A Novel Approach for the Integral Management of Water Extremes in Plain Areas

Due to the socioeconomical impact of water extremes in plain areas, there is a considerable demand for suitable strategies aiding in the management of water resources and rainfed crops. Numerical models allow for the modelling of water extremes and their consequences in order to decide on management strategies. Moreover, the integration of hydrologic models with hydraulic models under continuous or event-based approaches would synergistically contribute to better forecasting of water extreme consequences under different scenarios. This study conducted at the Santa Catalina stream basin (Buenos Aires province, Argentina) focuses on the integration of numerical models to analyze the hydrological response of plain areas to water extremes under different scenarios involving the implementation of an eco-efficient infrastructure (i.e., the integration of a green infrastructure and hydraulic structures). The two models used for the integration were: the Soil and Water Assessment Tool (SWAT) and the CELDAS8 (CTSS8) hydrologic-hydraulic model. The former accounts for the processes related to the water balance (e.g., evapotranspiration, soil moisture, percolation, groundwater discharge and surface runoff), allowing for the analysis of water extremes for either dry or wet conditions. Complementarily, CTSS8 models the response of a basin to a rainfall event (e.g., runoff volume, peak flow and time to peak flow, flooded surface area). A 10-year data record (2003?2012) was analyzed to test different green infrastructure scenarios. SWAT was able to reproduce the waterflow in the basin with Nash Sutcliffe (NS) efficiency coefficients of 0.66 and 0.74 for the calibration and validation periods, respectively. The application of CTSS8 for a flood event with a return period of 10 years showed that the combination of a green infrastructure and hydraulic structures decreased the surface runoff by 28%, increased the soil moisture by 10% on an average daily scale, and reduced the impact of floods by 21% during rainfall events. The integration of continuous and event-based models for studying the impact of water extremes under different hypothetical scenarios represents a novel approach for evaluating potential basin management strategies aimed at improving the agricultural production in plain areas.Fil: Guevara Ochoa, Cristian. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff"; ArgentinaFil: Masson, Ignacio. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff"; ArgentinaFil: Cazenave, Georgina. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff"; ArgentinaFil: Vives, Luis Sebastián. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff"; ArgentinaFil: Vazquez Amabile, Gabriel Gustavo. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales; Argentin

A Novel Approach for the Integral Management of Water Extremes in Plain Areas

Due to the socioeconomical impact of water extremes in plain areas, there is a considerable demand for suitable strategies aiding in the management of water resources and rainfed crops. Numerical models allow for the modelling of water extremes and their consequences in order to decide on management strategies. Moreover, the integration of hydrologic models with hydraulic models under continuous or event-based approaches would synergistically contribute to better forecasting of water extreme consequences under di erent scenarios. This study conducted at the Santa Catalina stream basin (Buenos Aires province, Argentina) focuses on the integration of numerical models to analyze the hydrological response of plain areas to water extremes under di erent scenarios involving the implementation of an eco-e cient infrastructure (i.e., the integration of a green infrastructure and hydraulic structures). The two models used for the integration were: the Soil and Water Assessment Tool (SWAT) and the CELDAS8 (CTSS8) hydrologic-hydraulic model. The former accounts for the processes related to the water balance (e.g., evapotranspiration, soil moisture, percolation, groundwater discharge and surface runo ), allowing for the analysis of water extremes for either dry or wet conditions. Complementarily, CTSS8 models the response of a basin to a rainfall event (e.g., runo volume, peak flow and time to peak flow, flooded surface area). A 10-year data record (2003–2012) was analyzed to test di erent green infrastructure scenarios. SWAT was able to reproduce the waterflow in the basin with Nash Sutcli e (NS) e ciency coe cients of 0.66 and 0.74 for the calibration and validation periods, respectively. The application of CTSS8 for a flood event with a return period of 10 years showed that the combination of a green infrastructure and hydraulic structures decreased the surface runo by 28%, increased the soil moisture by 10% on an average daily scale, and reduced the impact of floods by 21% during rainfall events. The integration of continuous and event-based models for studying the impact of water extremes under di erent hypothetical scenarios represents a novel approach for evaluating potential basin management strategies aimed at improving the agricultural production in plain areas.Facultad de Ciencias Agrarias y Forestale

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

The Molecular Origin and Taxonomy of Mucinous Ovarian Carcinoma

Mucinous ovarian carcinoma (MOC) is a unique subtype of ovarian cancer with an uncertain etiology, including whether it genuinely arises at the ovary or is metastatic disease from other organs. In addition, the molecular drivers of invasive progression, high-grade and metastatic disease are poorly defined. We perform genetic analysis of MOC across all histological grades, including benign and borderline mucinous ovarian tumors, and compare these to tumors from other potential extra-ovarian sites of origin. Here we show that MOC is distinct from tumors from other sites and supports a progressive model of evolution from borderline precursors to high-grade invasive MOC. Key drivers of progression identified are TP53 mutation and copy number aberrations, including a notable amplicon on 9p13. High copy number aberration burden is associated with worse prognosis in MOC. Our data conclusively demonstrate that MOC arise from benign and borderline precursors at the ovary and are not extra-ovarian metastases

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation