dataesr/scientific-paragraphs-categorization huggingface dataset:
The known motifs (Table  2 ) implemented in sHSPdb result from our analysis. The user can perform a similarity search by using BLASTP implemented in sHSPdb. This allows obtaining additional information and retrieving the best scoring sequences through the classical BLAST output interface. It is also possible to BLASTX a nucleotide sequence against sHSPdb to check the existence of similar or homologous sHSP in order, for example, to construct a putative sHSP cDNA sequence from EST. Many options are proposed for the output: (i) when entries have been retrieved, the information can be displayed in any of the views of the browse mode and the fields such as accession number, taxonomy, PFAM, CDD and InterPro numbers are linked to their original web site according to the relevant information found at the NCBI; (ii) the user can select or deselect any entry resulting from his search (« Toggle » option) and the final selection can be stored.
The statistical analysis tool Another original feature of sHSPdb is the possibility to dynamically generate plots of physico-chemical properties or amino acids usage of sHSP using different criteria. Therefore, one can immediately make multiple comparisons of self datasets. The user can access to statistical data through the menu « Statistical analysis ». This option displays « Class description » (examination Fig. 1  Browse interface for accessing the whole sHSPdb content. The « Search » mode provides an advanced search with multiple parameters and permits retrieval of very fine subsets of data. The user can also retrieve sequences by entering amino acids motif either exact or degenerated using regular expressions with sophisticated syntax. Among other information, the « Summary » option provides links to general databases, the name of the sHSP and its putative function.
Utility and discussion sHSPdb versus other sHSP datasets Poulain et al. [9]  reported about a dataset including 3787 sHSP sequences. However, this dataset contains some redundancy: 774 sequences from this dataset were aligned and 70 sequences are identical, corresponding to a redundancy of 9 %. It can be thus estimated that the entire dataset correspond roughly to 3400 different sHSP, i. e. , less than the number of sHSP contained in sHSPdb. Moreover, this dataset is limited to a flat PDF file with accession numbers and thus does not provide any details or functionalities. Our selection is very stringent since we excluded most of all files annotated « putative », « probable », « unknown », « hypothetical », « unnamed protein product », « synthetic construct », … in order to increase the quality of annotation even if less sequences were retained.
We did not consider fragments, i. e. , files annotated « partial ». Sequences with such annotation but additional indication and/or classifiable on the basis of a motif were retained. To our knowledge, there is only one other database dedicated to HSP (HSPIR: http://pdslab. biochem. iisc. ernet. in/hspir/shsp. php) which gather accession numbers and FASTA files for HSP protein sequences, and provides general information about the structure and function of the different families of HSPs  [25]. However, HSPIR contains only ≈ 1300 sequences of sHSP, and Fig. 2  The statistical analysis tool. One original feature of sHSPdb is the possibility to dynamically generate plots of physico-chemical properties or amino acids usage of sHSP using different criteria. Therefore, one can immediately make multiple comparisons of self datasets.
The user can access to statistical data through the menu « Statistical analysis ». This option displays « Class description » (examination of all physico-chemical properties or all amino acids usage or all combinations of amino acids for a given sHSP class) or « Class comparison » (comparison of a given physico-chemical property for the sHSP classes). Plots are automatically generated and are clickable to be resized offers access to only basic search and BLAST tools. sHSPdb appears thus as a unique comprehensive and highly implemented database for the computational and statistical analysis of sHSP.
Short example of use of sHSPdb: analysis of mitochondrial sHSP A phylogenetic tree of plants mitochondrial sHSP (class 6) has been built (Fig. 4 ). Five sequences correspond to mitochondrial sHSP from Arabidopsis thaliana (AAM63747, CAA67022, HS235_ARATH, HS23M_ARATH and HS2 6M_ARATH, all from class 6). The differences between AAM63747 and HS235_ARATH are [S/A]31 and [D/H]69 and the difference between CAA67022 and HS23M_AR-ATH is [D/H]143. The sequences of ACD and C-terminal domain are very similar (Fig. 5 ). The sequences of the Nterminal domain of HS235_ARATH and HS23M_ARATH are different from that of HS26M_ARATH and are more intrinsically disordered (negative FoldIndex for HS2 6M_ARATH). This may have a possible incidence on the dimer formation  [34, 35].
Conclusions sHSPdb harbors a comprehensive dataset available for sHSP, together with tools designed for their online analysis. To our knowledge, there is no equivalent database for sHSP. sHSP are classified into classes on the basis of various parameters, especially on the basis of amino acids motifs that discriminate the classes. sHSPdb thus  Future developments and perspectives: (i) sHSP physico-chemical properties and sHSP amino acids usage are statistically analyzed for all sHSP classes. We will thus be able to compare the three domains (i. e. , the N-terminal, the ACD and the C-terminal), thus bringing additional information to those already determined Fig. 4  Phylogenetic tree of plant mitochondrial sHSP. 91 sequences of mitochondrial sHSP from 47 plant organisms were analyzed. The characteristics of the sequences are: a length from 187 to 248 amino acids; an isoelectric point from 4.
620 to 10. 15; a net charge at pH 7 from -0. 041 to +0. 031; a FoldIndex from -0. 044 to 0. 138; a hydropathy (GRAVY) from -0. 908 to -0. 274. The alignment was made using Clustal-W and the figure was drawn using Dendroscope  [36]  Fig. 5  Alignment of three mitochondrial sHSP from Arabidopsis thaliana. The alignment was made using Multalin  [37]  and the figure was drawn using ESPript  [38]  by structural methodologies. (ii) We are currently developing software for the analysis of sequence submitted by the users in order to predict if it belongs to any of the sHSP classes. (iii) Since deciphering the molecular functions of sHSP is a major issue, we will provide lexical tools (dictionaries by alphabetic order or occurrence or synonyms…) for a better semantic analysis of the words that describe the known elements of the function of sHSP.
Eight adjacent readout lines (either anode or grid) therefore sampled a distance of 16 mm in x and y. Voltages on the grid and anode lines were sampled at 1 MHz providing information about the event in the drift direction z. With a negative ion drift speed of ∼5 cm/ms, this sampling rate corresponds to 50 µm spatial resolution in z. The 52  (41)  of the remaining wires form a grid(anode) veto on each side of this fiducial area against ionizing radiation from the sides of the detector, for each MWPC. Triggering of the data acquisition system occurred on individual anode lines. All lines were digitized from -3 ms to +7 ms relative to the trigger with 12-bit digitizers. The region bounded by the vetoes and the inner MWPC grid planes formed a fiducial volume of 0. 803 m 3. Each side of the detector was instrumented with an automated, retractable, ∼100 µCi 55 Fe calibration sources, which allowed regular monitoring of detector gain and performance.
e. no head-tail sensitivity). Blue circles correspond to data taken with a neutron source aligned with the z-axis. DRIFT has the best spatial resolution along the z direction. The measurements show a recoil sense sensitivity all the way down to the detector threshold of 50 keV r (the threshold has since been improved to 30 keV r ). The red triangles correspond to a combined data set with the neutron source aligned along the x and y axes, producing recoils that should exhibit no recoil sense asymmetry in the z direction. As expected, these measurements are consistent with zero. Figure from  [64]. stronger than the range component signature, as expected. As shown in Figure  4 , recoil sense recognition was achieved all the way down to the threshold (at the time) of 50 keV r. An extrapolation of the data suggest it is non-zero down to energies below threshold.
The Medipix chip family Medipix2 and Timepix  [179]  are the second generation of the Medipix chip family, fabricated in the IBM 250 nm CMOS process, with a 256 × 256 matrix of 55 µm square pixels. The two chips share a similar architecture. Medipix2 was originally conceived as a photon counting, X-ray imaging detector intended to exploit the superior noise performance and granularity of active pixel devices, compared to charge integrating systems based on conventional pad readout. Each Medipix2 pixel contains a charge-integrating preamplifier and a discriminator with a globally adjustable threshold, followed by mode control logic and a 14-bit counter. The chip is driven by an external shutter signal that determines if the pixel matrix is taking data or reading out. When the shutter is open, each pixel individually counts the number of particles above threshold and increments the counter.
The GEM-based Optical Detector A schematic of the detector is shown in Figure  19 , and a detailed description is provided in Ref. [39]. For the 100 Torr data the detector operated with a cascade of three standard, 7×7 cm 2 thin GEMs manufactured at CERN with a 140 µm pitch hexagonal hole pattern. At lower pressures (down to 40 Torr), a single or double stack of THGEMs (also manufactured at CERN) replaced the three standard GEMs. Several THGEMs were evaluated, with thicknesses of 0. 4 mm and 1 mm. The hexagonal-patterned holes had diameters and pitches of 0. 3 mm and 0. 5 mm, which was the smallest that CERN could make. The induction gap was defined by a wire grid plane with 1 mm pitch placed 3 mm above the last GEM in the cascade (GEM3 in Figure  19 ). The grid was made from 20 µm diameter gold-plated tungsten wires.
DMTPC detector configuration The DMTPC optical system employs CCDs and PMTs to image the micromegas-like TPC amplification region through a vacuum viewport (a window). DMTPC also implements charge readout, again external to the vacuum vessel. The charge channel comprises a fast amplifier connected via an electrical feedthrough to the TPC ground electrode, a pre-amplifier to the TPC anode electrode, and (optionally) a pre-amplifier to an electrically-isolated outer veto ring of the anode. The primary tracking information comes from the optical readout, while a complementary energy measurement comes from the single charge channel measurement on the TPC anode. The rise time of the charge mesh channel is used to distinguish particles with low dE/dx, e. g. from gamma scattering  [208]. The PMT signal is used to trigger the CCD data collection.
Detector (ii)  55 Fe source spectrum used to measure the peak voltage for the gain calculation. Bottom right: measured nuclear recoil candidate angle q recoil with respect to the neutron source for AmBe neutron calibration data in the m 3 detector. Inset (left) shows an example nuclear recoil event of candidate energy 60 keV r in the raw data (with no rebinning or filtering), while inset (right) shows the reconstruction from fitting this event. Intensity in units of digital CCD counts is indicated by color. was also used to prototype the charge readout scheme. To scale up to large volumes while preserving the direction measurement capability, multi-camera readout of a large amplification region is needed. The third prototype (iii) was instrumented with four cameras (and three PMTs) viewing a common amplification region, for a fiducial volume of 20L  [219].
Figure4: Measurements of the recoil sense in DRIFT-II as a function of energy. The y-axis quantifies the recoil sense reconstruction, with zero corresponding to correct sense reconstruction only half of the time (i. e. no head-tail sensitivity). Blue circles correspond to data taken with a neutron source aligned with the z-axis. DRIFT has the best spatial resolution along the z direction. The measurements show a recoil sense sensitivity all the way down to the detector threshold of 50 keV r (the threshold has since been improved to 30 keV r ). The red triangles correspond to a combined data set with the neutron source aligned along the x and y axes, producing recoils that should exhibit no recoil sense asymmetry in the z direction. As expected, these measurements are consistent with zero. Figure from [64].
Acknowledgements We acknowledge the support of the dation (NSF). This material is based up  NSF  under Grants Nos. 0600840 ,  060078  ions, findings, and conclusions or recom this material are those of the author(s) flect the views of the NSF. We also a the Science and  Technology Facilities Co ash Ltd. , the European Commissions's F  RII3-CT-2004-506222  and the European national Reintegration Grant number FP  J. Martoff  for useful discussions. Acknowledgements J. B. R. B. acknowledges the support of the  Alfred P. Sloan Foundation  ( BR2012-011 ), the  National Science Foundation  ( PHY-1649966 ), the  Research Corporation for Science Advancement  (Award # 23325 ), the  Massachusetts Space Grant  Consortium, and the  Wellesley College Summer Science program. I. G. I. and the Zaragoza group acknowledges support from the  European Research Council (ERC)  through the  ERC-2009-StG-240054 grant  ( T-REX  project) as well as from the  Spanish Ministry of Economy and Competitiveness  under grants  FPA2008-03456 ,  FPA2011-24058 , and FPA2013-41085-P.
This work was partially supported by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  657751. This work was supported in part by the  Office of High Energy Physics of the U. S. Department of Energy (DoE)  under contract  DE-AC02-05CH11231. F. I. acknowledges the support from the  Juan de la Cierva program. D. L. acknowledges support from the  National Science Foundation (NSF)  under grants  1407773  and  1506329. This work was partially supported by  KAKENHI   Grant-in-Aids for Young Scientist(A)  ( 16684004 ,  19684005 ,  23684014 ,  15H05446 )  KAKENHI Grantin-Aid for Scientific Research on Innovative Area  ( 26104005 ); KAKENHI  Grant-in-Aids for Scientific Research(A)  ( 16H02189 ); KAKENHI  Grant-in-Aids for Scientific Research (B) (21340063); KAKENHI  Grant-in-Aids for Challenging Exploratory Research  ( 23654084 , 15K13485 ); KAKENHI  Grant-in-Aids for JSPS Fellows ; from the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan.
This work was partially supported by  Program for Advancing Strategic International Networks to Accelerate the Circulation of Talented Researchers, JSPS, Japan  ( R2607 ). The  Royal Holloway, University of London  group is supported by  ERC   Starting Grant  number  ERC StG 279980  and  STFC  grant  ST/N00034X/1. D. P. S. I. acknowledges support from the  NSF  under grants  1407754 ,  1103511 ,  1521027  and  1506237 , and undergraduate student support from  Occidental's Undergraduate Research Center (URC)  summer program. S. V. and the  Hawaii  group acknowledge support from the  U. S. Department of Homeland Security  under Award Number  2011-DN-077-ARI050-03  and the  DoE under Award Numbers   DE-SC0007852  and  DE-SC0010504. Finally, we thank the anonymous referee for their helpful comments.
Some parametric and kernel methods; least absolute shrinkage and selection operator (LASSO), GBLUP, support vector machine regression (SVR) and RKHS regression were thereupon compared for their genomic predictive ability in the context of rice breeding using three real data sets. Among the compared methods, RKHS regression and SVR were often the most accurate methods for prediction followed by GBLUP and LASSO. An R function which allows users to perform RR-BLUP of marker effects, GBLUP and RKHS regression, with a Gaussian, Laplacian, polynomial or ANOVA kernel, in a reasonable computation time has been developed. Moreover, a modified version of this function, which allows users to tune kernels for RKHS regression, has also been developed and parallelized for HPC Linux clusters. The corresponding KRMM package and all scripts have been made publicly available.
On the other hand classical linear regression, which is a parametric method, rely on a prespecified functional relationship between covariates and the response. One objective of this paper is to provide readers with a clear and unified understanding of conventional parametric and kernel methods, used for genomic prediction, and to compare some of these in the context of rice breeding for quantitative traits. Another objective is to provide an R package named KRMM which allows users to perform RKHS regression with several kernels. The first part of the paper reviews the concept of regularized empirical risk minimization as a classical formulation of learning problems for prediction. The second part reviews the equivalence between some well-known regularized linear models, such as Ridge regression and LASSO, and their Bayesian formulations.
We show that many parametric methods can be framed as machine learning methods with simple kernels. In the last part we compare four methods which are LASSO, GBLUP, SVR, and RKHS regression for their genomic predictive ability in the context of rice breeding using three real data sets. Finally, we provide a simple and userfriendly R function, and a tuned and parallelized version of the latter, which allow users to perform RR-BLUP of marker effects, GBLUP and RKHS regression with a Gaussian, Laplacian, polynomial or ANOVA kernel. The corresponding KRMM package and all scripts have been made publicly available at https:// sourceforge. net/u/ljacquin/profile/.
Analyzed Data Sets and Prediction Methods Compared Three real data sets were analyzed. Four methods; LASSO, GBLUP, RKHS regression and SVR were applied to these data sets and traits, and hence a total of 60 situations were examined. R scripts were written to perform analyses with the four methods and are available on request. The glmnet  (Friedman et al. , 2010)  and kernlab  (Karatzoglou et al. , 2004)  packages were used for LASSO and SVR respectively. R scripts were written to solve GBLUP and RKHS regression. The expectation-maximization (EM) algorithm  (Dempster et al. , 1977; Foulley, 2002; Jacquin et al. , 2014)  was used to maximize the restricted likelihoods (REML) of the mixed models, associated to GBLUP and RKHS regression respectively, in order to estimate the associated variance parameters.
, 2008). A modified version of this function named Tune_kernel_Ridge_MM. R, which allows users to tune the rate of decay parameter for RKHS regression based on K-folds cross validation, has also been developed for Windows, Linux and parallelized for HPC Linux clusters. Finally, an R package named KRMM, associated to these functions, has also been developed. The KRMM package and all scripts are publicly available at https://sourceforge. net/u/ ljacquin/profile/. As conclusion, we recommend the use of kernel methods for genomic prediction, and selection, since the genetic architectures associated to quantitative traits are rarely known and can be very complex and complicated to model. Therefore, it seems more advisable to use data-driven prediction models, which can account for multiple orders of interaction, to assess the genetic merits of individuals.
Cervical spinal cord injury can disrupt connections between the brain respiratory network and the respiratory muscles which can lead to partial or complete loss of ventilatory control and require ventilatory assistance. Unlike current open-loop technology, a closed-loop diaphragmatic pacing system could overcome the drawbacks of manual titration as well as respond to changing ventilation requirements. We present an original bio-inspired assistive technology for real-time ventilation assistance, implemented in a digital configurable Field Programmable Gate Array (FPGA). The bio-inspired controller, which is a spiking neural network (SNN) inspired by the medullary respiratory network, is as robust as a classic controller while having a flexible, low-power and low-cost hardware design. The system was simulated in MATLAB with FPGA-specific constraints and tested with a computational model of rat breathing; the model reproduced experimentally collected respiratory data in eupneic animals.
FPGA Implementation (Open-Loop) The bio-inspired SNN controller was described in Very High Speed Integrated Circuits Hardware Description Language (VHDL) to define how to use and connect the hardware components available on the FPGA. The SNN controller was implemented on a Xilinx Spartan-6 FPGA (XC6SLX150: 4 Mbit full speed block RAM, 180 embedded multipliers, 92 k 6-input look-up tables and 184 kb distributed memory), hosted on a custom board, presented elsewhere  (Zbrzeski et al. , 2015). We used UART communication for storage and display of real-time variable data from the SNN. A sequencer coordinates two computing phases of the SNN that update and share two dynamical variables, r(t) and V m (t) (Figures  3A, B ). Both computing phases rely on the previously defined models, taking advantage of their simplicity and their computational efficiency.
Native activation was represented as a spaced sawtooth function and defined the pattern of the native model. Experimental data from one male Sprague Dawley rat (n = 1, 360 g)  (Siu et al. , 2015)  was used to determine the desired lung volume waveforms that were then used by the control system in the simulation studies. Data was collected with the approval of the Institutional Animal Care and Use Committee of Florida International University. The rat was maintained under eupneic conditions (21% O 2 , 0% CO 2 , balance N 2 ) through the use of a gas mixer (GSM-3, CWE Inc. ). The rat was anesthetized via IP delivery of pentobarbital (45 mg/kg) with supplemental isoflurane (0. 5-1%). A pulse oximeter monitored SpO 2 , while end-tidal CO 2 was monitored via a CO 2 analyzer (Capstar-100, CWE Inc.
The activity of this bio-inspired network is similar to experimental reports  (Richter, 1996) , as reflected in activity patterns of individual populations and in temporal sequencing and coordination across the network (Figure  5A ). The coordinated pattern of observed activity is as follows: the I dec activity increases quickly before slightly decreasing, this is followed by a slight then a fast increase of I inc activity; the I inc activity decreases abruptly when the IE activity quickly increases; this triggers E dec activity (and IE activity goes off); then E dec activity increases first quickly then slowly, which turns on EI activity to briefly activate I dec activity; and the activation sequence is repeated. The Matlab SNN computational model was designed to emulate the high level dynamic behavior as well as the exact data processing dynamics of the FPGA component.
ACKNOWLEDGMENTS The authors would like to acknowledge the technical support of  Ashwin MANGALORE  and  Gilles N'KAOUA. The authors would like to thank  Dr. Jennifer HASLER  for valuable discussions. This work was supported by the  US-French Collaborative Research in Computational Neuroscience program :  Agence Nationale pour la Recherche  ( ANR-13-NEUC-0001 ) and  National Institutes of Health  ( R01-NS086088 ).
Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agroecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible-near infrared (vis-NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global.