dataesr/scientific-paragraphs-categorization huggingface dataset:
Starting the optimization process Some tools were recently published to perform automated parametrization of software in untargeted metabolomics  (Libiseller et al. , 2015; Manier et al. , 2018). Because isotopologue extraction is performed with the same tools as feature extraction in untargeted metabolomics-i. e. XCMS in this study-, such tools can be also applied to untargeted isotopologue analysis. In this work we used the tool IPO  (Alboniga et al. 2020; Libiseller et al. , 2015) , which was specifically designed to parameterize XCMS, to provide starting parameter settings for isotopologue extraction. In compliance with the IPO guidelines, this was done with MS data collected on unlabelled samples-i. e. the unlabelled PT samples. The so-obtained parameters (Table  S2 ) were applied to extract the isotopologues from the labelled PT samples (benchmark isotopologues).
Nevertheless, seven reference isotopologues remained undetected in the optimized dataset, indicating that the automated process was slightly less efficient than manual extraction. Five of the missing isotopologues, the M0 and Mn of ADP and GDP and the Mn -1 of CDP, were not detected in any of the five PT sample replicates. The two others missing isotopologues (UDP M0 and CDP M1) were detected in only one replicate. The chromatographic signal appeared more intense in this replicate than in the others. Overall, the above data indicated not only that the number missed isotopologues was decreased after optimization process, but also that the detected isotopologues were much better defined.
Optimizing data processing in untargeted isotopic tracing studies therefore also means ensuring isotopologue abundances are properly measured. Because manual integration is somewhat arbitrary and automatic integration is imperfect regardless of the algorithm considered, the quality of isotopologue quantification was controlled and maximized throughout the optimization process by comparing isotopologue abundances in the benchmark isotopologues to those in the reference isotopologues. Two methods were used to evaluate quantification accuracy. We first compared the absolute abundances of individual isotopologues separately. Figure  S4  shows that the benchmark isotopologues have absolute abundances very close to those in the reference dataset. The isotopologue areas were closely similar whether integrated manually or automatically, indicating the reliability of automated isotopologue quantification after optimization.
Benchmark CIDs were calculated for the benchmark isotopologues-before (i. e. with IPO settings) and after parameter optimization-after reconstructing molecular isotopic clusters and correcting for naturally-occurring isotopes. The benchmark CIDs were then compared to the reference CIDs. The data are shown in full in the Supplementary Data  (Fig S5). The results obtained with the final, XCMS-optimized dataset are shown in Fig. 3B, C. For all 25 metabolites, the CIDs obtained after parameter optimization were in close agreement with the reference values (average error below 2%; Fig. 3B ). Figure  3C  compares the CIDs of selected metabolites with reference values before and after parameter optimization. The CIDs calculated from the initial non-optimized dataset are generally biased and show significant inter-replicate variability (e.
g. PRPP, Fig. 3C ). This is partly because many isotopologues go undetected with the IPO parameters, as mentioned above. After parameter optimization however, the CIDs were in good agreement with the corresponding values in the reference dataset, showing the benefit of the proposed optimization strategy. Interestingly, the CIDs of both malate and succinate, whose isotopologue abundances were overestimated in the optimized dataset (Fig. S4 ), were also closely consistent (Fig. 3C ). This means that although the MS signals of the two compounds were overestimated in the automatically extracted data, the quantitative relationships between isotopologues of the same compound were preserved. This observation points to a potential bias in interpreting the abundances of individual isotopologues from different metabolites to derive quantitative metabolic informatione.
Optimization of isotopologue clustering Isotopologue clustering consists in the grouping of extracted isotopologues into metabolite isotopic clusters (Fig. 2 ). Two different programs, geoRge and X13CMS  (Patti et al. 2014; Capellades et al. , 2016; Dange et al. , 2020) , were used to do this. Clustering was optimized in a similar fashion as the extraction process was (Fig. 2 ). The isotopic clusters of the 25 reference metabolites (reference clusters) were manually extracted from the optimal set of isotopologues. The software were used to automatically extract the 25 clusters (benchmark clusters) from the same dataset. The optimization consisted in adjusting software parameters to minimize the difference between benchmark clusters and reference clusters. The quality of clustering was evaluated from the proportion of correct clusters that were recovered.
The isotopologue mass deviation ("ppm" for X13CMS and "ppm. s" for geoRge) is the acceptable error in m/z measurements between successive isotopologues in the same isotopic cluster (the accuracy of isotopic distances), which should be the mass difference between 13 C and 12 C (1. 00335 m/z). The RT window ("RT window" for X13CMS and "rt. win. min" for geoRge), corresponds to the tolerance on the RTs of isotopologues from the same metabolite, which should in theory be exactly the same. The RT deviation was measured to vary between 0. 2 and 7. 2 in the isotopologue dataset obtained after XCMS optimization (Table  S5 , see 3. 3. 2). From these values, two different RT windows (5 and 10 s) were considered and the isotopologue mass deviation was varied from 1 to 10 ppm. The noise threshold was deliberately set at a low value (5000) to maximize peak extraction.
As reported previously  (Dange et al. , 2020) , many redundancies were observed in the clusters obtained with geoRge independently of the parameters used. This is due to the clustering algorithm of geoRge, which generates various clusters from the same set of isotopologues. The optimization for geoRge was therefore performed after manual curation of obvious redundancies in the geoRge dataset. For both programs, a low mass deviation threshold produced more incomplete clusters while increasing the mass deviation generated more corrupt clusters (Fig. 4 ). The missing species were most often the Mn isotopologues of nucleotides (ADP, ATP, CTP, GDP, UDP, UMP, UTP), which can be explained by the lower quality of the XCMS data for these species and their larger mass deviation (see Sect. 3. 3 and Table  S4 ).
Processing the data with a larger mass tolerance allowed these isotopologues to be recovered but also tended to generate corrupt clusters. An RT window of 10 s was found to yield a greater proportion of correct clusters than an RT window of 5 s for all isotopologue mass deviation values except 1 ppm, for which the proportion of correct clusters was the same with both. The number of corrupt clusters did not depend on the length of the RT window, regardless of the mass deviation used. These results are because while unlabelled and labelled samples were processed simultaneously, the heaviest isotopologues were only detected in the 13 C-enriched samples leading to a certain amount of variation in RTs. These results are based on a targeted search of 25-benchmark clusters and we assume that the observed errors are representative of the clustering process for the entire dataset.
The gain in data quality resulting from the optimization is illustrated in for the WT strain (Table  2 ). The fact that the proposed approach yields the same cluster precision for biological samples as for the reference material, confirms the efficiency of the optimization process. In total, 10,129 isotopologues were extracted by XCMS from the two E. coli strains and were further processed with geoRge and X13CMS to group some of isotopologues in isotopic clusters. As already observed, geoRge generated a significant number of redundancies from this dataset compared to X13CMS (Fig. 5A ), but after manual curation the number of clusters was similar with both software (1037 and 1133 for geoRge and X13CMS, respectively) (Fig. 5B ). Over all the distinct clusters detected (1180) with both programs (Fig.
5B ), a total of 990 clusters had the same basepeak, corresponding to an overlap of 84%, of which 797 were identical in both cluster length and isotopologue composition (Table  S7 ). Regarding the greater number of clusters detected by X13CMS than by geoRge, the 190 isotopic clusters identified by one program and not the other (147 by X13CMS and 43 by geoRge) mostly contained a single isotopologue possibly because of instrumental noise or the presence of unlabelled metabolites or other unresolved peaks. In isotope labelling experiments, isotopologue abundances can be interpreted either individually (e. g. the evolution of the M + 3 peak intensity of a metabolite) or relative to the complete isotopic cluster (isotopologue distribution), the latter approach being the most common way of describing labelling patterns in 13 C-fluxomics.
e. natural abundance), such that the only discriminating factor is the presence or absence of peaks. On the contrary, the 13 C-enriched samples are closely grouped by strain on the plots according to their isotopic composition. This demonstrates the significant impact of the ∆zwf mutation on flux distribution, which then significantly affects the isotopic composition of the metabolites. The WT and ∆zwf groups were analysed to identify the most discriminating labelling data between the two strains. The corresponding isotopic clusters were compared using Wilcoxon tests, with 207 (X13CMS) and 138 (geoRge) of these clusters having more than one significantly different (p ≤ 0. 025) isotopologue between strains. By exploiting an in-house database (containing 47 metabolites), 20 isotopic clusters could be assigned to metabolites with a level 1 confidence  (Creek et al.
Acknowledgements  MetaToul (Metabolomics & Fluxomics Facilities, Toulouse, France , www. metat oul. fr) and its staff members are gratefully acknowledged for technical support and access to mass spectrometry facilities. MetaToul is part of the national infrastructure MetaboHUB- ANR-11-INBS-0010  (www. metab ohub. fr). N. B. is grateful to the  Université Paul Sabatier, Toulouse, France , for being awarded a PhD grant. J. C. P. is grateful to the  French National Institute of Health and Medical Research (INSERM)  for granting a temporary full-time researcher position.
, 2014). Chimera detection relies on VSEARCH with de novo UCHIME method  (Edgar et al. , 2011; Rognes et al. , 2016). The innovative crosssample validation step from FROGS was used to confirm the chimeric status on all samples. Rare OTUs representing less than 0. 005% of all sequences were removed. Affiliation by blastn+ was performed using the SILVA v132 database for prokaryotes and the Protist Ribosomal 2 (PR2) database  (Guillou et al. , 2012)  for eukaryotes. The datasets for this study can be found in NCBI SRA database, Bioproject PRJNA735079.
Statistical analysis Cu concentrations in the incubation bottles as well as in the extracted biofilm, Kd and alpha diversity indexes were compared using a one-way analysis of variance (ANOVA) and Tuckey posthoc tests (p-value > 5%). Alpha diversity indexes (Chao 1, Shannon), clustering using Weighted Unifrac distances (Prokaryotes) and Unweighted Unifrac distances (Eukaryotes) followed by Principal Coordinates Analysis (PCoA) and PERMANOVA were performed using R Cran version (3. 5. 0. 4) with "phyloseq", "vegan" and "ggplot" program packages. The approach taking only into account the presence or absence of eukaryotic taxa, in addition to phylogenetic distances, was considered appropriate to account for possible biases associated with the limited quantification capacity of eukaryotic metabarcoding  (Kelly et al.
, most of them were microeukaryotes, as multicellular one's development should be limited by the size of MPs and the incubation time. Unfortunately, the taxonomic affiliation of the 18SrDNA gene (Whatever the data bank PR2 or Silva, data not shown) allowed the identification of only a few of OTUs to the genus level in biofilms on MPs (41% for PR2). Diatoms (Bacillariophyta), especially pennates, clearly dominated the most anthropized site (Pt12).
MATERIALS AND METHODS This study is based on retrospective analysis of prospectively acquired data from Rouen University Hospital. All patients had given written informed consent for data to be recorded and used for research, in accordance with the Declaration of Helsinki as revised in 2013 (World Medical Association [WMA], 2013). Approval was obtained from the French data protection authority (Commission Nationale de l'Informatique et des Libertés, CNIL No 817. 917), in compliance with French legislation. Ethical approval for the analysis of the data was obtained (CERNI No E2021-45).
Study Design and Statistical Analysis Consecutive patients with dyspeptic complaints were screened for FD according to Rome IV criteria. After descriptive statistics of the entire population, FD patients with normal and altered sleep quality according to the PSQI threshold were compared for their characteristics, QoL, and anxiety-depression scores. The same analyses were performed concerning insomnia, using the ISI results. Quantitative data were expressed by mean and standard deviation, and compared between subgroups using a non-gaussian unpaired t-test (Mann-Whitney test). Qualitative variables were for their part compared using Fisher's exact test. Multiple groups comparisons were performed using Kruskal-Wallis test with Dunn's multiple comparison post-test to compare all pairs of columns.
In addition, individual data brought by visual records are limited in time, because they require the observation of a single cattle by a single technician. For these reasons, few studies were carried out using this method. In a study submitted (https://assets. researchsquare. com/files/rs-717630/v1/5ea1 5e73-0352-47b4-be29-a9fd4b8f8a6d. pdf?c=1631886882; accessed on 28 June 2022), we have evaluated the impact of dipteran insects on Kamphaeng Sean bull-calves growing performances by comparison of feed intake, feed performances and weight gains of two groups of feeder cattle, exposed or not to the insects. The economic impact was drawn out and related to the fly density measured around the cattle pens using Vavoua and Nzi traps. In the present paper, we develop and validate a new methodological tool to measure a defense movement of cattle against biting flies.
In some instances, we present insect density in a more usual manner, as the "apparent density per trap" (ADT), which is usually calculated as the total insect record (IR) divided by the total number of traps  [33] : three in the present case. In our study, because we were using 2 types of traps (2 Vavoua and 1 Nzi), we used either IR or "ADT". In the midterm of the experiment, one night-time mosquito trapping session (from 18:00 to 06:00) was organized in order to evaluate the potential role of mosquitoes in the annoyance of cattle, using a CDC trap  [34]  hung 2. 5 m high in the center of pen B. The trapping box was changed every two hours for 6 trapping periods. Culicidae and other night flies possibly annoying cattle were identified and counted  [35, 36]. The results were compared with data obtained from the tail pedometers during the same 6 time periods.
Tail Flick Counting Methods Because the methodology of tail flick counts was established by visual records  [18, 20] , we used the visual records as a reference method. Visual records (VR): The cattle tail flick counts were estimated by sessions of visual records using a manual counter for 5 min, by one technician, for each of the 12 animals. These visual records (VR5m) were implemented 6 times a day, once during each of the 6 trapping periods of 2 h. Each animal was then observed for a total of 30 min/day (6 × 5 min), in a protocol derived from previously published results  [20]. The exact times at the beginning of each VR5m (accuracy of a minute, for example, 16:35) were registered to further compare VR5m scores with PR5m, the data obtained from the electronic devices (pedometer records = PR) for the same period of 5 min (in our example,  16:35-16:40).
The bit-counts (so-called "steps" recorded by the pedometers) of the animals were downloaded and saved three times per week, using the Fitbit ® smartphone application, for further data analysis. Continuous data obtained from the electronic devices were recorded from 24 August 2016 to 25 March 2017. The data were downloaded from the web site under several time units: (i) "PR5m" for periods of 5 min, to compare with VR5m; (ii) "PR2" for periods of 2 h (6 periods per day; 6 periods per night), to compare to VR5m and correlate to IR2; and (iii) "PR12n" and "PR12d", for periods of 12 h, respectively, for nocturnal records from 18:00 to 06:00, and diurnal records from 06:00 to 18:00, to evaluate the relative impact of diurnal versus crepuscular and nocturnal insects; and (iv) "PR24" for 24 h records.
Statistical Analyses All data were treated using R software under R-studio environment  [37]. Means are indicated with 95% confidence intervals. Mean comparisons were implemented using t-test. Insect records (IR) were established as the total of insects trapped by the 3 traps under two time units: (i) IR2: 2 h periods (6 periods a day, or 6 periods a night): IR2 was used to study correlation with PR and VR; and (ii) IR12d: 12 h period day time (from 06:00 to 18:00) or PR12n for night-time (from 18:00 to 06:00) for the analysis of the daily impact of diurnal (and occasionally nocturnal insects). Statistical analysis was conducted using the linear regression model, and interpretation was based on Pearson's linear correlation (r), p-value from the ANOVA and the coefficient of determination of the regression (R 2 ).
Temporal Distribution of Insects The temporal distribution of flies was studied along the day, as well as the variations along the season. During all our observations, biting flies had predominantly a bimodal activity. Hematophagous flies exhibited a peak of activity in the morning and another one at the end of the afternoon, as shown in Figure  4a ,b (black broken lines). However, depending on the temperature, relative humidity and rainfalls, other patterns could sometimes be observed (data not shown). Common flies generally exhibited more monotonous activity along the entire day, as shown on Figure  4b  (light grey broken line).
Modeling Pedometer Records (PR) and Insect Apparent Density per Trap (ADT = IR/3) Pedometers proved to be highly reliable tools in evaluating insect annoyance, due to their strong link with insect density (IR), obtained from the three traps catching flies in the surrounding area. Figure  7  shows the simple linear regression of the bit frequency recorded by the tail pedometers (PR/min) in the function of the mean apparent density per trap of hematophagous flies, in 2 h trapping sessions (ADT/2 h). The data were obtained from the mean pedometer records (PR2/120) of the animals from Group B, during the six 2 h periods of a day, and the insect records from the three traps divided by 3 (IR2/3 = ADT). The data of were grouped per 80 flies and re-ordered according to a growing ADT/2 h. The data were proven to be in normal distribution based on histogram (for ADT) and Q-Q plots (for PR).
99 for PR2/IR2 versus r = 0. 94 for VR5m/IR2). These results validate the use of pedometers both for tail flicks and insect density estimations. Based on the data collected, a strong linear relation was drawn between the apparent density per trap (ADT) of hematophagous flies, and the number of bits recorded per minute (PR/min), which allows a prediction of the ADT around cattle to be made with an accuracy of 99%. Thanks to the continuous recording enabled by pedometers, an unexpected activity was reported at night. The night trapping, implemented using a CDC trap, allowed us to establish a strong correlation between mosquito density and pedometer records. When considering the night and day additional bits induced by insects (Group B versus Group A), throughout the experiment, 62% of the defense movements could be attributed to biting flies, and 38% to mosquitoes (percentages extracted from the data presented in Figure  8b ).
Funding: This work was funded by the  Centre de Cooperation Internationale en Recherche Agronomique pour le Développement (CIRAD, France) ,  Kasetsart University (Thailand) , The  Thailand International Cooperation Agency (TICA 'One Health ') and the  French National Research Agency [Agence Nationale de la Recherche (ANR) ], under the  'FlyScreen' project'  ( ANR-15-CE35-0003 ).
VERCORS digital twin This inability to reproduce the moisture field with the "2018" identification lead to different investigations: • As explained in  [6] , the calibration of Granger model on mass-loss data is an ill-posed problem. The set of parameters might be inadequate for this reason. The calibration is now being improved by using other sources of data: mass-loss at different RHs, moisture profile measurements in large samples in VERCORS mock-up. • The model itself might be inadequate for the extrapolation from small laboratory samples to large samples. Therefore, a new model is currently tested on VERCORS mock-up based onăRichard-Fick's law  [14]. • The calibration procedure for the TDR sensor, linking permittivity to saturation, is also a complicated process  [13] , a precise temperature dependence correction is required.
Electronics searches We performed the electronic investigation of the relevant literature on July 10, 2021, for the publications during the last two decades. We did not use language restrictions. We sought trials in the Cochrane Library's Controlled Trials Registry and systematic review database, Embase, National Institutes of Health PubMed/MEDLINE, and Google Scholar databases. We used the following Keywords: "probiotic", "synbiotic", "bariatric surgery", "sleeve gastrectomy", "Roux-en-Y gastric bypass", "randomized-controlled trials" and "morbid obesity". We checked the reference list of relevant reviews for eligible clinical trials.
Summary of findings Two authors (MAD and MAC) independently assessed the evidence for the primary outcomes of this study. We used The Grading of Recommendations Assessment, Development, and Evaluation (GRADE)  [21]. We considered the study limitations constancy of effect, imprecision, indirectness, and publication bias. We assessed the certainty of evidence as high, moderate, low, or very low. If appropriate, we considered the following criteria for upgrading the evidence: large effect, dose-response gradient, and plausible confounding effect. We used the methods and recommendations described in sections 8. 5 and 8. 7 and chapters 11 and 12 of the Cochrane Handbook for Systematic Reviews oh Interventions. We used GRADEpro GDT software to prepare the Summary of findings tables. We explain the reasons for downgrading or upgrading the certainly of included studies using footnotes with comments.
Literature search 2658 papers were identified from the electronic database in the initial research. After full-text checking, we retained five eligible RCTs  (21) (22) (23) (24) (25)  published between 2009 and 2021 (Fig. 1 ). Eight studies were excluded for reasons: three trials compared probiotics with other interventions instead of placebo  [27] [28] [29] , one study had overlap with another trial  [30] , one trial did not report the outcome of interest  [31] , one study was a letter to the editor  [32]  and two studies were systematic reviews and meta-analyses  [11, 12]. The five identified studies were published as full papers in English. They involved 279 patients (141 patients in the probiotic group and 138 patients in the placebo group). Study characteristics with quality assessment and the Summary of evidence findings were reported in Tables  1  and 2 , respectively.