dataesr/scientific-paragraphs-categorization huggingface dataset:
DNA methylation. Global DNA methylation was measured on 512 samples using the Illumina EPIC microarray (Illumina, San Diego). Arrays were run at the UCLA Neuroscience Genomic Core (UNGC). DNA was extracted using the DNeasy kit (Qiagen #69506) according to the manufacturer's protocol and quantified using QUBIT broad range kit (Thermofisher #Q32852). At least 375 ng of DNA was submitted in 30 µl of ddH 2 O to UNGC for bisulfite conversion and hybridization using the Infinium Methylation EPIC BeadChip kit. Samples with DNA below 12. 5 ng/ul (~90 of 512 samples) were concentrated using SpeedVac Vacuum Concentrator (Thermofisher #SPD1030A-115) for <1 hour. Sample positions were randomized across six assay plates to avoid systematic batch variation effects on group or time-based comparisons. All DNA methylation data was processed in R (v4.
0. 2), using the 'minfi' package (v1. 36. 0). Quality control preprocessing was applied by checking for correct sex prediction, probe quality, sample intensities, and excluding SNPs and non-CpG probes. Of the 512 samples, 22 failed quality control and were excluded from further analysis, yielding a final analytical sample of n = 479. Data was then normalized using functional normalization (Fun Norm). Using the R package 'sva' (v3. 12. 0), both RCP and ComBat adjustments were applied to correct for probe-type and plate bias, respectively. After quality control, DNAm levels were quantified as beta values for 865,817 CpG sites.
DNA methylation clocks and related measures. We used DNA methylation data to calculate a series of measures broadly known as epigenetic clocks  15. We computed four clocks designed to predict the chronological age of the donor, Horvath1 (i. e. , PanTissue clock)  5  , Horvath2 (i. e. , Skin&Blood clock)  59  , Hannum  60  , and PedBE 61 clocks; two clocks designed to predict mortality, the PhenoAge 62 and GrimAge 63 clocks; a clock to measure telomere length, DNAmTL  64  ; a clock designed to measure mitotic age, MiAge  65  ; a clock trained to predict cellular senescence, DNAmSen (Levine et al. , unpublished), and two DNA methylation measure of the rate of deterioration in physiological integrity, DunedinPoAm  17  , and DundedinPACE 66. For the Horvath, Hannum, PhenoAge, GrimAge, and DNAmTL clocks, this dataset includes both the original versions of these clocks, calculated using the online calculator hosted by the Horvath Lab (https://dnamage.
genetics. ucla. edu/new) and versions developed using the methods proposed in Higgins-Chen et al. (https:// github. com/MorganLevineLab/PC-Clocks)  67. Briefly, this method replaces the clock's individual illumina probe measurements (5-500 CpGs) with the shared extracted variances among genome-wide CpGs from principal components (PC), yielding the PC-adjusted DNAmAges for each clock. Chronological age values used in the calculations of these clocks were the ages of the donors at the time of sampling. The MiAge clock was computed using the software published by in  65  (http://www. columbia. edu/~sw2206/softwares. htm). The Pace of Aging clocks, DunedinPoAm and DunedinPACE, were computed using the software published in  17, 66  (https://github. com/danbelsky/DunedinPACE).
Whole genome sequencing (WGS). Total genomic DNA was isolated for 94 samples across cellular lifespan using column based DNeasy blood and tissue kit (Qiagen #69504) and quantified using Qubit dsBR assay (ThermoFisher #Q32850). Sample quality assessment, library preparation, whole genome sequencing and data pre-processing was performed by Genewiz using standard Illumina workflow. Briefly, WGS paired-end (PE) reads with 2x150bp configuration were obtained from Illumina HiSeq platform and processed using SAMtools (v1. 2) and BaseSpace workflow (v7. 0). PE reads were aligned to hg19 genome reference (UCSC) using Isaac aligner (v04. 17. 06. 15) and BAM files were generated. Duplicate reads were identified and filtered using Picard tools (GATK). More than 80% of the bases were of high quality with a score >Q30.
com/l/rl/ YyZYM7n3). Cell media samples were collected at selected passages across cellular lifespan and frozen at -80 °C until analysed as a single batch. Thawed samples were centrifuged at 500 g for 5 min and supernatant transferred to a new tube. Media samples were ran undiluted, and the plates were incubated, washed, and signal intensity quantified on a Luminex 200 instrument (Luminex, USA) as per the manufacturer's instructions. Positive (>200 days aged healthy fibroblast) and negative controls (fresh untreated media) samples were used in duplicates on each plate to quantify and adjust for batch variations. Data was fitted and final values interpolated from a standard curve in xPONENT v4. 2. Cytokine concentrations were then normalized to the number of cells counted at the time of collection to produce estimates of cytokine production on a per-cell basis.
Data Records This multi-omics cellular lifespan dataset includes longitudinal data across 13 major biological outcomes, including cytological measures (growth rate, cell size), cellular bioenergetics (respiratory capacity, total energy consumption), transcriptomics (bulk RNA-seq), DNA methylation (EPIC array), whole genome sequencing (WGS), secreted factors (cytokines, cell-free DNA), and others (Fig. 2  and Table  2 ). These outcomes differ by the number of samples, repeat experiments, and length of timecourses for each donor line and treatment group (Figs. 3, 4 , Supplemental Fig. 1 , and Table  3 ). All expected hallmarks of cellular aging were observed in this model, including the upregulation of quiescence and senescence markers, and downregulation of genes associated with cell division (Fig.
5 ). Table  1  contains information about all cell lines used, including the biopsy site, sex and age of the donor, as well as known clinical and genetic information. Table  2  lists the assessed biological outcomes along with their dimensionality (number of parameters quantified), the number of samples available across all cell lines, their temporal frequency, and the total size or number of datapoints. For example, there is available transcriptomic (i. e. , gene expression) data quantified by RNA sequencing, which includes read counts for each annotated gene expressed as transcripts per million (TPM), on 360 samples collected on average every 11 days (min: 5 days, max 15 days), and each cell line has on average 8 timepoints (min:2, max:19). The transcriptomic dataset includes a total of 9.
88 million (genes x timepoints) datapoints; the total number of mRNA reads is 12. 6 billion, available in the source data for detailed analyses of transcripts (i. e. , alternative splicing). Although multiple treatments were used to perturb bioenergetic and endocrine pathways, the dataset is most extensive and is of highest temporal resolution for cell lines from healthy controls. Table  3  lists the five main phases for which the study was conducted (Phases I-V). The time periods and experimenters associated with each study phase. Data can be accessed, visualized, and downloaded without restrictions at https://columbia-picard. shinyapps. io/shinyapp-Lifespan_Study/. This simple interface allows users to select the donors of interest, select the experimental treatment(s), and visualize the time course data in a realtime-updatable display panel (Fig.
6 ). Users can select multiple donors and-or treatments simultaneously to visualize the effects of interest and explore the data. All data visualized is downloadable as a. csv file which can further be found directly at FigShare (18441998)  74. The app will be updated with new data as additional lifespan experiments and analyses are performed. The unprocessed RNAseq (GSE179848)  75  and EPIC DNA methylation array data (GSE179847)  76  can also be accessed and downloaded in full through Gene Expression Omnibus (GEO)  77. Additional EPIC DNA methylation data (n = 31 samples) collected on the same donor fibroblasts from earlier work  31  is available at (GSE131280)  78. Brightfield microscopy images can be downloaded at FigShare (18444731)  35  and raw Seahorse assay files can be found at FigShare (20277606)  55.
technical Validation The number of days per passage were systematically recorded (and can be derived from the 'Date_time_of_passage' variable, for each cell count timepoint in the database). Variation and deviations in the number of days between passages increase towards the latter part of the lifespan for all lines and treatments because of the limited number of cells once enter quiescence and senescence (Fig. 7A ). Two of the 6 healthy controls' growth curves were repeated 4 times, over a ~2 year period, in separate phases by different experimenters (see Table  3 ), confirming that the growth curves were reproducible (Fig. 7B ). Chronic DEX (Treatment 2) was repeated in study Phases I, II, and III. Hypoxia (Treatment 5) was repeated in study Phases III and V. SURF1-mutant cells were run in both study Phases II and V.
V. of 25% and 40% (range: 100,609 and 166 copies/ mL/10 6 cells, respectively) (Fig. 7E ). The IL6 ELISA assay contained 9 technical replicates across 8 plates of the same samples (HC3, passage 32, 189 days grown). An old media sample (robust extracellular IL6 levels) was selected as a positive control. Replicates had a C. V. of 49% (range: 101. 5 pg/mL/10 6 cells) (Fig. 7F ). The technical variation in DNAm on the EPIC array was also previously determined on six DNA replicates from HC5 (GEO #GSE131280)  78. DNAmAge computed from the combination of multiple probes or CpG sites showed a coefficient of variation, for selected clocks, of 3% for the Horvath1 (PanTissue) clock, 4% for the Horvath2 (Skin&Blood) clock, and 13% for the PhenoAge clock (Fig. 7G ). After PC-adjustment  67  based on the technical variation between samples was reduced to 2%, 8%, and 2%, respectively, indicating moderately robust technical validity at the single-CpG level, but high validity when multiple CpGs are combined into multivariate DNAm clocks.
The DNAm dataset also contains 3 replicate longitudinal experiments of HC1 & HC2 that can be used to quantify the variability in the longitudinal rates of epigenetic aging using the investigator's preferred clock(s) or individual CpGs. Technical variation for RNA-and DNA-based omics measures with > 100 samples (i. e. , RNAseq, DNAm, telomere length etc. ) was determined by distributing the same biological sample (healthy HC1 fibroblasts, untreated, passage 6, 21 days grown) across multiple plates and sequencing lanes. For RNAseq, the technical assay variation was determined from 7 biological replicates. The average coefficient of variation (C. V. , standard deviation divided by the mean) in normalized read counts across all genes was 1. 47% (range = 3. 42 normalized expression units (Fig. 7H ).
Normalization to cell numbers. The raw values for some features are influenced by the number of cells in the culture flask at each passage. For example, secretome data that includes extracellular levels of cytokines or cell-free mitochondrial DNA (cf-mtDNA) in the cell media are determined not only by the secretion rate, but also by the total number of cells contributing to the signal in the flask. Therefore, secreted factors levels are normalized to cell number at the time of harvesting media, which represents the amount of analyte released per cell counted. To obtain raw concentrations media concentrations, the investigator can multiply the normalized values (varia-ble_name_example e. g. 'IL6_ELISA_Upg_per_ml') by the cell count (variable_name_in_database_for_cellcount, e. g. 'Cells_counted_UmillionCells') at each time point.
Cell seeding density. In Phase V of the study, we improved our calculation of seeding numbers to ensure <80% confluency at each passage. This minor change in protocol could contribute to differences between replicate experiments and trajectories of metabolic rate and other potential differences between Phase V and earlier phases. Sample size and robustness of experimental treatments. As detailed in Supplemental Fig. 1 , this Cellular Lifespan dataset includes longitudinal assessments of treatment conditions in primary human fibroblasts from multiple independent donors, in some cases replicated multiple times. This is the case for untreated (Treatment #1), chronic DEX (#2), and hypoxia (#5) treatments. These time-series data enable robust modeling of time-related dynamics and treatment effects, using the user's preferred methods.
Other treatment conditions were either performed in a smaller number of donors, particularly HC1 (male) and HC2 (female), and/or for some treatments the timecourse experiment was not subsequently repeated. These conditions should be considered exploratory and may serve as preliminary data for subsequent experiments. All treatment conditions were conducted in at least two different donors, and each experiment contained multiple timecouse data points. The more limited domains of the dataset enable to calculate robust estimates of effect sizes, directionality of effects, and effects across two donors of different biological sex. Rate of epigenetic aging. To obtain stable estimates of epigenetic aging without potential artifacts attributable to the early effects of culture or before the onset of treatments, or to later alterations in the epigenome (i.
e. , non-linear trajectories on DNAmAge clocks) related to quiescence or senescence towards the end of life, rates of epigenetic aging were determined by taking the linear slope for each cell line from 25 to 75 days of growth. Future analyses. This dataset contains multi-modal data that can be used to investigate each of the hallmarks of aging (see Fig. 5 ). In particular, the high-resolution timecourses are ideal to model age-related trajectories with either linear or complex non-linear functions. These models could then be systematically classified and statistically examined using functional regression approaches, for example, to identify (groups of) parameters exhibiting similar related age-associated trajectories. Such parameters would indicate co-regulation and could inform subsequent mechanistic studies aiming to establish causal pathways, bioenergetic parameters, enzymes, or genes that drive specific aging trajectories.
Cell line metadata. Demographic, tissue of origin, and genetic characteristics of the primary human fibroblast cell lines used in the Cellular Lifespan Study dataset. The dataset includes 6 healthy control donors, and 3 donors with SURF1 mutations that alter mitochondrial respiratory chain complex IV assembly and function, and lead to early death in affected patients. 6. Contact Inhibition a. Rationale: Allowing cells to fill up the dish and enter a quiescent state allows for the experimentally untangle the role of cell division in time-dependent changes. Contact-inhibited fibroblasts continue to undergo morphological changes, exhibit >88% reduced division rate on average, and display skin-like tissue appearance after months in culture. b. Design & dose: After thawing and adjustment to culture environment cells are plated in multiple flasks at high density with marked collection points with the 0 time point collected 7 days after the initial plating.
Dimensionality of biological measures available in this dataset. Molecular, cellular and bioenergetic features, the technique or platform used to generate the data, their temporal resolution, and the dimensionality of datasets for each biological measure. & Values are averages with minimum and maximum number of timepoints (min, max). * The frequency of passaging differs by study Phase: Phase I, 5 days; Phase II, 5 days; Phase III, 7 days; Phase IV, 7 days; Phase V, 5 days.
This temperature, the dew/frost point temperature, is measured with a thermistor and the specific humidity is calculated. Occasional artifacts can be produced in CFH measurements after encounters with mixed-phase clouds if supercooled droplets freeze in the inlet tube of the instrument and subsequently re-evaporate in the dry stratosphere  (Jorge et al. , 2020). Potentially contaminated data were rejected from the analysis as described in  Brunamonti et al. (2019).
79, 0. 52, and 0. 52, for ChiWIS, FLASH, and FISH, respectively. ChiWIS reports an anomalously high mean value for clear-sky RH ice because the driest measurements were made at very high altitude where the cell pressure was unregulated and those data have been removed. If these data are included then the ChiWIS mean relative humidity in clear-sky is 0. 51 (see Fig. S9 ). For in-cloud periods, only ChiWIS and FLASH report measurements of relative humidity, with mean values of 1. 07 and 1. 05, respectively. Including the unregulated periods does not alter the in-cloud mean relative humidity because there were very few clouds sampled at Both ChiWIS and FLASH report very infrequent, but non-zero, measured points above the homogeneous nucleation threshold at very low temperatures (T < 190 K) during in-cloud periods.
These sub-diffraction-limit imaging techniques rely on digital image acquisition and deconvolution algorithms capable of localizing individual fluorescent probes in thousands of image frames and projecting the deconvolved images into a single plane. However, not all situations are amenable to super-resolution fluorescence imaging, because of computational limitations, or because one does not have access to advanced optical instruments and experienced personnel. Fast and efficient algorithms for resolution enhancement of conventional (fluorescence) images remain highly desirable  8. Examples include interpolation techniques  9  , learning-based algorithms  10  and reconstruction approaches based on deconvolution and regularization  11. Modern computer languages like Matlab, R and Python provide efficient matrix operations that make it easy to implement convolution with little programming.
Deconvolution is the inverse operation, estimating a source image from the observed image. It is a so-called ill-conditioned problem, but it can be tamed by using constraints, implemented as penalties  1, 12. Here we present an algorithm that works on arbitrary single images, and is very fast: zooming a 100 by 100 pixel image to 800 by 800 pixels takes less than a second on an average PC. The algorithm, which we call TurboZoom, can be coded in 30 lines of Matlab code. In the next section we describe the theoretical basis of fast deconvolution by ridge regression and how to implement it efficiently with the conjugate gradients algorithm. Section 3 presents some typical applications. In the final section we discuss options for further research.
Only for very low values, less than 10 -10 , instabilities occur. For κ = 0. 1 the estimate of x is strongly shrunk towards zero. Larger values of κ will lead to even more shrinkage. For κ = 0, Matlab reports a solution, but warns that the system is singular, and produces a result that looks more or less random. As Figure  1  shows, for κ = 0. 1 the estimate of x is strongly shrunk towards zero. Larger values of κ will lead to even more shrinkage.
The conjugate gradients algorithm In a modern computer language like Matlab, deconvolution of vectors by ridge regression is not challenging. One line of code does the work: X = (S' * S + kappa * eye(n)) \ (S' * y). Unless we have very small images this will not work with vectorization of matrices Y and X. As we have noted before, too much memory space and computing time are needed when we form vectors y and x and work on them. Conjugate gradients (CG) is an algorithm for iteratively solving systems of linear equations  14. It was developed for the standard case with a matrix and vectors. Its key property is that no matrix inversion is used, only products of matrices and vectors. For our application to images, we can adapt CG in such a way that only products of matrices are needed, and that these matrices are never larger than Y , S, S or X.
The Supplemental Information shows Matlab code for one-dimensional and two-dimensional CG. Here we discuss some key points. The conjugate gradients algorithm saves memory space, because we can work directly on the matrix X. Constructing a classical system of penalized equations would take much time and solving it even more. Another advantage is that we can set the number of iterations at a value that suits our purpose. For initial exploration of images, to locate interesting regions, a handful iterations will be enough. This is illustrated in Figure  5. We show the core of the code here. Listing 1. The core of the conjugate gradients algorithm for solving a linear system. function x = CG1(Q, y, delta) % Solve y = Qx with conjugate gradients % Convergence criterion delta % Initialize n = size(Q, 2); x = zeros(n, 1); r = y -Q * x; p = r; % Update solution for it2 = 1:100 q = Q * p; alpha = sum(r.
The result of super-resolution by a factor 8 is shown in the right panel. The PSF of the microscope was approximated by a two-dimensional Gaussian function with full-width at half-maximum (FWHM) of approximately 350 nm. Linearized DNA molecules that could hardly be distinguished on the original image can now be clearly resolved. This is of crucial importance as most methods employed for optical mapping of DNA rely on the quality of the signal traces extracted from these images (e. g. for database matching) and require a high accuracy of signal conversion. Another key aspect for DNA high throughput technologies is of course speed. The computation time to obtain the result in Figure  6  was approximately one second.
The early Eocene (∼56-48 Myr ago) is characterized by high CO 2 estimates (1,200-2,500 ppmv)    and elevated global temperatures (∼10°C-16°C higher than modern). However, the response of the hydrological cycle during the early Eocene is poorly constrained, especially in regions with sparse data coverage (e. g. , Africa). Here, we present a study of African hydroclimate during the early Eocene, as simulated by an ensemble of state-of-the-art climate models in the Deep-time Model Intercomparison Project (DeepMIP). A comparison between the DeepMIP pre-industrial simulations and modern observations suggests that model biases are model-and geographically dependent, however, these biases are reduced in the model ensemble mean. A comparison between the Eocene simulations and the pre-industrial suggests that there is no obvious wetting or drying trend as the CO 2 increases.
What are the physical mechanisms behind this precipitation response? 4. How do the DeepMIP models' early Eocene simulations compare with proxy data of African precipitation? Section 2 of this article briefly describes the experimental design followed by the DeepMIP models, gives a brief introduction to the models themselves, and describes the observational and proxy data used for comparative purposes. Section 3 presents the results, addressing each of the above questions. Section 4 summarizes and concludes.
The simulations varied in length (see Table  1 ) but were all at least 1,000 yr in length, with the climatologies, comprising the results discussed here, being calculated over the final 100 yr. At that point, all simulations should have had a global mean top-of-the-atmosphere net radiation balance of less than 0. 3 W m -2 (or a similar balance to that of the PI) and an SST trend of less than 0. 1°C century -1  (Lunt et al. , 2017). All of the output, details of which are given in  Lunt et al. (2017) , were uploaded to a centralized DeepMIP database.