dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, to make it more practical, we make our PSDS and ComSAR algorithms as an open-source TomoSAR package. To our knowledge, TomoSAR is the first public domain tool available to jointly handle PS and DS targets. We show that the compressed estimator outperforms other approaches in both simulation and experiments with Sentinel-1 data. The gains of the compressed estimator are due to the fact that the coherent integration within the mini-stacks attenuates both noise and short-lived components  [46]. In other words, the noise component of the data space is canceled out in the data compression, resulting in a better SNR of the compressed interferograms with respect to the original ones. Concerning the short temporal baselines, the result (see Section 4) yields a large error with time due to short-term coherences, which can be often biased.
Author contributions. MJG coordinated the study as part of LandClim II and PAGES LandCover6k, two research projects for which she is the overall coordinator and administrator. MJG, AKT, EG, FM, RF, ABN, AP and SS conceptualized the study and methodology. SS developed the REVEALS model and helped with all issues related to the application of the model and interpretation of results. EG, AKT, RF, FM, ABN and AP collected new pollen records from individual authors. JW provided part of the pollen records from the Mediterranean area (collected earlier for a separate project). LS, MS and ST provided unpublished pollen records. EG and AKT had the major responsibility of handling the pollen data files and collecting all related metadata. AKT collected new values of relative pollen productivity estimates (RPPs) in Europe.
1 ). Areas that overlapped with the established protected areas of Mantadia National Park and Analamazotra Special Reserve were excluded from the buffer zones. The outcome variable is the annual deforestation rate sourced from the Global Forest Change (GFC) dataset  34. Following Vieilledent et al. 45  these data were restricted to only include pixels classed as forest in a forest cover map of Madagascar for the year 2000  45, 55  , reducing the probability of false positives (whereby tree loss is identified in pixels that were not forested). The resulting tree loss raster was snapped to the forest cover 2000 layer to align cells, resulting in a maximum spatial error of 15 m. The GFC product  34  has been shown to perform reasonably well at detecting deforestation in humid tropical forests  63.
We also defined five additional variables (annual precipitation, distance to river, distance to cart track, distance to settlement and population density) and tested the effect of including these in the robustness checks. The additional covariates were so defined because they were of poorer data quality (population density and distance to settlement), correlated with an essential variable (annual precipitation and population density) or simply considered less influential (distance to river and distance to cart track; Supplementary Methods). Statistical matching was conducted in R statistics using the MatchIt package v. 4. 1 (ref. 67 ). To improve efficiency and produce closer matches we cleaned the data before matching to remove control units with values outside the calipers of the treated sample in any of the essential covariates (see Supplementary Methods for caliper definition).
Matching was run separately for each offset. The resulting matched datasets were aggregated by treated status (offset or control) and year to produce a matrix of the count of pixels that were deforested each year (2001-2019) in the offset and the matched control sample. Converting the outcome variable to a continuous measure of deforestation avoids the problem of attrition associated with binary measures of deforestation and is better suited to the framework of the subsequent regressions  70.
In a difference-in-differences analysis this assumes that, in the absence of the intervention, the treated sample would have experienced the same average change in outcomes over the before-after period as the control sample  72. Parallel trends in outcomes between treated and control before the intervention are an essential prerequisite for this assumption. We tested this for each matched offset-control dataset using the following formula: log(Count of deforestation + 1)i,t = β 0 + β 1 Yeart + β 2 CIi + β 3 (Year × CIit)+ ∈i,t (1) where the outcome is the log(y + 1)-transformed count of deforestation within sample i at year t and CI is a binary variable indicating whether the observation is from the offset (1) or control (0) sample. Parallel trends in deforestation between offset and matched control samples in the years before the intervention were present for all offsets except for CFAM (Supplementary Fig.
5 ). Consequently, CFAM could not be used in the site-based difference-in-differences analysis. However, its effect is still captured in the results from the fixed-effects panel regression as this is not based on an identifying assumption of parallel trends between groups in the pretreatment period  72. To estimate the impact of protection within each individual offset, we ran an ordinary-least-squares difference-in-differences regression for each matched offset-control dataset using the following formula: log(Count of deforestation + 1)i,t = β 0 + β 1 BAt + β 2 CIi + β 3 (BA × CI)i,t+ ∈i,t (2) where BA and CI are binary variables indicating whether the observation occurred before (0) or after (1) the intervention, in the offset (1) or control sample (0). Given the non-normal properties of count data and the presence of zero values, a log(y + 1) transformation was applied to the outcome variable  70, 73.
Data collection All raw data used in this study were secondary spatial data that are publicly available online. Data layers were compiled into a GIS using ArcMap version 10. 7. 1. All data were reprojected to WGS 1984 UTM Zone 38S. Some data were processed to produce a secondary output (eg. to calculate distance to the nearest feature). Covariate and outcome data values were sampled at sample pixel locations to produce the input data for the matching. Data was collated by K Devenish. Timing and spatial scale The study period is January 2001 to December 2019. This provides sufficient temporal coverage both before (at least 8 years) and after (at least 6 years) the offsets were protected to evaluate changes in outcomes. It also corresponds to the duration of annual deforestation data from the Global Forest Change dataset of Hansen et al (2013).
Reproducibility The code for the main matching specification was re-run several times by both KD and SD (using the same sample of pixels as the input data) and the same results were obtained each time. To test the robustness of our results to arbitrary modelling choices we repeated the analysis 116 times using 116 alternative matching model specifications, varying the combination of model parameters, matching distance measure and the inclusion of five additional covariates. We show that our results are robust to a range of alternative, valid model specifications. All input data and computer code used in this study are publicly available online on GitHub (link in the data availability statement) to enable reproduction.
acknowledgements We thank  A. Guizar Coutiño  and  H. Wauchope  for valuable advice on the methodology and  K. Goodenough  for her support, insightful discussions and comments;  J. Borner  and  D. Schulz  for sharing data allowing us to create Supplementary Fig. 10. This work was funded by the  Natural Environment Research Council Envision DTP  awarded to K. D. , the  British Geological Survey  and  Rothamsted Research's Transformation Fund. S. D. received financial support from the  PARSEC  project.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability We have both a data availability statement and a code availability statement: Data availability statement All input data used in this study are available in the GitHub repository accessible here: https://github. com/katie-devs/Biodiversity_offset_effectiveness. Code availability statement All computer code used in this study are available in the GitHub repository accessible here: https://github. com/katie-devs/Biodiversity_offset_effectiveness.
Escherichia albertii is a recently identi ed gastrointestinal bacterial pathogen of humans and animals which is typically misidenti ed and generally only detected during genomic surveillance of other Enterobacteriaceae. The incidence of E. albertii is likely underestimated and its epidemiology and clinical relevance are poorly characterised. Here, we whole genome sequenced E. albertii isolates from humans (n = 83) and birds (n = 79) in Great Britain and analysed a broader public dataset (n = 475) to address these gaps. We found human and avian isolates typically (90%; 148/164) belonged to host-associated monophyletic groups with distinct virulence and antimicrobial resistance pro les. Overlaid patient epidemiological data suggested that human infection was likely related to travel and possibly foodborne transmission.
g. pig, cat) and both terrestrial and marine wild mammal species (e. g. raccoon, seal, bat)  (8, 26, 27). Although the occurrence and signi cance to mammal host health remains uncertain, there is a growing body of evidence that avian hosts may act as a reservoir of infection  (21, 25). Thus, the extent of associated disease in birds and the relationship of bird and human infections requires further investigation. Here, we performed WGS analysis on E. albertii isolates from humans and birds in Great Britain (GB) from archives held at the UKHSA and the Zoological Society of London (ZSL), respectively, to investigate the epidemiology of this recently identi ed pathogen. The aims of the study were to integrate the phylogenetic and epidemiological data in order to gain insights into the ecology of E.
Bird isolates and epidemiological data collection Wild bird derived E. albertii isolates (n = 74) were obtained through scanning surveillance of dead wild birds conducted by ZSL over the period 2000-2019 inclusive (Supplementary table  2 ). Members of the public reported observations of wild bird mortality, typically in the vicinity of garden bird feeding stations. Carcasses were submitted from a subset of mortality incidents for post-mortem examination. Coverage was across Great Britain, although the majority of wild bird submissions and those from which E. albertii was isolated were from England (England n = 63 isolates, Wales n = 6, Scotland n = 5). Post-mortem investigations were conducted following a standardised protocol, supported by parasitological and microbiological examination as routine, combined with histological examination and other ancillary diagnostic testing as indicated based on macroscopic abnormalities.
Genome sequencing and quality control Isolates of E. albertii from UKHSA and ZSL were sequenced at GBRU according to previously described protocols  (28)  and deposited in the Sequence Read Archive (SRA) under the bioproject accession PRJNA315192 with the SRA accession numbers of individual isolates listed in Supplementary Table  1. Short read sequences were retrieved from the SRA and processed using Trimmomatic v0. 38  (30)  to trim adaptors and lter low-quality bases. FastQC v0. 11. 6 (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/) and MultiQC v1. 7  (31)  were used to assess the quality of reads.
Phylogenetic and clustering analysis Processed reads were mapped to the E. albertii strain 1551-2 reference genome (GenBank accession CP025317) (32) using BWA mem v0. 7. 17  (33). Alignment les were sorted and ltered using the SAMtools suite v1. 9-47  (34) , PCR duplicates were marked using Picard v2. 21. 1-SNAPSHOT MarkDuplicates (http://broadinstitute. github. io/picard/). The BCFtools suite v1. 9-80 (34) was used to identify sequence variants and lter variant les, in which low quality single nucleotide polymorphisms (SNPs) were removed if mapping quality < 60, Phred-scaled quality score < 30, read depth < 10 and variant allele frequency < 0. 7. BCFtools consensus was used to generate reference-based pseudogenomes for each isolate from the ltered SNP variants. Regions containing insertion sequences and phages (identi ed using the PHASTER web server https://phaster.
Construction of cgMLST tree with publicly available data To deepen the insights gained from the UKHSA and ZSL E. albertii isolates, we analysed their genome sequences in the context of publicly available E. albertii sequence data. Speci cally, additional publicly available E. albertii genome sequences accessible through Enterobase on the 7th of February 2022 (n = 475) were constructed alongside the data above into a core genome Multi Locus Sequence Type (cgMLST) tree using hierarchical clustering (HeirCC)  (41). Minimal metadata on source and country of origin was extracted from Enterobase alongside HeirCC level classi cations and visualised over the unrooted cgMLST tree using interactive Tree of Life (iTOL) v6. 5  (39). Metadata on isolate origin was manually curated into the following categories: human, avian (poultry, nonpoultry and not de ned); mammal (livestock, wildlife and companion species); food, water and undescribed sources.
1. 3 (46). Association of known AMR genes with related plasmid sequence were identi ed by extracting AMR-gene containing contiguous sequences from draft genome assemblies and comparison against the NCBI nonredundant database using MegaBlast. Detection of virulence genes was performed using ABRicate (https://github. com/tseemann/abricate), by which draft genomes were screened against the Virulence Factor Database with minimum nucleotide identity of 60% and minimum coverage of 60%. This screen comprised of virulence genes associated with E. albertii including stx, eae and cdtABC genes that encode Shiga toxin, intimin and CDT.
Genomic epidemiology of Escherichia albertii from humans and bird isolates To explore the genomic epidemiology of E. albertii among the human and bird isolates from GB, demographic features were overlayed on the bacterial population structure and statistical support for associations with metadata variables were evaluated. Speci cally, to determine the population structure of E. albertii within our dataset, a maximum likelihood phylogeny was constructed based on a SNP alignment of 26,594 bp (Fig. 1 ). BAPS identi ed eight clusters consistent with monophyletic clustering, with the exception of BAPS cluster 8, which was split across multiple regions of the tree (Table  1 , Fig. 1 ). Combining the epidemiological information with this population structure revealed distinct and separate phylogenetic clustering of bird and human isolates (p < 0.
Virulence pro les and associations with disease in bird hosts The eae gene was present in all but one isolate within the dataset, and the cdtA, cdtB, cdtC genes were present in > 94% (n = 153/162) isolates (Fig. 2 ). The stx2f gene was detected in 38 isolates, the majority (n = 37/38, 97%) of which were from wild birds in BACs, except for one human isolate (SRR6144114) belonging in BAPS 8. Among the wild birds, stx2f resulted in an increased odds of inferred clinical signi cance of infection (relative to equivocal and incidental combined) (OR = 10. 27, 95% CI = 2. 98-35. 45 p = 0. 0002). There was little evidence for confounding of the disease association by bird family (Fringillidae/Non-Fringillidae, Adjusted OR 10. 25 95% CI 2. 66-92. 78), a possible effect modi cation of the bird family (Strata speci c OR: OR = 12.
Antimicrobial resistance pro les in human and bird isolates To investigate the genotypic predictors of AMR among E. albertii isolates in this dataset, we looked for the presence of genetic determinants of AMR. Both horizontally acquired antimicrobial resistance genes (ARGs) and vertically inherited point mutations known to confer resistance or reduced susceptibility to various antimicrobials in E. coli were identi ed. ARGs were exclusively identi ed in human isolates, except for one captive zoo bird isolate in HAC BAPS cluster 8 (SRR13092475). Overall, human isolates were observed to carry more AMR genetic determinants compared to bird isolates, including a total of 25 ARGs and ve point mutations associated with resistance or reduced susceptibility to 10 different antimicrobial drug classes.
Point mutations in marR_S3N* and parE_I355T* were present in the majority of isolates tested in this study set, and resistance pro les were consistent across the dataset and impacted by the additional presence of other ARGs or point mutations (Table  2 )Global contextualisation of E. albertii from GB Supplementary Table  3. Results from BLASTn search of contiguous sequence identi ed in 14 Escherichia albertii isolates within a monophyletic clade. The ta lists plasmids that share 100% coverage and sequence identity with the 14,961 bp sequence from isolate SRR8838300 harbouring mph(A), sul1, blaDHA-1 an qnrB4. To place the human and bird E. albertii isolates from GB within the global context, we expanded the analysis to include additional isolates retrieved from publicly available data (n = 475, Methods, Supplementary Table  4 ).
g. Anatidae, Corvidae, Laridae) in the available global dataset, further surveillance is required to identify the role that different bird families play in the ecology of the bacterium. In conclusion, due to poor molecular diagnostic capabilities for E. albertii in both human and animal health laboratories, the true public health burden of E. albertii infection is likely to be underestimated. Furthermore, the lack of systematic surveillance data means that clinical severity and exposure risks are largely unknown. However, we leveraged available data to highlight the likely relevance of travel to regions with a high risk of GI infections, including an association with AMR, and a potential zoonotic component that is likely bird associated, but probably more so with poultry than with wild bird species.
To improve surveillance for E. albertii, we recommend increased deployment of molecular methods in medical and veterinary diagnostic laboratories, in conjunction with the systematic collection of epidemiological data. Maintaining close collaborations between medical and veterinary institutions, and the integration of human and animal surveillance datasets, is essential to better understand the source, transmission and risks to animal and public health of this recently identi ed pathogen. Declarations integrated into the Garden Wildlife Health project (2013-2019) (www. gardenwildlifehealth. org). We thank the veterinarians who conducted some of the wild bird post-mortem examinations included within this study (including Katie Beckmann, Lydia Franklinos, Joseph Heaver and Vicky Wilkinson) and Ed Fullick from the Animal & Plant Health Agency for assistance with information sharing.
Active mesh cell segmentation The 3D nuclei detection provides an approximate location of nuclei. In order to obtain final cell segmentation, the membrane information is essential. Edge-based deformable models  (11, 19)  are more appropriate choice for segmentation in this scenario. This is specifically advantageous in our case, since this formulation allows the mesh to coincide with image edge features and simultaneously forbid overlapping of meshes  (19). The active mesh model implemented in the bioimage analysis software Icy  (20)  is used for this purpose. The active contour evolution parameters are estimated in an automated manner as described in Refs. (11) and (19). We initialize the active mesh at the detected nuclei and evolve using the 3D membrane image I M. Each 2D slice in I M is multitask neural network membrane prediction b I m.
Neural network training We obtain 56-2D cropped images (dimension 255 Â 255) from the 3D scan of an embryo ventricular region. They are manually annotated to create nuclei and membrane ground truth. The 2D ground truth was generated in a semi-automated manner using Icy, a bio-image analysis software  (20). Ground truth for nuclei was generated by drawing manual contours. The membrane images are first enhanced using a hessian enhancement scheme and manual thresholding to obtain binary detection. An in-painting tool  (20)  is used to connect (disconnect) membrane regions to preserve continuity (remove noisy structures) of the structures. Finally, we compute the inverse of membrane ground truth and apply area-threshold to discard nonmyocardial regions. This forms the ground truth for the intermembrane prediction branch.
We perform a qualitative and quantitative comparison with four state-of-the-art methods to demonstrate the efficacy of our method. A dataset consisting of 500 images is created by employing various augmentations, such as horizontal and vertical flips, rotation, variation in brightness. The neural network is trained for 100 epochs with a learning rate of 10 À3 and a learning rate decay of 0. 9 at every 50 epochs. Adam Optimizer  (27)  is used to train the network. Details of the network architecture are shown in Figure  4. The filter dimensions (height and width) and the number of channels (Channel Size) at each convolution layer are shown in the aforementioned figure (above each convolution step). The channel size at each step is also marked above each block in the schematic. The dotted blocks denote the concatenation of channels.
Funding Statement. This work was supported in part by the  France-BioImaging (FBI) infrastructure  under Grant  ANR-10-INBS-04 , and the  Program PIA INCEPTION  under Grant  ANR-16-CONV-0005. The Meilhac Lab is supported by core funding from the  Institut Pasteur  and state funding from the  Agence Nationale de la Recherche  under the " Investissements d'avenir" program  ( ANR-10-IAHU-01 ) and an  FRM grant  ( DPC20171239475 ). D. D. was a recipient of fellowships from the  Region Ile-de-France  and  FRM. S. M. is an INSERM research scientist.
Genetic mapping in 2-generation crosses requires genotyping, usually performed with single nucleotide polymorphism markers arrays which provide high-density genetic information. However, genetic analysis on raw genotypes can lead to spurious or unreliable results due to defective single nucleotide polymorphism assays or wrong genotype interpretation. Here, we introduce stuart, an open-source R package, which analyzes raw genotyping data to filter single nucleotide polymorphism markers based on informativeness, Mendelian inheritance pattern, and consistency with parental genotypes. The functions of this package provide a curation pipeline and formatting adequate for genetic analysis with the R/qtl package.
Spurious or unreliable mapping outputs can result from defective SNP assays or wrong genotype interpretations. Therefore, raw data obtained from genotyping services must be curated before performing genetic analyses. Several tools exist for quality control of SNP genotyping arrays, including Illumina's GenomeStudio. R packages such as argyle  (Morgan 2015)  analyze hybridization intensity signals from MUGA arrays. The simple genetic structure of 2-generation crosses provides specific and efficient means for identifying spurious genotyping data, such as consistency with parental genotypes and expected Mendelian proportions. The R/qtl package includes functions to build genetic maps and check for genotype consistency (https://rqtl. org/tutorials/geneticmaps. pdf). However, this control is performed once genotypes have been imported and involves multiple steps of manual curation.