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Oliver Hofmann
Gang Fang: Subspace differential coexpression analysis
.. for the discovery of disease-related dysregulations - Oliver Hofmann
Standard analysis: targets the change of expression level (differential expression, DE) - Oliver Hofmann
Here: differential coexpression, changes in the coherence of (paired) expression - Oliver Hofmann
Existing DC work is full space (correlation difference of group genes in two classes) - Oliver Hofmann
Limited by heterogeneity, misses subspace patterns - Oliver Hofmann
Extension to subspace differential coepression, fraction of samples in class A, B that are co-expressed - Oliver Hofmann
Given n genes find all subet of genes for which subspace DC is greater than a threshold - Oliver Hofmann
(similar motivation to bi-clustering). - Oliver Hofmann
Direct mining of differential patterns - Oliver Hofmann
Use redefined SCC measure in an association-analysis approach (combinatorial search) - Oliver Hofmann
Validation in 3 lung cancer data sets and the combined data set - Oliver Hofmann
Statistical significance against random permutation test (gene pairs and phenotypes); yield 88 significant size-3 patterns - Oliver Hofmann
About half could not have found in full-space DC approaches - Oliver Hofmann
Ten-gene subspace DC pattern, not correlated in 92 cancer patients (90%), co-expressed in 63% of normal patients. Enriched with TNF-a/NFKb pathways - Oliver Hofmann
Can be used to study demographic, genetic differences in each class - Oliver Hofmann
Oliver Hofmann
Dennis Vitkup: Prediction of human disease genes
(Focus on analysis rather than prediction) - Oliver Hofmann
Picture of a bike as a model of human diseases: stuff that can go wrong, and how do components overlap. No handle bars, no steering.. but also no brakes. - Oliver Hofmann
[Well, this certainly is different] - Oliver Hofmann
Focus on disease genes vs disease mutations? OMIM with disease genes, higher probability that a random mutation in a disease gene will result in disease - Oliver Hofmann
Focus on mendelian, germ line inherited diseases - Oliver Hofmann
Parallel pathways vs duplicate genes and their (different?) contribution to organism robustness - Oliver Hofmann
Presence of duplicate significantly decreases probability of disease phenotype measured by conditional probability of a disease gene on the distance to the most similar gene - Oliver Hofmann
Genes with 90% sequence identity homologs 3 times less likely to harbor disease genes than those with remote homologs - Oliver Hofmann
Parallel pathways do not contribute to robustness against deleterious mutations - Oliver Hofmann
Mapping disease genes on PPI (Feldman, PNAS 2008), similar work from the Vidal lab: - Oliver Hofmann
intermediate PPI connectivity (degree) -> highest probability of _germ-line_ disease mutations - Oliver Hofmann
(need to be important, but not _that_ important) - Oliver Hofmann
Same is true for intermediae tissue distribution [would seem to make sense: no house-keeping genes, no genes essential for a vital tissue] - Oliver Hofmann
About 1/3 of disease genes are pleiotropic. Example of single gene with three (E.C.) functions or through different biological process (each domain involved in different process). Pleitropy depends on the biological processes, NOT the number of different functions (within one process) - Oliver Hofmann
Oliver Hofmann
Tomer Shlomi: Predicting metabolic engineering KO strategies
Living cells as factories for metabolic goals. Rational design or combinational approaches commonly used for engineering - Oliver Hofmann
Rational design requires solid prior knowledge. Model network function (PPI-Regulatory-Signaling-Metabolic) a trade-of between accuracy and scale - Oliver Hofmann
Kinetic models at one extreme, topological analysis at the other. Constraint-based models chosen here as a balanced approach - Oliver Hofmann
Developed in Palssn lab, UCSC. Predict metabolic reaction rates under steady state constraints - Oliver Hofmann
Usual constraints: mass balance, capacity, thermydynamic constraints, ... - Oliver Hofmann
Rely on selection pressure to drive the network towards maximized biomass production rate - Oliver Hofmann
Modify network such that metabolite of interest is maxmized as a result of biomass optimization (as a by-product) - Oliver Hofmann
OptKnock optimization problem (searches the knockout space that maximizes cellular objective along with metabolic production aim) - Oliver Hofmann
Tends to be overly optimistic (alternative paths); RobustKnock (here) tries to maxmize the minimal guaranteed chemical production rate - Oliver Hofmann
[Details on the math formulation of the optimization problem, should be in the paper] - Oliver Hofmann
Showcases sample results for different cases, including triple-knockouts in Ethanol (bio-fuel) production - Oliver Hofmann
Improved solution space boundaries for RobustKnock vs OptKnock - Oliver Hofmann
Ongoing work to develop microbe metabolic phenotypes, ... - Oliver Hofmann
Oliver Hofmann
Ihor R Lemischka: Systems level approaches to stem cell fate (Keynote)
Deriving genetic, epigenetic and dynamic blueprints of stem cells - Oliver Hofmann
Focus on mouse embryonic stem cells (ES) - Oliver Hofmann
Self-renew, differentiate (in vivo or vitro) into around 120 cell types including the germ line - Oliver Hofmann
Set of known key TFs and pathways (Oct4/Sox2 etc), or inhibition of Erk1/2 and GSK3. Nanog levels may define two different states of pluripotency - Oliver Hofmann
Nanong part of an auto-regulatory circuit maintaining pluri-potency - Oliver Hofmann
Knock down nanog, or sox2 or oct4 one gets blocks of gene expression changes that are superinposable (i.e., same target genes) - Oliver Hofmann
See Lemischka review in Nature Reiews on Mol Cell Biology - Oliver Hofmann
Measure network dynamics during changes in cell fate a requirement. Technical limitations restricted to snapshots at one potential regulatory level - Oliver Hofmann
Recap of the epigenetic molecular and temporal cell landscape - Oliver Hofmann
"Pinball origin of nanog" - Oliver Hofmann
How to convert the snapshots to movies: Lu et al, Nature 463 (2009) - Oliver Hofmann
ES cell lines to tune Nanog expression levels. Vector with Nanog shRNA, inducable promoter. Depend on Doxycycline for Nanog expression, tight control of levels (endogenous nanog knocked down, replaced by new version) - Oliver Hofmann
New publication (last week's nature?): Trace histone marks, Pol II, RNA, 1600 proteins by MassSpec. Day 0, Day 1/3/5 of ES cells - Oliver Hofmann
"Sobering results": if anything an anti-correlation on the expression level; activity of encoded proteins cannot be determined for this sample - Oliver Hofmann
Representation of high dimensional data sets: three dimensional heatmaps, interactive - Oliver Hofmann
Views on all known Nanog, Polycomb targets. Group them by RNA, Protein levels etc. Same option for gene sets (all TFs, drill down to targets of TFs) - Oliver Hofmann
Nanog snapshot (Wang, Nature 2006, Orkin lab) converted to a movie, split up into different time points - Oliver Hofmann
Stem cells: the movie -- coming soon (to the lab website) - Oliver Hofmann
Databases and software to follow (GATE), interfacing prior knowledge. Superimpose existing targets and observe how they relate to the experimental data - Oliver Hofmann
What happens when pulsing Nanog? Turn off, turn on a bit later (the theory of nanog levels and different pluripotency states) -- do you recover the pluripotent state? - Oliver Hofmann
nanog changes not reflected in changes of other pluripotency markers, work in progress at the single cell level - Oliver Hofmann
9 markers in 200 different ES cells monitored by qRT-PCR. Some with very narrow distribution (Oct4, Sox2); Nanog with widely distributed expression levels - Oliver Hofmann
[Love the visualization / interaction system, hopefully available at some point] - Oliver Hofmann
Nanog and Esrrb interacting at different regulatory levels. What happens when Esrrb is taken away (same approach as in Nanog)? - Oliver Hofmann
Strong discordance between mRNA/protein levels - Oliver Hofmann
Monitor landscape after Esrrb down-regulation - Oliver Hofmann
Expand pipeline to additional data types, conditions - Oliver Hofmann
Extended prior knowledge system as a predictive tool used to plot expression level changes after taking away Nanog or Essrb: while closely linked to each other (auto-regulation, PPI) strong changes in target fold changes - Oliver Hofmann
Functional validation by comparing against published RNAi hits - Oliver Hofmann
Transfect Nanog-GFP cells with shRNA, after 24h treat with RA+, follow with FACS - Oliver Hofmann
Knockdown of gene required for neuronal differentiation vs knockdown of gene required for self-renewal observable by reaction to RA stimulus - Oliver Hofmann
shRNA against 350 usual suspects. Hit Oct4, report goes away rapidly; hit components of RAR prolongs time in which Nanog is expressed - Oliver Hofmann
Rank targets by relative promoter construct expression - Oliver Hofmann
Identify SWI/SNF as a potential switch to dismante the pluripotency network - Oliver Hofmann
(a differentiation trigger) - Oliver Hofmann
[And yes, they are hiring -- quantitatively minded students, post docs and faculty] - Oliver Hofmann
Oliver Hofmann
Arjun Raj: Variability in gene expression
.. underlies incomplete penetrance in multicellular development - Oliver Hofmann
Why are individuals different? Excluding genetic differenes, what about random variation (e.g., in clones)? - Oliver Hofmann
(Random) Variation in gene expression leads to cell-to-cell variation in mRNA and protein number, examples from bacteria and yeast. How variable are multi-cellular organisms, how reliable is development? - Oliver Hofmann
Massive variation of gene expression between cells (Raj et al, 2006) - Oliver Hofmann
WT C.elegans with robust development (cell lineage fate) - Oliver Hofmann
Some fractions of mutants still reveal wild type in the case of incomplete penetrance - Oliver Hofmann
[Err. Slide text barely readable. Gene names likely to be off...[ - Oliver Hofmann
Track single mRNA in situ in individual embryos (quantitatively) - Oliver Hofmann
Focus on elt-2. Consistent in WT. In mutants late embryos generally do not express elt-2, but there are rare expression with the mutation yet almost WT-level expression - Oliver Hofmann
Track development stage by nucei count, track elt2, end-1, end-3, med-1.2, plot number of RNAs - Oliver Hofmann
(Mutation is nonsense skn-1) - Oliver Hofmann
med1/2 practically gone. Only remaining connection to elt2 is end-1 which is very heterogenous in mutant compared to WT. end-1 a controller of elt2, sometimes enough RNA/protein to switch on elt2, can be measured in transcript number counts required - Oliver Hofmann
Lower threshold of elt-2 expression results in lower penetrance of mutant phenotype - Oliver Hofmann
[had to skip the second example] - Oliver Hofmann
Oliver Hofmann
Antti Larjo: Simulating chemotactic and metabolic response
[@Golnaz: That's quick -- thanks for the pointers!] - Oliver Hofmann
Brief recap of chemotaxis in E.coli, tumble movement, adaption and sensitivity - Oliver Hofmann
Assays in capillary tubes, swarm plates - Oliver Hofmann
Switch to graphical notations for networks (SBGN, standardized notation). Still no fully executable semantics, concurrency, independency - Oliver Hofmann
Use a state-based methods (State Charts Harel 1987), extended classical state machines with hierarchy, orthogonal states, communication and history - Oliver Hofmann
Abstraction (clustering) allows higher-level representations - Oliver Hofmann
Two-tier formalism, link high-level representation with lower leel details (inter/intracellular); models fully executable - Oliver Hofmann
High level states: tumbling, growth states, run states, (Surviving group, metabolism group, flagellum group, ...) - Oliver Hofmann
Dynamic flux balance analysis, update of constraints at set intervals in the Rhapsody simulation environment - Oliver Hofmann
StochSim (Morton-Firth 1998) as stochastic simulator - Oliver Hofmann
Track asp concentration vs movement - Oliver Hofmann
State-based systems useful to bridge different levels of comprehension - Oliver Hofmann
Oliver Hofmann
Byung-Jun Yoon: Accurate and reliable cancer classification
Probabilistic inference of pathway activities - Oliver Hofmann
Expression (array) profiling as disease classifiers or progression estimators - Oliver Hofmann
Gene marker discovery: identification of differentially expressed genes challenging (small sample size vs size of feature set, inherent noise and heterogeneity) - Oliver Hofmann
Doubts on the usefulness of classifiers built on individual gene markers - Oliver Hofmann
Breast Cancer Metastasis data set (USA Dataset, Wang et al 2005; Netherlands Data set, van't Veer 2002). Each study with about 70 genes, three of which are shared. Low performance on cross-dataset comparison - Oliver Hofmann
How to design more robust classifiers for reproducible results? - Oliver Hofmann
Analyze at the level of functional modules (overcome the independent selection of marker genes) - Oliver Hofmann
Switch to pathway markers, joint analysis of expression levels of functionally related genes [I was wondering whether this was going to be GSEA, but they combine expression information] - Oliver Hofmann
Methods required to summarize pathway member gene expression levels. Previous results indicate that pathway comparison is more robust and provides insights into functional biology - Oliver Hofmann
Widely used methods: mean/median level, magnitude of first component of PCA, CORG (condition-responsive genes) uses mean of differentially expressed genes - Oliver Hofmann
Improve by using a probabilistic approach to measure pathway ativity and to identify the best markers - Oliver Hofmann
Assume expression level of gene x has different distributions in different phenotypes, calculate log-likelihood ratio between phenotypes given the expression level g(x); compare overall LLR as the sum of ratios of all pathway members - Oliver Hofmann
Combines support from each gene in the pathway (naive bayes model) [Assumes conditional independence of genes in a pathway?] - Oliver Hofmann
Compare to USA, Netherlands data set using MSigDB pathways, high t-test score indicates marker is effective in phenotype discrimination. Improved over mean/median, CORG - Oliver Hofmann
Rank markers based on t-score, discriminate power of top P% markers in second data set. Significantly higher t-score compared to CORG et al, particularly for top marker selection - Oliver Hofmann
Oliver Hofmann
Benjamin Logsdon: Regulatory network construction
... from genome-wide expression and genotype data - Oliver Hofmann
Probabilistic representation of networks. Standard methods for undirected graphs: UG inference, graphical lasso, scale for small sample sizes. - Oliver Hofmann
Scaling problem for DAGs given the sample size. Feedback relationships (cycles) require directed cycles but do even worse given the sample sizes - Oliver Hofmann
Aim: directed inference for realistic sample sizes, assumption: each expression trait has a perturbation and each perturbation only has one directed edge - Oliver Hofmann
If that is case the undirected graph can be used to infer a directed cyclic graph - Oliver Hofmann
Motivation: sample of n individuals from a population with m genetic loci, with phenotype p (gene expression products). As long as the changes are far away from each other assume they only have a cis-eQTL effect - Oliver Hofmann
Identify cis-eQTLs (SNPs and expression products) in population studies and use as input for the described method - Oliver Hofmann
[I am going to skip the notes on the isomorphism proof...] - Oliver Hofmann
Combine perturbation, mutation information, infer undirected graph s from the data, significant non-zero partial correlations from the undirected graph to infer the directed regulatory network - Oliver Hofmann
Preliminary results from HapMap (270 individuals, 4 populations) along with expression information from four immortalized lymphoblastoid cell lines - Oliver Hofmann
Survey of identified small networks with consistent biological function (cell cycle regulation, etc) - Oliver Hofmann
Oliver Hofmann
Diogo Camacho: Decoding small RNA networks in bacteria
ncRNA in bacteria: ubiquituos 50-350nt in length (longer than normal miRNA), 80 small RNAs identified/predicted. About 30 are Hfq-dependent sRNAs (stabilizes small RNA to the target) - Oliver Hofmann
Many involved in regulation of stress responses (Masse et al, 2003). - Oliver Hofmann
Usual problem of target detection (less developed than methods for sRNA detection) - Oliver Hofmann
Infer transcriptional regulatory networks from expression data, results in highly modular sRNA network with specific involvment in distinct processes - Oliver Hofmann
Start with the analysis of the Hfq-dependent group (best studied so far) - Oliver Hofmann
Confirmed known regulation (Iron metabolism, RhyB). Picked 20 targets including Lrp, regulates amino acid availability, compared to Lrp-indendent targets - Oliver Hofmann
Lrp-independent targets (and Lrp-regulated targets under different conditions) showed significant expression change. Some directly regulated by sRNA, others indirectly via sRNA effect on Lrp (and Lrp itself regulates small RNA) - Oliver Hofmann
Next: study the role of other small RNAs in stress responses, and identify novel small RNAs based on this combination of network/expression studies - Oliver Hofmann
Oliver Hofmann
Doron Betel: Comprehensive modeling of microRNA targets
Target determination one of the more difficult challenges. Extent of seed complementarity correlates with the extent of downregulation - Oliver Hofmann
Not necessary nor sufficient, explains the high false-positive rate - Oliver Hofmann
Additional contextual features can be utilized - Oliver Hofmann
mirSVR: regression model captures context and does not impose perfect seed complementarity - Oliver Hofmann
Ranks target sites, uses conservation as feature (not filter), scores can be interpreted as probability value of the extent of downregulation, and identifies functional non-canonical sites - Oliver Hofmann
Local and global features (seed, 3' binding, secondary structure accessibility at the target site, conservation, sequence length) - Oliver Hofmann
Grimson paper training data (9 miRNA transfection experiments) - Oliver Hofmann
Testing data: Linsley, 17 indepdendent miRNAs (with mRNA) and 5 miRNA with mass spec (Selbach) - Oliver Hofmann
Outperform target prediction methods [not sure against which algorithms this was tested] - Oliver Hofmann
Additional benefits: CDF plots by conservation, higher conservation coincides with higher downregulation - Oliver Hofmann
Scores linearly correlated with target down-regulation - Oliver Hofmann
mirSVR score can be converted into empirical probabiity of downregulation - Oliver Hofmann
Cross-linking experiment (PURE-CLIP) for direct identification of target sites, 20% non-canonical, compare to non-canonical sites not detected by CLIP; distinguishable by mirSVR - Oliver Hofmann
Scores and new targets soon at http://www.microRNA.org - Oliver Hofmann
Oliver Hofmann
Jef D Boeke: Building S. cerevisiae v2.0 (Keynote)
Probing genome placticity - Oliver Hofmann
Genome synthesis and analysis as a new way to perturb/control/dissect - Oliver Hofmann
Why would you design and construct a 12Mb genone (aka "the crazy project") - Oliver Hofmann
Determine the hidden rules of genome structure; does a synthetic organism become a new species (or how), what is the 'universe' of minimal genomes - Oliver Hofmann
Are repeats, introns essential? If we remove the introns and the splicing machinery is it still viable? - Oliver Hofmann
Generate a 'sleek' conditionally plastic genome with built-in capacity for expanding the genetic code - Oliver Hofmann
"An engineer's design of a genome" - Oliver Hofmann
Focus on chr3 and 9, starting with a modest first milestone - Oliver Hofmann
Requires new software BioStudio, built on DAS. Optimized genome editing, version control, genome shuffling, codon assignments, ... - Oliver Hofmann
Recoding minimal (synonymous mutations in coding regions), introduce "strategic" restriction sites, Church strategy to replace all TAG stop codons with TAA allows the insertion of a new additional amino acid (expansion of the genetic control) - Oliver Hofmann
Codons placed in essential genes as a safety switch as it requires a synthetic AA - Oliver Hofmann
A long term project with "gratifications along the way" - Oliver Hofmann
Remove repetitive elements, replace telomeres with synthetic repeats, delete all retrotransposons. Relocate all tRNA genes to their own special chromosomes (to ensure they do not interfer with the "nice sleek genome" - Oliver Hofmann
Integrate LoxPsym recombination sites through the genome into every 3'UTR of 5000 non-essential genes. Gene shuffling then regulated by Estrogen-regulated Cre site - Oliver Hofmann
Addresses genome restructuring, pulse Cre expression to gain a population of synthetic yeast with varied genome structures and content (the survivors). Deep sequence to determine the rules. - Oliver Hofmann
Status: work with company (CodonDevices), 90kb chromosomal arm in 30kb BAC chunks. Unfortunately took a year and a fortune. - Oliver Hofmann
Plan B: harness the power of undergrads - Oliver Hofmann
Deploy "build a genome" course. Interview to join, open access 24/7 lab access, TA guidance, grad-school style lab meetings and milestones ("no DNA? No A!") - Oliver Hofmann
Use GeneDesign framework to carve genome into smaller pieces or 750bp building blocks - Oliver Hofmann
Built by 16mer oligomer nucleotides. In year one first pass through entire chromosoe 3, 91% done - Oliver Hofmann
CodonDevices syn9R segment overview: 43 loxP sites, tRNAs gone, revised stop codons. Year to create, year to figure out how to integrate it and test for functionality - Oliver Hofmann
Inserted as circular vector, knocked off short arm of native chr9, tagged truncated chromosome, sporulation - Oliver Hofmann
PCRTags (or watermarking) to identify maximally recoded regions, primers vs WT and re-designed areas. Can track the changes in haploid / diploid genome - Oliver Hofmann
SynIXR strains with wild-type growth rates - Oliver Hofmann
43 loxP sites do not seem to have an impact on gene expression - Oliver Hofmann
Confirmed via Northern - Oliver Hofmann
Overall expression profiling with modest changes, not even in DNA damage response-related genes with the exception of telomeric region-genes (as they are no longer close to a telomar but now on a circular genome) - Oliver Hofmann
Survivors of Cre induction exhibit elevated phenotype diversity - Oliver Hofmann
(after a hundredfold reduction in viability as a result of the genome recombination) - Oliver Hofmann
Games to play: different paths genomes take when deleting non-essential genes (can be modeled); detect bypass repressor networks when combining essential gene deletion with genome reshuffling (select fast growers after knockout and recombination -- how did the network re-wire?) - Oliver Hofmann
Conclusion: 1% done, 99% to go - Oliver Hofmann
Oliver Hofmann
Franziska Michor: The cell of origin of human cancers (Keynote)
Tissue hierarchies: stem cell -> progenitors -> differentiated cells - Oliver Hofmann
Which cells accumulate the mutations leading to cancer? - Oliver Hofmann
[The origin of cancer stem cell problem] - Oliver Hofmann
Stem cell accumulates mutations necessary for tumorigenesis, or non-renewing sell acquires self-renewal capabilities and adds mutations for tumorigenesis - Oliver Hofmann
Determines treatment strategies: which cell population to target? - Oliver Hofmann
Mathematical model of tumorigenesis: symmetric, asymmetric cell divisions, differentiation - Oliver Hofmann
Monitor / model mutations over time - Oliver Hofmann
Application to myeloproliferatice neoplasms, driven by activating mutation in JAK2, does not confer properties of self-renewal - Oliver Hofmann
A cancer-stem cell (CSC) can emerge if JAK2 mutation arises in stem cell; calculate probability as a function of time (differential equation system) - Oliver Hofmann
(Model number of stem cells, cell divisions of progenitors, average time between divisions, time units measured, rate of apoptosis, rate of acquiring self-renewal or JAK mutation) - Oliver Hofmann
Mutate JAK in progenitor, gain self-renewal in any of the downstream clones - Oliver Hofmann
Mutate self-renewal in stem cell, gain JAK mutation downstream in progenitor cells - Oliver Hofmann
Result: progenitor cell the most likely cell of origin: self-renewal in progenitor cell, followed by JAK seems to be the dominant trajectory - Oliver Hofmann
Next steps: test model in mice, add mutations in all orders to see if the phenotype is recapitulated - Oliver Hofmann
Other cancer types more complex and require a whole series of mutations - Oliver Hofmann
PDGF-driven gliomas, can be modeled in mouse (RCAS/tv-a technology) - Oliver Hofmann
Tumor formation probability depends on the site of injection: SVZ 8/8, right hemisphere 10/11, Cerebellum 6/7 and with longer latency - Oliver Hofmann
Add parameters: number of self-renewing cells per niche, number of divisions transit amplifiers can undergo, probability of symmetric division, relative fitness of arf -/- SR cells, ... - Oliver Hofmann
Overexpression of PDGF confers properties of self-renewal to transit amplifiers - Oliver Hofmann
Leads to cell expansion / recruitment (factor by which the cell population expands) - Oliver Hofmann
Total probability of cancer initiation identical to probability of cancer initiation from self-renewing TA cells - Oliver Hofmann
Most important determinant: probability of acquisition of self-renewal. If it is zero the origin _has_ to be a stem cell. If larger than 10^-6 (one in a million) more likely to be a progenitor - Oliver Hofmann
Even for low number of division of TA cells more likely - Oliver Hofmann
Cell of origin: is it the cell that gains the last required mutation? - Oliver Hofmann
Are there cell-autonomous genetic alterations inducing self renewal? - Oliver Hofmann
Implications of resistance to therapy in self-renewing cells - Oliver Hofmann
What are the micro-environmental factors that induce self-renewal? - Oliver Hofmann
If stemness is reversible the CSC population is in a dynamic equilibrium - Oliver Hofmann
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