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As more people focus on sports, the strategy for clubs to advertise them to the whole world becomes crucial. Using a good marketing strategy on social media can help clubs grow tens of thousands of fans. If not, they might lose their supporter. This paper aims to help to explore good ways to help clubs market effectively. This paper used the case study method and comparative analysis method. First, this paper looked at a previous study about sports clubs and fans. The author found out that there is a blank space on how sports clubs can have better social media strategies to improve their fan base. Second, this research studied how positive and negative social media posts can influence clubs' perforce on social media. Finally, this paper analyzed the characteristics of successful and unsuccessful campaigns and suggested how sports clubs can post successful campaigns. This includes but is not limited to using historical figures, revisiting highlights throughout the clubs' history, hosting giveaways, sharing behind-scene stories, posting at the right time, collaborating with influencers, and going live. This paper believes that sports clubs can better engage with fans and create bigger business value by using these methods.

To date, most studies of police leadership have focused on lower and mid-level police managers, with few scholars having studied the leadership performance of police chiefs. This study contributes to the literature with an empirical assessment and theoretical consideration of leadership at the top executive level of police departments. Police chiefs who attended professional training programs at the Bill Blackwood Law Enforcement Management Institute of Texas evaluated the prior police chief's performance of multiple duties such as directing the work of employees, initiating changes in the department and working with other agencies. Factor analyses of the data indicate the measures load onto three clusters, the composition of which are incompatible with any single theory of leadership. These findings indicate the executive leadership of a police department may be distinct from the leadership of other types of organizations. Incorporating insights from skill-based and situational leadership theories, it is suggested that police executive leadership may be conceptualized as an amalgam of conceptual, managerial and political skills that are shaped by and tailored to the audience and situation.

To improve calibrationless parallel imaging using pre-learned subspaces of coil sensitivity functions.A subspace-based joint sensitivity estimation and image reconstruction method was developed for improved parallel imaging with no calibration data. Specifically, we proposed to use a probabilistic subspace model to capture prior information of the coil sensitivity functions from previous scans acquired using the same receiver system. Both the subspace basis and coefficient distributions were learned from a small set of training data. The learned subspace model was then incorporated into the regularized reconstruction formalism that includes a sparsity prior. The nonlinear optimization problem was solved using alternating minimization algorithm. Public fastMRI brain dataset was retrospectively undersampled by different schemes for performance evaluation of the proposed method.With no calibration data, the proposed method consistently produced the most accurate coil sensitivity estimation and highest quality image reconstructions at all acceleration factors tested in comparison with state-of-the-art methods including JSENSE, DeepSENSE, P-LORAKS, and Sparse BLIP. Our results are comparable to or even better than those from SparseSENSE, which used calibration data for sensitivity estimation. The work also demonstrated that the probabilistic subspace model learned from TA subspace-based method named JSENSE-Pro has been proposed for accelerated parallel imaging without the acquisition of companion calibration data. The method is expected to further enhance the practical utility of parallel imaging, especially in applications where calibration data acquisition is not desirable or limited.

PURPOSE: To improve estimation of myelin water fraction (MWF) in the brain from multi-echo gradient-echo imaging data. METHODS: A systematic sensitivity analysis was first conducted to characterize the conventional exponential models used for MWF estimation. A new estimation method was then proposed for improved estimation of MWF from practical gradient-echo imaging data. The proposed method uses an extended signal model that includes a finite impulse response filter to compensate for practical signal variations. This new model also enables the use of pre-learned parameter distributions as well as low-rank signal structures to improve parameter estimation. The resulting parameter estimation problem was solved optimally in the Bayesian sense. RESULTS: Our sensitivity analysis results showed that the conventional exponential models were very sensitive to measurement noise and modeling errors. Our simulation and experimental results showed that our proposed method provided a substantial improvement in reliability, reproducibility, and robustness of MWF estimates over the conventional methods. Clinical results obtained from stroke patients indicated that the proposed method, with its improved capability, could reveal the loss of myelin in lesions, demonstrating its translational potentials. CONCLUSION: This paper addressed the problem of robust MWF estimation from gradient-echo imaging data. A new method was proposed to provide improved MWF estimation in the presence of significant noise and modeling errors. The performance of the proposed method has been evaluated using both simulated and experimental data, showing significantly improved robustness over the existing methods. The proposed method may prove useful for quantitative myelin imaging in clinical applications.

Purpose To develop and evaluate a novel method for reconstruction of high-quality sodium MR images from noisy, limited k-space data. Theory and methods A novel reconstruction method was developed for reconstruction of high-quality sodium images from noisy, limited k-space data. This method is based on a novel image model that contains a motion-compensated generalized series model and a sparse model. The motion-compensated generalized series model enables effective use of anatomical information from a proton image for denoising and resolution enhancement of sodium data, whereas the sparse model enables high-resolution reconstruction of sodium-dependent novel features. The underlying model estimation problems were solved efficiently using convex optimization algorithms. Results The proposed method has been evaluated using both simulation and experimental data obtained from phantoms, healthy human volunteers, and tumor patients. Results showed a substantial improvement in spatial resolution and SNR over state-of-the-art reconstruction methods, including compressed sensing and anatomically constrained reconstruction methods. Quantitative tissue sodium concentration maps were obtained from both healthy volunteers and brain tumor patients. These tissue sodium concentration maps showed improved lesion fidelity and allowed accurate interrogation of small targets. Conclusion A new method has been developed to obtain high-resolution sodium images with good SNR at 3 T. The proposed method makes effective use of anatomical prior information for denoising, while using a sparse model synergistically to recover sodium-dependent novel features. Experimental results have been obtained to demonstrate the feasibility of achieving high-quality tissue sodium concentration maps and their potential for improved detection of spatially heterogeneous responses of tumor to treatment.

This study examines the effects of attitudes toward the police and punitive ideology on Hispanic criminal justice majors’ interest in law enforcement careers. To date, relatively few studies of the...

Trust in police is crucial for optimal police performance. Accordingly, much scholarly attention has focused on examining correlates of trust in police in the U.S., particularly with respect to rac...

Li et al. demonstrate the feasibility of rapid, high-resolution, near whole-brain 3D MR spectroscopic imaging in acute stroke within real-world clinical environments. Metabolic maps obtained at 3 mm spatial resolution show that the lactate signal can distinguish between areas of infarction, penumbra and oligaemia., Impaired oxygen and cellular metabolism is a hallmark of ischaemic injury in acute stroke. Magnetic resonance spectroscopic imaging (MRSI) has long been recognized as a potentially powerful tool for non-invasive metabolic imaging. Nonetheless, long acquisition time, poor spatial resolution, and narrow coverage have limited its clinical application. Here we investigated the feasibility and potential clinical utility of rapid, high spatial resolution, near whole-brain 3D metabolic imaging based on a novel MRSI technology. In an 8-min scan, we simultaneously obtained 3D maps of N-acetylaspartate and lactate at a nominal spatial resolution of 2.0 × 3.0 × 3.0 mm3 with near whole-brain coverage from a cohort of 18 patients with acute ischaemic stroke. Serial structural and perfusion MRI was used to define detailed spatial maps of tissue-level outcomes against which high-resolution metabolic changes were evaluated. Within hypoperfused tissue, the lactate signal was higher in areas that ultimately infarcted compared with those that recovered (P

Objective: To enable non-invasive dynamic metabolic mapping in rodent model studies of mitochondrial function using 31P-MR spectroscopic imaging (MRSI). Methods: We developed a novel method for high-resolution dynamic 31P-MRSI. The method synergistically integrates physics-based models of spectral structures, biochemical modeling of molecular dynamics, and subspace learning to capture spatiospectral variations. Fast data acquisition was achieved using rapid spiral trajectories and sparse sampling of (k, t, T) -space; image reconstruction was accomplished using a low-rank tensor-based framework. Results: The proposed method provided high-resolution dynamic metabolic mapping in rat hindlimb at spatial and temporal resolutions of 4 ${}\times \text{4} \times{}$ 2 mm3 and 1.28 s, respectively. This allowed for in vivo mapping of the time-constant of phosphocreatine resynthesis, a well established index of mitochondrial oxidative capacity. Multiple rounds of in vivo experiments were performed to demonstrate reproducibility, and in vitro experiments were used to validate the accuracy of the estimated metabolite maps. Conclusions: A new model-based method is proposed to achieve high-resolution dynamic 31P-MRSI. The proposed method's ability to delineate metabolic heterogeneity was demonstrated in rat hindlimb. Significance: Abnormal mitochondrial metabolism is a key cellular dysfunction in many prevalent diseases such as diabetes and heart disease; however, current understanding of mitochondrial function is mostly gained from studies on isolated mitochondria under nonphysiological conditions. The proposed method has the potential to open new avenues of research by allowing in vivo and longitudinal studies of mitochondrial dysfunction in disease development and progression.

Purpose To accelerate T2 mapping with highly sparse sampling by integrating deep learning image priors with low-rank and sparse modeling. Methods The proposed method achieves high-speed T2 mapping by highly sparsely sampling (k, TE)-space. Image reconstruction from the undersampled data was done by exploiting the low-rank structure and sparsity in the T2 -weighted image sequence and image priors learned from training data. The image priors for a single TE were generated from the public Human Connectome Project data using a tissue-based deep learning method; the image priors were then transferred to other TEs using a generalized series-based method. With these image priors, the proposed reconstruction method used a low-rank model and a sparse model to capture subject-dependent novel features. Results The proposed method was evaluated using experimental data obtained from both healthy subjects and tumor patients using a turbo spin-echo sequence. High-quality T2 maps at the resolution of 0.9 × 0.9 × 3.0 mm3 were obtained successfully from highly undersampled data with an acceleration factor of 8. Compared with the existing compressed sensing-based methods, the proposed method produced significantly reduced reconstruction errors. Compared with the deep learning-based methods, the proposed method recovered novel features better. Conclusion This paper demonstrates the feasibility of learning T2 -weighted image priors for multiple TEs using tissue-based deep learning and generalized series-based learning. A new method was proposed to effectively integrate these image priors with low-rank and sparse modeling to reconstruct high-quality images from highly undersampled data. The proposed method will supplement other acquisition-based methods to achieve high-speed T2 mapping.

Purpose To accelerate the acquisition of J-resolved proton magnetic resonance spectroscopic imaging (1 H-MRSI) data for high-resolution mapping of brain metabolites and neurotransmitters. Methods The proposed method used a subspace model to represent multidimensional spatiospectral functions, which significantly reduced the number of parameters to be determined from J-resolved 1 H-MRSI data. A semi-LASER-based (Localization by Adiabatic SElective Refocusing) echo-planar spectroscopic imaging (EPSI) sequence was used for data acquisition. The proposed data acquisition scheme sampled k , t 1 , t 2 -space in variable density, where t1 and t2 specify the J-coupling and chemical-shift encoding times, respectively. Selection of the J-coupling encoding times (or, echo time values) was based on a Cramer-Rao lower bound analysis, which were optimized for gamma-aminobutyric acid (GABA) detection. In image reconstruction, parameters of the subspace-based spatiospectral model were determined by solving a constrained optimization problem. Results Feasibility of the proposed method was evaluated using both simulated and experimental data from a spectroscopic phantom. The phantom experimental results showed that the proposed method, with a factor of 12 acceleration in data acquisition, could determine the distribution of J-coupled molecules with expected accuracy. In vivo study with healthy human subjects also showed that 3D maps of brain metabolites and neurotransmitters can be obtained with a nominal spatial resolution of 3.0 × 3.0 × 4.8 mm3 from J-resolved 1 H-MRSI data acquired in 19.4 min. Conclusions This work demonstrated the feasibility of highly accelerated J-resolved 1 H-MRSI using limited and sparse sampling of k , t 1 , t 2 -space and subspace modeling. With further development, the proposed method may enable high-resolution mapping of brain metabolites and neurotransmitters in clinical applications.

The restorative justice movement has increased the rights of consciousness for crime victims and their families globally. Though the practice of family group conferences in Australia and New Zealan...

Purpose To enable simultaneous high-resolution mapping of brain function and metabolism. Methods An encoding scheme was designed for interleaved acquisition of functional MRI (fMRI) data in echo volume imaging trajectories and MR spectroscopic imaging (MRSI) data in echo-planar spectroscopic imaging trajectories. The scheme eliminates water and lipid suppression and utilizes free induction decay signals to encode both functional and metabolic information with ultrashort TE, short TR, and sparse sampling of k , t -space. A subspace-based image reconstruction method was introduced for processing both the fMRI and MRSI data. The complementary information in the fMRI and MRSI data sets was also utilized to improve image reconstruction in the presence of intrascan head motion, field drift, and tissue susceptibility changes. Results In-vivo experimental results were obtained from healthy human subjects in resting-state fMRI/MRSI experiments. In these experiments, the proposed method was able to simultaneously acquire metabolic and functional information from the brain in high resolution. For scans of 6.5 minutes, we achieved 3.0 × 3.0 × 1.8 mm3 spatial resolution for fMRI, 1.9 × 2.5 × 3.0 mm3 nominal spatial resolution for MRSI, and 1.9 × 1.9 × 1.8 mm3 nominal spatial resolution for quantitative susceptibility maps. Conclusion This work demonstrates the feasibility of simultaneous high-resolution mapping of brain function and metabolism with improved spatial resolution and synergistic image reconstruction.

Turnover of police chiefs is brisk, with the average tenure of a chief of police ranging from 4 to 6 years, but few scholars have examined factors which may impact police chief turnover. This study...

Deuterium magnetic resonance spectroscopic imaging (DMRSI) has recently been recognized as a potentially powerful tool for noninvasive imaging of brain energy metabolism and tumor. However, the low sensitivity of DMRSI has significantly limited its utility for both research and clinical applications. This work presents a novel machine learning-based method to address this limitation. The proposed method synergistically integrates physics-based subspace modeling and data-driven deep learning for effective denoising, making high-resolution dynamic DMRSI possible. Specifically, a novel subspace model was used to represent the dynamic DMRSI signals; deep neural networks were trained to capture the low-dimensional manifolds of the spectral and temporal distributions of practical dynamic DMRSI data. The learned subspace and manifold structures were integrated via a regularization formulation to remove measurement noise. Theoretical analysis, computer simulations, and in vivo experiments have been conducted to demonstrate the denoising efficacy of the proposed method which enabled high-resolution imaging capability. The translational potential was demonstrated in tumor-bearing rats, where the Warburg effect associated with cancer metabolism and tumor heterogeneity were successfully captured. The new method may not only provide an effective tool to enhance the sensitivity of DMRSI for basic research and clinical applications but also provide a framework for denoising other spatiospectral data.

To accelerate TThe proposed method achieves high-speed TThe proposed method was evaluated using experimental data obtained from both healthy subjects and tumor patients using a turbo spin-echo sequence. High-quality TThis paper demonstrates the feasibility of learning T

Purpose To achieve high-resolution mapping of brain tissue susceptibility in simultaneous QSM and metabolic imaging. Methods Simultaneous QSM and metabolic imaging was first achieved using SPICE (spectroscopic imaging by exploiting spatiospectral correlation), but the QSM maps thus obtained were at relatively low-resolution (2.0 × 3.0 × 3.0 mm3 ). We overcome this limitation using an improved SPICE data acquisition method with the following novel features: 1) sampling (k, t)-space in dual densities, 2) sampling central k-space fully to achieve nominal spatial resolution of 3.0 × 3.0 × 3.0 mm3 for metabolic imaging, and 3) sampling outer k-space sparsely to achieve spatial resolution of 1.0 × 1.0 × 1.9 mm3 for QSM. To keep the scan time short, we acquired spatiospectral encodings in echo-planar spectroscopic imaging trajectories in central k-space but in CAIPIRINHA (controlled aliasing in parallel imaging results in higher acceleration) trajectories in outer k-space using blipped phase encodings. For data processing and image reconstruction, a union-of-subspaces model was used, effectively incorporating sensitivity encoding, spatial priors, and spectral priors of individual molecules. Results In vivo experiments were carried out to evaluate the feasibility and potential of the proposed method. In a 6-min scan, QSM maps at 1.0 × 1.0 × 1.9 mm3 resolution and metabolic maps at 3.0 × 3.0 × 3.0 mm3 nominal resolution were obtained simultaneously. Compared with the original method, the QSM maps obtained using the new method reveal fine-scale brain structures more clearly. Conclusion We demonstrated the feasibility of achieving high-resolution QSM simultaneously with metabolic imaging using a modified SPICE acquisition method. The improved capability of SPICE may further enhance its practical utility in brain mapping.

OBJECTIVE: To enable non-invasive dynamic metabolic mapping in rodent model studies of mitochondrial function using (31)P-MR spectroscopic imaging (MRSI). METHODS: We developed a novel method for high-resolution dynamic (31)P-MRSI. The method synergistically integrates physics-based models of spectral structures, biochemical modeling of molecular dynamics, and subspace learning to capture spatiospectral variations. Fast data acquisition was achieved using rapid spiral trajectories and sparse sampling of (k, t, T)-space; image reconstruction was accomplished using a low-rank tensor-based framework. RESULTS: The proposed method provided high-resolution dynamic metabolic mapping in rat hindlimb at spatial and temporal resolutions of 4×4×2 mm(3) and 1.28 s, respectively. This allowed for in vivo mapping of the time-constant of phosphocreatine resynthesis, a well established index of mitochondrial oxidative capacity. Multiple rounds of in vivo experiments were performed to demonstrate reproducibility, and in vitro experiments were used to validate the accuracy of the estimated metabolite maps. CONCLUSIONS: A new model-based method is proposed to achieve high-resolution dynamic (31)P-MRSI. The proposed method’s ability to delineate metabolic heterogeneity was demonstrated in rat hindlimb. SIGNIFICANCE: Abnormal mitochondrial metabolism is a key cellular dysfunction in many prevalent diseases such as diabetes and heart disease; however, current understanding of mitochondrial function is mostly gained from studies on isolated mitochondria under nonphysiological conditions. The proposed method has the potential to open new avenues of research by allowing in vivo and longitudinal studies of mitochondrial dysfunction in disease development and progression.

Purpose To map brain metabolites and tissue magnetic susceptibility simultaneously using a single three-dimensional 1H-MRSI acquisition without water suppression. Methods The proposed technique builds on a subspace imaging method called spectroscopic imaging by exploiting spatiospectral correlation (SPICE), which enables ultrashort echo time (TE)/short pulse repetition time (TR) acquisitions for 1H-MRSI without water suppression. This data acquisition scheme simultaneously captures both the spectral information of brain metabolites and the phase information of the water signals that is directly related to tissue magnetic susceptibility variations. In extending this scheme for simultaneous QSM and metabolic imaging, we increase k-space coverage by using dual density sparse sampling and ramp sampling to achieve spatial resolution often required by QSM, while maintaining a reasonable signal-to-noise ratio (SNR) for the spatiospectral data used for metabolite mapping. In data processing, we obtain high-quality QSM from the unsuppressed water signals by taking advantage of the larger number of echoes acquired and any available anatomical priors; metabolite spatiospectral distributions are reconstructed using a union-of-subspaces model. Results In vivo experimental results demonstrate that the proposed method can produce susceptibility maps at a resolution higher than 1.8 × 1.8 × 2.4 mm3 along with metabolite spatiospectral distributions at a nominal spatial resolution of 2.4 × 2.4 × 2.4 mm3 from a single 7-min MRSI scan. The estimated susceptibility values are consistent with those obtained using the conventional QSM method with 3D multi-echo gradient echo acquisitions. Conclusion This article reports a new capability for simultaneous susceptibility mapping and metabolic imaging of the brain from a single 1H-MRSI scan, which has potential for a wide range of applications. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

The restorative justice model focuses on amending offender-victim relations. Compared to Western countries, China's criminal justice policy has relied on both formal and informal mechanisms in deal...

Objective: To investigate the use of pre-learnt subspace and spatial constraints for denoising magnetic resonance spectroscopic imaging (MRSI) data. Method: We exploit the partial separability or subspace structures of high-dimensional MRSI data for denoising. More specifically, we incorporate a subspace model with pre-learnt spectral basis into the low-rank approximation (LORA) method. Spectral basis is determined based on empirical prior distributions of the spectral parameters variations learnt from auxiliary training data; spatial priors are also incorporated as is done in LORA to further improve denoising performance. Results: The effects of the explicit subspace and spatial constraints in reducing estimation bias and variance have been analyzed using Cramer-Rao Lower bound analysis, Monte-Carlo study, and experimental study. Conclusion: The denoising effectiveness of LORA can be significantly improved by incorporating pre-learnt spectral basis and spatial priors into LORA. Significance: This study provides an effective method for denoising MRSI data along with comprehensive analyses of its performance. The proposed method is expected to be useful for a wide range of studies using MRSI.

PURPOSE: To accelerate the acquisition of J-resolved (1)H-MRSI data for high-resolution mapping of brain metabolites and neurotransmitters. METHODS: The proposed method used a subspace model to represent multidimensional spatiospectral functions, which significantly reduced the number of parameters to be determined from J-resolved (1)H-MRSI data. A semi-LASER based echo-planar spectroscopic imaging (EPSI) sequence was used for data acquisition. The proposed data acquisition scheme sampled (k, t(1), t(2))-space in variable density, where t(1) and t(2) specify the J-coupling and chemical-shift encoding times, respectively. Selection of the J-coupling encoding times (or, TE values) was based on a Cramer-Rao lower bound analysis, which were optimized for gamma-aminobutyric acid (GABA) detection. In image reconstruction, parameters of the subspace-based spatiospectral model were determined by solving a constrained optimization problem. RESULTS: Feasibility of the proposed method was evaluated using both simulated and experimental data from a spectroscopic phantom. The phantom experimental results showed that the proposed method, with a factor of 12 acceleration in data acquisition, could determine the distribution of J-coupled molecules with expected accuracy. In vivo study with healthy human subjects also showed that 3D maps of brain metabolites and neurotransmitters can be obtained with a nominal spatial resolution of 3.0 × 3.0 × 4.8 mm(3) from J-resolved (1)H-MRSI data acquired in 19.4 minutes. CONCLUSIONS: This work demonstrated the feasibility of highly accelerated J-resolved (1)H-MRSI using limited and sparse sampling of (k, t(1), t(2))-space and subspace modeling. With further development, the proposed method may enable high-resolution mapping of brain metabolites and neurotransmitters in clinical applications.

Purpose To develop a subspace learning method for the recently proposed subspace-based MRSI approach known as SPICE, and achieve ultrafast 1 H-MRSI of the brain. Theory and methods A novel strategy is formulated to learn a low-dimensional subspace representation of MR spectra from specially acquired training data and use the learned subspace for general MRSI experiments. Specifically, the subspace learning problem is formulated as learning "empirical" distributions of molecule-specific spectral parameters (e.g., concentrations, lineshapes, and frequency shifts) by integrating physics-based model and the training data. The learned spectral parameters and quantum mechanical simulation basis can then be combined to construct acquisition-specific subspace for spatiospectral encoding and processing. High-resolution MRSI acquisitions combining ultrashort-TE/short-TR excitation, sparse sampling, and the elimination of water suppression have been performed to evaluate the feasibility of the proposed method. Results The accuracy of the learned subspace and the capability of the proposed method in producing high-resolution 3D 1 H metabolite maps and high-quality spatially resolved spectra (with a nominal resolution of ∼2.4 × 2.4 × 3 mm3 in 5 minutes) were demonstrated using phantom and in vivo studies. By eliminating water suppression, we are also able to extract valuable information from the water signals for data processing ( B 0 map, frequency drift, and coil sensitivity) as well as for mapping tissue susceptibility and relaxation parameters. Conclusions The proposed method enables ultrafast 1 H-MRSI of the brain using a learned subspace, eliminating the need of acquiring subject-dependent navigator data (known as D 1 ) in the original SPICE technique. It represents a new way to perform MRSI experiments and an important step toward practical applications of high-resolution MRSI.

Purpose Social scientific research is having a substantial impact on eyewitness identification procedural reforms. Police agencies in the USA have changed their eyewitness practices based on the results of social scientific research. The purpose of this paper is to contribute new knowledge by using a unique set of data to describe detailed aspects of eyewitness identification procedures conducted as part of robbery investigations in Houston, TX. Design/methodology/approach Robbery investigators completed surveys following identification procedures conducted during a six-month period of time. The sample includes 975 identification procedures. The analysis describes important features of identification procedures and places results in the context of existing research. Findings Results show that photo spreads were the most frequently used lineup procedure and selection outcomes were similar to recent field studies conducted in the USA. Results also show that the type of procedure, presence of a weapon, cross-race identifications, and viewing opportunity were significantly correlated with selection outcomes. Originality/value Police are reforming their eyewitness identification procedures based on findings from social science research. The study measures and describe the characteristics of a large sample eyewitness procedures conducted by investigators in the field.

In theory, sentencing decisions should be driven by legal factors, not extra-legal factors. However, some empirical research on the death penalty in the United States shows significant relationships between offender and victim characteristics and death sentence decisions. Despite the fact that China frequently imposes death sentences, few studies have examined these sanctions to see if similar correlations occur in China’s capital cases. Using data from published court cases in China involving three violent crimes—homicide, robbery, and intentional assault—this study examines the net impact of offender’s gender, race, and victim–offender relationship on death sentence decisions in China. Our overall multiple regression results indicate that, after controlling for other legal and extra-legal variables, an offender’s gender, race, and victim–offender relationship did not produce similar results in China when compared with those in the United States. In contrast, it is the legal factors that played the most significant role in influencing the death penalty decisions. The article concludes with explanations and speculations on the unique social, cultural, and legal conditions in China that may have contributed to these correlations.

To map brain metabolites and tissue magnetic susceptibility simultaneously using a single three-dimensionalThe proposed technique builds on a subspace imaging method called spectroscopic imaging by exploiting spatiospectral correlation (SPICE), which enables ultrashort echo time (TE)/short pulse repetition time (TR) acquisitions forIn vivo experimental results demonstrate that the proposed method can produce susceptibility maps at a resolution higher than 1.8 × 1.8 × 2.4 mmThis article reports a new capability for simultaneous susceptibility mapping and metabolic imaging of the brain from a single

PURPOSE To develop a practical method for mapping macromolecule distribution in the brain using ultrashort-TE MRSI data. METHODS An FID-based chemical shift imaging acquisition without metabolite-nulling pulses was used to acquire ultrashort-TE MRSI data that capture the macromolecule signals with high signal-to-noise-ratio (SNR) efficiency. To remove the metabolite signals from the ultrashort-TE data, single voxel spectroscopy data were obtained to determine a set of high-quality metabolite reference spectra. These spectra were then incorporated into a generalized series (GS) model to represent general metabolite spatiospectral distributions. A time-segmented algorithm was developed to back-extrapolate the GS model-based metabolite distribution from truncated FIDs and remove it from the MRSI data. Numerical simulations and in vivo experiments have been performed to evaluate the proposed method. RESULTS Simulation results demonstrate accurate metabolite signal extrapolation by the proposed method given a high-quality reference. For in vivo experiments, the proposed method is able to produce spatiospectral distributions of macromolecules in the brain with high SNR from data acquired in about 10 minutes. We further demonstrate that the high-dimensional macromolecule spatiospectral distribution resides in a low-dimensional subspace. This finding provides a new opportunity to use subspace models for quantification and accelerated macromolecule mapping. Robustness of the proposed method is also demonstrated using multiple data sets from the same and different subjects. CONCLUSION The proposed method is able to obtain macromolecule distributions in the brain from ultrashort-TE acquisitions. It can also be used for acquiring training data to determine a low-dimensional subspace to represent the macromolecule signals for subspace-based MRSI. Magn Reson Med 79:2460-2469, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Building brain networks based on functional Magnetic Resonance Imaging (fMRI) signal is one of the efficient methods to study functional connectivity of human brain. Various methods of constructing brain network will lead to different results. It is wondered which method is reliable. Therefore, it is necessary to set up a synthetical framework of brain network analysis to study the functional connectivity. A joint analysis method of multi-level functional brain networks is proposed in this paper. These networks are constructed based on different correlation matrixes of fMRI signal between voxels and between anatomical areas (regions) of brain. They are called whole brain network of voxel-based and region-based, and local network of voxel-based inside brain regions. The joint analysis implements feature combination of global and local network attributes to measure or evaluate the brain region characteristics towards reducing uncertainty. The resting-state fMRI data of 37 subjects (22 normal subjects and 15 patients with spinal cord injury (SCI)) have been used to test the proposed method. Three-level functional connectivity networks are jointly analyzed to combine the two-type significant features, the significant differences between normal and patient, and the significant correlations between network features and clinic function scores of patient. The results of the features combination are validated by the specific Brodmann area (BA) regions characterized by the similar and the complementary features, and most of them belong to the dorsolateral prefrontal cortex (DLPFC) and correspond with SCI disease. Compared with network analysis of the commonly used voxel-based whole brain network, the proposed joint analysis method can provide more central, more robust and more reliable evidences. Overall, the proposed method takes advantages of different functional networks and shows the complete discovery to us by the consistency and mutual complementation of these kinds of networks. It would be a new network analysis method of human brain.

Research on brain machine interface (BMI) has been developed very fast in recent years. Numerous feature extraction methods have successfully been applied to electroencephalogram (EEG) classification in various experiments. However, little effort has been spent on EEG based BMI systems regarding familiarity of human faces cognition. In this work, we have implemented and compared the classification performances of four common feature extraction methods, namely, common spatial pattern, principal component analysis, wavelet transform and interval features. High resolution EEG signals were collected from fifteen healthy subjects stimulated by equal number of familiar and novel faces. Principal component analysis outperforms other methods with average classification accuracy reaching 94.2% leading to possible real life applications. Our findings thereby may contribute to the BMI systems for face recognition.

Brain computer interface (BCI) is a direct communication pathway between the human central nervous system and external devices primarily aiming at restoring damaged functions such as sight, hearing and movement. Although great achievements have been made for the development of reliable BCI systems to assist people with upper-limb disabilities, researches on BCI development related to lower-limb are still rudimentary. In the current study, based on the regularized common spatial pattern analysis (R-CSP) method and statistical dependency, we have developed an improved feature selection method for lower-limb movement pattern classification. High-resolution electroencephalogram (EEG) signals were recorded from four healthy male subjects undergoing real lower-limb movements. Compared to the conventional CSP, R-CSP, and PCA methods, the proposed method achieved the best average accuracy of 83.5% for single trial classification of left and right lower-limb movement. Our findings thereby have insightful implications for developing practical BCI systems for lower-limb movement.