dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was supported by  Programme Hospitalier de Recherche Clinique  ( PHRC 2004 ),  Ministère des Affaires Sociales et de la Sante, France. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. grants from  French Ministry of Health , during the conduct of the study; personal fees and non-financial support from  Biogen ,  Genzyme ,  Roche ,  Bayer ,  Teva  and  Novartis ; research grants from merck-serono and  Teva , outside the submitted work. Dr. Debouverie  reports personal fees and non-financial support from  Biogen-idec ,  Novartis ,  Merck-Serono and Genzyme , outside the submitted work. Dr. Vermersch  reports grants, personal fees and nonfinancial support from  Biogen ,  Roche ,  Merck-Serono , Novartis; personal fees from  Almirall  and  Bayer ; personal fees and non-financial support from  Genzyme-Sanofi , and  Teva ; grants and personal fees from  Bayer , outside the submitted work.
Hidden Data The word "hidden" comes from the HMM terminology. It can include different types of data sets, mainly model outputs. Here hidden data are selected as outputs of the 3D ROMS set up by Guillou et al. [26]  in the English Channel. Each level corresponds to the SPIM concentration at a particular depth. Eleven depth levels are thus considered in sigma coordinates. The model assumes that the flow is turbulent over a rough bed. The particle assemblages are furthermore considered as spherically non-cohesive quartz components and the solid particles are not affected by the water-sediment mixture  [26]. This 3D sedimentary ROMS model has been applied to the English Channel with spatial and temporal resolutions of 3 km and 30 s (with an output frequency of 1 h), respectively. The area selected for the model implementation extends from -3.
Self-Organizing Maps PROFHMM uses SOMs in order to classify the available concurrent vertical distributions into the hidden states of the HMM and all observation vectors into the observable states. The SOMs are unsupervised classification algorithms that cluster data into discrete classes  [36]. The discretization of the hidden and observable data has been done with 1100 states. By applying SOMs, we obtain a topological map containing states. Each state has a corresponding referent vector, whose value approximates the mean value of the data attributed to this class during the training. Two neighboring classes on the two-dimensional (2D) topological map will have referent vectors that are close in the data space. Thus, in PROFHMM, we have two maps, hidden and observable, respectively corresponding to the SOMs of the vertical SPIM and the SOMs of the input data, which are used to generate sequences of indexes of the hidden and observable states.
Results and Discussion The present work focuses on two different experiments of constructing long-term SPIM profiles using the PROFHMM method. The first experiment (experiment 1) derives vertical profiles of SPIM using four modeled variables: Hs, Hs50, Ubar and Vbar (from NEMO and WW3 operational models). The main advantage of experiment 1 was that we could validate the derived surface SPIM with satellite data. The second experiment (experiment 2) focused on the assimilation of satellite data along with the operational model outputs. For this purpose, we integrated the satellite SPIM as an observable variable along with the four variables used in experiment 1. The present work mainly concentrates on experiment 1 because of the significant importance of the validation of the derived product.
Output from Learning Phase The vertical sections of the SPIM for 15 months between the original ROMS model output and PROFHMM have been compared. These sections show good agreement with each other for all of the four stations. Figures  4 5 6 7 show the vertical section of the SPIM between ROMS and PROFHMM at points M2 and M3. The derived SPIM exhibited seasonal and tidal fluctuations in their magnitude for all of the four stations. The root-mean-square error (RMSE) and normalized root-mean-square error (NRMSE) between these two data sets (ROMS and PROFHMM) for the entire section of the learning phase have been performed using the following equations: RMSE(mg/l) = ∑ n i=1 (SPI M_ROMS i -SPI M_PROFHMM i ) 2 n (3) NRMSE(%) = RMSE SPI M_ROMS max -SPI M_ROMS min (4) The RMSE value of the SPIM between the ROMS and PROFHMM data for the M1 station was 1.
Time Series of Surface SPIM We have pointed out that the methodology works well in all of the four stations in the learning phase, relying on these four observable variables. The profiles of SPIM have been derived through PROFHMM for ≈11 years with a sampling interval of 1 h. The validation of SPIM at each depth is out of the scope of this article because of the unavailability of sedimentary validation data for the 11-year period considered for the present study. For this reason, we have selected the surface satellite SPI M_sat for the validation of our statistical model. The hourly SPI M_sat values have been derived by the linear interpolation of daily data. As it is smooth over a semi-diurnal tidal period, we have computed 25 h of running average time series of surface PROFHMM data for the comparison with SPI M_sat data.
We observed very high concentrations of SPIM during the winter and comparatively low SPIM during the spring and summer. This was observed throughout the time series for the four stations (Figure  8 ). The periods of turbid events are well explained/predicted by PROFHMM using only the significant wave height and barotropic currents. It can furthermore be seen that there is a better fit between SPI M_PROFHMM and SPI M_sat during the time of learning phase (the red colored rectangle in each panel of Figure  8 ). An increase in the length of hidden data (more than 15 months) would lead to a better fit between SPI M_PROFHMM and SPI M_sat all the way along the time series. The discrepancy between surface SPI M_PROFHMM and SPI M_sat is noticeable at point M3 in particular. The point M3 is located in the turbid area near the Isle of Wight, which exhibits strong near-surface SPIM with yearly averaged values of 10-15 mg/L  [26].
Conclusions In the English Channel, we demonstrated the feasibility of deriving vertical SPIM profiles over a long time period (≈11 years) while only using three local and average hydrodynamic parameters (Hs, Ubar and Vbar). In these highly dynamical waters, the SPIM-profile time evolution follows statistical properties that are given by a previous analysis of a shorter time series (≈1 year) hindcast coming from a dedicated hydro-sedimentary deterministic model. By nature, a priori statistics are sensitive to the range and magnitude of processes sampled by the hindcast simulation. For this reason, the choice of this hindcast must be representative of the final extension period of the statistical simulation. For instance, here we used hidden data from 27 September 2007 to 30 December 2008. This period included a winter storm observed as the most prominent extreme event in the last decade  [38, 39].
To isolate the sheer effect on neural responses of signal intensity decrease, due to sound spherical spreading and excess attenuation, neural responses to Synthetic stimuli were also recorded. In the Synthetic stimuli, the sound intensity matched that of the Natural calls at each propagation distance, but the SNR was constant (see details below). To prepare the Natural stimuli, we used a database of distance calls recorded from 32 zebra finches (16 females, 16 males) raised in the Equipe de Neuro-Ethologie Sensorielle Laboratory. The calls were recorded in a soundproof room using a microphone (Sennheiser MD-42) placed 0. 2 m above the cage and connected to a Marantz Professional Solid state recorder (PMD 670; sampling frequency 44,100 Hz). Each bird was recorded in the presence of two females placed 3 m away and used as an audience to minimize stress; the bird was stimulated with distance calls playbacks from previously recorded conspecific birds.
These experimental protocols were approved by the Jean Monnet University's animal care committee (authorization 42-218-0901-38 SV 09 to Equipe de Neuro-Ethologie Sensorielle Laboratory). We recorded 16 different calls from each individual to make our call database (total number of calls: 16 ϫ 32 ϭ 512 calls). The intensity of all the calls was normalized by matching the maximum values of the sound pressure waveforms. We recorded the propagated sounds from all the calls of this database in natural conditions on an open flat field. For these sound recordings, the calls were broadcast from a portable solid-state recorder (Marantz PMD671) connected to a MegaVox speaker (PB-35W) placed on a stool, 1. 30 m high. The speaker volume was set to obtain a sound level of 70 dB SPL at 1 m  (Vignal et al.
1A ), but the amplitude matched the amplitude of the Natural call at these 4 distances (Fig. 1B ). The database of Synthetic stimuli consisted of 16 different calls per individual for the last 4 propagation distances (16 calls ϫ 32 individuals ϫ 4 distances ϭ 2048 calls). Animal procedures and electrophysiological recording protocol. Eight adult zebra finches (4 males and 4 females) were used as subjects in the electrophysiology experiments. As explained in more detail by  Elie and Theunissen (2015) , extracellular recordings from ensemble of single units were obtained in urethaneanesthetized subjects, immobilized on a stereotaxic apparatus, using one or two 16 channel electrode arrays (Omn1010, Tucker-Davis Technologies) consisting of 2 rows of 8 electrodes (width 0. 5 mm, length 1. 75 mm, distance between two electrodes within one row: 250 m).
These snippets were sorted off-line to extract responses from single units (see below). For each recording site, calls from 4 or 8 different vocalizers from the same sex were broadcasted to the subject; sex was swapped from one site to the other in the same subject. The call identities selected for each site (i. e. , vocalizer names and call renditions) were selected randomly from the database; and for each call identity, the calls recorded at all 5 distances were used for the Natural stimuli, and (depending on the protocol type; see below) the synthetic calls for all 4 distances were used for the Synthetic stimuli. To limit the recording time for each site, the number of calls selected per vocalizer varied for each condition: two subjects were only tested with Natural stimuli (Nat-only protocol) and heard 8 different renditions of the calls of 8 vocalizers at each site (i.
Following guidelines found in the literature  (Vates et al. , 1996) , we chose to approximate the limit between CMM and CLM as being 800 m away from the midline. To compare histological localization of electrodes between subjects, we used the depth of the recording sites and the rostrocaudal distance of each electrode to the y-sinus as coordinates in a 2D sagittal representation of the electrode sites. Of the 6 subjects for which histological information was available, one was recorded in both hemispheres at the same time; as the penetration angle of the electrode array was different for the left hemisphere in this dual electrode recording compared with all other penetrations, data recorded in this hemisphere were not included in the 2D representations. As a result, 521 units were used in these 2D representations out of 1322 total.
This process yielded 1322 single auditory units, and all subsequent analyses were done on this dataset of single units. All units were tested with the Natural stimuli (Nat-only and NatϩSyn protocols), and 1083 units were tested with the Synthetic stimuli (NatϩSyn protocol). Briefly, the spike sorting was performed using a semiautomatic custom made program written in MATLAB (The MathWorks, RRID:SCR_ 001622) that used both unsupervised (k-means) and supervised clustering algorithms (Random Forest). In a first stage, we manually chose templates for single spike shapes by means of a GUI and exploratory cluster analysis using k-means algorithm. In a second stage, these templates were used to train a Random Forest, which was then used to classify the remainder of the spikes into single units, noise, or nonclassifiable units (multiunits).
The bootstrapping resampling technique was performed as follows: for each unit, we obtained 1000 bootstrap versions of the original confusion matrix containing information about all Natural stimuli, and of the original confusion matrices calculated per distance, by randomizing the assignment of the predicted stimuli. Each of these "scrambled" confusion matrices was then collapsed so that we could calculate the various values of mutual information: MI tot , MI bird , MI dist , and MI bird/dist. This process yielded for each original confusion matrix a distribution of values of mutual information that could be obtained by chance (MI Rand ). Finally, to obtain values of MI in bits per second, the information values were simply divided by the duration of the analysis window (0. 436 s). To provide more intuitive values for discrimination performance, we also calculated the percentage of correct classification above chance for each confusion matrix, by adding the joint probabilities of the diagonal of the matrix (which is exactly equal to taking the average of the conditional probabilities in this symmetric dataset), and subtracting the chance level.
We combined the anatomical location of each recording site (measured as the exact rostral/caudal distance from the y-sinus, the exact depth from the brain surface and as a medial (0. 3-0. 6 mm), middle (0. 7-1. 1 mm), or lateral (1. 2-1. 5 mm) position; see Histology and anatomical localization of electrodes) with the MI measures (MI bird and MI dist ) to obtain functional maps. To create these functional maps, we first averaged the mutual information values that occurred at the exact same depth and rostral/ caudal positions in each of the three medial/lateral positions (from single units recorded on the same electrode site) and then performed 2 d linear interpolation (using the MATLAB function griddata) to obtain information values at equally spaced grid points spanning the region of interest.
3 : Individual 1). In this example, the firing rate during the stimulus is higher than before or after the stimulus for both individuals from 2 to 64 m, but not at further distances. At 128 and 256 m, no specific spike pattern is discernable regarding a response to the stimulus for both individuals. However, the constant decrease in spike rate from short to long distances illustrates the fact that this unit codes propagation distance. The observations we made regarding the response of our illustrative neurons in Figures  3  and 4  can be quantified by calculating how vocalizer identification or distance can be discriminated from the neural response when it is described in terms of spike rates or spike patterns. We performed such calculations for our entire dataset but show the details of such calculations for our two example neurons first.
As seen in Figure  8B , this joint distribution of residuals and information was clearly not unimodal. To capture this feature, and to methodically assign units into the two groups described above for further analysis, we fitted this distribution for the highly discriminative units (MI tot Ͼ 0. 6 bits/s) using a Gaussian mixture model (fitgmdist function in MATLAB) with two clusters. The two Gaussians used in the fit have well-separated means and different axis in their covariance matrix (Fig. 8B ), indicating that the observed distribution is indeed bimodal. In summary, this unsupervised clustering analysis showed two well-separated groups of highly discriminative neurons, shown in red and blue in Figure  8B : the red cluster, with positive residual values (mean and SD: 0. 40 Ϯ 0. 36 bits/s), regrouped units showing higher discrimination ability for individuals (i.
On both plots, the units used to create Figures  3  and 5 , and Figures  4  and 6 , are highlighted (Unit 1 used in Figs. 3  and 5 ; Unit 2 used in Figs. 4  and 6 ). SNR as the one obtained from the call recorded at 2 m (see Materials and Methods). Figure  10  shows the mean decay curves for the neural discrimination (MI bird/dist ) (Fig. 10A ) and spike rate (Fig. 10C ) to the Natural and Synthetic stimuli at all distances and for all units that were tested with both types of stimuli (n ϭ 1083 of 1322 total). On the one hand, the decay curves for MI bird/dist are strikingly equivalent and a mixed-effects linear model using the distances and the type of stimuli as categorical predictors of MI bird/dist did not reveal any significant effect of the type of stimuli or of the interaction between the predictive factors while distance was highly significant (ANOVA on the model generated with fitlme in MATLAB with unit as a random factor: type of stimuli ).
The MI bird/dist was much greater for the bird-specific cluster than for the distance-specific cluster, but this is expected by construction. The decrease with distance of the spike rate for these two specific subsets also showed the same pattern as in the entire dataset: the rate decreased further for the Synthetic than for the Natural calls (bird-specific cluster: ). Moreover, although the spike rates at 2 and 16 m are not significantly different between the dis-tance-specific cluster and the bird-specific cluster, the rate for the distance-specific units drops fast with propagation distance, whereas the rate for the bird-specific units drops at a slower rate and remains elevated (and above the overall mean) for all propagation distances. F Stim 1, F Stim 1, Therefore, it appears that, for all units, the degradation of the units' discrimination performance with distance is mostly explained by the mere decrease in intensity rather than by the increase in signal degradation or the additional spikes due to background noise.
Selectivity is quantified using GS, where 1 corresponds to maximal selectivity and 0 to no selectivity (see Materials and Methods). Colored points indicate data points. Gray points indicate values obtained from model data (random) with similar information but randomly exploring the range of confusion matrices that could yield this information (see Materials and Methods). Solid lines indicate linear fits to the data. The fit for the model data is restricted to the same range as the fit performed for the actual data. The actual data are restricted to those obtained with Natural stimuli only. whether the maps of discrimination for distance and for vocalizers were different or correlated. For this purpose, we calculated for each mediolateral group the correlation between the pixel matrices obtained using MI bird and MI dist (e.
The neural discrimination of propagated familiar or learned calls might be even superior to the one reported here  (Jeanne et al. , 2011). Moreover, our neural data were obtained in urethane-anesthetized animals. Although some studies have shown limited effects of Red crosses represent the localization of each recording site for all tested subjects for which histological localization was possible (6 of 8 total), as a function of depth and caudorostral distance from the y-sinus. For each recording site, the mean MI bird value between the corresponding units is calculated, and the graph is constructed by interpolating these values. Color scale represents MI bird. All values are shown using responses to Natural stimuli only. The histological area is given for each recording site localization; when histological results from different birds yielded different area identifications and/or when recording sites were at boundaries, all 4 possible areas are given as a list from more to less probable.
Baxter) in synthetic air (200 ml/min) and placed on the animal handling station equipped with an individual mask providing 1. 5-2% (v/v) mixture of isoflurane and synthetic air. The position of the handling station was adjusted so that the central section of the tumor was being visualized and contrast agent was injected into the tail vein of the animal. signal of the contrast marker accumulating in the tumor tissue was recorded using the probe of 13-24 MHz frequency (Ms250, Visualsonics). Next, the peak-enhancement [contrast mean power as l. a. u. (linear arbitrary units)] and time-to-peak (sec) parameters were calculated using the Vevo LAB 1. 7. 1 software (Visualsonics). Immunohistochemical staining of aorta sections. For the double immunohistochemistry sequential staining of thoracic aorta (isolated from mice on the days 7, 14, 21, 28 or 33 and from healthy, non-treated animals) sections were fixed in buffered formalin and then cut into 4-µm thick slices that were subsequently dewaxed and rehydrated through gradient of unyhol and unyhol Plus (Bio-Optica, Mediolan, Italy).
Microscopic examination and photographs were captured using a bright field microscope (Olympus CX41) connected with a camera equipped with Olympus stream Image Analysis software (Olympus europe Holding GmBH, Hamburg, Germany). Blinded microscopic examination was performed at magnifications (x10 and x100) and described according to the below scheme: 0, no staining; 1+, delicate staining; 2+, average color staining; 3+, intense color staining; 4+, bright color staining. Masson's trichrome staining for collagen fibers of tumor and lung tissue. Tissues that were dewaxed and rehydrated in a descending alcohol series were incubated with Weigert's iron hematoxylin nuclear staining solution for 5 min (Merck Millipore, Darmstadt, Germany). After 5 min of flushing in tap water, slides were submerged in 1% acetic acid for 3 min.
subsequently, the sections were immersed respectively in Azoploxine solution for cytoplasm dyeing (5 min), Tungstophosphoric acid orange G solution for erythrocytes staining (1 min), and Light green SF solution for collagen and connective tissue visualization (5 min). The slides were washed in 1% acetic acid between each staining procedure for 3 min (Masson-Goldner staining kit, Merck Millipore, Darmstadt, Germany). In the last step, slides were washed in 1% acetic acid for an additional 5 min and left to dry. Afterwards histo-logical slides were covered with non-aqueous mounting agent (Neo-Mount anhydrous mounting medium; Merck KGaA, Germany) and a cover glass for further analysis. Microscopic examination and photographs were made using a bright field microscope (Olympus CX41) connected with camera equipped with Olympus stream Image Analysis software (Olympus europe Holding GmBH).
We used 25 ng of cDNA for a single reaction, and each sample was performed in triplicate in a single experiment (3 experiments were performed). Fold-change (RQ) of target cDNA was determined by calculating the differences in ∆∆CT values in reference to phosphoglycerate kinase 1 (Pgk1) by DataAssist 3. 01 software (freeware by Applied Biosystems). Western blotting Tissue preparation. Tumor tissue specimens were collected in liquid nitrogen and stored at -80˚C. To determine the protein expression via western blotting, frozen tumors were mechanically homogenized (Rotilabo, Carl Roth, Karlsruhe, Germany) in RIPA buffer (sigma-Aldrich Chemie GmbH) supplemented with a complete mixture of phosphatase and protease inhibitors (sigma-Aldrich Chemie GmbH) and kept on ice for 25 min. Lysates were purified via microcentrifugation at 14,000 x g for 10 min at 4˚C.