dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION The goal of EPAD study was to create a deeply phenotyped cohort of non-demented subjects to select potential participants for secondary AD prevention trials. 17 When classifying our dataset according to the ATN criteria, 2 the portion of potential trial-ready subjects, that is, individuals in the AD continuum, was 32. 5%, of which 21. 9% were A+T-N-, 3. 4% A+T-N+ (atypical AD), 5. 5% A+T+N-, and 1. 8% A+T+N+. Brain regional volumetric differences among A-T-N-and A+T-N-were seen in the basal forebrain, postcentral and middle occipital gyri, amygdala, entorhinal cortex, nucleus accumbens, and putamen, while differences further along the AD continuum were detected by RBANS neuropsychological tests, especially in the memory domain. SNAP showed neuropsychological and neurodegeneration profiles similar to individuals in the AD continuum, but demonstrated a higher male prevalence, lower prevalence of APOE ε4 carriers, and lower cerebrovascular burden (similar to A-T-N-).
Funding information This work has received support from the  EU/EFPIA Innovative Medicines Initiative Joint Undertaking EPAD  grant agreement n•  115736. AMW, VW, and FB have received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 666992. JDG holds a  "Ramón y Cajal" fellowship  ( RYC-2013-13054 ). FB is supported by the  NIHR UCLH  biomedical research center. WvdF and FB are recipients of a  JPND grand for E-DADS  (project number  733051106 ). Research of the Alzheimer Center Amsterdam is part of the neurodegeneration research program of  Amsterdam Neuroscience. The Alzheimer Center Amsterdam is supported by  Stichting Alzheimer Nederland  and  Stichting VUmc  fonds. WF is recipient of  ZonMW Memorabel  ( ABIDE ; project no  733050201 ), a project in the context of the  Dutch Deltaplan Dementie.
Fecal microbiota composition at diagnosis is associated with prognosis of early BC We determined the metagenomics (MG)-based composition of stools at diagnosis of early BC in a subgroup (N = 76) of a deep cohort called "CANTO" (for CANcer TOxicities, NCT01993498). The CANTO study aims at performing a long-term follow-up of 13 250 women treated for BC over a period of ten years in order to quantify and prevent chronic toxicities related to treatment (surgery, radiation therapy, chemo-hormono-therapy) (refer to Consort diagram, Supplementary Fig. S1 ). The present analysis focuses on a first set of 9595 pts Enrolled from 2012-2017 with sufficient follow-up and mature data in 2019. Among the 76 females that signed the informed consent to get MG stool analysis, the median age was 52 (Supplementary Table  S1 ).
1  and Fig. 2A-D   Conversely, some of the bacteria underrepresented in patients who were less susceptible to develop neurological side effects also featured in the list of commensals associated with good prognosis and/or healthy status (C. aerofaciens, C. comes, D. piger, R. torques, B. crossotus). However, it is noteworthy that most of the bacteria associated with dismal prognosis that were dominant pre-CT (and disadvantaged by CT) were especially abundant in the stools of patients who did not suffer from weight gain (C. bolteae, C. clostridioforme, C. aldenense, C. hathewayi, E. ramosum, E. coli, B. uniformis, Eisenbergiella tayi, spp from the Veillonella genus and Streptococci spp. ) (Supplementary Fig. S6 , Supplementary Table  S4 ). The microbiome functional potential was analyzed at the genetic level in the MG data base of our CANTO cohort using the HUMAnN3 database  [26].
Clinical study We enrolled patients from CANTO trial (for CANcer TOxicities NCT01993498), a prospective clinical cohort including non-metastatic breast cancer (BC). The CANTO study aims at performing a long-term follow-up of 13 250 women treated for BC over a period of ten years in order to quantify and prevent chronic toxicities related to treatment (surgery, radiation therapy, chemo-hormono-therapy). The present analysis focuses on a first set of 9595 pts enrolled from 2012 to 2017 with sufficient follow-up and mature data in 2019, of which 76 were included in the biological microbiota sub-study (refer to Consort diagram, Supplementary Fig. S1 ). Patients stools were collected before and after chemotherapy (adjuvant or neoadjuvant) at home, in kits including an anaerobic generator (Biomerieux).
Samples were frozen 4-24 h later at -80 °C at Gustave Roussy Cancer Campus in plastic tubes (Plastic vessel by 1000-Sarstedt) with or without RNA later. Plasma samples were collected for 29 BC females: 26 samples before chemotherapy and 29 after chemotherapy. In parallel, clinical data were collected prospectively at three time points: at diagnosis (baseline); 3-6 months after the end of primary treatment (i. e. , primary surgery, chemotherapy, or radiotherapy, whichever came last), and then 1 year post primary treatment. Antihuman epidermal growth factor receptor (HER2) therapy and hormonal therapy could be ongoing, if indicated. Toxicity data were collected at 3-6 months and 1 year after the end of primary treatment. Clinical characteristics and toxicities are described in supplemental tables (Supplementary Tables  1, 2 , and 3).
CANTO is coordinated by UNICANCER, the National Cooperative Group of French Cancer Centers. The study was approved by the national regulatory authorities and ethics committee (ID-RCB: 2011-A01095-36, 11-039). All patients enrolled in the study provided written informed consent, including consent for the biological data collection. Furthermore, we used the feces of 54 Italian HV from Instituto Nazionale dei Tumori center, whose feces were harvested according to the same procedures and analyzed together with 282 HV-derived samples selected from referenced public metagenomes.
These pipelines are based on a gene-reference database, thus protein-coding gene markers deriving from the collection of the +100k bacterial genomes (which we especially focused on) deposited in NCBI or ENA archives. With few gene markers available for each bacterial species (ranging from 50 to 400) it was thus possible to ascertain their presence and relative abundance (expressed in the interval 0-100%) within assembled shotgun samples. For the MetaOMineR analyses the gene abundance profiling was performed using the 9. 9 million gene integrated reference catalog of the human microbiome  [54]. Filtered highquality reads were mapped with an identity threshold of 95% to the 9. 9 million-gene catalog using  [55]. Bowtie 2 included in METEOR software  [56]. The gene abundance profiling table was generated by means of a two-step procedure using METEOR.
The gene abundance table was processed for rarefaction and normalization and further analysis using the MetaOMineR package  [57]. The gene abundance table was rarefied to 13 million reads per sample (a threshold chosen to include all samples, but 1 with 12. 5 million reads) by random sampling of 13 million mapped reads without replacement. The resulting rarefied gene abundance table was normalized according to the FPKM strategy (normalization by the gene size and the number of total mapped reads reported in frequency) to give the gene abundance profile table. Metagenomic species (MGS) are co-abundant gene groups with more than 500 genes corresponding to microbial species. 1436 MGS were clustered from 1267 human gut microbiome samples used to construct the 9. 9 million-gene catalog  [54] , as described  [58].
Differentially abundant MGS between different patients' groups were selected using the Wilcoxon test (p < 0. 05). Microbial gene richness (gene count) was calculated by counting the number of genes that were detected at least once in a given sample, using the average number of genes counted in ten independent rarefaction experiments. MGS richness (MGS count) was calculated directly from the MGS abundance matrix. For the MetaPhlAn3 analyses fastq files were cleaned/filtered as described above and underwent an additional filtering for possible human contaminants (reference database GRCh37/hg19) and contextual quality control using KneadData. This wrapper entangles Bowtie2 ("-very-sensitive" and "-dovetail" settings) to rule out contaminant sequences and Trimmomatic (sliding window 20, min-length 50) to exclude low-quality reads.
Statistical analysis All targeted and pseudo-targeted treated data were merged and cleaned with a dedicated R (version 3. 4) package (@Github/ Kroemerlab/GRMeta). Calculations and statistical tests were performed using R v3. 4. Wilcoxon-Mann-Whitney test was used to assess differences in concentration between two different groups. Data representation was performed with softwares R v3. 6 and Rstudio v1. 2. 1335 using tidyverse, dplyr, ggplot2, ggpubr, complexheatmap, and corrplot packages.
Acknowledgements LZ and GK are supported by the  Ligue contre le Cancer (équipe labellisée) ;  Agence Nationale de la Recherche  ( ANR ) -Projets blancs;  ANR  under the frame of E-Rare-2, the  ERA-Net for Research on Rare Diseases ;  AMMICa US23/CNRS UMS3655 ;  Association pour la recherche sur le cancer (ARC) ;  Association "Le Cancer du Sein , Parlons-en!";  Cancéropôle Ile-de-France ;  Chancelerie des Universités de Paris  ( Legs Poix ),  Fondation pour la Recherche Médicale (FRM) ; a donation by Elior;  European Research Area Network on Cardiovascular Diseases (ERA-CVD, MINOTAUR) ;  Gustave Roussy Odyssea , the  European Union   Horizon 2020 Project Oncobiome ;  Fondation Carrefour ;  High-end Foreign Expert Program in China  ( GDW20171100085 ),  Institut National du Cancer (INCa) ;  Inserm (HTE) ;  Institut Universitaire de France ;  LeDucq Foundation ; the  LabEx Immuno-Oncology  ( ANR-18-IDEX-0001 ); the  RHU Torino Lumière ; the  Seerave Foundation ; the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; and the  SIRIC Cancer Research and Personalized Medicine (CARPEM).
In this case, the load resistance R S is 20 times larger than the VO 2 metallic resistance R met ; thus, the capacitance charge time is much faster than its discharge (Figure  2D ). Oscillator's dynamics can be described by Kirchoff 's law as: C P dV out (t) dt = V DD (t) -V out (t) G VO 2 (t) - V out (t) R S (4) Note that despite the first order differential equation, oscillations can occur as Equations (1-3) describe the hysteresis behavior of G VO 2 (t). To solve the oscillator dynamics, we start from an initial insulating VO 2 state and solve numerically on Matlab the system of Equations (1-4) by using Euler's method and Newton-Raphson's algorithm for non-linear Equation (3). Figure  2D  shows an example where V out (t = 0) = 0 V, G VO 2 (t = 0) = 1/R ins , and V DD (t = 0) = 2. 5 V.
FUNDING The work from PT, JB, and PV's Labs has been supported by the  Centre National de la Recherche Scientifique (CNRS) ; the  Institut National de la Santé et de le Recherche Médicale National (INSERM) ;  Sorbonne Université, Faculté des Sciences et Ingénierie ;  Université de Bordeaux ;  Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE) ;  Université Côte d'Azur ; the  Agence Nationale pour la Recherche (ANR ;  ANR-15-CE16-001 ;  ANR-18-CE37-0003-02 ;  ANR-10-IDEX-03-02 , and  ANR-16-CE16-0022 ); the  Fondation pour la Recherche Médicale  (FRM;  DEQ20180339159  and  DPA20140629798 );  Institut de Recherche en Santé Publique (IReSP) Aviesan  APP-addiction 2019 ( 20II146-00 );  NARSAD Young Investigator Grants  from the  Brain and Behavior Foundation ; the BioPsy labex excellence cluster; and the  labex BRAIN.
Fetal hemodynamic parameters and blood samples The FHR, MAP, and IAP (IntelliVue MX700; Philips, Eindhoven, Netherlands) were continuously monitored and recorded using a data acquisition board (Physiotrace©; Estaris Monitoring, Lille, France) during the study period  [14]. MAP refers to the amniotic cavity pressure (MAP = observed MAP-observed IAP). We defined the percentage changes in FHR and MAP from baseline were calculated as (time point value-baseline value) × 100 / baseline value and were described as "change in FHR" and "change in MAP", respectively. The baseline is the value obtained just before the occlusion. FHR, MAP, change in FHR, and change in MAP during UCO were recorded with a resolution of 1 second. Moreover, we defined six time points, averaged over 10 seconds, to compare change in FHR and change in MAP to baseline: just before UCO (baseline); then at 10, 30, 60, 120, and 300 seconds after starting UCO.
A Kruskal-Wallis test were used for testing nonparametric data, posthoc analysis was performed with a Dunn's test with Bonferroni correction. A value of p < 0. 05 was considered significantly different. In each experiment, n represents the number of studied protocols. Data are expressed as mean ± standard error. Statistical analyses were performed using SPSS (v26, SPSS, Chicago, IL) and R software version 3. 4. 1 (available online).
METHODOLOGY We performed a pubmed-based literature systematic search (January 1968 -February 2021) using the following search string: ((tryptophan OR kynuren* OR "quinolinic" OR "xanthurenic acid" OR "anthranilic acid") AND ("serum" OR "plasma" OR "blood") AND ("brain tissue" OR "BBB" OR "blood brain barrier" OR "blood-brain-barrier" OR "CSF" OR "cerebrospinal fluid" OR "postmortem" OR "post-mortem" OR "MRI" OR "fMRI" OR "PET" OR "DTI") NOT (review [Publication Type])). Eligible papers were extracted from the PubMed database using the following inclusion criteria: 1) English language articles published in peer-reviewed journals, 2) Human studies including patients with a major psychiatric disorder reporting correlation coefficients between peripheral and central KP metabolites or association measures between at least one peripheral KP metabolite and symptom severity or brain imaging disturbances or treatment outcome, or 3) Human studies including healthy controls or non-psychiatric patients reporting correlation coefficients between peripheral and central KP metabolites, or 4) In-vivo or postmortem assessment of at least one KP metabolite peripherally and centrally.
Experimental Procedure We adopted the face-name PAL paradigm from a previous f MRI study (Figure  1 )  [30]. In the present study, the neutral faces were acquired from the CAS-PEAL Large Scale Chinese Face Database and were presented on the lab computer. There were 4 runs in the present paradigm. In the first two runs, two-word names were presented, with (a) run 1: female face-name pairs and (b) run 2: male face-name pairs. In the last two runs, three-word names were presented, with (a) run 3: female face-name pairs and (b) run 4: male face-name pairs. Each run started with a 15-s baseline, and consisted of 4 blocks presented following the order of encoding (repeated), retrieval (repeated), encoding (novel), and retrieval (novel) face-name pairs. In each block, 7 faces were randomly presented on the white background, each lasting for 4.
Before the experiment, the entire procedure was explained in detail by the investigator and the participants were given specific instructions on how to remember the face-name matches for later testing. During the experiment, the participants were required to sit in front of the computer (45 cm) without extraneous movements for about 24 min and the f NIRS data were collected simultaneously. At the beginning, a white fixation cross (+) was presented in the center of the visual field on a black background to ensure the concentration of participants on paradigm stimuli. During the encoding runs, seven faces with names printed underneath were shown, and the participants were asked to memorize as accurately as possible the face-name pairs presented on the screen. In the retrieval runs, a face without a name appeared on the screen, and the participants were required to report the paired name within the given time (7.
f NIRS Data Acquisition A 40-channel f NIRS system (Danyang Huichuang Medical Equipment Co. Ltd. , China) was used to measure the relative change in oxygenated hemoglobin (HbO). Beta values were computed within the bilateral prefrontal, superior parietal, inferior parietal, and middle occipital cortices. The f NIRS system operated at two wavelengths (750 and 850 nm) and recorded changes at a sample rate of 7 Hz. In total, 16 sources and 15 detectors were used and the optodes were placed in the cap based on the international 10-5 system for EEG electrode placement (Figure  2 ), with a black overcap covering all the optodes to avoid interference from environmental light  (Babak S et al. , 2010). The uniform distance between neighboring optodes was 3 cm. The 3D positions (MNI standard coordinates) of the sources and detectors, as well as the reference optodes, were calculated via a 3D digitizer (PATRIOT, Polhemus), which is based on a four-point positioning algorithm in 3D space relying on received signal strength indication.
f NIRS Data Preprocessing The raw f NIRS data were recorded during the face-name PAL paradigm using the NIRSmart acquisition software and then processed via the NIRSpark analyzing software. During the data preprocessing, the unrelated time intervals and artifacts induced by motion and environment were eliminated (automatic motion correction of NIRSpark analyzing software; the standard deviation of threshold = 6. 0; the amplitude of threshold = 0. 5). The light intensities were converted to optical densities and blood oxygen concentrations through the modified Beer-Lambert law. A bandpass filter (0. 01-0. 1 Hz) was then applied to remove both noise and interference signals (heart rate, breathing rate, and Mayer waves)  [32]. The initial time of the hemodynamic response function (HRF) was set to -2 s (i.
Data Analysis The generalized linear model (GLM) was employed to analyze channel-wise hemodynamic responses induced by the face-name PAL paradigm. The GLM established a canonical HRF for each experiment condition and each participant, and calculate the degree of matching between the experimental and ideal HRF values. GLM models were used to analyze HbO concentration, with the stage (encoding/retrieval), novelty (novel/repeated), and difficult level of face-name pairs (two-word name/three-word name) as independent measures. The beta value represented how much a regressor (e. g. , experimental manipulation) contributed to the f NIRS signal and whether the HRF model was reliable  (Pinti P et al. , 2017). Diffuse optical topography (Figure  3 ) of beta value was generated by Easy-Topo  [33, 34] , a toolbox running through MatLab software.
fNIRS Data Preprocessing The raw fNIRS data were recorded during the face-name PAL paradigm using the NIRSmart acquisition software and then processed via the NIRSpark analyzing software. During the data preprocessing, the unrelated time intervals and artifacts induced by motion and environment were eliminated (automatic motion correction of NIRSpark analyzing software; the standard deviation of threshold = 6. 0; the amplitude of threshold = 0. 5). The light intensities were converted to optical densities and blood oxygen concentrations through the modified Beer-Lambert law. A bandpass filter (0. 01-0. 1 Hz) was then applied to remove both noise and interference signals (heart rate, breathing rate, and Mayer waves)  [32]. The initial time of the hemodynamic response function (HRF) was set to -2 s (i.
Data Analysis The generalized linear model (GLM) was employed to analyze channel-wise hemodynamic responses induced by the face-name PAL paradigm. The GLM established a canonical HRF for each experiment condition and each participant, and calculate the degree of matching between the experimental and ideal HRF values. GLM models were used to analyze HbO concentration, with the stage (encoding/retrieval), novelty (novel/repeated), and difficult level of face-name pairs (two-word name/three-word name) as independent measures. The beta value represented how much a regressor (e. g. , experimental manipulation) contributed to the fNIRS signal and whether the HRF model was reliable  (Pinti P et al. , 2017). Diffuse optical topography (Figure  3 ) of beta value was generated by EasyTopo  [33, 34] , a toolbox running through MatLab software.
Additionally, mean activation of hemodynamic state (HbO concentration) was also computed and exported for postprocessing analyses. In this study, only HbO signals calculated by the modified Beer-Lambert law were used. HbO is less influenced by physiological noise and better reflect changes in the regional cerebral blood oxygenation induced by stimuli when compared to deoxygenated hemoglobin (Hb)  [35]. The path length factor was set at 6 for both 740 nm and 850 nm waves. For the exported data (HbO concentration and beta value), the analysis was performed using IBM SPSS (v. 23. 0). The normality distribution of data was checked by the Shapiro-Wilk test, as recommended for small sample sizes  [36, 37]. Then, the paired-sample t-tests (e. g. , encoding vs. retrieval, repeated pairs vs. novel pairs (novelty), two-word pairs vs.
Results Table  1  shows the significances of HbO concentration among channels in the different task conditions. The channels with significant differences in beta values (baseline vs. task condition) are shown in Table  2. Finally, the visualization of beta values in the encoding and retrieval phases is illustrated in Figure  3. For the exported data (HbO concentration and beta value), the analysis was performed using IBM SPSS (v. 23. 0). The normality distribution of data was checked by the Shapiro-Wilk test, as recommended for small sample sizes  [36, 37]. Then, the paired-sample t-tests (e. g. , encoding vs. retrieval, repeated pairs vs. novel pairs (novelty), two-word pairs vs. three-word pairs (difficulty level)) were used for data of normal distribution, and the Wilcoxon signed-rank tests were used for the data of non-normal distribution, with a significance level set at α = 0.
Data collection Upon enrolment, HCPs completed a paper 'screener' form providing information on their demographic characteristics and clinical experience. Patients also completed a paper demographic form, providing information such as age and sex, while HCPs completed a paper case report form for each patient, providing clinical characteristics such as the patient's primary cancer diagnosis, treatment history and concomitant conditions. These forms were administered by a site nurse, who also completed a visit information sheet to capture information about each visit, such as duration of consultation and whether it was the patient's first encounter with the consulting HCP. Before and after both study visits, participants independently completed various questionnaires (see Supplementary Material 2) selected based on the findings of a detailed literature review of existing instruments assessing communication during consultations and the experience and impact of pain.
We performed a third clinical study to F I G U R E 9 Impact of sodium acetylated hyaluronate at 0. 2% formulated in cream versus placebo on the reduction of coarse area from nasogenian wrinkles after 6 h of application using VISIA® CR2. 3 Analysis. Data are expressed in mean of ∆D0T6h (% ± SEM). Student's t-test was used with *p < 0. 05 demonstrate the efficacy of sodium acetylated hyaluronate at 0. 1% on skin texture. Indeed, the protection of the ECM network through the inhibition of its degradation improves skin structure and consequently wrinkles as previously demonstrated, but also has a smoothing effect. We then analyzed the effects of the active ingredient on skin texture after 2 months of application. We observed that sodium acetylated hyaluronate significantly decreased the roughness by 11% with respect to D0, whereas the placebo showed only a 2% decrease (Figure  11 ).