Oncogene-dependent apoptosis in extracts from drug-resistant cells

Many genotoxic agents kill tumor cells by inducing apoptosis; hence, mutations that suppress apoptosis produce resistance to chemotherapy. Although directly activating the apoptotic machinery may bypass these mutations, how to achieve this activation in cancer cells selectively is not clear. In this study, we show that the drug-resistant 293 cell line is unable to activate components of the apoptotic machinery-the ICE-like proteases (caspases)-following treatment with an anticancer drug. Remarkably, extracts from untreated cells spontaneously activate caspases and induce apoptosis in a cell-free system, indicating that drug-resistant cells have not only the apoptotic machinery but also its activator. Comparing extracts from cells with defined genetic differences, we show that this activator is generated by the adenovirus E1A oncogene and is absent from normal cells. We provide preliminary characterization of this oncogene generated activity (OGA) and show that partially purified OGA activates caspases when added to extracts from untransformed cells. We suggest that agents that link OGA to caspases in cells would kill tumor cells otherwise resistant to conventional cancer therapy. As this killing relies on an activity generated by an oncogene, the effect of these agents should be selective for transformed cells

Single view silhouette fitting techniques for estimating tennis racket position

Stereo camera systems have been used to track markers attached to a racket, allowing its position to be obtained in three-dimensional (3D) space. Typically, markers are manually selected on the image plane, but this can be time-consuming. A markerless system based on one stationary camera estimating 3D racket position data is desirable for research and play. The markerless method presented in this paper relies on a set of racket silhouette views in a common reference frame captured with a calibrated camera and a silhouette of a racket captured with a camera whose relative pose is outside the common reference frame. The aim of this paper is to provide validation of these single view fitting techniques to estimate the pose of a tennis racket. This includes the development of a calibration method to provide the relative pose of a stationary camera with respect to a racket. Mean static racket position was reconstructed to within ±2 mm. Computer generated camera poses and silhouette views of a full size racket model were used to demonstrate the potential of the method to estimate 3D racket position during a simplified serve scenario. From a camera distance of 14 m, 3D racket position was estimated providing a spatial accuracy of 1.9 ± 0.14 mm, similar to recent 3D video marker tracking studies of tennis

Asymptotic Limits and Zeros of Chromatic Polynomials and Ground State Entropy of Potts Antiferromagnets

We study the asymptotic limiting function $W({G},q) = \lim_{n \to \infty}P(G,q)^{1/n}$, where $P(G,q)$ is the chromatic polynomial for a graph $G$ with $n$ vertices. We first discuss a subtlety in the definition of $W({G},q)$ resulting from the fact that at certain special points $q_s$, the following limits do not commute: $\lim_{n \to \infty} \lim_{q \to q_s} P(G,q)^{1/n} \ne \lim_{q \to q_s} \lim_{n \to \infty} P(G,q)^{1/n}$. We then present exact calculations of $W({G},q)$ and determine the corresponding analytic structure in the complex $q$ plane for a number of families of graphs ${G}$, including circuits, wheels, biwheels, bipyramids, and (cyclic and twisted) ladders. We study the zeros of the corresponding chromatic polynomials and prove a theorem that for certain families of graphs, all but a finite number of the zeros lie exactly on a unit circle, whose position depends on the family. Using the connection of $P(G,q)$ with the zero-temperature Potts antiferromagnet, we derive a theorem concerning the maximal finite real point of non-analyticity in $W({G},q)$, denoted $q_c$ and apply this theorem to deduce that $q_c(sq)=3$ and $q_c(hc) = (3+\sqrt{5})/2$ for the square and honeycomb lattices. Finally, numerical calculations of $W(hc,q)$ and $W(sq,q)$ are presented and compared with series expansions and bounds.Comment: 33 pages, Latex, 5 postscript figures, published version; includes further comments on large-q serie

Spatial Statistics of Visual Keypoints for Texture Recognition

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Max-Margin Dictionary Learning for Multiclass Image Categorization

Abstract. Visual dictionary learning and base (binary) classifier train-ing are two basic problems for the recently most popular image cate-gorization framework, which is based on the bag-of-visual-terms (BOV) models and multiclass SVM classifiers. In this paper, we study new algo-rithms to improve performance of this framework from these two aspects. Typically SVM classifiers are trained with dictionaries fixed, and as a re-sult the traditional loss function can only be minimized with respect to hyperplane parameters (w and b). We propose a novel loss function for a binary classifier, which links the hinge-loss term with dictionary learning. By doing so, we can further optimize the loss function with respect to the dictionary parameters. Thus, this framework is able to further increase margins of binary classifiers, and consequently decrease the error bound of the aggregated classifier. On two benchmark dataset

Organometallic iridium(III) anticancer complexes with new mechanisms of action: NCI-60 screening, mitochondrial targeting, and apoptosis

Platinum complexes related to cisplatin, cis-[PtCl2(NH3)2], are successful anticancer drugs; however, other transition metal complexes offer potential for combating cisplatin resistance, decreasing side effects, and widening the spectrum of activity. Organometallic half-sandwich iridium (IrIII) complexes [Ir(Cpx)(XY)Cl]+/0 (Cpx = biphenyltetramethylcyclopentadienyl and XY = phenanthroline (1), bipyridine (2), or phenylpyridine (3)) all hydrolyze rapidly, forming monofunctional G adducts on DNA with additional intercalation of the phenyl substituents on the Cpx ring. In comparison, highly potent complex 4 (Cpx = phenyltetramethylcyclopentadienyl and XY = N,N-dimethylphenylazopyridine) does not hydrolyze. All show higher potency toward A2780 human ovarian cancer cells compared to cisplatin, with 1, 3, and 4 also demonstrating higher potency in the National Cancer Institute (NCI) NCI-60 cell-line screen. Use of the NCI COMPARE algorithm (which predicts mechanisms of action (MoAs) for emerging anticancer compounds by correlating NCI-60 patterns of sensitivity) shows that the MoA of these IrIII complexes has no correlation to cisplatin (or oxaliplatin), with 3 and 4 emerging as particularly novel compounds. Those findings by COMPARE were experimentally probed by transmission electron microscopy (TEM) of A2780 cells exposed to 1, showing mitochondrial swelling and activation of apoptosis after 24 h. Significant changes in mitochondrial membrane polarization were detected by flow cytometry, and the potency of the complexes was enhanced ca. 5× by co-administration with a low concentration (5 μM) of the γ-glutamyl cysteine synthetase inhibitor L-buthionine sulfoximine (L-BSO). These studies reveal potential polypharmacology of organometallic IrIII complexes, with MoA and cell selectivity governed by structural changes in the chelating ligands

SynthEye: Investigating the Impact of Synthetic Data on Artificial Intelligence-assisted Gene Diagnosis of Inherited Retinal Disease

PURPOSE: Rare disease diagnosis is challenging in medical image-based artificial intelligence due to a natural class imbalance in datasets, leading to biased prediction models. Inherited retinal diseases (IRDs) are a research domain that particularly faces this issue. This study investigates the applicability of synthetic data in improving artificial intelligence-enabled diagnosis of IRDs using generative adversarial networks (GANs). DESIGN: Diagnostic study of gene-labeled fundus autofluorescence (FAF) IRD images using deep learning. PARTICIPANTS: Moorfields Eye Hospital (MEH) dataset of 15 692 FAF images obtained from 1800 patients with confirmed genetic diagnosis of 1 of 36 IRD genes. METHODS: A StyleGAN2 model is trained on the IRD dataset to generate 512 × 512 resolution images. Convolutional neural networks are trained for classification using different synthetically augmented datasets, including real IRD images plus 1800 and 3600 synthetic images, and a fully rebalanced dataset. We also perform an experiment with only synthetic data. All models are compared against a baseline convolutional neural network trained only on real data. MAIN OUTCOME MEASURES: We evaluated synthetic data quality using a Visual Turing Test conducted with 4 ophthalmologists from MEH. Synthetic and real images were compared using feature space visualization, similarity analysis to detect memorized images, and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score for no-reference-based quality evaluation. Convolutional neural network diagnostic performance was determined on a held-out test set using the area under the receiver operating characteristic curve (AUROC) and Cohen's Kappa (κ). RESULTS: An average true recognition rate of 63% and fake recognition rate of 47% was obtained from the Visual Turing Test. Thus, a considerable proportion of the synthetic images were classified as real by clinical experts. Similarity analysis showed that the synthetic images were not copies of the real images, indicating that copied real images, meaning the GAN was able to generalize. However, BRISQUE score analysis indicated that synthetic images were of significantly lower quality overall than real images (P < 0.05). Comparing the rebalanced model (RB) with the baseline (R), no significant change in the average AUROC and κ was found (R-AUROC = 0.86[0.85-88], RB-AUROC = 0.88[0.86-0.89], R-k = 0.51[0.49-0.53], and RB-k = 0.52[0.50-0.54]). The synthetic data trained model (S) achieved similar performance as the baseline (S-AUROC = 0.86[0.85-87], S-k = 0.48[0.46-0.50]). CONCLUSIONS: Synthetic generation of realistic IRD FAF images is feasible. Synthetic data augmentation does not deliver improvements in classification performance. However, synthetic data alone deliver a similar performance as real data, and hence may be useful as a proxy to real data. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references