dataesr/scientific-paragraphs-categorization huggingface dataset:
Non-negative matrix factorisation We propose non-negative matrix factorisation (NMF) as a model for data with overlapping blinking sources. NMF finds an approximate factorisation of the dataset d(x,t) into a set of K individual non-negative PSFs w k (x) with corresponding non-negative intensity time traces h k (t): d(x,t) ≈ K ∑ k=1 w k (x)h k (t), (1) where x and t correspond to the spatial (pixel index) and time (frame index) coordinates, respectively. The matrix form of Eq. (  1 ) D D D ≈ W W W H H H, (2) restricted to matrices with non-negative entries, shows the matrix factorisation explicitly. In Eq. (  2 ) T frames of the dataset d(x,t) were reshaped into a N × T matrix D D D and the individual sources w k (x) and intensities h k (t) are reshaped into columns and rows of matrices W W W (N × K) and H H H (K × T ), respectively.
N denotes the total number of pixels in each frame. The non-negativity is a strong natural constraint for both the PSFs and the intensity time profiles. An important assumption is the reappearance of the sources in different time frames throughout the dataset. This creates the redundancy, which together with the non-negativity constraints allows us to recover both the shape and the intensity sequence for each individual source. These can be different and unique and are inferred directly from the data. Note the difference of NMF to blind deconvolution, where the goal is to estimate one unknown PSF shared by all sources. NMF is illustrated in Fig. 1(a). We used multiplicative updates  [12]  for W W W and H H H to solve Eq. (2) (the symbol " " denotes the element-wise division of matrices): w xk = w xk ∑ T t=1 h kt (D D D W W W H H H)H H H xk h kt = h kt ∑ N x=1 w xk W W W (D D D W W W H H H) kt.
NMF with iterative restarts (iNMF) NMF becomes challenging when applied to large datasets. Besides computational time and memory requirements, local minima further complicate the NMF optimisation  [14]. We address this partly by dividing the data into partially overlapping patches (typically 25×25 pixels with 5 pixels overlap), so that NMF is applied to each patch individually and then stitching the patches back together, blending the border pixels with weights linearly decreasing towards the border. Note that the individual patches are evaluated independently which permits a simple parallelization of the evaluation. We also address this problem via the iterative NMF algorithm described in the next paragraph. Although the Lee and Seung updates are convex with respect to W W W and H H H separately, they are non-convex in both simultaneously  [12].
The corresponding rows {h h h k k k } j k=1 are reused for the first j rows of initial values of H H H. The remaining Kj components are re-initialised from the uniform random distribution. The initial values of W W W for the ( j + 1)th iteration are therefore composed of the j sparsest components of the previous iteration and (Kj) randomly initialised components. The NMF algorithm is then run with this initialisation, updating all K components. The iterative procedure runs until j = K. iNMF represents a "soft" sparsity enhancement, which is in contrast with Hoyer's sparse NMF algorithm  [15] , where the desired sparsity of the components is explicitly enforced. Note that iNMF allows the recovery of the sources each having different sparseness. NMF requires knowledge of the number of sources K in the dataset.
We used a crude estimation of the upper bound on K from the sorted principal values of the data matrix D D D because, as shown on simulated data, iNMF progressively recovers the optimal number of PSFs if K is overestimated  [13]. The redundant components are used for approximation of background noise. Our iNMF algorithm written in MATLAB [16] can be downloaded from  [17]  or can be cloned from github [18].
If not specified otherwise, we left the parameters set to the published code default values. CSSTORM processes each input frame individually, independent of the rest of the dataset. This method tries to recover a sparse distribution of the active (ON) fluorophores in each frame considering a known PSF (shared by all sources). The output for each frame is an image showing the possible positions of the sources on a sub-pixel grid (8 times oversampled). For AP estimation we processed the sum of all CSSTORM output frames, which summarises all the estimated sources. The summed image was filtered with a Gaussian kernel (σ = 0. 8 pixel, which corresponds to σ = 8 nm). In the evaluation of the simulated data of randomly scattered sources we identified the local maxima stronger than 5% of the global maximum (green crosses in Figs.
Only the maxima above a threshold were considered for evaluation (green crosses in Figs. 3(b)-3(c) ). The threshold was set individually for each image, such that the number of local maxima roughly corresponds to the number of sources considered by the 3B analysis after the last iteration. The bSOFI evaluation was performed by Marcel Leutenegger and Stefan Geissbuehler using the SOFI software package. Following the bSOFI authors standard procedure, the data sequence for bSOFI evaluation in Fig. 4  were divided into two shorter subsequences each having half number of frames. We show the average of the evaluation of the two subsequences of the fourth order bSOFI image. For comparison shown in Fig. 3  the positions of the local maxima (stronger then 5% of the global maximum) were used as the estimates of the sources locations (green crosses in Figs.
We further tested the performance of the methods on a real biological specimen -tubulin fibres of a of HEp-2 cell imuno-labelled with QDs (QD625, Invitrogen) with emission peak  For our computer (Intel ® Core™2 Duo @ 2GHz processor with 3GB of RAM) the computational time for the simulated dataset (10 3 frames with 21 × 21 pixels 2 ) was: • iNMF: ∼ 20 mins for K = 50 sources and K restarts. • CSSTORM: ∼ 260 mins. • bSOFI: ∼ 1 min. • 3B: ∼ 12 hours for 30 iterations. Note that the iNMF results in Fig. 4  are averages of 10 iNMF evaluation. The computation time is therefore comparable (∼ 200 mins) to CSSTORM. To demonstrate that iNMF can also be used with standard fluorophores we analysed images of tubulin structures labelled with Alexa 647 (500 frames with 40ms/frame acquisition time). The reconstructed image is shown in Fig.
The number of sources was set to K = 22. We used "tanh" as the nonlinearity option in the fixed-point algorithm. The blue pixels indicate the negative values in the estimated sources. Most of the estimated components clearly combine several overlapping PSFs together. The overall quality is inferior to the iNMF results which can be reproduced with the iNMF software containing corresponding LM data. The shape of each PSF can be used for axial localisation of the source, using a technique based on cross-correlation with simulated PSFs  [19]  (see upper right inset of Fig. 7 (a)). Fig. 7(a)  shows a reconstruction of a neurone with neurotransmitter subunits labelled with QDs acquired on a standard wide-field microscope. The estimated axial position is colour-coded. Only the sources where the correlation index with the simulated PSF exceeds 0.
Acknowledgments We thank  Stefan Geissbuehler  and  Marcel Leutenegger  for providing the bSOFI algorithm and help with the bSOFI evaluation and  David Baddeley  and  Anja Huss  for providing us with data of three dimensional samples. We thank  Susan Cox ,  Martin Kielhorn  and  Kai Wicker  for interesting discussions. This work was supported in part by the grants  EP/F500385/1  and  B-B/F529254/1  to the  University of Edinburgh School of Informatics Doctoral Training Centre in Neuroinformatics and Computational Neuroscience  (www. anc. ac. uk/dtc) from the  UK Engineering and Physical Sciences Research Council (EPSRC) ,  UK Biotechnology and Biological Sciences Research Council (BBSRC) , and the  UK Medical Research Council (MRC).
The packed bed concept has been studied using Computation Fluid Dynamics (CFD) software tools. However, the flow regime is complex and it is necessary to carry out prototype measurements in order to test the concept and gain confidence in the design. An experimental program is planned using an induction heater power supply to generate heating of the individual spheres. A short but representative length of the target proposed will be produced for the tests. A well instrumented helium flow loop will be constructed to cool this target test piece. The individual target spheres and container walls will be instrumented in order to measure both the effect of the heating and the cooling efficacy and make comparisons with the CFD model.
The dominating limestone and dolomite rock in the 2 km broad and 10 km long syncline of the Garpenberg mine, oriented in the north-east direction, is not suitable for the excavation of the very large MEMPHYS type of detector caverns. However, the syncline is surrounded by granite which is of sufficient strength and homogeneity to allow the safe construction of large caverns. Bore holes have been drilled from the mine at 880 m depth out to the granite zone and the mean value of the uniaxial compressive strength of the granite bore core samples collected has been measured to be 206 MPa. The rock stress at 880 m depth has previously been measured. The main component of the stress is horizontal and directed to the north-west. Its magnitude is about 40 MPa. On our request Garpen Mining Consultant Inc has evaluated the geological and geotechnical data of the surroundings of the Northen Garpenberg mine.
Acknowledgements We are indebted to the  European Spallation Source AB  for the support we have received. We wish to thank engineering staff at the  Garpenberg and Zinkgruvan mines in Sweden  for valuable technical advice and information regarding these respective mines. This work has been partially supported by the  U. S. Department of Energy  under award number  DE-SC0003915. P. Coloma would also like to thank  R. Patterson  and  R. Rameika  for providing the NOvA simulation details, and  M. Bass ,  M. Bishai  and  E. Worcester  for providing simulation details for LBNE. E. Fernandez Martinez  acknowledges financial support by the  European Union  through the  FP7 Marie Curie Actions CIG NeuProbes  ( PCIG11-GA-2012-321582 ) and the  ITN INVISIBLES  ( PITN-GA-2011-289442 ), and the  Spanish MINECO  through the  "Ramón y Cajal" programme  ( RYC2011-07710 ), through the project  FPA2009-09017  and through the " Centro de Excelencia Severo Ochoa" programme  under grant  SEV-2012-0249.
The metabolic network of a cell represents the catabolic and anabolic reactions that interconvert small molecules (metabolites) through the activity of enzymes, transporters and non-catalyzed chemical reactions. Our understanding of individual metabolic networks is increasing as we learn more about the enzymes that are active in particular cells under particular conditions and as technologies advance to allow detailed measurements of the cellu-lar metabolome. Metabolic network databases are of increasing importance in allowing us to contextualise data sets emerging from transcriptomic, proteomic and metabolomic experiments. Here we present a dynamic database, TrypanoCyc (http: //www. metexplore. fr/trypanocyc/), which describes the generic and condition-specific metabolic network of Trypanosoma brucei, a parasitic protozoan responsible for human and animal African trypanosomiasis.
Global untargeted molecular profiling data sets (e. g. trancriptomics, proteomics and metabolomics data) are now being generated for trypanosomes and the effects of life cycle, environmental perturbation, specific genetic manipulation and drug action are being dissected in a systematic manner  (5). Interpreting and integrating these data to allow biological inference and hypothesis generation is a major challenge. Metabolic network-based methods offer a means to contextualise and integrate data to help inference of biological function  (6). Reliable, comprehensive databases collating information on metabolic networks and pathways are therefore crucial to optimize understanding derived from postgenomic data sets. Metabolic databases such as Leish-Cyc (7) (for Leishmania), and the Library of Apicomplexan Metabolic Pathways (8) (for apicomplexan parasites) are among the few examples where this information is available in parasitology.
Creation of a metabolic network database is achieved by gathering information on all of the metabolic transformations an organism can perform  (9). A first outline of this information is generally retrieved from genomic orthology. Genes coding for enzymes are identified through sequence similarity searches and then, using enzyme activity information, metabolic reactions catalyzed by these enzymes are added to the network database. Several automatic and semiautomatic tools are available to perform these genomebased metabolic network reconstructions  (10) (11) (12) (13). In spite of their undoubted utility, genome-based reconstruction has limitations since it is based primarily on sequence homology comparisons between the organism of interest and databases encompassing information from a multitude of organisms  (14).
Incorrect annotations readily propagate across databases (15). Moreover, evolution works through modification of function following alteration of genes encoding proteins. For instance, trypanosomes use N 1 ,N 8 bis-glutathionyl spermidine (trypanothione)  (16)  as a key cellular redox-associated metabolite. Trypanothione is retained in its reduced form by the enzyme trypanothione reductase (EC 1. 8. 1. 12). This enzyme is evolutionar-ily derived from glutathione reductase (EC 1. 8. 1. 7), with which it shows great homology. In the absence of accompanying biochemical evidence, genome annotations would simply predict trypanosomes as possessing a glutathione reductase, and metabolic reconstructions would assume trypanosomes employ canonical glutathione-based redox balancing. Cases like this highlight the necessity of refining genome-based metabolic reconstructions by incorporating advanced biochemical knowledge (15).
A HIGHLY CURATED DATABASE OF T. BRUCEI METABOLISM The T. brucei TREU 927 genome is 26 Mb in size, with a karyotype of 11 megabase chromosomes  (22)  and containing a predicted 9068 protein-coding genes. In a collaborative project between the International Trypanotolerance Centre in The Gambia and the Sanger Institute, the genome sequence was processed using the Pathologic metabolic network reconstruction tool of Pathway Tools  (23) , creating a Pathway/Genome Database (PGDB) where gaps (called 'pathway holes') in the predicted metabolic pathways were filled by hypothetical reactions, even without an obvious gene association. The result of this first automatic reconstruction was the starting point of the current TrypanoCyc database. An international consortium of investigators, expert in various aspects of trypanosome metabolism, was assembled to produce a highly annotated TrypanoCyc database.
As recommended by Thiele & Palsson  (24)  we started the Try-panoCyc initiative in 2012 with a two-day 'jamboree'. Each expert was offered a specific set of pathway(s) in his/her area of expertise to curate. A dedicated web interface, called TrypAnnot (a password protected part of the website available to annotators, not described here) stores submitted annotations in a curation database, making it possible to track all annotations, which are automatically taken from T. brucei cells contain multiple membrane-bounded organelles, including the mitochondrion and an unusual peroxisome-related organelle, the glycosome  (25, 26) , in which the first seven steps of glycolysis occur, as well as a series of other pathways  (19). Annotators, therefore, specify the sub-cellular localization of reactions, if known, in the annotation interface.
The reactions EC 4. 2. 1. 109 (methylthioribulose 1-phosphate dehydratase) and EC 3. 1. 3. 77 (5-(methylthio)-2,3-dioxopentyl-phosphate phosphohydrolase), required to complete the pathway, are included in the database, but assigned negative scores to highlight that they are undetectable in spite of previous predictions in the literature  (27). We consider it useful to keep such entries such that users of the database can find explicit reference to these reactions they might seek upon reading literature pertaining to these reactions. Since metabolic databases focus mainly on pathways and seldom consider sub-cellular compartments, they usually lack information on intracellular transport reactions. Currently, TrypanoCyc contains only 35 such reactions. This is because we did not incorporate transport reactions into our annotation platform and because experimental knowledge on intracellular transport processes is still sparse.
However, the dynamic nature of TrypanoCyc means additional anno-tation and incorporation of measured and probable transport reactions (e. g. taken from existing manually curated metabolic models of the closely related organism Leishmania major (29)) will form part of the iterative process of database refinement. We also perform gap filling in each compartment using graph approaches and testing metabolic scenarios as suggested (9,10) and successfully implemented for other organisms  (30). Many additional databases provide information that can complement metabolic network databases. Linking to these other data sources enhances our ability to learn about an organism's metabolism. TrypanoCyc, therefore, links to multiple databases including BRENDA  (31, 32) , expasy. org (33) , ExplorEnz (34), Pubmed and UniProt  (35).
The Trit-rypDB database  (36)  is the central resource for trypanosomatid genomes and associated functional genomics data, while GeneDB houses the sequence information gathered and annotated through the Wellcome Trust Sanger Institute  (37). For each gene, TrypanoCyc offers a direct link to the corresponding TritrypDB and GeneDB pages. The BioCyc library is a collection of 3563 PGDBs. Based on the quality of the PGDBs and the level of manual curation, this central repository classifies them into Tier 1 (highly curated), Tier 2 (moderately curated) and Tier 3 (non-curated) categories. Prior to the release of BioCyc v18. 1, only 6 PGDBs (EcoCyc  (38) , MetaCyc  (14) , Human-Cyc  (39) , AraCyc  (40) , YeastCyc and LeishCyc (  7 )) were published in the Tier 1 category. Due to the quality of information being made available on TrypanoCyc, it was included in BioCyc's Tier 1 category with the release of Bio-Cyc v18.
Browsing TrypanoCyc content and expert annotations As a Pathway Tools-based website, TrypanoCyc provides a dedicated web page for each metabolic network entity (pathways, reactions, metabolites, enzymes, proteins and genes). The reaction page architecture was, however, modified in order to allow additional annotation information. These include the annotation confidence score (Figure  1d ), stage specificity and compartmentation with links to key literature (Figure  1e ). A comment box is also included, containing detailed free-text information on the reaction. Search requests on database content can be made through a quick search box found at the top right-hand corner of the interface page, as well as through the advanced search options available from the menu bar. Each pathway representation is available with different levels of detail, the simplest view displaying only the reactions and metabolites while the detailed view displays all available information including the molecular structure of all metabolites involved.
Mining stage specific metabolism using cellular overview To exemplify the integration of molecular profiling data in the TrypanoCyc database we used published results from a Stable Isotope Labelling of Amino acids in Cell culture experiment, comparing protein levels in bloodstream form (BSF) and procyclic form (PCF) trypanosomes  (41). The data set contains 3552 gene IDs along with their relative protein levels in the two tested stages of T. brucei (expressed as log PCF/BSF values). A TrypanoCyc cellular overview shows enzymes that differ in abundance between the two life cycle forms (Figure  2 ; for step by step instructions see Supplementary Data S1). Mapping other molecular profiling data in TrypanoCyc can be achieved using the Pathway Tools Omics Viewer  (42) , which displays all pathways in a single representation.
Data sets can be loaded using the options listed on the right-hand side of the page (Supplementary Material S2 is a version of this data set in an Omics Viewer-compliant format). Figure  2a  shows an image of the overview after loading the proteomics data of  (41). Individual reactions can be viewed by moving the mouse over them and clicking the link in the pop-up dialog box. This opens the related reaction page containing the annotation table, giving access to specific TrypanoCyc annotators' comments about the enzyme and its activity as well as the generic information pertaining to that reaction in the MetaCyc database. For example, the overlaid data clearly show that the respiratory chain is upregulated in procyclic stages. Browsing the reaction page of any of those up-regulated proteins shows additional information from the annotators.
Using MetExplore to create user-defined sub-networks from TrypanoCyc To complement the classical pathway-oriented BioCyc representation of data, we also offer a novel way to visualize the content of TrypanoCyc via our MetExplore web server  (21)  (for step by step instructions see Supplementary Data S3). Each pathway page contains a hyperlink (Figure  3a ), that opens MetExplore with the selected pathway (Figure  3b ). Importantly, the MetExplore viewer takes into account the localization of reactions. For example, Figure  3b  shows how the glycolytic pathway is divided into two compartments (glycosome and cytosol represented by green and red boxes, respectively). Another advantage of MetExplore is that it provides a tabular representation of compartments, pathways, reactions, enzymes, genes and metabolites in the database.
It is also possible to filter these tables by compartments, pathways or reactions. For instance, by filtering simultaneously on the pentose phosphate pathway, TCA cycle, succinate shunt and glycolysis, only reactions and metabolites related to these pathways are displayed in their respective tables (Figure  3c ). The user can also add reactions of interest to a 'cart' (red box on Figure  3d ). It is then possible to visualize the content of this cart in the network representation. From Figure  3d , it is evident that the network perspective is much more effective in representing compartments and transport reactions. Furthermore, the glycosome (green box) and cytosol (red box) are demonstrably connected by a reaction involved in the succinate shunt (marked by a red arrow on Figure  3d ). For a more flexible representation MetExplore also offers a downloadable version of the Cytoscape visualization software  (43) , pre-loaded with the cart content.
CONCLUSION Since 2012, TrypanoCyc has been under extensive curation with the help of the scientific community and is now counted among the seven Tier 1 databases within the Bio-Cyc repository. Collaborative annotations help in improving the quality of the database by reducing errors, reducing the workload for individual annotators and also providing inferences from multiple perspectives given the various types of experts in the community. T. brucei metabolic plasticity allows the parasite to adapt to divergent nutritional environments offered by different hosts. For drug target identification, for example, focusing on enzymes and metabolic pathways expressed in the parasite-stages that are replicative in the mammalian host is critical. TrypanoCyc is the first comprehensive metabolic network database for parasites including stage specificity as a key component of the collected data.