dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions and perspectives. We evidenced that the FR-Fon oak forest can tap at least 390 mm of water from the soil. This is a large amount for a temperate forest, as compared to published estimates of SWHC based on measurements  (Bréda et al. , 1995; De La Motte et al. , 2020)  or the modelling  (Granier et al. , 2007)  of the soil water balance. This value is 1. 9 times higher than the estimate of SWHC (= 207 mm) retrieved for the FR-Fon grid point in the French consensus soil database  (Badeau et al. , 2010) , which is used in modelling studies predicting the future of French forests  (Cheaib et al. , 2012 ). Yet, several papers pointed recently the role of "deep" water in provisioning water to sustain ecosystem functioning, particularly during drought periods as the one encountered at FR-Fon in 2018.
e. spot overlapping should be minimal. The later commercialized grain reconstruction approach [GrainMapper3D software (XnovoTech), initially working in a Laue focusing geometry, L sd = L ss ] is based on forward modelling and has been shown to perform rather well in reconstructing grain orientations and shapes for various types of sample using multiple scanning strategies  (Bachmann et al. , 2019; Oddershede et al. , 2022). However, detailed implementation of this approach has not been reported, and it has been restricted to a specific instrument and requires a commercial licence, preventing its use on other, more widely available, laboratory instruments. To give a true boost to the use of grain mapping by LabDCT, we have developed grain reconstruction methods based on forward and back calculations.
For a typical LabDCT data set as shown in Fig. 3  with the magnified geometry, N proj = 121, N spots ' 210 and N hkl = 40, and vectorized computation of both N G and C rankings for one orientation takes about 0. 05 s using one single CPU core (Intel i7-10700). This means it would take 1638 s to compute 32 768 orientations and 13 107 s for 262 144 orientations using one CPU core. To reduce such long computation times to a more realistic number, parallel computing is thus required. Using an eight-core CPU such as is readily available nowadays, the computation time for N G ranking can be reduced to about 200 s. To accelerate the computation further, we used GPU computing (NVIDIA Tesla V100-PCIE 32 GB) with CUDA programming (no further tuning for specific hardware) and reduced the time to about 2 s for both N G and C rankings.
5(b) ]. Therefore, checking the availability of neighbouring voxels is not needed and the computation can be parallelized. For example, this can reduce the growth time for 10 000 voxels from about 400 to 2 s. Here, the length of the bounding box is determined by the sizes of the spots associated with the indexed seeding voxel for a specific geometry. In summary, we have developed three approaches for the grain reconstruction: (i) CPU-G, using only CPU computing with N G ranking and successive growth, which is slow but with the option to reconstruct and merge sub-volumes without using a GPU; (ii) GPU-G, using GPU computing with N G ranking and independent growth; and (iii) GPU-C, using GPU computing with C ranking and independent growth. All the implementations were coded in MATLAB coupled with CUDA programming.
Characteristics of the current reconstruction methods Comparisons of the reconstructed grain maps with the ground-truth data demonstrate that the algorithms can handle well LabDCT data with a large number of spots for different geometries. The reconstructed grains have good spatial and orientation accuracies. Although the reconstruction is currently limited to strain-free grains, the obtained grain map can be used as an input for resolving elastic lattice strains and intra-granular orientations, for which a fit to spot intensities will be required. A well known limitation imposed by serious spot overlap applies to the current reconstruction methods, similar to many previously reported reconstruction methods for 3DXRD and its variants (cf. Lauridsen et al. , 2001; Ludwig et al. , 2008; Bernier et al.
Conclusions Multiple grain reconstruction algorithms based on forward and back calculations have been presented in detail. Differing in their indexing strategy and computational implementation, three reconstruction methods have been developed, among which an efficient computation is achieved with GPU parallel computing. These grain reconstruction methods have been demonstrated on a synthetic data set containing 144 grains and moderate spot overlap for both Laue focusing and magnified geometries. Comparisons of grain maps show that, on average, the reconstructed orientation accuracy is 0. 03 , the error in the grain centre-of-mass position is within two pixels and the relative grain size difference is 4%. These methods are not limited to a specific instrument and can be applied to various types of LabDCT data sets acquired on different instruments.
To ensure foods are safe, food companies, food safety authorities, and governmental agencies work together to improve the control and prevent unwanted food contamination by either biological, chemical or physical agents, namely the hazards. Foodborne illnesses leading to diseases in humans are still frequently reported. To better protect infants and children from foodborne diseases, the SAFFI (Safe Food for Infant) project promoted by the European Union in collaboration with China, will develop an integrative approach to identify, assess, detect and mitigate risks associated with microbial hazards (MHs) and chemical hazards (CHs) in infant foods. The first stage to tackle this issue was to identify relevant hazards in infant foods. By collecting data from the literature, scientific reports, existing databases, and clinical studies and using these data to compile a list of relevant foodborne hazards.
For example at the farm level, good agricultural practices (GAP) are implemented. In addition, scientific evaluations of adverse effects caused by known biological or chemical hazards in various food commodities are also done to characterize the risk associated with the hazards and to give recommendations to control food safety. This process is called risk assessment and consists of 4 steps; 1 Hazard identification; In this step, hazards that may be present in a specific food commodity in given situations, have the potential to survive food processing techniques, and may cause disease in humans are identified. 19, 21 This information is either collected based on scientific literature, surveillance/epidemiological data, existing expert knowledge, food analysis databases, or predefined knowledge of known pathogen/food associations,  22  for example, Salmonella in eggs.
2 Hazard characterization; In this step, the effects caused by hazards present in foods are evaluated, either based on qualitative data or quantitative data. 19, 21 The relationship between hazard exposure (route, level, prevalence) and consequences of exposures are derived from foodborne outbreak data associated with illness, physicochemical properties of hazards, hazard infectivity/virulence/ capability to produce toxins, toxicological studies, or human exposure studies  22. Additionally, the immunological and physical statuses in humans of different age groups are considered, as the same hazard may have different effects for the young/old/pregnant/immunocompromised populations. 3 Exposure assessment; is the stage in which the probability and quantity of hazard intake via foods are evaluated qualitatively or quantitatively, or exposures from other sources if relevant.
Safe food for infants (SAFFI) in the EU and China To achieve the goal of reducing foodborne incidents in infants and children in the EU and China, an EU research project funded by Horizon Europe 2020, SAFFI (Safe Food or Infants in the EU and China) has been initiated, and the project group consists of 20 partners from different sectors i. e. academia, food safety authorities, infant food companies, paediatrics, technological and data-science companies.
SAFFI objective and approaches The main objective of SAFFI is to develop an integrative approach to identify, assess, detect and mitigate risks associated with MHs and CHs for infants and children (< age of 3) in the EU and China. The first subobjective is the identification of hazards (similar to the first stage "hazard analysis" of HACCP and the first stage "hazard identification" of risk assessment). The specific ambition behind this project is to establish generic and standard procedures for hazard identification (HI) and risk ranking (RR) within the infant food chain, which will be achieved by the three-steps data-driven approach. Firstly, data concerning MHs and CHs are collected based on literature, official reports published by governmental agencies, existing databases, clinical studies, and expert knowledge.
These data are used to compile a list of relevant MHs and CHs that have the potential to be present in the food chain and cause adverse health effects to young children. Secondly, the collected data are assembled and stored in structural databases that contain information like survival of MHs at different pH, temperatures, production of harmful toxins, or types of CHs generated during food processing procedures, etc. These specific pieces of information, namely the knowledge rules of the hazards, are prerequisites to understanding their relevant threat in a food product. Concomitantly, procedures for hazard identification are devised, which includes a second step to prioritize the hazards that are relevant for a specific food commodity. For MHs, this HI is based on the knowledge rules of selected organisms, i.
Four important sources used for microbial hazards (MHs) identification MHs in food products, with a special focus on infants and toddlers up to the age of 3 can be derived from public health databases, outbreak data, scientific literature, and expert knowledge. To obtain a list of relevant foodborne MHs in SAFFI, four different sources: (1) Outbreak data; (2) Recalled Foods; (3) public health impact; and (4) Expert knowledge of each MH were considered (see Fig. 2 ).
Four important aspects for chemical hazards (CHs) identification Dietary exposure assessment reports are a major source for chemical hazard identification. When assessments of a risk associated with chemical hazards in food for a population are required, it is important to have representative contamination data. One of the most efficient methods to obtain this data is conducting a Total Diet Study (TDS). Indeed, contamination data and exposure calculations present in TDS reports give insight into the occurrence and contamination level of CHs in food products as consumed and representative of the whole diet. These reports can be found on food safety agencies' websites or scientific databases such as PubMed or Scopus. Occurrence, exposure, adverse effects, and expert knowledge were the four important aspects considered to identify chemical hazards (Fig.
Similarly, procedures for risk ranking will be devised and implemented in an RR-DSS computational tool. The HI-DSS and RR-DSS tools will be validated using four case studies in following-up works and will be integrated and harmonized with the data obtained from the SAFFI mirror project in China. Together, these tools will be useful for food safety agencies, food companies, and risk assessors to identify and rank MHs and CHs in the entire infant food chain in Europe and China, and improve the control of these MHs and CHs.
ML employs algorithms to find patterns in data that are not obvious to humans. Regression and logistic regression (LR) are among the first supervised ML algorithms for creating predictive health models  [9] [10] [11]. ML algorithms are considered supervised if the output classes are labeled (e. g. , BTX treatment, yes/no). Thus, supervised ML prediction models can help identify patients who will undergo BTX treatment. Prediction models can predict the probability (Prob) of a condition (e. g. , BTX treatment) being present  [12] [13] [14]. In supervised ML algorithms, the output is obtained from labeled training samples. Through the training examples, the program learns a function (e. g. , logistic regression) that will predict new incoming patients with unknown conditions. In previous studies, we developed  [1]  and validated [15] "PredictMed," a supervised ML model to predict neuromuscular scoliosis and hip dysplasia  [10, 16] , gastrostomy placement  [17] , and identify factors associated with intellectual disability  [18]  and autism spectrum disorder  [19]  in individuals with CP.
PredictMed has also proven effective in predicting osteoarthritis in young adults using statistical data mining and machine learning  [9]. In the present study, we implemented and externally validated the BTX-PredictMed ML model to predict prognostic phenotypes of children with CP needing BTX treatment. Following the development of a prediction model, external validation is strongly recommended, that is, to evaluate the model's performance on other participant data not used for model development  [13, [20] [21] [22]. External validation requires that predictions about outcomes be made using the original model for each subject in the new dataset, and be compared with observed outcomes  [23]. In this study, we applied the same predictive model to patients from different centers and countries to evaluate the model's performance through external validation.
Limitations Limitations of this study include the limited number of patients and the retrospective analysis. This resulted in high specificity and accuracy of diagnosis, while sensitivity was moderate. The study of the predictive performance of the algorithm by increasing the number of patients and independent variables (>15) will be our next issue. For the current study, we had a limited cohort of patients to study (hundreds). We plan to study and fine-tune the model on a much larger number (thousands) to confirm and possibly improve the model's predictive performance. In this regard, PredictMed has already achieved excellent results in predicting osteoarthritis in adults using statistical data mining and machine learning  [9]. At this stage, we plan to calibrate this model on a much larger database to test its potential overfitting (e.
Measurements All data were collected from the Electronic Medical Records by the senior author. Medical notes were written by a multidisciplinary team that included pediatric neurologists, epidemiologists, pediatricians, orthopedic surgeons, and physiotherapists. Narrative notes were coded and filled in the database of BTX-PredictMed  [9, 20]. Data on diagnosis, etiology, type of spasticity, functional assessments, epilepsy, clinical history, and radiology were collected anonymously between 2005 and 2020. CP etiology was classified as  [1] : • antenatal: cerebral malformation, genetic, prematurity, infection, vascular; • perinatal: anoxic, infectious ischemic; • postnatal: postnatal anoxic/ischemic injury epilepsy, cranial trauma, infectious. Motor function was assessed using the Gross Motor Function Classification System (GMFCS) and the Manual Ability Classification System (MACS)  [15, 24].
Statistical Analysis We performed Fisher's exact tests and developed contingency tables [41] for identifying the distribution frequencies and the confidence intervals of factors associated with BTX treatments. Then, we used the web-based epidemiological calculators MedCalc ® statistical software 20. 123 and OpenEpi software 3. 01  [42, 43]  to calculate 95% confidence intervals, odds ratios, and Z-statistics (Table  2 ). The glm() function of the open-source software R 4. 2. 2  [44] [45] [46]  was used to predict each patient's probability of undergoing BTX treatment; the common thresholds for selecting relevant variables (with p-value < 0. 2)  [44, 45]  were employed as independent input variables in a bespoke multiple logistic regression model  [44, 46]. The binary dependent variable was the presence of BTX treatment (yes/no).
Next, we filtered out outliers following the interquartile range criterion and used loess smooth lines with a span value of 0. 9 (i. e. , width of the moving window when smoothing the data) and a 95% confidence interval. To have an estimate of the amount of growth the plants achieved during the different field seasons we calculated cumulative degree days using 0 o C as base temperature (according to Perry Miller et al. [27] ) and average daily temperatures: 𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑑𝑒𝑔𝑟𝑒𝑒 𝑑𝑎𝑦𝑠 = ∑(𝑚𝑒𝑎𝑛 𝑇𝑒𝑚𝑝) -𝐵𝑎𝑠𝑒 𝑇𝑒𝑚𝑝 𝑗 𝑖=0 i: beginning of the temporal window considered (e. g. , W9. 5 date) j: end of the temporal window considered (e. g. , sampling date at 7 days after W9. 5) Statistical analyses and codes to model carpel and ovary development were conducted in RStudio Version 4. 0. 3 and are available at: https://github.
Development and validation of the stigma and ovary convolutional neural networks Our aim was to develop an automated phenotyping method to detect and annotate the perimeter covered by the stigma hairs and the ovary to determine stigma area and ovary diameter across the life cycle of wheat carpels. To carry out automated image annotation and measurement, we trained a convolutional neural network (CNN) on a set of representative carpel images with manual annotations of the stigma perimeter (n = 86 images) and ovary perimeter (n = 121 images) using the UNet design  [22]  (Fig. 3A ). The training dataset spanned a random sample of seven genotypes, ranging from early carpel development (W9. 5 and earlier) to fully degenerated carpels (Fig. 1A ), and included both fixed and non-fixed carpels. After successfully training the networks, we obtained an adapted stigma CNN able to quantify the area covered by stigmatic hairs and an adapted ovary CNN that, after some post-processing of the network output, quantifies the diameter of the ovary (Fig.
3B ). To evaluate the performance of the resulting adapted CNNs we applied the network to an unseen set of 60 microscope images without manual annotations (Fig. 3C ). Subsequently, we manually annotated this new dataset using Fiji to provide a ground truth reference with which to compare to the CNN annotated outputs. We divided the cross-validation process according to the developmental stage of the carpels, sampling method (fixed or non-fixed), and the tissue of interest (Fig. 3C  and Supplementary Figure  4  for ovary CNN validation). We observed a high overlap in stigma areas and ovary diameters of fixed and non-fixed samples between the manual and automated annotations across all three developmental stages. There were no significant differences between the manual and automated annotations, apart for ovary diameter of the nonfixed samples at stage 3 (P = 0.
Considerations on the use of the stigma and ovary adapted CNNs The quantification of stigma area and ovary diameter cannot be easily determined from surface observations of wheat spikes and requires the dissection and microscopy of individual carpels. The manual annotation and quantification of microscopy images is cumbersome and often delays scientific discoveries. Deep learning-based approaches, such as Convolutional Neural Networks (CNNs), have emerged as a solution to perform image quantification in an automated, rapid, and less biased manner, lifting the burden of image analysis from researchers. In this work we developed two CNNs (both publicly available at https://github. com/marina-millan/ML-carpel_traits) that enable non-machine-learning experts to quantify stigma area and ovary diameter on their local computer in a matter of hours (Fig.
Acknowledgment We thank the  JIC Field Trials and Horticultural Services teams  for technical support in field and glasshouse experiments, the  JIC Bioimaging facility  and staff for their contribution to this publication and  Professor Lars Østergaard  (JIC) and  Dr. Azahara Martín  for feedback on the work. We also want to thank  James Simmonds  and  Tobin Florio  for their assistance during the field trials and Flozbox studio (https://flozbox-science. com/) for designing Fig. 2. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.
Funding This work was supported by the  UK Biotechnology and Biological Sciences Research Council (BBSRC)  through the  Designing Future Wheat  ( BB/P016855/1 ) and Genes in the Environment ( BB/P013511/1 )  Institute Strategic Programmes. Additional funding was provided by the  European Council  ( ERC-2019-COG-866328 ). MMB was supported by a  BBSRC   Norwich Research Park Biosciences Doctoral Training Grant  ( BB/M011216/1 ).
g. , stable isotopes, fatty acids) for a broad range of marine taxa and regions. To harness the potential of existing data, it would be necessary to assemble and standardize them, and make them readily accessible for the scientific community. Existing online diet databases of marine taxa (e. g. , Fishbase  22  , SeaLifeBase  23  , DAPSTOM  24  , Food Habits Database  25  ) are mostly limited to stomach contents and contain few records of mesopelagic organisms, while databases on trophic markers are taxonomically or geographically restricted (e. g. , MarTurtSI  26  , SCAR  27  ). Thus, large volumes of relevant data remain scattered in the literature or unpublished, making it difficult and laborious to find and reuse the data. To address this gap, project SUMMER (https://summerh2020. eu/) created MesopTroph, a georeferenced database of diet, trophic biogeochemical markers, and energy content of marine taxa compiled from published and non-published sources, aimed to support research on mesopelagic food webs.
MesopTroph comprises seven datasets: (i) diet compositions from stomach content analysis, (ii) stable isotopes (δ  13  C and δ 15 N), (iii) major and trace elements, (iv) energy density, (v) fatty acid trophic markers (FATM), (vi) trophic positions, and (vii) estimates of diet proportions (Tables  1  and 2 ). MesopTroph currently contains 5,149 data records assembled from 203 data sources, representing 72,821 specimens, 498 unique species or genera, 190 families, 17 classes, and eight phyla. While records encompass a wide range of taxonomic groups, most are from fishes (54%), mammals (18%), seabirds (10%), and malacostracan crustaceans (7%). MesopTroph covers all marine biogeographic realms identified in the eastern and central North Atlantic, and the Mediterranean  28. Records date back to 1885 but most information is from 1980's onwards.
MesopTroph is far from complete but we plan to expand it in the future as more sources are identified and added, and as new trophic data emerges from the SUMMER project. The database can be used to estimate the diet, trophic position, and consumption rates of individual taxa, and to model trophic interactions and energy flow through mesopelagic food webs. Such information is critical for investigating food web structure and inferring ecosystem functioning and resilience to fisheries and climate impacts. These data are also relevant to understand the role of mesopelagic organisms in carbon fluxes and sequestration. In conclusion, MesopTroph provides robust spatial and species coverage to support investigations into key ecosystem services provided by the mesopelagic community at a basin scale and help meet the growing demand for data for ecosystem-based management approaches.