dataesr/scientific-paragraphs-categorization huggingface dataset:
If this variability is under-represented in the test set {X test , y test }, this can generate overfitting, optimistic estimates of the performance of the models, and finally misleading conclusions in methods' comparisons  [21]. For preventing such effects, we decided to implement a "severe" split between the training and the test: in each of the 100 above repetitions, the {X train , y train } and {X test , y test } represented each half of the dataset (i. e. , n trrain /n = n test/ n = 1/2), while often a softer choice in machine leaning studies is n train /n = 2/3 and n test/ n = 1/3.
PLSR Averaging and Stacking Contrary to models PLSR and PLSR-P, models AVG, AVG-CV, -AIC, -BIC, and STACK were not preliminarily optimized on training sets. They were directly fitted on {X train , y train } after setting an "omnibus" maximal number of LVs (A in Equations (  2 ) and (  4 )). To keep the operational interest of the methods, A must be given a priori. A heuristic has therefore to be defined (this point is discussed in the final section). Based on our expert experiences on PLSR and our knowledge on the intrinsic heterogeneity of forages data, we defined the following heuristic rule: A = 50 LVs for training sizes n train > 400 observations and A = 30 LVs for training sizes n train ≤ 400 observations (in this paper, this concerned only WAL datasets). The decrease from 50 LVs to 30 LVs (n train ≤ 400) relates to the fact that small datasets cannot support dimensionalities that are too high.
Results The relative errors estimated for the different models are displayed in Figure  8. Larger standard errors were observed for the WAL datasets, probably due to their lower sample size. Overall, the three less performant models were PLSR-P, PLSR, and AVG-SHENK (except for TROP2 where AVG-SHENK was as performant as other averaging methods). For CV-, AIC-, and BIC-averaging, the effect of parameter α (Figure  9 ) was dataset and method dependent, without showing a clear pattern. For instance, removing the 30% less performant models in Equation (2) (i. e. , α = 0. 3) did not improve the predictions for BIC weighting (all the datasets), while this was more successful in other situations (e. g. , for CV-weighting). Appl. Sci. 2022, 12, 7850 11 of 15.
Results The relative errors estimated for the different models are displayed in Figure  8. Larger standard errors were observed for the WAL datasets, probably due to their lower sample size. Overall, the three less performant models were PLSR-P, PLSR, and AVG-SHENK (except for TROP2 where AVG-SHENK was as performant as other averaging methods). For PLSR, lower dimensionalities a were selected for WAL datasets (a ≤ 20) compared to the others, in relation to their lower size. As expected, PLSR-P also selected lower dimensionalities a than the usual PLSR (Table  5 ). Nevertheless, PLSR-P was always less efficient (Figure  8 ) than PLSR. This indicates that selecting high values a in PLSR (up to a = 50 LVs in certain repetitions; Table  5 ) did not generate overfitting on these data. Globally, averaging models showed better performances than stacking (Figure  8 ), even if both types of methods returned close relative errors.
On our forage data, building a top model over the (A + 1) PLSR models was therefore not advantageous when compared to a direct averaging of the predictions. For PLSR, lower dimensionalities a were selected for WAL datasets (a ≤ 20) compared to the others, in relation to their lower size. As expected, PLSR-P also selected lower dimensionalities a than the usual PLSR (Table  5 ). Nevertheless, PLSR-P was always less efficient (Figure  8 ) than PLSR. This indicates that selecting high values a in PLSR (up to a = 50 LVs in certain repetitions; Table  5 ) did not generate overfitting on these data. Globally, averaging models showed better performances than stacking (Figure  8 ), even if both types of methods returned close relative errors. On our forage data, building a top model over the (A + 1) PLSR models was therefore not advantageous when compared to a direct averaging of the predictions.
Within the averaging methods, computing model weights from CV-, AIC-, or BIC-error rates has tended to slightly increase the performances compared to a uniform average. Nevertheless, the differences were very low (e. g. , for LUS2 in Figure  8 , the relative error was 5. 0% for AVG-BIC vs. 5. 1% for AVG) with orders of magnitude that have no agronomic importance in practice. Differences between the weighting methods were also clearly dataset dependent (e. g. , in TROP2, AVG was almost equal to other averaging). Even with a uniform weighting, it is important to note that averaging was always better than the usual PLSR. Within the averaging methods, computing model weights from CV-, AIC-, or BICerror rates has tended to slightly increase the performances compared to a uniform average. Nevertheless, the differences were very low (e.
g. , for LUS2 in Figure  8 , the relative error was 5. 0% for AVG-BIC vs. 5. 1% for AVG) with orders of magnitude that have no agronomic importance in practice. Differences between the weighting methods were also clearly dataset dependent (e. g. , in TROP2, AVG was almost equal to other averaging). Even with a uniform weighting, it is important to note that averaging was always better than the usual PLSR. For CV-, AIC-, and BIC-averaging, the effect of parameter α (Figure  9 ) was dataset and method dependent, without showing a clear pattern. For instance, removing the 30% less performant models in Equation (2) (i. e. , α = 0. 3) did not improve the predictions for BIC weighting (all the datasets), while this was more successful in other situations (e. g. , for CV-weighting).
Discussion and Conclusions The major finding of this paper is the overall superiority of the averaging methods compared to the usual PLSR selecting dimensionality a by minimum RMSEPCV. In our evaluation, we chose a severe splitting of the data (test set = half of the data) to prevent optimistic estimates of performances, which can occur frequently when the study materials have complex structures. To check the consistency of the results, we replicated all the same computations presented in this paper but with the more usual splitting {training set = 2/3, test set = 1/3}. The same types of patterns were observed in the performances (not detailed in this text), in particular the superiority of the averaging methods to the usual PLSR. When using averaging or stacking, the maximal dimensionality A has to be set as input parameter.
In practice, A can be optimized based on each dataset {X, y}, as the dimensionality a in the usual PLSR. However, the methods have the advantage of allowing a strategy where A is set a priori as an omnibus value, with the expectation that this value will provide results that are sufficiently efficient even if not fully optimal for each dataset. Such a strategy was implemented in this work. The a priori choices A = 50 LVs (ntrain > 400).
Discussion and Conclusions The major finding of this paper is the overall superiority of the averaging methods compared to the usual PLSR selecting dimensionality a by minimum RMSEP CV. In our evaluation, we chose a severe splitting of the data (test set = half of the data) to prevent optimistic estimates of performances, which can occur frequently when the study materials have complex structures. To check the consistency of the results, we replicated all the same computations presented in this paper but with the more usual splitting {training set = 2/3, test set = 1/3}. The same types of patterns were observed in the performances (not detailed in this text), in particular the superiority of the averaging methods to the usual PLSR. When using averaging or stacking, the maximal dimensionality A has to be set as input parameter.
In practice, A can be optimized based on each dataset {X, y}, as the dimensionality a in the usual PLSR. However, the methods have the advantage of allowing a strategy where A is set a priori as an omnibus value, with the expectation that this value will provide results that are sufficiently efficient even if not fully optimal for each dataset. Such a strategy was implemented in this work. The a priori choices A = 50 LVs (n train > 400) and A = 30 LVs (n train ≤ 400) appeared relevant for our forages data. More generally, for any type of data, relevant orders of magnitude of A can be easily estimated on a few preliminary validation samples. For instance, with a few runs of CV on other datasets than those presented in this paper (containing lower heterogeneity and lower sample sizes; n train ≤ 200), we observed that A = 20 LVs was very efficient for these data.
This easy and fast calibration process can help the user to find a value of A that will be efficient "everywhere" on his data. On our data, the SHENK weights  [38]  did not provide good performances. This was not in adequation with the observations of Zhang et al. [11]  on smaller and less complex datasets. Stacking results were also disappointing. Optimizing the weights by a top regression model did not improve the prediction performances. Averaging with CV-, AIC-, and BIC-weighting appeared to be the best methods overall. Nevertheless, uniform weighting was often almost as performant. The AVG method has the advantage of being simpler and much faster to compute (no need to internally estimate training error rates on {X train , y train }, required to compute the variable model weights). AVG can therefore be recommended for a fast strategy, providing efficient results even if not always optimal.
Such pipelines fit a PLSR model for each new observation to predict, after having selected a neighborhood of this observation. Local PLSR are particularly efficient when the relation between y and X is nonlinear. This occurs for instance when the data contain clustering and the relation y = f(X) varies between clusters. As for PLSR, local PLSR can become time consuming to optimize when many datasets {X, y} have to be processed. Again, with the objective of automatized predictions, local AVG PLSR methods can represent fast and safe alternatives to methods requiring time consuming calibrations.
Molecular Fireworks: The high angular resolution 2-3 mm molecular line survey covering the central arc minute of the Fireworks Galaxy NGC 6946 NGC 6946 is a nearby, gas-rich, very actively star-forming double-barred spiral galaxy. We have observed a suite of 1-3 mm lines with the IRAM Plateau de Bure Interferometer (PdBI) [PI: Schinnerer] to assess the physical and chemical structure of the inner ⇠50 00 of this galaxy. This gives us one of the most comprehensive, high resolution (2-4 00 ⇡ 75-150 pc) molecular line dataset for a nearby galaxy center in the northern hemisphere (  [7] ; see Fig. 3 ). In this work, we identify three distinct regions of prominent line emission in the inner small scale bar (based on the work of  [21] ): the nuclear region and the northern/southern inner bar ends.
The necessity for a high-resolution, high-sensitivity multiwavelength view of M51 has led to a NOEMA+30m large program mapping emission from dense gas tracers in the 3mm window (e. g. 13 CO, C 18 O, HCN, HCO+, HNC, N 2 H + ). We present the first results from the ongoing observations in comparison to the PAWS CO(1-0) map in Fig. 4. Using the resulting ⇠2" resolution dense-gas maps, we will expand the cloudby-cloud analysis from PAWS to a set of important dense gas tracers which will bring critical insights into current models of star-formation theory by studying the dense gas properties.
Acknowledgements The authors gratefully acknowledge the  European France-(Manche) - England cross-border cooperation program INTERREG V A "SmartT ", co-financed by  ERDF , for financial support. This work was financially supported by  University of Rouen Normandy ,  INSA Rouen Normandy , the  Centre National de la Recherche Scientifique (CNRS) ,  European Regional Development Fund (ERDF) ,  Labex SynOrg  ,  Carnot Insitut I2 C, the graduate school for research Xl-Chem  ( ANR-18-EURE-0020 XL CHEM ), and.
Acknowledgements We thank the  French "Agence Nationale pour la Recherche"  (grant  ANR-19-CE07-0002-01  " ANTIDEPRIM ") for funding this study (including PhD grants of A. L. ). Jean-Christophe Jullian (BioCIS)  and  Rémi Franco (BioCIS)  are thanked for NMR assistance. Prof. David  R. Spring ( University of Cambridge ) is greatly acknowledged for providing experimental details.
Acknowledgements We thank  MINECO/FEDER of the Spanish Government  (project reference  PGC2018-098533-B-100  and  PID2021-127127NB-I00 ) and the  Junta de Andalucía, Spain  ( UMA18-FEDERJA057 ). We also thank the vibrational spectroscopy unit of the  Research Central Services (SCAI) of the University of Málaga. Financial support from the  National Science Center of Poland  ( DEC-2015/19/B/ST5/00612 , M. S. ) is gratefully acknowledged. L. F. thanks  Thomas Vives  from the  Ecole Nationale Supérieure de Chimie of Rennes , the  Shimadzu and Chiral Technology companies  for their support regarding the separation of the chiral molecule by.
For example the methods that seem to lie on the vicinity of experimental values are LC-ωHPBE, M05-2X, ωB97, BhandHLYP, M06-2X, M08-HX, LC-ωPBE and M11. Close to this cluster we can find two smaller neighbourhoods: one consists of ωB97X, SoGGA11-x, BMK and CAM-B3LYP, and the other one with OHSE2PBE, MN15 and ωB97XD. Even further from the reference data is the cluster that contains PW6B95, mPW1PW91, mPW1PBE, PBEh1PBE, PBE1PBE, HSEH1PBE, APFD, N12-SX, mPW3PBE, B3PW91, X3LYP, B1LYP, M05, MN15-L, tHCTHhyb and TPSSh. The O3LYP, M06-L, tHCTH and HCTH147 functionals form the last cluster lying at the edges. There are also functionals that are not grouped and they most of the time lie aside of the larger clusters. Our findings are in agreement with what has been found in existing studies, however dealing only with the HOMO and LUMO KS energy eigenvalues.
Acknowledgements M. K. , G. L. , J. B. and M. R. acknowledge the  MARS consortium  ( GRDF, GRTgaz and Teréga ) for financial support. Partial financial support to M. R. from the  Institut Universitaire de France (IUF)  is gratefully acknowledged. B. G. M and G. M. M acknowledge support from the  National Institute of General Medical Sciences  ( Award R35GM119702 ) of the  National Institutes of Health.
[1, 54] This observation is also seen for P and P* but the effect is not as pronounced, Figures  S2-S3 , Supporting Information, suggesting that bulk viscosity is influenced by the preparation method. The structures in these solutions were investigated using small angle neutron scattering (SANS). The data for the solutions of NDI-GF alone at pD 11 (pD due to deuteration of solvent), were fit to a flexible elliptical cylinder model with a large axis ratio implying that the self-assembled structures present are tape-like flat cylinders (Figure  S4  and Table  S2 , Supporting Information). [68] he data for 1-NapFF alone at pD 11 fit to a hollow cylinder model and the data is comparable with that previously reported (Figure  S5  and Table  S3 , Supporting Information). [8, 46, 69] S at high pD is best fit by combining a hollow cylinder and a flexible elliptical cylinder model, implying the data represent a simple addition of the two single components with dimension remaining similar to that of the individual components and hence self-sorting (Figure  S6  and Table  S4 , Supporting Information).
Solutions were prepared as previously described above using deuterated solvent and base. Gels were prepared as described above and transferred to 2 mm quartz cuvette immediately after the addition of GdL. The solutions were left to gel overnight before measurement. These were placed in a temperature-controlled sample rack during the measurements. Data were reduced using Mantid software and data fitted using SASview. Rheology: Rheological tests were performed using an Anton Paar Physica 101 rheometer. Strain, frequency, and temperature data were collected using a vane (ST10-4V-8. 8/97. 5) geometry and a cup measuring system. Viscosity measurements were performed using an Anton Paar Physica 301 rheometer using a cone plate geometry (75 mm diameter, 1. 0°a ngle, 50 μm).
Acknowledgements The authors would like to thank the  EPSRC  ( EP/S032673/1 ) and ( EP/N509668/1 ), and  UKRI  ( MR/V021087/1 ) for funding. The authors would like to thank  Dr Ana M. Fuentes Caparrós  for help running SANS experiments. The authors acknowledge beamtime allocation on D11 at  Institut Laue-Langevin  (ILL) https://doi. org/10. 5291/ILL-DATA. 9-10-1670,Grenoble. This work benefitted from SasView software, originally developed by the  DANSE  project under  NSF  award  DMR-0520547.
Acknowledgements We are grateful to the  CNRS , the  Ecole Nationale Supérieure de Chimie de Rennes. Umicore AG & Co  is acknowledged for a generous gift of gold complexes. This work was supported by the  Agence Nationale de la Recherche  ( ANR-16-CE07-0019  " Hel-NHC " grant to D. P. -B. ). L. C. , L. F. and Z. Z. thank the  King Abdullah University of Science and Technology (KAUST)  for supporting this work. For computer time, this research used the resources of the  KAUST Super-computing Laboratory (KSL) at KAUST. We are grateful to  Philippe Jéhan  and the  CRMPO core facility  for HMRS of Au-and Cu-complexes.
Methods of Analysis of the Reaction Products The contents of hydrogen, carbon oxides, and methane in the reaction products were determined by gas chromatography on a CRYSTALLUX-4000M chromatograph (META-CHROM, Russia) equipped with a thermal conductivity detector using high-purity argon [99. 998%, GOST (State Standard) 10157-79] with a flow rate of 10 mL/min as a carrier gas. A 1 m × 3 mm adsorption column packed with SKT activated carbon with a particle size 0. 2-0. 3 mm was used. The column, detector, and vaporizer temperatures were 120°C. The concentrations of the gases were determined basing on the calibration curves with the use of specialized NetChrom v2. 1 software. Hydrocarbon gases С 1 -С 5 were identified on a CRYSTALLUX-4000M chromatograph (META-CHROM, Russia) equipped with a flame ionization Liquid organic reaction products were identified by gas chromatography-mass spectrometry (GC-MS) and gas-liquid chromatography (GLC).
Our kinome-wide dataset was used to computationally annotate and identify the most likely protein kinases for every reported phosphorylation site in the human Ser/Thr phosphoproteome. For the small minority of phosphosites where the protein kinases involved have been previously identified, our predictions were in excellent agreement. When this approach was applied to examine the signaling response of tissues and cell lines to hormones, growth factors, targeted inhibitors, and environmental or genetic perturbations, it revealed unexpected insights into pathway complexity and compensation. Overall, these studies reveal the full extent of substrate specificity of the human Ser/Thr kinome, illuminate cellular signaling responses, and provide a rich resource to link unannotated phosphorylation events to biological pathways.
INTRODUCTION Phosphorylation of proteins on serine, threonine, tyrosine, and histidine residues controls nearly every aspect of eukaryotic cellular function  [1] [2] [3] [4]. Misregulation of protein kinase signaling commonly results in human disease  [5] [6] [7] [8]. Deciphering the cellular roles of any protein kinase requires elucidation of its downstream effector substrates. The majority of kinase-substrate relationships that have been published to date, however, involve a relatively small number of well-studied protein kinases, while few, if any, substrates have been identified for the majority of the ~300 human protein Ser/Thr kinases within the human kinome  [9] [10] [11]. This lack of knowledge of kinasesubstrate relationships limits the interpretation of large mass spectrometry-based phosphoproteomic datasets, which to date have collectively reported over 200,000 Ser and Thr phosphorylation sites on human proteins  [12] [13] [14] [15].
To test this idea, we used publicly available MS datasets from cells collected in the absence or presence of various perturbations and scored all phosphorylation sites with our atlas of serine/threonine kinase motifs. Kinase motifs significantly enriched or depleted following experimental treatment were then represented as volcano plots of motif frequencies and adjusted p-values (Fig. 4a ). Using this approach, we found that sequence motifs corresponding to the most direct target of a genetic or chemical perturbation were among the most significantly regulated (seen, for example, with genetic deletion of the secreted primordial casein kinase FAM20C (Fig. 4b ))  57. When quantitative phosphoproteomics data from HepG2 cells lacking FAM20C  58  were analyzed by our kinome-wide dataset, the most downregulated kinase recognition motif corresponded to that of FAM20C.
Similarly, when skeletal muscle-like myotube cells were stimulated for 30 minutes with isoproterenol 59 , the most upregulated phosphorylation motifs corresponded to multiple isoforms of cAMP-dependent protein kinase (PKA), canonical effector kinases downstream of the β1 and β2 adrenergic receptors (Fig. 4c )  60, 61. Of note, PKA motifs are highly similar to those of several other basophilic kinases yet we could identify their enrichment in this scenario. In addition, our comprehensive serine/threonine kinome motif collection elucidated secondary signaling events in a dataset from HeLa cells arrested in mitosis using the PLK1 inhibitor BI-2536 (Fig. 4d )  62  where, in addition to observing a striking downregulation of substrates containing the optimal PLK1 motif, we also noted significant upregulation of substrates phosphorylated by ATM and ATR.
The full collection of serine/threonine kinome motifs also allowed temporal dynamics of signaling to be resolved from time-resolved phosphoproteomic datasets. For example, motif-based analysis of phosphoproteomic data from insulin-treated 3T3-L1 adipocytes 70 revealed rapid activation of the phosphoinositide 3-kinase signaling pathway within 1 minute after insulin stimulation followed by subsequent activation of the MAPK pathway, together with downregulation of AMP-and cAMP-dependent protein kinases within 60 minutes (Fig. 4f ). Similarly, phosphoproteomic data analysis from LPS-stimulated dendritic cells  71  suggested marked upregulation at 30 minutes of a set of stress-activated kinases including the IKKs, JNK and p38 MAPKs, along with the MAPKAPK family of p38 effector kinases, followed within 4 hours by subsequent upregulation of the PIM kinases and suppression of the MAPKs in parallel with the downregulation of their upstream MAPK3Ks (MEKK1, MEKK2, and ZAK)  72, 73  , suggestive of a negative feedback loop (Fig.
DISCUSSION This work presents the full spectrum of substrate motifs of the human serine/threonine kinome and provides an unbiased global framework to further explore their cellular functions. Globally, these motifs are substantially more diverse than expected, suggesting a broader substrate repertoire of the kinome. Hierarchical clustering of this dataset reorganized the kinome into 17 motif-classes and introduced several novel shared motif features (Fig. 2 ). For kinases with similar motifs, we saw multiple cases where their minor differences translated into dramatically different substrate predictions and motif enrichments (Fig. S11 , Fig. 4c-g ) and rationalized how such biochemically similar kinases can have divergent biological roles. The serine/threonine kinases we profiled were, almost without exception, strongly discriminatory against specific motif features.