dataesr/scientific-paragraphs-categorization huggingface dataset:
For patients in which multiple samples were available, samples were collapsed to obtain patient-level data. Specifically, the mean gene expression value for each gene from multiple samples was used to represent patient-level gene expression value. Furthermore, patients with at least one sample with a TP53 mutation were considered TP53-mutant. Analysis of TP53 variant allele frequency and reported karyotypic abnormalities indicated that the vast majority of patients could be classified as "multi-hit", and therefore patients were classified as TP53-mutant or WT without taking into account TP53 allelic status. In total, 360 patients with TP53 mutation status (329 TP53-WT and 31 TP53-mutant) and RNA-sequencing data available were included for analysis. Of these, 322 patients had concomitant survival data available (294 TP53-WT and 28 TP53-mutant).
The TCGA cohort consisted for 200 de novo AML patients represented by one sample each, out of which 151 patients had TP53 mutation status (140 TP53-WT and 11 TP53mutant) and RNA-sequencing data available, and were included for analysis. Of these, 132 patients had concomitant survival data available (124 TP53-WT and 8 TP53-mutant). second approach, the same analysis was performed, but TP53 multi-hit cells transcriptionally defined as leukaemic stem cells (falling in the leukaemic stem cell-like cluster, Fig. 2a , middle) were specifically selected; this gene-set is comprised of 102 genes ("p53LSC"; TableS4a). Next, lasso cox regression with 10-fold cross-validation implemented in the glmnet R package was used to identify p53-all-cells and p53-LSC genes that were associated with survival and to estimate their respective regression coefficients  79.
Survival analysis For each gene expression signature, patients were first split using the median gene expression signature score. This resulted in two groups of patients, namely patients with high expression scores (greater than or equal to the median) and patients with low expression scores (lower than the median). The Cox proportional hazards regression model implemented by the survival R package was fitted to estimate the hazard ratio associated with each feature. Log-rank test was used to test the differences between survival curves. The features analysed here were LSC17, p53-all-cells and p53-LSC signatures. Patients with low gene expression signature scores (below median) and patients with TP53 wildtype status were specified as the reference groups in the model. Kaplan-Meier curves were plotted using the survminer R package to visualize the probability of survival and sample size at a respective time interval.
Short-term liquid culture experiments and interferon treatment For short-term liquid culture differentiation experiments (Fig. 3j , Extended Data Fig. 7g, h  and IFNα (rhIFN-alpha-2a, PBL Assay Science; 11100-1) treatment (Fig. 4i ), 100-500 Lin - Medium was changed weekly and after 6 weeks of culture, cells were washed in IMDM+20%FCS and plated into 1. 4 mL of cytokine-rich methylcellulose (Methocult H4435, Stem Cell Technologies). Colonies were scored 14 days later under an inverted microscope, and each colony was classified according to its morphology as BFU-E (Burstforming unit erythroid), CFU-G (granulocyte), CFU-GM (granulocyte-macrophage), CFU-M (macrophage) or CFU-GEMM (granulocyte, erythrocyte, macrophage, megakaryocyte). Selected colonies were used for cytospin and genotyping as outlined below.
Evaluation of cell morphology Cell morphology from PDX models (Extended Data Fig. 3d ) and in vitro LTC-IC cultures (Extended Data Fig. 7e ) was assessed after cytospin of 50-100,000 cells onto a glass slide (5 min at 500 rpm) and May-Grünwald Giemsa staining, according to standard protocols. Images were obtained using an AxioPhot microscope (Zeiss).
Acknowledgments We are grateful to patients and donors; without their generosity, this study would not have been possible. We also thank  Steve Knapper , clinical study teams and other investigators involved in supporting sample collection, and  King's Health Partners Biobank  for providing access to samples. We thank  William Vainchenker  for his scientific input;  Zemin Ren  and  Timothé Denaes  for help with mouse experiments and  Sally-Ann Clark  for help with sorting. This work was funded by a  Medical Research Council (MRC)   Senior Clinical Fellowship  (A. J. M. ;  MR/L006340/1 ), a  CRUK   Senior Cancer Research Fellowship  (A. J. M. ;  C42639/A26988. ), a  Cancer Research UK (CRUK)   DPhil Prize Studentship  ( C5255/A20936 ) to A. R-M, a  British Spanish Society Scholarship  to A.
R-M. , a  MRC Single- Cell Biology  ( MR/M00919X/1 ). The authors would also like to acknowledge the contribution of the  WIMM Sequencing Facility , supported by the  MRC Human Immunology Unit  and by the  EPA  fund ( CF268 ), the  Gustave Roussy  flow cytometry platform and mouse facility. We also thank the  Oxford Genomics Centre at the Wellcome Centre for Human Genetics  (funded by  Wellcome Trust  grant reference  203141/Z/16/Z ) for the generation and initial processing of the OmniExpress SNP array data. The results published here are in whole or part based upon data generated by the TCGA Research Network (https://www. cancer. gov/tcga) and the BeatAML team. The views expressed are those of the authors and not necessarily those of the  National Health Service (NHS) , the  NIHR  or the  Department of Health.
Cell lineage gene signature scores For each sample, a given cell lineage gene signature score was computed as the mean expression values of the individual genes belonging to the cell lineage gene signature. Here, the gene signature scores for two cell lineages were computed, namely myeloid and erythroid populations. Two gene sets for each cell lineage were compiled. The first gene set was based on cell lineage markers previously reported in the literature whereas the second gene set was based on cell lineage markers derived from analysing a published single-cell dataset  70. Genes from each score are described in TableS3. For the former approach, six erythroid genes (KLF1, GATA1, ZFPM1, GATA2, GYPA, TFRC; Fig. 2e , Extended Data Fig. 5h ) and seven myeloid genes (FLI1, SFPI1, CEBPA, CEBPB, CD33, MPO, IRF8; Fig.
2f ) were identified. For the latter approach, the expression values of erythroid and myeloid cell clusters were first compared separately against all other cell clusters using Wilcoxon ranked sum test. The erythroid cluster consisted of the early and late erythroid populations while the myeloid cluster consisted of granulocyte, monocyte, and dendritic cell populations. Erythroid and myeloid-specific gene signatures were defined as genes having FDR values < 0. 05 and log2 fold change > 0. 5 in >=20 and 17 comparisons, respectively. In total, 100 erythroid genes and 135 myeloid genes were identified from this single-cell dataset (TableS3), and were used to compute the scores presented in Extended Data Fig. 5c-f.
Leukaemic stem cell (LSC) signature score The 17-gene leukaemic stem cell (LSC17) gene set was retrieved from Ng et al  31. For each sample, the LSC17 score was defined as the linear combination of gene expression values weighted by their respective regression coefficients. To identify TP53-sAML leukaemic stem cell signatures from our TARGET single-cell dataset, two different approaches were used. First, differentially expressed genes were identified as overexpressed in all Lin -CD34 + TP53 multi-hit cells regardless of their transcriptional classification ("p53-all-cells") versus myelofibrosis, healthy donor and TP53-WT preleukaemic cells; this gene-set consists of 30 genes (TableS4a).
Supplementary Tables TableS1. Clinical and genetic details from healthy donors and patients included in the study. TableS2. Differentially expressed genes between TP53 multi-hit HSPCs and TP53-WT cells. TableS3. Genesets used to calculate gene expression signature scores in TARGET-seq and publicly available bulk-transcriptomic datasets. Table  S4. Differentially upregulated genes in TP53 multi-hit cells (globally or LSCs) and genes selected by lasso regression to derive p53-all-cells and p53-LSC signatures.
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ++ + + + + + + + + + + + + + + + + + ++ + + + + + + + + + + + + ++ + + + + + + + + Extended Data Figure 6 a TCGA (full dataset) Strata + + score. bi=low score. bi=high b BeatAML (TP53-WT) Strata + + score. bi=low score. bi=high c TCGA (TP53-WT) Strata + + score. bi=low score. bi=high Survival probability Survival probability Survival probability 0. 25 0. 50 0. 75 1. 00 0. 00 0. 25 0. 50 0. 75 1. 00 + 6 + + 7 score. bi=high + 8 Survival probability High score 5 + + ++ ++ + 4 + + + score. bi=low 3 ++ ++ + ++ p53LSC signature 1 2 + + + 0 ++ + + ++ + + + ++ + + + + + + ++ + ++ Strata + HR: 2. 11 Low score p=0. 001 + + ++ + + + ++ + + + ++ + ++ ++ + + + ++ ++ + + + + + + + + + ++ + ++ ++ + + + Survival probability 0. 25 0.
88 p=0. 008 0. 00 Time 0. 00 Time 0. 00 Time Strata Strata score. bi=high score. bi=low score. bi=high score. bi=low High Low Strata 0 66 66 0 Number at risk 1 2 3 Time 4 40 28 18 12 29 13 8 4 1 2 3 4 Time (years) 5 6 1 5 66 40 28 18 12 6 66 29 13 8 4 1 0 1 2 3 4 5 Time Number at risk 6 3 1 6 3 1 6 7 1 0 7 1 0 7 Strata 8 0 0 8 0 0 8 score. bi=high score. bi=low score. bi=high score. bi=low 0 158 85 35 11 1 2 3 136 43 6 1 0 1 2 3 Number at risk 4 Time 5 6 2 1 1 4 5 158 85 35 11 6 2 136 43 6 1 1 1 6 1 1 6 1 1 7 0 1 7 0 1 8 0 1 8 0 1 9 0 0 9 Strata Strata score. bi=high score. bi=low score. bi=high score. bi=low 0 0 0 66 58 0 Number at risk 1 2 3 Time 4 40 28 18 12 28 13 8 4 1 2 3 4 Time 66 40 28 18 12 58 28 13 8 4 0 1 2 3 4 Number at risk 5 6 1 5 6 1 5 6 3 1 6 3 1 6 7 1 0 7 1 0 7 8 0 0 8 0 0 8 d BeatAML (full dataset) Time e Time BeatAML (sAML patients) Time LSC17 (Ng et al, 2016) LSC17 (Ng et al, 2016) (1.
Biorepository We collected serum samples from patients in two separate cohorts: (1) Patients who self-identified with diabetes mellitus at the Palo Alto Medical Foundation from 2011-2012 (N= 250); and (2) patients who were consented for the Stanford Precision Biobank regardless of status of diabetes mellitus or kidney disease (N=2579). From these cohorts, we extracted data from the electronic health record and maintained a deidentified, secure database. We verified patients with type 2 diabetes using physician recorded diagnosis (ICD-9 codes 250. X0, 250. X2), abnormal laboratory values according to American Diabetes Association guidelines [two of any hemoglobin A1c ≥ 6. 5%, fasting blood glucose ≥ 126 mg/dL, random blood glucose ≥ 200 mg/dL, or oral glucose tolerance test ≥ 200 mg/dL] 91 or use of any anti-diabetic medications.
Flow Cytometry We permeabilized formalin-fixed glomerular and tubulointerstitial single cells with 0. 25% Tween-20 in PEB buffer at room temperature for 30 minutes and blocked cells with Fc Block (clone 2. 4G2, BD Biosciences, San Jose, CA) diluted in 0. 25% Tween-20 on ice for 10 minutes. We stained cells with fluorescent conjugated antibodies (Supplementary Table  2 ) on ice for 30 minutes. After washing, we resuspended cells in 200 µL PEB buffer for flow cytometry analysis on a Scanford Flow Cytometer in the Stanford Shared FACS Facility. We used Flowjo 10 software (Becton Dickinson, Ashland, OR) to analyze data. For CD45(+) cells, CD68(+)Ly6G(-) cells were classified as macrophages, CD68(-)Ly6G(+) as neutrophils, and CD68(-)Ly6G(-) cells as others (likely lymphocytes).
Statistics For statistical comparisons with normal data distribution, we used unpaired Student's t-test as appropriate for two columns, parametric one-way ANOVA as appropriate for more than two columns, and two-way ANOVA for grouped tables. We used Bonferroni correction to adjust for multiple comparisons as indicated and used Dunnett T3 correction in one-way ANOVA assuming different standard deviations. For non-normal data distribution, we used Mann-Whitney test for two columns and Kruskal-Wallis test for more than two columns. For podometrics, we analyzed data with Minitab Statistical Software v19 (Minitab 17 Statistical Software (2010), Minitab, State College, PA). We assessed differences between two groups with unpaired, two-sample Student's t-test. We adjusted by Welch's correction for unequal variances when necessary.
For RNAseq analysis, we compared DESeq2-normalized, log-transformed gene counts (log2(normalized counts + 1)) for comparisons as indicated, and used the q-value function from q-value R package with default settings to run adjustment for multiple comparisons. We used the online software Metascape (http://metascape. org/) tool to perform gene ontology (GO) analysis 96 using genes with q-values less than 0. 05 as inputs. We ran this analysis with default parameters to infer a hierarchical clusterization of significant ontology terms into a tree based on Kappa-statistical similarities among gene memberships. We selected the term with the best q-value within each cluster as its representative term. We retained ontology clusters with a q-value of less than 0. 05 as significantly enriched. We designated statistical significance at a 2-sided p-value < 0.
Emory University Integrated Genomics Core subcloned mouse Esm-1 over-expression plasmids. The  Stanford Shared FACS Facility  provided flow cytometer, training, and sorting services. The  Stanford Animal Histology Service  provided services for paraffin embedding and sectioning. The  Stanford Canary Center Imaging Facility  provided the  Nanozoomer Digital Scanner. This work utilized computing resources and differential gene expression analysis provided by the  Stanford Genome Center. The authors thank  Drs. Alan Pao ,  Glenn Chertow , and  Philippe Lassalle  for helpful discussions and critical review of the manuscript. XZ was funded, in part, by a  Larry L. Hillblom Foundation   Postdoctoral Fellowship award  and the  Holmgren Family Foundation. LEH was funded by two  NIH  training grants ( T32 DK007357 ,  T32 AI07290 ) and an  American Heart Association / Enduring Hearts postdoctoral fellowship  ( 17POST33660597 ).
XY and AR were funded by  R01 DK116567. XXW, KM, and ML were funded by  R01 DK116567  and  NIH   R01 DK127830. PS was funded by  R01 DK114485. JSM was funded by a  Veteran's Administration Merit Award  ( I01 CX001971 ). Financial support for this work was also provided, in part, by the  NIDDK Diabetic Complications Consortium  (DiaComp, www. diacomp. org, DK076169 ). VB was funded, in part, by the  NIDDK  ( R01 DK091565 ).
Hidden state models are among the most widely used and efficient schemes for solar irradiance modeling in general and forecasting in particular. However, the complexity of such models -in terms of the number of states -is usually needed to be specified a priori. For solar irradiance data this assumption is very difficult to justify. In this paper, an infinite hidden Markov model (InfHMM) is introduced for short-term probabilistic forecasting of solar irradiance, where the assumption of fixed number of states a priori is relaxed and model complexity is determined during the model training. InfHMM is a non-parametric Bayesian model (NPB) indexed with an infinite dimensional parameter space which allows the automatic adaptation of the model to the ''correct'' complexity. This facilitates the automatic adaptation of the model to all weather conditions and locations.
Posterior inference for InfHMM is performed using the Markov chain Monte Carlo algorithm, namely the beam sampler. Data from 13 different sources are used to validate the proposed model and subsequently it is compared to two well-established models in the literature: Markov-chain mixture distribution (MCM) and complete-history persistence ensemble (CH-PeEn) models. Important results are found, that cannot be derived from the existing finite models, such as the variation of the number of states within and across sites. The comparison of the models shows that the InfHMM is more consistent in term of the forecasting horizon. For reproducibility of the methodology presented in this paper, we have provided an R script for the InfHMM as supplementary material.
Data For a thorough validation of the method, different climate and weather conditions are required. Therefore, datasets from 11 Baseline Surface Radiation Network (BSRN) stations are selected, in addition to 2 other datasets widely used in the literature. BSRN is the world's largest solar radiation monitoring network, providing freely available data to researchers around the world  (Driemel et al. , 2018). Currently, the BSRN has a total of 71 stations, covering a wide range of climate types. In 1988, the World Meteorological Organisation created BSRN and started collecting data in 1992 with 9 initial stations. The reader is invited to visit the website http://BSRN. awi. de for a detailed history and further information on BSRN. BSRN data downloading and quality control (QC) are performed using the free R-package SolarData  (Yang, 2018 (Yang, , 2019b)).
Datasets from BSRN are selected by automatic filtering to ensure that, after QC, data availability is at least 300 full days (without gaps) for each available year. The most recent year for each selected location is used. The two additional data sets are from Norrköping, Sweden and Hawaii, USA and they are extensively used in solar irradiance forecasting  (Munkhammar et al. , 2017 (Munkhammar et al. , , 2019)). All the used datasets are carried out with a resolution of 1 min. See Fig. 1  and Table  1  for a detailed station metadata. In order to obtain more stable results and a fair split of the available data into training and test datasets, we followed the configuration presented in  Munkhammar et al. (2019). It is a high skill score configuration, and does not depend on data seasonality.
In the following, we will describe the building blocks of the InfHMM as closely as possible to the language of solar irradiation engineering, with always keeping an eye on the readability of the paper. The key property of the InfHMM is that the number of hidden states is countably infinite. It is worth to mention that data is not obliged to visit infinitely many states, as it would appear at first glance. The reason is that data can only visit at most  different states, where  is the length of the observed time-series-the case where each observation is generated by a different state. In practice, the number of states is considerably less than. Generating samples from InfHmm requires a transition matrix  of infinite dimension and an infinite number of means and variances for the emission distributions.
Inference To sample from the InfHMM, we use the beam sampler introduced in Van Gael et al. (2008). The main idea of the beam sampler is to introduce auxiliary variables  1∶ in such a way that, conditional on them, the number of possible trajectories is finite. This stochastic truncation allows the application of the forward-filter backward sampler (FFBS). After initializing the parameters, the full beam sampler routine is: Full details of each of the posterior sampling steps of the beam sampler can be found in the appendix of  Maheu and Yang (2016). However, to facilitate the uptake of the InfHMM, an R script is provided as supplementary material to this paper. Data: 𝜅 1∶𝑁 Result: 𝑆 1∶𝑁 , 𝛱 = {𝜋 𝑖𝑗 } 𝑖,.
Forecast verification metrics In this paper we verify both deterministic and probabilistic forecasts for the first 15 horizons. The forecast horizon is the length of time in the future for which forecasts have to be issued. In this paper, we also verified the performance of the InfHMM at different short-term forecast horizons. To do this, we chose to represent the first fifteen future points as the target of the forecast. Each point is a horizon. The datasets used in this paper are carried out with a resolution time of 1 min, therefore the target horizons are: 1 min, 2 min, 3 min, ⋯, 15 min. For probabilistic verification, we use the continuous ranked probability score (CRPS), the rank diagram (RaD), the reliability diagram (ReD) and the resolution score (RS) to verify the sharpness, calibration and resolution of probabilistic forecasts.
Time complexity of infhmm The time complexity of the InfHMM is the same as that of the FFBS algorithm, except that for each iteration the visited states are sampled by slicing the transition matrix. If  states are represented, the worst complexity is ( 2 ). The InfHMM running time grows quadratically with the number of states and linearly with length of the data. Regarding the coding process, for maximum speed, we have coded the majority of the InfHMM parts in C++ language and used R language as an interface to facilitate the visualization and testing of the InfHMM.
Conclusion In this paper, an infinite hidden Markov model is proposed for shortterm clear sky index forecasting. It is a nonparametric Bayesian model indexed with an infinite-dimensional space of parameters. The main benefit of the InfHMM is its automatic adaptation to the correct model  complexity and size, suggesting thus an automatic adaptation of the model to all weather conditions and locations. In other words, InfHMM only requires a historical time series of the clear sky index, then it can be applied worldwide. The posterior inference using Markov chain Monte Carlo algorithm, namely beam sampler, is applied in this work. Datasets used to validate the model are recorded at 13 solar irradiance monitoring stations around the world and InfHMM is compared to two known and tested models in the literature: the MCM and CH-PeEn model.
We are at a time of considerable growth in the use and development of transcriptomics studies and subsequent in silico analysis. RNA sequencing is one of the most widely used approaches, now integrated in many studies. The processing of these data may typically require a noteworthy number of steps, statistical knowledge, and coding skills which is not accessible to all scientists. Despite the undeniable development of software applications over the years to address this concern, it is still possible to improve. Here we present DEVEA, an R shiny application tool developed to perform differential expression analysis, data visualization and enrichment pathway analysis mainly from transcriptomics data, but also from simpler gene lists with or without statistical values. Its intuitive and easy-to-manipulate interface facilitates gene expression exploration through numerous interactive figures and tables, statistical comparisons of expression profile levels between groups and further meta-analysis such as enrichment analysis, without bioinformatics expertise.
Nevertheless, most of these powerful packages are command-line based or demand coding knowledge and are therefore out of reach for scientists with limited computational training. Besides, analyses can be started at different points in the workflow, from raw or partially analysed data from different tools, to individual lists of favourite final features. This is often a limiting factor for the use of rigid tools that import only a single data type. Hence, providing flexible user-friendly tools for the analysis and visualization of gene expression data can help researchers to convert high-throughput genomics into basic science research insights. To bridge this gap, an increasing number of software tools are being released, based on intuitive, point-and-click, graphical interfaces. Frameworks such as R Shiny,  4  an R package, facilitate the creation and release of interactive web tools.