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

Algebraic Cayley Graphs over Finite Fields

A new algebraic Cayley graph is constructed using finite fields. Its connectedness and diameter bound are studied via Weil's estimate for character sums. These graphs provide a new source of expander graphs, extending classical results of Chung

Tac2Structure: Object Surface Reconstruction Only through Multi Times Touch

Inspired by humans' ability to perceive the surface texture of unfamiliar objects without relying on vision, the sense of touch can play a crucial role in robots exploring the environment, particularly in scenes where vision is difficult to apply, or occlusion is inevitable. Existing tactile surface reconstruction methods rely on external sensors or have strong prior assumptions, making the operation complex and limiting their application scenarios. This paper presents a framework for low-drift surface reconstruction through multiple tactile measurements, Tac2Structure. Compared with existing algorithms, the proposed method uses only a new vision-based tactile sensor without relying on external devices. Aiming at the difficulty that reconstruction accuracy is easily affected by the pressure at contact, we propose a correction algorithm to adapt it. The proposed method also reduces the accumulative errors that occur easily during global object surface reconstruction. Multi-frame tactile measurements can accurately reconstruct object surfaces by jointly using the point cloud registration algorithm, loop-closure detection algorithm based on deep learning, and pose graph optimization algorithm. Experiments verify that Tac2Structure can achieve millimeter-level accuracy in reconstructing the surface of objects, providing accurate tactile information for the robot to perceive the surrounding environment.Comment: Accepted for publication in IEEE Robotics And Automation Letter

A supramolecular radical cation: folding-enhanced electrostatic effect for promoting radical-mediated oxidation.

We report a supramolecular strategy to promote radical-mediated Fenton oxidation by the rational design of a folded host-guest complex based on cucurbit[8]uril (CB[8]). In the supramolecular complex between CB[8] and a derivative of 1,4-diketopyrrolo[3,4-c]pyrrole (DPP), the carbonyl groups of CB[8] and the DPP moiety are brought together through the formation of a folded conformation. In this way, the electrostatic effect of the carbonyl groups of CB[8] is fully applied to highly improve the reactivity of the DPP radical cation, which is the key intermediate of Fenton oxidation. As a result, the Fenton oxidation is extraordinarily accelerated by over 100 times. It is anticipated that this strategy could be applied to other radical reactions and enrich the field of supramolecular radical chemistry in radical polymerization, photocatalysis, and organic radical battery and holds potential in supramolecular catalysis and biocatalysis

Descriptions of five new species of Helina Robineau-Desvoidy (Diptera: Muscidae) from China

In this paper, five new species belonging to the genus Helina Robineau-Desvoidy are described and illustrated, viz H. combinisetata sp. n., H. discombinisetata sp. n., H. flavipulchella sp. n., H. subsetiventris sp. n., H. xingkaiensis sp. n. Revised couplets for the key to known Chinese species are given

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

Numerical Simulation of Fragment Separation during Rock Cutting Using a 3D Dynamic Finite Element Analysis Code

To predict fragment separation during rock cutting, previous studies on rock cutting interactions using simulation approaches, experimental tests, and theoretical methods were considered in detail. This study used the numerical code LS-DYNA (3D) to numerically simulate fragment separation. In the simulations, a damage material model and erosion criteria were used for the base rock, and the conical pick was designated a rigid material. The conical pick moved at varying linear speeds to cut the fixed base rock. For a given linear speed of the conical pick, numerical studies were performed for various cutting depths and mechanical properties of rock. The numerical simulation results demonstrated that the cutting forces and sizes of the separated fragments increased significantly with increasing cutting depth, compressive strength, and elastic modulus of the base rock. A strong linear relationship was observed between the mean peak cutting forces obtained from the numerical, theoretical, and experimental studies with correlation coefficients of 0.698, 0.8111, 0.868, and 0.768. The simulation results also showed an exponential relationship between the specific energy and cutting depth and a linear relationship between the specific energy and compressive strength. Overall, LS-DYNA (3D) is effective and reliable for predicting the cutting performance of a conical pick

TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images

In the remote sensing field, Change Detection (CD) aims to identify and localize the changed regions from dual-phase images over the same places. Recently, it has achieved great progress with the advances of deep learning. However, current methods generally deliver incomplete CD regions and irregular CD boundaries due to the limited representation ability of the extracted visual features. To relieve these issues, in this work we propose a novel Transformer-based learning framework named TransY-Net for remote sensing image CD, which improves the feature extraction from a global view and combines multi-level visual features in a pyramid manner. More specifically, the proposed framework first utilizes the advantages of Transformers in long-range dependency modeling. It can help to learn more discriminative global-level features and obtain complete CD regions. Then, we introduce a novel pyramid structure to aggregate multi-level visual features from Transformers for feature enhancement. The pyramid structure grafted with a Progressive Attention Module (PAM) can improve the feature representation ability with additional inter-dependencies through spatial and channel attentions. Finally, to better train the whole framework, we utilize the deeply-supervised learning with multiple boundary-aware loss functions. Extensive experiments demonstrate that our proposed method achieves a new state-of-the-art performance on four optical and two SAR image CD benchmarks. The source code is released at https://github.com/Drchip61/TransYNet.Comment: This work is accepted by TGRS2023. It is an extension of our ACCV2022 paper and arXiv:2210.0075