dataesr/scientific-paragraphs-categorization huggingface dataset:
We applied the signed distance function (SDF) for representing the depths of atoms in a macromolecule. The calculations of SDF values were performed on grid points in a rectangular box that accommodates the macromolecule. The depth for an atom inside the molecule was then obtained as a result of tri-linear interpolation of SDF values at the nearest grid points surrounding the atom. For testing the performance of present program Adepth, we have constructed an artificial molecule whose atomic depths are known as the gold standard for accuracy assessments. On average, our results showed that Adepth reached an accuracy of 1. 6% at 0. 5 A ˚of grid spacing, whereas the current reference server DEPTH reached 7. 5%. The Adepth program provides both depth and height representations; it is capable of computing isosurfaces for atomic depths and presenting graphical view of macromolecular shape at some distance away from the surface.
Another advantage of Adepth relies in its integrity on identification of surface atoms and quantification of atomic depths in one run. Validation of the program was performed on a well-designed molecular structure with known depth information (Figure  1 ). Applications of Adepth includes definition of protein skins with a user-defined thickness, calculations of atom-atom overlaps in macromolecular assemblies  (14)  and a graphical visualization of depth using iso-surfaces in VMD  (13). Another useful application of Adepth concerns the assessment of tip-convolution effects in atomic force microscopy (AFM) imaging  (15). In the range of grid spacing 0. 5-1 A ˚, Adepth has been shown to be more accurate than available servers that were tested in the present study.
ACKNOWLEDGEMENTS The authors thank  Muriel Giansily-Blaizot  ( Hopital Saint-Eloi, Montpellier ) for useful discussions on FVIIa,  Michael Odorico ,  Jean-Marie Teulon  and  Pierre Parot  ( CEA-SBTN Marcoule ) for AFM imaging and  Arnaud Martel  ( CEA-GIPSI Saclay ) for the website developments. The atomic co-ordinates of gold-standard molecule can be downloaded from the server website.
RPs identified in this manner are categorised into families, unambiguously annotated, and a detailed description of their features generated, using an integrated software pipeline. P2RP results can then be outputted in user-specified formats. Conclusion: Biologists have an increasing need for fast and intuitively usable tools, which is why P2RP has been developed as an interactive system. As well as assisting experimental biologists to interrogate novel sequence data, it is hoped that P2RP will be built into genome annotation pipelines and re-annotation processes, to increase the consistency of RP annotation in public genomic sequences. P2RP is the first publicly available tool for predicting and analysing RP proteins in users' sequences.
In this manner, RPs can be divided into families, and family membership then correlates with mechanism of action rather than biological function. Several on-line databases are now available which provide the results of such classification approaches as applied to whole genomes. For example, P2CS  [15] , P2TF  [4] , MiST2  [16]  and DBD  [17]. However there are remaining problems with RP annotation. Many RPs contain multiple domains, and some domains are found in multiple categories of RP. This has led historically to the mis-annotation of many proteins. For instance, SAB1964 is an RR from Staphylococcus aureus RF122, yet it is annotated as a 'two component sensor protein' , while YPA_3835 is a HK from Yersinia pestis Antiqua, which is annotated as an ' ATPase-like ATP-binding protein'. Currently, 1.
5% of all proteins now classified as RRs in the P2CS database  [15] , were originally described in some way as 'sensor kinase' proteins in the annotated genome files as retrieved from Refseq/Genbank. This problem is exacerbated by the current lack of a community-defined consensus set of categorisation criteria, or even a consensus naming system, for multi-domain RPs. However, this has been accomplished for a subset of RPs (RRs), by Galperin  [13, 14]. Due to their multiplicity within genomes and their multi-domain architectures, RPs are non-trivial to identify and annotate. Currently, the annotation of regulatory genes/proteins in individual genomes and databases is often idiosyncratic, misleading or wrong, confounding between-genome comparisons, and naming conventions are also typically different between genomes/databases.
There is consequently a profound need for the adoption of a consistent and harmonised categorisation and annotation system for RPs, which can be applied to any sequence dataset, whether newly derived sequences needing annotation, or previously annotated sequences which might benefit from reannotation  [3]. We have therefore developed P2RP (Predicted Prokaryotic Regulatory Proteins)primarily to help increase (re-) annotation consistency of RPs in published genomes, and for experimental biologists who wish to investigate regulatory genes in their novel sequence data. P2RP accepts two types of input -DNA and protein sequences. For nucleotide queries there is an initial gene prediction step (using MED-Start) to generate a proteome, although. gbk (GenBank) files can also be inputted. Predicted and supplied proteomes are then screened for the presence of particular TF/TCS domains, and proteins categorised and annotated according to their domain architecture  [15, 18].
Implementation A graphical representation of the P2RP process is shown in Figure  1. Users can upload sequences into P2RP as. gbk (GenBank) files, or as multiple FASTA files of protein or DNA sequences (for instance whole metagenomes). In the case of nucleotide FASTA files, the sequence is first run through MED-Start  [19] , a prokaryotic gene-finding algorithm, to define a set of potential gene sequences. The putative genes are then translated to constitute a proteome for RP prediction. The identification of RP candidates in protein sequences is accomplished by domain analysis of each predicted protein, using RPSBLAST, as previously developed for P2CS and P2TF  [4, 15, 18]. The pool of domains used to search for RP proteins was manually selected from the literature and extracted from within Pfam  [20]  and SMART  [21]  databases.
P2RP is a set of PHP scripts (PHP: Hypertext Preprocessor, a server-side scripting language designed for Web development), designed to search the numerous combinations of RP modules and to categorize RP proteins into families based on similarity and/or domain architecture. To circumvent the prediction of false-positive RPs, a post-analysis process is implemented. For instance, enzymes erroneously classified as RP proteins and enzymes containing DNAbinding domains (for instance transposases) are discarded or categorized as 'Other DNA-binding Proteins' (ODPs) respectively. The secondary structure of RP proteins is computed using the PSIPRED method  [22]. The result of the protein structure prediction is presented as a summary of the number of strands and helices and their location on the protein sequence, on which the identified domains are highlighted.
In addition, for follow-up analysis, external links to other web servers are available, including links from results to the P2CS and P2TF databases of genomic/metagenomic RPs. P2RP results can be outputted for download in a user-specified format, as an Excel worksheet (see Additional file 1 and Additional file 2 for examples), FASTA file or as tab-delimited text. To keep each user session private, every user query is given an ID, which allows later retrieval of results, using the 'Search' menu. The data are stored on the server for one month. For easy navigation, in addition to the homepage, each P2RP page contains a navigation bar that allows users to execute a new RP analysis, to search for a previous job, to access a help page or to contact the authors.
Annotation and classification of RPs The classification strategy for TCS proteins was based on that implemented by the P2CS database of genomic and metagenomic TCSs  [15, 18]. Possession of one of an established list of TCS domains defines proteins as being TCS components. Identified TCS proteins are then classified based on domain composition  [13] , for example the presence of HATPase and HisKA domains leads to classification of a protein as a HK. At this stage proteins are classified as RR, HK or phosphotransfer proteins (which usually shuttle phosphoryl groups between receiver domains). RRs are then assigned to families according to the nature of any output domain present on the protein, for instance any RRs containing a HTH_8 DNA-binding domain are classified as PrrA family members, while HKs are sub-classified as classic, hybrid, unorthodox, CheA or 'possible incomplete' (those lacking an obvious phosphorylatable His residue).
In addition to TCS proteins, P2RP also identifies and categorises DNA-binding proteins (classing them as TFs), if they give hits to a compiled pool of DNA-binding domains from the Pfam and SMART libraries, in a scheme developed initially for P2TF  [4]. TFs are then divided into families according to their domain architecture as proposed previously  [4, 17, 24, 25] , and as implemented in the P2TF database  [4]. The P2RP analysis then identifies TF proteins also containing a CheY-like receiver (phosphoacceptor) domains and annotates them as RRs. SFs were divided into 3 sub-groupings; RpoN, RpoD and ECF (extra-cytoplasmic function) SFs. OCSs were defined as proteins that contain sensory 'input' and DNA-binding 'output' domains but lack histidine kinase and receiver domains characteristic of two-component systems  [2].
TFs with only a DNA-binding domain are named transcriptional regulators (TRs). OCSs, TRs, RRs and other non-SF DNA-binding proteins are then divided into 76 families depending upon which domains are present in the proteins. For instance MerR family members contain MerR DNA-binding and B12-binding domains (a protein domain which binds to cobalamin (vitamin B 12 )). Validation of the categorisation process was achieved by comparing P2RP output with manually curated datasets, as has been described for P2TF and P2CS  [4, 15, 18]. Since P2RP implements the same algorithms developed for P2CS and P2TF, validation statistics (including sensitivity and specificity) for TCS prediction and TF prediction can be obtained from the help pages at www. p2cs. org and Figure  1  The P2RP workflow. If users upload a nucleotide sequence query there is an initial gene-prediction step to yield protein sequences for analysis.
Protein sequences (whether predicted by P2RP or inputted by the user), are then screened for the presence of developer-specified sets of conserved domains using SMART and Pfam domain profiles. Proteins with hits to appropriate domain profiles are then used to populate a 'P2RP Database'. Assignment of P2RP Database proteins to specific categories and sub-categories of RPs is then achieved using 'Domains combination and classification' algorithms. The results of the RP analysis can then be viewed as an interactive webpage or exported in a user-defined format. www. p2tf. org respectively. Hundreds of inputs were analysed during testing (amino acid and nucleotide sequences), and beyond the authors, fourteen individuals were involved in the testing and validation (including undergraduate students of Aberystwyth University registered on module BS33120 Molecular Genetics of Microbes).
Conclusion Regulatory processes are fundamental to how microbes alter gene expression in response to environmental changes such as those encountered during infection. Regulation is achieved by RPs such as transcription factors and two-component system proteins. RPs are relatively difficult to annotate because of their multi-domain nature and their paralogy within most prokaryotic genomes. P2RP provides users with the opportunity to investigate and consistently annotate RPs within novel sequence data, or to re-annotate published sequences. It is hoped that this will prove a useful resource to experimental biologists, in addition to increasing consistency in the annotation of RPs in public databases -potentially being used routinely within annotation pipelines.
Acknowledgements We are very grateful to the following students at  Aberystwyth University studying module BS33120 (Molecular Genetics of Microbes) , for testing the server functionalities as part of their studies:  Aimee Abbiss ,  Danielle Aspland ,  Jonathon Challinor ,  Jenna Bowen ,  Simon Davis ,  Anya-May Hope ,  Christine Lockey  and  Thomas Savage. We would also like to thank  Wafa Achouak ,  Marie Bertrand ,  Gilles De Luca ,  Sylvain Fochesato ,  David Lalaouna  and  Catherine Santaella  for useful comments and extensive testing of the web server. We are grateful to DSV/IBITEC-S/GIPSI team and particularly  Arnaud Martel  and  Jean-Marc Le Failler  for the hosting server installation.
In this article, we present the redesign of a traditional model of polymer folding, which has been extensively used in the field of chromosome modelling  [Langowski and Heermann, 2007]. These models are known to suffer from poor efficiency when considered in situations that are similar to in vivo because of the strong confinement of chromosomes. So far, "simple" code parallelization has mainly been set up using tools such as OpenMP  [Dagum and Menon, 1998]  or  MPI [Message Passing Interface Forum, 1993]. More recently, parallelization of dynamical models have been realized on GPU architecture, demonstrating the usefulness of such technology for the modeling of long polymers  [Reith et al. , 2011]. In the same spirit, our algorithm is mainly meant to exploit massively parallel processors, more particularly NVIDIA CUDA-enabled GPUs.
, 2008]. In our case, we have decided to cut any communication between the host and the device, except synchronizations. Indeed, GPUs cannot have all their threads synchronized at one point, at the time of writing, due to hardware constraints. On the other hand, some stages of the simulation need the data to be in a particular state before they can achieve their part of the computation. The only way to synchronize all the threads on a GPU is to wait for the completion of the kernel that has launched them. Each simulation step is consequently divided into several parallel stages, launched consecutively on the GPU. Such a design enables us to finely tune the parallelism grain of the simulation: at each stage the parallelism grain is the most adapted to the kernel that is to be launched. As a result, communications are shrunk to their utter minimum from the host to the device.
Results Thanks to the log files generated by the application, which contain the 3D coordinates of each joint, we can obtain a 3D representation of chromosomes using third-party software such as gnuplot. Figure  7  is the output drawing generated by gnuplot for a circular chromosome after 100 million iterations. All the results presented in this section have been obtained through simulations run on homogeneous machines equipped with the following configuration: Intel Core i7-2600k 3. 40GHz CPUs and NVIDIA GeForce GTX590 GPUs.
Simulation Setup. Lipid bilayers were modeled using the coarse-grained MARTINI force field version 2. 1  [12]. All simulations were performed with the GROMACS 4. 5. 5 software package  [46]  at the temperature of 320 K. Recommended simulation parameters for MARTINI force field were used  [12]. In all simulations a Berendsen barostat with the relaxation constant of 5 ps was used for semi-isotropic pressure coupling. The pressure of 1 atm was maintained separately in  plane and in  direction. An asymmetric membrane containing neutral dioleoylphosphatidylcholine (DOPC) or anionic dioleoylphosphatidylserine (DOPS) lipids was constructed. Each monolayer of the model membrane consists of two rectangular blocks of lipids of different type. Positions of the blocks in the second monolayer are inverted with respect to the first one.
In practical computations the spectra   (  ) and   (  ) contain a number of the short wavelengths components, which produce large chaotic oscillations of derivatives and (). Such artifacts could be eliminated by performing lowpass filtering of the spectra and keeping only the components with  >. The value of the cut-off wavelength   should be chosen empirically for each particular membrane system. In this work the value   = 15 nm was chosen. Figure  4  shows the membrane midline and the membrane curvature computed with our algorithm for one of trajectory frames. It is clearly seen that the membrane midline is reconstructed precisely and that the membrane bends are correctly recognized as the regions of large curvature. The algorithm developed above was implemented in C++ programming language using Pteros molecular modeling library  [27].
Flip-Flop Transitions of Cholesterol. The histogram of the times between successive flip-flop transitions clearly suggests an existence of two very different flip-flop times (data not shown). Fitting this histogram with (2) reveals the characteristic flip-flop times  1 = 4. 1 ± 0. 0045 ns and  2 = 288. 8 ± 0. 34 ns. The time  1 is very close to the interval between successive frames in MD trajectory (4. 0 ns), which is unlikely to be a coincidence. The most plausible explanation of this fact is the following. The flipflop transition of cholesterol molecules is detected if the head group of cholesterol appears in one monolayer in the previous frame but in the other monolayer in the current frame. The orientation of cholesterol molecules and the depth where their head groups are located were not recorded for technical reasons.
Background: Picking images of particles in cryo-electron micrographs is an important step in solving the 3D structures of large macromolecular assemblies. However, in order to achieve sub-nanometre resolution it is often necessary to capture and process many thousands or even several millions of 2D particle images. Thus, a computational bottleneck in reaching high resolution is the accurate and automatic picking of particles from raw cryo-electron micrographs. Results: We have developed "gEMpicker", a highly parallel correlation-based particle picking tool. To our knowledge, gEMpicker is the first particle picking program to use multiple graphics processor units (GPUs) to accelerate the calculation. When tested on the publicly available keyhole limpet hemocyanin dataset, we find that gEMpicker gives similar results to the FindEM program.
In the past, the particles in EM micrographs were picked manually, but this is not practical to reach sub-nanometre resolution or to resolve conformational changes within molecules. Modern digitial imaging technology combined with automated high-throughput data collection techniques now allow both higher resolution and unlimited sizes of 2D datasets to be captured. Hence, a major bottleneck in reaching atomic resolution in 3D reconstruction by cryo-EM is now the accurate and automated picking of particles from the raw EM micrographs. Many methods have been proposed for automatic cryo-EM particle picking  [3, 4]. Amongst the most popular are those that use particle templates to facilitate particle recognition. A template is usually a noise-free representation of the particle in a particular orientation.
Recently, GPUs have also been used to accelerate single particle reconstruction  [22] , tomographic reconstruction  [23] , and subtomogram averaging  [24]. With these observations in mind, we designed gEMpicker to be able to adapt easily to different hardware configurations, ranging from a modest workstation with one or two attached GPUs to large CPU-based or GPU-based clusters with tens or even hundreds of processors, and we have endeavoured to ensure that its performance increases linearly with the computational resources available. Here, we present particle picking speed-up results obtained on four different computational platforms, and we demonstrate the practical utility of the approach using the publicly available keyhole limpet hemocyanin (KLH) dataset. To our knowledge, gEMpicker is the first particle picking program to use multiple modern graphics processor units (GPUs) to accelerate FFT-based NCC calculations.
The computational platforms In this study, computational experiments were carried out using four different computational platforms, representing four possible hardware configurations. Dirac is a high performance workstation equipped with a contemporary GPU, Mbiserv is a modern GPU server (4 GPUs), Adonis is a small GPU cluster (8 CPU nodes, 16 GPUs), and Griffon is a medium-sized CPU cluster (64 CPU nodes). Dirac, Adonis, and Griffon all have pairs of quadcore CPUs, whereas Mbiserv has two hex-core CPUs. We included Mbiserv in our experiments because it was the only machine available to us which is directly connected to four GPUs. Further details of these machines are given in Table  1. Although gEMpicker uses essentially the same code on all platforms, some of the compiler directives will necessarily differ when compiling it for a CPU or GPU cluster.
Case study: keyhole limpet hemocyanin This section demonstrates the practical utility of gEMpicker using the publicly available keyhole limpet hemocyanin (KLH) dataset a. This annotated dataset was used previously to assess the performance of several automatic particle picking algorithms in a particle picking "bakeoff " experiment  [4]. This relatively small dataset consists of 82 defocus pairs of high-magnification images of size 2048 × 2048 of KLH particles, the locations of 1042 side-view particles picked manually by a human expert (Mouche's picks), and a preliminary 3D reconstruction. Each defocus pair contains an image acquired at near-tofocus conditions and an image acquired at far-from-focus conditions. KLH is a homo-oligomeric didecamer with D5 point group symmetry  [37]  and exists in two isoforms, KLH1 and KLH2.
These masks were then rotated through 360°in steps of 4°to make the corresponding masks for the rotated templates. It should be noted that even though we could benefit the KLH's symmetry property to effectively reduce the number of templates and masks to 46 without much performance loss, we keep using 180 templates and masks to facilitate comparison with the existing benchmark  [8]. In order to suppress intensity variations due to noise, the micrographs of the KLH dataset were convoluted with a 4 × 4 averaging filter. Each filtered micrograph was then correlated with each of the 180 templates using the algorithm in gEMpicker. The final correlation map for each micrograph contains the global maximum correlation scores for the 180 templates. Finally, gEMpicker extracts the peaks from the resulting 82 global maps using a procedure similar to that of FindEM  [8]  to identify the template that appears at each peak location.
As an example, Figure  7  shows two global correlation maps of a far-from-focus micrograph from the KLH dataset using side-view and top-view templates separately. In these maps, gEMpicker calls a picked particle whenever a local minima of such a map (here, negative values) falls below    a certain threshold (-0. 3 in this case, similar to the value used in  [8] ). It is worth noting that a receiver-operatorcharacteristic (ROC) analysis (see Figure  8 ) of the results shows that for the KLH dataset using a template rotation step size of 9°gives almost the same level of picking performance as 4°. Thus, correspondingly faster picking speeds may actually be achieved in practice. We then used gEMpicker to pick particles from the 82 far-from-focus micrographs in the KLH dataset. This gave 1249 side-view particles, which contain 979 (i.