dataesr/scientific-paragraphs-categorization huggingface dataset:
After fragmentation of the mRNA cDNA synthesis was performed. Sequencing adapters were then ligated to the cDNA fragments, and the resultant product was subject to PCR amplification. Clustering and DNA sequencing was performed using the Illumina HiSeq 4000. All samples were subjected to pairedend sequencing with a read length of 151 nucleotides. Processing of the raw RNA-seq was carried out by GenomeScan using custom in-house scripts. Read quality was assessed using an inhouse QC v1 tool and Trimmomatic v0. 30 was used to filter out reads of low quality and any contaminating adapter sequencers  36. The reads that passed the quality control steps were then aligned to the homo sapiens reference genome (GRCh37) using Tophat v 2. 0. 14  37. Next, featureCounts from the Subread package was used to calculate the number of reads that aligned to each gene producing an unnormalized gene count matrix  38.
The unnormalized count matrix was then normalized for library size using the trimmed mean of M-values (TMM) and then converted to count per million values using edgeR  39. The median number of reads per sample that aligned to known genes was 45 (IQR: 36-74) million reads. Samples with less than 15 million reads aligning to known genes, and one low-quality sample, were discarded. Deconvolution of the bulk RNA-sequencing was performed using CIBERSORTx  17. The CIBERSORTx module "Cell Fractions" was used to enumerate the proportions of distinct cell subpopulations in the bulk RNA-Seq profile. This is performed by providing a matrix that has signature genes derived from either single-cell transcriptomes or sorted cell populations. For the deconvolution of the EPISTOP RNA-Seq the publicly available LM22 signature matrix was used.
Quantification of data was performed using the computer program LinRegPCR  45  in which linear regression on the Log (fluorescence) per cycle number data is applied to determine the amplification efficiency per sample. Interplate variability was normalized using the inter-plate calibrator assay (UniSp3 IPC) according to the manufacturers' guidelines. The starting concentration of each product was divided by the starting concentrations of two 'spike-in' miRNAs (cel-miR39 and UniSp6) that were added during RNA isolation and cDNA synthesis, respectively, to protect against technical variation and to normalize expression patterns. Targeted metabolite analysis of serum samples was performed as described  46. Briefly, 100 µL of isolated serum was stored at -80 °C until processing; samples were thawed at 4 °C and spiked with 10 µM alanine D3 and succinic acid D4 and then centrifuged for 10 min at 14,000x g.
Buffer A was composed of 20 mM ammonium hydroxide/20 mM ammonium acetate (pH = 9. 0) in 95:5 water:acetonitrile. Peak areas from the total ion current for each metabolite SRM transition were integrated using MultiQuant v3. 0 software (AB/ SCIEX) and used as the basis for quantitative analysis. All metabolites with >50% missing values across all 357 samples were dropped from analysis, leaving a total of 249 metabolites for further consideration. Repeated measurements for some of the control samples were performed as a quality check between acquisition batches. To remove observed batch and Vigabatrin effects in the metabolite data set a correction was applied. Individual samples were annotated with their respective batch number and whether the subject was on vigabatrin. A Z-score correction of samples from individual batches was performed on log2-transformed peak area values followed by un-Z-scoring using the standard deviation and mean of all samples prior to Z-scoring.
We estimated the random and fixed effects using lme4 and lmerTest packages in R. We have treated the patient's code as a random effect and the age as a fixed effect with the application of the following formula: lmerTest :: lmerðintensity ∼ sample age w + ð1jcodeÞÞ By transforming the equation, we can derive a formula for intensity correction, depending on the sampling time and accounting for inter-patient variability: i corr = i À age ð Þ t * a À b ID ð2Þ where: i -intensity t -time of sampling a -fixed effect for each week of age b -model intercept (random effect for each patient) ID -patient's ID The assessment of developmental patterns in analytes involved several steps. The first step was to apply the Kruskal-Wallis Rank Sum test, alongside Dunn's post hoc test and Benjamini-Hochberg correction for multiple comparisons for each analyte and age category.
14 additional subjects were excluded because they had received Vigabatrin on a presymptomatic basis, and never developed seizures. Additionally, for 7 patients no comparable sample was available for inclusion. Hence, a total of 65 samples were analyzed, 54 from patients who developed seizures during the 2 year follow-up period, and 11 who did not. Sample size fluctuated due to missing data. Multiple data types were included in this analysis: Proteomics, metabolomics, transcriptomics, miRNA data, genotype information for 86 SNPs (Supplementary Data 1), TSC mutation status, and clinical data. The 86 SNPs were selected based on review of published genetic association studies (review performed using PubMed literature database in April to May 2019). Selected SNPs had to have an allele associated with predisposition to epilepsy of any kind: focal or generalized epilepsy (including absence and juvenile myoclonic epilepsy), infantile spasms, or febrile and other types of seizures (Supplementary Data 11).
The association finding had to be made in: (i) at least one genome-wide association study (GWAS), genome-wide meta-analysis association analysis, genome-wide mega-analysis association study, or candidate SNPs meta-analysis association study; or (ii) at least two independent candidate SNP association studies (Supplementary Data 11). SNPs were selected from studies performed in populations of any ethnicity, with no limits on population allele frequency or predicted risk effect, but had to be statistically significant. SNP genotypes were extracted from the EPI-STOP WGS data  9  , and were classified into two categories, either (i) major allele homozygote or (ii) heterozygote or minor allele homozygote. For the classifier analysis each dataset was processed in the following way: first, constant features (columns with constant values spread across the whole dataset, or, with variance equal to zero) were excluded.
Next, each variable was standardized (transformed to common scale), so the final values in each column had zero-mean and unit variance. More extensive filtering was used for the RNA-Seq dataset, since there were ~63,000 initial values. In the first filtering step, only genes with more than three reads in at least 75% of samples were retained. In the second step, a 2-fold median change between the seizure and no-seizure groups was required. Together, these filters reduced the number of RNA species under consideration to 267. Analytes were selected for inclusion in the classifier analysis using univariate Area-under-the curve (AUC) metric to assess the ability of each variable to predict seizure appearance. Analytes had to meet an AUC threshold > 0. 6 to be retained. In addition, no more than 30 analytes of each type could be selected, and they were down-sampled for inclusion based on AUC rank.
Matthews Correlation Coefficient ranges from -1 (complete disagreement between prediction and observation) to 1 (complete agreement), with 0 meaning lack of correlation. As the final metric for each classifier we chose the mean test MCC value obtained from Monte-Carlo cross-validation technique. Additionally, for each model, we present mean misclassification error (MMCE), positive predictive value (PPV) and negative predictive value (NPV). Missing values in the negative predictive value are related to the low number of negative examples in the test dataset. Due to the limited number of negative examples (n max for no seizures group = 12), the classifier sometimes incorrectly labeled all cases as positives, which caused the lack of true/false negatives (patients without seizures) in the formula for NPV calculation.
Due to the lack of an independent dataset for validation, we validated our results with a permutation test. To calculate the sampling distribution under the null hypothesis, we permuted the outcome variable (seizure status) and repeated the whole experiment 30 timesstarting from feature selection to a model evaluation with subsampling (subsampling was done 50 times for each model to reduce the computation time). This resulted in about 16 million models. The test-statistic was set to the mean test MCC. The significance level was set at 0. 05. A p value represents the fraction of models with random treatment allocation where the mean test MCC was equal or higher than in the models created for original data. Based on the permutation results, the calculated test statistics for this experiment had to exceed the mean MCC test equal to 0.
73 to be considered significant. The volume of data collected in the EPISTOP project consisted of about 33 TB. The experiment was designed in an R environment (version 4. 1. 0) using tidyverse, mlr, parallel and plotly packages (among all). We used packrat for package version control. The dedicated computing server consisted of 40 cores, 216 GB RAM and an additional 216 TB for data storage. Complete code for the.
Acknowledgements The authors wish to thank all of the numerous support personnel who enabled enrollment of subjects, collection of samples, etc. for EPISTOP, and the infants and parents who participated in EPISTOP. EPISTOP was supported by the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ;  EPISTOP , grant agreement no. 602391 , SJ), the  Tuberous Sclerosis Alliance  (no. 02-13 , DJK), and  Polish Ministerial funds for science  (years  2013-2019 ).
Data availability The raw RNA-Seq data generated for this study are held at the European Genome-phenome Archive (EGA) under the accession number: EGAS00001007264. Access to this data is controlled by a data access committee. Please email JDM at james. mills@ucl. ac. uk or DJK at dk@rics. bwh. harvard. edu for further information. All other large data files have been combined into supplemental Data files S1A and S1B, which has been placed in our github repository along with the code for doing this analysis (https://github. com/JagGlo/molecular_EPISTOP), which has https://doi. org/10. 5281/zenodo. 8389826  53. Source data are provided with this paper.
Briefly, this study was part of the EPISTOP project (Long-Term, Prospective Study Evaluating Clinical and Molecular Biomarkers of Epileptogenesis in a Genetic Model of Epilepsy-Tuberous Sclerosis Complex, ClinicalTrials. gov NCT02098759). It was carried out from March 2014 to October 2018 at 9 sites in Europe and 1 site in Australia. The study was approved by local ethics committees at all study sites, and caregivers of all participants signed informed consent before enrollment. It adhered to the International Conference on Harmonization Guidelines for Good Clinical Practice and the Declaration of Helsinki.
2010 ), (3) the High Acuity Wide field K-band Imager (HAWK-I;  Pirard et al. 2004; Casali et al. 2006; Arsenault et al. 2008; Kissler-Patig et al. 2008; Paufique et al. 2010; Siebenmorgen et al. 2011 ), (4) the VLT Ultraviolet and Visual Echelle Spectrograph (UVES;  Dekker et al. 2000) , (5) the X-ray Multi-Mirror Mission (XMM-Newton;  Jansen et al. 2001; Struder et al. 2001) , and (6) the Atacama Large Millimeter/submillimeter Array (ALMA;  Wootten & Thompson 2009). The relatively small separation of the bright quasars aids in collecting these multiwavelength data sets, as the region of interest (≈2′ × 2′) fits almost entirely within a single FOV for most of the instruments. The survey was launched with our large European Southern Observatory (ESO) program (ESO PID 1100. A-0528, PI: M. Fumagalli) using VLT MUSE to observe this region, which we have dubbed the MUSE Ultra Deep Field (MUDF).
This will provide a more complete census of their physical properties and reveal the role of low-mass galaxies in enriching the gas. Specifically, we aim to characterize the gas kinematics, density, temperature, dust content, and metallicity, and reveal how these physical properties are shaped by different environments at various redshifts. In this third paper, we expand our study of the MUDF to incorporate recently acquired HST observations, and provide the custom-calibrated data and source catalogs for use by the community. We describe the program design and acquisition of the HST observations in Section 2, and we detail our customcalibration of the direct imaging in Section 3. In Section 4 we describe our process for identifying sources in the images and measuring their photometric properties, and in Section 5 we describe our simulation-based methods for extracting the slitless grism spectroscopy.
Program Overview We first provide an overview of the HST observations in the MUDF, followed by a more detailed description of the program design and data acquisition. In the NIR, we have observed this field with WFC3/IR onboard HST, obtaining 90 orbits of G141 grism spectroscopy and F140W imaging (Program ID 15637, PI: M. Rafelski & M. Fumagalli), resulting in the deepest HST grism survey ever conducted for a single field. The direct imaging consists of observations through the F140W filter to identify sources in the dispersed grism images, as well as with the F125W filter to constrain their spectral slopes. In the optical, we take advantage of archival HST WFPC2 imaging through the F702W and F450W filters (Program ID 6631, PI: P. Francis), which provide useful constraints on the morphological and photometric properties of sources in the field.
The data were downloaded from the MAST archive on 2021 March 11, and were processed using the recently improved WFC3/IR Blob Flats  (Olszewski & Mack 2021) , and the most recent bad-pixel masks that were updated in the CDRS database 20  the day before. The 178 individual exposures were checked for quality, and 17 exposures with guiding or other issues were excluded. The remaining 161 exposures, each with an exposure time of ∼93 s, were then aligned and drizzled using the TWEAKREG (v1. 4. 7) and ASTRODRIZZLE (v3. 1. 8) tools within DRIZZLE-PAC (v3. 1. 8). In order to align the exposures, we first ran the UPDATEWCS function with the USE_DB option set to False. This removes the default WCS coordinates that cause the direct images and grism exposures to be misaligned, and restores the most recent HST guide star based WCS coordinates.
A limit of m F140W = 19 was experimentally determined so that the segmentation of each object best matches the extent of its emission in the imaging. The deep and shallow segmentation maps and catalogs were then combined into single data products by taking objects brighter than m F140W = 19 from the shallow run, replacing their Note. The ASTRODRIZZLE parameters used to create the F140W mosaic (Section 3). These settings were also used for the other HST filters, except that the FINAL_PIXFRAC was set to 0. 7 for the F702W and F450W filters, and the COMBINE_TYPE was set to "minmed" for the F702W filter. Note. The SOURCEEXTRACTOR parameters used for all HST filters to create the MUDF catalog. The rms maps were corrected for correlated pixel noise (see Section 3. 3), without which the equivalent deep and shallow thresholds are 1.
We examined the 558 grism exposures and discarded 15 with poor image quality due to jitter during the orbit or satellite trails that could not be masked fully. We used the latest available HST WFC3 G141 grism configuration files (see  Pirzkal et al. 2016; WFC3-ISR 2016-15)  and the improved background subtraction described in Pirzkal & Ryan (2020; WFC3-ISR 2020-04). The latter allowed us to estimate and remove the variable He I emission from the Earth's atmosphere. This He I contamination varies during the course of the observations, as described in Section 2. 2, and we carefully removed it before conducting on-the-ramp fitting of the multiple WFC3 reads of each exposure in order to produce a final calibrated image in units of electrons s -1. We then extracted the grism data using the techniques described above with our segmentation map and source catalog (Section 4), resulting in G141 spectra for 1499 sources.
In the case of objects with only a single emission line, we used the absence of other lines in either WFC3 or MUSE to determine the correct redshift. Specifically, when Hα λ6563 Å is observed in the grism, then [O III] λ5007 Å is typically detected in MUSE, while at higher redshifts, [O III] is in the grism and the [O II] λ3727 Å doublet is resolved in MUSE. We will release the emission line catalog in a forthcoming publication, and the resulting redshifts for the sample are shown in Figure  11. The spectral dispersion of WFC3 and MUSE differ by a factor of ∼20, so it is important to consider differences in the redshifts derived from fitting each data set. In the inset of Figure  11. The redshift distribution of sources in the MUDF catalog, with confirmed spectroscopic redshifts for 419 objects.
High Level Science Products In Table  7  we document the files that are publicly available as HLSPs from the MAST portal. The data products encompass three categories: imaging, spectroscopy, and catalogs. The imaging includes our science drizzles, PSF models, convolution kernels, PSF-matched drizzles, and the segmentation map. The spectroscopy includes the calibrated 1D spectra and the 2D spectral images. Our catalogs include the multifilter photometric catalog created with SOURCEEXTRACTOR and the STATMORPH morphological catalog. Detailed descriptions of the data reduction and analysis techniques used to produce these data products are contained in Sections 3-6, and their key characteristics are provided in Tables  1 2 3 4 5 6. We reiterate that users may wish to apply their preferred correction for Galactic extinction to the photometry, although it is very small for this field (see Section 4.
2). In Table  7  we provide a list of the filename extensions for each type of data product, with files provided in the Flexible Image Transport System (FITS) format. The catalogs are provided in simple tabular text file formats. These HLSPs can be accessed from the MAST portal using the DOI 10. 17909/81fp-2g44. The files are also described on the MAST project web page at https://archive. stsci. edu/hlsp/mudf.
Summary The MUSE Ultra Deep Field hosts two associated quasars at z ≈ 3. 22 that provide a unique laboratory for connecting the physical properties of galaxies seen in emission with their surrounding gas observed in absorption. We have observed this field over 90 orbits in a single pointing with the HST WFC3/ IR G141 grism and F140W filter, comprising the deepest HST grism survey ever conducted for a single field. We combined WFC3 (F140W, F125W, and F336W) and archival WFPC2 (F702W and F450W) imaging to detect 3375 sources, with 1536 having both spectroscopic and photometric coverage. The F140W and F336W mosaics reach depths of m AB ≈ 28 and 29, respectively, providing NIR and rest-frame ultraviolet data for 1580 sources, and we reach 5σ continuum detections in the grism spectra for objects as faint as m AB ≈ 27.
By using the long-wavelength coverage of the MUSE and WFC3 spectroscopy, we derived robust spectroscopic redshifts for 419 galaxies between z ≈ 0-6. We have generated custom-calibrated science images, photometric and morphological catalogs, and high-legacy value data products for the MUSE Ultra Deep Field that are available to the community through the MAST portal. These data products enable numerous studies aimed at advancing our models of galaxy formation and evolution in different environments. Specifically, we aim to connect the physical properties of the IGM and CGM traced in absorption with the galaxies discovered in emission to obtain a full census of the gas in and around galaxies in field, group, and candidate protocluster environments. Our forthcoming investigations include connecting the metal enrichment of the IGM and CGM with the gas phase metallicities of low-mass galaxies over cosmic time.
This first prototype uses radial contiguous surface-mounted magnets and its performance is then analyzed using finite element analysis (FEA), showing a relatively high torque ripple ratio. The rotor magnets are then arranged in a quasi-Halbach configuration and simulations are performed with different stator slot openings and different ratios of the tangential part of the magnet in order to quantify the effect of each of these two quantities in terms of average torque, torque ripples and harmonics of the back-electromotive force at no load. Since the design and optimization of this motor is finite element-assisted, a coupling process between FEA Flux software and Altair HyperStudy is implemented for optimization. This method has the advantages of high accuracy of the magnetic flux densities and electromagnetic torque estimates, and especially the torque ripples.
Based on an analytical approach with radial PM magnetization, a prototype is first designed and sized according to the specifications of an urban traction application. The rotor magnets are then arranged in a quasi-Halbach configuration and simulations are carried out with different stator slot openings and different ratios of the tangential part of the magnet in order to quantify the sensitivity effect of these two variables in terms of average torque, torque ripples and harmonics of the back-electromotive force at no-load. As the design and optimization of this motor are finite element-assisted, a coupling process is carried out between FEA Flux software and Altair HyperStudy for direct optimization using the GA single-objective algorithm  [29]. This method has the advantage of the magnetic flux densities and electromagnetic torque estimates, and especially the torque ripples, being more accurate.
Symbol Parameters Value λ r = π p r (15) The first prototype is sized with contiguous magnet configuration and a ratio of stator slot opening to stator pitch λ ss /λ s equal to 50%. Figure  5  shows a view of the stator and rotor of the prototype, and its main characteristics are given in Table  4. Otherwise, the rotor pitch angle 𝜆 is defined as: The first prototype is sized with contiguous magnet configuration and a ratio of stator slot opening to stator pitch 𝜆 /𝜆 equal to 50%. Figure  5  shows a view of the stator and rotor of the prototype, and its main characteristics are given in Table  4. 𝜆 = 𝜋 𝑝 𝑟 (15) (a) (b)    Different calculations were conducted on the designed prototype at no load and under rated load conditions using Flux 2D software. In order to limit the number of unknowns, and thus, the calculation time, only a partial model (half machine) is built.
Design and Optimization Method HyperStudy is a multidisciplinary design study and optimization software for engineers and designers. This software proposes different optimization approaches (DOE, Fit, Optimization and Stochastic), thus making it a very interesting way to efficiently study the design of an electric machine when coupled to an FE model such as Flux 2D. As depicted in Figure  19 , in such coupling between the two software packages, Hy-perStudy will modify the values of the selected prototype input parameters (geometrical or physical) in specified ranges, leading to different prototypes that are then modeled and.