dataesr/scientific-paragraphs-categorization huggingface dataset:
Methodology Graph Theory provides us with a powerful toolkit for the modelling and treatment of data which exhibit pairwise relations, such as a transportation network. In the case of the data derived from the Lloyd's Shipping Index, we treat each port as a node of the network, and the edges (the connections between the ports) are added if we observe at least one movement of a ship from port A to B in a given time period. We obtain in this way an undirected and unweighted graph G = (N, E), with the set of nodes (N) and edges (E). In this work we do not take into account the intensity of movements among the ports; that is, each edge in the network has a weight of 1 and contributes equally to the scores that we obtain while performing calculations on the network. This is certainly a simplification, which overlooks the intensity of flows, by looking only at the existing connections which we can observe in the maritime network.
However, we do observe some drops in 1920, 1946, and, most surprisingly in the year 2000, when the drop is the largest in the whole period under study. The last drop is especially puzzling, because the beginning of the 21st century is known to be the period of increased globalization and increased optimization performed by maritime operators. It is possible that the effect which we observe in the year 2000 is due to the limited amount of data which has been used for this study, or is linked to the fact that since the year 2000 we started to observe an important trend in ship upscaling, which has led to exclusion of smaller ports that were unable to handle such large vessels. If the studies of the fuller dataset confirm that the average random walk discovery started to deteriorate in the 21st century, we would have a really interesting phenomenon to explain.
Conclusion In the present paper we have constructed a network of maritime connections thanks to the data extracted from the Lloyd's Shipping Index, a database containing information on daily movements of ships of almost the entire world fleet. Our data cover the years between 1890 until 2000, where we have information about the movements of ships during at least 2 weeks in regular intervals of 5 years. This data enabled us to construct snapshots of the network of sea connections at different moments in time and to follow its evolution. Most of the existing studies of real world networks focus on the static networks due to scarcity of quality data. One of the few examples of such studies is the work by  Leskovec et al. (2007)  who find that real world networks tend to follow two laws, that is densification and shrinking diameters.
Acknowledgments The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP/2014-2020 )/ ERC  Grant Agreement n. 31384. A part of this work was done by  Nishant Rai  during his internship at the INRIA Gang Project -we would like to thank  Laurent Viennot  and  Adrian Kosowski  for their supervision of Nishant Rai's work.
This work was supported by the  French National Research Agency (Labex STORE-EX : grant  ANR-10-LABX-0076 ). We thank the  University of Bordeaux  for the use of the cluster Avakas. We are grateful for the computing resources on OCCIGEN (CINES) obtained through the project x2015097321. B. J. M. acknowledges support from the  Royal Society  ( UF130329 ).
Acknowledgements This study was partly supported by the  XRMON  project ( AO-2004-046 ; Grant no:  20288/06/NL/VJ ) of the  MAP program  of the  European Space Agency (ESA)  and by the  French National Space Agency (CNES ; convention n o  150515/00 ). The authors express their gratitude to  Dr. Olivier Minster  and  Pr. Bernard Zappoli  for their continuous support. The authors wish to acknowledge all the international partners who were involved in these projects: these scientific results could not have been achieved without their collaboration.
Light reflected by the EFPI is collected with a high-speed spectrometer (Ibsen Photonics I-MON-512-USB), operated on the 1520-1596 nm bandwidth. A LabVIEW™ program has been developed for data acquisition (DAQ), recording data from the spectrometer and switch and displaying the estimated output pressure; the software adjusts also the integration time of the spectrometer. The software also compensates the uneven spectrum of the SLED source, and adjusts the sampling frequency to approximately 10 Hz. EFPI calibration was performed in an air-filled tank, testing for pressure levels up to 100 kPa, and dynamically using a 60-cm burette filled with water, for fine detection of pressure up to 5. 8 kPa. The pressure sensitivity, measured as air-gap compression as a function of applied pressure, is estimated as 1.
2 pm/ ˝C. In absence of fluctuations of refractive index of the outer medium, pressure measurement yields accuracy better than 0. 1 kPa  [25]. The results of the calibration are in line with the previous pressure sensing system presented in  [25]  and developed by University of Limerick, which makes use of the same splicing method and capillary/diaphragm structure. from the optical source is directed to the probe through a 50/50 fiber-optic coupler. Light reflected by the EFPI is collected with a high-speed spectrometer (Ibsen Photonics I-MON-512-USB), operated on the 1520-1596 nm bandwidth. A LabVIEW TM program has been developed for data acquisition (DAQ), recording data from the spectrometer and switch and displaying the estimated output pressure; the software adjusts also the integration time of the spectrometer.
Photoluminescence spectra of the samples were acquired in steady-state at room temperature using a Nanometrics RPM2000 rapid photoluminescence mapping system. Current density vs. voltage (J-V) curves were measured under illumination at 25°C with a Keithley 2400 sourcemeter coupled with ReRa Tracer 3. 0 software. The light source used was a calibrated AM1. 5G spectrum LOT solar simulator equipped with a filtered xenon lamp.
Acknowledgements P. Calka is grateful to the Alexander von Humboldt foundation for granting her PostDoc fellowship. IHP authors gratefully acknowledge the financial support from the  Deutsche Forschungsgemeinschaft (DFG)  for the  RRAM  project under contract " SCHR 1123/7-2 ". The authors would also like to thank  Jens Katzer  (FIB) and  Andreas Schubert  (TEM) from  IHP.
The method exploits the information contained in the correlation between responses to produce highly accurate estimates of both quantal and dynamic parameters from the same recordings. The method also provides important conceptual and technical advances over existing state-of-the-art techniques. In particular, the repetition of the same stimulation in identical conditions becomes unnecessary. This paves the way to the design of optimal protocols to estimate synaptic parameters, to the quantitative comparison of synaptic models over benchmark datasets, and, most importantly, to the study of repetitive transmission under physiologically relevant patterns of synaptic activation.
(23) with I(y) given by: I(y) ϭ y Ϫ 3 2 •exp ͭ Ϫ q•͓y Ϫ q•(S k Ϫ Ϫ S k ϩ )͔ 2 2 q 2 y Ϫ (R k Ϫ y) 2 2 n 2 ͮ. (24) Re-estimation formulas have to be evaluated numerically. The EM algorithm was implemented in Cϩϩ. For multidimensional root finding, we used the Brent-Dekker algorithm and a derivative-free version of Powell's hybrid algorithm, as provided by the GNU Scientific Library  (Galassi et al. , 2009). The code is available from the authors upon request.
In the bottom panel of Figure  3B , we report the experimental (black curve) and the synthetic CVs (red curve; error bars represent 95% confidence interval). The model parameters were not selected to reproduce the average responses or the CVs, but rather to maximize the probability of the occurrence of the actual trains of responses observed in the experiment. Nevertheless, as can be seen, the experimental data are well reproduced by the model. We estimated the synaptic parameters for all the connections in our dataset. In 6 of 69 cases, the estimation procedure returned values for one or more parameters that were judged to be problematic. In four cases, the estimation procedure returned values for one or both the time constants (i. e. , D and F ) that were several orders of magnitude larger than the longest timescale at which the synaptic connections were probed.
For sets of responses generated by the model, one expects z out to be uniformly distributed between 0 and 1. The distribution of the average (over all trials) z out value across the dataset is shown in Figure  3D. As can be seen, most of the values are between 0. 4 and 0. 5. For only 5 of 63 connections, the distribution of z out values obtained from the leave-one-out procedure was statistically different from the uniform distribution (Kolmogorov-Smirnov test, p ϭ 0. 01). For three of these connections, Ͻ25 trials were available. No connection with Ն30 trials (n ϭ 10) showed a distribution of z out values that was statistically different from the uniform distribution. The leave-one-out procedure also allowed us to evaluate the stability of the estimation procedure, which we quantified by computing the average coefficient of variation of the estimates.
Quantifying the uncertainty of the parameter estimates We used a standard re-sampling procedure (i. e. , parametric bootstrap;  Efron and Tibshirani, 1994)  to quantify the uncertainty of the parameter estimates. For each set of parameters obtained from a synaptic connection in our dataset, we generated 500 synthetic experiments with the same settings as in the original experiment (i. e. , same stimulation protocol, number of repetitions, and baseline noise). We then re-estimated the parameters for each synthetic experiment and computed their relative errors with respect to the original set of parameters. We show in Figure  4A  the resulting distributions of relative errors for the same connection shown in Figure  3A. All the distributions peaked at ϳ0, with averages very close to 0 and relative SDs Ͻ0.
3. This shows that, at least for this con-nection, our method returns unbiased and accurate estimates of the parameters. We see in Figure  4B , where we plot the SD versus the bias of the relative error for each parameter and each connection, that this is, in fact, the case for the majority of connections in our dataset. For some connections, however, the estimates of q and/or F are quite inaccurate. Large relative errors in the estimates of q are due to the connections for which the baseline noise is comparable or larger than the quantal noise. Large relative errors in the estimates of F are due to connections for which the (experimentally) chosen interstimulus interval was ill suited to resolve the facilitation time course. For instance, in depression-dominated connections (i. e. , F Ͻ D ) the facilitation time constant cannot be reliably estimated if the interstimulus interval is much longer than F.
Generality and robustness of the method In this study, we have chosen the stochastic TM model as the underlying generative model  (Fuhrmann et al. , 2002). Several considerations motivated this choice: (1) the TM model has a small number of free parameters and yet is able to describe very diverse patterns of transmission; (2) The dataset we have analyzed was previously analyzed with the TM model, which allows for direct comparison; and (3) the stochastic TM model has been widely used in theoretical investigations of functional/computational implications of synaptic variability; thus, it appeared relevant to asses to which extent the model captures variability in real synapses. Our method, however, is general and can be extended to accommodate more biophysical detail, such as calcium-dependent docking rates  (Dittman et al.
, 2000)  or the presence of multiple vesicle pools  (Rizzoli and Betz, 2005). Also, we emphasize that our method is not restricted to electrophysiological recordings and could be adapted to optical recordings  (Yuste et al. , 1999; Oertner et al. , 2002; Trigo et al. , 2012). The frequencies used to probe synaptic transmission in our dataset were relatively low, and, accordingly, we have neglected postsynaptic receptor desensitization. However, desensitization is known to significantly contribute to short-term depression at higher stimulation frequencies  (Trigo et al. , 2012). This is especially relevant when experimentally probing synaptic connections with the Poisson protocol where interstimulus intervals can be very short. Our framework can be extended to include desensitiza-tion by making the quantal size dependent on spiking history.
Hallermann et al. (2010a,b)  adopted a similar approach to compare the explanatory power of different models of repetitive transmission on the same dataset. Loebel et al. (2009)  devised a somehow reversed procedure where, in a first step, least-squares fitting returns an estimate of the dynamic parameters, and of the product of the number of release sites and the quantal size (the so-called absolute synaptic efficacy). In the second step, another least-squares fitting returns the number of release sites (and thus the quantal size) that best reproduces the coefficient of variations of the synaptic responses. All these methods proceed in a stepwise (and more or less laborious) manner using the estimates obtained in one step as fixed parameters in the next step. In this way, however, estimation errors in one step are propagated, and potentially amplified, in the next step.
Likelihood (once properly corrected for the differing number of free parameters) constitutes a theoretically principled metric to compare both the descriptive and predictive powers of different models over the same benchmark datasets. Our method, thus, paves the way to factorial testing of models of synaptic transmission, an approach that is increasingly and fruitfully being used in neuroscience and cognitive science  (Pinto et al. , 2009; Daunizeau et al. , 2011; van den Berg et al. , 2014).
In particular, the Pam-Form™ code  [5]  based on an explicit F. E. M. approach and the AniForm™ code  [18]  based on an implicit approach. Recent papers analyze the thermoforming processes of thermoplastic prepregs. In  [21]  the influence of the different process parameters was experimentally evaluated. The process simulation presented in  [22]  is based on a non-orthogonal constitutive model originally developed by Yu et al. [23]  and implemented in a user subroutine of code Abaqus Explicit™. The simulations of a double dome geometry thermoforming were compared to the experiments, but wrinkles are not simulated since membrane and truss elements are used. In  [18] , simulations using AniForm™ software are compared to the experiments in cases of thermostamping unidirectional and woven textile reinforced prepregs.
Application to the analyzed thermoplastic prepreg The non-deformed mesoscopic structure was determined using Xray micro-computed tomography (μCT) (Fig. 5a )  [31, 32]. Then the geometric is modeled using the TexGen software developed at the University of Nottingham as a modeling pre-processor for textiles  [33]. The local direction of the fibers is provided by TexGen and is later used to manage the anisotropy of the meshes (Fig. 5 ). The chosen homogenization method implies that the thermal properties of the constituents at the mesoscopic scale (fiber yarns and matrix) are known. Outside the yarns, it is assumed that the behavior of the polymer is isotropic but depends on the temperature. The thermal conductivity of PA 6. 6 (k p ), is given Fig. 6   [34]. The conductivity of glass fibers (k f ) will be assumed to be constant and equal to 1 Wm -1 K -1  [35].
Numerical simulations and comparison with experimental process The constitutive model described above has been implemented in the finite element code Plasfib  [53]  based on an explicit dynamic approach. The blank is discretized by triangular shell elements without rotational degrees of freedom. The curvatures of the element are computed from the position of the neighbor elements  [54] [55] [56]. The coupling between the thermal and mechanical problem is obtained by performing alternately forming simulations (that modify both the boundary of the thermal problem and the values of the thermal conductivity as described in Section 3) and thermal computations (that modify the mechanical properties). The simulation of the composite part thermoforming presented in Section 2. 3 is performed. The processing time is 19 s (for the forming stage).
Acknowledgements We would like to thank  Dr Satoshi Kawada ,  Dr Hirohito Ohata  and the  Material Analysis Station (NIMS)  for ICP and XPS measurements. This work was supported by the  International Center for Young Scientists (ICYS) , the  Japanese Society for the Promotion of Science (JSPS)  and the  World Premier International Research Center Initiative  ( WPI Initiative ),  MEXT, Japan. Dr Sanchez-Ballester  and  Dr Rydzek  contributed equally to this work.
Acknowledgments We acknowledge the funding through the  SMARTCANCERSENS  project ( FP7-PEOPLE-2012-IRSES)  under the grant agreement No. 31805 , the SEA-on-a- CHIP  project ( FP7-KBBE ) under the reference  614168 , the  NATO  project,  SPS  ( NUKP. SFPP984173 ) and the  HEARTEN  under the grant agreement No. 643694.
Background: Microorganisms constitute a reservoir of enzymes involved in environmental carbon cycling and degradation of plant polysaccharides through their production of a vast variety of Glycoside Hydrolases (GH). The CAZyChip was developed to allow a rapid characterization at transcriptomic level of these GHs and to identify enzymes acting on hydrolysis of polysaccharides or glycans. Results: This DNA biochip contains the signature of 55,220 bacterial GHs available in the CAZy database. Probes were designed using two softwares, and microarrays were directly synthesized using the in situ ink-jet technology. CAZyChip specificity and reproducibility was validated by hybridization of known GHs RNA extracted from recombinant E. coli strains, which were previously identified by a functional metagenomic approach.
GHs have been used in many industries such as in paper production, textiles, detergents, feed and food  [4, 33]  as well as to promote healthy human nutrition and prevent diseases  [17]. In the last decade, cellulases and more recently hemicellulases have been considered for biorefining  [23, 30]. The discovery of GHs has been considerably accelerated with the metagenomic and metatranscriptomic approaches, which allow the identification of new enzymes in an unprecedented manner. GH exploration is largely facilitated by the existence of the CAZy database (CAZy; www. cazy. org). This database describes the families of enzymes that catalyze the breakdown, biosynthesis or modification of carbohydrates and glycoconjugates. In the CAZy database, GHs are classified into families based on amino acid sequence similarities and others conserved features  [7, 25, 26, 39].
GH-are classified in 135 families and represent approximately 47 % of the entire database. (April 2016)  [7]. The vast majority of currently known GH are from bacterial origin. DNA microarrays are widely used to profile gene expression and represent a relevant tool to study expression of key enzymes and monitor physiological changes of pure cultures or microbial communities  [12, 18, 28, 42, 46, 50, 68]. This approach can also be useful to link microbial diversity to ecosystem processes and functions  [22, 29, 67]. In this study, we developed the first microarray tool, termed CAZyChip, to quickly and accurately explore, at transcriptomic level, the GH composition of environmental samples. The CAZyChip provides snapshot views of the enzymes expressed by a single microorganism or more interestingly by microbial consortia derived from complex and various ecosystems.
The biochip gives an opportunity to highlight enzyme cooperation along with the plant biomass degradation pathway. The present study demonstrates that the CazyChip represents a unique, robust and yet generic tool to dynamically analyze the expression of a large variety of GHs in parallel. The current version of this biochip allows the detection of 55,220 bacterial annotated GHs and contains the signatures of all bacterial GH in all families available to date in the CAZy database in addition to 53 CE sequences. The CAZy chip was validated using characterized enzymes from gut metagenomic libraries of different species, which were chosen for their known abilities to degrade plant cell walls. The encoding sequences of the enzymes of interest were recovered from microbiome of worm (Pontoscolex corethrurus), human, rumen, and termites these latter include fungus-growing (Pseudacanthotermes militaris), wood-feeding (Nasutitermes corniger), or soil-wood feeding (Termes hispaniolae).
Custom microarray design The design of oligonucleotides for the microarray was performed using either the Agilent e-Array online portal (https://earray. chem. agilent. com/earray/) or, when sequences were rejected by eArray, the ROSO software  [16, 52]. When the design of 60-mers were impossible, a 40-mer or a pair of 25-mers associated with inert nucleotidic linkers was generated. For each targeted CAZyme gene (GH and CEs), three different 60-mer probes were designed and for each probe. The Agilent probe design algorithm assigned a BC score, which reflects uniqueness, secondary structure considerations, GC content and thermodynamic parameters, that predicts hybridization quality on the basis of their nucleotidic composition  [19]. Five grades of BC scores were defined and indicated the quality of the designed probes.
These different scores were, from the best to the worst: BC_1, BC_2, BC_3, BC_4 and BC_Poor. A total of 180,000 probes, including 4848 Agilent internal positive or negative control probes, were selected and synthesized in situ, on a glass slide using Agilent SurePrint technology to obtain a high-density DNA microarray tool on 4x180 K format (Agilent Technologies, Massy, France)  [32]. The full description of the CAZyChip microarray has been deposited in the Gene Expression Omnibus (GEO) public database (GSE80173 study is at: http://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE80173).
Data processing The median signal of each spot in the hybridized arrays were determined and quantified using Feature Extraction software v11. 5. 1. 1. The data from all the microarrays were normalized using the "limma" package function "normalizeQuantiles" and the "quantile" method  [5, 56]. Normalization and statistical analyses of the data were performed using the Bioconductor packages (http:// www. bioconductor. org) and R software v3. 1. 3. For each sample, the normalized fluorescence intensities of the three experimental replicates were analyzed and the mean values, standard deviations and correlation coefficients (%CV) were calculated. To determine whether probes were specific and target genes present, limma one way ANOVA test was carried out with False Discovery Rate adjusted p value < 0.
Probe design To design a generic microarray for the high-throughput detection of bacterial CAZymes mainly composed of GH's, all of the bacterial GH protein sequences referenced in the CAZy database (www. cazy. org) up to January 2015 (133 families), were selected and their nucleotide sequences downloaded from the National Center for Biotechnology Information database (www. ncbi. nlm. nih. gov). We also selected sequences of interest obtained from human or termite guts and cow rumen metagenomic libraries created in our laboratory  [2, 10]. The initial dataset used for probe design contained a total of 55,220 sequences and for each gene we designed three non-overlapping probes, with the aim to validate at least one probe per GH for use in a future prototype. With the e-array software, probe design has been possible on 55,012 sequences with a BC score attribution.