dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors are grateful to the participants from civil service departments and their welfare, personnel, and establishment officers and all members of the Whitehall II study teams. The Whitehall II study has been supported by grants from the  National Institute on Aging, National Institutes of Health  ( R01AG056477  and  RF1AG062553 );  UK Medical Research Council  ( R024227  and  S011676 ); and the  British Heart Foundation  ( RG/16/11/32334 ). Mika Kivimaki  is supported by the  Medical Research Council  ( K013351 ,  R024227 , and  S011676 ),  UK ,  NordForsk , the  Academy of Finland  ( 311492 ), and  Helsinki Institute of Life Science. S éverine Sabia  is supported by the  French National Research Agency  ( ANR-19-CE36-0004-01 ).
Conflict of interest statement The authors have no conflicts of interest to declare. Author contributions: S. Kumaradev, A. Singh-Manoux, and S. Sabia developed the hypothesis and study design. S. Kumaradev and A. Fayosse performed statistical analysis. S. Kumaradev and S. Sabia wrote the first and successive draft of the manuscript. All authors contributed to review of the manuscript and approved the final version to be published. A. Singh-Manoux and M. Kivim äki obtained funding. S. Kumaradev, A. Fayosse, and S. Sabia had full access to the data and took responsibility for the integrity of the data and the accuracy of the data analysis. Whitehall II data, protocols, and other metadata are available to the scientific community. Please refer to the Whitehall II data sharing policy at https://www.
13 thical questions and probes elicited moral and ethical experiences and emotional stressors. We incorporated responses from all questions that were relevant to the research question. 13 Using complementary data gathering methods, we conducted semistructured interviews, along with analyses of aid industry discourse relevant to the ethical and moral themes. Originally, we conducted informal interviews with key players and informants as individuals possessing knowledge relevant to the research question, including those with background in aid operations and ethical, moral and philosophical ideologies to improve validity and accuracy. Interviews were audio-recorded and transcribed. Following the theoretical framework and conceptual model guiding the study,  13  two coders coded transcripts through open, inductive and selective coding.
They discussed discrepancies to improve reliability, analysed data, characterised and agreed on important emerging themes, and compared themes across cases to elucidate commonality and variability. Observational data and non-verbal communication (intonation, pauses and voice reactions) were also evaluated to enhance analysis. We used a qualitative descriptive approach to analyse data. The lead author has decades of experience in humanitarian operations working with major humanitarian organisations, as well as academic and research experience in the fields of accountability and ethics in the humanitarian sector. Although this background could potentially introduce biases into data analysis, it has largely improved its depth and rigour by better developing questions and probes, identifying and exploring views from key informants, and addressing the interrelationship between the ethics and accountability.
Acknowledgements Authors immensely thank humanitarian workers who participated in this study and shared their personal stories and reflections. Contributors RA made substantial contribution to this study, including conception and design, acquisition, analysis and interpretation of data, drafting and critical revision of the manuscript for important intellectual content, technical, and material support and supervision, and approval of final version of the manuscript. KL made substantial contribution to this study, including acquisition of data and interpretation of data, and approval of final version of the manuscript. All authors had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Wood density (WD) relates to important tree functions such as stem mechanics and resistance against pathogens. this functional trait can exhibit high intraindividual variability both radially and vertically. With the rise of LiDAR-based methodologies allowing nondestructive tree volume estimations, failing to account for WD variations related to tree function and biomass investment strategies may lead to large systematic bias in AGB estimations. Here, we use a unique destructive dataset from 822 trees belonging to 51 phylogenetically dispersed tree species harvested across forest types in Central Africa to determine vertical gradients in WD from the stump to the branch tips, how these gradients relate to regeneration guilds and their implications for AGB estimations. We find that decreasing WD from the tree base to the branch tips is characteristic of shade-tolerant species, while light-demanding and pioneer species exhibit stationary or increasing vertical trends.
Across all species, the WD range is narrower in tree crowns than at the tree base, reflecting more similar physiological and mechanical constraints in the canopy. Vertical gradients in WD induce significant bias (10%) in AGB estimates when using database-derived speciesaverage WD data. However, the correlation between the vertical gradients and basal WD allows the derivation of general correction models. With the ongoing development of remote sensing products providing 3D information for entire trees and forest stands, our findings indicate promising ways to improve greenhouse gas accounting in tropical countries and advance our understanding of adaptive strategies allowing trees to grow and survive in dense rainforests. Terrestrial plants account for 83% of the living carbon on Earth 1 , of which tropical forests are estimated to account for close to half 2 , principally contained within woody plant parts.
and fluxes at relevant scales, e. g. , at the stand, landscape or region level. Aboveground biomass (AGB) and the associated carbon content are generally assessed indirectly via allometric models based on simple tree measurements such as trunk diameter at breast height (DBH), total height or species average mean wood density values obtained from global databases  3. Allometric models are calibrated through destructive tree harvests requiring enormous amounts of fieldwork to cut down and weigh every tree compartment. Despite the growing number of studies aimed at calibrating allometric equations  [4] [5] [6]  , only a few thousand tropical trees have been sampled for this purpose so far, including very few large trees despite their specific architecture  7  and disproportionate contribution to forest AGB  8.
This destructive method therefore offers limited promise for providing representative allometric equations for the estimated three trillion trees on Earth  9. Terrestrial LiDAR scanning (TLS) has recently emerged as a nondestructive method not only for calibrating allometric biomass models  10, 11  but also for performing measurements of volume, and by conversion biomass, at the stand level  9. TLS is now being promoted by the Intergovernmental Panel on Climate Change (IPCC) in its Good Practice Guidelines (GPG) for national greenhouse gas inventories, as tropical countries aim to implement more efficient and precise methodologies  12. However, as we gain in precision and sample size with TLS, which will provide massive datasets of detailed tree volume data, there is also need for renewed care in using reliable WD values for the conversion of these volumes into AGB.
In AGB estimates, WD values are typically extracted from databases documented using samples taken at the tree base or from sawmill logs. These samples rarely if ever account for variation in WD within trees  43. Since TLS tree volume is directly multiplied by WD to obtain an AGB estimate, any bias in WD will be directly propagated in AGB estimates (average 9% overestimation of AGB estimates at the scale of 1-ha forest stands using tree basal WD, see Sagang et al. 41  ). As it is logistically challenging to sample WD and its vertical variation for each tree during forest inventories or LiDAR scanning campaigns, there is a critical need for a better understanding of vertical variations in WD across a wide range of tree species to improve AGB estimates. Here, we use a unique destructive dataset collating six sampling sites in different countries and contrasted types of terra firme forests across the Congo basin  5  to determine the vertical variation in WD among 822 individuals (sampled across size classes) representing a total of 51 tropical tree species; we examine possible evolutive or functional drivers and implications for AGB estimation from volumetric (TLS) data; and we propose a way to correct the aforementioned bias in a cost-efficient way.
Identifying correlates of WD vertical profiles. The relative vertical profiles in WD were strongly correlated with tree basal WD, with a significant correlation coefficient (r = -0. 71) between individual PCA1 scores and WD Stu (Table  1  and Fig. S1A ). This correlation was also observed at the species level using species-level WD obtained from a Global Wood Density database  13, 44  (WD GWD , r = -0. 65, Table  1  and Fig. S1B ). This relationship is similar to that previously reported by Hietz et al. 37  in the case of radial WD gradients [Panamanian moist forest and Ecuadorian rain forest, 304 species] and Sagang et al. 41  for vertical gradients [semideciduous forest of Cameroon, 15 species]. We observed a shift in the vertical WD profile from decreasing (negative PCA1 scores) to increasing (positive PCA1 scores) as species WD GWD decreased (vertical dashed lines in insets in Fig.
1% of trees exhibited the same sign along PCA1 as their species average. Similarly, 88% of the individuals presented the same sign as their species average for the difference between the tree stump and small branches. Our dataset includes only two genera with more than one sampled species, so we could not evaluate profile aggregation at the genus level. However, we found clear taxonomic overdispersion of tree PCA1 scores at the family level (K Blomberg = 0. 132, P = 0. 014, see methods), suggesting less similarity than expected by chance at the family (i. e. , strong interfamily variation) or higher taxonomic level. This finding is consistent with the phylogenetic aggregation patterns reported for WD, with decreasing similarity in WD from species to genus and low similarity in WD at the family level  26, 45.
9%) and terrestrial LiDAR (c. 11%) datasets and the 9% overestimation at the stand level found by Sagang et al. 41  with a subset of our data. Since the vertical profile is correlated with species mean WD, and the latter value is close to 0. 6 g cm -3 in wet tropical forests overall  44  , we may expect dominance of decreasing vertical WD patterns (see Fig. 1D ) in this ecosystem. However, this may not be the case in young secondary rainforests, where the abundance of pioneer species may cancel out or even invert vertical WD patterns at the stand level. The successional trend may be different in drier vegetation, where early successional species exhibit relatively denser wood, possibly to resist higher stress levels  51. This potential scenario has implications when estimating stand-level carbon content (emission factor) using volumetric data  12  , notably in the context of national forest or greenhouse gas inventories.
We evaluated the robustness of our models using a leave-one-out cross-validation procedure, where the models were calibrated on all sites except a focal site (corresponding to a country in this case) and were used to predict tree VWWD for that site. Overall, the cross-validation models proved robust with B < 5% and CVs only slightly higher than when focal sites were used in model calibration (Fig. S4 ). This suggests that WD correction models should be transposable to other sites. Table  2. Prediction models of whole-tree WD estimation (VWWD, in g. cm -3 ). The models are based on species average WD extracted from the Global Wood Density database (WD GWD in, g. cm -3 ), individual tree WD from stumps (WD Stu , in g. cm -3 ) and tree structure parameters (stem DBH in cm and the stem morphology index, Sm).
Models The role of intraindividual WD gradients had not previously been evaluated when applying terrestrial LiDAR scanning to estimate AGB. The existing studies either exhibited unexplained systematic bias  10, 52  or neglected the influence of vertical WD variations by using WD obtained from the Global Wood Density database in both predicted and reference AGB data  52. In this study, we showed that WD gradients were a large source of uncertainty in tree-level AGB estimations and that they propagated systematic errors (bias) across our datasets that were far from being negligible (c. 10%). As TLS technology will likely play an increasing role in the development of nondestructive AGB allometries  10, 11, 52  and the direct assessment of AGB at the stand level (notably for the calibration of upcoming satellite sensors  53  ), care should be taken not to let the gain in the precision of wood volume be offset by the use of biased WD estimates.
Here, we followed and generalized a seminal study  39, 41  in which the approach differed significantly from previous approaches  45, 46  in proposing to collect WD samples at specific locations in trees that would presumably be representative of the mean tree WD. Instead, we leveraged the relationships between WD estimates that are currently available (e. g. , from the Global Wood Density database), WD vertical variation patterns and data that are routinely collected during field inventories to develop correction models that can easily be implemented for standard scientific inventory data. These models substantially reduced the error of tree-level AGB estimations and virtually removed the bias across our datasets, and they therefore constitute an efficient solution to the challenges of estimating tree biomass from volumetric data.
Converting V obs to biomass using a single WD value such as the species average WD values found in the Global Wood Density database (hereafter, WD GWD  13, 46  ), produced an estimated dry biomass value (AGB est ). Volume-weighted wood density. Following Sagang et al. 41  , we computed the volume-weighted wood density (VWWD c , in g. cm -3 ) at the compartment level. For example, VWWD WD V V (1) c c c obs = × At the individual tree level, an unbiased estimator of wood-specific gravity is the sum of VWWD c , which we hereafter refer to as VWWD. In practice, multiplying V obs by VWWD yields the reference tree biomass, AGB obs. Terrestrial LiDAR data. The terrestrial laser scanning (TLS) data used in this study derive from  10  and come from 58 trees sampled at the Cameroon field site. The trees were scanned with a Leica C10 Scanstation prior to being destructively sampled within the framework of the PREREDD + project.
A minimum of three scans were performed around each targeted tree to obtain a sufficient point cloud density for high-quality reconstruction of tree structure. Quantitative structure models (QSMs) were generated using the SimpleTree algorithm  57  , and the QSMs were manually refined using dedicated software (refer to Momo et al. 10  for details). Species functional traits. We used COFOR-traits, a database of central African species functional traits extracted from local floras, academic papers and unpublished theses  [58] [59] [60]  , to assign the species regeneration guild (pioneer, light demanding nonpioneer and shade tolerant) to each species (Table  S1 ).
Converting tree volume (derived from either destructive data or QSMs) to biomass requires the use of a WD value that may not be representative of the whole-tree WD, leading to biased tree AGB estimation. We computed AGB est using tree-level WD recorded approximately at breast height (WD Stu ) and species mean WD from the literature (i. e. , WD GWD ), both of which are of potential interest for the routine conversion of TLS-derived tree volume to biomass. While both WD Stu and WD GWD neglect WD vertical variation within trees, WD Stu accounts for site-level and individual tree-level variations and is therefore expected to provide more accurate AGB est values. We characterized the deviations of AGB est from AGB obs across trees using a measure of bias (B) and a measure of total error (CV). B was calculated as the average of tree-level relative errors, b i : where AGB est.
Acknowledgements S. T. M. was supported by an  IRD grant  ( 2017-2019 ) and the  PREREDD + project  ( 07/2015-10/2016 ) and P. P. by the  ERA-GAS   3DforMod  project ( ANR-17-EGAS-0002-01p ). This study benefited from data from the regional project on  REDD + "Establishment of allometric equations for forest types in the Congo Basin " grant  N° TF010038  from the  Global Environment Facility  administered by the  World Bank  and implemented by COMIFAC. Data collection was carried out by  INDEFOR-AP (Equatorial Guinea) ,  IRET (Gabon) , the  University of Yaoundé 1 (Cameroon) , the  University of Bangui (CAR) , the  University of Marien Ngouabi (Congo)  and the  University of Kisangani (DRC). We are also grateful to the logging companies involved in the project ( COMALI, Rougier, ALPICAM-GRUMCAM, SEFCA ,  CIB-OLAM and CFT)  for their logistic support necessary for fieldwork and laboratory measurements.
In sum, the lack of mature gametes in both sexes of the Psmc3ip/Hop2 knockout mice is consistent with a role for PSMC3IP/HOP2 in meiosis  [67]. However, the possibility of a meiotic defect in the patients described above could not be directly assessed  [65]. Thus, current data do not allow the authors to show a direct impact of the described pathogenic variant on the meiotic role of PSMC3IP. A subsequent study screened a group of 50 Swedish patients with POI for mutations in PSMC3IP. No alteration could be detected in this cohort showing that PSMC3IP pathogenic variants are a rare cause of POI  [68]. A more recent study identified a truncating variant in a consanguineous POI family. A concomitant variant in the Caseinolytic Mitochondrial Matrix Peptidase Proteolytic Subunit (CLPP) gene was detected but it seems to be unrelated with POI in this specific family  [69].
Conclusions The overview of recent and some less recent work provided above highlights several actors whose pathogenic variants are responsible for POI and reveal the importance of a proper level of genomic stability in establishing and maintaining fertility. They also show the power of formal genetics of consanguineous families allied to NGS in the discovery of 'new genes' involved in POI. Meiosis is a very complex process that involves dozens of candidate factors  [14]  whose pathogenic variants can explain cases of POI. The cases discussed above Fig. 5  Network of interaction of some proteins involved in meiosis and DNA repair. It involves those mentioned in the text or retrieved from the CORUM Database and further analysis using STRING  [73]. The CORUM database, accessible at http://mips.
helmholtz-muenchen. de/genre/proj/corum/about/corum. html, was queried with the word 'meiosis'. The following list (REC8, SMC1b, SMC3, SYCP3, STAG3, MND1, PSMC3IP, SYCE1, SYCE2, SYCE3, CSB-PGBD3, MCM8, MCM9, MCM2-7, SPATA22, MEIOB, FANCM, MSH4, MSH5, HFM1, SYCP3, DMC1, RAD51, BRCA2, RPA1-2) was submitted to STRING to generate a graphical representation. The analysis was based on textmining, experiments and databases (interaction score 0. 4) and included up to 5 interactors of a given node not included in the initial list. The level of interaction evidence is given by the thickness of the edges. are mostly Mendelian. However, it is likely that genome-wide analyses will uncover patogenic variants in more than one gene, pointing to digenicity and perhaps oligogenicity. This might also contribute to explain the incomplete penetrance sometimes observed in carriers of well-known mutations involved in POI  [11].
Determination of the uncertainty Considering Eq. 1 and Eq. 2, the overall relative error on NO 3 concentration can be considered as the square root sections are estimated to be 3 % in the spectral range of interest  (Vandaele et al. 1997 and Orphal, Fellows and 265 Flaud, 2003). However, the uncertainty generated by the data treatment ∆fit, i. e. by the fit, which results mainly from the noise in the spectra, should also be taken into account. Because the nitrate radical is an unstable species, this uncertainty cannot be estimated by calculating the standard deviation on its concentrations measured for a long period of monitoring. It was therefore estimated by considering the noise of a NO 3 concentration profile and has been found to be ~ 3ppt for 10 of integration time. The overall absolute error on NO 3 concentration 270 is then expressed by the following formula: ∆𝑁 𝑁𝑂 3 = √(( ∆𝜎 𝑁𝑂 2 (𝜆) 𝜎 𝑁𝑂 2 (𝜆) ) 2 + ( ∆𝑁 𝑁𝑂 2 ,𝑟𝑒𝑓𝑙 𝑁 𝑁𝑂 2 ,𝑟𝑒𝑓𝑙 ) 2 + ( ∆𝜎 𝑁𝑂 3 𝜎 𝑁𝑂 3 ) 2 ) × 𝑁 𝑁𝑂 3 + ∆𝑓𝑖𝑡 (Eq.
Finally, this technique was used for the absolute rate determination of a well-documented reaction, trans-2-butene+ NO 3. The value of (4. 13 ± 0. 45) × 10 -13 cm 3 molecule -1 s -1 found in this study is in very good agreement with the previous absolute determinations. Moreover, the good sensitivity and the good time resolution represent excellent performances allowing the use of this technique for monitoring the NO 3 radicals when involved in fast 400 reactions. The IBBCEAS technique is now operational and will be used in further works, particularly to monitor NO 3 concentrations for absolute rate determinations of fast reactions of volatile organic compounds with NO 3 radicals. Data availability. Rate constant for the NO 3 oxidation of trans-2-butene is available Table  3. It is also available Research Infrastructures (EUROCHAMP-2020; grant no.
through the Library of Advanced Data Products (LADP) of the EUROCHAMP data center (https://data. eurochamp. org/data-access/ gas phase-rate-constants/, last access: 25 March 2020,Fouqueau et al. ,   2020a). Simulation chamber experiments which were used to retrieve these parameters and for the intercomparaison are available through the Database of Atmospheric Simulation Chamber Studies (DASCS) of the EUROCHAMP data center (https://data. eurochamp. org/data-access/chamber-experiments/, last access: 25 410 March 2020, Fouqueau et al. , 2020b). Author contributions. MCi, BPV and AF designed the IBBCEAS technique with the help of GM and DR. AF installed and did characterization experiments with the technical support of XL (optical development), MCa and EP (experiments), PZ and GS (data treatment).
MCi, BPV and AF wrote the article and AF was responsible for 415 the final version. All coauthors revised the content of the original manuscript and approved the final version of the paper. Competing interests. The authors declare that they have no conflict of interest. 420 Financial support. This work was supported by the French national programme LEFE/INSU and by the European Commission's Seventh Framework Programme (EUROCHAMP-2; grant no. 228335), H2020.
405 through the Library of Advanced Data Products (LADP) of the EUROCHAMP data center (https://data. eurochamp. org/data-access/ gas phase-rate-constants/, last access: 25 March 2020,Fouqueau et al. ,   2020a). Simulation chamber experiments which were used to retrieve these parameters and for the intercomparaison are available through the Database of Atmospheric Simulation Chamber Studies (DASCS) of the EUROCHAMP data center (https://data. eurochamp. org/data-access/chamber-experiments/, last access: 25 410 March 2020, Fouqueau et al. , 2020b). Author contributions. MCi, BPV and AF designed the IBBCEAS technique with the help of GM and DR. AF installed and did characterization experiments with the technical support of XL (optical development), MCa and EP (experiments), PZ and GS (data treatment).