dataesr/scientific-paragraphs-categorization huggingface dataset:
Flot2 Interactome Forms a Complex Interlinked Network As has already been pointed out, proteins found to interact with Flot2 in this study may also in many cases interact with each other, and thus it is possible that a given protein may be involved in several cellular functions. The situation gets even more complicated when direct interactors of each protein retrieved from the Associomic database are added to the list (Figure  5 ). Interestingly, it could be seen in Associomics that many of the Flot2 interactors found in this study also interact with other Flot2 interactors listed in Associomics. The most common interactors are NHL3, cornichon and IQD6. Since these proteins have several hundred interactors in Associomics, they could serve as real docking hubs for many proteins in the plasma membrane, and could thus be the crossroads or signposts of many pathways.
ACKNOWLEDGMENTS The authors would like to thank  Christopher Grefen ,  University of Tuebingen, Germany  and  François Chaumont  and  Timothée Laloux ,  Université catholique de Louvain, Belgium  for sharing vectors and the yeast strain used in SUS assay as well as  Kateřina Vltavská ,  Institute of Experimental Botany, Czech Republic , for her excellent technical assistance.
Genetic analysis of tumor DNA samples revealed that 38. 9% of patients had deletions inducing frameshifts, 44. 4% presented transversions and 16. 7% harboured transitions. All VHL mutations found were investigated Missense: pathogenic (unknown mechanism)  [90]  Benign (0. 000) # # Discordant PolyPhen predictions compared to the reported impact of VHL mutations in the literature. to determine their phenotypic impact on pVHL functions by searching four distinct databases (see Methods and Table  1 ). Additionally, all missense mutations found in our cohort were further investigated by using a polymorphism phenotyping program (PolyPhen). It is important to note that the 85% sensitivity and 44% specificity of PolyPhen predictions for loss-of-function mutations  [28]  may explain the discrepancies between the reported impact of a missense mutation found in the literature and the PolyPhen prediction obtained for the same mutation (see Table  1 ).
El-Heliebi et al. have also isolated CRC from RCC patients by blood filtration and used immunemolecular analyses to validate CRC cytopathological diagnosis. However, they did not use cancer-specific immunolabelling and molecular tests were addressed to clusters possibly including tumor and non-tumor cells  [27]. They concluded that cytopathology alone is not sufficient to allow reliable detection of CTC/CTM but do not provide any diagnostic immune-molecular guideline to identify them. We found a total of 18 different VHL mutations in our cohort, including 17 which had previously been described. This is not surprising since more than 500 VHL exonic mutations have already been reported in the literature. By interrogating four distinct databases, we determined that only one mutation found in our cohort had not been previously described (Table  1 ).
Reactions were incubated in a GeneAmp 9700 thermal cycler (Applied Biosystems, USA) at 95° C for five minutes before 35 cycles of amplification (including 30 seconds at annealing temperature) and a final extension step of five minutes at 72° C were applied. Final PCR (PCR2) products were then purified using the DNA Clean & Concentrator™-5 kit (Zymo Research, USA) as per manufacturer's instructions. Purified PCR2 products were diluted 1:10 in sterile water prior to sequencing of both strands (bidirectional Sanger sequencing) with Big Dye terminators version 3. 1 (Applied Biosystems, USA) as per manufacturer's instructions. Sequencing data were collected raw from a 3130xl Genetic Analyser (Applied Biosystems, USA) and analysed using both Sequencing Analysis ® and SeqScape ® softwares (Applied Biosystems, USA).
ACKNOWLEDGMENTS We would like to thank  Dr. Raoudha Doghri  for cytopathological assistance. This work was supported by funds from:  Fondation pour la Recherche Médicale ,  Fondation Bettencourt-Schueller ,  Fondation Lefort-Beaumont de l'Institut de France ,  INSERM ,  Université Paris-Descartes  and  Canceropôle PACA , ligue départementale 06 de lutte contre le cancer.
This lack of ground-truth data when performing data analysis is particularly unavoidable in neuroscience  [25]. It has thus become necessary to establish standard, model-free methods that, even if they do not contribute to our understanding of the data, set levels of performance that may be used to benchmark model-based approaches  [26, 27]. Machine Learning provides a large array of techniques to classify datasets that have demonstrated high level of performance in fields ranging from image processing to astrophysics  [28]. Using a supervised classifier, so-called gradient boosting  [26, 27] , we show how this method can determine an encoding model for predicting population spike trains knowing the stimulus input. We also show, in line with recently published work  [27] , how gradient boosted trees (XGB) can also be used as a very efficient decoding model that is retrieving the stimulus likelihood knowing the spiking activity of a population of neurons.
Scoring function To estimate the quality of a model, we used the pseudo-R 2 score: pR 2 ¼ 1 À ðy log y À yÞ À ðy log ŷ À ŷÞ ðy log y À yÞ À ðy log " y À " yÞ ð10Þ with y the target firing rate, ŷ the prediction, " y the mean firing rate  [35]. A value of 1 indicates a perfect model that reproduces entirely the dataset while a value of 0 indicates a model that is no better than the average value of the training set. To compute the pseudo-R 2 score, the data set was divided into a training set and a test set, a procedure known as cross-validation, that prevents the model from over-fitting the training set. For all the predictions of firing rates, we used an 8-fold cross-validation, i. e the training set was divided into 8 discontinuous partitions with each one serving successively as the testing set.
Dataset Neuronal recordings that are analyzed in this report were described in a previously published paper  [30]  and are available for download (https://crcns. org/data-sets/thalamus/th-1/). Briefly, multi-site silicon probes (Buzsaki32 and Buzsaki64 from Neuronexus) were inserted over the antero-dorsal nucleus (ADn) of the thalamus in 7 mice. In three of these animals, a second probe was lowered to the post-subiculum (PoSub). During the recording session, neurophysiological signals were acquired continuously at 20 kHz on a 256-channel Amplipex system (Szeged; 16-bit resolution, analog multiplexing). The wide-band signal was downsampled to 1. 25 kHz and used as the local-field potential signal. To track the position of the animals in the open maze and in their home cage during rest epochs, two small light-emitting diodes (LEDs; 5-cm separation), mounted above the headstage, were To find the optimal number of trees and the optimal depth of XGB, a grid-search was performed for each neuron using the Bayesian Information Criterion (BIC).
D Distribution of the set of optimal parameters for all neurons. Overall, a maximum number of 100 trees with a depth of 5 was used to learn and predict spiking activity as in A. https://doi. org/10. 1371/journal. pcbi. 1006041. g002 recorded by a digital video camera at 30 frames per second. The LED locations were detected online and resampled at 39 Hz by the acquisition system. Spike sorting was performed semiautomatically, using KlustaKwik (http://klustakwik. sourceforge. net/). This was followed by manual adjustment of the waveform clusters using the software Klusters (http://neurosuite. sourceforge. net/). In animals implanted over the antero-dorsal nucleus, the thalamic probe was lowered until the first thalamic units could be detected on at least 2-3 shanks. The thalamic probe was then lowered by 70-140 μm at the end of each session.
Learning neuronal firing in relation to behavioral data: Performance and optimal parameters We first sought to validate the approach of learning a predictive model of spike trains from behavioral data with a decision tree learning algorithm that does not include a predefined model of the training set. To this end, we analyzed a dataset of HD cells  [5, 30] , whose firing in relation to behavior is among the best characterized signals in the mammalian nervous system. We demonstrated first that gradient boosted trees predicts the firing of the neurons with high accuracy by establishing a direct correspondence between the raw behavioral data (in this case the HD angular value alone) and the instantaneous spiking of the neurons (Fig  2 ). Using a Generalized Linear Model to regress the spiking activity of a neuron on raw behavioral data, such as the HD angular values, necessarily fails as this relationship is not generally linear.
Interpreting the structure of the gradient boosted trees While the structure of a multi-layered neural nets (or other forms of deep architecture) after learning the classification of a dataset is notoriously unanalyzable  [48, 49, 50] , we show how the branching of the decision tree may be highly informative on how input data are matched to their output targets. The density of splits (or branching) across the series of trees was maximal in the range of inputs where firing rates vary the most. This could be interpreted as a maximum data splitting around the maxima of Fisher Information which is, for a Poisson process, directly related to the change in firing rate as a function of stimulus value, that is when spike trains are most informative about the encoded signal  [36, 51]. Although the relationship between tree branching and Fisher Information is, in our study, purely empirical, it is interesting to show, again, that unraveling the tree structure allows the understanding of how the data are learned by gradient boosting.
Potential for neuroscience and future work The potential of these methods to unravel the dynamics of biological neuronal networks is tremendous and will be the scope of further studies. For instance, tracking synaptic transmission in pairwise spike trains  [66] , uncoupling the phase-locking of neuronal spiking to concomitant and nested brain oscillations  [67, 68] , and determining the nature of the coordination in neuronal populations in relation to behavior  [6, 54]  are examples of the current challenges of data analysis in systems neuroscience. In addition, future improvements of brain-machine interface will require the development of reliable and robust tools to decode neuronal activity  [69, 70]. In summary, gradient boosted trees methods are potentially helpful tools to explore a dataset and make a prediction on the underlying biological processes which, in turn, can be tested with more classical methods.
Similar results were obtained from the examination of aurone flavonoids effects on MT  [18]. All these observations led in proposing the existence of a modulatory site on MT  [19, 20]. However, MT crystallographic reports (PDB ID: 2Y9W), even in the presence of an inhibitor (PDB: 2Y9X), did not offer any evidence for this assumption  [12]. In our recent publication, searching for non-specific binding sites on MT, we showed how each of phthalic acid (PA) and cinnamic acid (CA), as two modulators with different structures, could form a ternary complex of substrate/MT/modulator  [21]. The non-specific binding sites for PA and CA were named MTPb and MTPc, respectively. We demonstrated that the modulators could reach the active site through channels which connected the MTPb and MTPc to MTPa  [21].
Focused docking into HMT Crystal structure of native MT (PDB ID: 2Y9W) was derived from RCSB Protein Data Bank  [12]. Water molecules and the heteroatoms (including the ligands) were removed and polar hydrogens were added to the receptor by ADT program  [26]. Partial atomic charges were assigned by Kollman-united charges method. The original structure of LTy (CID: 6057) was derived from NCBI PubChem Compound Database. The Gasteiger charge was assigned to the ligand using ADT program. Docking simulation of LTy into HMT was carried out in the active site coordinates. The best pose with favorable binding free energy was selected as the initial structure in the MD simulations.
Blind and focused docking of LTy 1 into HMT Results of the blind docking of LTy 1 into HMT (2Y9W) were ranked with respect to the binding affinity. As anticipated, LTy 1 achieved the most stable binding pose when it was in MTPa. A focused docking of LTy 1 on a space restricted to the active site area confirmed the binding pose of LTy 1 in MTPa (Fig. 3A ) with the lowest binding energy of -6. 4 kcal mol -1. Similar to what was observed for LTy bound to Bacillus megaterium tyrosinase (BmT, PDB ID: 4P6S)  [35] , the hydroxyl group of LTy 1 was directed toward CuA (an equivalent position to ZnA in BmT) in MTPa (Fig. 3B ). The ligand orientation in MTPa is assumingly resulted from the steric interactions of LTy 1 with His61, His85, His263, Val283, and stabilized through weak van der Waals interactions with Phe264, Phe292 and two hydrogen bonds (Hbonds) with Asn260 (3.
But the later work by  [14]  has contradicted this hypothesis, and rather suggested the formation of cesium, sodium and molybdenum oxides. In light of the evidence for the possible formation of the Cs 3 Na (MoO 4 ) 2 quaternary phase  [1] , the mechanism of the interaction between liquid sodium and cesium orthomolybdate needs to be re-visited  [10]. To this end, the determination of the thermodynamic properties of Cs 3 Na(MoO 4 ) 2 is a necessity. In this work we report for the first time the determination of the standard enthalpy of formation and standard entropy of Cs 3 Na (MoO 4 ) 2 at 298. 15 K using solution calorimetry and low temperature thermal-relaxation calorimetry. The standard enthalpies of formation of Cs 2 MoO 4 and Na 2 MoO 4 were moreover measured with the same solution calorimeter, and compared to literature data to serve as a benchmark for the present studies.
Powder X-ray and neutron diffraction The X-ray diffraction measurements were carried out at room temperature (295 AE 3 3 K) using a PANalytical X'Pert PRO X-ray diffractometer mounted in the Bragg-Brentano configuration with a Cu anode (0. 4 mm Â 12 mm line focus, 45 kV, 40 mA). The X-ray scattered intensities were measured with a real time multi strip (RTMS) detector (X'Celerator). The data were collected by step scanning in the angle range 10°6 2h 6 120°with a step size of 0. 008°( 2h); total measuring time was about 8 h. Neutron diffraction data were recorded on the beamline PEARL at the Hoger Onderwijs Reactor at TU Delft  [16]. The sample was encapsulated in a vanadium container hermetically closed with a rubber o-ring. The data were collected at room temperature (295 AE 3 4 K), at a fixed wavelength (k = 0.
Enthalpy of formation of Na 2 MoO 4 Next, the enthalpy of formation of Na 2 MoO 4 was measured in NaOH solution. Using a similar procedure as previously, Na 2 MoO 4 and MoO 3 were dissolved in 0. 1 M and 0. 164 M NaOH solutions, respectively. The dissolutions were again instantaneous. The corresponding reaction scheme is shown in Table  4 , and the detail of the calorimetric results are listed in Table  5. The enthalpy of reaction (2) is equal to D r H o m ¼ D r H 2b À D r H 1b ¼ Àð66:47 AE 0:85Þ kJÁmol À1. MoO 3 ðcrÞ þ 2NaOHðslnÞ ¼ Na 2 MoO 4 ðcrÞ þ H 2 OðslnÞ ð 2Þ The enthalpy of reaction (4b) in Table  2  was derived from the enthalpy of formation of NaOH(aq) reported by Gunn  [29] , i. e. D f H o m (NaOH, aq, 298. 15 K) = À(470. 445 AE 0. 063) kJÁmol À1 , and the CODATA value for the enthalpy of formation of H 2 O(l).
15 K. The collected data do not exhibit any anomaly, and the applica-   tion of a 9 T magnetic field (not shown) does not affect the results, as expected for such insulating material. is equal to D r H o m ¼ D r H 2c þ D r H 3c À D n D þ n E1 þ n E2 þ n E3 /mol 13. 20 The thermodynamic functions of Cs 3 Na(MoO 4 ) 2 were derived at 298. 15 K by fitting the experimental data using the OriginPro 2015 software to theoretical functions below T = 10. 0 K  [34] , and a combination of Debye and Einstein heat capacity functions  [35] [36] [37]  from T = (10. 0 to 299. 6) K. The fitting was done with the Levenbergh Marquardt iteration algorithm, using Origin C type fitting function above T = 10. 0 K, and simple expression type function below T = 10. 0 K. The fitted data are shown with solid lines in Figs.
We used the HASO4 Broadband Shack-Hartmann sensor from Imagine Optic, allowing measurements over a broad spectral range from 400 nm to 1100 nm with an array size of 68 × 50. The spatial resolution of the Shack-Hartmann sensor is 105 µm. A small part of the 1030 nm beam is visible in the spectrum (Fig. 1(b) ) and also reaches the wavefront sensor, but its intensity relative to the white-light is low enough to not influence the calculation of the wavefront. The wavefront sensor was located ∼11 cm after the crystal with the neutral density filters mounted on its input opening. The colored filters were placed 1 cm to 2 cm after the crystal. The analysis of the recorded wavefronts was performed with Imagine Optics' WaveView software. The horizontal and vertical tilt and defocus components of each wavefront were automatically subtracted from the zonal reconstitution in order to observe only the wavefront aberrations.
Chimeric sequences were identified de novo, reference based, and then removed using usearch61  49. The non-chimeric sequences were then clustered into operational taxonomic units (OTUs) at 97. 0% sequence similarity using a closed reference-based picking approach with UCLUST software against the Greengenes database 13_8 of bacterial 16S rDNA sequences  50. When full assignment at the genus or species taxonomic level was not possible, g_ or s_ alone was used to designate genus or species, respectively. The mean number of quality-controlled reads was 28,675 ± 10,173 (mean ± SD) per mouse. After rarefaction at 6,000 reads per sample, bacterial alpha diversity was estimated using the Shannon index. OTUs with a prevalence < 5% were removed from the analysis. Results are represented as the mean ± SEM.
Acknowledgements The authors thank the Animex platform ( Mylène Levant  and  Baptiste Lecomte ), for the follow-up of the mice and the Plaimmo platform. We thank the  NED group  ( Olivier Zemb  and  Béatrice Gabinaud ) and the  GeT-PlaGe platform  for their help with the sequencing data. We thank  Muriel Thomas  for her help with 16S qPCR and  Veronique Robert  for her help with culture of bacteria. The laboratory INSERM U996 is a member of the  Laboratory of Excellence LERMIT  supported by a grant from the  Agence Nationale de la Recherche  ( ANR-10-LABX-33 ). This work was supported by  INSERM , the  Université Paris-Sud , the  Association Française pour l′Etude du Foie (AFEF) , the  Fondation Pour la Recherche en Alcoologie (FRA/IREB) , the  European Foundation for Alcohol Research (ERAB) , and the  Institut de Recherches Internationales Servier (IRIS).
Microstructural Analysis Before tensile testing, pieces of rolled materials were cut off for microstructural analyses. Samples were polished using SiC paper with grit size down to 4000 followed by a Al2O3 polishing with final particle size of 0. 1 µm. Microstructural analyses were carried out on SEM Jeol 6490, at 20 kV with a working distance of 21 mm. Electron back scattered diffraction (EBSD) analyses were performed by Oxford instrument detector with a minimal number of detected Kikuchi bands equal to 8 and a step size of 0. 4 µm. EBSD were studied in four cases: after annealing and after section reduction of ε = 0. 5, 1. 1 and 2. 2. EBSD maps, Figure pole (FP), inverse figure pole (IFP) and orientation density function (ODF) were obtained using ATEX software developed at LEM3  [29]. Texture components were also performed using a spread of 15° from their ideal orientation to compute their volume fractions.
Microstructural Analysis Before tensile testing, pieces of rolled materials were cut off for microstructural analyses. Samples were polished using SiC paper with grit size down to 4000 followed by a Al 2 O 3 polishing with final particle size of 0. 1 µm. Microstructural analyses were carried out on SEM Jeol 6490, at 20 kV with a working distance of 21 mm. Electron back scattered diffraction (EBSD) analyses were performed by Oxford instrument detector with a minimal number of detected Kikuchi bands equal to 8 and a step size of 0. 4 µm. EBSD were studied in four cases: after annealing and after section reduction of ε = 0. 5, 1. 1 and 2. 2. EBSD maps, Figure pole (FP), inverse figure pole (IFP) and orientation density function (ODF) were obtained using ATEX software developed at LEM3  [29]. Texture components were also performed using a spread of 15 • from their ideal orientation to compute their volume fractions.
For ROS monitoring, a luminol-peroxidase solution containing 100 µM, or 350 µM of the corresponding SRB or solvent (0. 5% ethanol) was added to each well. All SRBs contain the same amount of ethanol. The luminescence was read immediately after elicitation every 2 min with a Tecan Infinity F200 PRO for 720 min. Data presented are means of at least triplicate experiments ± standard error of the mean (SEM) with n = 6 for each experiment. (A,C,E) SRB 10i , SRB 14i and SRB 18i at 100 µM, respectively. (B,D,F) SRB 10i , SRB 14i and SRB 18i at 350 µM, respectively. (G) Control (ethanol). response between saturated/unsaturated SRBs were observed. SRB 14i were more active than SRB 14 and only SRB 10i and SRB 18i displayed a significant response on ROS assays (Fig. 3 ). Dose response experiments on SRB 14i showed that the minimal concentration necessary for this molecule to induce a robust ROS response in Arabidopsis was 50 µM (Fig.
5% ethanol (control) and monitored for extracellular ROS production for 720 min. Histograms were calculated as the total RLUs over 12 hours of monitoring. Data presented are means of at least triplicate experiments and SEM with n = 6 for each experiment. (B) Extracellular ROS production in rbohD mutant challenged with the SRB 14i at 350 µM or ethanol (control). Data presented are means of at least triplicate experiments ± standard error of the mean (SEM) with n = 6 for each experiment.
SRB 14i interacts with plant biomimetic plasma membrane. The atypical immune signature of SRBs raised the hypothesis of a direct interaction of the molecules with lipids from the plasma membrane instead of a receptor-dependent perception, as it has already been proposed for the amphiphilic bacterial elicitor surfactin  33. In order to assess the affinity of SRB 14i for plasma membrane lipids, its partitioning into PLPC/sitosterol (80:20) vesicles and its insertion into PLPC or sitosterol monolayers were determined by ITC and Langmuir monolayer experiments. ITC raw data (Fig. 10A ) displayed a gradual decrease of the positive heat flow signal over the course of the successive LUV injections. This profile is typical of a binding phenomenon. The thermodynamic parameters obtained from the fitting of the cumulative heat vs lipid concentration plot (Fig.
Protection assays. For the protection tests against B. cinerea (strain B05. 10) 9 , 6-weeks-old plants were leaf spray-elicited with 350 µM SRB solution. Two days after elicitation, 7 mm leaf discs were cut and placed in Petri dishes onto water-wet Whatman filter paper. B. cinerea was initiated from a silica gel crystal stock and cultivated in PDA plates at 22 °C for 2 weeks. Conidia were collected by adding 4 mL of conidia suspension (KH 2 PO 4 1. 75 g/L, MgSO 4 0. 75 g/L, Glc 4 g/L, peptone 4 g/L, Tween 20 0. 02% [v/v]) and filtered with cheesecloth to separate hyphae from conidia. The conidia suspension was adjusted to 10 5 conidia/mL, incubated for 9 h at 22 °C for germination and Arabidopsis leaf discs were inoculated with one drop of the conidia suspension. Petri dishes containing the inoculated leaf discs were parafilm-sealed and incubated at 22 °C for 4 days before quantifying the necrotic area with the ImageJ software  75.