Estimation of Spatiotemporal Sensitivity Using Band-limited Signals with No Additional Acquisitions for k-t Parallel Imaging.

PURPOSE: Dynamic MR techniques, such as cardiac cine imaging, benefit from shorter acquisition times. The goal of the present study was to develop a method that achieves short acquisition times, while maintaining a cost-effective reconstruction, for dynamic MRI. k - t sensitivity encoding (SENSE) was identified as the base method to be enhanced meeting these two requirements. METHODS: The proposed method achieves a reduction in acquisition time by estimating the spatiotemporal (x - f) sensitivity without requiring the acquisition of the alias-free signals, typical of the k - t SENSE technique. The cost-effective reconstruction, in turn, is achieved by a computationally efficient estimation of the x - f sensitivity from the band-limited signals of the aliased inputs. Such band-limited signals are suitable for sensitivity estimation because the strongly aliased signals have been removed. RESULTS: For the same reduction factor 4, the net reduction factor 4 for the proposed method was significantly higher than the factor 2.29 achieved by k - t SENSE. The processing time is reduced from 4.1 s for k - t SENSE to 1.7 s for the proposed method. The image quality obtained using the proposed method proved to be superior (mean squared error [MSE] ± standard deviation [SD] = 6.85 ± 2.73) compared to the k - t SENSE case (MSE ± SD = 12.73 ± 3.60) for the vertical long-axis (VLA) view, as well as other views. CONCLUSION: In the present study, k - t SENSE was identified as a suitable base method to be improved achieving both short acquisition times and a cost-effective reconstruction. To enhance these characteristics of base method, a novel implementation is proposed, estimating the x - f sensitivity without the need for an explicit scan of the reference signals. Experimental results showed that the acquisition, computational times and image quality for the proposed method were improved compared to the standard k - t SENSE method.

Properties of 3-methyl-TIQ and 3-methyl-N-propargyl-TIQ for preventing MPTP-induced parkinsonism-like symptoms in mice.

BACKGROUND: Selegiline, a therapeutic drug for Parkinson's disease (PD), structurally resembles the endogenous parkinsonism-related compound 1,2,3,4-tetrahydroisoquinoline (TIQ). In the present study, we evaluated the effects of 3-methyl-TIQ (3-MeTIQ) and 3-methyl-N-propargyl-TIQ (3-Me-N-proTIQ), selegiline mimetic TIQ derivatives, for preventing 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced parkinsonism-like symptoms in mice. METHODS: We evaluated the preventative effects of 3-MeTIQ and 3-Me-N-proTIQ on MPTP-induced bradykinesia and depletion of striatal dopamine (DA) and nigral tyrosine hydroxylase (TH)-positive cells. RESULTS: MPTP-induced bradykinesia was not different when mice were pretreated with 3-MeTIQ, except for the high-dose group. However, pretreatment with 3-Me-N-proTIQ significantly prevented the appearance of this akinesic status. MPTP-induced striatal DA and 3,4-dehydroxyphenylacetic acid reduction were significantly prevented by pretreatment with 3-Me-N-proTIQ, but not 3-MeTIQ, in a dose-dependent manner. On the other hand, levels of serotonin and its metabolite, 5-hydroxyindole acetic acid, in the striatum were increased following treatment with 3-MeTIQ. In addition, the MPTP-induced decrease in TH-positive cells in the substantia nigra was significantly reduced by pretreatment with 3-Me-N-proTIQ, but not 3-MeTIQ. CONCLUSIONS: These results suggest that not only does 3-Me-N-proTIQ have potential as a candidate compound for disease-modifying therapy for PD, but also the N-propargyl functional group plays an important role in neuroprotection.

Integrated state evaluation for the images of crystallization droplets utilizing linear and nonlinear classifiers.

In a usual crystallization process, the researchers evaluate the protein crystallization growth states based on visual impressions and repeatedly assign scores throughout the growth process. Although the development of crystallization robotic systems has generally realised the automation of the setup and storage of crystallization samples, evaluation of crystallization states has not yet been completely automated. The method presented here attempts to categorize individual crystallization droplet images into five classes using multiple classifiers. In particular, linear and nonlinear classifiers are utilized. The algorithm is comprised of pre-processing, feature extraction from images using texture analysis and a categorization process using linear discriminant analysis (LDA) and support vector machine (SVM). The performance of this method has been evaluated by comparing the results obtained using the method with the results obtained by a human expert and the concordance rate was 84.4%.

Evaluation of crystalline objects in crystallizing protein droplets based on line-segment information in greyscale images.

Several automated crystallization systems have recently been developed for high-throughput X-ray structure analysis. However, the evaluation process for the growth state of crystallizing protein droplets has not yet been completely automated. This paper proposes a new evaluation method for crystalline objects in automated crystallization experiments. The main objective is to determine whether a droplet contains crystals suitable for diffraction experiments and analysis. The evaluation method developed here involves extracting line-segment features from an image of the droplet and discriminating the state of crystallization using classifiers based on line features. In order to verify the efficacy of the proposed method, it was used to classify images obtained by an automated crystallization system.

Evaluation of protein crystallization states based on texture information derived from greyscale images.

In recent years, several projects have advanced research and development related to the automation of the protein crystallization process. However, evaluation of crystallization states has not yet been completely automated. In the usual crystallization process, researchers evaluate the protein crystallization growth states based on visual impressions and assign them a score over and over again. The method presented here automates this evaluation process. This method attempts to categorize the individual crystallization droplet images into five classes. The algorithm is comprised of pre-processing, feature extraction from images using texture analysis and a categorization process using linear discriminant analysis. The performance of this method has been evaluated by comparing the results obtained by using this method with the results from a human expert and the concordance rate was 90.6%.

Down-regulation of endodermal Shh is required for gland formation in chicken stomach.

During the development of the proventriculus (glandular stomach) of the chicken embryo, the endodermal epithelium invades into the surrounding mesenchyme and forms glands. The glandular epithelial cells produce pepsinogen, while the non-glandular (luminal) epithelial cells secrete mucus. Sonic hedgehog is expressed uniformly in the proventricular epithelium before gland formation, but its expression ceases in gland cells. Here we present evidence that down-regulation of Sonic hedgehog is necessary for gland formation in the epithelium using a specific inhibitor of Sonic hedgehog signaling and virus mediated overexpression of Sonic hedgehog. We also show that gland formation is not induced by down-regulation of Sonic hedgehog alone; a mesenchymal influence is also required.