Single-base deletion in GmCHR5 increases the genistein-to-daidzein ratio in soybean seed.

Novel mutant alleles related to isoflavone content are useful for breeding programs to improve the disease resistance and nutritional content of soybean. However, identification of mutant alleles from high-density mutant libraries is expensive and time-consuming because soybean has a large, complicated genome. Here, we identified the gene responsible for increased genistein-to-daidzein ratio in seed of the mutant line F333ES017D9. For this purpose, we used a time- and cost-effective approach based on selective genotyping of a small number of F2 plants showing the mutant phenotype with nearest-neighboring-nucleotide substitution-high-resolution melting analysis markers, followed by alignment of short reads obtained by next-generation sequencing analysis with the identified locus. In the mutant line, GmCHR5 harbored a single-base deletion that caused a change in the substrate flow in the isoflavone biosynthetic pathway towards genistein. Mutated GmCHR5 was expressed at a lower level during seed development than wild-type GmCHR5. Ectopic overexpression of GmCHR5 increased the production of daidzein derivatives in both the wild-type and mutant plants. The present strategy will be useful for accelerating identification of mutant alleles responsible for traits of interest in agronomically important crops.

In vitro anticancer activity of loquat tea by inducing apoptosis in human leukemia cells.

Fresh loquat leaves have been used as folk health herb in Asian countries for long time, although the evidence supporting their functions is still minimal. This study aimed to clarify the chemopreventive effect of loquat tea extract (LTE) by investigating the inhibition on proliferation, and underlying mechanisms in human promyelocytic leukemia cells (HL-60). LTE inhibited proliferation of HL-60 in a dose-dependent manner. Molecular data showed that the isolated fraction of LTE induced apoptosis of HL-60 as characterized by DNA fragmentation; activation of caspase-3, -8, and -9; and inactivation of poly(ADP)ribose polymerase. Moreover, LTE fraction increased the ratio of pro-apoptotic Bcl-2-associated X protein (Bax)/anti-apoptotic myeloid cell leukemia 1 (Mcl-1) that caused mitochondrial membrane potential loss and cytochrome c released to cytosol. Thus, our data indicate that LTE might induce apoptosis in HL-60 cells through a mitochondrial dysfunction pathway. These findings enhance our understanding for chemopreventive function of loquat tea.

Genetic analysis of ion-beam induced extremely late heading mutants in rice.

Two extremely late heading mutants were induced by ion beam irradiation in rice cultivar 'Taichung 65': KGM26 and KGM27. The F2 populations from the cross between the two mutants and Taichung 65 showed clear 3 early: 1 late segregation, suggesting control of late heading by a recessive gene. The genes identified in KGM26 and KGM27 were respectively designated as FLT1 and FLT2. The two genes were mapped using the crosses between the two mutants and an Indica cultivar 'Kasalath'. FLT1 was located on the distal end of the short arm of chromosome 8. FLT2 was located around the centromere of chromosome 9. FLT1 might share the same locus as EHD3 because their chromosomal location is overlapping. FLT2 is inferred to be a new gene because no gene with a comparable effect to that of this gene was mapped near the centromere of chromosome 9. In crosses with Kasalath, homozygotes of late heading mutant genes showed a large variation of days to heading, suggesting that other genes affected late heading mutant genes.

A pediatric case of single-level idiopathic cervical intervertebral disk calcification with symptom relapse 1 year after initial onset.

BACKGROUND: To date there has been only 1 reported case of the symptom relapse of pediatric idiopathic intervertebral disk calcification (PIIDC), as described by Yoon and colleagues in 1987, who reported symptom relapse in a patient with multilevel PIIDC. Thus, symptom relapse in patients with single-level PIIDC have not been reported. METHODS: We report here a case of single-level PIIDC with symptom relapse 1 year after the initial onset. RESULTS: The patient was a 7-year-old girl who developed cervical pain and fever up to 38°C without an obvious cause. Computed tomography (CT) revealed calcification in the C4/5 intervertebral disk space and in the epidural space at the C3-5 vertebral levels. The patient was diagnosed with PIIDC and treatment with oral nonsteroidal anti-inflammatory drugs was begun. Both cervical pain and fever gradually improved and resolved in approximately 1 week. CT obtained 6 months after the initial onset showed calcifications localized in the posterior area of the C4/5 intervertebral disk space and reduced epidural calcifications, which had nearly resolved. One year after the initial onset, the patient developed similar symptoms. CT revealed an enlarged calcified lesion in the epidural space. Thus, the patient was diagnosed with symptom relapse of PIIDC. Although there was enlargement of calcifications in the epidural space, there were no calcifications involving the intervertebral disk at the time of relapse. The patient was treated conservatively. Follow-up CT revealed that the lesion resolved with time. CONCLUSIONS: This report described a patient with single-level PIIDC and symptom relapse 1 year after the initial onset. In the case presented herein, calcifications of the intervertebral space had extruded into the epidural space, thus causing a symptom relapse. The patient was treated conservatively at the initial onset and at the time of relapse. The symptoms improved both times. Although patients with single-level PIIDC usually have an uneventful clinical course, it is necessary to be mindful of potential symptom relapse.

ReconU-Net: a direct PET image reconstruction using U-Net architecture with back projection-induced skip connection.

Objective.This study aims to introduce a novel back projection-induced U-Net-shaped architecture, called ReconU-Net, based on the original U-Net architecture for deep learning-based direct positron emission tomography (PET) image reconstruction. Additionally, our objective is to visualize the behavior of direct PET image reconstruction by comparing the proposed ReconU-Net architecture with the original U-Net architecture and existing DeepPET encoder-decoder architecture without skip connections.Approach. The proposed ReconU-Net architecture uniquely integrates the physical model of the back projection operation into the skip connection. This distinctive feature facilitates the effective transfer of intrinsic spatial information from the input sinogram to the reconstructed image via an embedded physical model. The proposed ReconU-Net was trained using Monte Carlo simulation data from the Brainweb phantom and tested on both simulated and real Hoffman brain phantom data.Main results. The proposed ReconU-Net method provided better reconstructed image in terms of the peak signal-to-noise ratio and contrast recovery coefficient than the original U-Net and DeepPET methods. Further analysis shows that the proposed ReconU-Net architecture has the ability to transfer features of multiple resolutions, especially non-abstract high-resolution information, through skip connections. Unlike the U-Net and DeepPET methods, the proposed ReconU-Net successfully reconstructed the real Hoffman brain phantom, despite limited training on simulated data.Significance. The proposed ReconU-Net can improve the fidelity of direct PET image reconstruction, even with small training datasets, by leveraging the synergistic relationship between data-driven modeling and the physics model of the imaging process.

Whole Reconstruction-Free System Design for Direct Positron Emission Imaging From Image Generation to Attenuation Correction.

Direct positron emission imaging (dPEI), which does not require a mathematical reconstruction step, is a next-generation molecular imaging modality. To maximize the practical applicability of the dPEI system to clinical practice, we introduce a novel reconstruction-free image-formation method called direct µCompton imaging, which directly localizes the interaction position of Compton scattering from the annihilation photons in a three-dimensional space by utilizing the same compact geometry as that for dPEI, involving ultrafast time-of-flight radiation detectors. This unique imaging method not only provides the anatomical information about an object but can also be applied to attenuation correction of dPEI images. Evaluations through Monte Carlo simulation showed that functional and anatomical hybrid images can be acquired using this multimodal imaging system. By fusing the images, it is possible to simultaneously access various object data, which ensures the synergistic effect of the two imaging methodologies. In addition, attenuation correction improves the quantification of dPEI images. The realization of the whole reconstruction-free imaging system from image generation to quantitative correction provides a new perspective in molecular imaging.

Animal PET scanner with a large field of view is suitable for high-throughput scanning of rodents.

OBJECTIVE: In preclinical studies, high-throughput positron emission tomography (PET) imaging, known as simultaneous multiple animal scanning, can reduce the time spent on animal experiments, the cost of PET tracers, and the risk of synthesis of PET tracers. It is well known that the image quality acquired by high-throughput imaging depends on the PET system. Herein, we investigated the influence of large field of view (FOV) PET scanner on high-throughput imaging. METHODS: We investigated the influence of scanning four objects using a small animal PET scanner with a large FOV. We compared the image quality acquired by four objects scanned with the one acquired by one object scanned using phantoms and animals. We assessed the image quality with uniformity, recovery coefficient (RC), and spillover ratio (SOR), which are indicators of image noise, spatial resolution, and quantitative precision, respectively. For the phantom study, we used the NEMA NU 4-2008 image quality phantom and evaluated uniformity, RC, and SOR, and for the animal study, we used Wistar rats and evaluated the spillover in the heart and kidney. RESULTS: In the phantom study, four phantoms had little effect on imaging quality, especially SOR compared with that for one phantom. In the animal study as well, four rats had little effect on spillover from the heart muscle and kidney cortex compared with that for one rat. CONCLUSIONS: This study demonstrated that an animal PET scanner with a large FOV was suitable for high-throughput imaging. Thus, the large FOV PET scanner can support drug discovery and bridging research through rapid pharmacological and pathological evaluation.

Deep learning-based PET image denoising and reconstruction: a review.

This review focuses on positron emission tomography (PET) imaging algorithms and traces the evolution of PET image reconstruction methods. First, we provide an overview of conventional PET image reconstruction methods from filtered backprojection through to recent iterative PET image reconstruction algorithms, and then review deep learning methods for PET data up to the latest innovations within three main categories. The first category involves post-processing methods for PET image denoising. The second category comprises direct image reconstruction methods that learn mappings from sinograms to the reconstructed images in an end-to-end manner. The third category comprises iterative reconstruction methods that combine conventional iterative image reconstruction with neural-network enhancement. We discuss future perspectives on PET imaging and deep learning technology.

Deep learning-based correction of cataract-induced influence on macular pigment optical density measurement by autofluorescence spectroscopy.

PURPOSE: Measurements of macular pigment optical density (MPOD) using the autofluorescence spectroscopy yield underestimations of actual values in eyes with cataracts. Previously, we proposed a correction method for this error using deep learning (DL); however, the correction performance was validated through internal cross-validation. This cross-sectional study aimed to validate this approach using an external validation dataset. METHODS: MPODs at 0.25°, 0.5°, 1°, and 2° eccentricities and macular pigment optical volume (MPOV) within 9° eccentricity were measured using SPECTRALIS (Heidelberg Engineering, Heidelberg, Germany) in 197 (training dataset inherited from our previous study) and 157 eyes (validating dataset) before and after cataract surgery. A DL model was trained to predict the corrected value from the pre-operative value using the training dataset, and we measured the discrepancy between the corrected value and the actual postoperative value. Subsequently, the prediction performance was validated using a validation dataset. RESULTS: Using the validation dataset, the mean absolute values of errors for MPOD and MPOV corrected using DL ranged from 8.2 to 12.4%, which were lower than values with no correction (P < 0.001, linear mixed model with Tukey's test). The error depended on the autofluorescence image quality used to calculate MPOD. The mean errors in high and moderate quality images ranged from 6.0 to 11.4%, which were lower than those of poor quality images. CONCLUSION: The usefulness of the DL correction method was validated. Deep learning reduced the error for a relatively good autofluorescence image quality. Poor-quality images were not corrected.

[Direct Positron Emission Imaging Using Ultrafast Timing Performance Detectors].

This is an explanatory paper on Sun Il Kwon et al., Nat. Photon. 15: 914-918, 2021 and some parts of this manuscript are translated from the paper. Medical imaging modalities such as X-ray computed tomography, Magnetic resonance imaging, positron emission tomography (PET), and single photon emission computed tomography, require image reconstruction processes, consequently constraining them to form cylindrical shapes. However, among them, only PET can use additional information, so called time of flight, on an event-by-event basis. If coincidence time resolution (CTR) of PET detectors improved to 30 ps, which corresponds to spatial resolution of 4.5 mm, directly localizing electron-positron annihilation point is possible, allowing us to circumvent image reconstruction processes and free us from the geometric constraint. We call this concept direct positron emission imaging (dPEI). We have developed ultrafast radiation detectors by focusing on Cherenkov photon detection. Furthermore, the CTR of 32 ps being equivalent to 4.8 mm spatial resolution is achieved by combining deep learning-based signal processing with the detectors. In this article, we explain how we developed the detectors and demonstrated the first dPEI using different types of phantoms, how we will tackle limitations to be addressed to make the dPEI more practical, and how dPEI will emerge as an imaging modality in nuclear medicine.

List-Mode PET Image Reconstruction Using Deep Image Prior.

List-mode positron emission tomography (PET) image reconstruction is an important tool for PET scanners with many lines-of-response and additional information such as time-of-flight and depth-of-interaction. Deep learning is one possible solution to enhance the quality of PET image reconstruction. However, the application of deep learning techniques to list-mode PET image reconstruction has not been progressed because list data is a sequence of bit codes and unsuitable for processing by convolutional neural networks (CNN). In this study, we propose a novel list-mode PET image reconstruction method using an unsupervised CNN called deep image prior (DIP) which is the first trial to integrate list-mode PET image reconstruction and CNN. The proposed list-mode DIP reconstruction (LM-DIPRecon) method alternatively iterates the regularized list-mode dynamic row action maximum likelihood algorithm (LM-DRAMA) and magnetic resonance imaging conditioned DIP (MR-DIP) using an alternating direction method of multipliers. We evaluated LM-DIPRecon using both simulation and clinical data, and it achieved sharper images and better tradeoff curves between contrast and noise than the LM-DRAMA, MR-DIP and sinogram-based DIPRecon methods. These results indicated that the LM-DIPRecon is useful for quantitative PET imaging with limited events while keeping accurate raw data information. In addition, as list data has finer temporal information than dynamic sinograms, list-mode deep image prior reconstruction is expected to be useful for 4D PET imaging and motion correction.

Fully 3D implementation of the end-to-end deep image prior-based PET image reconstruction using block iterative algorithm.

Objective. Deep image prior (DIP) has recently attracted attention owing to its unsupervised positron emission tomography (PET) image reconstruction method, which does not require any prior training dataset. In this paper, we present the first attempt to implement an end-to-end DIP-based fully 3D PET image reconstruction method that incorporates a forward-projection model into a loss function.Approach. A practical implementation of a fully 3D PET image reconstruction could not be performed at present because of a graphics processing unit memory limitation. Consequently, we modify the DIP optimization to a block iteration and sequential learning of an ordered sequence of block sinograms. Furthermore, the relative difference penalty (RDP) term is added to the loss function to enhance the quantitative accuracy of the PET image.Main results. We evaluated our proposed method using Monte Carlo simulation with [18F]FDG PET data of a human brain and a preclinical study on monkey-brain [18F]FDG PET data. The proposed method was compared with the maximum-likelihood expectation maximization (EM), maximuma posterioriEM with RDP, and hybrid DIP-based PET reconstruction methods. The simulation results showed that, compared with other algorithms, the proposed method improved the PET image quality by reducing statistical noise and better preserved the contrast of brain structures and inserted tumors. In the preclinical experiment, finer structures and better contrast recovery were obtained with the proposed method.Significance.The results indicated that the proposed method could produce high-quality images without a prior training dataset. Thus, the proposed method could be a key enabling technology for the straightforward and practical implementation of end-to-end DIP-based fully 3D PET image reconstruction.

Deep-learning-based fast TOF-PET image reconstruction using direction information.

Although deep learning for application in positron emission tomography (PET) image reconstruction has attracted the attention of researchers, the image quality must be further improved. In this study, we propose a novel convolutional neural network (CNN)-based fast time-of-flight PET (TOF-PET) image reconstruction method to fully utilize the direction information of coincidence events. The proposed method inputs view-grouped histo-images into a 3D CNN as a multi-channel image to use the direction information of such events. We evaluated the proposed method using Monte Carlo simulation data obtained from a digital brain phantom. Compared with a case without direction information, the peak signal-to-noise ratio and structural similarity were improved by 1.2 dB and 0.02, respectively, at a coincidence time resolution of 300 ps. The calculation times of the proposed method were significantly lower than those of a conventional iterative reconstruction. These results indicate that the proposed method improves both the speed and image quality of a TOF-PET image reconstruction.

Unbiased TOF estimation using leading-edge discriminator and convolutional neural network trained by single-source-position waveforms.

Objective.Convolutional neural networks (CNNs) are a strong tool for improving the coincidence time resolution (CTR) of time-of-flight (TOF) positron emission tomography detectors. However, several signal waveforms from multiple source positions are required for CNN training. Furthermore, there is concern that TOF estimation is biased near the edge of the training space, despite the reduced estimation variance (i.e. timing uncertainty).Approach.We propose a simple method for unbiased TOF estimation by combining a conventional leading-edge discriminator (LED) and a CNN that can be trained with waveforms collected from one source position. The proposed method estimates and corrects the time difference error calculated by the LED rather than the absolute time difference. This model can eliminate the TOF estimation bias, as the combination with the LED converts the distribution of the label data from discrete values at each position into a continuous symmetric distribution.Main results.Evaluation results using signal waveforms collected from scintillation detectors show that the proposed method can correctly estimate all source positions without bias from a single source position. Moreover, the proposed method improves the CTR of the conventional LED.Significance.We believe that the improved CTR will not only increase the signal-to-noise ratio but will also contribute significantly to a part of the direct positron emission imaging.

Performance evaluation of dedicated brain PET scanner with motion correction system.

OBJECTIVE: Various motion correction (MC) algorithms for positron emission tomography (PET) have been proposed to accelerate the diagnostic performance and research in brain activity and neurology. We have incorporated MC system-based optical motion tracking into the brain-dedicated time-of-flight PET scanner. In this study, we evaluate the performance characteristics of the developed PET scanner when performing MC in accordance with the standards and guidelines for the brain PET scanner. METHODS: We evaluate the spatial resolution, scatter fraction, count rate characteristics, sensitivity, and image quality of PET images. The MC evaluation is measured in terms of the spatial resolution and image quality that affect movement. RESULTS: In the basic performance evaluation, the average spatial resolution by iterative reconstruction was 2.2 mm at 10 mm offset position. The measured peak noise equivalent count rate was 38.0 kcps at 16.7 kBq/mL. The scatter fraction and system sensitivity were 43.9% and 22.4 cps/(Bq/mL), respectively. The image contrast recovery was between 43.2% (10 mm sphere) and 72.0% (37 mm sphere). In the MC performance evaluation, the average spatial resolution was 2.7 mm at 10 mm offset position, when the phantom stage with the point source translates to ± 15 mm along the y-axis. The image contrast recovery was between 34.2 % (10 mm sphere) and 66.8 % (37 mm sphere). CONCLUSIONS: The reconstructed images using MC were restored to their nearly identical state as those at rest. Therefore, it is concluded that this scanner can observe more natural brain activity.

A genome-wide microarray highlights the antiinflammatory genes targeted by oolong tea theasinensin A in macrophages.

Theasinensin A is one of the oolong tea theasinensins, which differ from green tea catechins and black tea theaflavins. In a previous study, we found that theasinesin A had a potential effect on antiinflammation since theasinensin A suppressed LPS-induced COX2 and PGE(2) production. To clarify the molecular mechanisms, we investigated the gene expression profiling in macrophage-like cells treated with theasinensin A through a genome-wide DNA microarray in the present study. Among 22,050 oligonucleotides, the expression levels of 406 genes were increased by ≥3-fold in LPS-activated RAW264 cells, 259 gene signals of which were attenuated by theasinensin A treatment (≥2-fold). Expression levels of 717 genes were decreased by ≥3-fold in LPS-activated cells, of which 471 gene signals were restored by theasinensin A treatment (≥2-fold). These genes were further categorized as "defense, inflammatory response, cytokines activities, and receptor activities," and some of them were confirmed by real-time polymerase chain reaction. Furthermore, pathways analysis revealed that theasinensin A regulated the relevant expression networks of chemokines, interleukins, and interferons to exert its antiinflammatory effects.

Anatomical-guided attention enhances unsupervised PET image denoising performance.

Although supervised convolutional neural networks (CNNs) often outperform conventional alternatives for denoising positron emission tomography (PET) images, they require many low- and high-quality reference PET image pairs. Herein, we propose an unsupervised 3D PET image denoising method based on an anatomical information-guided attention mechanism. The proposed magnetic resonance-guided deep decoder (MR-GDD) utilizes the spatial details and semantic features of MR-guidance image more effectively by introducing encoder-decoder and deep decoder subnetworks. Moreover, the specific shapes and patterns of the guidance image do not affect the denoised PET image, because the guidance image is input to the network through an attention gate. In a Monte Carlo simulation of [18F]fluoro-2-deoxy-D-glucose (FDG), the proposed method achieved the highest peak signal-to-noise ratio and structural similarity (27.92 ± 0.44 dB/0.886 ± 0.007), as compared with Gaussian filtering (26.68 ± 0.10 dB/0.807 ± 0.004), image guided filtering (27.40 ± 0.11 dB/0.849 ± 0.003), deep image prior (DIP) (24.22 ± 0.43 dB/0.737 ± 0.017), and MR-DIP (27.65 ± 0.42 dB/0.879 ± 0.007). Furthermore, we experimentally visualized the behavior of the optimization process, which is often unknown in unsupervised CNN-based restoration problems. For preclinical (using [18F]FDG and [11C]raclopride) and clinical (using [18F]florbetapir) studies, the proposed method demonstrates state-of-the-art denoising performance while retaining spatial resolution and quantitative accuracy, despite using a common network architecture for various noisy PET images with 1/10th of the full counts. These results suggest that the proposed MR-GDD can reduce PET scan times and PET tracer doses considerably without impacting patients.

Correction for the Influence of Cataract on Macular Pigment Measurement by Autofluorescence Technique Using Deep Learning.

Purpose: Measurements of macular pigment optical density (MPOD) by the autofluorescence technique yield underestimations of actual values in eyes with cataract. We applied deep learning (DL) to correct this error. Subjects and Methods: MPOD was measured by SPECTRALIS (Heidelberg Engineering, Heidelberg, Germany) in 197 eyes before and after cataract surgery. The nominal MPOD values (= preoperative value) were corrected by three methods: the regression equation (RE) method, subjective classification (SC) method (described in our previous study), and DL method. The errors between the corrected and true values (= postoperative value) were calculated for local MPODs at 0.25°, 0.5°, 1°, and 2° eccentricities and macular pigment optical volume (MPOV) within 9° eccentricity. Results: The mean error for MPODs at four eccentricities was 32% without any correction, 15% with correction by RE, 16% with correction by SC, and 14% with correction by DL. The mean error for MPOV was 21% without correction and 14%, 10%, and 10%, respectively, with correction by the same methods. The errors with any correction were significantly lower than those without correction (P < 0.001, linear mixed model with Tukey's test). The errors with DL correction were significantly lower than those with RE correction in MPOD at 1° eccentricity and MPOV (P < 0.001) and were equivalent to those with SC correction. Conclusions: The objective method using DL was useful to correct MPOD values measured in aged people. Translational Relevance: MPOD can be obtained with small errors in eyes with cataract using DL.

Deep learning-based attenuation correction for brain PET with various radiotracers.

OBJECTIVES: Attenuation correction (AC) is crucial for ensuring the quantitative accuracy of positron emission tomography (PET) imaging. However, obtaining accurate µ-maps from brain-dedicated PET scanners without AC acquisition mechanism is challenging. Therefore, to overcome these problems, we developed a deep learning-based PET AC (deep AC) framework to synthesize transmission computed tomography (TCT) images from non-AC (NAC) PET images using a convolutional neural network (CNN) with a huge dataset of various radiotracers for brain PET imaging. METHODS: The proposed framework is comprised of three steps: (1) NAC PET image generation, (2) synthetic TCT generation using CNN, and (3) PET image reconstruction. We trained the CNN by combining the mixed image dataset of six radiotracers to avoid overfitting, including [18F]FDG, [18F]BCPP-EF, [11C]Racropride, [11C]PIB, [11C]DPA-713, and [11C]PBB3. We used 1261 brain NAC PET and TCT images (1091 for training and 70 for testing). We did not include [11C]Methionine subjects in the training dataset, but included them in the testing dataset. RESULTS: The image quality of the synthetic TCT images obtained using the CNN trained on the mixed dataset of six radiotracers was superior to those obtained using the CNN trained on the split dataset generated from each radiotracer. In the [18F]FDG study, the mean relative PET biases of the emission-segmented AC (ESAC) and deep AC were 8.46 ± 5.24 and - 5.69 ± 4.97, respectively. The deep AC PET and TCT AC PET images exhibited excellent correlation for all seven radiotracers (R2 = 0.912-0.982). CONCLUSION: These results indicate that our proposed deep AC framework can be leveraged to provide quantitatively superior PET images when using the CNN trained on the mixed dataset of PET tracers than when using the CNN trained on the split dataset which means specific for each tracer.