dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation Cross-validation using the K-fold approach was applied to evaluate the classification performance. Using K-fold, the database is partitioned into K groups. In this study, since the database was relatively small, five-fold cross-validation was used (K = 5). In this method, one fold is left for the test and the remaining four folds are used to train the classifier; thus, the training subset and the test subset are disjointed. In such a way, the predictive power of the LDA was evaluated. To evaluate the statistical accuracy, the validation process was performed five times, and, in each repetition, a different fold was used for prediction. The performance of the binary LDA classifier was averaged and summarized in the confusion matrix, as illustrated in Table  3. The deviation in the accuracy of the classifier was calculated as the standard deviation of the LDA classifier in each fold.
Thus, the classification complexity of the precancerous and cancerous classes was higher compared to the classification between normal and cancerous cells and between normal and precancerous cells. Different projections onto different PC subspaces were examined and the best projections were found in the PC1-PC2 subspace, as presented in Figure  5. Following the PCA analysis, the LDA classifier was applied for the classification of the three different biological systems, the MBM-T, NIH/3T3, and NFC biological systems, with four databases. In the first database (database I), the characteristic vector of each cell was the average of three Raman spectra measured from the center, cytoplasm, and cell membrane. Meanwhile, in the second database (database II), third database (database III), and fourth database (database IV), the characteristic vector of each cell was the Raman spectrum measured from the center, cytoplasm, and membrane of the cell respectively.
At this level, PCA was used for dimensionality reduction; thus, the feature vectors were the coefficients of the PCs. The eigenvectors for the projection were calculated for the training set only. The required number of PCs is task-dependent: when the differences between the classes are large, a small number of PCs is required (simple classification), while, when the differences between the classes are minor, a large number of PCs is Following the PCA analysis, the LDA classifier was applied for the classification of the three different biological systems, the MBM-T, NIH/3T3, and NFC biological systems, with four databases. In the first database (database I), the characteristic vector of each cell was the average of three Raman spectra measured from the center, cytoplasm, and cell membrane.
Meanwhile, in the second database (database II), third database (database III), and fourth database (database IV), the characteristic vector of each cell was the Raman spectrum measured from the center, cytoplasm, and membrane of the cell respectively. At this level, PCA was used for dimensionality reduction; thus, the feature vectors were the coefficients of the PCs. The eigenvectors for the projection were calculated for the training set only. The required number of PCs is task-dependent: when the differences between the classes are large, a small number of PCs is required (simple classification), while, when the differences between the classes are minor, a large number of PCs is required (hard classification). Thus, when classification was performed between each of the couples, namely abnormal (cancerous and precancerous)-normal, cancerous-normal, and precancerous-normal, 2-6 PCs were sufficient.
When the classification was performed between the cancerous and precancerous categories, 10-20 PCs were required to enable the LDA classifier to achieve the best classification. The classifier's performance was evaluated according to its classification success rate (Acc) regarding the number of PCs for the classification of normal and abnormal cells for the first 25 PCs. As illustrated in Figure  6 , we created a graph of the success rate as a function of the number of PCs using databases I, II, III, and IV. required (hard classification). Thus, when classification was performed between each of the couples, namely abnormal (cancerous and precancerous)-normal, cancerous-normal and precancerous-normal, 2-6 PCs were sufficient. When the classification was performed between the cancerous and precancerous categories, 10-20 PCs were required to enable the LDA classifier to achieve the best classification.
The classifier's performance was evaluated according to its classification success rate (Acc) regarding the number of PCs for the classification of normal and abnormal cells for the first 25 PCs. As illustrated in Figure  6 , we created a graph of the success rate as a function of the number of PCs using databases I, II, III, and IV. After a certain PC number, the accuracy rate reached a plateau, which means that increasing the number of PCs did not affect the LDA accuracy rate. The smallest number of PCs that enabled the classifier to reach the plateau was chosen. Table  4  shows the classifier's performance when classifying two different categories The results show that Raman spectroscopy has the potential to detect any changes in cells that are likely to develop into cancer cells, with 93.
From the performance of the classifier, presented in Table  4 , it is evident that the classification of normal NFC cells and precancerous NIH/3T3 cells, and the classification of normal NFC cells and cancerous MBM-T cells, were the most successful for the database of average cells (database I), with 93. 7% and 96. 5% success, respectively. Additionally, although the classification of the measurements taken from the cytoplasm (database III) yielded slightly better results compared to the other measurement locations for both categories (c and d), the success rate of the classification was very high when taken from the three tested regions of the cell. These results are not surprising because, when normal cells are transformed, all the cell components, including the nucleus, cytoplasm, organelles, and membrane, undergo many changes.
The feature vectors in the LDA classifier of each cell were the average of three Raman spectra measured from the center, cytoplasm, and cell membrane (database I), which offered the highest success rate for differentiation. Table  5  shows that the 1195-600 region yielded slightly greater differentiation success than other spectrum ranges in discriminating between NFC, NIH/3T3, and MBM-T; NFC and NIH/3T3; and NFC and MBM-T. In contrast, the 1380-1196 cm -1 range, which represents mostly proteins and lipids, showed greater success when discriminating between NIH/3T3 and MBM-T. Cancer cells typically have altered energy metabolism, including increased resting energy consumption and increased sugar, lipid, and protein metabolism  [47]. Aerobic glycolysis is dominant in cancer cells, which means that even when oxygen is present, cancer cells mostly obtain their energy through glycolysis (the Warburg effect)  [48].
5 ± 2. 9 89. 7 96. 3 94. 5 92. 9 (b) NIH/3T3 versus MBM-T Database No. of NIH/3T3 No. of MBM-T No. of PCs Acc (%) SE (%) SP (%) PPV (%) NPV (%) I 136 152 13 81. 7 ± 3. 3 77. 9 85. 5 82. 8 81. 3 II 132 143 20 80. 2 ± 2. 9 78 83. 2 81. 1 80. 4 III 86 73 10 79. 9 ± 2. 5 74. 4 86. 3 86. 5 74. 1 IV 91 72 10 80. 2 ± 2. 1 82. 5 80. 6 81. 4 79. 5 (c) NFC versus NIH/3T3 Database No. of NFC No. of NIH/3T3 No. of PCs Acc (%) SE (%) SP (%) PPV (%) NPV (%) I 169 136 3 93. 7 ± 3. 6 98. 2 92. 6 94. 3 97. 7 II 169 132 5 87. 4 ± 3. 4 92. 9 84. 8 88. 7 90. 3 III 115 86 5 93. 0 ± 3. 2 94. 8 90. 7 93. 2 92. 9 IV 116 91 5 89. 3 ± 3. 1 90. 1 88. 2 92. 1 87. 2 (d) NFC versus MBM-T Database No. of NFC No. of MBM-T No. of PCs Acc (%) SE (%) SP (%) PPV (%) NPV (%) I 169 152 2 96. 5 ± 3. 5 98. 8 96. 1 96. 5 98. 6 II 169 143 6 90.
Electrochemical detection of bacterial growth with a polyaniline electrode Reference bacterial strains (Table  S1 ) were spiked into human blood at amounts consistent with the concentrations observed in patients (i. e. between 2 and 300 cfu per bottle containing 10 mL of blood). For each positive experiment (90 experiments in human whole bloods for 13 genus and 20 species), specific signals were recorded with all electrode materials without any false positive or false negative tests. To clearly demonstrate our strategy, only the behaviour of polyaniline is fully detailed in the article. Nevertheless, the whole data set was used to demonstrate the identification capabilities of the electrochemical tongue.
Multi-dimensional ELFs We also performed the bacterial growth detection with the three other electrode materials (Iridium oxide/carbon ink, Prussian blue ink and carbon ink) and each material yields specific ELF for each bacterial species (database is available on demand). To exemplify the diversity and multi-dimensional aspect of the responses, Figure  5  shows the different signals recorded with each electrode material for E. coli growth in human blood. It appears that Iridium oxide/carbon ink does not specifically detect the pH evolution of the environment as expected. The difference in behaviour between the polyaniline and the Iridium oxide/carbon ink electrodes is presumably related to the difference in structure and reactivity of both materials. Thus, polyaniline is 3D electroactive and an electronically conductive polymer whereas Iridium oxide shows only a surface pH response.
Identification by machine learning As aforementioned, we obtained multi-dimensional ELFs for each bacteria species that could be used for identification. Thereby, we worked with a machine-learning program to establish an ELF descriptor specific to the pathogens. We accurately performed identifications of the Gram and then of the genus to validate our approach. A more precise identification of the species seems to be accessible (according to our first results) but requires a bigger database that would be the object of a further clinical trial. In order to work only on these ELFs and not on the temporal data (which depends on the inoculum), it was decided to work in the spectral domain. For each potential curve, two types of data processing were applied. First, the potential curve was derived and then smoothed over 5 slip points (convolution) to eliminate measurement noise.
A Fast Fourier Transform (FFT) algorithm was then applied. In a second step, the potential curve was directly smoothed and then an FFT was applied. In this way, the whole electrochemical fingerprint, both in terms of potential and derivative of potential, was taken into account. All these working data become a vector, the descriptor of the blood culture experiment, featuring the signal magnitudes in the frequency domain (Figure  6|a ). The most relevant frequencies for identification, for each material and for each data type (from potential or from derivative), were chosen by iterative testing. The details of the technical choices for the design of this algorithm are presented in Supplementary Information 1. Gram identification was performed by classical cross-validation. However, for the genus classification, we used the LOBO (Leave One Bottle Out) cross-validation to evaluate our accuracies.
For every training of the algorithm, the database is split into two fractions: the training set that gathers the whole data set, minus one experiment (one bottle), and the validation set including the only remaining experiment. The latter is then classified by the trained algorithm. The process was repeated as many times as necessary, so that every experiment has been used at least once as a validation set. In this way, final averaged identification accuracy yields a more realistic insight into the performances in clinical settings, where an algorithm is trained on a database that does not include the blood culture to be tested. The genus is identified with 80% accuracy 6. 4 hours after positivity, and with 85% accuracy 7. 8 hours after positivity. Furthermore, we have also tested another type of classification: when the microorganism is identified at 99% of accuracy as a Gram negative, our algorithms are able to find the right genus (in this class) with 85% of precision in 5.
However, several points remain to be investigated. First, the nature of the redox species involved in the ELFs needs to be determined. Experiments on defibrinated horse blood have shown electrochemical fingerprints similar to those reported in this paper, on human whole blood (Figure  S13 ). The recorded behaviour is thus independent of the culture media, as verified with a Mycobacterium tuberculosis culture performed in Myco/F Lytic culture media (data not shown). Secondly, our approach has to be implemented in a clinical trial to enhance the database, the artificial intelligence algorithms and thus the identification accuracy. This change in experimental scale will allow testing of real infected bloods to definitely confirm the relevance of our method. Thirdly, other electrode materials should be considered, in order to improve the TTD or the identification accuracy.
A few minutes before starting the experiment, 10 mL of human blood was added to the vial with a known amount of bacteria (depending on the purpose of the experiment). Potentiometric electrochemical measurements were performed with an AUTOLAB potentiostat (Autolab Bv, The Netherlands) controlled by Nova 2. 11 software. A multiplexer was used in order to record one measurement every 5 minutes on each of the 8 working electrodes for several hours or days.
SURVEY METHODOLOGY This section describes the methodology followed in the writing of this manuscript: Selection of topic: This review's topic aims to explore how the distinct features of DESs can enhance the effectiveness of bioactive natural products Define the scope: At this point, it clearly defined the boundaries and scope of this review. That included the DESs application in bioactive natural product research divided into solubilization, stabilization, formulation media, and drug delivery to maintain logical coherence throughout the review. Extensive literature review: a literature search was conducted thoroughly across reputable scientific databases, including PubMed, Web of Science, Scopus, and pertinent academic journals keywords such as biomolecules, natural products, deep eutectic solvents, formulation, stabilization, solubilization, pharmaceuticals, and storage were included.
Besides, the studies published between 2003 and 2013 were considered to ensure the capture of the most relevant and recent studies in the field. Data extraction and analysis: Extracted data encompassed an array of crucial parameters, including the specific types of DESs utilized, the bioactive natural products studied, the wide-ranging applications investigated, the methodologies employed, and the resulting outcomes. To ensure a neutral and unbiased approach, a systematic method was adopted for data extraction. Thematic organization: The collected literature was thoughtfully organized thematically, with a strategic focus on highlighting the diverse facets of DESs applications. This organization encompassed distinct topics, ranging from solubilization and stabilization to formulation media and drug delivery.
Computer code Model simulations were performed with the finite element reactive transport code CORE 2D V5  (Molinero et al. , 2004; Zhang et al. , 2008; Samper et al. , 2009; Fernández, 2017; Águila et al. , 2020). CORE 2D V5 relies on several thermodynamic databases. ThermoChimie v10. a  (Giffaut et al. , 2014)  was used for the model presented here. CORE 2D V5 is based on the sequential iteration approach, considers the changes in porosity due to mineral dissolution/precipitation reactions and their feedback effect on the flow and transport parameters  (Samper et al. , 2009; Águila et al. , 2020). The code has been benchmarked against other reactive transport codes  (Poonoosamy et al. , 2018; Águila et al. , 2021).
Acknowledgements The research leading to these results was funded by  ENRESA  within the  Work Package ACED of EURAD (European Joint Programme on Radioactive Waste Management  of the  European Union ,  EC  grant agreement nº  847593 ), the  Spanish Ministry of Science and Innovation  ( PID2019-109544RB-I00 ) and the  Galician Regional Government  (Grant  ED431C2021/54 ). Funding for open access charge was provided by  Universidade da Coruña/CISUG. We thank  Diederik Jacques , leader of the  ACED Work Package , and the ACED members for many enriching discussions, suggestions and recommendations,  Erika Neeft  ( COVRA ) for providing the temperature curves, and the special editor and the two anonymous reviewers for their comments and corrections.
Acknowledgements The authors thank the  French "Agence Nationale de Recherche"  ( ANR-19-CE07-0032 ) for financial support and grants to A. P. We gratefully acknowledge the  MaSCA (Maison de la Simulation de Champagne-Ardenne, France)  for computational facilities. Dr. Francesca Ingrosso  ( Université de Lorraine, CNRS, LPCT , F -54000 Nancy, France ) is warmly thanked for discussions and proofreading.
Methods Survey administration. The survey was designed by the authors with a French and an English version. Our research procedure was in accordance with the relevant guidelines and regulations (1964 Declaration of Helsinki and its later amendments, methodological reference of CNIL for data management), and was approved by the Research Ethics Committee of Sorbonne University (https:// www. sorbo nne-unive rsite. fr/ en/ unive rsite/ our-commi tments/ resea rch-ethics-commi ttee, CER n°2021-067). Specifically, all data was confidential-no IP address or other identifying data was acquired. Eligible participants had to be more than 18 years old, have OCD symptoms and at least one experience with one of the following psychoactive drugs: LSD, ecstasy, MDMA, psilocybin, DMT, ayahuasca, ketamine, 2C-B, Salvia divinorum, mescaline, ibogaine.
Using the same scale, participants also had to rate the acute unpleasant effects on the 21 following items: Diarrhea, Constipation, Nausea, Vomiting, Headache, Sedation/Drowsiness, Tachycardia, Shaking, Feeling of heat, Feeling of coldness, Difficulty concentrating, Memory impairment, Indigestion/Stomach troubles, Discomfort, Anxiety, Sadness/Melancholy, Revival of traumatic memories, Terror/Paranoia/Panic, Depersonalization, Aggressive behavior towards yourself, Aggressive behavior towards others. Additional questions were asked that are not reported here. The full survey is available at the following link: https:// osf. io/ x2sk7/? view_ only= 5ef58 1a60b 7a4d5 685a2 c233a 0435a 15. Data transformation. Data were exported from RedCap and imported in R 3. 5. 1 (R Core Team 2018). All answers from the French version were translated to match the English one and the two datasets were merged.
Results Participants selection. A flow chart representing the selection procedure is shown in Fig. 1. During recruitment, 227 participants entered the survey. Thirty-three participants were discarded because of no consent, incoherent or missing responses, or incomplete questionnaire (see Methods for details). We then selected our population of interest based on OCD symptoms and kept for analyses the data from participants who had either a diagnosis of OCD performed by a health professional (n = 9), or an OCI-r score superior to 18 (n = 62)  29  or both (n = 103). The rationale was to include participants who had not been screened by a psychiatrist or a psychologist (participants without diagnosis but OCI-r > 18), and participants in remission when filling out the survey (participants with a diagnosis but OCI-r < 18).
0063 factor (appropriate for Psilocybe cubensis type of mushrooms according to  30  ). Following this transformation, the mean(SD) dose of psilocybin was 20. 30 (16. 74)mg, ranging from 1 to 88 and the mean(SD) dose of LSD was 131. 56 (69. 85)μg, ranging from 10 to 250 (Fig. 3B ). Since doses were exploitable in a subset of participants only, we looked for a variable available in all participants that could constitute a proxy for the dose, by running a linear regression with 3 factors (delay of onset, duration, intensity of acute effects; all GVIF < 2). We did not include the pleasantness of acute effects as a regressor since previous study did not observe its association with the dose  31. Five participants were removed from the analysis because of missing data (n = 4 for the delay of onset and n = 1 for the duration), leading to 48 participants included in the analysis.
Results showed that the intensity of acute effects was positively associated with the dose (β = 0. 02, t = 3. 48, P = 0. 001; Fig. 3C ). None of the other regressors significantly predicted the dose (Delay of onset: β less30-30to60 = 0. 09, t = 0. 34, P = 0. 73, β more60-30to60 = 0. 06, t = 0. 22, P = 0. 82; Duration: β 5to12h-more12h = 0. 37, t = 1. 28, P = 0. 21, β less5h-more12h = 0. 26, t = 0. 82, P = 0. 41). This analysis shows that in our dataset, the intensity of acute effects can be used as a proxy for the dose, with increasing doses leading to more intense experiences.
Predicting the magnitude of OCD improvement. To study which factors predicted the magnitude of OCD improvement, a linear regression including 6 independent variables (substance, mindset, context, intensity and pleasantness of acute effects, OCI-r score; all GVIF < 2) was used. One participant was removed from the analysis because of missing data, ending in 89 observations included. In our dataset, the magnitude of OCD improvement was positively associated with both the intensity (β = 0. 87, t = 3. 86, P adj = 0. 003; Fig. 4A ) and pleasantness (β = 0. 76, t = 3. 96, P adj = 0. 003; Fig. 4B ) of acute effects. None of the other factors was significant (substance: β Psilo-LSD = 5. 67, t = 0. 77, P adj = 0. 67; mindset: β Expectation-NoExpectation = 2. 80, t = 0. 36, P adj = 0. 81; setting: β Recreational-Noparticular = -9.
The quartz ring provided the distance between plates. We used the capacitor filled with the studied sample for the pressuredependent dielectric measurements, which was next placed in the high-pressure chamber and compressed using silicone oil. Note that the sample was only in contact with stainless steel during the measurement. The Unipress setup measured the pressure with a resolution of 1 MPa. The temperature was controlled within 0. 1 K by means of a Weiss fridge. To avoid cold crystallization and maintain the same history for each sample, each [P 666,14 ]-based IL was quenched to 201 K and then compressed to the glassy state. Afterward, the temperature was increased, and the dielectric data were collected on isothermal decompression. This procedure enables studies of ion dynamics in the glassy and supercooled liquid 2 state.
To investigate the microscopic structural changes during LLT on a larger scale, we employed X-ray diffraction (XRD) characterization with an energy of 12 keV. Small-angle X-ray scattering (SAXS) measurements were performed in the q range of 0. 13-0. 8 Å, while wide-angle X-ray scattering (WAXS) measurements were conducted in the q range of 0. 6-4. 9 Å. The measurements were performed on cooling and subsequent heating, and the twodimensional data were acquired by using independent detectors. The one-dimensional data were obtained by azimuthal integration using the pyFAI program. The temperature of the system was controlled by Linkam, and the cooling/heating rate was set to 10 K/ min. NMR Relaxometry Studies. The 1 H spin-lattice relaxation data of [P 666,14 ][DCA] were collected by a STELAR fast field cycling (FFC) relaxometer in the frequency range of 10 kHz to 25 MHz at 204 and 201 K.