Parallel processing model for low-dose computed tomography image denoising.

Low-dose computed tomography (LDCT) has gained increasing attention owing to its crucial role in reducing radiation exposure in patients. However, LDCT-reconstructed images often suffer from significant noise and artifacts, negatively impacting the radiologists' ability to accurately diagnose. To address this issue, many studies have focused on denoising LDCT images using deep learning (DL) methods. However, these DL-based denoising methods have been hindered by the highly variable feature distribution of LDCT data from different imaging sources, which adversely affects the performance of current denoising models. In this study, we propose a parallel processing model, the multi-encoder deep feature transformation network (MDFTN), which is designed to enhance the performance of LDCT imaging for multisource data. Unlike traditional network structures, which rely on continual learning to process multitask data, the approach can simultaneously handle LDCT images within a unified framework from various imaging sources. The proposed MDFTN consists of multiple encoders and decoders along with a deep feature transformation module (DFTM). During forward propagation in network training, each encoder extracts diverse features from its respective data source in parallel and the DFTM compresses these features into a shared feature space. Subsequently, each decoder performs an inverse operation for multisource loss estimation. Through collaborative training, the proposed MDFTN leverages the complementary advantages of multisource data distribution to enhance its adaptability and generalization. Numerous experiments were conducted on two public datasets and one local dataset, which demonstrated that the proposed network model can simultaneously process multisource data while effectively suppressing noise and preserving fine structures. The source code is available at https://github.com/123456789ey/MDFTN .

Feature shared multi-decoder network using complementary learning for Photon counting CT ring artifact suppression.

BACKGROUND: Photon-counting computed tomography (Photon counting CT) utilizes photon-counting detectors to precisely count incident photons and measure their energy. These detectors, compared to traditional energy integration detectors, provide better image contrast and material differentiation. However, Photon counting CT tends to show more noticeable ring artifacts due to limited photon counts and detector response variations, unlike conventional spiral CT. OBJECTIVE: To comprehensively address this issue, we propose a novel feature shared multi-decoder network (FSMDN) that utilizes complementary learning to suppress ring artifacts in Photon counting CT images. METHODS: Specifically, we employ a feature-sharing encoder to extract context and ring artifact features, facilitating effective feature sharing. These shared features are also independently processed by separate decoders dedicated to the context and ring artifact channels, working in parallel. Through complementary learning, this approach achieves superior performance in terms of artifact suppression while preserving tissue details. RESULTS: We conducted numerous experiments on Photon counting CT images with three-intensity ring artifacts. Both qualitative and quantitative results demonstrate that our network model performs exceptionally well in correcting ring artifacts at different levels while exhibiting superior stability and robustness compared to the comparison methods. CONCLUSIONS: In this paper, we have introduced a novel deep learning network designed to mitigate ring artifacts in Photon counting CT images. The results illustrate the viability and efficacy of our proposed network model as a new deep learning-based method for suppressing ring artifacts.

Cloning, Expression, and Functional Characterization of Two Highly Efficient Flavonoid-di-O-glycosyltransferases ZmUGT84A1 and ZmUGT84A2 from Maize (Zea mays L.).

The maize (Zea mays L.) glycosyltransferase family 1 comprises many uridine diphosphate glycosyltransferase (UGT) members. However, UGT activities and biochemical functions have seldom been revealed. In this study, the genes of two flavonoid di-O-glycosyltransferases ZmUGT84A1 and ZmUGT84A2 were cloned from maize plant and expressed in Escherichia coli. Phylogenetic analysis showed that the two enzymes were homologous to AtUGT84A1 and AtUGT84A3. The two recombinant enzymes showed a high conversion rate of luteolin to its glucosides, mainly 4',7-di-O-glucoside and minorly 3',7-di-O-glucoside in two-step glycosylation reactions in vitro. Moreover, the recombinant ZmUGT84A1 and ZmUGT84A2 had a broad substrate spectrum, converting eriodictyol, naringenin, apigenin, quercetin, and kaempferol to monoglucosides and diglucosides. The highly efficient ZmUGT84A1 and ZmUGT84A2 may be used as a tool for the effective synthesis of various flavonoid O-glycosides and as markers for crop breeding to increase O-glycosyl flavonoid content in food.

Transcriptomic analysis of wheat reveals possible resistance mechanism mediated by Yr10 to stripe rust.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a catastrophic disease that threatens global wheat yield. Yr10 is a race-specific all-stage disease resistance gene in wheat. However, the resistance mechanism of Yr10 is poorly characterized. Therefore, to elucidate the potential molecular mechanism mediated by Yr10, transcriptomic sequencing was performed at 0, 18, and 48 h post-inoculation (hpi) of compatible wheat Avocet S (AvS) and incompatible near-isogenic line (NIL) AvS + Yr10 inoculated with Pst race CYR32. Respectively, 227, 208, and 4050 differentially expressed genes (DEGs) were identified at 0, 18, and 48 hpi between incompatible and compatible interaction. The response of Yr10 to stripe rust involved various processes and activities, as indicated by the results of Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Specifically, the response included photosynthesis, defense response to fungus, metabolic processes related to salicylic acid (SA) and jasmonic acid (JA), and activities related to reactive oxygen species (ROS). Ten candidate genes were selected for qRT-PCR verification and the results showed that the transcriptomic data was reliable. Through the functional analysis of candidate genes by the virus-induced gene silencing (VIGS) system, it was found that the gene TaHPPD (4-hydroxyphenylpyruvate dioxygenase) negatively regulated the resistance of wheat to stripe rust by affecting SA signaling, pathogenesis-related (PR) gene expression, and ROS clearance. Our study provides insight into Yr10-mediated resistance in wheat.

A self-supervised guided knowledge distillation framework for unpaired low-dose CT image denoising.

Low-dose computed tomography (LDCT) can significantly reduce the damage of X-ray to the human body, but the reduction of CT dose will produce images with severe noise and artifacts, which will affect the diagnosis of doctors. Recently, deep learning has attracted more and more attention from researchers. However, most of the denoising networks applied to deep learning-based LDCT imaging are supervised methods, which require paired data for network training. In a realistic imaging scenario, obtaining well-aligned image pairs is challenging due to the error in the table re-positioning and the patient's physiological movement during data acquisition. In contrast, the unpaired learning method can overcome the drawbacks of supervised learning, making it more feasible to collect unpaired training data in most real-world imaging applications. In this study, we develop a novel unpaired learning framework, Self-Supervised Guided Knowledge Distillation (SGKD), which enables the guidance of supervised learning using the results generated by self-supervised learning. The proposed SGKD scheme contains two stages of network training. First, we can achieve the LDCT image quality improvement by the designed self-supervised cycle network. Meanwhile, it can also produce two complementary training datasets from the unpaired LDCT and NDCT images. Second, a knowledge distillation strategy with the above two datasets is exploited to further improve the LDCT image denoising performance. To evaluate the effectiveness and feasibility of the proposed method, extensive experiments were performed on the simulated AAPM challenging and real-world clinical LDCT datasets. The qualitative and quantitative results show that the proposed SGKD achieves better performance in terms of noise suppression and detail preservation compared with some state-of-the-art network models.

Low-Dose CT Image Synthesis for Domain Adaptation Imaging Using a Generative Adversarial Network With Noise Encoding Transfer Learning.

Deep learning (DL) based image processing methods have been successfully applied to low-dose x-ray images based on the assumption that the feature distribution of the training data is consistent with that of the test data. However, low-dose computed tomography (LDCT) images from different commercial scanners may contain different amounts and types of image noise, violating this assumption. Moreover, in the application of DL based image processing methods to LDCT, the feature distributions of LDCT images from simulation and clinical CT examination can be quite different. Therefore, the network models trained with simulated image data or LDCT images from one specific scanner may not work well for another CT scanner and image processing task. To solve such domain adaptation problem, in this study, a novel generative adversarial network (GAN) with noise encoding transfer learning (NETL), or GAN-NETL, is proposed to generate a paired dataset with a different noise style. Specifically, we proposed a method to perform noise encoding operator and incorporate it into the generator to extract a noise style. Meanwhile, with a transfer learning (TL) approach, the image noise encoding operator transformed the noise type of the source domain to that of the target domain for realistic noise generation. One public and two private datasets are used to evaluate the proposed method. Experiment results demonstrated the feasibility and effectiveness of our proposed GAN-NETL model in LDCT image synthesis. In addition, we conduct additional image denoising study using the synthesized clinical LDCT data, which verified the merit of the proposed synthesis in improving the performance of the DL based LDCT processing method.

MFCNet: A multi-modal fusion and calibration networks for 3D pancreas tumor segmentation on PET-CT images.

In clinical diagnosis, positron emission tomography and computed tomography (PET-CT) images containing complementary information are fused. Tumor segmentation based on multi-modal PET-CT images is an important part of clinical diagnosis and treatment. However, the existing current PET-CT tumor segmentation methods mainly focus on positron emission tomography (PET) and computed tomography (CT) feature fusion, which weakens the specificity of the modality. In addition, the information interaction between different modal images is usually completed by simple addition or concatenation operations, but this has the disadvantage of introducing irrelevant information during the multi-modal semantic feature fusion, so effective features cannot be highlighted. To overcome this problem, this paper propose a novel Multi-modal Fusion and Calibration Networks (MFCNet) for tumor segmentation based on three-dimensional PET-CT images. First, a Multi-modal Fusion Down-sampling Block (MFDB) with a residual structure is developed. The proposed MFDB can fuse complementary features of multi-modal images while retaining the unique features of different modal images. Second, a Multi-modal Mutual Calibration Block (MMCB) based on the inception structure is designed. The MMCB can guide the network to focus on a tumor region by combining different branch decoding features using the attention mechanism and extracting multi-scale pathological features using a convolution kernel of different sizes. The proposed MFCNet is verified on both the public dataset (Head and Neck cancer) and the in-house dataset (pancreas cancer). The experimental results indicate that on the public and in-house datasets, the average Dice values of the proposed multi-modal segmentation network are 74.14% and 76.20%, while the average Hausdorff distances are 6.41 and 6.84, respectively. In addition, the experimental results show that the proposed MFCNet outperforms the state-of-the-art methods on the two datasets.

X-ray CT image denoising with MINF: A modularized iterative network framework for data from multiple dose levels.

In clinical applications, multi-dose scan protocols will cause the noise levels of computed tomography (CT) images to fluctuate widely. The popular low-dose CT (LDCT) denoising network outputs denoised images through an end-to-end mapping between an LDCT image and its corresponding ground truth. The limitation of this method is that the reduced noise level of the image may not meet the diagnostic needs of doctors. To establish a denoising model adapted to the multi-noise levels robustness, we proposed a novel and efficient modularized iterative network framework (MINF) to learn the feature of the original LDCT and the outputs of the previous modules, which can be reused in each following module. The proposed network can achieve the goal of gradual denoising, outputting clinical images with different denoising levels, and providing the reviewing physicians with increased confidence in their diagnosis. Moreover, a multi-scale convolutional neural network (MCNN) module is designed to extract as much feature information as possible during the network's training. Extensive experiments on public and private clinical datasets were carried out, and comparisons with several state-of-the-art methods show that the proposed method can achieve satisfactory results for noise suppression of LDCT images. In further comparisons with modularized adaptive processing neural network (MAP-NN), the proposed network shows superior step-by-step or gradual denoising performance. Considering the high quality of gradual denoising results, the proposed method can obtain satisfactory performance in terms of image contrast and detail protection as the level of denoising increases, which shows its potential to be suitable for a multi-dose levels denoising task.

A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on 18F-FDG PET/CT images.

PURPOSE: To explore a multidomain fusion model of radiomics and deep learning features based on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images to distinguish pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP), which could effectively improve the accuracy of diseases diagnosis. MATERIALS AND METHODS: This retrospective study included 48 patients with AIP (mean age, 65 ± 12.0 years; range, 37-90 years) and 64 patients with PDAC patients (mean age, 66 ± 11.3 years; range, 32-88 years). Three different methods were discussed to identify PDAC and AIP based on 18F-FDG PET/CT images, including the radiomics model (RAD_model), the deep learning model (DL_model), and the multidomain fusion model (MF_model). We also compared the classification results of PET/CT, PET, and CT images in these three models. In addition, we explored the attributes of deep learning abstract features by analyzing the correlation between radiomics and deep learning features. Five-fold cross-validation was used to calculate receiver operating characteristic (ROC), area under the roc curve (AUC), accuracy (Acc), sensitivity (Sen), and specificity (Spe) to quantitatively evaluate the performance of different classification models. RESULTS: The experimental results showed that the multidomain fusion model had the best comprehensive performance compared with radiomics and deep learning models, and the AUC, accuracy, sensitivity, specificity were 96.4% (95% CI 95.4-97.3%), 90.1% (95% CI 88.7-91.5%), 87.5% (95% CI 84.3-90.6%), and 93.0% (95% CI 90.3-95.6%), respectively. And our study proved that the multimodal features of PET/CT were superior to using either PET or CT features alone. First-order features of radiomics provided valuable complementary information for the deep learning model. CONCLUSION: The preliminary results of this paper demonstrated that our proposed multidomain fusion model fully exploits the value of radiomics and deep learning features based on 18F-FDG PET/CT images, which provided competitive accuracy for the discrimination of PDAC and AIP.

Domain-adaptive denoising network for low-dose CT via noise estimation and transfer learning.

BACKGROUND: In recent years, low-dose computed tomography (LDCT) has played an important role in the diagnosis CT to reduce the potential adverse effects of X-ray radiation on patients, while maintaining the same diagnostic image quality. PURPOSE: Deep learning (DL)-based methods have played an increasingly important role in the field of LDCT imaging. However, its performance is highly dependent on the consistency of feature distributions between training data and test data. Due to patient's breathing movements during data acquisition, the paired LDCT and normal dose CT images are difficult to obtain from realistic imaging scenarios. Moreover, LDCT images from simulation or clinical CT examination often have different feature distributions due to the pollution by different amounts and types of image noises. If a network model trained with a simulated dataset is used to directly test clinical patients' LDCT data, its denoising performance may be degraded. Based on this, we propose a novel domain-adaptive denoising network (DADN) via noise estimation and transfer learning to resolve the out-of-distribution problem in LDCT imaging. METHODS: To overcome the previous adaptation issue, a novel network model consisting of a reconstruction network and a noise estimation network was designed. The noise estimation network based on a double branch structure is used for image noise extraction and adaptation. Meanwhile, the U-Net-based reconstruction network uses several spatially adaptive normalization modules to fuse multi-scale noise input. Moreover, to facilitate the adaptation of the proposed DADN network to new imaging scenarios, we set a two-stage network training plan. In the first stage, the public simulated dataset is used for training. In the second transfer training stage, we will continue to fine-tune the network model with a torso phantom dataset, while some parameters are frozen. The main reason using the two-stage training scheme is based on the fact that the feature distribution of image content from the public dataset is complex and diverse, whereas the feature distribution of noise pattern from the torso phantom dataset is closer to realistic imaging scenarios. RESULTS: In an evaluation study, the trained DADN model is applied to both the public and clinical patient LDCT datasets. Through the comparison of visual inspection and quantitative results, it is shown that the proposed DADN network model can perform well in terms of noise and artifact suppression, while effectively preserving image contrast and details. CONCLUSIONS: In this paper, we have proposed a new DL network to overcome the domain adaptation problem in LDCT image denoising. Moreover, the results demonstrate the feasibility and effectiveness of the application of our proposed DADN network model as a new DL-based LDCT image denoising method.

Theoretical foundation for designing multilayer Halbach array magnets for benchtop NMR and MRI.

Multilayer Halbach array magnets support portable NMR and MRI, but optimizing their design to maximize performance and minimize the use of expensive magnet materials is challenging. This is partly because our theoretical understanding of such arrays is incomplete and computationally intensive. Here we provide a theoretical description of the magnetic field distribution and we demonstrate that inhomogeneity is greatest along the z axis in multilayer Halbach array magnets. This allows the configuration of the multilayer Halbach array magnets to be optimized in a way that takes into account homogeneity, magnet volume, and magnetic flux density. At the same time, our description simplifies the design of multilayer array magnets, while accommodating the possibility of different outer radii, lengths for each layer array, or the presence of separation between the rings. We validated the theoretical description in simulations of a three-layer Halbach array magnet, then with a prototype three-layer 1-T Halbach array magnet. After adjusting the position of magnet blocks in the neighboring rings, we achieved homogeneity of 220 ppm for a standard 5 mm NMR tube while the inner diameter of the magnet is 20 mm. Our work provides a theoretical foundation for designing multilayer Halbach array magnets to maximize homogeneity and minimize the use of magnet materials.

The safety and effectiveness of endovascular treatment for patients with vertebrobasilar artery occlusions: according to the BEST and BASICS criteria.

Background: Whether endovascular treatment (EVT) is safe and effective for vertebrobasilar artery occlusion (VBAO) is yet incompletely understood. Two RCTs, the endovascular treatment versus standard medical treatment for vertebrobasilar artery occlusion (BEST) trail and the Basilar Artery International Cooperation Study (BASICS), concentrating on this field were recently reported. Objective: We use real-world registry data of VBAO to compare the outcome of EVT inside and outside the inclusion and exclusion criteria of the BEST and BASICS study to testify the feasibility of the selection paradigms of VBAO in these trials. Methods: Consecutive patients with VBAO receiving EVT involving 21 stroke centers were retrospectively included. The safety outcomes [3-month mortality, symptomatic intracranial hemorrhage (sICH), and effectiveness outcomes (the proportion of 3-month functional independence (mRS of 0-2) and favorable outcome (mRS of 0-3)] were compared between VBAO patients who meet or failed to meet the BEST/BASICS selection criteria for EVT. Results: Our study cohort consisted of 577 VBAO patients who underwent EVT. Of them, 446 patients had pc-ASPECTS ≧8. Successful reperfusion (mTICI 2b or 3) was achieved in 85.4% (n = 493). There were 418 patients fulfilling the BEST criterion for EVT and 194 fulfilling the BASICS criterion. Regression analysis indicated that adherence to BEST or BASICS criterion for EVT was not independently related to most of the safety and effectiveness outcome except that adherence to BEST was significantly associated with the 3-month favorable outcome (ORBEST: 1.742, 95% CI: 1.087-2.790). However, when we put pc-ASPECTS into both criteria with a cut-off value of 8, meeting both BEST criterion plus pc-ASPECTS and BASICS criterion plus pc-ASPECTS was independently related to 3-month functional independence (ORBEST: 1.687, 95% CI: 1.077-2.644; ORBASIC: 1.653, 95% CI: 1.038-2.631) and favorable outcome (ORBEST: 2.280, 95% CI: 1.484-3.502; ORBASIC: 2.153, 95% CI: 1.372-3.378). Conclusion: Our study indicated that, based on real-world data of EVT, adherence to BEST or BASICS criterion for EVT was not independently associated with the safety and effectiveness outcome except that adherence to BEST was significantly related to the 3-month favorable outcome. However, the BEST or BASICS selection criterion and pc-ASPECTS ≧8 might be better paradigms for EVT patient selection.

Prognostic Evaluation Based on Dual-Time 18F-FDG PET/CT Radiomics Features in Patients with Locally Advanced Pancreatic Cancer Treated by Stereotactic Body Radiation Therapy.

Background: 18F-FDG PET/CT is widely used in the prognosis evaluation of tumor patients. The radiomics features can provide additional information for clinical prognostic assessment. Purpose: Purpose is to explore the prognostic value of radiomics features from dual-time 18F-FDG PET/CT images for locally advanced pancreatic cancer (LAPC) patients treated with stereotactic body radiation therapy (SBRT). Materials and Methods: This retrospective study included 70 LAPC patients who received early and delayed 18F-FDG PET/CT scans before SBRT treatment. A total of 1188 quantitative imaging features were extracted from dual-time PET/CT images. To avoid overfitting, the univariate analysis and elastic net were used to obtain a sparse set of image features that were applied to develop a radiomics score (Rad-score). Then, the Harrell consistency index (C-index) was used to evaluate the prognosis model. Results: The Rad-score from dual-time images contains six features, including intensity histogram, morphological, and texture features. In the validation cohort, the univariate analysis showed that the Rad-score was the independent prognostic factor (p < 0.001, hazard ratio [HR]: 3.2). And in the multivariate analysis, the Rad-score was the only prognostic factor (p < 0.01, HR: 4.1) that was significantly associated with the overall survival (OS) of patients. In addition, according to cross-validation, the C-index of the prognosis model based on the Rad-score from dual-time images is better than the early and delayed images (0.720 vs. 0.683 vs. 0.583). Conclusion: The Rad-score based on dual-time 18F-FDG PET/CT images is a promising noninvasive method with better prognostic value.

CCN-CL: A content-noise complementary network with contrastive learning for low-dose computed tomography denoising.

In recent years, low-dose computed tomography (LDCT) has played an increasingly important role in the diagnosis CT to reduce the potential adverse effects of x-ray radiation on patients while maintaining the same diagnostic image quality. Current deep learning-based denoising methods applied to LDCT imaging only use normal dose CT (NDCT) images as positive examples to guide the denoising process. Recent studies on contrastive learning have proved that the original images as negative examples can also be helpful for network learning. Therefore, this paper proposes a novel content-noise complementary network with contrastive learning for an LDCT denoising task. First, to better train our proposed network, a contrastive learning loss, taking the NDCT image as a positive example and the original LDCT image as a negative example to guide the network learning is added. Furthermore, we also design a network structure that combines content-noise complementary learning strategy, attention mechanism, and deformable convolution for better network performance. In an evaluation study, we compare the performance of our designed network with some of the state-of-the-art methods in the 2016 NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge dataset. The quantitative and qualitative evaluation results demonstrate the feasibility and effectiveness of applying our proposed CCN-CL network model as a new deep learning-based LDCT denoising method.

Efficient and genotype independent maize transformation using pollen transfected by DNA-coated magnetic nanoparticles.

Current gene delivery methods for maize are limited to specific genotypes and depend on time-consuming and labor-intensive tissue culture techniques. Here, we report a new method to transfect maize that is culture-free and genotype independent. To enhance efficiency of DNA entry and maintain high pollen viability of 32%-55%, transfection was performed at cool temperature using pollen pretreated to open the germination aperture (40%-55%). Magnetic nanoparticles (MNPs) coated with DNA encoding either red fluorescent protein (RFP), ß-glucuronidase gene (GUS), enhanced green fluorescent protein (EGFP) or bialaphos resistance (bar) was delivered into pollen grains, and female florets of maize inbred lines were pollinated. Red fluorescence was detected in 22% transfected pollen grains, and GUS stained 55% embryos at 18 d after pollination. Green fluorescence was detected in both silk filaments and immature kernels. The T1 generation of six inbred lines showed considerable EGFP or GUS transcripts (29%-74%) quantitated by polymerase chain reaction, and 5%-16% of the T1 seedlings showed immunologically active EGFP or GUS protein. Moreover, 1.41% of the bar transfected T1 plants were glufosinate resistant, and heritable bar gene was integrated into the maize genome effectively as verified by DNA hybridization. These results demonstrate that exogenous DNA could be delivered efficiently into elite maize inbred lines recalcitrant to tissue culture-mediated transformation and expressed normally through our genotype-independent pollen transfection system.

Design of a rapid diagnostic model for bladder compliance based on real-time intravesical pressure monitoring system.

OBJECTIVE: The diagnosis of bladder dysfunction for children depends on the confirmation of abnormal bladder shape and bladder compliance. The existing gold standard needs to conduct voiding cystourethrogram (VCUG) examination and urodynamic studies (UDS) examination on patients separately. To reduce the time and injury of children's inspection, we propose a novel method to judge the bladder compliance by measuring the intravesical pressure during the VCUG examination without extra UDS. METHODS: Our method consisted of four steps. We firstly developed a single-tube device that can measure, display, store, and transmit real-time pressure data. Secondly, we conducted clinical trials with the equipment on a cohort of 52 patients (including 32 negative and 20 positive cases). Thirdly, we preprocessed the data to eliminate noise and extracted features, then we used the least absolute shrinkage and selection operator (LASSO) to screen out important features. Finally, several machine learning methods were applied to classify and predict the bladder compliance level, including support vector machine (SVM), Random Forest, XGBoost, perceptron, logistic regression, and Naive Bayes, and the classification performance was evaluated. RESULTS: 73 features were extracted, including first-order and second-order time-domain features, wavelet features, and frequency domain features. 15 key features were selected and the model showed promising classification performance. The highest AUC value was 0.873 by the SVM algorithm, and the corresponding accuracy was 84%. CONCLUSION: We designed a system to quickly obtain the intravesical pressure during the VCUG test, and our classification model is competitive in judging patients' bladder compliance. SIGNIFICANCE: This could facilitate rapid auxiliary diagnosis of bladder disease based on real-time data. The promising result of classification is expected to provide doctors with a reliable basis in the auxiliary diagnosis of some bladder diseases prior to UDS.

Natural variation of ZmCGT1 is responsible for isoorientin accumulation in maize silk.

Maize (Zea mays L.) silk contains high levels of flavonoids and is widely used to promote human health. Isoorientin, a natural C-glycoside flavone abundant in maize silk, has attracted considerable attention due to its potential value. Although different classes of flavonoid have been well characterized in plants, the genes involved in the biosynthesis of isoorientin in maize are largely unknown. Here, we used targeted metabolic profiling of isoorientin on the silks in an association panel consisting of 294 maize inbred lines. We identified the gene ZmCGT1 by genome-wide association analysis. The ZmCGT1 protein was characterized as a 2-hydroxyflavanone C-glycosyltransferase that can C-glycosylate 2-hydroxyflavanone to form flavone-C-glycoside after dehydration. Moreover, ZmCGT1 overexpression increased isoorientin levels and RNA interference-mediated ZmCGT1 knockdown decreased accumulation of isoorientin in maize silk. Further, two nucleotide polymorphisms, A502C and A1022G, which led to amino acid changes I168L and E341G, respectively, were identified to be functional polymorphisms responsible for the natural variation in isoorientin levels. In summary, we identified the gene ZmCGT1, which plays an important role in isoorientin biosynthesis, providing insights into the genetic basis of the natural variation in isoorientin levels in maize silk. The identified favorable CG allele of ZmCGT1 may be further used for genetic improvement of nutritional quality in maize.

Isoorientin Affects Markers of Alzheimer's Disease via Effects on the Oral and Gut Microbiota in APP/PS1 Mice.

BACKGROUND: There is growing evidence of strong associations between the pathogenesis of Alzheimer's disease (AD) and dysbiotic oral and gut microbiota. Recent studies demonstrated that isoorientin (ISO) is anti-inflammatory and alleviates markers of AD, which were hypothesized to be mediated by the oral and gut microbiota. OBJECTIVES: We studied the effects of oral administration of ISO on AD-related markers and the oral and gut microbiota in mice. METHODS: Eight-month-old amyloid precursor protein/presenilin-1 (AP) transgenic male mice were randomly allocated to 3 groups of 15 mice each: vehicle (AP) alone or with a low dose of ISO (AP + ISO-L; 25 mg/kg) or a high dose of ISO (AP + ISO-H; 50 mg/kg). Age-matched wild-type (WT) C57BL/6 male littermates were used as controls. The 4 groups were treated intragastrically with ISO or sterilized ultrapure water for 2 months. AD-related markers in the brain, serum, colon, and liver were analyzed with immunohistochemical and histochemical staining, Western blotting, and ELISA. Oral and gut microbiotas were analyzed using 16S ribosomal RNA gene sequencing. RESULTS: The high-dose ISO treatment significantly decreased amyloid beta 42-positive deposition by 38.1% and 45.2% in the cortex and hippocampus, respectively, of AP mice (P < 0.05). Compared with the AP group, both ISO treatments reduced brain phospho-Tau, phosphor-p65, phosphor-inhibitor of NF-κB, and brain and serum LPS and TNF-α by 17.9%-72.5% and increased brain and serum IL-4 and IL-10 by 130%-210% in the AP + ISO-L and AP + ISO-H groups (P < 0.05). Abundances of 26, 25, and 23 microbial taxa in oral, fecal and cecal samples, respectively, were increased in both the AP + ISO-L and AP + ISO-H groups relative to the AP group [linear discriminant analysis (LDA) >3.0; P < 0.05]. Gram-negative bacteria, Alteromonas, Campylobacterales, and uncultured Bacteroidales bacterium were positively correlated (rho = 0.28-0.59; P < 0.05) with the LPS levels and responses of inflammatory cytokines. CONCLUSIONS: The microbiota-gut-brain axis is a potential mechanism by which ISO reduces AD-related markers in AP mice.

TaMYB29: A Novel R2R3-MYB Transcription Factor Involved in Wheat Defense Against Stripe Rust.

Members of the R2R3-MYB transcription factor superfamily have been implicated in plant development, improved disease resistance, and defense responses to several types of stresses. To study the function of TaMYB29 transcription factor-a member of the R2R3-MYB superfamily-in response to an avirulent race of stripe rust pathogen, Puccinia striiformis f. sp. tritici (Pst), we identified and cloned the TaMYB29 gene from wheat cultivar (cv.) AvS+Yr10 following infection with Pst. The TaMYB29 protein, comprising 261 amino acids, contains two highly conserved MYB domains. We first showed that TaMYB29 is a transcription factor, whose transcriptional levels are significantly induced by salicylic acid (SA), abscisic acid (ABA), jasmonic acid (JA), ethylene (ET), and Pst. The results showed that TaMYB29 is involved in the wheat response to stipe rust. The overexpression of the TaMYB29 gene resulted in the accumulation of reactive oxygen species (ROS) and pathogen-independent cell death in Nicotiana benthamiana leaves. The silencing of TaMYB29 gene in wheat cv. AvS+Yr10, containing the stripe rust resistance gene Yr10, promoted hyphae growth, significantly downregulated the expression of pathogenesis-related (PR) genes, and substantially reduced the wheat resistance to Pst compared with the non-silenced control. In addition, the accumulation of hydrogen peroxide (H2O2) significantly decreased, and the activity of catalase, an enzyme required for H2O2 scavenging, was elevated. Altogether, TaMYB29 positively regulates the defense response against stripe rust in wheat AvS+Yr10 by enhancing H2O2 accumulation, PR gene expression, and SA signaling pathway-induced cell death. These results provide new insights into the contribution of TaMYB29 to the defense response against rust pathogens in wheat.

Deep attention branch networks for skin lesion classification.

BACKGROUND AND OBJECTIVE: The skin lesion usually covers a small region of the dermoscopy image, and the lesions of different categories might own high similarities. Therefore, it is essential to design an elaborate network for accurate skin lesion classification, which can focus on semantically meaningful lesion parts. Although the Class Activation Mapping (CAM) shows good localization capability of highlighting the discriminative parts, it cannot be obtained in the forward propagation process. METHODS: We propose a Deep Attention Branch Network (DABN) model, which introduces the attention branches to expand the conventional Deep Convolutional Neural Networks (DCNN). The attention branch is designed to obtain the CAM in the training stage, which is then utilized as an attention map to make the network focus on discriminative parts of skin lesions. DABN is applicable to multiple DCNN structures and can be trained in an end-to-end manner. Moreover, a novel Entropy-guided Loss Weighting (ELW) strategy is designed to counter class imbalance influence in the skin lesion datasets. RESULTS: The proposed method achieves an Average Precision (AP) of 0.719 on the ISIC-2016 dataset and an average area under the ROC curve (AUC) of 0.922 on the ISIC-2017 dataset. Compared with other state-of-the-art methods, our method obtains better performance without external data and ensemble learning. Moreover, extensive experiments demonstrate that it can be applied to multi-class classification tasks and improves mean sensitivity by more than 2.6% in different DCNN structures. CONCLUSIONS: The proposed method can adaptively focus on the discriminative regions of dermoscopy images and allows for effective training when facing class imbalance, leading to the performance improvement of skin lesion classification, which could also be applied to other clinical applications.