dataesr/scientific-paragraphs-categorization huggingface dataset:
Life cycle inventory In this section, life cycle inventory (LCI) data are given in terms of a 1 kg reference mass for each of the main materials: L-PheMEHCl, Z-Asp acid, aspartame, and neotame. Fig. 2  shows the main synthesis steps (red boxes) and further synthesis sub-steps to go from a material to the Ecoinvent database (black boxes). Also shown are the materials consumed (left hand side, black text) and emitted (right hand side, blue text). Numerals in brackets after the materials are recovery rate percentages, with those subject to assumption denoted by a '*'. Further details of recovery rates are given in Supplementary Material, Table  S9. Materials without a recovery rate are completely consumed or emitted without recovery. Materials which were subject to scenario modelling are shown in red text.
(1990). Multiple resins were explored by  Mirviss et al. (1990) , including some derived from "strong base anionic exchange resins". However, production routes for the resins could not be further identified, and no direct equivalents were found in available LCI databases. Moreover, the quantity of resin used was not well defined, and was stated as being "recycled 4 times without any loss in activity". Therefore, for baseline impact calculations, it was assumed the resin is re-used indefinitely, and omitted from the LCA. However, resin consumption is explored in scenario modelling (Section 5. 1).
Neotame Neotame synthesis (box 4, Fig. 2 ) data were derived from US 5,511,508  (Claude and Tinti, 1996). LCI data are given in Table  4. Aspartame is reacted with 3,3-dimethyl butyraldehyde in the presence of acetic acid in water, methanol and 5% platinum catalyst on activated carbon. The product is hydrogenated to produce neotame and by-product water. Nitrogen purging is used before and after hydrogenation. Lastly, the solution has its pH raised to 5 by NaOH. The solid product is washed with hexane. No production pathway for 3,3-dimethyl butyraldehyde could be found to the Ecoinvent database. Therefore, cyclohexanol was used as a proxy because its formula is also C 6 H 12 O. However, environmental impact can be due to other factors, such as functional group, and sensitivity to change in proxy is explored in Section 5.
98 × 10 -2 kgCu-eq/kg, with contributions from aspartame synthesis of 4. 75 × 10 -2 kgCu-eq/ kg, Z-Asp acid 1. 90 × 10 -2 kgCu-eq/kg, and L-PheMEHCl 3. 33 × 10 -2 kgCu-eq/kg. Finally, in terms of WC, net impact of aspartame production is 4. 90 × 10 -1 m 3 /kg, with contribution of aspartame synthesis of 1. 46 × 10 -1 m 3 /kg, Z-Asp acid 1. 28 × 10 -1 m 3 /kg, and L-PheMEHCl of 2. 16 × 10 -1 m 3 /kg. The impact is reasonably evenly distributed across each of the three groupings shown in Fig. 3. Across all impact categories, aspartame synthesis accounts for an average of 33. 1% net impact, Z-Asp acid 23. 1%, and L-PheMEHCl 43. 8%. For comparison, environmental impact of aspartame from the World Food LCA Database (WFLDB)  (Nemecek et al. , 2019)  is 89. 60 kgCO 2 -eq/kg for GWP, 18. 51 m 2 acrop-eq/kg for LU, 4.
Scenario 2: electrical energy consumption For baseline results, electrical energy consumption was estimated based upon mass of materials moved when stirring or pumping. However, it is inevitable that there are more demands for electrical energy within a production process. It was also found to be lower than that reported in the aspartame model from the WFLDB (Section 3. 5). Therefore, a scenario explored the effect of an increase by 2-orders of magnitude in electrical energy consumption, making it well in excess of that used in the WFLDB model. Fig. 9  shows relative change in environmental impact of 1 kg aspartame (black) and 1 kg neotame (red), across all impact categories, for increase by 2-orders of magnitude in electrical energy consumption. Supporting numerical data are given in Supplementary Material, Tables  S16  and S17.
Scenario 3: resin consumption For baseline results, consumption of the D-PheME racemization resin was omitted. Therefore, in this scenario, consumption of resin in racemization of D-PheME to L-PheME was modelled using a proxy anionic exchange resin from the Ecoinvent database. No data for quantity used per mol of D-PheME, nor recovery rates were specified in  Mirviss et al. (1990)  for a single resin. Therefore, as per  Mirviss et al. (1990, p657) , a 1:2 mass ratio of resin to D-PheME (itself approx. half of the final product mass) as given for one resin, and a 25% recovery rate (which is a cautious minimum derived from the quote "the [resin] could be recycled 4 times without any loss in activity") for another resin. Net consumption of resin is modelled as 187. 5 g per 1 kg L-PheMEHCl. Fig.
Scenario 5: neotame proxy models When modelling neotame production, a production route for 3,3dimethyl butyraldehyde could not be found. Therefore, the baseline results were generated using cyclohexanol as a proxy. For this scenario, alternative proxies were chosen from the Ecoinvent database based upon similarity to 3,3-dimethyl butyraldehyde either by structure, number of atoms, or active groups: 2-methyl-1-butanol, 2,3-methybutane, 3methyl-1-butyl acetate, and benzaldehyde. Fig. 12  shows relative change in environmental impact of 1 kg neotame using an alternative proxy for cyclohexanol in place of 3,3dimethyl butyraldehyde: 2-methyl-1-butanol (black), 2,3-methylbutane (red), 3-methyl-1-butyl acetate (blue), and benzaldehyde (green). Supporting data are given in Supplementary Material, Table  S20.
Fig. 13  shows the relative impact of 1 kg SE aspartame (black and red bars) and 1 kg SE neotame (blue and green bars) compared with that of 1 kg sugar from the Agri-footprint 6. 1 (AFP) and Ecoinvent 3. 3 (EI) databases using a global mix of 80% sugar from cane and 20% from beet (ISO, 2020; OECD/FAO, 2021). Error bars are based upon standard deviation from the sensitivity analysis conducted in Table  7 , and further reported in Supplementary Material, Table  S21 , alongside other supporting data. Results show that both aspartame and neotame have the potential to reduce environmental impact for a given level of sweetness compared with sugar. Impact is lower across all categories, however the large uncertainty in IR should be noted. Neotame has lower impact than aspartame, showing that any impact due to the extra neotame synthesis step is offset by increase in sweetness from 200-to 8000-times that of sugar.
5 g aspartame and 995 g sorbitol, to give both sweet taste and technical function. Aspartame and sorbitol combined like this create at least 2. 34 kgCO 2 -eq/kg. Likewise, 0. 125 g neotame used alongside 999. 875 g sorbitol creates an impact of at least 2. 21 kgCO 2 -eq/kg. This compares to a global mix of sugar of 1. 39 kgCO 2 -eq/kg as per the Agri-footprint 6. 1 database. This highlights that more study is required to understand environmental ramification of NNSs use in formulations, and more widely within whole diets, to best understand entire life cycle impacts of producing and consuming NNSs.
Limitations and further research The LCA presented is for aspartame and neotame production, and multiple sources of uncertainty in data have been highlighted. Further research is needed to reduce these uncertainties. For example, reduced yields, unless otherwise stated, were assumed to be due to unreacted reagents, and not through creation of side-products. This is unlikely to be the case, and to account for this effectively, more knowledge is required about potential side-products. Likewise, materials quantities were derived from literature which reports experimental production processes, and not large-scale, optimized industrial processes. Therefore, material use may not be optimized, leading to a potential overestimate in this study. Neither set of information was available for this study and is an area for future investigation, preferably in collaboration with industry.
2023). No samples from raccoon dogs, civets, or porcupines on sale in the market were tested by qPCR, and no serology from animals in the market has been described. However, Liu et al. reported the genetic detection in environmental samples of several animal genera of potential interest  (Liu et al. 2023 ). Here, we analyze the data from the market generated and shared by Liu et al. using multiple novel genomic approaches. We first demonstrate that SARS-CoV-2 genetic diversity within the Huanan market reflects the tMRCA of the global pandemic, a finding consistent with its emergence within the market. We characterize the genetic material from mammals present in market metatranscriptomes at the species level, and thereby document their presence in samples and stalls with SARS-CoV-2. We further identify multiple additional viruses most likely shed by live mammals sold at the market.
Mammalian wildlife species detected in five SARS-CoV-2 positive samples from a wildlife stall and in other wildlife stalls Environmental samples with viral RNA can also contain genetic evidence of the mammalian hosts that shed that virus. We stringently mapped reads to a dereplicated database of eukaryotic mitochondrial genomes to quantify the abundances of animal mitochondrial DNA (mtDNA) in each environmental sample. Five SARS-CoV-2-positive samples from wildlife stall A contained mtDNA from raccoon dogs, hoary bamboo rats (Rhizomys pruinosus), and European rabbits (Oryctolagus cuniculus). Amur hedgehog and Malayan porcupine mtDNA was present in four samples, Reeves's muntjac (Muntiacus reevesi) and Himalayan marmot (Marmota himalayana) mtDNA in three, and one sample contained masked palm civet mtDNA (Figure  2D ; Supplementary Table  S6-S7 ).
To confirm species identification, we generated de novo contig assemblies and performed BLAST against a custom WGS database (Supplementary Table  S13 ) made from available genome assemblies of species known to be at Huanan market. The BLAST results identified human and wildlife species consistent with those described above (Supplementary Table  S14 ). Of the eighteen species reported by Xiao et. al. (Xiao et al. 2021 ) to be present in the four Wuhan city markets they surveyed, we confirmed genetic signatures of eleven at the species level at the Huanan market and an additional two at the genus level (Figure  3A  and Supplementary table  S15 ); Siberian weasel (Mustela sibirica) was rare in the market, and was absent from any of the January 12 th wildlife stall samples. We did not detect mtDNA sequence reads from American mink (Neogale vison), red squirrel (Sciurus vulgaris), Pallas' squirrel (Callosciurus erythraeus), complex-toothed flying squirrel (Trogopterus xanthipes) or Asian badger in any of the environmental samples.
Notably none of these, except Asian badger, was observed at the Huanan market specifically in November 2019  (Worobey et al. 2022 ). Additionally, while Xiao et al. reported sales of Chinese bamboo rat (R. sinensis), we identified abundant mtDNA from the hoary bamboo rat (R. pruinosus) with only one sample containing trace amounts of R. sinensis mtDNA (Supplementary Table  S15 ). This is likely due to a visual species misidentification based on physical morphology rather than genetic confirmation. We found no evidence for the presence of any bat or pangolin genetic material, the two known hosts of sarbecovirus relatives of SARS-CoV-2, in the Huanan market. In contrast, the presence of mtDNA of Myotis bats was previously reported  (Liu et al. 2023 ). To check this, we replicated this methodology by mapping reads to the Barcode of Life Data System COX1 gene database, which identified only 8 reads that mapped to any Myotis sequence with no mismatches.
In addition, SARS-CoV-2 detected in nonwildlife stalls was very likely shed by humans, rendering univariate correlations including these samples inappropriate. Experimentally, the overall sampling scheme of the market sampling was also imbalanced. All wildlife stall samples from January 12 th were sequenced regardless of their qPCR positivity, while other sequenced samples were predominantly qPCR positive and from elsewhere in the market (Supplementary Table  S3 ). Therefore, species present in wildlife stalls are disproportionately overrepresented in the sequenced negative set, and this sampling design will cause wildlife species to artifactually appear negatively correlated with SARS-CoV-2 (Figure  S4 ). As all wildlife stall samples collected on January 12 th had been sequenced regardless of their SARS-CoV-2 positivity, we conducted a correlational analysis of relative species abundances in these samples (n=70) as this could represent a balanced dataset for informing which host had shed the virus detected therein.
Wildlife stalls and SARS-CoV-2 positive samples contain other mammalian viruses associated with the animal trade The presence of animal viruses with predictable host ranges provides evidence of animals productively infected with viruses in the Huanan market at the end of 2019. By mapping sequencing reads to a custom database of human and animal viruses with stringent filtering, we identified several mammalian viruses present in the market (Supplementary Table  S17 ). Humanspecific viruses were rare, even at a threshold of one read per sample. We found human coronavirus 229E in one stall and human respiratory syncytial virus (subgroup B) in another. Other detectable human viruses were dsDNA viruses such as human polyomavirus 6, human papillomaviruses, and human herpesviruses (Supplementary Table  S17 ).
SNVs associated with two raccoon dog haplotypes collected in Vietnam in 2013 were absent from all samples. This result suggests that the detected raccoon dogs in the Huanan market in late 2019 were not N. p. ussuriensis and were a distinct population from those sold in markets in cities and raised on fur farms in northern China. It is unknown how far south the wild or farmed range of the subpopulations detected here extends due to a lack of genetic data for N. procyonoides in southern China. These data are consistent with a geographic origin of the raccoon dogs in the Huanan market in central or southern China, from which a viral transmission chain within the animal trade could have arisen after a spillover from a bat reservoir south of Wuhan.
The experimental susceptibility of civets is unknown, but Paguma larvata cells are susceptible to pseudotyped VSVs expressing the SARS-CoV-2 Spike protein in vitro  (Li et al. 2023). However, the susceptibility of bamboo rats, Malayan porcupines, and Amur hedgehogs remains unknown, and these species should also be prioritized for susceptibility testing. Of the other species in the market, marmots may be an unlikely conduit for SARS-CoV-2 considering that their usual range is at very high elevations  (Wu et al. 2023) , and muntjac deer have a truncated ACE2 gene without a signal peptide sequence that suggests a lack of susceptibility (GenBank accession: NP_001358344. 1). Siberian weasels  (Boklund et al. 2021; Shi et al. 2020) , foxes  (Porter et al. 2022) , and greater hog badgers  (Davoust et al.
Serological testing of the oldest animals (for instance the breeding stock) in source farms might provide additional information of transient circulation, as has been observed in mink farms infected with SARS-CoV-2  (Rasmussen et al. 2021). Future studies to clarify the susceptibility status of all of these species using in vitro approaches and liveanimal infection experiments, should also be prioritized. The limited viral and serological sampling of these species in Southeast Asia and southern China  (Wang et al. 2022; Wang et al. Mitochondrial mapping. All eukaryotic mitochondrial genomes were downloaded from NCBI's RefSeq and GenBank databases to build a custom mapping mitochondrial database. GenBank sequences with "partial", "gene", "genome assembly", and "chromosome" in the description and those smaller than 12 Kb were removed.
Genomes were clustered by Mash  (Ondov et al. 2016)  distances, first at 98% identity, preferentially selecting RefSeq genomes as cluster representatives. Reads were mapped with Bowtie2 to the 98% identity genome index and mapped reads with >=95% identity, MAPQ>=20, and mapping lengths >= 40 were retained using a custom Python script (count_reads98. py). For paired read samples, a mapped read pair was counted as a single observation. Next, a second round of reference genome clustering at 93% identity was performed, preferentially selecting the cluster representative as the genome with the highest sum of covered bases across all market samples. Reads were mapped again with Bowtie2 to this '93% clustered genome index' and counted using a custom Python script (count_reads93. py) and similar cutoffs as described above.
The resulting hits were filtered to Metazoa and assigned taxonomy with the Taxoniq package. To assess the potential differential impact of rRNA depletion on different species, we queried the mitochondrial genomic positions of the 16S and 12S rRNAs for all mammalian species observed in the market. A custom Python script was used to count mapped reads filtered in the same way as above, except also excluding all reads that overlapped with the genomic positions of the 16S or 12S for each mitochondrial genome. Mapping and analysis of environmental samples. We enhanced the market geospatial map from Worobey et al. (Worobey et al. 2022 ) using data on environmental samples taken from the market  (Liu et al. 2023) , including both SARS-CoV-2 positive and negative samples. We could precisely locate 783 of the 819 samples from inside the Huanan market.
The resulting sample locations and associated metadata were integrated with reported qPCR results and the results of our mNGS mapping for downstream analyses. Samples were grouped by stall to calculate the fraction of positive samples or the average proportion of reads associated with the species of interest. Overground and city drainage paths were plotted in accordance with published drainage maps  (Liu et al. 2023). Spatial relative risk analyses of environmental samples. As in  Worobey et al. (Worobey et al. 2022) , spatial relative risk analyses were performed for SARS-CoV-2 and other key viruses using the "sparr" package available in R  (Davies, Marshall, and Hazelton 2018) , with linear boundary kernels for edge correction and bandwidth selection using least-squares cross validation.
For analyses including market drains, we used a wider market boundary that included the drain sites outside of the market building. We studied variation in the relative risk quantity r(z)=f(z)/g(z) at each position z, where f(z) is the test distribution and g(z) is the control distribution, and tested the null hypothesis H0: r(z) = 1, against the alternative hypothesis of increased relative risk, H1: r(z) > 1. We then plotted an asymptotic p-value approximation P(z), a pointwise estimate of statistical significance. Correlational analyses. Spearman's rank correlation coefficients and p-values were calculated using the scipy package between animal species abundances (mitochondrial mapping results) and SARS-CoV-2 mapped read counts, both normalized to total number of reads per sample after preprocessing.
Reported conclusions were robust against normalization method (total reads, mapped reads, or no normalization). Species were included in analysis if they were identified in three or more samples. Reported correlation coefficients and 95% CI were estimated by bootstrapping with 1000 permutations. P-values for statistical significance of correlations of mammalian species and SARS-CoV-2 in wildlife stall data were corrected for multiple hypotheses using the Benjamini/Hochberg procedure implemented in the statsmodel package. Viral mapping and assembly. A viral database of all viruses deposited in the RefSeq database was created and supplemented with viral genomes from two recently published studies  (Cui et al. 2023; He et al. 2022 ) that reported viruses from wildlife animals. This set of genomes was clustered by Mash distances at >95% nucleotide identity.
Low-complexity regions of these viruses were detected using dustmasker 1. 0. 0 with default settings. Reads from each sample were mapped to this dereplicated viral database using Bowtie2. The resulting mappings were filtered with a custom Python script (count_reads_viral. py) that counted reads and/or read pairs and covered bases of each viral genome with the following filters: mapping quality >30, read alignment length >95%, read percent identity to the reference >97%, and base pairs mapping to within 200 bp of the contig edges, or to low-complexity regions, were ignored. Only viral genomes with a breadth of coverage of at least 500 genomic nucleotides in at least one market sample were retained. To assemble consensus genomes for the raccoon dog amdovirus, bamboo rat coronavirus, and civet kobuvirus, we used a reference-guided co-assembly approach due to low sequencing coverages of viruses in the data using a custom Python script (get_viral_consensus.
py). Reads from all samples with at least 500 bp of genomic breadth of coverage of the most closely related reference genome were pooled, and the consensus genotype of all mapped reads at each genomic position was used to infer the consensus genomes. Reference positions with genotype ties or no mapped reads were filled with 'N'. To identify Influenza H9N2 partial genomic fragments, we first noticed reads mapped to multiple Influenza A genome fragments in sample SRR23971532. Because our viral read mapping based approach does not distinguish closely related segmented virus subtypes, we reconstructed the partial consensus sequences for these segments from the sample and performed a BLAST against the NR database. The PB2 sequences, for which we had the best coverage, were more closely related to H9N2 sequences; other fragments hit H9N2 and H7N9 equally well.
Viral phylogenetics. We collated viral genome sequences from Genbank (299 amdoparvovirus, 283 kobuvirus) and aligned them with the Huanan Market sequences using MAFFT v. 7. 490  (Katoh and Standley 2013)  with default parameters. For amdoviruses we proceeded with phylogenetic inference using the full genome alignment, but downsampled to the RdRp-encoding region of the kobuvirus RNA genome. We inferred a maximum likelihood tree with IQ-TREE 2 v2. 0. 7  (Minh et al. 2020) , using a GTR+F+G4 model with 1000 bootstrap replicates. The trees were midpoint rooted. We aligned the Huanan Market bamboo rat coronavirus sequence with 54 Embecovirus full genomes from NCBI using MAFFT v. 7. 490 with default parameters. Because the market sequence is fragmented, we removed all regions from the alignment where it consisted of Ns, leaving a concatenated alignment of 29,468 nucleotides.
Mitochondrial genotype reconstruction. A reference-guided mapping based approach was used to reconstruct mitochondrial consensus genomes from each sample. Reads were mapped to the eukaryotic mitochondrial database as described above, and for mammalian wildlife species, the consensus base at each position was used to infer the consensus genome with a custom Python script (mt_consensus_genomes. py), filling in all reference positions without coverage with 'N'. Mitochondrial genomes are shared via the GitHub repository for this work. Cytochrome B phylogenetics. We collected 44 published raccoon dog mitochondrial sequences and aligned them to the reference (NC_013700. 1) using MAFFT (options --auto --keeplength -addfragments). As most of these were only of the cytochrome B (cytB) gene, we performed phylogenetic inference using only the cytochrome B gene.