dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics All statistics were performed using IBM SPSS Statistics (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25. 0. Armonk, NY: IBM Corp). To compare total number of offspring and longevity of females provided with hosts, we used general linear model ANOVAs with species as main factor. Offspring numbers were log-transformed to meet assumptions of equal variance. If the effect of species was significant, means were compared using Tukey multiple comparison tests (Fig. 1 ).
Acknowledgements The authors with to thank  Arjen de Jong  for help in collecting gelines in the field. Photos of the gelines in Fig. 1 are by  Tibor Bukovinszky. Roel Wagenaar  is thanked for the rearing of Cotesia glomerata and the three Gelis species. Data Accessibility Upon  acceptance of the manuscript all data will be archived in the Dryad Respository.
The 3D organization of the genome plays a key role in many cellular processes, such as gene regulation, differentiation, and replication. Assays like Hi-C measure DNA-DNA contacts in a high-throughput fashion, and inferring accurate 3D models of chromosomes can yield insights hidden in the raw data. For example, structural inference can account for noise in the data, disambiguate the distinct structures of homologous chromosomes, orient genomic regions relative to nuclear landmarks, and serve as a framework for integrating other data types. Although many methods exist to infer the 3D structure of haploid genomes, inferring a diploid structure from Hi-C data is still an open problem. Indeed, the diploid case is very challenging, because Hi-C data typically does not distinguish between homologous chromosomes.
Introduction The 3D organization of the genome plays an important role in regulating basic cellular functions, including gene regulation  [30, 34] , differentiation  [21, 12] , and the cell cycle  [27]. Chromosome conformation capture techniques such as Hi-C measure the frequency of interactions between pairs of loci, thereby allowing a systematic analysis of genome structure. Although Hi-C contact matrices yield valuable insights, modeling and visualizing genome structures in 3D can unveil relationships and higher-order structural patterns that are not apparent in the raw data  [13, 37, 27, 24]  by providing a humanly interpretable 3D structure, orienting genomic regions relative to various nuclear landmarks, and serving as a framework for integrating other data types  [5]. Embedding contact count data in a 3D Euclidean space can also reduce noise in the underlying Hi-C data.
Previous methods to inferring chromatin structure from population Hi-C data fall into one of two broad categories. "Ensemble" approaches create populations of 3D structures that jointly explain the observed Hi-C data  [29, 37, 8, 17, 20, 36, 15, 25, 39, 40, 19]. Theoretically, structural ensembles can mimic the heterogeneity of cells in a population. However, these methods are frequently underdetermined because there are often more parameters to estimate for a large population of cells than data points. Ensemble models can also be difficult to validate and interpret. "Consensus" approaches, on the other hand, make the assumption that bulk Hi-C data can be accurately summarized in a single, consensus 3D structure  [13, 10, 35, 38, 2, 23, 16]. Modeling a single structure tends to be less computationally demanding than modeling an entire population of structures.
Furthermore, the resulting model has the advantage of relatively straightforward visualization and interpretation. For either ensemble or consensus approaches, a particular challenge is presented by Hi-C data derived from diploid organisms. As in most high-throughput sequencing experiments, a typical Hi-C experiment does not produced phased data; that is, the data does not distinguish between allelic copies. Thus, an observation of a single Hi-C contact between loci i and j corresponds to one of four possible events: either copy of locus i coming into contact with either copy of locus j. Any 3D inference method that aims to model diploid genomes must accurately account for this allelic uncertainty. A variety of strategies have been developed to account for diploidy in Hi-C 3D models. In general, ensemble models face less of a challenge on this front, since the two allelic copies can be treated like additional members of the ensemble.
Among consensus methods, by far the most common approach is to assume that the two homologous copies of a given chromosome share the same 3D structure  [38, 23, 41 ] and then to model each chromosome separately. We are aware of only three previous attempts to model diploidy in non-ensemble methods. Previously, we described an extension of our PASTIS software to handle the near-haploid cell line KBM7  [3]. We proposed to infer jointly the distribution of contact counts between homologs and the 3D structures by maximizing a constrained and relaxed likelihood. However, this relaxation is unsatisfying, as it yields non-integer counts modeled as random Poisson variables. More recently, two separate research groups have developed methods for modeling diploid genomes from single-cell data  [7, 33].
However, these methods cannot be directly applied to bulk Hi-C data, which is much more widely available. In this work, we propose a method to infer diploid consensus 3D models from Hi-C data. Our approach builds upon PASTIS  [38] , which infers 3D models by using a Poisson model of Hi-C counts coupled with a simple biophysical model of polymer packing. The key idea of extending PASTIS to infer diploid genomes is to explicitly model the uncertainty of allelic assignments for each observed read. We consider two distinct settings: the more challenging setting where the data is fully ambiguous, and the setting where a subset of the reads can be mapped to a single parental allele. To assist in inference, we incorporate several constraints into our objective function, reflecting our prior knowledge of genome architecture.
Method Hi-C experiments involve sequencing pairs of interacting DNA fragments. Specifically, cells are cross-linked, DNA is digested using a restriction enzyme, and interacting fragments are then ligated together. Fragments are subsequently sequenced through paired-end sequencing, and each mate is associated with one interacting locus. Hi-C data can then be summarized in a symmetric n × n contact count matrix C, where each row and column corresponds to a genomic locus and each matrix entry c ij to the number of time those two loci have been observed to interact. For diploid organisms, reads from homologous chromosomes cannot be distinguished from one another, and the resulting Hi-C matrix aggregates contact counts from homologous chromosomes into a single Hi-C matrix (Figure  1 ). The challenge of inferring diploid structures from Hi-C data lies in disambiguating the contact counts from the two homolog chromosomes.
Modeling contact counts of diploid structures with a Poisson model We propose to extend PASTIS to diploid genomes by leveraging the properties of each type of Hi-C contact map: ambiguous, partially ambiguous, and unambiguous. Let us first take a closer look at the common scenario, where the data is fully ambiguous. For a given ambiguous contact count matrix C A , each observed contact count c A ij between a given pair of loci (i, j) corresponds to the sum of four different unambiguous contact counts (Figure  1 ): c A ij = :Φ( )=i p:Φ(p)=j c U p , (2) where Φ : [1, n] → [1, m] is the mapping that associates bead with locus i. Leveraging the property that the sum of i Poisson variables of intensities λ i is a Poisson variable of intensity i λ i , we model the interaction count as c A ij ∼ Poisson   b i b j β A :Φ( )=i p:Φ(p)=j d α p   , ( 3 ) where m is the number of loci, n is the number of beads, d ,p is the Euclidean distance between beads and p, and β A is a scaling factor determined by the coverage of the ambiguous contact count matrix.
Simulated Hi-C data To validate our approach, we generated 10 simulated genomes with coverage, number of beads, and ratios of disambiguated contact counts corresponding to those of Hi-C data from the mouse Patski cell line (described in Section 2. 4. 2) at 500 kb resolution. We also generated additional sets of 10 simulated genomes with the same number of beads, varying the proportion of ambiguous, unambiguous, and partially ambiguous contact counts. To simulate "true" structures, we applied a random walk algorithm. This algorithm places beads successively along each chromosome, constraining each bead to lie within a given distance of the previous bead, provided the new bead does not overlap with any of the previously placed beads and that the entire homolog fits within a sphere of a predefined radius.
Real Hi-C data We applied our method to publicly available in situ DNAse Hi-C of Patski fibroblast mouse kidney cells  [11]. This line was derived from F1 female embryos, obtained by mating a BL6 female with a Spretus male. The BL6 female had an Hprt mutation, so hypoxanthineaminopterin-thymidine medium was used to select for cells with X chromosome inactivation on the maternal allele. All real Hi-C data used for this project is available at available at https://noble. gs. washington. edu/proj/diploid-pastis/.
Constraints improve ambiguous inference First, we assessed the accuracy of our method on simulated datasets (Section 2. 4. 1) using ambiguous data alone, with and without our proposed constraints. Because of the lack of disambiguated contact counts, we expected this inference task to be difficult. Our results demonstrated that the two sets of constraints -bead connectivity and homolog separationare necessary for successful inference. In the absence of the constraints, ambiguous inference performed poorly (Figure  2 ). Specifically, inferred homolog structures overlapped one another, and adjacent beads sometimes had large gaps between one another. The homolog separation constraint (Equation  7 ) and the bead connectivity constraint (Equation  6 ) were specifically designed to address these problems.
Discussion Three-dimensional structural inference of diploid genomes is a challenging problem because most Hi-C data is inherently ambiguous and does not discriminate between contact counts from the two homologs of a given chromosome. Even in the rare cases when parental genotype information is available, only a minority of reads can be disambiguated. As a consequence, many inference methods have modeled a single structure per diploid chromosome  [38, 23, 41]. Such an approach assumes that the two homologous copies of a given chromosome have the same 3D structure and prevents structural inference of more than one chromosome at a time. Because of these limitations, the degree of structural similarity between homologous autosomes is not currently well understood. In this work, we show how to carry out true diploid structural inference by modifying the objective function of PASTIS, a previously published haploid inference method  [38].
4. Cells were voltage clamped to -60 mV using an Axopatch 200B amplifier (Axon Instruments) and membrane currents were recorded. Glass pipettes of resistance between 3 and 8 MΩ were filled with intracellular solution containing (in mM): 140 KCl, 10 HEPES, 3 Na 2 ATP, 0. 2 Na 2 GTP, 5 EGTA and 3 MgCl 2 , pH 7. 4. Data were acquired with a 2 kHz acquisition rate and low-pass filtered with a 4-pole Bessel filter at 1 kHz. Data acquisition and analysis were performed using pCLAMP 10 software (Axon Instruments). Photoswitching. Illumination was applied to the entire field of view using either a Polychrome V monochromator (TILL Photonics) through a 20× objective, or a Lambda DG4 high-speed wavelength switcher (Sutter Instruments) with 380 nm and 500 nm filters through a 40× objective. pClamp software was used for both data acquisition and control of illumination.
Imaging was with 100 ms acquisition time (10 Hz) with an Andor iXon 897 EMCCD camera (Figs. 1-6 and Supplemental Figs. 1 2 3 4 5 ) or with 10 ms acquisition time (100 Hz) with a Photometrics Prime 95B cMOS camera (Fig. 7  and Supplemental Fig. 6 ) using Lumidyne LD555 as donor and Lumidyne LD655 as acceptor (Förster radius ~52 Å). Dyes were conjugated to benzyguanine and benzylecytosine to allow for labeling of SNAP and CLIP proteins, respectively. smFRET data analysis. Single-molecule intensity traces showing single-donor and single-acceptor photobleaching with a stable total intensity for longer than 5 s were collected (20-30% of total molecules per imaging area). Individual traces were smoothed using a nonlinear filter 68 with following filter parameters: window = 2, M = 2 and P = 15. Each experiment was performed >4 times to ensure reproducibility.
Acknowledgements We thank  J. P. Pin  for the SNAP-and CLIP-fused versions of mGluR2 and 7, the  UC Berkeley Molecular Imaging Center's Holly Aaron  for help with confocal imaging, members of the  Isacoff lab  for helpful discussion and the lab of  Christopher Chang  for equipment used to separate conjugated fluorophores. This work was supported by  R01 GM117051 (E. Y. I. )  and the  Centre National de la Recherche Scientifique  (F. A. and I. M. ).
Gene Sequences The full set of EMBL CDS sequences was downloaded in July 2015 from ftp://ftp. ebi. ac. uk/. Species were clustered by its corresponding species and strain or subspecies (when available). To avoid overrepresented subsets of sequences (e. g. , housekeeping genes from organisms for which only few sequences are included in the data set), species for which only few sequences were available were discarded. More specifically, species for which there were >1,000 genes in the EMBL CDS data set for that same species were selected for further analysis. The final set of EMBL CDS sequences analyzed consisted in 1,625 genomes, which included over 14 million sequences. The distribution of sequences per phylum can be found in supplementary table  S2 , Supplementary Material online.
Codon Autocorrelation Codon autocorrelation standard deviations were computed as described in  Cannarozzi et al. (2010). Briefly, for each sequence, the number of consecutive pairs of codons for a same amino acid were counted. The expected numbers of pairs were computed as the products of the frequencies of the individual codons. Codons were Z-transformed by Codon Usage Signatures and Autocorrelation. doi:10. 1093/molbev/msz124 MBE subtracting the expected counts from the observed and divided by the standard deviations from the expected value. The results were expressed as standard deviations from the expected value. Code for computing codon autocorrelation can be found in https://github. com/enovoa/codon Autocorrelation.
Acknowledgments E. M. N. was supported by a long-term postdoctoral fellowship from the  Human Frontier Science Program  ( LT000307/2013-L) , and is currently supported by a  Discovery Early Career Researcher Award  ( DE170100506 ) from the  Australian Research Council. I. J. was supported by  R01 HG004037  and  GENCODE Wellcome Trust  grant  U41 HG007234. We thank all members of the  Kellis lab  for their valuable comments and suggestions.
Methodology e occurrence of CJ1 events was diagnosed applying the modified Bonner criteria on GDAS (Global Data Assimilation System) data provided by NCEP (National Centers for Environmental Prediction) for the period 2001-2005 as detailed in  [13]. e AErosol RObotic NETwork (AERONET, http:// aeronet. gsfc. nasa. gov) provides a freely available, unique, continuous, and high-quality database for the study of aerosol features and behaviour. Detailed information on the retrieval, inversion algorithms, and quality-control procedures can be found in  [21, 22, [25] [26] [27] [28]  or on the AERONET web page. Version 3 AOD data, which have been released recently  [29] , are available at three data quality levels: Level 1. 0 (unscreened), Level 1. 5 (cloud-screened and quality controlled), and Level 2.
is approach considers changes in optical and physical properties by merging them into mean values; such smoothing contributes to enhance the results of the comparison. e significance of the null hypothesis: "aerosol properties (AOD500nm, AE_440-870nm, and volume size distributions) on biomass-burning days are statistically the same as on no-burning days" was tested. For each case, the significance was checked by means of the p value (p < 0. 05 for statistically significant variations at the 95% confidence level). ree-dimensional backward trajectories reaching CEI-LAP-BA were obtained with the HYSPLIT 4. 9 model (HYbrid Single-Particle Lagrangian Integrated Trajectory) of the Air Resources Laboratory (ARL), National Oceanic and Atmospheric Administration (NOAA)  [30, 31]. e model was run using meteorological data from the Final Run analysis for the Southern Hemisphere (FNL-SH) provided by ARL/NOAA.
Secondly, for the corresponding date, daily AOD500nm from the AERONET database was required to be greater than the climatological monthly mean. irdly, at least one of the four trajectories was required to have originated at or travelled over the area, herein considered as the "biomass-burning source region," which includes north-eastern Argentina, Paraguay, Bolivia, and the Brazilian states of Goias, Mato Grosso, Mato Grosso do Sul, and Rondônia. e fourth and final step consisted of verifying whether biomass was being burnt along the path of the air parcels. To this end, satellite imagery provided by the Instituto Nacional de Pesquisas Espaciais (INPE, National Institute for Space Research), Brazil (http:// www. inpe. br/queimadas/bdqueimadas) was analysed to determine the number and location of fire spots.
Data Availability e GDAS meteorological data are available at https://www. ncdc. noaa. gov/data-access/model-data/model-datasets/globaldata-assimilation-system-gdas from NOAA's National Climatic Data Center. AERONET information is available at https://aeronet. gsfc. nasa. gov/. e HYSPLIT model and the meteorological files to drive the simulations can be found at https://www. ready. noaa. gov/HYSPLIT. php.
| Badger data collection Direct observations of badgers (M. meles), that is, presence data, were collected by wildlife protection officers from the French National Hunting and Wildlife Agency (i. e. , ONCFS). This organization implements the "small carnivorous logbook program" (SCSL), a program conducted over the whole of metropolitan France, and which asks wildlife protection officers to report all dead and living animals belonging to small carnivorous species (in particular Mustelidae) that they randomly meet during their fieldwork  (Calenge et al. , 2015). Detections of dead or living animals were recorded mainly along roads, when the officers were driving by car (e. g. , police patrol missions). Although this program began in 2001 and is still running, we restricted our dataset to 4 years of  detections, between 2006 and 2009.
Because we needed to make reasonable the assumption of constant relative abundance over the study period, we had to define a shorter study period. Moreover, due to several logistics constraints in recent years (smaller number of officers, smaller budget for gasoline, etc. ), the program has collected a much smaller volume of data in recent years. The years TA B L E 1 Environmental variables (n = 13) calculated for each spatial unit (i. e. , small agricultural region) in metropolitan France b "Base de Données Géographique des Sols de France", geographical dataset (www. gissol. fr). c Digital soil mapping from habitat-response models  (Rutgers et al. , 2016). d "Relevé Parcellaire Graphique", geographical dataset (www. geopo rtail. gouv. fr). e Global Climate Data-Free climate data for ecological modeling and GIS  (Hijmans et al.
Most relevant potential food resources attractive to badgers were also recorded, that is, the percentage cover of (2016), as a proxy of the major potential food resource for badgers  (Cleary et al. , 2011; Mouches, 1981). To describe the climate of each SAR, we used 24 temperature and precipitation maps of average monthly temperature and rainfall. We applied a principal component analysis using the ade4 package  (Dray & Dufour, 2007)  operating in R software (R Development Core Team, 2017) to reduce the number of variables related to climate. The first three principal components were retained measuring, respectively, the intensity of alpine (Alpi. ), continental (Conti. ), and oceanic/Mediterranean (Coastal) climates of SARs (detailed in Appendix S1). To characterize human pressure, we chose the percentage of urbanized area in each SAR (Urban.
Methodological Framework The software program utilized to carry out the modeling and the LCIA (life cycle impact assessment) comparison is "OpenLCA" (  [8] ), taking, as functional unit of the analysis, one metric tonne of produced refractory bricks "at gate" and considering the two product systems depicted in Figures  2  and 3 , for the traditional MgO-C and "Carbonless" bricks, respectively. Within the software, the LCA process database adopted for the modeling of the product systems is "ecoinvent ® 3. 4", in particular in the form of "Unit Processes" and with the system modeling approach "Allocation, Cut-Off by classification". In the terminology of this database, there is a slight terminological difference with the international standard  [9] , in that, according to the definition, "an ecoinvent activity dataset represents a unit process of a human activity and its exchanges with the environment and with other human activities.
" (  [10] ). Therefore, the term "activity" is to be intended as synonym of "process". As regards the localization of processes or of utility providers (energy or transport) comprised in the ecoinvent ® 3. 4 database, the general rules adopted are the following: if the dataset exists as a version with a specific national localization, this version is used; if the process is localized in Europe, the "RER" or "Europe without Switzerland" version of the dataset is used, if it exists ("RER" stands for Europe according to the database documentation); finally, if the process is localized outside Europe or if the "RER" dataset does not exist, then the "RoW" (Rest of the World) dataset or the "GLO" (Global) dataset are used. description of the processes comprised in the depicted product system are illustrated, along with the adopted references of their Life Cycle Inventories (LCIs).
Processes for Alumina-based Materials On the basis of information provided by industrial partners, the production site of both "Tabular Alumina" and "Reactive Alumina" is located in the same plant in France and it is assumed that, for both processes, the starting material is calcined alumina, produced in the same site. For this last process the LCI is given by the dataset in the database "aluminum oxide production | cut-off, U -GLO", with no transport associated, as the process is carried out on-site (the starting material is aluminum hydroxide, available on global markets). Tabular alumina is made up of grains of alpha alumina with a diameter from 50 to 300 µm, formed by crystalline growth during sintering. The production process, according to  [25] , starts with the grinding of a purified synthetic calcined alumina from the Bayer process, which is then agglomerated in a special ball-forming process, with water only, without additives.
As regards the considered time-frame, for example, for the impact indicator "Climate Change" it is limited to 20 years in the "individualist" perspective, to 100 years in the "hierarchist" perspective and to 500 years in the "egalitarian" one. The implementation of the methodology "ReCiPe" is carried out in the software adopting the ecoinvent database version 3. 4 Impact Assessment methods, within the framework specified in  [15].