dataesr/scientific-paragraphs-categorization huggingface dataset:
https://doi. org/10. 5194/essd-14-411-2022 Earth Syst. Sci. Data, 14, 411-447, 2022 Human water use is accounted for through the representation of the impact of water impoundment in man-made reservoirs and of net water abstractions (i. e. total abstractions minus return flows) on water flows and storages. The reservoir operation algorithm implemented in WaterGAP is a slightly modified version of the generic algorithm of  Hanasaki et al. (2006)   (Döll et al. , 2009). Based on a preliminary version of the Global Reservoir and Dam (GRanD) database  (Lehner et al. , 2011) , the model accounts for the largest 1082 reservoirs. The reservoir filling phase is simulated based on the first operational year and the storage capacity. Net water abstractions are simulated for five water use sectors (irrigation, livestock farming, domestic use, manufacturing industries, and cooling of thermal power plants) and subsequently subtracted from the surface water and groundwater storage compartments  (Müller Schmied et al.
For P2 (GRACE-Argo era) the SLB analysis may employ the SLBC_cci steric dataset, which is also shown in Fig. 12. Again, interannual variations of GMSL (black curve) and the sum of components (dark-red curve) agree largely in their sequence of positive and negative deviations from a longterm evolution, with the exception of the early Argo years of 2003 and 2004. Figure  11b  shows (in blue) the misclosure of the SLB when using this SLBC_cci steric dataset (and the individual mass contribution assessments). The percentage of misclosures within the 1σ , 2σ , and 3σ ranges of combined uncertainties are 84. 8 %, 99. 4 %, and 100. 0 %, respectively, indicative, again, of a narrower distribution than allowed for by the assessed uncertainties. For the case of using the GRACE-based OMC, the monthly budget assessment over P2 is illustrated in Fig.
Different columns adopt alternative estimates of some of the budget elements (as indicated by line labels and footnotes), while adopting the same estimates for other elements. The estimates of total sea level, the steric contribution, and the GRACE-based OMC refer to the ocean between 65  The OMB misclosure varies interannually between roughly -2 and +2 mm (bold curves in Fig. 11a ). The SLB misclosure varies interannually between roughly -6 and +4 mm (bold curves in Fig. 11b, c ) depending on which steric product and which way of estimating OMC are used. Errors of the datasets on GMSL, the steric contribution, GRACE-based OMC, and LWS are most likely responsible for these interannual misclosures. The glacier and ice sheet time series involve relatively small interannual variations (cf. Fig.
Data availability A compiled dataset of time series of the elements of the GMSL budget and of the OMB together with their uncertainties is freely available for download at https: //doi. org/10. 5285/17c2ce31784048de93996275ee976fff (Horwath et al. , 2021). The version 2. 2 dataset (ESACCI_SLBC_TimeSeriesOfSeaLevel BudgetElements_v2. 2. csv) is an update of the previous version 2. 1 dataset, where the update concerns the update of GRACE OMC estimates outlined in Sect. 3. 3. The single file in CSV (comma-separated value) format contains the time series presented in Figs. 10, 12, and 13. These time series are all at an identical monthly sampling, resulting from interpolation of the original time series where necessary. Uncertainties were partly recalculated from the original data products (as described in Sect.
Conclusions and outlook This study assessed CCI data products related to the SLB; advanced the generation of new time series of SLB elements based on satellite Earth observation and modelling; and integrated, within a consistent framework, the products into an analysis of the OMB and the SLB. The consolidation, improvement, and exhibition (in Figs. 1, 3 4 5 6 7 8 ) of the uncertainty characterisation for every budget element were central to this study. The datasets and analyses presented here document both achievements and limitations identified within the SLBC_cci study.
Advances on data products on individual budget elements For the GMSL, the use of the averaged ESA CCI 2. 0 gridded sea-level data was enhanced by the incorporation of the uncertainty estimate over each GMSL time step from  Ablain et al. (2019). Three major sources of errors were considered in the composition of a variance-covariance matrix to obtain GMSL uncertainty. The GMSL trend uncertainty over 1993-2016 (after correcting the TOPEX A drift) is assessed as 0. 24 mm yr -1 (1σ ). https://doi. org/10. 5194/essd-14-411-2022 Earth Syst. Sci. Data, 14, 411-447, 2022 For the steric-sea-level change, we developed a formaluncertainty framework around the estimation of steric height from Argo profiles, including propagation to gridded and time series products. The framework includes simple models to estimate each uncertainty source and their error covariance structures.
This new dataset (cf. Shepherd et al. , 2019)  shows that ice losses are dominated by the Pine Island Glacier and Thwaites Glacier basins in West Antarctica, where mass losses (expressed as equivalent GMSL contribution) have increased from 0. 04 ± 0. 01 mm yr -1 in 1992-1997 to 0. 36 ± 0. 03 mm yr -1 in 2012-2017. For the LWS contribution, the version of the global hydrological model WaterGAP (version 2. 2d) was developed and applied, which includes the commissioning years of individual reservoirs to take into account increased water storage behind dams as well as regionalised model parameterisations to improve the simulation of groundwater depletion  (Müller Schmied et al. , 2021). Comprehensive insights into the model sensitivity to choices of irrigation water use assumptions and climate input data were acquired, enabling a first uncertainty estimation.
The SLB misclosure (GMSL minus the sum of assessed contributions) is between -6 % and +6 % of the GMSL rise. Ranges quoted here arise from different options of assessing the contributions. Uncertainties given in Tables  3  and 4  are not repeated here. We cannot attribute the statistically insignificant misclosure of linear trends. We tentatively attributed interannual features of misclosure to errors in some of the involved datasets. When the SLBC_cci steric product is used, a SLB misclosure in the early years of Argo of 2003-2006 is likely due to an underestimation of the steric-sea-level rise associated with the global sampling error in conjunction with the constraints towards a static climatology, as discussed in Sect. 3. 2. 2. An interannual misclosure feature between 2006 and 2010 might be related to the SLBC_cci steric product and the WaterGAP model making the impression of a temporary slowdown in sea-level rise in 2006-2008 with subsequent recovery in 2008-2010, which is not as pronounced in the GMSL record.
Contact angle analysis of polymeric substrates Static contact angle measurements were obtained using the sessile drop method and an OCA 15 Plus goniometer (DataPhysics Instruments, Germany) with a highperformance image processing system (DataPhysics Instruments, Germany). Three microliters of either deionized water or diiodomethane were added using a motor driven syringe at room temperature (RT). The values of surface free energy were calculated by the Owens, Wendt, Rabel, and Kaelble (OWRK) method  [46]  that discerns polar and dispersive components of the surface energy, using the SCA20 version 2 software (DataPhysics Instruments, Germany). At least six measurements of each condition were performed per group.
Cells were washed twice in PBS and resuspended in 2% FBS in PBS. Sytox blue (Invitrogen, Ireland) was used as viability dye. Cells were analyzed using a fluorescenceactivated cell sorting (FACS) equipment (BS FACSCanto™ II Cell Analyser, BD Biosciences, UK) and the percentage of positive cell populations were calculated using FlowJo software v10 (TreeStar Inc. , USA). Flow cytometry analysis revealed that hBMSCs (Figure  S1 ) and hBMSCs-SF (Figure  S2 ) expressed high percentage of positive and low percentage of negative mesenchymal stem cell markers.
Samples with RIN values of > 8 were used for qPCR experiments. Samples with RIN < 8 were excluded from the study. 1 μg total RNA was reverse transcribed using the iScript™ cDNA Synthesis Kit (Bio-Rad Laboratories, Ireland). 5 ng cDNA were subsequently analyzed by qPCR on a StepOnePlus™ Real-Time PCR System (Thermo Fisher Scientific, Ireland), using TaqMan primer probe assays (IDT, Belgium) and TaqMan Gene Expression Mastermix (ThermoFisher Scientific, Ireland). The TaqMan primer probe assays used are listed in Table  S2. The amplification conditions were 50 • C for 2 min, 95 • C for 10 min, followed by 40 cycles of 95 • C for 15 sec and 60 • C for 1 min. qBasePlus v. 2. 4(Biogazelle NVBelgium) was used to perform geNorm analysis to determine the optimal number of reference genes. CQ values were analyzed and normalized relative quantities (NRQs) were calculated by normalizing the data to the expression of three validated endogenous control genes (EIF2B1, HPRT1, TBP) with qBasePlus v.
Statistical analysis Data are expressed as mean ± standard deviation. All experiments were conducted at least in three independent replicates. Statistical analysis was performed using GraphPad v6. 01(GraphPad Software Inc. , USA). One-or two-way ANOVA was used for multiple comparisons and a Tukey post hoc test was used for pairwise comparisons after confirming that the samples followed a normal distribution (Kolmogorov-Smirnov test) and had equal variances (Bartlett's and Levene's test for homogeneity of variances). When either or both of these assumptions were violated, nonparametric tests were used for multiple (Kruskal-Wallis test) and pairwise (Mann-Whitney test) comparisons. Statistical significance was accepted at p < 0. 05.
676338, the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme, grant agreement No. 866126 and the European Union's Horizon 2020 research and innovation Widespread: Twinning programme, grant agreement No. 810850. This publication has emanated from research supported in part by grants from Science Foundation Ireland (SFI) under Grant numbers 15/CDA/3629 and 19/FFP/6982 and Science Foundation Ireland (SFI) and European Regional Development Fund (ERDF) under grant number 13/RC/2073_2. E. M. F. acknowledges to the project TERM RES Hub -Infraestrutura Científica para a Engenharia de Tecidos e Medicina Regenerativa, Ref Number NORTE-01-0145-FEDER-02219015. The authors would like to acknowledge the significant contribution of Dr Oonagh Dwane in the writing and management of all grants.
Quasi-elastic neutron scattering (QENS) from bulk-water at 300 K, measured on the IRIS backscattering neutron spectrometer (ISIS, UK), is interpreted using the jump diffusion model (JDM), a "minimalistic" multi-timescale relaxation model (MRM) and molecular dynamics simulations (MD). In the case of MRM data analysis is performed in the time domain, where the relaxation of the intermediate scattering function is described by a stretched Mittag-Leffler function, E α (-(|t|/τ) α ). This function displays an asymptotic power law decay and contains the exponential relaxation function as a special case (α = 1). To further compare the two approaches, MD simulations of bulk water were performed using the SPCE force field and the resulting MD trajectories analysed using the nMoldyn software. We show that both JDM and MRM accurately describe the diffusion of bulk water observed by QENS at all length scales, and confirm that MD simulations do not fully describe the quantum effects of jump diffusion.
To avoid such a priori assumptions, alternative approaches for interpreting QENS data are urgently needed. Having that in mind, a "minimal model approach", where the multi-scale relaxation of the water molecules can be described without any prior information  [12] , has been recently proposed and successfully used to analyse the dynamics of confined water molecules in proteins and clay minerals  [13, 14, 15]. This said analysis shows that the new method can capture the full dynamics of the confined water with the benefit that it rationalizes extensive sets of QENS data in terms of a minimal number of parameters. Here we apply both methods to analyse bulk water, H 2 O, at 300K. To further understand the differences and similarities between the two approaches, a complementary technique is needed.
Molecular dynamics (MD) simulation is the method of choice since it provides detailed information about the dynamics of molecules and atoms of complex systems within the length and time scale probed by QENS. Considering the system of bulk-water, we can then compare the motion of water molecules seen by MD trajectories with those probed experimentally and, as a "side effect", verify the simulations  [16]. For this purpose, MD simulations have been performed for bulk-water at 300K using the LAMMPS Molecular Dynamics Simulator with the Velocity-Verlet algorithm as its numerical solver. To directly compare the measured QENS spectra to the MD results, the conversion of the trajectories and velocities to either S (Q, ω) or its Fourier transformed F(Q, t) was carried out using the program package nMoldyn  [17, 18, 19, 20].
"Minimalistic" multi-scale relaxation model The QENS data were also analyzed in the time domain using a "minimalistic" multi-timescale relaxation model (MRM)  [12, 13, 14]. The intermediate scattering function required for such analysis was obtained by Fast Fourier transforming the experimental QENS spectra. In the fitting procedure the generic form of the intermediate scatting function, Eq. 8, is assumed to have a (symmetrized) relaxation function of the form of a stretched Mittag-Leffler (ML) function  [12, 13, 14]  (the Q-dependence is omitted here),  14 ) which can be considered a generalization of the exponential function and admit EIS F(Q) > 0. We note that, ϕ (+) ML (t) ≡ E α (-(|t|/τ) α ) (0 < α ≤ 1, τ > 0), ( E α (z) = ∞ k=0 z k Γ(1 + kα) (15) is an entire function in the complex plane  [21] , where Γ(z) is the generalized factorial  [22] , and that the exponential function is retrieved in the special case α = 1.
Experimental details and data reduction Data on bulk-water were collected using IRIS, a cold neutron indirect backscattering spectrometer installed at the ISIS Spallation Neutron and Muon source. The spectrometer provides an intrinsic energy resolution of 17. 5 µeV (FWHM), corresponding to an upper experimental observation of ≈ 200 ps for those scattered neutrons energy analysed at λ = 6. 3Å  [8]. The bulk-water sample was mounted in a cylindrical sample holder and measurements were conducted at 300K. The measured dynamic structure factor is inevitably resolution broadened, which can be mathematically expressed through the convolution integral S m (Q, ω) = +∞ -∞ dω ′ R(Q, ω -ω ′ )S (Q, ω), (20) where the index "m" stands for "measured". A vanadium standard, that can be considered a completely elastic and isotropic material, is used to define the intrinsic resolution function of the spectrometer, R(Q, ω), and calibrate the measured data.
Data analysis with the jump diffusion model The measured QENS spectra were first analysed using Eq. (  10 ) and the Mantid software package  [25]. Here a single Lorentzian relaxation is assumed and fast vibrational motions accounted for by adding a linear background term. As a result only a few priors were needed to analyse the data. The result of the fit and obtained parameters, are shown in Figs. 2a  and 2b. The translational diffusion coefficient, obtained by fitting γ(Q) using Eq. 12, was found to be D t = 2. 38 • 10 -9 m 2 /s and the residence time τ 0 = 0. 61ps, corresponding to a jump length of about 1 Å. These values are in agreement with the literature  [4, 6]. The quality of the fit can be further verified by inspecting Fig. 3.
Compute and deconvolve the intermediate scattering function. Assuming that the resolution function, Eq. 20, is symmetric in ω, the "measured" intermediate scattering function is computed using a discrete approximation of the inverse Fourier transform F (+) m (Q, t) = +∞ -∞ dω e iωt S (+) m (Q, ω). By writ- ing F (+) m (Q, t) = R(Q, t)S (+) (Q, t) according to the convolution theorem of the Fourier transform, we obtain, F (+) (Q, t) = F (+) m (Q, t)/R(Q, t) for |t| < t c. ( 22 ) The cutoff time is determined by the resolution ∆E (FWHM) of the instrument, writing t c = ℏ/2∆E. An explicit illustration can be found in Ref. [23]. 3. Parameter fit. Once the experimental data was reduced following the steps described above, the deconvolved F (+) (Q, t) was fitted using Eq. (  8 ) using the IMinuit package  [32].
Comparing the results obtained in frequency and time domain We start the comparison between the results of the QENS data analyses with the two models by resuming that the. Both sets of HWHM data are fitted using, Eq. 12, where the fit results are presented above/below the fit in a corresponding color. c) MRM fit of the symmetrized, normalized, and deconvolved intermediate scattering function, F (+) (Q, t), according to Eq. 8. d) Water dynamics parameters of the QENS spectra as a function of Q obtained from the MRM model fit of F (+) (Q, t): i) α(Q), ii) the EIS F(Q), and iii) τ(Q). The lack of Q-dependence and zero value for the EIS F(Q) reflects that, as expected, the water molecules are not spatially constrained. JDM describes the relaxation of the intermediate scattering function with a single exponential function, Eq.
Reproducing the modelled spectra by MD simulations Knowing that the MD simulation is comparable with the QENS data up to a certain point in time and space, we now evaluate the MD simulation output against the modelling approaches used to analyse the measured QENS data. Using the MRM to inspect the MD results is straightforwardly done by fitting the simulated F(Q, t) to Eq. 8. To consider the JDM, a free single exponential function is used to describe the diffusive motion. The results are depicted in Fig. 5a , where the two fitted models are plotted against the experimental data for two Q-values. We observe that the fitted curves follow the data in F (+) (Q, t) well. However, by analysing Fig. 5b , where the fitted models F(Q, t) f it are transformed into S (Q, ω) f it and compared with the measured spectra, both models capture the measured QENS spectra particularly well for low Q-values only.
The juxtaposition of the QENS data and the MD simulation for bulk-water showed that the MD simulation describes well the translational dynamics of the water relaxation. However, further investigation revealed that in reality the MD simulation cannot completely capture the discrete jumps occurring at more localized length scales where quantum effects becomes significant. More importantly, by revisiting the modeling of QENS from bulk water, we show that the MRM, a new "minimalistic" energy landscape-based method for QENS analysis, can be used to accurately describe the data at all length scales. This is confirmed by comparing the Chudley-Elliot relaxation time and the relaxation time scale τ(Q) obtained from MRM, defining the median relaxation rate through λ 1/2 (Q) = 1/τ(Q), and showing that these values follow on the same master curve.
523A>T (p. K175*), in TNNI1 (NM_003281. 4), which was confirmed by Sanger sequencing (Figure  , C ). The nonsense variant was segregated among II-1, III-1, and III-2. In addition, the reverse transcription-PCR products of mRNA from the patient's muscle showed the presence of both the mutated and normal transcripts. The altered amino acid residue (p. K175) is within the tropomyosin-binding site near the C-terminus, which is highly conserved in the troponin family and is evolutionarily conserved. 1 This variant is not registered in any public databases, including gnomAD, dbSNP151, HGMD Professional 2021. 1, Human Genome Variation Database, ToMMo, ClinVar, ESP6500, and 1000 Genomes.
This study was supported partly by  Intramural Research Grants  ( 2-5  and  30-9 ) for Neurological and Psychiatric Disorders of  NCNP  and by  AMED  under Grant Number  20ek0109490h0001. Disclosure Y. Nishimori  reports no disclosures relevant to the manuscript. A. Iida received grants from  Intramural Research Grants  ( 2-5  and  30-9 ) for Neurological and Psychiatric Disorders of  NCNP  and  AMED  under Grant Numbers 20ek0109490h0001 and  Joint Usage  and  Joint Research Programs , the  Institute of Advanced Medical Sciences, Tokushima University  grant number  2020 ,  2A19. M. Ogasawara ,  M. Okubo ,  Y. Yonenobu ,  M. Kinoshita , and  K. Sugie  report no disclosures relevant to the manuscript. S. Noguchi  received grants from  Intramural Research Grants  ( 2-6  and  3-9 ) for Neurological and Psychiatric Disorders of  NCNP  and  AMED  under Grant Number  20ek0109490h0001.
No other clinically relevant variants were identified in patients who had whole-exome sequencing or panel testing. Study participants were initially enrolled between 2000 and 2019, and comprehensive clinical data were collected in the form of existing medical records and self-report clinical history questionnaires. Clinical data were collected at the time of enrollment directly from referring physicians, by medical records release procedures, and/or from the patients themselves and were updated over time when feasible. Medical records included but were not limited to neurology notes, muscle pathology reports, EMG reports, imaging reports, pulmonary function testing, sleep studies, cardiac evaluations, and inpatient hospitalization records. A systematic review of muscle pathology reports was supplemented by the manual review of slides when available, and all scoring and interpretation were performed by 1 coauthor (A.
H. B. ) to ensure consistency of interpretation. The self-report questionnaires focused on birth, developmental history, and current health and physical abilities and were answered by the patient or parent/guardian at 1 or more time points. In addition to these existing records, prospectively collected updated self-report data were obtained in 2021 by phone-administered questionnaire for 26 participants who were available for follow-up. Available records for each case were reviewed, and cross-sectional and longitudinal data were manually extracted and entered into a RedCap  36, 37  database for standardized reporting and descriptive Glossary AD = autosomal dominant; CMT = Charcot-Marie-Tooth; CNM = centronuclear myopathies; PH = pleckstrin homology; ULN = upper limit of normal; XLMTM = X-linked myotubular myopathy.
Participants This study included 42 patients (52% male and 48% female) from 34 families. Patients were born between 1939 and 2017, and the median age at enrollment was 30. 9 years (range 0-69. 5 years) and at the last report was 39. 4 years (range 0. 95-75. 8 years). Over the 20 years since data collection began, 7 (17%) patients died after enrollment (1 due to complications of CNM, 1 due to heart failure, 1 due to pulmonary failure, and 4 unspecified). Additional demographics are provided in eTable 1, links. lww. com/NXG/A547. Patients had one of 8 different pathogenic heterozygous missense variants in DNM2 (Figure  1A ; eTable 2, links. lww. com/NXG/A547). Twenty-two participants had a family history consistent with AD inheritance, mutations were proven de novo in 7, and 13 were sporadic cases (i.
Glu368Lys, p. Phe372Ser, p. Ala618Asp, and p. Ser619Leu mutations. These variants are all located at the autoinhibitory interface between the PH and middle domains (Figure  1A ), where disease-causing mutations are proposed to release autoinhibition of dynamin-2 and promote protein oligomerization, supporting the "gain-offunction" mechanism suggested for DNM2-related CNM. 51 is study was limited by its largely retrospective nature. Data were collected from multiple sources for comprehensive phenotyping, but this did result in some inconsistencies between patient-reported data and medical records. Due to a focus on collection of neuromuscular records and incomplete collection of medical records, especially from older patients, findings related to other organ systems may have been missed. Recall bias was also likely present in patient-reported data.