dataesr/scientific-paragraphs-categorization huggingface dataset:
Model performance Results for best models from all the different CI methods are shown in Table  2. All CI methods perform well on the data sets. The multiple linear regression (MLR) model, shown in Table  3 , does not represent the process adequately (15% error), suggesting that nonlinearity needs to be addressed with a robust method. Figure  1  shows the predicted vs observed graph for the MLR model. Hence, to find a suitable model, decision trees, ANNs, and GP algorithms were used. RGP was performed as an early-stage experiment at this point. Nonetheless, RGP shows results comparable to those from other methods, which is evidence that evolutionary computational methods are quite robust and can fit complex data well.
external validation Data from additional tableting experiments was used to test the trained models. Tablets were made with MCC. The external data set was particularly challenging for the models because roll compaction conditions were kept constant in the training data set while being systematically changed in the external data set. Table  1  shows the results of external validation. The models were able to predict porosity with substantial accuracy using unseen input data. The external data set is composed of experiments with two different roll compactor tooling (cheek plates and rim rolls) and two different roll compaction forces (4 kN and 8 kN) for each. The variety of roll compactor settings introduces variance in the external data set unknown to the trained models. Although CI systems are not known for their extrapolation abilities,  42  the models presented herein are able to extrapolate successfully in a small range.
Scale bars: 100 μ m. In E and J, morphometric analyses were performed by ImageJ, as described in Materials and Methods. notwithstanding enhanced E2 production. This treatment becomes efficient in stimulating follicular growth in our mouse colony when circulating FSH has returned to moderate levels, after 21 dpn (data not shown). Our findings are consistent with observations showing that preantral/early antral follicles display a slower growth at 14 dpn than at older prepubertal ages in the mouse  26  , but in apparent contradiction with a study in mouse models of follicle tracing  37. In addition, an inverse relationship has been observed between antral follicle count and FSH levels in ewe lambs during mini-puberty  39. Our study may provide some cues to understand why the aberrant elevation of postnatal FSH concentrations for several months following birth in prematurely born infant girls is associated with enhanced E2 levels but with decreased number of antral follicles  8, 40.
Samples were run on a Bioplex-200 instrument (Bio-Rad, Marnes-La-Coquette, France) and concentrations were calculated using a five parameter logistic fit curve (5PL) generated from the standards by the Bio-Plex Manager 6. 1 software (Bio-Rad, Marnes-La-Coquette, France). The sensitivity of the assays was 32 pg/ml for FSH and 3. 2 pg/ml for LH. The inter-assay coefficient of variation was 5. 4% for FSH and 3. 2% for LH. The intra-assay coefficient of variation was 7. 6% for FSH and 5. 9% for LH. Cultures of postnatal ovaries. Ovaries from 12 dpn female mice were placed on cell culture inserts (Millicell #PICM01250, Millipore, Guyancourt, France) on the top of 400 μ l of culture media containing RPMI without red phenol, fetuin (Sigma, #F2379), insulin (Sigma, #I0516), transferrin (Sigma, #T8158), sodium selenite (Sigma, #S5261) and BSA (Euromedex, Souffelweyersheim, France) in 24-well plates for 24 hours.
Acknowledgements The authors wish to thank the following investigators for providing reagents:  Dr S. Mazaud-Guittot  (IRSET, Rennes),  Dr P. Loget  ( Service d'anatomopathologie, CHU de Rennes ),  Drs N. Di Clemente ,  J. Y. Picard  and  R. Counis  ( University Paris 7 ). They acknowledge the technical assistance of  M. Surenaud  for gonadotropin assays ( Hopital Henri Mondor, France ),  S. Vandormael-Pournin  for the superovulation procedure ( Institut Pasteur, France ),  N. Coudouel  for tissue sectioning and all the technicians of the Buffon animal facility ( University Paris Diderot, France ) who contributed to this work. The authors also thank  Dr S. Bouret  ( Institut du Cerveau et de la Moelle, France ) and  Dr V. Simon  ( University Paris Diderot ) for valuable discussions on this project.
In this paper we investigate the use of the natural anticipatory behaviour of upper body to detect the intention to turn during locomotion, and to command turning with a lower limb assistive robot. This natural behaviour of the upper body may make humanrobot interaction more intuitive than previous control methods, and improve the performance of robotic rehabilitation and prostheses applications. First, we record and characterise the head anticipatory behaviour in locomotion in healthy participants. Then, we use the recorded data to develop a method to detect a turning movement before it is started based on the head anticipatory behaviour. We suggest that the turning anticipatory behaviour of the upper body can be efficiently used to control lower limb assistive robotic devices based on real time head and pelvis motion measurements and provide user with a natural human-robot control interface.
Methods This section describes the experimental protocol, the proposed turning detection algorithm and a set of validation methods used to evaluate the algorithm's performance. Subsections 2. 1 and 2. 2 describe the experiment which was used to collect human turning data. These data was then used for generating turning behavior clusters for the proposed head-based turning detection method described in subsection 2. 3. A simple pelvis yaw threshold based method is described in subsection 2. 4 which was used as an alternative turning detection approach for performance comparison with the proposed method. Subsections 2. 5, 2. 6 and 2. 7 describe performance evaluation tests for different turning angles, multiple turns and for anticipatory turning control of an assistive robotic system, respectively.
Data analysis The recorded time history of head and pelvis yaw were first analysed to identify the start and end of the turning movement for the head and pelvis during locomotion. Fig. 2  shows the time history plots for head and pelvis yaw of a typical subject. In Fig. 2A , we observe that the head turns earlier than the pelvis during left or right turning, and these results are in accordance with previous findings on head anticipatory behaviour  [11, 12]. To determine the time instants when turning starts head and pelvis yaw behaviour was modelled as a time-delayed second order system described with the following transfer function: W (s) = ω 2 s 2 + 2ωξs + ω 2 • e -T•s (1) where ω is the natural frequency, ξ damping ratio, and T • time delay. Identification of the transfer function parameters was done using the MATLAB identification toolbox (Mathworks, Natick, MA, US) for each trial of each subject.
Anticipatory head based turning detection Let us now describe how the head turning anticipatory behaviour during locomotion can be used to detect turning before it actually happens. Head yaw and pelvis yaw measurements collected during the experiment with the different subjects and plotted against each other are shown in Fig. 3. Five relatively clear sets of data can be obtained: walking straight when head is straight, walking straight with head turned left, walking straight with head turned right, walking after turning left and walking after turning right. We aimed to develop a method to distinguish between all these sets and detect turning as fast as possible after it has been initiated. It is important to note here that the data points located between left and right turning activities are not located along the {y = x} diagonal line which corresponds to head anticipatory behaviour.
The turning detection algorithm was realized in MATLAB (Mathworks, Natick, MA, US), which used the recorded during experiments timehistory of head and pelvis yaw for a given subject as an input and calculated the turning detection time for the recorded data. Each walking data for a given subject which was being used for turning detection, was not used for forming the clusters. That was done in order to test the robustness of the proposed detection method and to demonstrate that the method is not subject-specific and can efficiently work with the data sets of different subjects. The turning was detected when the identified class of the tested point changed from walking straight to turning.
2 were employed. Turning behavior was modelled with step responses of the second order transfer function  (1)  with the head parameters ω h = 4. 03 rad/s and ξ h = 1. 17, and the pelvis parameters ω p = 3. 00 rad/s and ξ p = 0. 83. Anticipatory behaviour of the head was modelled by delaying the pelvis turning by averaged anticipatory time calculated from all subjects data (528±385 ms). For our simulation the turning initiation time was set to t = 3 s. To emulate natural pelvis and head oscillations in horizontal plane during straight walking before the turning is initiated a sinusoidal signal was added to the head and the pelvis yaw trajectories: for the pelvis with amplitude 12 • and frequency 0. 74 Hz, and for the head with amplitude 3 • and frequency 0. 60 Hz. These values for amplitude and frequency were obtained as averaged parameters from the fast Fourier transform analysis of the straight walking data of all subjects.
Turning to different angles We have used the simulated turning trajectories as described in subsection 2. 6 with the experimental data based clusters to evaluate the performance of the proposed turning detection method. Fig. 9A  illustrates generated head and pelvis yaw behaviour for turning to 30 • and 60 •. Both trajectories contain straight walking (t ≤3 s) when the head and the pelvis are oscillating in antiphase, as it is observed in the experimental data. Turning initiation was programmed for t=3 s, which was followed by the head and the pelvis turning to the final orientation. The set of 70 trajectories was used to test the turning detection with the proposed and pelvis-based methods. The threshold for pelvis based method was set to 13. 00 • as it corresponded to maximal amplitude of simulated straight walking pelvis movements which is a more conservative testing condition than it was in the experimental tests.
LISA Pathfinder is a technology demonstration mission for the space-based gravitational wave observatory, LISA. It demonstrated that the performance requirements for the interferometric measurement of two test masses in free fall can be met. An important part of the data analysis is to identify the limiting noise sources. [1]  This measurement is performed with heterodyne interferometry. The performance of this optical metrology system (OMS) at high frequencies is limited by sensing noise. One such noise source is Relative Intensity Noise (RIN). RIN is a property of the laser, and the photodiode current generated by the interferometer signal contains frequency dependant RIN. From this electric signal the phasemeter calculates the phase change and laser power, and the coupling of RIN into the measurement signal depends on the noise frequency.
The WHOQOL-100 results were expressed by the scores attributed to each facet and domain. In the descriptive analysis, the categorical data were summarized in absolute and relative frequencies and the numerical data in average, standard deviation (SD) and standard error of the mean (SEM). In the figures, the data were presented as mean ± standard error. The differences between the averages of the quantitative variables (different domains of the QoL questionnaires) were evaluated using the Kruskal-Wallis test, with Dunn post-test, using the program GraphPad prism 5®. Multiple correlation analyses were also carried out to identify the strongest correlation betweeen the domains and the global QOL. The value of the regression parameter indicating significant correlation was defined as> 0. 3. In all tests, significance was set at 5%.
Methods used Extensor carpi ulnaris (ECU) and extensor carpi radialis (ECR) right were measured. A portable sEMG signal analysis system (Noraxon 1-08) and bipolar electrodes (FIAB F3010) were used. Electrodes were placed as recommended by  Zipp (1982). The signals were band-pass filtered in the 10-1000 Hz frequency range, sampled at 2000 Hz and amplified 1000 times. The root-mean-square values were calculated over 100-ms sliding windows in 0. 5-ms steps. Maximum voluntary electrical (MVE) activity was performed for each muscle to standardise the sEMG signals. The right wrist extension (EXT) and abduction-adduction (AA) angles were measured using a goniometer (SG110 biaxial model, Penny and Giles). Data were continuously recorded based on a 200-Hz sampling frequency.
Functional analysis of the genes potentially regulated by HBx We first performed a functional enrichment analysis of the whole genome repertoire of the genes potentially regulated by HBx in HBV replicating HepG2 cells. Figure  1a  shows the categories enriched, respectively, among the KEGG and Reactome Biological Pathways; the Gene Ontology (GO) Biological Processes and the InterPro families, a database that integrates diverse information about protein   S6  families, domains and functional sites. Overall, the results show a significant enrichment in genes involved in cell metabolism, chromatin dynamics and cancer, but also in biological pathways that have been associated with the control of HBV replication [i. e. Ras/Src  [19] , calcium transport, endocytosis]. It is important to underline that, since the HBx ChIP-Seq dataset was generated in HBV replicating HepG2 cells, which are derived from a primary liver tumor, and that an enrichment in genes belonging to the metabolism, chromatin dynamics and cancer pathways is often found in many immortalized and transformed cell lines, a number of the HBx genomic binding sites we found might reflect a bias for transcriptionally active chromatin regions in the "tumor cell" chromatin environment.
Independent anti-HBx ChIP experiments confirmed HBx recruitment, with a 3 to 8-fold enrichment relative to the control IgG ChIPed samples, to the promoter regions of selected genes belonging to different functional categories [Hsa04144: Rab1A, Rab2B, Rab5B; GO:0006357: SREBP2, Beclin1; Has04010: Traf2; Hsa05200: Stat1; IPR013032: Lama3; GO:0045892: Dnmt3B] in HBV replicating HepG2 cells (Fig. 1b ) and in HBV-infected NTCP-HepG2 cells (Additional file 1: Figure  S2a ) and primary human hepatocytes from 2 different donors (Additional file 1: Figure  S2b ). Validated PCR primers for genomic target sites that were negative for anti-HBx ChIP-Seq peak assignment showed no enrichment in the HBx immunoprecipitated chromatin, thus confirming the lack of off-target HBx recruitment and the specificity of the anti-HBx ChIP (Additional file 1: Figure  S3 ).
5c ) in the same region of the miR-224 promoter, that is accompanied by an hypoacetylation of Histone 4 lysines (Fig. 4d ). We also show that exogenously expressed HBx repressed miR-224 promoter in a transient luciferase reporter assay (Fig. 5d ). Altogether these results strongly suggest the recruitment of HBx as part of a transcriptionally inactive NFkB/p65 complex on the miR-224 promoter, which is responsible for the silencing of miR-224 transcription. Next, to confirm that the inhibition of HBV replication by miR-224 is indeed the result of a direct targeting of HBV 3. 5Kb/pgRNA, the 5 putative seed sequences identified by the RNAhybrid software in the HBV genome (Fig. 6a ) were cloned at the 3′UTR of the Renilla Luciferase gene in the pRL-TK vector (Fig. 6b ). Co-transfection of the different Renilla Luciferase constructs together with pre-miR-224 showed a significative inhibition of the Luciferase activity for constructs containing seed 1 and seed 4, thus confirming that HBV is a direct target of miR-224 which inhibits viral replication by directly binding HBV genome on seed 1 and seed 4 (Fig.
ChIP results are expressed as Fold Induction (FI) of the % of Input and the histograms show the mean from three independent experiments; bars indicate SD. b-c miRNA profiles were analyzed by real-time qPCR, and normalized with respect to RNU38, in mock, HBV-wt and HBx-mt replicating HepG2 cells (b) and by Taqman PCR-arrays in HBV infected (12 dpi) PHHs (c). Data represent means ± SD from at least three independent experiments performed in duplicate. d HBx recruitment impacts on H4 histone acetylation of neighboring chromatin promoters. Anti-AcH4 ChIPs were performed and analysed as in a). P-values: * 0,02 ≤ P < 0,05; ** 0,01 ≤ P < 0. 02; *** 0,005 ≤ P < 0. 01 the non-tumor tissues  [43]. Although it would be challenging to obtain a formal demonstration in vivo, it is tempting to speculate that, in the early phases of HBV infection, HBx binding at or near the p65/NFkB sites in the miR-224 promoter leads to the repression of miR-224 expression, that would be detrimental for viral replication, whereas HBx inactivation leaves the miR-224 promoter free to respond to TNF/LT-NFkB signaling in HCCs and highly displastic nodules.
ChIP-Seq data analysis Reads (36 bp) were mapped to the UCSC human genome hg19 using Bowtie (version 0. 12. 9) [48]. Uniquely aligned reads with no mismatches were retained for 2)  [49] , following the protocol described in Feng et al. [50]. Peak-calling was performed against a reference input sample from the same HepG2 cell line. Home-made Perl scripts (www. biocomputing. it; "Tools" section; Scripts related to Guerrieri et al. ) were used to locate HBx candidate binding sites along the genome, taking into account three categories: promoters (10 kb upstream or 1 kb downstream the transcriptional start site (TSS) of genes), intragenic (from 1 kb after TSS up to the transcript end site (TES)) and intergenic. For microRNAs, in particular, the promoter HBx binding sites were located within the range of (-5000; +1000) bps relative to the TSS.
Taqman® array microRNA cards Total RNA is extracted from PHH at different time points after HBv infection with TRIzol® reagent (Life Technologies) according to manufacturer's instructions. The Megaplex™ RT primer human pool A + B v3. 0 (Life Technologies) was used for miRNA-specific reverse transcription. RNA quality and quantity were monitored by ethidium-bromide staining and by UV absorbance. The Megaplex™ RT product was then loaded on Taqman® microRNA cards A + B and run performed with default thermal cycling conditions (Taq-man® array user bulletin -Life Technologies). Data were collected through SDS 2. 3 software and analyzed through RQ Manager 1. 2 program (Life Technologies).
Acknowledgements None Funding This work was supported by grants from: the  Italian Ministry of University and Research (MIUR-FIRB) , the  Italian Ministry of Health  ( Ricerca Finalizzata :  RF 2010-2317822 ), the  CARIPLO Foundation , the  University of Lyon-St Etienne (PALSE PROGRAM) , the  Agence National de la Recherche (ANR@TRAC-TION) , the  Center for Life NanoSciences of the Italian Institute of Technology (CLNS-IIT ) to ML; ANRS to ML and FZ; KAUST [ KUKI1-012-43 ] and  Epigenomics Flagship Project -EPIGEN  to AT;  DevWeCan French Laboratories of Excellence Network (Labex , Grant # ANR-10-LABX-61 ) to FZ; the  Gilead Sciences Research Scholars Program in Liver Diseases  to LB. FG, LL and LB are recipients of research contracts from  IIT.
Cognitive impairment is also associated with poorer outcomes for those on dialysis [6] These are even worse when there is co-existing dementia with a 2-year survival rate of 20% compared with approximately 70% in patients without dementia  [7]. A recent meta-analysis of more than 50000 patients with predialysis CKD showed that these patients have poorer cognition than those with normal renal function  [8]. Transition to dialysis appears to increase the rate of cognitive decline [9]. This mirrors the rapid increase in cardiovascular risk seen once patients start dialysis  [10]. In this issue of the Clinical Science, Angermann and colleagues present thought-provoking data related to cognitive impairment in haemodialysis patients  [11]. In a cross-sectional observational study, they performed cognitive testing in approximately 200 self-selected patients.
Their haemodynamic assessments included measures of arterial stiffness using the gold standard of pulse-wave velocity (PWV). Both epidemiological and clinical data show that damage to large arteries contributes to the increased cardiovascular risk observed in CKD  [12]. Arterial stiffness causes an elevation in central systolic blood pressure, increasing left ventricular workload with the gradual development of left ventricular hypertrophy, and also causes a fall in diastolic blood pressure, impairing coronary blood flow. It is an important independent predictor of all-cause and cardiovascular mortality in patients with CKD and it progresses most rapidly in patients on dialysis [13,14]. In the current study, the authors demonstrate an independent relationship between cognitive impairment and arterial stiffness in this patient group.
SNPs and INDELs selection Most SNPs and indels for testing were selected after a Linkage scan (r = 0. 5) and quality control with Plink version 1. 9  [43]  using whole genome sequencing data. These data were obtained from a merged dataset between the African populations data from the 1000 Genomes Project combined with low fold coverage (8-10x) whole genome shotgun data generated from 230 residents living in regions (DRC, Guinea Conakry, Ivory Coast and Uganda, European Genome Archive A accession number) where trypanosomiasis is endemic  [44]. The 88 SNPs and 8 indels loci were selected by two strategies: 1) by linkage scan of SNPs and indels (r 2 < 0. 5) across the gene; 2) by selection of SNPs and indels with published associations with HAT. Linked SNPs were identified for IL6, IL4, IL8, IFNG and HLA-G genes.
Genotyping Samples which had low DNA concentration or did not satisfy the quality control criteria were excluded prior to genotyping. Genotyping was performed by two commercial service providers: 1) "Plateforme Genome Transcriptome" at INRA of Bordeaux in France; 2) LGC Genomics Hoddesden, UK with approximately 1μg of genomic DNA per sample. At INRA, genotyping was carried out with a Multiplex design (two sets of 40 SNPs or indels) using Assay Design Suite v2. 0 (Agena Biosciences). For each SNP and indel, the genotyping was done with the iPLEX Gold genotyping kit (Agena Biosciences) for the Mass-Array iPLEX genotyping assay according to the manufacturer's instructions. Products were detected on a Mass-Array mass spectrophotometer and data were obtained in real time with Mass-Array RT software (Agena Biosciences).
SNP clustering and validation was carried out with Typer 4. 0 software (Agena Biosciences). A summary of the candidate genes, and SNPs and indels is shown in the supplementary data S1 Table. Some SNPs and indels that failed genotyping at INRA and some additional SNPs and indels were genotyped at LGC Genomics, Hoddesden, UK where SNPs and indels were genotyped using the PCR based KASP assay  [45].
Analysis This was a case-control study where no familial controls were collected during sampling. The raw genotypic data were converted to PLINK format and quality control (QC) procedures implemented using the PLINK v1. 9 package  [43]. The Spearman Chi-square test was used to compare frequencies of observed and expected genotypes under Hardy-Weinberg equilibrium (HWE) and LD using R/Rstudio version 3. 3. 2 (2016-10-31)-'Sincere Pumpkin Patch' and Plink  [43]. After quality control and filtering, poorly performing SNP loci with missing genotypes (!10) and samples with missing loci (!4) were removed. In addition, all loci with a MAF below 1% or a HWE P value < 1 × 10 -4 were removed. SNP in linkage with adjacent SNP (r > 0. 5) were also pruned. These filters are as described by Anderson et al.