Several groups produce estimates of the Earth's time-varying gravitational field with data provided by the NASA/DLR Gravity Recovery and Climate Experiment (GRACE) mission. These unprecedented highly accurate global data sets track the time-variable transport of mass across and underneath the surface of the Earth and give insight into secular, seasonal, and sub seasonal variations in the global water supply. Knowledge gained from these products can inform and be incorporated into ocean and hydrological models and advise environmental policy planning. Therefore, a complete understanding of the accuracy and variations between these different fields is necessary, and the most accurate possible solutions desired. While the various gravity fields are similar, differences in processing strategies and tuning parameters result in solutions with regionally specific variations and error patterns. This study analyzed the spatial, temporal, and spectral variations between four different gravity field products. The knowledge gained in this analysis was used to develop an ensemble solution that harnesses the best characteristics of each individual field to create an optimal model. Multiple methods were used to combine and analyze the individual and ensemble solutions. First a simple mean model was created; then the different solutions were weighted based on the formal error estimates as well as the monthly deviation from the arithmetic mean ensemble. These ensemble models as well as the four individual data center solutions were analyzed for bias, long term trend, and regional variations between the solutions, evaluated statistically to assess the noise and scatter within the solutions, and compared to independent hydrological models. Therefore, the form and cause of the deviations between the models, as well as the impact of these variations, is characterized. The three ensemble solutions constructed in this analysis were all effective at reducing noise in the models and better correlate to hydrological processes than any individual solution. However, the scale of these improvements is constrained by the relative variation between the individual solutions as the deviation of these individual data products from the hydrological model output is much larger than the variations between the individual and ensemble solutions. / text

Metabolism in cancer cells is adapted to meet the proliferative needs of these cells, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this work, we use the Ensemble Modeling (EM) framework to gain insight and predict potential drug targets for tumor cells. A metabolic network consisting of 58 reactions is considered which accounts for glycolysis, the pentose phosphate pathway, lipid metabolism, amino acid metabolism, and includes allosteric regulation. Experimentally measured metabolite concentrations are used for developing the ensemble of models along with information on established drug targets. The resulting models predicted transaldolase (TALA) and succinate-CoA ligase (SUCOAS1m) to display a significant reduction in growth rate when repressed relative to currently known drug targets. Furthermore, the synergetic repression of transaldolase and glycine hydroxymethyltransferase (GHMT2r) showed a three fold decrease in growth rate compared to the repression of single enzyme targets.

Metabolism in cancer cells is adapted to meet the proliferative needs of these cells, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this work, we use the Ensemble Modeling (EM) framework to gain insight and predict potential drug targets for tumor cells. A metabolic network consisting of 58 reactions is considered which accounts for glycolysis, the pentose phosphate pathway, lipid metabolism, amino acid metabolism, and includes allosteric regulation. Experimentally measured metabolite concentrations are used for developing the ensemble of models along with information on established drug targets. The resulting models predicted transaldolase (TALA) and succinate-CoA ligase (SUCOAS1m) to display a significant reduction in growth rate when repressed relative to currently known drug targets. Furthermore, the synergetic repression of transaldolase and glycine hydroxymethyltransferase (GHMT2r) showed a three fold decrease in growth rate compared to the repression of single enzyme targets.

Piggvarens (Scophthalmus maximus) val av habitat i Östersjön har modellerats utifrån provfiskedata och miljövariabler. Vid totalt 435 stationer i Östersjön har data samlats in i form av provfiske, CTD-mätningar (konduktivitet, temperatur och djup) och videofilmer. Genom att analysera videofilmerna från havsbotten i Östersjön har den klassificerats efter fyra olika förklaringsvariabler: täckningsgrad mjukbotten, strukturbildande växter, övriga alger och täckningsgrad blåmusslor. Ytterligare sex förklaringsvariabler har samlats in från mätningar och befintliga kartor: bottensalinitet, bottentemperatur, djup, siktdjup, vågexponering och bottenlutning. Dessa tio förklaringsvariabler har använts i tio olika enskilda statistiska modelleringsmetoder med förekomst/icke-förekomst av piggvar som responsvariabel. Nio av tio modeller visade på bra resultat (AUC > 0,7) där CTA (Classification Tree Analysis) och GBM (Global Boosting Model) hade bäst resultat (AUC > 0,9). Genom att kombinera modeller med bra resultat på olika sätt skapades sex ensemblemodeller för att minska varje enskild modells svagheter. Ensemblemodellerna visade tydligt fördelarna med denna typ av modellering då de gav ett mycket bra resultat (AUC > 0,949). Den sämsta ensemblemodellen var markant bättre än den bästa enskilda modellen. Resultaten från modellerna visar att största sannolikheten för piggvarsförekomst i Östersjön är vid grunt (< 20 meter) och varmt (> 10 oC) vatten med hög vågexponering (> 30 000 m²/s). Dessa tre variabler var de med högst betydelse för modellerna. Täckningsgrad mjukbotten och de två växtlighetsvariablerna från videoanalyserna var de tre variabler som hade lägst påverkan på piggvarens val av habitat. Med en högre kvalitet på videofilmerna hade de variablerna kunnat klassificeras i mer specifika grupper vilket eventuellt gett ett annat resultat. Generellt visade modellerna att denna typ av habitatmodellering med provfiske och miljödata både är möjlig att utföra. / The turbots’ (Scophthalmus maximus) selection of habitat in the Baltic Sea has been modeled on the basis of fish survey data and environmental variables. At a total of 435 stations in the Baltic Sea, data was collected in the form of fish survey data, CTD (Conductivity, Temperature and Depth) measurements and videos. By analyzing the videos from the seabed of the Baltic Sea, four different explanatory variables have been classified: coverage of soft bottom, structure-forming plants, other algae and coverage of mussels. Another six explanatory variables have been collected from measurements and existing rasters: salinity, temperature, depth, water transparency, wave exposure and the bottom slope. These ten explanatory variables have been used in ten different species distribution modeling methods with the presence/absence of turbot as a response variable. Nine out of ten models showed good results (AUC > 0.7) where the CTA (Classification Tree Analysis) and GBM (Global Boosting Model) performed the best (AUC > 0.9). By combining the models with good performance in six different ensemble models each individual models’ weaknesses were decreased. The ensemble models clearly showed strength as they gave a very good performance (AUC > 0.94). The worst ensemble model was significantly better than the best individual model. The results of the models show that the largest probability of occurrence of turbot in the Baltic Sea is in shallow (< 20 m) and warm (> 10 ° C) water with high wave exposure (> 30,000 m²/s). These three variables were those with the highest significance for the models. Coverage of soft bottom and the two vegetation variables, from the video analyzes, had the lowest impact on the turbots’ choice of habitat. A higher quality of the videos would have made it possible to classify these variables in more specific groups which might have given a different result. Generally, the models showed that this type of modeling of habitat is possible to perform with fish survey and environmental monitoring data and generates useful results.

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An investigation into using attention mechanisms for better feature extraction in wildfire spread prediction models. This research examines the U-net architecture to achieve image segmentation, a process that partitions images by classifying pixels into one of two classes. The deep learning models explored in this research integrate modern deep learning architectures, and techniques used to optimize them. The models are trained on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. This research concludes that when augmenting U-net with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, employing ensemble modeling reduces bias and variation, leading to more consistent and accurate predictions. When inferencing on wildfire propagation at 30-minute intervals, the architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1%.