dataesr/scientific-paragraphs-categorization huggingface dataset:
Longitudinal study The 1-year follow-up was assessed using A β42/A β40 ratio at M12 and A β-PET imaging at M24. Bootstrapped PLS-VIP analysis selected 25 of the 84 regions ( Table  3  ). These regions were mainly located in the frontal cortex and the cingulate cortex, including the bilateral insula. Likewise, the 2-year follow-up investigation on A β42/A β40 ratio at M0 and A β-PET imaging at M24 selected 19 of the 84 regions ( Table  4  ) mainly located in the frontal cortex and the cingulate cortex including right insula. Results showed a left hemisphere dominance for the 1-year follow-up analysis but not the 2-year follow-up (see Supplementary material for more details). On the testing dataset, we observed a significant negative association between A β42/A β40 ratio and amyloid-PET in all the regions selected over a 1-year follow-up ( Table  3  ) and a 2-year follow-up ( Table  4  ).
Cross-sectional study Measures of cortical FDG-PET showed signifcant pairwise correlations with cortical regions ( Fig. 1 B ). The minimum regional pair- wise correlation was 0. 50, the maximum was 0. 98, and the median 0. 82. Bootstrapped PLS-VIP selected 16 regions over 84 ( Table  5  ). These regions were mainly located in the temporal cortex. Investigation on the testing dataset showed that A β42/A β40 ratio was not significantly associated with FDG-PET measure in any of these regions ( Table  5  ).
Longitudinal study Bootstrapped PLS-VIP investigation on FDG-PET imaging at M24 identified 21 regions associated with baseline A β42/A β40 ratio and 16 regions with M12 A β42/A β40 ratio. Investigation on the testing dataset showed that A β42/A β40 ratio was not significantly associated with regional FDG-PET measure over a 1-year ( Table  S3  ) or a 2-year follow-up ( Table  S4  ).
, 2020 ; Dubois et al. , 2018 ). In summary, our DMN-related divergent results and believe that this evidence-based conceptual construct can also explain the apparent DMN-related inconsistent association over time-points in our study. We have based our hypothesis on the presence of cognitive reserve-related resiliency dynamics in our cohort dataset by capitalizing on extensive evidence and data-driven conceptual frameworks previously published  ( Babiloni et al. , 2020 ; Dubois et al. , 2018 ; Gaubert et al. , 2019 ; Hohman et al. , 2016 ; Negash et al. , 2013 ). In this conceptual framework lies the potential clinical value of our proposed study design. In fact, the PLS-based whole-brain approach could be used not only for investigating network dysfunction in AD trajectories but also resilience in individuals displaying incipient molecular signatures of AD or any other neurodegenerative disease.
The PLS-VIP method main advantages for AD clinical research We believe that our workplan is easily replicable and shows good intrinsic operability. Of note, we used a set of variables with known covariance to test the performance of the modeling approach. The true potential of our approach is for exploratory purposes besides validation studies. PLS can be considered as a generalization of multiple regression since, for identical problem formulations, they both produce similar answers  ( Cramer, 1993 ). Unlike multiple regression, PLS can handle extensive datasets that may contain more predictors than subjects  ( Cramer, 1993 ; Krishnan et al. , 2011 ). In addition, PLS is a very versatile method that provides relevant tools for different settings of clinical investigation. For instance, in AD clinical research, PLS was used for diagnostic classification tasks  ( Aguilar et al.
Study limitations We acknowledge that different atlases, including cytoarchitectonic and probabilistic maps, have been published lately and some of them are expected to perform better, especially for those regions with no clear macro-anatomical landmarks. This methodological limitation explains why we did not include subcortical regions in the present study. The present study does not include a long-term neuropsychological clinical follow-up of participants, which represents a limitation. Moreover, although education is widely considered a component of the cognitive reserve we thought not to overload our PLS-based approach and region models with too many covariates, considering that the dataset is not large enough. To expand on the clinical potential of our approach, we set out to extend the multi-modal biomarker and neuropsychological follow-up of the individuals involved in the present study.
In addition, we aim at expanding the dataset through a multicentric study where we would use PLS to explore trajectories of decline versus resilience, also keeping education levels into model. This future study would include an independent validation cohort, the lack of which represents caveat itself. To compensate this issue, we carried out several additional analytical steps (training/test datasets, bootstrap, correction for multiple tests, and internal reproduction at different timepoints) to increase confidence in the findings. We also acknowledge the lack of a test cohort of MCI and dementia individuals with AD pathophysiology as a major caveat of our study since it may have supported data interpretation from a biological standpoint and to draw some clinical conclusions. In this sense, we set out to perform a large-scale longitudinal multicenter study including at least one discovery and one independent validation cohort spanning the entire AD continuum, and including SMC and MCI either converters or stable.
Acknowledgments The research and this manuscript was part of the translational research program "PHOENIX ", awarded to HH , and administered by the  Sorbonne University Foundation  and sponsored by la  Fondation pour la Recherche sur Alzheimer. The study was promoted by  IN-SERM  in collaboration with  ICM, IHU-A-ICM  and  Pfizer  and has received support within the  "Investissement d'Avenir"  ( ANR-10-AIHU-06 )  French program. The study was promoted in collaboration with the " CHU de Bordeaux " (coordination CIC  EC7 ), the promoter of Memento cohort, funded by the  Foundation Plan-Alzheimer. The study was further supported by  AVID/Lilly. CATI is a French neuroimaging platform funded by the  French Plan Alzheimer  (available at http://cati-neuroimaging. com ). HH is an employee of  Eisai Inc.
5% (v/v) at RT for 30 minutes. After incubation with primary antibodies for 1 or 2 overnights (4°C) and PBS washes, sections were incubated with the appropriate secondary antibodies for 2 hours (RT): Alexa Fluor 488 goat anti-rat for BrdU, 1:1000; Alexa Fluor 568 donkey anti-goat for DCX, 1:1000; Alexa Fluor 594 donkey anti-rabbit for GFAP, Olig2 and NeuN, 1:1000; Thermo Fisher Scientific). Cell nuclei were stained with 4′,6-diamidino-2-phenylindole (1:1000; Sigma Aldrich), and slides were mounted with PermaFluor Aqueous Mounting Medium (TA-006-FM; Lab Vision, Thermo Fisher Scientific). For cell quantification, confocal images (×40) of SVZ and OB were obtained by the confocal microscope Olympus FluoView FV1000 (Olympus). SVZ and OB images were analysed using the Olympus FluoViewTM FV1000 software.
| Ultrasonic vocalizations Measurement of ultrasonic vocalizations (USVs) was performed as previously described with some modifications. 45 Briefly, each animal was placed in a cage for 24 hours. Then, the animal was in close proximity with a female animal, and USVs were recorded for 15 minutes using the Avisoft-Recorder (version 5. 1. 04) and manually analysed with AvisoftSAS Lab Pro (version 5. 1. 22, Avisoft Bioacoustics).
IS and LP are holders of Portuguese Foundation for Science and Technology (FCT) grants (CEECIND/02231/2018 for IS; IF/01079/2014 for LP). CD is a recipient of PhD fellowship of PhDOC program. LP wants to acknowledge the financial support from the Nature Research Award for Driving Global Impact -2019 Brain Sciences. This work has been funded by National funds, through the Foundation for Science and Technology (FCT) -project UIDB/50026/2020 and UIDP/50026/2020; and by the.
Images were acquired with either a Leica SP5 or SP8 confocal microscope. For VTA cell counting, every second section (containing VTA) was counted by stereology using Stereo Investigator software (MBF Bioscience Inc. ) at a 40X magnification. Amygdala were imaged at 20X and the density of fiber staining was assessed by the mean grey value option with ImageJ software. The infralimbic mPFC were imaged at 20X and 40X and cell number and staining intensity was measured with ImageJ and in-house macros.
ACKNOWLEDGEMENTS AND DISCLOSURES We would like to thank  Chantal Dubreuil ,  Jessica Apulei , and  Lucía Jiménez Alonso  for technical assistance. Funding was provided by  Fondation Bettencourt Schueller ,  European Research Council Advanced  Grant (HOMEOSIGN,  ERC-2013-ADG-339379  to A. P. ), and  Agence Nationale de la Recherche  Grant ( ANR-11-BLAN-069467  to A. P. ). A. P. is cofounder of BrainEver for development of homeoprotein-based therapeutics. The other authors report no biomedical financial interests or potential conflicts of interest.
This requires multicentric studies on well-characterized clinical cohorts. Extensive multicentric databases, which include healthy subjects as well as patients, will be needed to collect enough data to accurately perform these types of analyses. To conclude, SPECT and PET molecular imaging is currently at a turning point and is consolidating its position in the diagnostic strategy of parkinsonism. The development of novel targeted radiopharmaceuticals, the improvement in performance of technical imaging systems, and the rapid evolution of new image analysis approaches improve the diagnosis of neurodegenerative parkinsonism. These innovations may potentially lead to more accurate disease detection, enabling earlier diagnosis and treatment.
Within this context, quantitative models of disease progression, Disease progression Models referred to as DPMs, have been proposed  (Fonteijn et al. , 2012; Jedynak et al. , 2012; Nader et al. , 2020; Oxtoby et al. , 2017; Schiratti et al. , 2015) , to quantify the dynamics of the changes affecting the brain during the whole disease span. These models rely on the statistical analysis of large datasets of different data modalities, such as clinical scores, or brain imaging measures derived from MRI, Amyloid-and Fluorodeoxyglucose-PET  (Bilgel et al. , 2015; Burnham et al. , 2020; Donohue et al. , 2014; Y Iturria-Medina et al. , 2016; Koval et al. , 2018). In general, DPMs estimate a long-term disease evolution from the joint analysis of multivariate time-series acquired on a short-term time-scale.
To this end, we model the joint spatio-temporal variation of different modalities along the history of Alzheimer's disease by identifying a system of ODEs governing the pathological progression. This latent ODEs system is specified within an interpretable lowdimensional space relating multi-modal information, and combines clinically-inspired constraints with unknown interactions that we wish to estimate. The interpretability of the relationships in the latent space is ensured by mapping each data modality to a specific latent coordinate. The model is formulated within a Bayesian framework, where the latent representation and dynamics are efficiently estimated through stochastic variational inference. To generate hypothetical scenarios of amyloid lowering interventions, we apply our approach to multi-modal imaging and clinical data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
, 2018). The length of follow-up varies between 0 and 16 years. Further information about the data are available on https://adni. bitbucket. io/reference/, while details on data acquisition and processing are provided in Section Data acquisition and preprocessing. We show in Table  1A  socio-demographic information for the training cohort across the different clinical groups. Table  1B  shows baseline values and annual rates of change across clinical groups for amyloid burden (average normalized AV45 uptake in frontal cortex, anterior cingulate, precuneus and parietal cortex), glucose metabolism (average normalized FDG uptake in frontal cortex, anterior cingulate, precuneus and parietal cortex), for hippocampal and medial temporal lobe volumes, and for the cognitive ability as measured by ADAS11.
Data acquisition and preprocessing Data used in the preparation of this article were obtained from the ADNI database. The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. For up-to-date information, see www. adni-info. org. We considered four types of biomarkers, related to clinical scores, gray matter atrophy, amyloid load and glucose metabolism, and respectively denoted by cli, atr, amy and met. MRI images were processed following the longitudinal pipeline of Freesurfer  (Reuter et al. , 2012) , to obtain gray matter volumes in a standard anatomical space. AV45-PET and FDG-PET images were aligned to the closest MRI in time and normalized to the cerebellum uptake. Regional gray matter density, amyloid load and glucose metabolism were extracted from the Desikan-Killiany parcellation  (Desikan et al.
, 2019). For instance, pathologists have shown tau deposition in brainstem nuclei in adolescents and children  (Kaufman et al. , 2018) , and clinicians are currently investigating the pathological effect of early tau spreading on Alzheimer's disease progression  (Pontecorvo et al. , 2019) , raising crucial questions about its relationship with amyloid accumulation, and the impact on cognitive impairment  (Cummings, Blennow, et al. , 2019). In this study, 190 subjects underwent at least one Tau-PET scan. However, when considering the subjects for whom there exists one visit in which all the data modalities were available, the number of patients in the study cohort decreased to 33. This low sample size prevented us from estimating reliable trajectories for this biomarker. It is also important to note that among the 190 subjects with at least one Tau-PET scan, only 19 of them had one follow-up visit.
Funding This work has been supported by the  French government , through the  UCA JEDI  and  3IA Côte d'Azur Investments in the Future  project managed by the  National Research Agency  (ref. nANR- 15-IDEX-01  and  ANR-19-P3IA-0002 ), the grant  AAP Santé 06 2017-260 DGA-DSH , and by the  INRIA Sophia-Antipolis-Méditerranée , "NEF" computation cluster.
Materials and Methods Participants 19 right-handed participants (8 males; age = 28 years, SD = 6) took part in the study. All had normal or corrected-to-normal vision, were under no medical treatment, no known neurological history, and were naive as to the purpose of the study. Each participant provided a written informed consent in accordance with the Declaration of Helsinki (  2013 ) and the Ethics Committee on Human Research at NeuroSpin (Gif-sur-Yvette, France). Six subjects were excluded a priori from the analysis due to the failure of the digitization procedure (1 participant), data which were too noisy (1 participant), three subjects did not reach the 60% performance criterion in the control task (for one or two conditions) and one subject did not have enough trials in one condition.
Statistical analyses were conducted with R suite (R core team, 2013). A two-way repeatedmeasure ANOVA was conducted on the probability of perceiving the second flash in intermediary position with factors of condition (2: test, control) and sequence direction (2: inward, outward). A twoway repeated-measure ANOVA was also conducted on reaction times with factors of perceptual report (3: illusion, no-illusion, control) and sequence direction (2: inward, outward). Reaction times were computed from the onset of the last flash in the sequence. Post-hoc Bonferroni-corrected paired t-tests were computed as well as the Bayes factor for paired t-tests (BayesFactor package with R).
Combined MEG-EEG and anatomical MRI data acquisition Electromagnetic brain activity was simultaneously with EEG and MEG and recorded in a magnetically shielded room at a 1000 Hz sampling frequency with a high-pass filter of 0. 3 Hz. A wholehead Elekta Neuromag Vector View 306 MEG system (Neuromag Elekta LTD, Helsinki) in upright position and equipped with 102 triple sensor elements (one magnetometer and two orthogonal planar gradiometers) and an Easycap EEG cap with 60 sensors were used. Horizontal, vertical electrooculogram (EOG) and electrocardiogram (ECG) were recorded during the session. The reference electrode for EEG was placed on the top of the participant's nose. Participants' head position was measured before each block with four head position coils (HPI) placed over the frontal and mastoid areas.
MEG-EEG data preprocessing Signal space separation (SSS) was applied to decrease the impact of external noise  (Taulu & Kajola, 2005). SSS correction, head movement compensation, and bad channel interpolation were done using MaxFilter Software (Elekta Neuromag) after visual inspection of the raw data. The anatomical brain data were imported with BrainVisa software, segmented with the FreeSurfer image analysis suite (http://surfer. nmr. mgh. harvard. edu/), and then used to generate the 3-layers boundary elements model (BEM) surfaces needed to constrain the forward model. Structural MRI and MEG/EEG data were realigned during the co-registration step with mrilab (http://mrilab. sourceforge. net) and MNE-Python suite  (Gramfort, et al. , 2014). Finally, ocular and cardiac artefacts were corrected by rejecting ICA components computed separately for EEG and MEG data, which correlated with ECG/EOG events.
Epoching The MEG-EEG data analysis was performed with MNE-Python  (Gramfort, et al. , 2014) , Matlab vR2017a (MathWorks) and the Fieldtrip toolbox  (Oostenveld, Fries, Maris, & Schoffelen, 2011). Trials whose reaction time was inferior to 200ms, or superior to 4s, from the offset of the second stimulus, were rejected (~2% of rejection rate). The trials were split following the perceptual outcome: in test trials, participants could either perceive the illusion (illusion condition) or perceive the correct stimuli (noillusion condition); in control trials, only the correctly perceived trials were considered. The data were band-pass filtered between 0. 8 and 35 Hz and down sampled to 100 Hz. Epochs were locked on the first stimulus onset, span from -0. 3 s to +1. 2 s post-stimulus, and no baseline correction was applied.
Phase analysis We investigated whether the phase of pre-stimulus alpha activity influenced the perception of a test trial as illusory or veridical, by using the Phase Opposition Sum (POS) index  (VanRullen, 2016). This index is based on the inter-trial phase coherences measured over trials of each condition (ITC ILL and ITC noILL ) and over all trials (ITC all ): POS = ITC ILL + ITC noILL -2. ITC all. To investigate the statistical significance of the POS index, we used a permutation procedure (VanRullen, 2016) on the pre-stimulus period (-600 to -200 ms). We first built a null distribution for each individual by shuffling the trial labels and re-computing the associated POS over all sensors and time points (n = 1000). We then built a null distribution at the group level by randomly picking one sample per individual from the previous distributions, averaging the POS data points over individuals and repeating these operations (n = 10 000).
Study population Sepsis patients (SP) were prospectively enrolled as part of a larger randomized controlled clinical trial assessing the immunomodulatory effect of clarithromycin in sepsis, with an exploratory component (INCLASS study, NCT03345992). Patients were recruited from three intensive care units (ICUs; 2nd Department of Critical Care Medicine, ATTIKON University Hospital, Intensive Care Unit; LAIKO General Hospital and Intensive Care Unit, KORGIALENEIO-BENAKEIO General Hospital; and the 4th Department of Internal Medicine, ATTIKON University Hospital in Athens, Greece). The study was approved by the Greek national ethics committee (approval 52,086/2017 and amended 62,985/2019) and the National Organization for medicines (approval 51,239/01-06-2017 and amended on 26-06-2019). Before enrollment, all subjects or their legal representatives signed and dated the informed consent form.
4 mg/kg equivalent prednisone daily over the last 15 days or other immunosuppressive therapy, known active neoplasms or other conditions unrelated to sepsis that compromised short-term survival (1 month), neutropenia <1000/mm3, known allergy to macrolides, previous participation in the study, and administration of a macrolide for the current infection. Data collected upon enrollment included demographic characteristics (age, sex) and site of primary infection; the initial severity was assessed by the acute physiology and chronic health disease classification system II (APACHE II; range: 0-71), as well as the SOFA score (range: 0-24)  [28]  at 24 h. The 28-day mortality and occurrence of secondary infections during ICU stay were also assessed. For the present study, blood sampling was done once upon enrolment, before allocation to randomized treatment.
Gene expression analysis Briefly, after on-column mRNA extraction, expression was evaluated using a custom 93-gene panel, comprising genes related to the immune host response (Supplementary Table  S1 ) using the Nanostring technology. Further details on molecular detection are provided in Supplementary Methods. Data treatment and normalization was realized with nSolver analysis software (version 4. 0, NanoString technologies) as previously described  [24]  (Supplementary Methods). In order to compare the effect of the stimulation among immunocompetent and septic populations, results are expressed in counts (analysis within a population) and in fold change induction (ratio of stimulation condition -either LPS or MPLA-to control condition -NUL).
Statistical analysis Results are expressed as median and interquartile ranges [IQR] for continuous variables. Comparisons were made using parametric (t-test) and non-parametric t-test (Mann-Whitney or Wilcoxon matched-pairs signed rank test), as appropriate. The false discovery rate method was used to adjust p-values in gene analysis. The Spearman test was used for correlation analysis. Statistical analyses were conducted using Graph-Pad Prism® software (version 6; GraphPad software, La Jolla, CA, USA) and R (version 3. 5. 1). A p-value ≤0. 05 was considered as statistically significant. All p-values are two-sided. Principal component analysis (PCA) was carried out using Genomics Suite 7 (Partek, St. Louis, MO, USA).
Acknowledgements We thank  Maria Roumpoutsou ,  Ioannis Mitrou ,  Athanasios Karageorgos ,  Nikolaos Antonakos ,  Georgia Damoraki ,  Aikaterini Pistiki ,  Theologia Gkavogianni ,  Christelle Compagnon  and  Lucille Boisselier  for their help in patient recruitment and lab assistance. We thank  Isabelle Mosnier  for her technical assistance on NanoString molecular biology. We thank  Philip Robinson  for helpful reviewing of the manuscript.
Finally, we obtain lots of data for each subject (150 trials for each of the 14 conditions). Altogether, we are able to obtain unique "gap-metric" functions, the ERPcomponent equivalent of a psychometric function for 3 major aspects of N1 morphology: N1 latency, N1 single-trial jitter, and topographical similarity to the N170. In addition, reaction times measured across gap conditions also allows us to assess the functional consequences of gap-related waveform changes. Our experimental design allows us to isolate the effect of a single masking stimulus on variability in N1 morphology. All these allow us to answer the first question and part of the second question. Considering that applying only one forward mask per trial is not typical in the majority of RP studies (but see:  Dien, Brian, Molfese & Gold, 2013) , to completely answer the second question, typical RP was also measured with the RSS paradigm as a control condition and compared with all the other 14 conditions.