic status, rather than being in the “high” group, females were more likely to be in the “middle” group with an odds ratio of 2.50 (95% CI = 1.69–3.69) and in the “low” group with an odds ratio of 2.00 (95% CI = 1.27–3.13).Regarding health-related behaviors, females had lower odds ratios in physical activity compared with the “no activity” group: 0.43 (95% CI = 0.34–0.54) for the “1–4 days per week” group and 0.19 (95% CI = 0.13–0.28) for the “5 or more days per week” group. Breakfast consumption frequency showed that females had lower odds ratios compared with the “no consumption” group: 0.96 (95% CI = 0.76–1.21) for the “1–4 days per week” group and 0.67 (95% CI = 0.47–0.86) for the “5 or more days per week” group. Current alcohol consumption and sexual experience were both higher among females compared with males, with odds ratios of 1.34 (95% CI = 1.07–1.68) and 1.34 (95% CI = 1.05–1.71), respectively.Among the psychosocial characteristics, females had higher odds ratios for GAD compared with the “normal” group: 1.16 (95% CI = 0.89–1.50) for the “mild” group, 1.52 (95% CI = 1.06–2.18) for the “moderate” group, and 1.93 (95% CI = 1.27–2.92) for the “severe” group. Perceived stress was lower among females in the “yes” group than in those in the “no” group, with an odds ratio of 0.74 (95% CI = 0.58–0.94). Females had higher odds ratios for suicidal ideation compared with the “no” group, with an odds ratio of 1.69 (95% CI=1.34–2.13).This study examined the health-related behaviors and psychosocial characteristics of female adolescent smokers using data from the 17th KYRBWS (2021). Among the 2407 adolescent smokers, 69.2% were male and 30.8% were female. No significant differences in smoking-related characteristics were found based on the gender of smoking adolescents. Comparing our findings with those of a study by Lee and Song , which eIn 2020, the proportion of smoking initiation before middle school was 15.7% (17.7% for boys and 10.6% for girls) . By 2021These findings align with those by Kim , who repRegarding school level, high school students (78.9%) were found to smoke more than middle school students (21.1%), which is consistent with previous studies ,35. HoweA study analyzing smoking-cessation trends among 50,228 smokers from 17 European countries found that those who started smoking before the age of 16 were less likely to quit smoking . AnotherCompared with male adolescent smokers, female adolescent smokers had an intermediate or low subjective socioeconomic status, and a higher proportion of female students experienced economic difficulties due to COVID-19. However, in the multivariate analysis, subjective economic status emerged as the only significant variable, indicating that the perception of household economic level during normal times is more important than the short-term changes in economic status due to crises such as COVID-19. Previous studies have identified both low ,41 and hGreen et al.  reportedn = 318) in grades 9 through 12 in three schools in southwestern Ontario found a significant difference between male non-smokers (60.4%) and currently smoking students (31.9%) in terms of eating breakfast daily; however, there was no statistical difference among female students, who had concerns about weight gain [Regarding health-related behaviors, a lower proportion of female adolescent smokers engaged in physical activity and ate breakfast, compared with male adolescent smokers. This finding is similar to that of Kaczynski et al. , who repght gain .Korean adolescent smokers were found to have a significantly lower likelihood of consuming fruits, vegetables, and dairy products; however, they consumed significantly more fast food compared with non-smokers . AdolescSmoking among adolescents not only poses health risks but is also correlated with other addictive behaviors and deviant actions, such as alcohol consumption and drug abuse ,46,47. TFemale adolescent smokers showed lower stress levels but higher rates of GAD and suicidal ideation, compared with male adolescent smokers. Previous studies have found that psychological and social characteristics such as stress ,25,51,52This study offers valuable insights into the relationship between health-related behaviors and the psychological and social characteristics specific to female adolescent smokers, emphasizing the significance of incorporating sex-specific factors in evaluating such aspects. Based on these findings, it is evident that the development and implementation of smoking-cessation programs for adolescent smokers should address sex-specific smoking-related factors.It is important to note that this study was based on cross-sectional data from a single year, limiting its ability to establish causality, despite identifying associations between variables. Moreover, the use of secondary data variables has certain limitations. Therefore, future research should aim to conduct longitudinal studies that explore the causal factors influencing smoking among female adolescent smokers, considering diverse variables such as parental smoking, peer smoking, depression, and other factors related to adolescent smoking.Furthermore, the stress variable used as a psychosocial characteristic factor in this study consisted of a single question, which may compromise the validity of the tool. Although it has been used as a factor influencing adolescent smoking in previous secondary data analyses , it is nThis study examined the smoking-related characteristics of female adolescent smokers compared with those of their male counterparts. The findings revealed that the majority of female adolescent smokers were middle school students, with a significant portion reporting subjective socioeconomic statuses categorized as intermediate or low. They exhibited lower levels of physical activity and breakfast consumption, while demonstrating higher rates of health-risk behaviors such as current alcohol consumption and sexual activity. Moreover, female adolescent smokers showed a higher prevalence of GAD compared with male adolescent smokers, and the smoking rate increased with higher levels of anxiety. Although they perceived lower stress levels, they also reported a higher frequency of suicidal ideation. Consequently, comprehensive health education programs should be designed to encompass reproductive health promotion behaviors, including physical activity, breakfast habits, alcohol consumption, and sexual education. Moreover, attention should be paid when addressing psychological and social concerns, such as anxiety and suicide. To improve smoking prevention among female adolescent smokers, it is necessary to develop tobacco control strategies and programs to delay or deter smoking initiation in this population.


Conventional bulk indicators such as Norg/TOC ratio and δ13Corg were insufficient for fully determining the sources of sedimentary OC. Therefore, we employed a four-end-member approach, using Δ14Corg, δ13Corg, and lignin phenols to assess the relative contributions of petrogenic, soil-derived, plant-derived, and marine OC to the sedimentary OC pool. The analyzed fjord sediments consisted, on average, of 59.0 ± 28.1% petrogenic OC, 16.8 ± 12.1% soil-derived OC, 2.5 ± 2.2% plant-derived OC, and 21.8 ± 18.5% marine OC. This approach highlights the substantial contributions of petrogenic and aged soil-derived OC to present-day sedimentary OC in Svalbard fjords. Considering predicted global warming, accelerated inputs of petrogenic and soil-derived OC into fjords due to rapid glacier retreat may significantly impact the active carbon cycle and potentially contribute to CO2 emissions to the atmosphere, depending on burial efficiency.Svalbard fjords are recognized as hotspots for organic carbon (OC) burial and storage due to their high sedimentation rates, which effectively trap terrestrial sediments and inhibit extensive OC remineralization. In this study, we investigated surface sediments (n = 48) from eight Svalbard fjords, along with bedrock (n = 17), soil (n = 28), and plant (n = 12) samples, to identify the sources of sedimentary OC in these fjords using geochemical parameters. All examined surface sediments from the fjords showed a depletion in  Among these aquatic critical zones, fjords, mainly located in the mid-high latitudes, represent a relatively small fraction of the global continental margin (< 0.1%)3. Nevertheless, they serve as biogeochemical hotspots that effectively trap and store substantial amounts of organic carbon (OC)5. Annually, approximately 18 million tons of marine and terrigenous OC are buried in glacially depressed basins within fjords, accounting for approximately 11% of the global marine OC burial6 and 17 ± 12% of the global terrestrial OC burial7. As a result, the OC burial per unit area in fjords largely exceeds the global ocean average, making them a sensitive system for sequestering CO2 and regulating the global carbon cycle over geological time6.In recent decades, the ‘land-to-ocean aquatic continuum’ between ‘terra firme’ terrestrial ecosystems and the open ocean has received considerable attention as aquatic critical zones in regulating the global carbon cycle on annual to centennial timescales8. In recent decades, the fjords on the west coast of Spitsbergen have undergone significant hydrographic changes attributed to the intensified West Spitsbergen Current (WSC), which brings warm and saline Atlantic water into the fjords11. Furthermore, the Svalbard archipelago has experienced rising air temperatures and increased precipitation, leading to the retreat of marine- and land-terminating glaciers and increased freshwater discharge15. Due to increased WSC inflow and freshwater runoff, these hydrographic changes can potentially affect marine primary productivity and trigger the release of substantial amounts of terrestrial sediments to the fjords16. Consequently, these changes can alter the sources of sedimentary OC and significantly impact long-term OC burial in fjord sediments. Hence, the Spitsbergen fjords are regarded as areas susceptible to global warming18, making them ideal sites for studying the responses of high-Arctic fjords to current and projected future climate change.The high-Arctic Svalbard archipelago , with glaciers covering approximately 57% of its land masses, is located at the gateway between the North Atlantic and Arctic Oceans  and isotopic (δ13Corg) parameters23. However, these approaches often assume that the selected end-member  values represent the entire Svalbard region and treat all land-derived OC as a single terrestrial source. Nonetheless, it's important to recognize that land-derived OC is itself heterogeneous, comprising OC derived from both plants and soils , and bedrocks (referred to as “petrogenic” OC). Consequently, relying solely on conventional bulk organic parameters does not adequately capture the diverse sources of terrestrial OC in the sedimentary OC pool. Furthermore, only a limited number of studies have investigated surface sediments at the molecular level in Svalbard fjords, aiming to gain insights into different terrestrial OC sources25. Notably, the relative proportion of petrogenic and aged soil-derived OC to the total OC pool in Svalbard fjords remains largely unknown, despite their potential implications for active regional carbon cycling26. Therefore, a more comprehensive study investigating the contributions of petrogenic and soil-derived OC across Svalbard fjords is still necessary.Previous studies in Svalbard fjords have examined the spatial variability of OC composition in surface sediments and assessed the relative proportions of marine and terrestrial OC using bulk elemental  collected from eight Svalbard fjords Fig. . Our anaThe mean grain size of the fjord surface sediments was between 5.8 μm and 16.7 μm (average ± standard deviation (SD); 9.9 ± 3.2 μm, n = 48), and a predominant proportion of silt (63.7 ± 6.5%), followed by clay (28.1 ± 8.8%), and sand (8.2 ± 7.2%) was observed (Supplementary Table tot) and total organic nitrogen (Norg) contents ranged from 0.01 to 0.26 wt.% and 0.01 to 0.20 wt.%, respectively  content in the fjord surface sediments varied between 0.12 wt.% and 2.80 wt.% .In the plant samples collected around Longyearbyen, the TOC contents varied between 40.8 and 60.4 wt.% , while the lowest value was found in Wijdefjorden at an average value of − 15.2 ± 0.6 (n = 6). Notably, the detrital εNd in Hornsund showed a wide range of values from − 24.9 to − 10.1 .In this study, we newly measured neodymium isotopes from detrital components of surface sediments (n = 19) and compiled previous measurements (n = 29)We reported the total lignin phenol concentration (λ) as the sum of eight lignin-derived monomeric phenols, including vanillyl , syringyl , and cinnamyl  units normalized to TOC. In the surface sediments of the fjords, λ ranged from 0.001 to 0.52 mg/g OC, with an average of 0.14 ± 0.15 mg/g OC (n = 48). The highest concentration was observed in Wijdefjorden , while the lowest value was found in Hornsund  in Svalbard fjords. δ13Corg is another tool commonly used to identify sedimentary OC sources in Svalbard fjords32. The Norg/TOC ratio versus δ13Corg plot suggests that the fjord surface sediments, as observed in previous studies of Svalbard fjords19, represent a mixture of terrestrial and marine OC  to determine the quantitative proportions of sedimentary OC remains challenging.Previous studies conducted in Svalbard fjords have revealed the presence of inorganic nitrogen (N) bound in clay minerals, which accounted for up to 70% of the N OC Fig. . In gene20 and incorporating our new data, we attempted to assign terrestrial and marine OC end-member values based on the correlations between the Norg/TOC ratio and δ13Corg, as well as between δ13Corg and %Norg (defined as Norg/Ntot %), respectively  for fine-grained ice-rafted debris (IRD) samples collected in Kross/Kongsfjorden. These δ13Corg values were relatively enriched compared to the soils collected around Kross/Kongsfjorden, which varied from − 27.1 to − 25.2‰ 29. Additionally, our newly acquired soil data from the Ny-Ålesund region exhibited similarly depleted δ13Corg values ranging from − 26.4 to − 17.5‰  used for terrestrial or marine primary production, originating from the atmosphere, exhibits a positive 14C signal39, as demonstrated at Alert during the period of 2015–2019 with an average Δ14C value of 9.1 ± 6.4‰40. Therefore, fjord surface sediments that predominantly contain modern terrestrial or marine OC should not exhibit a strong depletion in 14C, assuming the absence of abiotic processes causing 14C offsets. As suggested by the previous study29, the significant depletion of 14C in the fjord surface sediments suggests that recently fixed terrestrial and marine OC alone is not the exclusive contributor to sedimentary OC. Instead, a substantial amount of old OC was also contributed, which could not be revealed solely by the Norg/TOC ratio and δ13Corg.The Δ14C-depleted OC in sedimentary OC in Svalbard fjords, we defined two OC components: ‘petrogenic’ OC, representing radiocarbon-‘dead’ OC sourced from bedrocks, and ‘biogenic’ OC, encompassing biomass fixed via photosynthesis in terrestrial and marine environments7. By assuming the end-member values of Δ14Cpetrogenic =  − 1000‰ and Δ14Cbiogenic = 9.1‰, we estimated the relative proportions of ‘14C-assessed’ petrogenic and biogenic OC to the sedimentary OC pool. The end-member Δ14Corg value of biogenic OC was assumed to be represented by the 14C composition of atmospheric CO2 at Alert during the period of 2015–201940, taking into account the sampling period of our surface sediments  in the sample, Fmsample is the measured 14C composition of the sample expressed as the fraction of modern C, Fmbio is the 14C composition of biogenic OC, and OCpetro is the content of petrogenic OC in wt.%. Although this approach was successfully applied to the Beaufort Sea43, we did not observe a similar linear relationship between the TOCsample and TOCsample × Fmsample in Svalbard fjords v lower than 0.3 are commonly associated with relatively fresh vascular plant detritus, whereas ratios exceeding 0.5 are typically observed in extensively altered soils with significantly depleted Δ14Corg signatures45. Many of the surface sediments in Svalbard fjords exhibited (Ad/Al)v ratios well above 0.5  of the soil profiles and those collected at shallower depths (0–10 cm) in the vicinity of the retreating land-terminating glacier in the Ny-Ålesund region v ratio, employing a Monte Carlo (MC) analysis57. In the initial stage, we constrained the end-member values for each OC source  and soil-derived OC  to the sedimentary OC pool. Our study demonstrated that the four-source apportionment approach based on Δ14Corg, δ13Corg, and the (Ad/Al)v ratio can provide reasonable estimates of the relative contributions of different OC sources to sedimentary OC in Svalbard fjords. However, further investigation is necessary to address the issue of overlapping end-member values of δ13Corg, particularly in Kross/Kongsfjorden, by analyzing additional background samples such as soils, subglacial sediments, and fjord sediments collected in the vicinity of tidewater glacier fronts. This further analysis would help in better constraining the end-member values for each parameter considered and improving the accuracy of the apportionment approach.To estimate the relative proportions of petrogenic, soil-derived, plant-derived, and marine OC in the sedimentary OC of Svalbard fjords, we utilized a four-source apportionment approach based on Δ7. In such environments, the effective burial of OC derived from more recently fixed terrestrial and marine biomass in fjord sediments can remove OC from the active carbon cycle. However, high-latitude fjords with glacier-dominated catchments, like those in Svalbard, experience the export of previously sequestered petrogenic OC to complex fjord systems58. This old OC can be remineralized during transport or within the fjords, thus impacting the active carbon cycle as a source of CO2 to the atmosphere60. In the case of the Svalbard fjords, the marine-terminating glaciers have undergone rapid retreat in recent decades61. As a result, there has been an increased export of OC from the hinterland to the fjords62, which is expected to accelerate in the near future due to projected climate warming6. Consequently, the amplified input of petrogenic and aged soil OC to Svalbard fjords may contribute to atmospheric CO2, despite its refractory nature, if this old OC is not effectively reburied but instead undergoes remineralization63. This positive carbon feedback mechanism in Svalbard fjords requires further investigation.In temperate fjords with vegetation-dominated catchments, such as New Zealand fjords, the input of petrogenic OC is negligible14Corg values and a wide range of Norg/TOC and δ13Corg values. These observations suggest that the sedimentary OC sources in Svalbard fjords cannot be adequately explained by a simple binary mixing model considering only marine and terrestrial OC. To overcome this limitation, we applied a MC approach, utilizing Δ14Corg, δ13Corg, and (Ad/Al)v values, to estimate the relative contributions of different OC sources to the sedimentary OC pool. This approach allowed us to discern the proportions of petrogenic, soil-derived, plant-derived, and marine OC in the sediment samples. Our analysis provides important insights into the sources of OC in Svalbard fjords, with a particular emphasis on the substantial contributions of petrogenic and aged soil-derived OC to the recent sedimentary OC pool. Furthermore, our study highlights the potential implications of predicted climate warming on the active carbon cycle in the Svalbard fjords. With the anticipated acceleration of glacier retreat, there will be an increased export of petrogenic and aged soil-derived OC to these fjords. This influx of OC has the potential to release additional CO2 into the atmosphere, thereby influencing the dynamics of the active carbon cycle25. Furthermore, the increased influxes of land-derived OC could alter ecosystem structure and function in Svalbard fjords, as demonstrated in Greenland fjords64.In this study, we conducted a comprehensive analysis of the OC sources in surface sediments from Svalbard fjords, employing a combination of bulk and molecular geochemical parameters. Our findings revealed that the surface sediments in Svalbard fjords exhibit depleted Δ3) from seven Svalbard fjord systems: Wijdefjorden (n = 6), Woodfjorden (n = 2), Dicksonfjorden (n = 2), Isfjorden (n = 2), van Mijenfjorden (n = 6), Hornsund (n = 24), and Hambergbukta (n = 3). These samples were obtained during Korea-Norway International joint cruises in Svalbard conducted in 2015, 2016, 2017, and 2019 aboard R/V Helmer Hanssesn of UiT The Arctic University of Norway  and the 5–10 cm depth of soils (n = 4) with three replicates each in Longyearbyen , and the plant species were identified by Yoo Kyung Lee from the Korea Polar Research Institute (KOPRI). These materials are deposited at KOPRI. Furthermore, in addition to the 14 bedrock samples used by Jang et al.27, we collected three coal samples near Ny-Ålesund in 2022. Please refer to Supplementary Table We collected a total of 45 surface sediment samples using a giant box corer  oxidized with 5 mL of H2O2 (35%) were analyzed for grain sizes smaller than 63 μm using a Mastersizer 3000 laser particle size analyzer  at KOPRI. The analytical precision was as follows: D(10): 37.5 ± 0.3 μm, D(50): 71.4 ± 0.2 μm, and D(90): 104.0 ± 0.0 μm. Mean grain size and sediment sorting were estimated according to Folk and Ward67.The grain size distributions were determined following a procedure previously reported by Kim et al.43. In brief, the TOC content of samples decalcified with 10% HCl for 24 h, the nitrogen contents in both bulk  and KOBr/KOH-treated  samples, and carbon isotopic compositions were determined at KOPRI using an elemental analyzer  coupled with an isotope ratio mass spectrometer . The carbon isotope ratios of TOC (δ13Corg) were reported using δ notation (per mil) with respect to the Vienna Pee Dee Belemnite (VPDB). The analytical precision was better than 0.5 wt.% and 0.5‰ for carbon and 0.5 wt.% for nitrogen. Accelerator mass spectrometry (AMS) radiocarbon (14C) analyses of TOC were performed at the MICADAS radiocarbon laboratory at the Alfred-Wegener Institute  and the Center for Applied Isotope Studies at the University of Georgia , following standard procedures. Radiocarbon results were presented in Delta notation , as defined by Stuiver and Polach68.Bulk elemental and stable isotope analyses were conducted following the methods described by Kim et al.27. Briefly, sediments (~ 100 mg) were chemically leached to obtain detrital fractions by removing the carbonate and authigenic fractions. All detrital fraction analyses were performed using a thermal ionization mass spectrometer  at KOPRI. The Nd isotope ratios were normalized to a 144Nd/146Nd value of 0.7219 to correct for mass fractionation. The analytical precision was 0.3 εNd units for the measured Nd isotopic compositions.Sequential extraction from bulk sediments and Nd isotopic analysis of the remaining detrital fractions were conducted according to the procedure described by Jang et al.43. Briefly, microwave-assisted alkaline CuO oxidation was carried out on ~ 400 mg of the samples using a Microwave Digestion System  at 150 °C for 1.5 h. All analyses were performed at KOPRI using an Agilent 7890B GC coupled to a 5977B Series Mass Selective Detector  operating in single ion monitoring (SIM) mode using a DB1-MS capillary column . The analytical precision associated with the lignin phenol concentrations was typically less than 10%.CuO oxidation of the samples was and analyses were conducted following the method described by Kim et al.69. The PCA of the lignin phenol concentrations was performed using R software version 4.0.3, specifically utilizing the FactoMineR package. To estimate the relative contributions of petrogenic, soil-derived, plant-derived, and marine OC to sedimentary OC in the fjords, a mixing model based on a Monte Carlo (MC) approach was employed v values  was described as normal distributions with mean and standard deviations. The MC analysis was performed using R software version 4.0.3, with the MixSIAR package.PCA was performed to provide an overview of the distribution of lignin phenols in fjord surface sediments, plants, and soils. When some components fell below the detection limit, a value of one-half of the minimum value detected for that variable across the entire dataset was assigned as the limit of detectionoyed see . This moThe authors promise that the experimental research and field studies on plants complied with relevant institutional, national, and international guidelines and legislation.Supplementary Information.


Natural flavonoids are renowned for their exceptional antioxidant properties, but their limited water solubility hampers their bioavailability. One approach to enhancing their water solubility and antioxidant activity involves the use of cyclodextrin (CD) inclusion. This study investigated the impact of CD inclusion on the three primary radical scavenging mechanisms associated with flavonoid antioxidant activity, utilizing apigenin as a representative flavonoid and employing density functional theory (DFT) calculations. Initially, the optimized geometries of CD−apigenin inclusion complexes were analyzed, revealing the formation of hydrogen bonds between CD and apigenin. In less polar environments, the inclusion complex strengthened the bond dissociation enthalpies of hydroxyl groups, thereby reducing antioxidant activity. Conversely, in polar environments, the inclusion complex had the opposite effect by lowering proton affinity. These findings align with experimental results demonstrating that CD inclusion complexation enhances flavonoid antioxidant activity in aqueous ethanol solutions. Free radicals, comprising reactive oxygen species characterized by unpaired electrons, are prevalent in the human body and can inflict substantial harm on biomacromolecules like lipids, proteins, and DNA ,2. Such Antioxidants play a crucial role in capturing free radicals and mitigating their damage. Among these antioxidants, flavonoids constitute a widespread category of polyphenolic compounds present in everyday foods like fruits, vegetables, cereals, and bee products. Flavonoids are effective scavengers of various free radicals, including hydroxyl, peroxyl, and lipid peroxyl radicals ,7. Theirα−CD), seven (β−CD), eight (γ−CD), or more glucopyranose units [α−CD, β−CD, and γ−CD are 0.47–0.53 nm, 0.60–0.65 nm, and 0.75–0.83 nm, respectively [Like other phenolic compounds, flavonoids possess relatively low water solubility and are susceptible to in vivo enzymatic degradation , factorsse units . They fese units . The cavectively . This unectively ,15. Reseectively ,17. Addiectively ,19.Antioxidants function to protect against free radicals through three primary mechanisms: Hydrogen Atom Transfer (HAT), Single Electron Transfer−Proton Transfer (SET−PT), and Sequential Proton Loss Electron Transfer (SPLET) ,21,22. WIn this study, we conducted comparative analyses to elucidate the antioxidative activity difference of flavonoids before and after CD inclusion complexation through the mechanisms of HAT, SET−PT, and SPLET. Apigenin , among tα−CD and β−CD was sourced from the Cambridge Crystallographic Data Centre. The positions of hydrogen atoms within α−CD and β−CD were optimized using the B3LYP/6-31G* method [α−CD−apigenin, and β−CD−apigenin complexes, were computed using the M062X/6-311+G** method. The solvent effect was incorporated utilizing the SMD continuum solvent model [All computations were executed employing the Gaussian 16 package . The str* method ,33. For * method , ensurin* method . Single-nt model .ρBCP), the Laplacian of electron density (∇2ρBCP), Lagrangian kinetic energy (GBCP), potential electron density (VBCP), and energy density (HBCP) at the BCP, are employed to investigate the properties of the hydrogen bond. In this study, the identification of BCPs in hydrogen bonds and detailed topological analysis were conducted using the Multiwfn 3.5 suite [The atoms in molecules (AIM) theory serves as a valuable tool for studying hydrogen bond properties . This th.5 suite .(1)HAT mechanism:The details of the three mechanisms are as follows ,21,22:(1(2)SET−PT mechanism:(3)SPLET mechanism:+, and ArO− represent for free radical, neutral flavonoid, flavonoid radical, flavonoid cation radical, and flavonoid anion, respectively. The reaction enthalpies related to Equations (1)–(5) are usually denoted as follows:In Equations (1)–(5), R•, ArOH, ArO•, ArOH•BDE: bond dissociation enthalpy (BDE) related to (Equation (1)). The calculated equation for BDE is:IP: ionization potential (IP) related to (Equation (2)). The calculated equation for IP is:PDE: proton dissociation enthalpy (PDE) related to (Equation (3)). The calculated equation for PDE is:PA: proton affinity (PA) of phenoxide anion (Equation (4)). The calculated equation for PA is:ETE: electron transfer enthalpy (ETE) related to (Equation (5)). The calculated equation for ETE is:+, e− and H• were obtained from the references [The calculated solvent phases enthalpies for Hferences ,40,41.α−CD−apigenin complex and the β−CD−apigenin complex are depicted in β−CD and apigenin is observed to be stronger than that between α−CD and apigenin. Hydrogen bonding is an important interaction in the inclusion process. We employed the sum of van der Waals atomic radii for hydrogen and oxygen (2.5 Å) to determine the presence of hydrogen bonds in the CD−apigenin complex [Before proceeding with the analysis of various molecular descriptors, we conducted an exploration of the conformational properties of apigenin and the CD−apigenin complex. The most stable structures of the  complex . For theρBCP, ∇2ρBCP, GBCP, VBCP, and HBCP at the BCP for the hydrogen bonds in both apigenin and the CD-apigenin complex are presented in The distances between the interacting hydrogen atom and oxygen atom within the complexes have been carefully labeled. These distances, which are less than 2.5 Å, unequivocally confirm the presence of intermolecular hydrogen bonds between apigenin and CD. To delve deeper into the nature of these hydrogen bonds within the complex, we examined the topological parameters utilizing AIM theory. The values of ρBCP values, ranging from 0.010 to 0.054 a.u., fall within the established range for hydrogen bonds [ρBCP serves as a numerical indicator of hydrogen bond strength, with higher values indicating stronger bonds. Consequently, the intramolecular hydrogen bond (O5−H5⋯O4) in apigenin weakens in the CD−apigenin complex. Notably, the O4 atom emerges as the predominant interaction site, characterized by the highest ρBCP value of O4⋯H−O among all hydrogen bond interactions.The analysis in en bonds . ρBCP se2ρBCP values observed in this study (ranging from 0.035 to 0.171 a.u.) aligns with established values for hydrogen bonds [2ρBCP serves as an indicator of the hydrogen bond type, with negative values denoting shared-type interactions and positive values denoting closed-shell interactions [2ρBCP value reported in The range of ∇en bonds , confirmractions . Consequ(1)2ρBCP > 0 and GBCP + VBCP > 0;Weak hydrogen bond: ∇(2)2ρBCP > 0 and GBCP + VBCP < 0;Medium hydrogen bond: ∇(3)2ρBCP < 0 and GBCP + VBCP > 0.Strong hydrogen bond: ∇The strength of a hydrogen bond can be classified into three types :(1)Weak 2ρBCP. The O5−H5⋯O4 intermolecular hydrogen bond in apigenin and the bonds between β−CD and apigenin exhibit negative GBCP + VBCP values, indicating their moderate strength. In contrast, the hydrogen bonds involving α−CD and apigenin display positive GBCP+VBCP values, signifying their relatively weaker strengths.HBCP can be used to distinguish whether a hydrogen bond is of a covalent or electrostatic nature [HBCP implies the hydrogen bond possessing a dominant character of covalent interactions. While the positive value indicates the electrostatic dominant nature of a hydrogen bond [HBCP is negative for the hydrogen bonds in apigenin and β−CD−apigenin complex, whereas the HBCP is positive for the hydrogen bonds in the α−CD−apigenin complex. Therefore, the hydrogen bonds in apigenin and β−CD−apigenin complex possess a dominant character of the covalent interactions, whereas those in α−CD−apigenin complex are electrostatic dominant.The sign of c nature . The neggen bond . From TaThe primary orbitals involved in chemical reactions are the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO). β−CD−apigenin and α−CD−apigenin. This finding unequivocally establishes apigenin monomer as having the most robust electron-donating capability. The complexation with cyclodextrins, particularly α−CD, diminishes the electron-donating potential of apigenin.The energy of the HOMO serves as a crucial parameter in representing the antioxidative activity of phenolic compounds, capturing both hydrogen atom/proton donation and electron transfer processes employed to trap free radicals. Phenolic compounds with higher HOMO energy exhibit stronger antioxidative activity. According to frontier orbital theory, the flavonoid’s most active redox site is identified by a high density of the HOMO. In our study, the HOMO orbitals are localized in three hydroxyl positions within apigenin. Consequently, all hydroxyl groups in apigenin are susceptible to attack by electrophilic agents like free radicals ,26,29,30The HAT mechanism is defined by the BDE. A lower BDE indicates an easier reaction and, consequently, a higher antioxidative activity. The calculated BDE values for the hydroxyl groups in apigenin are compiled in α−CD−apigenin > β−CD−apigenin. Consequently, apigenin appears to be the most effective scavenger following the HAT mechanism, and the complexation with cyclodextrin diminishes its antioxidative capacity.α−CD−apigenin complex, the homolytic cleavage of 7−OH requires breaking an intermolecular hydrogen bond  serves as the criterion for distinguishing thermodynamically favored processes. However, for the studied mechanisms, the absolute values of the entropic term (−T∆rS) only differ by a few units of kJ/mol, causing the free energies to be slightly shifted compared to the corresponding enthalpies (∆rH).Ordinarily, free energy (∆Chloroform phase: BDE < IP < PA;Benzene phase: BDE < PA < IP;Pyridine, ethanol, acetonitrile, DMSO and water phases: PA < BDE < IP.In multiple-step mechanisms, the first step holds thermodynamic significance. Enthalpies such as IP and PA are related to the initial step of SET−PT and SPLET mechanisms. By examining the lowest BDE, IP, and PA values presented in The significantly lower BDE in apigenin compared to the corresponding IP and PA indicates the dominance of the HAT mechanism in the benzene and chloroform phases. In contrast, in the pyridine, ethanol, acetonitrile, DMSO, and water phases, the lowest PA of the hydroxyl groups in apigenin is significantly lower than the corresponding lowest BDE and IP. Hence, the SPLET mechanism emerges as the most thermodynamically probable reaction pathway in polar solvents.α−CD−apigenin < β−CD−apigenin. Similarly, the sequence of the lowest PA is as follows: β−CD−apigenin < α−CD−apigenin < apigenin monomer. In the HAT and SPLET mechanisms, the antioxidative activities of flavonoids are determined by the lowest BDE and lowest PA, respectively. Therefore, in the benzene, and chloroform phases, the antioxidative activity of apigenin can be ranked in the following order: apigenin monomer > α−CD−apigenin > β−CD−apigenin. In polar phases, the order is reversed.Based on the findings from Based on the above discussion, it can be deduced that in the benzene and chloroform phases, the complexation with CD reduces the antioxidative activity of apigenin, whereas the opposite result occurs in polar phases. This conclusion is consistent with experimental findings indicating that CD inclusion complexation enhances the antioxidative activity of flavonoids in aqueous ethanol solutions ,19.We should also stress that experimentally observed behavior is not solely related to observed thermodynamics, but also the kinetics of radical scavenging play important roles , althougβ−CD and apigenin was found to be stronger than that between α−CD and apigenin. Hydrogen bonds between α−CD and apigenin exhibited weak strength and an electrostatic dominant character, whereas those between β−CD and apigenin demonstrated medium strength and a dominant covalent interaction character. These observations contribute valuable insights into the antioxidative activity differences in flavonoids and their CD inclusion complexes.This study aims to elucidate the differences in antioxidative activity between flavonoids and CD inclusion complexes using HAT, SET−PT, and SPLET mechanisms. Apigenin, a representative flavonoid, was selected for analysis employing DFT calculations. Prior to investigating various molecular descriptors, we explored the conformational properties of both apigenin and the CD−apigenin complex. Our analysis revealed the presence of closed-shell type intramolecular hydrogen bonds between apigenin and CD. The interaction between Upon examining the optimized structures, it became evident that all hydroxyl groups in apigenin were susceptible to attack by free radicals. However, complexation with CD was found to diminish the hydrogen atom donating and electron transfer abilities while enhancing the proton donating ability of apigenin. The HAT mechanism predominates in the benzene and chloroform phases, whereas the SPLET mechanism emerges as the most thermodynamically probable reaction pathway in polar solvents. Consequently, in the benzene and chloroform phases, the complexation with CD diminishes the antioxidative activity of apigenin, whereas the opposite result occurs in polar phases.This conclusion aligns with experimental findings demonstrating that CD inclusion complexation enhances the antioxidative activity of flavonoids in aqueous ethanol solutions.


An assessment of temporal variations in the diversity and abundance of nosZ denitrifiers was performed to establish the relative importance of these bacteria in denitrification. Sampling was conducted at the Arabian Sea Time Series (ASTS) location and a quantitative PCR (qPCR) analysis was performed. We detected a high abundance of the nosZ gene at core OMZ depths (250 m and 500 m), indicating the occurrence of denitrification in the AS-OMZ. The maximum abundance of the nosZ gene was observed during the Spring Intermonsoon (SIM) at 250 m (1.32×106 copies L–1) and 500 m (1.50×106 copies L–1). Sequencing analysis showed that nosZ denitrifiers belonged to the classes Alpha-, Beta-, and Gammaproteobacteria. Taxonomic analysis revealed that most OTUs were affiliated with Pseudomonas, Rhodopseudomonas, and Bradyrhizobium. Diversity indices and richness estimators confirmed a higher diversity of nosZ denitrifiers at 250 m than at 500 m during all three seasons. The present results also indicated that dissolved oxygen (DO) and total organic carbon (TOC) are critical factors influencing the diversity and abundance of the nosZ-denitrifying bacterial community.A significant amount of nitrous oxide (N The nosZ gene has two distinct clades, Clade I  and Clade II . Nitrous oxide genes associated with conventional denitrifiers are grouped as Clade I nosZ genes.Nitrous oxide reduction is the final step in the denitrification pathway, and the only known process for the utilization of Nse nosZ; , a coppenosZ gene is not always affiliated with denitrifying microbes; the recent discovery of novel nosZ sequences in non-denitrifying bacterial groups  location. We targeted the nosZ gene from DNA extracts to assess community and temporal variations. Furthermore, season- and depth-wise differences in the distribution of these bacteria were examined using quantitative PCR (qPCR). This information is critical for recognizing the responses of denitrifiers to changing environmental factors. Additionally, the identification of these bacteria will provide insights into the influence of nosZ denitrifiers on nitrogen transformation and fluxes.The climatic impact of marine nitrous oxide emissions due to deoxygenation have led to appeals for a more detailed understanding of the biological sink and sources of this gas in the ocean. The key to assessing the budget of nitrous oxide in OMZs is clarifying the distribution and abundance of microorganisms involved in the production and consumption of Ni.e. the Spring intermonsoon (SIM), Fall intermonsoon (FIM), and Northeast monsoon (NEM). Water was processed through sterivex filters , filled with 1.7 mL of storage buffer  and stored at –80°C until DNA extraction.Water samples were collected from the ASTS location using Niskin bottles on a CTD rosette from the surface (5 m), deep chlorophyll maxima (DCM) (~43–50 m), core OMZ depths (250 and 500 m), and 1,000 m during three seasons Physicochemical parameters  in every water sample were measured using different sensors mounted onto the CTD rosette. The standard Winkler titration method  was usedNucleic acid extraction from water samples was performed according to standard methods . The ampNosZ gene amplicons were purified, cloned into the PGEM-T Easy Vector (Promega), transformed inside high efficiency JM109 cells, and grown overnight at 37°C on LB/X-gal/IPTG plates. A minimum of 40 clones were selected per plate for colony PCR. Temperature conditions for colony PCR were as follows: an initial denaturation step at 94°C for 10 min, followed by 30 cycles at 94°C for 1 min, annealing at 55°C for 1 min with an elongation step at 72°C for 1 min, and a final extension at 72°C for 10 mins. PCR products were purified, measured, and sequenced using 15–50 ng of amplicons, adding 10 pmol each of the nosZ1F and nosZ1R primers in an ABI 3130 Genetic Analyzer (Applied Biosystems). Temperature conditions for sequencing were an initial denaturation at 96°C for 1 min, 30 cycles at 96°C for 10 s, annealing at 60°C for 45 s, elongation at 60°C for 4 mins, and a final extension at 60°C for 1 min.NosZ gene sequences were assembled using DNA Baser sequence assembly software version 2. VecScreen was used to eliminate vector contamination. Non-chimeric consensus sequences without vector and primer residues were submitted to the National Center for Biotechnology Information (NCBI) database to obtain accession numbers and were used in additional analyses. Sequences were classified using 1,000 pseudo-bootstrap replications at a bootstrap value of 80% (standard error of only 1.3%). Alignments were trimmed using Gblocks ( Gblocks . Clone snosZ gene) were assigned to operational taxonomic units (OTUs) by the average neighbor rule  with 1,000 replicate bootstrap analyses.Sequences . A plasmid carrying the nosZ gene fragment was cloned using the pCR4-TOPO vector and confirmed by sequencing. Ten-fold serial dilutions of a known copy number of plasmid DNA were subjected to a qPCR assay in triplicate to generate a standard curve and calculate the qPCR efficiency of the nosZ gene. A standard curve was generated by plotting threshold cycle values versus log10 of gene copy numbers. The slope, y-intercept, and coefficient of determination (r2) were assessed. The efficiency of amplification (E) was calculated using the following equation: E=–1+10(–1/slope).Quantification of the nosZ gene was quantified in triplicate. Each reaction contained a mixture of DNA (4 μL), the primer pair nosZ1F/nosZ1R (0.5 μL), and 5× qARTA Green qPCR Mix (12.5 μL). PCR cycles were performed according to the standard protocol  and gene copy numbers for every individual season. One-way analysis of variance (ANOVA) was performed for each physicochemical parameter, OTU, and copy number. Paired differences between depths within each season were tested using Tukey’s post hoc tests. Principal component analysis (PCA) with varimax rotations for the above-described physicochemical parameters was performed to reduce the number of inter-correlated variables. Multiple regression analyses were conducted to investigate the relationships between OTUs, copy numbers (dependent variables), and principal component scores (predictor variables) obtained from PCA. Statistical analyses were conducted using IBM© SPSS 23.0. Each result was shown as the mean±standard deviation (SD).Spearman’s correlation coefficient was used to evaluate the relationships between physicochemical parameters , and richness estimators (Chao 1 and ACE) were evaluated using MOTHUR. Non-parametric richness estimators were used to extrapolate the total richness of clone libraries from the observed number of OTUs. Diversity and richness estimators were calculated for individual clone libraries.nosZ gene sequences obtained in the present study are KX784867 to KX784885, KX911214 to KX911243, KY065372 to KY065445, and KY100043 to KY100090.NCBI accession numbers for the 171 –1 at 250 m to 5.85 μmol L–1 at 500 m. The concentration of nitrate was higher at core OMZ depths during all three seasons and ranged between 15.33 to 32.79 μmol L–1. The highest concentrations of nitrite (2.57 μmol L–1) and ammonia (NH4+ 1.01 μmol L–1) were noted at 250 and 500 m during the NEM and SIM, respectively. The concentrations of DO, NO2– NO3–, and NH4+ during all three seasons are shown in Temperature, salinity, pH, and total organic carbon (TOC) were consistent, while the concentrations of DO and nutrients varied at core OMZ depths . The avenosZ gene (259-bp amplification product) was only positive for samples collected at 250 and 500 m in all seasons. Among the 210 clones sequenced, 171 non-chimeric nosZ gene sequences were obtained and the following clone libraries were built: SIM-250, SIM-500, FIM-250, FIM-500, NEM-250, and NEM-500.Evolutionary differences between OTUs and representative sequences were assessed by phylogenetic analyses. PCR amplification of the nosZ clones showed 80–93% identity with one another and 77–88% similarity to sequences in GeneBank. All OTUs obtained in the present study matched the sequences of the phylum Proteobacteria in the NCBI database. Taxonomic analyses reveals that most nosZ OTUs were associated with Pseudomonas, Rhodopseudomonas, Bradyrhizobium, and Alphaproteobacteria. A small percentage of bacteria was affiliated with Azospirillum, Achromobacter,Cupriavidus, Nisaea, Thalassobaculum, Sinorhizobium, Herbaspirillum, Burkholderia, and Alcaligenes. The phylogenetic analysis showed that OTUs in the present study closely matched the environmental sequences obtained from the suboxic zone of the AS, deep-sea waters of the Mediterranean Sea, the crop soil and wetland sediment of Mexico, terrestrial subsurface sediments, a marine aquaculture biofilter, and paddy soil.The sequences of Rhodopseudomonas  and Pseudomonas . Azospirillum (2 OTUs), Bradyrhizobium (2 OTUs), Achromobacter (2 OTUs), and Cupriavidus (2 OTUs) each contributed 12% of the total number of OTUs. Bacterial sequences belonging to the genera Nisaea (1 OTU), Thalassobaculum (1 OTU), and class Alphaproteobacteria (1 OTU) formed OTUs with a smaller number of clones.Among the 87 clones sequenced from nosZ250m, 17 OTUs were obtained , the majBradyrhizobium , Pseudomonas , and Alphaproteobacteria . Thalassobaculum (1 OTU), Rhodopseudomonas (1 OTU), Sinorhizobium (1 OTU), Azospirillum (1 OTU), Herbaspirillum (1 OTU), Burkholderia (1 OTU), and Alcaligenes (1 OTU) were the other minor OTUs present.Thirteen OTUs were generated from nosZ500m (84 sequences) , the majAlpha-, Beta-, and Gammaproteobacteria , Azospirillum , and Pseudomonas . Three OTUs (11.1%) each were associated with Rhodopseudomonas (19 sequences), Sinorhizobium (19 sequences), and Achromobacter (19 sequences). One OTU (6 sequences) each was affiliated with the genera Thalassobaculum, Nisaea, and Burkholderia and the class Alphaproteobacteria , followed by the FIM (5) and NEM (5)  . At 500  the NEM . The numnosZ gene were plotted and qPCR efficiency (81.93%) was calculated. The efficiency reading was used as a reference to calculate the concentrations of the gene in environmental DNA samples. At surface and DCM depths, the copy numbers of the nosZ gene were negligible, ranging between 0.05 and 0.09×106 copies L–1 and between 0.07 and 0.09×106 copies L–1, respectively. At 250 m, the abundance of nosZ was the highest during the SIM (1.32×106 copies L–1), followed by the NEM (0.63×106 copies L–1) and FIM (0.49×106 copies L–1). At 500 m, the abundance of the nosZ gene was the highest during the SIM (1.50×106 copies L–1), followed by the NEM (0.63×106 copies L–1) and FIM (0.41×106 copies L–1). Overall, the highest copy number of the nosZ gene was detected at 500 m during the SIM (1.50×106 copies L–1). The abundance of the nosZ gene at 1,000 m varied from 0.05 to 0.08×106 copies L–1. nosZ genes.Standard curves for the P<0.01), nitrite , and TOC  negatively correlated with nosZ gene copy numbers, whereas ammonia  showed a positive correlation. Environmental parameters did not correlate with the abundance of the nosZ gene during the FIM, except for nitrite , which showed a negative correlation. DO  negatively correlated with the distribution of the nosZ gene during the NEM, while this correlation was positive for nitrite  . The third component (PC3) explained 23.13% of variance and included NO2– (0.947)  . A one-wP<0.001) . Tukey’s 500 m  during a (0.947) . Multipland NH4+ .nosZ denitrifiers was the highest during the SIM, followed by the FIM and NEM. Diversity was the highest at 250 m during all sampling seasons. The non-parametric estimators, Chao 1 and ACE showed a higher number of OTUs at 250 m during all seasons. The SIM (250 m) and NEM (250 m) had the highest number of OTUs  triggering intense denitrification  of the AS-OMZ, the nosZ genes in regions associated with the OMZ of the Eastern Tropical Pacific, the AS, and in oxygenated surface waters of the Arctic and Southern Oceans . Nevertheless, we acknowledge that the PCR primers used in the present study may have been biased towards the detection of Clade I nosZ genes and, thus, we may have underestimated the real abundance of nosZ genes in our samples. Nevertheless, the present results indicate that marine nosZ denitrifiers (Clade I) inhabit core AS-OMZ depths and play an equal and significant role at the ASTS, leading to a high percentage of fixed nitrogen loss.Although mostly detected in the soil ecosystem  isolated from the Mediterranean Sea, which is potentially involved in denitrification , the role of which remains unknown . PCR with a higher concentration of DNA did not yield positive amplification for any of the samples taken from depths of 5 m, DCM, or 1,000 m. Therefore, nosZ genes at these depths were limited and hard to detect via conventional PCR. Additionally, nosZ denitrifiers in the AS-OMZ. The higher abundance of nosZ denitrifiers during the SIM may be attributed to organic carbon in the OMZ being a significant substrate that supports the existence of denitrifying and anammox bacteria , whereas the nirS primers used in previous studies (We herein reported a higher abundance of NnosZ denitrifiers during different seasons. The presence of denitrifiers at core OMZ depths (as is implicit in derived diversity indices and the richness estimators ACE and Chao 1) indicated that low concentrations of oxygen, nitrite, and ammonia provided an ideal environment for the presence of nosZ denitrifiers  Diversity and Quantitative Detection of Clade I Type https://doi.org/10.1264/jsme2.ME22056Supplementary Material