dataesr/scientific-paragraphs-categorization huggingface dataset:
The optimal temperature for activity of each of the enzymes studied is reported in Fig. 2. Only enzymes from Medee Lake were considered for correlation analysis (see Results and Discussion section for details), and thus the enzymes from other site are shown in different colours. To prove that such a thermal-pressure correlation exists in Lake Medee, we polymerase chain reaction-amplified candidate genes from the two EST-positive clones (the ones containing MGS-M1 and MGS-M2 ESTs), and we were able to successfully express and produce in soluble form three proteins: a glycosidase (GLY; herein named MGS-M3), an aldo-keto reductase (AKR; MGS-M4) and an (L)-lactate dehydrogenase (LDH; MGS-M5). According to the CAZY database (http://www. cazy. org; Cantarel et al. , 2009)  and considering structural similarities, MGS-M3 is related to family 3 of GLYs, whereas MGS-M4 belongs to the gluconic reductase subfamily 5 of AKRs, and MGS-M5 matches lactate/malate dehydrogenases rather than other Rossman foldcontaining enzymes.
Source organisms of investigated sequence-encoded ESTs We finally investigated the presumptive microbial origin of the genes encoding enzymes under investigation (Table  S4 ). A search against the GOHTAM database  (Menigaud et al. , 2012)  revealed compositional similarities between the DNA fragments containing the genes for MGS-M1 to MGS-M5 and several bacterial strains that are most likely from uncharacterized species belonging to Firmicutes. Genes in the DNA fragment encoding MGS-M2 to MGS-M5 share similarity with the corresponding genes of the Tenericutes-like bacterium Haloplasma contractile  (Antunes et al. , 2008 (Antunes et al. , ,2008)). This bacterium was recently isolated from deep-sea sediment samples and is a unique 'transiting form' of bacteria that has been placed on the phylogenetic tree between Firmicutes (Bacilli and Clostridia) and Mollicutes (Mycoplasmas and other strictly symbiotic bacteria).
2. 3 (Roche). The genemark software  (Lukashin and Borodovsky, 1998 ) was employed to predict potential protein-coding regions (open reading frames with ≥ 20 amino acids) from the sequences of each assembled contig, and deduced proteins were screened via blastp and psi-blast  (Altschul et al. , 1997). MGSencoding enzymes were deposited in GenBank under the accession numbers KF831414 -KF831421.
5, 0. 2 M ammonium sulphate, 25% (w/v) PEG 3350, tobacco etch virus protease; MGS-M5 -0. 1 M sodium cacodylate pH 6. 5, 0. 2 M calcium acetate, 9% PEG8 K, trypsin protease. The crystals were cryo-protected with reservoir solution supplemented with either 12% glycerol, 15% ethylene glycol or Paratone-N oil prior to flash freezing in an Oxford Cryosystems Cryostream. Diffraction data were collected at 100K and the Cu Kα emission wavelength using a Rigaku HF-0007 rotating anode with a Rigaku R-AXIS IV++ detector. Diffraction data were reduced with either hkl3000  (Minor et al. , 2006)  or xds  (Kabsch, 2010)  and scala  (Evans, 2006). The structures were determined by molecular replacement using the following Protein Databank (PDB) codes: MGS-M1 -3HXK, MGS-M2 -2XUA, MGS-MT1 -3V9A, MGS-M4 -4FZI, and MGS-M5 -1LDN.
bioinf. manchester. ac. uk/naccess); residues with > 10 Å 2 SASA were assigned as surface residues. The difference in hydrophobic core burial was calculated by comparing the apolar buried surface area in the hydrophobic core using NACCESS with the unfolded state SASA calculated by the unfolded server (http://folding. chemistry. msstate. edu/utils/unfolded. html)  (Creamer et al. , 1997). Multiple sequence alignments were constructed using the clustalw2 tool (http://www. ebi. ac. uk/clustalw/index. html) integrated into the bioedit 7. 0. 9. 1 software  (Hall, 1999)  and geno3d  (Combet et al. , 2002). X-ray diffraction statistics can be found in Table  S6. The structural coordinates reported will appear in the PDB under accession codes 4Q3K, 4Q3L, 4Q3M, 4Q3N and 4Q3O.
Construction of a neighbour-joining tree and oligonucleotide usage pattern analysis Multiple protein alignments were performed using the clustalw program built into the bioedit software version 7. 0. 9. 0  (Hall, 1999). Phylogenetic analysis of protein sequences was conducted with the mega 4. 0 software  (Tamura et al. , 2007)  using the neighbour-joining tree method and sampling of 1000 trees for bootstrapping and Poisson correction. DNA sequences of contigs were searched against all sequenced bacterial chromosomes, plasmids and phages for oligonucleotide compositional similarity using the gohtam web tool  (Menigaud et al. , 2012).
While the similarity in overall structure confidently categorises MGS-M2 into family V with MCP hydrolases, the distinct features of its active site imply that MGS-M2 is not an MCP hydrolase, as was further confirmed by assays  (Alcaide et al. , 2013). Finally, the size of the active site of MGS-M2 is consistent with the experimentally determined substrate range in this study, as pNP esters as large as dodecanoate can be accommodated (Supporting Information Fig. S2 ). The structure of MGS-MT1 is consistent with its classification in family IV (hormonesensitive lipase, HSL) because its closest structural homologues in the PDB database are HSLs from thermophilic archaea, including AFEST from Archaeoglobus fulgidus  (PDB 1JJI; De Simone et al. , 2001) , EST2 from Alicyclobacillus acidocaldarius  (PDB code 1EVQ; De Simone et al.
The structure of MGS-M4 has a TIM-barrel fold  (Dellus-Gur et al. , 2013)  and was very well conserved with the structures of more than 50 NADPH-dependent aldo-keto reductases from the Pfam family PF00248 (Z-scores between 17-21 and RMSD values between 0. 8 and 1. 2 Å over the full 274 residue MGS-M4 sequence) (Supporting Information Fig. S3 ). The binding of NADPH by PF00024 family enzymes involves atoms from 12 residues in a positively charged pocket; in MGS-M4, 11 of these 12 residues are conserved, and a positively charged pocket is present, suggesting that the binding of NADPH to this enzyme would be largely similar to that in its structural homologues. Aldo-keto reductases have substrate binding sites with diverse size, depth and charge, based on which different substrates can be accommodated.
Acknowledgements The authors gratefully acknowledge the financial support provided by the  European Community  project  MAMBA  ( FP7- KBBE-2008-226977 ). This work was further funded by grant  BIO2011-25012  from the  Spanish Ministry of Economy and Competitiveness  (formerly MICINN). HT, OVG and PNG acknowledge  European Commission for 'MicroB3 ' grant ( FP7-OCEAN. 2011-2 (contract Nr 287589)). This work received support from the  Government of Canada through Genome Canada  and the  Ontario Genomics Institute  (grant  2009-OGI-ABC-1405  to A. F. Y. and A. S. ) and from the  U. S. National Institutes of Health  (grants  GM074942  and  GM094585  to A. S. through the  Midwest Center for Structural Genomics). We thank  Dr. C. Méndez-García  for her excellent support for the preparation of heat map figures and  Dr.
Thus, a polynomial approximation is considered in order to calculate the values of these parameters. Based on this approximation, we provide an analytical performance evaluation, in terms of bit error rate (BER) and channel capacity, of a VBLAST MIMO-OFDM system under a Rayleigh fading channel and memoryless nonlinear HPA model. Finally, we introduce a compensation method that aims to estimate and mitigate simultaneously the HPA nonlinearity and RF crosstalk at the receiver side. This proposed method is based on the theoretical analysis done in this paper. Results were supported with MATLAB simulation of a VBLAST MIMO-OFDM system under a Rayleigh fading channel. The remainder of this paper is organized as follows: In Section 2, the system model is introduced. The approximation of the HPA model is studied in Section 3.
Nonlinear MIMO-OFDM system with RF crosstalk The detected signal at the receiver level in the presence of HPA nonlinearity and crosstalk can be expressed as X n; i ð Þ ¼ H H n; i ð ÞH n; i ð Þþσ 2 n I Â Ã -1 H H n; i ð Þ K 0 H n; i ð ÞX n; i ð ÞþK 0 αH n; i ð Þχ n; i ð Þ ½ þH n; i ð ÞD n; i ð ÞþN n; i ð Þ ð56Þ Then, the instantaneous SNR of the received signal is defined as γ NLþcross ¼ K 0 j j 2 σ 2 x H n; i ð Þ j j 2 H n; i ð Þ j j 2 σ 2 d þ K 0 j j 2 α j j 2 σ 2 x À Á þ σ 2 n ð57Þ Proceeding in the same way as in the previous case, we considerer that ℵ ¼ H n; i ð Þ j j 2 ; γ c ¼ K 0 j j 2 σ 2 x and γ e ¼ K 0 j j 2 α j j 2 σ 2 x , Equation 57 becomes γ NLþcross ¼ γ c ℵ ℵ σ 2 d þ ℵ γ e þ σ 2 n ¼ f ℵ ð Þ ¼ γ ð58Þ The instantaneous SNR in the nonlinear with crosstalk can be expressed by γ ¼ γ c g γ ð Þ g γ ð Þσ 2 d þ g γ ð Þγ e þ σ 2 n ð59Þ Similar to the nonlinear case, we can deduce g(γ) and g′(γ): g γ ð Þ ¼ γσ 2 n γ c -γσ 2 d -γγ e ð60Þ g′ γ ð Þ ¼ γ c σ 2 n γ c -γσ 2 d -γγ e À Á 2 ð61Þ and similar to the previous case, we can obtain a closedform expression for the pdf p γ NL þcross : p γ NL þcross γ ð Þ ¼ γ c σ 2 n Ω γ c -γσ 2 d -γγ e À Á 2 exp- γσ 2 n Ω γ c -γσ 2 d -γγ e À Á ! 0≤γ≤ γ c σ 2 d þ γ e 0 otherwise 8 > < > : ð62Þ Therefore, the BER expression of the M-QAM nonlinear MIMO-OFDM system with crosstalk is given by BER NLþcross ¼ Z γ c σ 2 d þγ e 0 4 log 2 M Q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 3γ b log 2 M M-1 r ! γ c σ 2 n Ω γ c -γσ 2 d -γγ e À Á 2 exp- γσ 2 n Ω γ c -γσ 2 d -γγ e À Á ! dγ ð63Þ The integrals presented in Equations 55 and 63 are computed using MATLAB in order to determine the average BER, because the calculation of these integrals is intractable.
It is also worth adding that mathematical programming is the other key issue in numerical assessment of limit analysis problem. Discrete upper bound limit analysis results in a minimization problem involving linear or nonlinear programming. Linear programming problems can be applied for piecewise linearization of yield criteria, but an important number of additional variables is often needed. However, most of the yield criteria for plates can be formed as an intersection of cones for which the limit analysis problem can be solved efficiently by the primal-dual interior point method  [10, 11]  implemented in the MOSEK software package  [12]. This algorithm was proved to be a very effective optimization tool for the limit analysis of structures  [4, 6, 7, [13] [14] [15] [16] , and therefore it will be used in our study.
http://www. apjcen. com/1/1/12 Now the optimization problem (31) becomes a problem of minimizing a sum of norms as λ + = min m p NG i=1 ωi |J i | ρ i s. t ρ i = C T B i ẇ ∀i = 1, NG B ec ẇ = b ec (43) In fact, a problem of this sort can be reformed as a SOCP problem by introducing auxiliary variables t 1 , t 2 ,. , t NG λ + = min m p NG i=1 ωi |J i | t i s. t ⎧ ⎪ ⎨ ⎪ ⎩ ρ i ≤ t i ∀i = 1, NG ρ i = C T B i ẇ ∀i = 1, NG B ec ẇ = b ec (44) where the first constraint in Equation 44 represents quadratic cones. The total number of variables of the optimization problem is N var = NoDofs + 4 × NG where NoDofs is the total number of the degrees of freedom (DOFs) of the underlying problem. As a result, the optimization problem defined by Equation 44 can be effectively solved by the academic version of the Mosek optimization package  [12].
For the computational cost, it is estimated based on N var versus total optimization steps (Mosek times). Results are plotted in Figure  10. It is seen that the total number of variables increases quickly when increasing the degree of basis functions. Optimization problems solved using the Mosek academic software show very fast convergence in about 13 to 16 step iterations with computing times ranging from 2 to 18 s only. Although the analytical solution is unknown, this benchmark has been investigated by many authors. The earliest numerical performance of the upper and lower bounds was proposed by Hodge and Belytschko  [1]. Tables  2  and 3   the present solutions using quadratic and cubic elements versus elemental meshes. The present solutions fall between lower and upper bound published ones.
LC-MS analysis Separation of the Fc/2 fragments was carried out on an Acquity system (Waters, Milford, MA, USA) coupled to a UV detector and an electrospray mass spectrometer (Synapt G2S, Waters, Milford, MA, USA). Around 20 g of sample were injected on a Pursuit Diphenyl 150 mm × 2. 0 mm column (Agilent, Santa Clara, CA, USA) equilibrated at 70 • C and operated at a flow rate of 200 l/min. An elution gradient was applied using solvent A (0. 1% TFA in water) and solvent B (90% MeCN, 10% water and 0. 1% TFA), after an isocratic elution at 10% B for 5 min, B was raised to 38% in 10 min and to 44% in an additional 48 min. The column was then washed for 4 min at 80% of B and further equilibrated during 9 min at 10% B. The mass spectrometer was operated in the positive resolution mode and data were recorded from m/z 200 to 2000.
Among such locations were supermarkets and grocery stores, fast food restaurants/pizza (FFR), full-service restaurants (FSR), school cafeterias, and vending machines, among many others. The fast food industry generally segments fast food restaurants into 8 different types: burger, pizza, sandwich, chicken, Mexican, Asian, and fish restaurants, as well as coffee/snack shops  [15]. However, the NHANES dataset does not distinguish among different FFRs by restaurant type  [10, 16]. The present study provides the first analysis of children's consumption patterns by the type of FFR. Custom algorithms, developed by the authors, subdivided the NHANES FFR/pizza category into 8 different types. Meals and snacks consumed at each type of restaurant were then analyzed for energy, sodium, added sugars, and solid fats.
Dietary intake data sources Data analyses were based on the first 24-h recall from 4 cycles of the nationally representative National Health and Nutrition Examination Survey (NHANES) for the years 2003-2010. The NHANES 24-h dietary recall utilizes a multi-pass method, where all foods and beverages consumed in the preceding 24-h, from midnight to midnight were reported. The name, time and place of consumption for each eating occasion were also measured. For children 4-5 y, a parent or guardian completed the dietary recall. For children 6-11 y, the child was the primary respondent, but the parent was present and able to assist. For children 12-19 y, the child was the primary respondent, but could be assisted by an adult  [18]. Data from the MyPyramid Equivalents Database (MPED) were used to assess intakes of added sugars and solid fats  [19].
The MPED 2. 0 database was updated for use with more recent NHANES cycles by imputing the MPED equivalents for a limited number of foods (n = 291). Energy from solid fats was estimated as 9 kcal/g of solid fat. Since the MPED database provided added sugars in teaspoon equivalents, this value was converted to energy (1 tsp = 16 kcal)  [20]. The 5 principal locations of origin for all foods and beverages were obtained from NHANES data. These were: grocery stores, fast food restaurants (FFR), full-service restaurants (FSR), school cafeterias, and other  [21]. The ''other'' category included foods or beverages from someone else or as a gift, child-care centers, sports/recreational facilities, vending machines, and grown or caught among other categories  [21]. Since no further data about the FFR category are provided in NHANES data, a multi-step algorithm was developed by the authors to assign FFR meals and snacks into one of 8 market segments as defined by the restaurant industry  [15].
The FFR segmentation The 8 FFR segments were burger restaurants (e. g. , McDonald's or Burger King), pizza restaurants (e. g. , Pizza Hut or Domino's), sandwich restaurants (e. g. , Subway or Quiznos), chicken restaurants (e. g. , KFC or Chick-fil-A), Mexican restaurants (e. g. , Taco Bell), Asian restaurants (e. g. , Panda Express), fish restaurants (e. g. , Long John Silver's) and coffee/snack shops (e. g. , Starbucks or Baskin-Robbins). The NHANES data included meals and snacks from national chains as well as regional or local restaurants: the specific brand names provided above are for reference only. FFR meals were assigned to one of the eight pre-defined segments based on the foods and beverages consumed at that meal. First, unique meals were defined as eating occasions that occurred at the same time and at the same location (i.
These meals were randomly assigned to each segment according to a deterministic probability based on weights from sales data for the 50 largest chains. For example, all chicken and burger establishments selling a chicken nugget/strip product were identified and assigned a weight to each segment that randomly divided such meals according to weighted sales. This approach assumes that the relative revenues from each product were similar across different brand segments. For the chicken strips/nugget meal component, 84. 3% were assigned to the burger segment while 15. 7% were applied to the chicken segment  [15]. The reliability of the algorithm was evaluated by an independent coder who assigned 138 random meals (412 individual food items) into the 8 FFR segments. The chance-corrected concordance (Kappa) was estimated at 0.
Items consumed at burger restaurants may include burgers but may also include fries, chicken, salad, beverages, milkshakes, desserts, fruit and coffee. Thus, burgers alone contributed 1. 2% of energy to the diets of the 6-11 y age group and 1. 9% of energy to the 12-19 y age group, as reported previously  [16]. By contrast, the total contribution of calories from burger restaurants was much higher: 6. 2% overall, 5. 4% for 4-11 y olds, and 6. 8% for 12-19 y olds. The present estimates, based on nationally representative federal datasets, were generally consistent with FFR segment sales, as published by industry sources  [15]. Among FFRs, burger restaurants are the leading segment, as reflected in the consumption data. In 2009, total sales for burgers among leading national chains were $66 billion (or 47.
ACKNOWLEDGMENTS We acknowledge the following sources for funding:  ANR-08-GENM-022-001  (to P. L. );  ANR-09-GENM-014-002  (to P. W. );  Australian Research Council  (to R. J. H. );  Natural Sciences and Engineering Research Council of Canada  (to D. S. );  CNR-ENEA   Agrifood  Project  A2 C44 L191  (to  G. Gi. );  FINEP-Qualicafé, INCT-CAFÉ  (to A. C. A. );  NSF  grants  0922742  (to V. A. A. ) and  0922545  (to R. M. ); and the  College of Arts and Sciences, University at Buffalo  (to V. A. A. ). We thank  P. Facella  ( ENEA ) for  Roche 454 sequencing and Instituto Agronômico do Paraná  ( Paraná, Brazil ) for fruit RNA. This work was supported by the high-performance cluster of the  SouthGreen Bioinformatics platform (UMR AGAP) CIRAD  (www. southgreen. fr). The C. canephora genome assembly and gene models are available on the  Coffee Genome Hub  (http://coffee-genome.
org)and the CoGe platform (www. genomevolution. org). Sequencing data are deposited in the European Nucleotide Archive under the accession numbers CBUE020000001 to CBUE020025216 (contigs),  HG739085  to HG752429 (scaffolds), and  HG974428  to HG974439 (chromosomes). Gene family alignments and phylogenetic trees for BAHD acyltransferases and NMTs are available in the GreenPhylDB (www. greenphyl. org/cgi-bin/index. cgi)under the gene family IDs  CF158535  and  CF158539  to CF158545, respectively. We declare no competing financial interests.
Methods Amplification and sequencing. Partial amplification of the NS5B (nucleotide position 7915-8303 330 bp) was performed as described elsewhere  [10]. Molecular characterization was first carried out by sequence analysis and phylogenetic studies of HCV NS5B region. A partial amplification of E1 region (nucleotide position 482-1320, 830 bp was also performed as described previously  [11]  in a subset of positive samples. Positive samples were purified, and bidirectional sequence analysis was performed. Consensus nucleotide sequences were used for phylogenetic studies. Phylogenetic analysis. The NS5B or E1 sequences were added to a set of representative HCV genotypes and subtypes for the same gene region available in the databanks (n. 100). The DNA alignments were generated with Clustal W and followed by further phylogenetic analysis using the Neighbor Joining program using 1,000 bootstrap replication with in the Mega package  [12].
Phylogenetic Analysis Comparison with published reference sequences from Genbank showed that 130 (98%) of the Egyptian sequences clustered with genotype 4, and that 3 (2%) clustered with genotype 1. Most cases (105 out of 133; 77%) clustered with the subtype 4a Egyptian  Figure  1  shows the phylogenetic tree corresponding to the 105 subjects infected by HCV genotype 4a. The proximity of strains according to families and houses is suggested although bootstrap value for each familial or household cluster was always below 50%.
reference sequence ED43. The remaining sequences formed 5 distinct groups with strong bootstrap support. Two of these groups clustered with previously described Egyptian sequences identified as subtypes 4m and 4o (n = 2 and 3, respectively),and one with an undescribed yet subtype (provisionally called subtype 4u; n = 20). The last group (n = 3) clustered as a monophyletic group (bootstrap value 99%) corresponding to genotype 1g (n = 2) and 1b (n = 1). The NS5B phylogenetic classification of the Egyptian isolates was confirmed by partial amplification of the E1 region (position 482-1320) in 66 subjects. Altogether these data indicate the presence of a new subtype of type 4. Sequence access numbers are EF694393-EF694525 for the NS5B region and EF694321-EF694386 for the E1 region.
Constructing the full luminance profile Now that the control meshes for the B-spline surfaces are defined, the full luminance profile can be constructed. This is achieved by rendering the surfaces and projecting their depth values. This could be efficiently done by using the depth image produced by an off-screen rendering process with OpenGL or any other renderer. Since we did not have any requirements on performance, we used a MATLAB implementation with a naïve recursive evaluation of the surfaces. The resulting, smooth discretised surface will not correspond exactly with the input edge. There will be a small number of pixels that are incorrectly categorised. That is: a pixel on the 'wrong' side of the edge may be adjusted, when it should have been left untouched; or a pixel on the 'correct' side of the edge may be left untouched when it should have been adjusted.
We believe however that the selection of images (see the supplementary material) sufficiently demonstrates the flexibility of our enhancement technique. To select comparable enhancement strengths between the two methods, a series of images with varying λ values were produced for Trentacoste et al. 's method and their global contrast computed. Matching pairs for each λ were selected such that they minimised the difference between global contrast within the two methods for each image. The enhancement extent was fixed to 4. 2 degrees of visual angle (assuming a 0. 5m viewing distance), which corresponded to σ = 250px in our method. The experiment was designed in Matlab using the Psychophysics Toolbox and performed on a laptop display with the resolution of 1366 × 768. 17 participants (7 female, 10 male, age mean 43.
The autopilot board : Gumstix Verdex Pro XL6P In its future version, our robot, a quadrotor, will be equipped with a Gumstix, which is a Linux-based Computer-On-Module (COM). This processor board is equipped with an XScale-PXA270 processor cadenced at 600MHz with 128MB of DRAM and 32MB of Flash memory. This powerful micro computer was chosen to make our robot fully autonomous in terms of computational resources. In addition, the Gumstix can be easily managed via a wifi connection and a console application (such as PuTTY) from the host computer.