dataesr/scientific-paragraphs-categorization huggingface dataset:
Electrode Coordinates and Labeling Algorithm Electrode coordinates were computed using a volumetric image coregistration procedure. Using Freesurfer scripts 1  , the preoperative T1-weighted MRI (showing the brain anatomy) was aligned with a postoperative CT (showing electrode locations). Electrode coordinates were manually determined from the CT and placed into the native space. To take into account misalignment between MRI and CT scans due to craniotomy in patients implanted with subdural grid electrode arrays, we applied an energy-minimization procedure to project grid contacts onto the cortical surface  (Dykstra et al. , 2012). An electrode labeling algorithm 2  was used to assign a labeled brain region to each electrode using the Freesurfer's DKT 40 atlas  (Klein and Tourville, 2012)  in combination with a subcortical mapping enabled through Freesurfer.
Data Epoching Data were epoched using a time-window of 0. 3 s. Data during the finger tapping task were epoched in 0. 3 s windows around each correct button press. Data from the ∼4 min-long rest session was epoched into consecutive (non-overlapping) 0. 3 s-long timewindows, and data from each 30 s-long sleep epoch was epoched into consecutive (half-overlapping) 0. 3 s-long time-windows. Trials exhibiting values > 10 a. u. were excluded. On average (± SD), the percentage of rejected 0. 3 s long trials was 1. 87% (±2. 07), 1. 43% (±1. 06), 6. 18% (±4. 80), and 7. 33% (±7. 07), during the task, rest, sleepPre and sleepPost sessions, respectively.
Support Vector Machine Approach We implemented support vector machine (SVM) binary classification algorithms  (Boser et al. , 1992; Cortes and Vapnik, 1995)  using Matlab (Statistics and Machine Learning Toolbox). The SVM training algorithm searches for an optimal hyperplane that maximizes the margins between the nearest features of different classes. Different kernel methods can be used, such as linear, radial basis function or polynomial kernels. The classification approach was performed in 2 consecutive steps schematized in Figure  1B. Please note that we also implemented Linear Discriminant Analysis classification algorithms that confirmed the results we observed with SVM. Step1: Train an SVM Model Using Wake Data and Cross-Validate the Model An SVM model was built using the labeled waking data ("motor" and "rest"-trials).
We used a linear kernel in the function fitscvm implemented in Matlab (parameters: "KernelFunction, " "linear"; "Standardize, " true; "KernelScale, " 1). Classification performance was calculated using 5-fold crossvalidation. The data set (i. e. "motor-" vs. "rest-trials") was first randomly partitioned into 5 equal sized subsamples. Then, of the five subsamples, four subsamples were used as training data while the remaining subsample was retained as validation data. The cross-validation process was repeated five times, with each of the subsamples used once as the validation data. Each of the five trained classifiers was then applied on its corresponding validation data, and its predictive accuracy computed (i. e. the percentage of trials correctly classified). Finally, the mean of the five predictive accuracies was calculated and defined as the decoding accuracy (DA).
The functions crossval followed by kfoldLoss implemented in Matlab were used to crossvalidate the model. Step 2: Apply the Validated SVM Model on Sleep Data The SVM model (from step1) was used to predict the class labels ("motor" or "rest") of the unlabeled "sleep"-trials using Matlab function predict. For each sleep epoch scored as NREM sleep, a label was assigned to each of its accepted "sleep"-trials (0. 3 slong each, 50% overlap, see data epoching above). Finally, the proportion of motor-votes was computed per 30 s-long NREM epochs and in respect to the sleep period (sleepPre vs. sleepPost).
Statistical Analysis SPSS software (v22, IBM SPSS Statistics, Armonk, NY, United States) and Matlab were used to perform statistical analysis. Memory performance extracted from blocks 2:3, 9:10, and 12:13 were compared using repeated measures ANOVA (twotailed, p < 0. 05, Greenhouse-Geisser correction), and post-hoc comparisons using paired-sample t-test (two-tailed, p < 0. 05). For each participant, the proportions of motor-votes for sleepPre and sleepPost were compared using a Wilcoxon rank sum test (left-tailed since we hypothesized an increase in the proportion of motor-votes during sleepPost in comparison with sleepPre, p < 0. 025). The effect size r of the test was computed by dividing the z-statistic value by the square root of the total number of observations. At the group level, the median proportion of motor-votes during sleepPre and sleepPost were compared using a pairedsample t-test (two-tailed, p < 0.
Statistical analysis The normal distribution of data was analysed for each continuous variable using kurtosis and skewness. The Student's unpaired t-test, Mann-Whitney U-test, and Pearson chi-squared test were used to compare two independent factors. BCR-free survival (BCRFS) and metastases-free survival (MFS) curves were generated using the Kaplan-Meier method. A log-rank test was used for pairwise comparisons of survival. Two sets of multivariable Cox proportional hazards regression models were fitted. The preoperative model was limited to clinical and epidemiological preoperative features and the postoperative model included all features available for selection. Discrimination was evaluated using the Harrell's concordance index (C-index). Statistical significance was set at P < 0. 05. All tests were two-sided.
Therefore, we tested the methylation status of the pri-miRNAs using m 6 A-RNA immunoprecipitation followed by sequencing (m 6 A-IP Seq). This method is not an equivalent of the commonly used MeRIPSeq (32), as we avoided the fragmentation of polyA RNA. We validated this protocol using spiked-in methylated and nonmethylated controls (SI Appendix, Fig. S2 ). We further tested the robustness of our data by comparing them to previously published data by Shen et al. (19)  and Anderson et al. (33). In our dataset, we were able to identify 14,870 genes whose transcripts  (15, 562)  are depleted in the mta mutant, indicating that these gene transcripts are methylated. Upon comparison of our data to the published datasets, we found an overlap of 2,868 (80%) and 3,100 genes (65%) between our data and data from Shen et al.
(19)  and Anderson et al. (33) , respectively; 930 genes were common among all three datasets. A Venn diagram showing these overlaps is presented in SI Appendix, Fig. S3. miRNAs can be encoded within intergenic regions as independent transcriptional units or can be embedded within proteincoding genes sharing transcriptional units with their host protein genes  (34). For our analysis from the m 6 A-IP Seq data we selected transcripts of only those miRNA genes that are independent transcriptional units. Using this approach, we identified transcripts of 11 MIR genes that were enriched more than 1. 5-fold in WT vs. mta, indicating that they are m 6 A-methylated in WT plants (hence can be immunoprecipitated with m 6 A antibody), and lack this methylation in the absence of MTA (Fig. 2A  and SI Appendix, Fig.
S4 ). All of the identified pri-miRNAs that passed the enrichment threshold were either absent or had significantly reduced abundance in the mta IP sample relative to WT IP. As an orthogonal approach to verify that these pri-miRNAs were bona fide targets for MTA directed methylation, we carried out RNA IP (RIP) using an anti-GFP antibody and the GFPtagged MTA (35S:MTA-GFP) plant line. As a control, the plants expressing only GFP in the WT Col-0 background were used. RIP was performed on the nuclear fraction and followed by RT-qPCR on oligo(dT) primed cDNA. Eighteen pri-miRNAs (10 from our m 6 A-IP Seq data and 8 randomly chosen) were selected for verification by RT-qPCR, and of these all but 4 showed a statistically significant enrichment (P < 0. 05) in the MTA-GFP sample when compared to the GFP control (Fig.
However, MTA may also stimulate Microprocessor assembly by its direct interactions with TGH, which in turn interacts with HYL1 and stimulates/stabilizes HYL1 binding to double-stranded regions of pri-miRNAs. Upon comparison of our results to the previously published data regarding miRNA levels in the tgh mutant, we found a 45% overlap within down-regulated miRNAs in both tgh and mta mutants, despite these two datasets originating from two different tissues (46) (SI Appendix, Fig. S11 ). These 23 miRNAs that are down-regulated in both mta and tgh mutants could be regulated by the direct MTA-TGH interactions. However, the mechanism of this MTA-TGH interaction-related alteration of miRNA biogenesis requires further studies. Here, we also provide evidence that MTA interacts with RNA Pol II phosphorylated at both Serine 5 and Serine 2.
Materials and Methods Plant Material. A. thaliana WT Col-0, mta mutants (26), MTA-GFP (p35S:MTA-GFP), and GFP plants were sown on Jiffy pots and stratified for 2 d in dark at 4 °C. Thereafter, the plants were grown in plant growth chambers at 22 °C with 16-h light and 8-h dark cycles (70% humidity, 150-to 200-μmol m -2 s -1 photon flux density). Rosette leaves from 4-wk-old plants were harvested, immediately flash-frozen in liquid nitrogen, and used or stored at -80 °C until further use. sRNA Sequencing and Analysis. sRNA fraction from 4-wk-old A. thaliana WT Col-0 and mta mutant plants was used to prepare libraries that were sequenced at Fasteris on the HiSeq 4000 platform. A detailed description of the library preparation and data analysis can be found in SI Appendix, Supplementary Materials and Methods.
m 6 A-RNA IP of pri-miRNAs and Sequencing. PolyA enriched RNA from 4-wk-old A. thaliana WT Col-0 and mta mutant plants was used to perform m6A IP. Libraries were prepared from RNA before IP (input) and after (IP). A detailed description of the library preparation and data analysis can be found in SI Appendix, Supplementary Materials and Methods. Data Submission and Availability. The data obtained in this study (sRNA and m 6 A-IP Seq) has been deposited under the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) accession no. GSE122528. The PIP-seq data used for the secondary structure analysis of miRNA transcripts can be found under NCBI GEO accession no. GSE108852. RIP. Transgenic Arabidopsis line p35S:MTA-GFP and mta mutants were grown along with the GFP and WT Arabidopsis lines.
RIP was performed as described by Raczynska et al. (63). A detailed protocol for RIP and data analysis can be found in SI Appendix, Supplementary Materials and Methods. PIP-Seq Analysis to Access RNA Structure Scores of miRNA Processors. A total of two biological replicates of PIP-seq, including single-stranded RNA-seq and double-stranded RNA-seq libraries, for leaves five to nine from 4-wk-old WT and mta mutant plants were constructed as previously described  (37, 39). The raw data has been deposited under the GEO accession no. GSE108852. Refer to SI Appendix, Supplementary Materials and Methods for the detailed protocol and data analysis pipeline. Immunolocalization. The immunolabeling experiments using the Duolink PLA fluorescence protocol were performed on isolated nuclei of 4-wk-old A.
ACKNOWLEDGMENTS. This work was funded by the  Polish National Science Centre  (Grants  UMO-2019/32/T/NZ1/00122 ,  UMO-2017/27/N/NZ1/00202 ,  UMO-2016/23/B/NZ9/00862 ,  UMO-2016/23/D/NZ1/00152 ,  UMO-2013/10/A/NZ1/00557 ). The authors also received financial support from the  Initiative of Excellence-Research University  ( 05/IDUB/2019/94 ) at  Adam Mickiewicz University, Poznan, Poland , and from the  Poznan RNA Research Centre. Work in the R. G. F. 's laboratory was supported by the  Biotechnology and Biological Sciences Research Council  (Grant  BB/M008606/1 ). Work in B. D. G. 's laboratory was supported by the  National Science Foundation  (Grants  MCB-1623887  and  IOS-1444490 ).
In Vivo Tumor Accumulation and Tumor Penetration of SOD-Fe 0 @Lapa-ZRF As shown in Figure  S13b , SOD-Fe 0 @Lapa-Z group exhibited relatively weak fluorescent signals in the tumor region. In contrast, the fluorescence signal of SOD-Fe 0 @Lapa-ZRF group in tumor tissue was stronger than the SOD-Fe 0 @Lapa-ZR group after 4 h postinjection, indicating the significant tumor targeting ability of SOD-Fe 0 @Lapa-ZRF. Then, mice were sacrificed at 24 h postinjection, and tumors and major organs were collected for ex vivo imaging. The fluorescent signal of SOD-Fe 0 @Lapa-ZRF distributed in tumor tissues was significantly higher than that of other organs (Figure  S13c ). Semiquantitative data showed that the fluorescence intensity of Figure  3. In Vitro Induced Oxidative Damage to Tumor Cells by SOD-Fe 0 @Lapa-ZRF.
(A) Schematic illustration of cancer cells redox levels under the action of SOD-Fe 0 @Lapa-ZRF. (B) The cellular uptake of FITC-loaded SOD-Fe 0 @Lapa-Z, SOD-Fe 0 @Lapa-ZR, and SOD-Fe 0 @Lapa-ZRF in 4T1 cells. (C) 4T1 cells viability after treated with ZIF-8 and ZRF for 24 h. (D) 4T1 cells viability after incubation with SOD-Fe 0 , Lapa, and Lapa-Z for 24 h, as determined by CCK-8 assays. (E) Cytotoxic effect of SOD-Fe 0 @Lapa-Z, SOD-Fe 0 @Lapa-ZR, and SOD-Fe 0 @Lapa-ZRF on 4T1 cells. (F) CLSM images of calcein-AM/PI stained 4T1 cell after incubation with different agents. The scale bar is 100 μm. (G) Apoptosis analysis of 4T1 cells induced by SOD-Fe 0 , Lapa, SOD-Fe 0 @Lapa-Z, SOD-Fe 0 @Lapa-ZR, and SOD-Fe 0 @Lapa-ZRF with or without dicoumarol, and detected by flow cytometry. Data are shown as mean ± SD (n = 3).
In Vivo Fluorescence Imaging The female Balb/c mice with 4T1 tumors were performed in vivo imaging studies. When the tumor reached 100 mm 3 , the mice were intravenously injected with FITC-labeled SOD-Fe 0 @Lapa-Z, SOD-Fe 0 @Lapa-ZR, and SOD-Fe 0 @Lapa-ZRF (with equivalent Lapa of 2 mg kg -1 ). At 2, 6, 12, and 24 h after administration, and the fluorescent images were observed using VIS-FL imaging system (Cambridge Research & Instrumentation; Woburn, MA) at different time intervals. The mice were sacrificed 24 h post administration, and the tumors as well as normal tissues were harvested for ex vivo imaging. The region-of-interests were analyzed by using Living Image software.
Acknowledgements The authors are grateful for the financial support from the  National Natural Science Foundation of China  ( 21574148 ,  21104097 , and  31870943 ),  Natural Science Foundation of Guangdong Province  ( 2014A030313152 ), the  Science and Technology Project of Guangdong Province  ( 2018A050506019 ),  Science and Technology Program of Guangzhou  ( 201904010151 ), the  Fundamental Research Funds for the Central Universities  ( 21619418 ),  Postdoctoral Fund of the First Affiliated Hospital of Jinan University  ( 809018 ), Special Funds for the Cultivation of Guangdong College Students' Scientific and Technological Innovation ("Climbing Program" Special Funds) , and the  Intramural Research Program, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health.
5 J/ cm 2 , and spot size of 29 μm has been used for these analyses. The ETH-prototype GeoLas laser ablation system equipped with an Excimer ArF (193 nm) COMPex 102F laser source (Coherent, Germany) was used for the MI analyses. A laser repetition rate of 10 Hz, on-sample energy densities in the range of 5-10 J/cm 2 , and spot sizes between 10 and 30 μm, based on the different inclusion sizes, were used. In general, the inclusion diameter was slightly smaller than the selected spot size in order to ablate completely the inclusion and avoid the unrepresentative sampling related to heterogeneity therein. This paper reports a limited version of the MI data set of  Borghini et al. (2018)  with a selection of the inclusions with the strongest signal (i. e. highest signal-to-background ratio). Both laser systems were coupled with an Element XR (Thermo Fischer, Germany) sector-field ICP-MS.
The carrier gas consisted of high-purity He (5. 0 grade, 0. 5-1. 0 L/ min), to which was admixed make-up Ar gas (6. 0 grade, 0. 9-1. 0 L/min). For all analyses, the instrument was optimized based on the ablation of the NIST SRM612 glass (43 µm raster at 5 µm/s, 10 Hz, and 3. 5 J/cm 2 ) for highest sensitivity on the high mass range, low production of oxides ( 248 ThO + / 232 Th + <0. 4%), and U/Th of ~1. The intensities were acquired in low-resolution, peak jumping, and triple detector mode, with a dwell time of 10 ms for all elements (total sweep times were 750 ms for minerals and 782 ms for MIs). The list of isotopes used for each element is reported in the data repository of  Borghini et al. (2018)  for the different types of analyses. F I G U R E 2 The glass reference material NIST SRM610  (Jochum et al.
, 2011)  has been used for drift correction and the quantification of the inclusion analyses, ablated at the same conditions as the unknowns, except for an energy density of ~5 J/ cm 2 and spot size of 40 μm for MI analyses (for more details see data repository of  Borghini et al. , 2018). The USGS glass reference materials, GSD-1G and GSE-1G, were used as validation reference materials (respectively for minerals and MI analyses) to check the accuracy and reproducibility of the analyses. All data processing was performed offline. The acquired time-resolved signals were processed using the software SILLS  (Guillong, Meier, Allan, Heinrich, & Yardley, 2008). EMP concentrations were used as internal standards for deconvolution of host and inclusion signals and relative sensitivity correction for both mineral phases and inclusions.
%, that is, it is an omphacite (Jd ≥ 20-25 mol. %), as commonly observed in eclogite. By contrast, pyroxenite clinopyroxene is commonly augite-diopside or Cr-diopside. The investigated eclogites have features more compatible with the oceanic crust than with the mantle peridotites and pyroxenites, notably the low Cr, Ni contents, and Mg#, despite being enclosed in mantle rocks. The major element contents are indeed generally very similar to N-MORB, that is, oceanic crust. For example, Na 2 O generally fits in the range of common lower oceanic crust  (2. 23-3. 27 wt%; Coogan, 2014). However, since MgO is lower than in N-and E-MORB, KLA and RUB samples display a typical Fe-rich basaltic composition. Such MgO values are in the range of low-MgO mantle garnet pyroxenite when compared with the database of Isabel Varas-Reus,  Garrido, Marchesi, Bosch, and Hidas (2018) , and their Mg# is the lowest with respect to the other pyroxenites and kyanite eclogite of the Moldanubian Zone.
ACKNOWLEDGEMENTS We are grateful to PD  Dr. Lutz Hecht  and  Peter Czaja  for help during the analyses and to  Christine Fischer  for the sample preparation. The authors acknowledge  Nadia Malaspina ,  Károly Hidas , and  Jana Kotková  for the manuscript constructive reviews and  Bernardo Cesare  for the editorial handling. We thank the  German Federal Ministry for Education and Research and the Deutsche Forschungsgemeinschaft  (projects  FE 1527/2-1  and  FE 1527/2-2  to S. F. ).
General The model of the CHS beams was built using the FE software Abaqus  [20]. At the exception of Hartig et. al  [21] , the few attempts made in the literature to model the behaviour of hollow timber sections did not aim at fully capturing the fracture of the timber observed in Figure  2 , see  [4, 13]  for instance, as the compressive sudden failure mode was not always observed. In Hartig et. al  [21] , continuous damage mechanics  [22] [23] [24] [25] [26]  was employed to model a vehicle impact on moulded wooden tubes. The model predicted well both the observed failure modes, and the acceleration and velocity versus time relationships of the vehicle after impact. The stress developing in the sections and the section capacity (impact force), however, were not numerically verified.
Nonlinear geometric and material conditions and quasi-static implicit dynamic analyses were considered in the models. To improve convergence, viscous regularisation within the cohesive zone element definition was employed. This regularisation was verified to negligibly influence the results, with the energy dissipated by viscoelasticity calculated by the software (ALLCD) at peak load representing less than 0. 1% of the total internal energy of the system (ALLIE). Note that in the model, only the deterministic size effect  [41, 42]  is considered from the size of the fracture process zones. The statistical size effect  [43] [44] [45] , outlining the difference in volumes between the specimens used to measure the material properties in Table  1  and the tested CHS, is ignored. Further investigation would be needed to understand how this effect influences the capacity of the CHS.
In a design situation, Mo can be calculated through finite strip analyses for anisotropic material  [47]  and available software, such as CUFSM  [48]. The second slenderness ratio λy_p is based on the "plastic" yield moment My_p, defined as the moment at which the stress at the extreme fibre in tension reaches ft_L, assuming an elastic-perfectly plastic material in compression and no cracks developing in the timber, as shown in Figure  8 (b). While the calculation of My_e is straight forward, an algorithm must be developed to find My_p, such as in  [2]. λy_p is defined as, __ _ _ y p t L yp o c L Mf Mf   (5) Values of Mo, My_e and My_p for all analyses are provided in Table  5. Slenderness ratios and their associated "yield" and "plastic" moments are commonly used in structural engineering when developing design equations, such as in  [49].
82, 0. 05 m 3/ m 3 respectively and no bias). The new retrieval approaches were then applied to a large database covering a rainfed field in Morocco and 18 plots of rainfed and irrigated wheat of the Kairouan plain (Tunisia) and compared to other classical techniques of SSM retrieval including simple linear 3 relationships between SSM and. The method based on the WCM and the -AGB relationships also provided with slightly better results than the others on the validation database (r=0. 75, RMSE=0. 06 m 3 /m 3 and bias=0. 01 m 3 /m 3 over the 18 plots of Tunisia) but the simple linear relationships performed also reasonably well (r=0. 62, RMSE=0. 07, bias=-0. 01 in Tunisia for instance). This study opens perspectives for high resolution soil moisture mapping from Sentinel-1 data over south Mediterranean wheat crops and in fine, for irrigation scheduling and retrieval through the assimilation of these new products in an evapotranspiration model.
VH/VV appears more stable in time than VH or VV and more sensitive to the wheat growth cycle. Furthermore, it was correlated to the fresh biomass and able to discriminate cereals from non-cereals surface dominated crops  (Mattia et al. , 2015; Veloso et al. , 2017). the vegetation contribution in the WCM. The approach is developed on two monitored irrigated wheat fields located in Morocco and evaluated on a large database covering 20 rainfed and irrigated plots in Morocco and Tunisia. The next section describes the satellite and the in situ measurements, the backscattering models and the retrieval algorithm. Results are presented in section 3 including the experimental analysis of Sentinel-1 time series, the forward modeling of the backscattering coefficient and the SSM retrieval. In section 4, the main conclusions and perspectives are drawn.
Soil measurements Automatic measurements of SSM were acquired at a 30 min time step from Time Domain Reflectometry sensors (TDR Campbell Scientific CS616) buried at a depth of 5 cm. In order to get a measurement representative of the Sentinel-1 pixel, the soil moisture observations were taken as the average of two sensors data that were installed between and under the drippers. The gravimetric technique was used to calibrate the TDR measurements. Measurements were carried out, over Field 1 and Field 2, for five days in order to cover a wide range of soil moisture values (0. 08 to 0. 33 m 3 /m 3 ). Three samples were collected per day and per field at the TDR installation depth using a 392. 5 cm 3 aluminum core. A linear regression was established between the volumetric water content and the square root of the TDR time response.