Mapping Susceptibility to Debris Flows Triggered by Tropical Storms: A Case Study of the San Vicente Volcano Area (El Salvador, CA)

In this study, an inventory of storm-triggered debris flows performed in the area of the San Vicente volcano (El Salvador, CA) was used to calibrate predictive models and prepare a landslide susceptibility map. The storm event struck the area in November 2009 as the result of the simultaneous action of low-pressure system 96E and Hurricane Ida. Multivariate Adaptive Regression Splines (MARS) was employed to model the relationships between a set of environmental variables and the locations of the debris flows. Validation of the models was performed by splitting 100 random samples of event and non-event 10 m pixels into training and test subsets. The validation results revealed an excellent (area under the receiver operating characteristic (ROC) curve (AUC) = 0.80) and stable (AUC std. dev. = 0.01) ability of MARS to predict the locations of the debris flows which occurred in the study area. However, when using the Youden’s index as probability threshold to discriminate between pixels predicted as positives and negatives, MARS exhibits a moderate ability to identify stable cells (specificity = 0.66). The final debris flow susceptibility map, which was prepared by averaging for each pixel the score of the 100 MARS repetitions, shows where future debris flows are more likely to occur, and thus may help in mitigating the risk associated with these landslides

.Factores que favorecen la adherencia a los controles de crecimiento y desarrollo de niño sano.

Objetivo: Realizar una búsqueda sistemática de la literatura científica sobre los factores que favorecen la adherencia a los controles de crecimiento y desarrollo de niño sano. Método: se realizó una revisión documental de literatura científica, de tipo narrativo la búsqueda fue restringida a publicaciones realizadas con fechas de publicación entre 2010 y 2021, seleccionando la información según criterios de inclusión y exclusión, se llevó a cabo la depuración de artículos a través del flujograma PRISMA y criterios CASPe, la búsqueda se desarrolló en EBSCO, PUBMED, LILACS y LITERATURA GRIS. Resultados: entre los artículos científicos encontrados y tomados para la realización de esta investigación se identificaron diferentes factores adherentes al cumplimiento de control y crecimiento de niño sano, siendo los principales: el factor educativo, económico y social y por último la edad donde estos reflejan una mayor adherencia al seguimiento del cuidado infantil. Conclusiones: esta investigación realizada a través de la búsqueda sistemática de la literatura científica dio respuesta al objetivo y pregunta PICO de investigación demostrando que hay factores que favorecen la adherencia de los controles de crecimiento y desarrollo en niños. Palabras claves: Factores, Adherencia, Control, Favorecer, Crecimiento y Desarrollo, Niño Sano

Dual and Specific Inhibition of NAMPT and PAK4 By KPT-9274 Decreases Kidney Cancer Growth

Kidney cancer (or renal cell carcinoma, RCC) is the sixth most common malignancy in the United States and one of the relatively few whose incidence is increasing. Because of the near universal resistance which occurs with the use of current treatment regimens, reprogrammed metabolic pathways are being investigated as potential targets for novel therapies of this disease. Borrowing from studies on other malignancies, we have identified the PAK4 and NAD biosynthetic pathways as being essential for RCC growth. We now show, using the dual PAK4/NAMPT inhibitor KPT-9274, that interference with these signaling pathways results in reduction of G2-M transit as well as induction of apoptosis and decrease in cell invasion and migration in several human RCC cell lines. Mechanistic studies demonstrate that inhibition of the PAK4 pathway by KPT-9274 attenuates nuclear β-catenin as well as the Wnt/β-catenin targets cyclin D1 and c-Myc. Furthermore, NAPRT1 downregulation, which we show occurs in all RCC cell lines tested, makes this tumor highly dependent on NAMPT for its NAD requirements, such that inhibition of NAMPT by KPT-9274 leads to decreased survival of these rapidly proliferating cells. When KPT-9274 was administered in vivo to a 786-O (VHL-mut) human RCC xenograft model, there was dose-dependent inhibition of tumor growth with no apparent toxicity; KPT-9274 demonstrated the expected on-target effects in this mouse model. KPT-9274 is being evaluated in a phase I human clinical trial in solid tumors and lymphomas, which will allow this data to be rapidly translated into the clinic for the treatment of RCC. Mol Cancer Ther; 15(9); 2119-29. ©2016 AACR

Dual and Specific Inhibition of NAMPT and PAK4 By KPT-9274 Decreases Kidney Cancer Growth

Kidney cancer (or renal cell carcinoma, RCC) is the sixth most common malignancy in the US and one of the relatively few whose incidence is increasing. Due to the near universal resistance which occurs with the use of current treatment regimens, reprogrammed metabolic pathways are being investigated as potential targets for novel therapies of this disease. Borrowing from studies on other malignancies, we have identified the PAK4 and NAD biosynthetic pathways as being essential for RCC growth. We now show, using the dual PAK4/NAMPT inhibitor KPT-9274, that interference with these signaling pathways results in reduction of G2/M transit as well as induction of apoptosis and decrease in cell invasion and migration in several human RCC cell lines. Mechanistic studies demonstrate that inhibition of the PAK4 pathway by KPT-9274 attenuates nuclear β-catenin as well as the Wnt/β-catenin targets cyclin D1 and c-Myc. Furthermore, NAPRT1 downregulation which we show occurs in all RCC cell lines tested makes this tumor highly dependent on NAMPT for its NAD requirements, such that inhibition of NAMPT by KPT-9274 leads to decreased survival of these rapidly proliferating cells. When KPT-9274 was administered in vivo to a 786-O (VHL-mut) human RCC xenograft model, there was dose-dependent inhibition of tumor growth with no apparent toxicity; KPT-9274 demonstrated the expected on-target effects in this mouse model. KPT-9274 is being evaluated in a phase 1 human clinical trial in solid tumors and lymphomas which will allow this data to be rapidly translated into the clinic for the treatment of RCC

Susceptibility analysis for seismically-induced landslides: application to the 2001 earthquakes in El Salvador (C.A.)

The geodynamic context in which El Salvador is located, made of a convergent structure characterized by the interaction among six different plates, together with the lithological characteristics of the outcropping rocks and soils (mainly corresponding to deeply weathered acid pyroclastites, basic effusive rocks and volcanic ashes), are responsible for the very high seismically- induced landslide susceptibility of the country. These predisposing factors were decisive on the occurrence of thousands of seismically-induced landslides caused by two huge earthquakes on 13th January and 13th February 2001, which triggered thousands of landslides in the country. In particular, the February event (6.6M, onshore and intraplate at a depth of 10 km) triggered 5,371 landslides in an area of around 300km2. These gravitational phenomena took the form of debris slides, earth slides and debris flows and affected several inhabited areas damaging infrastructures and crops and causing, respectively 844 and 315 fatalities. Thanks to aerial photos taken soon after the days following both the two earthquakes and made available by the CNR (Centro Nacional de Registros - Instituto Geográfico y del Catastro Nacional), associated landslide maps have been prepared, where each phenomenon is represented by a landslide polygon and its LIP (Landslide Identification Point), located in the crown of the landslide. In particular, static landslide susceptibility models were prepared for the Ilopango (1594 landslides in an area of around 40km2) and the San Vicente (1602 landslides in an area of around 108 km2) sectors, by regressing the spatial distribution of the 13th February seismically-induced landslides on a set of explanatory variables obtained by a geologic map and a 10m pixel DTM (Digital Terrain Model). At the same time, shaking-dependent models were prepared by including also PGA (Peak Ground Acceleration) and the epicentral distance (ED) among the predictors. For both the two areas a marked increase of performance was observed (AUC from 0.70 to 0.75, for Ilopango, from 0.73 to 0.77, for San Vicente) from the static to the shaking-dependent models, highlighting the role of the seismic acceleration in the triggering of the landslides both in activating the susceptible sites and in lowering the score threshold for slope failures occurrences. Besides, for the Ilopango sector, a rainfall-induced susceptibility model was also prepared, exploiting a landslide inventory available for the 2009 IDA/12E storm events. The obtained score was then Powered by TCPDF (www.tcpdf.org) combined with PGA and ED to predict the spatial distribution of the seismically induced landslides, obtaining a higher performance than the relative basic model (AUC = 0.75). The results obtained from the research demonstrate suggest the possibility to couple the susceptibility scores obtained from static modelling to the expected mechanical shaking for the seismically-induced susceptibility assessment. The whole modelling was carried out by applying MARS (Multivariate Adaptive Regression Splines) analysis through RStudio and SAGA GIS freeware software

Predicting Earthquake-Induced Landslides by Using a Stochastic Modeling Approach: A Case Study of the 2001 El Salvador Coseismic Landslides

In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images allowed us to map 6491 coseismic landslides, mainly debris slides and flows that occurred in volcanic epiclastites and pyroclastites. Four different multivariate adaptive regression splines (MARS) models were produced using different predictors and landslide inventories which contain slope failures triggered by an extreme rainfall event in 2009 and those induced by the earthquakes of 2001. In a predictive analysis, three validation scenarios were employed: the first and the second included 25% and 95% of the landslides, respectively, while the third was based on a k-fold spatial cross-validation. The results of our analysis revealed that: (i) the MARS algorithm provides reliable predictions of coseismic landslides; (ii) a better ability to predict coseismic slope failures was observed when including susceptibility to rainfall-triggered landslides as an independent variable; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) an incomplete inventory of coseismic slope failures built just after the earthquake event can be used to identify potential locations of yet unreported landslides