dataesr/scientific-paragraphs-categorization huggingface dataset:
Information on PPIs is thus crucial for developing such antifungals, and the yeast two-hybrid (Y2H) system can play a pivotal role in this  (10). For several organisms, large-scale or genome-wide Y2H studies to investigate PPIs have been published, contributing to an in-depth knowledge of, e. g. , protein functions and cellular pathways  (11) (12) (13) (14). However, PPI studies are almost nonexistent for C. albicans  (15, 16). The BioGRID database reports only 163 interactions  (15) , while the STRING database does not even mention C. albicans as a searchable organism  (17) , despite being one of the most studied fungal pathogens and even serving as a model organism  (18). The lack of studied interactions is mainly due to C. albicans having a nonstandard CUG codon usage (translating CUG as serine rather than leucine most of the time), a low transformation efficiency, and unstable episomal plasmids, hindering the application of commonly used techniques such as the Y2H system  (19) (20) (21) (22) (23).
The high-throughput PPI screen was performed in a matrix setup in which bait protein Pho85 was mated with 1,646 random preys. Of these 1,646 prey-expressing strains, a total of 1,478 (90%) mated successfully with the Pho85 bait-expressing strain and formed tetraploids surviving on SC-Leu-Arg. From these 1,478 tetraploid strains, 15 showed promising growth on SC-Met-Cys-His and/or SC-Met-His medium. These 15 potential interactions were then manually retested. Only 5 of those 15 showed (weak) growth compared to the background growth and were kept for the second validation step (see Data Set S1 in the supplemental material). This final step led to one clear interaction partner: Pcl5, a cyclin (Fig. 3 ). Interestingly an interaction between the yeast orthologues of Pho85 and Pcl5 was already shown in S.
albicans protein interaction network and perhaps to the development of novel, very specific antifungals. The confirmed PPI found in this study will be uploaded to the BioGRID database  (58) , making it possible to extend the protein-protein interaction network in C. albicans. With the results presented here, we show the Candida community the potential of the C2H system to investigate PPIs. C2H high-throughput screening can be applied to detect multiple novel interactions, while the low-scale C2H system can be used to study putative interactions.
MATERIALS AND METHODS Strains and growth conditions. C. albicans strain SC2H3  (19)  and its derivate mating-competent strains SC2H3a-pWOR1 and SC2H3␣-pWOR1  (32)  were used for the C2H system. These strains are deposited in the Belgian Co-ordinated Collections of Microorganisms (BCCM). Accession numbers are given in Table  2. Strain SC2H3 was used for the cotransformation validation step 2 of the high-throughput study. Strains SC2H3a-pWOR1 and SC2H3␣-pWOR1 were used for the high-throughput study and validation step 1. Strains were cultured at 23°C or 30°C on YPD medium (1% yeast extract, 2% peptone, 2% dextrose, and 2% agar), Spider, or synthetic complete (SC) dropout medium from US Biological, respectively; SC-arginine (SC-Arg) for prey-plasmid-harboring strains; SC-leucine (SC-Leu) for bait-harboring strains; SC-Arg-Leu for strains with both prey and bait plasmids; SC-methionine-histidine (SC-Met-His); and/or SC-methionine-cysteine-histidine (SC-Met-Cys-His) to check for potential interactions.
ACKNOWLEDGMENTS We thank  Nico Vangoethem  for help with preparation of the figures and  Ilse Palmans ,  Tom Adriany , and  Selien Schots  for technical assistance. Financial support was obtained from the  Interuniversity Attraction Poles Programme  initiated by the  Belgian Science Policy Office  ( IAP P7/28 ) and by the  KU Leuven Research Council  ( C14/17/063 ). C. D. acknowledges support from the  French Government 's  Investissement d'Avenir program  ( Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases ,  ANR-10-LABX-62-IBEID ). C. A. M. and C. D. acknowledge support from the  Wellcome Trust  ( 088858/Z/09/Z ). C. A. M. acknowledges support from the  MRC Centre for Medical Mycology  ( MR/N006364/1 ) and the  University of Aberdeen.
Data availability. All plasmids and strains necessary to perform the C2H test are available from the BCCM collection (http://bccm. belspo. be). The collection/accession numbers are given in Tables  1  and 2. The pDONR207-XX plasmids containing the C. albicans ORFs (ORFeome) are available at the Centre International de Ressources Microbiennes (CIRM; https://www6. inra. fr/cirm_eng/) (32). The "empty" Gateway-adapted bait and prey plasmids (pCHB-GC and pCHP-GC), the generated bait plasmids (pC2HB-Pho85 and pC2HB-Pcl5), and the interacting prey plasmids (pC2HP-Pcl5 and pC2HP-Pho85) (and their plasmid maps) are accessible from the Belgian Co-ordinated Collections of Microorganisms (BCCM; http://bccm. belspo. be). All LMBP accession numbers are provided in Tables  1  and 3.
anopheles  [3]. The cgMLST profile of our isolate was compared to those publicly available in the Elizabethkingia cgMLST database on the Institut Pasteur server (http:// bigsdb. pasteur. fr/elizabethkingia/). Cluster analysis based on cgMLST profiles, see Fig. 1 , showed that the isolate belonged to sublineage 11, which was defined with strain CIP 60-59 (CDC 3375; ATCC 13255; NCTC 10586; CCUG 4321; LMG 12873) as a reference. Strain CIP 60-59 was isolated from the CSF of a premature infant who died  [13] ; however, the two strains of sublineage 11 have different alleles at 299 loci out of 1513 loci called in both strains. This result clearly shows that AAUH 98722 (our isolate) and CIP 60-59 are genetically distinct. The assemblies were submitted to ResFinder 3. 0 (http://cge. cbs. dtu. dk/services/Res-Finder/) to analyse them for the presence of antimicrobialresistance genes  [14].
We will search MEDLINE (PubMed), GreenFILE (EBSCOhost), Cumulative Index to Nursing and Allied Health Literature (EBSCOhost) and ScienceDirect as electronic database as well as hand-search publications for grey literature. This review will therefore provide evidence on this current topic, one which could have practical implications for policy-makers involved in choices which are more conducive to healthy living. Ethics and dissemination No primary data will be collected since all data that will be presented in this review are based on published articles and publicly available documents, and therefore ethics committee approval is not a requirement. The findings of this review will be presented at workshops and conferences, and will be submitted for publication in a peer-reviewed journal.
Open Access data selection Search for electronic databases We will search MEDLINE (via PubMed), GreenF ILE (via EBSCO host), Cumulative Index to Nursing and Allied Health Literature (via EBSCOhost) and Science Direct as electronic databases. To conduct this scoping review, we will use a structured search strategy based on controlled vocabulary and relevant key term. The key terms for the inclusion criteria are related to green space, urban space and cancer. The search terms and equations for MEDLINE can be found in online supplementary appendix 1 and will be adapted for other databases. We will also screen reference lists of included studies.
Search for other resources Google and Google scholar will be also examined. We will also hand-search websites of key organisations involved in addressing and reporting research on green spaces (WHO, Agency for Healthcare Research and Quality, National Institute for Health and Care Excellence (NICE), Medical Research National Institute (INSERM), French National Cancer Institute, The Institute of Cancer Research, Commission for Architecture and the Built Environment, national urbanism agencies, etc) and grey literature databases (eg, OpenGrey). We will screen all the reference lists of included studies (backward search) for new article and search articles (forward search) that have cited the included studies (Web of Science).
data management We will download the references retrieved from the electronic database searches and from the hand searching to Covidence (https://www. covidence. org/) and remove duplicates. All identified references screened independently by two reviewers (from pool of five authors: MP, ML, MG, JP, J-PR) using a three-stage approach to reviewing the title, abstract and full text. The results of this selection process will be documented in a standardised flowchart. Any potential disagreement will be recorded and resolved by further discussion. We will keep a record of reasons for excluding studies. Two authors will independently extract information for the first stage. Any potential disagreement will be recorded and resolved by further discussion. For the other stages, the members of the research team will screen the full text and extract the relevant data.
To support this process, we will pilot test a data extraction form and test it on five studies. It will be modified when necessary to ensure comprehensiveness and comparability between results. Tables will be prepared in Excel sheets to extract the data where relevant information will be reported data synthesis We do not plan to perform meta-analyses and statistical methods of synthesis in this review due to the heterogeneity of the identified evidence, The purpose of a scoping review is to aggregate the findings and provide a narrative synthesis. Quantitative data will be report using descriptive numerical summary analysis. A qualitative synthesis will be used to describe the key characteristics of urban green spaces and cancer populations, the relationships between urban green spaces and cancer.
If additional data emerge, they will be reported with the findings. We will summarise publications and Open Access their characteristics in tables to support the narrative synthesis. We will create a table that will constitute our map of the literature to present interventions and outcomes that aim to address relationships between urban green spaces and cancer. We will use the method described by the International Initiative for Impact Evaluation (http://www. 3ieimpact. org/ en/ evidence/ gapmaps/) to build the table. We will use the approach of level of scientific evidence to drive the conclusions on the relationship between urban green space and cancer. We will assess the quality of the body of evidence from all the included studies and reports. We will adapt the 'evidence for corroboration' grading system developed by NICE  38  for each characteristic identified (see table  1 ).
Data collection Process A questionnaire-based survey (general survey) was performed to collect general information on farm production and farm management practices, such as the number of cattle per farm, the number of calves and adult cattle per farm, the unit price of one dose of a bivalent vaccine, the cattle live weight price per kg, the price of an insemination service, and the milk price per liter. This survey was performed from June to October 2014 in the eight districts of the study area as mentioned above, with the help of a group of 15 veterinary students from Nong Lam University, Ho Chi Minh City. The students were trained about the questionnaire structure and face-to-face interview method by two certified lecturers from Nong Lam University 1 month before performing the field survey.
Farmer within two types of cattle production (dairy and beef) in eight districts mentioned above were invited to participated in questionnaire-based survey. The total number of interviewed farms per district was based on the cattle population density in each district. A stratified sampling for farms selection was performed based on the type of cattle production (dairy or beef) with a limit of 10 questionnaires per production type per village. Data about the financial impact of FMD (relative costs of control FMD case in cattle at the farm-level) were collected in farms with FMD suspicion which were detected during study period (animal having clinical signs of the disease that were recognized by farmer). Indeed, a series of focus group and individual interviews took place from November 2015 to April 2016 in the framework of a study on the topic of participatory surveillance in sentinel villages in Duc Hoa and Duc Hue district of Long An province.
Focus group interviews of 10-15 farmers per village were implemented to identify farms present suspected cases of FMD. Those suspected farms were then being the subject to individual semi-structured interviews to collect data on FMD financial impact. The results of the participatory surveillance study were reported apart, and financial impact data collected from infected farms were presented and used in this paper. In those farms, general data on disease management, control methods, disease impact, and all related costs were first collected using a standardized questionnaire. The first part of the questionnaire included general questions on the number of cattle at risk, number of disease cases, number of deaths due to the disease, number of premature slaughters, number of cattle destroyed, number of cattle vaccinated, vaccine type used, and actual vaccination practices applied in the farm.
Financial Impact Survey A total of 69 focus group interviews were organized in 32 villages of Duc Hoa and Duc Hue districts with the participation of 702 farmers. 129 farms located in 14 villages were then detected as suspected farms and being subject for individual interview using financial impact survey. The investigation demonstrated that in case of being infected by FMD, 43. 8% of the cattle in the three production types received treatment with only modern medicine rather than local medicine (11. 5%) or with both modern and local medicine (20. 9%). Local medicine was especially used in the beef production type (92. 6% of cases). The incidence rates and incidence risks of FMD in cattle farm estimated from the collected data and literature using Eqs 1-5 were presented in Table  5. Other data on the financial impact of FMD outbreaks at the farm-level was summarized in Table  6.
3%) (2). As a consequence, the mortality variable in literature was used in our calculation instead of the one found by the survey. The possible explanation for the difference between literature data and the survey findings was described as follow. FMD infected animals may have secondary infections during recovery time (digestive troubles, hemorrhagic septicemia, etc. ), which could delay or impede their recovery or even lead to their death in some instances. In case cattle do not recover well or die from a secondary infection, they are sent to slaughterhouse, as a consequence of FMD infection, even if FMDV does not directly cause their death. Subsequently, they were reported as death due to FMD to the research team. Moreover, high mortality was mainly observed in dairy farms using highly efficient cattle breeds which are more sensitive to the disease, in comparison to local breeds or crossbreeds used in beef farms.
This training-modeling phase of the CNN is realized beforehand with an existing very large database of images, manually classified into a large array of possible classes (e. g. , dog races, guitar types, professions, and plants). By being built on this large database, the CNN is assumed to select a very good feature space because it is now capable of sorting very diverse images into very diverse categories. The assumption is somehow grounded by the existence of common features (blobs, tubes, edges. ) in images from natural scenes. The study of these common features in natural scenes is a well-established issue in computer vision, which has been investigated for instance in gray-level images  [22, 23] , color images  [24, 25] , and even in three-dimensional (3D) images  [26]. It is thus likely that, since our images to be classified share some common features with the images of the database used for the training of the CNN, the selected features will also operate efficiently on our images.
Material The pre-trained CNN we used was developed by  [28] , trained on the Imagenet database. We used this CNN because it is one of the most general ones (some CNN were trained on more specific cases such as face recognition), and thus it can be expected to be more efficient for transfer learning in our problem. This network is composed of 22 layers and yields a total of 1000 features. Convolutional filters applied to the input image in the first layer of the CNN can be seen in Figure  1. These filters appear very similar to wavelets  [29]  oriented in all possible directions. This is likely to enhance blob-like or tube-like structures such as the tubular roots or grainy blobs of the soil found in our X-ray tomography. The classifier used was a linear SVM. It was chosen after comparing the performance on our data by cross-validation with all other types of classifiers available in Matlab.
Computation was run on Matlab R2016A, on a machine with an Intel (R) Xeon (R) 3. 5 GHz processor (Intel, Santa Clara, CA, USA), 32 GB RAM, and a FirePro W2100 AMD GPU (AMD, Santa Clara, CA, USA). Algorithm 1 Proposed machine learning algorithm for image segmentation. All Computer tomographic (CT) data was measured with an individually designed X-ray system at the Fraunhofer EZRT in Furth, Germany using a GE 225 MM2/ HP source Aerotech axis system (Aerotech, Pittsburg, CA, USA) and the Meomed XEye 2020 Detector (MEOMED, Přerov, Czech Republic) operating with a binned rectangular pixel size of 100 µm. The source was operated at 175 kV acceleration voltage with a current of 4. 7 mA. To further harden the spectra, 1-mm-thick copper pre-filtering was applied. The focus object distance was set to 725 mm and the focus detector distance to 827 mm, resulting in a reconstructed voxel size of 88.
9 µm. To mimic the data quality typically occurring in high-throughput measurement modes, only 800 projections with a 350-ms illumination time were recorded within the 360 degrees of rotation. This resulted in a measurement time of only 5 min for scanning the whole field of view of about 20 cm. The pot used for the measurement was a PVC (Polyvinyl Chloride) tube with a 9-cm diameter, and only a small partial volume in the middle part of the whole reconstructed volume was used to reduce the simulation time. The roots used as the reference in experiment in Section 3 and as experimental data for the experiment in Section 4 were maize plants of the type B73. During the growth period, the plants were stored in a Conviron A1000PG growth chamber. The temperature within the 12 h period of light was 21 • C and 18 • C during the night.
Acknowledgments: This work received supports from the  French Government  supervised by the  Agence Nationale de la Recherche  in the framework of the program Investissements d' Avenir  under reference  ANR-11-BTBR-0007  ( AKER program ). Richard SCHIELEIN acknowledges support from the  EU   COST action  'The quest for tolerant varieties: phenotyping at plant and cellular level ( FA1306 )'.
ACKNOWLEDGMENTS The authors would like to thank all the participants of LACCAVE and the contribution of stakeholders all through the project. Without their work, this paper and many others would not have been possible. We dedicate this contribution to our estimated colleague, friend and co-author Eric Lebon who passed away in December 2016. This work was supported by the  LACCAVE  and PERPHECLIM projects funded by the  meta-program Adaptation of Agriculture and Forests to Climate Change (AAFCC)  of the  French National Institute for Agricultural Research (INRA).
In addition to the processes structuring free-living communities, host-associated microbiota are directly or indirectly shaped by the host. Therefore, microbiota data have a hierarchical structure where samples are nested under one or several variables representing host-specific factors, often spanning multiple levels of biological organization. Current statistical methods do not accommodate this hierarchical data structure and therefore cannot explicitly account for the effect of the host in structuring the microbiota. We introduce a novel extension of joint species distribution models (JSDMs) which can straightforwardly accommodate and discern between effects such as host phylogeny and traits, recorded covariates such as diet and collection site, among other ecological processes. Our proposed methodology includes powerful yet familiar outputs seen in community ecology overall, including (a) model-based ordination to visualize and quantify the main patterns in the data; (b) variance partitioning to assess how influential the included host-specific factors are in structuring the microbiota; and (c) co-occurrence networks to visualize microbe-to-microbe associations.
In doing so, our proposed model can straightforwardly accommodate and discriminate among any measured host-specific factors. Over the past few years, there has been an increase in model-based approaches aimed specifically towards the analysis of host-associated microbiota (see, e. g. ,  Grantham, Reich, Borer, & Gross, 2017; Xia et al. , 2013; Xu, Paterson, & Xu, 2017; Zhang et al. , 2017). To our knowledge however, our proposed model is the first to explicitly and transparently account for the aforementioned hierarchical structure that is inherent in data on host-associated microbiota (Figure  1 ). Other key features of the proposed model, which are inherited from JSDMs and latent factor models, include the following: (a) parsimonious modelling of the high-dimensional correlation structures typical of hostassociated microbiota; (b) model-based ordination to visualize and quantify the main patterns in the data; (c) variance partitioning to assess the explanatory power of the modelled host-specific factors and their influence in shaping the microbiota; and finally (d) cooccurrence networks to visualize microbe-to-microbe associations.
, 2014). The authors constructed a host phylogeny from 18S rRNA gene sequences (downloaded from GenBank) by implementing a relaxedclock model in MrBayes. The data have a hierarchical structure with n = 90 samples nested within S = 20 host species and L = 4 collection sites. Host species are then further nested under one of R = 2 ecotypes. The response matrix had already been filtered to only include OTUs (defined at 97% similarity) with at least 500 reads, but we further removed OTUs with <20 presences across samples, resulting in m = 187 modelled OTUs. The second data set consists of 59 Neotropical bird species with a total of 116 samples from the large intestine. Host species were collected from 12 lowland forests sites across Costa Rica and Peru (Supporting Information Figure  S2 , for original study see  Hird, Sánchez, Carstens, & Brumfield, 2015).
The metadata include bird taxonomy and several covariatesincluding dietary specialization, stomach contents and host habitat. The authors sequenced and used the mitochondrial locus ND2 to reconstruct the host phylogeny by implementing a partitioned GTR + Γ model in BEAST. Similar to the sponge data set, this data set has a hierarchical structure with n = 116 samples nested within S = 59 host species and L = 12 collection sites. We filtered the response matrix to include OTUs (defined at 97% similarity) with at least 50 reads and 40 presences across samples, resulting in m = 151 modelled OTUs. Of the full list of covariates available, we included diet, stomach content, sex, elevation and collection site as explanatory predictor variables in our model. While diet and geography have been shown to influence the human gut microbiota (see, e.