dataesr/scientific-paragraphs-categorization huggingface dataset:
and MEDESS-GIB Ocean Sci. Discuss. , https://doi. org/10. 5194/os-2018-150 Manuscript under review for journal Ocean Sci. Discussion started: 8 January 2019 c Author(s) 2019. CC BY 4. 0 License. (EU MED Program; http://www. medess4ms. eu/ ; Sotillo et al. , 2016) multi-platform experiments as well as other experiments gathered in the In Situ Thematic Centre (INS TAC) products from CMEMS were used. The processing of these data is analogue to that applied to the global product (see section 3. 2).
retrieved by the drifters (Figure11. be) present a correlation coefficient larger than 0. 40 in most of the boxes. Correlations larger than 0. 50 are mainly located in the southernmost part of the basin where a stronger mesoscale activity occurs; namely the Alboran Sea (DS1), the Algerian Basin (DS3 and DS4), the Sardinian Channel (DI1), the Sicily Strait (DI3), the Ionian Sea (boxes DJ7, DJ8 and DJ5), and the Cretan passage (DH3). The overall RMS difference between both datasets ranges between 8 -11 cm/s whilst it reaches 20 cm/s in DS1 due to the strong dynamics of this area. Slightly large errors are obtained when comparing the DT2014 product with drifter observations (figure not shown). Furthermore, drifter data collected in boxes DS1, DS3 and DS4 present the largest variability due to the aforementioned mesoscale activity.
Data availability The datasets are available from the CMEMS web-portal (http://marine. copernicus. eu/services-portfolio/access-to-products/) and the C3S data store (https://cds. climate. copernicus. eu). Level 2 (GDR) input data are provided by CNES, ESA, 5 EUMETSAT and NASA. The MEDESS-GIB dataset is available through the PANGAEA (Data Publisher for Earth and En-vironmental Science) repository, with the following doi:10. 1594/PANGAEA. 853701. The AlborEx dataset is available at the SOCIB web page (http://www. socib. eu).
Acknowledgements 10 The DT2018 reprocessing exercise has been supported by CNES/SALP project, CMEMS and C3S services funded by the European Union. Global L3 Sentinel-3 production is coordinated by EUMETSAT and funded by the European Union. Solid earth tide Elastic response to tidal potential  [Cartwright and Tayler, 1971] ,  [Cartwright and Edden, 1973]  Mean Sea.
Protein blots For SDS-PAGE, protein samples were subjected to the gel electrophoresis at room temperature (12% acrylamide, 375 mM Tris-HCl, pH 8. 8, and 0. 1% SDS) followed by transfer blotting onto nitrocellulose membrane. For Phos-tag SDS-PAGE, proteins from kinase assays were subjected to Phos-tag gel electrophoresis [6% acrylamide, 375 mM Tris-HCl, pH 8. 8, 50 lM Phos-tag (Wako), and 100 lM MnCl 2 ] at 4°C followed by transfer blotting. After incubation with the primary and secondary antibodies, the immuno-blots were exposed and observed using a Bio-Rad Image Analyzer. Relative protein levels were quantified with the Image Lab software (Bio-Rad).
Sample preparation and LC-MS/MS data acquisition The protein mixtures after kinase assays were reduced with dithiothreitol, alkylated with chloroacetamide, and digested with trypsin. Subsequently, the digested samples were desalted using StageTips with C18 Empore disk membranes (3 M;  Rappsilber et al, 2003) , dried in a vacuum evaporator, and dissolved in 2% ACN, 0. 1% TFA. Samples were analyzed using an EASY-nLC 1200 (Thermo Fisher) coupled to a Q Exactive Plus mass spectrometer (Thermo Fisher). Peptides were separated on 16 cm frit-less silica emitters (New Objective, 0. 75 lm inner diameter), packed in-house with reversed-phase ReproSil-Pur C18 AQ 1. 9 lm resin (Dr. Maisch). Peptides were loaded on the column and eluted for 50 min using a segmented linear gradient of 5-95% solvent B (0 min: 5%B; 0-5 min-> 5%; 5-25 min-> 20%; 25-35 min-> 35%; 35-40 min-> 95%; 40-50 min-> 95%; solvent A 0% ACN, 0.
MS data analysis and PRM method development Raw data from DDA acquisition were processed using MaxQuant software (version 1. 5. 7. 4, http://www. maxquant. org/; Cox and Mann, 2008). MS/MS spectra were searched by the Andromeda search engine against a database containing the respective proteins used for the in vitro reaction. Trypsin specificity was required and a maximum of two missed cleavages allowed. Minimal peptide length was set to seven amino acids. Carbamidomethylation of cysteine residues was set as fixed, phosphorylation of serine, threonine and tyrosine, oxidation of methionine, and protein N-terminal acetylation as variable modifications. The match between runs option was disabled. Peptide spectrum matches and proteins were retained if they were below a false discovery rate of 1% in both cases.
The DDA approach only enabled the identification of T142. To analyze the putative phosphorylation sites at T184 and T535, a targeted approach was employed. Raw data from the DDA acquisition were analyzed on MS1 level using Skyline (version 4. 1. 0. 18169, https://skyline. ms; MacLean et al, 2010) and a database containing the respective proteins used for the in vitro reaction. Trypsin specificity was required and a maximum of two missed cleavages allowed. Minimal peptide length was set to seven maximum length to 25 amino acids. Carbamidomethylation of cysteine, phosphorylation of serine, threonine and tyrosine, oxidation of methionine, and protein N-terminal acetylation were set as modifications. Results were filtered for precursor charges of 2, 3, and 4. For each phosphorylated precursor ion, a respective non-phosphorylated precursor ion was targeted as a control, and several precursor ions from the backbone of ASY1 recombinant protein were chosen as controls between the different samples.
Acknowledgements We acknowledge the Salk T-DNA collection, the  GABI-Kat T-DNA collection , the  Arabidopsis Biological Resource Center (ABRC) , and the  European Arabidopsis Stock Centre (NASC)  for providing seeds of T-DNA lines used in this report. We thank  Dr. Maren Heese  ( University of Hamburg ) and four anonymous reviewers for their comments and constructive feedback on this manuscript. This work was supported by core funding of the  University of Hamburg. The IJPB benefits from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ).
Data availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD011035 (http://proteomecentral. proteome xchange. org/cgi/GetDataset?ID=PXD011035). The results of the mass spectrometry with a targeted approach analyzed using Skyline have been deposited to the Panorama Public (dataset link: https://panoramaweb. org/ASY_phosphorylation. url). Expanded View for this article is available online.
As datasets grow larger and more complex, modern data analysis requires solving high-dimensional estimation problems with very many parameters. Developing algorithms up to the task and understanding their limitations has become a major challenge in computer science, machine learning, statistics, signal processing, communications and related elds. In the present contribution, we address this challenge in the case of generalized linear estimation models (GLMs)  [1, 2]  where data are generated as follows: Given a n-dimensional vector X * , hidden to the statistician, he/she observes instead a m-dimensional vector Y where each component reads Y µ = ϕ 1 √ n [ΦX * ] µ , A µ , 1 ≤ µ ≤ m , (1) where Φ is a m × n "measurement" or "data" matrix, the random variables (A µ ) iid ∼ P A account for noise (or randomness) of the model.
For comparison, we also show the results of GAMP (black dots) and, in blue, the performance of a standard out-of-the-box solver. Left: Perceptron, with ϕ(x) = sign(x) and a binary Rademacher signal. While a perfect generalization is information-theoretically possible starting from α IT ≈ 1. 249, the state evolution predicts that GAMP will achieve such perfect prediction only above α AMP ≈ 1. 493. The results of a logistic regression with ne-tuned regularizations with the software scikit-learn  [65]  are shown for comparison. Middle: Perceptron with Gauss-Bernoulli distribution of the weights. No phase transition is observed in this case, but a smooth decrease of the error with α. The results of a logistic regression are very close to optimal. Right: The symmetric door activation rule with parameter K chosen in order to observe the same number of occurrence of the two classes.
Empirical comparison with general purpose algorithms In this section we argue that many cases that t into the setting of the present paper could serve as useful benchmarks for existing machine learning algorithms. We believe that the situation is perhaps similar to Shannon coding theorems that have driven algorithmic developments in error correcting codes, achieving the Shannon bound being the primary goal in many works in communications. In machine learning, classi cation is a natural task and algorithms are usually benchmarked using open access databases. In current state-of-theart applications of machine learning we usually have very little insight about what is the sample complexity, i. e. how many samples are truly needed so that a given generalization error can be achieved. In our setting the situation is di erent: We can present samples (y µ , Φ µ ) to generic out-of-the-box classi cation algorithms and see how their performances compare to the information-theoretic optimal performance and to the one of the GAMP algorithm that is ne-tuned to the problem.
In Fig. 2  we present examples of state-of-the-art classi cation algorithms compared to our results. In the left and center panels we compare the optimal and GAMP performances to a simple logistic regression, ne-tuned by manually optimizing the ridge penalty (for 2 regularization) and LASSO penalty (for a sparsityenhancing 1 regularization) with the software scikit-learn  [65]. We observe that while for the GB case the logistic regression is comparable to the performance of GAMP, for binary weights perfect generalization is not achieved close to the GAMP phase transition. In the right panel of Fig. 2  we study classi cation for labels generated by the symmetric door channel. A general purpose algorithm would not know about the form of the channel. A neural network with only two hidden units is in principle able to represent the corresponding function (each of the hidden neurons can learn one of the two planes that separate data in the symmetric door function).
A more intriguing question is whether a more generic multi-layer neural network is indeed able to learn this rule and how many samples does it need? In the example used in Fig. 2 , using the software Keras  [66]  with a tensor ow backend, we show the performance of a network with two hidden layers, ReLU activation and dropout (the details for this particular run can be found on the github repository  [62] ). The symmetric door function thus provides a challenging benchmark that could be used to study how to improve performance of the general purpose multilayer neural network classi ers. In the SI we provide additional examples comparing the optimal performance to general-purpose algorithms for regression.
249(1), the state evolution predicts that GAMP will allow such perfect prediction only from α = 1. 493  (1). The results of a logistic regression with ne-tuned ridge penalty with the software scikit-learn  [65]  are shown for comparison. Middle: Perceptron with Gauss-Bernoulli coe cients for the signal. No phase transition is observed in this case, but a smooth decrease of the error with α. The results of a logistic regression with ne-tuned 1 sparsity-enhancing penalty (again with  [65] ) are very close to optimal. Right: The symmetric door activation rule with parameter K = 0. 67449 chosen in order to observe the same number of occurrence of the two classes. In this case there is a sharp phase transition at α = 1 from a situation where it is impossible to learn the rule, so that the generalization is not better than a random guess, to a situation where the optimal generalization error drops to zero.
However, GAMP identi es the rule perfectly only starting from α s = 1. 566(1) (GAMP error stays 1 up to α stab = 1. 36, see the black dashed curve). Interestingly, this non-linear rule seems very hard to learn for other existing algorithms. Using Keras  [66] , a neural network with two hidden layers was able to learn approximately the rule, but only for much larger training set sizes (shown in inset, the Keras/tensor ow code for this particular run can be found on the github repository [62]).
The results of a LASSO with ne-tuned 1 sparsityenhancing penalty (with  [65] ) are very close to optimal. Middle: Here we analyze a ReLU output function ϕ(x) = max(0, x), still with a Gauss-Bernoulli signal. Now there is an information-theoretic phase transition at α = 2ρ = 0. 4, but GAMP requires α s = 0. 589(1) to reach perfect recovery. We show for comparison the results of maximum likelihood estimation performed with CVXPY -a powerful python-embedded language for convex optimization  [84] -using two methods that are both amenable to convex optimization: In CVX-1 we use only the non-zero values of Y, and perform a minimization of the 1 norm of x subject to Y µ = Φ µ • x for µ ∈ {1,. , m} such that Y µ = 0, while in CVX-2, we use all the dataset, with the constraint that Y µ = Φ µ • x for µ ∈ {1,.
Figure2: Optimal generalization error in three classi cation problems versus the sample complexity α, the size of the training set being αn. The red line is the Bayes-optimal generalization error (9) while the green line shows the (asymptotic) performances of GAMP as predicted by the state evolution (10). For comparison, we also show the results of GAMP (black dots) and, in blue, the performance of a standard out-of-the-box solver. Left: Perceptron, with ϕ(x) = sign(x) and a binary Rademacher signal. While a perfect generalization is information-theoretically possible starting from α IT ≈ 1. 249, the state evolution predicts that GAMP will achieve such perfect prediction only above α AMP ≈ 1. 493. The results of a logistic regression with ne-tuned regularizations with the software scikit-learn [65]  are shown for comparison.
Figure4: Generalization error in three classi cation problems as a function of the number of data-samples per dimension α. The red line is the Bayes-optimal generalization error, while the green one shows the (asymptotic) performances of GAMP as predicted by the state evolution (SE), when di erent. For comparison, we also show the result of GAMP (black dots) and, in blue, the performance of a standard out-of-the-box solver, both tested on a single randomly generated instance. Left: Perceptron, with ϕ(x) = sgn(x) and a Rademacher (±1) signal. While a perfect generalization is information-theoretically possible starting from α = 1. 249(1), the state evolution predicts that GAMP will allow such perfect prediction only from α = 1. 493 (1). The results of a logistic regression with ne-tuned ridge penalty with the software scikit-learn [65]  are shown for comparison.
Now there is an information-theoretic phase transition at α = 2ρ = 0. 4, but GAMP requires α s = 0. 589(1) to reach perfect recovery. We show for comparison the results of maximum likelihood estimation performed with CVXPY -a powerful python-embedded language for convex optimization [84] -using two methods that are both amenable to convex optimization: In CVX-1 we use only the non-zero values of Y, and perform a minimization of the 1 norm of x subject to Y µ = Φ µ • x for µ ∈ {1,. , m} such that Y µ = 0, while in CVX-2, we use all the dataset, with the constraint that Y µ = Φ µ • x for µ ∈ {1,. , m} such that Y µ = 0 (as before) and the additional restriction Φ µ • x ≤ 0 for µ ∈ {1,. , m} such that Y µ = 0. In both case, a perfect generalization is obtained only for α 1. Right: The sign-less output function ϕ(x) = |x|.
Acknowledgments J. B. acknowledges funding from the  SNSF  (grant  200021-156672 ). F. K. and L. Z. acknowledge funding from the  ERC  under the European Union's 7th  Framework Programme Grant Agreement   307087-SPARCS  and under  Horizon 2020 Research and Innovation Programme  Grant Agreement 714608-SMiLe. F. K. and N. M. acknowledge support from the  ANR-PAIL. Part of this work was done while L. M. was visiting EPFL.
Methods The CCGS Amundsen 2016 expedition and Green Edge project in Baffin Bay Sampling aboard the CCGS Amundsen during the Green Edge campaign extended from 09 June to 10 July 2016. The sampling locations were distributed along seven longitudinal transects across the ice edge, each at a different latitude (Figure  2 ). The total data set comprises around 135 sampling stations. For an impression of the general region, additional maps can be found in the supplemental material (Figures S1. 1-S1. 4).
Hydrographic sampling At each hydrographic station, vertical profiles of water column properties were made using a Conductivity-Temperature-Depth sensor system (CTD, Seabird SBE-911 plus), installed onboard the CCGS Amundsen which also included a Seapoint SCF Fluorometer for detection of chla, and a WetLabs C-Star transmissometer to measure beam attenuation as an indicator of biomass. In total, over all 135 sampling stations, 200 CTD casts were conducted. CTD data processing was done by the Amundsen Science Team following standard procedures  (Guillot, 2016). For the purpose of this study, temperatures are always given as conservative temperature, T C , defined according to the TEOS-10 standard (IOC, SCOR, IAPSO, 2010); the same applies to the use of S A for absolute salinity. The maximum temperature of each vertical profile is a reliable tracer for water masses of Atlantic origin  (Tang et al.
Light A photosynthetically available radiation (PAR; mol photons m -2 d -1 ) level of 0. 415 mol m -2 d -1 has been found to bound the lower extent of the vertical phytoplankton distribution in the North Pacific subtropical gyre  (Letelier et al. , 2004)  and has been used in the North Atlantic for the same purpose  (Boss and Behrenfeld, 2010). To assess the underwater light climate, we computed the depth of this isolume by combining 1) 40 profiles of multispectral underwater irradiance using a "Compact Optical Profiling System" (C-OPS, Biospherical Instruments, Inc. ) and 2) a composite dataset of daily above-surface irradiance, PAR 0+. Lacour et al. (2017)  derived a lower value of 0. 1 mol m -2 d -1 for the lower extent of possible phytoplankton growth for the subpolar North Atlantic. Qualitatively, the two criteria agree, with isolumes at the 0.
Modelling by  Pavlov et al. (2017, their Figure 9)  in and around an underice phytoplankton bloom indicates that the ratio E o : E d is within the range of 1. 3-1. 6, meaning that by using E d , we underestimate the depth of any isolume by a factor of at most approximately log (E o /E d )/K d (PAR) ≈ 1-7 m, where K d (PAR) ≈ 0. 07-0. 25 m -1 is the diffuse attenuation coefficient of PAR as observed in our dataset. Again, this analysis serves to show that the isolume depths discussed in this paper are conservative. Additional caveats include that the light field under a heterogeneous cover of ponded ice floes and leads between them is notoriously difficult to parameterize  (Massicotte et al. , 2018) , with spatial heterogeneity of the ice cover  Frey et al. , 2011; katlein et al. , 2015)  and horizontal spreading of light under the ice  (Ehn et al.
, 2011)  being major issues. Nor is it clear whether the physiological response of the phytoplankton is governed by the daily average light field or by sub-daily variations, e. g. , through exploitation of or disruption by peak irradiance in leads. In summary, the isolume depths computed in this paper should be seen as indicative of the depth range where growth definitely was possible, rather than the physiological maximum. Incoming above-surface irradiance PAR just above the sea surface was estimated using both (1) in situ data recorded on the ship meteorological tower (Kipp & Zonen PAR Lite; sampling frequency 1 min -1 ) and (  2 ) an atmospheric radiative transfer model (SBDART,  Ricchiazzi et al. , 1998). SBDART was first validated against in situ measurements (Figure  S4. 5), and then used to derive 24-hour mean irradiance at a given location before the hydrographic station.
B: The color scale shows salinity, the contour lines temperature as indicated on the top of the panel. C: Surface nitrate concentration and PAR irradiance at 3 m depth. D: Phytoplankton standing stock integrated from the surface to the isolume (grey line) and from the surface to 80 m depth (green line). E: Vertical layers of interest: The nitracline; the extent of the surface layer, h BD , which places a limit on the extent of the mixing layer; the chl-a fluorescence maximum depth; the depth of maximum beam attenuation, a proxy for biomass, and the depth of the 0. 415 mol m -2 d -1 isolume. The estimated depth of winter overturning is shown for reference. Shaded areas and error bars show 25 and 75% quantiles. DOI: https://doi. org/10. 1525/elementa. 357. f9 Because the summer ice edge retreat in Baffin Bay was so steady, the usual problem of confounding time and space variability was tractable in our dataset.
Our findings clearly demonstrate that in order to understand dynamics and constraints of Arctic ice edge blooms, we need to consider not only the in situ conditions sample by sample, but also the temporal coupling between them. involved in the Green Edge campaign for their contribution to field work and data collection. They include in particular Pascal Guillot and the Amundsen Science Data team for the CTD data processing and quality control (UQAR-ISMER). Hervé Claustre, Celine Dimier and Josephine Ras conducted the HPLC analysis for total chlorophyll-a. Tonya Burgers and Brent Else kindly provided the shipboard PAR measurements. Pascale Bouruet-Aubertot and Yannis Cuypers organized and supervised SCAMP sampling and data processing. Caroline Sévigny deployed the SCAMP during the second half of the cruise.
Acknowledgements The project is conducted under the scientific coordination of the  Canada Excellence Research Chair on Remote sensing of Canada's new Arctic  frontier and the  CNRS & Université Laval Takuvik Joint International Laboratory  ( UMI3376 ). We thank officers and crew of CCGS Amundsen and  Marie-Hélène Forget , and  Joannie Ferland  for planning the field work, and all other scientists and technicians.
f. : 46; U = 245; p = 0. 28; Mann-Whitney test) and frequency (d. f. : 46; U = 240; p = 0. 24; Mann-Whitney test; Fig. 5b ). NMDAR mEPSCs, recorded at + 40 mV in the presence of CNQX, also had very similar amplitudes (d. f. : 17; U = 37; p = 0. 59; Mann-Whitney test). However, Fmr1 -/y mice showed a 54% increase in NMDAR mEPSC frequency compared with WT mice (d. f. : 17; U = 18; p = 0. 03; Mann-Whitney test; Fig. 5c ). These data indicate that while AMPAR-containing synapses number and strength are unaltered in Fmr1 -/y mice, they possess ~50% more NMDAR containing synapses. Fmr1 -/y L4 SCs are hyperexcitable due to lower HCN currents. While these observed changes in synaptic properties reveal differences in dendritic spine function, alone they do not reveal how neurons integrate excitatory inputs leading to hyperexcitability.
g Average spike probability in response to 5 and 10 Hz stimulation. Statistics shown: *p < 0. 05, **p < 0. 01. All data are shown as mean ± SEM and source data for all plots are provided as a Source Data file (1. 7 mm outer/1 mm inner diameter, Harvard Apparatus, UK) on a horizontal electrode puller (P-97, Sutter Instruments, CA, USA), which when filled with intracellular solution gave a pipette resistance of 4-5 MΩ. Unless otherwise stated, all V-clamp recordings were performed at V M = -70 mV. All signals were filtered at 10 kHz using the built in 4-pole Bessel filter of the amplifier, digitized at 20 kHz on an analogue-digital interface (Digidata 1440, Axon Instruments, CA, USA), and acquired with pClamp software (pClamp 10, Axon Instruments, CA, USA). Data were analysed offline using the open source Stimfit software package  54  (http:// www.