Forced Current Excitation in Selectable Field of View Coils for 7T MRI and MRS

High field magnetic resonance imaging (MRI) provides improved signal-to-noise ratio (SNR) which can be translated to higher image resolution or reduced scan time. 7 Tesla (T) breast imaging and 7 T spine imaging are of clinical value, but they are challenging for several reasons: A bilateral breast coil requires the use of closely-spaced elements that are subject to severe mutual coupling which leads to uncontrollable current distribution and non-uniform field pattern; A spine coil at 7T requires a large field of view (FOV) in the z direction and good RF penetration into the human body. Additionally, the ability to switch FOV without the use of expensive high power RF amplifiers is desired in both applications. This capability would allow reconfigurable power distribution and avoid unnecessary heat deposition into human body. Forced-Current Excitation (FCE) is a transmission line-based method that maintains equal current distribution across an array, alleviating mutual coupling effects and allowing current/field replication across a large FOV. At the same time, the nature of this method enables selectable FOV with the inclusion of PIN diodes and a controller. In this doctoral work, the theory of FCE is explained in detail, along with its benefits and drawbacks. Electromagnetic simulation considerations of FCE-driven coils are also discussed. Two FCE-driven coils were designed and implemented: a switchable bilateral/unilateral 7T breast coil, and a segmented dipole for spine imaging at 7T with reconfigurable length. For the breast coil, shielded loop elements were used to form a volume coil, whereas for the spine coil, a segmented dipole was chosen as the final design due to improved RF penetration. Electromagnetic simulations were performed to assist the design of the two coils as well as to predict the SAR (specific absorption rate) generated in the phantom. The coils were evaluated on bench and through MRI experiments in different configurations to validate the design. The switchable breast coil provides uniform excitation in both unilateral and bilateral mode. In unilateral mode, the signal in the contralateral breast is successfully suppressed and higher power is concentrated into the breast of interest; The segmented dipole was compared to a regular dipole with the same length used for 7T spine imaging. The segmented dipole shows a large FOV in the long mode. In the short mode, the residual signal from other part of the dipole is successfully suppressed. The ability to switch FOV and reconfigure the power distribution improves the B1 generated with unit specific absorption rate towards the edge of the dipole, compared to the regular dipole

Forced Current Excitation in Selectable Field of View Coils for 7T MRI and MRS

High field magnetic resonance imaging (MRI) provides improved signal-to-noise ratio (SNR) which can be translated to higher image resolution or reduced scan time. 7 Tesla (T) breast imaging and 7 T spine imaging are of clinical value, but they are challenging for several reasons: A bilateral breast coil requires the use of closely-spaced elements that are subject to severe mutual coupling which leads to uncontrollable current distribution and non-uniform field pattern; A spine coil at 7T requires a large field of view (FOV) in the z direction and good RF penetration into the human body. Additionally, the ability to switch FOV without the use of expensive high power RF amplifiers is desired in both applications. This capability would allow reconfigurable power distribution and avoid unnecessary heat deposition into human body. Forced-Current Excitation (FCE) is a transmission line-based method that maintains equal current distribution across an array, alleviating mutual coupling effects and allowing current/field replication across a large FOV. At the same time, the nature of this method enables selectable FOV with the inclusion of PIN diodes and a controller. In this doctoral work, the theory of FCE is explained in detail, along with its benefits and drawbacks. Electromagnetic simulation considerations of FCE-driven coils are also discussed. Two FCE-driven coils were designed and implemented: a switchable bilateral/unilateral 7T breast coil, and a segmented dipole for spine imaging at 7T with reconfigurable length. For the breast coil, shielded loop elements were used to form a volume coil, whereas for the spine coil, a segmented dipole was chosen as the final design due to improved RF penetration. Electromagnetic simulations were performed to assist the design of the two coils as well as to predict the SAR (specific absorption rate) generated in the phantom. The coils were evaluated on bench and through MRI experiments in different configurations to validate the design. The switchable breast coil provides uniform excitation in both unilateral and bilateral mode. In unilateral mode, the signal in the contralateral breast is successfully suppressed and higher power is concentrated into the breast of interest; The segmented dipole was compared to a regular dipole with the same length used for 7T spine imaging. The segmented dipole shows a large FOV in the long mode. In the short mode, the residual signal from other part of the dipole is successfully suppressed. The ability to switch FOV and reconfigure the power distribution improves the B1 generated with unit specific absorption rate towards the edge of the dipole, compared to the regular dipole

Optimal Distributed Beamforming for MISO Interference Channels

We consider the problem of quantifying the Pareto optimal boundary in the achievable rate region over multiple-input single-output (MISO) interference channels, where the problem boils down to solving a sequence of convex feasibility problems after certain transformations. The feasibility problem is solved by two new distributed optimal beamforming algorithms, where the first one is to parallelize the computation based on the method of alternating projections, and the second one is to localize the computation based on the method of cyclic projections. Convergence proofs are established for both algorithms.Comment: 7 Pages, 6 figures, extended version for the one in Proceeding of Asilomar, CA, 201

Screening of Potential Hub Genes in Glioma Progression Based on Bioinformatics Analysis

Objectives: Glioma is the most common primary tumor of the central nervous system, and its therapeutic effect is not optimistic. In recent years, related therapeutic technologies have developed rapidly, but unfortunately, the improvement of clinical therapeutic effect is not satisfactory. In addition to conventional therapies, there are some attractive therapies, such as biological therapy (immunotherapy), gene therapy, etc[1]. Therefore, searching for potential target genes of glioma is of great significance for developing new therapeutic directions and designing new biomarkers[2]. Methods: Download gene expression data set, GSE137902 gelatin and GSE13790 matrix through NCBI-G to screen overlapping differential expression genes (DEGs). In order to identify central genes from these genes, we conducted protein protein interaction (PPI) network. To further explore the potential mechanism of central genes in glioma, we performed gene ontology (GO) and Kyoto Gene and Genome Encyclopedia (KEGG) analysis. Then get the intersection of key genes according to five algorithms of Closeness Degree EPC MCC Stress. The intersection is obtained through GSE117423, GSE188256 and GSE90598 in geo database, and finally verified through Receiver Operating Characteristic (ROC) curve. Results: A total of 1274 differentially expressed genes are identified, and then 309 genes are obtained by intersection of the two. 16 Hub genes were obtained, and then the intersection of the two genes with GSE117423, GES188256 and GSE90598 genes was verified to obtain the key gene TIMP1 of glioma. Made the ROC curve of key gene.The intersection with hub gene was determined to identify TIMP1 as the key gene. Conclusion: The DEGs and Hub genes and signal pathways found in this study can confirm that the key gene TIMP1 is closely related to the occurrence and evolution of glioma, and provide candidate targets for the diagnosis and treatment of glioma

Autoregressive Diffusion Model for Graph Generation

Diffusion-based graph generative models have recently obtained promising results for graph generation. However, existing diffusion-based graph generative models are mostly one-shot generative models that apply Gaussian diffusion in the dequantized adjacency matrix space. Such a strategy can suffer from difficulty in model training, slow sampling speed, and incapability of incorporating constraints. We propose an \emph{autoregressive diffusion} model for graph generation. Unlike existing methods, we define a node-absorbing diffusion process that operates directly in the discrete graph space. For forward diffusion, we design a \emph{diffusion ordering network}, which learns a data-dependent node absorbing ordering from graph topology. For reverse generation, we design a \emph{denoising network} that uses the reverse node ordering to efficiently reconstruct the graph by predicting the node type of the new node and its edges with previously denoised nodes at a time. Based on the permutation invariance of graph, we show that the two networks can be jointly trained by optimizing a simple lower bound of data likelihood. Our experiments on six diverse generic graph datasets and two molecule datasets show that our model achieves better or comparable generation performance with previous state-of-the-art, and meanwhile enjoys fast generation speed.Comment: 18 page

End-to-End Stochastic Optimization with Energy-Based Model

Decision-focused learning (DFL) was recently proposed for stochastic optimization problems that involve unknown parameters. By integrating predictive modeling with an implicitly differentiable optimization layer, DFL has shown superior performance to the standard two-stage predict-then-optimize pipeline. However, most existing DFL methods are only applicable to convex problems or a subset of nonconvex problems that can be easily relaxed to convex ones. Further, they can be inefficient in training due to the requirement of solving and differentiating through the optimization problem in every training iteration. We propose SO-EBM, a general and efficient DFL method for stochastic optimization using energy-based models. Instead of relying on KKT conditions to induce an implicit optimization layer, SO-EBM explicitly parameterizes the original optimization problem using a differentiable optimization layer based on energy functions. To better approximate the optimization landscape, we propose a coupled training objective that uses a maximum likelihood loss to capture the optimum location and a distribution-based regularizer to capture the overall energy landscape. Finally, we propose an efficient training procedure for SO-EBM with a self-normalized importance sampler based on a Gaussian mixture proposal. We evaluate SO-EBM in three applications: power scheduling, COVID-19 resource allocation, and non-convex adversarial security game, demonstrating the effectiveness and efficiency of SO-EBM.Comment: NeurIPS 2022 Ora

The measurement of masses of OB-type stars from LAMOST DR5

The measurements of masses and luminosities of massive stars play an important role in understanding the formation and evolution of their host galaxies. In this work, we present the measurement of masses and luminosities of 2,946 OB-type stars, including 78 O-type stars and 2,868 B-type stars, based on their stellar parameters (effective temperature, surface gravity, and metallicity) and PARSEC isochrones model. Our results show that the median mass and luminosity of the 2,946 OB-type stars are 5.4 M$_{\odot}$ and log(L/L$_{\odot}$)=3.2 with the median relative error of 21.4$\%$ and 71.1$\%$, respectively. A good agreement between our results estimated by using our method and those derived by using the orbital motions of binary stars from the literature is found for some B-type stars. In addition, we also fit the mass-luminosity relation of B-type stars by using our derived mass and the luminosity from $Gaia$ DR3.Comment: 12 pages, 10 figures, 1 table, accepted for publication in ApJ

Identification of Blue Horizontal-Branch Stars From LAMOST DR5

We construct a new catalog of the blue horizontal-branch (BHB) stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR5 dataset, which contains 5355+81 BHB stars at high Galactic latitude (($|Glat|>20^{\circ}$). We combine the spectral line indices with a set of Balmer line profile selection criteria to identify the BHB stars. During the selection process, we use the line index of \ion{Ca}{2}\,K to exclude the metal-rich A-type dwarfs. We obtain their atmospheric parameters by cross-matching our BHB stars with the catalog provided by \citet{Xiang2022}. The results show that our sample is consistent with the theoretical $T_{\rm eff}$-log\,$g$ evolutionary tracks of the BHB stars, indicating that our method is robust for identifying BHB stars from the LAMOST spectra. Their spatial distribution indicates that most of our BHB stars are located in the inner halo or the disk of the Milky Way. Combined with other BHB samples from the literature, the BHB stars can cover a large Galactic volume, which makes it a better probe for studying the kinematics, dynamics, and structural characteristics of the Milky Way.Comment: accepted by ApJS.15 pages, 18 figure

Nitrogen Removal in a Horizontal Subsurface Flow Constructed Wetland Estimated Using the First-Order Kinetic Model

We monitored the water quality and hydrological conditions of a horizontal subsurface constructed wetland (HSSF-CW) in Beijing, China, for two years. We simulated the area-based constant and the temperature coefficient with the first-order kinetic model. We examined the relationships between the nitrogen (N) removal rate, N load, seasonal variations in the N removal rate, and environmental factors—such as the area-based constant, temperature, and dissolved oxygen (DO). The effluent ammonia (NH4 + -N) and nitrate (NO3 −-N) concentrations were significantly lower than the influent concentrations (p \u3c 0.01, n = 38). The NO3 −-N load was significantly correlated with the removal rate (R 2 = 0.96, p \u3c 0.01), but the NH4 + -N load was not correlated with the removal rate (R 2 = 0.02, p \u3e 0.01). The area-based constants of NO3 −-N and NH4 + -N at 20 ◦C were 27 ± 26 (mean ± SD) and 14 ± 10 m·year−1 , respectively. The temperature coefficients for NO3 −-N and NH4 + -N were estimated at 1.004 and 0.960, respectively. The area-based constants for NO3 −-N and NH4 + -N were not correlated with temperature (p \u3e 0.01). The NO3 −-N area-based constant was correlated with the corresponding load (R 2 = 0.96, p \u3c 0.01). The NH4 + -N area rate was correlated with DO (R 2 = 0.69, p \u3c 0.01), suggesting that the factors that influenced the N removal rate in this wetland met Liebig’s law of the minimum

The Applications of Finite Element Analysis in Proximal Humeral Fractures

Proximal humeral fractures are common and most challenging, due to the complexity of the glenohumeral joint, especially in the geriatric population with impacted fractures, that the development of implants continues because currently the problems with their fixation are not solved. Pre-, intra-, and postoperative assessments are crucial in management of those patients. Finite element analysis, as one of the valuable tools, has been implemented as an effective and noninvasive method to analyze proximal humeral fractures, providing solid evidence for management of troublesome patients. However, no review article about the applications and effects of finite element analysis in assessing proximal humeral fractures has been reported yet. This review article summarized the applications, contribution, and clinical significance of finite element analysis in assessing proximal humeral fractures. Furthermore, the limitations of finite element analysis, the difficulties of more realistic simulation, and the validation and also the creation of validated FE models were discussed. We concluded that although some advancements in proximal humeral fractures researches have been made by using finite element analysis, utility of this powerful tool for routine clinical management and adequate simulation requires more state-of-the-art studies to provide evidence and bases