dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantitative RT-PCR (RT-qPCR) assays confirmed that all target genes were efficiently suppressed by the dsRNA molecules (Dataset S3). All 135 CP dsRNAs were injected into the thorax of fifthinstar nymphs. In general, the injection of two different dsRNAs targeting one gene caused the same phenotype. RNAi against 15 CP genes (NlugCpr6, NlugCpr47, NlugCpr56, NlugCpr61, NlugCpr62, NlugCpr64, NlugCpr69, NlugCpr73, and NlugCpr83 from the CPR family; NlugCPLCP2 from the CPLCP family; NlugCPAP1-E, -H, -K, and -N from the CPAP1 family; and NlugCPAPn-7 from the CPAPn family) led to lethal phenotypes with high mortality, indicating their essential roles in BPH integument structure and normal development. The survival rate of the treated BPHs was <20% 9 d after dsRNA treatments, compared with ∼90% survival rate of BPHs treated with dsRNA targeting the mock gene encoding green fluorescent protein (dsGFP) (Fig.
Knockdown of the common region of NlugCPAP3-C5a and -C5b or of the specific region of NlugCPAP3-C5b resulted in very thin body shape and ∼90% death rate after adult emergence, but knockdown directed against the specific region of NlugCPAP3-C5a showed no obvious phenotype (see more information in SI Appendix, section 3). Since parental RNAi has been demonstrated to occur in BPHs (10), we conducted the RNAi experiments on the newly emerged female adults before their ovaries became mature to observe the phenotypes of egg production and embryos from the next generation. A total of 68 CP genes (either relatively highly expressed in the ovary or expressed obviously during embryogenesis) were selected for RNAi experiments (Dataset S4). The average number of total eggs produced by one female adult and the average egg hatchability after RNAi were used to assess the function of each corresponding CP.
DR, developmental retardation; LEH, low egg hatchability; LEP, low egg production ability; ME, malformed eyes; NM, second-third nymph molting. Phenotypes in the brackets indicate TEM observation results: C-en, curved and disordered endocuticle; L-en, loose and thick endocuticle; T-en, thin and disordered endocuticle; T-ex, thin exocuticle. *The phenotypes listed were significantly decreased according to statistical test (Dataset S4). Phenotypes I-IV were classified as shown in Fig. 3. † Egg hatchability was <10%. ‡ The phenotypes were lethal. These results indicate that the functions of CPs during nymphal stages through the adult stage were relatively constant and consistent. However, the functions of CPs at egg/embryo developmental stage were dramatically different from that of nymphal or adult stages.
We estimated the expression level of each transcript using FPKM (fragments per kilobase of exons per million fragments mapped) values (Dataset S5) and found that BPH CP genes had highly tissue-specific expression patterns (FPKM). A total of 126 genes (90. 0%) were highly expressed in the integument (Fig. 5A , branch d). The remaining 14 genes (10. 0%) had their highest expression level in other tissues: 7 in the ovaries, 4 in the testis, and 3 in the gut (Fig. 5A,  branches a, b,  and c ). Intriguingly, knockdown of four genes (NlugCpr3, NlugCpr8, NlugCpr10, and NlugCpr90) highly expressed in the ovaries significantly decreased egg hatchability. To study the developmental expression patterns of N. lugens CP genes, we selected four time periods (egg, fourth-instar, fifth-instar, and adult stages) covering three developmental stages (egg, nymph, and adult).
nlm. nih. gov/, BioProject PRJNA177647; 27,571 coding protein sequences) and transcriptomic databases (https://www. ncbi. nlm. nih. gov/sra, accession no. SRX023419; 21,908 coding protein sequences). The full-length cDNA sequences were obtained from transcriptomic databases, and most of them were confirmed by RT-PCR. The full cDNA sequences of some CP genes were cloned by using RACE core sets (catalog nos. 6107 and 6106; TaKaRa) according to the manufacturer's instructions. The primers used are shown in Dataset S1. Sequence Analysis. The ORF prediction was performed on the Softberry website. The SMART program (smart. embl-heidelberg. de/) was used for the identification of modular domains. The signal peptide prediction program SignalP 4. 1 server (www. cbs. dtu. dk/services/SignalP/) was used to predict Nterminal signal peptides.
The multiple sequence alignment of the CPLCP family proteins was carried out by using ClustalX software  (18). The phylogenetic trees were constructed via the neighbor-joining method by using the MEGA6. 06 program  (19). Homologous relationships were determined by bootstrap analysis based on 1,000 or 5,000 replications, as each legend describes. Cuticle Protein Sample Preparation for UPLC-MS/MS. Four different BPH cuticle tissue samples were prepared for UPLC-MS/MS analysis. Cast first-instar nymph cuticles (CC1) were collected from ∼12,000 first-instar nymphs. Cast third-to fifth-instar nymph cuticles (CC3-5) were collected from ∼700 fifth-instar BPHs, 200 fourth-instar BPHs, and 200 third-instar BPHs. The BPHs were maintained in a polycarbonate box of 20 cm in length, 14 cm in width, and 9 cm in height with fresh rice seedlings.
The regions were designed to have no other similar sequences in the genome (SI Appendix, section 3). The purified DNA template was used to produce dsRNAs, and the primers were designed to contain the T7 RNA polymerase promoter at both ends (Dataset S1). The dsRNA was then synthesized through RT-PCR amplification by using the MEGA script T7 High Yield Transcription Kit (catalog no. AM1334; Ambion), according to the manufacturer's instructions. The concentration and quality of dsRNAs were measured with NanoDrop 2000 (Thermo Fisher Scientific), and the size of the dsRNAs was verified via electrophoresis in a 1% agarose gel. The quantities of dsRNA injected into second-and fifth-instar nymphs and adults were ∼25, 200, and 250 ng, respectively. Female adult BPHs treated with dsRNAs were crossed to healthy male BPHs for 3 d and then transferred onto fresh rice seedlings in glass tubes.
A C K N O W L E D G E M E N T S This study was supported by the Agence National de la Recherche (ANR) GeoSMEC contract ANR-12-BS06-0016. The paper was completed at Oxford with support for MT from the Natural Environment Research Council large grant, Looking Inside the Continents from Space (NE/K011006/1). We are grateful to Eric Dunham and an anonymous reviewer for their comments that improved our manuscript. We also thank J. -P. Ampuero for providing the SEM2DPACK code (available at https://sourceforge. net/projects/s em2d/) and for his useful comments on how to add the new constitutive model to the SEM code.
accelerating trend observed over the last decade  4. This also represents a major concern for the three million residents of the nearby city of Naples (Italy). The renewal of the Campi Flegrei activity is driven by repeated injections of magmatic fluids into the hydrothermal system  4. Over the last 65 years, this has led to progressive pressurization Black and red dotted lines, the two nested caldera formed by eruptions that occurred ~39 and ~15 ky ago  [16] [17] [18]. This map was generated using the Esri ArcMap 10. 2 software (http://www. esri. com), with the GIS database from Bechtold, et al. 60. (b) South view of Bocca Grande and Bocca Nuova (red and blue arrows, respectively, photo Marceau Gresse, ISTerre) with the evolution of (c) temperature (°C) and (d) CO 2 /H 2 O mass ratio. Continuous line, an averaged value, performed with 2 month moving windows.
The data and analytical methods used are available in Chiodini, et al. 4, 8. Analytical errors were assessed as lower than 3% by these authors. that generated a total cumulative ground uplift of ~4 m  19, 20  , along with thousands of earthquakes and the enlargement of the degassing area  8, 21. This activity is mostly focused underneath the Solfatara volcano (Fig. 1a ). Numerous multidisciplinary studies have been carried out over the last decade to image this active crater. Several electrical resistivity surveys  [22] [23] [24] [25] [26]  , seismic tomography  [27] [28] [29]  and petrophysical studies  30, 31  have inferred two main hydrothermal structures of the Solfatara: the liquid-dominated plume of the Fangaia mud pool, and the gas-dominated area beneath the main fumaroles. Using seismic reflection, Bruno, et al.
Among these, 17 ERT profiles included 19,861 measurements taken across the fumarolic area to improve the spatial resolution of the subsoil structure (Table  S1  in SI). Six radial and two circular resistivity profiles centered on the two main fumaroles, Bocca Grande and Bocca Nuova, allowed us to obtain resolution down to 1 m. The resistivity inversion was performed using the E4D code  54  , with an initial resistivity model based on an Audio-Magnetotellurics data inversion. More details about the ERT acquisition, its processing and inversion are available in Gresse, et al. 26. A sensitivity map of the resistivity model is also presented in Fig. S3  in SI.
Acoustic noise localization. The localization of continuous seismic sources in hydrothermal systems is performed using a beamforming technique called MFP  40, 55, 56. For tremor activity, many overlapping events are recorded, and therefore individual time-picking algorithms are not efficient for identification and localization. MFP is based on the phase coherence of recorded time windows that consist of incoherent noise sources recorded by a geophone network  57  , and therefore is adapted to localize dominant acoustic sources with numerous individual events issuing from the same area. Using a ground-velocity model, MFP is used to create a synthetic acoustic field for each test source of a 3D grid, and to compare this with the data recorded with geophone arrays. Finally, MFP defines the probability of the source position for each element of the grid, where the highest correlations indicate the best localization.
Acknowledgements Most of the computations presented here were performed using the Froggy platform of the CIMENT infrastructure (https://ciment. ujf-grenoble. fr), which is supported by the  Rhône-Alpes region  ( GRANT CPER07_13 CIRA ), the OSUG@2020 labex (reference  ANR10 LABX56 ) and the  Equip@Meso project  (reference  ANR-10-EQPX-29-01 ) of the program Investissements d' Avenir supervised by the  Agence Nationale pour la Recherche. This study was supported by the  Med-Suv  project. MED-SUV received funding from the  European Union   Seventh Program  for research, technological development and demonstration under the  FP7 ENV. 2012. 6. 4-2call  and grant agreement N°  308665. The authors are grateful to  Steven Ingebritsen  and the two anonymous reviewers for their useful comments and suggestions that greatly helped to improve and clarify the text.
Data availability. The geophysical datasets generated and analyzed during this study are available from the corresponding author upon request. The 3D electrical resistivity forward and inverse problem was performed with the open-source code E4D (Copyright 2014, Battelle Memorial Institute), which is available online at: https://e4d. pnnl. gov/Pages/Home. aspx. The TOUGH 2 model was post-processed using TOUGH2Matlab, a free program that is available online at http://esd1. lbl. gov/research/projects/tough/licensing/free. html (written by Antonio Rinaldi). The compositions of these fumarole emissions at Campi Flegrei are available from the literature, up to December 2015  4. Within one year the authors intend to publish all of the data from this paper and Gresse, et al. 26  in the open journal Scientific Data.
Feature Selection Scores In the feature selection context, we dispose a dataset of N color texture images represented in a D-dimensional feature space. We denote X, the associated data matrix where x r i is the rth feature value (r = 1,. , D) of the ith color image I i (i = 1,. , N). X =        x 1 1. x r. x r N. x D N        =        x 1. x i. x N        = f 1. f r. f D. Each of the N rows of the matrix X represent a color texture x i = x 1 i ,. , x D i ∈ R D , while each of the D columns of X define the feature f r = x r 1 ,. , x r N T ∈ R N.
Histogram Selection Scores In the histogram selection context, we dispose a dataset of N color textures images. Each image I i (i = 1,. , N) is characterized by D histograms. The whole data is summarized by the matrix H as: H =        h 1 1. h r 1. h D 1. h 1 i. h r i. h D i. h 1 N. h r N. h D N        =        h 1. h i. h N        = h 1. h r. h D where h r i is the rth histogram computed from the ith color texture image I i. It is defined by h r i = h r i (1),. , h r i (k),. , h r i (Q) where Q is the histogram bin number. The ith row of H represents a set of D histograms h i corresponding to the image I i and whose dimension is (D × Q). For each column, h r = h r 1. h r i. h r N T regroups the values of the rth histogram across the N images. The histogram selection scheme evaluates each histogram h r in order to select the most discriminant one among the D candidate histograms.
Histogram Selection To evaluate a supervised color texture classification scheme, it is usual to divide the considered database into a learning and a testing image subset. The learning subset is used to train the classifier during the learning stage, whereas the testing subset is used during the classification stage to evaluate the performances of the proposed method. In the histogram selection framework, the learning stage aims to build a low dimensional discriminating subspace thanks to labelled or unlabelled training data. Different models are proposed in order to evaluate the relevance of the candidate subspaces  [37]. The wrapper model uses the classification accuracy as discriminating power of the candidate subspaces. When a classifier such as the nearest neighbor is considered, it involves to decompose the learning subset into a training and a validation subsets.
Although this model is time consuming and classifier-dependent, it gives good results and determines easily the dimension of the selected subspace by searching the best classification accuracy. On the contrary, filter models evaluate the relevance of the candidate subspaces without classifying the images. They are less time consuming but the determination of the dimension of the subspace to be selected is not so easy. To obtain a good compromise between dimension selection, computation time and classification result, embedded models are preferred  [38]. These approaches combine a filter model to determine the most discriminating subspaces at different dimensions and a wrapper model to determine the dimension of the selected subspace  [6]. The approach used in this paper is an embedded histogram selection scheme which requires to split up the initial image database in a training, a validation and a testing image subset, according to a holdout decomposition.
Experiments In this section, the proposed histogram selection scores are compared thanks to three benchmark color texture image sets: Outex-TC-00013, USPTex, and NewBarkTex. Outex-TC-00013 is composed of 68 color texture images acquired under controlled conditions by a 3-CCD digital color camera and the size of which is 746 × 538 pixels  [39]. Each of these 68 textures is split up into 20 128 × 128 disjoint sub-images. Among these 1360 sub-images, 680 are used for the training subset and the remaining 680 are considered as testing images. The Outex-TC-00013 image test suite can be downloaded at http://www. outex. oulu. fi/index. php?page=classification. USPTex set is a more recent database  [40]. It contains 191 natural color textures acquired under an unknown but fixed light source. As for Outex-TC-00013, these images are split up into 128 × 128 disjoint sub-images.
Since the original image size is here 512 × 384 pixels, this makes a total of 12 sub-images by a texture. For our experiments, this initial dataset of 2292 sub-images is split up in order to build a training and a testing image subset: 6 images are considered for the training and the 6 others are used as testing images. This decomposition is available at https://www-lisic. univ-littoral. fr/~porebski/USPtex. zip. The Barktex database includes six tree bark classes, with 68 images per class  [41]. Even if the number of classes of this database is limited to six, the textures of these different classes are close to each other and their discrimination is not easy. To build the NewBarkTex set, a region of interest, centered on the bark and whose size is 128 × 128 pixels, is first defined. Then, four sub-images whose size is 64 × 64 pixels are extracted from each region.
We thus obtain a set of 68 × 4 = 272 sub-images per class. To ensure that color texture images used for the training and the testing images are less correlated as possible, the four sub-images extracted from a same original image all belong either to the training subset or to the testing one  [42] : 816 images are thus used as training images and the remaining 816 as testing images. The NewBarkTex image test suite can be downloaded at https://www-lisic. univ-littoral. fr/~porebski/NewBarkTex. zip. These sets do not require to consider specific illuminant or rotation invariant texture descriptors since the goal of this paper is to reveal the contribution of the proposed histogram selection scores independently of the texture descriptor invariance to the observation conditions. Let us note that the considered texture benchmark databases are composed of only two image subsets according to a holdout evaluation method, whereas the considered histogram selection scheme needs three subsets as explained in Section 4.
Comparison of the Histogram Selection Scores In this section, four histogram selection scores are compared on Outex-TC-00013, USPTex, and NewBarkTex sets:      These figures show that the accuracy obtained thanks to the unsupervised AL-score globally outperforms the results obtained by the AV-score, for the three databases and whatever the considered color space. In the same way, the ASL-score outperforms the ICS-score in the supervised context. These results confirm the high performances obtained thanks to the Laplacian scores in the context of feature selection  [8]. For histogram selection, the interest of the similarity term to capture the intrinsic properties of the data is also demonstrated. These figures also show that the ASL-score globally gives the highest accuracy, followed very closely by the unsupervised AL-score.
These tables confirm the interest of selecting LBP histograms: The selection improves the classification accuracy by on average 0. 52% on OuTex, 7. 70% on USPTex and 6. 32% on BarkTex, while reducing the number of considered histograms. We can also see that the performances reached thanks to the different scores are very close to each other, especially for the OuTex and USPTex databases. For the color space that gives the best rates (RGB for Outex-TC-00013 and NewBarkTex and YUV for USPTex), several scores give the higher performances and the ASL and the AL scores always appear among the best scores. For NewBarkTex which is a more challenging set, the AL, ASL, and ICS scores give exactly the same best accuracy with the same optimal dimension. The difference between these three scores appears more for subspaces with a little dimension: From Figure  3 , we can notice that the ASL and the AL scores seek faster the better histograms specially for the YUV and I 1 I 2 I 3 color spaces.
Comparison of the Histogram Ranks In this section, an analysis of the histogram ranking is done. Table  5  shows the histogram ranking obtained thanks the considered scores on Outex-TC-00013, USPTex and NewBarkTex sets. Each row of this table shows the histogram ranking in the considered color space using the specified histogram selection score, for the three image sets. For example the first row shows that, in the RGB color space, using the AV-score and the OuTex database, the first selected histogram is the number 2 ((C 2 , C 2 )), followed by the histogram 4 ((C 1 , C 2 )),. and finally the histogram 9 ((C 3 , C 2 )) is the last selected. The bold values correspond to the selected histogram subspace for which the best accuracy is achieved for each of the three color texture sets. This table shows that the histogram ranking is very variable according to the considered color space or score.
After 2 h of incubation at 30 °C, induction was stopped by washing the cells into IPTG-free M9G medium 1. E. coli cultures were introduced into microfabricated valves (Fig. 1a ) and the slits device (Fig. 1c ) by hand using a syringe. Each set of experimental conditions was tested on multiple devices, typically producing 10 3 to 10 4 tracks (Supplementary Table  1 ). Light microscopy. Bacterial cells were imaged by an automated inverted microscope (Leica DMI-4000B) with an external Leica EL6000 light source for fluorescence imaging. Time-lapse images were taken by a Photometrics CoolSnap EZ CCD camera (Fig. 1e ). The microscope and stage were controlled by Micromanager software  37. Fluorescent spots were detected and tracked by a custom-built script in Matlab. Details about particle-tracking can be found elsewhere  4.
The images were taken immediately after applying the pressure to the control channel. Typical lag times between applying the pressure were 10 s to ensure that the microscope was properly focused. Movies are acquired at a frame rate of five frames per second for 100 s. Calculation of D app and α. The ensemble-averaged MSD was computed using all of the tracks reported in Supplementary Table  1 , using time lags up to 10 seconds to avoid the presence of ballistic trajectories for the DNA loci  6. The linear regression to log(MSD) versus log(Time lag) was computed in MATLAB using the fit function and the 95% confidence intervals were computed using the confint function.
Acknowledgements This work was supported by  HFSP  ( RGY0070/2014 ). Fabrication was performed in the  Minnesota Nanofabrication Center , which receives partial support from the  National Science Foundation  through the  National Nanotechnology Infrastructure Network. We thank  Dr. Estelle Crozat-Brendon  for assistance with the culturing of the CJW4617 strain and and  Dr. Michal Wlodarski  for discussions.
GEOTRACES Trace elements Isotopes Electronic atlas IDP2017 A B S T R A C T The GEOTRACES Intermediate Data Product 2017 (IDP2017) is the second publicly available data product of the international GEOTRACES programme, and contains data measured and quality controlled before the end of 2016. The IDP2017 includes data from the Atlantic, Pacific, Arctic, Southern and Indian oceans, with about twice the data volume of the previous IDP2014. For the first time, the IDP2017 contains data for a large suite of biogeochemical parameters as well as aerosol and rain data characterising atmospheric trace element and isotope (TEI) sources. The TEI data in the IDP2017 are quality controlled by careful assessment of intercalibration results and multi-laboratory data comparisons at crossover stations. The IDP2017 consists of two parts: (1) a compilation of digital data for more than 450 TEIs as well as standard hydrographic parameters, and (2) the eGEOTRACES Electronic Atlas providing an on-line atlas that includes more than 590 section plots and 130 animated 3D scenes.
The digital data are provided in several formats, including ASCII, Excel spreadsheet, netCDF, and Ocean Data View collection. Users can download the full data packages or make their own custom selections with a new on-line data extraction service. In addition to the actual data values, the IDP2017 also contains data quality flags and 1-σ data error values where available. Quality flags and error values are useful for data filtering and for statistical analysis. Metadata about data originators, analytical methods and original publications related to the data are linked in an easily accessible way. The eGEOTRACES Electronic Atlas is the visual representation of the IDP2017 as section plots and rotating 3D scenes. The basin-wide 3D scenes combine data from many cruises and provide quick overviews of large-scale tracer distributions.