Abstract: The exchange of water and sediments between the Magdalena River and the Zapayán wetland is regulated by the complex interactions that exist between these important water bodies. The effect of these interactions in the Zapayán Water System (ZWS) are mainly the flood propagation, the water circulation pattern and the concentration of nutrients. The ZWS plays an important role in the regulation of floods for the villages and towns located downstream of it. The Zapayán wetland and it connection channel are located on the right bank of the Magdalena River 150 km upstream of its mouth in Bocas de Ceniza. In this study hydrodynamic interactions between the Magdalena River and the Zapayán wetland are investigated gathering the proper knowledge about the responses of this wetland to the flood events in the river by a series of regression equations generated from the output of a physically based model of the ZWS. The model calibration and validation processes were carried out by comparing the observed flood extent derived from a satellite image and the flood extent generated by the model. Four hydraulic indicators of the ZWS were analyzed using the physically based model results: water level, flood extent, overtopping flow area and the river-wetland discharge, and using the water level in the Magdalena River as independent or input variable, 18 regression equations were derived to describe and quantify the river-wetland interactions. Results show that the interaction of the Magdalena River and the Zapayán wetland depends on the head differences between these water bodies and the velocity of the water ascension in the river. The proposed equations are very promising, as they can be applied in combination with data of the Forecasting and Early Warning System of Colombia, generating information that will help the local authorities, stakeholders and decision makers, to reduce flood risk in the ZWS region. Resumen: El intercambio de agua y sedimentos entre el río Magdalena y la Ciénaga de Zapayán está regulado por las complejas interacciones que existen entre estos cuerpos de agua. El efecto de estas interacciones en el Sistema Hídrico de Zapayán (SHZ) son principalmente la propagación de inundaciones, el patrón de circulación del agua y la concentración de nutrientes. El SHZ desempeña un papel importante en la regulación de las inundaciones en los pueblos y ciudades situados aguas debajo de este. La Ciénaga de Zapayán y su caño se ubican en la margen derecha del río Magdalena 150 km aguas arriba de su desembocadura en Bocas de Ceniza. En este estudio se investigan las interacciones hidrodinámicas entre el río Magdalena y la Ciénaga de Zapayán reuniendo el conocimiento adecuado sobre las respuestas de esta a las inundaciones en el río mediante una serie de ecuaciones de regresión generadas a partir de la salida de un modelo hidrodinámico del SHZ. Los procesos de calibración y validación del modelo se llevaron a cabo comparando la extensión de la inundación observada derivada de una imagen satelital y la extensión de la inundación modelada. Se analizaron cuatro indicadores hidráulicos del SHZ utilizando los resultados del modelo hidrodinámico: nivel del agua, extensión de la inundación, área de flujo desbordante y descarga río-humedal, y utilizando el nivel del agua en el río Magdalena como variable independiente o de entrada, se generaron 18 ecuaciones de regresión derivadas para describir y cuantificar las interacciones río-ciénaga. Los resultados muestran que la interacción del río Magdalena y la Ciénaga de Zapayán depende de las diferencias de cabeza hidráulica entre estos cuerpos de agua y la velocidad de ascensión del agua en el río. Las ecuaciones propuestas son muy prometedoras, ya que pueden aplicarse en combinación con datos del Sistema de Pronóstico y Alerta Temprana de Colombia, generando información que ayudará a las autoridades locales, actores y administradores, a dar mejor manejo al SHZ.

Abstract: Antibiotic resistance is driven by selection, but the degree to which a bacterial strain’s evolutionary history shapes the mechanism and strength of resistance remains an open question. Here, we reconstruct the genetic and evolutionary mechanisms of carbapenem resistance in a clinical isolate of Klebsiella quasipneumoniae. A combination of short- and long-read sequencing, machine learning, and genetic and enzymatic analyses established that this carbapenem-resistant strain carries no carbapenemase-encoding genes. Genetic reconstruction of the resistance phenotype confirmed that two distinct genetic loci are necessary in order for the strain to acquire carbapenem resistance. Experimental evolution of the carbapenem-resistant strains in growth conditions without the antibiotic revealed that both loci confer a significant cost and are readily lost by de novo mutations resulting in the rapid evolution of a carbapenem-sensitive phenotype. To explain how carbapenem resistance evolves via multiple, low-fitness single-locus intermediates, we hypothesised that one of these loci had previously conferred adaptation to another antibiotic. Fitness assays in a range of drug concentrations show how selection in the antibiotic ceftazidime can select for one gene (blaDHA-1) potentiating the evolution of carbapenem resistance by a single mutation in a second gene (ompK36). These results show how a patient’s treatment history might shape the evolution of antibiotic resistance and could explain the genetic basis of carbapenem-resistance found in many enteric-pathogens.

Abstract: The transition from armed conflict to post-conflict periods in tropical countries can have significant impacts on natural ecosystems and their services. Colombia, a country currently transitioning into a post-conflict period, faces significant challenges arising from the peace agreement. These challenges are related to rural land development, socio-economic recovery, stabilization of the agricultural frontier, and the conservation of its tropical forests. Therefore, understanding the role of ecosystem services becomes important for sustainable land development strategies that prioritize the well-being of local communities and the conservation of tropical forests and the services they supply. The objective of this thesis was to explore the supply of ecosystem services in a post-conflict tropical forest region and assess the implications of different rural landscape alternatives on ecosystem services supply and local communities’ livelihoods, using El Capricho Environmental Unit for Peace and Social Services (El Capricho UAPSS) from San José del Guaviare, Colombia, as a case study. To achieve the research objective, the study employed as research methods the analysis of land cover maps and ecosystem services analysis, including the bundle analysis. The data collection methods included an expert assessment through the application of an online survey and literature review. Pastures were identified as the major land cover type in the baseline landscape, followed by secondary vegetation and dense forest. Three rural landscape alternatives were determined for future landscape development: agroforestry, forest conservation, and intensification of extensive livestock farming and timber extraction, also referred to as livestock & timber intensification. The study identified 22 main ecosystem services in the study area, categorized into nine provisioning, nine regulating and maintenance, and four cultural services. The findings revealed the high importance of water supply, livestock, water cycle regulation, and crops, among other services, for local communities. The assessment of ecosystem services showed that more natural land covers, such as dense forest and fragmented forest, exhibited higher capacities to supply ecosystem services compared to highly human-modified land covers. A possible trade-off between livestock service and other ecosystem services was also observed. Furthermore, the study assessed the ecosystem services supply within the baseline landscape and the three alternatives. For the baseline landscape, it was found that the more homogenous landscape pattern benefits some ES at the expense of others, highlighting the high potential supply of livestock and carbon sequestration. The agroforestry and forest conservation alternatives, characterized by landscape heterogeneity, showed a more balanced bundle of ecosystem services with a high potential supply. In contrast, the livestock & timber intensification alternative, characterized by landscape homogeneity, showed a decline in the potential supply of most ecosystem services. The implications of these findings for local communities suggest the importance of maintaining a diverse range of ecosystem services. Recommendations for sustainable rural landscape development include the promotion of agroforestry practices, forest conservation efforts, and careful management of livestock. Additionally, other economic activities can emerge by making use of ecosystem services with high potential supply, such as nature-based tourism, nature photography, and payment for ecosystem services schemes. This can support local communities’ livelihoods and provide new income opportunities and income diversification. These recommendations, combined with community involvement and monitoring systems, can help rural areas in post-conflict tropical forests achieve a high level of rural development and preserve ecosystem services.

Abstract: Abandoned mines are generally associated with the release of heavy metals into the environment. Through mine water, numerous streams and rivers in Scotland are currently severely polluted with potentially toxic metals. To determine the magnitude of the contamination by Pb, Zn, Cu, and Cd in the Glengonnar catchment, along with their spatial variation and potential risk to human health and surface water, sediment and water samples were collected at different locations along a section of the catchment. Heavy metals concentrations were determined by MP – AES and AAS, their spatial variation was observed through SEM – EDS, and the risk assessment was conducted through RISC5 software and Remedial Targets Methodology. Concentrations of metals were higher in sediments (up to 70,668.41 mg/kg Pb, 3,802.48 mg/kg Zn, and 1,078.60 mg/kg Cu) than in water (up to 2,385.16 ?g/L Pb, and 334.52 ?g/L Zn), as a result of the speciation and the adsorption-distribution coefficient of metals, together with the pH of the water. Metals, as free ions and in particulate form, were found to be present at Pb processing and Pb mining areas, respectively. Risk assessment revealed that Pb is the metal of greatest concern and concentrations are prone to harm human health. No detrimental effects to human health were related with concentrations of Zn and Cu in water or sediments. Concentrations of Pb, Zn and Cu in sediments represent a considerable risk to the aquatic environment of the surface water. Consequently, historical legacy of the mining operations at Leadhills means that risk to human health and surface water is present and is likely to continue; reason why adequate mitigation techniques are required in order to decrease pollution and dispersion of heavy metals.

Abstract: Changes in respiratory rate have been found to be one of the early signs of health deterioration in patients. In environments where diagnostic tools and medical attention are scarce, the monitoring of the respiratory signal becomes crucial to timely detect life-threatening conditions. This signal can be measured using wearable technology; however, the use of such technology is often hampered by the low quality of the recordings. Therefore, to apply these data in diagnosis, it is important to determine which parts of the signal are of sufficient quality. This study aims to evaluate the performance of a signal quality assessment framework, where two machine learning algorithms (support vector machine-SVM, and convolutional neural network-CNN) were used. The models were pretrained using data of patients suffering from chronic obstructive pulmonary disease. The generalization capability of the models was evaluated by testing them on data from a different patient population, presenting normal and pathological breathing. The new patients underwent bariatric surgery and performed a controlled breathing protocol, displaying six different breathing patterns. Data augmentation (DA) and transfer learning (TL) were used to increase the size of the training set and to optimize the models for the new dataset. The effect of the different breathing patterns on the performance of the classifiers was also studied. The SVM did not improve when using DA, however, when using TL, the performance improved significantly (p<0.05) compared to DA. The opposite effect was observed for CNN, where the biggest improvement was obtained using DA. The models presented a low performance for shallow, slow and fast breathing patterns. These results suggest that it is possible to classify respiratory signals obtained with wearable technologies using pretrained machine learning models. Resumen: Se ha encontrado que cambios en la frecuencia respiratoria son uno de los primeros signos de deterioro de la salud en pacientes. En entornos donde las herramientas de diagnóstico y la atención médica son escasas, el monitoreo de la señal respiratoria se vuelve crucial para detectar de manera oportuna condiciones que amenazan la vida. Esta señal se puede medir mediante tecnología portátil; sin embargo, el uso de dicha tecnología a menudo se ve obstaculizado por la baja calidad de las señales. Por lo tanto, para aplicar estos datos para diagnóstico, es importante determinar qué partes de la señal tienen una calidad suficientemente buena. Este estudio tiene como objetivo evaluar el rendimiento de un marco de evaluación de calidad de señal, donde se utilizaron dos algoritmos de aprendizaje automático (máquina de soporte vectorial-SVM y red neuronal convolucional-CNN). Los modelos fueron preentrenados utilizando datos de pacientes que padecen enfermedad pulmonar obstructiva crónica. La capacidad de generalización de los modelos se evaluó probándolos en datos de una población de pacientes diferente, que presentaban respiración normal y patológica. Los nuevos pacientes se sometieron a cirugía bariátrica y realizaron un protocolo de respiración controlada, mostrando seis patrones de respiración diferentes. Se utilizó aumento de datos (AD) y transferencia de aprendizaje (TL) para aumentar el tamaño del conjunto de entrenamiento y optimizar los modelos para el nuevo conjunto de datos. Se estudió también el efecto de los diferentes patrones de respiración en el rendimiento de los clasificadores. La SVM no mejoró al usar AD, sin embargo, al usar TL, el rendimiento mejoró significativamente (p<0.05) en comparación con AD. Se observó el efecto contrario en CNN, donde la mayor mejora se obtuvo con AD. Los modelos presentaron un bajo rendimiento para patrones de respiración superficiales, lentos y rápidos. Estos resultados sugieren que es posible clasificar las señales respiratorias obtenidas con tecnologías portátiles mediante modelos preentrenados de aprendizaje automático.

Abstract: Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions. Resumen: La entropía de transferencia (TE) se ha utilizado para identificar y cuantificar las interacciones entre sistemas fisiológicos. Existen diferentes métodos para estimar la TE, pero no hay consenso sobre cuál funciona mejor en aplicaciones específicas. En este estudio, se compararon cinco métodos (lineal, k-nearest neighbors, fixed binning with ranking, kernel density estimation, and adaptive partitioning). La comparación se realizó en tres modelos de simulación (lineal, no lineal y dinámicas lineales + no lineales). A partir de las simulaciones, se encontró que el mejor método para cuantificar las diferentes interacciones fue el adaptive partitioning. Este método se aplicó luego a datos de un estudio de polisomnografía, específicamente a las señales de ECG y respiratorias (flujo nasal y esfuerzo respiratorio alrededor del tórax). Se probó la hipótesis de que los componentes lineales y no lineales de las interacciones cardiorespiratorias durante el sueño ligero y profundo cambian con la etapa del sueño. Diferencias significativas, después de realizar un análisis de subrogados, indican un aumento en la TE durante el sueño profundo. Sin embargo, se encontró que estas diferencias dependían del tipo de señal respiratoria y la frecuencia de muestreo. Estos resultados resaltan la importancia de seleccionar las señales apropiadas, el método de estimación y el análisis de subrogados adecuados para el estudio de las interacciones cardiorespiratorias lineales y no lineales.

Abstract: There are too many reinforced concrete (RC) columns built before mid-1970s without sufficient transverse reinforcement. By now, we understand quite well the importance of transverse reinforcement in allowing a column to maintain its integrity under large displacement reversals in the nonlinear range of response. Poorly confined RC columns undergo a fast decay in resistance due to formation of criss-crossing inclined cracks, which can cause an abrupt failure or more gradual disintegration and trigger collapse of the structure. Those columns need to be strengthened to increase their drift capacity. Although there are several alternatives to retrofit RC columns, they often require specialized workmanship and equipment, and involved installation procedures. An easy-todesign and easyto-implement retrofit technique is examined here. It consists of external posttensioned clamps fastened around the column. Results of tests on full-scale RC columns furnished with the proposed clamps suggest the clamps can be effective in increasing column shear strength and drift capacity. Resumen: Muchas columnas de concreto reforzado construidas antes de mediados de la década de 1970 tienen insuficiente refuerzo transversal. Hace solo un par de décadas que se entendió la importancia del refuerzo transversal para permitir que una columna mantenga su integridad bajo grandes desplazamientos en el rango de respuesta no lineal. Las columnas de concreto reforzado mal confinadas sufren una rápida disminución de la resistencia debido a la formación de grietas, que pueden causar una falla abrupta o una desintegración más gradual y desencadenar el colapso de la estructura. Es necesario reforzar dichas columnas para aumentar su capacidad de deriva. Aunque existen varias alternativas para reforzar columnas de concreto reforzado, a menudo requieren mano de obra y equipo especializados, además de procedimientos de instalación complicados. Aquí se examina una técnica de reforzamiento fácil de diseñar e implementar. La técnica consta de abrazaderas presforzadas externas fijadas alrededor de la columna. Los resultados de laboratorio de las columnas de concreto reforzado a gran escala equipadas con las abrazaderas propuestas sugieren que las abrazaderas pueden ser efectivas para aumentar la resistencia al corte y la capacidad de deriva de la columna.

Abstract: SEPSIS-3 IN PREGNANT WOMEN IN LABOUR Abstract Introduction: Maternal sepsis represents 10.7% of maternal deaths worldwide, ranking as the third cause of mortality after haemorrhage and hypertensive disorders. Despite its high incidence, it still lacks a gold standard diagnosis. The latest update that proposed new sepsis criteria and scores, Sepsis-3, did not mention pregnancy as part of the study population. This study aims to evaluate the inclusion or exclusion of Sepsis- 3 pregnancy. Methods: This is a literature review of the Sepsis-3 database looking for the inclusion or exclusion of pregnancy from the latest update in sepsis and the basis of current definitions and guidelines. In addition, I reported the retrospective measurement of qSOFA and SOFA scores in 100 pregnant patients in labour from the EPIFEVER-2 cohort at the Royal London Hospital compared to their infection status. Results: Sepsis-3 investigators evaluated the predictive validity of the SOFA and qSOFA score for mortality in sepsis among nearly 1 million patients, however, pregnant women were not included. The SOFA and qSOFA score overlap with the normal physiological changes of pregnancy and labour, in the EPIFEVER-2 cohort 16% of uninfected pregnant patients during labour scored 1 point on qSOFA and 17% 1 point on SOFA score. The highest SOFA score of 3 points was obtained in a patient with bleeding rather than infection. Conclusion: Pregnant patients were not included in the Sepsis-3 databases. Therefore, qSOFA and SOFA score have not been validated for use in this population. Furthermore, as they are not modified for the normal physiological changes of pregnancy, detection of patients without infection has been reported. Further validation of qSOFA or modified SOFA scores as sepsis-specific maternal warning scores is expected in future research.

Abstract: Soil available water capacity (AWC) is a key function for human survival and well-being. However, its direct measurement is laborious and spatial interpretation is complex. Digital soil mapping (DSM) techniques emerge as an alternative to spatial modeling of soil properties. DSM techniques commonly apply machine learning (ML) models, with a high level of complexity. In this context, we aimed to perform a digital mapping of soil AWC and interpret the results of the Random Forest (RF) algorithm and, in a case study, to show that digital AWC maps can support agricultural planning in response to the local effects of climate change. To do so, we divided this research into two approaches: In the first approach, we showed a DSM using 1857 sample points in a southeastern region of Brazil with laboratory-determined soil attributes, together with a pedotransfer function (PTF), remote sensing and DSM techniques. In the second approach, the constructed AWC digital soil map and weather station data were used to calculate climatological soil water balances for the periods between 1917–1946 and 1991–2020. The result showed the selection of covariates using Shapley values as a criterion contributed to the parsimony of the model, obtaining goodness-of-fit metrics of R2 0.72, RMSE 16.72 mm m?1, CCC 0.83, and Bias of 0.53 over the validation set. The highest contributing covariates for soil AWC prediction were the Landsat multitemporal images with bare soil pixels, mean diurnal, and annual temperature range. Under the current climate conditions, soil available water content (AW) increased during the dry period (April to August). May had the highest increase in AW (?17 mm m?1) and decrease in September (?14 mm m?1). The used methodology provides support for AWC modeling at 30 m resolution, as well as insight into the adaptation of crop growth periods to the effects of climate change.

Abstract: Older Colombians face significant adversities: poverty, violence and displacement. However, there is evidence that Latinos are often resilient. We examine resilience in older Colombians living in poverty using an ecological framework that identifies three levels: individual; community; and societal. In this paper we examine data from 16 semi-structured interviews with older Colombians that explore resilience within the context of poverty. We analyze our data using three stages: (1) modified grounded theory; (2) assignment of resilience status; (3) identification of components of the ecological framework which contribute to resilience in these participants. The most striking feature is that some participants are able to adapt to their situation, demonstrating resilience, whilst others are not. Individual characteristics such as psychological and material resources contribute to resilience. At the community level, family, social support, participation and cohesion promote resilience. Finally, at the societal level, social and welfare services, finance, religion and social policy, are important factors. These different levels of resilience are co-dependent, and we illustrate how this is so. We suggest that older Colombians living in poverty often demonstrate resilience, but that more can be done to enhance their lives. This includes interventions at the individual and community levels alongside changes in social policy.