dataesr/scientific-paragraphs-categorization huggingface dataset:
For affinity capture, all samples were washed with PBS and peptides were released by trypsin (100 ng μl -1 in 10 mM HCl) and supernatants were collected, dried and dissolved in 0. 1% formic acid. All affinity-captured samples were measured on a Q-Exactive mass spectrometer (Thermo Fisher Scientific) with an MS1 resolution of 70,000, an AGC target of 3 × 10 6 and a maximum injection time of 100 ms over a scan range of m/z 350-1,500. A data-dependent top-12 method was used for HCD MS/MS with a normalized collision energy of 25 at a resolution of 35,000. Precursor ions were isolated in a 1. 2-Th window with an AGC target value of 1 × 10 5 with a maximum injection time of 120 ms. Dynamic exclusion was set to 40 s. Samples were analyzed using Mascot (Matrix Science, v. 2. 6. 2) with the SwissProt database (downloaded 4 February 2019) assuming trypsin with at maximum two miscleavages.
Variants were prioritized with the following approach: First, any coding variant (excluding synonymous variants) with a GnomAD frequency across all represented populations <0. 01, in homozygosity or compound heterozygosity with another relevant variant and supported by at least two forward and two reverse reads and at least eight reads coverage, were evaluated. Second, all variants categorized by the automatic application of the ACMG criteria  63  by InterVar  64  or classified in ClinVar 65 as 'pathogenic' or 'likely pathogenic', in homozygosity or compound heterozygosity with another relevant variant, were considered and evaluated. Third, variants with a dbscSNV_ADA or dbscSNV_RF scores > 0. 6 in the annovar annotation using the database prepared and described previously  66  were evaluated.
gsea-msigdb. org/gsea/msigdb/index. jsp 'MSigDB Collections' on 28 December 2020. Circos, including chord plots, were created using the circlize package v. 0. 4. 13 (ref. 71 ). The UniProt portal was accessed on 24 February 2021 to scrape protein localization data. https://doi. org/10. 1038/s42255-022-00720-8 Extended Data Fig. 2  | Biochemical assessment of MMUT activity and propionate incorporation activity supports diagnosis of affected individuals. a, Scatter plot of maximal, that is supplemented with adenosylcobalamin (AdoCbl) or hydroxocobalamin (OHCbl), activity of the MMUT enzyme and the propionate incorporation assay. b, Boxplots of MMUT enzyme activity with and without AdoCbl supplementation measured in biologically independent fibroblast samples (mut 0 , n = 119; mut -, n = 29; other MMA, n = 46; unaffected, n = 3).
Acknowledgements This project was funded by the  ETH  domain strategic focus area ' Personalized Health and Related Technology ' (https://www. sfa-phrt. ch). This work received financial support from the  Swiss National Science Foundation  ( 31003A_175779  to M. R. B. and  310030_192505 ) to D. S. F. ) and from the  University Research Priority Program  of the  University of Zurich ITINERARE-Innovative Therapies in Rare Diseases. P. F. was supported by the Filling the Gap grant awarded by the  Medical Faculty, University of Zurich , as well as by the  Swiss National Science Foundation  ( P500PB_211038 ) and the  European Molecular Biology Organization  ( ALTF 263-2022 ). X. B. was partially funded by  ETH core funding  (to G. R. ). R. J. M. was supported by the  Holcim Stiftung Wissen. We acknowledge the  Functional Genomics Center Zurich of the University of Zurich  for RNA-seq of mouse brain tissues and bioinformatics support, as well as for support with glutamine isotope studies.
Data availability Access to the raw genomic and transcriptomic data is restricted due to ethical concerns. Data can be made available upon reasonable request to D. S. F. within 3 months following an established data transfer, use agreement and ethical approval. The MS proteomics data (. raw files) have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the MassIVE partner repository (https://massive. ucsd. edu) with dataset identifiers MSV000088791 and PXD038225. Metabolomics MS raw data for human fibroblast measurements have been uploaded to the MassIVE data repository (https://massive. ucsd. edu) with dataset identifier MSV000089082. IP-MS raw files have been deposited to the ProteomeXchange Consortium via the MassIVE partner repository (https://massive.
Backward Trajectories The backward trajectories were computed using the Flexible Trajectories (FLEXTRA; https://folk. nilu. no/~andreas/flextra. html, accessed on 27 May 2022) software  [47, 48]  using ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF)  [49]  obtained through the flex-extract (v7. 1) interface running in gateway mode as an ECMWF member-state user. FLEXTRA input data every 6 h were interpolated to a horizontal resolution of 1 • × 1 • , covering longitudes from -179 • to 180 • and latitudes from -10 • to 90 • , and 137 in the vertical resolution. FLEXTRA was configured to a destination coordinate of 16. 5 • W 28. 3 • N (IZO location) and to four pressure levels (500, 600, 770 (IZO typical ground pressure), and 900 hPa). We obtained a backward trajectory coordinate every 20 min with a 3D wind configuration.
Satellite Data In the current work, we used Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) AOD data at 550 nm. This version updates the NASA previous satellite-era (from 1980 onward) reanalysis system to include additional observations and improvements to the Goddard Earth Observing System version 5 (GEOS-5). The spatial resolution of the MERRA-2 AOD is 0. 5 • × 0. 625 •  [50]. (More information at https://giovanni. gsfc. nasa. gov/giovanni/, accessed on 25 October 2022). The volcanic SO 2 total column amounts were obtained from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor (S-5P) platform. The S-5P has been orbiting in a sun-synchronous polar orbit with an equator crossing at 13:30 local solar time, with a high spectral covering from ultraviolet to shortwave infrared wavelengths and a spatial resolution of 5.
Data Availability Statement: The EKO MS-711 data are available on request from the Izaña WMO-CIMO testbed. The BSRN Izaña radiation measurements are available at https://doi. org/10. 159 4/PANGAEA. 882526 (accessed on 23 December 2022). Data from AERONET used in the present study can be obtained from https://aeronet. gsfc. nasa. gov (accessed on 23 December 2022). The vertical soundings can be downloaded from http://weather. uwyo. edu/upperair/sounding. html (accessed on 23 December 2022). The lidar MPL images used in this study can be downloaded from https://mplnet. gsfc. nasa. gov/ (accessed on 23 December 2022).
2. 1. 21 Site Description The datasets used in this work were obtained at the IZO, managed by the Izaña Atmospheric Research Center (IARC) of the Spanish State Meteorological Agency (AEMET) (http://izana. aemet. es, accessed on 15 March 2022). The IZO is located on the island of Tenerife (Canary Islands, Spain, at 28. 3 • N, 16. 5 • W, and 2400 m a. s. l. ). The IZO joined the WMO Background Atmospheric Pollution Monitoring Network (BAPMoN) in 1984 and the WMO Global Atmosphere Watch (GAW) program in 1989. Moreover, the IZO has collaborated with different international atmospheric networks, e. g. , the NDACC (Network for the Detection of Atmospheric Composite Change; http://ndsc. ncep. noaa. gov, accessed on 25 March 2022) since 1999 and the GAW-PFR (Precision Filter Radiometer Network; http://www.
Data Collection A quasi-randomized experimental approach was used to quantify the differences between the CMJ, HJ, and BJ in concentric and eccentric jump parameters. The experimental design is depicted in Figure  1. Every subject completed 3 laboratory visits separated by at least 48 h. All visits were scheduled at the same time of day (±1 h). The first visit began with measurements of body weight, body height, and body composition performed using an electronic column scale with a fitted stadiometer (Seca 769, Seca 220; Seca Ltd. , Hamburg, Germany) and bioelectric impedance (InBody 720; Biospace Co. , Ltd. , Seoul, Republic of Korea), respectively. The subjects then completed a standardized warm up which included a single set per exercise of in-place running (30 ground contacts per leg), 10 in-place bilateral pogo jumps, dynamic unilateral stretches of hip, knee, and ankle muscles (3 repetitions per leg, per exercise), 10 bodyweight squats, followed by reverse lunges, side lunges, unilateral stiff-legged deadlifts, and supine lying unilateral hip raises for 5 repetitions without added load per leg for each exercise.
After the subjects jumped over the hurdle or onto the box, they stepped back to the starting position and prepared themselves to perform the next repetition at the end of the 10-s period. A visual depiction of the set-up including the athlete, linear position transducer, force plate(s), box, and hurdle is shown in Figure  2. The ground reaction force data of all maximal jumps were recorded using two synchronized piezoelectric force plates (Kistler 9286BA; Kistler Instruments Inc. , Winterthur, Switzerland). The force plates were placed side-by-side on the ground to measure CMJs. The first force plate, used to measure propulsive forces for both HJ and BJ, was positioned on the ground separated from the hurdle or box by 15 cm. The second force plate was used to measure landing forces. The position of the second force plate was on the ground 5 cm behind the hurdle or on top of the box 5 cm from the edge closest to the subject.
The ground reaction force data of all maximal jumps were recorded using two synchronized piezoelectric force plates (Kistler 9286BA; Kistler Instruments Inc. , Winterthur, Switzerland). The force plates were placed side-by-side on the ground to measure CMJs. The first force plate, used to measure propulsive forces for both HJ and BJ, was positioned on the ground separated from the hurdle or box by 15 cm. The second force plate was used to measure landing forces. The position of the second force plate was on the ground 5 cm behind the hurdle or on top of the box 5 cm from the edge closest to the subject. The ground reaction forces were recorded using sampling frequencies of 1000 Hz, a 16-bit A/D board amplifier, and BioWare v5. 3. 2. 9 software (Kistler Instruments Inc. , Winterthur, Switzerland).
A custom MATLAB program (1. 8. 0. 121; MathWorks, Natic, MA, USA) was used to calculate peak propulsive forces (PF) in vertical, horizontal, and resultant directions as maximal ground reaction force produced in each direction during the propulsive phase, total impulsion time as the time from the beginning of the countermovement to the take-off, and peak impact force as maximal resultant ground reaction force produced during the landing phase. A threshold of 20 N was used to identify individual phases of the jump. The velocity and displacement data of all maximal jumps were recorded using a linear position transducer (GymAware Power Tool; Kinetic Performance Technology Pty. Ltd. , Canberra, Australia). The string of the linear position transducer was attached to the wooden dowel 30 cm from the right shoulder to the end of the dowel  [15].
The linear position transducer was placed on the ground directly below the end of the string for CMJ, and in the middle of the horizontal distance between the force plates in line with the attachment of the string for HJ and BJ. The attachment of the string and the position of the linear position transducer was selected due to the technical restrictions of the equipment used and the motion necessary to complete the measured task. However, the linear position transducer used in this study automatically corrects the data for horizontal displacement and allows the three conditions to be compared. The correct position of the string was checked before every jump. Additionally, one researcher observed the movement of the dowel during the data collection. Any trials with notable rotational dowel movements, deviation from horizontal dowel position, or other cases of failed trial (i.
Statistical Analysis Data acquired during 3 repetitions of each jump type were averaged for each subject and used for analysis. The Shapiro-Wilk test and Quantile-Quantile plots were used to test the data for normality of distribution. Means and standard deviations (SDs) were The ground reaction forces were recorded using sampling frequencies of 1000 Hz, a 16-bit A/D board amplifier, and BioWare v5. 3. 2. 9 software (Kistler Instruments Inc. , Winterthur, Switzerland). A custom MATLAB program (1. 8. 0. 121; MathWorks, Natic, MA, USA) was used to calculate peak propulsive forces (PF) in vertical, horizontal, and resultant directions as maximal ground reaction force produced in each direction during the propulsive phase, total impulsion time as the time from the beginning of the countermovement to the take-off, and peak impact force as maximal resultant ground reaction force produced during the landing phase.
A threshold of 20 N was used to identify individual phases of the jump. The velocity and displacement data of all maximal jumps were recorded using a linear position transducer (GymAware Power Tool; Kinetic Performance Technology Pty. Ltd. , Canberra, Australia). The string of the linear position transducer was attached to the wooden dowel 30 cm from the right shoulder to the end of the dowel  [15]. The linear position transducer was placed on the ground directly below the end of the string for CMJ, and in the middle of the horizontal distance between the force plates in line with the attachment of the string for HJ and BJ. The attachment of the string and the position of the linear position transducer was selected due to the technical restrictions of the equipment used and the motion necessary to complete the measured task.
Statistical Analysis Data acquired during 3 repetitions of each jump type were averaged for each subject and used for analysis. The Shapiro-Wilk test and Quantile-Quantile plots were used to test the data for normality of distribution. Means and standard deviations (SDs) were calculated for all variables and repeated ANOVA measures were performed to assess the data. A Greenhouse-Geisser correction was used in instances where sphericity was not assumed. Pairwise comparisons were performed using a Holm-Bonferroni follow-up when appropriate. Alpha level for significance for all tests was set at ρ ≤ 0. 05. Effect sizes (Cohen's d) were calculated and interpreted as trivial (<0. 20), small (0. 20 to 0. 49), moderate (0. 50 to 0. 79), and large (≥0. 80)  [16]. For the sake of clarity and concision, only moderate and large effect sizes will be discussed in further sections.
For example, acute patellofemoral pain seems to decrease an athlete's ability to effectively absorb impact force  [28]. In turn, increased magnitudes and rates of eccentric forces developed in the patellofemoral joint during landing were significantly correlated with increased patellofemoral pain in young symptomatic women  [29]. Therefore, according to our data, coaches can effectively use BJ to reduce peak impact force by ~51% compared to CMJ, while not decreasing concentric performance when decreased eccentric loading is warranted. Although the present study provided data for evidence-based decision-making when prescribing three plyometric exercises, much is still unknown regarding this topic. For example, the effect of an enhanced stretch-shortening cycle by performing multiple continuous repetitions, fatigue resulting from a higher volume set, the effect of various box heights on IF, and kinematic analysis of the hip, knee, and ankle joint movements during the jump were not assessed in this study.
To do so, we performed comprehensive DA experiments where three datasets (in situ measurements of net carbon exchange and latent heat fluxes, spaceborne estimates of the normalized difference vegetation index, and atmospheric CO 2 concentration data measured at stations) were assimilated alone or simultaneously. We thus evaluated their complementarity and usefulness to constrain net and gross C land fluxes. We found that a major challenge in improving the spatial distri-bution of the land C sinks and sources with atmospheric CO 2 data relates to the correction of the soil carbon imbalance.
, 2008; Zobitz et al. , 2014; Forkel et al. , 2014; Bacour et al. , 2015) , have also been used to constrain the model parameters at various spatial scales. Finally, atmospheric CO 2 mole fraction measurements have been assimilated to provide valuable information on large-scale net ecosystem exchange (NEE)  (Rayner et al. , 2005; Koffi et al. , 2012). In the early days of DA studies, most focused on the assimilation of a single data stream (e. g. , targeting only NEE). Then, assimilations with multiple C-cycle-related datasets were soon considered  (Moore et al. , 2008; Richardson et al. , 2010; Ricciuto et al. , 2011; Keenan et al. , 2013; Thum et al. , 2017; Knorr et al. , 2010; Kaminski et al. , 2012; Kato et al. , 2013; Bacour et al. , 2015; Peylin et al. , 2016). The underlying motivation behind assimilating multiple data streams is that using a greater number and diversity of observations should provide stronger constraints on model parameters, including a wider range of processes, hence resulting in a greater reduction in model uncertainty.
However, many previous studies that assimilated multiple datasets hardly considered potential incompatibilities between the model and the observations (although see  Bacour et al. , 2015; Thum et al. , 2017) , which may result in a deterioration of model agreement with other observations not included in the assimilation. In addition, only a few have quantified the actual benefit of assimilating multiple datasets compared to the single data stream assimilations, in particular in the context of globalscale C cycle DA experiments. The assimilation of multiple data streams can be done either sequentially, in which one observation type is assimilated at a time, or simultaneously (joint assimilation approach or "batch" strategy as defined in  Raupach et al. , 2005) , where the model is calibrated with all data included in the same optimization (e.
g. ,  Richardson et al. , 2010; Kaminski et al. , 2012; Schürmann et al. , 2016). Although with model parameters and observations described by probability distributions, simultaneous and sequential assimilations could theoretically lead to the same result  (Tarantola et al. , 2005) , this is not the case in practice for complex problems. Incomplete or incorrect description of the error statistics may result in large differences between simultaneous and stepwise approaches (see  Kaminski et al. , 2012; MacBean et al. , 2016). In addition, model nonlinearities also tend to exacerbate these potential differences. Simultaneous assimilation is considered to be more optimal in the context of optimizing TBM parameters as it maximizes the consistency of the model with the whole of the datasets considered  (Richardson et al.
, 2016)  and hence the weight that each dataset has on the optimization outcome (although specific weighting approaches may be envisioned, as in  Wutzler and Carvalhais et al. , 2014, or Oberpriller et al. , 2021). Another major challenge, as highlighted by  MacBean et al. (2016)  or  Oberpriller et al. (2021) , concerns inconsistencies between observations and model outputs, which are usually not accounted for in common bias-blind  (Dee, 2005)  Bayesian DA systems relying on the hypothesis of Gaussian errors. Indeed, most studies do not attempt to identify systematic errors in the observations and/or in the model and to correct for them. The likely impact of model-data biases on the parameter optimization is then a degraded model performance as well as an illusory decrease in the estimated model uncertainty  (Wutzler and Carvalhais, 2014; MacBean et al.
, 2016; Bacour et al. , 2019a). The present study aims to go a step forward in the assessment of how assimilating multiple C-cycle-related data streams impacts and changes the constraint on net and gross CO 2 flux simulations at the global scale. To do so, we further advance from the sequential assimilation of  Peylin et al. (2016)  (referred to as the "stepwise" approach hereafter) by implementing a simultaneous assimilation framework with the same data streams: net carbon fluxes (net ecosystem exchange -NEE) and latent heat fluxes (LE) measured at eddy-covariance sites across different ecosystems, satellite-derived normalized difference vegetation index (NDVI) at coarse resolution for a set of pixels spanning the main deciduous vegetation types, and monthly atmospheric CO 2 concentration data measured at surface stations worldwide.
The study relies on the variational DA framework designed for the ORCHIDEE global vegetation model  (Krinner et al. , 2005) , here associated with a simplified version of the LMDz atmospheric transport model (Atmospheric General Circulation Model of the Laboratoire de Météorologie Dynamique;  Hourdin et al. , 2006)  based on precalculated transport fields for assimilating atmospheric CO 2 concentration data. ORCHIDEE and LMDz are the terrestrial and atmospheric components of the Institut Pierre Simon Laplace (IPSL) Earth System Model  (Dufresne et al. , 2013). By conducting different assimilation experiments in which each data stream is assimilated alone or in combination (for all combinations of datasets), the research questions that we address in this study are as follows: 1. What impact does the combination of different data streams assimilated have on the reduction in model-data misfit, and to which extent are the model predictions improved (or degraded) with respect to the other data streams that were not assimilated? 2.
Improve the realism of the prior error statistics on parameters by making them consistent with the prior model-data mismatch. 2. Quantify the observation influence of each of the three data streams on the joint assimilation in which all three datasets were included in the optimization. Throughout the presentation of the results, we discuss implications of each assimilation experiment for our ability to accurately constrain gross and net CO 2 fluxes. In the final section we propose some perspectives for other modeling groups wishing to implement global-scale parameter DA systems to constrain regional-to global-scale C budgets. 2 Materials and methods.
Model setup The setup of the simulations performed with ORCHIDEE depends on the data assimilated. The model is run at site scale for the assimilation of eddy-covariance measurements, at a spatial resolution of 0. 72 • for the assimilation of the satellite NDVI data and at the resolution of the atmospheric transport model LMDz (3. 75 • × 2. 5 • ) for the assimilation of atmospheric CO 2 measurements. The Olson land cover classification at 5 km is used to derive the PFT fractions at each spatial resolution but for the flux tower simulations where the proportion of each PFT is set based on expert knowledge. For satellite pixels and global simulations, OR-CHIDEE is forced using the 3-hourly ERA-Interim gridded meteorological forcing fields  (Dee et al. , 2011 ) (aggregated at 3. 75 • × 2. 5 • when assimilating atmospheric CO 2 concentrations).
For the flux tower simulations, the model is forced by local measurements of the meteorological variables at a half-hourly time step. For each spatial resolution, a prior spin-up simulation was performed by recycling available forcing data. The objective was to bring the different soil carbon reservoirs to "realistic" values, though the spin-up runs result in neutral net carbon flux by construction. Each spin-up simulation was then followed by a transient simulation (starting from the first year of measurement for each data stream), accounting for the secular increase of atmospheric CO 2 concentrations; for the global simulations, only a short transient simulation from 1990 to 1999 was performed.