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Denials management is too important to a provider's financial health to be addressed haphazardly. The Six Sigma performance-improvement methodology can ensure your organization receives the full payment to which it is entitled.
We present an interactive and accurate collision detection algorithm for deformable, polygonal objects based on the streaming computational model. Our algorithm can detect all possible pairwise primitive-level intersections between two severely deforming models at highly interactive rates. In our streaming computational model, we consider a set of axis aligned bounding boxes (AABBs) that bound each of the given deformable objects as an input stream and perform massively-parallel pairwise, overlapping tests onto the incoming streams. As a result, we are able to prevent performance stalls in the streaming pipeline that can be caused by expensive indexing mechanism required by bounding volume hierarchy-based streaming algorithms. At runtime, as the underlying models deform over time, we employ a novel, streaming algorithm to update the geometric changes in the AABB streams. Moreover, in order to get only the computed result (i.e., collision results between AABBs) without reading back the entire output streams, we propose a streaming en/decoding strategy that can be performed in a hierarchical fashion. After determining overlapped AABBs, we perform a primitive-level (e.g., triangle) intersection checking on a serial computational model such as CPUs. We implemented the entire pipeline of our algorithm using off-the-shelf graphics processors (GPUs), such as nVIDIA GeForce 7800 GTX, for streaming computations, and Intel Dual Core 3.4G processors for serial computations. We benchmarked our algorithm with different models of varying complexities, ranging from 15K up to 50K triangles, under various deformation motions, and the timings were obtained as 30 approximately 100 FPS depending on the complexity of models and their relative configurations. Finally, we made comparisons with a well-known GPU-based collision detection algorithm, CULLIDE [4] and observed about three times performance improvement over the earlier approach. We also made comparisons with a SW-based AABB culling algorithm [2] and observed about two times improvement.
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Aligning distantly related protein sequences is a long-standing problem in bioinformatics, and a key for successful protein structure prediction. Its importance is increasing recently in the context of structural genomics projects because more and more experimentally solved structures are available as templates for protein structure modeling. Toward this end, recent structure prediction methods employ profile-profile alignments, and various ways of aligning two profiles have been developed. More fundamentally, a better amino acid similarity matrix can improve a profile itself; thereby resulting in more accurate profile-profile alignments. Here we have developed novel amino acid similarity matrices from knowledge-based amino acid contact potentials. Contact potentials are used because the contact propensity to the other amino acids would be one of the most conserved features of each position of a protein structure. The derived amino acid similarity matrices are tested on benchmark alignments at three different levels, namely, the family, the superfamily, and the fold level. Compared to BLOSUM45 and the other existing matrices, the contact potential-based matrices perform comparably in the family level alignments, but clearly outperform in the fold level alignments. The contact potential-based matrices perform even better when suboptimal alignments are considered. Comparing the matrices themselves with each other revealed that the contact potential-based matrices are very different from BLOSUM45 and the other matrices, indicating that they are located in a different basin in the amino acid similarity matrix space.
Data Envelopment Analysis (DEA) was used to measure efficiency of residential mental health facilities. The sample consisted of 50 half-way houses, 8 nursing homes, and 32 sheltered homes. In total, 68 facilities belonged to the public sector and were 22 supervised by private non-profit organizations. Variables chosen to characterize production were: structure size (m2), staff, salaries and operational costs, and the output measure was patient numbers. An input oriented DEA model, allowing for variable returns to scale, was applied and units were ranked according to a benchmarking approach. Mean efficiency, for the whole sample, was 73.2% and 18 best practice units were found, on average, 33.1% over-efficient. The other 72 were under-performing, with 54 appearing more than 20% inefficient. The mean efficiency scores for public and private non-profit units were 68.8 and 86.6%, respectively, and significantly different (p < 0.001). Results suggest that efficiency improvements are possible with better use of resources but more research employing various data sets is required.
A number of relatively low-cost processors are now available which employ architectural features previously found only on very expensive supercomputers. The speed of these computers makes it possible to reduce the time to solution of certain problems and to resolve large problems that previously were too time consuming to perform locally. A brief overview of this type of processor is given and the implications for computations in molecular biophysics are discussed in reference to the results of a benchmark that uses the refinement of a protein structure from X-ray crystallographic data.
BACKGROUND: Despite technological improvements, percutaneous coronary intervention (PCI) remains limited by restenosis requiring further revascularization procedures during the ensuing year. New technologies aiming to reduce restenosis are expensive and may increase net healthcare costs. Economic evaluations of such therapies have been performed, but have been hindered by the need to assess the disutility of short-term health care events and repeat coronary revascularization as well as the lack of benchmark standards for intermediate health outcomes. The contingent valuation approach may offer particular advantages when evaluating treatments that improve short-term health outcomes. OBJECTIVE: To examine patients' willingness to pay (WTP) for treatments that may reduce the risk of restenosis and repeat revascularization after PCI. METHODS: We used a contingent valuation approach to evaluate WTP among participants in two large clinical trials evaluating new PCI devices. The baseline scenario described a 30% probability of repeat revascularization following the initial procedure. Patients were asked to indicate, using a close-ended (referendum) question, their out of pocket WTP for an improved treatment that would reduce this risk. Three different prices (500 dollars, 1000 dollars, and 1500 dollars) and three levels of absolute risk reduction (10, 20, and 30%) were randomly varied creating nine sub-samples of patients. Patients' responses were analyzed using both parametric and non-parametric methods. RESULTS: 1642 patients completed the WTP question. The WTP medians for the 10 and 20% risk reductions were 273 dollars and 366 dollars, respectively; the median WTP for the 30% risk reduction was significantly higher at 1162 dollars (P<0.001). Higher household income (OR=1.57, P<0.001) was independently associated with a higher WTP. CONCLUSIONS: Although short-lived, avoidance of coronary restenosis may have considerable value to patients undergoing percutaneous coronary interventions. These findings may have important implications for emerging technologies such as drug-eluting stents.
Most organismal traits result from the complex interplay of many genetic and environmental factors, making their prediction difficult. Here, we used machine learning (ML) models to explore phenotype predictions for 223 traits measured across 1011 genome-sequenced Saccharomyces cerevisiae strains isolated worldwide. We benchmarked a ML pipeline with multiple linear and non-linear models to predict phenotypes from genotypes and gene expression, and determined gradient boosting machines as the best-performing model. Gene function disruption scores and gene presence/absence emerged as best predictors, suggesting a considerable contribution of the accessory genome in controlling phenotypes. The prediction accuracy broadly varied among phenotypes, with stress resistance being easier to predict compared to growth across nutrients. ML identified relevant genomic features linked to phenotypes, including high-impact variants with established relationships to phenotypes, despite these being rare in the population. Near-perfect accuracies were achieved when other phenomics data mostly in similar conditions were used, suggesting that useful information can be conveyed across phenotypes. Overall, our study underscores the power of ML to interpret the functional outcome of genetic variants.
There has been much debate about the benefits of supervised versus unsupervised learning on genomes. Determining which is better in what contexts requires developing comprehensive benchmarks spanning functional and evolutionary tasks. Importantly, such benchmarks need large sample sizes to enable well-powered ranking of models. Having developed and applied such a benchmark here (GUANinE v1.1), we conclusively demonstrate each paradigm offers key advantages and outperforms on certain tasks. In accordance with training, supervised sequence-to-function models exhibit strong performance when annotating functional states characterized by chromatin accessibility or histone marks, while self-supervised language models outperform on evolutionary conservation. Our hundreds of new evaluations in this v1.1 expansion provide evidence for a tradeoff between input context size and model parameter count for a fixed compute budget, which we depict with new metrics such as kiloparameters/base pair. We also construct two new large-scale variant interpretation tasks in v1.1: cadd-snv measuring deleteriousness, and clinvar-snv measuring clinical pathogenicity. We find that conservation scores, and by extension, genomic language models, predict deleteriousness well, but successfully translating deleteriousness predictions to pathogenicity remains challenging. GUANinE v1.1 newly evaluates dozens of pretrained genomic models, and we conclude that moderate-context hybrid or post-trained language models may define the next era of machine learning in genomics.
We have analyzed the performance of majority voting on minimal combination sets of three state-of-the-art secondary structure prediction methods in order to obtain a consensus prediction. Using three large benchmark sets from the EVA server, our results show a significant improvement in the average Q3 prediction accuracy of up to 1.5 percentage points by consensus formation. The application of an additional trivial filtering procedure for predicted secondary structure elements that are too short, does not significantly affect the prediction accuracy. Our analysis also provides valuable insight into the similarity of the results of the prediction methods that we combine as well as the higher confidence in consistently predicted secondary structure.
Despite the acute shortage of cadaveric organs for kidney transplantation, more than 10% of cadaveric kidneys are discarded each year because of marginal quality. Transplant recipients' access to these kidneys and to information about their quality is limited. A Monte Carlo model was developed to simulate the operations of an organ procurement organization over a 10-yr period. Donor and recipient characteristics were generated from the United States Renal Data System. Kidneys were assigned one of five possible grades, which were determined by calculating the relative risk of graft failure associated with donor characteristics and HLA matching for every donor-candidate pair. Modeled were recipient decisions to accept or reject a kidney on the basis of the relative change in quality-adjusted life years (QALY). Compared were the United Network of Organ Sharing (UNOS) policy, the UNOS expanded donor criteria policy, two benchmark policies (one equity driven and the other efficiency driven), and a hybrid policy that incorporated recipient choice into the UNOS algorithm. Sensitivity analyses for major input variables were performed. Compared with UNOS, an algorithm that incorporated recipient choice predicted a 6% increase in QALY, a 12% decrease in median waiting time, a 39% increase in the likelihood of transplantation, and a 56% reduction in the number of discarded kidneys. Benefits were observed across categories of age, gender, and race. Incorporating recipient choice in kidney transplantation would improve equity, efficiency, and QALY of the end-stage renal disease population.
Multimedia modelers from the United States Environmental Protection Agency (EPA) and the United States Department of Energy (DOE) collaborated to conduct a detailed and quantitative benchmarking analysis of three multimedia models. The three models--RESRAD (DOE), MMSOILS (EPA), and MEPAS (DOE)--represent analytically-based tools that are used by the respective agencies for performing human exposure and health risk assessments. The study is performed by individuals who participate directly in the ongoing design, development, and application of the models. Model form and function are compared by applying the models to a series of hypothetical problems, first isolating individual modules (e.g., atmospheric, surface water, groundwater) and then simulating multimedia-based risk resulting from contaminant release from a single source to multiple environmental media. Study results show that the models differ with respect to environmental processes included (i.e., model features) and the mathematical formulation and assumptions related to the implementation of solutions. Depending on the application, numerical estimates resulting from the models may vary over several orders-of-magnitude. On the other hand, two or more differences may offset each other such that model predictions are virtually equal. The conclusion from these results is that multimedia models are complex due to the integration of the many components of a risk assessment and this complexity must be fully appreciated during each step of the modeling process (i.e., model selection, problem conceptualization, model application, and interpretation of results).
The Los Alamos code MCNP4A (Monte Carlo N-Particle version 4A) is currently used to simulate a variety of problems ranging from nuclear reactor analysis to boron neutron capture therapy. A graphical user interface has been developed that automatically sets up the MCNP4A geometry and radiation source requirements for a three-dimensional Monte Carlo simulation using computed tomography data. The major drawback for this dosimetry system is the amount of time to obtain a statistically significant answer. A specialized patch file has been developed that optimizes photon particle transport and dose scoring within the standard MCNP4A lattice geometry. The transport modifications produce a performance increase (number of histories per minute) of approximately 4.7 based upon a 6 MV point source centered within a 30 x 30 x 30 cm3 lattice water phantom and 1 x 1 x 1 mm3 voxels. The dose scoring modifications produce a performance increase of approximately 470 based upon a tally section of greater than 1 x 10(4) lattice elements and a voxel size of 5 mm3. Homogeneous and heterogeneous benchmark calculations produce good agreement with measurements using a standard water phantom and a high- and low-density heterogeneity phantom. The dose distribution from a typical mediastinum treatment planning setup is presented for qualitative analysis and comparison versus a conventional treatment planning system.
The application of boosting technique to regression problems has received relatively little attention in contrast to research aimed at classification problems. This letter describes a new boosting algorithm, AdaBoost.RT, for regression problems. Its idea is in filtering out the examples with the relative estimation error that is higher than the preset threshold value, and then following the AdaBoost procedure. Thus, it requires selecting the suboptimal value of the error threshold to demarcate examples as poorly or well predicted. Some experimental results using the M5 model tree as a weak learning machine for several benchmark data sets are reported. The results are compared to other boosting methods, bagging, artificial neural networks, and a single M5 model tree. The preliminary empirical comparisons show higher performance of AdaBoost.RT for most of the considered data sets.
This special report provides an overview of the ASHE Hospital Maintenance Staffing Study, which was conducted by the society in 1994-95 through the efforts of the ASHE Facilities Management Committee. The intent of the study was to develop a standard staffing equation to assist hospitals in determining appropriate maintenance staffing levels based on current practices of American Hospital Association member hospitals. Throughout the study, extensive survey data were collected and over 200 variables were considered. Statistical analyses were performed to determine which factors were most closely related to variations in staffing patterns, and whether staffing could be reliably predicted by a standard staffing equation. As with any benchmarking or other similar efforts, the results of this study should be approached with caution. There is variability in the data and the variability may change over time. However, the study has shown that high-quality predictability is possible Health care facilities will be able to use the resulting model to determine whether their staffing levels are significantly out of line and may help point to contributing factors.