dataesr/scientific-paragraphs-categorization huggingface dataset:
To our knowledge, miR-940 had never been reported as differentially express in breast cancer. This absence might be explained by the fact that miR-940 is never one of the most differentially expressed microRNA in these datasets even though its expression trend is consistent. Here, we demonstrated for the first time its capacity to modulate cell cytoskeleton and reduce RPE1 cell migration and invasion and showed that its expression is reduced in breast cancer (Figure  5 ). Also in agreement with our results, it was recently shown that miR-612 exerts an inhibitory effect on hepatocellular carcinoma, proliferation, migration, invasion, and metastasis. Moreover, miR-612 appears to be involved in both the initial and final steps of the metastatic cascade by suppressing local invasion and distant colonization  51.
Methods Target prediction datasets. The flat file of DIANA-microT version 3. 0 (July 2009)  13  was downloaded from the web site http://diana. cslab. ece. ntua. gr/microT/. The database consists of genome-wide computationally predicted associations between microRNAs and their predicted targets in ensemble id format. Only Homo sapiens gene-microRNA information was considered. Scores and multiple binding sites were not taken into account (miTG score. 0), so that the lowest possible level of false negative prediction was considered. Under this restriction, there are 555 microRNAs that are predicted to regulate 18,986 different genes. The DIANA-microT dataset comprised our main dataset for network building. To compare the network outcome with that of another prediction algorithm, TargetScan v6.
We considered for this construction 551 nodes and 2,911 edges, as for the meet/min 0. 5 network. The second graph was a scale-free graph based on the Barabasi-Albert model  24  considering 551 nodes; at the end of the construction, the network will have scale-free properties as defined by Barabasi and Albert. The final graph is a real network of a human protein-protein interaction map based on yeast two-hybrid  25. The graphs were built using the package igraph  55  from the R statistical environment  56  and visualised using Cytoscape with the unweighted spring embedded layout  57. Assorted club deciphering. First, the hubs of the network were sorted out according to the degree of each node. We then looked at the induced subgraph formed by the i th first sorted hubs (beginning with the first two hubs).
Ultimately, 11 hubs were considered, defining two ''assorted clubs''  27  : the first comprising 8 microRNAs, and the second comprising 3 microRNAs. The 12 th hub was isolated from the 11 first hubs. Ontology enrichment calculation for microRNA clusters. The ontology enrichments were calculated using the R package topGO  30. All levels of gene ontology (GO) were considered: biological process (BP), molecular function (MF) and cellular component (CC). For BP terms, only those annotated for less than 5,000 genes and more than 10 genes were considered. To calculate the enrichment, Ensembl ids were used with DIANA-microT v3 predictions, and Entrez ids were used with TargetScan v6. All genes predicted by the respective algorithms were used as background genes for the two enrichment analyses. The ontology enrichment in terms of biological processes, molecular function and cellular component categories was built on genes that were shared by at least 50% of the group of microRNAs (e.
1% Triton X-100 in cytoskeleton buffer pH 6. 1 and fixed in 4% paraformaldehyde in cytoskeleton buffer for 15 min at room temperature. They were then rinsed twice with PBS and incubated in 0. 1 M ammonium chloride in PBS for 10 min. Cells were then blocked with 3% BSA in PBS Ca21 Mg21 for 30 min. Images were taken with an upright microscope (BX61; Olympus, Tokyo, Japan). Fluorescent images of myosin and actin staining are maximal projections of different aligned cells acquired with oil immersion objectives at 1003 (NA 5 1. 4) mounted on a piezo ceramic (Physics Instruments, Lederhosen, Germany). Five individual and typically representative images can be found in Supplementary Figure  S13. The microscope was controlled with Metamorph software (MDS Analytical Technologies, Toronto, Canada). The images were processed using ImageJ software  60.
30). The quantification of the number of cells that reached the lower side of the insert was performed either semi-automatically using ImageJ software  60  or manually. Four independent experiments with varying initial numbers of deposited cells (to better emphasise either the increase or decrease in the number of cells passing through the holes) were conducted. Each condition (miR-612, miR-661, miR-940, and siRNA-AllStars) was represented in triplicate, leading to 30 observations per experiment and per condition (Supplementary Figure  S14 ). To pool the four experiments together, each condition of each experiment was normalised to the median number of cells in the siRNA-AllStars condition (median siRNA{AllStarsx ), considering all replicates: Ñx,y,t ~log10 N x,r,t median siRNA{AllStarsx ð2Þ where N is the number of counted cells and N ˜the normalised number of cells for each condition, replicate and experiment; x is the experiment (1, 2, 3 or 4), r, the replicate for each experiment (1, 2 or 3), and, the different conditions (siRNA-AllStars or mimics of miR-612, miR-661 or miR-940).
Evolution of the average width of the wounds was finally normalised in relation to the initial wound width and pooled for every transfection condition. Expression data. To assess for microRNA expression in different tissues, samples from 8 different normal tissues were downloaded from GEO. The 8 downloaded datasets comprised GSE19505 (Prefrontal cortex and liver), GSE23527 (Skeletal muscle), GSE24205 (Blood), GSE25508 (Lung), GSE31309 (Breast), GSE34933 (Prostate), and GSE38309 (Colorectal mucosa). The raw data were downloaded and extracted from GEO. Datasets showing negative numbers were adjusted by adding a constant such that the minimum observed value was equal to 1 and then log2 transformed. Finally, a quantile normalization was applied  68. Cytoscape was used with the DIANA-microT network to visualize microRNA expression levels for each dataset separately.
Represented on the networks is the median value of each microRNA expression in control tissues. Min and max observed expression on each dataset were used to define the legend limits with a continuous gradation from white to red. The white limit was set as the median expression on each dataset so that not all nodes would be highlighted. As a consequence, only microRNAs with expression 50% over all miRNA expression(s) in a dataset (moderately to highly expressed on a chip) are coloured. Differences in colour spreading thus reflect the difference in expression score distribution across datasets (Supplementary Figure  S15 ). For the differential expression analysis, the raw data of the three breast cancer microRNA expression datasets were retrieved on Gene Expression Omnibus (GEO)  63  under the ID GSE31309, GSE38867 and GSE44124.
Only datasets with at least 20 samples were considered. As such, GSE31309 comprises 105 samples with 57 healthy controls. GSE38867 is composed of 28 samples with 7 normal tissues, and finally GSE44124 is built of 53 samples with 3 pools of normal tissue. All data were log2 transformed and quantile normalised  64  using either affyPLM package  65  or AgiMicroRna package  66  in R, depending on the data type. After data normalisation, the package Limma 67 was used to calculate P-values for differential expression of miR-940 in breast cancer tissues against normal tissues. As we investigated only one microRNA per dataset, no correction for multiple testing was applied. All calculations in this paper were conducted in the statistical environment R  56. Box-and-whisker plots show the lower and upper quartiles (25-75%) with a line at the.
Crystallization and data collection The frataxin from P. ingrahamii (pFXN) was expressed, purified and crystallized as described in our previous article  [11]. Briefly, crystals were obtained at 24 °C by the hanging drop method, and the drop was a 1:1 mix of protein (17 mg/ml in 20 mM Tris-HCl, 100 mM NaCl, pH 7. 0) and reservoir solution (200 mM sodium acetate, 200 mM MgCl 2 , 27. 5 % polyethylene glycol 4000, pH 4. 8). For soaking experiments, crystals of pFXN were incubated in the crystallization solution supplemented with 200 mM CoCl 2 or 200 mM EuCl 3 , and incubated for 5 h. The X-ray diffraction data were collected at 100 K on the X06DA beamline at the Swiss Light Source (SLS) (Switzerland), using a Pilatus 2 M detector (Dectris Ltd. , Baden, Switzerland). Prior to data collection, crystals were cryoprotected by soaking in 200 mM sodium acetate pH 4.
Structure solution, refinement, and validation The crystal structures were solved by molecular replacement (MR) using the coordinates of pFXN in the "apo form" Protein Data Bank (PDB) entry 4HS5  [11]. In the case of the cobalt-soaked crystal, MR was performed using Phaser  [24]  as implemented in the Phenix suite  [25] , whereas in the case of europium-soaked crystal, MR was obtained using Phaser, but implemented in the MrBump program  [26]  from the CCP4 suite  [27]. Refinement was carried out using REFMAC5  [28]  interspersed with manual model building using Coot  [29]. Metal atoms were identified from anomalous difference Fourier maps. B-factors of protein atoms were treated using TLS refinement (one group per chain). Final refinement was carried out using Phenix. The stereochemical quality of the model was checked using the Molprobity server  [30].
Structural analysis Protein-metal contacts and intermolecular contacts including symmetry-related molecules were calculated with the CONTACT program in the CCP4 suite. Normalized temperature factors (B′) were calculated with the formula B′ = (B -〈B〉)/σ(B), where B is the B-factor (the isotropic equivalent when the protein was solved with TLS refinement), 〈B〉 and σ(B) are the mean and the standard deviation of B-factors, respectively. The NOC program (http:// noch. sourceforge. net/) was used for structure superposition and root mean square deviation (RMSD) calculation. Glu105, which is the last residue, was omitted in all contact calculations because it is not visible in the Eu derivative structure. Model validation from statistical analysis of electron density maps, using the RSZD+, RSZD-and RSZO scores, was performed using the EDSTATS program in the CCP4 suite  [31].
An equivalent UV-visible titration was carried out to correct for the inner filter effect, and the corrected fluorescence intensity at 330 nm (F corr ) was determined from the observed intensity (F obs ) using the equation  [32] : The stoichiometry (p) and apparent dissociation constant (K D ) were obtained from a non-linear regression procedure, using a model of equivalent and independent binding sites to fit to the data  [33]. First to all, the F corr was used to calculate the fractional signal change (Y): where y 0 is the F corr at a given iron concentration, y b and y f are the F corr when the binding sites are fully occupied and unoccupied, respectively. Fractional signal change was related to the iron concentration through the equation: F corr = F obs × 10 (A 295 +A 330 )/2. where L t and P t are the total ligand and protein concentrations, respectively.
Hydrodynamic behavior The aggregation state was investigated by static light scattering using a miniDawn instrument (Wyatt Technology) coupled to a size-exclusion Superose-12 column (GE Healthcare). The elution buffer was 20 mM sodium acetate, 100 mM NaCl, pH 4. 8. Protein concentration was 790 µM and the experiment was carried out at room temperature (~25 °C) and at a 0. 4 ml/min flow rate. Data analysis was performed using the Astra 6. 0 software (Wyatt Technology).
Acknowledgments This work was supported by the  Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT) , the  Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) ,  Universidad de Buenos Aires (UBACyT) , the  CNRS , the  INSERM , the  Université de Strasbourg , the  Région Alsace , the  Hôpital Civil de Strasbourg, Instruct  [part of the  European Strategy Forum of Research Infrastructures (ESFRI) ] and the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01. We specially thank the  IGBMC Structural Genomics Platform  staff (in particular,  Pierre Poussin Courmontagne  and  Dr. Alastair McEwen ). The crystallographic experiments were performed on the X06DA beamline at the  Swiss Light Source ,  Paul Scherrer Institut, Villigen, Switzerland.
2), transmembrane protein 127 (TMEM127; NM_017849. 3), and von Hippel-Lindau gene (VHL; NM_000551. 3)] were analyzed using a combination of next-generation sequencing, Sanger sequencing and MLPA, as previously described  (13, 14). In addition, fumarate hydratase (NM_000143) was studied in a subset of patients. Tissue DNA analysis with PCR and sequencing was carried out according to standard protocols (Applied Biosystems). The sequences were analyzed using Mutation Surveyor (version 4. 0. 6; Softgenetics). In silico analysis of variants was performed using the Polyphen2 (http//:genetics. bwh. harvard-. edu) and ALAMUT 2. 2. 0 (http://www. interactive-biosoft ware. com/) softwares.
Immunohistochemistry Immunostaining for GHRH was performed using GHRH antibody 451-7 (Lyon, France), 1:2000 dilution, as previously described  (16, 17). Pheochromocytomas of patients with the MEN1 mutation were stained for menin using a rabbit polyclonal antimenin antibody (Abcam; ab2605, dilution 1:500), as previously described  (18). Mouse pancreas showing islets and pheochromocytomas of patients without any known germline mutation were used as a positive control. SDHA and SDHB immunostaining was performed using a mouse monoclonal anti-SDHA antibody (2E3GC12FB2AE2, ab147159, dilution 1:200; Abcam) and a rabbit polyclonal anti-SDHB antibody (HPA002867, dilution 1:200; Sigma-Aldrich), as previously described  (19). Further immunostaining was performed using the antimitochondrial antibody 113-1 recognizing a 60-to 65-kDa nonglycosylated membrane protein (Merck Millipore; dilution 1:150) and an antibody directed against the endoplasmic reticulum lectin 1 (ERLEC1; dilution 1:100; Novus Biological).
Acknowledgments We are grateful to  Professor Kalman Kovacs  ( Toronto, Canada ) for the advice on the histological features of the SDH-related pituitary adenoma tissue and to  Michael Weedon  ( Exeter, UK ) for the statistical help. Address all correspondence and requests for reprints to: Professor Márta Korbonits, Department of Endocrinology, Barts and the  London School of Medicine and Dentistry , Charterhouse Square, London  EC1M 6BQ , United Kingdom. E-mail: m. korbonits@qmul. ac. uk. N. D. was supported by a grant from the  Royal Society. Disclosure Summary: S. E. is a  Wellcome Trust Senior Investigator. The other authors have no conflicting interests.
Data collection In Vietnam, dengue cases are detected and reported following the guidelines of Ministry of Health (2011) on surveillance, diagnosis and treatment of dengue. The case definition for suspected dengue infection is based on an acute febrile illness, lasting from 2 to 7 days with a minimum of two clinical symptoms among headache, retro-orbital pain, myalgia, arthralgia, rash, hemorrhagic manifestations, and leucopenia  [11, 12]. A single blood sample of a suspected dengue case may be sent for laboratory confirmation by either serology test (IgM ELISA) or molecular methods  [11]. For all the dengue-notified cases in Cat Ba, we obtained the following routinely collected information by the dengue control program of Vietnam from the Hai Phong Preventive Medical Center: age, gender, occupation, household address, and date of disease onset.
Dengue virus characterization We had access to six positive dengue plasma samples isolated from Cat Ba patients tested by the Department of Virology, National Institute of Hygiene and Epidemiology. Serotypes of the collected samples were verified by Real-Time (Taqman) RT-PCR (Reverse Transcription Polymerase Chain Reaction) at OUCRU research laboratory in Hanoi. There were two samples with sufficient viral RNA (Ct value < 30) to be included for E gene sequencing. The E gene was amplified using 3-fragments conventional PCR according to previously described methods  [9, 13, 14]. Amplified fragments with a DNA concentration of at least 50 ng/ml were shipped to Macrogen Inc. (Seoul, South Korea) for sequencing. Genetic relationships of Cat Ba E gene sequences were compared to DENV3 E gene sequences from GenBank and DENV3 sequences isolated from 249 denguepositive patients admitted to the National Hospital of Tropical Diseases (NHTD) in 2013, Hanoi.
These ER were visualized as a matrix with temporal distances in rows and spatial distances in columns. The ER(s) were then visually translated to a map of clusters of dengue cases in the area. To group geocoded dengue points into the clusters by space and time, we considered a threshold of space-time distance so that the observed number of pairs of dengue cases should be at least two times higher than the average number of pairs of dengue cases occurred randomly in each clusters, namely excess risk should be greater than 2. The statistical analyses were developed in C++ and R using "Rcpp" package  [22]  for R version 3. 0. 2 (R Foundation for Statistical Computing, Vienna, Austria). The source code is available at https://github. com/thanhleviet/knox. Maps were made with ArcMap (ESRI 2011.
Ethics statement This study was considered as a component of the public health response to the dengue outbreak in Cat Ba in accordance with National Dengue Prevention and Control Program, 2010 -2015 and part of the responsibility of the National Institute of Hygiene and Epidemiology. Thus, the requirement for ethical review was waived. We ensured data used for this analysis remained anonymous and that the collected GPS locations of dengue household were sufficiently inaccurate (within 20 m of the actual residence) to ensure that patients could not be identified through GPS location. Dengue positive samples from National Hospital of Tropical Diseases were part of the dengue serotype surveillance, which was approved by National Hospital of Tropical Diseases ethics committee. As the surveillance did not collect patient identifiers, written consent was not required.
The final cluster analysis revealed a total of 10 pairs of 88 (79 %) cases within the selected threshold (Fig. 2b ). The clusters were numbered chronologically using the date of the index cases of each cluster. There were seven clusters  [1, 2, 4-6, 8, 10]  on the mainland of the island, and 3 clusters  [3, 7, 9]  in the floating village. Cluster 3 had the highest numbers of cases (24 patients) and lasted for 31 days. Characteristics of each cluster are described in Table  1. We were able to sequence two DENV3 serum samples (CB01 and CB02). These two sequences were compared with 82 DENV3 sequences isolated from 249 dengue positive patients admitted to NHTD, and 523 sequences downloaded from GenBank. In the first resultant phylogenetic tree, many sequences from GenBank were identical. For clarity, in the final analysis we selected 62 reference sequences having closest genetic distance to our sequences.
Acknowledgements We would like to thank the  Department of Entomology, National Institute of Hygiene and Epidemiology ; staff of  Hai Phong Preventive Medicine Center and Cat Ba Health Center  for their support on collecting dengue cases and mosquito trapping. We also thank  Le Hong Dang , student of  Hanoi Medical University  for his contribution to collecting GPS of dengue cases. MC is supported by the  "Biodiversity and Infectious Diseases" CNRS GDRI. This research is supported by the  Wellcome Trust, UK  and by the  IRD, France  (EID young team).
5 in practice), respectively; and R ¼ 0. 4 mm is the radius of our AFM cantilever tip (see typical fit in Fig. 3 a ). The Hertz fit of the force-displacement curves was obtained using the NANOSCOPE ANALYSIS software (Bruker). Almost all the fits had a coefficient of determination R 2 > 0. 99. The Hertz model assumes isotropy of the material, whereas the cell wall is anisotropic, and so should be considered as an orthotropic material with three moduli and three shear moduli. For instance, it has been shown that the onion epidermis is not isotropic  (26). As discussed in Milani et al. (13) , the modulus that we deduce here is likely to be closer to the smallest modulus, i. e. , the modulus in the direction perpendicular to the cell wall. We note, however, that the estimation of pressure is rather insensitive to the values of Young's modulus and of Poisson's ratio (for instance, changes of 20% in the value of modulus induce changes of 3% in the estimation of pressure).
Apparent stiffness The value of the apparent stiffness is obtained from the slope of the linear part of the force-depth curve. In practice, we used data with a force ranging from 75% to 100% of the maximal force (see Fig. 3 a ). The fit was obtained using the NANOSCOPE ANALYSIS software (Bruker). Almost all the fits had a coefficient of determination R 2 > 0. 99. In order to remove viscouslike behavior (see Results and Discussion), we defined the stiffness, k, as the average of the extension slope and the retraction slope. These slopes are shown in Figs. S2  and S3.
3  b. The mean (k M ) and Gaussian (k G ) curvatures are computed from the partial derivatives of z as 8 > > > > > > > > < > > > > > > > > : k M ðx; yÞ ¼ À z 2 ;x þ 1 z ;yy À 2z ;x z ;y z ;xy þ z 2 ;y þ 1 z ;xx 2 z 2 ;x þ z 2 ;y þ 1 3=2 k G ðx; yÞ ¼ z ;xx z ;yy À z 2 ;xy 1 þ z 2 ;x þ z 2 ;y 2 ; (3) where we used the notation z; a ¼ vz=va. The corresponding PYTHON script (https://www. python. org/psf/) is provided in the Supporting Material.
Discussion of the values of pressure In the following we analyze all data obtained in ultrapure water, i. e. , 41 onion epidermal cells from seven different peels (corresponding to seven different days) and five different onion bulbs. All values of pressure per cell are shown on Fig. 4 b , where cells are grouped by peel. The value of pressure in a given cell was computed from the average of 5-9 measurement locations. The standard deviation of pressure in each cell was low (~0. 01 MPa), confirming that our interpretation of the experiments yields a cellbased feature. We also found that the prediction for pressure is quite insensitive to the values of modulus and thickness, allowing us to use a single value of thickness for all experiments and to neglect the mechanical anisotropy of the cell wall.
ACKNOWLEDGMENTS We thank  Mathieu Colombani  and  Yoe ¨l Forterre  for demonstrating the pressure probe,  Bruno Moulia  for discussions about plasmolysis and osmoregulation in onion,  Ludovic Bellon  for discussions about the AFM calibration, and  Vincent Mirabet  for his help with curvature extraction from AFM topographic data. This work was supported by the  European Research Council  ( Starting Grant  PhyMorph No. 307387  to A. B. ).
However, a more thorough study concerning the optimal choice of the reconstruction method is left as a future work. c) Noisy data: In practice, we deal with data corrupted by noise (e. g. , instrumental, quantization, and channel). In the noisy case, an MPEGx-based approach requires denoising of at least the I and P frames in each GOP, since they are used together with the reconstructed residuals to obtain the rest of the P and B frames. On the other hand, one of the main advantages of CS is its inherent property to act as a denoising process by suppressing the reconstructed nonsparse part of the residual introduced by the noise. Thus, in our CVS system the denoising of only the I-frames should suffice. For the denoising, a doubledensity dual-tree complex DWT thresholding technique was employed 3.