dataesr/scientific-paragraphs-categorization huggingface dataset:
2 percentage (%) 10 0 10 20 30 40 -17. 9% 0 10 20 30 40 9. 78 full 9,341 7,500 8,456 378,249 391,775 383,295 100,000 200,000 300,000 Lung 51. 2 45. 6 2,500 14. 0 % number 8. 1 0 5,000 Lung 112,132 106,530 108,265 0 number of identifications 7,500 6,667 Jurkat 5,000 Jurkat 6,335 100,000 200,000 300,000 7. 8 31. 2 39. 2 13. 4 0 2,500 81,243 % specific peptides % single-protein CCs tot nb CCs D. 53,992 80,273 53,638 80,373 53,575 0 spectra peptides nb proteins/nb genes (per multi-protein CC) B. Lung Jurkat 0 20 40 reduced full full database reduced reduced full database post-hoc filter reduced DB search full full DB search percentage (%) reduced reduced 0 25 50 75 0 25 50 75 percentage (%) TDCvalid PSMs at 1% F E.
The current research explores how this model can account for human neurophysiology of narrative processing in much more extended narratives, increasing from the order of 10 1 to 10 2 -10 3 words. Here, instead of looking at trained readout responses, we directly examine the high dimensional representations of word embeddings within the recurrent reservoir, which is a high dimensional non-linear integrator. We compare these representations with those from human neurophysiology in human fMRI. It is important to acknowledge that this research would not be possible without the open science approach in modern neuroscience and machine learning. Data and algorithms used in the analysis of the human neuroscience are publicly available  [8, 44, 45] , as is python code for developing the reservoir model  [46] , and for creating word embeddings for narrative transcripts  [39].
[8]  to the trajectory of reservoir states generated by feeding the embeddings for the words in these texts into the reservoir. The HMM takes as input the neural trajectory to be segmented, and the number of segments, k, to be identified, and produces a specification of the most probable event boundaries. The HMM model is available in the python BrainIAK library described in the Materials and Methods section. Independent of the integration provided by the reservoir, there is abundant rich information in the embeddings themselves. It is thus of interest to determine if this information alone is sufficient to allow good segmentation based on the embeddings alone. Likewise, it is crucial to demonstrate that the NIR model's performance is different from something very simple like feeding the embeddings into a linear integrator (LI) model.
Using material from these experiments, we set out to compare HMM segmentation in two conditions: The first is the fMRI signal from humans who watched the same 24 minute movie, and the second is the recurrent reservoir activity when the Narrative Integration Reservoir is exposed to the text transcript of the 18 minute recall of that same movie as in  [47]. Thanks to their open data policy  [8, 9, 44, 45, 47] , we have access to fMRI from human subjects who watched the episode of Sherlock, along with a transcript of the recall of this movie. For fMRI we use data from Chen et al. 2017 that was recorded while subjects watched the first 24 minutes of the Sherlock episode (see Materials and Methods). For input to the NIR we use the transcript of the 18 minute audio recall of this 24 minute film from episode from  [47].
We use the transcript to generate a sequence of word embeddings from Wikipedia2vec which is the input to the Narrative Integration Reservoir. The resulting trajectory of activation patterns in the reservoir can be segmented using the HMM. In parallel the HMM is used to segment human fMRI from subjects who watched the corresponding movie. This allows a parallel comparison of the HMM segmentation of human brain activity and of Narrative Integration Reservoir activity, as illustrated in Fig 3. fMRI Data from 16 subjects who watched the 24 minute move were compared with state trajectories from 16 NIR reservoirs exposed to the transcript from the 18 recall of the movie. The movie duration of 24 minutes corresponds to a total recording of 946 TRs. For each subject, the HMM was run on the fMRI data from the angular gyrus (AG) which has been identified as an area that produces related event processing  [45, 47]  and segmentation  [8]  for watching a movie and listening to recall of the same movie.
Baldassano et al. [8]  determined that the optimal segmentation granularity for AG is between 50-90 segments for the 1976 TRs in the 50 minute episode, corresponding to a range of 24-43 segments for the 946 TRs in the 24 minute fMRI data we used. We thus chose k = 40 for the HMM segmentation, as a value that has been established to be in the optimal range for AG. The summary results of the HMM segmentation of the NIR and fMRI trajectories are presented in For the Narrative Integration Reservoir, 16 instances were created using different seed values for initialization of the reservoir connections, and each was exposed to the word by word narrative of the recall of the movie. For each word, the corresponding Wikipedia2vec 100 In order to visualize the temporal correspondence between the reservoir and the fMRI, we can calculate the probability that reservoir state and an fMRI TR are in the same event (regardless of which event it is).
In the current research we focus our analysis directly on the rich high dimensional states in the reservoir itself. That is, we do not train the reservoir to perform any transformation on the inputs. Instead, we analyze the activity of the reservoir neurons themselves. The basic discrete-time, tanh-unit echo state network with N reservoir units and K inputs is characterized by the state update equation: xðt þ 1Þ ¼ ð1 À aÞxðtÞ þ a � f ðWxðtÞ þ W in uðtÞÞ ð3Þ where x(n) is the N-dimensional reservoir state, f is the tanh function, W is the N×N reservoir weight matrix, Win is the N×K input weight matrix, u(n) is the K dimensional input signal, α is the leaking rate. The matrix elements of W and W in are drawn from a random distribution. The reservoir was instantiated using easyesn, a python library for recurrent neural networks using echo state networks (https://pypi.
BrainIAK HMM model The HMM model is described in detail in Baldassano et al. [8]  and is available as part of the BrainIAK python library, along with example jupyter notebook that corresponds to  [8]  (https://github. com/brainiak/brainiak/tree/master/examples/eventseg). Given a set of (unlabeled) time courses of (simulated or real) neural activity, the goal of the event segmentation model is to temporally divide the data into "events" with stable activity patterns, punctuated by "event boundaries" at which activity patterns rapidly transition to a new stable pattern. The number and locations of these event boundaries can then be compared between human neural activity and simulated Narrative Integration Model activity, and to ground truth values. The model is run on a given fMRI or reservoir trajectory, and requires specification of the expected number of segments, k.
Model code and data This research is realized in the open code spirit, and indeed benefitted from open code and data for the fMRI experiments  [9, 44]  and the HMM segmentation model  [8] , and for development of the reservoir model  [46] , and the language model for word embeddings  [39, 40]. The Narrative Integration Model code in python, and all required data is available on GitHub https://github. com/pfdominey/Narrative-Integration-Reservoir/. The fMRI data for 16 subjects in the comparison of human and Narrative Integration Reservoir event segmentation originates from the study of  [45]. Data from the angular gyrus for the first 24 minutes of the Sherlock episode were derived from this data and provided by Baldassano https://figshare. com/articles/dataset/Sherlock_data_for_OHBM/12436955.
(2019) , effectively couples a snow spectral albedo model with a narrowband atmospheric radiation scheme. This method (Spectral-to-NarrOWBand ALbedo module; SNOWBAL) allows the coupling of the radiative transfer model TARTES (Two-streAm Radiative TransfEr in Snow,  Libois et al. , 2013)  with the European Centre for Medium-Range Weather Forecasts (ECMWF) radiation McRad scheme based on the shortwave Rapid Radiation Transfer Model (RRTMsw) embedded in RACMO2  (Mlawer et al. , 1997; Clough et al. , 2005; Morcrette et al. , 2008; ECMWF, 2009). They used the first 12 of the 14 predefined representative wavelengths (RWs, for every 14 bands of RRTMsw) dependent on irradiance distribution and albedo within a spectral band to calculate the narrowband albedo and radiation absorption in each (sub)surface snow layer.
One of the perspectives would be to integrate this method into a radiative scheme of a global or regional climate model in order to drastically reduce the calculation time and to largely improve the albedo calculation compared to more common broadband and/or narrowband calculations. Code and data availability. The VALHALLA v1. 0 development and data presented and described in this article are available for download at https://doi. org/10. 5281/zenodo. 4570565 (Veillon et al. , 2021). TARTES is available as a python module at https://pypi. org/ project/tartes/ (last access: 19 November 2021,  Picard and Libois, 2021). SBDART model is available at https://github. com/ paulricchiazzi/SBDART (last access: 19 November 2021, Ricchiazzi, 2018). Author contributions. FV, MD, and MF started this project and developed the method.
Acknowledgements. The authors are grateful to  Ghislain Picard  and  Quentin Libois  for discussion on the VALHALLA method. The authors are also grateful to the two referees,  Joseph Cook  and  Christiaan Van Dalum , for very helpful and relevant comments on the paper. Financial support. CNRM/CEN is part of  Labex OSUG@2020  ( ANR-10-LABX-0056 ). This work was partly funded by  CNES APR MIOSOTIS  and by  ANR  grant  EBONI  ( ANR-16-CE01-0006 ). Marie Dumont  has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme (IVORI , grant agreement no. 949516 ). Review statement. This paper was edited by  Fabien Maussion  and reviewed by  Christiaan van Dalum  and  Joseph Cook.
Outcomes Outcomes included cumulative incidence, mortality, and number of hospital admissions. The probabilities of hospital admission or death were strati ed by age and adjusted for comorbidities, including, obesity, diabetes, chronic cardiac diseases, and chronic respiratory diseases, based on hazard ratios calculated using data from Institut Pasteur  23  and from the OpenSAFELY cohort study. 16 To re ect improved care of patients with COVID-19, we reduced the risk of death by an average of 10% in the model, regardless of age, starting July 1, 2020 to t observational data. 24, 25 Delays between infection, symptom onset, hospital admission, death or recovery were based on prior reports. 23, 26, 27 ccine E cacy COVID-19 vaccine e cacy was assessed using published results for the BNT162b2 mRNA COVID-19 Vaccine from P zer/BioNTech.
8 ). We thus assessed the localization of the proteins implicated in endocytosis in the lines expressing the iDePP system. MAP-mCH-dOCRL induction caused a decrease in both clathrin-adaptor protein AP-µ2 (AP-µ2-green fluorescent protein (AP-µ2-GFP); Fig. 4a-c ) and Src homology 3 (SH3)-domain-containing protein 2 at the plasma membrane (SH3P2-GFPs; Extended Data Fig. 8 ), while the localization of clathrin light chain 2 (CLC2-GFP) was not affected at the plasma membrane, nor at the trans-Golgi network (TGN) (Extended Data Fig. 8 ). These results suggest that CLC2 is likely recruited to the plasma membrane not only by AP2 but also by additional adaptor proteins whose localizations are not fully dependent on PI(4,5)P 2 , such as the TPLATE complex  15. The amphiphilic dye FM 4-64uptake experiments confirmed that bulk endocytosis was reduced in the line expressing MAP-mCH-dOCRL compared with the controls, with a reduction of vacuolar staining after one hour of FM 4-64 incubation (Fig.
Methods Sequence alignment. The human HsOCRL 234-539 and the Drosophila dOCRL amino acid sequences were aligned using T-Coffee software (http://tcoffee. crg. cat/ apps/tcoffee/do:regular) and the fasta_aln files obtained were then treated with BoxShade (https://embnet. vital-it. ch/software/BOX_form. html) and Inkscape (https://inkscape. org) programs. Strategy. We initially tested four systems that were previously published in mammalian literature and had used the following phosphatases: Inp54 (from or MAP-mCH-dOCRL dead (right panel) on FM 4-64 uptake (d), 1 hour after the beginning of the incubation (±10 min; images were colour coded in Green Fire Blue as in Fig. 2 ) and related quantification of the vacuolar staining 1 hour after FM 4-64 incubation (e). f, Dynamics of F-actin cytoskeleton labelled by Lifeact-yFPv in root cells expressing MAP-mCH-dOCRL or MAP-3xmCH after 5 µM dex (20 hours; top panel), and relative kymographs extracted from a time course over 2 min (bottom panel).
For root-hair phenotyping, five-day-old plantlets grown on root-hair medium, ½ MS medium, 2% sucrose, 0. 6% phytagel, 0. 01% myo-inositol and vitamins (0. 5 mg l -1 of nicotinic acid and vitamin B6, 0. 1 mg l -1 vitamin B1 and 2 mg l -1 glycine), were transferred to 5 µM dexamethasone-containing root-hair medium. Roots were imaged with a manual stereomicroscope Nikon SMZ18 (magnification: ×7. 5 to ×135, zoom ratio 18:1, LED diascopic illumination base, LED fluorescence light source (CoolLED pE-300 Lite)), equipped with a Hamamatsu ORCA-Flash9. 0 LT camera (Nikon NIS acquisition software) and controlled by a Dell Precision 3630 computer. For cotyledon analysis, seeds were sterilized and sown on Arabidopsis medium (MS medium without sugars and vitamins) for seven days. Seedling were then carefully transferred to MS medium (without sugars and vitamins) supplemented with 5 µM dexamethasone.
The values were transferred to Excel to obtain the graph. Shoot apical meristem and NPA-induced meristem projected images were obtained by using the MorphoGraphX software (https://www. mpipz. mpg. de/MorphoGraphX). The signal projections were generated by extracting the fluorescent signal at the surface of the meristem (between 2 and 5 µm from the meristem surface) and by projecting it onto the cellular mesh. Projections of root epidermal cells were obtained using the Fiji 'Maximum intensity projection' tool on z-stacks of 21 slices 0. 5 µm distant from each other. Dissociation indexes of membrane lipid fluorescent biosensors were measured and calculated as previously described  12. Briefly, we calculated 'indexNoDex' in the mock condition, defined as the ratio between the fluorescence intensity (mean grey value function of Fiji software) measured in two elliptical regions of interest (ROIs) from the plasma membrane region (one in the apical/basal plasma membrane region and one in the lateral plasma membrane region) and two elliptical ROIs in the cytosol.
Statistical analysis. For dissociation index and anisotropy, we performed all our statistical analyses in R (version 3. 6. 1, R Core Team, 2019), using the RStudio interface and the packages ggplot2 (ref. 47 ), lme4 (ref. 48 ), car  49  , multcomp  50  and lsmeans  51. For each model, assuming normality, we plotted residual values against normal quantiles to confirm their normal distribution. Graphs were obtained with R and RStudio software and customized with Inkscape. Details can be found in We observed a strong correlation between MAP-mCH-dOCRL expression and its impact on mCIT-2xPH PLC localization. Indeed, in the centre of the shoot meristem, MAP-mCH-dOCRL was not expressed and mCIT-2xPH PLC labelling clearly accumulated at the cell periphery, probably representing the plasma membrane.
We were concerned that this domain, that underwent natural selection in an endotherm organism with set-point temperature of 37 °C, would be less active in Arabidopsis thaliana, a plant of temperate climate which we usually grow at 21 °C. Therefore, we decided to use the phosphatase domain of dOCRL, Drosophila melanogaster homolog, as Drosophila are ectotherm and live at ~ 20 °C. Alignment of OCRL1 234-539 and dOCRL allowed us to identify dOCRL 168-509 as the homolog region of OCRL1 generally used in optogenetic systems. b, Coomassie blue staining monitoring purification of recombinant dOCRL phosphatase domain (His-SUMO-dOCRL 168-509 -His) from Escherichia coli. We first assessed dOCRL activity, which has not been reported so far, even though in vivo data suggest a conserved PI(4,5)P 2 5-phosphatase activity.
Acknowledgements We are grateful to the  SiCE group  (RDP), in particular  T. Gaude  and  V. Bayle  ( RDP, Lyon ), and to  Y. Boutté  ( Laboratoire de Biogenèse Membranaire ),  D. Van Damme  ( Vlaams Instituut voor Biotechnologie (VIB) ),  T. Stanislas  (ZMBP),  F. Besnard  and  N. Doll  (RDP), for comments and discussion. We also thank former interns  L. Courgeon  ( ENS de Lyon ) and  A. Bauer  ( ENS de Lyon ), who performed preliminary experiments during the early steps of this project and helped with plant labour, and  K. Grünwald. We thank  J. Berger ,  P. Bolland  and  A. Lacroix  from our plant facility. We acknowledge the contribution of  Structure Fédérative de Recherche Biosciences  ( UMS3444/CNRS ,  US8/ Inserm ,  ENS de Lyon, Université Claude Bernard Lyon 1)  facilities:  C. Lionet ,  E.
Chatre  and  J. Brocard  at the  LBI-PLATIM-MICROSCOPY  for assistance with imaging. We are also grateful to  E. Russinova  ( VIB ) for kindly providing us with ES9-17;  S. Bednarek  for sharing markers and for discussion and  M. Uyttewaal  ( INRAE ) for the MBD-GFP marker. We thank  E. Isono  ( University of Konstanz ) for sharing SH3P2-GFP's transgenic line with us. We also thank  R. Boisseau  ( OBEE department, University of Montana ) for help with the statistical analysis. This work was supported by the  European Research Council  (no. 3363360 -APPL under FP/2007-2013, Y. J. ;  EU-FET 828753 , T. M. ), the  Netherlands Organisation for Scientific Research  ( NWO 867. 15. 020; 711. 017. 005 ;T. M. ),  Austrian Academy of Science  through the  Gregor Mendel Institute (Y. B. ) ,  French National Research Agency ANR  (caLIPSO;  ANR-18-CE13-0025-02 ; Y.
Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source Python-based tool to estimate the fluxes of various GHGs Published by Copernicus Publications on behalf of the European Geosciences Union. and reactive species both at the global and regional scales. It will allow for running different atmospheric transport models, different observation streams and different data assimilation approaches.
Collaborative efforts towards unified systems are already happening in other data assimilation communities, with, for example, the Object-Oriented Prediction System (OOPS; coordinated by the European Centre for Medium-Range Weather Forecasts, UK) or the Joint Effort for Data Integration (led by UCAR/JCSDA; https://www. jcsda. org/jcsda-project-jedi, last access: 23 August 2021). The Data Assimilation Research Testbed (DART;  Anderson et al. , 2009)  is also an example of collective effort proposing common data assimilation scripts for diverse applications (e. g. , Earth system or reactive species inversions;  Gaubert et al. , 2020). The Community Inversion Framework (CIF) is an initiative by members of the GHG atmospheric inversion community to bring together the different inversion systems used in the community, and it is supported by the European Commission H2020 project VERIFY.
Computation modes in the CIF The present version of the CIF includes three main categories of inversion methods: (1) analytical, i. e. , algebraic solution of the unbiased Gaussian Bayesian problem; (2) ensemble methods with the ensemble square root filter (EnSRF); and (3) variational with two examples of minimizing algorithms (M1QN3 and CONGRAD). Other types of data assimilation methods (e. g. , direct sampling of probability density functions through Monte Carlo approaches) are also used by the community. The choice of implementing the three aforemen-tioned methods first is motivated by their dominant use and because these three use very different approaches for solving the Bayesian inversion problem, i. e. , with/without random sampling of probability distributions and with/without the use of the adjoint of the observation operator.
For very dense observations, such as datasets from new-generation highresolution satellites, the sequential assimilation of observations may not be sufficient, or at least methods may be needed to make the observation errors between sequential assimilation windows independent, e. g. , by applying a whitening transformation to the observations to form a new set with uncorrelated errors as suggested by  Tippett et al. (2003). The challenge is exacerbated for long-lived species such as CO 2 , for which assimilation windows must be long enough to maintain the propagation of information on the fluxes over long distances through transport; propagating a covariance matrix from assimilation windows to assimilation windows as accurate as possible could in principle limit the later issue, as https://doi.
General rules The Community Inversion Framework project follows the organization scheme of Fig. 3. A centralized website is available at http://community-inversion. eu, last access: 23 August 2021. The website includes all information given in the present paper, as well as further documentation details, practical installation instructions, tutorials and examples of possible set-ups. To foster the collaborative dynamics of our project, all open-access scripts and code files are available on a GitLab server at http://git. nilu. no/VERIFY/CIF, last access: 23 August 2021, where updates are published regularly. The frozen version of the code, documentation and data used for the present publication is available in  Berchet et al. (2021). The repository includes the documentation, sources for the CTMs implemented in the CIF and the Python library pyCIF.
-Data vectors. These store all information on inputs for pyCIF; this vector is used by the observation and target vector classes to build themselves. -Minimizers. These are the algorithms used to minimize cost functions, including M1QN3 and CONGRAD algorithms so far. -Simulators. These are the cost functions to minimize in variational inversions; they only include the standard Gaussian cost function so far. -Domains. These store information about the CTM's grid, including coordinates of grid cell centres and corners, vertical levels, etc. -Fluxes, fields and meteo-data. These fetch, read and write different formats of inputs for CTMs (surface fluxes, 3D fields and meteorological fields respectively); so far they include only inputs specific to included CTMs, but they will ultimately include standard data streams, such as widely used emission inventories or meteorological fields such as those from ECMWF.
-Measurements. These fetch, read and write different types of observation data streams; they only include the World Data Centre for Greenhouse Gases so far (https: //gaw. kishou. go. jp/, last access: 23 August 2021) but classical data providers such as ICOS (http://icos-cp. eu, last access: 23 August 2021) or ObsPack  (Masarie et al. , 2014)  will also be implemented in the CIF. Satellite products, in particular the formatting of averaging kernels and other metadata, should also be included in the CIF in the near future as they play a growing role in the community. Details on metadata and operations for each class are given in the Supplement, Table  S1. Our objective was to design a code that is fully recursive in the sense that modifying some instance of a class does not require users to update other classes calling or being called by the modified class.