dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was performed during the first author's postdoctoral fellowship at the  Geological Survey of Finland (GTK). The authors would like to thank  S. Töllikkö  ( GTK ) for careful preparation of polished sections for LA-ICPMS analysis and  A. Henttinen  ( GTK ) for her precious help during HPIC analysis. M. Cuney (GeoRessources) provided samples from  U deposits. P. Nurmi ,  R. Lahtinen ,  F. Molnár  and  H. O'Brien  (GTK) are greatly acknowledged for having encouraged this work. Robert Ayuso  is warmly acknowledged for careful editorial handling. Detailed reviews by  Mathias Burisch  and an anonymous reviewer greatly helped improving an earlier version of the manuscript.
The scalar-data task is simple and can be performed by the low-power general CPU, whereas the multimedia task is computation intensive and is appropriate to run on the specific units such as the DSP or GPU (Graph Processor Unit). By equipping more than one type of microcontroller on the node, the features of the WMSN hardware can better match that of the software. Consequently, the energy and delay performance of the WMSN node can be improved. In terms of the discussion above, we design a fire detection WMSN node MiLive-v2 as is shown in Figure  8. Three primary microcontrollers are equipped on this node. One is the 8-bit low-power tiny microcontroller AVR ATmega128rfa1, which can be used to run the simple tasks. One is the 32-bit powerful ARM microcontroller ARM1176JZF, which can be used to run the more complicated tasks.
The other is the DSP unit, which can be specially used to perform the image processing. These microcontrollers can communicate with each other through the GPIO ports, and any microcontroller can control the working status (sleep, active, or power off) of the others by sending the control commands. If a microcontroller has no tasks to run for the moment, it can enter the idle status to save the energy. If it has no tasks to run for a long time, it can be powered off directly. The BitCloud software firmware  [34]  is burned on the AVR microcontroller while the Linux firmware is burned on the ARM microcontroller.
This is because the circuit construction of the AVR core is simpler and less energy cost will be required by its operations. The scalar-data packet is more energy efficient to be transmitted by the Zigbee module rather than the Wi-Fi module. This is because Zigbee is a specific low-cost, lowpower, wireless mesh network standard dedicated to lowbattery devices  [35]. However, for the transmission of large amount of multimedia data, the Wi-Fi module will be more appropriate. To evaluate the optimization performance of multicore MiLive-v2, we run the fire detection application, respectively, on the MiLive-v2, the single-core ARM platform, and the single-core AVR platform. The energy cost and execution time on these platforms are shown in Table  3. From the result, it is seen that the single-core AVR platform has the highest energy cost and longest execution time.
Acknowledgments Our thanks go to the support from  National Natural Science Foundation of China  (Grants nos. 61702387 ,  61672398 ,  61771354 ),  Provincial Science & Technology Pillar Program of Hubei  (Grants nos. 2017AAA027 ,  2017AAA042 ),  Provincial Science & Technology International Cooperation Program of Hubei  (Grant no. 2017AHB048 ),  Key Natural Science Foundation of Hubei Province of China  (Grants nos. 2015CFA069 ,  2017CFA012 ), and  Natural Science Foundation of Hubei Province of China  (Grant no. 2017CFB302 ).
In this study, IBM SPSS v20 and R were used to conduct the analysis. The Statistical G Power program was used to calculate sample size. We estimated a minimum total sample size of 84 to achieve an effect size of 0. 35, the power of 0. 8 and type 1 error of 0. 05. Descriptive statistics are given via tables. Chi-square test of independence was used to detect the significant relationships between nominal variables. To test the differences between means, t-test, one-way ANOVA for normally distributed data and Mann-Whitney U test and Kruskal-Wallis H test for nonparametric data were used. To detect from which groups the difference originated, Tukey's honestly significant difference and Dunn's tests were used.
Target Site Selection The genome sequence of E. coli K-12 was obtained from the KEGG database  (Ogata et al. , 1999). For secondary and tertiary structure prediction of the targeted mRNAs, the Mfold  (Zuker, 2003) , and RNAComposer  (Popenda et al. , 2012)  software were used, respectively. To ensure target specificity of PNAs, the target sites were examined for uniqueness within the E. coli K-12 genome using the on-line search tools GenoList  (Lechat et al. , 2008)  and RiboScanner  (Górska et al. , 2016) , with special consideration given to the start codon region that includes the consensus Shine-Dalgarno sequence.
qPCR was performed using SYBR R Green Master Mix (Bio-Rad Laboratories Inc. , United States) according to the supplied instructions. The level of the transcript of the essential gyrA gene was used for normalization  (Rocha et al. , 2015). The gene-specific primer pairs used for qPCR are listed in Supplementary Table  3. Amplification was performed in a Biorad CFX96 Touch TM Real-Time PCR Detection System (Bio-Rad Laboratories Inc. , United States). Relative quantification of gene transcription was performed using the CT method  (Livak and Schmittgen, 2001). The presented data represent averages of triplicate determinations, performed at least two times. Statistical analyses were performed with one-way ANOVA with Fisher's test for multiple comparisons. Differences between treated and non-treated control samples were considered not significant (P ≥ 0.
Then the mask was moved at a velocity of 5-10 μm/s, according to the contact line recession speed. Faster or slower velocities caused only a small perturbation in wrinkle order, maintaining the wavelength and alignment the same. Film Characterization. The films were characterized with optical and electronic microscopy. The thickness was determined through the use of an OM Contour GTK optical profilometer. Data were analyzed with the Vision 64 software under white light illumination vertical scanning interferometry mode (VSI). Films were cut at different points to assess the different regions and to get an average film thickness. Wavelength Determination. The wrinkle spacing (i. e. , wave length, λ) was determined through a Gaussian analysis of distributions of 100 measures of distances extracted from 5 pictures per sample.
Mitochondrial DNA sequencing and multiple alignment. Genomic DNA was isolated from peripheral blood samples using a DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer's protocols. Double-indexed genomic libraries were constructed for each sample and capture-enrichment for mtDNA was carried out as described previously  12, 13. Sequencing was carried out on the Illumina platform and reads were processed and quality control measures carried out as described previously  14. Reads were mapped to the Reconstructed Sapiens Reference sequence (RSRS)  15, 16  using an in-house alignment program and a multiple sequence alignment was performed using MAFFT  16.
Data analysis. Haplogroups were assigned using HaploGrep2  17  with PhyloTree mtDNA tree Build 17  18. Note that in the text haplogroup labels without an asterisk include all downstream subhaplogroups, whereas haplogroup labels with an asterisk exclude all downstream subhaplogroups. For instance, B4 refers to all haplotypes belonging to haplogroup B4, while B4* refers to haplotypes assigned to B4, but not assigned to any of the defined subhaplogroups within B4. A Bayesian skyline plot (BSP) and maximum clade credibility (MCC) trees were constructed using BEAST 1. 8, based on Bayesian Markov chain Monte Carlo (MCMC). The software jModel test 2. 1. 7  19 was used to select the best model for creation of the BEAST input file by BEAUTi v1. 8  20. The mtDNA genome was partitioned into the coding and noncoding regions with respective mutation rates of 1.
Hereafter we use "MSEA" to refer to this dataset. In total, 2742 complete mtDNA sequences were employed to construct the parsimony trees of complete mtDNA sequences by haplogroup, using the mtPhyl program (https:// sites. google. com/site/mtphyl/). Median-joining networks of the major haplogroups were constructed with Network 5 (www. fluxus-engineering. com) and visualized in Network Publisher, and QGIS was used to construct contour maps depicting haplogroup frequencies (https://www. qgis. org/en/site/).
Results and Discussion Vietnamese mtDNA haplogroups. We sequenced the entire mtDNA genome from 609 unrelated Vietnamese individuals to an average read depth of 840X (range: 80-5249). After alignment against the RSRS and haplotype assignment using Haplogrep2 and PhyloTree Build 17  4, 18  , 399 distinct sequences (haplotypes) belonging to 135 haplogroups were identified, all belonging to the two macro-haplogroups M and N (Table  S2  in Online Resource 1). Of the 135 haplogroups, 46 (33. 82%) are singletons. Overall, F1 is the predominant haplogroup (19. 38%) followed by B4 (17. 41%), M7 (9. 36%), B5 (7. 22%), and M71 (6. 08%); these haplogroups are also common in other MSEA populations  6, 11, 23, 28  (Table  S3 , S4 in Online Resource 1). The distribution of major haplogroups with respect to language family is shown in Figure  S2  (Online Resource 2) both for the Vietnamese populations alone and in combination with the MSEA dataset (including the present Vietnamese data).
Among the language families in MSEA, the AA groups are the most distinctive, as they have the lowest frequency of B4 and (along with AN groups) a relatively high frequency of M71 (Figure  S2b , Online Resource 2). Novel Vietnamese mtDNA lineages. To identify Vietnamese-specific lineages (clades or branches consisting of sequences only from Vietnam), we constructed phylogenetic trees relating 2742 entire mtDNA genome sequences (including 609 newly sequenced mitogenomes from the present study and 2133 previously reported sequences from MSEA). Several previously undescribed sub-branches of haplogroups A, B, C, D, F, M and N were identified (Figures S5-S16 in Online Resource 2). In total, 111 novel lineages unique to Vietnam were found in the dataset. The majority of these belong to haplogroups B, F and M (25, 26 and 29, respectively); these are major haplogroups of MSEA, accounting for 76.
The phylogeography of haplogroup D. Haplogroup D is found in Northeast Asia and Central Asia  40, 41  and is also one of the founding haplogroups of the New World  42, 43  ; in MSEA it coalesces at ~38. 5 kya (Table  S6  in Online Resource 1). Phylogenetic analysis reveals four new sub-branches (labelled 1-4 in Figure  S9 , Online Resource 2). Of 174 sequences from haplogroup D in the MSEA dataset, 59% belong to D4. This haplogroup is distributed in groups from all five language families and network analysis shows a signature of an expansion, with two haplotypes shared by groups from different language families: one shared between AA and TK groups from Thailand, and the other between HM and ST groups from Vietnam (Fig. 4a, b ). D5 is also found in groups from all   S1  in Online Resource 1).
Acknowledgements We thank all sample donors for contributing to this research. We thank  Bui Quang Thanh ,  Alexander Hübner ,  Vladimir Bajić ,  Wibhu Kutanan  and  Murray Cox  for valuable advice and kind support. This research was funded by the  Ministry of Science and Technology, Vietnam  ( DTDL. CN-05/15 ) and by the  Max Planck Society. BP acknowledges the  LABEX ASLAN  ( ANR-10-LABX-0081 ) of  Université de Lyon  for its financial support within the program " Investissements d' Avenir"  ( ANR-11-IDEX-0007 ) of the French government operated by the  National Research Agency (ANR).
Study design In vitro experiments were planned and executed in the context of controlled laboratory experiments and with the help of expert staff if required (e. g. confocal analysis and z-stacks; Lysotracker assay). For each of the indicated experiments/assays we performed: (i) multiple (minimum of two) experimental and biological replicates and (ii) multiple (minimum of two) independent experiments, in order to generate an adequate number of samples in each group. The experimental sample size was determined on the basis of previously collected data in the same experimental settings and by the interim analysis of results. No data were excluded from the analysis, unless in the presence of a clear technical flaw (e. g. incomplete transfer of protein on nitrocellulose membranes; unexpected results in the positive/negative controls included in the experiments).
Image acquisition and analysis Samples were visualized with Nikon Eclipse Ni microscope using double laser microscopy with Nikon Plan-Fluor (×20 -air and ×40 -air magnification) objective lens (Nikon, Monacalieri, Italy). Images were acquired using a camera (Nikon DS Fi2) and NIS-Element F4. 00. 00 acquisition software (Nikon). Immunopositive area of acquired images (expressed in pixels) was calculated using the ImageJ software. ND samples were used to set the threshold. The immunopositive area was normalized to the number of nuclei in the same field. At least 10 fields/ sample with 20-30 cells/field were quantified in n = 3 experiments in triplicate.
Confocal and Z-stack Co-labeling between sulfatide (Sulf) and differentiation markers (β-tubulin III, GFAP, CNPase) was analyzed in differentiated cultures. Z-stacks were recorded utilizing a PerkinElmer UltraVIEW ERS Spinning Disk Confocal (PerkinElmer Life Sciences, Inc. ) using a ×63/NA1. 4 Plan-Apochromat oil-immersion lens (Carl Zeiss, Jena, Germany) with a 405 nm diode laser, 488 nm argon laser and a 568 nm krypton laser excitation wavelengths. Sequential confocal images were collected at 0. 25 μm intervals covering 14 μm depth (56 total scanning images for each channel). The 3D signals and the orthogonal projection representations were obtained by means of Volocity Software (v5. 2. 1; PerkinElmer-Improvision, Lexington, MA, USA). Images were imported into Adobe Photoshop CS4 (USA) and adjusted for brightness and contrast.
Statistical analysis In vitro cell counts, data from qRT-PCR, western blot, mass spectrometry, enzyme activity, and data obtained following the quantification of the immunopositive area by the ImageJ software were analyzed with Graph Pad Prism 6. 0c for Macintosh and expressed as the mean ± standard error of the mean (SEM, with n ≥ 3). Unpaired t test (statistical significance: p < 0. 05) was used to compare two independent groups. One-way ANOVA (followed by Bonferroni post-test) or Kruskal-Wallis test (followed by Tukey's or Dunn's post-test) were used for statistical analyses of more than two independent groups in the presence of Gaussian or non-Gaussian distribution of data (assessed by Kolmogorov-Smirnov test), respectively. The number of samples and the statistical test used are indicated in the figure legends.
Acknowledgements We acknowledge  Cesare Covino  for help with image acquisition,  Valeria Berno  and  Desire' Zambroni  for help with Lysotracker analysis,  Carla Panzeri  for electromicroscopy,  Fabrizia Guarnieri  for Synaptotagmin1 antibody,  Giovanni Palazzo  for MEA experiments and analysis,  Vania Broccoli  for helpful discussion and critical reading of the paper. Human brain tissues were obtained from the  NIH NeuroBioBank ; https://neurobiobank. nih. gov. Part of this work was carried out in ALEMBIC, an advanced laboratory established by the  San Raffaele Scientific Institute  and the  Vita-Salute San Raffaele University. This work was funded by  Fondazione Telethon , projects  TGT11B02  and  TGT16D02  to A. G. This work was also partially supported by unrestricted grants from  "Cinque per mille e Ricerca Corrente, Ministero della Salute " to M.
(i) B. pertussis isolates should be selected from different geographical regions and be epidemiologically unrelated. (ii) Equal numbers of isolates from vaccinated (n ϭ 15) and unvaccinated individuals (n ϭ 15) should be collected. The selection of isolates should be made from individuals younger than 5 years of age where possible. (iii) For those countries with large numbers of isolates in their collections, the isolates should be randomly selected in addition to the above criteria. The data that were collected included the original code of the isolate, the country, the date of collection, and the city, and the characteristics of patients from whom B. pertussis was isolated. The patient characteristics included sex, age, vaccination status, the number of doses received, and hospitalization status  (8).
(14)  and Litt et al. (15). The results were expressed as the MLVA type (MT), e. g. , MT18, MT27, etc. Reference strains with known MTs were included as positive controls in each run. New MTs were submitted to the MLVA database (http://www. mlva. net/bpertussis/default. asp) administrator for nomenclature. PFGE. All isolates were analyzed according to the standardized recommendations for the typing of B. pertussis with minor modifications using XbaI (R0145S; New England BioLabs, USA) as a restriction enzyme  (8, 16, 17). PFGE profiles were defined as individual profiles with distinct DNA band patterns (at least one band difference) and were designated BpSR1, BpSR2, BpSR3, etc. (18, 19). Isolates with new profiles were designated EU4_1, EU4_2, etc. according to the study name. A cluster analysis was performed with the unweighted pair group method with average linkages (UPGMA), with 1% band tolerance and 1% optimization settings.
French strain FR3693 (negative for PRN) and purified PRN were used as the controls. Vaccination status. During the period 2012 to 2015, 130 (49. 1%) of the infected individuals were vaccinated and 135 (50. 9%) were unvaccinated. Statistical analysis. BioNumerics software version 6. 6 (Applied Maths, Sint-Martens-Latem, Belgium) was used to calculate the PFGE cluster analysis. Chi-square tests for P values between vaccinated and unvaccinated subjects were calculated using GraphPad Prism version 4. 0 (San Diego, CA, USA). Twotailed P values of Ͻ0. 05 were considered significant. The Simpson diversity index (SDI) was calculated based on the formula D ϭ 1 Ϫ Αn(n Ϫ 1)/N(N Ϫ 1), where n indicates the number of individual profiles and N is the number of all profiles.
9%) of 265 isolates. Second was ptxP3-prn2-Fim3-MT27-BpSR10, with 37 (13. 9%) isolates. Third was ptxP3-prn2-Fim3-MT27-BpSR11, with 34 (12. 8%) isolates and fourth, ptxP3-prn2-Fim2-MT27-BpSR10, with 20 (7. 5%) isolates. We compared these main profiles with the vaccination statuses of the subjects. We did not find any significant difference (all P Ͼ 0. 1) between vaccinated and unvaccinated individuals, e. g. , the most common profile, ptxP3-prn2-Fim2-MT27-BpSR3, was found in isolates from 30 vaccinated and 20 unvaccinated subjects (P ϭ 0. 1184). Pertactin deficiency. Of the 265 isolates included in the EUpert IV collection, 66 (24. 9%) were found to be PRN negative, whereas the corresponding frequency was only 6. 4% in the EUpert III collection (data not shown).
DISCUSSION In this study, we analyzed 265 B. pertussis isolates collected from nine European countries during the period 2012 to 2015 and compared the results to those from three previous EUpert studies starting in late 1990s. Although the numbers of participating countries in each study varied, Finland, France, the Netherlands, and Sweden have participated in all four studies. Our study showed that the dominant alleles ptxA1, ptxP3, and prn2 in circulating strains are different from those used for the production of ACVs in European countries (for Denmark, data not available)  (21). However, country-based genetic differences of B. pertussis isolates were identified especially with PFGE analyses. The serotype has also changed from Fim3 to Fim2 in several countries, although ACVs used in many of these countries do not contain any fimbrial antigens.
40±0. 03 Ga are estimated to be 0. 047±0. 005 and 0. 033±0. 005, respectively. For the Mesoarchean samples, the combined 146,147  Sm-142,143 Nd and 176 Lu-176 Hf data are consistent with a near-chondritic mantle source. On the other hand, Nulliak ultramafic rocks were derived from a mantle reservoir with superchondritic Lu/Hf and Sm/Nd. The Nulliak parent reservoir, however, does not plot on the ε 176 Hf-ε 143 Nd mantle array defined by modern oceanic basalts. Instead, the Nulliak source more likely belongs to a distinct array defined by Eo-and Meso-Archean komatiites. These results are interpreted in the framework of a simple model of crystallization of a primordial magma ocean. It appears that the fractionation observed in the mantle source of Nulliak was most likely generated by crystallization of a garnet-bearing assemblage in the shallow mantle, above the transition zone rather than by perovskite fractionation in the lower mantle.
The chosen partition coefficients for this model are compiled from the literature and reported in Table 4  (Salters and Longhi, 1999; Draper et al. , 2003; Corgne, 2004; Corgne et al. , 2004; Corgne and Wood, 2004; Corgne et al. , 2012). Whenever possible, partition coefficients were selected from experiments using pyrolitic or chondritic starting compositions at the appropriate pressure range (i. e. 23-25 GPa for bridgmanite, ferropericlase and Caperovskite, 15-25 GPa for majorite, 2. 8-9 GPa for garnet). When high pressure data were unavailable (clinopyroxene, orthopyroxene), partition coefficients at 2. 8 GPa from  Salters and Longhi (1999)  were used. At lower mantle pressures, Lu/Hf and Sm/Nd fractionation are controlled by bridgmanite and Ca-perovskite, whereas at upper mantle pressure, garnet and clinopyroxene have the strongest influence.
The sole exception concerns the Isua boninites, for which the mean 147 Sm/ 144 Nd and 176 Lu/ 177 Hf values were determined from the mantle source parameters calculated individually for each sample; the uncertainties shown represent the 2 S. D. variations of these mean values. Note that model ages of differentiation for the mantle sources of Kaapvaal komatiites (i. e. T d in Equation (  1 )) cannot be inferred from 146,147 Sm-142,143 Nd, either due to absence of 142 Nd data or due to an apparent decoupling of the 146 Sm-142 Nd and 147 Sm-143 Nd systems  (Puchtel et al. , 2016). Accordingly, the time-integrated Lu/Hf and Sm/Nd values of their mantle sources cannot be estimated with as much confidence as for the Isua and Nulliak sources and should be considered first-order estimates. The blue and yellow envelopes represent the calculated compositions of magma ocean cumulates predicted to crystallize at lower and upper mantle pressures, respectively (See Section 5 and Figures  8 9 ).
on the ICP-MS work. We thank Béatrice Luais and Albert Galy for technical discussions during the development stages of this work. We also thank Emilie Thomassot and all members of the Saglek Block expedition as well as those who participated in its organization. We are grateful to the financial support from Agence Nationale de la Recherche (grant ANR-11-JS-0012) and the Région Lorraine, through a doctoral fellowship allocated to P. M.
The libraries were then normalized on specific beads according to the Nextera XT protocol (Illumina), pooled into a single library and then loaded onto the reagent cartridge. Automated cluster generation and Paired-End sequencing with dual index reads was performed in a single 39-hour run in a 2 × 251-bp. Total information of 9. 8 Gb was obtained from a 1,165 K/mm 2 cluster density with 88% (18,993,000 clusters) of the clusters passing quality control filters. Within this pooled run, the index representation of R. ornithinolytica strain Marseille-P1025 was determined to be 5. 51%. The 1,046,713 Paired-End reads were filtered according to the read qualities. Genome annotation and comparison. The sequencing reads were assembled using the A5 assembler  25. Then, a step of finishing was done using the Mauve software  26  and CLC bioserver.