dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, the MS/MS spectra were converted to. mgf format resulting in 195 different spectra of which 131 are unique compounds. This. mgf files were submitted to GNPS libraries  7, 8, 39  employing the 'Batch Upload of Annotated Spectra' workflow (https://ccms-ucsd. github. io/GNPSDocumentation/batchupload/). Additionally, another submission was done employing 217 MS/MS. mgf files of commercial standards obtained from Watrous et al. study focused on enzymatic oxylipins  6. This building procedure was summarized in Figure  1.
Technical Validation The validation of the molecular networking of NEO-PUFAs was accomplished through four distinct strategies. Firstly, the topology of the molecular network generated using the NEO-MSMS standards as input was examined to determine if the uploaded MS/MS data could highlight structural similarities among the standards present in the database. Next, we generated four feature-based molecular networks for each PUFAs, encompassing all types of oxidation. Additionally, a feature-based network was established, including all four fatty acids, to identify potential compounds based on observed connections within the molecular network from which was created our putative library. Moreover, the examination of oxPUFA data played a crucial role in identifying specific putative compounds which were subsequently added to the putative library.
Dereplication of the NEO-MSMS against the "putative Watrous oxylipins" available on the MASSIVE repository In the Watrous studies  6, 50  , more than 500 "unknown or putative oxylipins" were discovered, and as initially hypothesized in this manuscript and one of the purpose for this NEO-MSMS library, we believed that some of them could be related to NEO-PUFAs. We subsequently reanalyzed the data presented by Watrous et al. in 2019 through a conventional molecular network, using the same parameters and incorporating our library NEO-MSMS. However, the MS/MS files available present a mass error that can exceed 0. 04 m/z and could not be used to match lipid databases for predicting known fragmentations (simple loss of water or CO2 are already off target to match any formulae of oxylipins). Nevertheless, the power of molecular networking is that by increasing the mass error tolerance to 0.
Usage Notes The NEO-MSMS library can be used directly in the GNPS environment for the different molecular networking analysis. Also, it is available as a set of. mgf files compatible with MZmine3 libraries. oxPUFA files could be used for conducting classical molecular networking analysis with another samples as we demonstrated for serum and algae samples and dereplicate oxylipins. Additionally, the oxPUFA files will also be located in the MASSIVE repository as GNPS dataset allowing their use with GNPS MASST  51. With this tool, the oxPUFA data can be re-analyzed and used for the annotation of unknowns as putative NEO-PUFA in future lipidomic studies with special value for the novel separation approaches employing sophisticated chiral LC  52  or ion mobility  53, 54.
ACKNOWLEDGMENT This work was supported by  l'Institut de Chimie (INC)  via an  International Emerging Action  fund and the  U. S. National Science Foundation  ( CHE-2102550 ). We also thank  CNRS , the  French Ministry of Research, ICBMS , and the  Université Claude Bernard Lyon 1  for financial support. The NMR and  Mass Centres  of the  Université Claude Bernard Lyon  1 are thanked for their contribution. We thank the  Schimmel Family Endowed Scholarship Fund  for a  Graduate Fellowship  (C. Z. R. ).
Acknowledgements For financial support of this work, we acknowledge the  CEA ,  CNRS , the  European Research Council  ( ERC Consolidator Grant  Agreement no. 818260 ), as well as  VEOLIA  and  SIAAP. Vincent ROCHER  and  Sabrina GUERIN  ( SIAAP ) and  Guillaume BASLER  and  Arnaud SELAS  ( VEOLIA ) are warmly thanked for fruitful discussions regarding the valorisation of organic waste and their digestion to methane.
Acknowledgement CJ, LM and NC thank the  Australian Research Council  for funding. Moreover, this material is based upon work supported by the  Air Force Office of Scientific Research  under award number  FA2386-21-1-4048. LM is a senior member of the  Institut Universtaire de France  and thanks the  Alexander von Humboldt Foundation. LFL acknowledges the support of an  Australian Government Research Training Program  ( RTP)   International Scholarship. Part of this research was undertaken on the MX1 beam line at the  Australian Synchrotron , Victoria, Australia. Keywords. sila-acid anhydride, 1,2-disilylene, abnormal N-heterocyclic silylene, magnesium(I), radical.
Transmission electron microscopy (TEM) The microstructure of the materials (opened to air) was probed by TEM (FEI Talos F200X) equipped with a highbrightness field-emission gun, a high-angle annular dark field (HAADF) detector, and a large collection-angle EDX detector. The operation voltage of the instrument was set to 200 kV in scanning transmission electron microscopy (STEM) mode. The specimens for electron microscopy were prepared by dipping the Cu grids coated with carbon (TedPella, 300 mesh) into the powder. Ga K-edge X-ray absorption spectroscopy XAS measurements were performed at the BM31 station of the Swiss-Norwegian beamlines (SNBL) at the European Synchrotron Radiation Facility (ESRF). Data collection was carried out at the Ga K-edge using continuous scanning in transmission mode with a double-crystal Si(111) monochromator.
In situ XAS experiments were carried out in a quartz capillary reactor (diameter 1 mm; wall thickness = 0. 1 mm) whereby the material was placed between two quartz wool plugs. The material was subjected to a dehydroxylation treatment by flowing synthetic air (10 mL min -1 ) through the reactor while heating the material from room temperature to 650 °C (5 °C min -1 ) using an air blower. The material was kept at 650 °C for 1 h and then cooled down while the reactor was flushed with N 2 (10 mL min -1 ). For ex situ XAS measurements, materials were placed inside quartz capillaries (diameter 1. 5 mm; wall thickness 0. 1 mm) and sealed with wax and vacuum grease inside a N 2 -filled glovebox (H 2 O, O 2 < 1 ppm). All ex situ measurements were conducted at room temperature. The Demeter software was used for data processing.
The spectral energy was calibrated using a Zn foil (9659 eV). Extended X-ray absorption fine structure (EXAFS) data were fitted in R space between 1 and 3. 0 Å with a k-weight of 3. The Fourier transform (FT) of the EXAFS oscillations was obtained for a k-space range of 3-11 Å -1. The initial FEFF paths (Ga-O and Ga-Ga) and the amplitude reduction factor (S 0 2 ) were generated by the Artemis software using β-Ga 2 O 3 as the reference model. The continuous Cauchy wavelet transform (CCWT) analysis was performed with MATLAB software using an open script downloaded from http://www. univ-mlv. fr/~farges/ waw. 26  The Cauchy order was set to 20 while analyzing the long-range EXAFS data in R space from 0. 5 to 4 Å. To yield a better resolution of the CCWT images, we increased the Cauchy order to 150 for short-range R space (2-4 Å) analysis.
The composition of the outlet gases was analyzed by a 4-channel compact gas chromatography system (CompactGC 4. 0, Global Analyser Solutions) equipped with two thermal conductivity detectors (TCD) and two flame ionization detectors (FID). The data points were collected every 7 min. To assess the activity of the catalysts after reoxidation, a regeneration step was incorporated every 5 h of time-onstream. The regeneration step involved the flowing of synthetic air (30 mL min -1 at 550 °C, obtained by mixing dry N 2 and dry O 2 ) for 1 h, followed by a flushing step with N 2 (20 min, 30 mL min -1 ) before starting a new PDH cycle. The fractions of the gas-phase products (C x,out ) were determined by calibrating the GC with a gas mixture of known fractions. The formation rate of products (F x,out , mol min -1 ) was calculated based on the gas fractions measured in the outlet flow.
The high dispersion of Ga in Ga1 (650-air) is shown by EDX mapping (Fig. S8  and S9 †). We note, however, that an amorphous GaO x film with Ga sites in a tetracoordinate environment, that is, with a fitted CN of the first coordination sphere of 3. 7(3) has been reported. 54  To further differentiate between the contributions of Ga-Ga and Ga-Si scattering paths in the second sphere of Ga sites in Ga1 (500-air) and Ga1 (650) , a continuous Cauchy wavelet transform analysis of the EXAFS data was performed. 55  CCWT analysis correlates interatomic distances (r) and wave vector (k) spaces, thereby enabling the distinction between scattering atoms with different atomic numbers located at similar distances from the Ga site. Atoms with higher atomic numbers (such as Ga) scatter more strongly at higher k values than atoms with lower atomic numbers (such as Si).
Acknowledgements This work was supported by  ETH Zürich  through a  doctoral fellowship  to Z. C. ( ETH-40 17-2 ). We are grateful to the  Scientific Centre for Optical and Electron Microscopy (ScopeM, ETH Zürich)  for providing access to electron microscopy facilities. The  Swiss Norwegian Beamlines (SNBL)  at the  European Synchrotron Facilities (ESRF)  are acknowledged for providing access to the BM31 beamline. We thank  Dr. Dragos Stoian  for assistance with X-ray absorption spectroscopy measurements. We thank the  Pan-European Solid-State NMR   Infrastructure for Chemistry-Enabling Access (PANACEA) program  (funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  101008500 ) for provision of the measurement time. This publication was created as a part of  NCCR Catalysis  (grant agreement no.
Methods, MD simulation details Each studied system consists of an aggregate of 2500 F8H16 molecules (mimicking an isolated experimentally observed hemimicelle) placed on one of the surfaces of a water slab. Construction of the simulation boxes followed a previously established procedure  [27]. Briefly, simulation boxes of equilibrated slabs of water (a film of liquid water with 2 explicit water-vacuum interfaces perpendicular to the z axis) were prepared at the desired temperature and density. The vertically aligned F8H16 molecules, in their minimum energy configuration, were packed in a cylinder using the open-source software Packmol  [44]  and placed near the surface of water, with the CH chains facing the water surface. The system was then equilibrated in the NVT ensemble for at least 2 ns, followed by 20 ns NVT production runs, always at 298.
In the case of the OPLS-AA force field the unlike dispersion interactions are calculated using geometric combining rules, except for the interactions between hydrogenated and fluorinated atoms; the unlike energy interaction (ε HF ) is reduced by 23 % and the unlike size interaction (σ HF ) is increased by 0. 035 %. As in previous work, the SPC/ E  [54]  water model was used for all systems. Although the purpose of this work did not include testing different water models, a simulation run was made using the TIP4P/2005. The obtained hemimicelle displays a similar structure as those simulated over SPC/E water (Figure  S4 ). All used parameters are collected in Table  S1. The simulations were performed using the GROMACS (version 2020. 6)  [55]  open-source software. The simulation conditions and remaining algorithms are the same as those used before for the simulations of the OPLS aggregate  [27].
For some of the simulations, a planar and non-deformable water surface has been generated by restraining the water molecules to a well-defined layer in the zz direction. This was achieved by subjecting the water molecules to a position dependent potential, that is zero in the central region of the layer and then increases steeply, according to a harmonic function (force constant = 1000 kJ mol -1 nm -1 ), as the frontiers of the slab are reached. The width of the slab was chosen according to the experimental density of water. The simulation trajectories were analysed using GROMACS built-in capabilities, in-house written programs and, for the computation of spatial distribution functions (SDFs), the open-source software TRAVIS (version May 4, 2020)  [56, 57]. The snapshots of the simulations were rendered using the open-source web viewer Mol*  [58]  and the SDFs' representations were obtained using the Visual Molecular Dynamics (version 1.
Acknowledgements P. S. acknowledges funding from  Fundação para a Ciência e a Tecnologia (FCT)  through research grant  SFRH/BD/149192/2019. G. M. C. S. acknowledges funding from  FCT , Grant  SFRH/BD/123565/2016. Centro de Química Estrutural  is funded by  FCT  through grants  UIDB/ 00100/2020  and  UIDP/00100/2020.
Human samples Samples were generated following the clinical protocol "In Vivo Alzheimer Proteomics (PROMARA)" (ClinicalTrials Identifier: NCT02263235), which was authorized by the French ethical committee CPP Sud-Méditerranée IV  (#2011-003926-28)  and by the ANSM agency (#121457A-11). Enrolled patients (group a) were hospitalized in neurosurgery unit due to subarachnoid hemorrhage and received a temporary ventricular derivation of the CSF. The experiment protein turnover started 8 to 19 days after initial, medical ventricular drainage and normalization of CSF clinical chemistry analysis (normal CSF protein content lies in the 0. 2-0. 4 g/L range  (Roche et al. , 2008) ). Additional patients (group b) were hospitalized in neurology in relation with cognitive impairment etiologic investigation.
Note that in the case of protein turnover, only ratios are modeled and hence and are dimensionless. They are related by the following ordinary differential equations (ODEs): , ( 1 ) with. Figure  1C  illustrates a typical dataset with the and curves. It is important to observe that , which relates to tracer availability for protein synthesis, essentially acts as a scale parameter, whereas , the clearance/degradation rate primarily acts as a shape parameter that conditions protein half-life. Due to the generally large ratio of intensities between and , we have shown in that noise causes an almost uniform vertical shift of the observed RIA values  (Lehmann et al. , 2019). Therefore, we proposed an algorithm that include the computation of an optimal shift along with the parameters and to adjust to the data.
SUPPLEMENTARY DATA Tables S2-S5 are provided as supplementary data. Table  S1  is in Supplementary Information (SI). All the individual protein data for all the patients are provided as supplementary data. Preprocessed input data, control plots, and the scripts implementing the Bayesian models are available from Zenodo (10. 5281/zenodo. 8425322). MRM data are available from https://panoramaweb. org/jGcF67. url. Blood plasma whole transcriptome data are available from https://panoramaweb. org/Xpd6J5. url. CSF whole transcriptome data were made public already  (Lehmann et al. , 2019).
It is widely agreed that people make irrational decisions in the presence of irrelevant distractor options. However, there is little consensus on whether decision making is facilitated or impaired by the presence of a highly rewarding distractor or whether distraction effect operates at the level of options' component attributes rather than at the level of their overall value. To reconcile different claims, we argue that it is important to incorporate consideration of the diversity of people's ways of decision making. We focus on a recent debate over whether people combine choice attribute in an additive or multiplicative way. Employing a multi-laboratory dataset investigating the same decision making paradigm, we demonstrated that people used a mix of both approaches and the extent to which approach was used varied across individuals.
Results This study analysed empirical data acquired from human participants (five datasets; N=144) performing the multi-attribute decision-making task described in Figure  1   (Chau et al. , 2014; Gluth et al. , 2018). Participants were tasked to maximise their rewards by choosing between options that were defined by different reward magnitudes (𝑋) and probabilities (𝑃) depicted as rectangular bars of different colours and orientations, respectively (Figure  1b ). The task involved, as a control, Two-Option Trials, on which participants were offered two options. It also involved Distractor Trials, on which two chooseable options were presented alongside a distractor option which could not be selected. The two chooseable options and unchosen distractor option are referred to as the higher value (𝐻𝑉), lower value (𝐿𝑉), and distractor value (𝐷𝑉), based on their utility.
We found that the distractor effect, reflected in the (𝐷𝑉 -𝐻𝑉)𝑇 term, was significantly greater in the Multiplicative Group than the Additive Group (Figure  5 ; 𝑡(142) = 3. 792, 𝑝 = 0. 00022). Critically, the distractor effects were significant within each of the individual groups but bore opposite signs: the distractor effect was positive in the Multiplicative Group [𝛽 = 0. 105, 𝑡(70) = 3. 438, 𝑝 = 0. 000991]but negative in the Additive Group [𝛽 = -0. 0725, 𝑡(72) = -2. 053, 𝑝 = 0. 0437]. Finally, we performed two additional analyses that revealed comparable results to those shown in Figure  5. In the first analysis, reported in Supplementary Figure  1 , we added an 𝐻𝑉 + 𝐿𝑉 term to the GLM, because this term was included in some analyses of a previous study that used the same dataset  (Chau et al.
Multi-attribute decision-making task and datasets The current study re-analysed five published datasets of empirical data based on a multi-attribute decision-making task  (Chau et al. , 2014; Gluth et al. , 2018). The experimental task involved participants making a decision between two options in the absence (Two-Option Trials) or presence (Distractor Trials) of a third distractor option that could not be chosen. During each trial, two or three stimuli associated with different reward magnitudes and probabilities (represented by colours and orientations) were randomly presented in selected screen quadrants (Figure  1 ). Immediately following stimulus onset (0. 1 s), options that were available for selection were surrounded by orange boxes, while the distractor option that could not be selected was surrounded by a purple box.
The choice of the participant was indicated by the change in colour of the surrounding box from orange to red. At the end of each trial, the edge of each stimulus changed to yellow if the choice was rewarded, and to grey if the choice was not rewarded. A total of 144 human participants were included in the analysis with data from the original fMRI dataset (N = 21;  (Chau et al. , 2014) ) and additional replication experiments (Experiments 1, 2, 3, and 4) performed by Gluth and colleagues (N = 123;  (Gluth et al. , 2018) ).
Acknowledgments We thank the  Ginesis Lab (GIN, Fealinks ,  Labcom Programme  2016,  ANR 16LCV2-0006-01 ) for their help with data management and processing. We are also indebted to  Violaine Verrecchia  and  Marc Joliot  for their help in data analysis. Our thanks also go to  Marie Guerlain ,  Annie Bardon-Lay , and her team members for their help with the face painting. Warm thanks also go to all those who agreed to participate in our experiments. 7.
Acknowledgement We are thankful to the sports club  Elain Gymnique Rouennais ( Rouen, France ), which agreed to participate in the study with its 'Top 12' women's team. In particular to the club's Manager  Jean Manuel Gouvy , for his con dence, for sharing the objectives of the research protocol and for passing on his motivation to all the club's staff. We have to express our appreciation to the team's coaches,  Stephanie Bretin  and  Eric Pasquelin  for sharing their pearls of wisdom with us during the course of this research. Special thanks to the gymnasts for their professionalism and determination, despite their young age.
A major strength of computational cognitive models is their capacity to accurately predict empirical data. However, challenges in understanding how complex models work and the risk of overfitting have often been addressed by trading off predictive accuracy with model simplification. Here, we introduce state-of-the-art model analysis techniques to show how a large number of parameters in a cognitive model can be reduced into a smaller set that is simpler to understand and can be used to make more constrained predictions with. As a test case, we created different versions of the Connectionist Dual-Process model (CDP) of reading aloud whose parameters were optimized on seven different databases. The results showed that CDP was not overfit and could predict a large amount of variance across those databases.
Indeed, the quantitative performance of CDP was higher than that of previous models in this area. Moreover, sloppy parameter analysis, a mathematical technique used to quantify the effects of different parameters on model performance, revealed that many of the parameters in CDP have very little effect on its performance. This shows that the dynamics of CDP are much simpler than its relatively large number of parameters might suggest. Overall, our study shows that cognitive models with large numbers of parameters do not necessarily overfit the empirical data and that understanding the behavior of complex models is more tractable using appropriate mathematical tools. The same techniques could be applied to many different complex cognitive models whenever appropriate datasets for model optimization exist.
1  Thus, the number of parameters in a computational model is completely constrained by the underlying theory that is being modelled. Yet, some computational models, like those of the connectionist dual process family (see below), have been criticized for over-fitting the data  (Seidenberg, 2012; Seidenberg & Plaut, 1998). The argument of over-fitting has been presented in various ways in the literature. First, since the publication of  CDP+ (2007) , there has been a strong emphasis on a model's ability to account for item-level variance in large-scale databases. Indeed, CDP+ accounted for 17% of the item-level variance in the  Spieler and Balota (1997)  database, while its competitors (DRC and PDP) remained below 4% of the variance. However, it is easy to argue that CDP+'s larger number of parameters allowed its authors to "tweak" some of them to obtain better fits.
Finally, there has been an argument about how much variance there is to be explained in the empirical data  (Adelman et al. , 2013; Perry et al. , 2010a; Rey et al. , 2013; Seidenberg & Plaut, 1998). That is, largescale databases might be contaminated by undesirable sources of error variance and to understand how well a model is performing, one would need to estimate the amount of experimental noise that models cannot or should not try to account for  (Rey et al. , 2013; Seidenberg & Plaut, 1998). In this respect,  Perry et al. (2010a)  looked at the variance that is common across the same items in different databases. They found that performance to identical items across four different databases correlated moderately, with r values ranging between. 42 and. 68 (i. e. , 17. 6% and 46. 2% of the variance).
Sloppy parameters The SPA technique was initially proposed by  Gutenkunst et al. (2007a). The basic idea is that it allows one to quantify the extent to which different parameters affect a model's behavior. The key question is which parameters have strong effects on behavior and which have only small effects. This question will be addressed by optimizing CDP on major published data sets of reading aloud. Parameters in the model can then be varied to examine the effect they have on model performance. This will allow us to examine which parameters affect model fit across different datasets and which have a restricted or no effect at all. This will give insights into how complex CDP really is and which parameters are important for the predictions it generates. It is worthwhile noting that prior model optimization is essential for conducting SPA.