dataesr/scientific-paragraphs-categorization huggingface dataset:
For clarity, we restrict the alphabet to Q = 3 amino-acids, {A, C, D}, plus the gap. The alternation of white and gray cell backgrounds helps to track the transformation (e. g. C → 2 → 010). Typically, MSAs of protein families are such that in every column (i. e. residue position) there appears a number of distinct residues smaller than or equal to Q = 20. Here, we did not not consider a restriction of the alphabet to the residues actually occurring, and we used instead the same encoding for all residues. pre-processing step to improve the prediction, for either PSICOV or our model, and therefore all results presented in this work do not include this additional filtering. For the second question (identification of interaction partners), we have used the data of  [18] , thus having the possibility to directly compare with previous results.
In summary (for details see  [18] ), this data comes from 769 bacterial genomes, scanned using HMMER2 with the Pfam 22. 0 HMMs for the Sensor Kinase (SK) domain HisKA (PF00512) and for the Response Regulator domain Response reg (PF00072)  [49] , resulting in 12,814 SK and 20,368 RR sequences. A total of 8,998 SK-RR pairs are found to be cognates, i. e. to be coded by genes in common operons, while the rest are so-called orphans. For statistical inference, cognates sequences are concatenated into a single MSA, each line containing exactly one SK and its cognate RR.
Figure2. True positive rate plotted against number of predicted pairs. Data for plmDCA [15]  (green) and PSICOV version 1. 11 [12]  (red) was obtained using the code provided by the authors with standard parameters as found in the distributed code, except that PSICOV was run with the -o flag to override the check against insufficient effective number of sequences. The true positive rate is an arithmetic mean over 50 Pfam families (see Table2for the list); thin lines represent standard deviations.
Pfam ID Description PDB PF00001 7 transmembrane receptor (rhodopsin family) 1f88, 2rh1 PF00004 ATPase family associated with various cellular activities (AAA) 2p65, 1d2n PF00006 ATP synthase alpha/beta family, nucleotide-binding domain 2r9v PF00009 Elongation factor Tu GTP binding domain 1skq, 1xb2 PF00011 Hsp20/alpha crystallin family 2bol PF00012 Hsp70 protein 2qxl PF00013 KH domain 1wvn PF00014 Kunitz/Bovine pancreatic trypsin inhibitor domain 5pti PF00016 Ribulose bisphosphate carboxylase large chain, catalytic domain 1svd PF00017 SH2 domain 1o47 PF00018 SH3 domain 2hda, 1shg PF00025 ADP-ribosylation factor family 1fzq PF00026 Eukaryotic aspartyl protease 3er5.
i. e. every point in the reference system is computed using graphics primitives. The result of this computation is what is being displayed on the users device. The main advantage of this representation includes, independence from the rendered image size, its compacity and required data to reconstruct a vectorized picture cost the same price in size term. When a picture or a video is live recorded, the raster format is used. Meantime, vectorization is the format of choice mainly for Computer-Aided Design (CAD) softwares. Due the increase of heterogeneity of display devices in our daily lives (IPTV, smartphones, tablets. ) multimedia content is now often included in web pages. Whatever the screen's size, display picture quality is maximal. Building a raster representation of a vector picture is a trivial task, but the reverse process is not obvious  (Sun et al.
Implementation Video encoding is a quite heavy task even on most recent computers. The choice of a native programming language is then obvious. Because of code source portability but also external available libraries, the preferred choice for our implementation is C/C++. In order to accelerate the developping time and reduce the source code size to maintain, some well known multimedia libraries were used, such as FFmpeg and libx264 for decoding, encoding and manipulate the different kind of codec and containers. Image processing related operations are done by OpenCV, and our basic vectorization software uses the GNU Scientific library (GSL). The whole program is available as a standalone executable capable of transmoding any kind of videos, or as a library for direct splitting and encoding.
Real time processing Our first tests, presented in Table  3  reveal that real time processing is possible with videos assuming quite little spatial resolutions (240p). Using this spatial resolution, a 29 framerate is possible for the music clip. This framerate isn't reachable for all tested videos, as an example the documentary framerate is only about 7 pictures per second. In order to over- Another way of increasing performance, especially in the image processing world is to use the GPU. This aspect has been brought to our program using OpenCV GPU related functions.
coli O157:H7. The dataset was analyzed by using five independent clustering methods to minimize potential biases and to increase confidence in the results. Our analyses collectively indicated significant variations in microbiome composition between super-shedding and non-shedding cattle. Super-shedders exhibited higher bacterial richness and diversity than nonshedders. Furthermore, seventy-two operational taxonomic units, mostly belonging to Firmicutes and Bacteroidetes phyla, were identified showing differential abundance between these two groups of cattle. The operational taxonomic unit affiliation provides new insight into bacterial populations that are present in feces arising from super-shedders of E. coli O157:H7.
coli O157:H7 from colonizing the intestinal tract in a manner that leads to the super-shedding state. To test this, we employed Bacterial Tag-Encoded FLX Amplicon Pyrosequencing Analysis (bTEFAP)  [13]  to generate over 200,000 partial 16S rRNA gene sequences from fecal samples collected from 11 super-shedders (SS cattle) and 11 cotemporary pen mates which were negative for E. coli O157:H7 (NS cattle). Sequence data was processed to 1) depict the bacterial community structure for fecal samples from SS cattle, 2) compare the bacterial community profiles for SS and NS cattle and 3) identify the operational taxonomical units (OTUs) differentially present in each group.
Sequence data processing Raw sequence read data were quality-filtered using Mothur v1. 23  [16]  to remove sequences less than 200 nucleotides (nt), sequences containing homopolymers. 8 nt, mismatches in the barcode or primer,. 1 ambiguous nt, or regions in which an average quality score below 30 was obtained using a moving window of 50 nt. The remaining sequences were aligned to the SILVA-based bacterial reference alignment  [17]  using the Needleman-Wunsch algorithm  [18]. Potential chimeric sequences were removed using UCHIME in reference mode  [19]. Finally, sequencing noise was reduced by a pre-clustering step implemented in Mothurs v1. 23  [16]. Sequence data have been submitted to the short-read archive (Bio Project SRP040473 -accession numbers for individual animal samples sequence data are SRX498462, SRX498463, SRX498466, and SRX498471 -SRX498489).
Sequences were filtered to a fixed length of 180 nt and those with an expected error probability greater than 0. 5 were discarded. Chimeras were checked against the ''Gold'' database based on UCHIME  [19] , and reads were clustered at 97% sequence identity into operational taxonomic units (OTUs). For all clustering methods a dissimilarity level of 0. 03 was used to bin OTUs, a resolution that corresponds to the species level. To assess similarity among the different methods, two reference OTUs datasets were built by mapping quality-filtered, processed sequences against the Greengenes reference OTUs database  [25]  downloaded from http://greengenes. lbl. gov/Download/ Sequence_Data/Fasta_data_files/Caporaso_Reference_OTUs/ gg_otus_4feb2011. tgz using USEARCH  [26]  with a global identity threshold of 97% and 90%, respectively.
Statistical analysis The pyrotag libraries for each sample were normalized based on subsampling to the lowest number of reads (16; Table  1 ). The different OTU matrices obtained by the above methods were imported into the R version 2. 14 statistical computing environment (http://www. r-project. org/). Subsequent analyses including Good's coverage, Richness and Shannon Diversity Index and visualizations were performed in R using functions from the picante version 1. 3  [29] , vegan version 2. 03 (http://r-forge. rproject. org/projects/vegan/), phyloseq version 1. 14  [30]  and ggplot2 version 0. 8. 9  [31]. Bacterial community dissimilarity was quantified using Bray-Curtis distance. The Bray-Curtis distance matrix was used to test if the bacterial communities of nonshedders and super-shedders were statistically different by running the ANOSIM function in the vegan package with 1000 permutations.
7% (86,591) of the sequences could not be classified. For all samples, the bacterial communities were dominated by Firmicutes, accounting for 53. 9% of the dataset (60,845/112,926 sequences), followed by Bacteroidetes (35. 6%; 40,185 sequences), Spirochaetes (2. 2%; 2,521 sequences) and Proteobacteria (1. 7%; 1,892 sequences) (Figure  1 ). The remaining 14 phyla accounted for less than 2. 3% of the dataset. At the genus level, Prevotella (Bacteroidetes phylum) was identified most frequently in fecal samples, accounting for up to 10. 5% of the entire dataset. All of the taxa within each of the five taxonomic levels are listed in Table  S1. The classifier-based analysis revealed that the composition of bacterial communities differed among steers. Only 5/18 phyla (Bacteroidetes, Firmicutes, Proteobacteria, Spirochaetes, Tenericutes), 8/33 classes (Bacilli, Bacteroidia, Betaproteobacteria, Clostridia, Erysipelotrichi, Gammaproteobacteria, Mollicutes, Spirochaetes), 5/57 orders (Bacteroidales, Burkholderiales, Clostridiales, Erysipelotrichales, Spirochaetales), 12/77 families (Alcaligenaceae, Bacteroidaceae, Clostridiaceae, Clostridiales FamilyXIII incertae sedis, Erysipelotrichaceae, Lachnospiraceae, Peptococcaceae, Prevotellaceae, Rikenellaceae, Ruminococcaceae, Spirochaetaceae, Veillonellaceae) and 9/97 genera (Alistipes, Bacteroides, Clostridium, Eubacterium, Oscillospira, Prevotella, Ruminococcus, Sutterella, Treponema) were shared by all 22 samples.
Eight OTUs classified at the genus level were preferentially abundant in SS: with seven OTUs assigned to Prevotella (OTU_333, OTU_386, OTU_700, OTU_748, OTU_1560, OTU_2540 and OTU_2651) and one to Alistipes (OTU_1765). Differentially abundant OTUs identified as Tenericutes and Proteobacteria, were represented by two OTUs (OTU_446 and Mothur, Esprit-Tree, CROP, UPARSE and Otupipe were used to bin 16S pyrotags into OTUs. Otupipe2 and Otupipe4 matrices were generated with MinSize parameter set to 2 or 4, respectively. In addition, 16S pyrotags were mapped into against a full-length 16S reference dataset to generate OTU matrices at 97% (RefMap97) or 90% (RefMap90) identity. The plots contain data from super-shedders (SS) and non-shedders (NS). ANOISM was used to test for similarities between SS and NS steers, generating correlation (R) and significance (p) values.
coli O157:H7 to explore possible mechanisms of bacterial interaction. The statistical power needed to detect differential features for OTUs relies heavily on the number of sequences representing each OTU. Thus it is not surprising that most differentially abundant OTUs (i. e. , 69 out of 72) are members of the two dominant phyla (Firmicutes and Bacteroidetes), which combined accounted for 89. 5% of the dataset. However, the less abundant taxa may also play a role in the development of the SS state. Two OTUs from the phylum Tenericutes, which accounted for 0. 2% of the total data, were also identified as differentially abundant between SS and NS steers. Although with the current sequencing depth, we can see a general trend of alpha and beta diversity between SS and NS, it is still difficult to recover all of the differentially abundant features.
Despite the high sequencing depth (average of 10,000 sequences per sample) and Good's coverage (average of 86. 9%), more than half of the OTUs were represented by less than 5 sequences. Microbial populations in cattle feces are highly diverse from individual to individual. Our dataset was estimated to cover more than 85% of the bacterial diversity present in feces (Figure  2 ). The bacterial community in all steers was dominated by Firmicutes, accounting for 53. 9%, followed by Bacteroidetes at 35. 6% of total sequences, a pattern observed by others  [11, 12, 38]. With this depth, however, only a small proportion of OTUs (,1%, or 10-50 OTUs) were shared by all 22 samples at 97% similarity level (Figure  5 ; Table  S3 ). Shanks et al. [12]  obtained similar results with fecal samples from 30 cattle using 16S rRNA gene pyrosequencing, in which only 9/9,201 OTUs (0.
015% of the dataset (17 pyrotags) and 16S pyrosequencing analysis was unable to discriminate between strains of E. coli. Further comparative studies focusing on E. coli populations could be useful to evaluate the importance of commensal stains in SS and NS cattle. Interestingly, the genome of E. coli O157:H7 is 25% larger and contains ,1400 more genes than generic E. coli K-12  [54]. Among these genes, many are related to virulence but the function of. 60% is unknown, but they may confer specific traits that increase the competitiveness of E. coli O157:H7 within the GIT of cattle. The recent sequencing of 26 E. coli O157:H7 strains may aide in the identification of gene clusters that contribute to its prevalence within the GIT of cattle  [54]. In conclusion, significant differences in the composition of fecal microbiota of E.
Thaumatin crystallization Thaumatin I, a plant sweet protein from Thaumatococcus daniellii (T7638, molecular mass 22 kDa) was purchased from Sigma Aldrich (Milan, Italy). Thaumatin protein monolayers were deposited onto glass slides by the Langmuir-Schaefer (LS) method, which enables to obtain a highly packed and ordered Thaumatin monolayer with a surface density of ~9. 69·10 10 molecules/mm 2 , corresponding to ~10. 32 nm 2 /molecule, which fits well with the molecule geometric features from the Protein Data Bank (PDB code 3DZR) and from AFM measurements of LS film  [35, 36]. The 100 ml of filtered (0. 45-mm filter unit, Millipore, Carrigtwohill, Ireland) protein solution of concentration 1 mg/ml were spread onto the air-water interface of the Langmuir-Blodgett (LB) with a Hamilton syringe (Reno, NV).
Data reduction The Fit2D software package was used for data reduction  [45]. GISAXS patterns were analyzed with the IsGISAXS software [46-  50] which is dedicated to simulation of scattering from supported nanostructures. The scattering cross section is expressed in terms of island form factor and interference function and the specificity of the grazing-incidence geometry is stressed, in particular in the evaluation of the island form factor in the distorted-wave Born approximation (DWBA). A full account of size and possible shape distributions is given in the decoupling approximation (DA), where sizes and positions are not correlated, and in the local mono-disperse approximation (LMA). Two types of island repartitions on the substrate can be implemented: disordered systems characterized by their particle-particle pair correlation functions, and bi-dimensional crystalline or paracrystalline systems of particles.
Acknowledgements This work was supported by  FIRB Nanobiosensors  ( ITALNANONET RBPR05JH2P_003 ) of  MIUR (Ministero dell'Istruzione, Università e Ricerca)  to  Professor Dr. Claudio Nicolini University of Genoa , and by a grant Funzionamento by  MIUR (Ministero dell'Istruzione, Università e Ricerca)  to  Fondazione El. B. A. Nicolini  (FEN). We thank  ESRF  for cooperation and precious support. Dr. Nicola Luigi Bragazzi  is from 5 th July 2012 also Resident in Public Health at the  School of Public Health, Department of Health Sciences (DISSAL), University of Genoa, Via Antonio Pastore 1, Genoa 16132  (Italy).
Karstified and glaciological hydrological systems share some distinctive features that motivate this approach. Specifically, they both consist of systems with an inefficient drainage component and a more efficient one which is activated only under some water head conditions  (Hubbard and Nienow, 1997; White, 1999; Gulley et al. , 2012). The model is tuned and validated by performing a set of three experiments of increasing complexity using data obtained on the Haut Glacier d'Arolla. Data sets containing both hydrological and ice flow observations are rare. The Haut Glacier d'Arolla data set is one of the most complete, although it suffers from some non-synchronous measurements. To our knowledge, this is the first time that results from a three-dimensional coupled hydrology-ice flow model are compared to a data set including both hydrology and ice flow data for a winter to summer transition.
Conclusions We have presented a new hydrological model especially designed to be coupled to an ice flow model. This hydrological model is based on a double continuum approach and solves the same set of equations using different parameters for both the inefficient and efficient drainage systems. The two systems are coupled so that the total amount of water is conserved, and an ad hoc scheme is proposed to activate the efficient drainage system where the water pressure exceeds the overburden ice pressure. In our approach, the channels are not represented individually but in a continuous manner, presenting the advantage of not requiring a very fine description of the basal topography. In this paper, the hydrological model and its coupling to an ice flow model are validated by performing a series of three applications of increasing complexity using the data set of Haut Glacier d'Arolla.
project ( ANR-06-VULN-016-01 ) funded by the  Agence Nationale de la Recherche (ANR). Parts of the work were carried out under the HPC- Europa2  project (project number  228398 ) with the support of the  European Commission -Capacities Area -Research Infrastructures. The authors would like to thank  Neil Arnold  for providing the modelled water inputs on  Haut Glacier d'Arolla. The three anonymous reviewers are thanked for their constructive comments that helped to improve the paper quality. Edited by:  I. M. Howat The publication of this article is financed by  CNRS-INSU.
Background: Health inequalities can be tackled with appropriate health and social policies, involving all community groups and governments, from local to global. The objective of this study was to carry out a scoping review on social and health policies or interventions to tackle health inequalities in European cities published in scientific journals. Methods: Scoping review. The search was done in "PubMed" and the "Sociological Abstracts" database and was limited to articles published between 1995 and 2011. The inclusion criteria were: interventions had to take place in European cities and they had to state the reduction of health inequalities among their objectives. Results: A total of 54 papers were included, of which 35. 2% used an experimental design, and 74. 1% were carried out in the United Kingdom.
However, only 1. 7% of the documents published in the PubMed database related to Epidemiology or Public Health mention inequalities or determinants of health in the title or abstract  [13]. The majority of these health inequalities studies have been conducted to describe and analyse inequalities and are not focused on policies or interventions to reduce them in urban areas  [14] [15] [16] [17]. Although some countries are increasingly working to achieve health equity, there is little literature describing effective experiences and even less at the urban level. Given that the majority of the world population lives in cities, where health inequities are increasing  [4]  and that local governments have widely ranging capacities to intervene upon them, an updated review on published interventions to reduce health inequalities in cities may be helpful for future related actions.
Data sources A scoping review  [18, 19]  was carried out to select papers on policies or interventions to tackle inequalities in health in European urban areas published in scientific journals. We sought primarily to perform a mapping of the published papers; therefore, the main objective was not to assess the quality. Consequently, we opted for a scoping review which has been described as a process of mapping the existing literature or evidence base, exploring it without describing findings in detail  [18, 19]. In this complex topic, a scoping review is an important and necessary first step before undertaking a more intensive knowledge synthesis because it helps identify appropriate parameters and potential scope  [19]. A bibliographic search was done in the National Library of Medicine's PubMed database and in the CSA Sociological Abstracts database.
Biomedical and social databases were chosen to cover different fields and disciplines related to the topic. In addition, a manual search in the reference lists of the papers selected through these two electronic databases was performed  [20]. These sources were combined in order to capture as much relevant information as possible. Table  1  illustrates the search strategies and the inclusion and exclusion criteria. The search was limited to papers related to European cities and published between January 1995 and March 2011. Additional requirements for papers were: 1) presentation of the execution and/or results directly related to interventions or policies to reduce health inequalities (interventions had to state the reduction of health inequalities among their objectives), 2) written in English, Spanish or French, and 3) published in peer-reviewed journals.
Study selection Figure  2  represents the data extraction flow chart. The searches in Pubmed and Sociological Abstracts were carried out on the 1st of April 2011, and identified 1162 papers (849 papers from Pubmed and 313 from Sociological Abstracts). All these citations were systematically screened and evaluated by one author to exclude publications irrelevant to the inclusion criteria. Therefore, according the  Pubmed search, 231 papers related to European cities had been published in the study period. Two reviewers independently screened these 231 abstracts, and 81 papers were selected to be completely reviewed. Finally, 46 of these papers were included in the study. With reference to the Sociological Abstracts search, 165 papers were retrieved from peer-reviewed journals. Nevertheless, only 20 of them were related to European cities and published in the study period.
Data extraction, variables and data analysis The following data were extracted from each publication: authors, year of publication, goal of the paper, study design, city and year of the intervention, target population of the intervention, intervention description, evaluation of the intervention and results or health outcomes of the paper. This information for each paper was captured in a table displayed in the annex of this review (see Additional file 1: Tables  S1-S5 ), and in a database. Five researchers participated independently in the study selection and the data extraction. The main characteristics of the documents were classified and are summarized in Tables  2  and 3 , taking into account the following variables: -Type of intervention to reduce health inequalities: 1) Promotion of health behaviours, 2) Healthy settings (where people actively use and shape the environment; as for example, school, workplace, neighbourhood and community), 3) Socioeconomic context (economic factors, employment and working conditions, public services and social transfers), 4) Physical context (climate, built-up environment and environmental characteristics) and 5) Combined approach.