Registre telemàtic per administracions públiques

El projecte neix amb la finalitat de reduir aquests costos, creant una infraestructura que permeti realitzar els tràmits amb les Administracions Públiques per via telemàtica. D'aquesta forma se suprimeix la necessitat d'acudir presencialment a una oficina de l'Administració, suposant una gran avantatge per ambdues parts, especialment en quant al cost temporal

How Criticality of Gene Regulatory Networks Affects the Resulting Morphogenesis under Genetic Perturbations

Whereas the relationship between criticality of gene regulatory networks (GRNs) and dynamics of GRNs at a single cell level has been vigorously studied, the relationship between the criticality of GRNs and system properties at a higher level has remained unexplored. Here we aim at revealing a potential role of criticality of GRNs at a multicellular level which are hard to uncover through the single-cell-level studies, especially from an evolutionary viewpoint. Our model simulated the growth of a cell population from a single seed cell. All the cells were assumed to have identical GRNs. We induced genetic perturbations to the GRN of the seed cell by adding, deleting, or switching a regulatory link between a pair of genes. From numerical simulations, we found that the criticality of GRNs facilitated the formation of nontrivial morphologies when the GRNs were critical in the presence of the evolutionary perturbations. Moreover, the criticality of GRNs produced topologically homogenous cell clusters by adjusting the spatial arrangements of cells, which led to the formation of nontrivial morphogenetic patterns. Our findings corresponded to an epigenetic viewpoint that heterogeneous and complex features emerge from homogeneous and less complex components through the interactions among them. Thus, our results imply that highly structured tissues or organs in morphogenesis of multicellular organisms might stem from the criticality of GRNs.Comment: 34 pages, 17 figures, 1 tabl

Additional file 7: Table S2. of A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping

The names of the discovered microbial taxa using four methods to examine the sustained effects of LDP on microbial profiles. Discovered taxa without a name are excluded. (DOCX 15 kb

Additional file 1: Table S1. of A powerful microbiome-based association test and a microbial taxa discovery framework for comprehensive association mapping

The permutation-based method to estimate P values for the test statistics, TSPU(γ), TaSPU, QMiRKAT(k), QOMiRKAT, and MOMiAT [25, 31, 37]. (DOCX 23 kb

Additional file 12: of A highly adaptive microbiome-based association test for survival traits

Figure S11. Power estimates for individual MiRKAT-S tests through different software facilities, OMiSA and MiRKATS (via permutation). The censoring scheme, Ci ~ Unif(0,10), and the same effect directions, where βj ∈ Λ is a vector of the elements sampled from Unif(0,1) (blue), Unif(0,2) (yellow), or Unif(0,3) (red), for a small sample size (n = 50) were surveyed. KU, K0.5, KW, and KBC, indicates the use of unweighted UniFrac, generalized UniFrac with ϴ = 0.5, weighted UniFrac, and the Bray-Curtis dissimilarity kernels, respectively. (PDF 6 kb

Additional file 6: of A highly adaptive microbiome-based association test for survival traits

Figure S5. Power estimates for the individual and adaptive tests. The censoring scheme, Ci ~ Unif(0,5), and the same effect directions, where βj ∈ Λ is a vector of the elements sampled from Unif(0,1) (blue), Unif(0,2) (yellow), or Unif(0,3) (red), for a small sample size (n = 50) were surveyed. KU, K0.5, KW, and KBC, indicates the use of unweighted UniFrac, generalized UniFrac with ϴ = 0.5, weighted UniFrac, and the Bray-Curtis dissimilarity kernels, respectively, for MiRKAT-S [24]. (A: 10 most abundant OTUs are associated. B: 10 random OTUs are associated. C: 10 least abundant OTUs are associated. D: OTUs in a chosen cluster are associated.). (PDF 9 kb

Additional file 10: of A highly adaptive microbiome-based association test for survival traits

Figure S9. Power estimates for individual MiRKAT-S tests through different software facilities, OMiSA and MiRKATS (via analytic p-value calculation). The censoring scheme, Ci ~ Unif(0,5), and the same effect directions, where βj ∈ Λ is a vector of the elements sampled from Unif(0,1) (blue), Unif(0,2) (yellow), or Unif(0,3) (red), for a large sample size (n = 100) were surveyed. KU, K0.5, KW, and KBC, indicates the use of unweighted UniFrac, generalized UniFrac with ϴ = 0.5, weighted UniFrac, and the Bray-Curtis dissimilarity kernels, respectively. (PDF 6 kb

Additional file 9: of A highly adaptive microbiome-based association test for survival traits

Figure S8. Power estimates for individual MiRKAT-S tests through different software facilities, OMiSA and MiRKATS (via analytic p-value calculation). The censoring scheme, Ci ~ Unif(0,10), and the mixed effect directions, where βj ∈ Λ is a vector of the elements sampled from Unif(− 1,1) (blue), Unif(− 2,2) (yellow), or Unif(− 3,3) (red), for a large sample size (n = 100) were surveyed. KU, K0.5, KW, and KBC, indicates the use of unweighted UniFrac, generalized UniFrac with ϴ = 0.5, weighted UniFrac, and the Bray-Curtis dissimilarity kernels, respectively. (PDF 6 kb

Additional file 8: of A highly adaptive microbiome-based association test for survival traits

Figure S7. Power estimates for individual MiRKAT-S tests through different software facilities, OMiSA and MiRKATS (via analytic p-value calculation). The censoring scheme, Ci ~ Unif(0,10), and the same effect directions, where βj ∈ Λ is a vector of the elements sampled from Unif(0,1) (blue), Unif(0,2) (yellow), or Unif(0,3) (red), for a large sample size (n = 100) were surveyed. KU, K0.5, KW, and KBC, indicates the use of unweighted UniFrac, generalized UniFrac with ϴ = 0.5, weighted UniFrac, and the Bray-Curtis dissimilarity kernels, respectively. (PDF 6 kb

Additional file 4: of A highly adaptive microbiome-based association test for survival traits

Figure S3. Power estimates for the individual and adaptive tests. The censoring scheme, Ci ~ Unif(0,10), and the same effect directions, where βj ∈ Λ is a vector of the elements sampled from Unif(0,1) (blue), Unif(0,2) (yellow), or Unif(0,3) (red), for a small sample size (n = 50) were surveyed. KU, K0.5, KW, and KBC, indicates the use of unweighted UniFrac, generalized UniFrac with ϴ = 0.5, weighted UniFrac, and the Bray-Curtis dissimilarity kernels, respectively, for MiRKAT-S [24]. (A: 10 most abundant OTUs are associated. B: 10 random OTUs are associated. C: 10 least abundant OTUs are associated. D: OTUs in a chosen cluster are associated.). (PDF 9 kb