dataesr/scientific-paragraphs-categorization huggingface dataset:
Postprocessing of IDIF The motion-and partial volume-corrected IDIF was interpolated with a step length of 1 using a "Piecewise Cubic Hermite Interpolating Polynomial" to match the blood sampling times. All corrections were applied to the IDIF, with the AIF being considered as the reference  (3, 9). First, count rates from sampled arterial blood were scaled using the cross-calibration factor between the PET/MR and the on-site g-counter to express the AIF in the same units as the PET data (Bq/mL). Second, a plasma IDIF was derived on the basis of the individual plasma-to-blood ratios obtained from sampled arterial blood of the study subjects. Third, the delay between the AIF and the IDIF was corrected by shifting the IDIF curve to match the sampling times of the AIF. Finally, due to the difference in sampling location (ICA for IDIF and radial arteries for AIF), a monoexponential dispersion function with a tau value of 5 s (29) was convolved with the IDIF to mimic the dispersion effects.
Quantification of CMRGlc Calculation of cerebral metabolic rate of glucose (CMRGlc) for the test datasets was performed using a voxelwise Patlak graphical analysis (lumped constant 5 0. 65 (30)) using either the AIF or the IDIF. Analyses were performed using in-house-developed Matlab tools (Matlab R2018a; The Math-Works) that generate parametric images representing CMRGlc (umol/100 g/ min). In particular, a linear function was fitted to the Patlak-transformed data, including data from 25 min after injection until the end of the scan (8 data points). The resulting slope was then multiplied with the subject's plasma glucose level (umol/L) and divided by the lumped constant. By applying a gray matter (GM) mask derived from individually coregistered T1-weighted MR images, the average CMRGlc value in the GM was determined using either the AIF (CMRGlc AIF ) or the IDIF (CMRGlc IDIF ).
Assessment of cGAN Performance for Motion Compensation The quality of cGAN-aided motion correction of the dynamic frame sequence was assessed in relation to the sampled AIF. Specifically, IDIFs were determined from the test datasets (both original and simulated) using cGAN-aided motion vectors and compared with the AIFs using the area under the curve (AUC). Differences in GM CMRGlc values derived from the IDIF and AIF were assessed using the absolute percentage difference (j%Dj) between CMRGlc values: %D 5 CMRGlc IDIF 2CMRGlc AIF CMRGlc AIF x 100: Eq. 3 cGAN performance was assessed separately for the original dataset (n 5 6) and the simulated dataset (n 5 60).
During that time, cGAN-processed images derived from the very short frames (5 s) decreased the difference in histogram AUC by approximately 80% relative to histogram AUCs obtained from the original images. Subsequent improvements were minor (,5%) for longer and later frames with better count statistics. Supplemental Figure  4  demonstrates that cGAN-based IDIFs were closer to the reference standard in comparison to the IDIFs obtained without cGAN processing and using only standard frame-based motion correction (motion profile of the represented subjects in Supplemental Fig. 5 ). For the original dataset (n 5 6), the j%Dj between AUCs derived using the motion-corrected IDIF and the AIF was 1. 2% 6 0. 9%. The GM CMRGlc values determined using these 2 input functions differed by less than 5% (2.
4% 6 1. 7%) (Fig. 5 ). The quantitative difference in AUC and GM CMRGlc values between AIF and IDIF (before and after cGAN preprocessing) for individual datasets (n 5 6) with their respective augmentations (10 iterations) is depicted in Table  1. For the simulated datasets (n 5 6 3 10 iterations), the mean difference in AUC values between those obtained using the AIF and the IDIF using cGAN preprocessing was 0. 9% 6 0. 7%, whereas the difference in AUC values between AIF and the IDIF without cGAN preprocessing was 2. 9% 6 3. 1%. Moreover, IDIFs extracted from cGAN-preprocessed motion compensated data resulted in CMRGlc values closer to those obtained using the AIF, with an absolute difference of 2. 2% 6 1. 8% as compared with CMRGlc values determined without cGAN preprocessing of 3. 9% 6 3.
5%. The improved performance of cGAN-aided as compared with non-cGAN-aided motion correction can be also inferred from the smaller variance of both AUC and GM CMRGlc values in the case of cGAN preprocessing. Figure  6  shows representative images that were obtained by partitioning the reference image (55-60 min after injection) into 15-s subframes and the image quality of these subframes after cGAN processing. For the 10 3 30 s subframe dataset, the MI improved by 135% (from 0. 030 6 0. 003 to 0. 070 6 0. 001), whereas improvement was even greater for the 20 3 15 s dataset (improvement of 290%; from 0. 002 6 0. 003 to 0. 0700 6 0. 0001). Moreover, Figure  7  demonstrates the improvement in image quality of the reference image when 15-s subframes with artificially introduced random motion underwent standard frame-based motion correction and cGAN preprocessing before rigid-body motion correction as compared with non-motion-corrected summed subframes.
More specifically, the cGAN algorithm extracts the most likely relationships between low-count images (where the underlying distribution is ambiguous) and high-count images (where the underlying distribution is well defined) from the training dataset and applies the extracted relationships to new images. As a result, cGAN methodology is able to accurately predict generic image features (such as brain contours) from low-count images. The improved definition of brain contours then allows improved performance of conventional MI coregistration routines that strongly depend on well-defined imaging features. Images with dissimilar uptake pattern are typically encountered in dynamic studies when the tracer uptake pattern changes as a function of time during the frame sequence. Our results showed that structural information inherent to very early low-count images (,2 min after injection) is insufficient to generate an acceptable mapping with the reference frame, thus precluding the generation of a high-count image that could guide the derivation of an accurate motion vector.
e. , translation all axes , 1 mm, rotation ,1 in all axes)  (9, 10). Moreover,  even in the case of subject movement in this very early phase of the study, the error accrued in the integral under the blood time-activity curve remains negligible. We demonstrated this accuracy in our own test dataset, showing a mean value of ,1. 5% for the absolute difference between the IDIF and the sampled arterial blood curve, which translated to an average difference of ,3% for the calculated glucose metabolic rate in GM (Fig. 5 ). An exciting application of cGAN-aided motion correction is the possibility to address intraframe motion in static clinical PET scans. Clinical 18 F-FDG brain scans are usually performed at times. 45 min with a typical duration of 10-20 min. Such relatively long frames are sometimes subject to considerable patient motion artifacts, which impair image resolution and reduce image contrast important for differential diagnosis.
In this context, the question arises whether mappings are specific to one particular imaging system or whether they could be also applied to 18 F-FDG data obtained from other imaging systems. Our very preliminary data suggest that mappings might be transferrable to other imaging systems on the basis of the application of our mappings to a dynamic 18 F-FDG study (12 3 60 s, 4 3 120 s, 5 3 300 s) acquired using an external PET/CT system from a different vendor. The only requirement is that mappings should match their respective PET mid-times. Supplemental Figure  7  suggests that mappings might possibly be independent from the imaging system; however, this issue mandates further investigations that were not the focus of this work. One of the main drawbacks of the study is the low number of test datasets.
Although synthetic data with variable motion parameters were generated, they were still generated from the test datasets. Moreover, there are several other limitations that need to be considered when applying cGAN methodology in clinical applications. First, current implementations of cGAN processing are highly computationally intensive. The time to generate one (source-totarget) mapping pair is 17 h on a dedicated NVIDIA DGX Workstation with 1 3 32 GB Tesla V100 Volta GPU for a frame size of 344 3 344 3 127 voxels. However, once a mapping specific for a particular tracer is established via the training process, the time to apply this mapping to a low-count image of any individual subject is only 1 min. Another potential source of error might be intraframe motion in the reference frame. In this study, we did not correct for such motion artifacts because neither visual inspection of image quality nor close monitoring of the subjects during the last frame indicated noticeable subject motion.
Finally, our cGAN implementation requires a training set that consists of image pairs that are devoid of motion artifacts. Because our data were acquired on a fully integrated PET/MR system, simultaneously acquired MR navigators were used to correct the training set for motion. When translating these findings to other sites, the use of cycle GANs  (33) , which produce generic mappings from spatially noncorresponding data, may be a potential solution that voids the requirement of motion-corrected image pairs.
This work was supported by the Austrian Science Fund KLI482-B31. David Iommi and Zacharias Chalampalakis are funded by a Marie Skłodowska-Curie grant (no. 764458, EU Horizon 2020 research and innovation program). Lucas Rischka is recipient of a DOC Fellowship of the Austrian Academy of Sciences at the Department of Psychiatry and Psychotherapy, Medical University of Vienna. Without relevance to this work, Rupert Lanzenberger received travel grants or conference speaker honoraria within the last 3 years from Bruker BioSpin MR, Heel, and support.
ACKNOWLEDGMENTS We express our gratitude to  Vivian Tseng  from  The University of Applied Arts Vienna  for creating the graphical abstract for our article. To invite swift adoption of this novel approach by the research and clinical community, software codes developed for cGAN processing are free and available online: https://github. com/LalithShiyam/QIMPtools/tree/master/PANDA. This repository contains all software code that has been used to perform analyses in our work.
DISCLOSURE This work was supported by the Austrian Science Fund KLI482-B31. David Iommi and Zacharias Chalampalakis are funded by a Marie Skłodowska-Curie grant (no. 764458, EU Horizon 2020 research and innovation program). Lucas Rischka is recipient of a DOC Fellowship of the Austrian Academy of Sciences at the Department of Psychiatry and Psychotherapy, Medical University of Vienna. Without relevance to this work, Rupert Lanzenberger received travel grants or conference speaker honoraria within the last 3 years from Bruker BioSpin MR, Heel, and support.
ACCOST (Altered Chromatin COnformation STatistics) accomplishes this goal by extending the statistical model employed by DEseq, re-purposing the 'size factors,' which were originally developed to account for differences in read depth between samples, to instead model the genomic distance effect. We show via analysis of simulated and real data that AC-COST provides unbiased statistical confidence estimates that compare favorably with competing methods such as diffHiC, FIND and HiCcompare. AC.
Controlling FDR, as opposed to family-wise error rate, makes sense in the context of most 3D chromatin studies, which are often hypothesis-generating studies aimed to produce a large set of discoveries. Second, a good method must take into account the particular biases associated with the assay (such as Hi-C) that generated the data. As we will see, accomplishing this goal turns out to be very challenging. Third, because of the high cost associated with generating 3D chromatin data--in particular, the cost of sequencing deeply enough to characterize all pairs of loci in the genome--an ideal method will be capable of leveraging structure in the observed data to enable calling significant differential interactions even when only a single pair of experiments has been performed. Fourth, as with essentially any statistical testing procedure, an ideal method should maximize statistical power; i.
e. the method should identify as many differential interactions as possible, given the data, while still controlling the FDR. Note that, although multiple genome-wide 3D chromatin assays have been developed, most existing statistical testing frameworks focus on the first and still most widely used assay, Hi-C. Accordingly, we focus here on Hi-C analysis, though many of the methods described here may also apply directly to data generated using assays such as ChIA-PET, SPRITE, Hi-ChIP or PLAC-seq. Early Hi-C studies generally employed simple foldchange statistics to identify differential contacts  (8, 9) , but these methods have subsequently been supplanted by a variety of more statistically sophisticated techniques. • HOMER (10) implements a simple test for differential interactions, based on binomially-distributed counts.
HOMER can only consider comparisons between two libraries and cannot take into account replicate experiments. • ChromoR  (11)  carries out variance stabilization of Hi-C data using the the Haar-Fisz transform  (12) , which transforms Poisson distributed data into Gaussian distributed coefficients at multiple scales. The resulting coefficients are subjected to wavelet-based denoising. Chro-moR then analyzes the resulting transformed data using either change-point analysis for the purpose of detecting chromosomal aberrations or domain boundaries, or Bayes factor analysis for the detection of differential contacts. • HiBrowse (13) is a Python tool with a web interface that employs the edgeR RNA-seq package  (14)  to compare pairs of Hi-C experiments. • The R package diffHic (15) also uses the edgeR statistical framework but dispenses with the web interface.
DiffHic offers a range of functionality, including mapping, quality filtering, binning, normalization and estimation of statistical confidences for differential contacts. Notably, the package is able to accommodate complex experimental designs, including paired or blocked designs and experiments involving more than two groups. • FIND (  16 ) uses a spatial Poisson model to measure differences in chromatin loops, with the aim of relaxing the independence assumption between adjacent loci. • HiCcompare  (17)  normalizes the data and then tests for differential interactions on the normalized data directly, using a Z-score comparison. HiCcompare does not leverage replicate experiments. For all of these methods, a key challenge is accurately accounting for the unique features of Hi-C data. For example, several widely used methods can normalize Hi-C data to account for positional biases that arise due to GC content, mappability and density of restriction-enzyme sites along the genome  (18, 19).
Variants of these normalization routines are integrated into all of the methods outlined above. A second prominent and potentially problematic feature of Hi-C data is the 'genomic distance effect,' in which pairs of loci that are close together along the genome are very likely to be observed in contact simply due to the random polymer looping behavior of DNA. The genomic distance effect manifests itself most obviously in the form of a marked enrichment of high counts near the diagonal in the Hi-C contact matrix. This work was primarily motivated by the observation that the genomic distance effect can vary markedly between two Hi-C experiments. The result, in a Hi-C matrix showing log fold-changes in contact counts, is an enrichment of apparently differential contacts far from the diagonal (Figure  1A ).
Data sets For validation and comparison of methods, we used Hi-C data from human, mouse and the malaria parasite Plasmodium falciparum. • The human Hi-C data is derived from two cell lines, GM12878 and IMR90  (23). The data were downloaded in text form using the juicer dump command of Juicer tools  (24). Replicates are numbered as in Supplementary Table  S1  of (  23 ), heading 'biological replicate number. ' • Two mouse Hi-C data sets were analyzed. The two data sets were generated in the same lab, but were produced for two different studies. The first data set is derived from mouse embryonic stem cells (mESCs)  (25) , and the second is from mouse cortex (26). • The Plasmodium Hi-C data represents parasites at two distinct stages in the P. falciparum life cycle, trophozoite and sporozoite  (27).
Because the ICE procedure is based on multiplying each pairwise contact count by two locus-specific bias terms, and because these bias terms are similar in the two experiments, the log fold change values are quite similar before and after ICE normalization. In contrast, the pattern associated with the genomic distance effect is successfully removed by ACCOST's normalization (Figure  2D-E ). To quantitatively confirm that ACCOST correctly accounts for differences in the genomic distance effect, we simulated two Hi-C matrices with different distance decay curves. We generated counts from Poisson distributions with means that decay as illustrated in Figure  3. Note that we did not employ a negative binomial distribution because we did not want the simulations to favor ACCOST. The significance of differences between simulated datasets was then assessed using ACCOST and three existing methods (diffHiC, FIND and HiCcompare).
Because the only differences in the simulated data sets arise due to changes in the genomic distance effect, all methods should produce uniform (null) P-values. Empirically, we observe nearly uniform P-values for both ACCOST and diffHiC (Figure  4 , top row). In contrast, both FIND and HiCcompare are misled by the genomic distance effect, with FIND assigning many very small P-values, and HiCCompare deviating both above and below the expected uniform distribution (Figure  4 , bottom row). We further compared ACCOST to diffHic by assessing the significance of changes in contact counts between GM12878 and IMR90 cells. These cells derive from two markedly different cell types (B-lymphocyte and fibroblast, respectively) and have been observed to have different short-and long-range contact patterns  (23) , consistent with differing genome organization at multiple scales.
Analysis of Plasmodium Hi-C data As an illustration of how ACCOST might be applied in practice, we ran the software on Hi-C data from two stages in the P. falciparum life cycle: trophozoite, the most transcriptionally active stage in the erythrocytic cell cycle of the parasite, and sporozoite, the stage responsible for the transmission of the disease from mosquito to human. Malaria  transmission specifically occurs when sporozoites are injected from the mosquito's salivary gland into the human skin during blood feeding. ACCOST highlights several changes in interactions between these two stages, one of them particularly interesting in the right arm of chromosome 8 (Figure  6 ). In this chromosome, two clusters of genes interact significantly more at the sporozoite stage. One of these cluster involves genes specifically expressed at the sporozoite stage, including the sporozoite invasionassociated protein 2 and the sporozoite and liver stage tryptophan-rich protein.
DISCUSSION We have demonstrated that ACCOST provides unbiased confidence estimates for differential Hi-C contacts by correctly controlling for the genomic distance effect caused by the polymer behavior of DNA. ACCOST thus enables biologists to better interpret Hi-C data by focusing on the subset of differential contacts that exhibit the most statistically significant differences. As is apparent from Figure  6 , many of the observed differences exhibit complex structures related to known features of Hi-C data, such as loops, stripes, domains, and compartments. A clear direction for future work is to use ACCOST as a building block in a scheme to identify large, potentially irregularly shaped regions of dif-ferential contact. A second useful direction for future work would be extensions to handle differentiation or cell cycle time series data.
Plotting dispersion curves (wavenumber) for isotropic plate by SAFE method A Matlab program is established to compute dispersion curves. The wavenumber and the phase velocity are assumed to be functions of the frequency-thickness product (fe). The dispersion curves are calculated for an aluminum plate with thickness 1 mm, Young's modulus 69 GPa, Poisson's ratio 0. 33, and mass density 2700 kg/m 3. To verify the accuracy of the method, we compare the plotted dispersion curves by the SAFE method with those plotted by the DISPERSE program  [14]. The software, developed by Imperial College NDT Laboratory, permits us to create dispersion curves. It is based on the global matrix method; the displacements and stresses are described by a material layer matrix. By satisfying the boundary conditions at each interface, the individual layer matrices are assembled.
Sample and Data Collection We draw our sample from the financial analysis made easy (FAME) database, which provides information for companies in the UK and Ireland. We applied three criteria in our search strategy: 1) firms should be based in England; 2) the number of employees should not exceed 250; and 3) active firms not in receivership nor dormant. In total, 52,568 firms within the FAME database meet our search criteria. Given the resource constrains to contact firms for data collection, we randomly selected a sampling frame of 5,000 firms from the FAME database. With the samples selected, 1,542 firms provide contact details for their top executives such as business owners, chief executive officers, and managing director. We targeted the top executives of SMEs because they are more likely to have better understanding of firms' strategic orientation and operations  (Covin and Wales, 2019).
We contacted these firms in May 2015 through an email inviting them to participate in this research but reached only 1,388 firms as some of the addresses were not valid due to some executives having moved to other firms or retired. We collected 157 online survey responses after three rounds of follow-ups. Due to missing data on focal variables, we dropped 47 cases. Additionally, 22 firms that have less than 10 employees were also removed to ensure the samples used in our study have relative comparable resources and capabilities for EO and ambidexterity  (Audia and Greve, 2006; Plambeck, 2012). This leads to a final sample of 88 SMEs used in our study. In particular, the average firm age was 35. 5 years. Among the 88 SMEs, 36 of them are small firms with the number of employees ranging from 10 to 50 employees; the remaining 52 cases are medium-sized firms with employee numbers ranging from 51 to 250 employees.
Data Analysis Method Using fsQCA 3. 0 software (UC, 2017), we calibrated all variables into fuzzy membership scores ranging from 0 to 1  (Ragin, 2008). A membership score 0 designates "full non-membership", while 1 corresponds to "full membership", and 0. 5 represents the "cross-over point". Following prior fsQCA research  (Hudson and Kühner, 2013) , we set the mean value as the cross-over point, a value with one standard deviation below the mean as full non-membership or fully out, and a value with one standard deviation above the mean as full membership or fully in. Since fsQCA automatically excludes cases with a membership score of 0. 5 due to maximum ambiguity  (Ragin, 2008) , we added a 0. 001 to cases at the cross-over point to ensure they are not neglected during analysis  (Fiss, 2011).