dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Statistical analyses were performed and plots were generated using R software v. 3. 4. 2 (R Core Team, 2018). Pearson's correlation coefficients (r) were calculated from the means of all genotypes and Holm's correction was applied for the evaluation of correlation significance. Parameters of non-linear models (i. e. , logistic curves and exponentials) were adjusted using the nls function (non-linear least squares)  (Bates and Watts, 1988; Bates and Chambers, 1992). Linear regression models were fitted with the lm (linear models) function. Type II analyses of variance (ANOVA) were performed using the "car" package of R, and Tukey's post-hoc procedure was used to compare means. ANOVA assumptions were tested using the Shapiro-Wilk and Levene's tests. Hotelling (1931)  T-squared distribution test was used to test the multiple parameters of the non-linear models.
Genotypic Variation in NUE_DM and Its Components A high and significant genotypic variability in NUE_DM was identified using the LR15 and GR18 datasets, since the genotype effect explained up to 68% of the overall variation in NUE_DM, depending on the site and climate year (Table  3 ). In the GR18 experiment, the genotype effect on NUE_DM variance remained strong and significant throughout the crop cycle, except at BBCH 30-32 where it was not significant. In contrast, in the LR15 experiment, the genotype effect decreased progressively during the crop cycle, leveling off around 10% during the reproductive phase. In both experiments, the highest and most significant genotypic effect was observed during the vegetative phase, suggesting that the NUE_DM genotypic variability was determined from the beginning of the vegetative phase.
Deciphering Genotypic Variation in NUpE-Related Processes Under Low N Conditions The above results highlighted NUpE as the main contributor to NUE_DM shaping, especially under low N conditions and as an important driver of its genotype variability during the autumn growth. This raises the question of identifying the key processes underlying this trait. NUpE depends on the quantity of N accumulated in the plant, itself driven by two root processes: ability to absorb N per unit of cumulative fine-root biomass (SNU) and ability to maximize exchange area with the soil through root system development, that we approximated by fine-root growth. The study was focused on low-N conditions and carried out on the GR18 dataset that characterizes several genotypes with sufficient sampling frequency to ensure the establishment of reliable cumulative fine-root growth dynamics.
ACKNOWLEDGMENTS The article's English was revised by the Editor du jour company. The authors acknowledge  Solenn Guichard ,  Archange Uwantege , and  Christelle Franchet  for their strong involvement in the experimental set up and supervising, as well as the group Serres, the team Yield under abiotic challenges of IGEPP, and the technical team of ECOSYS for their help. We acknowledge the  BrACySol Biological Resource Center (INRAE Ploudaniel, France)  for providing the seeds used in this study.
Microsatellite DNA was analyzed using an ABI Prism genetic analyzer with 0. 1-0. 15 μl of PCR product, 25. 15 μl of ABI master-mix (25 μl formamide, 0. 15 μl standard size T486). Allele size was determined using GeneScan ® 500 TAMRA dye size standard and GeneScan ® 3. 1 software (Applied Biosystems). Statistics and reproducibility. Genetic data were analyzed using the software package GDA  63. Relatedness was estimated following  64  using the software Relatedness v4. 2  65  and GenAlEx v6. 51b2 66 with standard errors of means obtained by jackknifing by groups. Confidence limits for the fixation coefficients were obtained by bootstrapping over loci (5000x). From fixation coefficients F, we calculated the proportion of sib-mating α using F = α/(4-3 α)  67, 68. The Mantel test was made using R software-4.
Acknowledgements This work was supported by  DAAD  ( PROCOPE 57128510 ) and  DFG  ( He1623/42 ). We thank  Jean-Luc Mercier ,  Larissa Kalb ,  Sarah Koller ,  Christopher Schreier ,  Çiğdem Ün  and  Tobias Wallner  for help in the field as well as  Sarah Koller  and  Anaïs Chanson  for taking part in the analysis of microsatellites.
III. COMPUTATIONAL DETAILS The computational methods investigated here (HF, MOM, TpCCD, VpCCD, DOCI, and FCI) have been implemented as standalone mathematica modules,  152  which makes them easily interconnectable and modifiable depending on the actual purpose. These are provided in an accompanying notebook available for download from Zenodo at http://doi. org/10. 5281/zenodo. 4971904. All the calculations have been performed in the restricted formalism. 4] [155]  The convergence threshold (based on the DIIS commutator) was set to 10 -10 a. u. for the restricted HF (RHF) calculations, while the convergence thresholds (based on the maximum absolute value of the gradient) for the cluster amplitude and orbital optimization procedures were both set to 10 -6 a. u.
SUPPLEMENTARY MATERIAL Included in the supplementary material are a standalone mathematica notebook gathering modules for the computational methods investigated here (HF, MOM, TpCCD, VpCCD, DOCI, and FCI), raw data for each figure, orbitals obtained at various levels of theory, and files containing the one-and two-electron integrals for the systems studied in the present manuscript. hereafter we denote the CC wave function and its energy as |Ψ CC ≡ |Ψ and E VCC ≡ E. In this case, the derivative of the VCC energy functional with respect to the cluster amplitudes reads where we have used the assumptions that both the cluster amplitudes and orbital coefficients are real to simplify the second to last equality. Then, we differentiate the residual with respect to the cluster amplitudes to obtain the Jacobian matrix elements required to target saddle points.
However, these datasets are limited to resolutions much coarser than the scale of activity (many are 100+m/pixel, the best is CRISM with 18m/pixel) and only capture surface exposures. In many areas, a thin layer of dust is enough to obscure the underlying layers. In such areas, geologic unit mapping can provide some constraints, based on extrapolation from visible outcrops, topography, and radar analysis. In situ compositional data, as collected by the Mars rovers and landers, allows for much more detailed measurement of surface and nearsubsurface properties, and coupling this with the global perspective provided through orbital observations has been key to constraining interpolative analyses. Such analyses are of high importance for meteorological condition studies. Existing/continued orbital data provides a look at global circulation and atmospheric features, such as clouds.
f, HEK293T cells were transiently transfected by plasmids to overexpress different PEX2 mutants. IP and IB were conducted at the non-reducing condition. Experiments were repeated three times. g-i, PEX2 mutants were overexpressed in HEK293T cells. After 48 h, cells were collected after 0. 5 mM H 2 O 2 treatment for 2 h. PEX2 mutants were enriched through IP for IB using the indicated antibodies. Experiments were repeated four times. we mutated each arginine into lysine in the K0 mutant to obtain 15 site-specific lysine-only mutants. General proteasomal degradation of ATGL was dependent on multiple lysine residues (Extended Data Fig. 4a ); however, only the K92-only mutant was responsive to PEX2 modulation (Fig. 2h  and Extended Data Fig. 4b, c ). To obtain a spatial resolution, we measured ATGL ubiquitination levels in the LD and cytosol fractions from iBAs in both a basal and stimulated state.
In accordance with our previous results, we showed that PEX2 ubiquitinates ATGL in both LD and cytosol fractions to regulate its levels and lipolytic activity (Extended Data Fig. 4d-g ). Instead, ). e, iBAs with PEX2-FLAG-EGFP expression were treated with 0. 1 μM Iso and collected at the indicated time points. IP and IB were conducted using the indicated antibodies (n = 4, F = 11. 01). f, iBAs were transfected with Nc and Pex2 siRNA. After 48 h, cells were stimulated with 0. 1 μM Iso for 24 h at the indicated doses. IB was conducted to check protein levels using the indicated antibodies (n = 4). g-i, iBAs were treated with 100 μM of various FAs. After 48 h, cells were collected for IB analysis or lipolysis measurement was performed (n = 5, F = 14. 35 (g), 16. 51 (h) and 7. 892 (i)). Results are shown as the mean ± s.
Images were obtained using an Olympus FLUOVIEW 3000 confocal microscope and processed with ImageJ. Patient liver biopsies. Human liver biopsies were collected in two cohorts and were analysed for data integration. The liver biopsies of one cohort were obtained during the work-up of liver disease diagnostics in the outpatient clinic of the Division of Gastroenterology and Hepatology, University Hospital Basel, Switzerland. The study was carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki 2013, seventh revision) and was approved by the local ethics committee (EKNZ 2014-362). Written informed consent was obtained from all patients. Five to ten millimeters of liver biopsy cylinders were immediately snap-frozen by immersion in liquid nitrogen and stored in liquid nitrogen vapours until processing.
The human liver biopsies of the second cohort were obtained from participants of the Biological Atlas of Severe Obesity cohort, an ongoing prospective cohort study for the longitudinal assessment of metabolic outcomes after weight loss surgery (NCT01129297), which was approved by the CHRU Lille Ethical committee and were compliant with French National Ethics Committee guidelines. The study design has been described previously in detail  43. Clinical patient information is described in Supplementary Table  3.
For the cell culture experiments, at least three technical replicates were used. Results are presented as the mean ± s. e. m. A two-tailed unpaired Student's t-test was applied to comparisons of two groups. Analysis of variance (ANOVA) was applied to comparisons of multiple groups using a Dunnett correction post-hoc analysis. A non-parametric Spearman test was used for the correlation analysis. All statistical analyses were performed using GraphPad Prism 7 software. Statistical differences are indicated as exact P values. Reporting Summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article. were overexpressed in HEK293T cells or co-expressed with ATGL-FLAG, followed by siRNAs transfection. IP and IB were conducted using the indicated antibodies.
(v) Fatty acid uptake rate in the livers of Acox1KO and Pex2KO mice was determined via fluorescence measurement in the liver extracts 30 min after tail vein injection of Bodipy-palmitate at the dose of 10 μg/mouse (N = 5). (w) Fatty acid esterification rate in the livers of Acox1KO and Pex2KO mice was determined via fluorescence measurement in the liver lipid droplet fraction 1 h after tail vein injection of Bodipy-palmitate at the dose of 10 μg/mouse (N = 5). Results are shown as mean ± SEM and analyzed using a Student's two-sided t test. Clinical trial registration  EKNZ 2014-362 and NCT01129297  Study protocol Protocol of Basel cohort:Liver biopsies of one cohort were obtained during the work-up of liver disease diagnostics in the outpatient clinic of the Division of Gastroenterology and Hepatology, University Hospital Basel, Switzerland.
Acknowledgements L. D. was supported by a  European Molecular Biology Organization  long-term postdoctoral fellowship (no. ALTF 50-2017 ). F. P. , B. S. and P. L. received grants from  Agence Nationale pour la Recherche  (nos. ANR-16-RHUS-0006-PreciNASH  and  ANR-10-LABX-46 ). B. S. is a recipient of an  Advanced ERC grant  ( 694717 ). Work was supported by a grant from the  Swiss SNF  (C. W. ). The working model presented in Fig. 7k was created with BioRender.
Introduction Deep Convolutional Neural Networks (DCNNs) have demonstrated throughout the recent years their remarkable performance in handling a variety of problems in the fields of image processing and computer vision  [1, 2]. In particular, DCNNs are becoming a major tool for visual recognition modern tasks such as image classification, segmentation, semantic segmentation and so on. Considering the global dimension that DCNNs have taken, predominant implementations often require large datasets, which is not always possible in many domains. Regarding that, the interest in small sample learning (SSL) is increasingly growing  [3]. Novel network topologies and training methodologies are required to address this issue. In the literature, there are many approaches that attempt to face out the SSL paradigm.
The data augmentation approach tries to compensate the lack of samples by applying adequate transformations to the original dataset  [4]. Besides expanding the size of the initial dataset, distorted images improve the generalisability of the network. Another approach is knowledge transfer of fully trained networks that can be used to fit small data-sets  [5]. One could transfer knowledge from similar tasks or even from uncorrelated tasks  [6]. Essentially, generic pattern matching tasks, created from unlabeled data, have been relevant for SSL  [7]. To handle limited data, unsupervised pre-training strategies, consisting of making fine adjustments to the initialization parameters of the network, have shown promising results as well  [8]. In this paper, we focus on specific DCNN architectures using only convolutionnal layers.
These architectures referred to as fully connected neural networks (FCNNs) are mainly used for semantic segmentation or image prediction  [9]. In this context, an important strategy which has been proposed to address SSL issue is to combine small neural net models and patch-based training  [10] [11] [12]. First the input size of the model (ie. the number of input neurons) is reduced providing a global model with less parameters. And second, the training images are cut into small patches to feed the network. Besides requiring smaller architectures, the patch sampling strategy allows to enlarge the dataset by providing much more independent samples to train the network. At inference time, an arbitrary large target image can be processed by dividing it into patches and merging the corresponding inferred results to reconstruct a big prediction image.
Experiments We evaluate our distance transform-based enhancement and patch sampling strategy for two datasets. One is a state-of-theart example on 2D segmentation of electron microscopic (EM) images of the brain, while the other is about predicting the electric field from binary images of heterogeneous materials. Each dataset requires a different task. In particular, image segmentation and prediction. Results are evaluated using adequate scoring metrics, namely: VRand for image segmentation and PSNR, UIQ, SSIM for image prediction.
2D segmentation of EM images of the brain The training dataset is a part of the public ISBI 2012 EM segmentation challenge  [29]. The aim is to precisely segment an EM image, where, pixels inside a cell area have value 1 and pixels at the boundaries between neurite cross sections have value 0. A set of 30 training images 512 Â 512 ð Þpixels along with their ground truth annotations are made public for participants. Another set of 30 validation images of the same resolution is available. Its corresponding ground truth data is kept secret by the organizers. The latter is used to evaluate the performance of the proposed algorithms. Comparisons between some of the existing state-ofthe-art methods, such as, the original U-Net network instead of the patch-wise procedure, also, with and without data augmentation (mainly, affine transformations), are investigated and compared to our approach.
To extract training, validation and inference images, the original training dataset is randomly shuffled into k ¼ 5 equal sized samples. Each subsample contains 26 training images, 3 validation images and 1 inference image. The outcome estimation is the average of the 5 predicted images. Thus, with the chosen K and s parameters (see Section 3. 2), one network is trained on a dataset of 11466 patches and is validated on 1323 patches. The sigmoid activation function f x ð Þ ¼ 1= 1 þ e Àx ð Þis computed over the final feature map. The network is trained using the Adam  [30]  optimizer and binary cross entropy loss function defined as: L y; y 0 ð Þ¼À1=N Â P N i¼1 y log y 0 i À Á À Á þ 1 À y ð Þlog 1 À y 0 i À Á y and y 0 being ground truth and predicted patches, respectively. It was noticed that the stability of accuracy and loss values requires a number of epochs P 25.
A specific algorithm described in  [34]  uses an original construction method which allows to run wide simulations with the least computational cost. We follow the guidelines of the latter algorithm to generate our training images. In this framework, a multi-scale microstructure is modeled by volume fractions that define aggregates (V v;inc of inclusion areas), grains inside and outside the inclusion areas (V v and V v;out , respectively). The training dataset is made of 500 2 pixel images. The parameters R ¼ 20 (radius of spheres), V v;inc ¼ 0:4; V v ¼ 0:6 and V v;out ¼ 0:7 are fixed for the whole image set. Based on the foregoing microstructures and phase dielectric properties, we use a numerical scheme to estimate the electric field. This method lies on several works, namely  [35] [36] [37] 31].
Labeled images are representations of electric field response E x ð Þ estimated inside and outside microstructures. For dielectric constants of the phases of the binary microstructure, 0. 1 and 100 (no imaginary part) are used for the black and white pixels respectively. The resulting electric field response module is converted to 8bit format (256 values) by uniform sampling. The training is performed on a dataset of 4800 training and 800 validation patches. Only binary and grayscale images are used, colored illustrations are shown solely for clarity purposes. The experimental setup is akin to the one described for the EM images of the brain. The network reaches its optimal performance after one epoch and a batch size 4. Dataset generations and homogenization codes can be found in the open access software ''plug im!"  [38]  (see Figs.
The a measures the importance of both the merge and split errors. In fact, they can be derived from the equation above. We have: V Rand a¼0 ¼ V Rand split and V Rand a¼1 ¼ V Rand merge. Finally, P k s 2 k and P k t 2 k are appropriate normalization. The used script allowing the computation of V Rand is the one that is proposed in the website of the challenge  [29].
Shift invariance On the electric field dataset, we conduct shift transformations from À5 to þ5 pixels (in the x's axis) applied to all patches of validation images. Given a fully trained U-Net on the original dataset, we firstly compare the binary cross entropy value for each shift value. The goal is to evaluate the ability of the network to predict translated images from non translated training data. Fig. 7  shows that the minimum error value coincides with the prediction of a non translated image, and, thence, the network smoothly looses accuracy proportionally to increasing shift values. An alternative approach would consists of feeding a trained network a prediction image I p that yields a prediction field F p. We select a patch X p of the size W Â H centered at position x; y ð Þ, the network result is an estimated patch field f p at position x; y ð Þ.
EM segmentation challenge ISBI 2012 Building upon the experimental setup described in Section 5, two experiments are conducted: Patch-wise U-Net The first aim is to investigate the effect of training the network on the enhanced image dataset. Unlike the binary microstructures case, where there is a plenty of untextured, void areas that made the contribution of the distance map information, intuitively useful. This application is based on grayscale and highly textured images. Besides, the difference between the original and the transformed images is not clear from a visual point of view as shown in Fig. 6. Therefore, the small change in the pixel values of the image is expected to have a proportional improvement on the quality of the resulting probability maps. Which is not big, but sufficient to prove the interest of adding the distance  transform.
Such results will allow to show the contribution of the distance transform, even for a challenging dataset in terms of textural complexity. The sample is prepared differently, with and without the distance map. Correspondingly, probability maps are evaluated and compared to the ground truth segmentation images using the V Rand metric. From the results shown in Table  2 , more specifically by comparing the uniform sampling strategy results, it is clear that the V Rand error has improved by training the network on the enhanced image dataset instead of the original images. U-Net At this stage, a comparison with a state-of-the-art data augmentation method is achieved. It was found that random elastic deformations are very efficient to overcome over-fitting in the case of small data samples. Examples are illustrated here  [20]  and here  [41]  for the ISBI 2012 segmentation challenge dataset.
In our experiment, the following transformations are used: rotation, width and height shift, shear, zoom and horizontal flip. Slight changes induced by the former transformations makes the model more robust and generalizable. The use of these operations creates artificially new images. We started with 26 samples from the original challenge dataset. With data augmentation, the model reaches its optimal performance for 1000 steps per epoch. Each operation is defined on a range of values. The number of the possibly generated samples is limited by the allowed number of pixels. For instance, height and width shifts operations are restricted by the dimensions of the patch W Â H ð Þ. For a height shift range h s ¼ 0:05, the number of the generated samples is equal to GS i ¼ h s Â 2 Â H, considering the two translation directions, top to bottom and vice versa.
Electric field estimation For this dataset, we investigate the effect of the enhanced images to the task of image prediction. As pointed out in the method section, the distance map enriches the information to be extracted. A comparison of the PSNR, UIQ and SSIM indices is performed for two types of training data. Primarily, the network is trained on the original dataset. Afterwards, another training is performed on the enhanced image dataset. A visual comparison is shown in Fig. 6  and the values of the evaluation metrics are presented in Table  3. A concluding remark is that the PSNR, UIQ and SSIM indices have improved remarkably in the second case.
74 vs 0. 83, p=0. 005), (0. 73 vs 0. 80, p=0. 036), (0. 74 vs 0. 83, p=0. 008), respectively). The delayed calculation of these scores did not significantly improve our accuracy for predicting poor functional outcomes. Conclusion Delaying the calculation of prognostication scores in acute ICH until day 4 improved prediction of 6-month mortality but not functional outcomes. Trial registration number ClinicalTrials. gov Registry (NCT02175225).
Open access the National Institute of Neurological Disorders and Stroke (grant number U01NS074425). Access to the data can be obtained through a formal proposal to the iDEF authors. Patient population iDEF was a prospective, multicentre, placebo-controlled, double-blinded, phase two randomised clinical trial that assessed the safety of deferoxamine mesylate (DFO) in patients with acute ICH. 9  The trial was conducted at 40 hospitals in Canada and the USA, and recruited patients between November 2014 and 2017. Patients enrolled were between the ages of 18 and 80 years, presenting with spontaneous, supratentorial ICH, and were administered either DFO or placebo within 24 hours of haemorrhage onset. Infusions were continued for 3 consecutive days (24 hours apart). Key exclusion criteria included suspected secondary causes for ICH, infratentorial location, coagulopathy (defined as international normalised ratio >1.