Energy Spectrum Extraction and Optimal Imaging via Dual-Energy Material Decomposition

Inferior soft-tissue contrast resolution is a major limitation of current CT scanners. The aim of the study is to improve the contrast resolution of CT scanners using dual-energy acquisition. Based on dual-energy material decomposition, the proposed method starts with extracting the outgoing energy spectrum by polychromatic forward projecting the material-selective images. The extracted spectrum is then reweighted to boost the soft-tissue contrast. A simulated water cylinder phantom with inserts that contain a series of six solutions of varying iodine concentration (range, 0-20 mg/mL) is used to evaluate the proposed method. Results show the root mean square error (RMSE) and mean energy difference between the extracted energy spectrum and the spectrum acquired using an energy-resolved photon counting detector(PCD), are 0.044 and 0.01 keV, respectively. Compared to the method using the standard energy-integrating detectors, dose normalized contrast-to-noise ratio (CNRD) for the proposed method are improved from 1 to 2.15 and from 1 to 1.88 for the 8 mg/mL and 16 mg/mL iodine concentration inserts, respectively. The results show CT image reconstructed using the proposed method is superior to the image reconstructed using the standard method that using an energy-integrating detector.Comment: 4 pages, 4 figures in The 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference Recor

Exploiting CLIP for Zero-shot HOI Detection Requires Knowledge Distillation at Multiple Levels

In this paper, we investigate the task of zero-shot human-object interaction (HOI) detection, a novel paradigm for identifying HOIs without the need for task-specific annotations. To address this challenging task, we employ CLIP, a large-scale pre-trained vision-language model (VLM), for knowledge distillation on multiple levels. Specifically, we design a multi-branch neural network that leverages CLIP for learning HOI representations at various levels, including global images, local union regions encompassing human-object pairs, and individual instances of humans or objects. To train our model, CLIP is utilized to generate HOI scores for both global images and local union regions that serve as supervision signals. The extensive experiments demonstrate the effectiveness of our novel multi-level CLIP knowledge integration strategy. Notably, the model achieves strong performance, which is even comparable with some fully-supervised and weakly-supervised methods on the public HICO-DET benchmark

BH3 mimetic ABT-737 sensitizes colorectal cancer cells to ixazomib through MCL-1 downregulation and autophagy inhibition.

The proteasome inhibitor MLN9708 is an orally administered drug that is hydrolyzed into its active form, MLN2238 (ixazomib). Compared with Bortezomib, MLN2238 has a shorter proteasome dissociation half-life and a lower incidence and severity of peripheral neuropathy, which makes it an attractive candidate for colorectal cancer treatment. In the present study, we observed that MLN2238 induced autophagy, as evidenced by conversion of the autophagosomal marker LC3 from LC3I to LC3II, in colorectal cancer cell lines. Mcl-1, an anti-apoptotic Bcl-2 family protein, was markedly elevated after treating a colorectal cancer cell line with MLN2238. We proved that inhibiting Mcl-1 expression enhances MLN2238 induced apoptosis and negatively regulates autophagy. Co-administration of BH3 mimetic ABT-737 with MLN2238 synergistically kills colorectal cancer cells through MCL-1 neutralization and autophagy inhibition. Furthermore, the synergistic killing effect of the combination therapy is correlated with P53 status in colorectal cancer. These data highlight that the combination of ABT-737 with MLN9708 is a promising therapeutic strategy for human colorectal cancer