dataesr/scientific-paragraphs-categorization huggingface dataset:
The benefits of this optimization are illustrated in Figure  S11  for the aCompCor50% variant of the CF dataset. preorthogonalization schemes produced benefits compared to the standard approach, with the complete orthogonalization yielding the best results, particularly in shifting the QC-FC correlations toward zero. A second and critical benefit of such an optimization is specific to the aCompCor50% variant. When the optimization was used, we saw a marked reduction of the number of extracted PCs, that is, a reduction of the components required to fulfill the 50% variance criteria, as illustrated in Figure  S11D. This result shows that the majority of the components extracted without the optimization explained variance that was already accounted for by the other regressors within the model, particularly by the sine/cosine basis functions.
The number of used PCs has a direct impact on the nominal tDoF, which is a critical parameter particularly in short acquisitions, such as in the CNP dataset. Indeed, we note that without the optimization we could not perform aCompCor50% on the CNP dataset, since without the gain in residual tDoF provided by the preorthogonalization scheme the denoised matrix was not invertible. In summary, the optimization we adopted increased both the efficacy of the method and, in the case of the aCompCor50% variant, its efficiency. While the optimized approach was first introduced with connectivity toolbox (Whitfield- Gabrieli & Nieto-Castanon, 2012), research in denoising optimization surprisingly has not explored it thus far  (Ciric et al. , 2017; Muschelli et al. , 2014; Parkes et al. , 2018; Shirer et al.
However, we ruled out such possibility since Δrms (DVARS) was calculated in a noise-only mask where no neuronal meaningful difference is expected. ICA-AROMA was also associated with a considerable loss of tDoF, particularly for the CF dataset where the number of explanatory variables matched that of 24RP + aCompCor50% (an average of 82 ICs were classified as noise), while for the CNP dataset the number was similar to that of 24RP + 8WM&CSF (30 noise-classified ICs). These numbers are at odds with previous evaluation studies focused on resting-state data, where an average of 10 to 20 ICs were classified as noise in 5 to 8 min-long datasets  (Ciric et al. , 2017; Parkes et al. , 2018). The discrepancy is mainly related to the different run lengths, being the CNP and CF datasets roughly two and four times longer than those of previous studies, respectively.
Indeed, while the number of ICs classified as noise naturally tends to increase with the complexity of the structured noise, it also increases as a function of the number of components in which the data are decomposed (due to the more likely splitting of noise ICs in sub components with similar features). The algorithm for automatic dimensional estimation used in ICA-AROMA (which is the same of MELODIC, FSL,  Beckmann & Smith, 2004; Jenkinson, Beckmann, Behrens, Woolrich, & Smith, 2012)  tends to decompose longer datasets in higher dimensionalities, thus explaining the discrepancy with previous studies. Setting an a priori dimension for data decomposition may help in reducing the loss of tDoF in long or short-TR experiments, yet further research is needed to both determine the optimum decomposition number and to evaluate the ensuing classification performance.
| Considerations on the denoising framework A critical aspect in BOLD data cleaning is the order and method used for frequency filtering, nuisance regression, and volume censoring. In the current work, we opted for a single linear regression model that performs the three steps simultaneously, as recommended in  (Jo et al. , 2013). The simultaneous approach provides several benefits. When censoring is used, the simultaneous denoising avoids spreading motion-contaminated signals back and forward in time as in the case of filtering followed by volume deletion  (Carp, 2013). Moreover, the simultaneous denoising provides an upper limit on the number of censored volumes, because the estimation of the model is constrained by the nominal tDoF; namely, it is not possible to obtain a residual series when there are fewer observations (i.
Furthermore, censoring provided minor benefits compared to GSR and came at the great cost of reduced network identifiability. These results provide important indications for denoising data composed of multiple steady-state conditions (see Table  3 ), yet many The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding bodies. T A B L E 3 Overview of major findings and recommendations Use the preorthogonalization procedure to increase the noise prediction power and to reduce the number of extracted components (see Figure  S11 ) aCompCor (24RP+ aCompCor) Lower number of consumed tDoF compared to aCompCor50% Use the preorthogonalization procedure to increase the noise prediction power (see Figure  S11 ) ICA-AROMA Indeed, when denoising the entire series, the zero value matches the mean-centering of the series, thus, the volumes set to zero do not contribute to covariance.
from each functional condition, and finally plotting the task-associated change, that is, Δrms(DVARS NOISE ) = rms(DVARS NOISEjTASK )rms (DVARS NOISEjREST ). The noise only mask contained voxels on the edge of the brain and was constructed by applying a 3 voxel-level dilation to the subject-specific whole-brain mask (3dmask_tool, AFNI) and subsequently removing any voxels in the original mask; by construction, the NOISE mask does not contain voxels used to compute FC or to extract confounding signals. Ideally, in the case of a perfectly cleaned dataset, no difference in rms(DVARS NOISE ) between the two functional conditions is expected. Thus, effective pipelines should yield zero-centered distributions of Δrms(DVARS NOISE ).
Pearson's correlation between mFD and FC for all possible pairs of nodes, yielding a distribution of QC-FC correlations. Such distribution was computed separately for each functional condition (i. e. , mFD rest vs. FC rest and mFD task vs. FC task ). In addition, in order to understand to what extent the task-associated changes in FC are related to the task-associated difference in head movement, we computed a QC-FC distribution by correlating the per-subject ΔmFD = mFD task -mFD rest with the per-subject ΔFC = FC task -FC rest. In perfectly cleaned dataset, no intersubject variability in FC is expected to be explained by in-scanner motion, thus, a good cleaned dataset should yield a zero-centered QC-FC distribution with small standard deviation. We extracted the median of the absolute distribution to evaluate both the centering and spread of the distribution.
2. 8. 5 | Effect of motion on task-associated change in within-network FC One of the main findings we obtained analyzing the CF dataset was a marked task-associated reduction of the internal synchronization of several large-scale networks (Tommasin et al. , 2018). Here, we reevaluated this finding using different pipelines in order to assess the extent to which the processing may influence the reported reduction in FC. With this aim, we computed the within-network FC of six ICAderived networks (dorsal attention (DAN), default mode (DMN), frontoparietal (FPN), somatomotor (SMN), ventral attention (VAN),.
characterized both by more volumes with extreme FD values and by an overall shift of the FD distribution toward higher values F I G U R E 1 Evaluation of in-scanner head movement for the CF dataset. (a) FD series for each session and subject, with sessions from the same subject plotted with the same color. (b) Distribution of FD values in 9 bins of different width, showing the marked difference in the distribution of FD values between rest and task epochs. (c) Task-averaged mFD versus rest-averaged mFD. (d) Percentage of volumes above various FD thresholds computed separately at rest (left panel) and task (right panel). The dotted vertical and horizontal lines mark, respectively, the 0. 2 mm and 25% threshold that we used in denoising pipelines employing censoring. Percentage values above the red lines have less than 5 min of residual data.
shows the FD series from two typical high-motion subjects (mFD REST 0. 2 mm, one for each dataset) alongside DVARS GM series extracted from four denoising models plus a simple model with only trends and band-pass regressors (BP, black line). Large FD-concurrent fluctuations in BOLD intensity, which were prominent during rest epochs, were largely suppressed by 24RP regression. A further, albeit less marked, improvement was obtained by adding aCompCor and even more by switching to aCo-mpCor50%. ICA-AROMA showed a good but not consistent performance, occasionally failing to decouple the fluctuations in the two QC series (see e. g. , volumes 150, 430, and 550 in Figure3a, and volumes 125 in Figure3b). On the contrary, fluctuations in BOLD intensity not ostensibly related to fluctuations in FD series, mostly present during task epochs (especially in the CF dataset), were effectively mitigated both by aCompCor-based models and by ICA-AROMA, while they were mainly unaffected by 24RP regression.
associated changes in DVARS NOISE. The box plots show the distribution of the difference in rms(DVARS NOISE ) between task and rest-i. e. , Δrms(DVARS NOISE ) = rms(DVARS NOISEjTASK )rms(DVARS NOISEjREST )-for all considered pipelines, separately for CF (a) and CNP (b) datasets. Δrms(DVARS NOISE ) values, calculated separately for each run (40 and 120 runs for the CF and CNP dataset, respectively), are colorcoded based on the task-associated difference in mFD. At the bottom of each box is reported the median of the absolute Δrms(DVARS NOISE ), with the smallest value highlighted in blue. On top of each box the asterisks mark whether the mean of the distribution is significantly different from zero as indicated by a one-sample t-test (performed after averaging runs from the same subjects); ***p <.
19). F I G U R E 3 QC series for a representative high-motion subject within the (a) CF and (b) CNP dataset. In each main panel, the first row shows the FD series, while the second row shows DVARS GM series calculated after applying five different denoising models. BP (band-pass, black line) is a denoising model containing only trends and band-pass regressors Finally, among all considered pipelines, P-censoring yielded the lowest median absolute correlations (median jrj <. 118).
Using GSR greatly reduced the spread of the distributions, yielding approximately 10 times-lower median absolute values. Among GSR-based pipelines, F I G U R E 4 QC-FC plots for evaluating the across-subject relationship between motion (mFD) and connectivity estimates under different denoising strategies for the CF dataset. The top panels (a) show the distribution of QC-FC correlations along with the absolute mean value of the correlations, aiming at quantifying the centering of the distributions. The middle panels (b) show the median value of the absolute QC-FC correlations, which takes into account both the centering and the spread of the distribution. The bottom panels (c) show the Spearman's correlation between QC-FC correlations and the Euclidean distance between pairs of nodes, indexing distance-dependent artifacts.
Results of the community-based analyses are reported in Figures6 and 7for the CF and CNP dataset, respectively. Each figure shows the subject-specific modularity (Panels a) and the across-subject similarity of the identified network partitions (Panels b). In general, these F I G U R E 5 Censoring analysis (Δr) to evaluate residual artifacts in the CF dataset associated with the most moving volumes of each subject. For each run, Δr values were obtained by subtracting FC estimated using the least motion-affected volumes (FC jLM ) from FC estimated using the most motion-contaminated volumes (FC jHM ), where in both cases the top/bottom 20% of volumes were discarded. From the run-specific Δr values, two quantities were extracted: (a) the mean, which indexes residual global artifacts, and (b) the distance-dependent effect of motion of FC estimates, obtained by calculating the Spearman's correlation between Δr and the Euclidean distance between pairs.
I G U R E 6 Results of the network modularity analysis for the CF dataset. (a) The box plots show the across-subject distribution of modularity for each functional condition (REST and TASK) and for the differential condition (Δ = TASK -REST). The 40 runs (20 subjects × 2 runs) composing the box plots are color-coded based on mFD, for rest and task conditions, or based on ΔmFD, for the Δ comparison. At the bottom of each box plot is reported the median of the distribution, with the largest values that are color-coded based on the functional condition. (b) Partitions' similarity across subject, assessed via z-score of Rand index 1/mFD, so that partitions estimated from high motion subjects were penalized. Results of the weighted similarity, reported in Figure S8, are in agreement with the unweighted variant, which suggests that partitions' similarity results are not driven by stereotyped patterns of motion.
I G U R E 7 Results of the network modularity analysis for the CNP dataset. (a) The box plots show the across-subject distribution of modularity for each functional condition (REST and TASK) and for the differential condition (Δ = TASK -REST). The 120 runs composing the box plots are color-coded based on mFD, for rest and task conditions, or based on ΔmFD, for the Δ comparison. At the bottom of each box plot is reported the median of the distribution, with the largest values that are color-coded based on the functional condition. (b) Partitions' similarity across subject, assessed via z-score of Rand index performed poorly according to the selected benchmarks, displaying either high association between motion and FC or unbalanced residual motion artifacts between functional conditions. The inclusion of the global signal among confounding variables substantially improved many benchmarks and virtually equalized global motion artifactsbetween conditions.
, 2013; Yan et al. , 2013)  and, although the increase in effectiveness may be dependent on the amount of motion in the dataset, the modest reduction in tDoF (12 additional explanatory variables compared with 12RP), supports its use as default set of realignment-based regressors. Adding mean tissue-based signals with expansion terms (i. e. , 8WM&CSF) to the 24RP model yielded a minor but consistent improvement, which, however, was outranked by the use of aCo-mpCor. Of note, 24RP + 8WM&CSF and 24RP + aCompCor pipelines have a similar cost in terms of tDoF, being composed of 8 and 10 confounding signals extracted from tissue compartments, respectively.
F I G U R E 6 Results of the network modularity analysis for the CF dataset. (a) The box plots show the across-subject distribution of modularity for each functional condition (REST and TASK) and for the differential condition (Δ = TASK -REST). The 40 runs (20 subjects × 2 runs) composing the box plots are color-coded based on mFD, for rest and task conditions, or based on ΔmFD, for the Δ comparison. At the bottom of each box plot is reported the median of the distribution, with the largest values that are color-coded based on the functional condition. (b) Partitions' similarity across subject, assessed via z-score of Rand index 1/mFD, so that partitions estimated from high motion subjects were penalized. Results of the weighted similarity, reported in Figure S8, are in agreement with the unweighted variant, which suggests that partitions' similarity results are not driven by stereotyped patterns of motion.
F I G U R E 7 Results of the network modularity analysis for the CNP dataset. (a) The box plots show the across-subject distribution of modularity for each functional condition (REST and TASK) and for the differential condition (Δ = TASK -REST). The 120 runs composing the box plots are color-coded based on mFD, for rest and task conditions, or based on ΔmFD, for the Δ comparison. At the bottom of each box plot is reported the median of the distribution, with the largest values that are color-coded based on the functional condition. (b) Partitions' similarity across subject, assessed via z-score of Rand index performed poorly according to the selected benchmarks, displaying either high association between motion and FC or unbalanced residual motion artifacts between functional conditions. The inclusion of the global signal among confounding variables substantially improved many benchmarks and virtually equalized global motion artifactsbetween conditions.
T A B L E 2 Motion characteristics for the CF and CNP datasets CF dataset CNP dataset Measure Functional condition p-values Functional condition p-values Rest Task Rest Task mFD (mm) Number of runs (subjects) 40 (20) 40 (20) 120 (120) 120 (120) Mean 0. 114 0. 074 7. 8 × 10 -5 0. 106 0. 080 4. 4 × 10 -8 SD 0. 042 0. 029 0. 072 0. 061 Min 0. 060 0. 032 0. 033 0. 024 Max 0. 25 0. 16 0. 49 0. 44 Percentage of volumes with FD >0. 2 Number of runs (subjects) 40 (20) 40 (20) 120 (120) 120 (120) Mean 10. 1 2. 9. 0027 9 5 5. 4 × 10 -5 SD 9. 8 4. 0 15 10 Min 0 0 0 0 Max 47 15 83 79 FD series across all runs (mm) Number of volumes 11,200 11,200 18,240 18,240 SD 0. 10 0. 064 0. 15 0. 16 Skewness 5. 1 5. 5 12 21 Kurtosis 50 61 323 754 Note: p-values are obtained via two-sample paired t-tests (in the case of CF, after averaging runs belonging to the same subjects).
926 × 10 -5 GA 3 -0. 00665GA 2 + 0. 250GA -3. 0189 9 , t ∈ [0, 1] corresponds to GA ∈ [22weeks, adult] , y is the distance from the top surface, which is calculated for four vertices of each tetrahedron and would be averaged, H i is the initial cortical thickness. When the relative tangential growth tensor G is initialized, the brain solid starts to grow and the deformation gradient is formed, then the corresponding elastic force can be calculated. The resultant force (the sum of the elastic force and the contact force) is applied as the nodal force on each node of the mesh to produce the deformation of the brain solid. The Python code used in this study is available at https:// github. com/ rouss eau/ Brain Growth.
Discussion The biomechanical model based on the differential tangential growth hypothesis has been used for the realistic simulation of the early expansion and folding process of the human cerebral cortex  6, 7. Therefore, such biomechanical model can be used to study the relationship between biophysical parameters and severe cortical folding malformations which are thought to be associated with neurodevelopmental diseases. In this work, we have implemented the model of Tallinen et al. 6, 7  in Python which is available at https:// github. com/ rouss eau/ Brain Growth, used here to investigate the impacts of the several cortical growth models, the initial cortical thickness and the initial geometry onto the cortical surface morphology in an attempt to answer the questions raised in the introduction.
Following the incubation for 24 h at 37 • C, we obtained morphologically different colonies (differing by color, size, shape) in each plate from only 78 swabs. Well isolated colonies, growing on the selective media, were taken separately and purified. This step allowed us to obtain a total of 152 isolates that were identified using the matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) method (MicroflexTM; Bruker Daltonics, Bremen, Germany), with the flexcontrol and the biotyper 3. 0 software (Bruker Daltonics).
Molecular Characterization of Resistance Genes The molecular characterization of carbapenemase encoding genes (bla KPC , bla IMP , bla VIM , bla NDM , bla OXA-48 , bla OXA-23, bla OXA-24 and bla OXA-58) and for the OprD gene was performed by conventional polymerase chain reaction (PCR) using specific primers as described previously  [11, 12] , followed by sequencing based on the Sanger method. All bacteria were also screened for β-lactamases (bla CTX-M , bla TEM , and bla SHV ) genes and for mcr-1, 2, 3, 4 and 5 genes  [13, 14]. The amplicons of each tested gene were sequenced using BigDye1 terminator chemistry on an automated ABI 3130 sequencer (PE Applied Biosystems, Foster City, CA, USA). Sequences were compared with those available in the database (GenBank). The sequences were then aligned using the npsa_clustalW, the Network Protein Sequence Analysis (https://npsa-prabi.
Whole-Genome Sequencing and Genomic Comparison Genomic DNA was extracted by EZ1 Extraction using the Kit EZ1 ® DNA (Qiagen, Courtaboeuf, France). Whole-genome sequencing (WGS) was performed by Illumina MiSeq sequencer (Illumina, San Diego, CA, USA) for one carbapenem-resistant P. aeruginosa isolate (97H ST285), which was found OXA-48 encoding gene positive by PCR. This WGS was assembled by A5 assembler. The PROKKA software was used for the annotation, the ARG-ANNOT program was used to identify antibiotic resistance genes, and the Plasmid Finder program was used to detect the presence of plasmids in the genome of the isolate. For phylogenetic analysis, the conserved proteins of carbapenemase encoding genes in relation to those of many bacterial families, downloaded from the NCBI assembly database (BlastP), were selected and independently aligned using MUSCLE to cluster homologous genes.
12% identity (AQA26426. 1). The whole-genome sequencing (WGS) was assembled into 65 scaffolds and 65 contigs leading to a genome size of 6′224′225-bp and GC content of 66. 5%. Genomic analysis revealed that this isolate belonged to the ST285 clone. This isolate did not contain any plasmid. We detected by ARG-ANNOT program that this pathogen exhibit 100% nucleo-.
12% identity (AQA26426. 1). The whole-genome sequencing (WGS) was assembled into 65 scaffolds and 65 contigs leading to a genome size of 6 224 225-bp and GC content of 66. 5%. Genomic analysis revealed that this isolate belonged to the ST285 clone. This isolate did not contain any plasmid. We detected by ARG-ANNOT program that this pathogen exhibit 100% nucleotide identity with the published OXA-50 family oxacillin-hydrolyzing class D beta-lactamase OXA-494 from P. aeruginosa (WP_003118452. 1). The variant sequence bla OXA-494 gene differs from OXA-50 by two amino acids (D109E and R167H). In addition, to investigate the phylogenetic relationship of this bla OXA-494 variant from the 97H isolate, a phylogenetic tree with proteins belonging to the variants of OXA-50 and representatives of the main groups of oxacillinases families was reconstructed.
As shown in Figure  2  of the phylogenetic tree, our identified and annotated sequence and OXA-50 variants, grouped together with proteins as the OXA-50 family, where thus demonstrating their membership. Additionally, the analysis of the obtained sequence of the OprD encoding gene, as well as the corresponding protein sequence among this P. aeruginosa 97H isolate, showed the installation of amino acids substitutions in seventeen different positions (S57E, S59R, I210A, E230K, S240T, N262T, A267S, A281G, K296Q, R310G, V359L, N375S, N376S, V377S, G378S, Y382A, and G383C) when compared to the wild-type porin sequence from strain PAO1. Moreover, the genome comparison with the available sequences of P. aeruginosa in the NCBI database showed that this pathogen exhibits other antibiotic resistance genes which were identified as: for beta-lactams bla TEM-116 , for aminoglycoside agly aph3-iib and for phenicol phe catb7 , respectively, with percentage identities 100.
000%, 97. 89% and 98. 91%. To date, this study presents the first report published of this result in Tunisia. Diagnostics 2021, 11, x FOR PEER  REVIEW 10  G378S, Y382A, and G383C) when compared to the wild-type porin sequence from st PAO1. Moreover, the genome comparison with the available sequences of P. aeruginos the NCBI database showed that this pathogen exhibits other antibiotic resistance ge which were identified as: for beta-lactams blaTEM-116, for aminoglycoside aglyaph3-iib and phenicol phecatb7, respectively, with percentage identities 100. 000%, 97. 89% and 98. 91% date, this study presents the first report published of this result in Tunisia.