dataesr/scientific-paragraphs-categorization huggingface dataset:
1. The major steps of correlated cryo light and electron microscopy using two independent correlation markers. The process begins with sample preparation (step 1a) and vitrification by rapid plunge-freezing (step 1b). The grids are subsequently imaged by light and fluorescence cryo microscopy (cryoFM) (step 2a) in different channels. Each set of FM images is merged according to the accurate positions of TetraSpeck microspheres (step 2b). The correlation with cryoEM data starts with a rough alignment (step 3a). Then, blue FluoSpheres are used as reference points to assign new coordinates to the EM images (step 3b). Finally, the location of the feature of interest is calculated and tomograms are acquired. smaller blue FluoSpheres. Using algorithms developed for locating point-like fluorescent signals, it was possible to identify and determine the position of each TetraSpeck microsphere in the different channels (Fig.
Multi channel cryoFM image alignment We implemented a procedure for fluorescent channel alignment in MATLAB. All steps were fully or half automated for an efficient workflow, and accessible via a graphical user interface. The initial step of the alignment and correlation procedure was to load the fluorescent images of the different channels into MATLAB. In most cases, it was convenient for further handling to crop the field of view to the region of interest (e. g. the part of a cell that is flat enough for EM). In those cases, care should be taken to include a sufficient number of alignment markers of each kind, i. e. TetraSpeck microspheres and blue FluoSpheres (see above). In a second step, the shift between the different channels due to thermal or mechanical instabilities of the cryo sample stage, but also those induced by optical imperfections of the light microscope (e.
Using cryoFM as a guideline for EM acquisition In this section of the workflow (Fig. 1  step 3a), the feature of interest previously identified in LM is traced through a series of EM images of increasing magnification towards a final high magnification 2D projection image (as described in most CLEM methods). Importantly, automated EM data acquisition over large areas was used in order to (i) limit the electron dose, (ii) provide confidence in the correlation, and (iii) screen several regions of interest rapidly. As part of the optimised workflow, the autogrid-mounted EM grids were transferred to our 'Polara' electron microscope in dedicated sample cartridges that allow the fitting of autogrid-mounted EM grids  [18]. In order to re-locate previously selected grid squares, the entire grid was analysed using low magnification montage  For each set of images, the shift between red/blue and green/blue fluorescence images was measured and corrected according to TetraSpeck microspheres of 200 nm diameter.
The alignment precision s A is given in nm. Data corresponding to the maximum, minimum and average values were measured on 8 data sets recorded on plunge-frozen cells infected with Ad5-488 viruses and 8 data sets recorded on a plunge-frozen microsphere sample. A total of 3 to 20 TetraSpeck microspheres were used per image for the calculations. (LMM) imaging of the EM grid as available in SerialEM  [31]. The cell of interest is located by the alphanumeric identifier marks of the finder grid (Fig. 4 , arrow in A1 and A2). Next, a third or half of the cell is imaged via a higher magnification montage (HMM) in the same fashion as the LMM (Fig. 4, B1 ). HMM maps typically covered an area of 80 mm Â 80 mm. Due to the beam rotations and alignment errors inherent when switching between low and higher magnification ranges in the EM, careful attention must be paid at this step.
Post cryoEM acquisition fiducial based correlation To correlate between the cryoLM and EM images, we used a previously described procedure implemented in MATLAB  [24, 25]. Based on the Control Point Selection Tool of MATLAB, the same FluoSpheres are selected in both images. These are used for the linear conformal transformation between the two coordinate systems. Then, the precise positions of the events of interest identified in the fluorescence image can be plotted into the EM image. To determine the error of the correlation precision, we used a modified version of the correlation procedure described previously  [24, 25] , omitting the correction for the sample deformation between LM and EM required for plastic embedded specimens. Briefly, the 100 nm FluoSphere fiducials were used as reference points to assign new coordinates to EM images.
5C. Tomography helped to unveil the structure and identity of the targeted feature, which at the level of the EM projection image had remained ambiguous due to the overlay of the virus with numerous cellular structures. The tomogram slice shown Measurements were performed on four data sets acquired on a plunge-frozen EM-grid with only FluoSpheres and TetraSpeck microspheres (sample 1). Sample 2 additionally contained plunge-frozen cells infected with Ad5-488 viruses. 4 to 10 FluoSpheres of 100 nm were picked manually on each image and used as fiducials for coordinate transformations. The correlation precision s C is calculated as described by Kukulski et al. [23]  (see Section 2). Three cryoLM and cryoEM data sets were acquired on a vitrified cellular sample infected with Ad5-488 viruses. TetraSpeck microspheres of 200 nm diameter were used to measure the alignment precision s A between green and blue images.
Acknowledgements We thank  Jürgen Plitzko  and the correlative team at the  MPI of Biochemistry  for sharing experience and supporting the cryostage 2 hardware;  Martin Schorb  and  John Briggs  for sharing their coordinate transformation software. We thank  Cathy Whittle  for help in tissue culture. This work was supported by the  Human Frontiers Science Programme (HFSP ;  RGY0079/2009-C  to K. G. ), a  Wellcome Trust Senior Research Fellowship  (to K. G. ), a  Wellcome Trust   JIF award  ( 060208/Z/00/Z ) and a  Wellcome Trust  equipment grant ( 093305/Z/10/Z ) to the  Oxford Particle Imaging Centre , the  Wellcome Trust  core award  090532/Z/09/Z  to the  Wellcome Trust Centre for Human Genetics ,  Wellcome Trust  grant  091911  to the  Micron Advanced Bioimaging Unit ,  European Molecular Biology Organisation  grant  ALTF 642-2011  to P.
Our characterization however suggests us how to adopt a two-phase approach (Produceand-Choose, or PAC) in which we first produce many valid bundles, and then we choose k among them. For the first phase we devise two ad-hoc clustering algorithms, while for the second phase we adapt heuristics with approximation guarantees. We also devise another approach which is based on first finding a k-clustering and then selecting a valid bundle from each of the produced clusters (Cluster-and-Pick, or CAP). We compare experimentally the proposed methods on a large real-world database of user-generated restaurant reviews from Yahoo! Local, exploring their performance under a variety of settings. Our experiments show that when diversity is highly important, CAP is the best option, while when diversity is less important, a PAC approach constructing bundles around randomly chosen pivots, is better.
Following this observation, in Section 4 we devise two ad-hoc clustering algorithms: the first one based on constrained hierarchical clustering, and the second one inspired by k-nn clustering. In Section 5 we introduce a different approach which is based on first finding a k-clustering and then selecting a valid bundle from each of the produced clusters and in Section 6 we present an integer programming formulation of our problem, which may be used to obtain the exact solution. In Section 7 we compare experimentally the proposed methods on a large database of user-generated restaurant reviews from Yahoo! Local. We assess both quality and efficiency. We show that our heuristics produce good results according to our optimization objective, comparing them with the results of the LP implementation within a Branch-and-Cut framework.
Algorithm 2 provides the pseudocode for the Choose phase of our method. It receives in input a set of valid bundles according to Definition 1. This Algorithm 2 ChooseBundles Input: k, γ, and the bundle weighted graph G = (V, E) where ∀S ∈ V : ω(S) = ∑ u,v∈S s(u, v), and ∀(S i , S j ) ∈ E : ψ(S i , S j ) = 1-max u∈Si,v∈Sj s(u, v). Output: A set S of k valid bundles. 1: Define w(u, v) = γ 2(k-1) (ω(u) + ω(v)) + (1 - γ)ψ(u, v). 2: S ← V 3: while S > k do 4: u ← argmin [u∈S] ∑ v∈S w(u, v); 5: Remove u from S 6: return S set of bundles is represented as a complete weighted graph, where nodes are bundles. Nodes and edges are weighted with intra-bundle cohesion and inter-bundle separation respectively. Given this graph and the parameters γ and k, this is an instance of MAXIMUM EDGE-NODE SUBGRAPH that can be reduced to an instance of MAXIMUM EDGE SUBGRAPH by setting the edges' weight as in line 1.
Experimental framework Dataset: Our experiments are conducted on a database extracted from a recent sample of Yahoo! Local 2 containing user-contributed restaurant reviews. We picked all cities for which there are at least 100 restaurants with reviews. This left us with 38,530 restaurants distributed over 149 US cities, with the largest cities being New York (over 2,000 restaurants) and Los Angeles (over 1,000 restaurants). Most cities in our sample have between 300 and 500 restaurants. Each restaurant has one of the following 3 average meal prices: cheap ($10), moderate ($20) or expensive ($30). Each restaurant has multiple values for cuisine, drawn from 291 possible cuisine values, including American, Italian, Cajun, Omelets, Contemporary, Sandwiches, etc. The restaurant with the highest number of cuisine types has 34 cuisines ranging from Californian to Spanish.
RELATED WORK Composite Retrieval: The notion of composite retrieval was proposed with different semantics in recent work  [7] ,  [3] ,  [18] ,  [9] ,  [22]. CARD  [7]  is a framework for finding top-k recommendations of packages of products or services. A language similar to SQL is proposed to specify user requirements an the combination of atomic costs. Recommended packages are of fixed size. In  [3] , the problem is to query documents and build packages formed by multiple entities such as a city, a hotel or an airline. A package in that case is of fixed size (as a simple case of our notion of budget). In  [18] , the authors explore retrieving bundles of items in the form of a star (e. g. , an iPhone and all its accessories). A bundle is subject to budget and item compatibility constraints (e.
Diversity: Our work is related to result diversification in Web search, database queries, and recommendations. Diversifying Web search results and recommendations aims to achieve a compromise between relevance and result heterogeneity. In  [15] , the authors adopt an axiomatic approach to diversity that aims to address user intent. They show that no diversification function can satisfy all axioms together and illustrate that with concrete examples. In  [2] , taxonomies are used to sample search results in order to reduce homogeneity. In the database context, Chen and Li  [8]  propose to post-process structured query results, organizing them in a decision tree for easier navigation. In  [20] , a hierarchical notion of diversity in databases is introduced, and efficient top-k processing algorithms are developed.
CONCLUSIONS AND FUTURE WORK In many complex search and browsing applications returning item bundles is more appropriate than ranked lists. In this article, we present composite retrieval as an alternative to ranked lists and formalize the problem as finding the k highest scoring item bundles. Our formulation ensures complementarity among items in the bundles and diversity between retrieved bundles. We show that the problem is NP-hard and explore various heuristics, validating them on Yahoo! Local, a real-world database of user-generated restaurant reviews. In our future work, we plan to explore personalized composite retrieval (e. g. , retrieve item bundles that are most compatible with my interests, find the best item bundles including a specific item, find the best bundle compatible with a given item).
In order to explore such enzyme capabilities for pathway design, present metabolic, enzyme and reaction databases like MetaCyc  (15) , KEGG  (16) , BRENDA  (17) , CHEBi  (18)  and RHEA  (19)  can provide a wealth of information. Online tools, such as PathPred  (20) , can predict plausible pathways both for biodegradation and biosynthesis of secondary metabolites from a set of reaction rules based on the KEGG RPAIR database (based on RDM patterns)  (21). The authors proposed a score for each pathway as the average of the individual scores of each reaction given by the similarity between the query compound atoms and the matched compound atoms in the RDM patterns, which are a collection of chemical structure transformations generated for each reaction. However, RDM reaction rules are limited to biosynthesis of secondary metabolites and aspects such as toxicity or yields are not taken into account.
Metabolic Tinker  (22) , in turn, is a web server for designing pathways between two given compounds. Metabolic tinker is based on the CHEBi and RHEA databases for pathway feasibility estimation by using thermodynamics. However, a main limitation in Metabolic Tinker is the fact that the user needs to provide the source and the target, which imposes a practical limit on the number of pathways that can be explored online. Another limitation of Metabolic Tinker is that the pathway search is not based on reactions rules but on a graph search of connected compounds expanded by means of a compound similarity threshold. Another similar tool is From Metabolite to Metabolite (FMM)  (23) , although it does not exploit full pathway information, such as thermodynamics. Here we introduce the XTMS web server, an online tool which provides access to the set of available pathways that can be imported into E.
Implementation The server runs under apache on a Linux machine running Ubuntu. The server was written in python by using the Django framework in combination with SQLite 3. It uses MetaCyc version 16. 0 (15) and enzyme sequences were taken from the KEGG database  (16). Chemical format interconversions were computed by using Openbabel  (29). The RetroPath algorithm  (27)  was written in Perl for generation of the Extended Metabolic Space and in C with calls to the efmtool  (35)  for pathway enumeration.
RESULTS AND DISCUSSION A better understanding of the metabolic space and particularly the scope of metabolites that can be synthesized and transformed by enzymes is critical for future development in metabolic engineering and synthetic biology. To that end, the XTMS web server explores the regions in the extended metabolic space that can be reached through metabolic engineering. The computational procedure available at the web server provides a good balance between combinatorial complexity and predictive power from promiscuous reactions. For instance, the extended metabolic space that XTMS provides for diameter d = 12 was generated starting from a set of reaction rules and known metabolites from the MetaCyc database that comprised 6093 metabolites connected through 6078 reactions. After running the algorithm that performs the metabolic extension for 50 iterations, it converged to a total of 27 743 reactions, i.
e. almost five times larger than the size of the initial network. A valuable result from XTMS is the determination of the extent of the chemical space that can be reached through biochemical transformations from the chassis organism. Based on the application of the retrosynthesis principles for scope determination  (7)  and pathway enumeration by using elementary modes  (36) , we found 2182 heterologous compounds in the database that could be connected through metabolic pathways to the E. coli chassis. Concerning pathway enumeration, the number of pathways can substantially change from one diameter of the Extended Metabolic Space to another one. As the diameter d decreases, more pathways can be considered through new reactions. A case that illustrates this situation is the production of glycocholate (glycocholic acid).
In order to obtain this metabolite, three alternative heterologous pathways are proposed: (i) cinnamate 4-hydroxylase (EC 1. 14. 13. 11); (ii) tyrosine ammonia lyase (EC 4. 3. 1. 5); (iii) the two-step conversion hydroxyphenylpyruvate reductase (EC 1. 1. 1. 237) followed by 3-(4-hydroxyphenyl) lactate hydrolase (Table  1 ). We compared the results of XTMS for this example with those from the FMM server. FMM was not able to find routes to produce raspberry ketone from 4coumarate, neither for the target product or for its precursor p-hydroxybenzalacetone. We compared also the pathways found by FMM to produce 4-coumarate from E. coli metabolites, being able to identify both trans-cinnamate and tyrosine, but not the route that starts from hydroxyphenylpyruvate. Therefore, the advantages of using XTMS for this example are several: (i) XTMS was able to identify routes going from endogenous metabolites in the chassis to the target compounds, showing a better coverage of reactions databases and a good flexibility since the specific precursors do not need to be specified in advance; (ii) XTMS provided more alternative routes than FMM that can produce the target compound; (iii) XTMS ranks pathways in order to make easier the choice of the best pathways to implement; (iv) XTMS suggests enzyme sequence candidates for all the reactions in the pathway.
(i) Reactions and metabolites: MetaCyc version 16. 0; (ii) Metabolism of chassis organism: EcoCyc version 16. 1; (iii) Gibbs energies: MetaCyc version 17. 5; (iv) E. coli model for flux balance simulations: MODEL3023609334 (25) from BioModels database (26); (v) Enzyme sequence annotations: KEGG release 50. The curation process is detailed in Figure 1. Statistics of XTMS and third-party databases.
(iii) Number of steps: Number of enzymatic steps in the pathway; (iv) Number of putative steps: Number of enzymatic steps in the pathway that correspond to putative reactions predicted in the Extended Metabolic Space; (v) Estimated toxicity: Average toxicity log(IC50) for intermediate metabolites in the pathway, evaluated by using the Ecoli-Tox server (32); (vi) Maximum allowed yield: This value estimates the maximum available yield in the pathway based on estimated fluxes of the precursors of the pathway in a wild-type strain of E. coli obtained by flux balance analysis optimized for growth. The value is computed as the minimum yield; (vii) Estimated free Gibbs energy: Standard G Gibbs-free energy for reactions in kcal/mol were taken from MetaCyc database.
Experiments with Node Games Since H d (G) = dct i (G) dct(G) , we use the CADR algorithm to compute dct (G) and the PCS algorithm to compute dct i (G). We use graphs G obtained from indoor environments, which we represent by their floorplans. In Fig. 3  we present a floorplan and its graph representation. The graph is obtained by decomposing the floorplan into convex cells, assigning each cell to a node and connecting nodes by edges whenever the corresponding cells are connected by an open space. We have written a script which, given some parameters, generates random floorplans and their graphs. Every floorplan consists of a rectangle divided into orthogonal "rooms". If each internal room were connected to its four nearest neighbors we would get an M × N grid G. However, we randomly generate a spanning tree G T of G and initially introduce doors only 11 We do not deal with the adversarial cost of visibility because, while we can compute ct (G) with the CAAR algorithm, we do not have an efficient algorithm to compute ct i (G); hence we cannot perform experiments on H a (G) = cti (G)  ct(G).
The main objective of this study is to propose a methodological framework to simulate spatial population genetics of pathogen vectors in heterogeneous and changing landscapes, in order to link observed patterns with processes. We adapted the SimAdapt simulation software  [9]  to simulate the evolution of both neutral and adaptive genotypes of diploid, sexually reproducing pathogen vectors introduced into a landscape. This genetic model accounts for vector dispersal and adaptation to local conditions and is coupled to a cellular automaton allowing the representation of land-use and land-cover changes. It includes landscape features known to influence vectors' genetic structure (e. g. , roads, domestic, peridomestic and sylvatic habitats linked to possible loci under selection). Model simulations can be compared to field data of vector's spatial genetic structure.
At the interface across disciplines, this study provides to the community of epidemiologists a testable and applicable framework to foresee landscape modification consequences on vector dynamics and genetic structure, with potential implications for their surveillance and control. landscape resistance on infectious diseases and vectors. Technically, any GIS software provides the necessary features to define areas (habitat types), as well as additional layers for carrying capacity and resistance for migration. This information can be exported into SimAdapt as three different ''ascii'' files at a chosen resolution. Alternatively, the map can be built entirely from the graphical user interface (see Appendix S1 in  [9] ). Initialization of the simulation model. The second step is to initialize the parameters of the model as presented in Table  1.
The case of Triatoma brasiliensis in Northeastern Brazil As an example of application of our model in the context of vector-borne tropical diseases, we used genetic field data on T. brasiliensis (vector of the pathogen responsible for the Chagas disease) in the Caicomunicipality in Northeastern Brazil (Rio do Grande do Norte state). Triatomine individuals were sampled during a field work performed in March 2011. Domestic habitats consisted in houses, peridomestic habitats of the area 50 meters around houses and sylvatic habitats of areas at a minimum of 200 meters from any house. In total 126 individuals were collected in five different locations (populations) chosen to cover a range of distance between sampling points and habitat types (three sylvatic, one peridomestic and one domestic populations; see Figure  1A ).
Sensitivity analyses. Sensitivity analyses were performed to explore the effect of three variables related to vector dispersal on triatomine genetic differentiation coefficient (FST): i) the effect of migration rate from m = 0. 1 to 1 per generation; ii) the effect of Table  2. Pairwise fixation index (FST) between sampling points A, B, C, D and E (in bold italic when the associated p-value was below 0. 05), for the observed dataset in the Caicomunicipality in Northeastern Brazil. maximum dispersal distance from d = 1 to 5 grid cells per generation; and iii) the effect of attraction by domestic habitat from l = 0 to 10 (l representing a factor that reduce emigration from domestic habitat, i. e. when l = 10, m = m/10). The resulting sets of simulations were repeated 30 times to account for stochasticity.
We performed an additional set of 30 repetitions to ensure that the number of repetitions was representative of stochastic processes (Student tests, T mean = 0. 39, df mean = 296. 65, p min. 0. 23). All analyses were performed on a random sampling of 25 individuals at the five sampling locations (i. e. , 125 individuals in total; see  [38]  for a discussion on virtual sampling). Genotypes of sampled individuals for the 7 microsatellite loci were analyzed to calculate FST after 100 generations using Arlecore from Arlequin version 3. 5  [28]. This time was sufficient to observe a stabilization of the FST for all sets of simulations. Analyses of variance (ANOVA) were run to evaluate the effect of migration rate (m), dispersal distance (d) and domestic habitat attraction (l) on the FST using R software  [39].