dataesr/scientific-paragraphs-categorization huggingface dataset:
Evolutionary relationships were calculated using the Neighbor-Joining method. Evolutionary distances were computed using the Poisson correction method. All positions containing gaps and missing data were eliminated, and there was in total 292 positions in the final dataset. The tree is in scale with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. Bacterial (○), fungal (□), plant (Δ), insect (▲) and mammal (∎) sequences. Amoebae and C. elegans sequences are marked with a filled diamond (♦). Characterized GH20 enzymes from marine organisms are underlined.
softpedia. com/get/Science-CAD/BioEdit. shtml). The phylogenetic tree was constructed and visualized using MEGA v 7. 0. 26 (https://megasoftware. net/)  [55]. N-terminal signal peptide prediction was done by SignalP v 4. 1 with sensitive default cut-off values (http://www. cbs. dtu. dk/services/SignalP/) [85]. Promoter locations were predicted by SoftBerry tool BPROM (http://www. softberry. com/berry. phtml?topic=bprom&group=programs&subgroup=gfindb).
Acknowledgments:  Karina Jansen  ( Technical University of Denmark ) is thanked for general technical assistance,  Pernille K. Bech  and  Mikkel Schultz-Johansen  ( University of Copenhagen ) for providing the P. hydrolytica strain,  Corinna Schiano di Cola  for preparing autoinduction medium and  Tiina Alamäe  ( University of Tartu ) for fruitful discussions.
Funding: This research was funded by  Innovation Fund Denmark  to the project " OliGram. Design and gram scale enzymatic synthesis of human milk oligosaccharides ", grant number  1308-00014B  having P. S. as PI. D. T. is grateful to the  Novo Nordisk Foundation  for a postdoctoral fellowship (grant  NNF17OC0025660 ). The APC was funded by  University of Tartu Feasibility Fund  grant  PLTMRARENG13  to T. V. and the  NNF17OC0025660  grant to D. T.
Results Model formulation and fitting procedure. To capture the regional diversity of the response to the epidemic in Italy, we derive a network model where each node represents a different region and links capture fluxes of people traveling among the regions (see Fig. 1a ). Using a data-driven compartmental modelling approach, a set of ODEs is obtained describing the dynamics of six different compartments in each region (Susceptibles, Infected, Hospitalized, Quarantined, Deceased and Recovered); data analysis being used (see Methods) to define flows among compartments. The resulting model is then parameterized using a predictor-corrector algorithm applied to both a national aggregate model and to each of the twenty regional models, identifying the time points at which parameter values present significant changes.
To assess the economic costs of national and regional lockdowns we use official data and estimates from Italian governmental agencies  [23] [24] [25] [26]. Further details on the input data and the official repositories they were obtained from can be found at https://github. com/diBernardoGroup/Network-model-of-the-COVID-19. Regional effects of the national lockdown. Our approach successfully uncovers the regional effects of the national lockdown measures set in place by the Italian government initially in two northern regions (Lombardy and Veneto from the February 27, 2020), and then nationally from March 8, 2020 till May 4, 2020. We observe that notable parameter changes, detected automatically by our parameterization procedure (see Methods), occur as an effect of such measures with a certain degree of homogeneity across all regions (see Supplementary Fig.
14  for further details and function estimation). Validation is carried out by using the parameterized model to capture the available data for each window showing a mean squared error less than 10% over the entire dataset. The parameters identified in each window can also be used to provide model predictions of future trends of the epidemic disease as discussed in Supplementary Notes. Cost estimation. We estimate the cost of each regional lockdown as the sum of the costs for social care and the loss of added value. The costs for social care in each region were computed as the costs for layoff support ("cassa integrazione in   4. Shaded bands correspond to twice the standard deviation. Dashed red lines represent the fraction of the population that can be treated in ICU (T H i =N i ). Regions adopt lockdown measures in the time windows shaded in red while relax them in those shaded in green.
The loss of added value per day was taken from the values estimated by SVIMEZ (the Italian Association for the development of Industry in the South) in Table  3  of their online report  26. We then compute the daily costs of the lockdown and use it to estimate the total costs of each of the simulated scenarios. Data fitting and sensitivity analysis. All computational analyses and the fitting of data were performed using MATLAB and its optimization toolbox. To account for the inherent uncertainty associated to the COVID-19 epidemic, and hence to provide a better validation of the proposed intermittent strategies, each result reported in the manuscript is the output of 10,000 numerical simulations, where we varied the values of the model parameters using the Latin Hypercube sampling method  30.
(The case where ρ i is set to a lower value equal to 1. 5ρ i is shown for the sake of comparison in Supplementary Figs. 5  and 6. ) Also, when a region is in lockdown, we assume all fluxes in and out of that region are reduced by 70% of their original values to better simulate the actual reduction in people's movement observed during the lockdown in Italy (for further details see Supplementary Information). Such a reduction level was estimated qualitatively by considering the publicly available mobility data from Google (Google mobility data (https://www. google. com/covid19/mobility/)). National lockdown measures are modelled by setting all ρ i simultaneously to ρ i in all regions and reducing all fluxes by 70% while national reopening of all regions by setting all ρ i simultaneously to ρ i and restoring inter-regional flows to their prelockdown level.
To model the increase in the COVID-19 testing capacity of each region the parameter α i in region i is multiplied by a factor 2. 5, which corresponds to the average increase in the number of tests carried out nationally since the COVID-19 outbreak first started. Numerical simulations. All simulations were carried out in MATLAB with a discretization step of 1 day to match the available data sampling interval. Initial conditions for regional compartments were set as follows. Quarantined, Hospitalized, Deceased and Recovered are initially set to the datapoints available for May 3, 2020. The number of infected is set to the value I f estimated by our procedure for that date; Susceptibles are initialized to the resident population from which the other compartments are removed. Further details are given in Supplementary Notes.
Acknowledgements M. d. B. , M. C. and A. G. wish to acknowledge support from the  European Project FET-OPEN Cosy-Bio. M. d. B. , M. C. , P. D. L. and F. L. I. acknowledge support from the research project  PRIN 2017 "Advanced Network Control of Future Smart Grids " funded by the  Italian Ministry of University and Research  ( 2020-2023 ). P. D. L. and F. D. R. acknowledge support from the  Compagnia di San Paolo ,  Istituto Banco di Napoli-Fondazione  for supporting their research under the grant  STAR 2018 , project  ACROSS.
8 Kt, 130. 2 Kt, 127. 0 Kt, and 115. 6 Kt, respectively. The top three producers of grapes worldwide in 2018 were China, Italy and USA with the following kilotonnes: 13,397. 0 Kt, 8513. 6 Kt, and 6890. 9 Kt. According to the European food consumption database, northern countries, especially Finland and Latvia but also Spain and Romani are the biggest consumers of berries within Europe, consuming 1. 38 kg/year, 1. 24 kg/year, 1. 28 kg/ year, and 1. 17 kg/year. The strawberries (4. 77 g/day) are consumed most frequently, followed by blueberries (1. 34 g/day)  [42]. Berry fruits are popularly consumed not only in fresh and frozen forms but also as processed and derived products, including dried and canned fruits, yogurts, beverages, jams, and jellies, making the evaluation of their intake rather delicate when using questionnaires.
On the other hand, grapes, excluding wine, are mainly consumed as fresh fruits, and mean consumption in European countries, according to comprehensive European food consumption database, is 4. 93 g/day  [42]. Among them, Hungary and Romania consume the highest amount of grapes 9. 7 g/day and 8. 8 g/day, respectively  [42, 180]. Berry fruits have attracted considerable attention due to their potential benefits to human health. Botanical classification defines berry as a fruit with seeds produced from the ovary of a single flower. According to this definition category, berries include blueberries, cranberries, lingonberries, Ribes species, grapes, but also bananas, tomatoes or eggplants. On the other hand, strawberries, raspberries, and blackberries are not botanical berries, however enter this category in common nomenclature.
Selection of food groups For this review, grapes (Vitis vinifera) together with raisins (i. e. , dried grapes) were selected as widely consumed members of the Vitaceae family. The second family of fruits included in this work was botanical berries: blueberries, cranberries, blackcurrant and redcurrant, and commonly called berries: strawberries and Rubus fruits (raspberries and blackberries). The selection of berry types was based on the frequency of consumption reported in the FoodEx database  [42]  and in the literature  [110]. The chokeberries and goji berries, as consumed less frequently, were not included in this review.
Search for relevant FIB research papers An extensive literature search was carried out to collect all available information on the existing and new candidate BFIs for the selected fruits. The FIBRev protocol (Food Intake Biomarker Reviews) elaborated with guidance of the PRISMA statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and described in Pratico et al. [128]  was followed. Briefly, a primary search was performed in the three databases Scopus, PubMed central, and Web of Science to identify compounds that are already used as or may represent potential BFIs. Search for each berry type was performed independently. The name of the specific fruit and its botanical genus, i. e (strawberr* OR fragaria), (blackberr* OR rubus), (raspberr* OR rubus), (redcurrant OR ribes), (blackcurrant OR ribes), (blueberr* OR vaccinium), (cranberr* OR vaccinium), and (grape* OR rasin*) along with the common keywords: AND (urine or plasma or serum or excretion or blood) AND (human* OR men OR women OR patient* OR volunteer* OR participant*) AND (biomarker* OR marker* OR metabolite* OR biokinetics OR biotransformation OR pharmacokinetics OR bioavailability OR ADME) AND (Intake OR meal OR diet OR ingestion OR administration OR consumption OR eating OR drink*) were applied.
Identification and characterization of candidate BFIs A secondary search allowed to retrieve relevant information to assess the quality of every individual candidate biomarker, regarding specificity, biokinetics, dose-response relationship, robustness, and reliability of the method of analysis, in order to validate its use as BFI according to the scoring validating scheme established by Dragsted et al. [40]. The name of the candidate biomarkers and their synonyms were queried in the previously mentioned databases along with AND (biomarker* OR marker* OR metabolite* OR biokinetics OR biotransformation OR pharmacokinetics OR bioavailability OR ADME). Additionally, the compounds were searched manually in the online databases HMDB (https://www. hmdb. ca), FooDB (http://foodb. ca/), Phenol-Explorer (http://phenol-explorer.
eu/), Dictionary of Food Compounds (http://dfc. chemnetbase. com/faces/chemical/ChemicalSearch. xhtml), Duke's phytochemical and ethnobotanical databases (https://phytochem. nal. usda. gov/phytochem/ search), eBASIS (http://ebasis. eurofir. org/Default. asp), Knapsack (http://kanaya. naist. jp/knapsack_jsp/top. html), and PhytoHub (http://phytohub. eu) to determine all the possible dietary or metabolic origins of the candidate BFIs. Specific and non-specific metabolites were discussed in the text, while only the most plausible candidate BFIs have been reported in Table  1  including the information related to study designs and analytical methods. The non-retained compounds are listed in Table  2 , with main reasons for exclusion and references for an exhaustive presentation of the results.
Berry biomarkers From 852 citations, 75 human studies were selected for this review (Fig. 1 ), most of them employing a targeted analysis, while only 7 used an untargeted approach  [2, 5, 7, 31, 64, 94, 95]. Targeted analyses have mainly quantified anthocyanins in their native glycosidic or aglycone forms, as well as ellagitannins and their microbial metabolites. Some investigations also included quantification of phenolic acids and their conjugates. After careful application of the inclusion and exclusion criteria to the citations collected with the databases search, the numbers of human studies selected for this review were 22 studies out of 206 for blueberry intake  [2, 3, 12, 13, 32, 34, 37, 50, 81-83, 91, 103, 105, 106, 112, 137, 153, 160, 178, 185] ; 20 out of 137 for strawberries  [4, 5, 9, 14, 20, 22, 26, 31, 41, 48, 74, 75, 113, 126, 129, 141, 147, 148, 165, 179] , 7 out of 87 for raspberry  [70, 78, 86, 95, 98, 156, 184] , 5 out of 66 for blackberries  [47, 64, 89, 100, 161] , 13 out of 236 for cranberries  [7, 27, 49, 51, 94, 108, 119, 127, 135, [174] [175] [176] 183] , and 8 out of 61 for blackcurrant  [44, 76, 80, 105, 114, 115, 139, 164].
Funding This study was financially supported as a part of the  Food Biomarkers Alliance (FoodBall)  project and the  FoodPhyt  project, funded by  National Research Councils  under the umbrella of the  Joint Programming Initiative a Healthy Diet for a Healthy Life (JPI HDHL ), with grants from the  Italian national funding organization, Ministry of Education, University and Research ,  MIUR  (decreto n. 2075 of  18/09/2015 ) to MU and FM; from the  French Agence Nationale de la Recherche  (# ANR-14-HDHL-0002-02  and  ANR-19-HDH2-0002 ) to NVM and CM; a grant from the  French Agence Nationale de la Recherche   ANR-19-HDH2-0002-01  to CM; a grant from the  Ministry of Economy and Competitiveness (MINECO)  ( PCIN-2014-133-MINECO Spain ); an award from the  Generalitat de Catalunya's Agency AGAUR  ( 2017SGR1566 ); and funds from  CIBERFES  (co-funded by the  FEDER Program from EU)  to MGA and RL.
The manual delineation of region of interest (RoI) in 3D magnetic resonance imaging (MRI) of the prostate is time-consuming and subjective. Correct identification of prostate tissue is helpful to define a precise RoI to be used in CAD systems in clinical practice during diagnostic imaging, radiotherapy and monitoring the progress of disease. Conditional GAN (cGAN), cycleGAN and U-Net models and their performances were studied for the detection and segmentation of prostate tissue in 3D multi-parametric MRI scans. These models were trained and evaluated on MRI data from 40 patients with biopsy-proven prostate cancer. Due to the limited amount of available training data, three augmentation schemes were proposed to artificially increase the training samples. These models were tested on a clinical dataset annotated for this study and on a public dataset (PROMISE12).
Kohl et al. [13]  used a simple generative model where there was only one discriminator and one generator. Zabihollahy et al. [14]  segmented the prostate whole gland and zones from two modalities (T2W and ADC) based on the U-Net. Cheng et al. [15]  presented segmentation of the whole prostate and central gland from only one modality (T2) using nested networks. Grall et al. [4]  also adopted the conditional GAN architecture for prostate segmentation. Supervised methods outperform unsupervised approaches with regards to classification and segmentation tasks. However, supervised methods need a lot of data to be annotated by experts and clinicians and this is time consuming. Developing methods to improve performance of deep learning methods on small datasets may improve performance of complex models with small datasets by incorporating suitable augmentation schemes.
The contributions of this work are: • to incorporate three data augmentation schemes (super-pixel, noise addition and moving mean) to increase the amount of data in the training set; • to train three models including U-Net, cGAN and cycleGAN with limited amount of training data and compensate it by using the augmented training data • to compare their robustness on the publicly available prostate MRI dataset (completely unseen samples collected from different institution) and our clinically collected prostate multi-parametric MRI dataset (collected from the same institution). Our qualitative and quantitative results show that the cGAN outperformed the other two models (U-Net and cycleGAN) which implies that the proposed detection and segmentation model has the potential to be utilised as an alternative to double reading by clinicians  [24].
Datasets The MR Data from 40 patients with biopsy-proven prostate cancer was collected from the Norfolk and Norwich University Hospital, UK. This dataset includes 683 ADC (Apparent Diffusion Coefficient), 683 DWI (Diffusion Weighted Imaging) and 857 T2W (T2-Weighted) MRI slices. All images were obtained with a 3 Tesla magnet GE Medical Systems, using a phased array pelvic coil, with different fields of view while covering the whole prostate and seminal vesicles in both transverse and coronal planes. The T2 small field of view sequence obtained 3. 6 mm contiguous slices with a 22 × 22 cm field of view and 320 × 320 matrix. For the small field of view diffusion sequence, 3. 6 mm contiguous slices were obtained with a 24 × 12 cm field of view and a 160 × 80 matrix. The MRI protocol used was the same for all the images and as such the quality was similar for the images in this cohort.
There were between 16 to 32 axial slices for each case, in each modality in the dataset. All images were manually annotated by a radiologist experienced in MRI of the prostate to indicate the precise location of the prostate on a slice-by-slice basis in each modality. The radiologist segmented the prostate location on each modality separately using the Onis 2. 6 Professional software (http: //www. onis-viewer. com/ProductInfo. aspx?id=18). The dataset was divided into training and testing sets as 75% and 25% of the whole database based on patients, ensuring that there was no overlap between testing and training sets. For the training process, different combinations of training samples were generated by using augmented samples (covered in Section 2. 2). This augmentation was done separately for each modality.
The number of images created in each data augmentation step was equal to the number of raw images in that modality. To obtain objective results, the test set included only raw images. Examples of super-pixel, noisy and moving mean images are shown in Figure  1. In order to examine the generalisability of the models on another representative set of MR images acquired in a clinical setting, the publicly available dataset (PROMISE12)  [25]  was used. This dataset contains 50 cases including T2-weighted MR images of the prostate. This dataset is multi-center and multi-vendor and has different acquisition protocols, with various prostate sizes and appearances. Details of the acquisition protocols for the different centers is explained by Litjens et al. [25]. Due to different MRI protocols (e. g.
, differences in slice thickness, magnet strength, with/without endorectal coil), the quality of the images was different in this cohort. Figure  2  shows some slices from different data cohorts to show appearance differences. There is a spread in prostate sizes and appearance in all images in our clinical cohort and the public dataset. All images used in the testing phase were collected using different devices with different resolutions and scan protocols, while these variations were not included in the training process. We trained the models on our clinical study dataset but evaluated the performance of the models on our private clinical study dataset and the PROMISE12 public dataset. All images with different sizes were zero-padded (if needed) and scaled to 128 × 128 pixels.
Super-Pixel (SP) Approach Inspired by Tian et al. [17] , we propose to generate super-pixels to group similar pixels in prostate MR images and use them in the training dataset. The Simple Linear Iterative Clustering (SLIC) algorithm  [26]  was used to cluster the relevant pixels in prostate MR images in the combined three-dimensional gray and image plane space and efficiently generate compact super-pixels. A super-pixel is defined as an image patch that is better aligned with intensity edges. These super-pixels can produce highly irregular super-pixels on MR images, with widely varying sizes and shapes. Therefore, it is of critical importance to define a suitable number of clusters. The number of segments (clusters) was tested in the range [200, 1500] and chosen to be 1200 per image. This value was selected based on the empirical evaluation in order to achieve a uniform number of segments.
Conditional GAN (cGAN) The first GAN architecture that was introduced by Goodfellow et al. [28] , has a generator and a discriminator. The generator's training objective is to produce realistic images from a latent space or random noise for the targeted data distribution. The images generated by the generator are evaluated by the discriminator which classifies whether the generated images are accepted or not. The generator tries to fool the discriminator while the discriminator tries to detect the generator's falsely created images as much as possible. CGAN  [29]  is an extended version of GAN and the key idea behind it is to generate images that are indistinguishable from original images (desired output) using an adversarial loss. 3D prostate tissue detection and segmentation using MRI scans is implemented as finding the prostate tissue on each 2D MR scan in terms of its semantic label map representing the desired tissue in the image.
Training For each MR modality used in this study (DWI, T2W and ADC), raw images were combined with the augmented images (described in Section 2. 2) to create different types of datasets. Using each created dataset, cycleGAN (https://github. com/junyanz/CycleGAN) and cGAN (https://github. com/phillipi/pix2pix) models were trained. All the network training was performed within the Torch framework with a NVIDIA GeForce GTX 1080 GPU on Intel Core i7-4790 Processor. To achieve a better validation of the experiments, 4-fold cross validation was performed. The detection and segmentation performance of each trained model for the prostate tissue was evaluated only on the raw test images. All networks were trained with 70 epochs for the batch size = 1 as reported by Zhu et al. [30]. This number of epochs was observed to be sufficient for the generator's loss function to reach a steady state in which there were no more visually perceived improvements in the predicted masks.