An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks

Deep generative neural networks (DGNNs) have achieved realistic and high-quality data generation. In particular, the adversarial training scheme has been applied to many DGNNs and has exhibited powerful performance. Despite of recent advances in generative networks, identifying the image generation mechanism still remains challenging. In this paper, we present an explorative sampling algorithm to analyze generation mechanism of DGNNs. Our method efficiently obtains samples with identical attributes from a query image in a perspective of the trained model. We define generative boundaries which determine the activation of nodes in the internal layer and probe inside the model with this information. To handle a large number of boundaries, we obtain the essential set of boundaries using optimization. By gathering samples within the region surrounded by generative boundaries, we can empirically reveal the characteristics of the internal layers of DGNNs. We also demonstrate that our algorithm can find more homogeneous, the model specific samples compared to the variations of {\epsilon}-based sampling method.Comment: AAAI 202

An empirical Bayes model using a competition score for metabolite identification in gas chromatography mass spectrometry

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry (MS) based metabolite profiling has been increasingly popular for scientific and biomedical studies, primarily due to recent technological development such as comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS). Nevertheless, the identifications of metabolites from complex samples are subject to errors. Statistical/computational approaches to improve the accuracy of the identifications and false positive estimate are in great need. We propose an empirical Bayes model which accounts for a competing score in addition to the similarity score to tackle this problem. The competition score characterizes the propensity of a candidate metabolite of being matched to some spectrum based on the metabolite's similarity score with other spectra in the library searched against. The competition score allows the model to properly assess the evidence on the presence/absence status of a metabolite based on whether or not the metabolite is matched to some sample spectrum.</p> <p>Results</p> <p>With a mixture of metabolite standards, we demonstrated that our method has better identification accuracy than other four existing methods. Moreover, our method has reliable false discovery rate estimate. We also applied our method to the data collected from the plasma of a rat and identified some metabolites from the plasma under the control of false discovery rate.</p> <p>Conclusions</p> <p>We developed an empirical Bayes model for metabolite identification and validated the method through a mixture of metabolite standards and rat plasma. The results show that our hierarchical model improves identification accuracy as compared with methods that do not structurally model the involved variables. The improvement in identification accuracy is likely to facilitate downstream analysis such as peak alignment and biomarker identification. Raw data and result matrices can be found at <url>http://www.biostat.iupui.edu/~ChangyuShen/index.htm</url></p> <p>Trial Registration</p> <p>2123938128573429</p

Model-based peak alignment of metabolomic profiling from comprehensive two-dimensional gas chromatography mass spectrometry

<p>Abstract</p> <p>Background</p> <p>Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS) has been used for metabolite profiling in metabolomics. However, there is still much experimental variation to be controlled including both within-experiment and between-experiment variation. For efficient analysis, an ideal peak alignment method to deal with such variations is in great need.</p> <p>Results</p> <p>Using experimental data of a mixture of metabolite standards, we demonstrated that our method has better performance than other existing method which is not model-based. We then applied our method to the data generated from the plasma of a rat, which also demonstrates good performance of our model.</p> <p>Conclusions</p> <p>We developed a model-based peak alignment method to process both homogeneous and heterogeneous experimental data. The unique feature of our method is the only model-based peak alignment method coupled with metabolite identification in an unified framework. Through the comparison with other existing method, we demonstrated that our method has better performance. Data are available at <url>http://stage.louisville.edu/faculty/x0zhan17/software/software-development/mspa</url>. The R source codes are available at <url>http://www.biostat.iupui.edu/~ChangyuShen/CodesPeakAlignment.zip</url>.</p> <p>Trial Registration</p> <p>2136949528613691</p

Selective inhibition of pancreatic ductal adenocarcinoma cell growth by the mitotic MPS1 kinase inhibitor NMS-P715

Most solid tumors, including pancreatic ductal adenocarcinoma (PDAC), exhibit structural and numerical chromosome instability (CIN). Although often implicated as a driver of tumor progression and drug resistance, CIN also reduces cell fitness and poses a vulnerability that can be exploited therapeutically. The spindle assembly checkpoint (SAC) ensures correct chromosome-microtubule attachment, thereby minimizing chromosome segregation errors. Many tumors exhibit upregulation of SAC components such as MPS1, which may help contain CIN within survivable limits. Prior studies showed that MPS1 inhibition with the small molecule NMS-P715 limits tumor growth in xenograft models. In cancer cell lines, NMS-P715 causes cell death associated with impaired SAC function and increased chromosome missegregation. Although normal cells appeared more resistant, effects on stem cells, which are the dose-limiting toxicity of most chemotherapeutics, were not examined. Elevated expression of 70 genes (CIN70), including MPS1, provides a surrogate measure of CIN and predicts poor patient survival in multiple tumor types. Our new findings show that the degree of CIN70 upregulation varies considerably among PDAC tumors, with higher CIN70 gene expression predictive of poor outcome. We identified a 25 gene subset (PDAC CIN25) whose overexpression was most strongly correlated with poor survival and included MPS1. In vitro, growth of human and murine PDAC cells is inhibited by NMS-P715 treatment, whereas adipose-derived human mesenchymal stem cells are relatively resistant and maintain chromosome stability upon exposure to NMS-P715. These studies suggest that NMS-P715 could have a favorable therapeutic index and warrant further investigation of MPS1 inhibition as a new PDAC treatment strategy

Ordered mesoporous porphyrinic carbons with very high electrocatalytic activity for the oxygen reduction reaction

The high cost of the platinum-based cathode catalysts for the oxygen reduction reaction (ORR) has impeded the widespread application of polymer electrolyte fuel cells. We report on a new family of non-precious metal catalysts based on ordered mesoporous porphyrinic carbons (M-OMPC; M = Fe, Co, or FeCo) with high surface areas and tunable pore structures, which were prepared by nanocasting mesoporous silica templates with metalloporphyrin precursors. The FeCo-OMPC catalyst exhibited an excellent ORR activity in an acidic medium, higher than other non-precious metal catalysts. It showed higher kinetic current at 0.9a&amp;#65533;...V than Pt/C catalysts, as well as superior long-term durability and MeOH-tolerance. Density functional theory calculations in combination with extended X-ray absorption fine structure analysis revealed a weakening of the interaction between oxygen atom and FeCo-OMPC compared to Pt/C. This effect and high surface area of FeCo-OMPC appear responsible for its significantly high ORR activity.open251

An inhibitor of the mitotic kinase, MPS1, is selective towards pancreatic cancer cells

poster abstractThe abysmal five year pancreatic cancer survival rate of less than 6% highlights the need for new treatments for this deadly malignancy. Cytotoxic drugs normally target rapidly dividing cancer cells but unfortunately often target stem cells resulting in toxicity. This warrants the development of compounds that selectively target tumor cells. An inhibitor of the mitotic kinase, MPS1, which has been shown to be more selective towards cancer cells than non-tumorigenic cells, shows promise but its effects on stem cells has not been investigated. MPS1 is an essential component of the Spindle Assembly Checkpoint and is proposed to be up-regulated in cancer cells to maintain chromosomal segregation errors within survivable limits. Inhibition of MPS1 kinase causes cancer cell death accompanied by massive aneuploidy. Our studies demonstrate that human adipose stem cells (ASCs) and can tolerate higher levels of a small molecule MPS1 inhibitor than pancreatic cancer cells. In contrast to PANC-1 cancer cells, ASCs and telomerase-immortalized pancreatic ductal epithelial cells did not exhibit elevated chromosome mis-segregation after treatment with the MPS1 inhibitor for 72hrs. In contrast, PANC-1 pancreatic cancer cells exhibited a large increase in chromosomal mis-segregation under similar conditions. Furthermore, growth of ASCs was minimally affected post treatment whereas PANC-1 cells were severely growth impaired suggesting a favorable therapeutic index. Our studies, demonstrate that MPS1 inhibition is selective towards pancreatic cancer cells and that stem cells are less affected in vitro. These data suggest MPS1 inhibition should be further investigated as a new treatment approach in pancreatic cancer

An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer

<p>Abstract</p> <p>Background</p> <p>The nuclear transcription factor estrogen receptor alpha (ER-alpha) is the target of several antiestrogen therapeutic agents for breast cancer. However, many ER-alpha positive patients do not respond to these treatments from the beginning, or stop responding after being treated for a period of time. Because of the association of gene transcription alteration and drug resistance and the emerging evidence on the role of DNA methylation on transcription regulation, understanding of these relationships can facilitate development of approaches to re-sensitize breast cancer cells to treatment by restoring DNA methylation patterns.</p> <p>Methods</p> <p>We constructed a hierarchical empirical Bayes model to investigate the simultaneous change of gene expression and promoter DNA methylation profiles among wild type (WT) and OHT/ICI resistant MCF7 breast cancer cell lines.</p> <p>Results</p> <p>We found that compared with the WT cell lines, almost all of the genes in OHT or ICI resistant cell lines either do not show methylation change or hypomethylated. Moreover, the correlations between gene expression and methylation are quite heterogeneous across genes, suggesting the involvement of other factors in regulating transcription. Analysis of our results in combination with H3K4me2 data on OHT resistant cell lines suggests a clear interplay between DNA methylation and H3K4me2 in the regulation of gene expression. For hypomethylated genes with alteration of gene expression, most (~80%) are up-regulated, consistent with current view on the relationship between promoter methylation and gene expression.</p> <p>Conclusions</p> <p>We developed an empirical Bayes model to study the association between DNA methylation in the promoter region and gene expression. Our approach generates both global (across all genes) and local (individual gene) views of the interplay. It provides important insight on future effort to develop therapeutic agent to re-sensitize breast cancer cells to treatment.</p