dataesr/scientific-paragraphs-categorization huggingface dataset:
Conserved resides and conserved motifs distribution To further understand the possible physiological role and substrate specificity of BvAQPs, they were aligned and TMHs and conserved resides (NPA motifs, ar/R selectivity filter and Froger's position) were analyzed (Table  2 ). The data revealed that all BvAQPs had six characteristic TMHs for AQPs, required for transport function (Fig. S1 ). However, BvPIP1;1 showed partial loss of TM6. The resulting missing protein domains may cause dominant negative effect on protein functions, although this need to be confirmed. All PIPs and TIPs showed the dual typical NPA motifs, but some members of NIPs such as BvNIP5;1, BvNIP5;2 and BvNIP6;1 showed that the Alanine (A) in the third residue of the second NPA motif was replace d by a Valine (V). All SIPs and XIPs showed various third residues in the first NPA motif, in which Alanine (A) was replaced by Threonine BvPIP1;1 NPA NPA F H T R Q S A F W BvPIP1;2 NPA NPA F H T R Q S A * * BvPIP1;3 NPA NPA F H T R Q S A F W BvPIP2;1 NPA NPA F H T R Q S A F W BvPIP2;2 NPA NPA F H T R Q S A F W BvPIP2;3 NPA NPA F H T R Q S A F W PIP BvPIP2;4 NPA NPA F H T R M S A F W BvTIP1;1 NPA NPA H I A V T S A Y W BvTIP1;2 NPA NPA H I A V T S A Y W BvTIP1;3 NPA NPA H I A V T S A Y W BvTIP2;1 NPA NPA H I G R T S A Y W BvTIP2;2 NPA NPA H I G R T S A Y W BvTIP3;1 NPA NPA H I A R T A A Y W BvTIP4;1 NPA NPA H I A R T S A Y W TIP BvTIP5;1 NPA NPA N V G Y T S A Y W BvNIP1;1 NPA NPA W V A R F S A Y L BvNIP4;1 NPA NPA W V A R F S A Y I BvNIP4;2 NPS NPA W A A R L S A Y I BvNIP5;1 NPS NPV A I G R F T A Y M BvNIP5;2 NPS NPV A I A R F T A Y M BvNIP6;1 NPS NPV S I G R F T A Y F BvNIP6;2 NPA NPA S I G R Y T A Y M BvNIP6;3 NPA NPA S I A R Y T A Y L NIP BvNIP7;1 NPA NPA A V G R F S A Y F BvSIP1;1α NPT NPA I V P N M A A Y W BvSIP1;1β NPT NPA V V P N M A A Y W SIP BvSIP2;1 NPL NPA S N G S F V A Y W XIP BvXIP1;1 NPT NPA V S A R F C A F W P5 To explore further the diversity in each group, the conserved motifs were predicted (Fig.
g. ,  Minniti 1995; Barbuy et al. 1998; Ivanov et al. 2005 Ivanov et al. , 2017)). One of the goals of the VISTA Variables in the Vía Láctea (VVV) Survey was to search for such GCs, and measure their astrophysical parameters in the near-IR  (Minniti et al. 2010). Indeed, our previous VVV GC searches identified a few new Galactic GCs  (Minniti et al. 2011; Moni Bidin et al. 2011; Borissova et al. 2014; Minniti et al. 2017). However, the VVV Survey data could reveal many more such objects disguised in the central regions of the Milky Way, as current attempts at uncovering them have not been exhaustive. In this paper, we present a new way of searching for these objects, using selected cuts in the Wesenheit Ks-band magnitude W K versus J-Ks color-magnitude diagram (CMD) in order to create maps of bulge red giants and search for good GC candidates, for which we present deep near-IR CMDs to further investigate their GC nature.
Discussion The present discoveries prompt many interesting questions: Are these low luminosity clusters younger or do they have different chemical compositions than the most luminous GCs? What can these objects tell us about the survival of structures deep into the Galactic potential well? What fractions of the GC population belong to the bulge, disk, or halo? How many GCs are still missing in the Milky Way? There were 36 previously known GCs in the region mapped by the VVV Survey, and our candidates, if they are all genuine, augment the sample by more than 60%. This has only been possible thanks to the high quality near-IR images and photometry from the VVV Survey database. Considering that we have not thoroughly explored the Galactic plane and the outer bulge, we predict that the number of GCs in the region may be more than double the current estimate including the new candidates.
It would also be possible to measure metal abundances and radial velocities with APOGEE  (Blanton et al. 2017)  for the brightest candidates and with MUSE for the faintest ones (E. Valenti et al. 2017, in preparation). The velocities are needed in order to compute orbits after proper motion measurements are acquired. For some of the GC candidates, it may be possible to measure relative proper motions with the VVV data (Contreras Ramos et al. 2017). However, most of our GC candidates have red giants that are bright enough to have accurate absolute proper motion measurements with Gaia (E. Valenti et al. 2017, in preparation).
project staff and supply chain participants including bag manufacturers, vendors (i. e. wholesalers, semiwholesalers, and retailers), partner NGOs and research institutes, and PICS business consultants. Data collection focused on capturing history and current status of the PICS supply chain in each country; the key factors that influenced private sector actors' willingness to invest in building up the supply chain; the institutional environment that impacted business and marketing strategies; and 'market failures' that needed to be addressed in launching the supply chain. Other information collected was related to factors that hinder the sustainability of the supply chain, and strategies used by the project team at Purdue and partners in expanding the distribution system and in increasing bag sales.
This literature search resulted in 12 publications that have previously been presented in a meta-analysis study  [6]. Mean mGFR and standard deviations (SDs) from this meta-analysis study were used to calculate lower (LRL) and upper (URL) reference limits as mean mGFR 6 2ÂSD. Statistical analysis [descriptive statistics, t-tests, analysis of variance (ANOVA)] was performed with SAS 9. 4 (SAS Institute, Cary, NC, USA).
Results Table  1  and Figure  1  demonstrate the age decline for both male and female potential kidney donors in our dataset (n ¼ 633). No difference was observed between males and females in the defined age groups younger than 50 years of age. However, we noticed a difference between males and females older than 50 years, with a faster decline in females than in males, an observation that we also found in our meta-analysis study  [6]. When combining the mGFR data of males and females in age decades, then, for the decades 20-30 years fmean mGFR ¼ 107. , no difference between the mean mGFRs of these age groups was observed (ANOVA, P ¼ 0. 0814) and no difference in the age decades 20-30 years and 30-40 years was observed with a mean value of 107. 3 mL/min/1. 73 m 2 , as predicted from the FAS equation.
5th-97. 5th percentiles) of mGFR (iothalamate) for 365 healthy potential living kidney donors. In his analysis, Rule et al. did not observe differences between men and women. They implicitly assumed a linear renal decline starting at 18 years of age and regressed the mGFR data over the full age range of 18-71 years, observing a slope or decline of 4. 9 mL/min/1. 73 m 2 /decade. The FAS equation would predict a renal decline of 6. 3 mL/min/1. 73 m 2 over the same age range. Grewal and Blake  [19]  performed a study on 428 subjects (218 females, 210 males; age range 19-72 years) undergoing assessment as live kidney donors. GFR was evaluated from 51 Cr-EDTA  'Age' is defined as the mid-point of the age interval. LRL and URL are the means of the lower and upper limits calculated from the meta-analysis data  [6].
9 Â 0. 988 (AgeÀ40) mL/ min/1. 73 m 2 for those >40 years. This upper limit is much closer to the upper limits obtained for mGFR from the meta-analysis for younger adults. When this upper limit is applied to the 633 kidney donors of our dataset, we find 91. 8% lying between these reference limits, compared with 97. 2% when the SCr-based reference limits are used. The fraction of 5. 4% (97. 2-91. 8%) is completely located between the two upper limits, suggesting a skewed GFR distribution for (normal) healthy subjects. As limitations to our study, we must emphasize that the presented reference intervals are for Caucasians between 20 and 70 years of age, although we are not aware of differences in GFR due to ethnicity  [25]. Possible effects of a protein-rich diet on the mGFR are unknown, as this has not been considered as an exclusion criterion during patient recruitment.
Structural evaluation Circular dichroism (CD) measurements CD spectra were recorded on a Jasco 810 (Japan) dichrograph in quartz suprasil cells (Hellma) with an optical path of 1 mm for peptide in solution or in the presence of liposomes vesicles. Same concentrations of peptide (40 µM) were used for each condition. Spectra were obtained from 3 accumulations between 190 and 260 nm with a data pitch of 0. 5 nm, a bandwidth of 1 nm and a standard sensitivity.
Membrane interaction and internalization of CPP:siRNA nanoparticle GUV imaging by confocal microscopy For the microscopy experiments, the GUVs were placed into a glass bottom cell culture dish (Greiner bio-one) and incubated with 400 nM Atto633-RICK:20 nM siRNA-Cy3b. Confocal images of GUVs were immediately obtained with an inverted LSM780 multi-photon microscope (Zeiss). The obtained confocal images were projected and treated with the software ImageJ.
No emission bleed through was observed between the different channels observed with these acquisition parameters. Emission photons were collected on an EM-CCD camera (iXon Ultra) and images were recorded every 2 for 120 min and projected with the software Andor IQ3. These acquisition parameters provide an image of 512 × 512 pixels with a pixel size of 0. 15 µm. The image treatment was performed with the Imaris software for a 3D reconstruction.
In some case, we could also visualize the accumulation of Atto633-RICK at the plasma membrane over the whole incubation period (2 h), which indicated the strong affinity of RICK peptide for lipids, while siRNA was never found at the plasma membrane. Furthermore, longer incubation (>24 h) of the nanoparticles did not give rise to higher cytosolic accumulation of Atto633-RICK or siRNA-Cy3b (data not shown). In conclusion, we could state that nanoparticles had no lytic properties (no GUV or LUV destruction) and that the amphipathic peptide did not induce any membrane perturbation (no inner vesicle or tube formation) as reported by Ayala-Sanmartin and coll. for R9 and RW9  [55]. Finally, the RICK:siRNA internalization occurred immediately after applying the nanoparticles on cells. A minimal incubation time of 90 min should be sufficient to deliver the siRNA and then to induce a biological activity.
5a ). In this context, cytotoxicity evaluation was an important feature to avoid false-positive results because toxic effect would have an impact on both luciferase activities. Under the applied conditions (5000 cells per well), we observed at siRNA concentrations higher than 60 nM (=1. 2 µM RICK concentration) that the normalized results were no longer coherent based on the unspecific knock-down of RLuc (data not shown). In parallel, stability of the PBNs in the course of storage was investigated over the time with regard to future clinical applications. Although the DLS investigations have already indicated that nanoparticles were stable and conserved the same colloidal features after 72 h at 4 °C, we decided to control the FLuc knock-down efficiency of siRNA-loaded RICK PBN over the time.
c Gene silencing on the endogenic protein cyclin B1. siSCR scrambled siRNA, N. T. non-treated cells, siCycB1 siRNA anti-Cyclin B1, Vincu. Vinculine CADY-K:siCycB1, respectively). This latter result confirmed the more pronounced stability for RICK in serum, as already observed for other d-isoform peptides  [53]. All this data confirmed the high potential of RICKbased nanoparticles as a promising tool for siRNA delivery in vitro and in vivo.
Acknowledgements We thank  Dr. Eric Vivès  for many helpful suggestions/comments and for critically reading the manuscript and  Dr Cyril Favard  as well as  Naresh Yandrapalli  for their critical advice on GUVs experiments. We thank  Sylvain De Rossi  (Montpellier RIO imaging microscopy platform) for technical advices as well as  Guillaume Cazals  (technical platform of the  Institute of Biomolecules Max Mousseron, Montpellier, France ) for LC-MS and LC-MS/MS experiments.
Acknowledgments: The author is deeply indebted to the libraries (and librarians) that digitize rare and old texts before making them freely accessible on the internet. The author also wishes to gratefully acknowledge  Mina Bui-Jones ,  Emma Quilliam  and  Jean-Yves Mary  for their editorial assistance,  Jean-François Meister  for discussions on the case of his grandfather,  Marie-France Estadieu  from the  Société d'Histoire du Val de Villé  for a comprehensive document on Joseph Meister's case (now accessible at http://www. webcitation. org/6oOwdJq7l), Nick Veldhuis ,  Tallay Ornan ,  Mark Weeden  and  Mirjam Duijvestijn  for their help in accessing historical material,  Hervé Bourhy  and  Anne-Marie Moulin  for sharing their extensive knowledge of rabies and its history and providing much-needed guidance, and  Daniel Tarantola  for discussions on variolation and smallpox vaccination (and for providing half of the author's genome).
We reconstructed the natural history of each tumor, showing that the order in which driver events occur is not very constrained: a same driver mutation or copy-number alteration can be early in one tumor and late in another. However, general rules emerge, like the diminution of signature 16 in late subclonal mutations or the late acquisition of chromosome gains. The latter is in agreement with a recent pancancer analysis showing that chromosomal gains are typically acquired during the second half of clonal evolution  38. In particular, we identified several modes of duplication acquisition, multiple synchronous gains and whole genome duplications being very late events associated with larger tumors. A limitation of this study is the small sample size for some etiologies. By grouping our new WGS data set with the ICGC-Japan series, we obtained substantial sample sizes for the main etiologies in France and Japan: alcohol, HBV and HCV infection.
Samples sequenced at Integragen were sequenced on an Illumina Hiseq 2500 as paired-end 125 bp reads. Sequences were aligned to the hg19 version of the human genome using BWA  41  version 0. 7. 15. We used Picard tools version 1. 108 (http://broadinstitute. github. io/ picard/) to remove PCR duplicates and GATK 42 version v3. 5 for local indel realignment and base quality recalibration, as recommended in GATK best practices  43. We obtained an average depth of 92-fold for tumors (range 63-109) and 67-fold for matched non-tumor liver samples (range 35-116). Coverage and variant calling statistics for each sample are provided in Supplementary Table  2. Somatic mutation calling. We created a panel of normals (PON file) using GATK and we used MuTect2 to call somatic mutations (single nucleotide variants and small insertions and deletions) by comparing each tumor sample with its matched non-tumor counterpart and the PON file.
Based on visual inspection of 188 SNVs and 76 indels using the Integrative Genomics Viewer (IGV)  44  , we established the following post-filtering criteria. First we selected mutations covered by ≥6 reads in both the tumor and non-tumor samples, with <5% of variant reads in the nontumor sample and we excluded mutations belonging to the ENCODE Data Analysis Consortium blacklisted regions (http://hgdownload. cse. ucsc. edu/goldenPath/ hg19/encodeDCC/wgEncodeMapability/ wgEncodeDacMapabilityConsensusExcludable. bed. gz). We then processed differently SNVs and indels. For SNVs, we selected mutations with a MuTect2 filter flag among "PASS", "clustered_events" or "t_lod_fstar". To improve specificity in the calling of mutations with low variant allele frequencies (VAF < 0. 2), we quantified the number of high quality variant reads in the tumor (mapping quality ≥50, base quality ≥30) and the number of variant reads in the non-tumor sample with no quality threshold using bamreadcount (https://github.
We used cgpBattenberg  29  algorithm to reconstruct copy-number profiles from whole genome sequences of our 44 liver tumors. This method uses both coverage data and allele frequencies of germline SNPs to estimate the tumor cell content, ploidy and absolute copy-numbers of each allele across the genome. Besides, germline SNPs are phased and haplotype-based allelic frequencies are used to identify subclonal copy-number alterations. Mutational signature analysis. Ten mutational signatures were described so far in liver cancers  3, 11, 16  and are referenced in COSMIC database: signatures 1, 4, 5, 6,  12, 16, 17, 22, 23, and 24. To identify new mutational signatures in our series of 44 liver tumors, we first performed a de novo extraction of signatures using BayesNMF  46, 47  and EMu 18 methods.
BayesNMF extracted 10 signatures and Emu 9 signatures, which we then compared to the pan-cancer catalog of 30 signatures referenced in COSMIC database. The comparison was performed using the cosine similarity score, as previously described  3. Most signatures corresponded to one or a mixture of two already known signatures and displayed a cosine similarity >0. 75 with at least one of the ten COSMIC signatures known to be operative in liver cancers. However, two signatures were identified consistently by BayesNMF and EMu that did not correspond to any previously described signature (cosine similarity <0. 75). These new signatures were named N1 and N2 (Supplementary Fig. 1 ). Signature N1, characterized by T > C at NTT site, was encountered in a 67 year-old woman who developed an adenoma with carcinoma transformation on a non-cirrhotic liver with metabolic syndrome.
We used replication sequencing (Repli-seq) data generated by the ENCODE consortium for the liver cancer cell line HepG2 to define early and late-replicating regions as well as leading and lagging DNA strands as previously described  7. In short, replication timing deciles were defined using wavelet-smoothed Repli-seq signals downloaded from the ENCODE website. We used Repli-Seq signal peaks (replication initiation) and valleys (replication termination) defined by ENCODE to define leading and lagging strands based on the direction of the replication fork. To avoid ambiguous status, only regions with a peak-to-valley distance ≥500 kb were assigned strand information. This allowed to classify unambiguously 1. 5 Gb of the human genome. To estimate mutation rates within each replication timing decile, we divided the number of mutations by the informative genomic size of the decile, excluding 'N' bases.
Signature-wise analyses were performed similarly using only mutations attributed to a single signature with a probability ≥0. 7. Replicative strand biases were analyzed by comparing the number of mutations occurring on the leading and lagging strands, over the genome or within each replication timing decile. Transcription levels and transcriptional strand bias. To correlate mutational processes with gene expression, we first estimated the median expression level of GENCODE genes (version 24) in an in-house RNA-seq data set of 39 non-tumor liver samples. We used these expression levels to define 5 gene expression categories from no to high transcriptional activity. We created a separate class for very highly expressed genes, defined by an FPKM (fragments per kilobase of exons per million reads) value ≥ 100.
Computing codes. The functions created to perform this work and generate figures are available as an open-source R package, Palimpsest, available on Github: https://github. com/FunGeST/Palimpsest. Data availability. The sequencing data reported in this paper has been deposited in the EGA (European Genome-phenome Archive) database (accessions EGAS00001002091, EGAS00001002408 and EGAS00001000706) and the International Cancer Genome Consortium (ICGC) data portal (http://dcc. icgc. org/; release 24, April 2017).
Acknowledgements We thank  J. C. Nault  for critical review of the manuscript and  T. Shibata  for helpful discussions on mutational signatures. We also thank the  Reśeau National Centre de Ressources Biologiques (CRB) Foie , the  French Liver Biobanks network (INCa ,  BB-0033-00085 , Hepatobio bank), and the tumor banks of  CHU Bordeaux  ( BB-0033-00036 ) and  CHU Henri Mondor  for contributing to the tissue collection. This work was supported by  Institut National du Cancer (INCa)  with the  International Cancer Genome Consortium  (ICGC  LICA-FR  project) and  NoFLIC projects (PAIR HCC ,  INCa and ARC) ,  INSERM  with the  Cancer et Environnement (plan Cancer ) and  HETCOLI projects (Tumor Heterogeneity and Ecosystem program ). The group is supported by the  Ligue Nationale contre le Cancer (Equipe Labellisée) ,  Labex OncoImmunology  (investissement d'avenir),  Coup d'Elan de la Fondation Bettencourt-Shueller , the  SIRIC CARPEM and Fondation Mérieux.
Computing codes. The functions created to perform this work and generate figures are available as an open-source R package, Palimpsest, available on Github: https://github. com/FunGeST/Palimpsest. Data availability. The sequencing data reported in this paper has been deposited in the EGA (European Genome-phenome Archive) database (accessions EGAS00001002091, EGAS00001002408 and EGAS00001000706) and the International Cancer Genome Consortium (ICGC) data portal (http://dcc. icgc. org/; release 24, April 2017). URLs. R software, v3. 2. 3, http://www. R-project. org/; GATK4, http://www. broadinstitute. org/gatk/; Oncotator, http://www. broadinstitute. org/cancer/cga/Oncotator/; cgpBattenberg, https://github. com/cancerit/cgpBattenberg; ICGC data portal, https://dcc. icgc. org/; COSMIC mutation signatures database, http://cancer.
Many datasets describing contacts in a population suffer from incompleteness due to population sampling and underreporting of contacts. Data-driven simulations of spreading processes using such incomplete data lead to an underestimation of the epidemic risk, and it is therefore important to devise methods to correct this bias. We focus here on a non-uniform sampling of the contacts between individuals, aimed at mimicking the results of diaries or surveys, and consider as case studies two datasets collected in different contexts. We show that using surrogate data built using a method developed in the case of uniform population sampling yields an improvement with respect to the use of the sampled data but is strongly limited by the underestimation of the link density in the sampled network. We put forward a second method to build surrogate data that assumes knowledge of the density of links within one of the groups forming the population.
e. , of reported contacts that are not relevant for propagation events, can also arise. Here we focus on data incompleteness, but investigations of the impact of false positives would also be of clear interest]. To obtain such an estimation, a possibility is to try and construct surrogate datasets using only the information contained in the incomplete data such that these surrogate data, despite not being strictly equal to the original data, are "similar enough" with respect to the spreading process of interest. Here, "similar enough" means that the outcomes of simulations of spread using the surrogate data should be close to the ones using the real, complete data. This issue has been addressed in the case of uniform population sampling by Génois et al. 24  Uniform node sampling indeed maintains not only the network density, but also the whole contact matrix of densities that describes the structure of links in a population structured in distinct groups, such as classes in a school.