dataesr/scientific-paragraphs-categorization huggingface dataset:
We encourage researchers to build upon the proposed framework and functional dataset while tailoring it to their specific research needs. 772284 (IceCommunities). This research was also supported by Bio-diversa+, the European Biodiversity Partnership under the 2021-2022 BiodivProtect joint call for research proposals, co-funded by the European Commission (Grant Agreement no. 101052342 'PrioritIce-Vanishing habitats: conservation priorities for glacier-related biodiversity threatened by climate change') and with the funding organizations MUR and ANR. SG is funded by the Italian national inter-university PhD course in Sustainable Development and Climate change.
Statistics Invasion, proliferation, viability, morphology, and quantitative PCR (qPCR) assays were done with 3 technical and biological replicates. To compare multiple groups, 1-way ANOVA (parametric) or Kruskal-Wallis (nonparametric) tests were used for continuous outcome variables, with χ 2 and Fisher's exact tests used for categorical outcome variables. ANOVA or Kruskal-Wallis tests were followed by Tukey's or pairwise Wilcoxon's post hoc tests for comparisons between groups, respectively. Nonparametric 2-tailed t tests were used to compare 2 groups. NanoString data were analyzed using the DESeq2 package in R, through which a geometric mean is calculated for each gene across replicates and counts in each replicate are divided by the mean, with count outliers removed using Cook's distance analysis and the Wald test used to assess significance.
Invasion quantification For invasion analysis of spheroids in HA hydrogels, spheroids were imaged every 2 days using the Eclipse TE2000 Nikon Microscope with a Plan Fluor Ph1 ×10 objective. Images were acquired using NIS-Elements software (NIS-ELEMENTS AR 5. 42. 02). For each spheroid, invasion was calculated as (A f -A i )/A i ) where A f = final spheroid area and A i = initial spheroid area. Spheroid area was measured using ImageJ (NIH), and invasion was normalized to control spheroids. To analyze cells invading in devices, cells were imaged every 7 days using the Eclipse TE2000 Nikon Microscope with a Plan Fluor Ph1 ×10 objective. Images were acquired and stitched using NIS-Elements software (NIS-ELEMENTS AR 5. 42. 02). For each device, total cell reservoir area was outlined in ImageJ at each time point, and invasion was calculated using the same equation as above.
J Clin Invest. 2024;134(3): e170397  https: // doi. org /10. 1172/JCI170397  supported by the  National Science Foundation Graduate Research Fellowship Program  under Grant No. DGE 1752814. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. SK was supported by  R01CA227136 ,  R01CA260443 , and  R01GM122375. KJW was supported by  F31CA2228317. We thank  Mary West  of the  UC Berkeley Cell   and Tissue Analysis Facility (CTAF). Work was performed in the QB3 CTAF that provided the Cryostar NX70. Confocal imaging occurred at the  CRL Molecular Imaging Center  (RRID:SCR_017852) supported by the  Gordon and Betty Moore Foundation. We thank  Holly Aaron  and  Feather Ives  for microscopy advice.
Address correspondence to:  Sanjay Kumar , UC Berkeley, 108 Stanley Hall #3220, Berkeley, California 94720-3220, USA. Phone: 510. 666. 3317; Email: skumar@berkeley. edu. Or to:  Manish K. Aghi , UCSF, 1450 Third Street, Room HD-465, San Francisco, California 94158, USA. Phone: 415. 353. 3998; Email: manish. aghi@ucsf. edu. Acknowledgments MKA was supported by  R01CA227136 ,  R01NS079697 , and  R01CA260443. JHG was supported by a diversity supplement to  R01CA227136. EAA was supported by a  National Science Foundation Graduate Research Fellowship. This material is based upon work.
Methods A total of 147 Ae. japonicus specimens (77 females and 70 males) and 124 Ae. koreicus specimens (67 females and 57 males) were collected in South-West Germany. The left wing of each specimen was removed, mounted and photographed. The coordinates of 18 landmarks on the vein crosses were digitalised by a single observer. The resulting two-dimensional dataset was used to analyse the differences in the wing size (i. e. , centroid size) and wing shape between Ae. japonicus and Ae. koreicus by means of geometric morphometrics. To analyse the reproducibility of the analysis, the landmark collection was repeated for 20 specimens per sex and species by two additional observers.
Identi cation was con rmed by DNA barcoding. DNA isolation was performed from one leg per specimens adapting the protocol described by  Blattner et al. [56]. Individual legs were placed into 2 ml tubes and 180 µl ATL buffer (Qiagen) and 20 µl (20 mg/ml) Proteinase K (Qiagen) added followed by incubation overnight at 56 °C on a shaking thermomixer (400 rpm). Extraction with the DNeasy Blood & Tissue kit (Qiagen) was performed according to the manufacturer's protocol. Polymerase chain reaction (PCR) ampli cation of nad4 gene region was conducted with the protocol published by Fonseca et al. [51]. All amplicons were further processed with Sanger sequencing (LGC Genomics, Berlin, Germany), preprocessed with Geneious 7. 1. 9 (https://www. geneious. com) nally compared to GenBank sequences (http://blast.
Statistics The two-dimensional landmark coordinates were used to calculate the centroid size and the superimposed shape coordinates of each specimen with the "gpagen" function in the R package "geomorph", version 4. 0. 1  [62]. The centroid size is considered a proxy for wing size and was used to statistically compare the mean wing size of Ae. japonicus and Ae. koreicus through an analysis of variance (ANOVA). As mosquitoes are well known for sex-speci c differences in their wing size [63], the ANOVA was applied separately for the females and the males. The allometric effects of the centroid size on the wing shape were assessed with the "procD. lm" function using 1000 permutations  [62]. The superimposed wing shape coordinates were analysed by linear discriminant analyses (LDA) with the R package "MASS", version 7.
Observer effect The centroid size did not signi cantly differ between the three observers for both, females (linear model, F = 0. 276, Z = -0. 751, R² = 0. 005, p = 0. 776) and males (linear model, F = 0. 219, Z = -0. 848, R² = 0. 004, p = 0. 784). The mean Procrustes variance for the repeated measurements per specimen was 0. 000439 for the females and 0. 00034 for the males and thereby considerably smaller than the mean Procrustes variance observed within the two species Ae. japonicus (females: 0. 001686, males: 0. 001494) and Ae. koreicus (females: 0. 00094, males: 0. 001639). This indicates a relative low observer effect and was also con rmed by the leave-one-out classi cation with the wing data set of the repeated measurements, which yielded a similar accuracy as the dataset produced by a single observer for the females (95.
Acknowledgements We would like to thank  Nicole Sittig  for assistance in collecting the samples in the eld. Funding FGS, LR, KK and RL are funded by the  Federal Ministry of Education and Research of Germany (BMBF)  under the project  NEED  ( 01Kl2022 ) and  UL  by the  German Federal Ministry of Food and Agriculture (BMEL)  through the  Federal O ce for Agriculture and Food (BLE)  with the grant number  2819107A22.
g. NGO's), other scientists and the general public. Based on harmonized data from the HBM4EU Aligned Studies (2014-2021), the usefulness of our indicators was demonstrated for the age group children (6-11 years), using two case examples: one phthalate (Diisobutyl phthalate: DiBP) and one non-phthalate substitute (Di-isononyl cyclohexane-1,2-dicarboxylate: DINCH). For the comparison of age groups, these were compared to data for teenagers (12-18 years), and time periods were compared using data from the DEMOCOPHES project (2011)(2012). Our result indicators proved to be suitable for demonstrating the effectiveness of policy measures for DiBP and the need of continuous monitoring for DINCH. They showed similar exposure for boys and girls, indicating that there is no need for gender focused interventions and/or no indication of sex-specific exposure patterns.
, 2020a). To produce robust and scientifically sound answers to the policy questions, the indicators need to be based on harmonized and quality assured data  (Buekers et al. , 2018). Under HBM4EU, comparable HBM data with European wide exposure coverage from different countries have been aligned and collated under the HBM4EU Aligned Studies  (Gilles et al. , 2021 and Gilles et al. , 2022). This harmonized data set  (Esteban López et al. , 2021; Mol et al. , 2022)  has been used for further development of the indicators. Depending on which information they should provide final visualization of the indicators can be either in a number format, or as an infographic  (Buekers et al. , 2018). In this publication, we present the derived European HBM indicators for one selected phthalate Diisobutyl phthalate (DiBP) in children as a case study and the non-phthalate substitute Di-isononyl cyclohexane-1,2-dicarboxylate (DINCH).
Methods During a HBM4EU workshop in Copenhagen in June 2017, a selection of substances was performed to identify substances that are relevant for the translation into HBM indicators. This was done by the application of criteria concerning relevance with regard to various aspects and data quality (see below). Consequently, the following substances were selected for indicator development: bisphenols A, F, S (BPA, BPF, BPS), some per-and polyfluoroalkyl substances (PFAS), cadmium, a number of pesticides, several phthalates and the non-phthalate substitute DINCH. Additionally, for showing the applicability of result indicators for investigating time trends data from the German Environment Specimen Bank (ESB) has been used from the HBM4EU dashboard for two aprotic solvents (N-Methyl-2-pyrrolidone; NMP and N-Ethyl-2-pyrrolidone; NEP).
Available datasets for the derivation of HBM indicators For the derivation of HBM indicators there are several requirements with regards to harmonized and quality approved data, and only a few studies have been conducted to date harmonizing HBM data at the European level. The DEMOCOPHES project was a first HBM feasibility study in 17 European countries  (Den Hond et al. , 2015). DEMOCOPHES data comprises urine samples from children (6-11 years) in a selected study area (see Table  S2 ), collected between 2011 and 2012 and are currently available in the HBM4EU data repository. 2 This contains DEMOCOPHES data for DiBP from 8 countries and data for DINCH from 6 countries. DEMOCOPHES data for phthalates were quality controlled by a QA/QC scheme  (Schindler et al. , 2014). For the result indicators on "time patterns" (i.
For teenagers (12-18 years) data are available from 9 studies both for DiBP and DINCH. These data were used for the comparison of age groups (see Table  S4 ). Chemical analysis of the biomarkers in the HBM4EU Aligned Studies was quality controlled (Esteban  López et al. , 2021; Mol et al. , 2022)  and was done for phthalates and DINCH by 7 different analytical laboratories (Esteban  López et al. , 2021). Urine samples were either first morning urine samples or spot urine samples collected in the HBM4EU Aligned Studies. Urine samples taken in the frame of the DEMOCOPHES project in children were all first morning urine samples. All concentrations are given in μg/L and are not creatinine-adjusted. Since creatinine excretion is age dependent in children  (German Human Biomonitoring German HBM Commission, 2005)  we decided not to present the study results on a creatinine-adjusted basis.
Calculation of result indicators 4) for "time patterns" For this indicator, P50 values of phthalate metabolites for children (6-11 years) from the HBM4EU Aligned Studies (2014-2021) were compared to P50 values from the previous conducted DEMOCOPHES project  (2011) (2012). A difference in exposure was defined when no overlap of the 95% confidence intervals between P50 internal exposure values of the different studies was seen. An analogous indicator was also derived for the non-phthalate substitute DINCH. To give an overview on P50 values for the two periods of time, all the available data from the HBM4EU repository are shown and this resulted in 8 studies with data for DiBP and 6 studies with data for DINCH. Since only data from countries with studies available for both time periods have been compared, the number of studies for this comparison was 4 for DiBP and 3 for DINCH.
Visualization of indicators For the visualization of the result and impact indicators, the data have been graphed using the R statistical analysis software package (R Core Team, 2018). These graphs show P50 and P95 as bars and the HBM-GVs as dotted lines. For the result indicators, the indicators were stratified according to the selected parameters (e. g. sex and age). For the geographical comparison the studies from the respective countries have been assigned to one of the four regions according to the United Nations geoscheme in the HBM4EU Aligned Studies (United Nations, 1999,) (, i. e. North, South, East and West. The graphs showing the impact indicator with the focus on illustrating the percentage of participants exceeding the HBM-GV have been plotted using Microsoft Excel (version 2019).
Vogel et al. (2022a)  (this issue) analysed the individual data from HBM4EU and confirmed these findings for DiBP. However, when the authors used single years of age instead of the selected age groups of children and teenagers, they found lower levels for DiBP with increasing age. In contrast to our findings, differences in phthalate metabolite concentrations for different age groups have been reported in several studies, both when comparing children and adults but also for children and adolescents of different age groups  (Bastiaensen et al. , 2021; Den Hond et al. , 2015; Schwedler et al. , 2020a; Silva et al. , 2004). In the DEMOCOPHES project, levels of phthalate metabolites were in general higher in children than in their mothers. Furthermore, younger children (5-8 years) had higher levels of phthalate metabolites compared to older children (9-11 years)  (Den Hond et al.
Another influencing factor that may lead to an over-or underestimation is the wide range of sampling years (as has already been discussed above for the indicator on geographic differences). Here, time trend studies for both children and teenagers would be needed to adjust for possible time effects. For DINCH, our result indicators on age differences revealed higher exposure of children compared to teenagers. An age dependency of DINCH exposure was also observed by  Schwedler et al. (2020b) , who reported HBM data from the German Environmental Survey of Children and Adolescents  (GerES V, 2014 (GerES V, -2017)). The authors reported the highest geometric mean (GM) biomarker concentrations for the youngest children, while the concentrations decreased with increasing age. DINCH biomarker levels in children (3-5 years) were almost 3-fold higher than in teenagers (14-17 years)  (Schwedler et al.
, 2020b). DINCH has been developed for sensitive applications, such as children's toys, as young children likelyhave the closest contact with these. This assumption of increased exposure to toys could be confirmed by our indicator in the group of children. To summarize, the result indicator for age differences is suitable to highlight potential differences in exposure for different age groups. For DINCH and DiBP (in two studies) a higher exposure of children could be shown. The next step would be to determine the reasons for this higher exposure. Age differences may be related to differences in lifestyle behavior or differences in metabolism. Those reasons are relevant to know for taking further steps in chemical risk management. Further, with our indicators on age differences, we could demonstrate the necessity of a valid database for the derivation of this indicator.
, 2020b)  and studies from other European countries (e. g. Frederiksen et al. , 2020; Bastiaensen et al. , 2021). DINCH data from the literature also reported an increasing time trend of DINCH biomarkers (e. g. Kasper-Sonnenberg et al. (2019) ,  Lemke et al. (2021)  and  Schwedler et al. (2020b) ). Lemke et al. (2021)  reported that substitutes mimic the exposure behavior of REACH regulated phthalates. As a shift to non-regulated phthalates or substitutes (such as DINCH) takes place, an increase in measured concentrations for these substitutes can be followed. Vogel et al. , 2022b (this issue)  confirmed an increasing time trend for DINCH when analyzing HBM data from Denmark and Germany, including samples collected at different time points. The "time pattern" indicator is suitable for revealing differences in internal exposure over time.
Limitations Limitations regarding our indicators are mainly focused on data quality aspects and the availability of data. When using indicators for decision-making, the demand for data quality such as representativeness, reliability and comparability of data comes to the fore. It has to be noted, that in a few exceptional cases, data were used for the derivation of indicators that were not quality assured within HBM4EU  (Gilles et al. , 2021; Govarts et al. , 2022). Theses include 1) MiBP measurements for children in the PCB-cohort study (SK) and 2) MiBP measurements for teenagers from CELSPAC:TE (CZ) and the PCB cohort follow-up (SK). All data were included in the analysis of exposure determinants and risk assessment. It was not possible to derive indicators for socioeconomic status (SES), as data on these were not available in a sufficiently large number of studies.
General conclusions The first set of pan-European HBM indicators for phthalates and DINCH provide a valuable tool to highlight differences in exposure between geographical regions, age groups, sexes and periods of time, but also to assess health impacts by comparing exposure values with HBM-GVs. The corresponding policy-related questions developed under HBM4EU could be answered by our indicators. The HBM indicators can be compared with existing indicators based on environmental data, consumption data and food datasets. They are aimed at complementing other sets of indicators in the field of environment and health (EEA) and on human health (WHO). With these indicators, a first step has been made for processing HBM findings in a clear and comprehensible manner. Further steps are needed to further simplify these messages, possibly in an interactive graphic design, as it has been done for the indicators which present blood lead levels of children by the U.
S. EPA (US EPA, 2022). Therefore, the HBM4EU indicators demonstrate that they are useful tools for a direct and simple interpretation of HBM data that can help policy makers to answer policy-related questions for the respective compounds and thus enable them to set priorities for further actions on their way to a toxic-free environment under the EU's chemicals strategy for sustainability  (Ganzleben et al. , 2017). Lessons learned for deriving HBM indicators comprise aspects mainly related to data quality, namely: a) analytical methods should be quality assured and comparable between the studies/countries b) sampling should take place within a narrow time frame, especially for substances with a short half-life c) homogenous population groups should be compared to enable the mapping of existing differences for regions, sex or age groups d) samples should be representative for the parameter investigated: to map country differences a representativeness at national level should be the aim, to map differences between age groups these should be representative for the country e) exposure should be continuously monitored to enable time trend observations for the whole of Europe and thus create a database for deriving real "time trend indicators" at the European level f) the latest scientific findings and policy demands have to be considered, which also includes consideration of combination effects.
Funding The authors received funding from the EUHorizon 2020 framework (grant agreement No 733032). Additional financial support for the conduct of the studies is provided by each participating country. The Norwegian Institute of Public Health (NIPH) has contributed to funding of the Norwegian Environmental Biobank (NEB). The laboratory measurements have partly been funded by the Research Council of Norway through research projects (275903 and 268465). The funding of the German Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection is gratefully acknowledged. PCB cohort was supported by Ministry of Health of the Slovak Republic, grant no. 2012/47-SZU-11 and by Slovak Research and Development Agency, grant no. APVV-0571-12. PCB cohort follow-up received additional funding from the Ministry of Health of the Slovak Republic, program 07B0103.
Riksmaten Adolescents was performed by the Swedish Food Agency with financial support from the Swedish Environmental Protection Agency and the Swedish Civil Contingencies Agency. The BEA study was co-funded by the Spanish Ministry of Agriculture, Fisheries and Food and the Instituto de Salud Carlos III (SEG 1321/15). The CELSPAC study is supported by the MEYS (LM2018121, CZ. 02. 1. 01/0. 0/0. 0/17_043/0009632 and CZ. 02. 1. 01/0. 0/0. 0/ 15_003/0000469) and from the European Union's Horizon 2020 research and innovation programme under grant agreement (857560). This publication reflects only the author's view and the European Commission is not responsible for any use that may be made of the information it contains. NIRAS/ONDRAF (Belgian National Agency for Radioactive Waste and enriched Fissile Material), STORA (Study and Consultation Radioactive Waste Dessel) and MONA (Mols Overleg Nucleair Afval) financed the 3xG study.
3B ). Again macrophages or adipocytes were treated with the COreleasing compounds and CO levels determined in both cell types. As observed in setup 1, 73 % and 96 % of CO accumulated in adipocytes and 27 % and 4 % of CO in the underlying macrophages when 401 or 6b, respectively, were added to adipocytes on top (Protocol 3 in Fig. 3B ). In protocol 4, macrophages at the bottom exposed to 401 and 6b exhibited, respectively, 43 % and 95 % CO while 57 % and 5 % CO was detected in adipocytes on top (Fig. 3B ). Altogether, these data indicate that CO levels increase predominantly in the cell type coming in direct contact with the compounds, regardless of whether cells are growing at the top or bottom of the coculture system. However, a main difference is that treatment with results in approximately one third of the total CO accumulation in the cell type that did not come in direct contact with the compound (with the exception of Protocol 4 in setup 2), while for 6b the vast majority of Fig.
Chemical shifts are reported in parts per million (ppm) and referenced to the residual solvent peaks: δ 1H (DMSO-d 6 ) = 2. 50 ppm, δ 13C (DMSO-d 6 ) = 39. 5 ppm, δ 1 H (DMF-d 7 ) = 8. 03, 2. 92 and 2. 75 ppm, δ 13 C (DMF-d 7 ) = 163. 2, 34. 9 and 29. 8 ppm. Splitting patterns are indicated as follows: s, singlet; bs, broad singlet; d, doublet; t, triplet; q, quartet; m, multiplet. Spectra were processed and analysed with MestreNova software. Elucidation of the chemical structures was based on 1 H, 13 C, COSY and HSQC experiments for compounds 1-4. Elucidation of the chemical structures was based on 1 H, 13 C, COSY experiments for compounds 1-2. Elucidation of the chemical structures was based on 1 H and COSY experiments for compounds 3-6. Elemental analyses of C, H, N and S were performed on a Perkin-Elmer 2400 (BioCIS, Châtenay-Malabry, France).
Acknowledgements This work was supported by a grant from the  Agence National de la Recherche  ( ANR-19-CE18-0032-01 ). D. E. B. is supported by a  PhD fellowship  from the  University of Paris-Est Créteil. The authors are grateful for a  Visiting Fellowship  from  IMRB/INSERM  to Q. M. The authors thank all the personnel of the animal facility platform at IMRB.