The purpose of this work is to develop and validate a learning-based method to derive electron density from routine anatomical MRI for potential MRI-based SBRT treatment planning.
We proposed to ...integrate dense block into cycle generative adversarial network (GAN) to effectively capture the relationship between the CT and MRI for CT synthesis. A cohort of 21 patients with co-registered CT and MR pairs were used to evaluate our proposed method by the leave-one-out cross-validation. Mean absolute error, peak signal-to-noise ratio and normalized cross-correlation were used to quantify the imaging differences between the synthetic CT (sCT) and CT. The accuracy of Hounsfield unit (HU) values in sCT for dose calculation was evaluated by comparing the dose distribution in sCT-based and CT-based treatment planning. Clinically relevant dose-volume histogram metrics were then extracted from the sCT-based and CT-based plans for quantitative comparison.
The mean absolute error, peak signal-to-noise ratio and normalized cross-correlation of the sCT were 72.87 ± 18.16 HU, 22.65 ± 3.63 dB and 0.92 ± 0.04, respectively. No significant differences were observed in the majority of the planning target volume and organ at risk dose-volume histogram metrics (
> 0.05). The average pass rate of γ analysis was over 99% with 1%/1 mm acceptance criteria on the coronal plane that intersects with isocenter.
The image similarity and dosimetric agreement between sCT and original CT warrant further development of an MRI-only workflow for liver stereotactic body radiation therapy.
This work is the first deep-learning-based approach to generating abdominal sCT through dense-cycle-GAN. This method can successfully generate the small bony structures such as the rib bones and is able to predict the HU values for dose calculation with comparable accuracy to reference CT images.
Smart wearable robotic system, such as exoskeleton assist device and powered lower limb prostheses can rapidly and accurately realize man-machine interaction through locomotion mode recognition ...system. However, previous locomotion mode recognition studies usually adopted more sensors for higher accuracy and effective intelligent algorithms to recognize multiple locomotion modes simultaneously. To reduce the burden of sensors on users and recognize more locomotion modes, we design a novel decision tree structure (DTS) based on using an improved backpropagation neural network (IBPNN) as judgment nodes named IBPNN-DTS, after analyzing the experimental locomotion mode data using the original values with a 200-ms time window for a single inertial measurement unit to hierarchically identify nine common locomotion modes (level walking at three kinds of speeds, ramp ascent/descent, stair ascent/descent, Sit, and Stand). In addition, we reduce the number of parameters in the IBPNN for structure optimization and adopted the artificial bee colony (ABC) algorithm to perform global search for initial weight and threshold value to eliminate system uncertainty because randomly generated initial values tend to result in a failure to converge or falling into local optima. Experimental results demonstrate that recognition accuracy of the IBPNN-DTS with ABC optimization (ABC-IBPNN-DTS) was up to 96.71% (97.29% for the IBPNN-DTS). Compared to IBPNN-DTS without optimization, the number of parameters in ABC-IBPNN-DTS shrank by 66% with only a 0.58% reduction in accuracy while the classification model kept high robustness.
is a famous edible and medicinal herb, and polysaccharides are the main bioactive component in it. In this study, response surface methodology (RSM) combined with a Box-Behnken design (BBD) was used ...to optimize the enzyme-assisted extraction (EAE), ultrasound-microwave-assisted extraction (UMAE), and hot water extraction (HWE) conditions and obtain the polysaccharides named DHP-E, DHP-UM, and DHP-H. The effects of different extraction methods on the physicochemical properties, structure characteristics, and bioactivity of polysaccharides were compared. The differential thermogravimetric curves indicated that DHP-E showed a broader temperature range during thermal degradation compared with DHP-UM and DHP-H. The SEM results showed that DHP-E displayed an irregular granular structure, but DHP-UM and DHP-H were sponge-like. The results of absolute molecular weight indicated that polysaccharides with higher molecular weight detected in DHP-H and DHP-UM did not appear in DHP-E due to enzymatic degradation. The monosaccharide composition showed that DHPs were all composed of Man, Glc, and Gal but with different proportions. Finally, the glycosidic bond types, which have a significant effect on bioactivity, were decoded with methylation analysis. The results showed that DHPs contained four glycosidic bond types, including Glc
-(1→, →4)-Man
-(1→, →4)-Glc
-(1→, and →4,6)-Man
-(1→ with different ratios. Furthermore, DHP-E exhibited better DPPH and ABTS radical scavenging activities. These findings could provide scientific foundations for selecting appropriate extraction methods to obtain desired bioactivities for applications in the pharmaceutical and functional food industries.
Gaussian processes (GP) based extended target tracking (ETT) technique has attracted the attention of scientists since it can accurately estimate the kinematic states as well as the contour states of ...irregular-shape targets. Most traditional GP methods consider the ETT problem with a linear measurement model in Cartesian coordinates and ignore the input uncertainty of GP for simplicity. In many applications, however, measurements are generated with high-resolution sensors in polar coordinates. More importantly, the ignorance of input uncertainty in GP will exacerbate the tracking performance in these cases. In order to track an irregular-shape extended target in polar coordinates with input uncertainty, an improved GP-based probabilistic data association (IGP-PDA) algorithm is developed that includes the following enhancements: Firstly, a nonlinear measurement model is used and the unbiased converted measurement technique is invoked for the ETT problem. Secondly, the analytical form of statistical properties of the input uncertainty due to measurements noise and predicted error is derived. Thirdly, an IGP-PDA taking into account measurement origin uncertainty as well as input uncertainty is proposed, and three sub-optimal implementations are provided. Last, the posterior Cramer-Rao lower bound of ETT with input uncertainty is derived. Simulation results verify the effectiveness of the proposed method.
This study investigates the time-dependent rheological behavior of cemented paste backfill (CPB) that contains alkali-activated slag (AAS) as a binder. Rheological measurements with the controlled ...shear strain method have been conducted on various AAS-CPB samples with different binder contents, silicate modulus (Ms: SiO2/Na2O molar ratio), fineness of slag and curing temperatures. The Bingham model afforded a good fit to all of the CPB mixtures. The results show that AAS-CPB samples with high binder content demonstrate a more rapid rate of gain in yield stress and plastic viscosity. AAS-CPB also shows better rheological behavior than CPB samples made up of ordinary Portland cement (OPC) at identical binder contents. It is found that increasing Ms yields lower yield stress and plastic viscosity and the rate of gain in these parameters. Increases in the fineness of slag has an adverse effect on rheological behavior of AAS-CPB. The rheological behavior of both OPC- and AAS-CPB samples is also strongly enhanced at higher temperatures. AAS-CPB samples are found to be more sensitive to the variation in curing temperatures than OPC-CPB samples with respect to the rate of gain in yield stress and plastic viscosity. As a result, the findings of this study will contribute to well understand the flow and transport features of fresh CPB mixtures under various conditions and their changes with time.
Micro-nano biorobots based on bacteria have demonstrated great potential for tumor diagnosis and treatment. The bacterial gene expression and drug release should be spatiotemporally controlled to ...avoid drug release in healthy tissues and undesired toxicity. Herein, we describe an alternating magnetic field-manipulated tumor-homing bacteria developed by genetically modifying engineered Escherichia coli with Fe
O
@lipid nanocomposites. After accumulating in orthotopic colon tumors in female mice, the paramagnetic Fe
O
nanoparticles enable the engineered bacteria to receive and convert magnetic signals into heat, thereby initiating expression of lysis proteins under the control of a heat-sensitive promoter. The engineered bacteria then lyse, releasing its anti-CD47 nanobody cargo, that is pre-expressed and within the bacteria. The robust immunogenicity of bacterial lysate cooperates with anti-CD47 nanobody to activate both innate and adaptive immune responses, generating robust antitumor effects against not only orthotopic colon tumors but also distal tumors in female mice. The magnetically engineered bacteria also enable the constant magnetic field-controlled motion for enhanced tumor targeting and increased therapeutic efficacy. Thus, the gene expression and drug release behavior of tumor-homing bacteria can be spatiotemporally manipulated in vivo by a magnetic field, achieving tumor-specific CD47 blockage and precision tumor immunotherapy.
The industrial Internet of Things (IIoT) is growing quickly due to increasing deployment and integration of smart sensors, instruments, and devices, and software using wired or wireless networks. ...Through this integrated hardware-software approach, industrial practices will improve significantly, resulting in industrial intelligence for more efficient manufacturing. To realize such industrial intelligence, significant developments in IIoT big data processing and analysis are required to uncover and use hidden essential and valuable information of the production process. But large-scale, streaming, multiattribute IIoT data from production processes are noisy and have redundancies. Therefore, a suitable data processing technique such as tensor-train that can handle these IIoT data is needed. However, existing tensor-train decomposition methods are inefficient and cannot meet the processing demands of the large-scale IIoT big data. In this article, we propose an advanced (improved and highly efficient) distributed tensor-train (ADTT) decomposition method with its incremental computational method for processing IIoT big data. Finally, experiments are carried out on a typical and publicly available IIoT dataset-the bearing test data to verify and measure the performances of the proposed ADTT method.
Inspired by the recent discovery of the pentaquark states Pc(4450) and Pc(4380), which can be viewed as excited nucleon states with hidden charm, we study the three-body interaction of a kaon and a ...pair of DD¯⁎ in isospin 0 and 1. We show that the two body interactions stringently constrained by the existence of the Ds0⁎(2317), Ds1⁎(2460), X(3872), and Zc(3900), which are widely believed to contain large DK, D⁎K, and DD¯⁎ components, inevitably lead to the existence of a heavy K⁎ meson with hidden charm. Concrete coupled channel three-body calculations yield its mass and width as (4307±2)−i(9±2)MeV with I(JP)=1/2(1−). This state, if found experimentally, definitely cannot be accommodated in a qq¯ picture, and therefore presents a clear case of an exotic hadron.
The neural melanocortin receptors (MCRs), melanocortin-3 and -4 receptors (MC3R and MC4R), play essential non-redundant roles in the regulation of energy homeostasis. Interaction of neural MCRs and ...melanocortin-2 receptor accessory proteins (MRAPs, MRAP1 and MRAP2) is suggested to play pivotal roles in MC3R and MC4R signaling. In the present study, we identified two new human (h)
splice variants,
(465 bp open reading frame) and
(381 bp open reading frame). Human MRAP2s are different in C-termini. We investigated the effects of five isoforms of MRAPs, hMRAP1a, hMRAP1b, hMRAP2a, hMRAP2b, and hMRAP2c, on MC3R and MC4R pharmacology. At the hMC3R, hMRAP1a and hMRAP2c increased and hMRAP1b decreased the cell surface expression. hMRAP1a increased affinity to ACTH. Four MRAPs (hMRAP1a, hMRAP1b, hMRAP2a, and hMRAP2c) decreased the maximal responses in response to α-MSH and ACTH. For hMC4R, hMRAP1a, hMRAP2a, and hMRAP2c increased the cell surface expression of hMC4R. Human MRAP1b significantly increased affinity to ACTH while MRAP2a decreased affinity to ACTH. Human MRAP1a increased ACTH potency. MRAPs also affected hMC4R basal activities, with hMRAP1s increasing and hMRAP2s decreasing the basal activities. In summary, the newly identified splicing variants, hMRAP2b and hMRAP2c, could regulate MC3R and MC4R pharmacology. The two MRAP1s and three MRAP2s had differential effects on MC3R and MC4R trafficking, binding, and signaling. These findings led to a better understanding of the regulation of neural MCRs by MRAP1s and MRAP2s.
Edges tend to be over-smoothed in total variation (TV) regularized under-sampled images. In this paper, symmetric residual convolutional neural network (SR-CNN), a deep learning based model, was ...proposed to enhance the sharpness of edges and detailed anatomical structures in under-sampled cone-beam computed tomography (CBCT). For training, CBCT images were reconstructed using TV-based method from limited projections simulated from the ground truth CT, and were fed into SR-CNN, which was trained to learn a restoring pattern from under-sampled images to the ground truth. For testing, under-sampled CBCT was reconstructed using TV regularization and was then augmented by SR-CNN. Performance of SR-CNN was evaluated using phantom and patient images of various disease sites acquired at different institutions both qualitatively and quantitatively using structure similarity (SSIM) and peak signal-to-noise ratio (PSNR). SR-CNN substantially enhanced image details in the TV-based CBCT across all experiments. In the patient study using real projections, SR-CNN augmented CBCT images reconstructed from as low as 120 half-fan projections to image quality comparable to the reference fully-sampled FDK reconstruction using 900 projections. In the tumor localization study, improvements in the tumor localization accuracy were made by the SR-CNN augmented images compared with the conventional FDK and TV-based images. SR-CNN demonstrated robustness against noise levels and projection number reductions and generalization for various disease sites and datasets from different institutions. Overall, the SR-CNN-based image augmentation technique was efficient and effective in considerably enhancing edges and anatomical structures in under-sampled 3D/4D-CBCT, which can be very valuable for image-guided radiotherapy.
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