957 resultados para model-based reasoning processes


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Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion.

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The present paper introduces the topical area of the Polish-Swiss research project FLORIST (Flood risk on the northern foothills of the Tatra Mountains), informs on its objectives, and reports on initial results. The Tatra Mountains are the area of the highest precipitation in Poland and largely contribute to flood generation. The project is focused around four competence clusters: observation-based climatology, model-based climate change projections and impact assessment, dendrogeomorphology, and impact of large wood debris on fluvial processes. The knowledge generated in the FLORIST project is likely to have impact on understanding and interpretation of flood risk on the northern foothills of the Tatra Mountains, in the past, present, and future. It can help solving important practical problems related to flood risk reduction strategies and flood preparedness.

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As the understanding and representation of the impacts of volcanic eruptions on climate have improved in the last decades, uncertainties in the stratospheric aerosol forcing from large eruptions are now linked not only to visible optical depth estimates on a global scale but also to details on the size, latitude and altitude distributions of the stratospheric aerosols. Based on our understanding of these uncertainties, we propose a new model-based approach to generating a volcanic forcing for general circulation model (GCM) and chemistry–climate model (CCM) simulations. This new volcanic forcing, covering the 1600–present period, uses an aerosol microphysical model to provide a realistic, physically consistent treatment of the stratospheric sulfate aerosols. Twenty-six eruptions were modeled individually using the latest available ice cores aerosol mass estimates and historical data on the latitude and date of eruptions. The evolution of aerosol spatial and size distribution after the sulfur dioxide discharge are hence characterized for each volcanic eruption. Large variations are seen in hemispheric partitioning and size distributions in relation to location/date of eruptions and injected SO2 masses. Results for recent eruptions show reasonable agreement with observations. By providing these new estimates of spatial distributions of shortwave and long-wave radiative perturbations, this volcanic forcing may help to better constrain the climate model responses to volcanic eruptions in the 1600–present period. The final data set consists of 3-D values (with constant longitude) of spectrally resolved extinction coefficients, single scattering albedos and asymmetry factors calculated for different wavelength bands upon request. Surface area densities for heterogeneous chemistry are also provided.

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We integrated research on the dimensionality of career success into social-cognitive career theory and explored the positive feedback loop between occupational self-efficacy and objective and subjective career success over time (self-efficacy → objective success → subjective success → self-efficacy). Furthermore, we theoretically accounted for synchronous and time-lagged effects, as well as indirect reciprocity between the variables. We tested the proposed model by means of longitudinal structural equation modeling in a 9-year four-wave panel design, by applying a model comparison approach and indirect effect analyses (N = 608 professionals). The findings supported the proposed positive feedback loop between occupational self-efficacy and career success. Supporting our time-based reasoning, the findings showed that unfolding effects between occupational self-efficacy and objective career success take more time (i.e., time-lagged or over time) than unfolding effects between objective and subjective career success, as well as between subjective career success and occupational self-efficacy (i.e., synchronous or concurrently). Indirect effects of past on future occupational self-efficacy via objective and subjective career success were significant, providing support for an indirect reciprocity model. Results are discussed with respect to extensions of social-cognitive career theory and occupational self-efficacy development over time.

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Today many business processes are based on IT systems. These systems are exposed to different threats, which may lead to failures of critical business pro-cesses. Thus, enterprises prepare themselves against threats and failures of critical IT systems by means of Business Continuity Management (BCM). The phe-nomenon of outsourcing introduces a new dimension to BCM. In an outsourcing relationship the client organization is still responsible for the continuity of its processes but does not have full control over the implemented business continuity measures. In this paper we build a research model based on institutional and assimilation theories to describe and explain how and why BCM is assimilated in outsourcing relationships. In our case studies we found evidence that primarily coercive and normative pressures influence the assimilation of BCM in outsourcing relationships and support the explanation of variation across enterprises. Mimetic pressures seem to influence the assimilation but do not explain variations.

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This chapter proposed a personalized X-ray reconstruction-based planning and post-operative treatment evaluation framework called iJoint for advancing modern Total Hip Arthroplasty (THA). Based on a mobile X-ray image calibration phantom and a unique 2D-3D reconstruction technique, iJoint can generate patient-specific models of hip joint by non-rigidly matching statistical shape models to the X-ray radiographs. Such a reconstruction enables a true 3D planning and treatment evaluation of hip arthroplasty from just 2D X-ray radiographs whose acquisition is part of the standard diagnostic and treatment loop. As part of the system, a 3D model-based planning environment provides surgeons with hip arthroplasty related parameters such as implant type, size, position, offset and leg length equalization. With this newly developed system, we are able to provide true 3D solutions for computer assisted planning of THA using only 2D X-ray radiographs, which is not only innovative but also cost-effective.

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Periacetabular Osteotomy (PAO) is a joint preserving surgical intervention intended to increase femoral head coverage and thereby to improve stability in young patients with hip dysplasia. Previously, we developed a CT-based, computer-assisted program for PAO diagnosis and planning, which allows for quantifying the 3D acetabular morphology with parameters such as acetabular version, inclination, lateral center edge (LCE) angle and femoral head coverage ratio (CO). In order to verify the hypothesis that our morphology-based planning strategy can improve biomechanical characteristics of dysplastic hips, we developed a 3D finite element model based on patient-specific geometry to predict cartilage contact stress change before and after morphology-based planning. Our experimental results demonstrated that the morphology-based planning strategy could reduce cartilage contact pressures and at the same time increase contact areas. In conclusion, our computer-assisted system is an efficient tool for PAO planning.

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Context. On 12 November 2014 the European mission Rosetta succeeded in delivering a lander, named Philae, on the surface of one of the smallest, low-gravity and most primitive bodies of the solar system, the comet 67P/Churyumov-Gerasimenko (67P). Aims. The aim of this paper is to provide a comprehensive geomorphological and spectrophotometric analysis of Philae's landing site (Agilkia) to give an essential framework for the interpretation of its in situ measurements. Methods. OSIRIS images, coupled with gravitational slopes derived from the 3D shape model based on stereo-photogrammetry were used to interpret the geomorphology of the site. We adopted the Hapke model, using previously derived parameters, to photometrically correct the images in orange filter (649.2 nm). The best approximation to the Hapke model, given by the Akimov parameter-less function, was used to correct the reflectance for the effects of viewing and illumination conditions in the other filters. Spectral analyses on coregistered color cubes were used to retrieve spectrophotometric properties. Results. The landing site shows an average normal albedo of 6.7% in the orange filter with variations of similar to 15% and a global featureless spectrum with an average red spectral slope of 15.2%/100 nm between 480.7 nm (blue filter) and 882.1 nm (near-IR filter). The spatial analysis shows a well-established correlation between the geomorphological units and the photometric characteristics of the surface. In particular, smooth deposits have the highest reflectance a bluer spectrum than the outcropping material across the area. Conclusions. The featureless spectrum and the redness of the material are compatible with the results by other instruments that have suggested an organic composition. The observed small spectral variegation could be due to grain size effects. However, the combination of photometric and spectral variegation suggests that a compositional differentiation is more likely. This might be tentatively interpreted as the effect of the efficient dust-transport processes acting on 67P. High-activity regions might be the original sources for smooth fine-grained materials that then covered Agilkia as a consequence of airfall of residual material. More observations performed by OSIRIS as the comet approaches the Sun would help interpreting the processes that work at shaping the landing site and the overall nucleus.

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The events of the 1990's and early 2000's demonstrated the need for effective planning and response to natural and man-made disasters. One of those potential natural disasters is pandemic flu. Once defined, the CDC stated that program, or plan, effectiveness is improved through the process of program evaluation. (Centers for Disease Control and Prevention, 1999) Program evaluation should be accomplished not only periodically, but in the course of routine administration of the program. (Centers for Disease Control and Prevention, 1999) Accomplishing this task for a "rare, but significant event" is challenging. (Herbold, John R., PhD., 2008) To address this challenge, the RAND Corporation (under contract to the CDC) developed the "Facilitated Look-Backs" approach that was tested and validated at the state level. (Aledort et al., 2006).^ Nevertheless, no comprehensive and generally applicable pandemic influenza program evaluation tool or model is readily found for use at the local public health department level. This project developed such a model based on the "Facilitated Look-Backs" approach developed by RAND Corporation. (Aledort et al., 2006) Modifications to the RAND model included stakeholder additions, inclusion of all six CDC program evaluation steps, and suggestions for incorporating pandemic flu response plans in seasonal flu management implementation. Feedback on the model was then obtained from three LPHD's—one rural, one suburban, and one urban. These recommendations were incorporated into the final model. Feedback from the sites also supported the assumption that this model promotes the effective and efficient evaluation of both pandemic flu and seasonal flu response by reducing redundant evaluations of pandemic flu plans, seasonal flu plans, and funding requirement accountability. Site feedback also demonstrated that the model is comprehensive and flexible, so it can be adapted and applied to different LPHD needs and settings. It also stimulates evaluation of the major issues associated with pandemic flu planning. ^ The next phase in evaluating this model should be to apply it in a program evaluation of one or more LPHD's seasonal flu response that incorporates pandemic flu response plans.^

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As the requirements for health care hospitalization have become more demanding, so has the discharge planning process become a more important part of the health services system. A thorough understanding of hospital discharge planning can, then, contribute to our understanding of the health services system. This study involved the development of a process model of discharge planning from hospitals. Model building involved the identification of factors used by discharge planners to develop aftercare plans, and the specification of the roles of these factors in the development of the discharge plan. The factors in the model were concatenated in 16 discrete decision sequences, each of which produced an aftercare plan.^ The sample for this study comprised 407 inpatients admitted to the M. D. Anderson Hospital and Tumor Institution at Houston, Texas, who were discharged to any site within Texas during a 15 day period. Allogeneic bone marrow donors were excluded from the sample. The factors considered in the development of discharge plans were recorded by discharge planners and were used to develop the model. Data analysis consisted of sorting the discharge plans using the plan development factors until for some combination and sequence of factors all patients were discharged to a single site. The arrangement of factors that led to that aftercare plan became a decision sequence in the model.^ The model constructs the same discharge plans as those developed by hospital staff for every patient in the study. Tests of the validity of the model should be extended to other patients at the MDAH, to other cancer hospitals, and to other inpatient services. Revisions of the model based on these tests should be of value in the management of discharge planning services and in the design and development of comprehensive community health services.^

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The genomic era brought by recent advances in the next-generation sequencing technology makes the genome-wide scans of natural selection a reality. Currently, almost all the statistical tests and analytical methods for identifying genes under selection was performed on the individual gene basis. Although these methods have the power of identifying gene subject to strong selection, they have limited power in discovering genes targeted by moderate or weak selection forces, which are crucial for understanding the molecular mechanisms of complex phenotypes and diseases. Recent availability and rapid completeness of many gene network and protein-protein interaction databases accompanying the genomic era open the avenues of exploring the possibility of enhancing the power of discovering genes under natural selection. The aim of the thesis is to explore and develop normal mixture model based methods for leveraging gene network information to enhance the power of natural selection target gene discovery. The results show that the developed statistical method, which combines the posterior log odds of the standard normal mixture model and the Guilt-By-Association score of the gene network in a naïve Bayes framework, has the power to discover moderate/weak selection gene which bridges the genes under strong selection and it helps our understanding the biology under complex diseases and related natural selection phenotypes.^

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Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^

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Phosphorus cycling in the ocean is influenced by biological and geochemical processes that are reflected in the oxygen isotope signature of dissolved inorganic phosphate (Pi). Extending the Pi oxygen isotope record from the water column into the seabed is difficult due to low Pi concentrations and small amounts of marine porewaters available for analysis. We obtained porewater profiles of Pi oxygen isotopes using a refined protocol based on the original micro-extraction designed by Colman (2002). This refined and customized method allows the conversion of ultra-low quantities (0.5 - 1 µmol) of porewater Pi to silver phosphate (Ag3PO4) for routine analysis by mass spectrometry. A combination of magnesium hydroxide co-precipitation with ion exchange resin treatment steps is used to remove dissolved organic matter, anions, and cations from the sample before precipitating Ag3PO4. Samples as low as 200 µg were analyzed in a continuous flow isotope ratio mass spectrometer setup. Tests with external and laboratory internal standards validated the preservation of the original phosphate oxygen isotope signature (d18OP) during micro extraction. Porewater data on d18OP has been obtained from two sediment cores of the Moroccan margin. The d18OP values are in a range of +19.49 to +27.30 per mill. We apply a simple isotope mass balance model to disentangle processes contributing to benthic P cycling and find evidence for Pi regeneration outbalancing microbial demand in the upper sediment layers. This highlights the great potential of using d18OP to study microbial processes in the subseafloor and at the sediment water interface.

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