dataesr/scientific-paragraphs-categorization huggingface dataset:
For the analysis of axon length, the NeuronJ plugin of ImageJ software was used, as previously described  [29]. Briefly, the length of a maximum of 40 axons (mean 24. 1 ± 1. 5 axons) for both normal and underfed was measured for each explants. The global axon growth for each condition was calculated as the mean of the eight quantified explants. The global axon growth for the three treated conditions were normalized against their respective controls (basal condition) for each plate due to the variability of the explant cultures. Since pups were pooled for the explants culture, each plate was considered as one single experiment (n = 1) for statistical analyses, and data are presented as the mean of relative axon growth calculated from various experiments (n = 6-9 experiment depending of the test, the number is given in results and legends).
Underfeeding alters the IGF-I signaling pathway The main elements of the IGF-1R signaling pathway were measured by Western blot analysis, despite the highly limited amount of protein that could be harvested from micro-dissected arcuate nuclei from normally fed or underfed 7 day-old pups. As with explants cultures, micro-dissected arcuate nuclei were harvested from both sexes. Overall, underfed pups had similar IGF-1R protein levels in their arcuate nucleus as age-matched normally fed pups (Fig 5A and 5B right panel). In addition, a similar induction of IGF-1R activation (p-IGF-1R/IGF-1R ratio) was observed after 15 min of IGF-I stimulation in both underfed and normally fed pups relative to the basal condition (Fig 5A  and 5B , left panel). These data suggest that both total IGF-1R levels and its activation were not altered by nutritional restriction.
This study was supported by the  Institut National de la Santeet de la Recherche Medicale (INSERM) , l' UniversitePierre et Marie Curie (Sorbonne Universite  ´s,  UPMC Univ Paris 06), the institute of Cardiometabolism and Nutrition (IHU ICAN), Sandoz-France laboratories , and the Premup foundation. P. M. was supported by an  ANR  grant ( ANR-2010-BLAN-1415-01 ). E. M. was supported by  CDD-Inserm. L. D. was supported by a PhD grant from the  French Ministry for Education and Research Training  and from the  Societe Francaise d'Endocrinologie et Diabètologie Pediatrique (SFEDP, grant Sandoz Biopharmaceuticals France ).
The ongoing coffee crisis in Latin America and the Caribbean highlights the need for reliable assessments of coffee yield loss for governments, banks, and development agencies in order to implement appropriate economic responses  [2]. This crisis has been triggered by a severe outbreak of coffee leaf rust (Hemileia vastarix Berkeley and Broome) combined with the inefficient management of coffee plantations, which have led to a decrease in production estimated between 30% and 50% in some countries, negatively affecting the incomes and food security of families involved directly or indirectly with coffee activities  [2, 15, 16]. The quantification or reliable estimation of yield losses is a major challenge and requires trustworthy methods  [1, 4]. Our research was a first attempt aimed at (i) quantifying primary and secondary coffee yield losses through field experimentation, taking into account a three-year dataset on foliar pests and diseases of coffee, and (ii) identifying the most important predictors of coffee yields and yield losses, through structural equation modeling.
PiecewiseSEM permits testing whether the equations used to explain intermediate and final response variables are independent and a reflection of the true paths involved, for which P-value must be higher than the significance threshold (we chose α = 0. 05) to ensure a good fit of the modeling. PiecewiseSEM also provides the Fisher's C statistic and the likelihood degrees of freedom (K) to calculate the Akaike's information criterion (AIC = C + 2K)  [23]. Given that this modeling approach considers the influence of predictors of the previous year, it was necessary to use data of two consecutive years for each plant. We used data from 2014 and 2015 only, as no information was available for 2011 and 2012, and the number of dead branches was not assessed in 2013. Contrary to previous analysis, no treatments were excluded.
However, since the estimation of yield losses makes sense only if plants have yield components >0, only plants with yield components >0 were used. Models were then constructed with data of only 82 plants out of the original 144. We used linear mixed models to estimate the actual yield for 2015 and to model intermediate predictors influenced by those of the previous year (2014); in all cases, the plot was declared as random effect. The list of models included in PiecewiseSEM was as follows: where Yact is actual coffee yield per plant; sAUDPC is the standardized area under the disease progress curve of pests and diseases (we included the sAUDPC of each pest and disease individually and also the sAUDPC P&D -all pests and diseases together-); DeadB is the number of dead productive branches; (n) represents the year of interest (2015 in this case), and (n-1) represents the previous year (2014).
We ran the PiecewiseSEM on this list in the software R  [19]  with the package 'piecewise-SEM'  [23]. Different combinations of predictors in the models were tested, verifying that the P-value was P >0. 05 to ensure a good fit, and suppressing predictors with no significant effects (P >0. 05) or with very low effects (i. e. , low coefficients <0. 001) in each test. Which means that different lists of models were run until we selected the best list, based on two criteria: the list that showed the lowest values of AIC and the highest values of R 2 provided by the Piecewise-SEM. Finally, based on the significance and on the coefficient of each predictor involved in the best list, also provided by the PiecewiseSEM procedure, we identified the most important predictors of yields, and of primary and secondary yield losses.
In all these cases, yield losses were assessed in the current year, and therefore we assumed that they were primary yield losses. The ranges of these primary yield losses are wide, depending on the type of crop/variety and pests and diseases. In our study, we provided new quantitative data indicating that primary yield losses on a perennial crop such as coffee can reach 26% due to a set of foliar pests and diseases. To our knowledge, our study was the first attempt to quantify secondary yield losses, which could have important implications for perennial crops if they are as high as the one we estimated for coffee (38%). In accordance with the hypothesis proposed by Avelino and colleagues  [2, [30] [31] [32] , our study confirms that the attacks of foliar pests and diseases have delayed impacts in coffee.
Measurements can be easily done at three critical times: dead productive branches just after the current harvest, fruiting nodes after the next flowering, and finally, the dead productive branches at the end of the next harvest. The data needed to estimate primary and secondary yield losses can therefore be obtained in the lapse of one year. These predictors could be included in linear mixed models to estimate actual yields, using random effects to consider the influence of local production factors. The random effects can improve the estimations, especially when working in large areas (with diverse production factors). Once the equations are obtained, the predictors representing reduction factors (dead productive branches in this case) can be set as "zero" to estimate attainable yields, and then calculate yield losses (attainable yield-actual yield).
7 software (Molecular Devices, Sunnyvale, CA, USA), using the Scan-Slide option (10% overlap). To identify IL-7 in the infected mucosa, immunostaining experiments were performed using polyclonal antibodies directed against IL-7, cytokeratin, and ezrin (polyclonal antiserum obtained from P. Mangeat; CNRS UMR 5539, Montpellier, France). Acquisitions were performed with 20× objective on a DMI6000B Leica microscope with MetaMorph 7. 7 software. The antibodies used for immunohistofluorescence labeling are listed in Table  S4  in Supplementary Material.
Quantitative image analysis Analyses were performed using the ImageJ software. 1 In order to accurately quantify nuclei (DAPI), CD3 + cells (Alexa Fluor ® 546), or CD4 + , or CD8 + cells (Alexa Fluor ® 488), images were acquired over the entire thickness of the sample with 13-20 z-stack images and the best focuses were chosen. In order to count immune cells in large areas, the LP, submucosa (SM), and regions containing epithelial cells (E) or lymphoid follicles (LF) were manually defined. Pixels corresponding to E + LF zones were excluded from the process. These excluded zones were replicated on all treated images. After a denoising process, a manual threshold was applied on CD3 + , CD4 + , and CD8 + stains to identify immune cell surfaces. CD3 -CD4 + and CD3 -CD8 + stained surfaces, as well as CD3 + CD4 + and CD3 + CD8 + costained surfaces, were quantified.
statistical analysis The non-parametric Spearman rank correlation test was used to investigate the relationship between parameters. The nonparametric Mann-Whitney test was used to compare different groups of macaques, and the non-parametric Wilcoxon rank sum test was used to compare data from the same macaques at different time points before and after SIV infection. All statistical analyses were performed with Real Statistics 2 add-in to Microsoft Excel software. In two-tailed tests, p values of 0. 05 or lower were considered significant.
resUlTs local il-7 Overexpression coincides with siV infection in the small intestine Intestinal tissue and LNs were sampled from five healthy and nine SIV-infected Chinese rhesus macaques sacrificed at days 3 (n = 3), 7 (n = 2), 10 (n = 2), or 14 (n = 2) following intravenous infection with 50 AID50 of SIVmac251. In these tissues, we first evaluated IL-7 expression by measuring mRNA levels using a highly sensitive qRT-PCR assay. Interestingly, IL-7 transcription significantly rose by day 3 in the ileum. Maximal IL-7 transcription was observed at day 7-10 post-infection (pi) (p = 0. 009; Figure  1A ). IL-7 expression returned close to baseline values at day 14. IL-7 mRNA levels were also increased by day 3 in the duodenum and the jejunum, slightly increased at day 10 in the large bowel and remained stable in the LNs, suggesting that the increase in IL-7 transcription was mostly confined to the small bowel in acutely SIV-infected macaques (data not shown).
Finally, memory CD4 + T-cell cycling was detectable at day 17 pi (Figure  7B ). In the same samples, we evaluated cell surface expression of CCR6 (CCL20 receptor) and CCR9 (CCL25 receptor), both of which are involved in intestinal T-cell homing. We also assessed the cell surface expression of β7 integrin, which, in association with α4 integrin, acts as a specific docking molecule to gut endothelial cells. While the frequency of CCR6 + β7 + cells remained constant in all T-cell subsets, we observed a gradual decrease in the frequency of CCR9 + β7 + cells throughout the follow-up period, leading to a twofold to fourfold reduction at day 14 (Figure  7C ). Similarly, a twofold reduction in the frequency of CCR9 + cells within β7 + CD4 + and CD8 + T-cells was observed at day 14 (data not shown).
Acknowledgments: We thank the Fondation pour la recherche médicale (FRM; www. frm. org;équipe  FRM 2011 DEQ20110421299 ) and the  National French Agency for Research against HIV and Hepatitis Viruses  (ANRS; http://www. anrs. fr/; AAP 2014-2 ), which supported the work with the salary of LG. Author Contributions: Both authors wrote the manuscript and shared the bibliography.
Stimuli and experimental procedure All the EEG markers described here were derived from the same multidimensional protocol, acquired within a single EEG session lasting 1. 5 h. Auditory stimuli were presented via headphones. Each trial consisted of a cue followed by a target with a fixed stimulus onset asynchrony (SOA) of 550 ms (Fig. 1A ). The target was a brief tone composed of three superimposed sinusoidal tones (500, 1000 and 2000 Hz), lasting 50 ms with a 7 ms rise and a 7 ms fall time. There were three randomly interleaved cue types: the participant or patient's own first name, another first name, and a non-vocal control matched to the participant's own name. The participants' own names and the other names were all digitally recorded with the same female voice in the same "calling" intonation, using the software Audacity.
For each participant, the "other name" was matched in gender, number of syllables (1 2 or 3) and duration to the participant's own name, but with no similar syllables. Names from close relatives or friends were excluded. Each non-vocal control was created in Matlab by modulating a characteristic frequency of the corresponding own name (mean frequency of the spectrum) and two of its harmonics with the amplitude envelope of the own name stimulus. In this way, the non-vocal stimulus preserved some physical characteristics of the own name but did not sound like a voice  (Holeckova et al. , 2006). For each participant, the three cues were matched in peak amplitude and duration, and lasted between 400 ms and 500 ms across participants. Each participant performed 3 blocks with a predictive context (75% of congruent trials) and 3 blocks with non-predictive context (50% of congruent trials).
EEG Acquisition and Preprocessing We recorded high-density EEG sampled at 250 Hz and referenced to the vertex using a 257-channel system (Electrical Geodesics Inc. , Oregon, USA). Preprocessing was performed using the Fieldtrip software  (Oostenveld et al. , 2011)  and custom scripts in Matlab. Continuous data were band-pass filtered between 0. 1 and 20 Hz for control subjects, and between 0. 5 and 20 Hz for patients, because patients showed important low-frequency noise. Data were then segmented from 750 ms before to 1000 ms after each target "beep". In a first pass of semi-automatic artifact rejection, trials and channels whose variance across a session exceeded a certain threshold were rejected, using the Fieldtrip artifact rejection tool (3000 μV 2 for control subjects, 5000 μV 2 for patients, corresponding to standard deviations of 55 μV and 71 μV respectively).
Individual statistics of the GFP For each experimental contrast, the presence of an effect was tested on the Global Field Power (GFP) which is the root summed square of the voltage of all electrodes at each time point  (Lehmann and Skrandies, 1980). The GFP of the contrast between two conditions provides a compact summary of the time course of an effect. GFPs were corrected for a baseline period of 200 ms before cue onset. To test for the significance of an effect in GFP, we performed twosteps statistics: at the level of individual samples, and then at the level of temporal clusters, based on the non-parametric cluster-level statistics developed in the fieldtrip software  (Maris and Oostenveld, 2007)  adapted to temporal clusters  (Chennu et al. , 2013). At the first level we estimated the p-value of the effect at each time point using Monte Carlo permutations (1000 randomizations).
Acknowledgments We thank  Dominique Morlet  and  Sebastien Marti  for methodological support and advice. We thank  Viola Störmer ,  Paolo Bartolomeo ,  Sebastien Marti ,  Athena Demertzi  and two anonymous reviewers for useful comments. This study was supported by a  Marie Curie Reintegration Grant  to  Claire Sergent  ( FP7-PEO-PLE-ERG-2008 ), the  ANR  grant " CogniComa " ( ANR-14-CE-0013-03 ), the  Fondation pour la Recherche Médicale  ('Equipe  FRM 2015' grant ),  the program 'Investissements d'avenir'   ANR-10-IAIHU-06 , the  ANR   14-CE15-0013-04 , and the ' Recovery of consciousness after severe brain injury Phase II ' grant of the James S. McDonnell Foundation.
Molecular-based allergy diagnosis yields multiple biomarker datasets. The classical diagnostic score for allergic bronchopulmonary aspergillosis (ABPA), a severe disease usually occurring in asthmatic patients and people with cystic fibrosis, comprises succinct immunological criteria formulated in 1977: total IgE, anti-Aspergillus fumigatus (Af) IgE, anti-Af "precipitins," and anti-Af IgG. Progress achieved over the last four decades led to multiple IgE and IgG(4) Af biomarkers available with quantitative, standardized, molecular-level reports. These newly available biomarkers have not been included in the current diagnostic criteria, either individually or in algorithms, despite persistent underdiagnosis of ABPA. Large numbers of individual biomarkers may hinder their use in clinical practice.
MAteriALs AND MetHODs The statistical analysis was performed retrospectively using flowcharts and laboratory data from 39 ABPA-free CF patients with detectable sIgE to Af extract (Af-CF group) and 10 ABPA patients (7 asthmatics, 3 CF; 2 children) from the Adult and Pediatric Regional Centers for Cystic Fibrosis (RCCF) and the Pulmonology Departments of Marseille, Southern France. Patient demography and detailed sIgE and sIgG4 data were previously described  (17). Data from one ABPA patient (CF background, 12-year-old male) diagnosed with ABPA in July 2016 were included. sIgE, sIgG, and sIgG4 for Af extract and recombinant allergens Asp f 1, Asp f 2, Asp f 3, Asp f 4, and Asp f 6 were measured with Thermo Fisher ImmunoCAP (Uppsala, Sweden). The detection thresholds were 0. 01 mgA/L for sIgG and sIgG4 and 0.
ethics statement Recombinant Af allergen sIgE and sIgG (4) determination was part of the regular medical care since their commercial release. Patients received written laboratory workup reports. The study was based on a retrospective, non-interventional review of medical charts and laboratory results. According to the French law  (18, 19) , ethical approval and patient consent were not necessary for this type of study, while patients were informed and retained the right to oppose the use of their anonymized medical data for research purposes.
stAtisticAL ANALYsis First, we performed a hierarchical classification on principal components, as previously described  (20, 21). All the variables (biomarker measures) were included in this analysis and were equally weighted. A principal component analysis (PCA) was the first preprocessing step to explore the biomarker on the mixed data set, which takes into consideration relationships between biomarkers. Then, the coordinates of each variable in the first 20 principal components, which summarize 95% of the information, were used to perform a hierarchical ascendant classification (HAC). This method provides classes according to the immunological profile using an objective non-supervised classification technique that allows classifying independently from diagnosis. Furthermore, using the first 20 principal components is a way of denoising the data, thus yielding a more robust classification  (21).
resULts The PCA-HAC approach identified three clusters within the population, with a homogenous cluster 1 comprising 34/39 Af-CF  patients, cluster 2 comprising 6/10 ABPA patients, and cluster 3 including both ABPA and Af-CF patients (Figure  1 ). Classification and regression tree multivariate analysis method applied to current biomarkers of ABPA (total IgE, sIgE to Af, ELISA IgG to Af, and Af precipitins) resulted in a classification based on three parameters only: total IgE, ELISA IgG, and sIgE. Together, these three results allowed correct classification of 38/39 Af-CF patients and 3/10 ABPA patients. The remaining seven ABPA patients were correctly classified with a probability of 88% (Figure  S1  in Supplementary Material). Classification and regression tree was sequentially performed on datasets of either sIgE, IgG or IgG4 responses to Af molecules Asp f 1, Asp f 2, Asp f 3, Asp f 4, and Asp f 6.
The molecular sIgE dataset allowed proper classification of 35/39 Af-CF patients and 8/10 ABPA, the latter with an 89% probability (Figure  S2  in Supplementary Material). Datasets of either IgG or IgG4 responses to molecules Asp f 1, Asp f 2, Asp f 3, Asp f 4, and Asp f 6 resulted in 70-80% of incorrect classification of ABPA patients (not shown), in line with previous IgG4 results  (17). Finally, the classification tree built using the three molecular datasets together (sIgE, sIgG, and sIgG4) performed better, and adequately classified 35/39 Af-CF and 7/10 ABPA patients. The number of equivocally classified Af-CF or ABPA patients was reduced to 4 and 3, respectively (Figure  2 ).
etHics stAteMeNt Recombinant Af allergen sIgE and sIgG (4) determination was part of the regular medical care since their commercial release. Patients received written laboratory workup reports. The study was based on a retrospective, non-interventional review of medical charts and laboratory results. According to the French law  (18, 19) , ethical approval and patient consent were not necessary for this type of study, while patients were informed and retained the right to oppose the use of their anonymized medical data for research purposes.
MRI Acquisition and Analysis MRI data were acquired at the Oxford Centre for Functional MRI of the Brain (FMRIB) using a 3-Tesla, Siemens Magnetom Verio (Erlangen, Germany) scanner with 32-channel head coil. DTI scans were acquired with an echo planar imaging sequence (60 diffusion weighted directions, b-value 1500s/ mm 2 ; five non-diffusion weighted images, b-value 0s/mm 2 , with one b0 volume acquired in the reversed phase encoded direction) with repetition time 8900ms, echo time 91. 2 ms, field of view 192 mm, and voxel dimensions 2. 0 mm isotropic. The susceptibility induced off-resonance field was estimated from a pair of b0 scans using the FSL tool topup  [Andersson et al. , 2003]. This information was fed into the FSL tool eddy, where data was corrected for subject movement and eddy current-induced distortions  [Andersson and Sotiropoulos, 2016]  and for movement induced signal voids (outliers)  [Andersson et al.
, 2016]. BIANCA classifies the image's voxels based on their intensity and spatial features, where the intensity features were extracted from FLAIR, T1 and FA images (additional options used: local average intensity within a 3D kernel of size 5 3 voxels; MNI coordinates as spatial features, with a weighting factor of 2; 24 manually segmented images as training dataset). The output image represents the probability per voxel of being WMH. The total WMH volume was calculated from the voxels exceeding a probability of 0. 9 of being WMH and located within a white matter mask, adjusted for the total intracranial volume. All image analysis was performed using tools from the FMRIB Software Library (FSL, version 5. 0; http://www. fmrib. ox. ac. uk/fsl). Voxel-wise analysis of DTI data was carried out using Tract Based Spatial Statistics (TBSS)  [Smith et al.
RESULTS Of 534 participants recruited for the Whitehall II Imaging Sub-Study (2012-2014), 448 were included in analyses of current sleep quality, and 398 in analyses examining the persistence of sleep quality. Attrition of participants is provided in Supporting Information Figure e-1. Participants excluded because of missing data were not different in age, sex, education or MoCA score, compared with participants with full datasets (Supporting Information Table e-I). One hundred and forty-seven participants (33%) had PSQI scores 6 at the time of the MRI assessment and were classified as reporting poor sleep quality (Table  I , Supporting Information Fig. e-2 ). Poor and good sleep quality groups were not significantly different in age or education, but the poor sleep quality group contained a greater proportion of female participants.