dataesr/scientific-paragraphs-categorization huggingface dataset:
Blood DNA collection and DNA methylation analysis Blood samples (5 to 15 ml of blood) from CCC patients were collected in EDTA tubes. Genomic DNA was isolated using standard salted methods and the methylation analysis was done using the same protocol as tissue DNAs. Blood DNA methylation data 138 patients were selected randomly from our Chagas bank. It included 48 asymptomatic subjects, 46 moderate CCC patients and 44 severe CCC patients (Supplementary Table  1 ). The age and sex ratio were not significantly different between the 3 groups (age mean and ratio female/male for all phenotypes: asymptomatic: age: 57. 63, ratio =1; moderate: age: 56. 89, ratio=1. 14; severe: age: 59. 59, ratio=0. 95). In a second time, these 138 samples were randomly distributed between the training (70%) and validation (30%) cohorts.
This random distribution was done in such a way that the age and sex ratio was still not so different between the groups in the two subcohorts (Training cohort (age mean and ratio female/male for all phenotypes: asymptomatic: age: 62. 45, ratio=1; moderate: age: 60. 13, ratio=1. 14; severe: age: 57. 18, ratio=0. 86), validation cohort (age and ratio female/male for all phenotypes: asymptomatic: age: 52. 82, ratio=1; moderate: age: 53. 67, ratio=1. 13; severe: age: 62, ratio=1)). The methylation data are available under the reference: (GEO accession: GSE191082).
Biomarker identification for disease forms Since data contains a lot of features (736,661), feature selection was performed in two steps, on the training group only. The scripts used for the following steps are available on Github (https://github. com/TAGC-ComplexDisease/ biomarkersChagas). First, the delta beta (difference of beta means) was computed between the two phenotypes of interest. Only the CpGs having at least 10% methylation differences were retained. Then, a machine learning (ML) analysis was done in Python with Scikit-learn library. Four supervised ML methods were considered: decision tree, random forest, logistic regression and linear SVM (Support-Vector Machine, a linear classificator). For each method, recursive feature elimination (RFE) was performed, and the best model (best accuracy) with the minimal set of feature was selected using 10-time crossvalidation.
Symptomatic cardiac form prediction After feature selection based on delta beta values, 86 CpGs were selected. Among all the tested models, linear SVM seems to have the better prediction on training dataset with the minimal number of features (Figure  2A ). According to this analysis, the SVM was trained with 35 features (Supplementary Table  2 ). The model parameters optimization was performed using a grid search where the L2 penalty varies between 0. 01 and 10. Finally, with a L2 penalty of 1, 42 of 44 patients phenotype of the validation dataset were correctly predicted (accuracy = 0. 95), with a sensitivity of 0. 96 and a specificity of 0. 94 (area under the curve: 0. 996) (Figure  1A ). Those 35 features are mainly located in the body of genes (n=20), or in intergenic regions (n=11). Particularly, 3 CpGs are located in LHX6, and 3 in POU6F2.
Chagas cardiomyopathy stage prediction After feature selection based on delta beta, 108 CpGs were selected. Among all the tested models, random forest seems to have the better prediction on training dataset with the minimal number of features (Figure  2B ). According to this analysis, the SVM was trained with 33 features (Supplementary Table  3 ). The model parameters optimization was performed using a grid search where the number of estimators varies between 50 and 200. Finally, 150 estimators, 27 of 28 patients phenotype (accuracy = 0. 96) of the validation dataset were correctly predicted, with a sensitivity of 1 and a specificity of 0. 93 (area under the curve: 1) (Figure  1B ). Those 33 features are mainly located in 18 intergenic regions. Other CpGs are located in 15 genes, and more precisely in 6 gene body and 7 promoter regions.
Funding This work was supported by the  Institut National de la Santeé t de la Recherche Medicale (INSERM) ; the  Aix-Marseille University  (grant number: AMIDEX "International_2018" MITOMUTCHAGAS); the  French Agency for Research (Agence Nationale de la Recherche-ANR  (grant numbers: " Br-Fr-Chagas ", "landscardio"); the  CNPq (Brazilian Council for Scientific and Technological Development) ; and the  FAPESP (São Paulo State Research Funding Agency Brazil  (grant numbers:  2013/50302-3 ,  2014/50890-5 ); the  National Institutes of Health/USA  (grant numbers:  2 P50 AI098461-02  and  2U19AI098461-06 ). This work was founded by the  Inserm Cross-Cutting  Project  GOLD. This project has received funding from the  Excellence Initiative of Aix-Marseille University  -A* Midex a French "Investissements d'Avenir programme"-Institute MarMaRa   AMX-19-IET-007.
, 2008) , and experiences multiple disturbances, including species invasion, and habitat loss  (Ibanez et al. , 2019; Jaffré, Bouchet, & Veillon, 1998; Jaffré, Rigault, & Dagostini, 1998; Pellens & Grandcolas, 2009). Therefore, furthering ecological knowledge in this context holds high value for local conservation plans by helping to evaluate forest restoration programs, which are of high importance due to mining activity in New Caledonia  (Losfeld et al. , 2014). In this study, a large taxonomic inventory was conducted during both day and night to measure the overall abundance, species richness, composition, and structure of the cricket communities. Data were collected in three ecological stages defined by a gradient of microclimate and vegetation characteristics (forest, preforest, and shrubland) in the south of the New Caledonian archipelago's main island, on ultramafic soil.
| Environmental variables During cricket sampling, environmental attributes were measured in each parcel, including the percentage of bare ground, the percentage of three vegetation layers (herbaceous, shrub, and tree), the number and diameter (dbh) of stems, the vegetation height, and the plant species richness. Canopy openness was extracted from photos taken with a 180° hemispherical (fisheye) lens in each parcel corner and in its center. From the photos, canopy openness was then calculated using the Gap Light Analyzer software (version 2. 0, Simon Fraser University, Cary Institute of Ecosystem Studies) by estimating the percentage of light in the forest overstorey  (Frazer et al. , 1999). Daily temperature (°C) and relative humidity (%) were recorded every 30 minutes for 15 days at a 50-cm height above the ground using waterproof thermo-hygrometer sensors (HOBO U23 Pro v2;.
| Statistical analysis Two datasets were created for statistical analyses. The first included the environmental attributes averaged per site, while the second described the cricket assemblages, defined here as the number of individuals per species averaged per site. All statistical analyses were performed using R (software; R Core Team, 2019), with the type I error set at 5%. To evaluate differences between ecological stages a Kruskal-Wallis test followed by the post hoc Conover test for multiple comparisons with the Bonferroni correction was applied  (Conover & Iman, 1979). This test was run separately on species richness (number of species), overall abundance (total number of individual crickets), and environmental variables. This test was computed using the function posthoc. kruskal. conover.
This analysis was similarly conducted on cricket assemblages and summarized in four ways: (1) cricket species abundances, (2) stridulatory cricket species abundances, (3) presence or absence of cricket species, and (4) presence or absence of stridulatory cricket species. The function metaMDS was used to perform the Bray-Curtis distance and the NMDS analyses, while the permutation test was performed using the function envfit for environmental data. Lastly, the permutation test applied on the distance matrix of cricket assemblages was performed using the function adonis. These three functions are from the "vegan" R package  (Oksanen et al. , 2019 ). The function s. class from the "ade4" R package was used for the graphical scatter diagram representation of the ecological stages in the NMDS space  (Dray & Dufour, 2007).
, 2012). The Bray-Curtis dissimilarity was decomposed in species contribution. This analysis was performed using the function simper to the "vegan" R package  (Oksanen et al. , 2019). The R code used to compile, manipulate, and analyze the data is available as Appendix S4 and https:// github. com/agasc/ Anso-et-al-2022-Biotr opica.
006). NMDS showed a clear succession of cricket assemblages along the ecological succession (stress = 0. 034, two dimensions). Eleven environmental variables significantly contributed to the ordination model with a high significance for vegetation height and mean temperature (p < =. 01). Considering the presence or absence of the cricket species, the ecological stages explained 60% of the total variation of the cricket assemblages (permutation test: R 2 = 0. 6, p =. 001). For that dataset, thirteen significant variables were identified for the ordination model, with seven of high significance (p < =. 01): percentage of shrub layer, percentage of bare ground, vegetation height, percentage of canopy openness, maximum temperature, daily temperature variation, and daily humidity variation (Figure  2d ).
Fourthly, calling cricket communities reliable bioindicators of the surrounding environment and their analyses can reveal major environmental perturbation. In the present work, community analysis was performed only with stridulating crickets revealing the same succession-based specialization as compared to the analyses using all species. As shown by  Gasc et al. (2018) , the presence of the invasive ant Wasmannia auropunctata resulted in a significant reduction in cricket species and their acoustic activity. In contrast with other methods, such as fogging or traditional taxonomic inventories, the ecoacoustic approach is advantageous as it is noninvasive, easy to implement, allow data collection over large temporal and spatial scales, and in remote places  (Sueur & Farina, 2015). An acoustic approach for biodiversity monitoring is meaningful in species-rich ecosystems, such as tropical rainforests  (Deichmann et al.
de l'Environnement de la Province Sud for providing permit collection. This study was partly funded by a Ph. D. grant, "Bourse d'encouragement à la recherche universitaire," attributed to JA by the Government of New Caledonia. Field and lab work were partly supported by a funding attributed to HJ by Agence Nationale de la Recherche (ANR) through the ERA-Net BiodivERsA Project (FFII, JE 288/7-1) and by a grant from the Grand Observatoire du Pacifique Sud: AAP GOPS 2013 / "Bioacoustique des grillons de Nouvelle-Calédonie. " The laboratory procedures were supported by two Action Transversale du Muséum (ATM) research programs: "Biodiversité actuelle et fossile. Crises, stress, restaurations et panchronisme: le message systématique," and "Barcode. " We would like to thank the two anonymous reviewers for their constructive comments.
Purpose FDOPA PET shows good performance for the diagnosis of striatal dopaminergic denervation, making it a valuable tool for the differential diagnosis of Parkinsonism. Textural features are image biomarkers that could potentially improve the early diagnosis and monitoring of neurodegenerative parkinsonian syndromes. We explored the performances of textural features for binary classification of FDOPA scans. Methods We used two FDOPA PET datasets: 443 scans for feature selection, and 100 scans from a different PET/CT system for model testing. Scans were labelled according to expert interpretation (dopaminergic denervation versus no dopaminergic denervation). We built LASSO logistic regression models using 43 biomarkers including 32 textural features. Clinical data were also collected using a shortened UPDRS scale.
Results The model built from the clinical data alone had a mean area under the receiver operating characteristics (AUROC) of 63. 91. Conventional imaging features reached a maximum score of 93. 47 but the addition of textural features significantly improved the AUROC to 95. 73 (p < 0. 001), and 96. 10 (p < 0. 001) when limiting the model to the top three features: GLCM_Correlation, Skewness and Compacity. Testing the model on the external dataset yielded an AUROC of 96. 00, with 95% sensitivity and 97% specificity. GLCM_Correlation was one of the most independent features on correlation analysis, and systematically had the heaviest weight in the classification model. Conclusion A simple model with three radiomic features can identify pathologic FDOPA PET scans with excellent sensitivity and specificity.
Patients Two datasets were retrospectively analysed, an exploration dataset and a testing dataset. The exploration dataset consisted of all patients consecutively referred for striatal FDOPA PET/CT to the nuclear medicine department of the Centre Antoine Lacassagne, Nice, France, from January 8, 2020, to April 14, 2021. Inclusion stopped after reaching 450 subjects. We excluded six patients with an abnormal acquisition time, as it was shown to affect textural features  [18, 19]. We also excluded one patient who had poor scan quality (visually unreadable). The final number of patients in the exploration dataset was 443, with 171 scans (39%) interpreted as positive and 272 scans (61%) interpreted as negative. Clinical data were collected from patients on the day of their referral, using a shortened version of the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS)  [20]  (detailed in the electronic supplementary material).
Twenty-five motor and non-motor symptoms were quantified between 0 and 4, for a maximum total score of 100. The second dataset was reserved for testing the model and comprised 100 patients referred to the same centre between May 10, 2019, and August 28, 2019, with scans acquired on a different PET/CT system (see below). Patients were selected in a consecutive manner, and the same exclusion criteria were applied. After reaching 60 negative scans, only positive scans were included in order to reproduce the positive/negative ratio of the exploration dataset. No clinical data were collected in this group.
Image acquisition For the exploration dataset, FDOPA PET/CT images were acquired on a Biograph Vision 600 (Siemens, Erlangen, Germany). The images were obtained after a period of 85-95 min following the intravenous injection of 3 MBq/kg of 3,4-dihydroxy-6-[ 18 F]fluoro-L-phenylalanine (DOPAVIEW® AAA company). PET acquisition time was 6 min. The acquisition matrix size was 512 × 512. Images were reconstructed using OSEM, with 12 iterations and 5 subsets, and a Gaussian filter with FWHM = 3 mm was applied. Time-of-flight (TOF) correction was applied, but not point spread function (PSF) correction. The reconstructed voxel size was 0. 709 × 0. 709 × 2. 00 mm. For the test dataset, FDOPA PET/CT images were acquired on a Biograph mCT 40 (Siemens, Erlangen, Germany). The images were obtained after a period of 85-95 min following the intravenous injection of 2 MBq/kg of 3,4-dihydroxy-6-[ 18 F]fluoro-L-phenylalanine (DOPAVIEW® AAA company).
PET acquisition time was 10 min. The acquisition matrix size was 512 × 512. Images were reconstructed using OSEM, with 6 iterations and 24 subsets, and a Gaussian filter with FWHM = 4 mm was applied. No TOF or PSF corrections were applied. The reconstructed voxel size was 0. 795 × 0. 795 × 2. 03 mm. In both cases, protein-containing foods were banned 4 h prior to the procedure, as per EANM guidelines. Patients with no contraindications received 100 mg of carbidopa 1 h before injection  [21] ; the number of patients who did not receive carbidopa was 22 in the exploration dataset (5%) and 4 in the test dataset (4%). For all scans, ground truth was the interpretation of a nuclear medicine specialist with extensive experience in FDOPA imaging (J. D. ). This expert had access to clinical data, MRI data when available and semi-quantification software based on automatic positioning of atlas-derived striatal VOI and comparison of the SOR to a reference database (Siemens Scenium Striatal analysis).
To assess texture robustness, several computation settings were tried. All histogram features were computed after absolute discretisation  [24, 25]  between a lower bound of 0 and an upper bound of 10, using either 32, 64 or 128 grey levels (GLs)  [26]. Voxel size was either reconstructed to be isotropic (1 × 1 × 1 mm or 2 × 2 × 2 mm)  [27] , or left with default values. For the features derived from the grey-level co-occurrence matrix (GLCM), three different distances were tried: 1, 2 and 5  [14]. Thus, in total, the 43 features were computed 21 times for all 443 patients in the exploration dataset (Table  1 ). Independently for each dataset, features were standardised by removing the mean and scaling to unit variance.
The LASSO performs its own feature selection by setting the coefficients of less useful features to zero, which allows for good performance even against a large number of features  [32]  while ultimately yielding a simple and understandable model  [33]. To optimise the model parameters, we used the SAGA solver, a popular variant of the stochastic average gradient solver adapted for the LASSO  [34]. We systematically performed fivefold cross-validation and the regularisation strength was selected by the crossvalidation for each iteration. Furthermore, we performed 1000 bootstrap resamples in order to estimate the confidence interval and the probability of variable selection  [35]. After performing feature selection on the exploration dataset, the chosen features were tested on the test dataset, using the optimal parameter combination and a standard logistic regression model.
We reported AUROC, sensitivity, specificity and balanced accuracy. Descriptive statistics including the mean of features and the AUROC of individual features were only computed for one parameter set. This set was chosen according to the best performance reached by the models, using all features. This was also the reference set for the CCC. For the clinical data only, we had to compensate for missing values using mean imputation. All statistical tests and model building were performed on Python version 3. 7. 11 using free and open-source packages. The source code is available on: https:// github. com/ tirol ab/ FDOPA-PET-analy sis.
Results The population is described in Table  2. For the exploration dataset, out of 443 patients, 171 (39%) showed unilaterally or bilaterally reduced uptake, while 272 (61%) had a normal FDOPA distribution. Overall, 56% were male, 95% were right-handed and 19% had diabetes. At the time of referral, 16% of patients were taking antiparkinsonian medication, and 8% were taking antipsychotic drugs. The median age was 74, with IQR 68-80. Five percent of patients could not receive carbidopa before injection. When comparing positive and negative patients, we found the sex ratio, as well as the proportion of patients with diabetes, and the proportion of patients taking antiparkinsonian drugs or antipsychotic drugs, to be significantly different. In the test dataset, the proportion of positive and negative scans was identical to that of the exploration dataset, by design.
The sex ratio, the mean age and the proportion of patients who received carbidopa were also similar to the exploration dataset. No data was collected on the presence of diabetes, right-or left-handedness or drugs taken. The mean (SD) SUVmean across all VOIs was 2. 61 (0. 76) and mean (SD) SUVmax was 4. 56  (1. 25). Mean (SD) VOI volume was 12. 9 mL (3. 7). The histograms of the conventional features showed considerable overlap between the "positive" and "negative" populations; some are shown in Fig. 1a. ROC curves were drawn to estimate the predictive value of features in isolation (Fig. 1b ). The features with the highest AUC were GLCM_Correlation (93. 91), Skewness (93. 22) and Sphericity (91. 91). The mean clinical score was 22 for patients with negative scans and 20 for patients with positive scans.
The mean (SD) AUC score was 96. 06 (1. 79) using cross-validation on the exploration dataset. There was a significant difference between this score and that of the best-performing "conventional" model (p < 0. 001). Finally, our "simplified" model was trained on the entire exploration dataset and tested on the testing dataset. The AUROC was 98. 21 with a balanced accuracy of 95. 83%, a sensitivity of 95. 00% and a specificity of 96. 67%.
Finally, we tested our results on an external dataset with the same positive/negative ratio, as per expert recommendations  [33, 43]. With 95. 00% sensitivity with 96. 67% specificity, we showed that our model still performs well on scans acquired with a different PET/CT system and a different protocol, as long as features are normalised beforehand. To our knowledge, this is the first radiomics study to explore the diagnosis of dopaminergic denervation on FDOPA PET. For DAT SPECT, Rahmim et al. [15, 16]  showed the potential of radiomics for the prediction of the UPDRS score; they also found Correlation to be a feature of interest, but only for the MOCA score. Before them, Martinez-Murcia et al. [14]  had published promising results comparing several feature selection methods, but did not identify Correlation as relevant for distinguishing positive from negative DaTSCANs.
Correlation GLCM = ∑ i ∑ j (i -i ) • (j -j ) • GLCM(i, j) i • j Our study has several strengths. The cohort design ensured that our dataset was close to the target population, with patients scanned at an early stage and an authentic ratio of positive and negative scans. We included a large number of patients, complying with the expert recommendation of including more than ten patients for each feature  [13]. Our classification followed the visual analysis of an expert helped by the most recent semi-quantitative analysis software. The simplicity of the 40% isocontour method is likely to ensure a good interobserver reproducibility, although we did not test it. We studied feature correlation and robustness, and assessed their usefulness using a series of simple but powerful models. Lastly, we tested our model on scans acquired on a different PET/CT system with different parameters, which is an essential part of the radiomics process  [43, 44].
To account for the differences of the two systems, we scaled features independently for the exploration and testing dataset. The similarity of our results on the two sets is in favour of the good generalisability of the model. It should be noted, however, that in order to reproduce our results in a different centre, a minimum of 30 patients will be necessary to ascertain the mean and variance of each feature in the new centre, in order to apply standardisation on the features before using the model. We should also point out some limits to this study. Firstly, we chose to average the features computed independently on both striatal regions. Thus, the difference between patients with strongly asymmetric dopaminergic denervation and healthy patients was less noticeable than if we had chosen the most pathological side for each patient: this could have reduced our power in this study.
Conclusion Using a LASSO approach, we were able to train a model based on only three conventional and textural features, which could predict dopaminergic denervation as visually assessed by a medical expert, with a mean AUROC of 96. 06. Testing the model on an independent dataset yielded high performances, with an AUROC of 96. 00, a sensitivity of 95. 00% and a specificity of 96. 67%. Combining textural and conventional features significantly improved the model compared to using conventional features alone. The most important textural feature for the models was GLCM_Correlation which we found to be independent and robust. Further research is needed to confirm the clinical usefulness of our model for the diagnosis of dopaminergic denervation on FDOPA PET scans.