dataesr/scientific-paragraphs-categorization huggingface dataset:
Water Balance Analysis Each and every model, depending on the resolution and origin of the data used in its setup, will have a different representation of the fluxes. One way to highlight this is to look at the main elements of the water balance, that is, precipitation, evapotranspiration, and streamflow. The model differences in water balance components in the 10 studied catchments are depicted using the Turc-Budyko diagram (Figure  5 ). Water yield is generally similar between the two model setups (with the exception of two catchments in which runoff coefficient between the models differs  Water Resources Research maximum by 0. 1); however, the aridity index differs in a consistent way, with higher values in the GR6J setup. Higher potential evapotranspiration values are estimated in the E-HYPE than in the GR6J setup (on average 380-mm difference).
These differences in the aridity indices indicate that the model states (i. e. , soil moisture) might vary between the two models, further affecting the forecasts. In both E-HYPE and GR6J, potential evapotranspiration is estimated using the modified Jensen-Haise model but with the existing E-HYPE parameterization leading to higher potential evapotranspiration than in GR6J. Moreover, in E-HYPE, parameters are identified for each land use type against satellite-based potential evapotranspiration data (Moderate Resolution Imaging Spectroradiometer global data set;  Mu et al. , 2011) , and annual potential evapotranspiration from E-HYPE and Moderate Resolution Imaging Spectroradiometer should thus match over the entire model domain. In model identification, parameters can compensate for meteorological biases and reach similar streamflow results nonetheless.
Results show that in all seasons and for both models, the highest gain in skill comes from the initial hydrologic conditions. The gain is higher in spring and summer than in autumn and winter and can be observed up to 25 weeks. GCM forecasts yield some additional gain for the first month in the continental setup, with almost equivalent gain to ICs in the autumn and winter seasons. However, for GR6J, the GCM forecasts do not yield gain, except for the very first weeks of winter. Here, it is assumed that the gain in E-HYPE forecasting skill using GCM forecasts is due to the meteorological data set used in the calibration and warmup. Indeed, ECMWF System 4 forecasts are bias adjusted to Hydro-GFD, which was also used in the E-HYPE calibration and warmup. In the case of GR6J, which was calibrated and warmed up with local observations, the large-scale data set is not close enough to local observations for bias adjustment to bring the forecasts closer to the local model climatology.
, 2015). Nevertheless, a forecasting service setup, initialized and run with consistent meteorological data sets can reach similar performances to catchment-tailored services by learning from the system's dynamics and potential biases. E-HYPE takes better advantage of bias-adjusted GCM seasonal forecasts, because the data set used as reference in the bias-adjustment was also the one used in the setup and initialization of the Water Resources Research model. For the catchment-specific model, climatology could yield more or equivalent information than GCM forecasts bias-adjusted based on large-scale reanalysis. Indeed, ESP, which is the model fed with climatology, has been shown to be already a good setup, which can be hard to outperform  (Arnal et al. , 2018). For hydroclimate service users, this means that bias adjustment based on local observations is crucial in order to gain from meteorological forecasts.
The two datasets are spatially consistent (R=0. 64) and exhibit similar variance (slope =1. 1±0. 1). The cloud-sliced UT NO 2 has a small positive offset in background UT NO 2 (intercept =2. 3±1. 2 pptv). On average, cloud-sliced UT NO 2 is 17 % more than the true cloudy UT NO 2 , but this depends on the spatial resolution of the retrieved cloud-sliced product. Regression slopes increase from 0. 87±0. 03 for cloud-sliced UT NO 2 obtained at 2∘×2. 5∘ to 1. 4±0. 2 at 8∘×10∘, and the cloud-sliced UT NO 2 is 4. 1 % less than the true cloudy UT NO 2 at 2∘×2. 5∘ and 37 % more at 8∘×10∘. The increasingly positive bias with spatial resolution is because a greater relative proportion of clusters with NO 2 vertical gradients ≥0. 33 pptv hPa -1 are retained at coarser resolution. The percentage retained relative to all clusters with more than 10 data points and uniform overlying stratospheric NO 2 increases from 9 % for 2∘×2.
, 2007 (Loyola et al. , , 2018b)). In ROCINN, clouds are modelled as multiple optically uniform layers of light-scattering water droplets (the clouds-as-layers or CAL model). We convert the ROCINN-clouds-as-layers (ROCINN-CAL) cloud-top heights to pressures for cloud-slicing and comparison to FRESCO-S. FRESCO-S data are quality screened using the same qa_value threshold (0. 45) as NO 2. A qa_value threshold of 0. 5 is used for OCRA and ROCINN-CAL. Snow and/or ice scenes potentially misclassified as clouds by FRESCO-S are identified as differences in reported scene and surface pressures >2 %, as in van der A et al. (  2020 ), as having snow cover >80 % or classified as permanent ice cover. In what follows, we distinguish the two cloud-sliced TROPOMI UT NO 2 products as FRESCO-S and ROCINN-CAL.
, 2011; Silvern et al. , 2018). Strict filtering applied to cloud-slicing removes most scenes where the increase in NO 2 with altitude exceeds 33 pptv across 280-180 hPa, based on the synthetic experiment with GEOS-Chem. The influence of more than a 10-year gap between the OMI and TROPOMI UT NO 2 datasets on the comparison is challenging to quantify, due to paucity of routine measurements of NO 2 in the UT. The contribution of changes in lightning activity should be small, as interannual variability is small (<5 %), and there is no discernible trend in the longterm record of satellite observations of lightning flashes  (Schumann and Huntrieser, 2007). According to most climate models, there is an increase in lightning flashes due to global warming  (Romps et al. , 2014; Finney et al. , 2016) , though almost all of these models use the same positive relationship between lightning flashes and cloud-top heights first proposed by  Price and Rind (1992).
Conclusions We have developed new products of NO 2 in the upper troposphere (UT; ∼8-12 km) by cloud-slicing partial columns of NO 2 from the space-based TROPOMI instrument. This involves regressing partial NO 2 columns against cloud-top pressures and converting regression slopes to UT NO 2 mixing ratios. We first refined and tested representativeness of cloud-sliced UT NO 2 by applying cloudslicing to synthetic partial columns from the GEOS-Chem model. Synthetic cloud-sliced UT NO 2 values are spatially consistent (R=0. 64) with the synthetic truth, but preferentially sampling cloudy scenes and substantial data loss contribute to a resolution-dependent positive bias in cloud-sliced UT NO 2 of 11 %-22 %. Before applying cloud-slicing to TROPOMI, we evaluated TROPOMI with Pandora total columns at high-altitude sites (Mauna Loa, Izaña, Altzomoni) and Pandora and MAX-DOAS free-tropospheric columns at Izaña.
Acknowledgements The authors are grateful to  Piet Stammes  and colleagues for retrieval of the FRESCO-S cloud product and  Diego Loyola  and colleagues for retrieval of the OCRA and ROCINN-CAL cloud product. The PGN is a bilateral project supported with funding from  NASA  and  ESA. The Pandora measurements in Altzomoni were possible thanks to technical assistance from  Alejandro Bezanilla. Financial support This research has been supported by the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (through the  Starting Grant  awarded to  Eloise A. Marais ,  UpTrop  (grant no. 851854 )). The Pandora measurements in Altzomoni were possible thanks to financial support of  Conacyt-AEM  (grant no. 275239 ). Robert G. Ryan  has received financial support from the  Australian Research Council Centre of Excellence for Climate Extremes  and the  Albert Shimmins Memorial Fund  through the  University of Melbourne.
Ethics statement Animal tumor model studies were performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. Protocols used were approved by the Institutional Animal Care and Use Committee (IACUC) at Tufts University School of Medicine for studies using murine Lewis lung carcinoma (LLC) cells (Protocol: #P11-324) and at Roswell Park Cancer Institute (RPCI) for studies using human LM2-4 LUC+ breast carcinoma cells (Protocol: 1227M). Institutions are AAALAC accredited and every effort was made to minimize animal distress  [15]. For the breast data measured by fluorescence, guidelines for animal welfare in experimental oncology as recommended by European regulations (decree 2013-118 of February 1, 2013) were followed.
Mice experiments The experimental data comprised three data sets. Animal tumor model studies were performed in strict accordance with guidelines for animal welfare in experimental oncology and were approved by local ethics committees. Precise description of experimental protocols was reported elsewhere (see  [15]  for the volume measurements and  [41]  for the fluorescence measurements). Breast data measured by volume (N = 66). This dataset is publicly available at the following repository  [42]. It consisted of human LM2-4 LUC+ triple negative breast carcinoma cells originally derived from MDA-MB-231 cells. Animal studies were performed as described previously under Roswell Park Comprehensive Cancer Center (RPCCC) Institutional Animal Care and Use Committee (IACUC) protocol number 1227M  [15, 24].
Breast data measured by fluorescence (N = 8). This dataset is publicly available at the following repository  [44]. It consisted of human MDA-MB-231 cells stably transfected with dTomato lentivirus. Animals were orthotopically implanted (80,000 cells at injection) into the mammary fat pads of 6-week-old female nude mice. Tumor size was monitored regularly with fluorescence imaging. The data comprised a total of 64 observations. To recover the fluorescence value corresponding to the injected cells, we computed the ratio between the fluorescence signal and the volume measured in mm 3. We used linear regression considering the volume data of a different data set with same experimental setup (mice, tumor type and number of injected cells). The estimated ratio was 1. 52 � 10 9 photons/(s � mm 3 ) with relative standard error of 11.
Lung data measured by volume (N = 20 ). This dataset is publicly available at the following repository  [45]. It consisted of murine Lewis lung carcinoma cells originally derived from a spontaneous tumor in a C57BL/6 mouse  [46]. Animals were implanted subcutaneously (10 6 cells at injection) on the caudal half of the back in anesthetized 6-to 8-week-old C57BL/6 mice. Tumor size was measured as described for the breast data to a maximum volume of 1. 5 cm 3. The data was pooled from two experiments with a total of 188 observations.
Specifically: log ðθ i Þ ¼ log ðμÞ þ η i ; η i � N ð0; ωÞ: The choice of a log-normal distribution ensured the positivity of the parameters without adding any constraint. Moreover, the ratio of two log-normal distributions is a log-normal distribution. We considered a combined residual error model e i j , defined as e i j ¼ ðs 1 þ s 2 f ðt i j ; θ i ÞÞε i j ; where ε i j � N ð0; 1Þ are the residual errors and σ = [σ 1 , σ 2 ] is the vector of the residual error model parameters. In order to compute the population parameters, we maximized the population likelihood, obtained by pooling all the data together. Usually, this likelihood cannot be computed explicitly for nonlinear mixed-effect models. We used the stochastic approximation expectation minimization algorithm (SAEM)  [17] , implemented in the Monolix 2018 R2 software  [47].
e. , the population parameters ϕ), Pðy i jθ i Þ ¼ R R PðV i I ÞPðy i jθ i ; V i I ÞdV i I is the likelihood, defined from Eq (4), and Pðy i Þ ¼ R R p Pðθ i ÞPðy i jθ i Þdθ i is a normalization factor. The predicted distributions of extrapolated growth curves and subsequent âi were computed by sampling θ i from its posterior distribution (8) using Pystan, a Python interface to the software Stan  [38]  for Bayesian inference based on the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo  [36]. The sampling procedure depends on the evaluation of the likelihood Pðy i jθ i Þ, which relies itself on V i I. Therefore, V i I was sampled from its distribution for each realization of the posterior distribution. Predictions of âi were then obtained from (5), considering the median value of the distribution.
Different data sets were used for learning the priors (training sets) and prediction (test sets) by means of k-fold cross validation, with k equal to the total number of animals of the dataset (k = N, i. e. leave-one-out strategy). At each iteration we computed the parameters distribution of the population composed by N -1 individuals and used this as prior to predict the initiation time of the excluded subject i. The Stan software was used to draw 2000 realizations from the posterior distribution of the parameters of the individual i.
Population analysis of tumor growth curves The population approach was applied to test the descriptive power of the exponential, logistic and Gompertz models for tumor growth kinetics. The number of injected cells at time t inj = 0 was 10 6 , therefore we fixed the initial volume V inj = 1 mm 3 in the whole dataset  [15]. We set (t I , V I ) = (t inj , V inj ) as initial condition of the equations. We ran the SAEM algorithm with the Monolix software to estimate the fixed and random effects  [47]. Moreover, we evaluated different statistical indices in order to compare the different tumor growth models. This also allowed learning of the parameter population distributions that were used later as priors for individual predictions. Results are reported in Table  1 , where the models are ranked according to their AIC (Akaike Information Criterion), a metrics combining parsimony and goodness-of-fit.
g. V inj = 1 for the breast data measured by volume). Assuming that the cells don't change their proliferation kinetics when implanted, this value should thus in theory be equal to the in vitro proliferation rate (supposed to be the same for all the cells of the same cell line), denoted here by λ. The value of this biological parameter was assessed in vitro and estimated at 0. 837  [24]. In support to our quantitative assumptions, we indeed found estimated values of α i close to λ (fixed effects of 0. 58, see Table  2 ). However, most of the values of α i were smaller than λ in the majority of the cases (Fig  3A ). We postulated that this difference could be explained by the fact that not all the cells will be successfully grafted when injected in an animal. Under such assumption the SGR at the initial time, to be compared with λ, would not be given by α i anymore.
Missense variant 3D modelling GAD67 protein modelling and prediction of the effects of the p. (Glu509Lys) variant were generated using bioinformatic tools from HOPE  (Venselaar et al. , 2010). The model was generated based on a homologous structure of yeast GAD67 (PDB ID: 2OKJ) and built using the Yasara/WHAT IF Twinset. Structural information was collected using information from WHAT IF Web services, the UniProt database and the Reprof software. development could be evaluated. One patient (Case 1, Patient A-III:1) was only first examined at the age of 6 months. The remaining patients all had neonatal developmental and epileptic encephalopathies (DEE) with seizure onset in the first months of life. No ictal EEG recordings were available for re-evaluation in the context of this study. Therefore, seizure classification was based on description of seizures in medical records.
Discussion We describe 11 patients, from six independent families, presenting early-onset DEE and a variable association of cleft palate, omphalocele, equinovarus and/or more widely distributed joint contractures, caused by bi-allelic variants of GAD1. Four of six families carried a homozygous variant leading to a truncated protein (Supplementary Fig. 5 ), supportive of a complete loss-of-function mechanism. GAD1 constraint scores calculated on the gnomAD dataset are also in line with an autosomal recessive inheritance of lossof-function variants, as the pRec score  (Lek et al. , 2016)  (probability of being intolerant to bi-allelic loss-of-function variants) is 0. 934, above the 0. 9 threshold to consider a gene intolerant to bi-allelic variants. In addition to nonsense and frameshift variants, we found one deletion of a highly conserved lysine [c.
MaxEntScan: http://hollywood. mit. edu/burgelab/maxent/ Xmaxentscan_scoreseq. html GeneSplicer: http://www. cbcb. umd. edu/software/GeneSpli cer/gene_spl. shtml SpliceAid2: http://www. introni. it/spliceaid. html RCSB Protein Data Bank: https://www. rcsb. org/structure/ 2OKJ.
Funding The study was supported by grants from the  Swedish Research Council  (H. T. ), the  Swedish Society of Medicine  (H. T. ),  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement no. 608473 (H. T. ) ,  the Eurocores program  of the  European Science Foundation  (S. W. ), the  BOF-University of Antwerp  ( FFB180053 ) (S. W. ),  FWO-FKM  ( 1861419N ) (S. W. ), the  European Research Council  ( ERC Starting Grant TREAT Cilia , grant No  716344 ) (M. S. ) and  Yale Center for Mendelian Genomics and GSP Coordinating Center (NHGRI  and  NHLBI  ( UM1HG0 06504 ,  U24 HG008956 : The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institutes of Health ) (K.
Interaction Confidence Scoring A confidence score for Predicted Biological Score (PBS) was attributed to each interaction as previously described  [33]  (Data S1). The PBS score represents the probability of an interaction being non-specific and is computed to assess the interaction reliability. PBS is an e-value, primarily based on the comparison between the number of independent prey fragments found for an interaction and the chance of finding them at random (background noise). The value in the scores A to D varies between 0 and 1 (A < 1e -10 < B < 1e -5 < C < 1e -2. 5 < D < 1). Several thresholds were arbitrarily defined in order to rank the results in the following scores:.
• A: Very high confidence in the interaction. • B: High confidence in the interaction. • C: Good confidence in the interaction. • D: Moderate confidence in the interaction. This category is the most difficult to interpret because it mixes two classes of interactions: (i) false-positive interactions and (ii) interactions hardly detectable by the Y2H technique (e. g. , low representation of the mRNA in the library, prey folding, prey toxicity in yeast). • E: Interactions involving highly connected (or relatively highly connected) prey domains, warning of non-specific interaction. The total number of screens performed on each organism is taken into account to set this connectivity threshold to 20 interactions to different bait proteins in the entire human database. They can be classified in different categories: (i) prey proteins that are known to be highly connected due to their biological function and (ii) proteins with a prey interacting domain that contains a known protein interaction motif or a biochemically promiscuous motif.
Annotation of Identified AKR2-Interacting Proteins DomSight (Hybrigenics Services) was applied to compare the bait fragment and the selected interacting domain (SID) of the prey proteins with the functional and structural domains generated by Pfam (https://pfam. xfam. org), SMART (http://smart. embl-heidelberg. de), TMHMM (http://www. cbs. dtu. dk/services/TMHMM/), SignalP (http://www. cbs. dtu. dk/services/SignalP/), and COILS (https: //embnet. vital-it. ch/software/COILS_form. html) algorithms (Figure  S1 ). N/A annotations (Data S1) were not considered as interactions. Annotations for biological process and subcellular localization were performed according to Uniprot (https://www. uniprot. org; searched on October 2018). Comparison with previously published datasets for AKR2-interacting proteins were performed using datasets for human AKR2 (http://www.
humanmine. org/humanmine/portal. do?externalids=55122&class=Gene&origin= FlyMine)  [19] [20] [21] [22] [23]. For validation, AKR2-protein interactions were searched in the public databases STRING (https://string-db. org/cgi/input. pl?sessionId=BIqHpYhivYPx&input_page_show_search= on) and NCBI (https://www. ncbi. nlm. nih. gov/gene?Db=gene&Cmd=DetailsSearch&Term=55122) reporting interactions using alternative interactome models such as ingenuity pathway analysis (IPA).
If the base was of two notes, we established for its expression a different metrical scheme: dotted-quarter-note-plus-eighth-note followed by a quarter-note (GCG = DRR), or by two eighth-notes (GCC = DRMR). When the algorithm provided the same melodic formula for a double base (GGA = SL; GGC = SLDR) and for a single base (UCA = S; UCU = SL), we opted for the metric scheme half-plus-quarter-note in the first case and dotted-quarter-note-plus-dotted-quarter-note in the second case. In this way, each codon had a unique rhythmic and melodic definition. All sound files and scores were prepared using the Finale (v. 2018) program (https://www. finalemusic. com). Finding the best match by trial and error produced musical ensembles. Audio files were uploaded and can be found at https://freesound. org/people/josedelafuente/sounds/478998 to 479009.
Twenty-four AKR2-protein physical interactions have been described before in human kidney, colon, lung, and mammary epithelial cells  [19] [20] [21] [22] [23]. Of them, only three proteins were among the identified proteins in human placenta with very high (A; AKR1) or moderate (D; maspardin (SPG21) and E3 ubiquitin-protein ligase LNX (LNX1)) interaction scores (Figure  2B , Table  1 , and Data S1). In STRING and NCBI databases of AKR2-protein interactions using Y2H and other alternative interactome models such as IPA, the interactions of AKR2 with AKR1, RNF10, zinc finger protein 862 (ZNF862), LNX1, and SPG21 were identified.
The results showed that both AKR2-RNF10 and AKR2-THRAP5 but not AKR2-IFN-α1 musical ensembles (Figure  S4 ) predicted protein interactions coinciding with those found by Y2H (Figure  5C ). Twenty-four AKR2-protein physical interactions have been described before in human kidney, colon, lung, and mammary epithelial cells  [19] [20] [21] [22] [23]. Of them, only three proteins were among the identified proteins in human placenta with very high (A; AKR1) or moderate (D; maspardin (SPG21) and E3 ubiquitin-protein ligase LNX (LNX1)) interaction scores (Figure  2B , Table  1 , and Data S1). In STRING and NCBI databases of AKR2-protein interactions using Y2H and other alternative interactome models such as IPA, the interactions of AKR2 with AKR1, RNF10, zinc finger protein 862 (ZNF862), LNX1, and SPG21 were identified.
As shown previously by genetic approaches, musical ensembles supported the fact that AKR/SUB in different species are evolutionarily related and structurally conserved, and also predicted AKR2-protein interactions coinciding with those found by genetic approaches such as the Y2H screening used here. The methodological approach proposed here for quantum vaccinomics further advances the potential of the vaccinomics pipeline used before for the identification of candidate protective antigens  [29, 68, 69]. Furthermore, if combined with network analysis for the integration of interactomics and regulomics datasets  [70]  and big data machine learning algorithms to identify candidate protective antigens and epitopes  [71] , quantum vaccinomics would result in designing chimeric antigens based on protective epitopes in SID from vector-and pathogen-derived regulatory proteins.