dataesr/scientific-paragraphs-categorization huggingface dataset:
Uncertainties We will first review the different sources of uncertainties relevant for this dataset, before discussing the scale used and correction and flagging of data. Uncertainties in the data reported originate from the water collection and storage in bottles (Sect. 2. 1), the uncertainties resulting from the experimental laboratory setup and analysis protocols (Sect. 2. 2), and the uncertainties in the internal standards which are used in the experimental setup (Sect. 2. 3).
However, due to various changes at LOCEAN, there has been a long backlog at times, with some samples having been stored in the cold room for 5 years or more. The longest storage time was for OISO-18 data collected in 2010 and analyzed 9 years later in 2019. Storage time was also very long for most samples from cruises  OISO-21, OISO-22, OISO-23, OISO-25, and OISO-26 (southern Indian Ocean, 2012 to 2016). Before analysis, samples are checked for obvious signs of evaporation, such as low water level or salt crystals around the bottle's neck. We tested whether the samples in common cap bottles change during storage by aging three reference waters of the same deep equatorial Atlantic origin over 2 years in a laboratory room which is not air-conditioned and without securing the common caps with parafilm.
Since 2016, seawater samples have been most often directly measured using a wire mesh (liner) to limit the spreading of sea salt in the vaporizer (https://www. picarro. com/sites/default/files/ Salt%20Liner%20App%20Note_180323_final. pdf, last access: November 2021). G. Reverdin et al. :  The CISE-LOCEAN seawater isotopic database  We most commonly used a Picarro L2130-i CRDS, but at times, a Picarro L2120-i CRDS was used, resulting in a larger standard deviation, in particular for δD. On both CRDS analyzers, when repeatability of the different injections of the sample was not sufficient or the daily run presented an unacceptably drift, the samples were analyzed at least a second time. In that case, either the best value or an average of the different values was retained. The typical daily run at LOCEAN currently includes one or two reference water samples followed by three freshwater standards at the beginning to establish a slope calibration, as well as regularly interspersed reference water samples afterwards (usually, from Kona Deep mineral water with a value close to 0.
02 ‰ in δ 18 O (0. 10 ‰ in δD for the L2103-i). In the current database, the data retained are the ones obtained with the empirical approach. If a significant drift in the reference water values is noticed throughout the run, it is corrected, usually by adjusting the data linearly between the successive values of the reference water (Fig. 1c  and d ). We thus assume that the estimated drift is independent of the δ 18 O and δD values. In addition, between 2017 and 2019, the response slope of the Picarro CRDS was adjusted by interpolating between the three-point slope estimate (based on three internal standards) at the beginning and at the end of the runs when that was deemed possible. However, this adjustment was discontinued in 2020 because the last internal standard samples were often not as reliably measured, with values more sensitive to the number of injections, probably as a result of salt deposits in the liner.
Often, when this happens, there is also a larger scatter between the different injections either for this sample or the initial in-house standards. Running the set of samples again or a selection of them sometimes evidences isotopic shifts that can exceed 0. 05 ‰ in δ 18 and 0. 2 ‰ in δD. Repeating the analysis helps mitigate this source of uncertainty. But, this has not always been done, except for datasets on which there was a specific emphasis.
(2017c)  as well as on complementary tests with the different wire meshes used more recently and between duplicated IRMS-CRDS samples. The values we report are thus internally consistent but could present differences with datasets processed in other institutions without this proposed adjustment or with other changes of scale of up to 0. 10 ‰ in δ 18 O and 0. 20 ‰ in δD, as indicated in  Benetti et al. (2017c). We thus expect that adjusted LOCEAN CRDS δ 18 O data would be higher (more enriched in heavy isotopes) than these other CRDS and more common IRMS data.
Correction and flagging of samples having probably breathed In regions where there is enough information in the LO-CEAN dataset to establish an average relationship between d-excess and salinity  (Benetti et al. , 2017b) , a large breathing of a sample during storage can be detected using its d-excess value, which is then too low compared to the expected relationship. This was recently checked on a set of 10 water samples originating from salinity bottles collected in the surface North Atlantic in 2021 on MV Tukuma Arctica that did not have the usual plastic insert and thus had evaporated as witnessed by the comparison of salinity with thermosalinograph records. These samples indeed present higher practical salinity (S), d-excess lower than expected, and δ 18 O and δD higher than the expected values, estimated by average linear fits of d-excess versus salinity and δ 18 O versus S for this region.
One set of bottles was analyzed by CRDS at LOCEAN and the other set by IRMS for δ 18 O at Geozentrum Erlangen. Among the 32 LOCEAN samples, 11 show indications of breathing and have been slightly adjusted based on their negative d-excess deviation. The comparison between the 32 LOCEAN and Geozentrum Erlangen isotopic values suggests that the adjustment we applied to 11 of the LOCEAN data results in decreasing the standard deviation of the δ 18 O differences between the two sets from 0. 060 ‰ to 0. 041 ‰. The adjustment of the 11 LOCEAN samples also decreased the standard deviation in the differences between d-excess and d-excess estimated from the dexcess versus S relationship derived for the entire LOCEAN dataset from 0. 25 ‰ to 0. 15 ‰. As a comparison, when the set is restricted to the 21 non-adjusted LOCEAN samples, the corresponding standard deviations for the δ 18 O differences between LOCEAN and Geozentrum Erlangen values and the d-excess differences to the expected d-excess versus S relationship were 0.
043 and 0. 14 ‰, respectively. These values are very close to what is found for the set of 32 LOCEAN samples including the 11 adjusted samples, suggesting some homogeneity in the adjusted dataset. For earlier IRMS analyses at LOCEAN, we base the identification of possible evaporated data on excessive scatter in the δ 18 O versus S scatter plots or between successive data compared to what we have previously measured in regions with repeated cruises, and outliers (6 %) are flagged as probably bad. The smaller (by half) proportion of flagged IRMS analyses than for the CRDS analyses suggests either that this validation missed some evaporated IRMS samples, that these earlier data had evaporated less than the more recent ones (some were analyzed sooner after collection), or that the IRMS runs had smaller uncertainties than the latter CRDS runs.
Validation As discussed in Sect. 2, in addition to random errors or to issues related to evaporation of samples, there is the possibility of shifts between subsets of the data due to the different internal standard waters, methods of processing, and adjustment (for CRDS) or conversion from the activity to the concentration scale (for IRMS). We thus need to compare this database with data analyzed in other laboratories and evaluate time series when the data have been repeated in time at the same location. In particular, the LOCEAN dataset contains a limited number of samples for different cruises in deep-water masses that are unlikely to have experienced much change in their isotopic composition over the last 50 years due to their weak ventilation and small salinity variability. Examining data in such deep waters can thus provide a test of consistency between subsets of the LOCEAN data or relative to other datasets.
Within the LOCEAN dataset, relevant deep waters have been sampled in different years (in the southern Indian Ocean -OISO cruises, in the equatorial Atlantic -PIRATA cruises, and in the North Atlantic subpolar gyre -mostly OVIDE cruises), with statistics presented in Table  2. These comparisons on a limited set of cruises, but corresponding to analyses done throughout the last 22 years of the spectrometry platform, suggest that internally the δ 18 O dataset is coherent in time to within 0. 035 ‰ (after an adjustment applied to LOCEAN IRMS data, which most of the time was +0. 09 ‰ to adjust to CRDS data). For δD, the period of comparison is more limited with data from Picarro CRDS only since 2010, and the standard error of yearly δD averages is typically on the order of 0. 15 ‰. The comparison also highlights cruises with more noisy data than others.
09 ‰ adjustment that was applied to recent CRDS salty water samples analyzed since 2015 at LO-CEAN based on  Benetti (2017c) , an adjustment that was not done on CRDS or IRMS datasets produced in other facilities. In summary, these external comparisons, together with the internal consistency tests on the LOCEAN database in a few regions, suggest that the LOCEAN δ 18 O dataset is within +0. 035 ‰ absolute accuracy, at least when averaged spatially or in time (Table  2 ). Individual data have larger uncertainties as discussed before because of the instrumental and internal standard uncertainty (resulting in a total uncertainty of usually less than 0. 05 ‰ in δ 18 O) as well as possible aging and/or evaporation during collection and storage. We are not able to provide similar comparisons for δD or d-excess, as the database for comparison is very reduced.
Data distribution Figure  3  presents the spatial distribution of the LOCEANanalyzed data close to the surface, with the largest data collection being in the North Atlantic (Fig. 3a ) (in particular, with OVIDE cruises since 2002 and the SURATLANT ship of opportunity dataset since 2011), the tropical Atlantic (in particular, the EGEE and PIRATA cruises since 2005), and the southern Indian Ocean (Fig. 3b ) (OISO cruises since 1998). Table  3  reports the number of valid data points by depth range, which indicates that the emphasis in this set has been on near-surface data (58 % of the δ 18 O data above 40 m depth, 13 % between 40 and 200 m depth, and only 12 % at 1000 m or deeper). There is less valid δD than δ 18 O data, the difference corresponding to IRMS-measured data, which correspond to 25 % of the total number of water samples in the database.
Time series We illustrate the dataset with time series of June (or July) data between 50 and 55 • N in the eastern North Atlantic subpolar gyre (NASPG) collected mostly during the OVIDE cruises (Fig. 4 ). This scatter plot of cruise-averaged S and δ 18 O indicates a near alignment of the values. It is striking that the strongest negative (fresher and lighter) anomalies in 2016 fit rather well on the regression line (in red) for water samples in the southwestern NASPG. This regression line  is derived from data from the 47-55 • N, 30-49 • W region, excluding very low-salinity data from seasonal sea ice melt or from shelf waters, and is very similar to the distribution in  Frew et al. (2000). Thus, this reinforces the hypothesis of  Holliday et al. (2020)  that the strong freshening present in the eastern subpolar gyre in 2016 originated from the transport of Arctic fresh water from the western boundary current into the eastern basins and not from local rainfall, which would have likely resulted in higher δ 18 O at the same "low" salinity such as depicted by the black line  (Frew et al.
, 2000; Camille Risi, personal communication, 2021). The SURATLANT surveys provided a seasonal sampling of water isotopes between late 2011 and 2019 along the western flank of the Reykjanes Ridge in the central part of the gyre (53-56 • N, 38-44 • W). Annual summaries of these data are provided in Fig. 5a. There is less alignment of the interannual values on the average southwestern NASPG linear regression line than for the OVIDE surveys (Fig. 4 ). However, there is some aliasing of the seasonal cycle in the an-nual averages (see  Reverdin et al. , 2018a) , which contributes to the scatter, as well as noise in the data and natural variability. On this plot the freshest year appears to be 2017, in agreement with an analysis using a much more complete salinity dataset  (Reverdin et al. , 2018b).
Conclusions Instrumental uncertainty in individual data in this dataset is as low as 0. 03 ‰ in δ 18 O and 0. 15 ‰ in δD for most runs, with occasional much larger uncertainties. One needs to add to that the uncertainties in the internal standards that are used to convert measured values into the VSMOW scale. Different comparisons suggest that the internal standard values have almost always remained defined within at most 0. 03 ‰ for δ 18 O and 0. 2 ‰ for δD since 2012. There was, however, a short-term larger difference found for the most negative standard (equal to 0. 1 ‰ for δ 18 O), most likely related to the readjustment of the instrument to laboratory conditions in May 2021. When using the CRDS Picarro L2130-i, we also found periods with quite uncertain analyses, in particular due to salt or particle deposits in the vaporizer or filters.
These samples could often be run again afterwards to reach lower resulting uncertainty. Finally, there is the issue of possible evaporation during collection and storage. When the analysis is done on a CRDS, we are usually able to detect possible biases larger than 0. 05 ‰ in δ 18 O by comparing d-excess with the expected dexcess derived from regional d-excess-S linear relationships. Attempts were made here to correct δ 18 O and δD when the resulting uncertainty did not exceed 0. 05 ‰ and 0. 1 ‰, respectively. In particular, this was done for some OISO cruise samples which were analyzed many years after collection, in the case of faulty caps being used, or in the case of caps that were not properly closed and wrapped with parafilm. This is certainly a strong source of quality loss for part of the database, and "small" effects may have remained undetected.
Possible long-term drifts due to changes in internal standards, storage, instrumentation, and protocols are difficult to estimate. This is done here by checking the consistency of different subsets of the database, for instance when time series can be obtained (such as in the southern Indian Ocean or North Atlantic subpolar gyre), by comparison with duplicate data analyzed in other laboratories, or by comparison with other datasets in deep regions commonly sampled. These comparisons are encouraging. On one hand, they suggest that the internal consistency in the database is usually within an uncertainty of 0. 03 ‰ for δ 18 O and 0. 15 ‰ for δD. On the other hand, although other datasets sometimes differ by much more with a large scatter between the 18 comparisons (with a standard deviation of 0.
055 ‰ for δ 18 O), the average difference (+0. 082 ‰) found with them is close to the adjustment that is applied to the LOCEAN data to report them on the concentration scale (+0. 09 ‰ for δ 18 O analyzed with a salt liner since 2015). Of course, there is still the possibility of errors and biases in subsets that could not be compared in a similar way, such as surface samples collected from ships of opportunity or sailing vessels in the tropics, that could result from different handling of the samples during collection and more uncertain storage conditions. There are also small errors originating from memory effects in the Picarro CRDS runs that could be better corrected and taken into account  (Vallet-Coulomb et al. , 2021). We also illustrated the possibility of using this dataset to investigate ocean variability.
Of course, the interest of a data archive is to merge datasets from different institutes such as this one, while retaining similar accuracy. This was attempted with the Global Seawater Oxygen-18 Database at GISS  (Schmidt et al. , 1999) , although biases between subsets of this mostly δ 18 O dataset remain at a level that makes the overall analysis of variability difficult to carry out. The few comparisons we could do suggest that differences with other datasets are at times large. Effort to correctly adjust for these differences and produce a larger coherent archive is required to get full use of the data collected. There is still a need for more and better-calibrated seawater isotope data to reconstruct tropical hydroclimate variability, such as formulated for the tropical coral archives by the PAGES CoralHydro2k Project, or for high-latitude studies of the various sources of fresh water in the ocean, including continental runoff, sea ice, iceberg melt, and air-sea exchanges.
This water mass is regularly sampled and has not been strongly ventilated recently. In 1998-2015 during German-led cruises, these waters presented an average δ 18 O value close to +0. 28 ‰ (after removing suspiciously high data from a cruise in 2011 and large positive outliers in 2012; Paul Dodd, personal communication, 2020). The LOCEAN database contains seven δ 18 O samples close to the bottom across Fram Strait from the MSM76 cruise on RV Maria S Merian in 2018, with an average (SD) value close to +0. 395 (0. 035) ‰, thus averaging higher by 0. 115 ‰ than the other set in 1998-2015. We extracted individual profiles from the GISS Global Seawater Oxygen-18 Database  (Schmidt et al. , 1999)  that can be compared with the LOCEAN station data in deep and old water masses. In the southern Indian Ocean, for example, numerous profiles collected during 1993-1994 cruises (CIVA1  (Archambeau et al.
, 1998) , ADOX1, SWINDEX, ADOX2) suggest that LOCEAN δ 18 O in the deep layers are higher by 0. 10 ‰ to 0. 17 ‰ depending on the cruise. There is also one GEOSECS 1978 station with a single deep value within 0. 01 ‰ of nearby OISO stations, as well as some 1984 (IN-DIVAT1) and 1996 (CIVA2) station data with larger uncertainties that indicate higher LOCEAN δ 18 O values by 0. 15 ‰ to 0. 22 ‰, depending on how outliers are identified and removed. In the North Atlantic, there are data from three cruises that can be directly compared with LOCEAN data, focusing on deep waters with T -S properties close to the ones of the LOCEAN dataset. Comparison with one GEOSECS 1972 station south of Greenland suggests higher δ 18 O LOCEAN values by ∼ 0. 060 ‰ (there is a small salinity shift between the two profiles that required adjusting the LOCEAN δ 18 O value to the same salinity based on the average δ 18 O-S relationship).
Data from four stations of the CONVEX1991 cruise  (Frew et al. , 2000)  indicate higher δ 18 O in the LO-CEAN dataset by ∼ 0. 090 ‰ (after adjustment done to consider small salinity differences). On the other hand, data close to the northeastern Atlantic deep-water layer from stations collected in June 1995 in the southern Labrador Sea  (Khatiwala et al. , 1999)  do not show a significant difference with LOCEAN stations closer to southern Greenland (southern Irminger Sea) at a similar salinity. In the equatorial Atlantic there are deep data from two GEOSECS stations collected in October 1972 and February 1973 that can be compared with the LOCEAN data (mostly near 1000-2000 m depth). These limited comparisons (often at large distance but at a similar salinity) suggest that LOCEAN values are larger than the GEOSECS δ 18 O by 0.
Acknowledgements. Data were measured at the CISE-LOCEAN facility housed by the LOCEAN laboratory and part of the OSU ECCE Terra analytical services. Support by OSU ECCE Terra, by LOCEAN, and by various French national institutes and programs is gratefully acknowledged (including INSU/CNRS, IFREMER, IPSL, IRD, IPEV, LEFE program, ANR GEOVIDE), as is support by different French "Services nationaux d'Observation", such as PI-RATA, SSS, and OISO/CARAUS. Many of the data originate from research cruises on French Research vessels: RV Suroit, Thalassa, Atalante, Marion Dufresne 2, and Tara. Some data were collected during research cruises on non-French vessels, such as MIDAS in 2013 as well as BOCATS1 in 2016 and BOCATS2 in 2021 on the Spanish RV Sarmiento de Gamboa, HUD2014007 on the Canadian RV Hudson, JR302 in 2014 and JR16004 in 2017 cruises on the UK HMS James Clarke Ross, the Arctic cruises in 2006-2008 and 2013 along with the 2020-2021 Microbiome cruise on French SV Tara, the Nordic seas MIZEX cruises in 2002-2004 on Swedish RV Oden, the 2017 SPURS2 cruise on RV Revelle, and the 2018 eastern Greenland cruise MSM76 on German RV Maria S.
Subsequently, trio WES analysis of P4 and his parents identified a single heterozygous c. 2128A>G, p. Arg710Gly DNM1L variant that had arisen de novo in the proband. The c. 2128A>G, p. Arg710Gly variant was classified as "likely pathogenic" using the ACGS/ACMG criteria PS2_moderate, PS3_moderate, PM2_moderate, PP3_supporting, and PP4_supporting. Finally, DNA from P5 was subject to singleton WES analysis which revealed the same single heterozygous c. 1201G>A, p. Gly401Ser DNM1L variant that was present in P1. All DNM1L variants have not been previously reported pathogenic and were absent from gnomAD database (https://gnomad. broadinstitute. org/). The DNM1L variants were confirmed by Sanger sequencing, and analysis of parental samples was undertaken either as part of the trio WES pipeline, or by targeted Sanger sequencing which supported the de novo occurrence of a DNM1L variant in each clinically affected child.
In silico structural modelling of DRP1 variants Three of the five patients (P1, P2, and P5) exhibited single-nucleotide variations, c. 1201G>A, p. Gly401Ser (G401S), or c. 1088G>A, p. Gly363Asp (G363D), in the DRP1 stalk domain  (Fig 1B)  which has been shown to play a key role in dimerization and self-assembly essential for fission  (Fr öhlich et al, 2013; Francy et al, 2017; Kalia et al, 2018). Analysis of the cryoEM structure of DRP1 in co-complex with one of its recruiting proteins, MID49 (PDB:5WP9), suggests that both residues are located at the dimer interface  (Fig 2A). Indeed, a quadruple mutant G401-404 AAAA has been shown to promote disruption of tetramers (or any higher order oligomers) and the formation of stable dimers under certain conditions for DRP1 and other dynamin related proteins  (Gao et al, 2010 (Gao et al, , 2011;; Faelber et al, 2011; Ford et al, 2011; Fröhlich et al, 2013).
In addition, the mitochondrial network length was analysed using immunofluorescence labelling of fixed patient and age-matched control fibroblasts using TOM20 antibodies. The Columbus (PerkinElmer) software system was used to  (B) Quantification of mean mitochondrial network length via MiNa using ImageJ n > 20 fields from two independent experiments, calculated by multiplying mean branch length and mean number of branches per network. Non-parametric one-way ANOVA and Dunn's multiple comparisons using GraphPad Prism were used to calculate statistically significant differences between groups. (C) Mitochondrial network length using immunofluorescence analysis of fixed paediatric control (C1), adult control (C2), and DNM1L patient (P1-P4) fibroblasts labelled with TOM20 antibodies. The Columbus (PerkinElmer) software was used to quantify the hyperfusion of patient mitochondrial networks relative to controls and a minimum of 5,500 mitochondria were analysed for each case.
Detailed analysis of mtDNA nucleoids stained with PicoGreen using Columbus software (PerkinElmer) revealed marked differences in the proportion of enlarged nucleoids (area > 1. 5 μm 2 ) in P1 (p. Gly401Ser) and P2 (p. Gly363Asp) compared with control (Fig S1C ). There was no difference in nucleoid size ratio between P3 (p. Leu230dup) and control  (Fig S1C). Although, upon visual examination P4 (p. Arg710Gly) nucleoids appeared enlarged compare with controls, we were not able to accurately quantify the individual puncta because of increased levels of lipofuscin present in these cells  (Fig S1D). Altogether, assessment of patient fibroblasts demonstrated that the de novo variants identified in P1 (p. Gly401Ser) and P2 (p. Gly363Asp) cause mitochondrial network hyperfusion, leading to mitochondrial enlargement and nucleoid clustering which is indicative of impaired nucleoid distribution and segregation.