dataesr/scientific-paragraphs-categorization huggingface dataset:
These different distribution shapes reflect that SC emp networks are diluted matrices with a few strong connections only, while FC emp networks have a higher and more uniform density of connections. Virtual and bivirtual SC connectomes tend to have fatter right tails (and even displaced mode peaks for SC MFM ), reflecting that, in absence of any arbitrary sparsification strategy, completion pipelines generate surrogate SCs without the sparsity constraint and, thus, with less near-zero link weights. Such systematic discrepancy, well visible in Extended Data Figure  6 -1, however, does not prevent correlations between single subject-specific connectivity traits to remain strong, which is a necessary condition for personalized predictive information preservation.
The close relationship between the original data and the respective virtual cohorts (already studied in Fig. 6  for individual instances of bivirtual connectomes) is visually manifested in Figure  9A  where a distance-respecting nonlinear t-SNE projection  (Maaten and Hinton, 2008)  has been used to represent in two dimensions the virtual cohorts of surrogate virtual FC MFM s associated to the 88 subjects with available SC emp in the ADNI dataset (among which, thus, also the 12 of the SC 1 FC subset). Every dot corresponds here to the two-dimensional projection of a high-dimensional virtual dual FC MFM (100 different virtual FC MFM s have been generated starting from each one of the 88 SC emp connectomes). Clusters of dots (color-coded by their nature, of control subjects or MCI and AD patients) are visually evident in the projection indicating that the distance between dual connectomes within each virtual cohort is smaller than the distance between dual connectomes belonging to different cohorts.
We also plotted, for comparison, the cloud of the projected FC emp connectomes for the twelve subjects of the ADNI SC 1 FC dataset for which it was available, and connected these projections via a thin line to the projection of one of their virtual FC MFM images in the corresponding subjects' virtual cohorts. The projections for all the FC emp connectomes seem to collapse in a single additional cluster close to the center of the global t-SNE map. This collapse manifests that empirical connectomes and virtual connectomes live in different spaces, as previously stressed (Fig. 7A ). Eventually, when projecting a sample composed of hundred more virtual than empirical connectomes, the two-dimensional rendering of the original high-dimensional metric relations is dominated by virtual connectomes.
Discussion We have here demonstrated the feasibility of connectomic dataset completion using algorithms based on mean-field computational modeling. In particular, we have completed an ADNI gold standard connectomic dataset and verified that analogous completion performance could be reached on a control healthy aging dataset. We have then shown that machine learning classifiers trained on virtual connectomes can reach comparable performance to those trained on empirical connectomes. This renders the classification of novel empirical connectomes via classifiers trained exclusively on virtual connectomes possible. Furthermore, the generation of virtual and bivirtual dual connectomes is a procedure preserving at least some personalized information about detailed network topology. As a consequence, virtual cohorts offer an immense opportunity to enable or unblock, and, in perspective, possibly improve machine learning efforts on large patient databases.
Incomplete datasets for clinical research are certainly among the factors contributing to slow progress in the development of new diagnostic and therapeutic tools in neurodegenerative diseases and AD in particular. Our data completion procedures provide a step forward toward "filling dataset gaps" since they allowed us to infer FC when only SC was available or SC when only FC were Figure  9. The Virtual cohorts. We created virtual cohorts of surrogate FC data, generating 100 different FC MFM matrices for each of the 88 subjects in the ADNI dataset with an available SC emp. A, Shown here is a low-dimensional t-SNE projection of the resulting 8800 virtual FC MFM s, colored depending on the associated subject label (blue for control subjects, yellow for MCI patients, and red for AD patients).
For the subjects in the ADNI FC 1 SC subset, we also projected the actual empirical FC emp connectome and link their projections to one virtual connectome within the cohort for the matching subjects. All FC emp connectomes appear grouped in a single cluster, since all far away to connectomes in dual space (they belong to a different space, so appear as "distant" in this projected view emphasizing differences within virtual space). However, virtual cohorts interrelations reproduce an exploded view of the fine structure of this All FC emp cluster. Virtual connectomes within a same virtual cohort are closer between them than connectomes belonging to different cohorts since they maintain a strict relation to their empirical counterparts and are thus good candidates for data augmentation applications.
B, We show, on top, example alternative connectomes within a representative cohort for a single subject that could be used as alternative identity preserving distorted connectomes for data augmentation applications, analogously to slightly distorted versions of object images (on the bottom) used to boost training of object classifiers. available. Such procedures for data completion could easily be implemented within popular neuroinformatic platforms as TVB. TVB provides practical graphical interfaces or fully scriptable code-line environments for "plugand-play" large-scale brain network behavior, signal emulation, and dataset management, including simulating SC and FC with adjustable complexity MFMs or SLMs  (Sanz-Leon et al. , 2013). In this way, capitalizing on the software built-in capabilities, even the more elaborated nonlinear completion algorithms could become accessible to nonexpert users with only a little training.
This effective "reinjection" of information could potentially compensate for unavoidable loss, compare "data processing inequality"  (Cover and Thomas, 2006) , along the algorithmic processing chain represented by completion. This could be a possible explanation for the superior performance of nonlinear methods in the ADNI dataset completion. For this compensation to happen, however, the guess about the right working point should be close to reality. In this paper we were implicitly supposing that all the subjects have the same working point of dynamic operation (e. g. , the same distance from critical rate instability;  Hansen et al. , 2015). Now, pathology or aging may precisely be also altering this working point itself, making of our assumption in MFM-based completion only an approximation.
For instance, the distributions of matching between empirical and virtual community structure in FC connectomes for the healthy aging dataset (Extended Data Fig. 8-1B ) are clearly bimodal, indicating that the used completion ansatz may be more appropriate for certain subjects than for others. Thus, diverse working points of dynamic operation for different subjects, here not accounted for, may contribute to the inferior performance of nonlinear methods in the healthy aging dataset. We defer to future studies considerations about how to further optimize the selection of a working point. When both empirical SC and FC were available, we could measure the quality of reconstruction achieved by our models. The correlation reached between empirical and reconstructed connectivity matrices is only moderate, however.
e. , remarkable matching between, e. g. , the original SC (or FC) and the bivirtual SC bi-MFM (or FC bi- MFM ) generated via the intermediated FC MFM (or SC MFM ) step. This self-consistent correspondence is not limited to generic correlations but captures actual personalized aspects of detailed network topology (Table  3  and Fig. 8  for the ADNI dataset and Table  4  and Extended Data Fig. 8 -1 for the healthy aging dataset). Second, classification performance reached based on empirical data could be nearly equated by classifiers trained on virtual or bivirtual dual connectomes (Fig. 7 ). Therefore, even if the reconstruction quality of our model-based completion procedures is modest, a meaningful relationship with the original seed data are still maintained, even after two steps of virtual completion.
The use of simple models has the additional advantage of being less computationally expensive to simulate. SLMs are even simpler and faster to run than our basic MFMs and their performance was better than the one of nonlinear models in many aspects when dealing with the healthy aging dataset. Note that SLMs have been shown to be very performing in rendering static aspects of FC in other contexts as well  (Hansen et al. , 2015; Messé et al. , 2014). However, linear models were down-performing on the ADNI dataset, while nonlinear models performance seemed more stable across datasets. This shows once again that linear and nonlinear models may capture different facets of the actual, possibly unknown empirical connectomes and that there is an interest in computing and sharing both type of surrogates, given their potential complementarity.
g. , K = 96, corresponding to the number of regions in the used parcellation (see Materials and Methods) for which connectivity strengths were computed in the ADNI dataset classification problem, is comparable to the number of subjects in the considered dataset (N = 88 or 178, respectively, for ADNI subjects with available SC emp or FC emp ). Therefore, it is not surprising that high performances are difficult to access, even when using classification approaches specially adapted to this situation, as in our case. Superior classification performance could be potentially reached via a more careful feature selection  (Guyon and Elisseeff, 2003)  that goes beyond the scope of the current study. Hopefully, future attempts to classification will be able to approach more robustly these tendential performances.
Given the high degree of personalized correspondence between real and dual connectomes (compare Table  3  for the ADNI dataset and Table  4  for the healthy aging dataset), we are confident that any performance level reached by future classifiers trained in real space could be closely approached by classifiers trained in dual virtual and bivirtual spaces. In perspective, the use of virtual connectomes could become beneficial to the training of machine learning algorithms in a further way. The use of a wider ensemble of surrogate date with statistical distributions of multidimensional features equivalent to the original data are a common practice in machine learning, known as data augmentation  (Yaeger et al. , 1997; Taylor and Nitschke, 2018) , as previously mentioned. Data augmentation is, e.
g. , very popular in object recognition (where surrogate training data are produced by clipping or variously transforming copies of the original training images). Data augmentation aims to expand the training dataset beyond the initially available data to boost the learning by a classifier of the target categories (e. g. , object identities). Crucial for dataset augmentation applications is that the surrogate data generated are not just identical to the actual data with some added noise but are genuinely new and can serve as actual good guesses for alternative (unobserved) instances of data-points belonging to the same category (compare Fig. 9B ). Indeed, if information cannot be created  (Cover and Thomas, 2006) , redundant information can nevertheless improve the performance of decoding and classification  (Guyon and Elisseeff, 2003).
Figure1. Connectomic information extracted from the ADNI dataset has gaps. A, The different dataset releases by the ADNI consortium include a variety of information relative to different biomarkers and imaging modalities. Here, we focus on structural and fMRI features and, chiefly: T1, DTI (allowing to extract empirical structural connectomes), and rsfMRI BOLD time series (allowing to extract empirical functional connectomes). B, Matrices SC emp and FC emp summarizing connectomic information about, respectively, SC and FC are obtained via elaborated multistep processing pipelines, using various software including FreeSurfer, FSL, ANTS, and MRtrix3. C, The total number of subjects in healthy aging dataset is 49 between the ages of 18 and 80 (mean = 42. 16 6 18. 37; 19 male/30 female) in which with approximately equal number of subjects they were divided into four categories (I:IV).
Figure2. From MFM to connectomic data completion. A, We present here a graphical summary of the various computational simulation and inference strategies used in this study to bridge between different types of connectivity matrices. Mean-field simulation and the associated analytic theory can be used to generate virtual FC, through simulations of resting-state whole-brain models embedding a given input SC connectome (ascending arrows). Algorithmic procedures, that may still include computational simulation steps, can be used to perform the inverse inference of a virtual SC that is compatible with a given input FC (descending arrows). Both simulation and inference can be performed using simpler linear (green arrows) or nonlinear (blue arrows) approaches. When the input SC (or FC) connectomes used as input for FC simulation (or SC inverse inference) correspond to empirical connectomes SC emp (or FC emp ), derived from T1 and DTI (fMRI) images, then model simulation (inversion) can be used to complete gaps in the dataset, whenever FC emp (or SC emp ) is missing.
F, Third criterion: in the best fitting zone, resting-state FC MFM display a relatively richer dynamics than in other sectors of the parameter space. This gives rise to an "dFC matrix" (correlation between time-resolved FC observed at different times) which is neither random nor too regular but displays a certain degree of clustering (see lower cartoons). The emergence of complex dynamics of FC can be tracked by an increase in the clustering coefficient of the dFC matrix extracted from simulated resting-state dynamics. G, The boxplot shows the distribution of correlations between the actual FC emp and FC MFM estimated within the best fitting zone for all subjects from the SC emp 1 FC emp ADNI subset and the aging dataset. See Extended Data Figure3-1 for linear SC-to-FC completion and Extended Data Figure3-2 for dependency of MFM best fit zone on additional parameters.
7 after 2000 iterations) and the SC* at the last iteration is used as virtual surrogate SC MFM. B, The boxplot shows the distribution of correlation between SC emp and SC MFM for all subjects in the SC emp 1 FC emp ADNI subset and the healthy ageing dataset. C, The correlation between SC emp and SC MFM can vary using different random initial connectomes SC* (0). Here, we show a boxplot of the percent dispersions of the correlation values obtained for different initial conditions around the median correlation value. The fact that these dispersions lie within a narrow interval of 62. 5% indicates that the expected performance is robust against changes of the initial conditions. See Extended Data Figure4-1 for linear FC-to-SC completion.
Figure5. Bivirtual connectomes. This figure shows the correspondence between empirical and bivirtual SC and FC pairs, both when using chained linear (SLM-based) and nonlinear (MFM-based) completion procedures. A, For 88 subjects from the ADNI-subset with only SC emp available, considering the linear bivirtual completion chain SC emp to FC SLM to SC bi-SLM , we obtained a median correlation between SC emp and SC bi-SLM equal to 0. 63 and 0. 92 for 49 subjects from the healthy ageing dataset (green boxplot); simultaneously, considering the nonlinear bivirtual completion chain SC emp to FC MFM to SC bi-MFM , we obtained a median correlation between SC emp and SC bi-MFM equal to 0. 58 for the ADNI datast and 0. 64 for the healthy ageing dataset (blue boxplot). B, For 168 subjects from the ADNI-subset with only FC emp available, considering the linear bivirtual completion chain FC emp to SC SLM to FC bi-.
Figure6. Intersubject distances for empirical-bivirtual pairs. We show here the distances between the empirical SC emp (or FC emp ) of different subjects and the intersubject distances for their corresponding pairs of subjects from bivirtual SC bi-MFM (or FC bi-MFM ). A, B, For the ADNI dataset the correlation between the intersubject distances in real and dual spaces for SC (between SC emp and SC bi-MFM ) were significant and equal to 0. 39, and for FC pairs (between FC emp and FC bi-MFM ) equal to 0. 43. C, D, The same intersubject distances for the healthy aging dataset were measured, with correlation values equal to 0. 53 and 0. 40 for SC and FC empirical-bivirtual pairs, respectively.
Figure8. Correspondence of network topology between empirical and their bivirtual dual connectomes (ADNI dataset). The bivirtual dual connectomes share the same nature (SC or FC) of the corresponding empirical connectome. Therefore, network topology can be directly compared between empirical and bivirtual SCs or empirical and bivirtual FCs. A, B, We show here scatter plots of connectivity strengths (top center), local clustering coefficients (top right) and local centrality coefficients (bottom center) for different brain regions and subjects, plotting feature values for empirical connectomes versus their bivirtual counterparts. We also show histograms over different subjects of the relative mutual information (normalized between 0 and 1, the latter corresponding to perfect matching) between the community structures (bottom right) of empirical connectomes and their bivirtual duals.
The  Canadian Institutes of Health Research  is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the  Foundation for the National Institutes of Health. The grantee organization is the  Northern California Institute for Research and Education , and the study is coordinated by the Alzheimer's  Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the  Laboratory for Neuro Imaging at the University of Southern California. D. B. was supported by the  European   Union Innovative Training Network "i-CONN " ( H2020 ITN 859937 ); V. J. was supported by the  European Union 's  Horizon 2020 Framework Program for Research and Innovation  under the  Specific Grant  Agreement No. 785907  ( Human Brain  Project  SGA2 ) and  H2020 Research and Innovation Action grants VirtualBrainCloud ; and A.
L Figure2. From MFM to connectomic data completion. A, We present here a graphical summary of the various computational simulation and inference strategies used in this study to bridge between different types of connectivity matrices. Mean-field simulation and the associated analytic theory can be used to generate virtual FC, through simulations of resting-state whole-brain models embedding a given input SC connectome (ascending arrows). Algorithmic procedures, that may still include computational simulation steps, can be used to perform the inverse inference of a virtual SC that is compatible with a given input FC (descending arrows). Both simulation and inference can be performed using simpler linear (green arrows) or nonlinear (blue arrows) approaches. When the input SC (or FC) connectomes used as input for FC simulation (or SC inverse inference) correspond to empirical connectomes SC emp (or FC emp ), derived from T1 and DTI (fMRI) images, then model simulation (inversion) can be used to complete gaps in the dataset, whenever FC emp (or SC emp ) is missing.
This prompted us to test the effect of the absence of VTC4 on chlorophyll accumulation under -Fe+P and -Fe-P conditions. Under -Fe-P, mutants with a vtc4 null allele were still chlorotic, similarly to pht4;4 and in contrast to wild-type plants (Fig. 3b, c ). These data show that AsA contributes to preventing chlorosis in -Fe-P conditions. Next, we tested whether the chlorotic phenotype is due to variations in AsA levels. In wild-type, AsA levels decreased significantly under -Fe+P at 52 h after the transfer relative to control (+Fe+P), whereas no change was detected under -Fe-P (Fig. 3e ), suggesting that AsA levels were associated with -Femediated chlorosis. To test whether AsA levels were associated with chlorosis in general, we measured AsA levels in AsA synthesis (vtc4) mutant plants. Under +Fe+P, vtc4 plants accumulated 35% less AsA than wild-type plants, and AsA levels remained unchanged in response to -Fe+P or -Fe-P stress (Fig.
4a  and Supplementary Fig. 10B , and Supplementary Data 2). Furthermore, adding AsA to -Fe+P mimicked -Fe-P response in preventing downregulation of the photosynthesis genes (Fig. 4a , Supplementary Fig. 10B , and Supplementary Data 2). While under -Fe+P PRGs are expressed in pht4;4 to similar levels to that of wild-type plants, PRGs were downregulated in pht4;4 plants under -Fe-P as well as -Fe+P supplemented with AsA (Fig. 4a  and Supplementary Fig. 10B ). Taken together, these data indicate AsA regulates the expression of PRGs in a pht4;4-dependent manner. bZIP58 regulates the expression of photosynthesis-related genes (PRGs) in a Fe-dependent manner. We next sought to determine how -Fe+P affected expression of the 32 PRGs. To look for potential transcriptional regulators of these genes, we screened candidate genes from the GWAS analysis.
Athaliana. BioMart. plantsmart 27 (Bioconductor) packages. In DESeq2, P values from the Wald test were corrected for multiple hypothesis testing using the Benjamini and Hochberg method. A transcript was considered differentially expressed if the adjusted P value <0. 05. Volcano plots were generated using the EnhancedVolcano package (version 1. 6. 0) (Bioconductor) with a default cut-off of log2(fold change) >|2| and adjusted P value <10e -6. DEGs having a P value of 0 were converted to 10 -1 × lowest nonzero P value. Turnbull staining. Arabidopsis plants (Col-0 genotype) were grown on 0. 5× MS medium. Fourteen days after germination, plants were vacuum infiltrated with Turnbull staining solution (4% (v/v) HCl and 4% (w/v) K-ferricyanide) for 15 min and then incubated for 45 min at room temperature on an orbital shaker  55, 56.
Acknowledgements The authors thank the members of the  Rhee lab ;  Benoit Lacombe  and  HONUDE team (INRAe)  for their comments on the manuscript and helpful discussions. The authors are grateful to  Jean-Francois Briat ,  Stéphane Mari  and  Diego Melo-Almeida  for helpful discussion on Fe staining in Arabidopsis leaves. We thank the  ICP-MS/TIMS Facility  within  Stanford University  for assistance with the ICP-MS measurements, and the  Stanford Functional Genomics Facility  for assistance with RNA sequencing (Stanford, CA). The GWAS analysis was made possible using expression data generated by Kawakatsu et al. 17. This work was.
f Athymic nude mice were injected subcutaneously with PANC1 or MIAPaCa2 cells for 7 days and then treated with IKE (40 mg/kg, i. p. , once every other day) in the absence or presence of ZZW-115 (5 mg/kg, i. p. , once every other day) or liproxstatin-1 (10 mg/kg, i. p. , once every other day) at day 7 for 2 weeks. Tumor volumes were calculated weekly (n = 5 mice/group; two-way ANOVA with Tukey's multiple comparisons test on all pairwise combinations). g Indicated mPDAC cells were treated with erastin (10 µM) or RSL3 (1 µM) in the absence or presence ZZW-115 (2 µM) for 24 h and then cell death was assayed (n = 3 well/group, two-way ANOVA with Tukey's multiple comparisons test on all pairwise combinations). ns: not significant. Data in a-e and g are presented as mean ± SD. The results in a-g are representative of those from 2 to 3 independent experiments.