dataesr/scientific-paragraphs-categorization huggingface dataset:
For this step, it was used a dataset of complete public genomes described in item 2. 4. Afterwards, the number of genes per KEGG pathway for each MAG was estimated, as described above, compared, and clustered in a heatmap plot using the Pheatmap package for Rstudio. Then, the list of genes from all MAGs was mapped to KEGG pathways using the KEEGmapper tool. All the maps (map00620, map04727, map00400, map00380, map00250, map00920, map02010, map02060, and map00480) used for the reconstruction and visualization of MAGs integrated metabolic pathways were initially generated by Kanehisa laboratories  [32].
The GenBank ID for the 17 selected proteins is available in Table  1. For this step, cell adhesion-related proteins were submitted on INTERSPPI -Human&Bacteria v3 (10. 1021/acs. jproteome. 9b00074) (accessed on 02/03/23) to predict PPI, considering a 97% of identity against the platform databases and 95% interaction probability cutoff. Networks were plotted in the Rstudio igraph package and Python networkx and pyvis packages.
Sensory testing is conducted with a group of human assessors, or panellists, who evaluate specific product attributes and provide qualitative and quantitative responses. The data can be used to find effective ingredients and create optimal formulas  [14]. Panels can be formed of naïve assessors who do not undertake any formal training or selection or expert assessors with demonstrated sensory sensitivity, training, and experience to make consistent and repeatable assessments  [15, 16]. Different sensory evaluation methods can be grouped into three main types: discriminative, descriptive, and hedonic. Discriminative tests focus on product differences and can be performed by trained or untrained assessors  [17, 18]. Descriptive methods aim to give global information about the product and typically require a trained expert panel to characterise specific features and score their perceived intensity.
Statistical Analysis Two types of statistical methodologies were used in this study: one related to the research methodology for experimental designs and the other for the statistical processing of data from the sensory panels. Design of experiment (DoE), a set of methods for validating a hypothesis using statistical models, was used in Panel I. A screening plan was carried out to investigate the role of the taste-masking excipients, comparing their individual roles and the synergy between the factors of variation-in this study, the quantities of the raw materials. The DoE was a complete two-level factorial design with central repetition, according to  Montgomery (2001)   [25]. Table  3  shows the experiment matrix (as encoded by Box) and the three experimental factors: Vanilift™, sucralose, and citric acid (expressed in %).
For the purposes of this study, the minimum and maximum concentrations of these excipients were chosen as follows: 1. 1% ≤ Vanilift™ ≤ 2. 2%; 0. 5% ≤ sucralose ≤ 1. 0%, and 2. 0% ≤ citric acid ≤ 4. 0%. The re-alised design was based on a two-level full factorial (2 3 ) design with a central point to test the repeatability and curvature of the effects model. High (+1) -1 -1 -1 A: Vanilift TM 1. 1 2. 2 1 -1 -1 B: Sucralose 0. 5 1. 0 -1 1 -1 C: Citric acid 2. 0 4. 0 1 1 -1 -1 -1 1 1 -1 1 -1 1 1 1 1 1 0 0 0 Design-Expert ® software (version 12; Stat-Ease, Inc. , Minneapolis, MN, USA) was used to generate and compute the models and statistics. In addition to the analysis of Variance (ANOVA), a Pareto chart of the effects was drawn. This statistical treatment is specific to DoEs based on Hadamard matrices and consists of using studentised effects to compare the main effects and interactions.
Microbial gene count table Reads were cleaned using ALIENTRIMMER v0. 4 to (i) remove resilient sequencing adapters and (ii) trim lowquality nucleotides at the 3' side using a quality and length cutoff of 20 and 45 bp, respectively. Cleaned reads were subsequently filtered using a human reference sequence (human genome version: Homo_sapiens_chm13_ t2t_v1_1) with an identity score threshold of 90%. Gene abundance profiling was performed using the 8. 4 million gene catalogue of the human oral microbiome [https:// doi. org/ 10. 15454/ WQ4UTV]. Filtered high-quality reads were mapped with an identity threshold of 95% to the 8. 4 million-gene oral catalogue using Bowtie2 (v2. 3. 5) included in METEOR v3. 2 software (https:// forge mia. inra. fr/ metag enopo lis/ meteor). A gene abundance profiling table was generated through a two-step procedure using METEOR v3.
Data analysis A principal component analysis (PCA; biplot representation) of the covariance matrix provided a multidimensional representation of the links between the scores obtained for the different tastes. The participants were represented using different symbols depending on their age and sex. A second PCA was performed on saliva and lingual film species to depict the dataset variability. By correlating principal components to descriptive variables, such as age, sex and salivary flow, we aimed to evaluate whether species-major information is linked to these participants characteristics. Spearman correlation tests were used to evaluate the significance of correlations. Correlations between phylum, genus and species expression quantified in saliva and lingual film were tested for each of the 5 tastes using the Spearman correlation test.
The classifiers were PLS DA (discriminant analysis) models built on a training set comprising 80% of the participants and tested on a test set of participants (the remaining 20%). This data split (training and test sets) was reproduced a hundred times to remove any potential bias due to the randomized splitting between two sets. With each data splitting, the AUC (Area Under the Receiver Operating Characteristics) on the test set was used as predictive score to determine classifier performances. An excellent model has AUC near to 1 whereas a poor model has an AUC near 0. When AUC is 0. 5, the model has no class separation capacity. The CARET R package was used to compute these classifier models (https:// CRAN. R-proje ct. org/ packa ge= caret). A flow chart of the different steps of the study is presented Fig.
Comparison between saliva and lingual film microbiota The objective of the present work was to elucidate whether oral microbiota composition is related to taste perception. We focused on two ecosystems, the salivary and lingual film microbiota, which is present on the tongue dorsum and cover the taste papillae. The latter is suspected to modulate the concentration of taste compounds in the vicinity of taste receptors  7  , whereas the resting saliva microbiota may reflect the various niches of the oral cavity and can be relevant as a future source of markers. We first identified 666 different species, which is comparable with the current number of 774 oral species reported in the expanded Human Oral Microbiome Database  26. This represents an important diversity, especially considering that we studied participants from the same living area.
Funding This work was supported by grants from the  French "Investissements d' Avenir" program , project  ISITE-BFC  (contract  ANR-15-IDEX-0003 ) TOM, the  French Institut Carnot Qualiment (project MORGoM) , the  Conseil Régional Bourgogne, Franche-Comte  ( PARI grant ) and  FEDER (European Funding for Regional Economical Development).
Chronic food restriction was associated with increased plasma levels of acyl-ghrelin (B) and decreased levels of LEAP-2 (C) independently of running wheel in home-cage. AG/LEAP-2 ratio was higher in FR and FRW mice compared to ad libitum fed controls with no effect of the running wheel. Progressive refeeding lead to a significant decrease in AG plasma levels (F) and increase in LEAP-2 levels (G) leading to decreased AG/ LEAP-2 molar ratio in refed FR and FRW mice. n = 8 animals per group. After refeeding, blood samples of the FRW group were assayed for AG in 7 mice only because of insufficient blood volume extracted at sacrifice for one animal. Data are presented as mean G SEM plus individual values and were analyzed using three-way ANOVA (A), two-way ANOVA (B-D) or RM ANOVA (E-G) as described in Table  S1.
Participants were five healthy females aged between 20 and 29 years old, all with normal BMI between 18. 8 and 22. 8 kg/m 2 (mean G SEM = 20. 40 G 0. 73 kg/m 2 ). AG and LEAP-2 levels were measured following a 15-h fast, and after participants were refed. Refeeding lead to a significant increase in blood glucose levels (p = 0. 044, Figure  S1E ), a decrease in plasma levels of AG (p = 0. 019, Figure  S1F ). LEAP-2 levels increased in four participants, while one exhibited an opposite pattern of regulation (ns, Figure  S1G ). Finally, AG/LEAP-2 molar ratio significantly decreased with refeeding in healthy female volunteers (p = 0. 034, Figure  S1H ). Taken together, our data suggest that AG and LEAP-2 levels undergo similar regulation patterns in female mice and women after acute fasting and refeeding.
These patients were excluded from subsequent analyses. Demographic and clinical data are summarized in Table  S2. All patients attained a normal body mass index (BMI) at discharge (V2) and their BMI had significantly increased with refeeding (p < 0. 001, Figure  3A ). At discharge, patients had lower symptom severity on the EDI-2 scale (p < 0. 001) and a lower Flow chart of the longitudinal study in patients suffering from AN 42 patients following the inclusion criteria were admitted in the inpatient program so far. Among them, 39 patients were initially included in the study and 3 patients had to be excluded because the delay between admission and inclusion exceeded 8 days. During hospitalization, 9 patients were excluded because they dropped out of the inpatient program, and one was excluded because of somatic complications.
Patients with anorexia nervosa (AN) Thirty women aged from 19 to 53 years old (26. 22G1. 79), with DSM-5 criteria of AN, were included during their admission in a department specialized in eating disorders (CMME, GHU Paris Psychiatrie et Neurosciences). Twenty-nine patients went through the entire protocol so far, inclusion and follow-up are still ongoing. The protocol was approved by national Ethical Committee (CPP19. 07. 26. 54412). Written informed consent was obtained for all participants on inclusion. All data were recorded anonymously.
The running wheel activity with wheel revolution and time spent running was recorded every 10 min. Running wheel activity was divided into nighttime activity (18:00 to 06:00) usual active phase for rodent, food anticipatory activity (13:30 to 17:00) described as an active phase in anticipation to food delivery in FR mice, and total activity on 24h. Data from running wheel activity were extracted with an Excelâ macro to obtain cumulative activity data for these three time-periods and was used to validate the use of the running wheel by all animals from ALW and FRW groups. Cages of AL and FR groups were equipped with similar wheels locked with metal clamps in order to control for cage enrichment. Body weight was monitored every weekday during the protocol and blood glucose was measured three times per week to ensure tolerance to FR (FreeStyle Optium Neo, Abbott, The Netherlands).
ACKNOWLEDGMENTS This work was supported by a grant from  Fondation de France  ( N 00081239-Remano  to P. G. ),  Agence Nationale de la Recherche  ( ANR-19-CE37-0020-01  to V. T. ) and supported by an  INSERM  promotion ( C18-39 ). ChloeTezenas du Montcel  is the recipient of a PhD fellowship funded by '' Fondation pour la Recherche Medicale '' ( Espoir de la Recherche ,  N FDM202006011161 ). Jingxian CAO  received a doctoral fellowship from the  UniversiteParis Cite ,  ED BioSPC. We warmly acknowledge,  Etienne Kimmel ,  Marie Fadigas , and the nurses from  Clinique des Maladies Mentales et de l'Encephale (GHU Paris Psychiatrie et Neurosciences, Hopital Sainte-Anne, Paris, France)  for the care of the patients and their contribution to the clinical study. We would like to thank  Dr Macarena Cuenca  and all members from the  ''Centre de Recherche Clinique'' and ''Centre de Ressources biologiques'' (GHU Paris Psychiatrie et Neurosciences, Hopital Sainte-Anne, Paris, France)  for their help in conducting this study.
Considering the results obtained from all tubers, hardness of boiled yam was found to be related to DM and GalA (Table  1 ). However, it is important to note that correlations were not significant when hardness was low (between 1. 5 and 5. 9 N, 80% of total dataset). This led to the conclusion that 20% of the dataset, with hardness > 6 N, made the correlation significant. Hence, further work is required with samples of more contrasting hardness to confirm or rule out this tendency. Nevertheless, considering DM and GaIA contents as predictive variables, a model for hardness was developed by linear regression (Fig. 2 ). This model satisfactorily predicts hardness (r 2 = 0. 76 and root mean square error = 0. 37) for this particular set. Again, such a model must be tested and adjusted using a larger set of samples exhibiting larger differences in terms of hardness after cooking.
ACKNOWLEDGEMENTS We are grateful for the grant opportunity  INV-008567  (formerly  OPP1178942 ):  Breeding RTB Products for End User Preferences  (RTBfoods), to the  French Agricultural Research Centre for International Development (CIRAD), Montpellier, France , by the  Bill & Melinda Gates Foundation (BMGF) : https://rtbfoods. cirad. fr. We acknowledge  Hernán Ceballos  for the comments that significantly improved the manuscript, as well as  Aliénor Dutheil de la Rochère ,  Caroline Garcia ,  Nouha Bouaziz  ( INRAe Avignon, France ),  Jean-Luc Irep ,  David Lange  ( INRAe Guadeloupe, France ),  Thierry Bajazet  and  Mathieu Lechaudel  for experimentations, data treatments, and supplying fresh yam. Important contributions to experimental work by  Luis Londoño ,  Jorge Luna ,  Maria Alejandra Ospina  and  Cristian Duarte  ( Alliance Bioversity-CIAT, Cali, Colombia ) and  Pham Thi Ha Giant  ( CIRAD ) are gratefully acknowledged.
a b s t r a c t This article describes data related to the research paper entitled "Concurrent vs. retrospective temporal data collection: Attack-evolution-finish as a simplification of Temporal Dominance of Sensations?"  [1]. Temporal sensory perception data of five dark chocolates that vary in cocoa content were collected from 129 consumers who evaluated the samples in two sessions, using a different sensory evaluation method in each session. A within-subject design was set-up to compare the two data collection methods: consumers in Panel 1 (36 men and 32 women aged 19 to 63 years old) started with the Temporal Dominance of Sensations (TDS) method, and consumers in Panel 2 (35 men and 26 women aged 19 to 61 years old) started with the Attack-Evolution-Finish dominance (AEF-D) method. For each chocolate, consumers had to report the sensations they perceived either concurrently (TDS) or retrospectively (AEF-D) to the tasting.
After the descriptive task, consumers were asked to rate their liking for chocolates on a 9-point discrete scale. Finally, consumers had to answer questions related to the difficulty of the descriptive task. The dataset includes information on consumers' gender, age and frequency of consumption of dark chocolates. The dataset can be reused by sensometricians to compare methods or develop new statistical models for data analysis. It can also be reused to compare at the individual level declarative sensory measures collected either concurrently or retrospectively to tasting. Thus, the impact of cognition (due to memorization, stress or complexity of measurements) on sensory description and liking can be investigated. More specifically, this dataset can be help understand how the dynamics of perception of texture, mouthfeel and flavour attributes are integrated when using static measures.
© 2023 The Author(s Two consumer panels (129 consumers in total) evaluated the samples in the sensory booths of the ChemoSens platform, using the TimeSens©  [3]  software, version 2. 0. Two temporal sensory evaluation methods were used by consumers to describe their temporal perception: Temporal Dominance of Sensation (TDS  [4]  ) and Attack-Evolution-Finish Dominance (AEF-D  [1]  ). Liking scores were rated using 11-point discrete scales (between 0 and 10). Items related to the perception of the tasks were evaluated using 5-point Likert scales  [5].
Data format Raw Description of data collection In two sessions spaced 48 hours apart, five samples of dark chocolates were evaluated by all consumers separated in two panels (same samples in each session, within-subject design). The consumers had first to qualitatively describe their temporal perception of each sample by selecting in a predefined list of attributes (Astringent, Bitter, Cocoa, Dry, Fat, Floral, Fruity, Melting, Sour, Sticky, Sweet, Woody) those they perceived as dominant. Half started with TDS in session 1, half by AEF-D, and the order was reversed in session 2. When using TDS, the evaluation task was concurrent to the tasting, the times of citations of each attribute and the duration of the tasting were recorded. When using AEF-D, the evaluation task was retrospective to the tasting and only the duration of tasting was recorded.
Data Description The dataset is provided as an Excel file (data. xlsx) including seven sheets. Subjects provides information about the consumers. "Panel": name of the panel to which the participant has been assigned. "Subject": unique code of the participant. "Gender": gender of the participant (M: male or F: female). "Age": age of the participant. Products provides information about the dark chocolates evaluated by the participants. "Product": code of the product. The 2-digit number corresponds to the percentage of cocoa. TDS and AEF-D provide data collected with the corresponding temporal evaluation methods. "Subject", "Product": see above. "Attribute": dominant attribute clicked by the participant (Astringent, Bitter, Cocoa, Dry, Fat, Floral, Fruity, Melting, Sour, Sticky, Sweet, Woody + START and STOP in TDS).
Participants Participants were preselected from a population registered in the ChemoSens Platform's Pan-elSens database (declared to the relevant authority, Commission Nationale Informatique et Libertés -CNIL, authorization number 1148039). The inclusion conditions for participating in this study were as follows: being between 18 and 65 years old; not suffering from food or non-food allergies; not being pregnant or breastfeeding and not following a restrictive diet incompatible with the consumption of sugar; being a regular consumer of dark chocolate (at least once every two weeks). The purpose of the study was explained via an information sheet sent by email. The participants have to accept the conditions and fill out a written informed consent form before to be included. One hundred and forty consumers were selected and randomly assigned to one of two panels, with a constraint of balance in gender and age between panels.
Data Collection The consumers participated in sessions of approximately one hour in the sensory lab of ChemoSens at the Centre for Taste and Feeding Behaviour, Dijon, France. In session 1, consumers in panel 1 firsts attended a collective briefing (groups of 16 persons) aiming at explaining the procedure and demonstrating how to report their perception using the TDS method. The concept of dominance was presented as "the sensation that catches the attention at any time", and the panel leader shown how to interact with a TDS measurement screen while simulating a tasting. To ensure their understanding of the task, the participants were invited to ask any question. Then, they were installed in individual booths equipped with computers running TimeSens© V2 software, the data acquisition program.
As previously reported, we observed that OB-and lENT-projecting piriform neurons can be distinguished based on their laminar positioning and morphology  (Diodato et al. , 2016  Figure  1b , d and data not shown). We then analyzed EYFP fluorescence on axonal processes in coronal sections through the brain. As expected, in mice in which CAV2-Cre was injected into the OB, we detected dense labeling of EYFP-positive axons in the granule cell layer of the OB (Figure  1e ). In addition, we observed EYFP-positive axons in the mPFC, the OT, and the insular cortex (IC) (Figure  1f ). These data suggest that OB-projecting neurons send axon collaterals to several other target areas in the anterior telencephalon. In contrast, targeting lENT-projecting neurons resulted in only minimal labeling of the OB, the mPFC, the OT, and the IC (Figure  1h, i ).
Laser output was maintained at 7-10 mW as measured at the end of the fiber. The conditioning apparatus was a rectangular chamber (9 cm W x 57 cm L x 16 cm H) with a stainless-steel rod floor. Each half of the conditioning apparatus was connected to an electrically operated switch, which was connected to an aversive stimulator (115 V, 60 Hz, Med Associates) and to a microcontroller board (Mega 2560, Arduino), allowing foot-shock to be applied independently to either side. The testing apparatus was a rectangular chamber (9 cm W x 57 cm L x 16 cm H) with a white PVC floor. Before each training session, laser beam output intensity and shape were adjusted and electrical current in the rod floor was measured with an aversive stimulation current test package (Med Associates). Photostimulation and foot shock were controlled using Arduino 1.
8. 3 open software. Experimental animals were allowed to habituate to the apparatus for 5 min. The conditioning paradigm consisted of 3s of photostimulation (20 Hz square-wave shape, 25ms pulses) followed immediately by a 0. 5s, 0. 65 mA foot shock. Foot shock was applied only when the animal was in or near either end of the apparatus, forcing the animal to run to the opposite side. Photo stimulation/shock pairings were spaced 3-4 min apart. Each of the two training sessions consisted of 10 photo simulation/foot shock pairings, for a total of 20 pairings. Photo stimulation was applied 7 times over the testing session, every 3-4 min. All sessions were video-recorded. Videos recorded during the testing session were analyzed during the 10 seconds time period before and after stimuli presentation using a custom-written Matlab script to quantify: maximum speed before and after stimuli presentation, distance run before and after stimuli presentation and reaction time after stimuli presentation.
Acknowledgments We thank  A. F. for the help with conceptualization, original draft writing, supervision, and funding acquisition. We thank  G. Dugué ,  Y. Dupraz  and  G. Paresys  for their help with optogenetics and the setup of the behavior asset. We thank members of the  Fleischmann lab  for their input during scientific discussions. This work was supported by grants from the  Fondation pour la Recherche Médicale   AJE201106. I. V was supported by a  PhD international fellowship  ( Ref. SFRH/BD/91460/2012 ) from the  Foundation for Science and Technology, Portugal  and D. B. was supported by a graduate scholarship from  École des Neurosciences de Paris Île-de-France.
| Participants Thirty healthy normosmic subjects (14 men, 16 women; mean age 23 years; age range 18-34 years) volunteered for the experiment. We estimated the sample size utilizing G*Power software  (Faul et al. , 2007). Within the difference between two dependent means in matched pairs, our sample size obtained a medium effect size of 0. 53 with α level set to 0. 05 and power set to 0. 8. Prior to the study normal olfactory function was ascertained using the odor identification part of the "Sniffin' Sticks" test battery  (Oleszkiewicz et al. , 2019). Pregnant women were excluded because pregnancy may change olfactory function  (Hook, 1978). Participants provided written consent after having been informed about the aims and possible risks of the study. All investigations were performed according to the Declaration of Helsinki.