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Supplementary Table 1 | Investigational and approved drugs against K-RAS identified mutations. Specific K-RAS mutation, name of the drug, status (approved or investigational), identification code (NCT) and clinical studies with links and phases are included. Intervention is included if the drug is given in combination with others. Supplementary Table 2 | Gene functions of thirteen selected deregulated genes. Supplementary Table 3 | Upregulation details of cell surface-related genes analyzed. Name of the gene, mean of expression in mutant and wildtype K-RAS tumors, fold change (FC), direction and p-value are included. Supplementary Table 4 | Kaplan-Meier survival values of cell surface-related genes. Table includes the name of the gene, the hazard ratio (HR) (in blue, significant good prognosis, and in red, bad one), p-value and fold discovery rate (FDR) for FP and in LUAD patients., Targeting K-RAS-mutant non-small cell lung cancer (NSCLC) with novel inhibitors has shown promising results with the recent approval of sotorasib in this indication. However, progression to this agent is expected, as it has previously been observed with other inhibitors. Recently, new immune therapeutics, including vectorized compounds with antibodies or modulators of the host immune response, have demonstrated clinical activity. By interrogating massive datasets, including TCGA, we identified genes that code for surface membrane proteins that are selectively expressed in K-RAS mutated NSCLC and that could be used to vectorize novel therapies. Two genes, CLDN10 and TMPRSS6, were selected for their clear differentiation. In addition, we discovered immunologic correlates of outcome that were clearly de-regulated in this particular tumor type and we matched them with immune cell populations. In conclusion, our article describes membrane proteins and immunologic correlates that could be used to better select and optimize current therapies., Peer reviewed

Crop Wild Relatives (CWR) are a valuable source of genetic diversity that can be transferred to commercial crops, so their conservation will become a priority in the face of climate change. Bizarrely, in situ conserved CWR populations and the traits one might wish to preserve in them are themselves vulnerable to climate change. In this study, we used a quantitative machine learning predictive approach to project the resistance of CWR populations of lentils to a common disease, lentil rust, caused by fungus Uromyces viciae-fabae. Resistance is measured through a proxy quantitative value, DSr (Disease Severity relative), quite complex and expensive to get. Therefore, machine learning is a convenient tool to predict this magnitude using a well-curated georeferenced calibration set. Previous works have provided a binary outcome (resistant vs. non-resistant), but that approach is not fine enough to answer three practical questions: which variables are key to predict rust resistance, which CWR populations are resistant to rust under current environmental conditions, and which of them are likely to keep this trait under different climate change scenarios. We first predict rust resistance in present time for crop wild relatives that grow up inside protected areas. Then, we use the same models under future climate IPCC (Intergovernmental Panel on Climate Change) scenarios to predict future DSr values. Populations that are rust-resistant by now and under future conditions are optimal candidates for further evaluation and in situ conservation of this valuable trait. We have found that rust-resistance variation as a result of climate change is not uniform across the geographic scope of the study (the Mediterranean basin), and that candidate populations share some interesting common environmental conditions., Peer reviewed

The dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Please, read the full ODbL 1.0 license text for the exact terms that apply. Users of the dataset are free to: Share: copy, distribute and use the database, either commercially or non-commercially. Create: produce derivative works from the database. Adapt: modify, transform and build upon the database. Under the following conditions: Attribution: You must attribute any public use of the database, or works produced from the database. For any use or redistribution of the database, or works produced from it, you must make clear to others the license of the original database. Share-Alike: If you publicly use any adapted version of this database, or works produced from an adapted database, you must also offer that adapted database under the ODbL., This work was supported by PROMETEO/2018/066 from ‘Conselleria d’Educació’ (Generalitat Valenciana, Comunitat Valenciana, Spain), grant PID2021-125858OB-100 and the Severo Ochoa Excellence Program CEX2021-001189-S funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”. EM-G was recipient of a predoctoral grant FPU18/02019 funded by MCIN/AEI/ 10.13039/501100011033 and by “ESF Investing in your future”. SG holds a ‘Juan de la Cierva Incorporación’ grant (IJC2020-042749-I) from the Spanish ‘Ministerio de Ciencia e Innovación’, funded by The European Union–NextGenerationEU., Fig 1: Controlled fungal infection assays on orange fruits -- Fig.2: P. chrysogenum Terb resistant mutants on PDA + PDA terb -- Fig.3: Controlled fungal infection assays on orange fruits -- Fig.4: Measurement of luciferase and nanoluciferase luminescence signals for transformants expressing luciferase under different promoters after 2 days of growth in PDB -- Fig. 5: Measurement of luciferase and nanoluciferase luminescence signals for transformants expressing luciferase under different inducible promoters after 4 days of growth in PdMM – Fig. 6: Measurement of luciferase and nanoluciferase luminescence signals for Penicillium digitatum transformants expressing luciferase under dCas9-regulated GB_SynP synthetic promoter and the corresponding dCas9 activation system (samples named as "+dcas9"), after 2 days of growht in PDB., Peer reviewed

S1 Fig. Cellular automaton model accurately predicts the experimentally observed evolution of early cln3. A) Scheme of the algorithm used in Model A (Fig 2A). B) Number of generations and times required to obtain <98% of cells with 220 rDNA repeats and an average generation increase (Delta) between 0.3 and 2 copies. A Delta between 1.2 and 1.4 fits the experimental results (between 160 and 180 generations). S2 Fig. Cellular automaton model by assuming that SIR2 repression linearly decreases. In this case, the cells with 125 copies divide 100% into a daughter with an amplified copy number by a given Delta factor and another cell with no amplification (125 copies). However, the cells with >125 copies have a linear increasing tendency (from 1% to 126 copies to 100% with 220 copies) to divide into two cells with no amplification. The table and the plot show the number of generations required for every possible integral Delta value. Note that only Delta >11 fits the number of experimentally observed generations. S3 Fig. Cellular automaton model by assuming that growth rate differences between cells have different rDNA copy numbers for models B. Scheme of the algorithm used for Models B (Fig 2D and 2E). The figure shows that the growth rate increases (generation time decrease, GTI -9 s). A model for a lowering growth rate would be similar, but with a GTI of +9 s. S1 Table. List of the yeast strains used in this work. S1 Appendix. Algorithm used in Model A1. This appendix describes the pseudocode of Model A1. Both the implementation details and source codes for all the models can be downloaded from https://www.uv.es/varnau/modelo/MODEL_A.c., Peer reviewed

A processing tool for the spaceborne GNSS-R complex waveform products available at IEEC's GOLD-RTR server. The project provides an example for accessing, searching and analyzing the compelx waveform product derived from the GNSS-R raw IF data collected by different spaceborne missions (e.g. TDS-1, CYGNSS, BuFeng-1, SPIRE)., Peer reviewed

Extended synoptic situation and flood events for each synoptic pattern., Peer reviewed

The supplementary material provide technical details about our numerical methods and convergence tests as requested by the referees. Appendix A: Covariant evolution equations Appendix B: Code validation, Peer reviewed

Crop Wild Relatives (CWR) are a valuable source of genetic diversity that can be transferred to commercial crops, so their conservation will become a priority in the face of climate change. Bizarrely, in situ conserved CWR populations and the traits one might wish to preserve in them are themselves vulnerable to climate change. In this study, we used a quantitative machine learning predictive approach to project the resistance of CWR populations of lentils to a common disease, lentil rust, caused by fungus Uromyces viciae-fabae. Resistance is measured through a proxy quantitative value, DSr (Disease Severity relative), quite complex and expensive to get. Therefore, machine learning is a convenient tool to predict this magnitude using a well-curated georeferenced calibration set. Previous works have provided a binary outcome (resistant vs. non-resistant), but that approach is not fine enough to answer three practical questions: which variables are key to predict rust resistance, which CWR populations are resistant to rust under current environmental conditions, and which of them are likely to keep this trait under different climate change scenarios. We first predict rust resistance in present time for crop wild relatives that grow up inside protected areas. Then, we use the same models under future climate IPCC (Intergovernmental Panel on Climate Change) scenarios to predict future DSr values. Populations that are rust-resistant by now and under future conditions are optimal candidates for further evaluation and in situ conservation of this valuable trait. We have found that rust-resistance variation as a result of climate change is not uniform across the geographic scope of the study (the Mediterranean basin), and that candidate populations share some interesting common environmental conditions., Peer reviewed

Crop Wild Relatives (CWR) are a valuable source of genetic diversity that can be transferred to commercial crops, so their conservation will become a priority in the face of climate change. Bizarrely, in situ conserved CWR populations and the traits one might wish to preserve in them are themselves vulnerable to climate change. In this study, we used a quantitative machine learning predictive approach to project the resistance of CWR populations of lentils to a common disease, lentil rust, caused by fungus Uromyces viciae-fabae. Resistance is measured through a proxy quantitative value, DSr (Disease Severity relative), quite complex and expensive to get. Therefore, machine learning is a convenient tool to predict this magnitude using a well-curated georeferenced calibration set. Previous works have provided a binary outcome (resistant vs. non-resistant), but that approach is not fine enough to answer three practical questions: which variables are key to predict rust resistance, which CWR populations are resistant to rust under current environmental conditions, and which of them are likely to keep this trait under different climate change scenarios. We first predict rust resistance in present time for crop wild relatives that grow up inside protected areas. Then, we use the same models under future climate IPCC (Intergovernmental Panel on Climate Change) scenarios to predict future DSr values. Populations that are rust-resistant by now and under future conditions are optimal candidates for further evaluation and in situ conservation of this valuable trait. We have found that rust-resistance variation as a result of climate change is not uniform across the geographic scope of the study (the Mediterranean basin), and that candidate populations share some interesting common environmental conditions., Peer reviewed

Crop Wild Relatives (CWR) are a valuable source of genetic diversity that can be transferred to commercial crops, so their conservation will become a priority in the face of climate change. Bizarrely, in situ conserved CWR populations and the traits one might wish to preserve in them are themselves vulnerable to climate change. In this study, we used a quantitative machine learning predictive approach to project the resistance of CWR populations of lentils to a common disease, lentil rust, caused by fungus Uromyces viciae-fabae. Resistance is measured through a proxy quantitative value, DSr (Disease Severity relative), quite complex and expensive to get. Therefore, machine learning is a convenient tool to predict this magnitude using a well-curated georeferenced calibration set. Previous works have provided a binary outcome (resistant vs. non-resistant), but that approach is not fine enough to answer three practical questions: which variables are key to predict rust resistance, which CWR populations are resistant to rust under current environmental conditions, and which of them are likely to keep this trait under different climate change scenarios. We first predict rust resistance in present time for crop wild relatives that grow up inside protected areas. Then, we use the same models under future climate IPCC (Intergovernmental Panel on Climate Change) scenarios to predict future DSr values. Populations that are rust-resistant by now and under future conditions are optimal candidates for further evaluation and in situ conservation of this valuable trait. We have found that rust-resistance variation as a result of climate change is not uniform across the geographic scope of the study (the Mediterranean basin), and that candidate populations share some interesting common environmental conditions., Peer reviewed