Predicting the outcome of renal transplantation

ObjectiveRenal transplantation has dramatically improved the survival rate of hemodialysis patients. However, with a growing proportion of marginal organs and improved immunosuppression, it is necessary to verify that the established allocation system, mostly based on human leukocyte antigen matching, still meets today's needs. The authors turn to machine-learning techniques to predict, from donor-recipient data, the estimated glomerular filtration rate (eGFR) of the recipient 1 year after transplantation.DesignThe patient's eGFR was predicted using donor-recipient characteristics available at the time of transplantation. Donors' data were obtained from Eurotransplant's database, while recipients' details were retrieved from Charite Campus Virchow-Klinikum's database. A total of 707 renal transplantations from cadaveric donors were included.MeasurementsTwo separate datasets were created, taking features with <10% missing values for one and <50% missing values for the other. Four established regressors were run on both datasets, with and without feature selection.ResultsThe authors obtained a Pearson correlation coefficient between predicted and real eGFR (COR) of 0.48. The best model for the dataset was a Gaussian support vector machine with recursive feature elimination on the more inclusive dataset. All results are available at http://transplant.molgen.mpg.de/.LimitationsFor now, missing values in the data must be predicted and filled in. The performance is not as high as hoped, but the dataset seems to be the main cause.ConclusionsPredicting the outcome is possible with the dataset at hand (COR=0.48). Valuable features include age and creatinine levels of the donor, as well as sex and weight of the recipient

Fuel treatment planning: Fragmenting high fuel load areas while maintaining availability and connectivity of faunal habitat

Reducing the fuel load in fire-prone landscapes is aimed at mitigating the risk of catastrophic wildfires but there are ecological consequences. Maintaining habitat for fauna of both sufficient extent and connectivity while fragmenting areas of high fuel loads presents land managers with seemingly contrasting objectives. Faced with this dichotomy, we propose a Mixed Integer Programming (MIP) model that can optimally schedule fuel treatments to reduce fuel hazards by fragmenting high fuel load regions while considering critical ecological requirements over time and space. The model takes into account both the frequency of fire that vegetation can tolerate and the frequency of fire necessary for fire-dependent species. Our approach also ensures that suitable alternate habitat is available and accessible to fauna affected by a treated area. More importantly, to conserve fauna the model sets a minimum acceptable target for the connectivity of habitat at any time. These factors are all included in the formulation of a model that yields a multi-period spatially-explicit schedule for treatment planning. Our approach is then demonstrated in a series of computational experiments with hypothetical landscapes, a single vegetation type and a group of faunal species with the same habitat requirements. Our experiments show that it is possible to fragment areas of high fuel loads while ensuring sufficient connectivity of habitat over both space and time. Furthermore, it is demonstrated that the habitat connectivity constraint is more effective than neighbourhood habitat constraints. This is critical for the conservation of fauna and of special concern for vulnerable or endangered species

An optimisation approach for fuel treatment planning to break the connectivity of high-risk regions

Uncontrolled wildfires can lead to loss of life and property and destruction of natural resources. At the same time, fire plays a vital role in restoring ecological balance in many ecosystems. Fuel management, or treatment planning by way of planned burning, is an important tool used in many countries where fire is a major ecosystem process. In this paper, we propose an approach to reduce the spatial connectivity of fuel hazards while still considering the ecological fire requirements of the ecosystem. A mixed integer programming (MIP) model is formulated in such a way that it breaks the connectivity of high-risk regions as a means to reduce fuel hazards in the landscape. This multi-period model tracks the age of each vegetation type and determines the optimal time and locations to conduct fuel treatments. The minimum and maximum Tolerable Fire Intervals (TFI), which define the ages at which certain vegetation type can be treated for ecological reasons, are taken into account by the model. Examples from previous work that explicitly disconnect contiguous areas of high fuel load have often been limited to using single vegetation types implemented within rectangular grids. We significantly extend such work by including modelling multiple vegetation types implemented within a polygon-based network to achieve a more realistic representation of the landscape. An analysis of the proposed approach was conducted for a fuel treatment area comprising 711 treatment units in the Barwon-Otway district of Victoria, Australia. The solution of the proposed model can be obtained for 20-year fuel treatment planning within a reasonable computation time of eight hours

Applied Geometry Optimization of an Innovative 3D-Printed Wet-Scrubber Nozzle with a Lattice Boltzmann Method

In contrast to conventional dry separators, new types of wet scrubbers with innovative nozzle geometries are capable of separating submicron particles with comparatively low pressure drop. As those geometries can easily be adapted using 3D-printing manufacturing, an applied geometry optimization can lead to a fast and cost-efficient product development cycle. In this study, the lattice Boltzmann method is used to optimize the pressure drop associated with a novel nozzle design. Simulated pressure drop data are validated with experimentally determined ones. By replacing originally installed ellipsoid-shaped bluff bodies with foil-shaped structures according to the 4-digit NACA-series, an optimization approach regarding the resulting pressure drop is described

Backward masked fearful faces enhance contralateral occipital cortical activity for visual targets within the spotlight of attention

Spatial attention has been argued to be adaptive by enhancing the processing of visual stimuli within the ‘spotlight of attention’. We previously reported that crude threat cues (backward masked fearful faces) facilitate spatial attention through a network of brain regions consisting of the amygdala, anterior cingulate and contralateral visual cortex. However, results from previous functional magnetic resonance imaging (fMRI) dot-probe studies have been inconclusive regarding a fearful face-elicited contralateral modulation of visual targets. Here, we tested the hypothesis that the capture of spatial attention by crude threat cues would facilitate processing of subsequently presented visual stimuli within the masked fearful face-elicited ‘spotlight of attention’ in the contralateral visual cortex. Participants performed a backward masked fearful face dot-probe task while brain activity was measured with fMRI. Masked fearful face left visual field trials enhanced activity for spatially congruent targets in the right superior occipital gyrus, fusiform gyrus and lateral occipital complex, while masked fearful face right visual field trials enhanced activity in the left middle occipital gyrus. These data indicate that crude threat elicited spatial attention enhances the processing of subsequent visual stimuli in contralateral occipital cortex, which may occur by lowering neural activation thresholds in this retinotopic location