Additionally, we investigated if the welfare status varied on a regional basis.Knowing the national status of animal welfare, one can identify welfare problems and set a benchmark against which improvements can be compared. Such a status is potentially invaluable for tangible, sustained animal welfare improvement. The objective of this cross-sectional study was to report the status of animal welfare in Norwegian loose-housed dairy herds as assessed using the Welfare Quality® assessors. This article presents the herd prevalences of common welfare issues in dairy production in Norway, as well as integrated welfare scores. To determine whether welfare status varied regionally in Norway, generalized linear modeling was used to estimate the mean welfare score for five regions in the four Welfare Quality® principles: A. Good feeding, B. Good housing, C. Good health, and D. Appropriate behavior. These estimated mean welfare scores and their 95% confidence intervals were subsequently assessed for significant variation.In total, 155 herds in eight of Norway's eleven counties were assessed by six trained Welfare Quality® principle Good housing (p < 0.01).Encouraging findings included the low mean herd prevalence of ‘very lean' cows (3.0%) and the high proportion of cows (59.8%) which could be touched during avoidance distance testing, indicating a positive relationship between stockpeople and their cattle. Challenges affecting the welfare of Norwegian dairy cows were also identified. Of particular concern were issues related to the cows' environment such as prolonged times needed to complete lying down movements and integument alterations. No herd was completely free of changes to the integument and, on average, 77.9% of each herd were affected either mildly or severely. Animal welfare did not appear to vary much between the five regions assessed. Our investigation revealed significant regional variation between two regions (Trøndelag and Vestlandet North) in only the Welfare QualityThe almost complete absence of regional variation demonstrates that animal welfare status generally varies most at herd level. In conclusion, both welfare challenges and encouraging findings were identified in loose-housed Norwegian dairy herds. To improve animal welfare, herd-specific interventions are most likely to be effective in these herds. In turn, these criteria scores are combined to generate a score from 0 to 100 for each of the four principles. These four principle scores are ultimately combined to assign the herd to one of four categories: “Excellent”, “Enhanced”, “Acceptable”, or “Not classified”. A full description of the score integration can be found in the WQ® protocol  may vary depending on the regional climate. In turn, this variation may influence dairy cow welfare. If welfare is found to vary significantly from region to region, this could have important implications both for the provision of a national status and for targeting regionally specific welfare interventions.We hypothesized that the score for the four overarching WQTo our knowledge, a primarily animal-based welfare assessment of this scale has never been previously conducted in Norwegian loose-housed dairy herds. Specific topics relevant to dairy cow welfare have been studied in Norwegian dairy herds, for example skin lesions ; mastiti® protocol, aligns with the high legislative standards and consumer expectations in Norway. Such information may be of interest to dairy industry stakeholders in other countries as they consider how to address growing public concern about animal welfare. Additionally, should regional variation be detected, it would facilitate the implementation of regionally specific welfare interventions in Norway. Awareness of the potential for regional variation could prove useful beyond Norwegian conditions, in any country with a welfare status which may similarly vary.Previous studies have indicated that, contrary to what has happened in other European countries, Norwegian dairy producers have not faced a problem of consumer distrust. Animal welfare in Norway has been primarily governed and regulated by the issuing of statutory regulations that often go beyond EU standards . The legThe herds were selected from the Norwegian Dairy Herd Recording System (NDHRS), operated by Mimiro AS, a subsidiary of TINE SA. TINE SA is Norway's largest producer, distributor, and exporter of dairy products , represen = 2950) (n = 1244) using SAS 9.4 software (n = 311). Following inspection of the list of selected herds, two herds were deemed ineligible for participation as they were no longer producing milk. A list of active selected herds (n = 309) was prepared. From the target population of all loose-housed Norwegian dairy herds ( = 2950)  a list wsoftware . The lis® specifies that herds should not be assessed until at least 28 days after routine claw trimming. Additionally, for practical reasons, lactating cows must be housed during the visit. Due to these constraints, scheduling practicalities, budgetary limitations, and potential participant hesitancy we anticipated it would be possible to visit ~50% of the eligible selected herds.To minimize the influence of routine claw trimming on lameness scoring, WQAll 155 herds participated voluntarily. Their geographical distribution can be seen in Initially, all 309 active selected herds were emailed by the TINE Member Center, the group responsible for internal communication with members of TINE SA. The email contained information about the project and a link to a web-based questionnaire with four questions . After cAll active selected herds were plotted geographically using GPS coordinates in Google Maps™ . To ensuIn areas where an insufficient number of herds were recruited passively, herds which had not explicitly stated they did not wish to participate in a questionnaire response were contacted by telephone. Their participation was requested and if they were willing a visit was arranged. These herds were classified as actively recruited because their recruitment required a direct telephone conversation rather than expressing interest of their own volition through the questionnaire. They were contacted by the assessors in order of appearance on the list of active selected herds until visits were arranged with ~50% of herds in an area.n = 43), Østlandet South (n = 38), Østlandet North (n = 29), Vestlandet South (n = 24), and Vestlandet North (n = 21). The five regions and their herds are shown in For the purpose of investigating regional variation, the 155 herds were assigned to one of five regions corresponding to their administrative district within TINE SA. These administrative districts are based on traditional geographical divisions of Norway. The five regions were Trøndelag  using photographs and videos. The percentage agreement with the silver standard reference values ranged from 73 to 100%. The IOR was evaluated using statistical techniques by the course provider and deemed to be acceptable for all measures except for those related to behavior observations. This was consistent with the prior experience of the course provider. For this reason, extra attention was placed on these behavioral assessments during the shadowed visits which followed the course.Six assessors, three veterinarians and three ethologists were trained to use the WQ® certified.Each assessor performed one to two complete shadowed visits accompanied by a more experienced assessor. These shadow visits occurred prior to undertaking visits independently. Two herds not included in the list of randomly selected herds were used for shadow visits but not included in the analysis. All assessors were subsequently WQ® assessment was performed for each herd between April 2021 and January 2022 as described by the WQ® protocol . The body condition scoring system used by WQing grid  adjusteding grid  was used® definitions.Some data were self-reported by the farmer from on-farm records or memory. These included the number of cases of dystocia, the number of downer cows, and the number of cows which died, were euthanized or were emergency slaughtered on-farm during the 12 months prior to the visit. Additionally, the number of days of pasture access per year and the number of hours of pasture access during those days was self-reported. The definition of pasture extended to any vegetation covered outdoor area, even if it did not contribute to the cows' nutritive requirements. Self-reporting was preferred as this aligned most accurately with the WQn = 7), the mean value from the other herds was used instead as this was considered by the authors to be broadly reflective of Norwegian conditions. When mortality data were unavailable through self-reporting, the mortality records for the farm were accessed through the NDHRS and these data used. For every herd, data for the average number of lactating animals and the number of calvings which took place in the herd during the 12 months prior to the visit were retrieved from the NDHRS following the on-farm assessment. These data were used to calculate the percentage mortality, dystocia, and downer cows during that time period. Somatic cell count (SCC) data for each cow for the last three milk recordings prior to the visit were also obtained from NDHRS and the percentage of cows with a SCC ≥400,000 cells per milliliter in at least one of these milk recordings was calculated.When dystocia data were missing in the dataset  and observed prevalences of common welfare issues and other welfare related herd level summary data (n = 34) were reported in table format, organized within the structure of WQ® as described in The integrated WQGeneralized linear modeling (GLM)  was usedgladder” command. The link function corresponding to the most normally distributed transformation was included as a component of the model. The Gaussian family function was included in all four models due to the distribution of the dependent variable or its chosen transformation.The distribution of the dependent variable and its transformations, based on a subset of the ladder of powers , were scP < 0.20 was used during univariable screening, however, biological plausibility took precedence throughout the model building process. Independent variables were retained following univariable screening process regardless of their statistical relationship to the dependent variable if it was biologically justifiable. Potential biological relationships were identified using directed acyclic graphs ; milking system (AMS or parlor); recruitment method (passive or active), and herd size (continuous). Herd size, as the only continuous independent variable, was plotted against the dependent variable using locally weight scatterplot smoothing (LOWESS)  to assesDespite training and IOR testing, significant assessor effects were detected during univariable screening for the models B. Good housing, C. Good health, and D. Appropriate behavior. Furthermore, the distribution of herds visited by different assessors was not even across the five regions. It was concluded that assessor effects could act as a confounder for potential regional effects. Therefore, it was necessary to include the assessor variable in all models intended to determine the extent of regional variation.2 Test scores and their significance values  and the remaining 11% used milking parlors (n = 17). The proportion of the cow assessed for individual level measures was between 46 and 100% per herd. On average, 74% of the eligible lactating cows and heifers in each herd were assessed individually. The mean proportion of NRF in the herds assessed was 92.7%. There were slightly more actively recruited herds (51.5%) than passively recruited herds (48.4%).The 155 herds assessed had an average herd size of 52 cows with a range from 18 to 117 cows. AMS were present in 89% of the herds (n = 86), were classified as “Enhanced” based on their WQ® assessment. Of the remaining herds, 43.2% (n = 67) were placed in the “Acceptable” category and 1.3% (n = 2) were deemed “Unclassified”. None of the herds attained the highest WQ® classification of “Excellent”.More than half of the herds, 55.5% ; moderate problem (5.2–6.3 s); or severe problem (>6.3 s). Of the herds assessed, 15.5% of herds were classified as normal, 41.3% as having a moderate problem, and 43.2% a severe problem. That herds were classified across all three categories highlights the variation at herd level. The mean time across all herds was only slightly below the border between a moderate and a severe problem.WQ® as having a severe problem. In one herd in particular, nine out of ten recorded lying down movements resulted in a collision.As can be seen in ® as having a severe problem, meaning they had more than 5% of their animals lying partly or completely outside the designated lying area. While a quarter of cows in one herd were recorded as lying out, more than half of the herds had < 3% of cows lying partially or completely outside the lying area as demonstrated by the median score in The proportion of animals in each herd observed lying partly or completely outside the lying area was similarly skewed toward lower prevalences. A little over one fifth of the herds assessed were classified by WQ® cleanliness classifications, the mean prevalence for all herds for dirty lower and upper hindlegs would be classified as a severe problem. The mean prevalence of dirty udders would be classed as a moderate problem. There was not a single herd where all of the lower hind legs were scored as clean, with all herds having some cows with dirty lower hind legs. In 21 herds over 90% of the cows were scored as having dirty lower hind legs.The scoring for all three cleanliness measures followed an approximately normal distribution. As described in Injuries were frequently observed, as can be seen in ®, “warning” and “alarm”. If the warning threshold is surpassed, it indicates that there may be a problem in the herd, and it could be beneficial to investigate this further. If the alarm threshold is breached, then it is recommended by the WQ® protocol that action be taken to address the problem identified.The criterion absence of disease is made up of ten disease-related measures which are reported in High somatic cell count and ocular discharge, both clinical signs of disease issues, were most commonly identified. The mean herd prevalences of nasal discharge, diarrhea, dystocia, downer cows, and mortality were all < 10%. Vaginal discharge and hampered respiration were very rarely encountered. On average, 0.1 coughs were detected per cow per 15 minutes of observation time. The prevalence of these clinical measures varied between herds. For all contributary measures, there were herds in which no animals scored were affected by that particular issue. In other herds, as many as half of all animals scored were affected, depending on the measure.As shown in The mean prevalence of cows with a somatic cell count >400,000 in at least one of last three recordings prior to the visit was over 15%. This exceeded the warning threshold, as described above, and was just below the alarm threshold of 17.5%.The prevalences of nasal and ocular discharge showed a seasonal pattern when plotted against the month of the visit. Both increased during the late spring and summer months before decreasing in the autumn.The criterion score for absence of pain induced by management procedures was the same for all 155 herds assessed.The scoring of this principle was approximately normally distributed and varied between herds through a range of over 50 points. Of the four criteria contributing to this principle, good human-animal relationship was the most highly scored, followed, respectively, by expression of social behaviors, positive emotional state, and expression of other behaviors.The criterion expression of social behavior is scored based on the number of agonistic behaviors observed per cow per hour. These behaviors are divided into two measures, headbutts and displacements, with fighting, chasing, and chasing-up being considered together with displacements. The scores obtained varied from herd to herd through almost the entire possible range of scores. The data were approximately normally distributed.® as pasture. This was the only WQ® score in the study that spanned the full possible range from 0 to 100 points.On average, the herds assessed allowed cows out to pasture for approximately three and a half months per year. The number of days varied from herd to herd, with two herds allowing their cows year-round access to a vegetation-covered outdoor area and five herds (at least one in each region) providing nothing that would be described by WQ® to one of four categories: the cow could be touched; the cow could be approached close than 50 cm but not touched; the cow could be approached between 50 and 100 cm; or the cow could not be approached closer than 110 cm. On average, almost two thirds of cows could be touched by the assessor. Approximately one third of individual cows could be approached closer than 50 cm but not touched. Less than 1% of cows could not be approached closer than 100 cm. The human-animal relationship did vary from herd to herd, however, as despite nearly half of herds scoring over 80 points, eight herds scored < 50 points.The criterion score for good human-animal relationship is based on the avoidance distance testing of a sample of individual cows. The outcome of each individual avoidance distance test is assigned by WQThe criterion score for positive emotional state was based on qualitative behavioral analysis of the herd. The scoring was approximately normally distributed and varied between herds with a range of 64.6 points from minimum to maximum.® principle scores for the five regions are reported in The estimated mean WQ® protocol. Just over half of the herds were classified as “Enhanced”, the second highest ranking within the WQ® assessment framework. The absence of any herds classified as “Excellent” is similar to the findings of other European studies  of the variation between Trøndelag and Vestlandet North.® principles. Regardless of whether the typical feed composition varies regionally, as we supposed, or not, it appears to meet the cows' nutritive requirements in most cases. We also assumed that the number of days per year at pasture would vary from region to region, but this assumption appears to be incorrect based on these data. The lack of variation on a regional basis, with one borderline exception, runs counter to our initial belief that welfare status would vary markedly from region to region. It has previously been suggested that when cows are kept in similar systems, their welfare is broadly similar from country to country . Recruitment method was therefore retained in the best fitted models for those principles in order to account for the participation bias potentially identified.® protocol provided an approach to reporting the national status of animal welfare in loose-housed Norwegian dairy herds. The status both presented encouraging findings and identified welfare challenges. Particularly positive were the low herd prevalences of very lean cows and the positive relationship between stockpeople and their cattle. The challenges of most concern were injuries arising from conflicts between the cows and their environments, clinical signs such as ocular discharge, and inadequate resting comfort.Our assessments using the WQ® principle level in Norwegian loose-housed herds did not vary on a regional basis in most cases. However, variations in dairy cow welfare were clearly present at a herd level. We conclude, therefore, that attempts to improve dairy cow welfare should be directed at individual herds rather than at a regional level.Our investigation revealed significant regional variation between just two regions in only one of the four principle scores assessed. The suggestion is that contrary to our initial belief, the welfare status at WQThe welfare of dairy cows is multidimensional and positive achievements in some respects may not compensate for deficiencies in other areas. Stakeholders in Norwegian dairy production should focus on herd-specific opportunities to improve animal welfare.conor.barry@nmbu.no.The datasets presented in this article are not readily available because of the need to maintain participant anonymity. Requests to access the datasets should be directed to CB, Animal ethics approval was not required as this was an observational study which did not influence the animal's normal way of life or cause anything other than an entirely temporary light pain or discomfort. This is in line with Section 4.7 of the Norwegian University of Life Science's Ethical Guidelines. Written informed consent was obtained from the owners of the participating herds for the participation of their animals in this study. This study complied with the requirements for data protection and written, informed consent in studies involving human participants described in Section 4.6 of the Norwegian University of Life Sciences' Ethical Guidelines. Written informed consent was obtained from all human participants for their participation in this study. Independent evaluation by the Regional Committee for Medical and Health Research Ethics was considered to not be applicable due to the absence of ethical concerns regarding human participation in this study.Conceptualization, study planning, and writing—review and editing: CB, KE-D, RG, SG, CW, and CK. Assessor training: CW. Data collection: CB, KE-D, RG, SG, and CK. Software, data analysis, visualization, and writing—original draft: CB, KE-D, and CK. All authors contributed to the article and approved the submitted version.


Gluten composition is an important quality parameter for wheat flour, because it is strongly correlated to baking quality. Wheat proteins are commonly extracted stepwise and analysed using RP-HPLC-UV to determine the gluten composition. This procedure is very time-consuming and labour-intensive. Therefore, a new, fast and easy method to quantitate gluten proteins was established using NIR spectroscopy (NIRS). PLS-regression models were calculated containing 207 samples for calibration and 169 for test set validation. Albumin/globulin (ALGL), gluten, gliadin and glutenin content was predicted with a root mean square error of prediction (RMSEP) of 2.01 mg/g, 6.09 mg/g, 4.25 mg/g and 3.50 mg/g, respectively. High-molecular-weight glutenin subunits (HMW-GS) and low-molecular-weight glutenin subunits (LMW-GS) were predicted with a RMSEP of 1.12 mg/g and 2.38 mg/g. The relative error was too high for ALGL, LMW-GS and HMW-GS, but that of gluten, gliadins and glutenins was in a range comparable to the reference method. Therefore, the new NIRS method can be used to estimate the gluten composition of wheat flour, including the gliadin/glutenin and the LMW-GS/HMW-GS ratio.  •Quantitation of gluten, gliadin and glutenin for quality assessment of wheat flour.•NIR spectroscopy is a fast and easy approach to quantitate gluten proteins.•PLS regression to predict gluten, gliadin and glutenin content.•NIRS is a suitable fast method to determine the gluten composition of wheat flour. The DumIdentification and quantitation of gluten proteins is often performed using a combination of extraction and chromatographic separation. Following a modified version of the Osborne fractionation, albumins and globulins (ALGL), gliadins and glutenins are extracted from the flour in three steps and analysed by RP -HPLC with UV-detection . The proAs gluten composition is an important quality trait, fast and easy methods for its determination are desirable. Near-infrared spectroscopy (NIRS) is a well-established, quick and comparatively cheap technique in food analytics . To quanBased on the most recent findings on bread making properties and protein composition, gluten quality should be integrated into the quality assessment of wheat flour . Therefo22.1In total, 376 different wheat samples were included in this study. The sample set contained the 82 flour samples described in detail by 2.2Crude protein content was determined according to ICC Standard no 167 (2000). For the Dumas combustion method 50 mg of flour were weighed into a tin foil cup and analysed with a Leco TruSpec nitrogen analyser . A conversion factor of 5.7 was used to calculate crude protein content.2.3Extraction and quantitation of wheat proteins was performed according to 2.4−1 and 3946 cm−1 (866 nm–2534 nm) with a resolution of 8 cm−1. The measurement spot of the spectrometer had a diameter of 10 mm. A rotating sample cup (diameter 97 mm) was used to maximize the analysed sample surface. The sample cup was filled with flour up to a height of approximately 4 cm. For each sample, 64 scans were measured. Background spectra were subtracted from sample spectra and measured every 10 samples.NIR spectra of the flours were measured using a Tango-R FT-NIR-spectrometer  in reflectance mode. The Tango-R measures wavenumbers between 11,545 cmReference spectra of water, wheat starch , vital gluten (Kröner-Stärke GmbH) and dried flour were measured to assign NIR absorption bands to flour constituents.2.5NIR spectra were measured using the OPUS 7.8 software . Raw files were exported and further analysed in R using the pls package for PLS-regression . As desc33.1The gluten, gliadin and glutenin content of the sample set containing 376 white wheat flours ranged from 47.7 mg/g to 132.7 mg/g, 32.1 mg/g to 95.3 mg/g and 15.6 mg/g to 46.2 mg/g, respectively . The ratTo generate reliable calibration models, the sample set needs to consist of representative samples. Box and whisker plots of the respective analytes show an even distribution of samples within the interquartile range . Samples3.2−1 and 5160 cm−1 were assigned to O–H bonds of water ) were analysed and compared to vital gluten (protein content 71.3 g/100g), wheat starch, water and the same flour sample dried to a moisture content of 1% to identify relevant wavenumbers for protein absorptions . Absorptof water . After dferences  B. Flourferences  and therhoulders . Peaks aterature . Spectra3.3−1 to 3946 cm−1.PLS-regression models were calculated for the quantitation of gluten proteins. For method development, different spectral ranges and pre-processing options were tested and PLS-regression models were calculated with different combinations of pre-processed spectra and spectral ranges. Leave-one-out cross validation was used to estimate the quality of the regression. Optimization of the regression models was performed by minimizing the RMSECV. To avoid overfitting, the number of PLS factors was chosen according to a permutation test. The test aims to choose a minimal RMSECV with maximal explained variance , gliadin (>80 mg/g) or glutenin (>38 mg/g) contents. When comparing the predicted and the reference values for gluten and gliadins, the contents were underestimated in case of high amounts  B and C.Prediction of the crude protein content using NIRS is already well-established  and was For ω5-, ω1,2-, α-, γ-, and ωb-gliadins the PLS-regression models had relative errors of up to 100% (data not shown) and were therefore not applicable for quantitation.−1 whereas the regression model for LMW-GS/HMW-GS ratio included wavenumbers from 6181 to 5542 cm−1 and 5175 to 4436 cm−1, respectively. The RMSEP of the PLS-regression for predicting the gliadin/glutenin ratio was 0.27, which corresponds to a relative error of 9.2–19.6%. For the ratio of LMW-GS/HMW-GS, a RMSEP of 0.27 was observed (8.5–14.8%). It is questionable, whether distinct PLS-regressions for the gliadins/glutenins and LMW-GS/HMW-GS ratios are necessary, because gliadins, glutenins and LMW-GS were predicted with acceptable error. Therefore, we compared the ratios predicted by a distinct PLS-regression model with those calculated from the respective predicted contents , cost-effective and non-destructive analytical technique is desirable for predicting quality parameters of wheat flour. PLS-regression models with acceptable errors were calculated to predict gluten, gliadin and glutenin content and thus a fast and simple quantitation of important quality traits of wheat flour was possible for the first time. The error of prediction for ALGL, LMW-GS and HMW-GS was larger than the acceptable error of the reference method. Nevertheless, all of the presented PLS-regression models can be used if an estimation of the respective content is required. The gliadin/glutenin and LMW-GS/HMW-GS ratios were calculated with the predicted gliadin, glutenin, LMW-GS and HMW-GS contents with adequate accuracy. Therefore, NIRS is an appropriate fast method to determine the gluten composition of wheat flour.10.13039/501100008465FEI was supported via AiF within the programme for promoting the Industrial Collective Research (IGF) of the Federal Ministry of Economic Affairs and Climate Action (BMWK), based on a resolution of the German Parliament. Project 20283 N. This work was additionally supported by funds of the 10.13039/501100005908Federal Ministry of Food and Agriculture (10.13039/501100005908BMEL) based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (10.13039/501100010771BLE) under the innovation support programme, project BigBaking (2818404B18). We acknowledge support by the KIT-Publication Fund of the 10.13039/100009133Karlsruhe Institute of Technology.This IGF Project of the Clemens Schuster: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft. Julien Huen: Conceptualization, Funding acquisition, Resources, Writing – review & editing. Katharina Anne Scherf: Conceptualization, Funding acquisition, Resources, Supervision, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


N = 556,112) to examine the extent to which absolute and relative intergenerational educational mobility has changed in Switzerland for women and men from the 1951–1990 birth cohorts. We show that there is significantly more upward than downward mobility, while a large proportion of individuals are laterally mobile. By looking at absolute mobility patterns by cohort and gender separately, we extend previous research and show that the decreasing absolute mobility rates are due to the changing educational composition of the parental generations. Following on from previous studies, we reveal that the observed trend toward less relative social mobility continues in the youngest cohorts. It is also worth noting that, while the father's educational attainment has a higher predictive power for children's education in all cohorts, the impact of the mother's education approaches that of the father. Overall, the mobility patterns of men and women converge very strongly over the cohort sequence. Beyond these substantive points, our study demonstrates the potential of using administrative data for social stratification research.In many societies, educational attainment determines social inequality in terms of life chances, and at the same time there is a strong link between social origin and educational success. Therefore, analysis of educational mobility patterns is a central concern for sociologists. In the context of societal changes, such as trend of modernization, educational expansion and significantly increased female participation in education, we use administrative data from different sources ( In many societies, there is a strong link between educational attainment and life chances , enabling us to calculate detailed subgroup analyses by birth cohort, gender and parental educational background. In addition, the data allow us to identify parents and children who no longer live in the same household. This enables us to draw more generalizable conclusions than previous studies using administrative data . They were able to show the following educational mobility for this group of people: 40% had the same education as their parents and 43% had attained a higher level of education, while 17% had attained a lower level of education. Using 2000 census data, Bauer and Riphahn . They found that, among natives for whom educational information was available both for the children and for the father, 65% were laterally mobile , 25% were upwardly mobile, and 10% were downwardly mobile. With respect to the mother's education, 57% were laterally mobile, 36% were upwardly mobile and 7% were downwardly mobile. The studies mentioned above have in common that they show a low degree of educational downward mobility for Switzerland. In addition, they reveal a high degree of lateral mobility and that there is significantly more upward mobility than downward mobility. These studies do not provide information on how these mobility patterns vary by gender or by birth cohort.As mentioned above, there is a lack of research on absolute intergenerational educational mobility in Switzerland. Levy et al.  presente Riphahn  examinedRecently, an increasing number of studies have been published on relative educational mobility. Many studies also examine the relationship between parental social background  and the education of their children, or specific educational transitions in the educational trajectory . Their findings suggest that relative mobility remained constant across these two birth cohorts. Based on a survey from 1991  and a second survey from 1999 , Bergman et al. , four age cohorts were differentiated  and their relative mobility parameters were estimated. According to these results, relative mobility was very stable across the age cohorts and there was virtually no change in relative mobility patterns. In a comparison of the countries studied, Switzerland had the fourth lowest relative mobility rate. Only Germany, Belgium and Slovenia had lower relative mobility rates.A study by Pfeffer , which iN = 25,858). They classified 30–69-year-olds into five different birth cohorts  and calculated the relationship between parental education and children's education, controlling for parental social class separately by gender and birth cohort. For men, they found an increase in relative intergenerational mobility between the 1945 and 1960 cohorts. For women, the process of improvement in relative mobility began in earlier birth cohorts and was more pronounced than for men. In the most recent cohort in the study (born around 1970), a decline in relative social mobility could be observed for both men and women. This inverted U-shaped relationship was more obvious for women than for men. Jann and Seiler . They examined the 1922–1982 birth cohorts, which they divided into five cohorts spanning 6–16 birth years. They also found a U-shaped pattern of relative mobility  for both genders. Relative educational mobility increased up to birth cohorts born around 1960, with relatively stable relative mobility patterns for birth cohorts born from 1950 to the mid-1960s. For birth cohorts born from the mid-1960s onward, a deterioration in relative mobility patterns was observed. Considering various control variables, they found that the U-shaped relationship was more pronounced for women than for men. For both genders, however, a deterioration in relative mobility patterns could be observed for birth cohorts from the mid-1960's onward.Jann and Combet  examinedd Seiler  also exaAlthough the findings of the research on relative educational mobility in Switzerland are not beyond dispute, the following conclusions can be drawn from the studies mentioned above. Most studies report stable or improving patterns of relative mobility for cohorts born between 1920 and 1960. For cohorts born from the mid-1960s onward, the results suggest that relative mobility patterns are deteriorating again, and that parental education is once again becoming a stronger determinant of children's education. A pattern of increasing relative mobility for the 1940s and 1950s birth cohorts, followed by stagnating or diminishing mobility rates for subsequent cohorts, has also been observed in other countries  equalize across cohorts between the genders.We use several data sources for our analyses. First, we use data from the 1970, 1980, 1990 and 2000 censuses ), general education schools , professional education and training (PET [5]) and university .To analyze the change in mobility patterns over time, we categorized birth years according to eight five-year birth cohorts: 1951–1955 (1), 1956–1960 (2), 1961–1965 (3), 1966–1970 (4), 1971–1975 (5), 1976–1980 (6), 1981–1985 (7) and 1986–1990 (8). It has to be emphasized that this definition of a cohort is not based on a theory. Rather, it is intended to be linked to existing studies in a pragmatic way  as a measure of relative mobility  to the log-likelihood of a model without explanatory variables (LL0). This pseudo-R2 can assume values between 0 and 1. The pseudo-R2 can be interpreted as an indicator of the improvement in model fit (measured using log-likelihoods) when comparing a model with explanatory variables  to a model without explanatory variables  across birth cohorts and different operationalizations of parental education. For the cohorts born between 1950 and 1965, relative mobility has remained stable regardless of the reference point of parental education.2 has increased from 0.04 to a maximum of 0.055. In relative terms, this is a significant increase. In absolute terms, however, it is rather small, suggesting that the effect of parental education on children's education has not changed substantially.Even if a slight deterioration in relative mobility across cohorts is revealed, further interpretation must consider that the pseudo-RThe aim of our contribution was to investigate absolute and relative patterns of educational mobility across birth cohorts for Switzerland. Special attention was paid to the detection of gender differences in mobility patterns. We were also interested in calculating these, not only by using the dominance approach  or the conventional approach (based on the father's highest education) – which are predominant in mobility analyses – but also by considering the mother's education. Furthermore, we were interested in the extent to which these patterns have changed across birth cohorts over the course of educational expansion.N = 556,112 observations. This approach allowed us to analyze a much larger sample than previous studies on intergenerational educational mobility in Switzerland  are less likely to be linked in the administrative data, and are thus less likely to be included in our analysis sample. Therefore, we have only considered individuals born in Switzerland after 1950. Many studies of intergenerational mobility impose such a restriction by place of birth, for example to ensure that individuals have gone through the education system in the country of interest  after signing a data use and linkage agreement. Further information can be found here: RN and RB contributed to conception and design of the study and organized the database. RN performed the statistical analysis and wrote the first draft of the manuscript. RB wrote the second draft of the manuscript. Both authors contributed to manuscript revision and read and approved the submitted version.


We present a quantum Otto engine model alternatively driven by a hot and a cold heat reservoir and consisting of two isochoric and two adiabatic strokes, where the adiabatic expansion or compression is realized by adiabatically changing the shape of the potential. Here, we show that such an adiabatic deformation may alter operation mode and enhance machine performance by increasing output work and efficiency, even with the advantage of decreasing work fluctuations. If the heat engine in the sudden limit operates under maximal power by optimizing the control parameter, the efficiency shows certain universal behavior,  Heat engines should ideally have good performance in finite time ,3,4,5,6,To describe the machine performance, there are usually two benchmark parameters: ,17,27,32 dilemma ,47,48,49 dilemma ,20,50,51Discreteness of energy levels, due to quantization, may significantly improve the performance of a quasi-static quantum Otto cycle ,52,53,54In this paper, we study a quantum version of the Otto engine model, which consists of a single particle confined in two different potentials and which works between two heat reservoirs of constant inverse temperatures m confined in a one-dimensional power-law trapping potential x direction withn. Thus, n. Throughout the paper we set x direction, the time-independent Schrödinger equation may be written as L, We consider a single particle with mass exponent , which iion withV(x)∼x3/θion withV(x)∼x3/θn for the single-particle system.The expressions for creation and annihilation operators , we , we 7), m at time n after a time period Based on the two-time measurement approach, the probability density function of the stochastic heat see also .x and y Γc(Γh) tod y H^=ω for In r, respectively, for r for given r increases. The behavior of curves for work fluctuations r is dependent on r, is linear, it becomes parabolic when For complete thermalization along each isochore, The coefficient of variation for power tability . CompariBoth n as per b,c. ThesEquation ] could bSince the power output, en limit ,60. It i=ηC2/[ηC−−ηCln14)], in We proposed an exact analytical description for the performance and fluctuations in these quantum engines at the low-temperature limit where only the lowest two energy levels are occupied. We showed that quantum heat engines, with adiabatic deformation, can run in the extended regimes where their counterparts, without adiabatic deformation, operate as heaters or refrigerators. Examining the efficiency and coefficient of variation of power, we found that an appropriate selection of two trapping potentials enables engines to be built that are capable of performing more stably and efficiently. We also showed that, even for a given trap in an isochore, the relative power fluctuations in our engines are significantly smaller than those of engines in the absence of adiabatic deformation, with higher efficiency than that of engines without adiabatic deformation. By tuning the energy gap between the ground and the first excited state, we found that the efficiency at maximum power is independent of shape deformation and shares the same universality with the CA efficiency. This optimized efficiency, however, can be realized in regions where engines experiencing no change in the shape of the potentials cannot operate as heat engines.N) confined in a Our approach can be directly used to describe an ensemble of many non-interacting particles (with particle number


Non-reducing polyketide synthases (NR-PKSs) account for a major share of natural product diversity produced by both Asco- and Basidiomycota. The present evolutionary diversification into eleven clades further underscores the relevance of these multi-domain enzymes. Following current knowledge, NR-PKSs initiate polyketide assembly by an N-terminal starter unit:acyl transferase (SAT) domain that catalyzes the transfer of an acetyl starter from the acetyl-CoA thioester onto the acyl carrier protein (ACP).Cortinarius rufoolivaceus, were biochemically characterized. These basidiomycete synthases lack a SAT domain yet are fully functional hepta- and octaketide synthases in vivo. Three members of the other clade of basidiomycete NR-PKSs (clade VIII) were produced as SAT-domainless versions and analyzed in vivo and in vitro. They retained full activity, thus corroborating the notion that the SAT domain is dispensable for many basidiomycete NR-PKSs. For comparison, the ascomycete octaketide synthase atrochrysone carboxylic acid synthase (ACAS) was produced as a SAT-domainless enzyme as well, but turned out completely inactive. However, a literature survey revealed that some NR-PKSs of ascomycetes carry mutations within the catalytic motif of the SAT domain. In these cases, the role of the domain and the origin of the formal acetate unit remains open.A comprehensive phylogenetic analysis of NR-PKSs established a twelfth clade from which three representatives, enzymes CrPKS1-3 of the webcap mushroom The role of SAT domains differs between asco- and basidiomycete NR-PKSs. For the latter, it is not part of the minimal set of NR-PKS domains and not required for function. This knowledge may help engineer compact NR-PKSs for more resource-efficient routes. From the genomic standpoint, seemingly incomplete or corrupted genes encoding SAT-domainless NR-PKSs should not automatically be dismissed as non-functional pseudogenes, but considered during genome analysis to decipher the potential arsenal of natural products of a given fungus.The online version contains supplementary material available at 10.1186/s40694-023-00164-z. The reactions were analyzed by LC–MS in negative mode for expected mass traces of 3. Curiously, both ArmB and ArmBΔSAT yielded a product eluting at a retention time . As the detected masses (m/z) were in perfect agreement with those of 3, we concluded that the product formed was the tetraketide pyrone , which arises through O-C cyclization of the ACP-linked tetraketide − (mass for unlabeled 3), m/z 173  and m/z 175 . In reactions containing unlabeled acetyl-CoA and [13C3]malonyl-CoA, TTL was dominant in the EIC for m/z 173, but detectable in traces within the m/z 175 EIC as well. This result indicated the dominant PKS product was formed by incorporation one unit of acetyl-CoA and three units of malonyl-CoA. When [13C3]malonyl-CoA was added as sole substrate, TTL was detected exclusively in the m/z 175 EIC, indicating tetraketide formation from four malonyl-CoA units. High-resolution mass spectrometry showed partially or fully 13C-labeled species and confirmed these results . As the product spectrum of ArmBΔSAT perfectly mirrored the one of full-length ArmB, we proved that ArmB is neither structurally nor catalytically dependent on the SAT domain. As previously shown for CoPKS4, malonyl-CoA is sufficient for product formation. Surprisingly, however, acetyl-CoA seems to be the preferred starter unit, even in the absence of the SAT domain. The observed in vitro formation of the TTL, although unusual, does not invalidate our conclusions about the use of starter units. Its synthesis may reflect a non-functional PT or TE domain caused by the extensive purification of large multi-domain enzymes  or were fully active when the SAT domain was artificially removed . Thus, mushroom PKSs seem to not follow established knowledge about the minimal domain architecture of NR-PKSs and the starter unit effect acetyl-CoA and that malonyl-CoA as sole substrate is sufficient to produce 1. This result suggests that the SAT domains of ACAS and possibly other clade V NR-PKSs may potentially also represent non-functional pseudodomains. However, a truncated SAT-domainless variant of ACAS has not been analyzed yet. We addressed this knowledge gap by creating genes encoding truncated variants of ACAS (ACASΔSAT), which we expressed heterologously in A. niger tNAL060 (acasΔsat based on genomic DNA) and tNAL066 (acasΔsat based on cDNA). As positive controls, the full-length variants of ACAS produced by A. niger tNAL059 (based on gDNA) and tNAL065 (cDNA) were added to our experiments. As ACAS depends on a discrete TE domain (ACTE) for product release, we based our in vivo assays on a double auxotrophic A. niger strain to co-express the genes for the TE domain. Genetic analyses by PCRs verified that the respective transgenes were properly integrated into the host genomes . Surprisingly, however, ACAS-activity was not restored in A. niger expression strain tNAL067  according to the manufacturer’s instructions and sequenced on a MinION flow cell malonyl-CoA (II), 5 mM [13C3]malonyl-CoA only (III) and a negative control lacking any substrate (IV). Reaction II was additionally performed with heat-inactivated enzymes  as negative controls. The reaction mixtures were incubated at 20 °C for 14 h. After lyophilization, the residue was dissolved in 50 µL MeOH and subjected to LC–MS (gradient II; Additional file Product formation assays were carried out as duplicates in 250 µL reactions containing 0.5 μM purified protein in phosphate buffered saline (PBS, 137 mM NaCl, 2.7 mM KCl, 10 mM NaFor detailed information on liquid chromatography and mass spectrometry, refer to the additional experimental procedures in the supplementary information (Additional file Evolutionary analyses were based on the sequences of KS domains of biochemically characterized basidiomycete or ascomycete NR-PKSs, which are listed in Additional file Additional file 1: Additional experimental procedures, seven supplementary tables, and ten additional figures with supporting research data.


SETD2 mutations have been documented in several cancers, including clear cell renal cell carcinoma (ccRCC). SETD2 deficiency is associated with cancer occurrence and progression by regulating autophagy flux, general metabolic activity, and replication fork speed. Therefore, SETD2 is considered a potential epigenetic therapeutic target and is the subject of ongoing research on cancer-related diagnosis and treatment. This review presents an overview of the molecular functions of SETD2 in H3K36me3 regulation and its relationship with ccRCC, providing a theoretical basis for subsequent antitumor therapy based on SETD2 or H3K36me3 targets.SET domain-containing 2 (SETD2) is a lysine methyltransferase that catalyzes histone H3 lysine36 trimethylation (H3K36me3) and has been revealed to play important roles in the regulation of transcriptional elongation, RNA splicing, and DNA damage repair.  Thernd SETD2 . Furthernd SETD2 . DespiteSETD2 mutations. SETD2 has been reported to accelerate ccRCC progression  is involved in the regulation of transcriptional activation and RNA splicing, as well as DNA repair and recombination , and gengression , 8 and igression . This pa2The human SETD2 gene is located in the p21.31 region of chromosome 3, where the copy number is frequently lost in many tumors. Thus, SETD2 is generally considered a tumor suppressor. The human SETD2 protein consists of several conserved functional domains, containing the AWS (associated with SET)-SET-PS (post-SET) domains, WW domains, SRI (Set2-Rpb1 Interacting domain), SETD2-hnRNP interaction (SHI) domains, and a large unstructured N-terminal domain  as the cofactor, providing an additional methyl . It is r2.2The SRI domain of 108 amino acids at the C-terminal end is the main region that interacts with RNA polymerase II (RNAPII), entering a transcription elongation phase. The SRI domain binds to RNAPII-C terminal domain (RNAPII-CTD) Ser5P and Ser2P  and prom2.3The WW domain comprises two conserved tryptophan (W) residues in the SETD2 C-terminus. The WW domain interacts with proline-rich or proline-containing motifs of other proteins mediating protein-protein interactions . For exa2.4The structure of the coiled-coil (CC) domain has been predicted by in-silico calculations to be a conserved motif that participates in protein-protein interactions in yeast and promotes protein homodimerization. In human SETD2, the predicted structure of the CC domain is much shorter . The SHI2.5in vivo and promote the abnormal methylation of Set2 to histones (via the AID-SRI axis (The AID domain in the middle region of Set2 (a yeast ortholog of human SETD2) suppresses SET domain activity, and the AID domain suppresses its catalytic activity when the SRI domain is lost. AID mutations usually lead to excessive activity of Set2 histones . The AIDhistones . It woulSRI axis . All the2.6in vivo, a property that is enhanced by its reduced degradation. The N-terminal region is conducive to the liquid-liquid phase separation of the protein, and the phase separation behavior of SETD2 intensifies with the removal of the N-terminal fragment  and is unstructured. SETD2 is an unstable protein that depends on the degradation of the proteasome . It was fragment . Thus, t33.1VHL inactivation occurs in 90% of all ccRCCs, and several mutations in tumor suppressor genes on chromosome 3p have been identified: PBRM1, BAP1, and SETD2  shows that SETD2-deficient negatively enriched the gene related to the mRNA splicing pathway  domain play an important role in recognizing H3K36me3. MORF4-related gene on chromosome 15 (MRG15) can bind to H3K36me3 , 60 and 3.2.3SETD2-deficient cancer cells failed to activate p53 and displayed lower cell survival in DNA damage (via combination with plant homeodomain (PHD) of Ring finger domains 1 (PHRF1), modulating non-homologous end-joining (NHEJ) and stabilizing genomic integrity (SETD2 knockdown cancer cells (SETD2-deficient cells exhibit microsatellite instability (MSI) with a high frequency of spontaneous mutations  by generating H3K36me3. Cell death occurs if DNA repair fails, and tumor development may arise from incorrect DNA repair. SETD2 facilitates DNA double-strand breaks (DSBs) repair by homologous recombination (HR), activating replication protein A (RPA) single-stranded DNA (ssDNA)-binding protein complex loading and the formation of RAD51 presynaptic filaments , 67, 68.A damage , 67, 68.A damage . ConsistA damage . AccordiA damage , 71. Alsntegrity . SETD2 her cells . The cryer cells . SETD2-dutations . Compareutations . Recent utations , 76 . Also, the actin cytoskeleton regulates autophagy dynamics . AutophaSETD2 in ccRCC cells reduces LC3-II expression, which is linked with abnormal cumulative ATG12 in free and complexes containing ATG12, except for the ATG5-ATG12 complex. Furthermore, SETD2-loss deregulates alternative splicing, which is related to increased ATG12 short isoform and reduced conventional ATG12 long isoform , cells lacking SETD2 have decreased interaction of the actin nucleation-promoting factor WHAMM with its target actin, actin filaments are required for initiation of autophagy in ccRCC, and autophagy markers LC3-II and p62 are decreased .+ T cells. Inhibition of autophagy or lysosomal production increases MHC-I expression, enhances the adaptive immune response, and inhibits the generation of tumors  during replication stress, which plays a crucial role in the DNA replication stress response and negatively regulates replication initiation, the deletion of H3K14me3 , 106. In3.2.8SETD2 is the main H3K36me3 methyltransferase in mammalian cells. Recent studies have suggested that SETD2 could also catalyze non-histone substrate methylation. During ccRCC mitosis, SETD2 trimethylates α-TubK40me3 and maintains genomic stability. Mono-allelic mutation of SETD2 results in α-TubK40me3 deficiency, leading to chromosome abnormalities and genomic instability exhibiting multipolar spindle formation, chromosome bridges, micronuclei, polyploidy, and multiple nuclei . SETD2, SETD2 increases protein translation-related gene expression and decreases eEF1A1 K165me3 and K318me1 in ccRCC, but SETD2 is associated with eEF1A1 methylation indirectly, SET domain of SETD2 regulated the expression of EEF1AKMT2 and EEF1AKMT3, EEF1AKMT3 methylates eEF1A1 on lysine 165 and EEF1AKMT2 methylates eEF1A1 on lysine 318  have a high probability of converting to RCC. However, there is a paucity of knowledge regarding how PKD can develop into RCC, necessitating further research into genetic alterations or the regulation of signaling pathways. Li et al. found that SETD2 deletion can lead to increased activation of the Wnt/β-catenin signaling pathway and promote epithelial-mesenchymal transition and tumor formation. SETD2 plays an essential role in the process of the conversion of PKD to ccRCC .Emerging evidence suggests that exosomal circRNAs might be potential cancer biomarkers –112. He 4SETD2 mutations have been identified in ccRCC  increased with DAC treatment. In the SETD2-knockdown tumor model, increased CD8+ T cell infiltration and fewer MDSC following combined treatment with DAC and anti-PD-L1. ccRCC with altered SETD2 gene provides preclinical support for a therapeutic target for DAC and anti-PD-L1 (SETD2 and LRP1B, to further explore (SETD2 in ccRCC is worthy of attention.As a tumor suppressor, iomarker . 5-aza-2ti-PD-L1 . A case  explore . Thus, tWith current innovations in genome engineering and proteomics, the role of SETD2 in normal cells and cancer will be better understood at the molecular level. Nonetheless, it is urgent to explore whether and how SETD2 regulates the molecular mechanisms of recurrence and ccRCC metastasis.Furthermore, SMYD5 and SETD5 were also demonstrated to catalyze H3K36me3 , 116. A In conclusion, the in-depth study of SETD2 during tumor formation and development is warranted for diagnosing, treating, and preventing tumors. It is anticipated that further epigenetics studies will reveal the regulatory pathway of SETD2 expression.YZ and LJ supervised the review study. MY, YZ, and LJ reviewed the literature and drafted the first draft. GW and KQ provided suggestions to improve the draft. MY, YX, and LJ edited the figures and tables. All authors contributed to the article and approved the submitted version.


In this paper, a router ownership inference method based on the classification of intra- and inter-domain links is proposed. In this method, the differentiating Internet Protocol (IP) address vector distance feature, the autonomous system relationship feature of the IP link, and the fan-in and fan-out features are designed to support the discrimination of IP link types. The use of additional information derived from the link type enriches the basis for router ownership inference and improves the accuracy of the inference result. Experimental results show that the accuracy reaches 96.4% and 94.6% on the two verification sets, respectively, which is 3.2–11.2% better than the existing typical methods.Research on router ownership inference is central to many Internet studies, such as network failure diagnosis, network boundary identification, network resilience assessment, and inter-domain congestion detection. The existing router ownership inference method  An AS is a set of routers run by one or more network operators with a single and clearly defined routing policy2. Recently, the total number of ASes worldwide is 109,4371. Router ownership inference, the technology aiming to assign routers to their operating ASes, is a challenge in Internet topology research.One of the main terminologies for analyzing the Internet ecosystem is the Autonomous System (AS)4, and then understand the composition of the Internet at a fine-grained level6. Besides, it helps network managers to maintain network performance, such as detecting congestion points that appear on links between ASes, analyzing the regularity of the Internet interdomain routing7 and inferring the delay on border links8. Additionally, it benefits network security researchers to identify AS border routers which are critical network infrastructures with great significance in application and safety.Accurate router ownership inference plays an essential role in many problem domains. First and foremost, it allows researchers to combine the router-level and AS-level Internet topologies, grasp the real connection details within and between ASes9, which will interfere with router ownership inference. Second, the Internet Protocol (IP) address subnet space of the inter-AS connection usually comes from only one of the two ASes10, i.e., IP addresses sharing between ASes, resulting in it challenging to identify the inter-domain connection accurately. Additionally, considering security and competition factors, Internet operators tend to hide their network topologies, making it difficult to obtain some ground truth for modeling.In the actual Internet, multiple factors interfere with the accurate inference of router ownership. First, routers may respond to traceroute probes with different interface addresses, including third-party addresses11 achieved 91.8%-98.8% accuracy when mapping AS boundaries in two Internet-wide traceroute datasets. However, this method has relatively few available constraints when inferring routers at the end of traceroute paths, so there is still room for improvement in its accuracy. In response to this problem, in this study, we call routers that appear in the back part of traceroute paths and maybe in the same AS with traceroute destination last-AS routers. For last-AS routers, a Link Classification-based method for Router Ownership Inference (LCROI) is proposed. LCROI first classifies intra-domain and inter-domain links by establishing a probability model with the type of IP link as the hidden variable. After that, the information implied in the link type can increase available constraints when inferring last-AS routers resulting in better accuracy.The current typical algorithm bdrmapITWe introduced three distinctive features to differentiate between intra-domain and inter-domain links. Using these features, we developed a probability model with link type as the hidden variable to classify IP links.A method for inferring router ownership, known as Link Classification-based Router Ownership Inference (LCROI), has been proposed. LCROI used link classification results to impose constraints on routers, resulting in higher accuracy in router ownership inference, as demonstrated in actual test results.The rest of this article is organized as follows: the “The main contributions of our work can be summarized as follows: Existing methods of router ownership inference rely on traceroute topology measurement, alias resolution, and IP-to-AS mapping technology. Routers have multiple interfaces with different IP addresses. Alias resolution technology can identify IP interfaces belonging to the same router. IP-to-AS mapping is a technology that maps IP addresses to their AS Numbers (ASN). The widely used IP-to-AS mapping method is to look up the origin IP-to-AS mapping table extracted from the Border Gateway Protocol (BGP) routing table, where each IP address is mapped to the origin AS of its longest matching prefix. A Multiple Origin AS (MOAS) conflict occurs when a particular prefix appears to originate from more than one AS.According to the application scope of the method, existing router ownership inference methods can be divided into two categories: methods for a single AS and methods for the entire Internet, as shown in Table 12 is for the single target AS. It first executes targeted traceroutes from VPs inside an AS and then combines the AS relationship data, using traceroute features and topology constraints to perform inference. It can only infer inter-domain connections related to the network hosting VPs and can be observed by VPs.The router ownership inference method for a single AS first selects the target AS to deploy Vantage Points (VPs) for the traceroute probe. Then it infers the owner of routers inside the target AS and border routers inside neighboring ASes. Bdrmap proposed by Luckie et al.14 chose the most frequently assigned AS in the origin AS set of the router. The method is simple and has insufficient accuracy. Chang et al.3 argued that a router is a border router if its interface addresses are assigned from multiple administration domains, and proposed the intersection rule, majority rule, and hole-filling heuristic. Their approach complements the method proposed by Tangmunarunkit et al.14. However, they did not verify their heuristics with any actual information of AS border routers. Claffy et al.15 believed that in provider-customer connections, the provider side provides the address space. This experience was used in the methods proposed by Huffaker et al.16, Pansiot et al.17, Motamedi et al.18, and others. Also, since it does not apply to all cases, other heuristics were supplemented to improve the inference accuracy. Huffaker et al.16 created an AS frequency matrix for each router. The matrix counted the number of interfaces (known and inferred) from each AS that appears on the router. Next, they described five different heuristics to produce an AS-router dual graph, including Single, Election, Neighbor, Customer, and Degree. The accuracy of their results needs to be improved. Pansiot et al.17 proposed a Router-to-AS algorithm based on probabilistic and empirical IP allocation rules to extract intra-domain topology from a large dataset collected with mrinfo. However, since only routers supporting Internet Protocol Version 4 (IPv4) multicast can reply to mrinfo, the scope of application of the method is limited. Motamedi et al.18 proposed Internet eXchange Point-assigned (IXP-assigned) IPs and Valley-free heuristics, and improved some of the previous heuristics as needed. If all of their heuristics fail to identify the AS owner, they would honor the original BGP-based AS ownership assignment for the concerned interfaces. Marder et al.11 created bdrmapIT, which combined bdrmap and their previous algorithm MAP-IT33 used for iteratively inferring inter-AS links at the interface-level graph, to infer router owners at the Internet scale. However, the inference accuracy can be improved somewhat for some routers.The router ownership inference method for the entire Internet uses heuristic methods to infer router ownership. Tangmunarunkit et al.11 method and gives a formal definition of the research problem in this paper.This section briefly introduces and analyzes the principle of the bdrmapIT11 is a classical approach that implements router ownership inference at the Internet scale. When it maps AS boundaries on two traceroute data sets, the accuracy is more than 90%. bdrmapIT11 method mainly includes three phases: constructing the graph, annotating the last hops, and graph refinement. A router-level directed topology is established in the graph construction phase based on the IP-level topology from traceroutes after alias resolution. The second phase infers the operators of routers that appear only at the end of traceroutes. This phase provides topological context for mappings in the graph refinement phase. The graph refinement phase maps routers observed in the middle of at least one traceroute path to their ASes, and continuous corrections are made through iteration. Finally, accurate AS inference results of all routers are obtained. The primary process of bdrmapIT is shown in Fig. The bdrmapIT11 combines bdrmap12 and MAP-IT33 to adapt bdrmap which targets a network to the iterative improvement framework of MAP-IT. This reduces bdrmap’s requirements for the number and location of VPs, and expands its scope of application (from a specific network to any number of networks). Moreover, it optimizes the limitation that MAP-IT does not use alias resolution and only performs AS-level boundary recognition at the interface level. Although bdrmapIT has achieved excellent results, it also has two shortcomings:The bdrmapIT(1) Fewer constraints are available in the phase of annotating the last hops.11. In this case, bdrmapIT considers that the last interface may be on the border router of the network containing the destination. So in the stage of annotating the last hops, bdrmapIT infers the operator of a router with no outgoing links to be its destination AS of the paths on which the router’s interfaces are observed. Since only the destination AS set is used as the candidate set, relatively few constraints are available. bdrmapIT may have some misjudgments for such routers. To better handle this type of router, it’s feasible to enrich topological constraints for inferring routers’ owners.When the last hop of a traceroute path is not the destination, it may be because the destination AS is configured with a firewall to prevent it from responding to other networks(2) The failure of experience can lead to errors.19, but there are violations of the valley-free rule.The bdrmapIT relies on empirical knowledge in Internet practice when mapping routers to their operating AS and infers the results by designing various rules. In practical applications, this rule-based approach can handle problems in some scenarios. Still, some exceptions are not defined by the rules, which will affect the processing effect of the method. For example, bdrmapIT may choose candidate ASes based on the valley-free assumptionTo improve the shortcomings of bdrmapIT, we consider adding available constraints for router ownership inference, that is, different types of IP links (inter-domain links and intra-domain links). Here, we first introduce related definitions and concepts. Then, our problem is formally defined.(Path Information):(IP Link): Each path can be divided into several end-to-end IP links (Network Topology): IP-level Topology P, and P.Router-level Topology (Mapping Function): IP-to-Router mapping IP-to-AS mapping Router-to-AS mapping (Intra-domain Link): The intra-domain link refers to the link between interfaces on routers in the same AS, that is,(Inter-domain Link): The inter-domain link refers to the link between interfaces on two border routers of connected ASes, that is,P, generate an IP-level topology last-AS routers to their owner ASes. The key stages involved include the design of IP link features, the classification of intra-domain and inter-domain links, and the inference of router ownership.The problem studied in this paper is: given a traceroutes set IP links can be classified into two types: inter-domain links and intra-domain links. Each kind of link has some observable features, and the probabilities of particular features occurring are different for various link types. The type of IP links in the IP-level topology derived by the traceroute probe is unknown. Still, appropriate features can be designed based on domain knowledge to distinguish between inter-domain and intra-domain links. Due to the anonymous nature of IP link types and the different probabilities exhibited by features, the IP link classification problem can be modelled as a probabilistic model with IP link type as the hidden variable, and the probabilities of given features occur for different link types as parameters.20. The EM algorithm can converge and give a set of stable parameters for the Naive Bayes classifier. Therefore, it is feasible to use Naive Bayes as the classification technique.The classification of IP link types relies on the accurate prediction of probabilistic model parameters. The Expectation-Maximization (EM) algorithm is suitable for estimating probability models with hidden variables. Therefore, in our problem, due to the existence of the hidden variable (IP link types), the EM algorithm is used to estimate the parameters iteratively. In each iteration of the EM algorithm, considering the possible correlation among features, we use the Naive Bayes classification techniqueIn summary, this paper proposes a router ownership inference method based on the classification of intra- and inter-domain links. The main idea is to design distinguishing features, use a probability model to classify IP links, and determine owner ASes of last-AS routers based on the voting mechanism. The principle framework of the method is shown in Fig. Step 1: Path preprocessing. First, to avoid mistaking the host as a router, the destination addresses of traceroute paths are ignored. That is, when extracting IP addresses from traceroute paths for alias resolution, only addresses that are intermediate hops in some paths are covered. After that, delete paths with only one IP address and paths with IP loops. In addition, delete IP addresses belonging to the BGP route server ASN provided by IXP for establishing multilateral peer-to-peer connections.Step 2: Network topology generation. Use the path processed in step 1 to generate an IP-level network topology.Step 3: IP-to-AS mapping. Use IP-to-AS mapping technology to map all IPs in the IP-level topology to their origin ASNs. For MOAS, to choose “the best” mapping for simplicity, the AS that appears as an origin AS most frequently in the source BGP table can be selected21.Step 4: Modeling. Model the IP link classification problem as a probabilistic model with IP link type as the hidden variable, and the parameters are the probabilities of particular features occurring for different link types.Step 5: IP link classification. Use the EM algorithm to estimate the parameters of the model, and the Naive Bayes classifier is used to calculate the probability that the IP link belongs to each category. After the convergence is reached, output the result according to the probability and threshold.Step 6: Candidate ASes voting. According to the IP link classification result obtained in step 5, the corresponding router-level link type is determined. A router-level topology containing link-type labels is constructed after alias resolution. Generate an intra-domain router set for each router, and combine the destination AS set to vote together to determine its owner AS.The main steps of the method are as follows: In the above steps, the key steps are IP link feature design in modelling, IP link classification, and candidate ASes voting, which are described in detail below.Based on domain knowledge, this section designs three features that distinguish between inter-domain and intra-domain links, including the AS relation feature of adjacent IP links, the fan-in and fan-out features, and the IP address vector distance feature of IP links.22.The link type also relates to the business relationship between ASes. Without considering the complex hybrid relationship, AS relationships can be categorized as provider-to-customer (p2c), customer-to-provider (c2p), peer-to-peer (p2p), and sibling-to-sibling (s2s)In our method, we use entclass1pt{minimasame’ if forward AS relation feature is the setp. The forward AS relation set is obtained after traversing all paths P. Similarly, the backward AS relation feature of IP link With regard to the IP link Ri was mapped to ASj. In Internet practices, when two ASes connect with each other by the point-to-point link, the IP subnet of the link  is typically from one of the two ASes’ address space23. It is worth noting that in p2c relationships, the provider usually supplies the address space for the IP subset. Moreover, when passing a p2c link in a traceroute probe, the customer’s router usually uses the IP address from the provider’s space to respond to the probe. In the example, AS1 is the provider of AS2, and AS1 provides the address space for the IP link between them, i.e., the inter-domain link in fact is forward AS relation feature, it may be an inter-domain link. Furthermore, if  both in the forward and backwardAS relation feature of a link, the link is likely to be an intra-domain link.We take a traceroute path as an example to analyze the role of the AS relation feature of adjacent IP links in the classification of IP links. As shown in Fig. fan-in feature) or after (fan-out feature) a particular link given its link type.In general, multiple probe paths passing through different routers in an AS will converge to the border router and cross the inter-domain link to enter its neighbor AS. Similarly, multiple probe packets traversing the inter-domain link to reach an AS will be distributed and forwarded to different routers inside the AS. Therefore, for an inter-domain link, it may have more previous or subsequent links with AS switch  feature of the link.For each IP link jth byte of 11. The histogram is shown in Fig. Considering that IP addresses within the same AS may be in the same or similar IP prefixes, while IP addresses belonging to different ASes may be far apart, the IP address vector distance feature is proposed. Specifically, the IP(v4) address is a 32-bit binary number that is typically split into four bytes. For each IP link IP address vector distance feature is used to indicate that the two IPs at the ends of an inter-domain link typically have a larger Euclidean distance than that of an intra-domain link. The IP address vector distance feature of the IP link It can be seen from Fig. mentclass2pt{minimWe use the EM algorithm to estimate the parameters of the probability model established in this paper. Specifically, first, calculate the conditional probability distribution of each feature for different link types, then use the Naive Bayes classifier to update the type of each link, and recalculate the feature distribution according to the new probability. Repeat the above two steps until convergence.The probabilistic model requires initial parameters. Therefore, first, simply use the IP-to-AS mapping data to generate initial type labels (intra-domain links or inter-domain links) for all IP links. The specific method is to consider the link without AS switch as an intra-domain link, and the link with AS switch as an inter-domain link. In addition, it is believed that the provider provides the address space used by the interconnection in the p2c link.The following pseudocode introduces the implementation of the probabilistic IP link classification algorithm.threshold. The output is the inferred type of each IP link. Firstly, for each feature in the feature vector, we calculate the conditional probability of the two link types separately. To solve the problem of zero probability, the algorithm uses Laplace (Add-1) Smoothing24 (Lines 2-5). We then compute the prior probability of the links’ types by counting the proportion of the two link types at the beginning. The prior probability of each link type is then multiplied by the conditional probability of all the features (Lines 6-11). Finally, recalculate the probabilities of the two link types. When the ratio of the probability of the inter-domain link to the intra-domain connection is greater than the threshold, it is inferred that the link is an inter-domain link; otherwise, it is an intra-domain link (lines 12-17). The process of link type inference and updating conditional probability distributions of features are repeated until convergence, i.e., the number of links that change the type label between the current iteration and the prior iteration decrease to a small value O(e), where e is the number of IP links in the network topology.The input of the algorithm is all IP links, initial types of IP links, the feature vector, a minimum value A router may have many interfaces, and a router-level link may have many corresponding IP links. For a router-level link, if one of its corresponding IP links is an inter-domain link, we think that the router-level link is an inter-domain link; otherwise, it is an intra-domain link. After assigning all the router-level links a type label, all the routers are divided into different ASes by inter-domain links in the router-level topology.intra-domain neighbors, while neighbor routers connected to it with the inter-domain link are called inter-domain neighbors. For a given router R, we first find its intra-domain neighbors. For the intra-domain neighbors, we choose routers without inter-domain neighbors, and continue to find their intra-domain neighbors. We repeat the process until there are no routers eligible for the requirement mentioned above. All the intra-domain neighbors found are added to the intra-domain router setR.For a router, we call neighbor routers connected to it with the intra-domain link R. We generate a list of candidate ASes R, containing the ASes to which IPs of all routers in Let R is represented by The set of traceroute destination addresses of all paths containing R consisting of ASes that all IPs in We can generate another candidate ASes list R. The election is held among ASNs in the list R to this AS.We use the voting method to determine the AS annotation for To verify the performance of the proposed method, we conducted experiments using publicly available topology probe data.This section introduces the experimental environments and tools, the public datasets used in the experiments, the methods for comparison, and the evaluation metrics of the experimental performance.The experiment results were obtained using a personal computer with an Intel Core i5-7400 Processor (3.5 Gigahertz), 4 Cores, and 16 Gigabytes of memory, running Windows 10 64-bit operating system. Additionally, when using Team Cymru’s IP to ASN mapping service, we used an AliCloud server running the CentOS 7 operating system with 2 virtual central processing units and 4 Gigabytes of memory. The programming language we utilized is python, and the integrated development environment is JetBrains PyCharm community edition 2019.1.2.The experiment requires traceroute probe data, IP-to-AS mapping data, IXP prefix list, routing server ASN list, AS relationship data, and verification set data used to verify the inference results.25, RouteViews26, Team Cymru27, Regional Internet Registry (RIR)28, Euro-IX29, PeeringDB30, etc.The primary experimental data mainly come from some Internet research institutions, management institutions, projects, and databases, including the Center for Applied Internet Data Analysis (CAIDA)31. Therefore, we also manually generate a verification set based on the DNS hostname data, that is, the TeliaSonera (AS1299)32 verification set. The reason for choosing this AS is that its DNS host names used for inter-AS connections often indicate the name of the interconnected ASes33.To verify the AS ownership inference result is not easy. The reason is that operators generally hide their network topology to avoid possible network security risks and commercial competition risks. Therefore, in the public data, the data in PeeringDB is first selected as the verification set. PeeringDB is an open, user-maintained network interconnection database designed to support the actual needs of network operators, but it is also a valuable source of information for researchers. In PeeringDB, we can find out the real member ASN of the router whose IP interface address is mapped to the IXP operator. In addition, researchers found that by analyzing the Domain Name System (DNS) host name, some interface information of the router can be obtained, which helps to determine the type and location of the router62.115.14.14 with the hostname planetel-ic-301155-mno-b2.c.telia.net. It’s mapped to AS1299 in IP-to-AS mapping data, but in fact, its router belongs to AS47217 whose AS name is PLANETEL,IT. So we find hostnames containing the string ‘-ic’, and match the string before ‘-ic’ with the names of all the ASes that may connect to TeliaSonera. On the other hand, for hostnames without the above-mentioned character (like snn-sec1-link.se.telia.net), we think these interfaces, as well as their routers, are in TeliaSonera. We can’t decode every hostname since some hostnames have no hints or exist ambiguous information.Specifically, the TeliaSonera verification set is manually generated by selecting interfaces in the paths associated with TeliaSonera. An example is interface Finally, to verify the IP link classification results, a verification set (TeliaSonera-1) containing a total of 26755 IP links is generated, of which 16,180 are inter-domain links and 10,575 are intra-domain links. To verify the AS ownership inference result, a verification set (TeliaSonera-2) containing a total of 2727 routers is generated, of which 1157 are in TeliaSonera and 1570 are in other ASes connected to it.The data sources, data scales, and main applications in the experiment are shown in Table 33, and bdrmapIT method11. As far as we know, the MAP-IT method is a classic method to infer inter-domain links between ASes. It is verified with real data, and the result has a high accuracy rate. The bdrmapIT method is a new method that implements router ownership inference on the Internet scale. When it mapped AS boundaries on two traceroute data sets at the Internet scale, the accuracy is more than 90%.Here, we introduce two baseline methods in detail: MAP-IT methodMAP-IT MAP-IT tries to infer the exact interface addresses used for point-to-point inter-AS links, as well as the specific ASes involved. MAP-IT combines evidence of AS switches from distinct traceroute traces, using the forward and backward neighbors set of each interface to infer. Each pass leverages prior inferences to refine existing inferences and to discover additional inter-AS link interfaces. Although MAP-IT does not include the alias resolution process, its inference is the AS of the interface’s router. Since MAP-IT only deduces the inter-AS links, for internal routers of ASes, we determined their ASes by IP-to-AS mapping.bdrmapIT The detailed introduction of the bdrmapIT method is as described in the “Analysis of bdrmapIT” section above.accuracy, i.e., the fraction of routers with correct inferences:For a router, the correct inference is mapping it to its operating AS. Conversely, if a router is assigned to an AS different from its real owner AS, the inference is considered to be incorrect. The evaluation metric in this experiment is the The accuracy of the IP link classification result would affect the AS ownership inference in the subsequent steps, so this subsection verifies it experimentally. The effect of IP link classification was verified on the TeliaSonera-1 validation set.True Positive (TP): both the inferred type and the true type are inter-domain links.False Positive (FP): the inferred type is the inter-domain link, while the true type is the intra-domain link.True Negative (TN): both the inferred type and the true type are intra-domain links.False Negative (FN): the inferred type is the intra-domain link, while the true type is the inter-domain link.The effect of the algorithm is related to the selection of the threshold in the last step. Therefore, we used the receiver operating characteristic curve (ROC curve) to improve the decision rule by observing the different effects of the algorithm for different thresholds. The local ROC curve is shown in Fig. The last step in the algorithm determines the result according to the relation between probabilities and the threshold, that is, if Precision, Recall, and F1 Score are selected as metrics to assess the performance, reflecting its ability to classify IP links. After counting the numbers of the above four cases respectively, Precision can be interpreted as how many links predicted to be inter-domain links are true inter-domain links, as shown in Eq. (Recall means how many of true inter-domain links are correctly inferred, as shown in Eq. (F1 Score is the weighted average of Precision and Recall, which is shown in Eq. (With a determined threshold of 0.09, n in Eq. .5\documen in Eq. .7\documeIn the candidate AS voting stage, there may be two scenarios in the voting process: a tie or a non-tie. In the case of a non-tie, a unique voting winner is selected, but in the tie scenario, more than one AS will win. The handling of the tie situation may affect the final result of the router ownership inference.In the experiment, when the vote was tied, we chose randomly among ASes with the most votes. Therefore, we ran our program ten times to consider the effect of the random selection strategy on the accuracy of router ownership inference results. The validation sets for the experiments were PeeringDB and TeliaSonera-2. The results are shown in Table It shows that the accuracy of the results is stable at around 96.4% and 97.8% on the two validation sets, respectively. This experiment indicates that the random selection strategy has a slight effect on the number of routers with inferences, but almost no effect on their accuracy. That is, our strategy for the candidate ASes has high accuracy and stability.This section compares LCROI with MAP-IT and bdrmapIT on the accuracy indicator, and the experimental results are shown in Fig. It can be seen from Fig. 40 and iffinder41 for alias resolution, and the second topology uses kapar42 besides MIDAR and iffinder. The two topologies differ in accuracy and completeness. The former has high confidence and resolves a few false positives in aliases, while the latter enriches the aliases but with more false positives.We also studied the impact of different alias resolution methods on the accuracy of our approach. The ITDK dataset contains two router-level topologies generated from the same IP-level topology using different alias resolution techniques. The first topology uses MIDARThe results in the above experiment use only midar and iffinder techniques for alias resolution. To determine the effect of less accurate but more enriched aliases on the experimental results, we used the second topology in ITDK to experiment. The results are shown in Fig. It can be seen from Fig. In general, the inference effect of LCROI is better than the two comparison methods. In particular, we analyze the possible reasons why LCROI can correct the wrong result of bdrmapIT.annotating last hops stage, since they appear only at the last hop of paths, bdrmapIT has no topological constraints other than the set of destination ASes for router ownership inference in its second stage, leading to some misjudgments of such routers. LCROI uses the router’s intra-domain router set and traceroute destination addresses to jointly construct lists of candidate ASes for voting, adding topological constraints to infer the routers mentioned above more accurately.For routers wrongly inferred in graph refinement. First, the type of links cannot be taken into account in bdrmapIT when summing the subsequent interfaces of routers, resulting in inter-domain neighbors being a possible interfering factor. If the number of inter-domain neighbors is higher than the number of intra-domain neighbors, a wrong inference may generate. Our method classifies the IP links and reduces confounding factors when voting. Second, the failure of experience can lead to errors. For example, when a router’s subsequent interface in a traceroute path is an IXP public peer address, bdrmapIT typically selects the candidate AS with the largest customer-cone based on traditional assumptions, ignoring violations of valley-free property, thus leading to the existence of misjudgments. Our approach reveals the characteristics of the data themselves rather than only relying on subjective judgments from experience, improving such distortions to some extent.There could be the following reasons responsible for incorrectly inferred routers in the stage We designed the IP address vector distance feature, the AS relationship feature of the IP link, and the fan-in and fan-out features to distinguish intra-domain links and inter-domain links. Based on this, we presented a probability model with the IP link type as the hidden variable to classify IP links. The use of the additional information derived from the link type enriches the basis for router ownership inference, and improves the accuracy of the inference result. Using two separate validation datasets to verify the result, the accuracy of our method ranges between 94.6% and 96.4%, which is slightly better than MAP-IT and bdrmapIT. We think that the more representative features may bring better classification performance on types of IP links, and further optimize the router ownership inference.In future work, we will pay attention to features that provide better classification effects of IP links. The complex hybrid AS relationships are also worth exploring. In addition, future research should be conducted in more realistic settings to further develop and confirm these initial findings.


Reactive oxygen species (ROS) are central to diverse biological processes through which organisms respond to and interact with their surroundings. Yet, a lack of direct measurements limits our understanding of the distribution of ROS in the ocean. Using a recently developed in situ sensor, we show that deep-sea corals and sponges produce the ROS superoxide, revealing that benthic organisms can be sources and hotspots of ROS production in these environments. These findings confirm previous contentions that extracellular superoxide production by corals can be independent of the activity of photosynthetic symbionts. The discovery of deep-sea corals and sponges as sources of ROS has implications for the physiology and ecology of benthic organisms and introduces a previously overlooked suite of redox reactants at depth. It is produced biotically through extracellular enzymatic catalysis by aerobic organisms and abiotically through photooxidation of dissolved organic matter and oxidation of reduced metals by oxygen (Reactive oxygen species (ROS) are a group of oxygen-containing molecules that influence myriad processes, from organismal growth to the geochemical cycling of redox-active metals and organic matter . SuperoxThe short half-life (seconds to minutes) of superoxide in seawater precludes its measurement within collected seawater samples. Thus, previous estimates of in situ concentrations and production rates were extrapolated from shipboard incubations and decay kinetics in filtered waters. These studies revealed that a substantial proportion of superoxide production within the marine water column was particle-associated, presumably microbial , 4. PartAlvin off the coast of California. In this study, we show the first measurements of superoxide associated with deep-sea organisms revealing corals and sponges as sources of ROS.Based on the discoveries of superoxide production via biological light-independent reactions, we predicted that measurable superoxide may also be produced in deep-sea environments. To test this hypothesis, we designed and constructed a deep-sea chemiluminescent sensor (SOLARIS) similar in design to DISCO but capable of in situ calibration and operation to 4,000 m. SOLARIS was initially used to characterize the waters of coastal California, demonstrating steady-state concentrations over a 600-m deep-water column, well below the chlorophyll maximum . SimilarSuperoxide concentrations measured using SOLARIS in bottom waters at Davidson Seamount  and the Channel Islands  ranged between 0.05 and 0.13 nM. At these depths, both sites exhibited negligible fluorescence and light. Surface water superoxide levels at these sites (∼8 to 16 nM)  measured, while the other five species were octocorals . The highest signals, reaching 7.9 nM, were associated with an Acanthogorgia sp., representing an 80-fold increase over background seawater. Superoxide within the sponge exhalent oscula ranged from 0.1 to 3.1 nM above background seawater. These data clearly indicate that corals and sponges can be net producers and sources of superoxide to the deep sea.We measured superoxide associated with 12 corals across 6 species and 7 sponges across 6 species over two 8-h Possible sources of superoxide within coral and sponge holobionts include the animals themselves, and their associated microbiomes . Althoug2−1 h−1 , once at Davidson Seamount and once in the Channel Islands.We measured extracellular superoxide in situ using the submersible oceanic chemiluminescent analyzer of reactive intermediate species (SOLARIS), previously described in Taenzer et al. . For detAlvin's hydraulically powered manipulator, with 7° of movement and controlled by a spatially correspondent position feedback mechanism, was used to precisely position the tip of SOLARIS's sampling wand within a few centimeters or less of the organism's surface. SOLARIS's pump pulled seawater through the wand into the reaction cell. We operated SOLARIS from a custom graphical user interface within Alvin.We collected ancillary data on the site geochemistry using a vehicle hosted conductivity-temperature-depth package and adjacent sensors . We alsopgad398_Supplementary_DataClick here for additional data file.