Resultados de investigación

1 table., Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research., Peer reviewed

Peer reviewed

Details on simulation and systematic uncertainties., Peer reviewed

Additional figures and tables., Peer reviewed

22 pages. -- Table S01. Statistics for multiple types of biological activity parameters in ChEMBL dataset. -- Table S02. Details of the metabolic networks of >40 organisms. -- Table S03. Average values of fk for the metabolic networks of >40 organisms. -- Table S04. Conditions includes in ChEMBL Dataset of Antibacterial Drugs vs MRN analysis. -- Table S05. Linear index based on atoms descriptors included in the model., Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research., Peer reviewed

Table S1. Regional models driven by the ERA-Interim reanalysis from the European Centre for Medium-Range Weather Forecasts, for the 1989-2008 period. Table S2. EURO-CORDEX Regional models driven by the CMIP5 GCMs. Also shown the approximate spatial resolution from each GCM taken from https://portal.enes.org/data/enes-model-data/cmip5/resolution. Figure S1. Daily precipitation Intensity (left) and frequency (right) distributions taken from the hindcast EURO-CORDEX RCMs and ERA-Interim reanalysis (1989-2008) for the Iberian Peninsula. Also shown the Iberian Gridded Dataset distribution for the same domain and period. All RCM data was previously interpolated into the IGD 0.1o regular resolution. As for Era-Interim, two PDFs are shown, one for the original resolution of the low-resolution and other interpolated into the IGD resolution. The dash point and the value written refers to the 95th percentile of the observations for NGD on the original resolution (blue) and interpolated into the ERA-Interim resolution (red). The time periods are (a) Year, (b) DJF, (c) MAM, (d) JJA and (e) SON. Figure S2. Yearly and seasonal distribution added values (DAV) of the Iberian Peninsula, between the RCMs and the ERA-Interim reanalysis for the 1989-2008 period, taken from the Hindcast EURO-CORDEX simulations, with the NGD regular dataset as reference for (a) daily precipitation intensity, considering the whole PDF shown in the left panels of Figure S1, (b) daily precipitation frequency considering the whole PDF shown in the right panels of Figure S1, (c) daily precipitation intensity extremes, only considering the values above the observational 95th percentile shown in Figure S1 left side and (d) daily precipitation frequency extremes, only considering the values above the observational 95th percentile shown in the right side of Figure S1. All model data were previously interpolated to 0.1oregular resolution from the observations. Figure S3. Daily precipitation Intensity (left) and frequency (right) for the historical driving CMIP5 GCMs and EURO-CORDEX RCMs for the Iberian Peninsula, considering the 1971-2005 period, where all results were previously interpolated into the observational grid. The dash point and the value written refers to the 95th percentile of the bservations. (a) Year, (b) DJF, (c) MAM, (d) JJA and (e) SON. Figure S4. Daily precipitation Intensity (left) and frequency (right) for the historical driving CMIP5 GCMs and NGD observations interpolated into each GCM resolution for the Iberian Peninsula, considering the 1971-2005 period. Also shown the PDF from the NGD observations at the original resolution. (a) Year, (b) DJF, (c) MAM, (d) JJA and (e) SON. Figure S5. Yearly and seasonal distribution added values (DAV) of the Iberian Peninsula, between the RCMs and the CMIP5 GCMs for the 1989-2008 period, taken from the Historical EURO-CORDEX simulations, with the NGD regular dataset as reference for (a) daily precipitation intensity, considering the whole PDF shown in the left panels of Figure S3, (b) daily precipitation frequency considering the whole PDF shown in the right panels of Figure S3, (c) daily precipitation intensity extremes, only considering the values above the observational 95th percentile shown in Figure S3 left side and (d) daily precipitation frequency extremes, only considering the values above the observational 95th percentile shown in the right side of Figure S1. All model data were previously interpolated to 0.1o regular resolution from the observations. The thick blue lines separate the RCMs driven by different GCM., Peer reviewed

Table S1. Regional models forced by the ERA-Interim reanalysis from the European Centre for Medium-Range Weather Forecasts, for the 1989-2008 period. Table S2. EURO-CORDEX Regional models driven by the CMIP5 GCMs. Also shown the approximate spatial resolution from each GCM taken from https://portal.enes.org/data/enes-model-data/cmip5/resolution. Figure S1. Maximum (left) and minimum (right) daily temperature distributions taken from the hindcast EURO-CORDEX RCMs and ERA-Interim reanalysis (1989-2008) for the Iberian Peninsula. Also shown the Iberian Gridded Dataset distribution for the same domain and period. All RCM data was previously interpolated into the IGD 0.1o regular resolution. As for Era-Interim, two PDFs are shown, one for the original resolution of the low-resolution and other interpolated into the IGD resolution. The dash point and the value written refers to either the 90th percentile for max temperature or the 10th percentile for min temperature of the observations for NGD on the original resolution (blue) and interpolated into the ERA-Interim resolution (red). The time periods are (a) Year, (b) DJF, (c) MAM, (d) JJA and (e) SON. Figure S2. Yearly and seasonal distribution added values (DAV) of the Iberian Peninsula, between the RCMs and the ERA-Interim reanalysis for the 1989-2008 period, taken from the Hindcast EURO-CORDEX simulations, with the IGD regular dataset as a reference for (a) maximum daily temperature, considering the whole PDF, (b) minimum daily temperature considering the whole PDF. (c) maximum daily temperature extremes, only considering the values above the observational 90th percentile from maximum temperatures and (d) minimum temperature extremes only considering the values below the observational 10th percentile from minimum temperatures. All model data were previously interpolated to 0.1o regular resolution from the observations. Figure S3. Maximum (left) and minimum (right) daily temperature distribution for the historical EURO-CORDEX RCMs driven by the CMIP5 GCMs, for the Iberian Peninsula, considering the 1971-2005 period, where all results were previously interpolated into the observational grid. The dash point and the value written refers to the 90th percentile of the observations for the maximum temperature and to the 10th percentile of the observations for the minimum temperature. (a) Year, (b) DJF, (c) MAM, (d) JJA and (e) SON. Figure S4. Maximum (left) and minimum (right) daily temperature distributions for the historical driving CMIP5 GCMs and IGD observations interpolated into each GCM resolution for the Iberian Peninsula, considering the 1971-2005 period. Also shown the PDF from the IGD observations at the original resolution. (a) Year, (b) DJF, (c) MAM, (d) JJA and (e) SON. Figure S5. Yearly and seasonal distribution added values (DAV) of the Iberian Peninsula, between the RCMs and the CMIP5 GCMs for the 1989-2008 period, taken from the Historical EURO-CORDEX simulations, with the IGD regular dataset as a reference for (a) maximum daily temperature, considering the whole PDF shown in the left panels of Figure S3, (b) minimum daily temperature considering the whole PDF shown in the right panels of Figure S3, (c) maximum daily temperature extremes, only considering the values above the observational 90th percentile from maximum temperatures and (d) minimum daily temperature extremes, only considering the values below the bservational 10th percentile from minimum temperatures. All model data were previously interpolated to 0.1o regular resolution from the observations. The thick blue lines separate the RCMs driven by different GCM., Peer reviewed

18 pages. -- Appendix S1. Multiple paternity in clutches of wild sea turtles. Only those references reporting the number of clutches with multiple paternity were retained in Figure 1. -- Appendix S2. Full-sibling family assignments determined by COLONY2 software across 217 individual hawksbill turtles. -- Appendix S3. Full-sibling parentage assignments were inferred using COLONY2 software across 217 individual hawksbill turtles collected from six nests at Treasure Island (Set A and Set C, n = 93 and n = 94, respectively, three nests each) and from a single nest at Bounty Island (Set B, n = 30), Fiji, in the South Pacific., Peer reviewed

SUPPLEMENTAL TABLE 1. Data table show the list of genes from Apoptosis and Survival Tier 1 commercial plate for genes expression in samples taken from control NRVM in I/R, and in NRVM transfected with mimics of miR-29a or miR-251_1*. A mix of 4 cultures was added to each pool. Results are expressed in log fold change. SUPPLEMENTAL TABLE 2. Primers designed for genes RT- PCR purchased from Merck-Sigma-Aldrich (USA). SUPPLEMENTAL FIGURE 1. Cartoons outlining the experimental protocols of ischemia and reperfusion (I/R) conducted in vivo in animal models and in vitro in isolated cardiac myocytes. SUPPLEMENTAL FIGURE 2. miRNAs relative expression assessed in infarcted heart’s risk zones of I/R and I/R+Ucn-2 animal model at 1 week. SUPPLEMENTAL FIGURE 3. Control experiments for the expression of miR-29a and miR-451_1*. SUPPLEMENTAL FIGURE 4. miRNAs relative expression assessed in risk zones of infarcted hearts., Peer reviewed

Figure S1: Characterization of MZ and MM CRISPR/Cas9 generated iPSCs and iPSC derived hepatic cells, related to Figure 1. Figure S2: MZ and ZZ iHeps Demonstrate Metabolic and Mitochondrial Dysregulation, related to Figure 3 and Figure 4. Figure S3: Transcriptional Heterogeneity and Branch-Specific Activation of the UPR In MZ and ZZ iHeps related to Figure 3 and Figure 5. Table S1: Results for differential expression testing, GSEA Hallmark pathways, and GO terms related to mitochondria, ER and ER stress for PiZZ6 ZZ, MZ and MM iHep pairwise comparisons, related to Figure 2. "Table S4: Results of differential expression testing, GSEA analysis by original identity and Enrichr-generated enrichments from the top 50 differentially expressed genes for each Louvain cluster and cluster 0 vs 1 comparisons from scRNA seq for PiZZ1 ZZ, MZ, MM and PiZZ6 ZZ, related to Figure 5." Table S5: Results of Enrichr-generated enrichments from the top 50 differentially expressed genes for each Louvain cluster from scRNA seq for PiZZ100 ZZ and MM, related to Figure 5., Peer reviewed