dataesr/scientific-paragraphs-categorization huggingface dataset:
V4 16S rRNA gene pyrosequencing and metagenomic analyses of the samples DNA extracted from three human faecal samples from the ELDERMET project (samples 176, 204 and 205) was analyzed by 454 pyrosequencing of the 16S rRNA V4 region amplicons on a 454 Genome Sequencer FLX Titanium platform as described by Claesson et al. [22]. Two of these samples (176 and 205) were also analysed by direct random shotgun sequencing of libraries with 91 bp paired-end Illumina reads and 350 bp insert size, further assembled using MetaVelvet  [36]  as described by Claesson et al. [5]. Raw metagenomic data are available at the MG-RAST server  [37]  with the following reference number 4491484. 3 and 4491423. 3. To determine the microbiota composition from the metagenomic samples, the rRNA sequences were affiliated using the RDP, SILVA and Greengenes database with a maximum Figure  1.
Probe design procedure using the PhylArray software (adapted from  [27] ). (  1 ) The creation of a database was an essential part of the procedure; making sure this database contained good quality, correctly affiliated sequences was crucial. (2)  The selection of a targeted taxonomic level and the reorganisation of the sequences so that they belonged to the correct taxon. (3) For each different taxon (e. g. family), a consensus sequence on the whole 16S gene sequence was constituted with all the sequences it contained. (4) The software then tested all the possible probe regions on the whole sequence using a 25 nucleotide sliding window with a step of 1 nucleotide. It selected the 5 regions with the best specificity and degeneracy for each taxon and developed all the probe combinations. (5) Finally, the software verified probe specificity performing a nucleotide BLAST against the initial database which allowed to distinct the specific from the explorative probes.
Quantitative PCR analysis Quantitative PCR analysis of three phyla (Firmicutes, Bacteroidetes, Actinobacteria) was performed using previously published primers (Table  1 )  [38, 39]. PCR reactions were performed in a final volume of 20 ml using Brilliant II Ultra-Fast SYBR Green qPCR Master Mix 2X (Agilent Technologies, Palo Alto, CA), in presence of 10 ng of template DNA, following the manufacturer's instructions. Quantitative PCR reactions were performed on the Mx3005P (Agilent Technologies, Palo Alto, CA). The thermocycling protocol consisted of an initial denaturation step at 95uC for 10 min followed by 40 cycles of denaturation at 95uC for 30 s, annealing at 61uC for 30 s and elongation at 72uC for 30 s, followed by a final step producing a dissociation curve. Data analysis was achieved using the Mx Pro qPCR software (Agilent Technologies, Palo Alto, CA).
HuGChip development and probe design The database used for probe design was initially developed by Rajilic-Stojanovicet al. [8]  and completed to achieve a curated database of 1,052 16S rRNA gene sequences, each corresponding to a distinct phylotype. The PhylArray probe design strategy (Figure  1 ) was used for each family in order to take into account the sequence polymorphism (available at http://g2im. u-clermont1. fr/HuGChip/). Five non-overlapping 25-mer regions were selected within each family. For each, the number of nondegenerate combinations varied from 1 up to 182, encompassing explorative probes. Such probes should, therefore, allow the detection of undescribed microorganisms belonging to the targeted taxon. This resulted in a set of 4,441 probes (Table  S1 ), spotted in triplicates and targeting 66 families (Table  2 ).
The specificity of each probe was tested against the curated database: 2,442 probes were specific and 1,919 were explorative. The remaining 80 probes were redundant, meaning probes which could crosshybridize with sequences of different families. Among them, 62 hybridized with sequences from families of the same order of the original target (Table  S2 ). Next, the probe set was also verified using the Greengenes and SILVA databases, leading to respectively 1,852 and 1,486 specific probes. This decrease is likely due to a comparison with an exhaustive repertoire of bacterial sequences, encompassing those from families unexpected or absent in the gut environment. Among the originally defined explorative probes, only 164 and 206 had counterparts in respectively Greengenes and SILVA databases, therefore justifying the word ''explorative'' for all the remaining probes.
The explorative probes which had counterparts in the databases were mostly specific for the intended family (respectively 141 and 136 probes accordingly to Greengenes and SILVA). The remaining 23 or 70 probes were specific for the order (Greengenes, 16 probes; SILVA, 30 probes), the class (none for Greengenes; 9 for SILVA) or the phylum (2 for Greengenes; 10 for SILVA).
In silico explorative probe assessment of the HuGChip In order to assess the relevance of the explorative probe design strategy, these probes were tested in silico with metagenomic data obtained from two human faecal samples. The results indicated that 7 explorative probes could hybridize (100% identity) with metagenomic sequences, 3 with sample 176 and 4 with sample 205. As seen in Table  3 , the MG-RAST affiliation of the detected sequences was in agreement with the family the probes targeted. Surprisingly, one MG-RAST affiliation was directly with a referenced strain, therefore not justifying that the probe was effectively explorative (Sequence #176-3): in fact, difference was due to the presence of ambiguous nucleotides (N) in sequences from the microarray database. Furthermore, another sequence (sequence #205-4) was detected in silico with a probe targeting the Streptococcaceae family while it was affiliated by MG-RAST as an uncultured bacterium (Table  3 ).
Comparison of HuGChip and amplicons pyrosequencing data DNA extracted from stool samples of 3 patients was characterized in parallel by amplicons pyrosequencing of the V4 hypervariable region of the 16S rRNA gene and the HuGChip. The results were analyzed at two different taxonomic levels, the family and the phylum level. For each taxon, the ratios of numbers of RDP classified sequence reads were compared with their corresponding relative abundance obtained with the microarray. Hierarchical clustering at family level for both techniques showed exactly the same clustering pattern (Figure  S1 ). Following this result, Pearson's coefficients were calculated as a measurement of linear correlation between sequence-based RDP assignments ratios versus HuGChip relative abundance of all common taxonomic groups for the phylum and family (Figure  2 ).
Comparison of the HuGChip with metagenomic data In order to avoid eventual bias from analyses limited to the V4 region, together with amplification bias, two of the samples mentioned above were also analyzed using random shotgun sequencing with two different levels of coverage: 14,869 sequences were obtained for the samples 176, and ,10 fold more for the sample 205 (140,766 sequences). This allowed two different sequencing depths in identified 16S rRNA features as provided by MG-RAST: 598 sequences for sample 176 and 1,458 for sample 205. The SILVA database was used to affiliate features at the phylum and family levels and results were compared to the HuGChip hybridization signals using the above criteria. Pearson correlation indicated a high similarity at both phylum and family level between the two technical approaches.
As indicated in Figure  4 , the average Pearson's correlation coefficient was of 0. 93 at the phylum level (respectively of 0. 92 and 0. 94 for samples 176 and 205) and of 0. 88 at the family level (respectively 0. 90 and 0. 85). The Greengenes and RDP databases were also used to compare the two techniques and revealed similar Pearson's correlation coefficients (data not shown). As previously, the differences relate (i) to the difficulty for the DNA microarray to detect some rare taxa, and (ii) to families detected with a relatively high abundance by the microarray which are not detected in the metagenomes. The abundance results of the three techniques were compared with qPCR for three main phyla present in the gut microbiota. Quantitative PCR analysis and comparison with HuGChip The qPCR technique was used here as a benchmark for quantitative analysis of the two most dominant phyla (Firmicutes and Bacteroidetes) present in faecal samples and a less abundant one (Actinobacteria).
In contrast to other microarrays, the HuGChip, with its probe design strategy, is a phylogenetic microarray which targets known bacteria, together with potent uncharacterised respresentatives of the corresponding families. Furthermore, the design strategy allows, for each family, the determination of five regions along the 16S rRNA gene, which are not pre-defined as for example in the HITChip strategy, but are selected to give the best reliability on microarray data analysis. Twenty-five mer probes have been shown to give the best specificity  [28, 43]  and thus, were selected for the HuGChip. Their specificities were first verified in silico using the sequence database used for the design indicating that the large majority of probes could be classified as specific or explorative. A small number of redundant probes were detected.
These probes were frequently specific of the taxonomic levels above the family (e. g. class or order). Such hierarchical hybridization has been reported previously for other microarrays  [21]. Furthermore, the probes were compared against bacterial databases containing sequences from different environments (e. g. soil, water, air, and human microbiota). The consequence was a decrease in probe specificity that might be attributed to bacterial species which had not been described in the human gut microbiota. Most of the explorative probes would not target known species, some (64 and 206 respectively for Greengenes and SILVA) could target known bacteria which were not originally detected in the human gut microbiota. Consequently, accordingly to Greengenes, only 23 of the total explorative probes were identified as hybridizing sequences from bacterial representatives from another family, including 5 targeting another phylum.
These numbers rose to respectively 70 and 21 representatives using SILVA data. In fact, these results are likely over-estimates as the human gut does not host all the bacterial identified so far in all the environments. Moreover, this could explain the different relative abundance of the unclassified sequences between the pyrosequencing of the V4 hypervariable region of the 16S rRNA gene sequence and the HuGChip (respectively 30,2% and 18,3%). Moreover, it was shown that 7 of the explorative probes of the HuGChip harboured 100% sequence identity and a correct taxonomic affiliation at the family level with sequences from the two metagenomes justifying their presence and benefits. These results showed that the probe design helped in minimizing the main limitations of microarrays: the detection of species which were not yet described and/or which were not included in databases used for the probe design.
Other microarrays limitations could be caused by the presence of ambiguous nucleotides (N) in sequences from databases due to sequencing bias and errors: these were also at least partly overcome in this study with the use of the HuGChip explorative approach. Using this strategy, the cross-hybridization of a sequence from another family cannot be excluded but is rather unlikely and if sometimes real, contributes weakly to the overall signal, at least an order of magnitude less  [20]. Using a mock community composed of 5 different families allowed setting the best threshold which had to be used with the HuGChip to analyze gut microbiota samples. As it has been shown that there are strong variations of hybridization signal intensity from probe-target duplexes with similar predicted duplexes  [21, 35, 40, 44, 45] , at least three of the five regions for each family have to show a probe signal to noise ratio above 12 to be considered present in the sample.
Taken into account that the HuGChip gave higher Shannon Diversity Index when compared with either 16S pyrosequencing or metagenomics argues preferentially for a better evaluation of low-represented families while dominant ones (Firmicutes and Bacteroidetes) seemed to be less prevailing. Altogether, the results showed that the HuGChip is a suitable tool to assess the human gut microbiota. Contrary to other microarrays, this tool contains explorative probes which allow the detection of unknown bacteria, without providing strong taxonomic evidences, but probably contributes to a better detection of low-represented families, and increases the specificity at the family level thanks to the use of 5 different regions per family. Pyrosequencing of the V4 region of the 16S rRNA gene provided an important amount of unclassified sequences, part of which may have been detected and affiliated by the microarray to a family: in fact, the presence of explorative probes based on 5 specific ''regions'' spread along the 16S rRNA gene and not restricted to a small variable region is a significant improvement as a majority of the explorative probes do not show counterparts in international database used for the affiliation of sequencing data.
Currently, the format and design of the HuGChip (8615k probes, three probe replicates) make it possible to analyze 16 different samples per run reducing costs and limiting inter microarray bias. Furthermore, the analysis of the data extracted from the microarray is not laborious compared to other high throughput techniques and stands on 5 different regions per family, increasing specificity. Microarrays are also a particularly well-adapted format to monitor the gut bacterial environment over the time and are a mean to give an alternative determination of the bacterial richness and abundance of a sample. Taken altogether, this suggests that the microarray should also be used to characterize and select the samples of interests in order to study them with next generation sequencing techniques.
H NMR Spectroscopy Each thawed urine sample (500 μl) was mixed with phosphate buffer (75 μl, 0. 5 M, pH 7. 0), D 2 O (75 μl), 3-(trimethylsilyl)propionic acid-d4 sodium salt (TSP) (5 μl in D2O, 0. 11 μmol), and sodium azide (5 μl in D 2 O, 12. 3 nmol). Resulting solutions were centrifuged; 500 μL supernatants were then transferred to 5-mm NMR tubes. All 1 H NMR experiments were carried out at 25 °C on a Bruker DRX 500 MHz spectrometer equipped with an HCN 1 H inverse detection probe with triple axis magnetic field gradients. 1 H NMR spectra were acquired using the standard one-dimensional NOESY pulse sequence with water presaturation during the recycle delay of 3 s and a mixing time of 100 ms. Each dataset was averaged over 32 transients using 32,000k points. The time domain data were Fourier transformed after multiplying by a 0.
Data analysis Primarily analysis was performed by calculating fold change values that are the ratios of 24hour excreted metabolite quantities for each rat at each supplementation week to their concentrations at the baseline week (prior to the treatment) and the ratios were presented with a colored heat map (Figure  2 ). The effects of GT and GT formulation to the metabolism of studied rats were also evaluated by comparing the mean concentration changes (versus baseline) among treatments using the generalized estimating equation modification to linear regression models to account for the longitudinal nature of the data. Statistical analyses were performed using SAS software (version 8. 2; SAS Institute Inc. , Cary, NC).
Results Figure  1  shows typical 1 H NMR spectra of urine from rats with four different treatments: 1) GT, 2) AA, 3) GT+AA and 4) Water (Control). Fourteen metabolites were identified and quantified: formate, hippurate, fumarate, urea, allantoin, creatinine, glucose, 2-ketoglutarate, dimethylglycine, citrate, succinate, acetate, alanine and lactate. Catechins and their metabolites were not detected by 1 H NMR, most likely due to their relatively low levels in urine. Preliminary analysis of the data showed that the AA treatment decreased glucose tolerance only slightly, GT and GT+AA increased glucose tolerance slightly (data not shown). Therefore metabolic changes seen were due to mechanisms other than changes in the degree of hyperglycemia. Figure  2  depicted the fold changes in 24-hour excreted quantities of metabolites as compared to baseline levels over time under the four treatments.
Egg parasitism Egg parasitism was quantified for the nine T. pityocampa populations studied by keeping egg batches in the laboratory separately, inside vials until emergence of the parasitoids ceased. All parasitoids that emerged were collected, identified, and counted. The eggs were then uncovered by removing the scales, and all hatched, unhatched, and parasitized eggs were counted. As some parasitoids could have emerged in the field before egg batch collection, the number of parasitized eggs (and thus, parasitism rate) was determined by counting the number of eggs with emergence holes of parasitoids. These data were used for the parasitism analyses, while the number and species of parasitoids that emerged in the laboratory, either in the current year or after undergoing the winter diapause, were used to determine the species frequencies.
, Quebec, Canada). Four biometric variables calculated by this software were selected as follows: (1) Area of the scale (Ain mm 2 ), (2) form coefficient (FORM = 4pA/P 2 where A = scale area and P = scale perimeter in mm). FORM varies between 0 and 1, 1 being a perfect circle and 0 a filiform object, (3) maximum straight width (Win mm), and (4) width to length ratio (W/L, L being the maximum straight length in mm). The WinSEEDLE software works with up to 12 color classes defined by the user, divided into groups of colors. Twelve color classes, representative of the color range found in the scales sampled, were thus selected, and three groups of colors (light, medium, and dark) were then defined, each group including four color classes. This software calculated the percentage of the area occupied by each class, assigning each pixel to one of the 12 classes defined.
Realized female fecundity The number of eggs per egg batch was used as an indicator of the realized female fecundity. This variable was analyzed for the nine populations by generalized linear models (GLM), using a Poisson log linear link function, considering the population factor. A maximum likelihood estimation procedure was used. Differences between populations were analyzed using a least significant differences (LSD) test (a = 0. 05). As we were not able to explicitly take the variable time into account in the procedure described above, although interannual variability of the environmental conditions can affect female fecundity, we conducted the same analysis to compare only the Leiria SP with the Leiria WP, using the data obtained for the two common years of sampling, 2000 and 2004. The factors year and population were considered, as well as their interaction.
The group of girls who were born in winter, spring, summer and autumn numbered 114, 110, 89 and119 respectively. The tested data were evaluated with unitrate analyses of variance using SPSS statistical package. Mean value, standard error, standard deviation and coefficient of variation were calculated. The significance of differences between mean values was evaluated using "t" test. Differences with an error below 5% were considered to be significant. Furthermore, a correlation analysis was used to evaluate the relationship between season of birth, body development and motor-related performance data. Age, body height, body weight, throwing a stuffed ball in one hand, twenty-metre dash, six minutes of continuous running, and obstacle race-test are interdependent variables of development and motor performances of young girls of this age.
The study was organised by, and both body measurement and motor test data were measured by the same teachers. Details of the tests were as follows: 20 m dash: 20 metres straight line was marked with starting-and finishing-line in the sport hall. Behind the starting line a running out possibility place was remained for the children. Children had to start from behind the startingline by word of command from standing position ad had to run with the utmost speed as far as 20 metres distance. Time was measured by stop clock with punctual of tenth sec. Stop clock was started at the time of word of command and stopped when the child's chest reached the horizon of finishing line. Two-two children were tested at the same time. Standing broad jump: Jumping was started from behind the jumping line and finished at the sponge arrival place.
They had to run among the medicine balls, later got through under the first obstacle. Then they had to jump up to the table to creep trough on it and got down on the other side of the table. After tuning the children had to step over the second obstacle then run back and change the two balls. Making way ahead children had to roll over the carpet, then stand up and run as far as the final line. One practice was allowed for children before the competition. Time of performance was measured with punctual of 0. 1 sec. Unitrate analyses of variance were used by SPSS programme to estimate the birth season and the age effect. Mean value, standard error, standard deviation and coefficient variation were calculated and "t" test was used for the evaluation of significance of differences between the main values.
As reference, we choose the PL designed by Yu et al. represented in Figure  1 (a)  [18] , which focuses a normal incident transverse magnetic (TM) plane wave at λ = 650 nm at a focal length of 3 µm. The electric field intensity map and its transverse profile at the focal point are shown in Figure  1(b ) and 1(c). The simulations were performed with COMSOL software  [20] , using m = -12. 9 + 1. 2i for the gold permittivity  [21]  and the scattering boundaries conditions. As we can see, the focal spot is sharp and well contrasted. The thickness of the gold layer is fixed at 450 nm, thus the phase delays are controlled by varying both the width and the position of the slits. This process leads to a 53-slit structure with aspect ratios of up to 37 which are hardly achievable. A much simpler HL was designed with the same focal length as shown in Figure  1.
Synthesis ZnCl 2 (purity 98 %; FLUKA) and [N(C 3 H 7 ) 4 ]Cl (purity 97 %; FLUKA) were dissolved in a HCl (1 M) aqueous solution in a molar ratio of 1:1. Colorless prismatic monocrystals of [N(C 3 H 7 ) 4 ] 2 Zn 2 Cl 6 compound were obtained by slow evaporation at room temperature. The single crystal was selected by using a microscope. After that, it has been washed by absolute ethanol and dried in vacuum desiccators 2 days before the measurements. The reactions sequence for the synthesis is shown in the following equation: 2 N C 3 H 7 ð Þ 4 Â Ã Cl þ 2ZnCl 2 ÀÀ À À À À À À À → HCl 1M ð Þ C 3 H 7 ð Þ 4 N Â Ã 2 Zn 2 Cl 6 Crystal structure X-ray data were collected at 298 K on colorless prismatic crystal of dimensions 0. 35×0. 23×0. 06 mm 3 using a Bruker AXS CCD area detector diffractometer, with MoKα radiation (λ=0.
71073 Å), in φ and ω scans mode. For unit cell refinement, 20,522 reflections with θ ranging from 1. 3°to 30. 5°w ere used. The crystal structure adopts a triclinic system with P1 space group (Z=4). Absorption corrections were based on multiple and symmetry-equivalent reflections in the data set using the SADABS program  [16]. The structure was solved by Patterson methods using SHELXS 97  [17]  and refined by least squares techniques with Crystals 2003  [18]. Anisotropic thermal parameters were employed for non-hydrogen atoms. With regard to the hydrogen atoms attached to carbon atoms, they were positioned geometrically, and initially refined with soft restraints on the bond lengths and angles to regularize their geometry, C-H in the range of 0. 93-0. 98 Å. Diamond computer program  [19]  was used to prepare drawings.