dataesr/scientific-paragraphs-categorization huggingface dataset:
Read mapping Quality control of FASTQ files was evaluated using the FastQC tool (version 0. 10. 1 www. bioinformatics. babraham. ac. uk/projects/fastqc). To remove any read originating from organelle circular genomes, reads were mapped against the mitochondria (NCBI GenBank Y08501. 2 for A. thaliana; GenBank NC_011033 for O. sativa) and chloroplast genomes (GenBank AP000423. 1 for A. thaliana; GenBank X15901 for O. sativa) using the program BOWTIE2 version 2. 2. 2  [62]  with-sensitive local mapping. Unmapped reads were considered for the next analysis and were mapped against a genome of reference, TAIR10 (The Arabidopsis Information Resource, http://www. arabidopsis. org) for A. thaliana, IRGSP1. 0 (International Rice Genome Sequencing Project version 5 http://rgp. dna. affrc. go. jp/E/IRGSP/Build5.
html) for O. sativa. The parameters used for the mapping were as follows:-sensitive local mapping, no multiple-mappings (-k 1) so that only the best hit is kept per read-pair. DNA from both mitochondria and chloroplast genomes are integrated in nuclear genomes. To completely eliminate these regions from our data, sequencing reads were simulated from organelle genome using the dwgsim program (version 0. 1. 10) and the. fasta files were mapped against the corresponding reference genome. A total of 816,300 bp and 1,697,400 bp were masked in A. thaliana and O. sativa, respectively, and TE containing regions cover 24,786,000 bp and 194,224,800 bp in A. thaliana and O. sativa, respectively. A. bam file with all genome regions corresponding to organelle integrated sequences was obtained for each species and was used to filter our alignment files using the intersect module of BEDTools version 2.
Statistical analysis For each species, the reference genome was split into consecutive windows of 100bp for each library and the coverageBED module of BEDTools was used to determine the read coverage depth of these non-overlapping windows. The coverage data was normalized by the total number of reads which mapped on the genome expressed in rpm and statistical analysis was performed on these files. First we determined covered regions using the Poisson distribution that best fits our data with a p-value <10 -5 for each library. All uncovered regions were removed from our coverage files. On the covered regions we applied a negative binomial distribution to identify peaks of higher coverage with a p-value <10 -3. Finally, regions corresponding to the peaks were selected and annotated using. gff files (S5 Table ).
de novo assembly Mobilome-seq reads were assembled de novo using the A5-miseq pipeline  [64]. For each library,. fasta and. bam files were obtained and the idxstats module of SAMtools was used to determine the read number corresponding to each assembled scaffold. The coverage data was normalized by the total number of reads used for the de novo assembly expressed in million reads (rpm). We applied a negative binomial distribution to identify significantly covered scaffolds (p-value < 0. 05). Filtered scaffolds were annotated using a BLAST analysis (-p -m 8) against organelle genomes and a TE database allowing for one hit per scaffold (-b 1 -v 1 options) and for an e-value < 10 -2. For A. thaliana we used the TE database based on TAIR10 annotation and established by H. Quesneville (www. arabidopsis.
Split-reads detection Reads spanning 2 LTR junctions constitute an evidence of a circular TE and were detected using a SR mapping strategy. Reads were aligned against the reference genomes using the segemehl software  [65]  with the following parameters: -S (SR mapping) -A 95 (accuracy of 95%) -U 24 (minimum score of 24) -Z 25 (minimum length of 25) -W 95 (alignment covered on 95% of the read). Split reads were collected from. bam files based on the FLAG field of each read, using the view module of SAMtools. Therefore, only reads which were not mapped in a proper pair (-f 14) and which have multiple primary alignments (-F 256) were considered as SR. The coverageBED module of BEDTools was used to determine the read coverage depth of these SR. bam files were visualized with IGV.
Candidate TEs To determine TE loci of interest in each library, we first filtered the. bam files obtained from bowtie2 mapping (for the DOC) for TEs covered for 90% of their length and with a DOC >10 rpm. Using the. bam files obtained from segemehl mapping we selected the TE loci with a SR coverage >10 rpm. We selected the TE loci fitting both criteria and which length is > 100bp (S6 Table ). The TE loci were visualized with Circos  [63]. The in-house developed code for DOC and SR detection and for the establishment of the candidate TE list can be accessed upon request.
Identification of PopRice subfamily To determine the evolutionary story of PopRice elements, a consensus sequence of PopRice was used to detect by BLAST all LTR-RTs from the IRGSP-1. 0 reference genome belonging to the same family (HSP>4000bp, minimum of 70% of identity, e-value < e -50 ). All selected sequences were aligned with MAFFT (http://mafft. cbrc. jp/alignment/server/). Alignments were analyzed using SEAVIEW (http://doua. prabi. fr/software/seaview) and all incomplete elements were removed. 47 elements were selected for the Osr4 family (comprising PopRice sequences) and a phylogenetic tree was built with PhyML and visualized with FigTree (http:// tree. bio. ed. ac. uk/software/figtree/).
Acknowledgments We would like to thank  Jerzy Paszkowski  and  Jon Reinders  for their support in the early development of the mobilome-seq strategy and for epi12 seeds, and our colleagues from the  IRD  and  LGDP laboratories  for stimulating discussions. We thank  Helène Vignes  at the  CIRAD  for her technical help with sequencing,  Nathalie Picault  for kindly providing the rice callus material,  Moaine El Baidouri  for his help with rice TE annotation, and  Pierre Larmande  for his help with Circos visualization.
Funding: SL is supported by a  French National Agency for Research PhD fellowship  ( ANR-13-JSV6-0002 ). This work was funded by  IRD , an  Agropolis   Fondation grant  ( Labex AGRO, "RetroCrop "  1202-041 ) and a young investigator grant from the  French National Agency for Research  ( ANR-13-JSV6-0002  "ExtraChrom") to MM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We 3D-printed lens holders to embed the focus-adjustable lenses in the HMD casing. The wiring for the video signal and power of the LCD panel is independent of the head-tracking and lens-controller signals of the HMD, allowing us to split the device into two independent modules around the autorefractor. See Figs. 1  and 3  for overviews of the design. The lenses are controlled via a USB interface; commands are sent in sync with each rendered frame to the lens controller. The controller software was written in C# around the original Google cardboard library, with a custom driver for the focus-adjustable lenses. We implemented the controller as an intuitive ad-hoc API, which is exposed to the graphics programmer with a "focus-at-distance" mode providing expected behavior (Sec. 2. 2). We implemented our API in Unity3D, allowing its use in any Unity3D application or game.
Lens Calibration and Adjustments It is important to ensure that the overall pipeline with its various components is correctly calibrated and adjusted. A short description of our procedure follows, more details can be found in supplemental material. Focus-adjustable Lens Calibration. We needed to know what electrical current value to send to each focus-adjustable-lens controller in order to achieve the desired focal power. We used a digital singlereflex (DSLR) camera to create a mapping between electric current and focal distance for both lenses. We found the relationship between current and focal power to be linear, so we fit the data via linear regression. The mappings differed for the two lenses due to imprecisions in the manufacturing process. Measurement Pipeline Calibration. We also needed to calibrate the accommodation measurements for the whole pipeline.
Apparatus and Design Data Acquisition. The autorefractor generates a file of spherical power values 4-6 times a second. We used a Unity3D application for rendering and it generated an SQL database with the current experimental settings and stimulus location. There were five database entries per second. We manually started the autorefractor recording and stimulus motion on their respective computers, which yielded approximate synchronization. Occasionally, the autorefractor stopped recording because the subject's cornea moved relative to the autorefractor's optical path. When this happened, we restarted the measurement. Blinks during measurements were assigned an undefined value. Stimulus. The experimental stimulus was a 3D forest scene with near and far elements (Fig. 5 ) and a target that was a 0.
Subjects. We tested five naive subjects (three males, mean age 25, SD 2. 1) who were 30 years of age or younger and had normal visual acuity and stereoacuity. We did not include older subjects because after age 30 most subjects have reduced accommodative range  [Duane 1912 ]. Accommodative behavior was very similar across subjects (as evidenced by the statistical results in Sec. 4. 3 and supplemental material), so we adopted the common practice in accommodation research of testing only a handful of subjects  [Kruger et al. 1993; MacKenzie et al. 2010 ]. The experiment lasted about an hour per subject. Each subject provided enough data to allow statistical comparisons of responses across conditions. The study was approved by the institutional ethics board at UC Berkeley.
Data Processing. To analyze the data, we registered the stimulus (rendering) database with the autorefractor data files using peak-to-peak synchronization. After registration, we applied corrections to the autorefractor data as determined from the calibration procedure described earlier. Consistent with normal practice in accommodation research  [Kruger et al. 1993; MacKenzie et al. 2010] , we determined the gain of the accommodative response from the measurement time series. Gain is the amplitude of the response at the stimulus frequency divided by the amplitude of the stimulus at that frequency. We estimated the gain by fitting the response data with a sinusoid at the stimulus frequency with amplitude, phase and DC offset as free parameters (using the Levenberg-Marquardt damped least-squares method).
Can Accommodation Predict Discomfort? Our working hypothesis is that the VA conflict drives discomfort, so we wanted to see whether the accommodation data can be used to make predictions about which conditions should be more comfortable than others. We could have used accommodative gain as a predictor (e. g. , lower gain predicts greater VA conflict and hence more discomfort), but it is not obvious how to do this for the monovision condition because the stimuli to the two eyes differed. Instead we computed the accumulated VA conflict for each eye in all conditions to see whether this predicts which conditions should be more comfortable. Accumulated VA Conflict. From the accommodation experiment, we know the accommodative response and vergence stimulus over time. We did not measure vergence responses, but they are generally quite accurate [Schor 1992], so we assumed that the vergence response was always equal to the vergence stimulus.
ACKNOWLEDGEMENTS We thank  Adobe  for generous donations. The work was partly supported by the  CRISP Inria Associated Team as part of the Inria@SiliconValley program  between the  GRAPHDECO Inria team  and  UC Berkeley BanksLab. Funding was also provided by  EU   FP7 ICT-611089 CR-PLAY ,  EU H2020 727188 EMOTIVE  and  NSF  award # BCS-1354029. We thank  D. M. Levi  for giving us access to his team's autorefractor,  J. Vera-Vilchez ,  S. Cholewiak  and  A. Djelouah  for useful discussions,  K. Mania  for proofreading, J-P. Merlet at  Inria  for use of the electronics lab and  P. Illes  for helping with the experiments.
The privacy of input data is kept due to the inner-product encryption scheme. Roughly speaking the data item on which a prediction must be made is encrypted. From the encrypted data, integer innerproducts are extracted and are used afterwards to produce the class of the input data item. The performance of the classification algorithm is evaluated on the MNIST database  (LeCun et al. , ). The paper is structured as follows. In Sec. 2, we recall machine learning and cryptographic back-ground that we need in this paper. Sec. 3 presents the protocol for performing classsification over encrypted data, that we introduce. Sec. 4 gives the experimental results we obtained with our protocol. Finally, Sec. 5 concludes this paper.
MNIST Dataset The performance of the privacy preserving classification methods proposed in this work, is tested using the MNIST dataset. The MNIST database is a collection of handwritten digit images (LeCun et al. , ). Fig. 1  presents sample images from it. Dataset images have size 784 = 28 × 28 and each pixel has 256 levels of grey. The handwritten digits are normalized in size and are centered. There are 10 output classes in the dataset (digits from 0 to 9). The MNIST dataset has been extensively used by the ML community for classifier validation. For a review of ML methods applied to MNIST, please refer to  (Bottou et al. , 1994; LeCun et al. , 1998).
Encrypt(PUB, w) PUB {v i } PUB, {sk v i } ct The following steps are repeated each time a user wants to send its data to a server: (1) A user encrypts a private data vector w with the Encrypt algorithm of the IPFE, and sends it to the server. (2) The server decrypts it with all of his secret keys {sk v i } 1≤i≤m using the Decrypt algorithm of the IPFE, and gets {ip i } 1≤i≤m such that ip i = v i , w , ∀1 ≤ i ≤ m. (3) The server uses a classification algorithm in order to predict the class of w using the inner products {ip i } 1≤i≤m. To illustrate the advantage of our construction, we now describe a realistic scenario. We assume that there is a pharmaceutical company which has constructed a classifier that takes as input medical and private pieces of information. The company does not want to divulge its secret classifier.
Combined Classifier The simplest way to classify is to use a linear classifier. The only thing to perform is the argMax({ v i , w } 1≤i≤m ) which give the class of the vector w. In order to increase the results of linear classification, we propose a combined classification method, in which linear classification is applied to encrypted data, then followed by a more complex classification algorithm (for example an ensemble method in our case but not limited to). For each piece of input encrypted data, several inner products are computed. These products are then used as input features for a second, more elaborate, classifier. In this way, we are able to perform classification of encrypted data with increased performance in terms of an evaluation metric (e. g. error rate). A linear one-vs. -rest classifier is applied on the dataset.
, 2013)  for field F * p computations. The experiments were performed on a regular laptop computer with an Intel Core i7-4650U CPU and 8GB of RAM. The sizes of the elements we manipulate in the IPFE instantiation are given in Table  1. The sizes are given in the context of a classification algorithm applied to 2 http://scikit-learn. org 3 The k-means clustering was used. 4 We shall note that the range of coefficients is not limited to [-127. 127] as regular signed 8-bit integers. The inner product encryption scheme allows to encrypt any integer modulo the prime order of the cyclic group (see Alg. 1). The decryption of the IPFE scheme is performed by computing a discrete logarithm. In our case, the inner products obtained by the application of the learned linear coefficients on the MNIST database belong to a small interval.
MRI CSF flow measurement protocol All MRI measurements were obtained with a General Electric 3T scanner (Signa HDxt, software 15. 0_ M4_0910. a). CSF flow data were collected at three vertebral levels, C2-C3, C7-T1 and T10-T11, using phase-contrast MRI with retrospective electrocardiogram (ECG) gating and 32 cardiac phases  [14]. Each slice had a thickness of 5. 0 mm and an in-plane resolution of 0. 54 × 0. 54 mm. Orientation of the slice was made perpendicular to the CSF flow direction and positioned vertically by intersection with a vertebral disk (i. e. C2-C3). A flip angle, TR, TE and VENC was used with a value of 25°, 13. 4, 8. 26 and 8 cm/s respectively. Detailed information on imaging parameters is provided by Baledent et al. [19].
CSF flow quantification Oscillatory cardiac-related CSF flow was quantified for the axial locations located at the vertebral disk at the C2-C3, C7-T1 and T10-T11 vertebral levels. As detailed in our previous studies  [14, 20] , Matlab was used to compute the CSF flow waveform, Q (t) , based on integration of the pixel velocities with Q(t) = ∑A pixel [V pixel (t)], where A pixel is the area of one MRI pixel, V pixel is the velocity for the corresponding pixel, and Q (t) is the summation of the flow for each pixel of interest. A smooth distribution of CSF flow along the spine was achieved by interpolating CSF flow between each axial measurement location  [21]. Similar to previous studies, the diastolic CSF flow cycle phase was extended in cases when necessary  [22]. For correcting eddy current offsets, the cyclic net CSF flow was offset to produce zero net flow over a complete flow cycle  [14].
Hydrodynamic analysis The hydrodynamic environment at 1 mm slice intervals along the entire spine was assessed by Reynolds number based on peak flow rate, Re = Q sys D H νA cs , and Womersley number based on hydraulic diameter. For Reynolds number, Q sys is the temporal maximum of the local flow at each axial interval along the spine obtained by interpolation from the experimental data and ν is the kinematic viscosity of the fluid. Similar to previous studies, CSF viscosity was assumed to be that of water at body temperature. To evaluate the presence of laminar flow, (Re < 2300), similar to previous studies in CSF and biofluids mechanics, Reynolds number was evaluated at peak systolic flow along the spine. Womersley number, α = D h 2 √ ω/ν, where ω is the angular velocity of the volume flow waveform ω = 2π/T, was used to quantify the ratio of unsteady inertial forces to viscous forces.
Discussion The intrathecal CSF space is a complex 3D fluid-filled geometry with multiple levels of anatomic complexity, the most salient features being the spinal cord, dura mater and dorsal and ventral spinal cord NR. An accurate anthropomorphic representation of these features is needed as a tool for development of in vitro and numerical models of CSF dynamics that can be used to inform and optimize CSF-based therapeutics. In this paper, we provide a detailed and downloadable anthropomorphic 3D model (Additional file 1) of the intrathecal CSF space that is licensed for re-use under the Creative Commons Attribution-ShareAlike 4. 0 International license (CC BY-SA 4. 0). CSF flow data, measured by PCMRI, is provided as a validation data set for numerical modeling. The model is characterized in terms of axial distribution of intrathecal CSF dynamics with detailed information on various hydrodynamic parameters including Reynolds number, Womersley number, hydraulic diameter and CSF velocities.
Segmentation of the intrathecal CSF space A variety of software exists to help reconstruct MRI DICOM image files in 3D. Many segmentation software platforms provide automatic segmentation algorithms that can deliver relatively quick visualizations but these segmentations are often not suitable to create 3D models that can be used for CFD modeling or easily exported for 3D printing  [33]. In this study, we used the open-source program ITK-SNAP ("The Insight Segmentation and Registration Toolkit", http://www. itk. org) that supports automatic, semi-automatic and manual approaches. The final model was constructed based on manual segmentation of each slice along the spine by an expert operator previously trained in intrathecal CSF segmentation procedures. Despite the popularity of CFD studies conducted in the SSS, there is a lack of detailed information on intrathecal segmentation methods based on anatomic MR imaging.
More research is needed to define the nature of CSF flow dynamics with respect to turbulence. Cerebrospinal fluid flow data was collected at three distinct axial locations along the spine for a single subject. Data from these three locations was spatial-temporally interpolated (Fig. 6b ) and used in combination with the geometry to quantify axial distribution of CSF dynamics along the spine (Fig. 7 ). While only representative of the single subject analyzed, the provided parameters give insight into CSF dynamics for a single healthy subject within a complete SC model containing detailed nerve root geometry. For example, the detailed geometry showed that Reynolds number varies significantly along the spine due to the presence of NR (see Fig. 7d  Reynolds number variation in cervical spine).
Cerebrospinal fluid flow data used for calculation of CSF dynamics along the spine was measured at three axial positions along the spine. An improved method would include measurement of CSF flow at more axial levels and with higher temporal resolution. The exact reproducibility of these CSF flow waveforms could be tested by conducting a reliability study on the same subject. In this study, cardiac-related CSF flow was quantified using retrospective gated PCMRI measurements. Therefore, Fig. 7  results indicate CSF hydrodynamics under cardiac-related CSF oscillations. Impact of the respiratory cycle on CSF flow dynamics could be quantified using real-time PCMRI  [80] [81] [82] [83].
Funding This work was supported by an  Intuitional Development Award (IDeA)  from the  National Institute of General Medical Sciences (NIGMS)  of the  National Institutes of health (NIH)  under Grant # P20GM1033408  and # 4U54GM104944-04TBD , The  National Institutes of Mental Health  Grant # 1R44MH112210-01A1 , and  University of Idaho Vandal Ideas  Project.
Materials and Methods This section describes all the hardware (Section 2. 1) and software (Section 2. 3) in sufficient detail to allow the interested reader to replicate the results. This is greatly simplified as all the software is published under a BSD open-source license (https://github. com/realms-team/). Section 2. 2 provides necessary information for understanding the low-power wireless networking technology we are using. Section 2. 4 describes the methodology we employ when deploying a new network, further allowing the interested reader to replicate the results.
metronomesystems. com/) is a multi-purpose, ultra-low mote. It features a 16-bit ARM programmable system on chip (PSOC), a versatile micro-controller capable to interfacing to virtually any sensor and actuator. Up to 40 different types of sensors can be interfaced to the PSOC. It also features a SmartMesh IP wireless module by Analog Devices, allowing it to transfer its sensor measurements to the base station in a reliable, ultra low-power and secure way. Additionally, the NeoMote contains an SD card for local backup, and a real-time clock (RTC) for timestamping. The NeoMote and sensor are powered by a 17 Ah Li-Ion battery, which is recharged by a solar panel 6. A 2. 4 GHz, 4 dBi, omnidirectional antenna is mounted on the top of the pole 7 to allow the wireless module to communicate. To install a sensor station, a steel U-channel is concreted into the ground.
Repeater Node The role of the repeater node (Figure  1b ) is to provide connectivity between the sensor stations and the base station and maintain the redundancy of the mesh network. Mechanically, it resembles the sensor station: it consists of a 5 m aluminum pole bolted to a U-channel. It is, however, much simpler than a sensor station, as it only contains a waterproof fiberglass enclosure 1 with a Metronome Systems Wireless Sensing Relay Board. This node contains only the SmartMesh IP wireless module and is powered by a 17 Ah primary battery. An antenna is mounted on the top of the box 2.