Implementation of simulation software packages for healthcare scheduling

Simulation methodology is considered an effective decision making tool in the process of patient scheduling in healthcare centers. In spite of the large number of simulation software packages that are commercially available, operation researches still are facing difficulties to implement simulation models in healthcare centers. In many cases, healthcare schedulers must spend extra efforts trying to mold scheduling requirements to conform to the features of the package. In this paper, we offer a systematic approach that can be used by practitioners to successfully adopt the available software simulation packages, develop simulation models, and transform their findings into practical scheduling rules. A radiology center in a Midwestern Hospital is used to illustrate the proposed methodology

Avalanche ruggedness of parallel SiC power MOSFETs

© 2018 Elsevier Ltd The aim of this paper is to investigate the impact of electro-thermal device parameter spread on the avalanche ruggedness of parallel silicon carbide (SiC) power MOSFETs representative of multi-chip layout within an integrated power module. The tests were conducted on second generation 1200 V, 36 A–80 mΩ rated devices. Different temperature-dependent electrical parameters were identified and measured for a number of devices. The influence of spread in measured parameters was investigated experimentally during avalanche breakdown transient switching events and important findings have been highlighted

Anatomy-driven modelling of spatial correlation for regularisation of arterial spin labelling images

Arterial spin labelling (ASL) allows blood flow to be measured in the brain and other organs of the body, which is valuable for both research and clinical use. Unfortunately, ASL suffers from an inherently low signal to noise ratio, necessitating methodological advances in ASL acquisition and processing. Spatial regularisation improves the effective signal to noise ratio, and is a common step in ASL processing. However, the standard spatial regularisation technique requires a manually-specified smoothing kernel of an arbitrary size, and can lead to loss of fine detail. Here, we present a Bayesian model of spatial correlation, which uses anatomical information from structural images to perform principled spatial regularisation, modelling the underlying signal and removing the need to set arbitrary smoothing parameters. Using data from a large cohort (N = 130) of preterm-born adolescents and age-matched controls, we show our method yields significant improvements in test-retest reproducibility, increasing the correlation coefficient by 14% relative to Gaussian smoothing and giving a corresponding improvement in statistical power. This novel technique has the potential to significantly improve single inversion time ASL studies, allowing more reliable detection of perfusion differences with a smaller number of subjects

Late-life brain perfusion after prenatal famine exposure

Early nutritional deprivation may cause irreversible damage to the brain and seems to affect cognitive function in older age. We investigated whether prenatal undernutrition was associated with brain perfusion differences in older age. We acquired Arterial spin labeling scans in 118 Dutch famine birth cohort members. Using linear regression analyses, cerebral blood flow was compared between exposed and unexposed groups in gray matter (GM) and white matter (WM), perfusion territories, the neurodegeneration-related regions anterior and posterior cingulate cortex and precuneus. Furthermore, we compared the GM/WM ratio and the spatial coefficient of variation as a proxy of overall cerebrovascular health. The WM arterial spin labeling signal and the GM/WM ratio were significantly lower and higher, respectively, among exposed participants (−2.5 mL/100 g/min [95% CI: −4.3 to −0.8; p = 0.01] and 0.48 [0.19 to 0.76; p = 0.002], respectively). Exposed men had lower cerebral blood flow in anterior and posterior cingulate cortices (−8.0 mL/100 g/min [−15.1 to −0.9; p = 0.03]; −11.4 mL/100 g/min [−19.6 to −3.2; p = 0.02]) and higher spatial coefficient of variation (0.05 [0.00 to 0.09; p = 0.05]). The latter seemed largely mediated by higher 2h-glucose levels at age 50. Our findings suggest that prenatal undernutrition affects brain perfusion parameters providing further evidence for life-long effects of undernutrition during early brain development

Reduction in Cerebral Blood Flow in Areas Appearing as White Matter Hyperintensities on Magnetic Resonance Imaging

The purpose of this study was to examine cerebral blood flow (CBF) as measured by arterial spin labeling (ASL) in tissue classified as white matter hyperintensities (WMH), normal appearing white matter, and grey matter. Seventeen healthy older adults received structural and ASL MRI. Cerebral blood flow was derived for three tissue types: WMH, normal appearing white matter, and grey matter. Cerebral blood flow was lower in WMH areas relative to normal appearing white matter, which in turn, was lower than grey matter. Regions with consistently lower CBF across individuals were more likely to appear as WMH. Results are consistent with an emerging literature linking diminished regional perfusion with the risk of developing WMH

Reduction in Cerebral Blood Flow in Areas Appearing as White Matter Hyperintensities on Magnetic Resonance Imaging

The purpose of this study was to examine cerebral blood flow (CBF) as measured by arterial spin labeling (ASL) in tissue classified as white matter hyperintensities (WMH), normal appearing white matter, and grey matter. Seventeen healthy older adults received structural and ASL MRI. Cerebral blood flow was derived for three tissue types: WMH, normal appearing white matter, and grey matter. Cerebral blood flow was lower in WMH areas relative to normal appearing white matter, which in turn, was lower than grey matter. Regions with consistently lower CBF across individuals were more likely to appear as WMH. Results are consistent with an emerging literature linking diminished regional perfusion with the risk of developing WMH

Partial volume correction in arterial spin labeling perfusion MRI: A method to disentangle anatomy from physiology or an analysis step too far?

The mismatch in the spatial resolution of Arterial Spin Labeling (ASL) MRI perfusion images and the anatomy of functionally distinct tissues in the brain leads to a partial volume effect (PVE), which in turn confounds the estimation of perfusion into a specific tissue of interest such as gray or white matter. This confound occurs because the image voxels contain a mixture of tissues with disparate perfusion properties, leading to estimated perfusion values that reflect primarily the volume proportions of tissues in the voxel rather than the perfusion of any particular tissue of interest within that volume. It is already recognized that PVE influences studies of brain perfusion, and that its effect might be even more evident in studies where changes in perfusion are co-incident with alterations in brain structure, such as studies involving a comparison between an atrophic patient population vs control subjects, or studies comparing subjects over a wide range of ages. However, the application of PVE correction (PVEc) is currently limited and the employed methodologies remain inconsistent.In this article, we outline the influence of PVE in ASL measurements of perfusion, explain the main principles of PVEc, and provide a critique of the current state of the art for the use of such methods. Furthermore, we examine the current use of PVEc in perfusion studies and whether there is evidence to support its wider adoption.We conclude that there is sound theoretical motivation for the use of PVEc alongside conventional, ‘uncorrected’, images, and encourage such combined reporting. Methods for PVEc are now available within standard neuroimaging toolboxes, which makes our recommendation straightforward to implement. However, there is still more work to be done to establish the value of PVEc as well as the efficacy and robustness of existing PVEc methods

The physics of spreading processes in multilayer networks

The study of networks plays a crucial role in investigating the structure, dynamics, and function of a wide variety of complex systems in myriad disciplines. Despite the success of traditional network analysis, standard networks provide a limited representation of complex systems, which often include different types of relationships (i.e., "multiplexity") among their constituent components and/or multiple interacting subsystems. Such structural complexity has a significant effect on both dynamics and function. Throwing away or aggregating available structural information can generate misleading results and be a major obstacle towards attempts to understand complex systems. The recent "multilayer" approach for modeling networked systems explicitly allows the incorporation of multiplexity and other features of realistic systems. On one hand, it allows one to couple different structural relationships by encoding them in a convenient mathematical object. On the other hand, it also allows one to couple different dynamical processes on top of such interconnected structures. The resulting framework plays a crucial role in helping achieve a thorough, accurate understanding of complex systems. The study of multilayer networks has also revealed new physical phenomena that remain hidden when using ordinary graphs, the traditional network representation. Here we survey progress towards attaining a deeper understanding of spreading processes on multilayer networks, and we highlight some of the physical phenomena related to spreading processes that emerge from multilayer structure.Comment: 25 pages, 4 figure

Imaging Patients with Psychosis and a Mouse Model Establishes a Spreading Pattern of Hippocampal Dysfunction and Implicates Glutamate as a Driver

The hippocampus in schizophrenia is characterized by both hypermetabolism and reduced size. It remains unknown whether these abnormalities are mechanistically linked. Here, in addressing these questions we used MRI tools that can map hippocampal metabolism and structure in patients and mouse models. In at-risk patients, hypermetabolism was found to begin in CA1 and spread to the subiculum after psychosis onset. CA1 hypermetabolism at baseline predicted hippocampal atrophy, which occured during progression to psychosis, most prominently in similar regions. Next, we used ketamine to model conditions of acute psychosis in mice. Acute ketamine reproduced a regional pattern of hippocampal hypermetabolism, while repeated exposure shifted the hippocampus to a hypermetabolic state with concurrent atrophy and pathology in parvalbumin-expressing interneurons. Parallel in vivo experiments using LY379268 and direct measurements of extracellular glutamate showed that glutamate drives both neuroimaging abnormalities. These findings show that hippocampal hypermetabolism leads to atrophy in psychotic disorder and suggest glutamate as a pathogenic driver

ExploreASL: An image processing pipeline for multi-center ASL perfusion MRI studies

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice