dataesr/scientific-paragraphs-categorization huggingface dataset:
Seed-Based FC Extents Seed-based group-level FC maps were obtained with datasets statistically thresholded with FDR-corrected p < 0. 001. Each estimated FC extent was limited with |Fisher(r)| > 0. 3. Note that Fisher(r) denoted Fisher-transformed Pearson correlation coefficient. Figure  4A  showed average FC maps derived from 10 seeds as group results of 9-min-length OE and ME datasets. ME FC maps showed similar or stronger clusters than the OE FC maps for seeds placed in PCG, Ins, and PCC. For seeds placed in Hipp and ACC, ME datasets detected additional clusters in FC maps as indicated by arrows in Figure  4A. Figure  4B  showed quantitative comparisons of the group-level FC extents with 9-min-length datasets, where ME produced broader FC extents with higher number of voxels than OE in most seed-based FC maps.
Especially, for Hipp and ACC seeds, FC extents of ME datasets were approximately up to 3 times larger than those of OE datasets. tasets, FC group difference of each ROI was visualized on the surface model as shown in Figure  3C. Four ROI regions, indicated by arrows in Figure  3C , had significant differences in functional connectivity, as well as in signal intensity. The locations of the four ROIs were consistent with the regions indicated in Figure  3B , where cortical coverage on the surface model was dramatically improved by echo combination. For visualization of the meaningful FCs from the four ROIs, i. e. , |Fisher(r)| > 0. 3, the BrainNet Viewer  [62]  was used to plot edges for inter-ROI functional connections derived from the four ROIs. In Figure  3D ,E, OE datasets showed a much smaller number of FCs from the red nodes than ME datasets.
Seed-Based FC Extents Seed-based group-level FC maps were obtained with datasets statistically thresholded with FDR-corrected p < 0. 001. Each estimated FC extent was limited with |Fisher(r)| > 0. 3. Note that Fisher(r) denoted Fisher-transformed Pearson correlation coefficient. Figure  4A  showed average FC maps derived from 10 seeds as group results of 9min-length OE and ME datasets. ME FC maps showed similar or stronger clusters than the OE FC maps for seeds placed in PCG, Ins, and PCC. For seeds placed in Hipp and ACC, ME datasets detected additional clusters in FC maps as indicated by arrows in Fig-  ure 4A. Figure  4B  showed quantitative comparisons of the group-level FC extents with 9min-length datasets, where ME produced broader FC extents with higher number of voxels than OE in most seed-based FC maps.
ICA-Based FC Detection Eight IC maps were identified as well-known intrinsic brain networks from 20 IC components. Group-level IC maps calculated by dual regression and permutation test with truncated OE/ME datasets are shown in Figure  5A. IC maps were derived by datasets thresholded by FDR corrected p-value < 0. 001. Visually, group-level IC maps from ME datasets showed stronger and broader connectivity than those from OE datasets. Sensitivity, false positive rate (FPR, as '1-specificity') and accuracy of OE/ME group IC maps were evaluated with a threshold of FDR corrected p < 0. 001. In Figure  5B , the averages of all three metrics across eight IC maps were plotted against scan length. ME datasets produced higher sensitivity than OE datasets through all scan times. Scan lengths shorter.
ICA-Based FC Detection Eight IC maps were identified as well-known intrinsic brain networks from 20 IC components. Group-level IC maps calculated by dual regression and permutation test with truncated OE/ME datasets are shown in Figure  5A. IC maps were derived by datasets thresholded by FDR corrected p-value < 0. 001. Visually, group-level IC maps from ME datasets showed stronger and broader connectivity than those from OE datasets. Sensitivity, false positive rate (FPR, as '1-specificity') and accuracy of OE/ME group IC maps were evaluated with a threshold of FDR corrected p < 0. 001. In Figure  5B , the averages of all three metrics across eight IC maps were plotted against scan length. ME datasets produced higher sensitivity than OE datasets through all scan times. Scan lengths shorter than 2 min failed to show acceptable levels of sensitivity for both groups as expected.
Echo combination has yet to fully recover signal dropout that intrinsically occurs in EPI acquisition. When it is combined with other susceptibility reduction techniques, such as z-shimming  [68] , it is expected to further improve cortical coverage. ICA-based fMRI de-noising methods, such as ICA-AROMA, MEICA, TEDANA, etc. [54, 69]  has shown the potential for data quality improvements, as well as the capability to subject-specifically separate signal versus noise/artifacts. These methods could provide noise and artifact component removal and, thus, improved performance for FC detection  [25, 70]. The primary focus of this work is to compare the benefit of only ME combination to the shorter (but comparable) TR of OE acquisition on the condition of high-spatial-temporal resolution implementation, especially when it is performed on the C3T fully using the high gradient slew rate.
However, the IC classifiers for these ICA-based de-noising methods were extensively studied with relatively higher SNR fMRI data, such as low-spatial-temporal resolution fMRI datasets. As described in previous studies  [22, 69, 71] , the classifiers could fail in case of ambiguous components and an expert/interpreter may be required to manually intervene and decide whether components were classified correctly. For these reasons, ICA-based de-noising methods were excluded in preprocessing of data cleaning in this study, which would be examined in future study. Instead of detecting ICAbased subject-specific artifact components, concat-ICA was applied to detect 'well-known' brain network components common to all subjects and all datasets. Echo combination served as a single extra pre-processing step for the ME fMRI.
All the other denoising steps remained the same between ME and OE acquisitions. Even without ICA-based denoising pre-processing, ME fMRI still outperforms OE fMRI based on several analysis methods. A limitation in this study was that the scan length of the ME acquisition was restricted by a scanner software limit on the total number of reconstructed images per scan. With the limit, the proposed ME imaging protocol (Table  1 ) was restricted to only 5 min per scan. To achieve a longer scan length, two ME scans had to be performed consecutively and concatenated together afterwards. Due to this, there is a potential loss in temporal continuity introduced by data truncation and concatenation. Since RS FC estimates are modulated by frequency dynamics with cycles on the order of several minutes  [72] , data truncation of any RS-fMRI dataset could cause biased reliability and reproducibility in FC estimate  [73].
As an alternative approach, data scrubbing, such as delta function regression has been suggested to minimize a loss in reliability and reproducibility on the FC estimate  [73]. Although the previous study demonstrated that data concatenation could improve the reliability of functional connectomics  [48] , it is still unclear how a potential loss in temporal continuity caused by the data concatenation would influence the FC estimate  [48]. With a recent software upgrade of the C3T scanner, the system currently allows a non-stop acquisition of the ME scan over 10 min. This enables comparison of concatenated data with data acquired without discontinuity. As part of future works, the effect of data concatenation with different preprocessing steps will be investigated. For future works, the enhanced sensitivity of ME fMRI will be applied to clinical RS-fMRI studies of aging and neurodegenerative diseases, such as dementia as the brain suffers from signal dropout in regions with iron deposition and increased partial volume effect due to brain atrophy  [74].
(C) Functional connectivity between 168 ROIs defined in individual anatomical images were examined. Through the comparison between OE and ME group-level functional connectivity, the numbers of significantly different FCs on each ROI were evaluated. The indicated regions in (B,C) shows differences both in functional connectivity and cortical coverage ratio, which are left and right medial orbital sulci (olfactory sulci) and anterior transverse collateral sulci. The functional connections from the four regions are visualized by BrainNet Viewer with (D) OE and (E) ME datasets.
Sensors 2023 , 20 Figure 5. 2023205 Figure 5. (A) Group-level ICA-derived brain networks of single-and multi-echo datasets with 9min-length datasets. Dual regression and permutation testing (N = 5000) were performed to evaluate individual-and group-level IC maps, respectively. (B) Sensitivity, false positive rate, and accuracy were evaluated with single-and multi-echo datasets in ICA-derived brain networks. Eight IC maps obtained from Concat-ICA were assumed as ground truth for the representative brain networks. The single-and multi-echo group-level IC maps like (A) were calculated with a variety of scan times and were compared to the ground truth IC maps. Mean and standard deviation of the three metrics (columns) were evaluated across the eight brain networks on each scan time length. Abbreviations.
In summary, we present PGGB, a new, modular, and conceptually straightforward approach to understanding sequence relationships between many complete genomes in both pangenomic and comparative genomics settings. Our approach provides a general framework for genome graph building techniques which we expect researchers will upgrade and extend in the future. By making it easy to build variation graphs, PGGB opens the door to diverse downstream population and evolutionary genetic methods that can consider all classes of sequence variation simultaneously. This will allow us to develop conclusive understanding of the links between genome, phenotype, and evolution in an era where the complete assembly of genomes becomes routine. Online content. PGGB is available at https://github. com/pangenome/ pggb.
Finally, we collect metrics and statistics for further analysis and visualization. To simplify reproducibility, here we provide a detailed summary of the evaluation process: 1. Extract FASTA file: The script starts by extracting the FASTA sequences from the pangenome graph (GFA format) using the odgi paths tool. 2. Take reference sequences: The script extracts the reference paths in the GFA file and creates a new FASTA file containing these sequences. 3. Identify variants with PGGB: The script then identifies variants in the pangenome graph using the vg deconstruct tool with appropriate options for haplotype-based variant calling from the graph and complex allele decomposition with BiWFA and VCFLIB. The final variants are saved in a VCF format file. 4. Pre-process the PGGB-based VCF files: The script pre-processes the VCF files, including normalizing alleles, removing insertions and deletions larger than 1 base pair, and removing the ALT allele if it is not present in the haplotype.
5. Identify variants with NUCmer: The script performs a pairwise sequence alignment between the reference and each contig in the pangenome using NUCmer. The script extracts SNPs from the NUCmer delta file using the show-snps command and generates VCF files for each aligned contig. 6. Merge variants by haplotype: The script then merges all VCF files for each haplotype generated by NUCmer to create a single VCF file per haplotype. 7. Variant evaluation: RTG Tools' vcfeval is used to evaluate the performance of PGGB-based variants and NUCmer-based variants by comparing true positives, false positives, and false negatives in shared callable regions. This is done for both non-waved and waved PGGB-based VCF files, allowing for a direct comparison of the performance of these variant calling methods.
8. Collect statistics: The script computes summary statistics, such as cision, recall, and F1 scores for each haplotype and writes them to TSV files. It also calculates the total number of variants called and the ratio of evaluated variants for both NUCmer and PGGB-based methods. 9. Organize output: Finally, the script organizes the output data, including VCF files, evaluation results, and statistics, into a specified output directory. Although imperfect due to our lack of ground truth in the context of whole genome alignment, this method provides a way to approximately compare the existing standard for whole genome pairwise alignment, MUMMER4, with PGGB. We focus on SNPs and omit comparison of structural variation for diverse reasons. First, we found extracting SVs from MUMMER4 output to be problematic and poorly-supported.
, 2013; Slowik et al. , 2010a). Slowik et al. (2010b)  reported that the uncertainty unbalance when coupling data from two different instruments can lead to unevenly representation of a data subset since there may be stronger internal correlations in one set versus the other. For example, in AMS data, there are characteristic m/z patterns due to the fragmentation process. This decompensation of the instruments' representation was tested by relatively changing the weighting of the uncertainty matrix of one of the instruments and assessing the scaled residuals. This work found that the solution with the lowest scaled residual means was obtained with a weighting of the PTR-MS dataset by 0. 7 for winter and 0. 8 for summer without changing the AMS dataset weight. Moreover,  Belis et al. (2019)  tuned the weighting of the instrumentally-established errors to provide the receptor model with optimum weighting for each instrumentation's data.
This study proposed to adjust the weights according to the scaled residuals and Q/Q exp outcomes. Thereupon,  Tong et al. (2022)  further explored the combination of two different instrumentation datasets by weighting their errors and assessing the outcomes by analysing the intersection between both datasets' scaled residuals histograms. That study showed that the most balanced solutions were found for weightings close to unity (scant weighting). The origins of submicron aerosol in Barcelona have been previously studied with a wide variety of instrumentation. Most of the studies are based on measurements with a TR of 24 h and with organic aerosol (OA) as a bulk species  (Pérez et al. , 2008; Brines et al. , 2019; In't Veld et al. , 2021). Pérez et al. (2008)  reported seven groups of pollutants classified by their major origin for the bulk PM 1 based on chemical analysis of offline PM 1 filter samples: Organic Matter (OM) + Elemental Carbon (EC), sea spray, crustal, ammonium, sulphate and nitrate.
The MTR-PMF technique allows for SA of data subsets with different TR to provide an improved description of the sources in terms of the contribution of a wider range of species and their time variation. The objective of the present study is twofold. It pursues in a better quantitative SA of the submicron aerosol sources, identifying different organic and inorganic soluble ions' together with black carbon (BC) and metal species, leading to a more complete source quantification and to the identification of additional potential sources. The submicron PM components used for this aim were: i. organic aerosol (OA), measured by a Quadrupole Aerosol Chemical Speciation Monitor (Q-ACSM); ii. nonrefractory secondary inorganic species (SIA), including SO 4 2-, NO 3 -, NH 4.
+ , and Cl -, also measured by a Q-ACSM; iii. BC measured by an aethalometer; iv. elemental species, from filter samples collected by high-volume samplers and analysed via ICP-AES and ICP-MS. This study also aims to examine the effect of the averaging of the high resolution (HR) data subset and of the uncertainty weighting of the datasets towards the reliance of MTR-PMF results. This parameter optimisation will provide guidance for future MTR-PMF users who seek for the optimal combined SA. To the authors' knowledge, this work provides the first study of these two parameter effects in a coupled manner from an analytical perspective to obtain the best possible SA of the submicronic aerosol.
Sampling site, period and instrumentation Measurements were performed at the Palau Reial site (PR; 41 • 23 ′ 15 ′′ N; 02 • 07 ′ 05 ′′ E; 80 m a. s. l. ), an urban background site located in NW Barcelona near one of the busiest avenues of Barcelona (Diagonal Avenue, Fig. S1 ). More details of this site can be found in  Minguillón et al. (2016)  and  Pey et al. (2010). The sampling period was from September 2017 to October 2018. The results from the deployed Q-ACSM and the aethalometer AE33 have been reported in  Via et al. (2021 ), Yus-Díez et al. (2021). High volume samplers were co-located and used to collect PM 1 filter samples, as described in In't  Veld et al. (2021). The combination of these three datasets has been prepared as input to PMF model both separately and for MTR-PMF.
9, 1. 68, respectively, from  Zotter et al. (2017). Posteriorly, the speciation of the BC regarding its origin into liquid fuel (BC lf ) and solid fuel (BC sf ) was obtained by applying the Sandradewi model  (Sandradewi et al. , 2008)  to these BC measurements. The measurement uncertainty for the absorption was set to 15% of the measurement  (Forello et al. , 2019)  for each of the seven wavelengths absorption. Error propagation was used for obtaining the uncertainty estimation of BC lf and BC sf. The data, generated at a 1-minute TR, was averaged into the 30-minute timestamps matching the Q-ACSM data to provide homogenised measurements. The ensemble of Q-ACSM (OA matrix and SIA) and aethalometer data (BC lf , BC sf ) will be hereinafter referred to as the high time resolution (HR) data subset.
(2021)  and  Via et al. (2021). Another quarter of each sample was water extracted and analyzed by ion chromatography for the determination of SO 4 2-, NO 3 -, and Cl -concentrations and by selective electrode for the determination of NH 4 + concentrations. A portion of the filter was analyzed by a thermal-optical method (Sunset OCEC analyser, Sunset Laboratory Inc. ) following the EUSAAR2 protocol  (Cavalli et al. , 2010)  for the EC and OC concentrations determination. The uncertainty of these measurements was calculated as described in  Escrig et al. , (2009). These species will be referred to as the low TR (LR) data subset.
2 and 2 (so called weak) were kept after multiplying their uncertainties by a factor of two (Table  S2 ). Therefore, the HR data subset includes OA mass spectra from m/z12 to m/z100, SO 4 2-, NO 3 -, NH 4 + , and Cl -, and BC sf and BC lf (17603 datapoints); and the LR data subset comprises Ca, Al, K, Mg, Na, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, As, Sn, Sb, and Pb (83 datapoints). The number of species and samples of the LR data subset provides a number of degrees of freedom per variable (72) in the 50-100 advised range  (Henry et al. , 1984). The elements selection was based on their signal-to-noise ratio values (0. 5 threshold) and stability throughout the period.
Instrument weighting In order to generate sensible SA results, a fair representation of all species and groups of them must be ensured. Although the signal-tonoise ratio (Table  S2 ) is comparable from one data subset to the other, the datasets can be uncertainty-unbalanced. An approach to evaluate the fairest uncertainty weighting is the assessment of the resulting PMF ratio of the error (e ij ) over the extent on which a measurement (x ij ) can be varied (σ ij ), i. e. , scaled residuals. The instrument-individual uncertainty adjustment has been widely used for balancing these species' scaled residuals  (Sofowote et al. , 2018) , which are expected to have a Gaussian distribution centred to zero  (Zhou et al. , 2004). However, less attention has been paid to the ensemble of all scaled residuals, which can lead to non-unimodal distributions especially if different instrumentation data is put together.
A balanced PMF solution has been defined as that whose magnitude of scaled residuals is independent of the instrument  (Slowik et al. , 2010a). Thus, the scaled residuals distribution should be unimodal. Tong et al. (2022)  showed that even for signal-to-noise-balanced input matrices and equally internally correlated ions within subsets, PMF residuals can be affected by the relative number of species included in each subset. To address this inherent unbalanced coupling of different instrument data, weighting each subset has been proven successful  (Crippa et al. , 2013; Tong et al. , 2022). The quantity C represents the scaling value for each of the data subsets' uncertainties (σ). In this study case, these Cs will be hereinafter called C HR and C LR , for the HR and LR datasets, respectively: σ HR ′ ij = σ HR ij C HR , σ LR ′ ij = σ LR ij C LR (2) The optimality assessment of a weighted PMF solution will be based on the similarity of the scaled residuals with those from PMF of the separate subsets.
The quantification of this similarity among the data subsets scaled residuals was proposed by  Tong et al. (2022) , assessing the overlap between the histograms of the scaled residuals of the different subsets, in this case, HR and LR (example depicted in Figure  S3 ). This quantity can be calculated by: F overlap = ∫ a -a min [ P HR ( s ij ) , P LR ( s ij ) ] (3) where P HR and P LR indicate the density of probability of occurrence of the scaled residuals value s ij , and a represents the integration limits. This integral is expected to cover the [0,1] range, being the extremes of this interval the null and full intersection, respectively. Defining F* overlap as the overlap of the scaled residuals histograms of the independent HR and LR PMFs, the optimal solution is, as defined in  Tong et al.
Workflow design With the purpose of assessing the MTR output sensitivity to the TR averaging and uncertainty weighting effects, a multi-approach procedure was designed, similarly to  Belis et al. (2019). Approach 1. HR SA. SA was performed to a data subset including the OA mass spectra, SIA species, and BC measurements separated into BC lf and BC sf. The TR of this data subset is 30 min. Approach 2. LR SA. SA was performed on the data subset that contains metals concentrations at a TR of 24 h every four days. Approach 3. Pseudo-conventional SA. SA was performed on a data subset that contains the metals, the SIA from Q-ACSM, and the BC and the OA as a bulk, all averaged to the LR data subset timestamps (24 h every four days). The BC was introduced as a species summing BC lf and BC sf , and the OA as the sum of all its m/zs to mimic the EC, OC concentrations that a conventional offline data subset would account for.
Thus, the 'pseudo-conventional' naming refers to the mimicking of this data coupling to a conventional offline approach. This approach provides the reference to compare the posterior MT-PMF results with this conventional offline precursor SA methodology. Approach 4. Base case SA. SA was performed on a data subset containing the HR data subset averaged to the LR subset timestamps (24 h every four days) and the LR data subset so that both data subsets are run under the same TR. This Approach does not apply uncertainty weightings. This data subset represents the most basic ensemble the MTR-PMF more refined results can be compared with. Approach 5. MTR-PMF. SA performed on the dataset including OA species, SIA compounds, BC lf , BC sf , and metals in their native TR using the MTR-PMF technique.
This data coupling requires performing first TR and C weightings assessment in order to find the most effective settings for both mathematical and environmental adequacy of the results. The determination of the highest suitability of these parameters' values was based on the scaled residuals histogram intersection between subsets. Whilst the resolution of the LR dataset remains constant (24 h every 4 days), the HR subset is averaged to different resolutions (R 1 , referring to the TR of the HR dataset): R 1 = 30 min. , 1 h, 2 h, 3 h, 6 h, 12 h, 24 h. Weighting scaling values combinations are: (C HR , C LR )=(1,0. 001), (1,0. 01), (1,0. 1), (1,1), (1,10), (0. 001,1), (0. 01,1), (0. 1,1), (10,1), (100,1), (1000,1). The C-weightings testing experiment was afterwards repeated in a narrower range of C-values amongst the determined best combinations resulting in optimal resolution.