Mark Twain, the Dialogic Imagination, and the American Classroom

Mark Twain is often read as a provincial realist or naturalist whose works are disseminated in simplified versions as children's stories or seen as humorous social criticism of the southern United States and its dialects. This article focuses on two of Twain’s novels—A Connecticut Yankee in King Arthur’s Court (1889) and No. 44, the Mysterious Stranger (published posthumously with various titles)—in order to focus on the more modern, less provincial, novelistic aspects of Twain’s writing. The theories of Mikhail Bakhtin provide the background for a characterization of the novelistic nature of these works in an effort to re-focus Twain criticism away from realist or naturalist analysis and toward semiotic and structural considerations. This essay functions as an introductory-level presentation of Bakhtinian analysis and Twain criticism, as well as a reimagining of the role of Twain’s writings in the classroom, especially in light of recent controversies surrounding the language used in works like The Adventures of Huckleberry Finn. Of paramount importance to this argument are the temporal, spatial, formal and thematic coordinates of the two books, and the assertion that they conform to Bakhtin's conception of the novel and how it radically differs from other forms

Preclinical Results of Camptothecin-Polymer Conjugate (IT-101) in Multiple Human Lymphoma Xenograft Models

Purpose: Camptothecin (CPT) has potent broad-spectrum antitumor activity by inhibiting type I DNA topoisomerase (DNA topo I). It has not been used clinically because it is water-insoluble and highly toxic. As a result, irinotecan (CPT-11), a water-soluble analogue of CPT, has been developed and used as salvage chemotherapy in patients with relapsed/refractory lymphoma, but with only modest activity. Recently, we have developed a cyclodextrin-based polymer conjugate of 20-(S)-CPT (IT-101). In this study, we evaluated the preclinical antilymphoma efficacy of IT-101 as compared with CPT-11. Experimental Design: We determined an in vitro cytotoxicity of IT-101, CPT-11, and their metabolites against multiple human lymphoma cell lines. In human lymphoma xenografts, the pharmacokinetics, inhibitions of tumor DNA topo I catalytic activity, and antilymphoma activities of these compounds were evaluated. Results: IT-101 and CPT had very high in vitro cytotoxicity against all lymphoma cell lines tested. As compared with CPT-11 and SN-38, IT-101 and CPT had longer release kinetics and significantly inhibit higher tumor DNA topo I catalytic activities. Furthermore, IT-101 showed significantly prolonged the survival of animals bearing s.c. and disseminated human xenografts when compared with CPT-11 at its maximum tolerated dose in mice. Conclusions: The promising present results provide the basis for a phase I clinical trial in patients with relapsed/refractory lymphoma

Ariel - Volume () Number 1

Copyright 1986 Arie

In Vivo Imaging of Transplanted Islets with ^(64)Cu-DO3A-VS-Cys^(40)-Exendin-4 by Targeting GLP-1 Receptor

Glucagon-like peptide 1 receptor (GLP-1R) is highly expressed in pancreatic islets, especially on β-cells. Therefore, a properly labeled ligand that binds to GLP-1R could be used for in vivo pancreatic islet imaging. Because native GLP-1 is degraded rapidly by dipeptidyl peptidase-IV (DPP-IV), a more stable agonist of GLP-1 such as Exendin-4 is a preferred imaging agent. In this study, DO3A-VS-Cys^(40)-Exendin-4 was prepared through the conjugation of DO3A-VS with Cys^(40)-Exendin-4. The in vitro binding affinity of DO3A-VS-Cys^(40)-Exendin-4 was evaluated in INS-1 cells, which overexpress GLP-1R. After ^(64)Cu labeling, biodistribution studies and microPET imaging of ^(64)Cu-DO3A-VS-Cys^(40)-Exendin-4 were performed on both subcutaneous INS-1 tumors and islet transplantation models. The subcutaneous INS-1 tumor was clearly visualized with microPET imaging after the injection of ^(64)Cu-DO3A-VS-Cys^(40)-Exendin-4. GLP-1R positive organs, such as pancreas and lung, showed high uptake. Tumor uptake was saturable, reduced dramatically by a 20-fold excess of unlabeled Exendin-4. In the intraportal islet transplantation models, ^(64)Cu-DO3A-VS-Cys^(40)-Exendin-4 demonstrated almost two times higher uptake compared with normal mice. ^(64)Cu-DO3A-VS-Cys^(40)-Exendin-4 demonstrated persistent and specific uptake in the mouse pancreas, the subcutaneous insulinoma mouse model, and the intraportal human islet transplantation mouse model. This novel PET probe may be suitable for in vivo pancreatic islets imaging in the human

Quality Enhancement of Highly Degraded Music Using Deep Learning-Based Prediction Models for Lost Frequencies

Audio quality degradation can have many causes. For musical applications, this fragmentation may lead to highly unpleasant experiences. Restoration algorithms may be employed to reconstruct missing parts of the audio in a similar way as for image reconstruction-in an approach called audio inpainting. Current state-of-The art methods for audio inpainting cover limited scenarios, with well-defined gap windows and little variety of musical genres. In this work, we propose a Deep-Learning-based (DL-based) method for audio inpainting accompanied by a dataset with random fragmentation conditions that approximate real impairment situations. The dataset was collected using tracks from different music genres to provide a good signal variability. Our best model improved the quality of all musical genres, obtaining an average of 12.9 dB of PSNR, although it worked better for musical genres in which acoustic instruments are predominant

A Cluster-Based Method for Action Segmentation Using Spatio-Temporal and Positional Encoded Embeddings

A crucial task to overall video understanding is the recognition and localisation in time of different actions or events that are present along the scenes. To address this problem, action segmentation must be achieved. Action segmentation consists of temporally segmenting a video by labeling each frame with a specific action. In this work, we propose a novel action segmentation method that requires no prior video analysis and no annotated data. Our method involves extracting spatio-Temporal features from videos in samples of 0.5s using a pre-Trained deep network. Data is then transformed using a positional encoder and finally a clustering algorithm is applied with the use of a silhouette score to find the optimal number of clusters where each cluster presumably corresponds to a different single and distinguishable action. In experiments, we show that our method produces competitive results on Breakfast and Inria Instructional Videos dataset benchmarks

Serial Diffusion MRI to Monitor and Model Treatment Response of the Targeted Nanotherapy CRLX101

Purpose: Targeted nanotherapies are being developed to improve tumor drug delivery and enhance therapeutic response. Techniques that can predict response will facilitate clinical translation and may help define optimal treatment strategies. We evaluated the efficacy of diffusion-weighted magnetic resonance imaging to monitor early response to CRLX101 (a cyclodextrin-based polymer particle containing the DNA topoisomerase I inhibitor camptothecin) nanotherapy (formerly IT-101), and explored its potential as a therapeutic response predictor using a mechanistic model of tumor cell proliferation. Experimental Design: Diffusion MRI was serially conducted following CRLX101 administration in a mouse lymphoma model. Apparent diffusion coefficients (ADCs) extracted from the data were used as treatment response biomarkers. Animals treated with irinotecan (CPT-11) and saline were imaged for comparison. ADC data were also input into a mathematical model of tumor growth. Histological analysis using cleaved-caspase 3, terminal deoxynucleotidyl transferase–mediated dUTP nick end labeling, Ki-67, and hematoxylin and eosin (H&E) were conducted on tumor samples for correlation with imaging results. Results: CRLX101-treated tumors at day 2, 4, and 7 posttreatment exhibited changes in mean ADC = 16 ± 9%, 24 ± 10%, 49 ± 17%, and size (TV) = −5 ± 3%, −30 ± 4%, and −45 ± 13%, respectively. Both parameters were statistically greater than controls [p(ADC) ≤ 0.02, and p(TV) ≤ 0.01 at day 4 and 7], and noticeably greater than CPT-11–treated tumors (ADC = 5 ± 5%, 14 ± 7%, and 18 ± 6%; TV = −15 ± 5%, −22 ± 13%, and −26 ± 8%). Model-derived parameters for cell proliferation obtained using ADC data distinguished CRLX101-treated tumors from controls (P = 0.02). Conclusions: Temporal changes in ADC specified early CRLX101 treatment response and could be used to model image-derived cell proliferation rates following treatment. Comparisons of targeted and nontargeted treatments highlight the utility of noninvasive imaging and modeling to evaluate, monitor, and predict responses to targeted nanotherapeutics