dataesr/scientific-paragraphs-categorization huggingface dataset:
Using phenocams to study the intra-community variability of leaf phenology A large part of the literature on forest leaf phenology is based on ground observation data. These data are historically the oldest and have been collected in national to continental databases (e. g. PEP725 Templ et al. , 2018 , NPN-usa Betancourt et al. , 2007)  that cover a period of several decades (e. g. from the 1950's in the PEP725 database). In the late 2000's, the use of "digital repeat photography" to document the phenology of plant canopies became widespread  (Richardson et al. , 2007 (Richardson et al. , , 2009)). These studies were initially based on the analysis of data obtained by automated photographic instruments, most often mounted on towers overhanging the canopy (i. e. phenological cameras or "phenocams").
Networks of phenocams have been set up (notably the PhenoCam Network,  Richardson, 2019 , see also  Wingate et al. , 2015) , which has enabled data to be centralized and harmonized. Image data are more complex than ground observation data. They have to be processed to extract an analysable phenological signal (e. g. critical dates in the development of the foliage). The development and public sharing of algorithms for processing phenocam images (e. g. R packages phenopix  Filippa et al. , 2016 and xROI Seyednasrollah et al. , 2019b)  has increased the use and impact of these data. Several studies have been dedicated to the comparison of critical phenological dates observed from the ground and inferred from the analysis of phenocam datasets at a common site (e. g. Keenan et al. , 2014; Soudani et al.
Processing phenocam images We obtained images from the phenocam dataset  (Seyednasrollah et al. , 2019b)  for 10 study sites (all the ENA sites plus Mill Haft). Images for the 7 other sites were provided by the site PIs. For each site, we delineated a mask (Region Of Interest, ROI) to delimit the deciduous vegetation zone in the phenocam scene (i. e. excluding roads, buildings, the sky and evergreen trees), thanks to the R package xROI  (Seyednasrollah et al. , 2019a). Since our aim was to work on ICVLo, we adopted a "pixel-based" approach  (Filippa et al. , 2016) , that has rarely been used to date for the analysis of phenocam images (but see  Ahrends et al. , 2008; Delpierre et al. , 2020; Richardson et al. , 2009). For this, we subdivided the ROI into sub-ROIs using a systematic hexagonal grid (Fig.
After establishing the date of leaf-out for each sub-ROI, we first cross-checked the minimum and maximum dates of leaf-out against phenocam images. In eight site-years out of 106 site-years present in the dataset, the analysis of the GCC data produced too early or too late leaf-out dates in some sub-ROIs as compared to our visual inspections of the phenocam images. We removed those outliers from the distributions of leaf-out dates determined at the sub-ROI scale. These outliers represented 3% of the sub-ROIs on average for the eight site-years considered. Then, we computed community-scale phenological metrics for each site-year in the dataset, namely the minimum, maximum and average leaf-out date (DoY), the leaf-out standard deviation (in days) and the amplitude (i. e. maximum-minimum, in days).
Meteorological data We retrieved air temperature and radiation data of each site from nearby meteorological stations for ENA sites (except Bull shoals, for which we used data from the DAYMET database  (Thornton et al. , 2021) ) as well as for two European sites (Alice-Holt and Mill Haft), and from the ICOS community for the other European sites ("Warm Winter 2020 ecosystem eddy covariance flux product", https://doi. org/10. 18160/2G60-ZHAK). In order to assess the influence of temperature conditions on ICVLo, we computed for each site-year the average temperature (Tmean, in °C) and the absolute minimum temperature occurring during the period of leaf-out. Preliminary analyses (Fig. S3 ) identified that ICVLo was more strongly related to the minimum temperature measured during the period extending from the 5th to the 95th percentile of the sub-ROI distribution of leaf-out (hereafter Tmin, in °C).
5B , average safety margins in the "earliest" sub-ROI were smaller than in the "latest" sub-ROI). In EUR, we detected 1 site-year (i. e. 2% of the EUR data, Table  6 ) for which the earliest sub-ROI was exposed to a frost below -3°C. In ENA, this rose to 4 site-years (6% of the ENA data), belonging to two sites, both located in the cooler ENA-Dfb zone (Fig. 1  inset). This result is consistent with calculations of a higher probability of frost exposure for North American, as compared to European species  (Zohner et al. , 2020) , and suggests that North American temperate forests, indeed, experience late frosts more frequently than European forests. Not only the frequency, but also the extent of exposure to frost of ENA vs. EUR forests differed in our dataset. When late frost struck ENA forests, it could affect a large proportion of the community (Table  6 ).
( Assistant Director of Engagement, Duke University) for their contribution. ND acknowledges funding from ANR (TAW-tree project, grant #ANR-23-CE01-0008-01). The development of Phe-noCam has been funded by the Northeastern States Research Cooperative, NSF's Macrosystems Biology program (awards EF-1065029 and EF-1702697), and DOE's Regional and Global Climate Modeling program (award DE-SC0016011). JL was funded by the China Scholarship Council (grant #202008330320). AK and FK were funded by the German Federal Ministry of Education and Research (BMBF) as part of ICOS, by the Deutsche Forschungsgemeinschaft (INST 186/1118-1 FUGG) and by the Ministry of Lower-Saxony for Science and Culture (DigitalForst: Niedersächsisches Vorab (ZN 3679)). MW acknowledges funding from the UK Forestry commission.
AN acknowledges funding from DOE Ameriflux Network Management Project, DOE NICCR 08-SC-NICCR-1072, DOE-TES 11-DE-SC-0006700, USDA Forest Service Eastern Forest Environmental Threat Assessment Center 08-JV-11330147-038 and 13-JV-11330110-081. JCD acknowledges funding from DOE DE-SC0023309, PHydrauCC #ANR-21-CE02-0033-02. JWM acknowledges funding from DOE DE-AC02-05CH11231, NSF LTER DEB-1832210. SOD acknowledges funding from NIFA (award #2021-67034-35129). KD acknowledges funding from National Science Foundation DEB-2044818 and USDA Hatch Project #MICL02716. KMH and GC were funded by the JABBS Foundation and the University of Birmingham. ARD acknowledges funding from DOE Ameriflux Network Management Project award to ChEAS core site cluster.
Statistical analysis Data was analyzed using Prism software V 8. 0. 1 (GraphPad, California). A Chi-square test was used to compare distribution of relative scores from each profile, a Mann-Whitney test was used to compare medians of each measured response reflecting cross- reactivity and an ANOVA test was used to determine any statistically significant difference between the means of the measured crossreactive response for all profiles. The significance level was set at p < 0. 05. HeatMap figures were made using CHM builder, available at https://build. ngchm. net/NGCHM-web-builder/  [23]. The Alluvial plot was created using R Studio.
iedb. org (march 2022) SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; MHC, major histocompatibility complex; iedb, immune epitope database and analysis resource; W, Wuhan strain; VoCs, variants of concern; B, Beta; D, Delta; O, Omicron. individuals showed a relatively lower ICC (0. 022) possibly due to the stringency of the test (appendix Q).
Indeed, the analyses were carried out using a commercially available kit (see M&M section), which has been validated and employed in 1691 published studies (catalog #3420-4APW-10, Mabtech). Furthermore, the characterization of the exposure history was conducted on an immunomodulatory antigen, described in other studies, located in the RBD region that was selected from the Immune Epitope Database  [4, 25]. This region is noteworthy for containing mutation sites specific to the four SARS-CoV-2 variants analyzed in this study. Abdo A. Elfiky highlighted its significance in the interaction of the SARS-CoV-2 virus with its receptor  [24]. The results provided by this study have enabled us to describe 16 distinct cellular response profiles established in the Antananarivo BTC after the three waves of COVID-19 that the country had undergone early 2022.
Plasma metabolome Plasma samples of pups (n = 19 controls, n = 19 PTU-treated) were extracted as described previously  (Hansen et al. , 2016)  with slight modifications as described in supplementary data. The analyses were performed on a nanoflow ultra-high performance liquid chromatography system (ThermoFisher Scientific) with a pre-concentration trap-column setup previously described  (Ma et al. , 2020). For the untargeted workflow the instrument was operated in data-dependent mode by automatically switching between MS and MS/MS fragmentation (further details provided in supp. info). Compound Discoverer software version 3. 2. 0. 421 (Thermo Scientific) was used for data processing and analysis (see suppl. info. for setting and parameters). Putatively annotated compounds were based on comparisons of the experimental MS2-spectrum with in-house spectral libraries (ID level 1) the online spectral library, mzCloud, (ID level 2).
Data analysis was conducted in TraceFinder v4. 1 (Thermo Scientific inc. ). For quantification, a six-point calibration curve (0. 24-7. 8 pmol/mL) run in duplicates was applied. The statistical analysis was performed using a mixed model in the package lme4  (Bates et al. , 2015)  with litter included as a co-variate. R version 3. 6. 3 incorporated in RStudio ver. 2022. 02. 3 was applied.
, 2020). Raw reads were mapped on the mouse genome (Grcm38/mm10 version) using Bowtie2 (Galaxy Version 2. 2. 6. 2)  (Langmead et al. , 2009). Count tables were prepared using htseq-count (Galaxy Version 0. 6. 1galaxy3)  (Anders et al. , 2015). Differential gene expression analysis was performed using Deseq2 (Galaxy Version 2. 11. 40. 7)  (Love et al. , 2014). The GSEA software (https://www. gsea-msigdb. org/gsea/ index. jsp)  (Subramanian et al. , 2005)  was used to evaluate the distribution of specific genesets in the lists of genes ranked by expression level (adjusted base counts produced by Deseq2) in each group of RNA sample.
Funding We acknowledge funding from  European Union 's  Horizon 2020 research and innovation program , under grant agreement No. 825753  (ERGO). This output reflects only the author's view, and the European Union cannot be held responsible for any use that may be made of the information contained therein. We also acknowledge the  Carlsberg Foundation  (grant no. CF20-0422 ) and MH acknowledge the financial support from  Aarhus University Research Foundation  ( AUFF-T-2017-FLS-7-4 ).
X-ray computed tomography has established itself as a crucial tool in the analysis of rock materials, providing the ability to visualise intricate 3D microstructures and capture quantitative information about internal phenomena such as structural damage, mineral reactions, and fluid-rock interactions. The efficacy of this tool, however, depends significantly on the precision of image segmentation, a process that has seen varied results across different methodologies, ranging from simple histogram thresholding to more complex machine learning and deep learning strategies. The irregularity in these segmentation outcomes raises concerns about the reproducibility of the results, a challenge that we aim to address in this work. In our study, we employ the mass balance of a metamorphic reaction as an internal standard to verify segmentation accuracy and shed light on the advantages of deep learning approaches, particularly their capacity to efficiently process expansive datasets.
Introduction Time-resolved (4D) operando experiments in µCT scanners have emerged as a promising way of studying solid-state reactions offering unprecedented insight into mineral phases and volume changes and the method is becoming a technique of choice for many geoscience problems because it provides information about both the spatial and temporal evolution of the microstructure of a sample. This technique can achieve a range of resolutions, with voxel sizes from millimetres to hundreds of nanometres. Underpinning any usefulness of these new insights is the accurate segmentation of individual phases into three-dimensional (3D) representations across often large datasets; once different phases are segmented and labelled, they directly aid in a quantitative understanding of all types of solid-state mineral reactions (metasomatic, diagenetic, metamorphic, and physico-chemical alteration)  (Fusseis et al.
More advanced machine learning techniques have been used successfully on a range of geoscience problems and offer better portability and applicability compared to histogram segmentation (e. g. for solid state reactions  (Marti et al. , 2021) ; crack detection  (Cartwright-Taylor et al. , 2022; Lee et al. , 2022; Reinhardt et al. , 2022) ; and one/two-phase flow experiments  (Phillips et al. , 2021) , but they also still fall short in achieving complete portability between various time-steps. While deep learning methods show promising potential for tackling the challenges in image segmentation of high-resolution, time-series datasets, they still need refinement for optimal performance. Deep learning algorithms are gaining popularity for analysing microstructures in biological and medical sciences  (Renard et al.
, 2020) , in engineering materials (e. g. ,  Müller et al. , 2021; Allen et al. , 2022)  and for the segmentation of deforming and reacting porous rock materials  (Da Wang et al. , 2021). However, regardless of their scientific domains, most studies focus on two-component systems, void and solid material classifications. In addition, some algorithms still rely heavily on adaptive filtering and simple thresholding operations  (Phan, 2021). This is a limitation because greyscale images contain limited information and this restricts how effectively a deep learning algorithm can perform, regardless of its complexity. This is most clearly seen in data that contain low contrast phases. Moreover, as grayscale image inputs evolve in time-series datasets the usefulness of any thresholds chosen is undermined.
Greater insight into microstructural changes can only be gained through the full segmentation of all mineral components based on grayscale and other considerations, like for example component morphology. This outlines a clear need for deep learning workflows to be further explored and optimised so that they can be better exploited in geosciences. In this paper, we explore the use of supervised deep learning to segment 4D synchrotron-based µCT datasets of dehydrating Volterra Alabaster (Fig. 1 ). We employ a 2D U-Net architecture  (Ronneberger et al. , 2015)  and demonstrate its capability to accurately segment the data into four phases: gypsum, bassanite, celestite, and pores. This model dehydration reaction has been monitored during experiments under different stress and pore fluid pressure conditions  (Gilgannon et al.
While the random forest algorithm initially can effectively segment features of interest in our dataset, its capability for generalisation to new, unseen data is limited  (Rezaee et al. , 2018). The inclusion of the deep learning step enhances the generalisation capability of our workflow. This results significantly improved accuracy and validity of the segmentation and labelling of the µCT data during the solid state reaction of gypsum to bassanite and pore space. We believe that this work demonstrates the potential of deep learning for volumetric image segmentation of complex materials. The method is generic and can be applied to other geoscience problems.
1 MPa). Time-resolved (4D) synchrotron microtomography (µCT) datasets were acquired during the experiments at SLS TOMCAT beamline using a pink beam with an energy peak at 27 KeV. For each µCT dataset, 1500 radiographs were collected over 180 • rotation in 2-4 s. The resulting radiographs had a voxel size of 2. 753 µm, and the resulting 3D µCT datasets had a size of 2016 × 2016 × 2016 voxels. The frequency rate of the tomoscopy was set to 60 seconds, and the experiments ran over 150-314 minutes, resulting in 2. 5 TB of data to be analysed. More details on the experiments can be found in  Gilgannon et al. , (2023).
Challenges of Segmenting Dehydrating Gypsum during Operando X-ray Microtomographies It is clear from Figure  2  that microstructural changes during the experiment can be readily identified by the human eye. However, it is also apparent from the evolving histograms in Figure  2a  that accurate segmentation of the four phases of interest (i. e. , gypsum, bassanite, pores, and celestite) cannot rely on simple histogram thresholding segmentation. Each 4D µCT dataset is extensive, containing more than 100 scans, each ranging from ∼5 to ∼15 GB of reconstructed data, depending on the scanning parameters. As the histograms of individual scans are clearly different for different time steps (Fig. 2a ), an automated segmentation of the evolving volumes based on a single histogram would yield inaccurate results, necessitating manual segmentation and the explicit selection of thresholds for each tomogram and for each experiment.
This laborious process inhibits the efficient analysis of large 4D datasets but also misses basic standards for reproducibility. This is further complicated by the fact that all synchrotron µCT images have a symmetrical vertical gradient in noise through the sample, first decaying and then increasing, which renders the application of a single set of thresholds even to a single µCT dataset problematic. Additionally, the homogeneity of the unreacted starting material intensifies artefacts such as rings which are problematic to handle for segmentation algorithms that are based solely on grayscale thresholds. As noted above, the human eye can distinguish different phases in the data and this suggests that a learning-based approach to semantic segmentation would be applicable to the dataset. It is becoming evident that we may also require information beyond grayscale values, such as the geometry of the feature of interest, for successful segmentation.
A Segmentation Workflow with Internal Standards To accurately segment large datasets of dehydrating gypsum samples, we used deep learning algorithms, which have entered the field of volumetric image segmentation through the implementation of convolutional neural networks (CNN). For this work, we used a specific implementation of 2D U-Net available in the Dragonfly™ software. This implementation has performed well on µCT images from fibre-reinforced ceramic composites  Badran et al. (2020). Our dehydrating gypsum datasets are To train the network, we selected 13 of the 2016 horizontal (XY) virtual slices from the synchrotron CT scan of sample VA19 time step 40 as "input" images. This specific time-step was chosen because it has sufficient volume of each phase we aim to segment; in images derived from either early or late steps of the experiment, the volume of at least one of the phases would be insufficient to achieve automatic segmentation.
e. , flip horizontally, flip vertically, rotate, shear, and scale). Specifically, we octupled the input data in order to render the neural network more robust, while at the same time compensating for deliberately using a small input dataset. This strategy allowed us to increase the network's ability to generalise while decreasing the potential danger of overfitting  (Shorten & Khoshgoftaar, 2019). By tuning the different CNN (hyper-)parameters and including augmented data, we improved the overall performance of the network (Fig. 3d ). However, the final segmentation still lacked accuracy: it can be seen that errors remained, for example the celestite was still identified as bassanite (Fig. 3d ). More importantly, this deep neural network model struggled when it was applied to new and more complex datasets: such as in the early and final stages of the reaction (where one of the two main phases was scarce, or absent).
Random Forest Classifier as Training Data In contrast, the use of ground truth data classified with a random forest classifier plus data augmentation performed exceptionally well and visually captured more of the features of the microstructure correctly (Fig. 3e ). A random forest classifier (Fig. 1 ) comprises numerous decision trees, each contributing a vote toward the class prediction for every voxel (more details on the random forest classifier are available in the Additional Information). The class receiving the most votes is allocated to the respective voxel. Filters are applied to the input images, generating filtered images that serve as features  (Reinhardt et al. , 2022). These features enable the classifier to differentiate between phases in the dataset. In this work, the random forest classifier was pre-set with morphological filters, 3x3 neighbour filter and Gaussian filter to perform identification of the phases in the training set.
Discussion and implications The application of deep learning to time-resolved micro-CT imaging offers a new tool for geoscientists studying rock deformation, metamorphic processes, and fluid-rock interactions. We successfully leveraged optimised deep learning methods to perform reliable and efficient segmentation of time-resolved volumetric images during the gypsum dehydration reaction. The approach outlined here not only streamlines data analysis by swiftly processing large datasets, but also enhances confidence in the robustness of results by ensuring high segmentation accuracy. Importantly, this accuracy is established in a robust way due to the three-component system under study: gypsum, bassanite, and water. We ascertained the accuracy of the chosen deep 310 learning model we compared the theoretical and measured molar evolution of gypsum to bassanite during dehydration.
This approach defines an internal standard, verifying that the segmentation method accurately captures the mineralogical changes occurring within the rock samples. Importantly, the robustness of this validation is based on the three-component nature of the system-gypsum, bassanite, and water (imaged as porosity the µCT data)-allowing for a non-circular and independent verification of our method's effectiveness. By harnessing the power of deep learning for image segmentation, we can extract more nuanced and precise information from µCT imaging datasets. This will enhance our understanding of geological processes and contribute to more accurate models of rock behaviour under different physical conditions.
Comparison with other segmentation approaches Accurate segmentation has long been a challenge across various scientific domains, from medical CT imaging to material sciences and engineering  (Withers et al. , 2021). Global segmentation methods, such as manual histogram thresholding, have long been go-to solutions for the segmentation of X-ray µCT tomographic images. However, three considerable drawbacks persist: (i) significant time commitment required, (ii) global techniques ignore local context and thus have an intrinsic potential for misclassification, and, therefore, (iii) the potential for compromised reproducibility  (Andrew, 2018)  As µCT imaging technologies evolve, resulting in larger datasets, the scalability and efficiency of manual segmentation methods become increasingly challenging  (Da Wang et al.