dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Data are expressed as Mean ± Standard Deviation (SD) or Median and Interquartile Range (IQR). In all cases, the Shapiro-Wilk test was used to verify the normality of the data. The statistical test used for individual analyses is provided in the figure legends. Graphics were performed using GraphPad Prism v. 8 software (GraphPad Software, San Diego, CA, USA) and statistical analysis using SPSS version 26 (IBM Inc, Armonk, NY, USA). For all analyses, a probability value of (p) < 0. 05 was considered statistically significant. The influence of diet and treatments, as well as the treatment administration time on the liver metabolomics profile, was assessed through heatmaps analysis using MetaboAnalyst v. 5. 0 (McGill University, Montreal, QC, Canada)  [47]. To analyze the circadian rhythms, we used the Cosinor-based rhythmometry method.
To do this, J. R. S-R created a script using PyCharm software (v. 2018. 2. 4, JetBrains s. r. o. , Prague, Czech Republic) with Python version 3. 7. 4. The circadian rhythm was estimated using the CosinorPy package (v. 1. 1) [48]. To determine the presence of a diurnal rhythm, we considered each gene expression model that fits cosine curves with a significance level of p < 0. 05. Additionally, this method enabled us to obtain rhythmicity parameters such as the MESOR (average value adjusted for diurnal rhythm), amplitude (the difference between a wave's peak and average), or acrophase (the time when the oscillation peak occurs) of the diurnal oscillations.
The ROS Detoxification System in the Liver Is Altered Due to the CAF To investigate the rhythmicity of gene expression related to the ROS detoxification system in the liver, we used the Cosinor method to study the expression of several key genes. Specially, we focused on Superoxide dismutase 1 (Sod1), mainly found in the cytoplasm, Superoxide dismutase 2 (Sod2), predominantly found in the mitochondria, and Catalase. Furthermore, the expression of Glutathione peroxidase 1 (GPx1) and Glutathione-disulfide reductase (GSR), which are essential components of the GSH cycle, were also examined. The data for ZT0-treated rats are represented in Figure  5  and Table  6 , while those for ZT12-treated rats are in Figure  6  and Table  7. Regarding ZT0-treated rats, Sod1 expression did not exhibit diurnal rhythmicity in either STD-VH or CAF-VH (Figure  5A , Table  6 ).
Acknowledgments: The authors would like to thank  Niurka Llópiz  and  Rosa Pastor (Tarragona) , for their help and technical assistance. We thank  Pol Herrero  and  Antonio del Pino of the Centre for Omic Sciences (COS) Joint Unit of the Universitat Rovira i Virgili-Eurecat  for their contribution to the metabolomics analyses. The graphical abstract and Figure 9 were designed with resources from Flaticon. com(accessed on  10 July 2023 ).
The libraries were endrepaired, A-tailed, adapter ligated, size selected, amplified, and finally purified. The libraries were quantified by Qubit and realtime PCR and size distribution was checked by bioanalyzer. The barcoded libraries were then pooled at equimolar concentrations and loaded on the Illumina NovaSeq 6000 Sequencing system (Illumina, San Diego, CA, USA) to obtain 150 bp paired end reads. For each sample, an average of 22 million filtered reads were obtained with a minimum of 19. 8 million reads per sample. The average mapping percentage of the filtered reads was 86% (Supplementary Table  2 ). The bioinformatic analysis of the RNA-Seq data was performed following our previously described protocol  (16). In brief, the quality of raw reads was assessed using the FastQC command line, and the tool fastp to filter the reads by considering the Phred quality score (Q ≥ 30).
The filtered reads were then aligned to the reference zebrafish genome downloaded from NCBI (release 106) using HISAT2, version 2. 2. 1 with default parameters. Read counts that belong to each gene were generated using featureCounts version 1. 5. 3. Differential expression of the genes across the treatment groups was determined by DESeq2 and transcripts with |Log 2 fold change| ≥ 1 and an adjusted p-value < 0. 05 (Benjamini-Hochberg multiple test correction method) were considered significantly differentially expressed. The gene ontology (GO) enrichment analyses were performed using the software Database for Annotation, Visualization and Integrated Discovery version 6. 8 with a p value of 0. 05 and minimum gene count of 2. The packages ggplot2, pheatmap and GOplot in R were employed to present the data.
We designed the primers for the selected genes using the Primer-BLAST tool in NCBI. The primers were then checked for secondary structures such as hairpin, repeats, self and cross dimer by NetPrimer (Premier Biosoft, Palo Alto, USA). The primers for the target genes are listed in Supplementary Table  3. Relative expression of selected genes was determined based on the geometric mean of three reference genes (eef1a1l1, rpl13a and actb1). The data were checked for assumptions of normality (Shapiro-Wilk) and homogeneity of variance (Bartlett's test). Based on the normality and equal variance assumption check results, the statistical difference was determined by Analysis of Variance (ANOVA) or Kruskal-Wallis test. The pair wise comparison between the treatments was done by Tukey's or Dunn test.
Light microscopy photomicrographs were taken with the Olympus BX61/Camera Color View IIIu (Olympus Europa GmbH, Hamburg, Germany) and the photo program Cell P (Soft Imaging System GmbH, Munster, Germany). The ImageJ software  (20)  was used for analysing the tissue microarchitecture. To understand the histopathological changes, we measured five parameters of the intestine features: number of eosinophils, goblet cell number, goblet cell size, villi length and width of lamina propria.
Level 2b: identification by accurate mass (with an acceptable deviation of 3 ppm), and MS/ MS spectra, Level 3: identification by accurate mass alone (with an acceptable deviation of 3 ppm). The obtained metabolomic data were analyzed employing MetaboAnalyst 5. 0  (22). The data were log-transformed and auto-scaled (mean-centered and divided by the standard deviation of each variable) before downstream analyses. Principal component analysis was performed using the mixomics package in R 4. 2. 1 to understand the differential clustering of the study groups. Metabolites with a |Log 2 fold change| ≥ 0. 6 and a p value of < 0. 05 are reported as significantly altered metabolites. The packages ggplot2 and pheatmap in R 4. 2. 1 were employed to prepare the illustrations in this article.
Since 2016, the Sentinel-2 constellation has provided a promising source of imagery, with 13 spectral bands in various frequencies at 10-60 m of spatial resolution, an image every 5 days and a consistent spatial coverage. Recently,  Marszalek et al. (2022)  evaluated the use of Sentinel-2 data to perform fieldlevel crop yield prediction and concluded that the use of all raw bands explained the observed variability in winter wheat yields well compared to the use of vegetation indices. Therefore, it requires further study, although it is known that vegetation indices can be easily saturated by a dense canopy  (Wang et al. , 2012). In addition to these two sources of information, biophysical parameters can also be derived from remote sensing images and have the benefit of mitigating the saturation problem.
One solution to obtain their estimation with global coverage is to use the SNAP (Sentinel Application Platform) biophysical processor  (Weiss and Baret, 2016). This tool uses an artificial neural network (ANN) pre-trained model, based on the PROSAIL  (Jacquemoud et al. , 2009)  simulation system, to inverse the biophysical variable from optical remote sensing data. The ANN weights are publicly available and accessible. This approach has become the standard one and was the first entry point for estimate biophysical variables from Sentinel-2 data  (Hauser et al. , 2021). Commonly used machine learning algorithms for crop yield prediction include empirical regression methods (e. g. Ridge), ensemble learning approaches (e. g. Random Forest, Gradient boosting),  Support Vector Regression, and Artificial Neural Networks (Mateo-Sanchis et al.
More precisely, we propose to (1) explore the possibility of accounting for seasonal changes in phenology by using thermal time instead of calendar time (2) identify the most relevant Sentinel-2-derived spectral information (3) evaluate different complementary machine learning algorithms for such task. Here, we exploit a multi-year corn production dataset with over 1150 observations of corn field-level yield averages, spanning the U. S. states of Iowa and Nebraska between 2017 and 2021, with an average of 230 fields per year. Experiments are being conducted to evaluate machine learning methods behaviour on ''out-of-year'' samples, using Sentinel-2 satellite image time series and temperature data derived from ERA-5 climate reanalysis, to monitor crop physiology and temperature regime respectively.
, 2012)  and are therefore not retained in the analysis. Band B2 was not included either, as this channel is affected by atmospheric scattering  (Marshall et al. , 2022). All the raw data are obtained via the EO-oriented open source Machine Learning (ML) framework eo-learn  (Sinergise, 2019). This framework provides a Python client to download and preprocess atmospherically corrected Sentinel-2 data at level 2A from the SentinelHub cloud platform. All raw bands pixels were resampled to 10 m bands reference grid using nearest neighbour interpolation. For each image, the pixels identified as saturated, shaded, or cloudy using the scene classification map (SLC) obtained by sen2cor (Main-Knorn et al. , 2017) were discarded in further analyses. Next, we calculate the percentage of pixels that were previously identified as invalid relatively to the total number of pixels in the field bounding box.
The temperature at 2 m above ground (a. g) is associated to each field observation using data from meteoblue API. 2  The data was initially generated from ERA-5 climate dataset, developed by ECMWF, 3  where climate data is simulated in a reanalysis model. It provides hourly estimates of temperature data from 1979 to present in a spatial grid of 31 km. Thermal time has the advantage of limiting the effects of potential temporal shifts from season to season or location to location  (Atwell et al. , 1999) , which can be detrimental to characterizing actual growing conditions that will then be exploited to predict corn yield  (Bojanowski et al. , 2022). This will result in smoother and more temporally consistent time series of canopy biophysical variables  (Duveiller et al. , 2013). Thermal time is typically measured in units called growing-degree days (GDDs), which denotes accumulating mean daily air temperatures at 2 m a.
Temporal characterization of corn growth The daily time series derived from satellite and thermal data are then aggregated into periods calculated from the sowing date, whether based on calendar or thermal time. For the first, we empirically set periods of 10 days, which gives approximately at most 15 periods. For the second, in order to have approximately the same number of periods as the calendar time, the accumulated GDD values are aggregated with a step of 216 in Fahrenheit (120 in Celcius). For both, we remove the periods where we have more than 90% missing values, giving us a total of 14 periods to cover the season. For the fields where the last period(s) have missing values, we impute them against the observed value of the previous period. From the calculated periods, we characterize the growth of the plant during the season by using variables derived from Sentinel-2 that allow us to monitor the biomass, in particular by using the LAI estimate.
Huber et al. (2022)  have recently shown that a feature-based representation derived from remote sensing can outperform even the most recent deep learning-based yield predictors. Artificial neural networks (ANNs) have been considered, even when dealing with small data sets, because they have the ability to capture complex relationships between input and output variables and generalize to new data. They can be improved by incorporating regularization techniques, such as weight decay and early stopping. However, the random uncertainty caused by the random initialization of deep learning methods can cause arbitrary errors on test samples that are not part of the training data distribution. To reduce the variability due to initialization, an unweighted average of the outputs of several individual ANNs, randomly initialized and trained on the same dataset, can alleviate this effect or even improve the results of the individual models  (Lang et al.
Instead, the SAE aggregates the predictions of individual models by taking the average of their outputs, resulting in improved performance compared to using any individual model alone  (Gorissen et al. , 2009). The ensemble strategy prediction involving RF, SVR, XGBoost and MLP  is implemented as follows: ŷ𝑆𝑇 𝐴𝐶𝐾 𝑁𝐿 = ŷ𝑅𝐹 + ŷ𝑆𝑉 𝑅 + ŷ𝑋𝐺𝐵𝑜𝑜𝑠𝑡 + ŷ𝑀𝐿𝑃 𝐸𝑁𝑆 4 (5) where ŷ , ŷ  , ŷ and ŷ  are the predicted values of the nonlinear regression methods (namely, RF, SVR, XGBoost and MLP  ), and ŷ   is the resulting prediction obtained by stacking the unweighted average of these four predictions. The algorithms RR, RF and SVR are available in the Python library scikit-learn  (Pedregosa et al. , 2011)  ; XGboost in the library xgboost  (Chen and Guestrin, 2016). To avoid overfitting, the hyperparameters of the algorithm are tuned using a grid search, where the root mean square error (RMSE) is defined as an objective function to be minimized in average over the validation sets.
Results In this section, we present the results of the different data processing, machine learning algorithms and validation strategies described in Section 2. We first examined how the two different validation strategies, namely RDM  and LOYO  , influence the behaviour of the machine learning methods in Section 3. 1. We then compared the use of thermal time versus calendar time for resampling time series observations in Section 3. 2. In addition, we assessed the explained yield variation by the models according to different Sentinel-2 derived spectral information, namely BANDS, VI , BANDS+VI and BP, previously introduced in Section 3. 3. Regarding the machine learning algorithms introduced in Section 2. 6. 1, we reported their behaviours according to the average performances obtained, as well as their dispersion around the mean, obtained in term of R-squared ( 2 ) in Section 3.
Comparison of validation strategies: mixing seasons vs. predicting a new season Before comparing the two validation strategies RDM  and LOYO  , we briefly recall how they were built. For both, approximately 20% of the dataset was devoted to the test set. The procedure was repeated five times to obtain average results and standard deviations. Therefore, each model presented in Section 2. 6. 1 had its set of hyperparameters (Table  3 ) for a given training/test split, which was obtained based on the average of the model scoring on the validation sets. We present the average metrics obtained for each method and their respective standard deviation in term of  2 and MAPE on the test set in Tables  4  and 5 , respectively. We observed that all methods achieved the best performance with RDM  compared to LOYO  , where the  2 at the field-level decreased from 0.
Comparison on sentinel-2 derived spectral information We then compared the different variables derived from the Sentinel-2 raw bands (Section 2. 6. 1) as inputs to the machine learning models, namely the raw bands (BANDS), vegetation indices (VI ), combined raw bands and vegetation indices (BANDS+VI ), and estimated biophysical variables (BP). We observed that there was no systematic benefit in terms of model evaluation to add vegetation indices to band time series as model inputs (BANDS+VI ) rather than considering only bands (BANDS). However, we noticed that the right choice of Sentinel-2 derived spectral information depends on the model evaluation strategy, i. e. , whether or not seasons were mixed in the test dataset. While we had little difference with RDM  validation, using BP as input performed most of the time better with LOYO  validation for all nonlinear methods, obtaining an  2 of 42% against at best 31% for the other input variables.
Conclusion This study analysed and compared the performance of data-driven methods for seasonal corn yield estimation from ground-based data in the U. S. using Sentinel-2, temperature and in-situ data. The capacity of estimating yield based on this corn yield seeds production dataset was shown to be influenced by various aspects, namely : (1) the way in which temporal dynamic is considered (thermal or calendar); (2) the Sentinel-2 derived spectral information (bands and/or vegetation indices, biophysical parameters); (3) the machine learning method that is chosen. Using biophysical parameters and resampled information over thermal time, our chosen ensemble learning approach explained 42% of the variation in yield, with an associated mean absolute error of 15. 2% for samples coming from years not represented in the training data.
The amount and complexity of data delivered by modern galaxy surveys has been steadily increasing over the past years. New facilities will soon provide imaging and spectra of hundreds of millions of galaxies. Extracting coherent scientific information from these large and multi-modal data sets remains an open issue for the community and data-driven approaches such as deep learning have rapidly emerged as a potentially powerful solution to some long lasting challenges. This enthusiasm is reflected in an unprecedented exponential growth of publications using neural networks, which have gone from a handful of works in 2015 to an average of one paper per week in 2021 in the area of galaxy surveys. Half a decade after the first published work in astronomy mentioning deep learning, and shortly before new big data sets such as Euclid and LSST start becoming available, we believe it is timely to review what has been the real impact of this new technology in the field and its potential to solve key challenges raised by the size and complexity of the new datasets.
• Deep learning to derive physical properties of galaxies (both posteriors and point estimates). These are applications in which deep learning is used to estimate galaxy properties such as photometric redshifts or stellar populations properties. Neural Networks are typically used to replace existing algorithms with a faster and more efficient solution, hence more suited for large data volumes. In addition, we also review applications in which deep learning is employed to derive properties of galaxies which are not directly accessible with known observables, that is, to find new relations between observable quantities and physical properties of galaxies from simulations. • Deep learning for assisted discovery. Neural networks are used here for data exploration and visualisation of complex datasets in lower dimension.
g. Bluck et al. 2022 ) among many others. However, we have made the choice not to include a detailed description of these works in this review. We will focus essentially on how deep learning has changed the landscape in the past half decade. What is different with deep learning and why a dedicated review? In many aspects, deep learning represents a change in the way we approach data analysis. Because, we now have access to large datasets and the computing resources are powerful enoughespecially thanks to Graphic Processing Units-we can move from an algorithmic-centred approach relying on manually engineered features to a fully data-driven unsupervised feature learning approach. This implies that instead of developing advanced domain specific algorithms for each task, we rely on a generic optimisation algorithm to extract the most meaningful features in an end-to-end training loop.
CNNs as state-of-the-art approach In the past years, the number of works using deep learning to classify galaxies based on their morphology has exploded, and CNNs have been used to classify the morphologies of galaxies in a variety of optical/Near-Infrared surveys (we address the radio domain in Section 3. 1. 2). a https://www. kaggle. com/. It demonstrates that deep learning has fast become the state-ofthe-art approach to estimate galaxy morphology in big datasets. Figure  3  illustrates the most common approach used for morphological classification. Images are fed into a series of convolutional layers which extract some summary statistics. The extracted statistics are then fed into a Multi-Layer Perceptron which maps them into a class. The employed loss function is usually a cross-entropy loss.
Radio galaxy morphology In addition to the Optical and Near-Infrared domains, the radio astronomy community has also been very active in developing and testing deep learning techniques for the classification of radio galaxies. These efforts are motivated by the forthcoming arrival of new radio facilities such as SKA b that will change the landscape by detecting hundreds of thousands of new radio galaxies. Similarly to what happened in the optical, the availability of large datasets with labels has enabled the community to extensively test deep learning for classification. The Radio Galaxy Zoo project  (Banfield et al. 2015)  used indeed citizen science to determine the host galaxy of the radio emission and the radio morphology of ∼170 000 galaxies. This therefore constitutes an excellent database for applying neural networks.
In that context, deep learning helps to reduce by several orders of magnitude the amount of required visual inspections but does not completely get rid of them. Despite these issues, deep learning has rapidly become the state-of-the-art technique to find strong lenses in large surveys. It has been successfully applied to multiple imaging surveys following very similar strategies as just described. The first work using CNNs for identifying lenses focused on the CFHTLS survey is  Jacobs et al. (2017). Petrillo et al. (2017)  and (  2019 ) applied a similar strategy for KIDS galaxies and  Jacobs et al. (2019)  extended the approach to DES. Huang et al. (2020)  focused on the DECALS datasets. Some other works focus more on simulations only in view of preparing future surveys. Lanusse et al. (2018)  demonstrated the performance of CNNs to detect lenses in LSST images and  Davies et al.
Some works have systematically compared different neural network architectures on the same dataset (e. g. Fielding, Nyirenda, & Vaccari 2021; Cavanagh, Bekki, & Groves 2021). The general conclusion is that, even if there are some differences in accuracy and in efficiency, all architectures fall in the same ballpark. A possible explanation for this is that astronomical images present in general less diversity than natural ones and hence relatively simple CNNs suffice to extract the relevant information. The works by  Katebi et al. (2019)  and  Lukic et al. (2019)  are among the few works in astronomy exploring the use of Capsule Networks  (Sabour, Frosst, & E Hinton 2017). Capsule Networks are proposed as an alternative to CNNs that incorporates spatial information about the features present in the images.
Beyond vanilla CNNs Labelled data A common bottleneck for deep learning based classification is the availability of labelled samples to train the supervised algorithms. In astronomy this is particularly delicate since the data properties (e. g. noise, resolution) change from one dataset to another so in theory the labelling process should be repeated for every new dataset. A number of works have addressed this issue with different approaches. The work by  Walmsley et al. (2020)  explores Active Learning as a way to reduce the amount of required examples for training. Active learning allows one to select the most informative examples for the model which are the ones showed to human classifiers. The work by  Walmsley et al. (2020)  is also the first to explore Bayesian deep learning as a way to both estimate uncertainties an also identify the most informative examples (Figure  6 ).