dataesr/scientific-paragraphs-categorization huggingface dataset:
Not only will it allow individual researchers to predict enzyme functions by comparing enzyme-coding genes in newly identified BGCs to a thoroughly documented dataset, it will also facilitate general advances in chemistry predictions. Substrate specificities of PKS acyltransferase domains and NRPS adenylation domains, as well as their evidence codes, will be registered automatically for all gene clusters. This will enable automated updating of the training sets for key chemistry prediction algorithms  19, 20, 21  , which can then be curated by the degree of evidence available, increasing the accuracy of predictions of core peptide and polyketide scaffolds. Also, because groups of genes associated with the biosynthesis of specific chemical moieties (such as sugars and nonproteinogenic amino acids) will be registered consistently, a continuously growing dataset of such sub-clusters will be available to use as a basis for chemical structure predictions.
In addition, MIBiG has the potential to greatly enhance the understanding of secondary metabolite biosynthesis in its ecological and environmental context: the connection of MIBiG to the MIxS standard should stimulate researchers to supply MIxS data on the genome and metagenome sequences that contain the BGCs. This will generate opportunities for a range of analyses, such as the biogeographical mapping of secondary metabolite biosynthesis  22  , thereby identifying locations and ecosystems harboring rich biosynthetic diversity. But even if the contextual data associated with the genome sequences cannot always be made MIxS compliant (perhaps because the origin of a strain can no longer be traced), the MIBiG standard itself provides a comprehensive reference dataset for annotating large-scale MIxS-compliant metagenomic data from projects such as the Earth Microbiome Project  23  , Tara Oceans  24  and Ocean Sampling Day  25.
Community annotation effort To accelerate the usefulness of new MIBiG-compliant data submissions, we initiated this project by annotating a significant portion of the experimental data on the hundreds of BGCs that have been characterized in recent decades. The resulting data will allow immediate contextualization of new submissions (see below) and comparative analysis of any newly characterized BGCs with a rich source of MIBiG-compliant data. Moreover, this annotation effort offered an ideal opportunity to evaluate the MIBiG standard in practice on a diverse range of BGCs. Hence, we carefully mined the literature to obtain a set of 1,170 experimentally characterized gene clusters: 303 PKS, 189 NRPS, 147 hybrid NRPS-PKS, 169 RiPP, 78 terpene, 123 saccharide, 21 alkaloid and 140 other BGCs. Compared to the 288 BGCs currently deposited in ClusterMine36012 and the 103 BGCs deposited in DoBISCUIT  14  , this presents a significant advance in terms of comprehensiveness.
We then annotated each of these 1,170 BGCs with a minimal number of parameters (genomic locus, publications, chemical structure and biosynthetic class and subclass). Subsequently, in a community initiative involving 81 academic research groups and several companies worldwide, we performed a fully MIBiG-compliant reannotation of 405 of these BGCs according to the information available in earlier publications and laboratory archives. (All participants of this annotation effort are either listed as coauthors of this article or mentioned in the Acknowledgments, depending on the size of their contribution. ) An initial visualization of the full data set arising from this reannotation is publicly available online at http://mibig. secondarymetabolites. org. Altogether, these submitted entries will function as a very useful seed dataset for the development of databases on secondary metabolism.
Planned implementation To allow straightforward and user-friendly access, the MIBiG standard will be implemented by multiple databases and web services for genome data and secondary metabolite research. For example, the MIBiG-curated dataset has already been integrated into the antiSMASH tool in the form of a new module  26  that compares any identified BGCs with the full MIBiGcompliant dataset of known BGCs. Moreover, a full-fledged database is currently under development that will be tightly integrated with antiSMASH and will build on the previously published ClusterMine360 framework  12. Additionally, MIBiG-compliant data will be integrated into the recently released Integrated Microbial Genomes Atlas of Biosynthetic Clusters (IMG-ABC) database from the Joint Genome Institute (https://img.
jgi. doe. gov/ ABC/)  27. Regular exchange of data will take place between the MIBiG repository and the IMG-ABC, antiSMASH and ClusterMine databases. Additional cross-links with the chemical databases ChemSpider  28  , chEMBL  29  and chEBI  30  are being developed so that researchers can easily find the full MIBiG annotation of the BGC responsible for the biosynthesis of given molecules. Finally, all community-curated data are freely available and downloadable in JSON format for integration into other software tools or databases, without any need to request permission, as long as the source is acknowledged. For submission of new MIBiG-compliant data by scientists in the field, we prepared an interactive online submission form (available from http://mibig. secondarymetabolites. org), which was extensively tested through the community annotation effort.
Data can also be submitted through the BioSynML plug-in  26  (http://www. biosynml. de) that was recently built for use in the Geneious software. In this way, MIBiG-compliant data can easily be integrated with the in-house BGC content management systems of individual laboratories or companies. Finally, it will be possible to submit updates to existing MIBiG entries based on peer-reviewed articles through dedicated web forms.
Future perspectives The MIBiG coordinating team within the GSC is committed to ensuring the continued support and curation of the MIBiG standard, in cooperation with its partners. Compliance with the standard and interoperability with other standards and databases will also be guaranteed within the GSC. In order to stay relevant and viable, MIBiG is projected to be a 'living' standard: updates will be made as needed to remain technologically and scientifically current. Coordination with relevant journals will be sought to make MIBiG submission of BGCs (evidenced by MIBiG accession codes) a standard item to check during manuscript review. To stimulate submission of MIBiG data during the process of publishing new biosynthetic gene clusters, unique MIBiG accession numbers are provided for each BCG that can be used during article review (including for data embargoed until after publication).
In spring 2013, the MIBiG project proposal was accepted by the board of the GSC to form a new checklist within the MIxS framework. Besides a number of general descriptors, it also includes pathway type-specific packages that function analogously, for different classes of biosynthetic pathways, to the way the MIxS environmental packages do for different environmental origins. An example MIBiG entry, describing the relatively simple hybrid NRPS-PKS biosynthetic gene cluster for isoflaivipuciine/dihydrolsoflavipudne from Aspergillus terreus. Fields without information have been omitted, and some JSON field abbreviations have been modified for clarity. The full entry is available from http://mibig. secondarymetabolites. org/ repository/BGC0001122/BGC0001122. json. The MIBiG data standard and submission system will lead to a continuously growing dataset (stored in the online MIBiG repository) that will be loaded into several databases and web services.
The lower part of the figure shows the threefold potential of MIBiG for the study of BGCs, which will make it possible to (1) systematically connect genes and chemistry by identifying which genes are responsible for the biosynthesis of which chemical moieties; (2) understand the natural genetic diversity of BGCs within their environmental and ecological context, by combining MIBiG-and MlxS-derived metadata sets; and (3) develop an evidence-based parts registry for engineering biosynthetic pathways and gene clusters through synthetic biology.
Acknowledgments M. H. M. was supported by a  Rubicon fellowship of the Netherlands Organization for Scientific Research (NWO ;  Rubicon 825. 13. 001 ). The work of R. K. was supported by the  European Union 's  Seventh Framework Programme  (Joint Call OCEAN. 2011-2: Marine microbial diversity-new insights into marine ecosystems functioning and its biotechnological potential)  under the grant agreement no. 287589  (Micro  B3 ). M. C. was supported by a  Biotechnology and Biological Sciences Research Council (BBSRC)  studentship ( BB/J014478/1 ). The GSC is supported by funding from the  Natural Environment Research Council (UK) , the  National Institute for Energy Ethics and Society (NIEeS; UK) , the  Gordon and Betty Moore Foundation , the  National Science Foundation  (NSF; US) and the  US Department of Energy.
DYNAMIC MODELING OF THE PLANT: THE ARROW ROBOT ARROW (acronym for Accurate and Rapid Robot with large Operational Workspace) is a 4-DOF RA-PKM intended to be used for fast lightweight machining tasks. It has two degrees of actuation redundancy since it is equipped with six linear actuators and has four degrees of freedom. All the actuators are aligned along the same axis and are split into two sets, each set consists of three actuators (cf. Figure  1 ). The actuators are connected to the end affecter through two pairs of simple arms and one pair of parallelograms. As its acronym indicates, the ARROW robot, developed in the framework of the French ANR-ARROW project, has been designed in the aim of creating a very fast and accurate PKM with large workspace and homogeneous performance throughout its workspace.
EXPERIMENTAL VALIDATION OF THE PROPOSED CONTROLLER To show the relevance of the proposed controller, real-time experiments have been conducted on ARROW robot. The experimental setup is illustrated in Figure  3  and the control scheme is developed using Simulink software from Mathworks Inc. The controller is compiled using the Real-Time Workshop toolbox before being loaded on the target PC. The latter is an industrial computer running xPC Target (also from Mathworks Inc. ) in real-time with a sampling frequency of 5 kHz. The moving platform of the robot has to perform several pointto-point displacements and rotations inside the workspace. The estimated parameters vector is initialized to 0 Θ(0) = [0, 0, 0, 0] T. The controller parameters of both controllers (the original L 1 adaptive controller and the augmented one) are summarized in Table  2.
In  [22] , the authors focus on the effect of silver nanoparticles on the immune system by analyzing the interaction between silver nanoparticles with viruses, bacteria, etc. In  [23] , Felicetti et al. provide a software platform, named BiNS2, able to simulate diffusion-based molecular communications inside blood vessels, in order to analyze the interactions of nanoparticles with the blood cells. The same simulator is also exploited in  [24] , where the authors describe a specific communication process happening inside blood vessels, atherogenesis. Another similar paper is  [25] , where a particle-cell hybrid model is developed to model nanoparticle transport, dispersion and binding dynamics in the blood. Finally, the authors in  [26]  focus on the interference aspects of the gold nanoparticles with the macrophage cells studied through a specific approach with microscopy techniques based on electrons and ions.
, 2007). An initial wavelet-based segmentation was performed on the images using a custom-made program provided by J. B. Sibarita (University Bordeaux Segalen, Bordeaux, France;  Izeddin et al. , 2012) , giving very good delineation of the dendritic spine outline, and putting the background to zero. The area covered by the dendritic spine or filopodium during the recording period (up to 15 min) was determined by a threshold on the maximal image projection of the whole image stack over time. Pairs of consecutive frames were subtracted from each other with an added constant (1000) to avoid negative pixel values (Supplemental Figure  S2a ). In these images, pixels with newly appearing structures are white, and disappearing structures are black. Highly motile structures will thus appear as highly contrasted on this type of image.
Actin and cadherin enrichments in filopodia and spines For freely moving dendritic filopodia and filopodia in contact with RFP-expressing axons and spines, actin-GFP images were segmented with a custom program to isolate actin-rich regions in the spine head, and the ratio of fluorescence from this region to the fluorescence in neighboring shaft regions was computed. For the observation of filopodia in contact with Ncad-Fc micropatterns, neurons were visualized on a Leica DM R upright epifluorescence microscope (Leica Microsystems, Wetzlar, Germany) equipped with a 63×/1. 32 NA objective and appropriate filter sets from Chroma (GFP: excitation S490/20 nm, dichroic 86100bs, emission S528/38 nm; tetramethylrhodamine isothiocyanate: excitation S555/28 nm, dichroic 101848, emission S617/73; Cy5: excitation S635/20, dichroic 101848, emission S685/40 nm).
Images were acquired with a CCD camera (HQ CoolSnap; Roper Scientific), using MetaMorph software. The enrichment factors of actin-GFP and RFP at the N-cadherin-coated micropatterns were measured on three randomly chosen dendritic areas of each neuron and calculated using an automatic program written in MetaMorph  (Mondin et al. , 2011). Briefly, the outlines of the patterns from the Cy5 channel corresponding to the micropatterned substrates were washed with NB supplemented with B27 (37°C) to remove unattached cells, and medium was replaced with conditioned medium previously incubated for 4 d in dishes containing a feeder glial cell layer. Neurons were cultured up to 3 wk at 37°C in a 5% CO 2 atmosphere. Neurons were transfected at DIV 7 using lipofection, according to the manufacturer's protocol (Effectene kit; Qiagen, Courtaboeuf, France), using 1 μg of total DNA per micropatterned coverslip or 1 μg per regular dish (four coverslips).
Microscopy observation of fixed cells and subspine distribution of proteins For observation of protein distribution within spines, cells were examined using a SP-5 Leica confocal microscope, using a 100×/1. 40 numerical aperture (NA) objective and a 100-μm pinhole aperture. GFP images were acquired using the 488-nm argon laser line and emission band 500-550 nm, and RFP images were acquired using the 532-nm laser line and emission band 560-600 nm. GFP and RFP images were segmented using a custom macro written in Meta-Morph software (Universal Imaging, Bedford Hills, NY), providing the coordinates of the GFP signals relative to the centroid of the spine obtained from the RFP signal. These values were normalized by the width and length of each spine, respectively. This semiautomatic image analysis performed for hundreds of dendritic spines provided a plot of the relative distributions of each protein within spines.
The output text file (x, y, t) was entered into a custom program run in MetaMorph, allowing the construction of stacks of images representing actin distribution over time  (Czöndör et al. , 2012). A zoom factor determined the pixel size of the reconstructed image (typically 30-100 nm). To measure the enrichment of actin at filopodia/spines, the whole image stack was summed into a single image, and the ratio of intensities in the filopodium/spine versus that in the dendritic shaft was computed. It took ∼100 s for the system to reach steady state, as characterized by a fairly constant number of actin molecules within spines. To model photobleaching, an intensity parameter was set to 0 for actin molecules localized in one of the two filopodia at a given time after steady state (150 s) and kept null thereafter.
ACKNOWLEDGMENTS We acknowledge  A. Bresnick ,  C. Holt ,  A. Matus ,  R. M. Mège , and  R. Tsien  for the generous gifts of reagents. We thank  Z. Karatas ,  C. Breillat ,  A. Frouin ,  D. Bouchet , and  N. Retailleau  for cell culture and  P. Gonzales ,  R. Sterling , and  A. Barbet  for technical assistance. We thank  C. Poujol ,  S. Marais ,  M. Mondin , and  P. Legros  at the  Bordeaux Imaging Center  for help in microscopy and image analysis,  J. B. Sibarita  for his image reconstruction program, and the  Genomic Transcriptomic Facility of Bordeaux  for DNA sequencing. The research leading to these results has received funding from the  Centre National de la Recherche Scientifique , the  Agence Nationale pour la Recherche  (grants Neuroligation, Synapse-2Dt, and Preciput), the  Conseil Régional Aquitaine , and the  Fondation pour la Recherche Médicale.
1 D , asterisks mark the location of the hotspots on kymographs, and numbered arrows point to some examples of actin trails). Quantitative data from these experiments are shown in Fig. 2. F-actin hotspots were distributed at 3. 6 ± 1. 8-µm intervals along axons (see Materials and methods for details) and lasted for an mean of ∼30 s (Fig. 2 A ). Note that the frequencies of actin trails (Fig. 2 B ) are orders of magnitude greater than the "actin waves" previously described in younger neurons  (Flynn et al. , 2009). Moreover, actin trails are not accompanied by axonal morphological fluctuations (as determined by simultaneous imaging of GFP:Utr-CH and soluble mRFP; see additional data linked to this article on the JCB DataViewer), and unlike actin waves, are independent of microtubules (see next section).
5  A′ shows an example). Next, we designed quantitative algorithms in MATLAB to determine the overlap between F-actin hotspots and stationary endosomes (see Mate-rials and methods for details). Data from these analyses suggest that many F-actin hotspots colocalize with stationary axonal endosomes (Fig. 5  C, left). There is some innate variability in the frequencies of F-actin trails from axon to axon (Fig. 2 B ; also seen with other cargoes, see  Tang et al. , 2013). We found that frequency of F-actin trails correlated with the number of stationary axonal endosomes (Fig. 5  C, right), further suggesting links between endosomes and actin dynamics. If actin trails were indeed dependent on vesicles, conditions attenuating axonal vesicles would also be expected to diminish actin trails. To test this, we incubated neurons with Brefeldin-A (BFA), a drug that blocks endoplasmic reticulum to Golgi transport and attenuates vesicle transport in axons (Fig.
Single-and dual-color live imaging of axons and synapses Live-imaging experiments with GFP:Utr-CH and LifeAct:mTurquoise-2/GFP were performed using an inverted epifluorescence microscope (Eclipse Ti-E; Nikon) equipped with CFI S Fluor VC 40× oil (NA 1. 30; Nikon) and CFI Plan Apochromat VC 100× oil  (NA 1. 40; Nikon)  objectives. An electron-multiplying charge-coupled device camera (QuantEM:512SC; Photometrics) and LED illuminator (SPECTRA X; Lumencor) were used for all image acquisition. The system was controlled by Elements software (NIS Elements AR 4. 13. 00 64 bit; Nikon). Immediately before live imaging, neurons were transferred to a Hibernate-E-Low Fluorescence media (Brainbits, LLC), supplemented with 2% B27, 2 mM GlutaMAX, 0. 4% d-glucose, and 37. 5 mM NaCl (live imaging media) and maintained between 35.
Exposures for both the green and red channels were set at 400 ms (20% LED power). For imaging en passant boutons, 9-12 DIV neurons were cotransfected with synaptophysin:dsRed and GFP:Utr-CH, and boutons were identified based on size of the synaptic vesicle cluster and morphology  (Wang et al. , 2014). Boutons along the primary axon, >150 µm from the soma were selected for imaging. Additionally, axons selected for imaging had at least two or more en passant boutons in proximity. Mitochondrial dynamics along the axons was imaged at 3-s intervals (500ms exposure) for a duration of 5 min as described earlier  (Tang et al. , 2012 (Tang et al. , , 2013)). Synaptophysin and NPYss vesicle dynamics along axons were imaged at 5 frames/s for 1 min. All video files were converted to 16 bit TIFF files in NIS Elements software.
Kymographs of these 16 bit TIFF files were generated using the Kymograph function in MetaMorph Image Analysis software (Version 7. 7, Molecular Devices, LLC). The brightness/contrast of kymographs was adjusted in MetaMorph (Molecular Devices). No other adjustments were made to any images unless otherwise mentioned. EGFP, mCherry, mRFP, dsRed, and mTurquoise fluorochromes were used for all live-imaging experiments.
After locating a suitable neuron using low-intensity illumination, epifluorescence images were acquired in both the green and far-red channels, following the axon up to ∼120 µm from the cell body. For single-color imaging, the sample was continuously illuminated at 647 nm (full power) and a series of 60,000-100,000 images (256 × 256 pixels, 15-ms exposure time) were acquired with an astigmatic lens into the optical path to determine the Z position of fluorophores  (Huang et al. , 2008). The N-STORM software was used for the localization of single fluorophore activations. After filtering localizations with more than 900 photons, the list of localizations was exported as a text file. Image reconstructions were performed using the ThunderSTORM ImageJ plugin (National Institutes of Health;  Ovesný et al.
, 2014)  in Fiji software  (Schindelin et al. , 2012). Custom scripts and macros were used to translate localization files from N-STORM to ThunderSTORM formats, as well as automate images reconstruction for whole images, detailed zooms and XZ transverse projections. Measurement of the spacing periodicity was performed by fitting multiple 1-µm axon stretches (n = 111) with a sinusoid, its period corresponding to an individual spacing measurement. The distribution of all spacing values were in turn fitted with a Gaussian curve, and the mean and standard deviation of this Gaussian fit are reported as the spacing measurement in the text.
Image analysis and colocalization algorithms To analyze GFP:Utr-CH kinetics, lines were drawn using the Meta-Morph Image Analysis Software on the kymograph starting from the origin of appearance of the polymer (actin trail) and ending where the polymer stops elongating. Hotspots were identified by intensity peaks in kymographs from maximum-projection images. To quantify the overlap of F-actin hotspots with stationary endosomes, corresponding kymographs from each axon (GFP:Utr-CH or pHRodo/Rab5/Lamp1) were generated. Vertical lines were drawn manually in MetaMorph onto corresponding kymograph pairs to designate F-actin hotspots (GFP:Utr-CH kymograph) and stationary endosomes (pHRodo/Rab5/ Lamp1 kymograph). Each stationary event had an x value and a range of y values corresponding to the spatial location and the duration of the events, respectively.
The x and y values were used as inputs for a custom MATLAB script, written to determine the percentage of overlap between F-actin hotspots and stationary endosomes. Each event in the control (GFP:Utr-CH kymograph) was compared with all events in the condition (pHRodo/Rab5/Lamp1 kymograph) by a double pass overlap test. The y values were first tested and only those events with 50% or more overlap between the control and condition groups were considered for the x overlap analysis. During the x overlap test, x values from the condition which fell within x ± 4 µm of the control were counted as positive. Only events which passed both these criteria were considered as positive. Percentage of overlap was generated using the number of events that overlapped between the control and condition kymographs, over the total number of events in the control kymograph.
Video 2 shows the transient F-actin dynamics in the circumference of an en passant bouton marked by the synaptic marker synaptophysin. Online supplemental material is available at http://www. jcb. org/cgi/content/ full/jcb. 201506110/DC1. Additional data are available in the JCB DataViewer at http://dx. doi. org/10. 1083/jcb. 201506110. dv.