dataesr/scientific-paragraphs-categorization huggingface dataset:
Results: We present Structuprint, a free standalone software that fully automates the rendering of protein surface maps, given -at the very least -a directory with a PDB file and an amino acid property. The tool comes with a default database of 328 descriptors, which can be extended or substituted by user-provided ones. The core algorithm comprises the generation of a mould of the protein surface, which is subsequently converted to a sphere and mapped to two dimensions, using the Miller cylindrical projection. Structuprint is partly optimized for multicore computers, making the rendering of animations of entire molecular dynamics simulations feasible.
Background Over the last two decades, the growth rate of the Protein Data Bank has been exponential. As structural data for biomolecules are increasingly made available, the study of homologous proteins can be performed not only at the level of sequence, but also at the level of threedimensional structure. This has led to the development of numerous sophisticated methods, concerning, among others, the analysis of structural evolution  [1]  and the structure-based design of new drugs  [2]. For the comparison of protein surfaces in particular, a family of methods is based on the reduction of the dimensionality of the system. The concept of projecting a three-dimensional protein structure to two dimensions was first introduced by Fanning et al. under the term 'molecular cartography'  [3]. They presented this notion as a novel method for studying the entire surface of a protein, emphasizing on the topography of antigenic sites.
To fill these gaps, we introduce Structuprint, a new tool for visualization of protein surfaces in two dimensions. Its name is a combination of the terms 'structure' and 'fingerprint' , alluding to the fingerprint-like figures that it generates (see Fig. 1  for an example). Structuprint can produce single 2D maps starting from a PDB file, or GIF animations from multiple files. It is designed with a focus on scalability and extensibility. The tool can utilize multiple CPU cores on GNU/Linux and OS X machines and can easily incorporate any physicochemical predictors provided by the user, other than those in its own default set. The following sections describe the design choices behind its algorithm, present the results from a benchmark and show three characteristic examples of use.
Amino acid properties database Values for 328 properties/descriptors were calculated for the 20 common amino acids with MOE 2010. 10  [11]  and were stored within an SQLite database. In particular, the database contains 11 categories of descriptors: i) 33 adjacency and distance matrix descriptors  [12] [13] [14] [15] [16]  (e. g. , Balaban's connectivity topological index  [14] ); ii) 41 atom/bond count descriptors  [17, 18]  (e. g. , the number of double bonds); iii) 18 conformation dependent charge descriptors  [19]  (e. g. , the water accessible surface area of polar atoms); iv) the 16 Kier and Hall connectivity and kappa shape indices  [20, 21]  (e. g. , the Zagreb index); v) 21 MOPAC descriptors  [22]  (e. g. , the ionization potential); vi) 48 partial charge descriptors (e. g. , the total positive partial charge); vii) 12 pharmacophore feature descriptors (e.
g. , the number of hydrophobic atoms); Fig. 1  The main steps of the algorithm executed by Structuprint. Here, a mould of the surface of the 3D structure of the leporine serum albumin ([PDB: 4F5V]) is first generated. The property values (e. g. , charge) of the amino acids below the mould are retained. Then, the dummy atoms consisting the mould are mapped onto a sphere. Finally, the sphere is projected onto a map using the Miller cylindrical transformation and a smoothing of the property values is performed. The elements of the upper half of the figure were rendered with UCSF Chimera viii) 11 potential energy descriptors (e. g. , the solvation energy); ix) 16 physical properties  [18, [23] [24] [25] [26] [27]  (e. g. , the molecular weight); x) 18 subdivided surface areas; xi) 94 surface area, volume, and shape descriptors (e.
User interfaces The default interface of Structuprint is a cross-platform, command-line interface (CLI). It consists of two executables: structuprint_frame and structuprint. The structu-print_frame executable produces a TIFF figure from a single input PDB file, using the R package ggplot2  [32]  for plotting. The structuprint executable is responsible for processing multiple superimposed PDB files -either serially or in a parallel manner -, generating a TIFF figure per input file and a final GIF animation, rendered with the Imager Perl module  [33]. Most parameters of the underlying algorithms can be modified by the user, such as the delay between animation frames, the background colour, and the appearance of ID numbers on final figures. A full descriptive list of the available parameters for both executables can be found in Structuprint's manual, distributed along with the application and also available from its website.
Parallelism On Unix-like systems (e. g. , GNU/Linux, OS X), Structuprint supports task parallelism when generating animations. Using the Parallel::ForkManager Perl module  [34] , Structuprint can take advantage of multiple CPU cores by assigning each input PDB file to a different processor. The simultaneous rendering of multiple individual frames considerably reduces the total execution time, allowing for visualization of entire molecular dynamics simulations within a reasonable time frame.
Benchmark To understand how execution time and memory consumption scale with the number of atoms in an input PDB file, we ran Structuprint against 700 randomly selected structures from the Protein Data Bank (Additional file 1). For simplification purposes, multi-model PDB entries were excluded, as a large proportion of the atoms would overlap in 3D space, being essentially indistinguishable. The benchmark was performed on a GNU/Linux system with an Intel Xeon E5-1650 v2 CPU at 3. 50 GHz and 31. 4 GB of memory. Structuprint was launched 10 times per PDB file and the execution time was measured as the median time for completion. Memory usage was measured similarly. We then performed linear regressions using execution time and memory consumption as dependent variables and number of atoms as the independent variable.
In both regressions, we applied a Box-Cox transformation  [35]  to the dependent variable to ensure that the residuals were normally distributed. The final fitted models are shown in Fig. 3. Execution time increases linearly with the number of atoms, whereas Fig. 2  Structuprint's Graphical User Interface. The main window is split between two tabs for preparation of 1) animations and 2) single static maps. The default parameters of the algorithm can be modified using the 'Advanced options' popup window. When Structuprint is rendering a figure, its progress is shown in a temporary terminal memory consumption increases with the square root of the atom count. For example, on the aforementioned system it took 88 seconds and 211 MB of RAM to generate a Structuprint figure for a relatively small protein with 2,461 atoms ([PDB:1YLP]).
Depiction of surface conservation The evolution of protein surfaces and the conservationor lack -thereof is another domain in which Structuprint can be applied. As an example, we performed a brief phylogenetic analysis of three orthologs of plastocyanin -a protein involved in electron transfer in photosynthesis  [38]  -for which crystallographic structures were available. The amino acid sequences of spinach plastocyanin (Spinacia oleracea [Swiss-Prot:P00289]) and those of two green algal species (Ulva pertusa [Swiss-Prot:P56274], Ulva prolifera [Swiss-Prot:P07465]) were retrieved from the UniProt database, along with the sequence of the spinach chloroplastic fructose 1,6biphosphatase ([Swiss-Prot:P22418]) that would be later used as an outgroup. The sequences were aligned with ProbCons 1. 12  [39]  and the best model of amino acid substitution was determined with RAxML 8.
1. 16 [40]. Ten maximum likelihood trees were then inferred with RAxML using the biphosphatase as the outgroup sequence, and the best scoring tree was selected. Next, 2D protein surface maps of the corresponding 3D structures ([PDB:1AG6, 1IUZ, 7PCY, 1SPI]) were produced with Structuprint, after performing a structural superposition. For this example we used a more complex descriptor, FASA_H: The results are shown in Fig. 4. There is significant conservation of both surface structure and hydrophobicity patterns among all three species, with the algal orthologs (Fig. 4b, c ) exhibiting greater similarity, as expected. Finally, the representation of the chloroplastic fructose 1,6-biphosphatase (Fig. 4d ) is vastly different from the others, highlighting the long sequence distance among these proteins.
Comparison of conformational changes, e. g. , due to mutations A third proposed application of Structuprint involves visually contrasting protein surfaces before and after events such as mutations, ligand binding, pH or temperature alterations. We exemplify this case using a mutant of Rop, a small regulatory protein from Escherichia coli with a native tertiary structure of a homodimeric four-helix bundle. The native structure has been shown to be disrupted by a single amino acid substitution (Ala31 → Pro) in the turn region  [41]. To show the consequences of this mutation, we generated Structuprint maps of the wild type protein ([PDB:1ROP]) and the A31P mutant ([PDB:1B6Q]) after superposition. Figure  5  illustrates the mutation-induced conformation change, comprising different surface shape and grouping of negatively charged residues.
Availability and requirements Project name: Structuprint Project home page: http://dgkontopoulos. github. io/ Structuprint/ Operating systems: Prebuilt packages and installers are available for GNU/Linux distributions (Ubuntu 14. 04, Debian 8, Fedora 22, CentOS 7, openSUSE 13. 2), Windows, and OS X. For all other operating systems, installation from the source code is required. The GUI is available by default only for GNU/Linux systems. Programming languages: Perl 5, R License: GNU GPLv3+ Any restrictions to use by non-academics: None.
Hybrid (IT/RS/ML) decoding complexity 5. 3. 1 Experimental conditions In order to evaluate the complexity of GLDPC-Staircase codes, we implemented a functional C language codec integrated in the OpenFEC. org environment, reusing its binary LDPC-Staircase and Hankel-RS over GF (2 8 ) software codecs. Then we conducted throughput tests on a Linux desktop, using kernel 2. 6. 27. 41/64bits, and an Intel Xeon CPU E5410@2. 33GHz processor (a single CPU core was used during experiments). Here also we use N 1 = 5, r L = 2 3 and a regular distribution of extra-repair symbols.
uate climate models under extreme forcing scenarios, in particular high ( > 800 ppmv) atmospheric CO 2 concentrations. Although a post hoc intercomparison of Eocene (∼ 50 Ma) climate model simulations and geological data has been carried out previously, models of past high-CO 2 periods have never been evaluated in a consistent framework. Here, we present an experimental design for climate model simulations of three warm periods within the early Eocene and the latest Paleocene (the EECO, PETM, and pre-PETM). Together with the CMIP6 pre-industrial control and abrupt 4× CO 2 simulations, and additional sensitivity studies, these form the first phase of DeepMIP -the Deep-time Model Intercomparison Project, itself a group within the wider Paleoclimate Modelling Intercomparison Project (PMIP). The experimental design specifies and provides guidance on boundary conditions associated with palaeogeography, greenhouse gases, astronomical configuration, solar constant, land surface processes, and aerosols.
Introduction There is a large community of Earth scientists with strong interests in "deep-time" palaeoclimates, here defined as climates of the pre-Pliocene (i. e. prior to ∼ 5 Ma). Recently, a growing community of modelling groups focussing on these periods is also beginning to emerge. DeepMIP -the Deeptime Model Intercomparison Project -brings together modellers, the data community, and other scientists into a multidisciplinary international effort dedicated to conceiving, designing, carrying out, analysing, and disseminating an improved understanding of these time periods. It also aims to assess their relevance for our understanding of future climate change. DeepMIP is a working group in the wider Paleoclimate Modelling Intercomparison Project (PMIP4), which itself is a part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6,  Eyring et al.
The chosen intervals -the Early Eocene Climatic Optimum (EECO) the Paleocene-Eocene Thermal Maximum (PETM), and the pre-PETM. The choice of a time interval on which to focus is based on a balance between (i) the magnitude of the anticipated climate signal (larger signals have a higher signal-to-uncertainty ratio, and larger signals provide a greater challenge to models), (ii) the uncertainties in boundary conditions that characterize the interval (small uncertainties result in more robust conclusions as to the models' abilities, and minimize the model sensitivity studies required to explore the uncertainties), and (iii) the amount and geographic distribution of palaeoclimate data available with which to evaluate the model simulations. We have chosen to focus on the latest Paleocene and early Eocene -∼ 55 to ∼ 50 Ma (the Ypresian stage), as it is the most recent geological interval characterized by high (> 800 ppmv) atmospheric CO 2 concentrations.
Land surface i. Vegetation: the vegetation in the DeepMIP palaeoclimate simulations should be prescribed as that in H14, which is included digitally as a netcdf file in the Supplement of H14 (Table  2 ; note that the BIOME4 vegetation should be used rather than the Sewall vegetation, and that groups may choose to base their vegetation either on the 27 biomes or the 10 megabiomes), and shown here in Fig. 4. Groups should make a lookup table for converting the H14 Eocene data set to a format that is appropriate for their model. To aid in this process, a modern vegetation data set is also provided in the Supplement of H14, using the same plant functional types as in the H14 Eocene reconstruction; in addition, the lookup table for the CLM  (Oleson et al. , 2010)  land model is provided as a guide in the Supplement of this paper.
Output format We strongly recommend that DeepMIP model output should be uploaded to the anticipated PMIP4 component of the CMIP6 database  (Eyring et al. , 2016) , distributed through the Earth System Grid Federation (ESGF). However, if this is not possible, then netcdf files of the variables in Appendix A, including Tables A1-A3, should be uploaded to the Deep-MIP Model Database, which will be set up if and when required. In any case, for the "highest priority" variables in Appendix A, Tables A1-A3, all months of the simulations should be retained, such that averages can be calculated from arbitrary years of the simulation, and such that equilibrium states can be estimated using the approach of  Gregory et al. (2004).
Climate proxies A major focus of DeepMIP will be to develop a new synthesis of climate proxy data for the latest Paleocene and early Eocene, focussing on the three targeted time intervals: pre-PETM, PETM, and EECO. The main focus of DeepMIP will be on temperature and precipitation proxies. Two working groups have been set up to compile these data from marine and terrestrial records. These groups will also work together to generate new data sets for poorly documented regions, such as the tropics, and will seek multiple lines of evidence for climate reconstructions wherever possible. The marine working group is excited by the possibility of using innovative analytical techniques (e. g. Kozdon et al. , 2013)  to recover robust estimates for sea-surface temperature from planktic foraminiferal assemblages within legacy sediment cores of the International Ocean Discovery Program.
Published data sets will be combined into an open-access online database. The EECO and PETM or pre-PETM marine compilations of  Lunt et al. (2012) ,  Hollis et al. (2012), and Dunkley Jones et al. (2013) , and EECO terrestrial compilations of  Huber and Caballero (2011)  provide a starting point for this database. One of the great challenges for these working groups will be to develop new ways to assess climate proxy reliability and quantify uncertainties. In some cases, it may be more straightforward to consider relative changes in proxies rather than report absolute values. Climate proxy system modelling  (Evans et al. , 2013)  coupled with Bayesian analysis (e. g. Khider et al. , 2015; Tierney and Tingley, 2014)  has great potential for improving estimation of uncertainties and directly linking our climate proxy compilation with the climate simulations.
Acknowledgements. We thank  NERC  grant  NE/N006828/1  for providing funds for the first DeepMIP meeting in Boulder, Colorado,  USA , in January 2016. Daniel J. Lunt  acknowledges the  NERC grant  " Cretaceous-Paleocene-Eocene: Exploring Climate and Climate Sensitivity " ( NE/K014757/1 ), and advanced  ERC  grant " The Greenhouse Earth System" (T-GRES , project reference  340923 ), awarded to Rich Pancost. Matthew Huber  acknowledges funding from  NSF   OCE-0902882. Michiel L. J. Baatsen ,  Henk A. Dijkstra , and  Anna S. von der  Heydt acknowledge support by the  Netherlands Earth System Science Centre (NESSC) , financially supported by the  Ministry of Education, Culture and Science (OCW) ,  024. 002. 001. We thank two anonymous reviewers whose comments were very useful in improving and clarifying the experimental design.
Features and Architecture Our Arpeggigon is a software realization of the reacTogon concept. The main features our Arpeggigon provides over the reacTogon are: -Multiple layers: one or more cellular automata run in parallel. Layers can be added, removed, and edited dynamically through a tabbed GUI. -Extended attributes for tokens, such as note length, accent, and slide. -Per-cell repeat count for local modification of the topology of the grid. -MIDI integration. -Saving and loading of configurations. Figure  2  shows a screenshot. Dynamic addition and removal of layers means that both the core logic of the application and the GUI must support structural changes while the application is running. Note the different kinds of tokens to the right of the grid. They can be dragged and dropped onto the grid to configure a layer, even while the Arpeggigon is running.
GUI and Interaction The GUI of the Arpeggigon is written using the cross-platform widget toolkit GTK+. The Arpeggigon does not generate any audio by itself; it needs to be connected to an external, MIDI-capable hardware or software synthesizer. MIDI I/O is handled by the JACK Audio Connection Kit. All code for interfacing with the external world is structured using reactive values and relations (RVR). Much of this code is of course monadic (in the IO monad). However, as it is mostly concerned with creating and interconnecting interface entities, the code has a fairly declarative reading as a sequence of entity definitions and specifications of how they are related. As a case in point, consider the following code for the system tempo slider: In essence, this code defines a box, a label, and a slider, and visually relates them by placing the last two inside the box.
Quantification of KCC2 Immunohistological Labeling Double fluorescent labelings were captured using frame-channel mode to avoid any cross talk between the channels. Each optical section resulted from two scanning averages. Excitation of the fluorochromes was performed with an argon ion laser set at 488 nm, and a helium/neon laser set at 575 nm. At high magnification, we only scanned the ventral CN (PVCN and AVCN regions), which exhibited reactive neurogenesis. We digitized stacks of 1 µm-thick optical sections. A custom program written in Matlab R (The Mathworks, Inc. ) was developed to analyze fluorescence at the plasma membrane of neurons. The background or non-specific immunofluorescence, was assessed by calculating the average fluorescence in a visually selected area devoid of neurons or any other stained structures.
From this region, we then derived a threshold equal to the average immunofluorescence plus three times the standard deviation. All data were then subtracted from this threshold value and only positive values were conserved for further analysis. A region of interest was then drawn around the neuronal plasma membrane of each cell body. The program calculated the average fluorescence within the region of interest over data that were 20% above the maximum values. This thresholding insured that all pixels taken for calculating the average was part of the plasma membrane and that the same criterion was used for all slices in all conditions (otherwise, the averaged fluorescence can depend on how large the region of interest was manually drawn around the plasma membrane). Also, to avoid any biases at the stage of extracting the averaged fluorescence level in each condition, the experimenter in charge of the analysis (M.
The main advantage of this method is that it clearly decreases costs, as library preparation for bar-coding is nowadays approximately the same price as sequencing. The drawback is that genotypes cannot be derived anymore. Instead, we have access to the allele frequency in the population, a result known as the allelotype. In this work, we present a method for the de novo identification, differential analysis and annotation of variants from RNAseq data in non-model species. It takes as input RNA-seq reads from at least two conditions (e. g. the modalities of the phenotype) with at least two replicates each, and outputs variants associated with the condition. The method does not require any reference genome, nor a database of SNPs. It can therefore be applied to any species for a very reasonable cost.
We first evaluated our method using RNA-seq data from the human Geuvadis project  (9). The great advantage of this dataset is that SNPs are well annotated, since the selected individuals were initially included in the 1000 genomes project  (10). This enables to clarify what is the precision and recall of our method, and how it compares to methods which require a reference genome or a reference transcriptome. We then applied our method in the context of nonmodel species. First we focused on Asobara tabida, an hymenoptera that exhibits contrasted phenotypes of dependence to its symbiont. Using RNA-seq data from two extreme modalities of the phenotype, we were able to establish a catalog of SNPs, stratify them by their impact on the protein sequence, and assess which SNPs had a significant change of allele frequency across modalities.
We further selected cases for experimental validation, and were able to confirm that the SNPs were indeed condition specific. We then applied our method on two recently diverged Drosophila species, D. arizonae and D. mojavensis. These species can still produce hybrids that are sterile. In this case, our method identifies differences of 1 nt, which are not Figure  1. With fasta/fastq input from an RNA-seq experiment, SNPs are found by KISSPLICE without using a reference. As KISSPLICE provides only a local context around the SNPs, a reference can be built with TRINITY, and SNPs can be positioned on whole transcripts. Some SNPs that do not map on the transcripts of TRINITY, called orphan SNPs, are harder to study but can still be of interest. We propose a statistical method, called KISSDE, to find condition-specific SNPs (even if they are not positioned) out of all SNPs found.