dataesr/scientific-paragraphs-categorization huggingface dataset:
We next compared it to the observed difference with 95% threshold cutoff value in order to detect traits under spatiallyvarying selection. In addition to Q ST -F ST analyses, we employed the DRIFTSEL R package  [128]  to test for signal of selection of traits while accounting for drift-driven population divergence and genetic relatedness among individuals (half-sib design). DRIFTSEL is a Bayesian method that compares the probability distribution of predicted and observed mean additive genetic values. It provides the S statistic as output, which measures the posterior probability that the observed population divergence arose under divergent selection (S∼1), stabilizing selection (S∼0) or genetic drift (intermediate S values)  [59]. It is particularly powerful for small datasets, and can distinguish between drift and selection even when Q ST -F ST are equal  [59].
Environmental correlation of outlier SNPs We tested associations between allelic frequency at 171 outlier SNPs and environmental variables across 28 populations, using Bayenv 2. 0  [40, 111]. Because environmental variables are highly correlated, we used the first two principal component axes from the environmental PCA analysis (PCenv1 and PCenv2) to run Bayenv 2. 0. This software requires a neutral covariance matrix, that we computed from the available dataset of 1000 neutral SNPs (S1 Table ). We performed 100,000 iterations, saving the matrix every 500 iterations. We then tested the correlation of these to the last matrix obtained, as well as to an F ST matrix calculated with BEDASSLE  [135] , as described in  [57]. For each outlier SNP, we compared the posterior probability of a model that included an environmental factor (PCenv1 or PCenv2) to a null model.
Acknowledgments We thank  Jessica Melique  for her help in gathering stomata data. We are very grateful to  Angelica Cibrian at Langebio (Cinvestav, Irapuato, Mexico)  who let us use her lab to lyophilize our samples, and store them. Valeria Souza  greatly helped us with logistic support in Mexico. Insights from  Delphine Legrand ,  Pierre de Villemereuil  and  Elodie Marchadier  were important for analyzing correlations between traits and to estimate heritabilities. We would like to thank warmly five anonymous reviewers for their insightful comments on our work.
This can be further complicated by assay resolution variation among platforms, and the level of differentiation required, in relation to species within a limited number of families, or single strains in an even narrower range of species. These problems in oligonucleotide probe design have been addressed through the creation of several software packages to aid in their design and verification, including OligoWiz platform, ArrayDesigner 2. 0, OliCheck, ORMA, ProbeMaker, ARB, and PathogenMIPer, to name a few. In the case of multiplex detection by long DNA probes (>40 base pairs) a uniform probe melting temperature is needed. The melting point essentially reflects the distribution of GC along the probe. Instead, with a shorter probe consisting of 20-40 base pairs, the uniform melting temperature is typically not necessary, as the range for the GC content is 40-60%.
difficile. A highly sensitive and rapid point-of-need nucleic acid lateral flow assay has also been developed for the direct detection of RPA products  [121]. For the lateral flow assay two biotinylated capture probes were immobilized on the test strip for each of the respectively test and control lines. On the test line, the immobilized probe is complemented with the 5 end of the amplified DNA, while the control line binds to the control probe, which is conjugated to the gold nanoparticles for visual detection. The gold nanoparticle-conjugated control probe also binds to the 3 -end of the test line-attached amplified DNA and thus forms a sandwich, signaling the presence of the analyte DNA through appearance of the red color on the test line. The test line was observable at DNA concentration as low as 30 pM, with the LoD of 1 × 10 -11 M using a smartphone camera and Image J software for detection, taking 15 min for the entire procedure.
Acknowledgments: Figure 4. Reprinted by permission from  Springer Nature ,  Microchimica Acta , Paper-based device with on-chip reagent storage for rapid extraction of DNA from biological samples,  Ruihua Tang ,  Hui Yang ,  Jane Ru Choi  et al. , 2017. DOI:  10. 1007/s00604-017-2225-0. Figure 5. Reprinted by permission from  Royal Society of Chemistry , A rapid and eco-friendly isothermal amplification microdevice for multiplex detection of foodborne pathogens,  Lab Chip ,  T. N. D. Trinh ,  N. Y. Lee , 2018, DOI:  10. 1039/C8LC00424B. Figure 7b: Adapted with permission from  Multiplex Biosensing Based on Highly Sensitive Magnetic Nanolabel Quantification: Rapid Detection of Botulinum Neurotoxins A, B, and E in Liquids. Alexey V. Orlov ,  Sergey L. Znoyko ,  Vladimir R. Cherkasov ,  Maxim P.
Funding: This work was supported by  Institut National de la Recherche Agronomique and Biosense , and in a part by the  ANTARES  project that has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 664387 , by the bilateral  Franco-Italian Galilée Grant   37184VG , and by the  TOX-detect One Health EJP Project of the European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No  773830.
This research determines the behaviour metrics and observations of different reactions of students during the lecture. Researchers analysed the duration of attention considering the aspects of quantifying body motion and estimation of gaze direction. Mainly, it represents essential criteria due to the provided results to evaluate interaction among teacher and students according to the resolution of incoming data to estimate observations. Finally,  Gillies (2008)  argues the importance of observations focused on how teachers can encourage discussions among learners and how they can work together applying the collaborative method. Thus, two major studies are presented: the first one describes the difference in teachers' verbal behaviours during cooperation among small groups. The second one presents benefits derived from assessments given by teachers to use communication skills to improve students' learning and thinking during cooperation.
The used indoor localisation technique is based on proximity. When teachers are positioned nearby the table of each collaborative group, the RFID anchor will automatically detect the tag's data while it contains its database. Hence, this basic approach requires a defined distance between the reader and the teacher's tag, which is typically under 1 m in the case of the wireless connection. An active RFID system ensures the simultaneous detection of several tags in the action zone. As shown in Figure  3 , five hardware readers and two teachers' tags are interconnected to make a network dedicated to the indoor positioning system. These hardware devices are linked to our middleware's gateway for data processing. As described above, the gateway is dedicated to manage the whole SLE via connecting devices and enabling services.
The controlled data of the joystick are associated with the position of command and can be monitored via the Arduino setup. However, the positioning system deployed indicates whether different groups of learners are enthusiastic about the required work and determines efficiently the learners' work progress. Through this experience, teachers could define the increased autonomy in cooperative group work situations to reflect and think strategically. This experiment aims essentially to examine the following: • How have learning been involved in the experience of collaborative learning in the SLE? • What is the role of the teacher's position in improving learners' performance? • How have learners used the feedback received from their teachers to improve their collaborative experience? • Are learners more interactive with the proximity position of the teacher? The presented study is conducted principally to analyse the effect of teacher's position on students' participation and achievements.
Results Meta-analysis. Here we report the first meta-analysis of GWAS in tomato using results of three publicly available GWAS panels: 163 tomato accessions from panel S  8  , 291 accessions from panel B  11  , and 402 accessions from panel T  6  (Fig. 1 ). We analyzed a large set of tomato flavor-related quality chemicals, including sugars, organic acids, amino acids, and volatiles measured in each of these panels. First, we used IMPUTE2 software  22  to increase the genomewide SNP densities of panel S 8 and panel B  11  , which were genotyped using SNP arrays (Online methods). After quality control (Supplementary Figs. 1 2 3 , Supplementary Tables  1  and 2 , Supplementary Data 1-3), a total of 209,152 and 252,414 SNPs was retained for panel S and B, respectively. Imputation greatly increased the density of genomic coverage (Supplementary Fig.
4 ) and revealed a similar genetic population structure compared a moderate content of organic acids, high intensity of volatiles contributing to liking while reducing those unpleasant volatiles with genotyped SNPs for both panels (Supplementary Figs. 5-12  and Supplementary Data 4-5). We used the Efficient Mixed-Model Association eXpedited (EMMAX) software for association tests for panel S and B  23  , as reported for panel T  6  (Online methods, Supplementary Fig. 13 ). After imputation, we observed a similar or slight statistical increase in terms of the significance and the number of associated loci compared with MLMM 24 (Supplementary Figs. 14-44) and no genomic inflation (λ < 1) was detected for most (83. 3%) of the traits (Supplementary Data 6). For panel T, which was characterized by 2,040,403 SNPs, the association tests had also been performed using EMMAX  6.
By combining the three separate studies, a total of 775 unique tomato accessions were used for the final meta-analysis of 31 flavor-related traits (2 sugars, organic acids, 10 amino acids, and 17 flavor-related volatiles). We performed the meta-analysis with two software: METAL using a fixed effect model and METASOFT  26  for those SNPs where heterogeneity occurred (I 2 > 25) using a random effect model. Manhattan plots and quantile-quantile (Q-Q) plots for all traits are shown in Supplementary Figs. 45-75. Meta-analysis identified a total of 305 significant loci (P < × 10 -7 for sugars, acids, and volatiles; P < 2. 99 × 10 -6 for amino acids), among which 211 were new (Supplementary Data 7). A total of 87 strong effect meta-QTLs were identified with high probability (P < 10 -9 ). Most of these loci passed the suggestive thresholds in at least one panel (Supplementary Figs.
14-75 ). Among the identified loci, 35 had a moderate to strong heterogeneity (I 2 > 25). We generated a local SQLite dataset for tomato (Online methods) and provided the LocusZoom plots for all the genome-wide significant associated loci (Supplementary Figs. 76-123 ). Among the 305 loci, 24 loci exhibited cis-eQTLs in a previous transcriptome-wide association study  12  in fruit tissue (Supplementary Data 7). Among the 211 associated loci, we identified 37 promising candidate genes (7 with significant cis-eQTLs  12  ) with functional annotations related to the pathways of flavor chemicals (Table  1 ). We performed a singular enrichment analysis for all associations using agriGO  27  (http://bioinfo. cau. edu. cn/agriGO/index. php). Up to 10 biological processes were significantly enriched (P < 0.
Source data of Fig. 4g -i are provided in a Source Data file Panel T consists of 402 tomato accessions from two separate panels  6. Panel T was genotyped by whole genome resequencing technology, generating a number of 2,014,488 SNPs passing quality control (MAF > 0. 05, missing rate < 10%). This panel includes five tomato types, including modern (51), transitional (50), cherry (27), heirloom (243), and wild species (27)  6. Phenotypes. A total of 31 flavor-related quality traits in tomato were analyzed for meta-analysis, including two sugars (fructose and glucose), two organic acids (citrate and malate), 10 amino acids, and 17 flavor-related volatiles. The 10 amino acids were asparagine, aspartate, GABA, glutamine, lysine, methionine, phenylalanine, proline, serine, and threonine. The 17 volatiles were (E)-2-heptenal (E2HEP), (E)-2-hexenal (E2HEX), (E)-2-pentenal (E2PEN), (E,E)-2,4-decadienal (EE24D), (Z)-3-hexen-1-ol (Z3H1X), (Z)-3-hexenal (Z3HEX), 1-octen-3-one (X1O3ON), 1-penten-3-one (X1P3ON), 2-methyl-1-butanol (X2M1BU), 3methyl-1-butanol (X3M1BU), 6-methyl-5-hepten-2-one (X6MHON), beta-ionone (BIONO), geranylacetone (GRACE), guaiacol (GUAIA), hexanal (XEXAN), phenylacetaldehyde (PHEAC), and methylsalicylate (METHY).
Sugars and organic acids were measured in all three panels. Amino acids were measured both in panel S and B, while flavor-related volatiles were measured both in panel B and T. Briefly, fructose and glucose in panel S were measured using the micro-method. Citrate and malate were measured by gas chromatography-mass spectrometry (GC-MS)  8. Data distribution was tested using the Shapiro-Wilk test and data with a non-normal distribution were Log 10 transformed. In panel B, these metabolites were measured within the Product Metabolism and Analytical Sciences Endogenous Metabolite Profiling Platform at Syngenta Jealott's Hill International Research Center, Bracknell, UK. Fructose and glucose were analyzed by high pH ion-exchange chromatography. Citrate and malate were analyzed using electrospray ionization-liquid chromatography (ESI-LC-MS/MS).
Fructose and malate were transformed using the Boxcox method. Citrate was transformed using the Log 10 method. In panel T, citrate and malate were measured using the citrate and malate analysis kits (R-Biopharm, Marshall, MI), according to the manufacturer's instructions  60. Measurements of amino acids and volatiles in panel S was measured using GC-MS by comparing with a database of authentic standards. Small organic acids and amino acids in panel B were analyzed using electrospray ionization-liquid chromatography (ESI-LC-MS/MS). Volatiles in panel T were first captured by headspace solid phase micro extraction (HS-SPME) coupled GC-MS. Reference panel for SNP imputation. A reference panel was selected from the 360 re-sequenced tomato accessions  19  to perform SNP imputation in panels S and B.
Among this panel, only accessions with genome coverage ≥90% and mean sequencing depth ≥4. 0 were kept. Wild tomato species were also removed, generating a total reference set of 221 accessions genotyped with 3,809,156 SNPs (Supplementary Table  1 ). Recombination map. A high-density recombination map is required for imputation and computing genomic partitions. However, the available tomato genetic maps EXPIM 2012 and EXPEN 2012  57  have a limited genomic coverage (~3500 mapped SNPs). In order to use a much denser genetic map, we developed a Python script to infer the corresponding genetic positions of the 3,809,156 SNPs in the reference panel. Before calculating the recombination rate, we first compared the physical vs genetic distribution patterns for each chromosome (Supplementary Fig. 1 ).
Data availability Data supporting the findings of this work are available within the paper and its Supplementary Information files. All new meta-analysis data associated with the paper are available in a repository [https://doi. org/10. 15454/TWFDYW]. The source data underlying Figs. 2c-g, 3c-h, and 4g-i and Supplementary Figs. 5a-f, 6a-d , and 124-126 are provided as a Source Data file. Additional datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
In particular, we applied optimism and pessimism scores as the users' personality to discover the influential users in the network. Considering that, we propagate the opinion based on two facts: 1) the impact of influential users, derived by the presence of an extremely confident individual in the network and 2) the impact of neighbors, induced by the presence of the users who have a connection with the current user. Then, we proposed the opinion aggregation of the group induced by the weighted averaging operator and fuzzy techniques. In order to evaluate the validity of the method, we used enormous data sets of Epinions and Etsy which are signed and unsigned, respectively.
1) THE EPINIONS DATA-SET: This data-set is based on the trust relationship of the users of the Epinions. com website. The users are usually the consumers and can rate the product of this website. Based on the users' preferences, they can trust or distrust each other (the links have the values of +1 or -1). The Epinions dataset includes 131, 828 users and 841, 372 links or connections between them. It is worth mentioning that for simplicity, we pruned the users without connections. Finally, we reached to a network with 49, 289 users and 507, 592 links for this data-set.
2) THE ETSY DATA-SET: Etsy is the global market-place for unique and creative (handmade) products which is crawled from ''https://www. Etsy. com'' website. It includes a broad domain of products consisting universe or special ones, unique handcrafted pieces, vintage jewels and etc. Also, based on the online verification (Hoover), the main rivals of this online shopping website are eBay, Craigslist and Amazon. The data-set has two main distinct information: 1) the graph and the connection of the users and 2) and the user rates on the goods (which implies their opinions). Similar to Epinions data-set, the range of the users' rates are integers in the interval of  [1, 5]. This website has several divisions for the products and for simplicity, we picked one group of the products (the home). In addition, the crawler is devised in C# programming language which fetches the data (the network of users and their opinions).
Subsequently, the three named methods will be evaluated by the credibility values. Each method ranks the users and nominates some users as the highest ranked ones (and as each method detected influential users) for different percentages. The credibility of these users will be added for each method and then compared with other methods to verify which one nominated the most credible users. The one who nominated the more credible users ranked the users correctly and found the influential users better than the other methods. Figures  2a  and 2b  show the values of users' credibility for each method with different percentages of top nominated influential users for both datasets. As it is indicated, the POPRank nominated users are more credible rather than the other two methods. This implies the fact that applying the personality of the users can effectively enhance the influential users' detection.
An elongation sensor (T-PTS HT Sensor Technology) which measures the piston stroke. This test bench is used to validate the theoretical dependance between the pressure measured inside the landing gear and the effort applied by the hydraulic jack (see part 3). The idea is to measure the pressure inside the damper according to the applied force so as to infer the sustained mass on the landing gear. For this purpose, a pressure sensor (EPTT5100) is embedded inside the oleo part of the shock absorber and a MEM's accelerometer (ADXL 345) is applied outside. Both are connected to a specific electronic board, containing a microcontroller to digitize and process data, and a Bluetooth module to send information to a smartphone interface (Figure  2 ). This Android application, connected to the main board via Bluetooth, displays a schematic view of the aircraft aiming to visualize the barycenter and the mass repartition on each landing gear.
The ADC is configured to measure and convert the different signals using an analog multiplexer which selects the input channels. The electronic board has been developed under the OrCAD software (Figure  6 ). First of all, to draw the electrical diagram, it was necessary to define new libraries in order to create the non-existent components and their footprints. Thus, the microcontroller and the Bluetooth module are defined as boxes containing the number of associated pins, while the sensors are modeled by multi-port terminals to connect the wires. Each component is associated with a specific footprint, and the software links the component footprint with the circuit symbol. Then, concerning the electronic board routing, the board includes two layers, a top and a bottom. The PCB design is accomplished by manually tracing PCB tracks, respecting the constraints related to track widths and spacing.
Human Machine Interface The application, developed for Android smartphones on the online software App Inventor, acts as a real Human Machine Interface allowing the user to visualize in real time the mass repartition on each landing gear and the center of gravity. It contains various features: 1. A Bluetooth connectivity part that allows to link two Bluetooth peripherals (the module and the smartphone). 2. A stopwatch that serves as a time counter for the application's use and as a checker in case of an application bug. 3. A data display section, including the coordinates of the aircraft's center of gravity, the percentage of load on each shock absorber, the temperature provided by the microcontroller and the acceleration values. 4. A dynamic part containing the aircraft's diagram and a cursor representing the aircraft's barycenter.
In this work, we present an attempt to estimate the reaction excitation function based on the measurements of thick target yield. We fit a function to experimental data points and then use three fitting parameters to calculate the cross-section. We applied our approach to 43 Ca(p,n) 43 Sc, 44 Ca(p,n) 44g Sc, 44 Ca(p,n) 44m Sc, 48 Ca(p,2n) 47 Sc and 48 Ca(p,n) 48 Sc reactions. A general agreement was observed between the reconstructions and the available cross-section data. The algorithm described here can be used to roughly estimate cross-section values, but it requires improvements.
48 Sc, as a radioactive impurity, is also listed here with reference to the analysis in this paper. In this work, we want to complement our research by evaluating the 43 Ca(p,n) 43  Sc, 44 Ca(p,n) 44g  Sc,  44 Ca(p,n) 44m Sc, 48 Ca(p,2n) 47 Sc and 48 Ca(p,n) 48 Sc cross-sections based on reported TTY measurements (the latter is not medically relevant, but 48 Sc production is important as it is a radioactive impurity). A similar attempt has already been proposed in  [21]  for the study of 34m Cl production. In this work, we verify this approach for above-mentioned reactions while employing a different, straight-forward numerical algorithm (our Python code is submitted in the Supplementary Materials to this paper).
Here, we describe the attempt to obtain the energy dependence of the cross-section (the excitation function) based on the experimental TTY exp (E) [MBq/µAh] values for different projectile energies E. These data are supplemented by an assumption TTY exp (E thr ) = 0, where E thr denotes the energy threshold for this reaction. The crucial factor is the choice of the function used to describe the TTY energy dependence. The number of parameters of the function used to fit the data should be restricted, as the number of the experimental data points is usually limited. Therefore, we propose a simple shape, TTY f it (E) = d + ac 2        √ π (b -E thr ) er f E -b a -a exp        -(E -b) 2 a 2               which fulfils several important criteria. This function is monotonically increasing, as TTY(E) should be.