dataesr/scientific-paragraphs-categorization huggingface dataset:
As described in detail in refs. 68, 69 , the DiroImager is an automated high-throughput platform that allows for high-resolution optical imaging of an entire 96-well microtiter plate. Data are reported as the EC50 (µM) calculated from the tested concentration series. For the Dirofilaria immitis larval development assays, freshly isolated L3s were cultured in wells of a 96-well microtiter plate with 10 L3s per well. All wells contained supplemented RPMI 1640 medium  68, 69  and a test compound at one of the following concentrations: 10 µM, 2 µM, 0. 4 µM, 0. 08 µM, 0. 016 µM and 0. 0032 µM. L3s exposed to DMSO only (1%) were used as negative controls. All drug concentrations were tested in duplicate and drug effects were evaluated after 72 h of incubation, after which DMSO-only controls are L4s and the shed L3 cuticles are evident in the wells.
Compounds that have dramatic acute effects retard or arrest the growth of the nematodes and they remain L3s, while other compounds have less severe effects and the nematodes grow to the L4 stage. Motility was scored for each sample in the following standard manner: The 20 worms total from the two duplicate sample wells are considered. If only one of the 20 worms moves (as detected by pixel displacement with the DiroImager), the score for that sample is considered as 5% motility. Data on a given day were considered as valid only if the DMSO-only controls exhibited at least 90% motility. Data is then reported as the EC50 (µM) calculated from the tested concentration series. We also conducted Dirofilaria immitis experiments in the lab of M. Zamanian (Univeristy of Wisconsin). For this experiment, shown in Supplementary Fig.
Haemonchus contortus culture and small-molecule assays The INVENesis Migration Trap Assay (MTA), previously described in ref. 72  , measures the effect of compounds on the third larval (L3) stage of Haemonchus contortus nematodes (the susceptible isolate, 'Weybridge', UK). Approximately 300 L3s were exposed to the drug or control treatments in each well of a 96-well plate. In all experiments, non exsheated L3s were exposed for 24 h to the compound in a solution of 1. 5% of DMSO and 0. 00425% of Tween 20. Larvae were then transferred to a migration plate, which is a 96-well plate allowing H. contortus L3s to migrate from a deposit area to a trap area through a corridor. The mobility of the worms in the trap area is monitored over a 21 min time window by an automated data acquisition system equipped with a Basler acA2000-50gm camera with which pixel displacement is measured.
6 × 150 mm Zorbax SB-38 column (5-μm particle size) and eluted with solvent and flow rate gradients over 8. 65 min. UV-Vis absorbance was measured every 2 nm between 190 and 602 nm. Using MATLAB (The MathWorks), absorbance intensity values were converted to three-dimensional heat-mapped chromatograms. A sample of 5 nmol pure Nemacol-1 was processed prior to worm samples to determine the compounds elution time and absorbance spectrum. All HPLC was performed a using an HP 1050 system equipped with an autosampler, vacuum degasser, and a variable wavelength diode-array detector. The solvent and flow rate gradients are indicated in the chart below. Data analysis was done using HP Chemstation software. The area under the curve (AUC) was automatically integrated by the software for quantification of Nemacol-1 absorbance peaks.
NMR characterization data were obtained at 293 K on a Varian Mercury 300 MHz, Varian Mercury 400 MHz, Bruker Advance III 400 MHz, Agilent DD2 500 MHz equipped with a 5 mm Xses cold probe or Agilent DD2 600 MHz. 1  H spectra were referenced to the residual solvent signal (CDCl 3 = 7. 26 ppm, DMSO-d 6 = 2. 50 ppm). 13 C{ 1 H} spectra were referenced to the residual solvent signal (CDCl 3 = 77. 16 ppm, DMSO-d 6 = 39. 52 ppm). Data for 1 H NMR are reported as follows: chemical shift (δ ppm), multiplicity (s = singlet, d = doublet, t = triplet, q = quartet, m = multiplet), coupling constant (Hz), integration. NMR spectra were recorded at the University of Toronto Department of Chemistry NMR facility (http://www. chem. utoronto. ca/facilities/nmr/nmr. html). Infrared spectra were recorded on a PerkinElmer Spectrum 100 instrument equipped with a singlebounce diamond/ZnSe ATR accessory in the solid state and are reported in wavenumber (cm -1 ) units.
Acknowledgements We are grateful for the strains given to us by the  Caenorhabditis Genetics Center (University of Minnesota ) and  James Rand (Oklahoma Center for Neuroscience). Christopher Evans  and  Andrew Moorhead  of the  NIH/ NIAID   Filariasis Research Reagent Resource Center  ( FR3 ) provided valuable advice on Dirofilaria purification and provided some parasite material. Some data analysis was performed using the compute resources and assistance of the  UW-Madison Center For High Throughput Computing (CHTC) (Dept of Computer Sciences). The following grants supported this work:  CIHR  grant  313296  (P. J. R. ),  CIHR  grant  173448  (P. J. R. ),  Canada Research Chair program  (P. J. R. ),  NSERC  grant  RGPIN-2020-04168  (M. L. ) and an  NIH/NIAID  grant  R01 AI151171  (M. Z. ).
Data availability Original data for all analyses presented are included in the Source data file. Sequences used herein include the following (NCBI reference sequence identifiers or otherwise stated): human NP_003046. 2; swine (Sus scrofa): XP_013838900. 2; cattle (Bos taurus): XP_002699016. 1; sheep (Ovis aries): XP_027818269; mouse (Mus Musculus): NP_068358. 2; rat (Rattus norvegicus): NP_113851. 1; zebrafish (Danio rerio): NP_001071018. 1; Trichuris trichiura: CDW52212. 1; Ancylostoma duodenale: KIH66835. 1; Necator americanus: XP_013297134. 1; Onchocerca volvulus: A0A2K6VZC1 (UniProt ID) Ascaris suum: AgB02_g088_t01 (WormBase ParaSite transcript ID); Dirofilaria immitis: nDi. 2. 2. 2. t09212 (WormBase ParaSite transcript ID); Haemonchus contortus: A0A7I4YIM0 (UniProt ID); C. elegans: NP_001379838.
Crop type maps identify the spatial distribution of crop types and underpin a large range of agricultural monitoring applications ranging from early warning of crop shortfalls, crop condition assessments, production forecasts, and damage assessment from extreme weather, to agricultural statistics, agricultural insurance, and climate mitigation and adaptation decisions. Despite their importance, harmonized, up-to-date global crop type maps of the main food commodities do not exist to date. To address this critical data gap of global-scale consistent, up-to-date crop type maps, we harmonized 24 national and regional datasets from 21 sources covering 66 countries to develop a set of Best Available Crop Specific masks (BACS) over the major production and export countries for wheat, maize, rice, and soybeans, in the context of the G20 Global Agriculture Monitoring Program, GEOGLAM.
Methods Several steps were required for developing the GEOGLAM-BACS. These included the following, which are further detailed below: • Dataset collection and selection of a base layer • Criteria evaluation and scoring • Product selection based on scores • Generation of a unified map (updated as new data become available) Data set selection. The Group on Earth Observation Global Agriculture Monitoring (GEOGLAM) required global crop-specific masks to provide accurate crop assessments for the primary crops (maize, wheat, soybean, and rice) that AMIS (Agricultural Market Information System) is focused on. In response to this requirement and acknowledging the need to ensure the spatial fidelity of crop locations while building upon years of community product development, we undertook a study of existing global, regional, and national crop-specific map products through exchanges with the various national and regional focal points that contribute to the Crop Monitor for AMIS  2  , the GEOGLAM expert networks, and literature reviews.
The top five global producers of each commodity (Table  1 ) were prioritized as those countries account for between 82-97% of global crop production. The GEOGLAM-BACS integrates 16 out of the 20 crop-type masks for these top producers. Several criteria were used to determine a product's fitness for inclusion within GEOGLAM-BACS and are described below. In countries where no specific crop type maps were available, the SPAM 2010 data remains. Selection criteria and scoring. Similar to the approach described by Waldner et al. 21 , five criteria were considered to guide the selection and evaluation of the crop specific maps. These consisted of seasonal specificity (SS), timeliness (Ti), spatial resolution (SR), accuracy (Ac), and data source (DS) (Tables  2 3 4 5 6 ). Each criterion has a range of scores with 4 or 3 as the highest score (depending on the criterion) and 1 as the lowest.
Spatial resolution criterion. A range of studies have characterized in detail the spatial resolution requirements for accurately mapping crop types, which account not only for field size but also account for field shapes, crop diversity, and overall landscape complexity  [26] [27] [28]. However, for the GEOGLAM-BACS products, we took a simplified approach and considered that the higher spatial resolution products would generally be able to characterize crop types better than the coarser resolution products. While we acknowledge that higher spatial resolution does not always translate to higher accuracy, they do often perform better when produced with local training and validation datasets which is the case for the higher-resolution products integrated into the GEOGLAM-BACS dataset. As such, the higher-resolution products (5-20 m) received the highest score (4) and the lowest-resolution products (>500 m) received the lowest score (1) (Table  4 ).
Generation of unified map. As the collection of national, regional, and global crop-specific masks are at a variety of spatial resolutions, all the masks were harmonized to a common resolution. The 0. 05-degree resolution of the climate modeling grid (CMG) MODIS products was selected for the standardized product. This resolution was chosen to match the wide variety of climate datasets available at the CMG level for agricultural monitoring. Additionally, it has been shown that when detailed in-season crop masks are not available over areas with crop rotations, a coarse resolution mask, such as the 0. 05-degree CMG, can be used to accurately monitor and yield forecast for specific crops using Normalized Difference Vegetation Index (NDVI) time-series data  22. The crop-specific SPAM 2010 products were converted to percent per crop, per pixel, and then resampled to CMG level from the original 10 × 10 km grid.
Data Records The GEOGLAM-BACS masks are a collection of five global Geotiff files containing flat rasters at 0. 05 degrees, one for each of the AMIS crops (Maize, Soybean, Rice, Winter Wheat, and Spring Wheat). A comprehensive metadata file accompanies the rasters. Values represent the percent of the specific crop per pixel, ranging from 0 to 100%. Within the rasters, values were converted to integers whereby the values are from 0 to 10,000, where 10,000 is equal to 100. 00%. This was done to preserve small variations across the landscape of less than 1% while reducing the size of the dataset. The GEOGLAM-BACS will be updated regularly as new and more recent products become available. The updated rasters and metadata files will be pushed to the Zenodo repository as well as hosted on www. CropMonitor.
All crop monitoring activities, including that of the GEOGLAM Crop Monitor, would be strengthened with quantitative validation that would empower more precise and risk-informed decision-making  36. Critical to this effort is expanding the collection and availability of ground data for training and validation. Advances have been made in generating and interpreting labeled datasets to assist in these efforts (e. g. 31, 32, 37 ), and should be further developed, particularly in data-sparse regions which often correlate with food insecurity  38. • The GEOGLAM-BACS are not currently produced in-season. This is a major priority for monitoring crops as they develop throughout the growing season, yield forecasting, producing within-season cropped area estimates, damage estimates from extreme weather, conflict, etc.
Usage Notes The primary use of this dataset is for underpinning EO-based, global, crop-specific condition monitoring for four of the major crop commodities in the major production/exporting countries, through the GEOGLAM Crop Monitor activity. As noted in the prior section (Technical Validation: Known Issues & Required Improvements), there is room for improvement concerning validation, extent, resolution, and seasonal specificity, and these limitations should be noted in the application of these data in different monitoring activities. Nevertheless, the data are already being operationally used within the GEOGLAM Crop Monitor Reports.
GEOGLAM-BACS, as indicated by its name, represents the best-available, most up-to-date globally harmonized dataset on the locations of the four major commodity crops wheat, maize, rice, and soybean. Still, there remain gaps as indicated by this technical validation, and enumerated briefly below: Latvia Field Block WFS service Maize, Soybean, Winter Wheat, Spring Wheat Field Scale 2019 N/A (not publicly accessible) Mexico Mexico SIAP crop mask Maize, Winter Wheat, Spring Wheat Field Scale 2019 N/A (not publicly accessible) Netherlands Basic Registration of Crop Parcels (BRP) 2019 Maize, Soybean, Winter Wheat, Spring Wheat Field Scale 2019 57 Pakistan UMD GLAD Pakistan Winter Wheat Winter Wheat 250 meter 2014 58 Russian Federation UMD/NASA Wheat Mapping Winter Wheat 500 meter 2019 42 South Africa Maize growing areas Maize Field Scale 2014 N/A (not publicly accessible) Ukraine UMD/NASA Wheat Mapping Winter Wheat 500 meter 2019 42 Uruguay Integrated Map of Coverage/Land Use of Uruguay Rice 10 meter 2018 59 USA USDA NASS CDL Maize, Soybean, Rice, Winter Wheat, Spring Wheat 30 meter 2019 60,61.
Finally, the application focuses on 13 test areas within the Eurométropole de Strasbourg, which has a very proactive policy in favor of active mobility. It is based on a geographic information system (GIS) and an associated database. The calculation of a synthetic score, which is then declined by mode of travel, by time step, and according to different criteria, aims to characterize differentiated situations within the urban space. In fine, putting the indicators into perspective with the socio-urban environment allows revealing socio-spatial disparities.
In the last phase, a normalized score between 0 and 100 is attributed to each criterion calculated previously, with 100 representing, for a given criterion, the highest result of all the meshes of the study territory. For example, the score for the "number of resources in the proximity zone" will be equal to 100 for the grid cell from which the highest number of resources in the study area can be accessed and 0 for the mesh from which no resources can be accessed. The average of the scores of all the criteria (as presented below) provides the synthetic indicator of usability at each point of the territory. The approach required reflection on the indicators and the development of a large localized database including the distribution of resources, infrastructure, roads, urban environment, etc.
Implementation of the Approach Within a GIS Implementing the approach to assessing the ergonomics of access to everyday life resources by itinerary explained in the previous sections requires several steps: • Selection of daily life resources. • Calculation of itineraries based on the principle of the shortest paths. • The selection and calculation of numerous indicators contributing to the ergonomics of access. • The elaboration of a synthetic indicator translated into scores. • To do this, an extensive localized database had to be created.
, 2014). Finally, the choice and classification of resources were the object of in-depth reflection and numerous debates, notably within the framework of the ANR RED project. The selection of everyday resources from the 2015 SIREN data was based on several parameters. First, we want to consider the resources that city dwellers frequently use in their daily lives on a daily, weekly, or monthly basis. In addition, there are other resources that are perhaps less frequently used, but their presence near the household is a desirable asset. Examples include public services (municipality, job center, etc. ) and health services (hospital activity, dentistry, etc. ). Consequently, resources that are occasionally required or are not necessary for everyday life have not been taken into account (clothing, cosmetics, etc.
). Future developments could include attributing weight to resources according to their probability of a visit. It would have been interesting to include other resources related to sociability, such as children's playgrounds, green spaces, associations or cash dispensers, banks, relay points, etc. However, various practical difficulties were encountered: nongeographical referencing, classification confused with other resources, difficult data verification, etc. , which led to their exclusion. The access ergonomics to everyday resources is then investigated to evaluate the accessibility of city inhabitants to the following five categories of resources: shops, schools, public services, health services, and leisure. Classes (subcategories from A to O) are defined for each category, taking into account more precisely the nature of the activities, the surface area of the activity, the number of employees, and the levels of demand by the population  (Tannier, 2014; Tannier et al.
The synthetic structure of this data model is composed of five families of data (hyperclasses): resources, territorial actors, territories (specific features of the territories and land use), infrastructure and facilities, and finally access costs. The elaborated database then includes the different elements necessary for the evaluation of the ergonomics of access in the area under study, taking into account the analysis method. The data used for this study concern the year 2015 and come mainly from INSEE or the Eurometropole de Strasbourg. Collecting and organizing this information into a structured database required several months: • The data related to the population are provided by INSEE • The data related to the territory and the network (boundaries, roads, etc. ) were obtained from Eurometropole de Strasbourg.
Itinerary Calculation The application of the method of assessing the ergonomics of access to resources required the use of ESRI's "ArcGis 10. 6" GIS software, and more specifically the "Network Analyst" module, which specializes in the calculation of itineraries. The starting points and resources were linked to the nearest roads within a 300 m radius. The following criteria were used to calculate the itineraries: • The trips made are considered door-to-door, from the point of departure to the resource, as if the chosen mode of travel is immediately available. However, parking is included in the criteria for assessing the ergonomics of access. For car drivers, the journey to the nearest parking space on foot was taken into account. • For cars and bicycles, it was necessary to respect the direction of circulation.
Application to the Eurometropole de Strasbourg The implementation focused on the Eurometropole de Strasbourg. The territory has 2645 inhabited meshes of 200 m × 200 m. To test the developed method, 12 study areas were selected (Fig. 7 ). The choice of the test areas is crucial for making comparisons within the territory and revealing socio-spatial disparities. Two selection criteria were used: on the one hand, the geographical distribution throughout the Eurometropole, taking into consideration the urban morphology structuring the territory (center, planned center, first and second peri-urban ring), and on the other hand, the socio-environmental characteristics of the different neighborhoods resulting from a typology based on population data (age, household size, socioprofessional categories, etc.
). Road safety plays a role in the choice of the mode, but it also reveals problems of incoherence in the design of urban and peri-urban spaces, a dysfunction of a territory linked to the choices of development and urban organization. The criteria of alternatives/choice in terms of resources (number and diversity) and modes of access (in particular public transport to be integrated into the study) are intended to reveal the possibilities that the territory provides to respond to the constraints of the moment or to the differentiated needs of a population with different socioeconomic profiles (range of goods and services). The application of the approach to 13 test areas, with the help of a GIS and an adapted database, has made it possible to detect disparities within the Eurometropole de Strasbourg to analyze the differentiated situations in detail, and to put them into perspective with the sociodemographic profiles.
Looking at the values of N T in all the tables, it is clear that in most simulations multiple crosses occur. The experiments were conducted on a DELL Inspiron 15 machine, equipped with a processor Intel(R) Core(TM) i5-8250U, operating system Linux, kernel version 5. 4. 0-89. All the implementations were programmed in C language, compiler gcc version 7. 5. 0. The SFNM is a particular case of a Multi Component System Model (MCSM). In a typical MCSM there is a set of individual components that cooperate with each other, so that the whole system provides some type of service. The individual behavior of the components is known, and the way they interact with each other is also known. But the global response of a MCSM, as a function of the set of components, is usually unknown and sometimes very difficult to determine.
These networks implicitly learn the varying landscape of haplotypic structure in order to capture complex correlation patterns along the genome and generate a wide diversity of plausible haplotypes. We performed comparative analyses to assess both the quality of these generated haplotypes and the amount of possible privacy leakage from the training data. As the importance of genetic privacy becomes more prevalent, the need for effective privacy protection measures for genomic data increases. We used generative neural networks to create large artificial genome segments which possess many characteristics of real genomes without substantial privacy leakage from the training dataset. In the near future, with further improvements in haplotype quality and privacy preservation, large-scale artificial genome databases can be assembled to provide easily accessible surrogates of real databases, allowing researchers to conduct studies with diverse genomic data within a safe ethical framework in terms of donor privacy.
Introduction Machine learning is an important staple in modern genomic studies. There have been numerous applications in demographic inference  [1] , detecting natural selection  [2] , genome-wide association studies  [3]  and functional genomics  [4] , many of which became state of the art  [5, 6]. In the recent few years, generative machine learning approaches for the genomics field have also begun to gain research interest thanks to algorithmic advances and widespread availability of computational resources  [7] [8] [9] [10] [11]. Broadly speaking, generative machine learning involves the utilisation of generative models which are trained to model the distribution of a given dataset so that new data instances with similar characteristics to the real data can be sampled from this learned distribution.