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

Abnormal Unsaturated Fatty Acid Metabolism in Cystic Fibrosis: Biochemical Mechanisms and Clinical Implications

Cystic fibrosis is an inherited multi-organ disorder caused by mutations in the CFTR gene. Patients with this disease exhibit characteristic abnormalities in the levels of unsaturated fatty acids in blood and tissue. Recent studies have uncovered an underlying biochemical mechanism for some of these changes, namely increased expression and activity of fatty acid desaturases. Among other effects, this drives metabolism of linoeate to arachidonate. Increased desaturase expression appears to be linked to cystic fibrosis mutations via stimulation of the AMP-activated protein kinase in the absence of functional CFTR protein. There is evidence that these abnormalities may contribute to disease pathophysiology by increasing production of eicosanoids, such as prostaglandins and leukotrienes, of which arachidonate is a key substrate. Understanding these underlying mechanisms provides key insights that could potentially impact the diagnosis, clinical monitoring, nutrition, and therapy of patients suffering from this deadly disease

Harnessing the Wisdom of the Confident Crowd in Medical Image Decision-making

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 investigated methods for improving the accuracy of a single decision maker and a group of decision makers by aggregating repeated decisions in different ways. 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 examined whether it is possible to improve individual-level performance by using the maximum confidence slating algorithm (Koriat, 2012b), which leverages metacognitive ability by using the most confident response for an image as the ‘final response’. We find maximum confidence slating improves individual classification accuracy for both novices and experts. Building on these results, we show that aggregation algorithms based on confidence weighting scale to larger groups of participants, dramatically improving diagnostic accuracy, with the performance of groups of novices reaching that of individual experts. In sum, we find that repeated decision making and confidence weighting can be a valuable way to improve accuracy in medical image decision-making and that these techniques can be used in conjunction with each other

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

The SREBP Pathway in Drosophila Regulation by Palmitate, Not Sterols

AbstractIn mammals, synthesis of cholesterol and unsaturated fatty acids is controlled by SREBPs, a family of membrane-bound transcription factors. Here, we show that the Drosophila genome encodes all components of the SREBP pathway, including a single SREBP (dSREBP), SREBP cleavage-activating protein (dSCAP), and the two proteases that process SREBP at sites 1 and 2 to release the nuclear fragment. In cultured Drosophila S2 cells, dSREBP is processed at sites 1 and 2, and the liberated fragment increases mRNAs encoding enzymes of fatty acid biosynthesis, but not sterol or isoprenoid biosynthesis. Processing requires dSCAP, but is not inhibited by sterols as in mammals. Instead, dSREBP processing is blocked by palmitic acid. These findings suggest that the ancestral SREBP pathway functions to maintain membrane integrity rather than to control cholesterol homeostasis

Direct PCR with the CDC 2019 SARS-CoV-2 assay: optimization for limited-resource settings

Abstract PCR-based diagnostics generally require nucleic acid extraction from patient specimens prior to amplification. As highlighted early in the COVID-19 pandemic, extraction steps may be difficult to scale during times of massive demand and limited reagent supply. Forgoing an extraction step, we previously reported that the N1 primer/probe-set of the widespread CDC COVID-19 assay maintains high categorical sensitivity (95%) and specificity (100%) with direct inoculation of viral transport media (VTM) into qRT-PCR reactions. In contrast, the N2 set demonstrated a prominent Ct delay and low sensitivity (33%) without extraction. In the current study, we have improved the performance of this modified CDC assay (in particular the N2 set) by incorporating N1/N2/RNase P multiplexing and dissecting the effects of annealing temperature, VTM interference, and inoculum volume. The latter two factors exerted a more prominent effect on the performance of N2 than N1, although these effects were largely overcome through elevated annealing temperature. This unextracted/multiplex protocol was evaluated with 41 SARS-CoV-2 positive and 43 negative clinical samples, demonstrating a categorical sensitivity of 92.7% and specificity of 100% versus the unmodified CDC methodology. Overall, this work offers a generalizable strategy to maximize testing capabilities for COVID-19 or other emerging pathogens when resources are constrained