Gradient-based algorithm for determining tumor volumes in small animals using planar fluorescence imaging platform

Planar fluorescence imaging is widely used in biological research because of its simplicity, use of nonionizing radiation, and high-throughput data acquisition. In cancer research, where small animal models are used to study the in vivo effects of cancer therapeutics, the output of interest is often the tumor volume. Unfortunately, inaccuracies in determining tumor volume from surface-weighted projection fluorescence images undermine the data, and alternative physical or conventional tomographic approaches are prone to error or are tedious for most laboratories. Here, we report a method that uses a priori knowledge of a tumor xenograft model, a tumor-targeting near infrared probe, and a custom-developed image analysis planar view tumor volume algorithm (PV-TVA) to estimate tumor volume from planar fluorescence images. Our algorithm processes images obtained using near infrared light for improving imaging depth in tissue in comparison with light in the visible spectrum. We benchmarked our results against the actual tumor volume obtained from a standard water volume displacement method. Compared with a caliper-based method that has an average deviation from an actual volume of 18% (204.34 ± 115.35 mm(3)), our PV-TVA average deviation from the actual volume was 9% (97.24 ± 70.45 mm(3); P < .001). Using a normalization-based analysis, we found that bioluminescence imaging and PV-TVA average deviations from actual volume were 36% and 10%, respectively. The improved accuracy of tumor volume assessment from planar fluorescence images, rapid data analysis, and the ease of archiving images for subsequent retrieval and analysis potentially lend our PV-TVA method to diverse cancer imaging applications

A Review of Particle Removal Due to Thermophoretic Deposition

Thermophoretic deposition is an important technique for particle removal. The thermophoretic force of the particles under an appropriate temperature gradient can achieve a good particle removal effect. At present, there have been many studies on the deposition mechanism of ultrafine particles under the action of thermophoresis. In this chapter, the development history and current research status of the research on the thermophoretic deposition effect of ultrafine particles are summarized, and the future direction of thermophoretic deposition is proposed

Call Sequence Prediction through Probabilistic Calling Automata

Predicting a sequence of upcoming function calls is important for optimizing programs written in modern managed languages (e.g., Java, Javascript, C#.) Existing function call predictions are mainly built on statistical patterns, suitable for predicting a single call but not a sequence of calls. This paper presents a new way to enable call sequence prediction, which exploits program structures through Probabilistic Calling Automata (PCA), a new program representation that captures both the inherent ensuing relations among function calls, and the probabilistic nature of execution paths. It shows that PCA-based prediction outperforms existing predictions, yielding substantial speedup when being applied to guide Just-In-Time compilation. By enabling accurate, efficient call sequence prediction for the first time, PCA-based predictors open up many new opportunities for dynamic program optimizations

Small Molecule Inhibitors of Metabolic Enzymes Repurposed as a New Class of Anthelmintics

We thank Qi Wang for her technical assistance related to clustering compounds and identifying representatives for screening. This work was supported by National Institute of Allergy and Infectious Diseases (NIAID) grant AI081803 to M.M. The study was also partly supported by NIAID grant AI056189 to R.V.A.Peer reviewedPostprin

An atlas of DNA methylomes in porcine adipose and muscle tissues

It is evident that epigenetic factors, especially DNA methylation, have essential roles in obesity development. Here, using pig as a model, we investigate the systematic association between DNA methylation and obesity. We sample eight variant adipose and two distinct skeletal muscle tissues from three pig breeds living within comparable environments but displaying distinct fat level. We generate 1,381 Gb of sequence data from 180 methylated DNA immunoprecipitation libraries, and provide a genome-wide DNA methylation map as well as a gene expression map for adipose and muscle studies. The analysis shows global similarity and difference among breeds, sexes and anatomic locations, and identifies the differentially methylated regions. The differentially methylated regions in promoters are highly associated with obesity development via expression repression of both known obesity-related genes and novel genes. This comprehensive map provides a solid basis for exploring epigenetic mechanisms of adipose deposition and muscle growth