dataesr/scientific-paragraphs-categorization huggingface dataset:
Systematic uncertainties Several tests were performed to identify possible ratedependent systematic effects that could have biased the halflife result. First, an increasing number of leading channels was removed from the start of the fit for each of the four runs, and the decay curve was refit with the same function described above. An example of a leading-channel removal plot for one of the runs is shown in Fig. 7. It shows no obvious deviation from the half-life value before the removal of high-rate data at the start of the decay curve. A cycle-by-cycle analysis was also carried out to determine the half-life for each of the 144 cycles. The decay curve from each cycle was fit using the three-parameter fit function employed in the analysis above. The 144 results from each cycle were averaged to obtain T 1/2 = 2.
During plant growth, the shoot apical meristem (SAM) gives rise to the primary stem and all aboveground (lateral) organs. In the central zone of the SAM, slowly dividing stem cells give rise to daughter cells that first make up the peripheral zone, then the organ primordia, and finally differentiate into lateral organs, such as leaves, stems, and flowers  (Gaillochet et al. , 2015). The meristem-to-organ boundary is a group of cells of distinct identity formed between the meristem and organ primordia and is characterized by specific gene expression profiles and morphology  (Aida and Tasaka, 2006; Rast and Simon, 2008). Two roles can be attributed to meristem-to-organ boundaries: (i) the initial separation of the emerging organ of the meristem; and (ii) the production of new tissues later during development, such as of axillary meristems and carpel marginal meristems  (Aida and Tasaka, 2006; Khan et al.
Gene expression analysis RNA was isolated from plant material using the Plant RNeasy Kit (Qiagen, Germany) following the manufacturer's instructions with the addition of a DNase treatment step. cDNA was synthesized from 1 µg RNA using the iScript reverse transcriptase kit (Bio-Rad). Quantitative real-time PCR (qRT-PCR) was performed on a LightCycler 480 (Roche Applied Science, USA) using Fast Start SYBR Green I fluorescent dye (Roche). At least three biological repeats and three technical repeats were used for each analysis. Expression data were normalized through two reference genes, UBC (At5g25760) and PP2A (At1g13320). For RT-PCR, cDNA was amplified with the Go-Taq PCR mix (Promega) using different amplification cycles, and loaded on an agarose gel containing SYBR Safe (Life Technologies).
Metabolite profiling Metabolite profiling was performed exactly as described by  Lisec et al. (2006) , using the modifications for root tissue described in  Joshi et al. , (2006). Metabolite identities were verified via comparison to spectral libraries of authentic standards housed in the Golm Metabolome Database  (Kopka et al. , 2005). Metabolite information is provided following recent recommendation standards (Supplementary Table  S2 ;  Fernie et al. , 2011).
TAX1 overexpression mildly affects the primary metabolome Given the link between TbTAX and plant metabolism  (Onrubia et al. , 2014) , the primary metabolome of TAX1 overexpressing seedlings was profiled. The changes observed in the primary metabolites were comparatively mild in the overexpressors with only two of the changes conserved across the genotype in leaves, namely phosphate and serine, and only one (again serine) in the roots. However, the extent of metabolic change was consistent with the degree of overexpression and the severity of the developmental phenotypes in the lines. When assessed at a pathway level, the leaf data clearly suggest an elevated rate of photosynthesis on a per gram fresh weight basis, with increases in pentose-and hexosephosphates as well as in sucrose. In addition, the intimately connected pathway of photorespiration appears to be upregulated as indicated by the above-mentioned increases in serine and also in glycerate.
Acknowledgements We thank  Patricia Springer  for providing seeds of lof1-1lof2-1 and pLOF2::GUS;  Paul Wintermans ,  Amparo Cuéllar Pérez ,  Astrid Nagels Durand , and  Robin Vanden Bossche  for excellent technical assistance;  Karel Spruyt  for help with photography; and  Annick Bleys  for help in preparing the manuscript. This work has been supported by funding from the  Research Foundation-Flanders  through the project  G005312  and the Special Research funds from  Ghent University  and the  National Research Foundation  (NRF) from  South-Africa  for a North-South "Sandwich"-type predoctoral scholarship to JC. LP is a postdoctoral fellow of the  Research Foundation-Flanders.
The authors thank the members of the  EDyP group  for fruitful discussions. J. G. 's group has been supported by the  French National Research Agency  ( ANR-11-PDOC-0011  and  ANR-10-INBS-08   ProFI Proteomics French Infrastructure ), and a  Marie Curie Action  ( Career Integration Grant , grant number  304003 ). M. C. is supported by a  FINOVI fellowship  from the  Région Rhône Alpes. The authors have declared no conflict of interest.
Acknowledgments We wish to acknowledge the technical contributions of  R. Hanni ,  P. Lutz ,  J. Rochet ,  C. Tognina  and  J. M. Vuilleumier. This work was supported by:  Swiss National Science Foundation  grant no. 142436 ,  European Research Council  grant no. 291242-HBAR-HFS  and the  Austrian Ministry for Science and Research, DFG  research grant,  Excellence initiative of Heidelberg University, Research Council of Norway, Bergen Research Foundation ,  Istituto Nazionale di Fisica Nucleare (INFN-Italy ).
Background: Autologous stem cell transplant (ASCT) is the standard of care for patients (pts) with relapsed/refractory Hodgkin lymphoma (HL), providing a cure for approximately 50% of pts; however, most pts with risk factors will progress post-ASCT  (Sureda 2005). Brentuximab vedotin comprises an antieCD30 antibody conjugated by a protease-cleavable linker to a microtubule-disrupting agent, monomethyl auristatin E (MMAE), and has an objective response rate of 75% in relapsed or refractory HL. The AETHERA trial was initiated to evaluate whether early treatment with brentuximab vedotin post-ASCT can prevent progression in pts with HL (Clinical-Trials. gov #NCT01100502). Methods: The AETHERA trial is a phase 3, randomized, double-blind, placebo-controlled, multicenter study. After ASCT, pts received brentuximab vedotin 1.
Decision Trees (DT): Decision trees are algorithms that allow the approximation of functions of discrete and continuous values, beginning with the generation of a set of rules. DTs classify the values that attributes (called instances) can take in a tree, where each node represents an instance of an attribute, and each branch descending from the node corresponds to one of the possible values that the attribute can take  [13]. In other words the DTs seek the instance that has the closest relationship with the target variable, dividing the dataset into subsets according to attribute. The procedure is repeated until the established criterion is satisfactorily met  [14]. Among the most popular algorithms for building DTs include: ID3  [15] , C4. 5  [16] , CART  [17] , CHAID  [18] , and QUEST  [19].
Nearest neighbor (K-NN): This algorithm proposed by Fix and Hodges  [30] , classify a new input data , according to the k training data closest to those around them, where k is an integer defined by the user. As such, the class assigned to the new input data will be the most voted class among the k nearest neighbors from the training dataset. However, a tie among the most voted classes is a possibility. In this case, to select the class to be assigned, it is necessary to use a method for resolving ties. Among the functions of methods for ties are assigning the class with the first nearest neighbor among those tied on votes; and selecting the class among those tied that has the lowest average distance to its neighbors  [7]. Taking the example of classification of rust-infected leaves presented above, the K-NN algorithm classifies the new input data by the value of k.
Related Work In this section, several existing approaches related to the stated research problem are considered. The proposals of these research works are oriented to the use of classifiers in diverse applications for the agricultural sector. The first of the approaches included in this category is documented in  [31]. This work consists of evaluating the risks posed by gray spot on corn leaves. The authors make use of logistic regression (LR) and classification and regression tree (CART) algorithms to predict the severity of the disease according to sowing date, amount of corn residue on the soil surface, maturity of the genotype, and resistance to gray leaf spot. The training data used were collected between 1998 and 2001, for a total of 332 cases. The results of the LR algorithm show that 70% of the examples were classified correctly, 77% for CART.
A second paper  [10]  proposes predicting the severity (percentage affected in each plant in a given area) of the fungus Pericularia oryzae, also called rice blast, which attacks rice crops. The training data used were collected between 2000 and 2004, involving data meteorological and pathology of plant. The authors use the SVM algorithm to solve this problem. However, they compare SVM with such other approaches as Conventional Multiple Regression (REG), Generalized Regression Neural Network (GRNN), and Back-Propagation Neural Network (BPNN). The results obtained for the algorithms mentioned in calculating the absolute percentage error (APE) indicate that SVM is the most accurate in predicting the severity of rice blast with %APE = 36. 66. The other algorithms presented the following results: GRNN with %APE =46.
Recent research efforts have focused on predicting the incidence of rust in coffee using supervised learning algorithms. The base training data used for these efforts was collected by  [34]  on the experimental farm of the Pro-Coffee Foundation in Varginha, Minas Gerais, Brazil over eight years (from October 1998 to October 2006). The final dataset includes 182 examples. This work is presented below. In  [35]  the authors develop a decision tree (DT) for cultivation areas with a high fruit load, in which the target variable (class), is determined by the rust infection rate (RIR), defined by the percentage of leaves infected monthly. In this respect the target variable is defined by three classes. The first class is given the name of reduction or allocation and occurs when RIR is less than or equal to 0%, while the second, named moderate growth, occurs when RIR is greater than 0% and less than or equal to 5%.
Equally  [43]  predicted tomato spotted wilt virus using random forest a specification of decision trees obtaining a minimum prediction error rate of 12. 25%. A Indo-Japan initiative  [44] ,  [45]  used Bayesian Networks (BN) in peanut crops of India for monitoring of pest and disease. First BN is utilized to assume the presence (or absence) of a particular feature in dataset that describe the thrip pest. Second BN found the ideal conditions of the Leaf Spot (LS) for a semi-arid tropic region. The results show that LS disease infection is strongly influenced by minimum temperature (18 -20 °C), prolonged duration of leaf wetness (7-10 h), high humidity (75% or more) and age of the crop. In fungal crops  [46]  predicted the distribution of diseases (T. tritici, A. pisi, U. hordei, P. halstedii, P.
All N-glycans start with a common core sugar sequence, (Man) 3 (GlcNAc) 2 -Asn-peptide and are classified into three types: (1) "oligomannose", in which only mannose residues are attached to the core; (2) "complex", in which "antennae" initiated by Nacetylglucosaminyltransferases (GlcNAcTs) are attached to the core; and (3) "hybrid", in which only mannose residues are attached to the Man␣1-6 arm of the core and one or two antennae are on the Man␣1-3 arm  (Varki et al. , 2009). N-glycans are found on many secreted and membrane-bound glycoproteins at Asn-X-Ser/Thr sequence. Analyses of protein sequence databases have revealed that about two thirds of the entries contain the consensus Asn-X-Ser/Thr sequence. It is estimated that at least two thirds of those sequences are likely to be N-glycosylated  (von der Lieth et al.
Although many studies have shown the importance of structural changes of sugar chains of glycoproteins during development  (Varki et al. , 2009) , limited data are available on the changes in the sugar chains that take place during ageing. Parekh et al. (1988)  were the first to investigate age-related Nglycosylation changes on human serum IgGs. Healthy males and females varying in age from 1 to 70 years old were studied. The relative incidence of agalactosyl (with both outer arms terminating in GlcNAc) N-glycans on total serum IgG decreased from birth to a minimum (at 25 years of age) and then increased with age. The relative incidence of digalactosyl structures varied inversely to the amount of agalactyl N-glycans and the relative incidence of monogalactosyl structures was constant. They concluded that galactosylation of the N-glycans on the human serum IgGs of normal individuals is an age-related molecular parameter  (Parekh et al.
Acknowledgements This work was partly supported by  French ANR  project (Grant No. ANR-11-BS04-0019 ), by  LABEX MATISSE through a post-doctoral grant  and a scholarship from  China Scholarship Council (CSC , Grant No. 2011618135 ). We thank  F. Gendron ,  J. -L. Cantin  and  J. Von Bardeleben  for access to the electromagnet facility and  M. Boukhicha  for preliminary work. We acknowledge the  Consortium des salles blanches dIle de France ,  M. Rosticher  and  J. Palomo  for access to clean room facilities and discussions with  F. Mauri ,  M. Calandra  and  T. Brumme.
de Part 2, Hantje Cantz, 2010. For two important early edited volumes with seminal scholarly and artistic reflections on video, art, and its social and political potential, see: Doug Hall and Sally Jo Fifer, eds, Illuminating Video. An Essential Guide to Video Art, Aperture Foundation, 1990; and Michael Renov and Erika Suderburg, eds, Resolutions: Contemporary Video Practices, University of Minnesota Press, 1996. See also: Chris Hill, ed, Rewind A Guide to Surveying the First Decade: Video Art and Alternative Media in the U. S. , 1968-1980, Video Data Bank, 1995; Abina Manning and Brigid Reagan published a revised edition in 2008.
de Part 2, Hantje Cantz, 2010. For two important early edited volumes with seminal scholarly and artistic reflections on video, art, and its social and political potential, see: Doug Hall and Sally Jo Fifer, eds, Illuminating Video. An Essential Guide to Video Art, Aperture Foundation, 1990; and Michael Renov and Erika Suderburg, eds, Resolutions: Contemporary Video Practices, University of Minnesota Press, 1996. See also: Chris Hill, ed, Rewind A Guide to Surveying the First Decade: Video Art and Alternative Media in the U. S. , 1968-1980, Video Data Bank, 1995; Abina Manning and Brigid Reagan published a revised edition in 2008. 7 Parikka, What is Media Archaeology?, p. 6. 8 See for a historical account of the alternative use of film by the pre war British labour movement: Bert Hogenkamp, Deadly Parallels: Film and the Left in Britain, Lawrence and Wishart, 1986; for an account of postwar efforts of the Left to mobilise film (and television) as an alternative medium, see: Bert Hogenkamp, Film, Television and the Left, 1950-1970, Lawrence and Wishart, 2000.
Data analysis Four datasets were analysed: (1) the identification and genotypes of the collected adult mosquitoes; (2) the results of susceptibility testing; (2b) the results for the subsample of these mosquitoes that were also genotyped; and (3) the results of the synergism bioassay. Dataset 1 was used to determine the spatial distribution of the kdr mutation. Dataset 2 to assess levels of resistance and their variations and dataset 2b and 3 their mechanisms. In order to assess the level of resistance at each site and for each year according to WHO standards, mortality rates was calculated after 24 h and their confidence intervals (CIs). If the mortality rate was <80 %, the mosquitoes were considered resistant; if the rate was 80-98 %, the mosquitoes were considered increasingly tolerant; and if the rate was >98 %, the mosquitoes were considered to be sensitive.
In addition, a probit log-time model of which only significant terms had been retained was adjusted on the first hour mortalities (kinetics) to determine 50 and 95 % knockdown times (KDT 50 and KDT 95 ) for each site and each year as well. In order to assess whether the levels of resistance varied significantly over time and space, a generalized linear model with binomial error structure (logistic regression) was used, with year, site and insecticide as the explanatory variables and 24 h mortality as the response variable. The design was balanced, with 100 mosquitoes per year, site and insecticide (N = 5399). Mechanisms of resistance were assessed from dataset 2b, from the relations between genotypes at the kdr and ace1 loci and mortality, and from dataset 3, which indicated the extent of oxidase resistance.
The sampling method for dataset 2b corresponded to a case-control design, and it was therefore analysed accordingly. Logistic regression was performed with mortality as a response variable, genotype as an explanatory variable and year, site, insecticide and insecticide-site interaction as control variables, as they were shown to affect mortality significantly in analyses of variation in levels of resistance over time and space. A rare event correction  [36]  was performed (with weights calculated using dataset 2) to allow for interpretable estimates of mortality for the different genotypes despite the case control sampling design. The logistf R library was used to compute the: confidence intervals of these mortalities from the profile-penalized log likelihood. The SE genotype was selected as the baseline for pairwise comparisons because it shares one allele with all the other genotypes (SS, SW and EE).
The significance of variables was tested with the drop1 R function. A logistic model was used on a combined subset of dataset 2 (bioassays) and dataset 3 (synergism assays) after DDT, permethrin and deltamethrin treatment in 2012. The response variable was mortality; the explanatory variables were insecticide, site and exposure to PBO before the insecticide (Yes/ No). Type II sum of squares was used to test significance.
Mechanisms of resistance All the mosquitoes genotyped and identified are An. arabiensis. None of them had the ace-1 resistant allele, but kdr resistant alleles were found. In the analysis of the relation between insecticide-induced mortality and genotype at the kdr locus (dataset 2b), all the explanatory variables, including genotype, were significant (χ 2 = 68. 86, df = 3, p = 7. 44e-15). As expected, the mortality rate of EE genotype mosquitoes was lower than that of SE mosquitoes (OR = 0. 225, 95 % CI = 0. 213-0. 239), while the mortality rate of SS mosquitoes was higher (OR = 1418, 95 % CI = 89-22,708). Note the latter OR is highly inaccurate because the mortality rate was close to 1. The mortality rate of the SW genotype was slightly higher than that of the SE genotype (OR = 3. 192, 95 % CI = 2.
631-3. 872). A trend to decreasing mortality was seen between 2010 and 2012 in each locality and for each kdr genotype (Additional file 3). The model used to analyse dataset 3 showed an effect of insecticide and site and their interaction, consistent with our previous results. Moreover, PBO and the interaction between site and PBO exposure had significant effects. Interestingly, the interaction between exposure to PBO and insecticide was not significant (Table  3 ). As the effect of PBO varied by site, a subset logistic regression was conducted to assess the effect of PBO for each site separately (Table  4 ). PBO exposure significantly increased mortality among mosquitoes from Pikine (OR = 1. 4, 95 % CI = 1. 3-1. 5) and Yarakh (OR = 1. 6, 95 % CI = 1. 4-1. 7) but not in Almadies (OR = 1. 0, 95 % CI = 0.
Funding This work was supported by the  French Research Institute for Development , the  Direction Générale de l' Armement. The funders had no role in study design, data collocation or analysis, decision to publish or preparation of the manuscript. Ousmane Ndiath  was supported by a  G4-Group Pasteur Bangui (CAR) fellowship  provided by the  Institut Pasteur International Network. Aurélie Cailleau  was supported by a fellowship from  Afrique  funded by the  Wellcome Trust.
Acknowledgements This work has been partially supported by  European Community  through the  FET-Proactive Project  " MoQuaS ", contract N. 610449, by the  Italian Ministry for Research (MIUR)  through the  FIR grant   RBFR13YKWX  and the  German Research Foundation  ( DFG Priority Program SPP 1459 Graphene ). We thank  Y. Hernandez  for help with the preparation of the turbostratic graphene samples.
(Bottom) When the nanotube loop is pushed against a flat surface, part of the nanotube is adsorbed on the surface due to Van der Waals interactions. The radius of curvature Ra at the last point of contact on the CNT with the substrate is fixed by an equilibrium between the adhesion of the adsorbed part and the bending of the free standing part of the nanotube. From the measurement of the AFM cantilever deflection d and sample position zs, the force F acting on the nanotube and its compression zc can be recorded. icon. We first present the samples (CNT and substrates), and the acquired data during approach-retract cycles: force, dynamic stiffness, compression. We then give the analyzing framework, and discuss the nanotube configurations during peeling. We finally conclude, giving quantitative values of the adhesion energy of a SWCNT on the five different substrates.
IV. CONCLUSION In this article, we present some experiments where a SWCNT loop, grown directly on an AFM tip, is pushed against various substrates. The adhesion force and dynamic stiffness of the nanotube in contact with the sample are recorded as a function of its compression. The experimental data are analyzed in the framework of an elastic loop absorbed on a flat surface. During retraction, we observe force plateaux characteristic of a peeling mechanism. The cross information between force and dynamic stiffness helps to understand the configuration of the nanotube during the peeling process: some of the plateaux are attributed to adhesion on the substrate, while others are hinting at adsorption on the AFM tip. Quantitative values are derived for the nanotube diameter, and its energy of adhesion per unit length on various substrates: graphite, gold, mica, platinum and silicon.
ACKNOWLEDGMENTS We thank  F. Vittoz  and  F. Ropars  for technical support,  L. Champougny ,  M. Geitner ,  A. Petrosyan ,  J. P. Aimé  and  Z. Sun  for stimulating discussions. This work has been supported by the  ANR  project  HiRe-sAFM  ( ANR-11-JS04-012-01 ) of the  Agence Nationale de la Recherche in France. Finally ,  T. J. LI  would give thanks to  Chinese Scholar   Council (CSC)  for financial support. The authors acknowledge the  Plateforme Nanofils et Nanotubes Lyonnaise of the University Lyon1.
The FWM efficiency is defined as the ratio between the average idler output power and the input probe power. These were evaluated by integration of idler and signal spectra and considering the duty cycle of the pulse train. Thus the data of Fig. 3 (a) and Fig. 3 (b) of the output spectra can be summarized in Fig. 4  for the two pump wavelengths (blue and red dots). Though the parametric conversion efficiency is not large, the change in the spectral position where phase matching takes place and the dependence on detuning of the gain spectral width are clearly seen. Larger efficiencies may be obtained with lower propagation losses, with a waveguide design in which scattering losses at long wavelength are lower and by improving the membrane structure so that larger poump powers can be tolerated.