dataesr/scientific-paragraphs-categorization huggingface dataset:
NMR titration experiments contain rich information on the thermodynamic, kinetic and structural aspects of protein-ligand interactions. Automated tools are required to process the large number of signals typically acquired in these experiments and facilitate quantitative interpretations. We present Interact, a Python script accessible within the Bruker BioSpin TopSpin TM software, which allows automated analysis of both 1D and 2D NMR titration experiments. Interact performs peak picking and annotation of the successive spectra and supports quantitative interpretation of changes in chemical shifts and linewidths induced by the ligand (e. g. to estimate dissociation constants) through different fitting procedures. Interact can be applied to all types of 1D and 2D NMR experiments and all nuclei, hence facilitating routine analysis of existing and forthcoming NMR titration data.
, 2008; Günther, 2002; Mittag et al. , 2003) , and only a few Matlab scripts are available for detailed analysis of particular titration experiments  (Günther, 2002; Kovrigin, 2012; Waudby, 2016). There is thus the place for a generic and flexible tool to process and analyze NMR titration experiments, preferably implemented in Bruker BioSpin TopSpin TM -the main acquisition software used by the NMR community -to provide an end-to-end solution in a unified framework. Here, we present Interact, a Python script accessible within TopSpin, which includes the following features: • Automated peak picking and annotation of successive 1D or 2D spectra. Signals can be processed individually (based on the spectral range of the currently displayed spectrum), or in batch (from a text file defining the spectral range of the signals of interest).
Plots are generated for visual inspection of fitted and experimental spectra. Processing results are stored in tabulated text files. Data integration. Interact integrates the results obtained for each signal in the different experiments to extract additional information on the protein-ligand interaction. Changes of chemical shifts (dωF1, dωF2, and the corresponding Euclidian distance E, Fig. 1 ) and linewidths (dσF1 and dσF2) induced by the different ligand concentrations are measured in every spectrum. These parameters are exploited further to infer structural and thermodynamic information via fitting procedures. For each signal, the dissociation constant (Kd) is estimated using a twostate interaction model described by the following function (Williamson, 2013): ( ) 2 max 4 2 P L Kd P L Kd P L E E P + + - + + -⋅ ⋅ = ⋅ ⋅ where E is the euclidean distance of the chemical shift caused by the ligand at concentration L, E max is the maximal euclidean distance (i.
Processing results are stored in tabulated text files. Implementation. This workflow has been implemented in Python programming language (http://python. org). Peak picking and annotation are performed by a TopSpin Python module, and data extraction, fitting and visualization are performed by external Python scripts. This enables seamless usage of Interact on Windows, MacOS and Linux, the three platforms supporting TopSpin. The source code is distributed under OpenSource license at http://github. com/MetaSys-LISBP/Interact.
Example Interact has been used to investigate the interaction between a short heparin oligosaccharide (degree of polymerization dp4) and the TauF4 fragment  (Fauquant et al. , 2011)  to better understand how the polyanion can drive the aggregation of Tau into fibers resembling the filaments that characterize patients with Alzheimer's disease  (Pérez et al. , 1996). Briefly, 7 samples were prepared with a fixed concentration of TauF4 (100 µM) and different concentration of the dp4 heparin fragment (varied from 0 to 800 µM). 2D TROSY NMR spectra were acquired and pre-processed in TopSpin. Using Interact, the entire dataset was automatically processed -including annotation, peak picking and fitting of a total of 259 signals (37 signals on each of the 7 spectra), and data integration -in less than 2 hours on a personal computer.
Methyl cyanide (CH 3 CN) and propyne (CH 3 CCH) are two molecules commonly used as gas thermometers for interstellar gas. They are detected in several astrophysical environments and in particular towards protostars. Using data of the low-mass protostar IRAS 16293-2422 obtained with the IRAM 30m single-dish telescope, we constrained the origin of these two molecules in the envelope of the source. The line shape comparison and the results of a radiative transfer analysis both indicate that the emission of CH 3 CN arises from a warmer and inner region of the envelope than the CH 3 CCH emission. We compare the observational results with the predictions of a gas-grain chemical model. Our model predicts a peak abundance of CH 3 CCH in the gas-phase in the outer part of the envelope, at around 2000 au from the central star, which is relatively close to the emission size derived from the observations.
g. ,  Mauersberger et al. 1991 ). Both molecules have also been detected in dense and cold cores (e. g. ,  Vastel et al. 2014; Gratier et al. 2016) , while CH 3 CN was also found towards a protoplanetary disk  ( Öberg et al. 2015). In this study, we focus on the study of these two complex molecules towards the solar-type protostar IRAS 16293-2422 (hereafter IRAS16293), using data obtained with the IRAM-30m telescope. This deeply embedded source, located in the ρ Ophiuchi cloud, is a binary composed of source A (South-East) and source B (North-West), which are separated by about 5 , i. e. about 750 au at a distance of about 141 pc  (Ortiz-León et al. 2017; Dzib et al. 2018). This object is at a very early stage of the star formation process (Class 0,  André et al. 1993 ) and has been characterized by the presence of numerous complex organic molecules in the warm inner regions of the two components, where the icy grain mantles thermally desorb  (Cazaux et al.
Line properties of CH 3 CN and CH 3 CCH In Table  1 , we provide some line properties. The spectroscopic data, extracted from the CDMS database  (Müller et al. 2005) , are from  Müller et al. (2015)  for CH 3 CN and  Cazzoli & Puzzarini (2008)  for CH 3 CCH. We used the CLASS software, from the GILDAS package 1 , to reduce and analyse the data. Gaussian fits were made to the detected lines following a local low (typically 0th) order polynomial baseline subtraction. Table  1  shows the results of these fits for the 5 observed lines of CH 3 CN and 8 observed lines of CH 3 CCH. Three CH 3 CCH observed lines are below the detection level and we report the 3σ detection limit in Table  1. For CH 3 CCH, the mean LSR velocity is 3. 7 km s -1 and the mean FWHM is 2. 3 km s -1 while for CH 3 CN, the LSR velocity is 3.
(2010)  (see their Fig. 7 ). Crimier et al. (2010)  have determined the density and temperature profiles (at large spatial scale) of the IRAS16293 protostellar envelope using continuum data (single dish and interferometric, from millimeter to MIR) and ISO water observations. They showed that the densities remain higher than 10 6 cm -3 even at a distance of 2000 au from the two central objects. This is higher than the typical critical densities for the observed transitions of CH 3 CN that can be computed using the available collisional coefficients from  Green (1986) , that cover a range between 8 × 10 4 cm -3 and 5 × 10 5 cm -3 at 60K. The LTE approximation should remain valid for our study. Collisional coefficients for CH 3 CCH are not known. The sampling of the posterior distribution function was carried out using the No-U-Turns (NUTS) Hamiltonian Monte Carlo sampler implemented in the Stan Probabilistic Programming Language  (Carpenter et al.
ACKNOWLEDGEMENTS VW's research is funded by an  ERC   Starting Grant  ( 3DICE , grant agreement  336474 ). The authors acknowledge the  CNRS program  " Physique et Chimie du Milieu Interstellaire " (PCMI) co-funded by the  Centre National d'Etudes Spatiales (CNES). LM also acknowledges the support from the  NASA   postdoctoral program. A portion of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration.
The sHBZ and usHBZ mRNAs that encode for the HBZ isoforms, sHBZ and usHBZ, have entirely different 5 untranslated regions (5 UTRs). The HBZ isoforms are 95% identical and differ only by seven amino acids at their N-terminus. Both HBZ isoforms exert similar but not identical roles in HTLV-1 infectivity, persistence and oncogenesis  (2, 3). HBZ transcripts have been detected in ATL cell lines and in primary cells harvested from patients with acute ATL; however, the sHBZ protein is more abundant in cells than the usHBZ  (4) (5) (6) (7) (8). A study shows that the expression of the sHBZ mRNA is higher (4-fold) than that of the usHBZ mRNA in HTLV-1 infected cells recovered from ATL patients  (7). These results contrast with another dataset showing that the level of both the sHBZ and usHBZ transcripts are similar in ATL cell lines and in primary cells harvested from ATL-patients (4).
Plasmids and template DNA The dl EMCV  (13, 14) , dl HTLV-1 IRES  (15) , dl HCV 1b  (14)  (herein referred to as dl HCV IRES), dl PV IRES  (13)  and dl HIV-1 IRES (13) were previously described. The RLuc  (16)  and FLuc  (17)  (herein referred to as Glo-FLuc) vectors were kindly provided by Dr Ricardo Soto-Rifo (Universidad de Chile, Chile). Plasmids expressing the functional wild-type (genotype A16) or an inactive mutant human rhinoviruses (HRV) 2A protease  (18, 19) , herein referred to as p2A-wt and p2A-mut were generously supplied by Dr Ann C. Palmenberg and Dr Kelly Watters (University of Wisconsin-Madison, USA). The DNA sequence that corresponds to the 5 UTR of the spliced (s) version and the unspliced (us) version of HBZ mRNA were recovered from the HTLV-1 K30 provirus (accession number: L03561), kindly provided by Dr B.
RNA quality analysis was made using the PROSize Data Analysis software. Samples used in the assay had a RNA quality number equal or over 8. When needed, RNA samples were stored at -80 • C. The RT-PCR assay was carried out using the SuperScript™ III One-Step RT-PCR System with Platinum ® Taq DNA polymerase kit (#52122; ThermoFisher Scientific) according to manufacturer's protocol, 5 ng of total RNA, and the primers Pforluc and p2anti as described in  (15, 26). The No-RT/PCR reaction was carried using the same amount of total RNA. Total DNA was extracted from COS-7 cells as described in  (23) , using the E. Z. N. A SQ Blood DNA Kit II (#D0714; Omega Bio-Tek, Inc, GA, USA) according to the manufacturers' protocols, and total DNA (50 ng) was used in each PCR reaction with primers Pforluc and p2anti and GoTaq ® Green Master mix (#M7122; Promega), according to the manufacturers' protocol, as described in  (26).
The resulting cDNAs were separated by capillary electrophoresis (Beckman Coulter, Ceq8000). Data were analyzed using the software QuSHAPE  (35). RNA probing was performed in triplicate for each RNA preparations. Significant SHAPE value differences in presence or absence of Mg 2+ were defined as in  (22). HTLV-1 sequences were recovered from the HTLV-1 database (http: //htlv1db. bahia. fiocruz. br/) and manually aligned using bioEdit (http://www. mbio. ncsu. edu/BioEdit/page2. html). 1M7 and NMIA reactivity maps and sequence alignment were used to model RNA secondary structure using RNAalifold  (36)  following the workflow described in  (37). RNA secondary structure was drawn using VaRNA  (38).
Data were expressed as relative luciferase activities (RLA). Similar to our in vitro findings, the FLuc activity driven from the sHBZ 5 UTR (set to 100%) was much higher (∼8-fold) than from the usHBZ 5 UTR (Figure  2A ). Importantly, equivalent levels of RLuc activity suggested that variations in FLuc activity were not attributable to differences in transfection efficiency (Figure  2A ). Comparable results were also obtained from transfecting in vitro transcribed capped and polyadenylated musFLuc or msFLuc mRNAs into COS-7 cells together with a capped and polyadenylated RLuc mRNA (Figure  2B ). The levels of in vitro transcribed RNA recovered from transfected cells were equivalent for both the musFLuc and msFLuc transfected COS-7 cells (Figure  2C ). Together, these findings indicate that the 5 UTR of the sHBZ mRNA promotes more efficient translation initiation than the 5 UTR of the usHBZ mRNA in vitro and in vivo.
Our results showed that differential expression of the sHBZ and usHBZ isoforms was regulated at the level of translation initiation (Figures  1  and 2 ). Translation from the 5 UTR of the usHBZ mRNA was inhibited by a yet undefined mechanism. Initially, we considered that the presence of multiple uAUGs present within the 5 UTR of the usHBZ mRNA could be responsible for inhibiting translation initiation (9); however, inhibition of protein synthesis was only marginally relieved by mutating the uAUGs present within the 5 UTR, never reaching the strength of the sHBZ 5 UTR (Figure  3 ). Further studies are required to fully understand the mechanisms regulating translation initiation mediated by the usHBZ 5 UTR. Nonetheless, from our data we can conclude that 40S ribosome recruitment is mediated by a cap-dependent mechanism and that recognition of the usHBZ initiation codon requires ribosome scanning (Figures  4, 5  and 6 ).
ACKNOWLEDGEMENTS We are grateful to  Dr B. Barbeau ,  A. C. Palmenberg ,  K. Watters ,  I. Brierley  and  R. Soto-Rifo  for kindly sharing plasmids and reagents. CJC and NC conducted this work as part of their PhD thesis,  Programa de Doctorado en Ciencias menci ón Microbiología, Facultad de Ciencias ,  Universidad de Chile.
Fifteen images were captured of each 30. 7×30. 7 µm field of view, five phase-shifted images per angle at each of three interference pattern angles. A full raw dual color image stack was acquired in 17s. Live-cell fluorescence microscopy over the cell cycle was achieved by performing time-lapse imaging. 3D SIM snapshots were captured at 5 min or 10 min (for fosfomycin-treated cells) time intervals to follow dynamics while minimizing photo-bleaching of the sample during the image acquisition. Multiple fields of view were imaged sequentially at each time point, allowing following up to 200 cells per experiment. Superresolved SIM images were reconstructed by the Nikon NIS-Elements software.
Analysis of cell shape dynamics The super-resolved SIM images were processed via a custom-made software package called sDADA (Shape Dynamics Automated Data Analysis). sDADA generates scatter plots, histograms and violin plots in order to study key parameters controlling the cell size and homeostasis, such as: elongation rate, constriction duration, length at birth, onset, onset time. sDADA extracts these parameters from the analysis of the cell shape dynamics thanks to semi-interactive modules for image segmentation, edge detection, cell filtering, cell tracking and statistical analysis (See Detailed image analysis workflow). The MATLAB-based software package is available together with its documentation upon request. For the E. coli data, the septum is much more vertical than in C. crescentus, therefore it is no longer possible to robustly measure the early and late constriction rates with sDaDa, moreover, these cell could not be synchronized.
Drift correction To simplify the tracking of cells over time, the time-lapse videos were drift-corrected using the Fiji plugin "Descriptor based registration (2d/3d + t)"  (Preibisch et al. , 2010). The dual-color experiment, registration of both channels was performed based on the drift observed in the red-channel (561nm). The description of the main features of the sDaDa program and of the general steps in a typical pipeline (Figure  S1 ) are presented below. The main pipeline stages of the software are (1) image segmentation and first edge detection guess, (2) cell tracking, (3) edge detection refinement and shape parameters measurement, (4) divisome ring identification (5) shape parameters correlation analysis and outputs display. More specifically, the segmentation step (1) is based on two processes: the first is based on the Otsu's thresholding method  (Otsu, 1979)  to distinguish background pixels from foreground one; the second group together a set of pixels by seeing which pixels are connected to each other.
The Otsu's method sets a proper threshold via maximizing the inter-class variance of the bi-modal histogram of the pixel intensities (foreground pixels and background pixels). Connected pixels that have a signal value higher than the threshold are tagged with the same number and identify as part of a cell only if they form an area bigger than 0. 5 µm2. We used two built-in MATLAB functions (graythreshold() and bwlabel()) for Otsu's image thresholding and to identify each individual cell. A microbeTracker function named model2mesh. m defines the first cell contour guess starting from the edge detection performed with buit-in MATLAB functions bwperim()and bwtraceboundary()). The model2mesh function retours two semi-contours, corresponding to the 'left' and 'right' sides of the cell. From the two semi-contours, the bacteria poles (the two farthest apart points on the contour) and the centerline (the average of the two half contour parts) can be easily identified.
Within the segmentation stage, the cell shape search, control and refinement is done thanks to an interactive tool. The user has several tools inspired by the MicrobeTracker  (Sliusarenko et al. , 2011)  approach to manipulate the region, such as removing parts, joining two regions, smoothing, expanding. The user can also choose to delete the current time point of the cell or to mark the cell as divided, after which it will no longer be followed. In order to track the same bacteria in successive frames (step (2)), the program performs a search based on a spatial analysis. The area and the barycenter position belonging to a cell in one frame is compared with the spatial distribution of pixels belonging to the possible corresponding cell in the following frame. The two regions correspond to the same cell if the difference between these two spatial distributions is lower than a user-defined tolerance parameter.
The user can generate a scatter plot for each possible couple of parameters combination (e. g. the scatter plots in Figure  3 ). Moreover, the user can generate a violin plot for each parameter to inspect its statistical distribution over the entire population (e. g. the violin plots in Figure  2 ). To conclude the program computes the correlation coefficient of each couple of parameters. Analysis of cell poles was performed using the Celltool software package (  (Pincus and Theriot, 2007) ; http://zplab. wustl. edu/celltool/). Image-derived cell shapes were converted into parametric spline curves  (Pincus and Theriot, 2007) , and centerlines were fit to each cell shape (as described in  (Sycuro et al. , 2010) ), again using Celltool. Cell poles were defined as the position on the cell outline closest to the ends of the centerline, and the curvature at that position was calculated from the first and second derivatives of the parametric spline x(t), y(t): curvature = (x′y″ -y′x″)/( x′ 2 + y′ 2 ) 3/2 , where prime and double-prime represent the first and second derivatives, respectively.
ACKNOWLEDGMENTS The authors would like to thank  Jan Skotheim  and  Devon Chandler-Brown  for critical reading of the manuscript,  Thierry Laroche  from the  EPFLBioimaging and Optics Core Facility  ( EPFL-BIOP ) for support in imaging, Manley lab members and Collier lab members for helpful discussions, and the  Thanbichler lab and Christine Jacobs Wagner Lab  for plasmid gifts. This work was supported by the  Swiss National Science Foundation  Project Grant  CR32I3_166330  (S. M. ),  European Research Council Starting  Grant  243016  (S. M. ), and  Marie Curie Intra-European Fellowship   PIEF-GA-2011-297918  (S. H. ).
DATA AND SOFTWARE AVAILABILITY All data and software used to support the results of this manuscript are available from the Lead Contact upon reasonable request. Original data is available on Zenodo, DOI: 10. 5281/zenodo. 1248441 and 10. 5281/zenodo. 1241005. Software is available on Zenodo, DOI: 10. 5281/zenodo. 1173751 and on github: https://github. com/LEB-EPFL/sDaDa. DATA AND SOFTWARE AVAILABILITY All data and software used to support the results of this manuscript are available from the Lead Contact upon reasonable request. Original data is available on Zenodo, DOI: 10. 5281/zenodo. 1248441 and 10. 5281/zenodo. 1241005. Software is available on Zenodo, DOI: 10. 5281/zenodo. 1173751 and on github: https://github. com/LEB-EPFL/sDaDa.
The Submatrix Selection Singular Value Decomposition (SMSSVD) method produces a denoised signal decomposition from a given data matrix. The SMSSVD method guarantees orthogonality between signal components in a straightforward manner and it is designed to make automation possible. We illustrate SMSSVD by applying it to several real and synthetic datasets and compare its performance to golden standard methods like PCA (Principal Component Analysis) and SPC (Sparse Principal Components, using Lasso constraints). The SMSSVD is computationally efficient and despite being a parameter-free method, in general, outperforms existing statistical learning methods. A Julia implementation of SMSSVD is openly available on GitHub (https://github. com/rasmushenningsson/SMSSVD. jl).
Introduction High throughput biomedical measurements, by design, normally capture multiple overlaid biologically relevant signals, but often also signals representing different types of biological and technical artefacts like e. g. batch effects. There exist different methods aimed at signal reconstruction and deconvolution of the resulting high dimensional and complex datasets, but these methods almost always contain parameters that need to be estimated or present other types of ad hoc features. Developed specifically for Omics data and more particularly gene expression data such methods include the gene shaving method  [6] , tree harvesting  [5] , supervised principal components  [1]  and amplified marginal eigenvector regression  [3]. They employ widely different strategies do deal with the ubiquitous P N (many more variables than samples) problem in omics data.
Our first observation for the theoretical foundation of SMSSVD is that the SVD of a linear map restricted to a hyperplane (linear subspace) share many properties with the SVD of the corresponding unrestricted linear map. Using this we show that, by iteratively choosing orthogonal hyperplanes based on criteria for optimal variable selection and concatenating the decompositions, we can construct a denoised decomposition of the data matrix. The SMSSVD method guarantees orthogonality between components in a straightforward manner and coincide with the SVD if no variable selection is applied. We illustrate the SMSSVD by applying it to several real and synthetic datasets and compare its performance to golden standard methods for unsupervised exploratory analysis: Classical PCA (Principal Component Analysis)  [7]  and the lasso or elastic net based methods like SPC (Sparse Principal Components)  [13].
Gene Expression Data Three Gene Expression data sets, two openly available with microarray data and one based on RNA-Seq available upon request from the original authors, were analyzed. Gene expression microarray profiles from a study of breast cancer  [2]  was previously used to evaluate SPC  [13] , but in contrast to their analysis, we use all 118 samples and all 22215 genes. Each sample was labeled as one of five breast cancer subtypes: 'basal-like', 'luminal A', 'luminal B', 'ERBB2', and 'normal breast-like'. In a study of pediatric Acute Lymphoblastic Leukemia (ALL), gene expression profiles were measured for 132 diagnostic samples  [12]. The samples were labeled by prognostic leukemia subtype ('TEL-AML1', 'BCR-ABL', 'MLL', 'Hyperdiploid (¿50)', 'E2A-PBX1', 'T-ALL' and 'Other'). Our final data set is from another pediatric ALL study, where gene expression profiling was done from RNA-Seq data for 195 samples  [9].