dataesr/scientific-paragraphs-categorization huggingface dataset:
These findings also suggest that S. Dublin in those regions are geographically segregated with clusters containing different matrices potentially emphasizing spreading the bacteria over the entire food chain, and within herds. Geographic locations showing a high diversity of Salmonella were found to be exchange areas with several cooperatives or a large concentration of markets where different bacteria from different geographical locations meet each other. Control measures must be put in place in these exchange areas to prevent the spread of different clusters of Salmonella found in humans. We appraise the benefit of a WGS cgSNP approach on closely related isolates and how the results from large datasets under proper control of the impact of breadth-ofcore erosion can bring to fathom strain contamination dynamics and empower sanitary and safety authorities in designing tailored safety policies.
ACKNOWLEDGEMENTS The authors would like to thank  Choreh Farrokh  for helping, mediating with the stakeholders, the operators and altogether advocating for the project and funding. Part of the computation was performed on the GAMeR HPC Cluster. The random selection group (RND) was initially labelled SELEC B and renamed for clarity. Selections A, C, D and E were however not renamed to ensure coherence with previous public and private communications.
Bitcoin miners are globally geo-located, facing very different energy costs, and employ hardware with unknown energy intensities. To overcome the significant constraints in estimating the carbon emissions of daily power consumption associated with Bitcoin's blockchain, here, we use machine learning (ML) methods, demonstrating their usefulness for pressing societal issues, such as climate change. A subset of ML methods, feed-forward neural networks are becoming increasingly popular due to their unrivaled performance in prediction tasks. Feedforward neural networks, also called multilayer perceptrons (MLPs), have been developed since the midtwentieth century, relying on joint advances from computer science, applied mathematics and information and probability theory. Their recent success stems from their theoretical ability to approximate unknown data generating processes (Universal Approximation Theorem and its variants), while handling large and complex datasets.
g. , the tight connection between the USD and the CNY markets; or (iv) supply-side factors for the costs incurred by Bitcoin and ASIC mining chips producers, related to rational for-profit mining decisions. The resulting novel input dataset for the period 1 January 2017-31 December 2019 covers a comprehensive set of factors found in the related literature  (Liu and Tsyvinski 2018; Kristoufek 2015; McNally et al. 2018; Jang and Lee 2017) , adding some novel supply-side ones, described next. 11 100.
Input Data Because Bitcoin prices determine the upper limit of CO 2 emissions generated by the break-even electricity consumption of rational Bitcoin network miners, we start with the predictors of Bitcoin prices identified in the literature. The complete list of variables (obtained from Bloomberg) is reported in Table  1  and explained as follows: 1. Commodity prices of gold, platinum and crude oil were included (XAUCurncy, CL1Comdty and PL1Comdty in Table  1 ) because of the common traits shared with cryptocurrencies such as limited supply and high price volatility, but also because it is believed that Bitcoin could serve as an alternative to these commodities either as a store of value or as a hedging instrument  (Dyhrberg 2016). The daily future price of crude oil and the spot prices of platinum (USD/ounce) and gold (USD/ounce) were obtained from Bloomberg.
The test data consisted of daily observations for (each of three) target output(s) and the P = 42 input variables between 1 November 2019 and 31 December 2019. An optimal ReLU DNN was fitted for each of the three different targets, corresponding to Equations (  8 )-(  10 ). Since our inputs were standardized, the current approach in the literature was nested within the ML approach when no input data were used, i. e. , when the inputs were evaluated at their means of zero ("Optimal ReLU, no inputs"). For each case, the out-of-sample mean absolute error (MAE), mean squared error (MSE) and square root of the MSE (RMSE) are reported, showing the predictive outperformance of CO BU 2 (third row) against (i) (first two rows), (ii) (fourth row) and (iii) (fifth and sixth rows). To perform a pairwise comparison in terms of predictive ability, a Diebold Mariano test was performed to obtain a test statistic of the difference in out-of-sample MSEs.
An example of an output variable y is the carbon footprint of Bitcoin mining, the input variables X of which are electricity prices, the energy efficiency of available mining hardware, drivers of Bitcoin prices, foreign currencies exchange rates against the USD, or the country-specific carbon intensities of electricity consumed, among others. Finally, examples of unobserved inputs z are the actual energy efficiency of mining hardware or the carbon intensities of different sources of electricity effectively employed. ML algorithms can be broadly categorized as unsupervised or supervised. Unsupervised learning algorithms aim at uncovering useful properties of the structure of the input dataset, i. e. , there is no y, and, given that the true data generating process (DGP) p data (X) is unknown, the goal is to learn it, or some useful properties of it, from a random sample of i = 1.
e. , applied unit-wise), mapping the output from the single hidden layer h The optimization (A5) finds the optimal depth L and number of hidden units per layer (or optimal width, layer-wise) { Z l } L l=1 given the network architecture size, Z = ∑ L l=1 Z l. Since the optimization (A5) is conditional on the architecture size, note that bigger and more complex datasets {y i , X i } N i=1 would naturally summon architectures with more hidden units, Z.
g. , IL11, ELFN2, and FBXO46) are poorly translated in mitosis. The IL11 mRNA for instance is the most down-translated mRNA transcriptome-wide in wildtype mitotic cells compared to interphase cells (Supplementary Data 6). Hence if these mRNAs are not translated in mitosis, the presence or absence of DENR cannot affect them. Therefore, although we do not know why our synthetic stuORF reporter does not increase its translation during mitosis, we believe the most likely explanation is that it is not being translated in mitosis for some technical reason. We present here in Supplementary Materials a few unbiased and comprehensive screens for pathways that might regulate the DENR•MCTS1 complex. We tested all serine/threonine kinases by in vitro kinase assay, we knocked down all hits from this kinase screen and assayed DENR•MCTS1 activity using the stuORF reporter in HeLa and MCF7 cells, and we systematically mutated all amino acids in DENR or MCTS1 which were reported in public databases to be post-translationally modified.
Finally, chemiluminescence was detected using ECL reagents and detected using a Biorad ChemiDoc imager. For immunoblot visualization and analysis ImageLab software version 5. 2. 1 was used. Antibodies. The following antibodies were used: ABLIM1 (WB: 1:1000, IF: 1:500, rabbit, bethyl-biomol A302-237-T), β-Actin (1:5000, mouse, Sigma #A2228), ATF-4 (1:1000, rabbit, Cell Signaling #11815), γ-tubulin (1:1000, mouse, Abcam #ab27074), Caspase 3 (1:1000, rabbit, Cell Signaling #9662), CDKL5 (WB: 1:1000, IF: 1:500, rabbit, abcam ab22453), CDT1 (1:1000, rabbit, Cell Signaling #8064), cleaved Caspase 3 (WB: 1:1000, flow cytometry 1:100, rabbit, Cell Signaling #9664), CUL1 (1:500, mouse, Invitrogen #32-2400), cyclin A2 (1:1000, rabbit, Cell Signaling #91500), cyclin B1 (1:1000, mouse, Cell Signaling #12231), cyclin E1 (1:1000, rabbit, Cell Signaling #20808), DENR (WB and IF: 1:2000, guinea pig, in-house production), pDENR_Ser73 (WB: 1:500, IF: 1:200, rabbit, custom-made by innovagen AB, Lund, Sweden), DUSP4 (WB: 1:1000, IF: 1:500, rabbit, abcam ab216576), FLAG (1:1000, rabbit, Sigma #F7425), FLAG-M2 (1:1000, mouse, Sigma #F3165), GAPDH (1:2000, rabbit, Cell Signallling #2118), Geminin (1:1000, rabbit, Cell Signaling #52508), pHH3 (Serine 10) (for WB: 1:500, rabbit, Cell Signaling #9701; for flow cytometry: 1:100, mouse, Cell Signaling #9706 S), MAP2K6 (WB: 1:1000, IF: 1:500, rabbit, Cell Signaling #8550), MCTS1 (1:1000, guinea pig, in-house production) a-raf (1:1000, rabbit, Cell Signaling #4432), c-raf (1:1000, rabbit, Cell Signaling #9422), ERK1/2 (1:1000, rabbit, Cell Signaling #4695), GST-HRP conjugate (1:5000, goat, GE Healthcare #RPN1236), pERK1/2 (T202/Y204) (1:1000, rabbit, Cell Signaling #4370), p-p90RSK (T359/Y363) (1:1000, rabbit, Cell Signaling #9344),RSK1/2/3 (1:1000, rabbit, Cell Signaling #14813).
RNA was extracted from the gel pieces and phosphorylated using T4 PNK. Deep sequencing libraries were prepared from these RNA fragments using the Bio-Scientific NEXTflex Small RNA-Seq Kit v3. Deep-sequencing libraries were sequenced on the Illumina Next-Seq 550 system. Data analysis. Adapter sequences and randomized nucleotides were trimmed from raw reads using cutadapt. rRNA and tRNA reads were removed by alignment to human tRNA and rRNA sequences using bowtie2 version 2. 3. 4. 2. Then, the remaining reads were aligned to the human transcriptome using BBmap. Metagene plots, single transcript traces and grouped analyses were carried out or created with custom software written in C available at https://github. com/aurelioteleman/ Teleman-Lab and the Zenodo repository (DOI 10. 5281/zenodo. 5751288).
Statistics. Theoretical Z vs Observed Z analysis was carried out in excel. The statistical tests used are indicated in the figure panels. Software. DNA sequence analysis was done with A Plasmid Editor (ApE) v2. 0. 49. 10. Statistical analyses were done with GraphPad Prism version 9. 0. 0 (86). Analysis of flow cytometry data were done with Guava Soft 3. 3. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements This work was supported by a  DFG grant  (project number  316695455 ) to A. A. T. , by the  DFG   SFB 1036  to A. A. T. , by a  DKFZ NCT3. 0   Integrative Project in Cancer Research  ( NCT3. 0_2015. 54DysregPT ) grant to A. A. T. , and by a  Cell Networks-Cluster of Excellence  ( EXC81 ) grant to K. C. v. H.
Results lncRNA expression according to breast cancer clinicopathological subtypes. To identify lncRNAs expressed by specific breast cancer subtypes or associated with clinicopathological features, we analyzed RNA-sequencing data from two large independent breast cancer cohorts: SCAN-B (n = 3455)  17  and TCGA-BRCA (n = 1095). We focused on lncRNAs annotated in the Ensembl 18 v93 noncoding reference transcriptome (Supplementary Fig. 1 ), and identified 4108 lncRNAs present in both cohorts, which are further analyzed in this study. A small number of lncRNAs (100 in SCAN-B, 37 in TCGA) were expressed >1 transcript per million (TPM) in all patients, but the majority of lncRNAs were expressed at lower levels. The two cohorts differ in the distribution of patients expressing lncRNAs>1TPM (Supplementary Fig.
3a ). Only lncRNA and mRNA with significant correlation (Bonferroni p-value < 0. 05) and -0. 4> Spearman's rho > 0. 4 in the TCGA (n = 1095) and SCAN-B (n = 3455) cohorts are used in the unsupervised clustering. In addition, we plot only lncRNAs and mRNAs with number of association higher than the mean value of association (Supplementary Fig. 4 ). Clusters are defined using cutree_rows = 3 and cutree_cols = 3. lncRNAs (x-axis) are annotated according to the differential expression analysis (Fig. 1 ). b, d Bar plot showing -log(FDR q. value) from a hypergeometric test (y-axis) of gene set enrichment analysis using Hallmark pathways of the MSigDB database. Input genes for GSEA are genes from mRNA-cluster A (n = 2890) (b), mRNA-cluster B (n = 1480) (c), and mRNA-cluster C (n = 667)(d). Boxplot of the coefficients from the generalized linear modeling of the expression of lncRNAs in the SCAN-B cohort using three variables into the same model, ESR1 mRNA (to reflect estrogen signaling (e)), fibroblast score (to infer fibroblast tumor content (f)) and lymphocyte score (to infer lymphocyte infiltration (g)).
9 ). We therefore further analyzed the promoters of lncRNA with no overlap with protein coding gene loci; either promoters of lncRNAs overexpressed in ER positive (n = 2320) or ER negative (n = 536) samples. We compared these two groups of promoters with respect to i) Chromatin accessibility measured by ATAC-seq in 74 TCGA-BRCA patients, ii) ChromHMM, chromatin genome segmentation, and iii) Transcription Factor (TF) -binding sites using the UniBind database  32. lncRNA promoters are accessible in an ER-status specific manner. We found lncRNA promoters to be accessible in a lineage specific manner, i. e. promoters of lncRNA overexpressed in ER positive tumors were more open (higher Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) signal) in ER positive samples than in ER negative samples.
10 ). Specific transcription factors binding sites are found at lncRNA promoters. We next sought for enrichment in transcription factor binding sites (TFBS) in lncRNAs promoters using the UniBind database  32. UniBind stores TFBSs with both experimental and computational evidence for direct TF-DNA interactions. We found ER + lncRNA promoters enriched for FOXA1 and ESR1 binding sites; TFs known to drive ER positive breast cancer (Fig. 4f , Supplementary Data 6). On the other hand, promoters of the lncRNAs highly expressed in ER negative tumors were enriched for BATF3, MAF, and RELA, components of the NF-κB TF complex, shown to be constitutively active in triple negative breast cancer  33  (Fig. 4g , Supplementary Data 6). To further assess the specificity of the TF binding according to length of the promoter chosen for the lncRNA, we assess three  a different sizes of promoters: TSS -300/+100 bp, TSS -500/ +100 bp, TSS -1000/+100 bp.
We correlated the levels of DNA methylation at CpGs and lncRNA expression for all CpGs and lncRNAs on the same chromosome in two cohorts for which DNA methylation and lncRNA expression were available TCGA-BRCA (n = 603) and OSLO2 (n = 279). As the OSLO2 lncRNA expression was measured by Agilent microarray 60 K, we focused on 1027 lncRNAs found in both cohorts (Supplementary Fig. 12 ). For both cohorts, we identified 26342 CpGs significantly inversely correlated with 396 lncRNA (Bonferroni corrected Spearman correlation p-value < 0. 05). We first tested in which chromHMM regions the CpGs whose DNA methylation was inversely correlated with lncRNAs were located and found them significantly enriched in enhancer regions (Fig. 5a , Supplementary Data 7). CpGs negatively correlated with lncRNAs highly expressed in ER positive tumors were found in open chromatin regions significantly more open in ER positive samples according to the TCGA-BRCA ATAC-seq data (Fig.
Discussion This study is, to our knowledge, the first to identify lncRNAs associated to clinicopathological features in breast cancer using two large independent cohorts. Combining the analysis of the SCAN-B and TCGA-BRCA RNA-seq data allowed us to assess the expression of more than 4000 lncRNAs with respect to breast cancer clinicopathological features and to report lncRNAs with robust association to clinical features across patient cohorts with remarkable concordance. We identified more than 2800 lncRNA genes, almost 70% of all the lncRNAs included in this analysis, with significant differential expression between ER positive and ER negative breast cancer. This is in line with the previous observations based on the TCGA-BRCA cohort alone  13. Characterization of lncRNA functions remains a critical challenge  37.
f, g Swarm plots showing enrichment of TF binding sites (-(log10(p-value) using Fisher's exact tests) on the y-axis for specific sets of promoters according to UniBind. TF names of the top 10 enriched TF binding sites data sets are annotated by colours. f Promoters of independent lncRNA overexpressed in ER positive cases and g promoters of independent lncRNAs overexpressed in ER negative cases. observation using single cell RNA-seq data and confirmed that many cluster 2 -lncRNAs were expressed by fibroblasts. One such lncRNA was MEG3, which was shown to contribute to the development of cardiac fibrosis  38. Further, the expression of the mature miRNAs hsa-miR-99a and hsa-miR-100 have previously been associated to fibroblasts in breast cancer  39. We found that the corresponding miRNA precursor transcripts which themselves are lncRNAs were part of cluster 2 and were indeed expressed in fibroblasts of the tumor breast microenvironment.
Methods Patient material. Two independent breast cancer cohorts with RNA-seq data were used; SCAN-B (n = 3455)  48  and The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) cohort (n = 1095)  49. A third independent cohort, the OSLO2 breast cancer cohort for which lncRNA expression were measured by Agilent 60 K array  50, 51  , was also included. SCAN-B cohort. The SCAN-B cohort  17, 48  is a consecutive observational cohort of resectable primary breast cancers from south Sweden. Patients included in this study were enrolled in the Sweden Cancerome Analysis Network -Breast (SCAN-B) initiative (ClinicalTrials. gov ID NCT02306096), approved by the Regional Ethical Review Board in Lund, Sweden (Registration numbers 2009/658, 2010/383, 2012/58, and 2013/459). All patients provided written informed consent prior to study inclusion.
All analyses were performed in accordance with patient consent and ethical regulations and decisions. Patient characteristics and clinicopathological features are described in  17  , and are according to current clinical definitions in Sweden. 3455 patients were identified with high quality RNA sequencing (RNA-seq) data and included in this analysis with the following clinical groups: ER positive (n = 2409), ER negative (n = 504), Her2 positive (n = 458), Her2 negative (n = 2845), Basal like (n = 341), Luminal A (n = 1769), Luminal B (n = 766), Her2 (n = 310), and Normal-like (n = 206) (Supplementary Data 8). RNA-seq library preparation and sequencing methods are described in  48. Quantification of gene expression was performed using kallisto 52 (v0. 46. 0) with 100 bootstrap samples (-b 100), using an indexed reference that combined all Ensembl  18  coding and non-coding sequences (Homo_sapiens.
GRCh38. cdna. all. fa and Homo_sapiens. GRCh38. ncrna. fa, Ensembl Archive Release 93 (July 2018)). Transcript abundance from kallisto were summarized to gene level expression using tximport  53  (v1. 16. 1) in R. lncRNAs were defined as genes in the Ensembl (v93) non-coding reference with a length above 200 bp. lncRNA expressed at >1 TPM in >5% of samples in the cohort (Supplementary Fig. 1 ), with an interquartile range >0. 1 (IQR function in R) were included in the downstream analysis (Supplementary Data 9). Hierarchical clustering of patients was performed using hclust as part of the pheatmap package (v1. 0. 12) in R with correlation distance and ward D2 as agglomeration method (Fig. 1a, b ). The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) cohort. The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) cohort, from here on named TCGA, has previously been described  49.
Clinical information for the TCGA was obtained from the UCSC Xena browser  54  (https://tcga. xenahubs. net/ download/TCGA. BRCA. sampleMap/BRCA_clinicalMatrix, curated survival endpoints; https://tcga-pancan-atlas-hub. s3. us-east-1. amazonaws. com/download/ Survival_SupplementalTable_S1_20171025_xena_sp; Full metadata), and PAM50 subtype information from  55  were obtained using the TCGAbiolinks package in R  56  (Version:2. 16. 3). After removing formalin-fixed, paraffin-embedded (FFPE) and duplicate samples 1095 patients were included in the analysis with the following clinical groups: ER positive (n = 807), ER negative (n = 237), Her2 positive (n = 114), Her2 negative (n = 650), Basal like (n = 190), Luminal A (n = 562), Luminal B (n = 209), Her2 (n = 82), and Normal-like (n = 40) (Supplementary Data 8).
To quantify lncRNA and protein coding gene expression, raw fastq files from the TCGA BRCA cohort were downloaded from https://gdc. cancer. gov/. Sample identifiers and clinical information is included in Supplementary Data 8. Quantification of gene expression was performed as described for SCAN-B with tximport v 1. 10. 1, and same filtering was applied as described above (Supplementary Data 10). DNA methylation data from TCGA  55  (level 3), probes with more than 50% missing values were removed, and further missing values were imputed using the function pamr. knnimpute (R package pamr) with k = 10. The Oslo2 breast cancer cohort. The Oslo2 breast cancer cohort has been previously described  39, 50, 51  and is a consecutive study collecting material from breast cancer patients with primary operable disease at several hospitals in south-eastern Norway.
Patients were included in the years 2006-2019. The study was approved by the Norwegian Regional Committee for Medical Research Ethics (approval number 1. 2006. 1607, amendment 1. 2007. 1125), and patients have given written informed consent for the use of material for research purposes. All experimental methods performed are in compliance with the Helsinki Declaration. The mRNA expression data have been previously published and can be obtained from GEO with accession number GSE58215  50. To accurately assign array probes to lncRNAs, published probe sequences (GEO Platform GPL14550) were aligned to Ensembl. 93 noncoding reference sequences (Homo_sapiens. GRCh38. ncrna. fa) using blast (ncbiblast-2. 6. 0). Probes where all 60 bp matched 100% to the reference were included in the analysis. In the case where several probes could detect the same lncRNA, the mean expression value was used.
A total of 4018 probes mapping to 3000 unique Ensembl gene IDs were included in the lncRNA analysis, and 1027 lncRNAs were detected in all three cohorts according to the filtering criteria described for TCGA and SCAN-B. The Illumina Infinium HumanMethylation450 microarray was used to measure the DNA methylation levels (GSE84207)  57, 58. Preprocessing and normalization involved steps of probe filtering, color bias correction, background subtraction and subset quantile normalization. The DNA methylation data have been previously published  36. Differential gene expression analysis. "scaledTPM" values from the tximport function were used to create a DGEList object using edgeR (v 3. 24. 3 (TCGA)/3. 30. 3 (SCAN-B)), and linear modeling (lmFit) and the empirical Bayes moderation function (eBayes) from the Limma/voom R-package (v 3.
38. 3/3. 44. 3) were used to define differentially expressed lncRNAs in the TCGA and SCAN-B cohorts. lncRNAs with Benjamini-Hochberg adjusted  59  p-values < 0. 05 were considered significant, and lncRNAs that were significant in both cohorts were included in the downstream analysis. lncRNAs referred to as ER + and ER-associated had higher expression in the respective clinical group in both cohorts (Supplementary Data 1). Survival analysis. Cox proportional hazards regression analysis was performed using the coxph function of the Survival package (v3. 3-1) in R with Overall Survival as endpoint. ER + and ER-patients were analyzed separately in the SCAN-B cohort and lncRNAs with p-value < 0. 05 after Benjamini-Hochberg (FDR) correction were used for validation in the TCGA BRCA cohort (Supplementary Data 2).