dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016). Following this result,  Kwok and Cunningham (2015)  estimate snow depth according to the following equation: h s (X, t, f M Y ) = h W 99 s (X, t)f M Y + 0. 5h W 99 s (X, t)(1 -f M Y ) (3) where h s (X, t, f M Y ) represents the time (t) and space (X)-varying W99 climatology and f M Y is the Multi Year Ice (MYI) fraction that they derive from the Advanced Scatterometer (ASCAT) following the study by  Kwok (2004). In the present study, we use a similar methodology to estimate snow depth except that we use a different dataset to derive MYI ratio. Indeed, the methodology based on ASCAT observations assumes a direct link between ice age and ice roughness, which is not necessarily true in every case. To overcome this issue, we use the National Snow and Ice Data Center (NSIDC) ice age dataset (http://nsidc.
org/data/docs/daac/nsidc0611-sea-ice-age/, Anderson and Tschudi (2014) ). In this product version, the ice age is calculated by tracking the ice through the comparison of adjacent passive microwave images as well as using wind forcing and buoys displacement information. In addition to not make the assumption of a constant correlation between ice age and ice roughness, this product has the advantage to be available during the entire Envisat mission  (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012). In the present study, monthly snow depth fields are therefore derived as follows: First, each ice pixel with ice older than 1 year is considered as MYI and pixels with ice younger than 1 year is considered as FYI. Then, we apply an average filter with a radius of 100 km (similarly to the freeboard datasets) and estimate the ratio of MYI observation within the filtering area to build MYI concentration maps.
Ancillary data Daily sea ice extent fields are available at a 12. 5 km resolution from NSIDC and are derived from AMSR-E and SSMIS passive microwave observations  (Anderson and Tschudi, 2014). The ice extent dataset is used to grid the freeboard height observations in regions where sea ice is present and to remove observations over open water areas. The Beaufort Gyre Exploration Project (BGEP) moorings provide high frequency (0. 5 Hz) measurements of sea ice draft since August 2003 thanks to a network of 2 to 4 moorings located in the Beaufort Sea (http://www. whoi. edu). The buoys are equipped among other instruments of an Upward Looking Sonar (ULS) that measures the distance from the mooring to the bottom of the ice with a 420 kHz beam sonar. Sea ice draft is then estimated by removing the depth of the mooring that is deduced from pressure measured every 40 seconds.
5 cm) consistent with previous studies  (Armitage and Ridout, 2015; Kurtz et al. , 2014) , which demonstrates that despite the ice surface diffusion difference between leads and ice floes, CryoSat-2 allows to produce a realistic freeboard height dataset. Regarding the seasonal variability, Envisat displays an unrealistic negative variation of -2. To analyze the link between ice surface diffusion and the freeboard height discrepancy between Envisat and CryoSat-2, the shape of the radar waveform echoes can be used. Indeed, surface diffusion due to ice roughness and snow volume scattering modifies the shape of waveform echoes, which allows to identify ice surface characteristics. Examples of typical Envisat waveforms are provided in Fig. 4. For specular surfaces, the radar signal back-scattered toward the satellite originates from a small surface around the nadir position  (Connor et al.
Only rough MYI areas display PP observations lower than 0. 1 during this period of the year. The average Envisat PP decreases progressively from 0. 14 in November to 0. 06 in April due to the ice growth, ice deformation and snow thickening that increase the ice surface diffusion. Looking at Fig. 3a   Despite this slight difference between the two winter periods, the strong linkage between the PP and the ∆fb shown in Fig. 3  and Fig. 5  supports the hypothesis that the discrepancy between the Envisat and CryoSat-2 freeboard height datasets is related to the variability of ice surface diffusion. Moreover, the unrealistic Envisat freeboard height estimates in contrast to the accurate CryoSat-2 estimates suggests that the discrepancy between Envisat and CryoSat-2 freeboard height fields is mainly driven by a larger impact of ice surface diffusion on Envisat waveform echoes as postulated in the introduction section.
However, ice surface diffusion has a higher impact on LRM altimeters as suggested in the present study, and the current Envisat freeboard estimates are strongly biased. To improve the Envisat freeboard height retrievals, a first approach could consist in developing a retracking algorithm model that takes into account the large variability of ice surface diffusion as operated in  Kurtz et al. (2014) , instead of using threshold retrackers. While such model could provide accurate measurement of sea ice freeboard height, it requires accurate knowledge on sea ice characteristics such as mean surface slope (diffusion), angular backscattering efficiency (specularity) and snow volume scattering properties, which are currently hardly measurable parameters. Another approach to improve the Envisat freeboard height retrievals would consist in correcting the Envisat freeboard dataset with the results obtained in the present study.
Comparison to BGEP ice draft measurements To assess the potential of the PP-correction approach to produce accurate ice thickness estimates, we now convert the CryoSat-2, Envisat and Envisat/PP freeboard height datasets to sea ice draft (thickness -freeboard) fields and compare the results to BGEP ice draft measurements. For this purpose, we estimate the monthly median altimetric ice draft within a radius of 50 km around each available mooring, which we compare to the corresponding monthly median mooring ice draft. To obtain accurate ice draft estimates, we first test three different sea ice densities (890, 900 and 910 kg. m -3 ) to convert CryoSat-2 freeboard height (see Tab. 3). For the three density values, the correlation coefficient is similar (0. 83) but the average RMSE is fairly different depending on which density value is used.
As suggested by Tab. 3, the best results are obtained with an ice density of 900 kg. m -3 (RMSE = 0. 2 m). Following this result, we use the 900 kg. m -3 value for all type of ice and for both CryoSat-2 and Envisat datasets to convert freeboard height to ice draft. In Fig. 6 , we show the monthly CryoSat-2 and Envisat/PP ice draft estimates as a function of the corresponding monthly BGEP ice draft. In color, we represent the MYI fraction for each observation. We also show in gray the uncorrected Envisat ice draft estimates. The CryoSat-2 ice draft estimates have a relatively low RMSE (0. 13 m) with in situ measurements during the 2010/2011 ice growth season (Fig. 6a ) and a higher RMSE (0. 25 m) during the 2011/2012 ice growth season (Fig. 6b ). during winter 2011/2012 (Fig. 6k ). This result demonstrates therefore the potential of the PP-correction to produce Envisat ice draft estimates as accurate as CryoSat-2 during the entire cross-calibration period.
The higher RMSE observed in To further assess the potential of the PP correction, it is necessary to verify if the Envisat/PP dataset is also valid out of the cross-calibration period. As the BGEP ice draft measurements are available from August 2003, the accuracy of the PPcorrection can be evaluated over most of the Envisat mission lifetime. The Envisat and Envisat/PP ice draft estimates are therefore computed over the 2003-2010 ice growth seasons and are compared with the corresponding mooring observations (Fig. 6c-i ). As during the 2010-2012 period, the Envisat/PP ice draft dataset displays a good agreement with the buoys observations (RMSE =0. 16 to 0. 30 m) relatively to the uncorrected Envisat dataset (RMSE = 1. 06 to 1. 43 m). In Fig. 6c ,h and i, underestimated values are characterized by a relatively low MY fraction.
Time series of circumpolar ice thickness and volume As shown in the previous sections, the PP-correction allows to obtain an improved Envisat freeboard height dataset over the entire Envisat mission. Furthermore, the PP-correction allows to cross-calibrate Envisat and CryoSat-2 freeboard height estimates and enable to combine the two datasets to produce a time series of ice thickness over more than a decade. In Fig. 7   et al. , 2002)  missions provide results during this period. As expected, the seasonal ice thickness variation between CryoSat-2 and Envisat during the 2010-2012 period is fairly identical for the 2 satellite missions, despite a higher estimation shown by Envisat at the beginning of the 2011/2012 winter. As shown in  Kwok et al. (2009)  and  Giles et al. (2008) , the average pan-Arctic and winter ice thickness is relatively thick.
, 2015). In the present study, we investigate the inconsistency between Envisat and CryoSat-2 freeboard height. We show that ice surface diffusion drives most of the difference between the two freeboard height datasets and that a correction based on the waveform PP can be used to improve Envisat freeboard height estimates. While the PP-correction allows to remove most of the bias between CryoSat-2 and Envisat freeboard height datasets, other sources of inconsistencies could explain the remaining difference between the 2 datasets. For instance, bright off-nadir reflexions have a higher impact on Envisat than on CryoSat-2. The reduced footprint of CryoSat-2 allows to filter out bright off-nadir reflections in the along-track direction, which enables to produce more accurate estimates of surface level position than with Envisat.
As this discrepancy between the two radar altimeters is not corrected by the PP-correction, the Envisat/PP freeboard height is likely less accurate than CryoSat-2's. Nevertheless, the relatively low RMSD observed between Envisat/PP and CryoSat-2 freeboard height (see Tab. 2) suggests that the bias driven by off-nadir reflections on the Envisat/PP freeboard height should be negligible. Another source of inaccuracy of the Envisat/PP dataset is the function used to correct the Envisat freeboard height (y(PP) in Eq. (  5 )). This function is obtained by fitting the freeboard height difference between CryoSat-2 and Envisat with the Envisat PP and is used to produce a CryoSat-2-like version of Envisat freeboard height. Thus, the accuracy of the Envisat/PP freeboard height depends strongly on the accuracy of the CryoSat-2 dataset.
5)The CryosphereDiscuss. , doi:10. 5194/tc-2016Discuss. , doi:10. 5194/tc- -293, 2017     Manuscript under review for journal The Cryosphere Published: 17 January 2017 c Author(s) 2017. CC-BY 3. 0 License. Where f b pp is the corrected Envisat freeboard height (hereafter Envisat/PP), PP is the Envisat pulse-peakiness, f b is the un-corrected Envisat freeboard height and and y(P P ) is the average relationship between PP and ∆fb shown in black in Fig. 5. First of all, the Envisat/PP dataset displays a realistic average freeboard height during the entire winter (see Tab. 1). Looking at Fig. 2a and 2c, Envisat freeboard height is very similar to the results obtained with CryoSat-2, with identical patterns in most Arctic regions. Only ice located north of the Candian Archipelago displays a discrepancy with a thicker CryoSat-2 freeboard height.
This discrepancy is due to the higher uncertainty on CryoSat-2 and Envisat freeboard height estimates over very diffuse surfaces as shown in Fig. 5for low PP observations. In Tab. 2, we show the freeboard height RMSD between Envisat and CryoSat-2 and between Envisat/PP and CryoSat-2. The Envisat/PP dataset displays a clear improvement with an average RMSD of ∼2 cm in contrast to the uncorrected Envisat dataset (∼16 cm). In addition to the spatial variability, the seasonal variation is clearly improved with a variation of 3. 0 cm between November 2010 and April 2011 and of 2. 0 cm between November 2011 and March 2012. These results demonstrate therefore that the relation between the Envisat PP and ∆fb allows to build a robust CryoSat-2-like version of Envisat freeboard height during each month of the common flight period of Envisat and CryoSat-2 missions.
Envisat freeboard height dataset (Envisat/PP) based on the relation between the freeboard height difference between Envisat m) and as accurate as CryoSat-2 (RMSE = 0. 13 -0. 25 m). To further assess the potential of the PP-correction, the Envisat and Envisat/PP ice draft datasets are extended to the 2003-2010 period and are similarly compared to BGEP ice draft measurements. As for the 2010-2012 period, the agreement with in situ measurements is higher for Envisat/PP (RMSE = 0. 14 -0. 29 m) than for Envisat (RMSE = 1. 08 -1. 32 m), which demonstrates the potential of the PP-correction to provide accurate freeboard height estimates over the entire Envisat mission. Another important result of this comparison is the strong ice thickness uncertainties 10 related to inaccurate snow depth parametrization.
Sections were incubated with a Cy3conjugated donkey anti-rat antibody (1:700 in 1% FBS) and counterstained with Hoechst 33342 (Molecular Probes, OR, USA) for 5 min and washed three times with PBS. Sections were mounted using Fluoromount G mounting medium (Southern Biotech, AL, USA). Slides were dried overnight and images were captured using a Nikon eclipse Ti-s inverted microscope. Images were processed using NIS elements 3. 0 imaging software (Nikon Instruments Inc. , NY, USA).
4. 2a  (35)  with the option alignEndsType EndToEnd. Only reads with unique mapping were considered for further analysis. Gene expression levels were calculated using htseq-count  (36)  with option intersection-strict and mm10 Refseq 3′UTR GTF annotations. Duplicate reads were filtered if they mapped to the same gene and had identical UMIs. Normalization and differential expression analysis was performed using the DESeq2 R-package (Bioconductor, https:// bioconductor. org/packages/release/bioc/html/DESeq2. html). Differentially expressed genes were defined as genes that had a significant adjusted p value (<0. 05) and at least twofold change. Differentially expressed genes in at least one of the comparisons were clustered using the k-means algorithm (Partek ® Genomics Suite software, Partek Inc.
acKnOWleDgMenTs The authors thank Dr. Tomer Meir Salame from the FACS unit in the Weizmann Institute of Science for his excellent technical support. The authors also thank the NIH Tetramer Core Facility, Emory University, Georgia, USA for providing CD1d-PBS57 loaded tetramers. This study was supported by the Atheroflux consortium (EU grant FP7-602222-2), the Israel Science Foundation (grant 1728/15), and the National Institutes of Health (grant GM076217). AS was supported by the Planning and Budgeting Committee program for outstanding post-doctoral researchers. AHF is the Joseph Meyerhoff Professor of Biochemistry at the Weizmann Institute of Science.
sUPPleMenTarY MaTerial The Supplementary Material for this article can be found online at http://www. frontiersin. org/article/10. 3389/fimmu. 2017. 01386/ full#supplementary-material. DaTa sheeT s1 | Raw RNAseq data and analysis of differentially expressed genes in livers isolated from wild-type (WT) and ceramide synthase 2 (CerS2)null mice with and without LCMV infection, and LCMV-infected CerS2-null mice after transfer of WT iNKT cells. The data have been uploaded to Gene Expression Omnibus (GEO), accession number GSE104205. reFerences.
For each layer type, we design a hardware block (hand-written VHDL) that is reused in the pipeline with different parameters. See the top of Figure  1  for a simplified schematic view of the implementation of each block type. Four main layer types are used: Sliding Window Layer (SWL), Neuron Layer (NL), Ternarization Layer (TL) and Max Pooling Layer (MPL). The TL exists because of the constraints introduced by the ternaryonly activations: the result of a neuron is a scalar and it must Throughout this paper, we use two networks with different dimensions, NN-64 and NN-128, respectively with n = {64, 128}. For instance according to Equation 1, the fifth NL has 2n neurons, hence the second SWL frame size has dimension z = 128 for NN-64 and z = 256 for NN-128. Our network NN-128 actually has same architecture than the network used in  [8]  except we increased the number of output neurons from 10 to 100 to enable using datasets with up to 100 classes.
A. Experimental Setup Experiments are performed on a VC709 FPGA board directly plugged in a PCI-Express slot of a workstation. This board is equipped with the Xilinx FPGA XC7VX690T. We highlight that the on-board 8 GB RAM is unused because only on-chip memory is used in our designs. most demanding layers are parallelized, which may be only a small fraction of the design resources. Similarly, inside the neurons themselves, only the size of the adder tree increases, not the entire neuron. Without parallelism, all weight memory banks are implemented within dedicated block RAM (BRAM) resources of the FPGA. Adding parallelism increases the amount of data that these memory banks have to produce at each clock cycle. Even though the storage needs (in bits) does not increase, the BRAM requirements increase to implement the required output width.
C. Power consumption We measure the power consumption using the core 1 V power supply rail of the FPGA, since our designs fit entirely inside the FPGA. To confirm that this way of doing the measure is correct, we also monitored the global board power (all supply rails measured through PMBus) and observed that it is higher than the core 1 V rail by a rather constant 4. 5 W for all designs. Results are presented in Figure  8. The figures related to NN-64 and NN-128 form two very distinct groups. For each of the two NN sizes, the power consumption is approximately a linear function of the design throughput, and varies little between datasets. This is due to our FPGA implementation not exploiting dataset sparsity (zero-activations and zero-weights) to reduce design activity. When neuron weights are packed inside large RAM banks, it is not possible to inhibit RAM read for selected positions.
V. R E L AT E D W O R K Our results are presented in Table  II , along with results from others FPGA-based works using the same datasets. For each dataset and neural network architecture, our platform provides the highest throughput and it outperforms accuracy of related works. Umuroglu et al. [13]  propose FINN, an FPGA implementation of NN-64 with binary weights on board ZC706. Their design can classify the datasets CIFAR10 and SVHN at a throughput of 21 900 fps. Our raw processing speed (frames per second) is higher than their, but this is exclusively due to our higher frequency (250 MHz instead of 200 MHz). This difference may be linked to us using a higher-performance FPGA technology (Virtex-7 where they use Zynq-7000). Our power efficiency (throughput per watt) is lower than their by 33-37%.
The communication interface is unknown. Their HLS-generated design runs at 90 MHz and processes the dataset CIFAR10 at 6218 fps. With our hand-written RTL, our frequency is higher (250 MHz) and our design is notably faster. But even if they used our frequency, their throughput would be only 17272 fps which is still much lower than our platform. Their design is presented as a 7. 663 TOP/s accelerator (with multiply and accumulate counted as different operations). We have 4. 19 TMAC/s for NN-64 and 8. 36 TMAC/s for NN-128, hence respectively 8. 38 TOP/s and 16. 72 TOP/s, an improvement of respectively 9. 4% and 118% over their design. In  [11] , Alemdar et al. propose ternary neural networks similar to our NN-64 and NN-128, but with 12-channel ternary input. Only a fixed parallelism of 8× is used in their FPGA version and the power consumption is based on pessimistic estimations.
Mass Spectrometry Cell-free supernatant was ultracentrifuged at 100 000g at 4°C for 70 minutes (Optima L-100 XP, Beckman Coulter) and concentrated with Amicon ultra filters, 50K (Millipore). Concentrate was denatured by adding sodium dodecyl sulfate (SDS) to a concentration of 1%, 100 mM dithiothreitol and heated to 95°C for 5 minutes. Removal of SDS, alkylation, and reduction were performed using the filter aided sample preparation protocol  [20]. Peptides were desalted and analyzed on a Q-Exactive mass spectrometer as previously described  [21]. Proteins were identified and quantified by MaxLFQ  [22]  by searching with the MaxQuant version 1. 5 against the Pseudomonas aeruginosa reference proteome database (Uniprot). Modifications included C carbamlylation (fixed) and M oxidation (variable).
Statistical Analysis Data are presented as mean ± standard error of the mean for at least 3 independent experiments. Statistical analysis was carried out using unpaired Student t test or one-way analysis of variance with Tukey posttest (GraphPad Prism 6 software). Survival curves were compared by log-rank (Mantel-Cox) test (GraphPad Prism 6). Statistical significance was denoted as P <. 05. Densitometry analysis was performed with Image J Software.
Using alkaline protease as an example to validate the results obtained by mass spectrometry, we performed casein zymography for levels of active alkaline protease in cell-free supernatant of P. aeruginosa grown in normoxia and hypoxia. Three P. aeruginosa isolates (B244J, U60088, and S4169) expressed a 50-kDa protease, which is consistent with alkaline protease  [24, 25]. A significant decrease in protease activity under hypoxic conditions was found in supernatants from the isolates from acute infections but not from the chronic infection (Figure  3B ). These data support the identification of reduced alkaline protease expression in clinical strains of P. aeruginosa derived from acute infections and exposed to hypoxia.
Acknowledgments. We thank  Christopher J. Schoefield  and  Gail Preston  for the PAO310 strain. Financial support. This work was supported by grants from the  National University of Ireland  (to B. S. ) and  Science Foundation Ireland  ( 11/PI/1005  to C. T. T. ). Potential conflicts of interest. All authors: No reported conflicts of interest. All authors have submitted the  ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
It is found in the rCRS is GenBank number NC_012920  (7) (8) (9) (10) (11)   [11] [12] [13] [14] [15]. Neanderthal 1 mtDNA genome  [11]. DENISOVA1 and DENISOVA2 mtDNA genomes  [12]. (a) DENISOVA1 alias "Denisova" in the original paper: Select item 9529523511. Homo sapiens ssp. Denisova isolate Denisova 8 mitochondrion, complete genome 16,573 bp circular DNA Accession: KT780370. 1 GI: 952952351 (b) DENISOVA2 alias "ALTAI" in the original paper: Select item 2926064082. Homo sp. Altai mitochondrion, complete genome 16,570 bp circular DNA Accession: NC_013993. 1 GI: 292606408. Chimpanzee mtDNA genome: >X93335. 1 Pan troglodytes mitochondrial DNA, complete genome (isolate Jenny)  [16].
Part 2: The exhaustive search for circular resonances of type (C+A)/TCAG Analysing 20 mtDNA mutations with reports of disease associations: Datas from MITOMAP database has provided by Fogleman et al. [18]  and organized by mtDNA location by Fogleman et al. [18]  (Supplementary Tables  1  and 2 , Figures  2 3 4   3  and the other 19 cases can be seen in Supplementary Tables  4  and 5       [  19, 20]  and can be seen in Supplementary.