dataesr/scientific-paragraphs-categorization huggingface dataset:
A second fruitful approach thus consists in inferring notable signal changes due to lesions from one acquisition to another directly from the MRI volumes of interest at the two time points, instead of from the two lesion maps  (9, (13) (14) (15) (16) (17) (18). Such a solution has the advantage of benefiting from all the information available at once and thus maximizing its ability to detect relevant signals of interest. Intuitively, by comparing scans from one session to another, we alleviate the problem from a part of confounding factors due to interindividual anatomical differences. Nonetheless, such a method needs databases of serial MR scans acquired at different time steps and with manually segmented new lesions, which are relatively uncommon as of now. In this project, we chose to develop a method following this second approach.
This method is briefly detailed in the four next subsections. First, we designed a training/testing dataset consisting of a set of pairs of FLAIR, T2-weighted (T2w) and T1weighted (T1w) acquisitions from 41 MS patients. Second, we set out a pre-processing pipeline so that data of a given patient are appropriately aligned and signal intensity is comparable from one acquisition to another. Third, we trained a deep neural network whose inputs consist, for a given patient, of the two sets of T1w, T2w, and FLAIR images and output consists of the softmax output map associated with the presence of new lesions at each voxel. Fourth, we implemented a few post-processing steps to produce a binary segmentation mask from the network softmax layer. The resulting trained model achieved a true positive rate of 0.
Building the Training and Testing Dataset We designed our segmentation module using a dataset from a previous clinical project (ClinicalTrials ID: NCT02117375) consisting of a set of MR scans from 41 patients acquired on two Siemens 3T MRI scanners (Magnetom Verio, VB17). For each of these patients, data consists of 3D T1w, 2D axial T2w, and 3D FLAIR imaging at two times temporally distant by 1 year. Acquisition parameters were: for 3D T1w: 4 min 30, 1 × 1 × 1 mm, TR = 1,900, TE = 2. 26, TI = 900, FA = 9, matrix = 256 × 256, GRAPPA2; for Axial DPw T2w: 2 min 12, 0. 7 × 0. 7 × 3 mm, TR = 6,530, TE = 9. 4/84, FA = 150, matrix = 320 × 320, GRAPPA2; for 3D FLAIR: 5 min, 1 × 1 × 1. 1 mm, TR = 5,000, TE = 399, TI = 1,800, matrix = 256 × 256, GRAPPA2. New lesions from the first acquisition to the second one were manually segmented by an expert and reviewed by another expert using the ITK-SNAP software (http://www.
itksnap. org/). This procedure provides the delineation of 152 new lesions. The dataset was split into a learning dataset (24 patients, 111 lesions) and a testing dataset (17 patients, 41 lesions). Data splitting was achieved by stratifying lesions according to their locations (deep white matter, periventricular, juxtacortical, brainstem, cerebellum) and optimizing patients repartition to achieve balanced (60%/40%) training and testing groups with respect to these characteristics.
Nevertheless, we did not observe any notable difference in results when modifying the choice of the reference (neither in training, nor in testing). Fourth, images are all cropped using the FLAIR baseline as a mask in order to reduce pointless data. Fifth, bias due to spatial inhomogeneity is estimated using the N4 algorithm  (20)  and removed from the data (animaN4BiasCorrection). Finally, for each pair of baseline and follow-up images (e. g. , FLAIR baseline and FLAIR follow-up) voxel intensities are jointly corrected using a 2 fold procedure: (i) first, their joint histogram is linearly rescaled so that it best fits the y = x line in a least square sense, (ii) second, a Nyul standardization  (21)  on an in-house multisequence template is applied independently on each acquisition (animaNyulStandardization).
Deep Neural Network Architecture and Learning The core of the segmentation module consists of a fully convolutional neural network that was trained to segment new lesions from a pair of preprocessed FLAIR, T1w, and T2w acquisitions. Specifically, we adopted the nnU-net framework proposed by  Isensee et al. [(22) , github. com/MIC-DKFZ/nnUNet] that enables training of a 3D U-Net  (23)  while automating the choice of the hyperparameter values. This framework has been shown to outperform a number of deep learning-based methods on a variety of segmentation tasks. Precisely, our 3D U-Net has 6 input channels (one for each sequence and each time point) of size  [160, 192, 64]. To fit this frame, each input image is first resampled to size [0. 5, 0. 5, 1. 1 mm] (median training image resolution) and then each set of 6 images (3 sequences for each of the 2 time points) is split into patches of such a size.
Finally, each such 6×  [160, 192, 64]  patch is processed independently and aggregated to others to form the final softmax outputs map. Data augmentation included: (i) isotropic rescaling, (ii) 3D rotation, (iii) mirroring in the sagittal plane, (iv) smooth elastic deformations and (v) intensity enhancements and attenuations on lesion voxels (modeling the diversity of signal change due to lesions). This network was trained to minimize the sum of Cross-Entropy and Dice loss over the training dataset and included a dropout based regularization (with probability = 0. 2). Training was performed using a stochastic gradient descent run over 1,000 epochs, each of them consisting of 250 minibatches. Learning was conducted on a GPU NVIDIA Quadro P6000, 24 GB and lasted 10 days. Prediction for a given patient lasted about 6 min (including pre and post-processing) on the same hardware.
Data Post-processing Once the neural network evaluated for a given pair of acquisitions, a binary segmentation map was obtained from the network softmax outputs using the following empirical procedure. First, the softmax outputs map is binarized using a threshold of 0. 01. Second, connected components (26connectivity) were extracted from the resulting binary map. Third, only connected components with volume larger than 12 mm 3 and including at least one voxel with softmax value >0. 1 were selected as new lesions in the final output mask. Last, preprocessed data and the corresponding segmentation mask were resampled to the original baseline FLAIR image slab and resolution.
The Visualization Module: Efficient and Adapted Reporting The CADIMS software has been designed to allow a fast and intuitive access to the preprocessed volumes and new lesions segmentation masks (Figure  3 , as well as Video available in Supplementary Material). It was built in collaboration with a neurologist and a neuroradiologist following MS patients to meet their clinical needs. It consists of a MRI viewer usable from a standard web browser. It has been developed using the AMI framework (https://github. com/FNNDSC/ami) for the visualization of medical images and integrated in an Angular application. It allows the visualization of DICOM images that are directly retrieved from the hospital PACS using DICOMWeb TM , the DICOM Standard for web-based medical imaging. As explained in section The transfer and storage modules: Servers interoperability and data access, the processed scans and the segmentation maps are transferred back via the telemedicine platform and are directly available in the viewer.
Data Sets Patients from 5 MRI centers were prospectively included in the MUSIC project. All patients were informed and written consents were obtained. All patients were included in the OFSEP ("Observatoire Français de la Sclérose en Plaques") cohort, registered on clinicaltrials. gov (NCT02889965) and compliant with French data confidentiality regulations. The study was approved by the relevant ethics committee. Inclusion criteria were chosen to target a population with a substantial number of active patients. They included (i) a.
Impact of the MUSIC Workflow on Routine Clinical Practice In this second experiment, we explored the added value of the overall MUSIC workflow in clinical practice. This experiment was conducted on 6 patients. Again, it was a 2-fold procedure. In the first phase, each expert was asked to visualize the MRI data and write a radiological report using the fully manual procedure currently in use. Hence, the images were viewed directly from the PACS and MRI for the two time points were manually roughly registered. The presence of new lesions was visually assessed and annotated in the radiological report, without any computer-aided tool. In the second phase of the experiment, 2 weeks later, each expert was asked to repeat the exercise via the MUSIC workflow (i. e. , from a user perspective, using the new lesion segmentation mask and realigned data in the dedicated web MRI viewer).
Automated New Lesion Segmentation Tools May Have Substantial Consequences on the Therapeutic Management of MS Patients Beyond lesion-wise statistics, our results suggest that the use of segmentation masks has also consequences at the patient level. Indeed, in the first experiment, it allows each expert to identify three to five supplementary active MS patients (i. e. , with at least one new lesion on MRI). This result, which may seem important, should be interpreted in relation to our dataset, including particularly active patients. This high activity rate is well-explained by our inclusion criteria, selecting patients at the early stage of the disease. Moreover, it would be interesting to evaluate the consequences on patient management by the clinician (in particular with respect to potential treatment changes).
In parallel, the design of methods using both a joint analysis of the baseline and follow-up acquisitions (as in the present work) and an analysis of each cross sectional segmentation probability maps, obtained from dedicated algorithms, could maximize the use of the information available in the different annotated databases  (34, 35). In particular, these latter segmentation probability maps could be obtained estimating the confidence maps associated with the presence of lesions  (10) , that have already shown their interests to detect new MS lesions. Sixth, our experiments were limited to follow-up with two time points and did not include settings with more time points. Our workflow can actually deal with such settings by processing the data sequentially, using the first baseline images as reference target for registration, and performing segmentations independently for each consecutive pair of acquisition sessions.
diagnosis of MS according to 2017 Mc Donald criteria (2); (ii) a disease duration <10 years; (iii) an Expanded Disability Status Scale (EDSS) score <4; (iv) a follow-up MRI available 10-16 months after the first MRI. Our evaluation dataset consists of 54 pairs (baseline and follow-up) of 2D or 3D FLAIR, T1w, and T2w scans acquired on 9 different 3T MR scanners from Siemens, Philips and General Electrics. Thirty out of the 54 studied MS patients had a followup scan on a different MR scanner than the first scan, and 22 out of these 30 on a MR scanner from a different manufacturer. The overall allocation between scanners is depicted in Table1. All data were acquired according to the OFSEP recommendations (6). The median and range FLAIR, T1w, and T2w spatial resolutions (in mm) were, respectively [1, 1, 1] (range [0.
ACKNOWLEDGMENTS Authors thank the  Observatoire Français de la Sclérose en Plaques (OFSEP)  which was supported by a grant provided by the  French State  and handled by the  Agence Nationale de la Recherche  within the framework of the  Investments for the Future program , under the reference  ANR-10-COHO-002 , by the  Eugène Devic EDMUS Foundation  against multiple sclerosis and by the  ARSEP Foundation. Authors thank  Damien Le Port  for his help to gather and document the dataset used in this study.
FUNDING This study received funding from  Biogen ,  Teva Santé ,  Novartis Pharma ,  Roche SAS ,  Merck Serono ,  Sanofi. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. As a part of the  MUSIC  project, the academic POCADIMS study (NCT03205280) was funded by the  Programme Hospitalier de Recherche Clinique-Interregional  ( PHRC-I) 2016 ,  Groupement Interrégional de Recherche Clinique et d'Innovation Grand Ouest. The study was supported by a grant from the  Institut des Neurosciences Cliniques de Rennes (INCR).
Spectral observations and reduction The galaxies analyzed in this study are selected from the ESO-VANDELS project  (ESO Large Program ID 194. A-2003(EK) , P. I. L. Pentericci and R. McLure) 1. VANDELS is, to date, the deepest optical spectroscopic survey of high redshift galaxies. We refer to the two introductory papers by  McLure et al. (2018)  and  Pentericci et al. (2018)  for all the details concerning the observations and data reduction, and highlight here only the main characteristics. The survey targeted ∼ 2100 galaxies at redshift z ≥ 1 in an area of the sky of 0. 2 deg 2 in total, in the UDS (Ultra Deep Survey) and CDFS (Chandra Deep Field South) fields around the CANDELS region  (Grogin et al. 2011; Koekemoer et al. 2011). The interesting targets for our goals are: (1) bright star-forming galaxies (SFG) with photometric redshift ranging 2.
This procedure yields fully wavelength and flux calibrated two and one dimensional spectra, corrected for atmospheric and galactic extinction, and normalized to the i-band photometry available for all targets. As described in  Pentericci et al. (2018) , since an artificial flux loss was observed in the extreme blue end of the spectra (λ < 5600 Å) when compared to broad-band photometry, an empirical correction estimated in a statistical way was applied in post-processing to ensure the correct flux density shape at lower wavelengths. This correction is in all cases no larger than 10-20% of the original flux density. After the reduction process, the VANDELS team was in charge for measuring spectroscopic redshifts z spec for all the observed targets. The derivation of z spec was made with the help of the EZ software package  (Garilli et al.
Photometry in VANDELS fields and stellar masses from SED fitting All targets in VANDELS have either space-based or groundbased photometric data available. The pointings in the UDS and CDFS fields are centered on the area covered by the CANDELS survey  (Grogin et al. 2011; Koekemoer et al. 2011). For these regions, we have deep optical+near-IR (ACS + WFC3/IR) HST images, Spitzer images, and H-band selected, PSF-homogenized photometric catalogs assembled by  Galametz et al. (2013) ;  Guo et al. (2013) , including total magnitudes in 6 and 10 space-based broad-band filters, in the UDS and CDFS fields respectively. In the same area, also photometric data from ground are available, so that we are able to cover the full range between band B (λ cen 4450 Å) and IRAC channel 2 (λ cen 4. 5µm). The limiting magnitude of this dataset is H AB < 27.
Systematics and uncertainties In the measurements of β, for each galaxy we could use from a minimum of two to a maximum of six bands (four on average) from the list of Table  1. We proved the stability of our values by removing for each galaxy one or two random photometric bands from the initial dataset (keeping at least three bands), and computing again the slope with the same ODR fitting procedure. This yields determinations that are in qualitative agreement with the original values based on the full available dataset, and do not have systematic discrepancies, indicating that our results are not driven by some specific bands adopted in the fit, and are stable against the exact number of photometric points that are used in the fit. We note that the wavelength range for fitting the UV slope does not contain strong emission lines that can significantly affect the photometry, as those bands possibly contaminated by the Lyα line have always been excluded at the beginning.
Zahid et al. (2017)  investigated the MZR at redshifts z ∼ 0. 08 (0. 027 < z < 0. 25) for star-forming galaxies extracted from the Sloan Digital Sky Survey (SDSS). Secondly,  Gallazzi et al. (2014)  analyzed a massselected sample of ∼ 70 galaxies at redshift ∼ 0. 7, deriving a MZR representative of the whole galaxy population, both starforming and quiescent. While a direct comparison to the latter cannot be performed as we probe systematically lower stellar masses, the dataset of  Zahid et al. (2017)  shows that there is a decrease of stellar metallicity by ∼ 0. 4 dex between z ∼ 0 and z ∼ 3 in all the mass range explored, from 10 9 to 10 10. 3 M. Remarkably, the slopes of the mass-metallicity relations found at such different cosmic epochs are very similar. Fitting a linear relation between log 10 (M * /M ) = 8.
5 (e. g. ,  Buat et al. 2012; Heinis et al. 2014; Pannella et al. 2015; Hathi et al. 2016; McLure et al. 2018) , with a 1σ scatter in β of the order of 0. 5. It is thus interesting to investigate with our large VANDELS sample how these quantites are related. In Fig. 6  we display, for our VANDELS selected subset #2, the UV slope as a function of stellar mass. We remark that we are taking here only those galaxies for which β is well constrained from the power-law fit to the available photometric data (see Section 2. 4. 1). Given the rapid evolution of β expected with cosmic time, we divided the main sample in two redshift bins, above and below the median redshift of the sample. Then we constructed additional subsamples of different stellar masses by requiring an equal number of objects for each subset, and we calculated the median β and the median absolute deviation (MAD) in all of these bins.
The effects of dust extinction are not included. As far as the chemical abundance is concerned, it is calculated considering the whole star-formation history of each model galaxy, adopting a yield (i. e. , the fraction of metals in stars that returns to the ISM during their lifetime) of 0. 02 and a recycled gas fraction of 0. 45, appropriate for the Chabrier IMF. The model also adopts the instantaneous recycling approximation (IRA), and it does not distinguish between SNII and SNIa chemical enrichment. This SAM has been shown to accurately predict the observed luminosity function of galaxies from the Local Universe to high-redshift  (Menci et al. 2002) , the quasar luminosity function up to z ∼ 4  (Menci et al. 2006) , and the color bimodality of galaxies  (Menci et al. 2005). In order to have a more manageable dataset, especially for the visualization, we studied the distribution of galaxies in the M * -Z * plane, using a grid with 20 bins in mass (from log 10 (M * /M ) = 8.
We notice that for stellar masses ranging 10 9 < M * < 10 10. 2 M , the stellar metallicities of the average star-forming galaxy population are also 0. 2 dex higher than GAEA predictions, with log 10 (Z/Z ) varying from -0. 8 to above solar. Overall, despite the different metallicity normalizations, the two relations have slopes that are remarkably in agreement with the observed MZR from  VAN-DELS data. This suggests that models connect the natural evolution from low-mass, more metal-poor galaxies to high-mass, more chemically enriched systems in a way that is consistent with our observations. In Fig. 10 -bottom we show the comparison between VAN-DELS data and GAEA models for the β -Z * relation. First, we notice that the range of UV slopes predicted by GAEA is consistent with the values found in our VANDELS selected sample.
6). The EWs of these two indexes, measured in VANDELS stacked spectra, are well in agreement with Starburst99 model predictions, yielding fully consistent metallicity values in all the range explored with our data. • The mass-metallicity relation (MZR) estimated from the above far-UV absorption indexes is consistent with the previous determination based on VANDELS data  (Cullen et al. 2019) , obtained through a global fitting to the full FUV spectrum with similar stellar models (Fig. 5 ). • The MZR does not significantly evolve from z ∼ 2 to z ∼ 3. 5, in agreement with  Cullen et al. (2019) : even though a slightly lower metallicity (by 0. 05 dex) is measured for typical starforming galaxies at redshift z ∼ 3. 5 compared to samples at lower z median ∼ 2. 5 (from our VANDELS sample) and ∼ 2 (from  Halliday et al.
• We contrast our findings with the predictions of two different SAMs, namely GAEA  (Hirschmann et al. 2016)  and  Menci et al. (2014). We first construct two mock galaxy catalogs with a redshift, stellar mass and SFR distributions matched to our VANDELS data. We show that these models predict a slope of the mass-metallicity relation that is in agreement with that found from VANDELS star-forming galaxies, even though their metallicity normalizations are higher by ∼ 0. 2 and ∼ 0. 4 dex, respectively. In addition, the slope of the Z * -β relation predicted by GAEA is consistent at 2σ level with our result. An offset of ∼ 0. 1-0. 2 dex between mass-weighted and FUV-weighted stellar metallicities, or a lower yield in  Menci et al. (2014)  SAM, could account for the different normalization between our data and model predictions.
Typically, the largest variations (up to 8%) occur at solar and super-solar Z *. The only exceptions are the indexes at 1853 and 1978 Å, which can show a decrease of EW up to 0. 25 Å. However, these indexes are not considered for our metallicity calculation. Finally, for an extremely flat IMF slope (α upper = -1. 65), the effects on the EW are not negligible for the majority of the absorption lines. In particular, for those adopted in this paper to derive the stellar metallicity, we observe a variation of EW (at fixed Z * ) of 0. 15 and 0. 3 Å for the 1501 and 1719 Å indexes, respectively, in the Z * range of our dataset. In other words, this IMF would produce a constant upward shift of stellar metallicity of 0. 1-0. 13 dex. However, we remark that this is a very unlikely IMF for typical star-forming galaxies at z ∼ 3 in the stellar mass range explored by VANDELS.
Principles The following sections explain: 1) how components of resistance were implemented in the downy mildew model, 2) how a sensitivity analysis was performed on both the individual and combined relative resistance (RR) parameters effects, and 3) how the experimental coefficients measured for the 16 grapevine varieties were used to simulate the varietal effect. The model was drawn and run with the STELLA® program  (Isee Systems, 2005). Briefly, the model represents the main epidemic processes of infection, latency, and infectiousness, as a flow of leaf sites through successive states, where a site is a portion of plant tissue that can sustain a given infection and potentially give raise to new ones  (Zadoks, 1971; Vanderplank, 1968). For modelling purposes, each site in the model has a fixed area.
Acknowledgments This study was supported by the  Doctoral School on the Agro-Food System (Agrisystem)  of the  Università Cattolica del Sacro Cuore (Italy). Authors want to acknowledge the valuable input of one anonymous Reviewer, reminding them of Gregory's (1948) multiple infection transformation. Funding Open access funding provided by  Università Cattolica del Sacro Cuore  within the  CRUI-CARE Agreement.
In men, CD exposure at adulthood was associated with an increased risk of prostate cancer  (34). Because populations are still exposed to CD, there is an immediate urge to assess the potential deleterious effects of CD, especially on fragile developmental organism. We hypothesized that in utero CD exposure affects the epigenetic mechanisms important for the control of gene expression and silencing. To investigate this hypothesis, we used umbilical cord blood samples obtained at birth from the TIMOUN mother-child cohort in Guadeloupe and applied improved ChIP-seq techniques. We analyzed only males in our study as in utero exposed boys have developmental problems related to fine motor skills  (32)  and men exposed during adult life have a high risk of prostate cancer development  (34). The data from human study were complemented with experimental work using the human Ke-37 cell line to identify the mechanism of action involved.