dataesr/scientific-paragraphs-categorization huggingface dataset:
The preparation of datasets The dataset used for ML modeling was built based on the experimental results (seen in Table  1 ). Material, layer thickness and printing temperature were taken as input parameters, whereas dynamic compressive strength was taken as the output. The dataset was used to train and test the ML models in order to explore the input-output correlations. To this end, the dataset was split into training and test data in the ratio of 9:4 (as discussed in Fig. 1 ), which were used to fit the model and to validate the results of the training protocol respectively. Considering that the amount of data in this study is relatively small and the distribution is relatively sparse, different data splitting methods may impact the performance of models. If the dataset used for model training misses important information or interactions of the data, the generalization of the model may be poor as reported in Ref.
[68]. In order to better compare different ML models, the training dataset was selected according to the full factorial experiment method because for the data with interaction as discussed in Fig. 3 , the full factorial experiment can better reflect the interaction  [43]. More specifically, as discussed in Fig. 1 , according to the fixed splitting method, Test 1 to 9 and 14 to 22 were the training set, and Test 10 to 13 and 23 to 26 were test set.
For RF and XGB, important hyperparameters n_estimators were tuned from 2 to 100. Random_state, the parameter controlling random choices, was tuned ranging from 0 to 100. After random_state is determined, the performance of the model for the certain group of datasets will be also fixed. The rest of the hyperparameters are the default settings. A KNN model was developed and its most important hyperparameters k (n_neighbors) was fine-tuned ranging from 1 to 3. An SVR model was built and a kernel function was used to map features that were not linearly separable into a higher dimensional space. Kernel functions radial basis function (rbf) and polynomial (poly) were compared to choose the most suitable one. The cost C function (C), which was used to mitigate over-fitting, was adjusted ranging from 1 × 10 5 s -1 to 1 × 10 7 s -1 ) by the grid search method.
A typical ANN consists of an input layer, an output layer, and one or more hidden layers. Each layer contains a different number of neurons. In this work, the input layer contains 3 neurons (each input parameter is characterized by one neuron), and the output layer contains 1 neuron. The numbers of layers and neurons in the hidden layer were adjusted according to the results of the model. The solver was set as lbfgs. The activation function was set as tanh. Before building the models, datasets for KNN and ANN were normalized to ensure convergence. The normalization formula is as follows: x ′ = (x -X min ) (X max -X min ) (7) Here, x ′ is the normalized data, x is original data, X max and X min are maximum and minimum values in the data column that x is in, respectively.
This result was consistent with the findings of Karimipour et al. [73]. They reported that SVR had further resistance to overfit in small-size datasets therefore SVR was a reliable method for the regression of small-size datasets. SVR can  map the non-linearly separable data to linearly separable data through appropriate kernel function  [74] , which may be one of the reasons for its good performance. In Fig. 4 , KNN showed a lower R 2 score in training set than test set. This is typical underfitting. KNN achieves pattern recognition and regression prediction by grouping data points that are close to each other. The small-size and sparsely-distributed data samples in this work may result in the poor performance of KNN. ANN presented the best R 2 score on both training set and test set than all the other models.
Comparison of experimental and predicted results In order to observe better the degree of conformity between the predicted results of each model and the experimental results, the prediction results of each model and the corresponding experimental results were displayed by means of plots and they are listed in ascending order of the prediction results of DT, as shown in Fig. 8. Note that the step sizes of the printing parameters of the predicting dataset are: printing temperature: 5 • C, layer thickness: 0. 01 mm. In Fig. 8 , it can be found that the prediction of DT is not continuous but the segmented function that presents a stepwise shape. When the values of the experimental data are small, the distribution of experimental points is more concentrated and the prediction result of DT fits better with the experimental points.
However, when the value of the data is larger, the data points are more scattered. This is determined by the process of DT as shown in Fig. 5. The    predicted curves of RF and XGB are similar, and both fit the experimental data better than DT. Compared to DT, the predicted result curves of RF and XGB also are step-like plateau functions, but they are more variable and have more plateau values. The curve of RF shows more variation compared to XGB. The KNN curve also varies in discontinuous jumps, and its values change more frequently compared to XGB and RF, but its changes seem to be too drastic where there are no experimental data points, which may be one possible reason for its lower R 2 score. The curves of SVR and ANN show similar characteristics, which are both jagged and change rapidly.
Comparison of models' accuracy and efficiency Fig. 11  shows the comparison of the accuracy and efficiency of all ML models. The R 2 score is an intuitive way to evaluate the performance of each model on the datasets, but it cannot directly quantify the error for specific data. In order to measure the practicality of the model for the problem, it is more appropriate to evaluate the model by its accuracy (or error) for the datasets. Fig. 11  (a) displays the MAE, RMSE, MedAE on the training set and test set of each model. In this figure, it can be found that errors of all the models are below 8%. DT and KNN presented higher errors than the other models. ANN presented the lowest errors than the other models may be due to it had backpropagation process which can lower error. SVR, RF and XGB showed lower errors.
This result was similar to the findings of Li et al. [77]. They evaluated the accuracy of individual base ML models such as SVR, RF in predicting the surface roughness of the 3D-printed specimens with relatively small data. The results found that the accuracy of SVR outperformed the other base ML learners. In addition, the performance of RF was comparable to the performance of SVR. The prediction errors of ANN, SVR and RF are almost all within 5%, which indicates that these three models are reliable and practical. The size of training datasets will largely impact the accuracy of ML prediction. Notice that relatively small datasets were used in the present work to provide an insight into certain fast AM design scenarios where large databases may not be available. The effects of dataset size will be addressed in the future.
In addition to accuracy, the response speed of the model is also important in real production. Fig. 11 (b)  shows the fitting time and prediction time of each model. It can be seen that the fitting time and prediction time of all models are within 0. 25 s, which may be due to the small number of data samples in this work. DT, SVR and KNN all had the shorter fitting time and prediction time. Among three tree based ML learners, XGB, RF presented relatively long fitting time than DT because they are ensemble models of DT (consisting of many DT processes as shown in Fig. 6 ). Similar results could be found in Hürkamp et al. 's study  [23]. They compared the efficiency of six ML models such as DT, RF and XGB in predicting the interface bond strength of thermoformed sheet. The results found that DT showed the greatest speed efficiency and RF had a very long training and prediction time compared with other models (Note that six ML methods in their study had no ANN).
Conclusion This work applied ML methods to analyze dynamic strength of 3Dprinted PP-based composites. The dynamic strength of 3D-printed specimens was studied by SHPB. Based on a small and sparse experimental dataset, six ML methods were applied to predict the dynamic strength of 3D-printed parts. The performance of six ML methods was compared. Among these models in this study range, DT and KNN showed overfitting and underfitting respectively, thus they were not recommended. ANN showed extremely high accuracy but with relatively lower computational efficiency. SVR had the advantages of high accuracy and fast prediction speed. However, the SVR was hardly interpreting how it made the prediction. Compared to SVR, tree learners were interpretable and can give the importance of input parameters, which was helpful for manufacturing.
Funding:  Wilhelm Sander Stiftung  (H. R. ; grant  2018-042. 1 );Mayent- Rothschild Foundation  and  INCa  (H. R. ). High-throughput sequencing was performed using the ICGex NGS platform of the  Institut Curie  supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  ( France Génomique Consortium ) from the  Agence Nationale de la Recherche  (" Investissements d'Avenir" program ), by the  ITMO-Cancer Aviesan (Plan Cancer III) , and by the  SiRIC-Curie program  ( SiRIC Grant   INCa-DGOS-465  and  INCa-DGOSInserm_12554 ). The Delattre and Adameyko teams are supported by the  ERC   Synergy program  "Kill-or-differentiate". Institutional Review Board Statement: The generation of cell cultures of the adrenal chromaffin cells and sympathetic neuroblasts from OF-1 mice was performed in accordance with the German animal welfare law and was declared to the Animal Welfare Officer of Goethe-University, Faculty of Medicine (Wa-2014-35).
Notably, among these symptoms, many are neurological, including fatigue, headaches, brain fog and memory loss  5. As the cases of persistent neurological symptoms have been increasing, numerous reports of patients with late-onset neurologic and neuropsychiatric symptoms are currently integrating scienti c databases. Recent studies have named this new clinical entity "Long COVID Syndrome" or "Post COVID 19 Neurological Syndrome -PCNS", which has not yet been studied or completely elucidated. Different descriptions of long COVID-19 have already been proposed, and the most common description include "symptoms lasting for more than three months after the rst symptom onset"  6. Here, we highlight that the term "long COVID-19" is often used to refer to symptoms that continue or develop after acute COVID-19  7.
INFORMATION SOURCES AND SEARCH STRATEGY We performed a systematic review of studies describing long-term neurological symptoms in patients with previous COVID-19 infection. From May to June 2021, we searched the Embase, Scopus and PubMed databases using the keywords "Long COVID"; "Neurological Symptoms", "Post-COVID"; "Neuropsychiatric Symptoms" and "COVID Long-Haulers" as well as synonyms that were combined using the operators "AND" and "OR".
From the whole set of data obtained, the most impactful neurological disorders were fatigue, anosmia and ageusia, with the main psychological disorders being impaired cognition, anxiety and depression. Given the process with which we conducted this study, these two areas could be differentiated because they are organic and mental in nature, but this has only been executed in a methodological and procedural capacity. The full scope of long-haulers burden is a complex and intricate matter and its aspects cannot be separately addressed on an individual level.
METHODS AND RESULTS: This cross-sectional analysis involved baseline data of 6332 participants. Food polyphenol content was estimated by a validated semiquantitative food frequency questionnaire and from the Phenol-Explorer database. Multivariable-adjusted linear regression models with serum uric acid (milligrams per deciliter) as the outcome and polyphenol intake (quintiles) as the main independent variable were fitted. Cox regression models with constant follow-up time (t=1) were performed to estimate the prevalence ratios (PRs) of hyperuricemia (≥7 mg/dL in men and ≥6 mg/dL in women). An inverse association between the intake of the phenolic acid class (β coefficient, -0. 17 mg/dL for quintile 5 versus quintile 1 [95% CI, -0. 27 to -0. 06]) and hydroxycinnamic acids (β coefficient, -0. 19 [95% CI, -0.
Study Population This study was based on the cross-sectional analysis of baseline data of participants included in the PREDIMED-Plus (Prevención con Dieta Mediterranea-Plus). This is an ongoing 6-year multicenter, parallel-group, primary cardiovascular prevention randomized trial, which is currently being conducted in 23 Spanish recruiting centers (universities, hospitals, and research institutes). The trial was registered at the International Standard Randomized Controlled Trial Number registry (ISRCTN89898870). The study protocol includes more detailed information and is available in previous publications  [23] [24] [25]  and at the website. 26 Because of the confidential nature of the data collected for this study, requests to access the data set from qualified investigators trained in human subject confidentiality protocols should be made to the PREDIMED-Plus trial steering committee.
Nonstandard Abbreviations and Acronyms A total of 9677 people were included in a run-in period to identify and select participants with greater probability of complying with the study protocol, of whom 6874 participants were eligible for the study and were included in the trial. After excluding participants with missing data for the main variables, such as serum uric acid, and with data of daily energy intake beyond predefined limits (<500 and >3500 kcal/d for women and <800 and >4000 kcal/d for men),  28  6332 participants were included in the present analysis (Figure  1 ). The data were analyzed using the available PREDIMED-Plus database, dated March 12, 2019.
Variables and Data Collection Data on age, sex, educational levels, anthropometric measurements, dietary habits, and lifestyle, as well as blood samples for biochemical analyses, were collected at the baseline visit. Anthropometric evaluations were measured according to the PREDIMED-Plus protocol. Body mass index was calculated as weight (kilograms) divided by the square of height (square meters). Self-reported information about sociodemographic and lifestyle habits, individual and family medical history, smoking status, medical conditions, and medication use was collected. Biochemical analyses were performed using overnight fasting blood samples by standard enzymatic methods to determine parameters such as fasting plasma glucose, triglycerides, cholesterol, creatinine, or serum uric acid.
Estimation of Dietary Polyphenol Intake Trained registered dietitians assessed baseline dietary intake with a semiquantitative 143-item Food Frequency Questionnaire,  32  which was previously validated in the Spanish population. 33, 34 ietary intake of the aglycone forms of polyphenols was estimated from the European Phenol Explorer database. 35 We have used aglycone equivalents for polyphenol intake estimation, instead of considering the total amount of individual chemical polyphenol species (glucosides, glucuronides) reported for each food. This procedure standardizes the data of the results of different analytical methods and facilitates cross-study comparisons. 36 olyphenol intake was calculated in milligrams per day, using food consumption data from the Food Frequency Questionnaire and the aglycone polyphenols content of each food contained in the Phenol-Explorer database.
No retention factors were applied in the calculation of the amount of polyphenol ingested. The data from Phenol-Explorer contains information on polyphenol concentration obtained from both chromatography and chromatography after hydrolysis analytical methods. Polyphenol intake values were adjusted for total energy intake according to the Willett residuals method  37  to obtain intake of polyphenols, which is not correlated to total energy intake, and were categorized in sexspecific quintiles (Qs).
Finally, other factors affecting polyphenol content could be a limitation: the estimation of their intake through the Food Frequency Questionnaire; the differences in their absorption and in their bioactivity  60  ; the synergies with other polyphenols, nutrients, or compounds  61  ; and factors related to climatic stress, geography, and storage conditions or losses during cooking. 62 The grouping for polyphenols instead of considering only individual compounds is another limitation, so important associations for individual compounds may have been missed. Nevertheless, the strengths of the study are the multicenter design, the large sample size, the availability of blood samples, and the high-quality detailed information collected by qualified personnel. Also, our database was built including all available information on polyphenol content in the Phenol-Explorer database, with a mixture of data extracted from chromatography and chromatography after hydrolysis data, and this was adjusted by energy intake using the residuals method.
Acknowledgments The authors thank all of the volunteers for their participation and the medical professionals for their contribution to the  PREDIMED-Plus trial. Author contributions: Conceptualization:  M. Rubín García ,  F. Vitelli-Storelli ,  V. Martín Sánchez. Methodology:  M. Rubín García ,  F. Vitelli-Storelli ,  A. Tresserra-Rimbau ,  S. Castro-Barquero. Formal analysis and investigation:  M. Rubín García ,  F. Vitelli-Storelli ,  L. Álvarez-Álvarez ,  V. Martín Sánchez. Writingoriginal draft preparation:  M. Rubín García. Writing-review and editing:  M. Rubín García ,  F. Vitelli-Storelli ,  L. Álvarez-Álvarez ,  M. Á. Martínez-González ,  J. Salas-Salvadó ,  D. Corella ,  Á. Hernáez ,  J. A. Martínez ,  Á. M. Alonso-Gómez ,  J. Wärnberg ,  J. Vioque ,  D. Romaguera ,  J. López-Miranda ,  R.
Study design This is a subanalysis of the Lithuanian Echocardiography Study of Dyspnoea in Acute Settings (LEDA, NCT03048032). LEDA was a prospective observational multicentre study performed in two Lithuanian university centres in collaboration with a research protocol of the international Global Research on Acute Conditions Team network. The enrolment period, inclusion/exclusion criteria, data collection and diagnosis adjudication have been previously described. 5 Briefly, adult patients with acute dyspnoea admitted to the emergency department at two Lithuanian university centres were enrolled; patients with acute coronary syndrome (ACS) occurring during the first 4 hours of admission were excluded. Demographic data, baseline medication, comorbidities, clinical signs and laboratory parameters at admission were recorded.
Furthermore, images for the RV deformation assessment were obtained from apical four-chamber view as described by Rudski et al. 7  Offline speckle tracking analysis was performed by the two-dimensional strain software in the EchoPac (V. 110. 1. 2 GE Healthcare, Norway) and QLAB (V. 9. 0, Koninklijke Philips, the Netherlands). Both the entire RV strain (including basal, mid, and apical segments of the RV free wall and interventricular septum) and RV free wall strain (excluding septal segments) were calculated. Left-sided parameters related to intracardiac congestion included E/e' ratio, left atrial volume index (LAVI) and left ventricular diastolic diameter (LVDD). Lung ultrasound was performed with a phased array transducer scanning in four chest sites bilaterally. 8 The total number of B-lines was recorded as the final result.
Open access For group comparison, Dunn's tests with the Bonferroni correction were performed. Performance of sCD146 and NT-proBNP in the prediction of IVC dilation and reduced collapsibility, B-lines and cardiac abnormalities were assessed conducting a receiver operating characteristic (ROC) analysis using the area under the ROC curve (AUC). Change in two AUCs was tested by DeLong et al. 9  The analysis was carried out using IBM SPSS Statistics for Windows, V. 23. 0 (IBM Corp) and MedCalc for Windows, V. 14. 8 (MedCalc software). All tests were two sided and a p value of <0. 05 was considered significant.
35 The relationship between venous stretch and sCD146 was demonstrated in an elegant experimental study by Arrigo et al. 3  The authors measured the level of sCD146 in patients with heart failure at baseline and after 90 min of unilateral forearm venous congestion, and documented a rapid release of sCD146 in response to congestion-mediated venous stretch that may reflect systemic congestion in chronic heart failure. Another study revealed a relationship between sCD146 and pulmonary congestion in the  Open access phase of which was independent from the severity of myocardial cell necrosis. 36 he present study extends the accumulated data by revealing a close association between the levels of sCD146 and clinical and echocardiographic evidence of congestion as well as cardiac morphology and function in an unselected population of acute dyspnoea.
Limitations This study has several limitations. First of all, the sample size is limited as it was not always possible to perform a high-quality echocardiography study on admission. Also, some patients were excluded from different parts of the analysis due to missing values in the patient history, which is often the case in a real-life emergency setting. Further research is needed to determine if sCD146 could potentially help to distinguish cardiac and non-cardiac dyspnoea as well as to guide diuretic treatment in different conditions. We do not have the data of deceleration time on echocardiography, which could give additional information on diastolic function. When assessing the association between blood biomarkers and congestion, we relied on clinical and echo-derived markers of volume overload instead of invasively measured intracardiac filling pressures.
Pixel segmentation of high-resolution RGB images into chlorophyll-active or nonactive vegetation classes is a first step often required before estimating key traits of interest. We have developed the SegVeg approach for semantic segmentation of RGB images into three classes (background, green, and senescent vegetation). This is achieved in two steps: A U-net model is first trained on a very large dataset to separate whole vegetation from background. The green and senescent vegetation pixels are then separated using SVM, a shallow machine learning technique, trained over a selection of pixels extracted from images. The performances of the SegVeg approach is then compared to a 3-class U-net model trained using weak supervision over RGB images segmented with SegVeg as groundtruth masks. Results show that the SegVeg approach allows to segment accurately the three classes.
Materials and Methods As shown in Figure  1 , this study investigates two approaches to segment images in three classes: green, senescent, and background. The first step consists of developing the SegVeg method that combines a binary U-net model (U-net 2C) to first separate vegetation from background. Then a SVM model will separate green from senescent vegetation, once the whole vegetation is extracted. This stage relies on two training datasets: fully annotated patches (Dataset #1 with 2-class entire masks) for the U-net 2C training and pixel labelled datasets for the SVM approach (Dataset #2). Once the SegVeg approach is set, it is used to build a third dataset of fully nonsupervised annotated patches (Dataset #3) and train a 3-class U-net model (Unet 3C) on the same RGB images present in Dataset #1.