Nanotransfection-based vasculogenic cell reprogramming drives functional recovery in a mouse model of ischemic stroke

Ischemic stroke causes vascular and neuronal tissue deficiencies that could lead to substantial functional impairment and/or death. Although progenitor-based vasculogenic cell therapies have shown promise as a potential rescue strategy following ischemic stroke, current approaches face major hurdles. Here, we used fibroblasts nanotransfected with Etv2, Foxc2, and Fli1 (EFF) to drive reprogramming-based vasculogenesis, intracranially, as a potential therapy for ischemic stroke. Perfusion analyses suggest that intracranial delivery of EFF-nanotransfected fibroblasts led to a dose-dependent increase in perfusion 14 days after injection. MRI and behavioral tests revealed ~70% infarct resolution and up to ~90% motor recovery for mice treated with EFF-nanotransfected fibroblasts. Immunohistological analysis confirmed increases in vascularity and neuronal cellularity, as well as reduced glial scar formation in response to treatment with EFF-nanotransfected fibroblasts. Together, our results suggest that vasculogenic cell therapies based on nanotransfection-driven (i.e., nonviral) cellular reprogramming represent a promising strategy for the treatment of ischemic stroke

The global abundance of tree palms

Aim Palms are an iconic, diverse and often abundant component of tropical ecosystems that provide many ecosystem services. Being monocots, tree palms are evolutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon sequestration and storage) and in terms of responses to climate change. We quantified global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change. Location Tropical and subtropical moist forests. Time period Current. Major taxa studied Palms (Arecaceae). Methods We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., ≥10 cm diameter at breast height) abundance relative to co‐occurring non‐palm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure. Results On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of long‐term climate stability. Life‐form diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many non‐tree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of above‐ground biomass, but the magnitude and direction of the effect require additional work. Conclusions Tree palms are not only quintessentially tropical, but they are also overwhelmingly Neotropical. Future work to understand the contributions of tree palms to biomass estimates and carbon cycling will be particularly crucial in Neotropical forests

The global abundance of tree palms

Aim: Palms are an iconic, diverse and often abundant component of tropical ecosystems that provide many ecosystem services. Being monocots, tree palms are evolutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon sequestration and storage) and in terms of responses to climate change. We quantified global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change. Location: Tropical and subtropical moist forests. Time period: Current. Major taxa studied: Palms (Arecaceae). Methods: We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., ≥10 cm diameter at breast height) abundance relative to co‐occurring non‐palm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure. Results: On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of long‐term climate stability. Life‐form diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many non‐tree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of above‐ground biomass, but the magnitude and direction of the effect require additional work. Conclusions: Tree palms are not only quintessentially tropical, but they are also overwhelmingly Neotropical. Future work to understand the contributions of tree palms to biomass estimates and carbon cycling will be particularly crucial in Neotropical forests

Optimización del proceso de abastecimiento de cemento a granel en Colombia, aplicación de IRP con restricciones particulares (OPAC)

Con mayor fuerza la analítica ha entrado a evolucionar los procesos logísticos dentro de la cadena de abastecimiento de múltiples y diversos productos, como el cemento. En esta industria es de gran importancia tener un proceso productivo eficiente a lo largo de su cadena, especialmente en los niveles tácticos y operativos donde se evidencia que los más altos costos provienen del transporte y almacenamiento por ser un producto tan voluminoso, pesado y económico. En este trabajo se realiza la propuesta de analítica prescriptiva a través del desarrollo un modelo integrado de optimización del nivel táctico y operativo conocido como IRP (problema de inventario y ruteo de vehículos) enfocado en las circunstancias de un proceso de distribución de cemento con las condiciones particulares de esta industria, aplicando la metodología VMI (inventario administrado por el proveedor). OPAC (optimización del proceso de abastecimiento de cemento) aborda este problema desde un enfoque táctico y posteriormente operativo, en donde el primer enfoque existen las particularidades de una flota no homogénea de vehículos, múltiples productos que no todos pueden ser producidos en una misma planta, capacidad finita de almacenamiento en clientes y centros de distribución (Hub’s), además existe la posibilidad de hacer recirculación, que dependerá de los tiempos de viaje (manejo de tiempo continuo) y el manejo de conductores relevo para hacer más eficiente el uso de los activos fijos desde orígenes que pueden ser distintas plantas y los destinos conocidos como clientes que tienen una demanda de diferente productos por satisfacer en diferentes periodos de tiempo. todo esto realizado en un periodo de planificación que cuenta con varios periodos (días), evidenciando el manejo del tiempo discreto con el objetivo de minimizar los costos totales de la cadena de suministro. Para el segundo enfoque se toma como base principal los resultados del modelo táctico, en este enfoque se busca incluir las condiciones de ventanas horarias de clientes (horarios de atención), definiendo operativamente como debe realizarse la secuenciación de visitas en cada uno de los periodos del horizonte de planificación con el objetivo de minimizar la penalidad de incumplimiento de las visitas fuera del horario de atención. Este conjunto de enfoques permite obtener un resultado exacto en el manejo de inventario y ruteo de vehículos mediante la programación matemática, específicamente la rama de programación lineal entera mixta, en un tiempo computacionalmente razonable, esta modelación es implementada en un software de modelación algebraica AIMMS, ejecutándolo para un caso de aplicación de abastecimiento de cemento en Colombia de una empresa transportadora, permitiendo obtener un modelo que abarca el 95% de la realidad mediante esta metodología, logrando un resultado comparable con la realidad del problemaAbstract: With greater force, analytics has begun to evolve the logistic processes within the supply chain of multiple and diverse products, such as cement. In this industry it is of great importance to have an efficient production process along its chain, especially at the tactical and operational levels where it is evident that the highest costs come from transportation and storage because it is such a bulky, heavy and economic product. In this work the prescriptive analytical proposal is made through the development of an integrated model of optimization of the tactical and operational level known as IRP (Inventory Routing Problem) focused on the circumstances of a cement distribution process with the individual’s conditions of this industry, applying the VMI methodology (Vendor Managed Inventory). OPAC (optimization of the cement supply process) addresses this problem from a tactical and later operational approach, where the first approach contains the peculiarities of a non-homogeneous fleet of vehicles, multiple products that can not all be produced in the same plant, finite storage capacity in customers and distribution centers (Hub's), there is also the possibility of recirculation, which will depend on travel times (continuous time) and the management of relay drivers to make more efficient the use of fixed assets from Origins that can be different plants and destinations known as customers that have a demand for different products to be satisfied in different periods of time. all this carried out in a planning period that has several periods (days), evidencing the management of discrete time with the objective of minimizing the total costs of the supply chain. For the second approach, the results of the tactical model are taken as the main base. This approach seeks to include the conditions of client time windows, defining operationally how visits should be sequenced in each of the horizon periods. of planning with the objective of minimizing the penalty of non-compliance of the visits outside the hours of attention. This set of approaches allows to obtain an optimal result in inventory Resumen y Abstract X management and vehicle routing through mathematical programming, specifically the branch of mixed whole linear programming, in a computationally reasonable time, this modeling is implemented in an AIMMS algebraic modeling software, With this, an implementation is carried out in a case of application of cement supply in Colombia of a transport company, allowing to obtain a result that covers 95% of reality through this methodology, obtaining a result comparable with the reality of the problem.Maestrí

Combination forecasting method using Bayesian models and a metaheuristic, case study

Planning of demand forecasting for perishable products is important for any type of industry that manufactures or distributes, especially if it has a seasonal behavior and a difficult to predict variability. This paper proposes a metaheuristic based on Ant Colony Optimization (ACO) for the combination of forecasts of multiple products, based on three models: Mixed Linear Model (MLM), Bayesian Regression Model with Innovation (BRM) and Dynamic Linear Bayesian Model (BDLM), which are part of the proposed combination whose process is based on minimizing the Mean of Absolute percentage Error (SMAPE) indicator. It is found that the BDLM and BRM methodologies obtain good results on an individual basis, being better BRM, however, the ACO algorithm designed yields a better result, facilitating an adequate prediction of the demand of several products of a company in the meat buffer sectorLa planeación de pronósticos de demanda de productos perecederos es importante para todo tipo de industria que los manufacture o distribuya, en especial, cuando ésta tiene un comportamiento estacional y variabilidad difícil de predecir. En este trabajo se propone una metaheurística basada en Optimización por Colonia de Hormigas (ACO) para la combinación de pronósticos de múltiples productos, basada en tres modelos: Modelo Lineal Mixto (MLM), Modelo de Regresión Bayesiana (BRM) y Modelo Bayesiano Lineal Dinámico (BDLM), los cuales hacen parte de la combinación propuesta cuyo proceso se basa en la minimización del indicador de Media de Error Absoluto Porcentual Simétrico (SMAPE). Se encuentra que las metodologías de BDLM y de BRM obtienen buenos resultados de forma individual siendo mejor esta última, no obstante, el algoritmo ACO diseñado arroja un mejor resultado, facilitando una adecuada predicción de demanda de varios productos de una empresa del sector de cárnico

Método de combinación de pronósticos usando modelos Bayesianos y una metaheurística, caso de estudio

Planning of demand forecasting for perishable products is important for any type of industry that manufactures or distributes, especially if it has a seasonal behavior and a difficult to predict variability. This paper proposes a metaheuristic based on Ant Colony Optimization (ACO) for the combination of forecasts of multiple products, based on three models: Mixed Linear Model (MLM), Bayesian Regression Model with Innovation (BRM) and Dynamic Linear Bayesian Model (BDLM), which are part of the proposed combination whose process is based on minimizing the Mean of Absolute percentage Error (SMAPE) indicator. It is found that the BDLM and BRM methodologies obtain good results on an individual basis, being better BRM, however, the ACO algorithm designed yields a better result, facilitating an adequate prediction of the demand of several products of a company in the meat buffer sector.La planeación de pronósticos de demanda de productos perecederos es importante para todo tipo de industria que los manufacture o distribuya, en especial, cuando ésta tiene un comportamiento estacional y variabilidad difícil de predecir. En este trabajo se propone una metaheurística basada en Optimización por Colonia de Hormigas (ACO) para la combinación de pronósticos de múltiples productos, basada en tres modelos: Modelo Lineal Mixto (MLM), Modelo de Regresión Bayesiana (BRM) y Modelo Bayesiano Lineal Dinámico (BDLM), los cuales hacen parte de la combinación propuesta cuyo proceso se basa en la minimización del indicador de Media de Error Absoluto Porcentual Simétrico (SMAPE). Se encuentra que las metodologías de BDLM y de BRM obtienen buenos resultados de forma individual siendo mejor esta última, no obstante, el algoritmo ACO diseñado arroja un mejor resultado, facilitando una adecuada predicción de demanda de varios productos de una empresa del sector de cárnicos