Determining Optimal Player Position, Distance, and Scale from a Point of Interest on a Terrain

In a three-dimensional virtual world that is presented as part of a virtual reality environment, a player is teleported from one location displaying a point of interest to a new location displaying a new point of interest. When positioning the player at the location within the three-dimensional virtual world displaying the new point of interest, a virtual reality application determines a player viewing position at the location in order for the player to view the point of interest in a consistent and optimized fashion. The determination made by the virtual reality application, comprehending factors of scale, altitude, and direction, provides the player with a view that is comfortable and makes the point of interest easily recognizable

Discriminating lymphomas and reactive lymphadenopathy in lymph node biopsies by gene expression profiling

<p>Abstract</p> <p>Background</p> <p>Diagnostic accuracy of lymphoma, a heterogeneous cancer, is essential for patient management. Several ancillary tests including immunophenotyping, and sometimes cytogenetics and PCR are required to aid histological diagnosis. In this proof of principle study, gene expression microarray was evaluated as a single platform test in the differential diagnosis of common lymphoma subtypes and reactive lymphadenopathy (RL) in lymph node biopsies.</p> <p>Methods</p> <p>116 lymph node biopsies diagnosed as RL, classical Hodgkin lymphoma (cHL), diffuse large B cell lymphoma (DLBCL) or follicular lymphoma (FL) were assayed by mRNA microarray. Three supervised classification strategies (global multi-class, local binary-class and global binary-class classifications) using diagonal linear discriminant analysis was performed on training sets of array data and the classification error rates calculated by leave one out cross-validation. The independent error rate was then evaluated by testing the identified gene classifiers on an independent (test) set of array data.</p> <p>Results</p> <p>The binary classifications provided prediction accuracies, between a subtype of interest and the remaining samples, of 88.5%, 82.8%, 82.8% and 80.0% for FL, cHL, DLBCL, and RL respectively. Identified gene classifiers include LIM domain only-2 (<it>LMO2</it>), Chemokine (C-C motif) ligand 22 (<it>CCL22</it>) and Cyclin-dependent kinase inhibitor-3 (<it>CDK3</it>) specifically for FL, cHL and DLBCL subtypes respectively.</p> <p>Conclusions</p> <p>This study highlights the ability of gene expression profiling to distinguish lymphoma from reactive conditions and classify the major subtypes of lymphoma in a diagnostic setting. A cost-effective single platform "mini-chip" assay could, in principle, be developed to aid the quick diagnosis of lymph node biopsies with the potential to incorporate other pathological entities into such an assay.</p

TP53 mutation status divides myelodysplastic syndromes with complex karyotypes into distinct prognostic subgroups

Risk stratification is critical in the care of patients with myelodysplastic syndromes (MDS). Approximately 10% have a complex karyotype (CK), defined as more than two cytogenetic abnormalities, which is a highly adverse prognostic marker. However, CK-MDS can carry a wide range of chromosomal abnormalities and somatic mutations. To refine risk stratification of CK-MDS patients, we examined data from 359 CK-MDS patients shared by the International Working Group for MDS. Mutations were underrepresented with the exception of TP53 mutations, identified in 55% of patients. TP53 mutated patients had even fewer co-mutated genes but were enriched for the del(5q) chromosomal abnormality (p 10%), abnormal 3q, abnormal 9, and monosomy 7 as having the greatest survival risk. The poor risk associated with CK-MDS is driven by its association with prognostically adverse TP53 mutations and can be refined by considering clinical and karyotype features

Improving Medical Image Decision Making by Leveraging Metacognitive Processes and Representational Similarity

Improving the accuracy of medical image interpretation is critical to improving the diagnosis of many diseases. Using both novices (undergraduates) and experts (medical professionals), we investigate methods for improving the accuracy of a single decision maker by aggregating repeated decisions from an individual in different ways. Our participants made classification decisions (cancerous versus non-cancerous) and confidence judgments on a series of cell images, viewing and classifying each image twice. We first applied the maximum confidence slating algorithm (Koriat, 2012), which leverages metacognitive ability by using the most confident response for an image as the `final response'. We also examined algorithms that aggregated decisions based on image similarity, leveraging neural network models to determine similarity. We found maximum confidence slating improves classification accuracy for both novices and experts. However, aggregating responses on similar images improves classification accuracy for novices and not experts, suggesting differences in the decision mechanisms of novices and experts

High-Dimensional Analysis of Acute Myeloid Leukemia Reveals Phenotypic Changes in Persistent Cells during Induction Therapy

<div><p>The plasticity of AML drives poor clinical outcomes and confounds its longitudinal detection. However, the immediate impact of treatment on the leukemic and non-leukemic cells of the bone marrow and blood remains relatively understudied. Here, we conducted a pilot study of high dimensional longitudinal monitoring of immunophenotype in AML. To characterize changes in cell phenotype before, during, and immediately after induction treatment, we developed a 27-antibody panel for mass cytometry focused on surface diagnostic markers and applied it to 46 samples of blood or bone marrow tissue collected over time from 5 AML patients. Central goals were to determine whether changes in AML phenotype would be captured effectively by cytomic tools and to implement methods for describing the evolving phenotypes of AML cell subsets. Mass cytometry data were analyzed using established computational techniques. Within this pilot study, longitudinal immune monitoring with mass cytometry revealed fundamental changes in leukemia phenotypes that occurred over time during and after induction in the refractory disease setting. Persisting AML blasts became more phenotypically distinct from stem and progenitor cells due to expression of novel marker patterns that differed from pre-treatment AML cells and from all cell types observed in healthy bone marrow. This pilot study of single cell immune monitoring in AML represents a powerful tool for precision characterization and targeting of resistant disease.</p></div