dataesr/scientific-paragraphs-categorization huggingface dataset:
The resulting library was cleaned with AMPure XP beads and quantified by qPCR with KAPA SYBR Fast universal for the Illumina Genome Analyzer Kit. The library size was determined by analysis using the Bioanalyzer 2100 DNA High Sensitivity Chip. The library was sequenced on the Illumina NovaSeq 6000 using 150 cycles and paired-end dual-index read chemistry. The version of NovaSeq control software was NVCS 1. 6. 0 with real-time analysis software, RTA 3. 4. 4. Postprocessing of the run to generate the FASTQ files was performed at the UCI IGB. ATAC-seq reads were aligned and processed following the ENCODE pipeline (www. encodeproject. org/atac-seq/) for paired-end sequencing reads. Reads were aligned to the mouse genome (mm9) using BowTie2 with the options of "-k 4 -X 2000. " Only properly paired reads were retained for analysis.
The Picard tool (http:// broadinstitute. github. io/picard/) was used to mark and remove duplicates. All samples were randomly down-sampled to 56,000,000 reads to exclude library size effects. Read coordinates were then transformed into the "BEDPE" format and fed to MACS2 for peak calling. The P value cutoff for MACS2 was 0. 001. Peaks that overlapped with any ENCODE mm9 "blacklist" regions were excluded. The reads density in "bedGraph" format was generated by MACS2 and then transformed to bigWig using the "bedGraphToBigWig" tool in the UCSC binary utility directory (http://hgdownload. soe. ucsc. edu/admin/exe/). TF footprint detection was conducted on two biological replicates using an HMM-based software "rgt-hint" that is able to distinguish members of the same TF family  (35)  with the option of "--atac-seq.
Metabolites were identified by automated comparison of the ion features in the experimental samples to a reference library of chemical standard entries that included retention time, molecular weight (m/z), preferred adducts, and in-source fragments as well as associated MS spectra and curated by visual inspection for quality control using software developed at Metabolon. Identification of known chemical entities was based on comparison to metabolomic library entries of purified standards  (109). General metabolite chemical classification (lipid, carbohydrate, etc. ) and subpathways (fatty acid synthesis, polyunsaturated fatty acid, etc. ) were determined by a combination of literature evidence and cross-reference to Metabolon's internal database. Statistical analyses, including PCA and ANOVA, were carried out with MetaboAnalyst 3.
0 (33) using prenormalized peak area under the curve values for each metabolite, which were then log-transformed and autoscaled. At each ZT, ANOVA with Fisher's least significant difference (FDR < 0. 1) determined metabolites altered in KO (WT versus KO, P < 0. 05) and restored in Liver-RE (KO versus Liver-RE, P < 0. 05; WT versus Liver-RE, not significant). Outliers were removed using the Grubbs' test. Two types of statistical analyses were performed: (i) significance tests and (ii) classification analysis. Standard statistical analyses were performed in ArrayStudio on log-transformed data. For analyses not standard in ArrayStudio, the R program (http://cran. r-project. org/) was used. Following log transformation and imputation of missing values, if any, with the minimum observed value for each compound, Welch's two-sample t test was used as a significance test to identify biochemicals that differed significantly (P < 0.
Acknowledgments: We dedicate this paper to the memory of Paolo, who was an extremely supportive and enthusiastic scientist and who will remain an enduring influence for us all. We also extend special thanks to our animal technician  S. Sato  and laboratory manager  W. Orquiz  for valuable help, as well as to all members of the  P. S. C. laboratory  for support. We would like to thank the  Genomics Unit at the CRG  for assistance with ChIP-seq. Funding: C. M. G. was supported by the  National Cancer Institute of the National Institutes of Health (NIH)  under award number  T32CA009054  and by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement  749869. K. B. K. was supported by  NIH-NINDS   F32DK121425 , and J. G. S. was supported by the  Zymo-CEM   Postdoctoral Fellowship (Zymo Research ).
P. -S. W. is supported by a  "Ramon y Cajal " contract ( RYC2019-026661-I ) from the  Spanish Ministry of Science and Innovation (MICINN). Research in the P. M. -C. laboratory is supported by  MINECO-Spain  ( RTI2018-096068 ),  ERC-2016-AdG-741966 ,  AFM, MDA-USA ,  La Marató /TV3 Foundation, LaCaixa -HEALTH-HR17-00040 , and  UPGRADE-H2020-825825  and  María-de-Maeztu-Program for Units of Excellence  to  UPF  ( MDM-2014-0370 ) and  Severo-Ochoa-Program for Centers of Excellence  to  CNIC  ( SEV-2015-0505 ). Research in the S. A. B. laboratory is supported by the  European Research Council (ERC) , the  Government of Cataluña  ( SGR grant ), the  Government of Spain (MINECO) , the  La Marató/TV3 Foundation , and The  Worldwide Cancer Research Foundation  (WCRF). Author contributions: C. M. G.
, K. B. K. , J. G. S. , P. B. , S. M. , S. A. B. , P. M. -C. , and P. S. -C. conceived and designed the study. C. M. G. , K. B. K. , J. G. S. , S. A. B. , P. M. -C. , and P. S. -C. wrote and edited the manuscript. C. M. G. , K. B. K. , J. G. S. , P. -S. W. , V. M. Z. , T. M. , K. S. , T. S. , P. P. , S. K. C. , and K. A. D. performed experiments. O. D. , A. K. and M. V. -D. provided technical support. D. L. , J. M. M. K. , C. V. , and M. M. S. performed bioinformatic correlation analyses. S. C. , M. S. , P. K. , R. C. , J. S. , and W. L. performed sequencing analysis. Competing interests: The authors declare that they have no competing interests. Data and materials availability: The RNA-seq, ATAC-seq, and ChIP-seq data have been deposited in the GEO with accession numbers  GSE158600 ,  GSE171432 , and  GSE171184  (RNA-seq); GSE154902 (ATAC-seq); and GSE169483 (ChIP-seq).
Metabolomic data are provided as data  S6. For cross-tissue gene correlation, all datasets, analysis, and walk-through are provided at https://github. com/marcus-seldin/endocrine_communication-nonrestored-clocks. All transcriptomic data for this project are publicly available on the CircadiOmics web portal (112,113) (http://circadiomics. igb. uci. edu/biocycle). Submitted 30 March 2021 Accepted 29 July 2021 Published 22 September 2021 10. 1126/sciadv. abi7828.
Results Prediction of variant abundance is computationally analogous to RNA transcript abundance estimation SARS-CoV-2 RNA fragments in wastewater originate from different infections, with potentially different viral lineages, pooled together into a single sample. After successfully extracting RNA from wastewater and sequencing SARS-CoV-2 genome fragments, the computational challenge is to assign reads to lineages and estimate relative abundance per lineage. This is analogous to RNA transcript quantification from RNA-Seq data, where the sequencing data consists of reads originating from different transcripts of a given gene, and the objective is to quantify the relative abundance per transcript (Fig  1a ). While pipelines for viral variant detection in clinical samples rely on individual mutation frequencies  14  using popular tools like V-pipe  15  or iVar  16  , RNA transcript quantification algorithms make use of the genome sequences without identifying specific mutations.
SARS-CoV-2 lineages are characterized by a combination of mutations, but additional variation is observed within lineages (Fig  S1 ). We provide kallisto with a reference set consisting of multiple genomic sequences per lineage, capturing the mutations specific to this lineage as well as withinlineage variation (Fig  1c ). Our constructed reference set includes 1-17 genome sequences per lineage, with a total number of nearly 1500 sequences for the 881 unique SARS-CoV-2 lineages present in the GISAID database at the time of download (9 March 2021). Including multiple sequences per lineage reduces biases related to within-lineage variation and potentially identifies any additional genomic signatures frequently seen in a given lineage. Finally, we filter out any predictions below a given percent abundance threshold to reduce noise and sum all predictions per lineage, which gives predicted abundances per lineage (Fig  1c ).
Kallisto predictions of variant abundance are accurate on simulated data To evaluate the accuracy of the predictions obtained through our pipeline, we created a collection of benchmarking datasets that resemble real wastewater samples. For each variant (B. 1. 1. 7, B. 1. 351, B. 1. 427, B. 1. 429, P. 1) we created a series of 33 benchmarks by simulating sequencing reads from a variant genome, as well as a collection of background (non-variant of concern/interest) sequences, such that the variant abundance ranges from 0. 05% to 100%. Analogously, we created a second series of benchmarks, simulating reads only from the Spike gene of each SARS-CoV-2 genome. We refer to the first set of benchmarks as "whole genome" and to the second set of benchmarks as "Spike-only". Finally, we performed these benchmarking experiments at different sequencing depths: 100x and 1000x coverage for the whole genome benchmarks, and 100x, 1000x, and 10,000x coverage for the Spike-only benchmarks (Table  1.
and Fig 2). Predicting variant abundance can be difficult when a variant is present at very low frequency, because of the high degree of similarity between lineages. On our simulated datasets, where we know the true frequency of each variant, we observe a background noise of 0. 01--0. 09% (Fig  S2 ), meaning that some sequences are falsely predicted to be present at 0. 01--0. 09% abundance. These false positives are likely due to shared mutations or conserved sequences between lineages. The level of background noise tends to be higher for whole genome benchmarks than for Spike-only benchmarks, because the majority of defining variant mutations are in the Spike gene (Figs  S1  and 2 ). In both cases, we apply a threshold of 0. 1% abundance to include a sequence in the lineage abundance computation and we only report the presence of a variant exceeding this threshold to avoid false positives.
This explains why our predictions are most accurate for P. 1, the most divergent lineage among the variants considered (Fig S1). Figure  2  (bottom) shows the relative frequency estimation error, defined as the absolute difference between true and estimated frequency, relative to the true frequency. We observe that relative frequency estimation errors are highest at low frequencies, where a small deviation in the absolute sense makes a large relative difference but are relatively stable at true variant frequencies of at least 1%. Besides underestimation, we also notice overestimation, particularly at low variant frequencies. This is due to shared sequence between the variant and the background lineages: in datasets where the variant is present at a low frequency, the background lineages must be present at relatively high frequencies.
We calculated these statistics for minimal variant abundance thresholds of 0. 1% and 1%. Increasing the minimal abundance thresholds reduces the false positive rate but increases the false negative rate. We generally observe more false negatives for datasets of lower coverage, because low-frequency variants become harder to detect. In terms of precision and recall, we note that, unsurprisingly, increasing the number of reads While for this study we primarily used kallisto  17  , we also evaluated performance for the software package salmon  18  , which takes a slightly different algorithmic approach to the same problem (Fig  S4 ) and found predictions were highly similar to those obtained with kallisto, the main difference being that salmon is slightly more conservative: it achieves higher precision (fewer false positives), at the expense of lower recall (more false negatives).
Observed PCR values of wastewater correspond to genome coverage We obtained primary sewage sludge samples from the wastewater treatment plant serving New Haven, CT, USA, every 2 days between January samples with a Ct value < 34 and at least 0. 5M reads aligned reach a genome coverage of at least 40%. To evaluate the impact of genome coverage on variant abundance predictions, we subsampled a dataset with maximal coverage (99% of the SARS-CoV-2 genome with >20x coverage, 1. 9M paired-end reads) to obtain datasets with reduced genome coverage by randomly selecting 20%, 40%, 60%, and 80% of amplicons, respectively, each of which we repeated 100 times. Figure  3c  shows the resulting abundance predictions for B. 1. 1. 7 per coverage value. We observe that the median predicted abundance is close to the predicted abundance at full coverage (dashed line in Fig 3c ) for all coverage values; however, variance is much larger in datasets with low coverage compared to datasets with high coverage, consistent with statistical predictions and prior work  16.
This indicates that datasets with low genome coverage can still result in accurate abundances, but the predictions are less reliable. Kallisto offers a bootstrapping feature, through which the sequencing data is resampled at least 100 times and variant abundances are predicted for each of these resampled datasets. The resulting predictions can subsequently be analyzed to obtain confidence intervals for the predicted abundance on the original dataset. For the New Haven sludge samples discussed here we obtained narrow confidence intervals (upper and lower errors <1% abundance), suggesting that predictions are very consistent (Fig  S6 ). However, this type of analysis captures only computational noise, and not technical noise (e. g. sampling bias). The fact that our predictions are more variable than the clinical data while confidence intervals from bootstrapping are narrow suggests that wastewater sequencing data is highly stochastic and not always representative of the infections in the population.
Wastewater abundances for different variants across the US match expected patterns The quality of datasets obtained through wastewater sequencing varies widely. RNA levels and Ct values fluctuate with the number of infections in a sewershed, which impact overall genome coverage and prediction accuracy. Other factors such as sampling approach, PCR inhibition, and amplification bias add to this variability. To validate our approach for general use, we applied our pipeline to predict variant abundance on a diverse collection of composite influent wastewater samples obtained from 25. Although these predictions may be false positives, at the time P1 was thought to be likely at such low prevalence that these cases were not picked up by the sequencing efforts in place.
Clinical frequencies and the GISAID data used for the national data above have strong biases and so are not themselves ground truth. Individual wastewater samples can be unreliable: the catchment and composition of an individual wastewater sample can include hospital or industrial inputs and is not necessarily representative of the population, and low infection levels, inhibitory compounds, and degradation of RNA can result in higher Ct values and associated genome coverage. Further, the potential that different variants have different viral shedding in waste is not taken into account nor are differences in shedding rates for vaccine breakthrough cases. The data here is consistent with highcoverage sequencing being well representative of the sample and the computation faithfully reconstructing it, therefore we believe the primary source of observed noise is the underlying noisiness correspondence between a given wastewater sample, the population as a whole, and the clinical cases.
These results do offer lessons for future development of wastewater sequencing methods. With perfect coverage (simulated data), Spike-only sequencing gives better predictions than whole genome sequencing. More broadly, as the variant-discriminating mutations are restricted to a subset of the amplicons in tiled sequencing, a strategy focusing sequencing depth proportionally to the potentially informativity of the tiles would likely yield the more accurate predictions per sequence read. In practice, whole genome sequencing is often necessary to obtain enough information versus restricting to Spike. These differences in sequencing protocols, as well as differences in population size and sampling techniques, make it challenging to compare data between locations. Another important lesson is the improved coverage with higher virus RNA concentrations (lower PCR Ct values).
Constructing a reference set We selected representative genomes per lineage from the GISAID database 4 , downloaded on 9 March 2021. As our samples are from wastewater collection systems across the US, we considered only reference sequences of US origin. After removing low-quality sequences (defined as having less than 29,500 non-ambiguous nucleotides) we randomly selected 1000 sequences per lineage for further analysis. We used minimap2 and paftools to align each of these sequences to the reference genome (MN908947. 3) and subsequently identify variation with respect to this reference  22. We then used VCFtools to compute allele frequencies within each lineage. Based on these allele frequencies, we selected sequences per lineage such that all mutations with an allele frequency of at least 50% were captured at least once.
Designing benchmarking experiments To evaluate the accuracy of our variant abundance predictions, we created benchmarks consisting of a selection of non-variant genomes (background) and one variant. In order to build benchmarks that reflect our real data as closely as possible, we selected the background genomes by taking all 11 sequences in GISAID collected in Connecticut at 2021-02-11 (the most recent collection date). For Spike-only benchmarks, we trimmed these sequences to keep only the Spike gene and simulated paired-end (2x150 bp) Illumina sequencing reads at equal abundance using ART  23. In addition, we randomly selected variant sequences from GISAID and simulated sequencing reads for the Spike gene of each variant (B.
Wastewater data preprocessing Before processing with kallisto, we first removed adapter sequences from the reads using Trimmomatic 24 , aligned the trimmed reads to a reference genome (GenBank MN908947. 3) with BWA-MEM v0. 7. 17  25  , and subsequently identified primer sequences using iVar v. 1. 3. 1  16  and removed these with jvarkit (http://lindenb. github. io/jvarkit/Biostar84452). Reads were aligned to a reference genome (GenBank MN908937. 3) using BWA-MEM v. 0. 7. 15  25.
Acknowledgements This work was supported in part by the  Pew Charitable Trusts , the  David and Lucile Packard Foundation ,  NIH NIGMS  award  R35GM133700 , and the  Alfred P. Sloan Foundation  (J. A. B. and M. B); CTSA Grant Number  TL1 TR001864  (M. E. P. and T. A. ); Fast Grant from Emergent Ventures at the  Mercatus Center at George Mason University  (N. D. G. ); CDC Contract # 75D30120C09570  (N. D. G. );  Yale CoReCT  pilot award (J. P. and N. D. G. ); and  NIH NIGMS  award  U54GM088558  (W. P. H. ). James McGann  and  Jim Griffin  were involved in developing the sequencing methodology at  Gingko Bioworks.
Data availability The raw SARS-CoV-2 sequencing data from New Haven wastewater (. fastq files) are available on NCBI SRA under Bioproject PRJNA741211. The clinical sequencing data can be accessed via covidtrackerct. com. The raw SARS-CoV-2 sequencing data from across the U. S. (. fastq files) are available on NCBI SRA under Bioproject PRJNA759260. The simulated wastewater sequencing data (. fastq files) for benchmarking are available on Zenodo (DOI: 10. 5281/zenodo. 5307070).
unreliable, the predicted variant abundances match expected patterns across the US from the times of sampling: B. 1. 1. 7 was predicted most abundantly in Florida; B. 1. 427and B. 1. 429were primarily found in California; and B. 1. 526was predicted most abundantly in New York and Connecticut. Other variants (B. 1. 351, P. 1) were not observed in GISAID for these states at the time of sampling and our predictions for these variants agree: B. 1. 351 was predicted to be present at very low frequency in 4 samples and absent in all other samples; P. 1 was predicted present in a single dataset at 1% abundance and absent in all others (Fig S8) but reduced genome coverage compared to sludge samples. Nevertheless, the same quality filtering parameters apply: Ct < 31 or Ct < 34 with at least 0. 5M reads aligned to select for samples with high coverage (Fig S7).
After this filtering step, we predicted variant abundance for the 30 remaining datasets (corresponding to 16 different locations across 8 states). Figure 5 shows the predictions for lineages B. 1. 1. 7, B. 1. 427, B. 1. 429, and B. 1. 526, along with the clinical lineage frequencies in the corresponding state (calculated treatment plants across the United States between late December 2020 and late January 2021 (Fig5). These samples had slightly higher average Ct values than the sludge samples analyzed above (33. 1 vs 32. 3), from GISAID in the 7-day window centered at the wastewater sampling date). Although data uploaded to GISAID has its own biases and individual towns do not necessarily reflect state-wide variant abundances, this is the only statistic we can compare against across all states.
Non-significant factors (P >. 05) were eliminated one at a time until all remaining factors had a significant association with active mobility (IMS ≥4). Goodness-of-fit was assessed using the Hosmer-Lemeshow test and discrimination using the area under the receiver operating characteristic (AUC-ROC) curve. Accuracy was considered good when the AUC-ROC curve ranged from 0. 70 to 0. 80, and excellent when it was greater than 0. 80. The adjusted odds ratio (OR) and 95% confidence interval (CI) were calculated for each factor that was significant in the logistic regression model. Data were analysed using SAS 9. 2 software (SAS Institute, Cary, North Carolina). Vasoactive drugs: dopamine, dobutamine, noradrenaline, epinephrine, milrinone, and levosimendan.
| LIMITATIONS The study sample was heterogeneous in that just under a third were surgical patients and only a small proportion (5. 5%) were trauma patients. The small representation of trauma and neurology patients can be explained by the inclusion criteria. Delirium was diagnosed only subjectively, because the CAM-ICU scale is not frequently used in our setting. 32 When the diagnosis of delirium is based on the charge nurse's subjective assessment, only hyperactive delirium is likely to be detected. Therefore, delirium was clearly underdiagnosed. It should be noted that hypoactive delirium remains unrecognized in 66% to 84% of hospitalized patients. 35 To standardize the data collection in the database, we decided to consider the APACHE II scale only, because it is the most widely used scale.
Active mobility according to days of intensive care unit stay Ethical and research approvals The study was approved by the Ethics and Clinical Research Committees of the participating centres. During the first 48 hours of admission, patients who were alert, cooperative, and oriented-or otherwise their next of kin-were asked to give their informed consent. If informed consent was obtained, data collection commenced on day 3 of the ICU stay. 5 | RESULTSWe analysed the data of 642 patients, corresponding to 10 046 patient-days and 80 ICUs, which represent 35% of the 228 ICUs in Spain, according to the database of the Spanish Society for Intensive Care Nurses. FigureS1shows a flow diagram with the patients' movement through the study.