dataesr/scientific-paragraphs-categorization huggingface dataset:
832738/ESCAPE. M. T. thanks the  Gruber Foundation  for its generous support to this research. M. T. was granted access to the  High-Performance Computing (HPC) resources of Centre Informatique National de l'Enseignement Supérieur (CINES)  under the allocations №  A0020101167  and  A0040110391  made by  Grand Équipement National de Calcul Intensif (GENCI). This work has been carried out within the framework of the  National Centre of Competence in Research PlanetS  supported by the  Swiss National Science Foundation. M. T. acknowledges the financial support of the SNSF. M. T. and F. F. thank the  LMD Generic Global Climate team  for the teamwork development and improvement of the model. M. J. W was supported by  NASA's Nexus for Exoplanet System Science (NExSS). Resources supporting the ROCKE-3D simulations were provided by the  NASA   High-End Computing (HEC) Program  through the  NASA Center for Climate Simulation (NCCS)  at  Goddard Space Flight Center.
J. H. M. acknowledges funding from the  NASA   Habitable Worlds program  under award  80NSSC20K0230. The authors acknowledge the help of  Andrew Ackerman  to set up the cloud diagnostics in ROCKE-3D. The  THAI GCM  intercomparison team is grateful to the  Anong 's  THAI Cuisine restaurant in Laramie  for hosting its first meeting on November 15, 2017. Numerical experiments performed for this study required the use of supercomputers, which are energy intensive facilities and thus have non-negligible greenhouse gas emissions associated with them. We estimate that the final versions of Hab 1 & Hab 2 experiments resulted in roughly ≈1280 kg kgCO 2 e, including ≈520 kg from ExoCAM runs, ≈80 kg from LMD-G, ≈650 kg from  ROCKE-3D  and  ≈30  kg from the  UM.
SCFA analysis in cecal samples Samples were water-extracted, and proteins were precipitated with phosphotungstic acid. A volume of 0. 1 μl of the supernatant was analyzed for SCFAs on a gasliquid chromatograph (Nelson 1020; Perkin-Elmer, St. Quentin en Yvelines, France) equipped with a split-splitless injector, a flame-ionization detector and a capillary column (15 m × 0. 53 mm, 0. 5 μm) impregnated with SP 1000 (FSCAP Nukol; Supelco, Saint-Quentin-Fallavier, France). The carrier gas (He) flow rate was 10 ml/min, and the inlet, column, and detector temperatures were 175, 100, and 280 °C, respectively. 2-Ethylbutyrate was used as the internal standard. Data were collected and peaks integrated using Turbochrom v6 software (Perkin Elmer, Courtaboeuf, France). Cecal SCFA concentrations are expressed as μmol/g of stool.
16S and ITS2 sequence analysis For 16S sequences, the sequences were demultiplexed and quality filtered using the QIIME2 version 2021. 2. 0 software package  [16]. The sequences were then assigned to OTUs using the UCLUST algorithm  [17]  with a 97% pairwise identity threshold and classified taxonomically using the Greengenes reference database (version 13. 8)  [18]. Rarefaction was performed on both datasets and used to compare the relative abundances of OTUs across samples. Alpha diversity was estimated using the Shannon diversity index or the number of observed species. Beta diversity was measured by a Jaccard distance matrix and was used to build principal coordinates analysis (PCoA) plots. The linear discriminant analysis (LDA) effect size (LEfSe) algorithm was used to identify taxa that were specific to a genotype  [19].
For ITS2 sequences, data were processed using the FROGS pipeline (available from http:// frogs. toulo use. inra. fr), established in Toulouse, France  [20] , for sequence quality control, filtering, and affiliation of taxa. The sequences were assigned to OTUs with a 97% threshold of pairwise identity and classified taxonomically using the UNITE ITS database (version 8_2)  [21]. The phyloseq package for R was used for alpha and beta diversity analyses as well as all illustrations. The Deseq2 package for R was used for differential analysis of OTUs with respect to the different phenotypes  [22]. The LEfSe algorithm was used to identify taxa that were specific to genotype. Deposition of the raw sequence data in the SRA database from the NCBI, the accession numbers are the following: PRJNA824403 and PRJNA824345.
To test this hypothesis, we treated D-1/2KO mice with a broad-spectrum antifungal drug (fluconazole) and characterized the effect of this treatment on DSS-induced colitis (Fig. 3C ). Again, as illustrated in Fig. 3D , modification of the fungal population did not affect the susceptibility of D-1/2KO mice to colitis. These data suggest that even though Dectin-1 and Dectin-2 are two fungal receptors, their effects on the susceptibility of D-1/2KO mice to DSS-induced colitis did not seem to be linked to an effect on the fungal population. To further characterize the fungal microbiota in mice deficient in both Dectin-1 and Dectin-2, we investigated the effect of double Dectin deficiency on the fungal microbiota composition by amplicon-based sequence analysis targeting ITS2. We analyzed the mycobiota of D-1/2KO and WTj mice at baseline.
However, we were able to isolate 5 different bacteria from the Lachnospiraceae family. The Blautia genus was the strongest hit identified by our LEfSe analysis (Fig. 4C ), but we were unable to isolate any Blautia strains from the feces of D-1/2KO mice. Thus, for the purpose of our analysis, we used a Blautia strain from an international collection: Blautia hansenii ATCC 27752. Before in vivo experiments, we used in vitro screening of BMDCs to select the best strains with potential anti-inflammatory effects. From this screen, we selected the strains BM38, BM62 and B. hansenii ATCC 27752 for in vivo experiments (Fig. 5A  and Supp Fig. 4A ) since these strains elicited lower IL-6 and TNF alpha (for BM38 only) production after coincubation with BMDCs. BM38 and BM62 isolates were identified using 16S sequence blast against the NCBI database.
Acknowledgements The authors thank  Gordon D Brown  for kindly providing the Dectin-1KO strains. The authors thank the employees of the animal facility at the IERP platform,  INRAE ,  UE0907  ( Jouy en Josas, France ) for their technical assistance. This work has benefited from the facilities and expertise of  @BRIDGe  ( Université Paris-Saclay ,  INRAE ,  AgroParisTech , GABI, 78350 Jouy-en-Josas, France).
Haplotype analysis Haploview version 4. 2 was used for the analysis of LD patterns and haplotype blocks as well as for the detection of tagging SNPs based on the genotype data of the 135 healthy Tunisian subjects. LD blocks were defined according to the haplotype block definition of Gabriel which defines a pair of SNPs to be in "strong LD" if the one-sided upper 95% confidence bound on D' is >0. 98 and the lower bound is above 0. 7  [21]. In each sample, the genotype frequencies for each SNP were checked for consistency between the observed values and those expected from Hardy-Weinberg equilibrium using the same software. We used the program PHASE version 2. 1 in order to estimate haplotypes distribution of BRCA1 and BRCA2 genes using genotype data of tagging SNPs. PHASE implements a Bayesian statistical method for reconstructing haplotypes from population genotype data  [22, 23].
Population structure and genetic diversity analysis Population structure and genetic differentiation were evaluated using principal component analysis (PCA) and fixation index (FST). PCA was performed using the toolset Plink1. 9. The two first PCAs were then plotted using the ggplot2 R package  [25]. When performing PCA analysis only SNPs with the following criteria were considered: HWE p-value > 0. 0001, have a genotyping call rate of greater than 95%, MAF �0. 05, and r2 < 0. 6. FST-values were calculated using Plink1. 9 based on the method of Weir and Cockerham  [19]. FST-values range from 0 to 1. High Fst implies a considerable degree of differentiation among populations while values near 0 indicate little genetic subdivision.
Functional effect prediction To predict the functional impact of the studied SNPs and to identify functional variants that could contribute to disease risk, several In Silico prediction tools have been used. Since most of the variations covered by SNP arrays are non-coding, we have used tools and databases providing annotation information on non-coding SNPs. To prioritize variations, we have proceeded as follows: 1) RegulomeDB, a database that annotates SNPs with known and predicted regulatory elements in the intergenic regions of the H. sapiens genome, was used to analyze the regulatory effects of noncoding variants  [26]. 2) Top ranked SNPs (RegulomeDB score = 1) were then investigated using the GTEx portal to evaluate the association of potential functional variants with gene expression in breast mammary tissue  [27]  3) SNPnexus  [28]  and VARAdb  [15]  databases were used to annotate variations and to retrieve regulatory features of the investigated SNPs while SNPinfo web server was used to predict the potential miRNA binding sites (https://snpinfo.
Functional annotation The vast majority of SNPs characterized from genotyping data are localized in untranslated regions. Given that non-coding elements can have diverse roles in the regulation of proteincoding genes, we performed functional annotation using RegulomeDB to identify potential functional variants. RegulomeDB uses a scoring system to classify variations. Lower scores indicate increasing evidence for a variant to be located in a functional region  [26]. To prioritize candidate functional variants only top ranked ones, those belonging to category 1, were considered for further analysis. These variations are likely to affect binding and are linked to the expression level of a target gene. These SNPs were then explored in the GTEx database to evaluate their association with gene expression in breast mammary tissue.
We have also determined tagging SNPs constituted with the most informative markers that can be used in upcoming large-scale breast cancer association studies in Tunisia. In the current report tagging SNPs were used to construct a haplotype Network for BRCA genes that included genotypic data from Tunisia and from 8 other human populations. The most frequent haplotypes identified for each gene are probably the ancestral ones from which all others have derived. Moreover, Tunisian haplotypes showed a heterogeneous distribution, some are shared with different populations and others are unique to the Tunisian population. These results reflect the mosaic genetic structure of the Tunisian population which may explain the genetic heterogeneity of breast cancer in Tunisia. This observation was further confirmed with PCA and Fst estimation.
This work was supported by  Qatar National Research Foundation  [ NPRP 08-083-3-031 ], the  Tunisian Ministry of Higher Education and Scientific Research and the Tunisian Ministry of Health. MB is a recipient of a  MOBIDOC fellowship , funded by the  EU  through the  EMORI program  and managed by the  ANPR. The funders efficient in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Image processing Signal compensation strategy was performed to compensate for the attenuation of ow signal in locations underlying pathological abnormalities in the retinal pigment epithelium/Bruch's membrane complex. In brief, an inverse transformation was applied to the en face structural image and a Gaussian smoothing lter (3x3 pixel kernel) was utilized to reduce the speckle noise. The en face OCTA CC image was then multiplied by the resultant, inverted, smoothed, corresponding structural image. 21  Image analysis was performed by ImageJ software version 1. 50 (National Institutes of Health, Bethesda, Maryland, USA).
Quantitative image analysis The GA border was delineated on the baseline en face structural fundus image which represents reconstruction of all signals coming from the A-scans, and the corresponding B-scans. 22  Successive, 100 µm wide, concentric rings were generated around the GA lesion border using the 'Edit -Selection -Enlarge' tool in ImageJ software, which automatically creates a border that follows the boundary of the GA lesion. The generated grid of rings, which is distinctive to each case, was applied to the compensated CC images at the same position around the GA lesion (Fig. 1 ). The 6x6 mm CC OCTA images (1024x1024 pixels) were binarized in order to calculate the FD% in each ring using the Phansalkar's local thresholding strategy with a 3-pixel radius (approximately 17. 6 microns).
23  The images were then analyzed with the "Analyze Particles" function. In accordance with previous reported methods, FDs with a diameter less than 24 µm were excluded from the analysis, as they are thought to fall within the normal range of spacing between choriocapillary vessels. 24  The entire process was repeated for the OCTA CC images exported from the three different slab locations; 11-21, 21-31 and 31-41 µm from the RPE-t line (Fig. 2 ). The entire analysis was conducted on all image data by two masked, independent Doheny Reading Center graders (AA, IB) to assess the repeatability for all measurements. GA yearly enlargement rate calculation: The GA border was segmented on the en face OCT fundus image of baseline and follow-up scans to measure the GA lesion size at both visits. Square root transformation was utilized to exclude the confounding effect of baseline lesion size on the GA enlargement rates.
Statistics Statistical analyses were performed using SPSS Statistics version 20 (IBM, Armonk, NY). The spearman's correlation coe cient was utilized to calculate the correlation between the FD% in each ring and the GA yER. Generalized estimating equations were utilized to adjust for the correlation between the two eyes of the same subject. Intraclass correlation coe cients (ICCs) were calculated to assess the agreement between the two grader's measurements. A P value < 0. 05 was considered to be statistically signi cant.
, 2020), frequency-domain electro-magnetic and self-potential survey  (Prudhomme et al. , 2019). In the urban environment, GPR survey is performed mostly due to di culty of survey due to surrounding conditions such as asphalt roads. However, GPR survey has disadvantage that the depth of detection is shallow. ERT has widely used in general also has di culties due to surrounding environment in case of urban region, however provides subsurface electrical images for a deeper depth. ERT had applied at various studies to interpret fractured rock and cavity locations (e. g. ,  Fountain et al. , 1975; Smith, 1986; Cardarelli et al. , 2006; Kim et al. , 2007; Ungureanu et al. , 2017; Kidanu et al. , 2020). In order to interpret resistivity data in the urban area occurring subsidence, considering the characteristics of exploration site is signi cant in interpreting resistivity data.
First, the geological characteristics of the survey site and the groundwater level change in the vicinity of the excavation area before and after subsidence were explained. Before analyzing the data obtained from the ERT in the vicinity of the excavation area, the effect of the excavation area on electrical response was analyzed using eld-scale 3D numerical model. In case of 2D inversion, we proposed data correction considering the effect of the excavation area and proved this method by numerical model. 2D inversion method considering the excavation effect was applied to the eld data and interpret weakness zone. In the 3D inversion, excavation effect was considered in in-house inversion algorithm.
2D inversion with correction of excavation effects The eld data in the subsidence occurring region was corrected for 2D inversion by using the ratio of resistivity change (Eq. (  1 )). The ratio was calculated through eld-scale modeling of homogeneous numerical excavation model considering the excavation size and survey line in the actual subsidence site. Field apparent resistivity data was also corrected by dividing by the ratio in the same way as the method used in the numerical test. Inversion of obtained raw data without any processing using a commercial software of DC2dpro showed a conductive zone of 50 ohm-m or less, which is spatially bigger than the excavation range (Fig. 9(a) ). Inversion of corrected data delineated the conductive anomaly more clearly at a depth of more than 10 m between 13th and 18th electrodes in narrower range along the survey line.
Rabies is endemic in Cambodia. For exposed humans, post-exposure prophylaxis (PEP) is very effective in preventing this otherwise fatal disease. The Institut Pasteur du Cambodge (IPC) in Phnom Penh was the primary distributor of PEP in Cambodia until 2018. Since then, and to increase distribution of PEP, two new centers have been opened by IPC in the provinces of Battambang and Kampong Cham. Data on bitten patients, who sometimes bring the head of the biting animal for rabies analyses, have been recorded by IPC since 2000. However, human cases are not routinely recorded in Cambodia, making it difficult to establish a human burden of disease and generate a risk map of dog bites to inform the selection of future PEP center locations in high-risk areas. Our aim was to assess the impact of accessibility to rabies centers on the yearly rate of PEP patients in the population and generate a risk map to identify the locations where new centers would be the most beneficial to the Cambodian population.
Data collection and management 2. 1. 1 Rabies surveillance data. Since 1998, IPC has made PEP available to dog-bite victims at its Phnom Penh location, initially for free, and then with a fee starting in 2010  [23]. In parallel, each patient was given a questionnaire upon arrival to collect information related to the characteristics of the attack and the victim(s), such as name, age, and address, in order to guide allocation of HRIG and the PEP regimen. These data were then completed over time with follow-up visit information, number of injections, and results from animal testing when the head of the biting animal was brought with patients. The victim's residence was recorded at the province level from 1998 to 2013, then at the district level from 2013 to 2016 (S1 and S2 Data). 2. 1. 2 Administrative and demographic data.
Using primary data covering transport infrastructure, land coverage, geographical features such as slope, altitude, and water bodies, this dataset estimates the travel speeds across the globe with a resolution of one square kilometer. Using the coordinates of PEP centers or provincial and district capitals and the friction raster, the R package "gdistance" computes the shortest travel time from any pixel on the map to the nearest point (PEP center or provincial/district capital) by assessing all possible paths between them  [48, 49]. This produces a raster of travel time from any given location to the nearest point with the same resolution as the friction raster. 2. 1. 4 Prediction scenarios for PEP patients. We considered five accessibility scenarios for predicting the expected number of PEP patients (Table  1 ).
This was confirmed by a preliminary analysis of the number of patients every month using a generalized additive model (GAM) with a smooth term over month, using the R-package "mgcv"  [54]  (S3 Fig). The smooth term for month from this model had no significant effect on the number of PEP patients (p-value = 0. 815). Therefore, we constructed all prediction models using a yearlevel temporal random effect only. Model selection was done using the Deviance Information Criterion (DIC) and the Watanabe-Akaike Information Criterion (WAIC). All models in this section and beyond used minimal non-informative priors as set by default in R-INLA. The default prior distribution for a Poisson model in R-INLA is a Gamma distribution with the following parameters Gam(1, 0. 00005)  [55]. Once the best fitting model was obtained, both at the provincial and district level, models were fitted using a dataset that was expanded to include the prediction year 2017.
This involved adding observations with demographic and accessibility data for 2017 but with NA values for the outcome, to be fitted when running the model. Due to the different spatial aggregation scales, resulting models were not directly comparable with DIC and WAIC. Therefore, we used intra-class correlation coefficients (ICC) to assess the agreement between observed and fitted values. These ICCs where computed using the R package "irr"  [56]. All data management and statistical analysis were conducted in R Version 4. 0. 3 [25].
Descriptive data Between 2000 and 2016, 293,955 PEP patient records were identified and associated to a given province, representing a rate of 12. 97 patients per 10,000 person-years. For another 85 records (0. 03%) the province could not be identified. From 2013 to 2016 we observed 85,780 records with an identifiable district, whereas a further 42 patients (0. 05%) could not be located. Yearly data are summarized in Fig 1. We observed two phases in PEP patient numbers with a plateau between 12,000 and 14,000 patients per year from 2000 to 2007, followed by a major increase to a second plateau between 20,000 and 22,000 patients per year from 2008 to 2016. Despite the Cambodian population increase during this time, the rate of PEP per 10,000 people also showed a major increase from 2007 to 2008.
The number of patients in 2019 represents an increase of 134% in Phnom Penh compared to 2017. If we assume a similar rate of increase nationwide due to this event, our total expected number would be closer to 33,600 patients in 2019, of which 6,400 would be in Battambang and 4,800 in Kampong Cham. Though we have few data points from the opening of the Battambang center to when these events unfolded, we do observe a similar trend. The center opened in July 2018 and only recorded 84 patients in its first two months of operation, but then averaged 172 patients per month from September through November. In December, the number of patients at the Battambang center jumped to 600 and continued increasing to a peak of 1,501 in April 2019 before coming back down to an average of 1,157 from May 2019 to December 2020.
This might be particularly true for bite injury victims which could have impaired mobility. Moreover, travel speed depends on accessibility to a vehicle, which is limited in a country where only 5% of households owned a car and 44% owned a motorbike in 2008, though this would have likely increased since  [43, 63]. However, in the absence of a more locally-specific mobility study, we assumed this would still be reflective of the broad accessibility issues for the country, and that if the assumptions from the Global Malaria Atlas project introduced a bias, this bias would be mostly similar throughout the country. Nevertheless, this does create unmeasured uncertainty around our estimates. Secondly, given the infrequency of census data, demographic data were estimated for most years using linear projections, which is a simplistic approach to demographic projections.
Data The paper relies on a comprehensive dataset from a series of surveys on food flows from the four West African cities Tamale (Ghana), Bamenda (Cameroon), Ouagadougou (Burkina Faso), and Bamako (Mali). In the four cities, transport by road is the major channel for food transport, and the only channel in Bamenda and Tamale. In the capital cities Ouagadougou and Bamako, additional transport channels are available, but at small scale or only for few selected products, such as the railway connecting Ouagadougou with Abidjan, Côte d'Ivoire that mainly transports imported goods from the port, and the waterway on the river Niger, connecting Bamako seasonally with locations in Guinea upstream and the country's northern downstream (Supplementary Materials). Data were collected between 2013 and 2017 in different seasons and with a focus on road surveys (Figure  2 ).