dataesr/scientific-paragraphs-categorization huggingface dataset:
7b. Figure  7c  shows the observed SAOD measured by OSIRIS. Over the winter months, there is a lack of observational data from mid-October 2009 until the beginning of 2010, particularly at high latitudes, that coincides with the polar night. A precise comparison for these months is therefore not possible. CESM1(WACCM) suggests that the sulfate SAOD remains at a fairly constant level over the Northern Hemisphere over the October-December 2009 period, then decreases quite quickly from February to April 2010. The degraded model SAOD shows reasonable agreement with the SAOD observed by OSIRIS, whilst the nondegraded model simulates much higher SAOD. This demonstrates that OSIRIS's limitations are crucial to the interpretation of its data. In Fig. 7 , the observed (OSIRIS) SAOD shows, however, a slightly stronger maximal magnitude than the degraded SAOD from the model.
A possible explanation may be that CESM1(WACCM) yields extinctions for sulfuric acid particulates only, whereas OSIRIS's observations ac- count for a more comprehensive SAOD that can include nonsulfate compounds in the lower stratosphere. To place a greater emphasis on sulfuric acid particulates due to the volcanic eruption, we convert all three data sets to anomalies. These anomalies were calculated by subtracting background conditions from the SAODs, for which averages calculated in the first week of June 2009 were used as an approximate reference. Figure  8  presents the same layout as Fig. 7  but now displays the SAOD anomalies over the same period (1 June 2009 until 31 May 2010). Again, a good accordance is found between the degraded model compared to OSIRIS (with the non-degraded model showing higher SAODs).
The agreement in SAOD anomalies in Fig. 8  is better than for the absolute SAODs in Fig. 7. This indicates that differences in the background aerosol content prior to the eruption may explain some of the model-measurement discrepancy in terms of SAOD maximum amplitude as highlighted in Fig. 7. Integrating anomaly data presented in Fig. 8  yields the Northern Hemisphere SAOD anomaly calculated at 750 nm over the year following the eruption, shown in Fig. 9. The dashed red line is SAOD simulated by the model. The plain red line indicates the same data after the OSIRIS bias degradation, and the blue line indicates SAOD from OSIRIS observations. Note that missing data in OSIRIS's measurements during winter were taken into account in the integration of the degraded model data, as shown by the shaded area in Fig.
018 on 12 July 2009 (Fig. 9 ), followed by a long decay with an e-folding time of ≈ 169 days. Conversely, OSIRIS shows a much fainter and later peak (≈ 0. 004 on 1 September 2009), with a quicker decay (efolding time of 52 days). The degraded model SAOD yields an e-folding decay time (51 days) that is very comparable to that from OSIRIS; the peak value is also similar in amplitude to OSIRIS's and is reached on 17 October. This is slightly later than in the OSIRIS data, although the plateau in SAOD during that period can account for this delay. Table  3  summarizes the SAOD e-folding times calculated in this study, along with values from previous studies  (Haywood et al. , 2010; Kravitz et al. , 2011) , and calculated using OSIRIS data. Different bands of latitude are explored. One can note that the e-folding times vary quite significantly between authors, including those computed from OSIRIS's data (it is likely that different versions of the OSIRIS data -v.
5. 05 up to v. 5. 07 for the present study -were used). The main point to be highlighted here is the fair consistency obtained between e-folding times computed on the CESM1(WACCM)'s degraded data and on OSIRIS retrievals in our study, as evident from the last two columns of Table  3. We find that both the saturation limit and the fact that extinction profiles may terminate well above the tropopause are significant sources of measurement bias that need to be taken into account in comparison of OSIRIS data to model studies.
Our study suggests that the presence of HCl leads to a delay in the oxidation of SO 2 to form sulfuric acid particles of about 2 days, with a 5-10 % increase in the modelled e-folding times for SO 2. We also find a better temporal accordance in SO 2 burden derived from satellite (IASI) data and our simulations when taking HCl into account. The additional surface area provided by volcanic particles catalyses reactions that can perturb stratospheric chemistry, including activation of stratospheric halogens, and can lead to strong reduction of NO 2 and modest depletion of ozone as highlighted by  Berthet et al. (2017)  for Sarychev Peak. Our simulations show that the co-injected volcanic HCl also affects the post-eruption stratospheric chemistry of ozone and NO x , depleting these species more severely than in simulations that account for SO 2 injections only.
Our results highlight that volcanic HCl emissions should be taken into account when simulating sulfur chemistry and stratospheric chemistry impacts from volcanic eruptions during which HCl is co-injected. The second major point highlighted by this paper is the treatment of limitations in SAOD derived from OSIRIS measurements: both a saturation effect and a varying minimum altitude in available OSIRIS data (i. e. extinction profiles may terminate well above the tropopause, in particular at high latitudes) were identified by  Fromm et al. (2014). We used a two-step model degradation process to reproduce these biases in the modelled data and found as a result very good agreement with the actual OSIRIS measurements following the volcanic eruption, reproducing both the magnitude and temporal evolution of the SAOD following the 2009 Sarychev eruption  (Figs.
Acknowledgements. This work was undertaken as part of  WP7  of the  VOLTAIRE LabEx (VOLatils -Terre, Atmosphère et Interactions -Ressources et Environnement ), convention number  ANR-10-LABX-100-01. The authors wish to thank the technical staff of the  CaSciModOT structure (Calcul Scientifique et Modélisation Orléans-Tours ), part of the  French national network of complex systems (RNSC -Réseau National des Systèmes Complexes ), along with the  CINES (Centre Informatique National de l'Enseignement Supérieur ), thanks to which the simulations could be completed. This work was granted access to the high-performance computing (HPC) resources of CINES under the 2014 allocation (c2014019129) made by  GENCI. The authors are grateful to the  "Centre National d'Études Spatiales" (CNES)  balloon launching team for successful operations and to the  Swedish Space Corporation at Esrange.
The  StraPolÉté  project has been funded by the  French "Agence Nationale de la Recherche " ( ANR-BLAN08-1-31627 ),  CNES , and the " Institut Polaire Paul-Émile Victor " (IPEV). The 2010 balloon observations in the frame of the  AEROWAVE  project have been supported by the  French CNRS-INSU Balloon Committee (CSTB). The  ESPRI-AERIS (formerly ETHER) database (CNES-INSUCNRS ) and the  CNES  "sous-direction ballon" are partners of the project. The authors also would like to thank the  NCAR/CESM  online discussion board for many helpful technical discussions that helped throughout this study, along with  Charles G. Bardeen  from  NCAR  for some discussion about the model,  Sophie Bouffiès-Cloché  from  IPSL  for providing the MERRA forcing data,  Adam Bourassa  from  University of Saskatchewan  for some discussion about OSIRIS data, and  Terry Deshler  for providing the Wyoming OPC data.
AcknowLedGMentS This work was supported by  French  state funds within the framework of the Cluster of Excellence MATISSE led by  Sorbonne Universités  and within the  INTERRVIE action of the 2016 CNRS-INSU TelluS program. We acknowledge support by the  Sorbonne Universités FormInnov SPIREM  project. We thank two anonymous reviewers and  Robert B. Heimann  for their thoughtful comments.
Specifically, we tested whether similar results would be found when using two anatomical atlases of different resolutions to define nodes: the Desikan atlas  30  (68 ROI's) and the Destrieux atlas  31  (148 ROI's). In addition, we compared two types of networksone based on cortical volumes and one on cortical thickness-and assessed how the number of subjects in each group would affect binary and weighted network findings. Understanding how these different choices affect graph measures derived from clinical data is crucial in order to correctly assess and compare graph theoretical findings. Investigating the impact group size and group composition have on network properties is of great importance in order to understand to what extent graph theoretical result acquired from a cohort can be generalized to a larger population.
Methods Subjects. Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni. loni. usc. edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer's disease (AD). AddNeuroMed is part of InnoMed (Innovative Medicine in Europe) and data was collected at six sites across Europe with the aim to develop novel biomarkers for AD and validate existing ones  47, 48. In this project we combined data from ADNI and AddNeuroMed to increase the sample size.
The inclusion and diagnostic criteria were similar in both studies. In short, to be eligible as a control a Mini Mental State Exam (MMSE) score > 24, a total Clinical Dementia Rating (CDR) of 0 and no signs of depression were required. The AD diagnoses were based on the criteria for probable AD of the National Institute for Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorder Association (NINDS/ ADRDA) together with CDR >0. 5. For both groups, no history of drug abuse, organ failure, significant neurological or psychiatric illness (except AD) was required. No MRI data was used in order to make the diagnoses. Written informed consent was given by all participants (or by authorized representatives) in the ADNI and AddNeuroMed cohorts, in accordance with the Declaration of Helsinki.
This was followed by deformation processes to register each subject to an individual spherical atlas based on cortical folding patterns  61  , parcellation of the cerebral cortex  62  and calculating thickness and volumes measures  60  from cortical regions specified by either the Desikan  30  or the Destrieux atlas  31. We extracted the cortical thickness and cortical volumes from 68 regions of the Desikan atlas and from 148 regions of the Destrieux atlas. All data was preprocessed through The Hive Database system (theHiveDB)  63  and all output data was quality controlled.
Network construction and graph analysis. The cortical thickness and volume measures extracted from FreeSurfer were used to define the nodes of the structural networks. Using the Desikan atlas, we built networks with 68 nodes, whereas for the Destrieux atlas we built networks with 148 nodes. The thickness and volume data from each region or node were corrected for the effects of age and gender using linear regression and the residual values were used to construct the connectivity matrices. For the cortical volumes, the intracranial volume was used as an additional covariate. The linear regressions were performed using data only from the control subjects. The reasoning behind generating a model based on controls only is that we wanted to detrend the effects of healthy aging and not the disease related changes we were interested in studying, as discussed in  50, 64.
Acknowledgements We would like to thank the  Swedish Foundation for Strategic Research (SSF) ,  The Swedish Research Council (VR) , the  Strategic Research Programme in Neuroscience at Karolinska Institutet  (StratNeuro),  Swedish Brain Power , the regional agreement on medical training and clinical research (ALF) between  Stockholm County Council  and  Karolinska Institutet, Hjärnfonden, Alzheimerfonden , the  Åke Wiberg Foundation and Birgitta och Sten Westerberg  for additional financial support. Data collection and sharing for this project was funded by the  Alzheimer's Disease Neuroimaging Initiative (ADNI)  ( National Institutes of Health  Grant  U01 AG024904 ) and  DOD ADNI (Department of Defense  award number  W81XWH-12-2-0012 ). ADNI is funded by the  National Institute on Aging , the  National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from the following:  AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation ;  Araclon Biotech ;  BioClinica, Inc.
fnih. org). The grantee organization is the  Northern California Institute for Research and Education , and the study is coordinated by the Alzheimer's  Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the  Laboratory for Neuro Imaging at the University of Southern California. Data used in preparation of this article were obtained from the  Alzheimer's Disease Neuroimaging Initiative  (ADNI) database (adni. loni. usc. edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni. loni. usc. edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.
Temperature and salinity were used to compute the potential density of sea water and determine the depth of the mixed layer where potential density is well homogenized (as a proxy for the layer where mixing is active). Following de Boyer Montégut et al. 13  , the mixed layer depth (H in m) was computed as the depth at which the change in potential density from its value at 10 m, Δσ θ , exceeded 0. 03 kg m -3this value of Δσ θ best tracked the region of weak stratification in our data set. The euphotic layer depth (H e in m), the depth below which the light level is too low to support photosynthesis, was computed as the depth at which the dailyaveraged photosynthetically available radiation drops below 0. 1 mol photons m -2 d -1 , corresponding to the lowest light levels at which the temperate diatom Phaeodactylum tricornutum has been observed to grow  14.
H ∂H ∂t , the time series of P ml , hPi, H, and H e were linearly interpolated on equally spaced 3-day time series, the average sampling period in our dataset. Then, a running average of 24-days was applied to the time series, so as to filter out short-term fluctuations and focus on the seasonal evolution of phytoplankton biomass. (The 24 days correspond to the spring bloom e-folding timescale as shown in Supplementary Note 4. ) The time series of r p and 1 H ∂H ∂t in Fig. 2d , e confirm that growth of phytoplankton populations begins in winter at a time when the mixed layer is still deepening ( 1 H ∂H ∂t >0). The net population growth rates are positive, but weak of order of 0. 01 d -1. During this phase of weak biomass accumulation, r p is of the same order as 1 H ∂H ∂t and the phytoplankton concentration P ml decreases only slightly, because the net population growth and dilution approximately balance.
Methods Float data processing. All floats used in this study were PROVOR CTS-4 profiling floats, equipped with a SEABIRD SBE41 CPCTD sensor, a Satlantic OC4 radiometer measuring downwelling irradiance at 380, 412, and 490 nm and Photosynthetically Available Radiation integrated between 400 and 700 nm, a WETLabs ECO PUK comprising a chlorophyll a fluorometer, a CDOM fluorometer and a backscattering sensor at 700 nm. The CDOM data were not used in this study. The 6901485-86 floats also included an AANDERAA optode oxygen sensor and the 6901485 float was equipped with a Satlantic SUNA nitrate sensor. The floats nominal mission included CTD and optical profiles from 1000 m to the surface. The sampling resolution was 10 m from 1000 m to 250 m, 1 m from 250 m to 10 m, and 0. 2 m from 10 m to the surface.
The upward casts were repeated every 1, 2, 3, 5, or 10 days depending on the mission and scientific objectives. The floats typically emerged to the sea around local noon. The CTD and trajectory data were quality-controlled using the standard Argo protocol  32. Following the BGC-Argo procedure  33, 34  , the optical raw signals (counts) were transformed into Chl, b bp and iPAR. The data were qualitycontrolled following the BGC-Argo quality control manual  35. In addition to this, all time series were visually inspected and we did not detect any sensor failure or drift over time. As suggested by Roesler et al. 36  , the Chl values were divided by a factor of 2 to account for a calibration systematic error in the WETLabs 413 ECO-series fluorometers. Fluorescence quenching was corrected using the method of Xing et al.
The daily-averaged sea surface photosynthetically available radiation (PAR(0 -) in mol photons m -2 d -1 ) was obtained by averaging Eq. (  1 ) over the length of the day. The irradiance was assumed to decay exponentially with depth as per the Beer-Lambert Law. The exponential decay rate, i. e. , the diffuse attenuation coefficient for photosynthetically available radiation, K (m -1 ), was derived from the float iPAR measurements by fitting a linear least square regression, constrained to pass through the origin, between lnð iPAR z;t¼noon ð Þ iPAR 0 À ;t¼noon ð Þ Þ and z in the upper 50 m-the average depth at which light intensity is 1% of its surface value in our dataset. Following the Beer-Lambert law, the euphotic layer depth, H e , was estimated as the depth at which the daily-averaged photosynthetically available radiation drops below a threshold value, PAR th , here taken as 0.
Acknowledgements A. M. and R. F. acknowledge  NSF  support through award  OCE-1155205  and H. C. acknowledges the support by the  European Research Council  for the remOcean project (grant424 agreement no. 246777 ) and ATLANTOS  EU  project (grant agreement  2014-633211 ) funded by  H2020 program. We thank the crew of the various cruises who deployed the  BGC-Argo  floats used in the present study. The work benefitted from many insightful conversations with  Mike Behrenfeld ,  Emmanuel Boss ,  Glenn Flierl ,  Stephanie Dutkiewicz , and  Griet Neukermans.
Data availability. These data were collected and made freely available by the International Argo Program and the national programs that contribute to it (http:// www. argo. ucsd. edu, http://argo. jcommops. org). The Argo Program is part of the Global Ocean Observing System. The BGC-Argo data used for this study can be downloaded from ftp://ftp. ifremer. fr/ifremer/argo.
This corresponds to 0. 4 mg/kg bw per day. Similarly, a LOAEL of 10 mg/kg feed was identified for carp, corresponding to 0. 5 mg/kg bw per day. This LOAEL was based on pathological alterations, changes in haematological parameters and reduced body weight gain. A NOAEL of 20 mg/kg feed was identified for catfish. This was based on reduced body weight gain and microscopic liver lesions. No data could be identified concerning the effects of FBs in cats, dogs or farmed mink. No data were available to establish a reference point for any modified form of fumonisin, for any of the animal species considered. The dietary exposure was estimated using a final data set of 18,140 feed samples on FBs (i. e. FB 1 , FB 2 and FB 3 ) representing most of the feed commodities with potential presence of fumonisin.
At the time of the evaluation, experimental data available for catfish and Nile tilapia suggested a NOAEL corresponding to 20 mg FB 1 /kg feed. A LOAEL of 2,000 lg/kg bw per day for FB 1 was identified for poultry based on increased Sa and Sa/So ratios in liver  (EFSA, 2005). In 2014, the EFSA CONTAM Panel developed a Scientific Opinion on the risks for human and animal health related to the presence of modified forms of certain mycotoxins in food and feed  (EFSA CONTAM Panel, 2014). The toxicity for animals and humans of metabolites and masked or bound forms of mycotoxins, including fumonisins, was evaluated. The EFSA occurrence database contained no data on modified fumonisins, and therefore occurrence was based on limited information reported in the literature. An estimation of the human dietary exposure and animal feed exposure compared with the exposure to the parent mycotoxins and assessments of the human and animal health risks was performed.
Feed occurrence data Following an European Commission mandate to EFSA, a call for an annual collection of chemical contaminant occurrence data in food and feed, including fumonisins, was issued by the former EFSA Dietary and Chemical Monitoring Unit (now DATA Unit) 3  in December 2010 with a closing date of 1 October of each year. The data submissions to EFSA followed the requirements of the EFSA Guidance on Standard Sample Description for Food and Feed (EFSA, 2010a); occurrence data were managed following the EFSA standard operational procedures (SOPs) on 'Data collection and validation' and 'Data analysis and reporting'. By the end of July 2017, a total of 18,273 analytical results from 8,057 samples on fumonisins in feed were available in the EFSA database. Data received after that date were not included in the data set used to estimate dietary exposure.
No data on the modified forms of fumonisins were available in the EFSA Chemical Occurrence database. Following the EFSA SOP on 'Data analysis and reporting' to guarantee an appropriate quality of the data used in the exposure assessment, the initial data set was carefully evaluated applying several data cleaning and validation steps. Special attention was paid to different parameters such as 'Sampling strategy', 'Sampling year', 'Sampling country', 'Analytical methods' and the 'Reporting unit'. Feeds were classified based on the catalogue of feed materials specified in the Commission Regulation (EU) No 68/2013 4. Analytical results were reported either on a whole weight basis or with a dry matter (DM) content of 88%. Before estimating dietary exposure, all results were converted into 88% DM mg/kg.
Methodologies 2. 2. 1. Use of default value for Fumonisins, modified forms and hidden forms included in the assessment 2. 2. 1. 1. Modified forms As described in Section 1. 3. 1 (Fumonisins, modified forms and hidden forms considered in this opinion) FB 1-3 as parent forms, modified forms of fumonisins and hidden forms of fumonisins have been included in the assessment, according to the available occurrence data. Due to the lack of information on their toxicity, the CONTAM Panel was unable to derive any relative potency factor (RPF) for modified fumonisins  (EFSA CONTAM Panel, 2018). In consideration of the lack of occurrence data for modified forms of fumonisins in the EFSA database, and since studies from the literature indicate a low occurrence (less than 10%) of these forms compared to the parent compounds, modified forms of FB 1-3 were not included in the exposure assessment.
Methodology for data collection and study appraisal In 2015, the CONTAM Panel received from European Commission the mandate for an assessment of the risk to animal health of fumonisins and their modified forms. In addition, a mandate was received to assess whether it is appropriate and feasible to set a group HBGV for fumonisin B 1 and B 2 and their modified forms identified in the CONTAM opinion on the risks for human health related to the presence of modified forms of certain mycotoxins in food and feed (EFSA CONTAM Panel, 2018), and to consider, if relevant, the appropriateness to use the parent compounds as a marker for presence and toxicity of fumonisin B 1 and B 2 and their modified forms. A call for a literature search and review was launched in March 2016 within the Framework Contract (FWC) No OC/EFSA/AMU/2014/01 Lot 2 Chemical/toxicological -FWC 6 with the aim of identifying and collecting relevant literature related to fumonisins and their modified forms to support preparatory work for the present opinion and that on HBGVs (EFSA CONTAM Panel, 2018).