dataesr/scientific-paragraphs-categorization huggingface dataset:
TE annotation refining Datasets of TE families (consensus + genomic TE copies) used in the reported analyses were refined in a second TEannot pipeline iteration that was carried out with the improved REPET V2 release. We filtered out poorly defined and redundant TE consensus sequences showing no full-length copies (i. e. copies covering more than 95% of the consensus sequence) in the genome. Hence, we decreased the complexity of our consensus libraries, and increased the number of TE copies found by some consensus sequences, without any significant loss of sensitivity. We also re-launched PASTEC  [33]  the new TE classifier implemented in the REPET V2 TEdenovo pipeline. TE consensus libraries and annotations in genomes are available at: https://urgi. versailles. inra. fr/download/fungi/TEs/.
Multiple sequence alignments of TE copies Multiple sequence alignments may be difficult to calculate if TE copies are fragmented and degenerated. To tackle this problem, we first performed pairwise alignments between each copy and its cognate consensus sequence, and then we used the consensus as a reference to derive a multiple alignment using refalign and refa-lign2fasta respectively (from REPET package). We filtered out the consensus families and TE copies < 400 bases to produce a dataset satisfying the fixed quality criteria for RIPCAL. We also filtered out copies with less than 80% identity with the consensus sequence from the pairwise alignment and we excluded TE families with less than five sequences in the multiple alignments (TE consensus + four TE copies). In order to address the problem of degenerated and nested TE copies, the "long join procedure" implemented in the TEannot pipeline allow the connection of 2 ore more fragments to generate a TE copy.
Search for proteins of the Dnmt1 family We used RID, Masc1, Masc2 and Dim-2 protein sequences  [14]  as queries for Blastp  [51]  analysis to search for genes encoding cytosine DNA methytransferase from the Dnmt1 subfamily in the fungal genomes studied. We first searched for genes with GeneName = RID or GeneName = Masc1 or GeneName = Masc2 in Uniprot using SRS at EBI (http://srs. ebi. ac. uk/). Of the 14 genes found, we kept six non-redundant ones (O13369_ASCIM Masc1 Ascobolus immersus; O42731_ ASCIM Masc2 A. immersus; B0B065_SORMA, RID, Sordaria macrospora; Q8NJV8_NEUTT, RID, Neurospora tetrasperma; Q8NJV9_NEUIN, RID, Neurospora intermedia; Q8NJW0_NEUCR, RID, Neurospora crassa). Using this set of six genes, we searched for similar genes in Uniprot with Blastp (e-value < 1e -10 ). We also searched the Microbotryum protein databank of the Broad Institute (http://www.
Phylogenomic analysis of the 44 Dnmt1 fungal proteins We performed an Interproscan analysis (Version 4. 8 at http://www. ebi. ac. uk/Tools/pfa/iprscan/),  [52]  were to identify domains specific to each fungal Dnmt1 gene. The input parameters of this program were iprscan, Nocrc false, and GO terms true. The applications were blastprodom, fprintscan, hmmpir, hmmpfam, hmmsmart, hmmtigr, profilescan, hamap, patternscan, superfamily, signalp, tmhmm, hmmpanther, and gene3d (Additional file 5). The PTHR10629 domain sequences from the Panther HMM domain database that were found in the studied genomes and other sequences from 15 ascomycetes and 8 basidiomycetes (including five and three of our study, respectively) were aligned with the T-Coffee multiple sequence alignment program  [53]  and analyzed phylogenetically with PhyML  [54].
The analysis was performed on the Phylogeny. fr platform  [55]  and comprised the following steps: (i) sequences were aligned with T-Coffee (v6. 85)  [53]  with the following pair-wise alignment methods: the 10 best local alignments (Lalign_pair) and an accurate global alignment (slow_pair). (ii) After alignment, ambiguous regions (i. e. those containing gaps and/ or poorly aligned regions) were removed with Gblocks (v0. 91b)  [56]  (using the following parameters: minimum length of a block after gap cleaning: 5, positions with a gap in less than 50% of the sequences were selected in the final alignment if they were within an appropriate block, all segments with contiguous, not conserved positions bigger than 8 were rejected, minimum number of sequences for a flank position: 55%. (iii) The phylogenetic tree was reconstructed using the maximum likelihood method implemented in the PhyML program (v3.
0 aLRT)  [57]. The WAG substitution model was selected assuming an estimated proportion of invariant sites (of 0. 191) and four gamma-distributed rate categories to account for rate heterogeneity across sites. The gamma shape parameter was estimated directly from the data (gamma = 1. 964). The reliability of the internal branch was assessed using the aLRT test (SH-Like). (iv) The phylogenetic tree was drawn and edited with TreeDyn software (v198. 3) [58]. In addition we performed a MEME search to identify conserved motifs  [59]  (version 4. 9. 1) in the 44 proteins. The parameters used were -nmotifs 30, -minw 6 and -maxw 50. Of the 30 motifs found, we filtered out those present in at least one protein of the two subgroups of Dnmt1. Results (html files and motifs in logo format) are provided in Additional file 6.
Acknowledgements We are grateful to the  Broad Institute  and particularly the leaders of the  Microbotryum violaceum sequencing project  ( Michael Perlin ,  University of Louisville ,  David Schultz ,  University of Louisville, Christina Cuomo, Broad Institute ) for generating and making available the genome assembly and the protein-coding gene data set, MVLG_04160, and for giving us permission to use this data in our study. We would like to gratefully acknowledge  Ludovic Mallet  for the re-annotation of transposable elements in Magnaporthe oryzae. We also are grateful to  Florian Maumus  for his helpful comments subsequent to the critical reading of the manuscript.
Acknowledgements. We thank  Andrew Conley  ( NCAR ) for help with mathematical fitting,  Albert Presto  ( CMU ) for providing the UV lamp spectrum, and  Duseong Jo  and  Rokjin Park  ( Seoul National University ) for providing the  GEOS-Chem model code  and for their assistance in developing the photolysis module for the model. This research was supported by the  National Center for Atmospheric Research , which is operated by the  University Corporation for Atmospheric Research  on behalf of the  National Science Foundation , and by  DOE  ( BER/ASR)  through grant  DE-SC0006711. We would like to acknowledge high-performance computing support from Yellowstone provided by  NCAR's Computational and Information Systems Laboratory. Any opinions, findings and conclusions or recommendations expressed in the publication are those of the author(s) and do not necessarily reflect the views of the  National Science Foundation.
Introduction Deep neural networks represent the state-of-the-art solution for virtually all relevant machine learning, computer vision, and speech recognition benchmarks  (Schmidhuber, 2015). Their advantage over shallow architectures lies in their ability to extract hierarchies of increasingly abstract relevant features, which give rise to a data representation that lends itself for task-specific optimization. Whereas convolutional networks  (LeCun et al. , 1998; Sermanet et al. , 2014)  currently outperform other architectures on many vision tasks, the alternative architecture of Deep Belief Networks (DBNs)  (Hinton and Salakhutdinov, 2006)  remains very popular due to its ability to learn from large unlabeled datasets  (Le et al. , 2012) , and because of its dual role as classifier and generative model of the data.
The energy efficiency of Minitaur measured on the digit classification task using the MNIST database is around 12. 48-18. 73 MSops/W while the CPU efficiency for a Core 2 Duo running the same DBN network is 1. 68 MSops/W. Based on Minitaur, we have recently presented an even more efficient implementation of deep neural networks on SpiNNaker  (Stromatias et al. , 2015a,b) , a hardware platform optimized for scalable eventbased simulations  (Furber et al. , 2014). This platform has a biologically-inspired architecture designed to enable low-power and low-latency massively parallel large-scale simulations of heterogeneous models of spiking neurons in real-time. A single SpiNNaker board with 48 Application-Specific Integrated Circuit chips (SpiNN-5), which is the building block for creating larger SpiNNaker machines, delivers up to 54.
27 MSops/W in real-time  (Stromatias et al. , 2013). Because of this massive parallelism and SpiNNaker's optimized communication infrastructure, spiking network implementations on SpiNNaker have beneficial scaling properties, allowing to overcome the latency, communication, and energy issues of conventional computing systems for realtime applications. However, gains in energy efficiency should not be outweighed by losses in classification performance due to computation with spikes instead of real numbers, or due to limitations of the hardware compared to conventional computers. The hope is that in networks of such large size, numerical imprecision would rather cancel out than accumulate. In the conversion of  O'Connor et al. (2013)  from digital to spiking DBNs in software only small performance losses and overall very good performance was reached.
These results also hold for Minitaur  (Neil and Liu, 2014). In the present article we present a full characterization of software implementations of spiking DBNs, developing a set of case studies to determine the impact of the hardware bit precision, the input noise, weight variance, and combinations on the classification performance of a deep network for handwritten digit recognition. These studies provide important guidelines for informing current and future efforts to develop custom large-scale digital and mixed-signal spiking network platforms such as SpiNNaker, TrueNorth, Neurogrid, Bluehive, and BrainScales  (Moore et al. , 2012; Pfeil et al. , 2012 Pfeil et al. , , 2013;; Benjamin et al. , 2014; Merolla et al. , 2014a,b). Although the present study addresses only the scenario of offchip learning, it also provides guidelines for the design of hardware learning circuits that can train synaptic weights in DBNs  (Mitra et al.
, 2012; Kuzum et al. , 2012; Cruz-Albrecht et al. , 2013; Serrano-Gotarredona et al. , 2013). Our results are in good agreement with a recent study of the impact of weight precision for bistable memristive synapses in a probabilistic learning task, which has revealed a surprisingly low level of precision required for reliable learning of handwritten digit patterns  (Bill and Legenstein, 2014). This paper is structured as follows: Section 2 describes the theory of spike-based DBNs, training methods, and methods to reduce bit precision and introduce parameter noise. Section 3 presents investigations on the impact of bit precision of the hardware platform and input noise on the classification performance of a spike-based DBN using the MNIST digit database. These investigations include the impact of silicon mismatch on the coding of the weight parameters, and present novel off-line training methods to overcome reduced hardware precision.
Database, Image Conversion to Spikes, and Input Noise Generation Training and testing of spiking DBNs was carried out on the well-known MNIST database of handwritten digits  (LeCun et al. , 1998) , which consists of 70,000 28 × 28 gray-scale pixel images, of which 10,000 are used as a test set. In order to convert the static images to spike trains, each pixel of an MNIST image is converted to a Poisson spike-train with a rate proportional to its intensity, while all firing rates are scaled such that the total firing rate of the population is constant  (O'Connor et al. , 2013). In order to determine the impact of input noise on the performance of DBNs, noise is introduced into the spike-train representation of each image by redistributing a percentage of spikes randomly across the whole input population  (Neil and Liu, 2014).
Event-Based Platforms Spiking DBNs were recently implemented on portable digital hardware platforms and neurally inspired hardware, which dissipate much less power than conventional processors. In particular, this has been shown by an FPGA implementation, dubbed Minitaur  (Neil and Liu, 2014) , and on the SpiNNaker spiking neural platform  (Stromatias et al. , 2015a,b). The software simulation results in this work are validated by comparing to spiking DBNs running on the SpiNNaker platform. The SpiNNaker platform  (Furber and Temple, 2008; Furber et al. , 2014)   Specific Integrated Circuit (ASIC) designed to enable realtime simulations of heterogeneous models of spiking neurons and synapses. The fundamental component of the system is the SpiNNaker Chip. It contains 18 identical and fully programmable ARM9 cores, each of which has access to a local 96KBs tightlycoupled memory (TCM), executing the runtime neural kernels and storing the neural parameters.
is a biologically inspired Application The building block of SpiNNaker machines is a single PCB comprising 48 SpiNNaker chips. This has proven to be capable of simulating a quarter million neurons with more than 80 million synapses. In terms of activity, the tested system is able to generate a dynamic activity of 1. 76 billion synaptic events (SE) per second while dissipating <1 W per chip (<40 W for the whole board,  Stromatias et al. , 2013). Interconnecting more of these boards together will form the final SpiNNaker machine, which will utilize ≈ 60, 0000 chips, for a total of more than a million ARM cores, and it aims at simulating 1% of the human brain in real-time  (Furber et al. , 2013). The networks simulated using the Brian software package can easily be mapped on to the SpiNNaker platform through an interface with a high-level simulator independent neural specification language called PyNN  (Davidson et al.
Results The performance of spiking DBNs with reduced precision or weight variability is assessed on a common benchmark task, the classification of handwritten digits from the MNIST dataset  (LeCun et al. , 1998). The MNIST dataset is divided into a 60,000 digit training set, and a 10,000 digit test set. The conversion of the 28×28 gray-scale pixel images into spike trains and training of DBNs are described in Section 2. 3. Simulation results of the spike-based DBN were obtained using the Brian spiking neural network simulator  (Goodman and Brette, 2008). In the following we characterize the impact of reduced bit precision and noise on the classification performance of spiking DBNs. In Section 3. 1 the impact of lower precision or higher input noise levels on fully trained networks during testing is demonstrated, along with an investigation of the possible reasons for the resulting performance curves in Section 3.
Comparison of Hardware Performance to Simulations In order to validate the simulation results presented in Section 3. 1 on actual hardware platforms, we ran simulations of spiking DBNs on the fixed-point, event-based digital platform  SpiNNaker. For the DBN with two hidden layers, Figure  1B , the double floating-point weights were truncated to a Q3. 8 fixed-point representation, and the input rates used to encode the MNIST images into spike trains were set to 1500 Hz. The classification accuracy achieved by the SpiNNaker platform on the MNIST testing set, is 94. 94% when the input noise is set to 0 and 88. 66% when the input noise is set to 60%. This is in good accordance with the noise-free simulation results obtained by  O'Connor et al. (2013) , and classification accuracies for simulations on Brian  (Goodman and Brette, 2008) , which reach 94.
95 and 88. 66%, respectively, for the same weight precision and input noise (Figure  4 ). This spiking DBN ran on a single SpiNNaker chip and generated an activity of <1 million synaptic events (SE) per second, which is well below the 36. 8 million The first column shows results for a software simulation in Brian with Q3. 8 precision, the second column shows results for SpiNNaker, which uses the same fixed point representation of weights. Differences in performance are almost negligible. SE a SpiNNaker chip can process  (Stromatias et al. , 2013). The comparison of results is summarized in Table  2. Our results indicate that the difference in the classification accuracy between SpiNNaker and Brian for the same bit resolution, input rates, and input noise levels is almost negligible and in the order of 0.
Note that here we are focusing on mapping networks that have been trained off-chip to neuromorphic hardware, rather than training networks on chip. This is because current training methods for deep networks from large datasets are optimized for exploiting conventional computing technology such as GPUs, but the execution on event-based platforms yields efficiency advantages as discussed previously. Our results show indeed that spike-based DBNs exhibit the desired robustness to input noise and numerical precision. The classification performance of the spike-based DBN on the MNIST digit database holds up even for bit precisions down to Q3. 3, which requires significantly fewer bits to represent the large parameter space of DBNs than in typical CPU systems. For example, the two-layer DBN has 642,510 synapses, which would require 4.
A validation was achieved by running the DBN for MNIST classification on the biologically-inspired massively-parallel fixed-point SpiNNaker platform, which uses less precise weights than standard software implementations. We have shown that the resulting performance of the network implemented on SpiNNaker is very close to the results from the software simulation with only a 0. 06% difference, despite the fact the SpiNNaker uses fixed-point arithmetic. For implementations on custom mixed-signal hardware systems one has to deal with the constraint that they can only offer reduced numerical precision in the synaptic weights  (Neftci et al. , 2011; Liu et al. , 2015). The level of mismatch in the individual synapses can be taken into account during design and reduced by methods such as clever layout strategies and increasing the transistor area.
Acknowledgments We acknowledge the following grants:  SNF  grant  200021_146608 ,  SNF  grant  200021_135066 ,  EPSRC  grant  BIMPA (EP/G015740/1 ),  EU  grant  SeeBetter  ( FP7-ICT-270324 ),  EU  grant  HBP (FP7-604102 ),  EU  grant  BrainScales-Extension  ( FP7-287701 ), and the  Samsung Advanced Institute of Technology. We also acknowledge the  Telluride Neuromorphic Cognition Workshop  where the initial discussions and work were carried out.
Objectives: In this paper, we present ProstateAnalyzer, a new web-based medical tool for prostate cancer diagnosis. ProstateAnalyzer allows the visualization and analysis of magnetic resonance images (MRI) in a single framework. Methods: ProstateAnalyzer recovers the data from a PACS server and displays all the associated MRI images in the same framework, usually consisting of 3D T2-weighted imaging for anatomy, dynamic contrast-enhanced MRI for perfusion, diffusion-weighted imaging in the form of an apparent diffusion coefficient (ADC) map and MR Spectroscopy. ProstateAnalyzer allows annotating regions of interest in a sequence and propagates them to the others. Results: From a representative case, the results using the four visualization platforms are fully detailed, showing the interaction among them.
The tool has been implemented as a Java-based applet application to facilitate the portability of the tool to the different computer architectures and software and allowing the possibility to work remotely via the web. Conclusion: ProstateAnalyzer enables experts to manage prostate cancer patient data set more efficiently. The tool allows delineating annotations by experts and displays all the required information for use in diagnosis. According to the current European Society of Urogenital Radiology guidelines, it also includes the PI-RADS structured reporting scheme.
For example, Vos et al. (30)  used 3D-T2 imaging to define specific regions of interest (ROI), which were subsequently used on diffusion-and perfusionweighted images to extract relevant features. The purpose was to train and classify the extracted set of features to calculate the likelihood of malignancy. Other related surveys have been focused on magnetic resonance spectroscopic data  (31). The rapid growth of computer-based clinical exams has seen a significant increase in the number of online medical imaging systems  (32, 33) , some of which being developed as web-based applications  (34). The main challenge is the access and the interaction between the hospital database and the distant users. Thus, the aim of our work was to develop a new architecture system allowing including a web-based application connected to a prostate image database.
In the field of biomedical informatics, one of the long standing problems is finding a way to share medical data across a variety of media. Inherently, medical data are generated by a multitude of sources  (35, 36). eXtensible Markup Language (XML) has emerged as a leading facilitator. Although the XML is provided with predefined tags, one of its advantages is its extensible use. Over the last few years, a set of standards in the medical domain has been developed, called medical markup language (MML) to allow the exchange of medical data between different medical information providers  (37, 38). Therefore, the XML schema of tags can be defined for each individual case. Finally, the inclusion of the XML databases facilitates the management of XML files by storing them in an efficient way  (39).
The tool is able to characterize the morphological (location, shape, and size) and the imaging features (mean and standard deviation of the signal, area, and center of mass) of a region within the image, defined and annotated by an expert. Furthermore, it calculates the signal-time curve in perfusion studies and displays the LCModel signal spectrum for spectroscopic analysis  (41). Besides, it allows the analysis of the same image by different experts, an essential feature in order to obtain a robust evaluation. The final outcome of the analysis is summarized in terms of the PI-RADS protocol. With regard to the proposed architecture for storage of medical images, this should guarantee the connection and a secured access to the server, the DICOM database, and the system storage of clinical diagnoses (XML database).
RESEARCH DESIGN Materials Three-dimensional T2-weighted fast spin-echo (TR/TE/ETL: 3600 ms/143 ms/ 109, slice thickness: 1. 25 mm) images (T2WI) were acquired with submillimetric pixel resolution in an oblique axial plane. DWI (TR/TE: 4200/ 101 ms) was performed using a pulsed gradient spin-echo technique with two b-values (100 and 800 s/mm 2 ). A parametric image, the ADC map, was directly generated (on the vendor's MRI consol) from the raw data on a pixel-by-pixel basis using the formula: S ¼ S 0 expð1 À bDÞ where S 0 represents signal intensity when b ¼ 0 and D is the ADC directly displayed as a gray scale number expressed in units of 10 À6 mm 2 /s. The protocol was completed with DCE-MRI performed using a fat suppressed 3D T1 VIBE sequence (TR/TE/Flip angle: 3. 25 ms/1. 12 ms/100; temporal resolution: 6 s/slab over approximately 5 min).
A bolus injection of Gd-DTPA (Dotarem, Guerbet, Roissy, France) was administered at a dose of 0. 2 ml Gd-DTPA/kg of body weight. Finally, three-dimensional MR spectroscopic data were acquired using a water and lipid-suppressed sequence. Data sets were composed of 16 Â 16 Â 16 phase-encoded spectral arrays giving nominal spectral resolution of 0. 14 cm 3. Spectral data were processed using a modified version of LCModel (S. Provencher Inc. , Asbestos, QC) for 1H spectroscopy of the prostate at 3T. Choline, creatine, and citrate were all quantified individually.