dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation root mean square errors were 54. 56, 63. 60, 7. 30, 59. 87, and 152. 89 mg/kg for Na, Ca, Mg, P, and K, respectively. The built models were applied on the Walloon milk recording large-scale spectral database, including 3,510,077. The large-scale predictions on this dairy herd improvement database provide new insight regarding the minerals' variability in the population, as well as the effect of parity, stage of lactation, breeds, and seasons.
To achieve this objective, large variability in the calibration dataset both regarding the reference values and the spectral data is essential  [27, 28]. For this reason, the first objective and the novelty of this work is to develop MIR models predicting the mineral content of milk from a large and variable dataset, including records coming from several countries, breeds, seasons, diets, and MIR spectrometers. No models presented in the literature  [20] [21] [22] [23] [24] [25] [26]  exhibit a multi-country approach, and the models presented could allow a wide range of utilization for the farmer and could be used for all the countries present in the calibration set. The second objective was to test the models on a country-independent validation dataset and to evaluate the impact of including local samples in the calibration dataset.
Data From 2005 to 2020, approximately 3000 milk samples were collected from individual cows in five different countries (Belgium, France, Germany, Luxembourg, and Austria). Those samples were collected by local DHI centers with the objective to maximize the variability, by sampling local specificities, diets, breeds, and management systems. Based on this methodology, the collected samples represented 13 dairy breeds: Abondance (ABO), Aubrac (AUB), dual-purpose Belgian Blue (BBL), Brown Swiss (BSW), Fleckvieh (FLE), Holstein-Friesian (HOL), Jersey (JER), Meuse-Rhine-Yssel-type Red and White (MRY), Montbéliarde (MON), Normande (NMD), Salers (SAL), Simmental (SIM), and Tarentaise (TAR). As the measurement of the mineral content by chemical analysis is costly and timeconsuming, we try to limit the number of analysis.
To gather robustness to the model while limiting the chemical analysis, only half of those samples were selected based on their spectral variability. Starting from the model developed by Soyeurt et al. [19] , new samples were added iteratively, by picking the most spectrally different samples. The sample selection was based on the standardized Mahalanobis distance, also called the Global H distance (GH), and the neighboring H distance (NH)  [27]. This led to a dataset of 1281 selected samples, with 502 samples from Belgium and Luxembourg, 388 samples from France, 291 samples from Germany, and 100 samples from Austria. All milk samples were collected following the guidelines edited by the International Committee for Animal Recording and with the use of a preservative to ensure the stability of milk physico-chemical properties before analysis.
Modelling The pretreatments consisted of applying a first derivative with a gap of five wavenumbers and a normalization of variables (autoscale). Then, to limit the presence of noisy areas, the spectral area selected was like the region selected by Grelet et al. [27] : 968. 1 to 1577. 5 cm -1 , 1731. 8 to 1762. 6 cm -1 , 1781. 9 to 1808. 9 cm -1 , and 2831. 0 to 2966. 0 cm -1. This represents the 212 first-derivative spectral points. Calibration and cross-validation were performed on dataset-merging records coming from Belgium, Luxembourg, France, and Germany (maximum of 1181 samples). Calibrations were performed with partial least square (PLS) regression. Cross-validations were performed using 10 folds randomly constituted. During those cross-validations, samples with an absolute residual higher than 2.
As the RPD aims to combine the variability of the model (i. e. , the SD of the calibration dataset) and the accuracy of predictions (i. e. , the RMSE in the cross-validation and validation steps), it was calculated using the SD of the calibration dataset and the RMSE in the cross-validation and validation steps. Then, in order to check the robustness of the forecasts (i. e. , to assess the relevancy of having data coming from a country where we want to make a prediction), a second calibration set was created by adding, to the first training set, 30 randomly selected Austrian samples. The modeling process was exactly the same and the validation was performed on the 70 remaining Austrian samples to validate the model with four countries (Belgium, Luxemburg, France, and Germany) and the other one with the Austrian data included.
Finally, all the samples were merged, and a third calibration dataset was created by randomly selecting 80% of randomly selected samples from the entire dataset (1024 of 1281 samples). Models were then validated with the remaining 20% of records. Another metric was used to evaluate our models. The concordance correlations coefficient (CCC) allowed us to underline potential slope or bias problems in the development of the mineral models. The CCC was calculated through the Equation (1). CCC = 2ρσ x σ y σ 2 x + σ 2 y (µ x + µ y ) 2 , ( 1 ) where x is referring to the reference, and y to the predicted values; µ are the means, σ is the covariance, and ρ is the correlation coefficient between the reference and the predicted. The different percentage of model improvement was calculated as stated in Equation (  2 ).
% = (X -X aust ) X aust , (2) where X can be the RMSECV, RMSEP, or RPD of model without any Austrian data included and X aust is with 30 Austrians included. All computations were carried out with the PLS toolbox v. 4. 11 (Eigenvector Research, Inc. ,Wenatchee, WA, USA), Winisi software (version 4. 6; Foss, Hillerød, Denmark), and scripts developed in Matlab v7. 5. 0 (The Mathworks, Inc. , Natick, MA, USA).
Large-Scale Phenotyping The developed MIR predictive models were finally applied to the DHI spectral database related to the southern part of Belgium and managed by the Walloon Breeding Association (Awé, Ciney, Belgium). This database included 3,510,077 spectra from 235,355 different cows and 1344 herds recorded from 1 January 2012 to 4 March 2020. Records had days in milk (DIM) ranging from 5 to 365 days, and a parity up to 18 and concerning 16 different cows breeds. This large-scale prediction allowed us to obtain information about the evolution of milk mineral prediction following the stage of lactation, month of tests, and parity. These effects were assessed by computing descriptive statistics.
Reference Dataset The descriptive statistics of the entire dataset are mentioned in Table  2. The various numbers of samples per mineral is related to the erroneous ICP-AES analysis, as mentioned previously. The main purpose of this study was to establish robust models who can be applied on a large application scale. Compared to the current study, the models published in the literature  [19, 23]  had a different standard deviation and mean. Indeed, these metrics vary according to the sampling. The models presented by Toffanin et al. [20]  exhibited relatively few differences in mean concentration for Ca and P (0. 84% and -5. 84%) with our models. A higher average was especially found with the mineral models presented by Visentin et al. [23]  (12. 45%, 17. 42%, 29. 60%, 1. 93%, -0. 86% for Na, Ca, Mg, P and K, respectively).
Both models had relatively similar cross-validation performances, but in cross-validation, RPD decreased from -1% to -13% when 30 Austrian samples were added in the training set. However, the validation RPD increased from 6 to 39%, except for Na, where RPD decreased by -16%. As stated in  [27] , it validates that inclusion of variability in the dataset can decrease the apparent accuracy (i. e. , in cross-validation) but improve the robustness and real performances of the models in validation. It consequently seems important to cover the variability of new samples to predict in order to limit extrapolation and ensure better predictions in routine. Indeed, when the inclusion of Austrian dataset is added, the RMSEP decreases by 1. 1%, 12. 8%, 11. 1%, 36. 7%, and 18. 7% for Na, Ca, Mg, P, and K, respectively.
For Na, Ca, and Mg, a slight difference was found between both external validations (Table  3 ), which explains the model being robust enough to predict the Austrian samples well. For the P model including Austrian samples in the calibration dataset, a large decrease of the RMSEP of validation was observed, confirming a better performance of the predicted model (i. e. , Higher RPD). The relatively high errors of prediction (for both external validation) for the K model revealed the incapacity of the model to accurately predict the Austrian samples. When 80% of samples in the calibration model were randomly chosen, the RMSEP for predicting K was 106. 7 mg/kg. This prediction error seems more consistent (than the validation with only Austrian samples) and confirms the differences from K content in the Austrian milk sample.
These results may be explained by the difficulty to predict the K content in milk. Even with a robust model including samples from four different countries, the external validation exhibited some deficiencies to validate Austrian samples for P and K. A decrease for RMSEP was revealed when 30 samples coming from the Austrian validation set was included in the calibration set. The results obtained in this study highlight the need for data collaboration and the interest of including local variability to obtain the most robust models. The best way to collaborate is to have a common database and standardized spectra allowing to taking into account the machine-dependent errors  [29]. Compared to the current study, the models published in the literature  [15, 19]  exhibited similar model performance (RPD) but exhibited higher RMSE.
Correlation between Mineral Traits and Traits Related to Animal Health and Milk Technological Properties The Pearson correlations were estimated to achieve two objectives. The first one was to compare the reference minerals dataset with the MIR predicted traits, and the second one was to compare the predicted mineral dataset with the MIR predicted traits (Table  4 ). Since the dataset created to develop the different models was not representative of the population as the selection of samples was not randomly conducted, the correlations given in this study cannot be generalized to the population. However, it was interesting to observe whether the relationships between traits stayed similar when using reference or predicted mineral contents even if the prediction accuracy was not high. Moreover, this allows to confirm the relationships with other predicted traits known to be related to animal health or the milk technological properties.
Reference Minerals Predicted Minerals Correlations above the diagonal between predicted minerals were performed on the large-scale prediction dataset (real population), and correlations below the diagonal between predicted minerals were performed on the calibration dataset; NAGase: N-acetyl-β-D-glucosaminidas; LDH: lactose dehydrogenase; RCT: rennet coagulation time; k20: curd-firming time; a30: curd firmness 30 min after rennet addition; ILCY: Individual laboratory cheese yield. The caseins were part of the phosphoproteins with 0. 85% of the phosphorous as phosphates. Unlike whey proteins, which do not contain any phosphorous, the phosphates are responsible for the ability to bind a large amount of calcium cations. This type of phosphate coming from the esterification of the hydroxyl group of serine was called organic phosphate  [37].
Month Influence on Milk-Predicted Mineral Composition The descriptive statistics derived from large-scale prediction from the Walloon DHI database are shown in Table  5. By monthly averaging those results, the annual evolution of concentration of the five main predicted minerals in milk were computed and shown in Figure  1. There were three types of predicted mineral annual tendencies: (1) sodium (2) calcium, magnesium, and phosphorus, and (3) potassium. Predicted sodium increased from February to September, with a slight decrease between May and July. During the year, the lowest amount of pNa was around February, and the maximum amount around September. A local minimum was observed in July. The second tendency exposed an annual behavior with a characteristic minimum during the summer and a significant increase between August and September.
Conclusions In conclusion, this study developed new predictive equations for Na, Ca, Mg, P, and K in milk from a large dataset, including records coming from multiple countries. The robustness of these equations was also demonstrated by external country validations in Austrian samples. The importance of including local variability in the model was tested by integrating 30 Austrian samples in the calibration dataset and validating with the remaining samples. Despite relatively good RMSEP for the model without the addition of the Austrian samples, RMSEP tends to decrease with the addition of the Austrian samples. This work shows the usefulness of including local variability to improve the robustness of a prediction equation. The correlation values exhibited strong links between Ca, Mg, and P and confirmed the interactions between these minerals and the protein content in milk.
Video data can be easily collected in laboratory experiments or ambulatory settings, including real-life scenarios. In the present study, we reanalyzed five data sets totaling 931 participants (Table  1 ). We used video recordings from previous studies that were focused on the functions of positive emotions and cardiovascular responses  [30] [31] [32] [33]. We focused on dimensional conceptualizations of emotion (positive/negative) to address research investigating emotional experiences and their effects upon motor activity  34. Thus, we focused on responses to positive, negative, and neutral stimuli. Moreover, we focused on two aspects of spontaneous emotional behaviors: direction (approach and avoidance behavior) and intensity (behavioral freezing/activation). The approach behavior is indicated by leaning forward and towards the stimulus, whereas avoidance behavior as moving away and reclining backward  35.
Results We accounted for five studies, presenting behavioral reactivity to 24 affective stimuli obtained from 931 participants with age ranging from 18 to 35 (M 22. 37, SD 2. 93). Of participants, 372 (40%) were female. The most frequently studied valence category was positive emotions (1676 reactions; 39. 5%), then negative emotions (1444 reactions; 34. 0%), and neutral conditions (1096 reactions; 25. 80%). The regression models fit data well for approach/avoidance behavior, PPp = 0. 480, 95% CI [-6. 368, 7. 436 Approach/avoidance behavior. We found that the approach/avoidance behavior differs between negative and positive stimuli, Δβ = -0. 050, 95% CI [0. 008, 0. 093], p = 0. 011. Individuals leaned backward more while watching negative stimuli (M -1. 92 cm, SD 4. 06 cm) than while watching positive stimuli (M -1.
Furthermore, we used multiple film clips to elicit single emotion, which provides additional variation in the manipulation strength. However, this approach also generalizes the impact of emotional valence and limits the effect of using specific stimuli. Researchers might use our approach to re-examine findings from other laboratory experiments on discrete emotions, in which behavioral reactivity was not the main focus. Given that behavioral reactivity (with the exemption of facial expressions) gained relatively low popularity in affective science, reexaminations of existing data may result in an effective way to increase the number of examined emotion. This study's strength is in the use of a novel method of movement analysis with a large number of participants. We evidenced that the analysis of head sway among participants in a seated position can reflect affective influences.
Procedure. All five experiments began with a 5-min resting baseline. Participants were asked to wait for five minutes without doing any unnecessary actions. During the experiments, we elicited emotions (Table  1 ), presenting affective pictures or film clips. Unlike previous studies that used the head movement of standing individuals as an index of emotional behavior  24, 26  , participants in our study sat on a chair with the back and armrests throughout all studies. Affective stimuli. Films. We used stimuli from the emotion-eliciting video clip databases to elicit emotions, with prior evidence of reliability and validity  [39] [40] [41] [42]. Each clip lasted two minutes. Within the sessions, clips were presented in a counterbalanced order. Table  1  presents which films were used to elicit emotions in the studies.
Measures. Apparatus. We recorded the head movements of each participant continuously using an HD camera mounted on the top of the PC screen. The distance from the camera to participants' head was approximately 65 cm (Fig. 2 ). The video data were analyzed using facial expression analysis software Quantum Sense (Quantum CX, Poland)  44, 45  , which uses computer vision algorithms to detect and determine the head positions in a three-dimensional space for each frame. Data was recorded with the sampling rate of 15 FPS in Study 1 and Study 2, 10 FPS in Study 3 and Study 4, and 24 FPS in Study 5. We also included leg movements in Study 5. We used four accelerometers (wGT3X-BT, Actigraph, USA) attached to left and right knee and left and right ankle to record physical activity continuously and unobtrusively.
To operationalize behavioral change of head sway to emotional stimuli, we used reactivity scores corrected for baseline levels. The decreased total body sway (the negative difference between baseline and affective stimuli) indicates behavioral freezing, whereas the increased total head sway (the positive difference between baseline and affective stimuli) indicates behavioral activation. In Study 5, we also calculated the magnitude of legs movement as the sum of all four joints sways in X, Y, Z-axis. The internal reliability for four joints movements was satisfactory, α = 0. 84. To test the impact of emotions on behavioral freezing/activation collected from two movement sources (head and legs), we converted the data from each source of behavioral activity into the z-scores, and we merged them into a single variable.
Acknowledgements The authors thank  Michał Kosakowski ,  Jolanta Enko ,  Martyna Dziekan , and other Psychophysiology and  Health Lab members at Adam Mickiewicz University  for helping in data collection. The authors thank  Katarzyna Janicka  (katarzyna. janicka. kj@gmail. com)for creating Fig. 2. Preparation of this article was supported by the National.
We are currently working on exploring the potential of HSI coupled with deep learning-based analysis of HS images to intraoperatively predict the ischemia-reperfusion damage. As a first step and a proof of concept, we focused on the ability of HSI to predict damage given by liver hypoxia ligating only the hepatic artery  [31]. This allowed us to exclude only the oxygen variable without the more complex composition of blood supply from the portal vein. Machine learning has recently been used for the automatic analysis of hyperspectral image data  [32] , mainly driven from the remote sensing community  [33, 34] , but now extending to a range of medical applications  [1]  such as automatic tumor detection  [35, 36]  and histopathological analysis  [37]. However, there have not been prior studies reporting on the use of machine learning models to automatically characterize liver reperfusion damage intraoperatively in a large field of view.
Study Design Sample size calculation was performed using the correlation between optical and biological data. The calculation was based on previous publications on bowel ischemia which showed a ρ correlation coefficient of -0. 7  [38, 39]. The required sample size in terms of paired values was 4, considering α = 0. 05 with a power (1β) = 0. 9. In the present study, 42 paired values StO 2 % and lactates values were obtained in 5 pigs in total. The AI model used a pixel by pixel (640 × 480) analysis per each of the 42 images providing a large dataset elaboration. The aim of the study was to predict liver viability through the analysis of hyperspectral images using artificial intelligence based on convolutional neural networks (CNNs) to (i) discriminate the liver from the rest of the tissues, (ii) recognize perfused from the non-perfused liver, (iii) predict the level of liver perfusion during the reperfusion phase, and (iv) predict biological data (Figure  1a-c ).
The control group is represented by the same treated pigs before hepatic artery ligation. The ischemic phase was held for 90 min, collecting optical and biological data every 30 min (Figure  1a, b ). The following reperfusion phase was monitored for 5 h, collecting data every hour. The hypercube extracted from hyperspectral images was used to train two CNNs (Figure  1c ). Finally, the generated AI score for the reperfusion phase was generated and the quantitative analysis of hyperspectral images was correlated with biological data. Histopathological evaluation and scoring were performed in a blinded fashion. Capillary lactate was sampled randomly through the liver surface. (c) Hyperspectral imaging was acquired providing the hypercube with a wavelength range from 500 to 1000 nm. Two artificial intelligence-based convolutional neural networks (CNNs) were trained to perform a segmentation that could identify the liver surface and analyze the same surface to predict perfused and not perfused livers during the ischemia phase.
Hyperspectral Imaging A CMOS push-broom scanning hyperspectral camera (TIVITA, Diaspective Vision GmbH, Am Salzhaff Germany) was used to generate HS images which were performed with a camera-specific software module from the same company. The three-dimensional hypercube is composed of a spatial resolution (x,y) plus a third dimension with the relative reflectance of each pixel (z). The range of the wavelength detected is 500-1000 nm with a 5 nm interval, totaling 100 wavelengths for every pixel. The scanning method is allowed through a slit-shaped aperture motorized with an internal stepper motor  [44]. The resolution of the hypercube is 640 × 480 pixels × 100 wavelengths. The acquisition is performed at ~40 cm of distance from the sample and monitored by a distance sensor Bluefruit Feather nRF52832 with Adafruit VL53LOx device (Adafruit, New York, NY, USA) orthogonal to the liver surface.
The light source is composed of 6 halogen lamps of 20 W (OSRAM Halospot 70, OSRAM GmbH, Munich, Germany). The HS camera takes ~6 s to perform the acquisition of the hypercube which is transferred to a PC where it is processed creating pseudo-color images. The relative reflectance I I 0 is converted in relative absorbance through the equation A = -ln I I 0. The device used in this experiment provides different algorithms (preset), that quantify the relative oxygen saturation (StO 2 %) of the microcirculation at a depth of ∼1 mm, and at deeper layers with the near-infrared (NIR) spectrum (3-5 mm)  [4]. Quantitative analysis of the StO 2 % and NIR index was performed intraoperatively using the TIVITA Suite software module over the whole liver surface. The methods and algorithms of the TIVITA system were explained in more detail by  Holmer et al.