Knocking out isopropylmalate synthase simultaneously improves grain appearance and nutritional quality in rice.

Grain appearance and nutritional quality are critical traits for rice marketing. However, how to simultaneously improve grain appearance (slender grain and low chalkiness) and nutritional quality (improved protein and amino acid contents) in rice remains a major challenge. Here, we show that knocking out rice isopropylmalate synthase genes OsIPMS1 and OsIPMS2 can improve both grain appearance and nutritional quality. We find that OsIPMS1 directly interacts with OsIPMS2 to form heterodimers. Meanwhile, we observe that OsIPMS1 and OsIPMS2 influence the expression of genes previously reported to be involved in the determination of grain size and nutritional quality in the developing panicles and grains. Furthermore, we show that Osipms1/2 double mutants exhibit significantly improved grain appearance and nutritional quality in polished rice in both the japonica (Wuyungeng 23) and indica (Huanghuazhan) varieties. Our findings indicate that OsIPMS is a useful target gene for breeding of rice varieties appealing for marketing and with health-benefiting properties.

OsNAC3 regulates seed germination involving abscisic acid pathway and cell elongation in rice.

Seed germination is a critical trait for the success of direct seeding in rice cultivation. However, the underlying mechanism determining seed germination is largely unknown in rice. Here, we report that NAC transcription factor OsNAC3 positively regulates seed germination of rice. OsNAC3 regulates seed germination involving abscisic acid (ABA) pathway and cell elongation. OsNAC3 can directly bind to the promoter of ABA catabolic gene OsABA8ox1 and cell expansion gene OsEXP4, which consequently activates their expressions during seed germination. We also find that the expression of OsEXP4 is reduced by ABA during seed germination in rice. OsNAC3 regulates seed germination by influencing cell elongation of the embryo through directly affecting OsEXP4 expression and indirectly ABA-medicated OsEXP4 expression. The OsNAC3 elite haplotype is useful for genetic improvement of seed germination, and overexpression of OsNAC3 can significantly increase seed germination. We therefore propose that OsNAC3 is a potential target in breeding of rice varieties with high seed germination for direct seeding cultivation.

Wavelet transform-based frequency self-adaptive model for functional brain network.

The accurate estimation of functional brain networks is essential for comprehending the intricate relationships between different brain regions. Conventional methods such as Pearson Correlation and Sparse Representation often fail to uncover concealed information within diverse frequency bands. To address this limitation, we introduce a novel frequency-adaptive model based on wavelet transform, enabling selective capture of highly correlated frequency band sequences. Our approach involves decomposing the original time-domain signal from resting-state functional magnetic resonance imaging into distinct frequency domains, thus constructing an adjacency matrix that offers enhanced separation of features across brain regions. Comparative analysis demonstrates the superior performance of our proposed model over conventional techniques, showcasing improved clarity and distinctiveness. Notably, we achieved the highest accuracy rate of 89.01% using Sparse Representation based on Wavelet Transform, outperforming Pearson Correlation based on Wavelet Transform with an accuracy of 81.32%. Importantly, our method optimizes raw data without significantly altering feature topology, rendering it adaptable to various functional brain network estimation approaches. Overall, this innovation holds the potential to advance the understanding of brain function and furnish more accurate samples for future research and clinical applications.

[18F]FDG PET/MRI combined with chest HRCT in early cancer detection: a retrospective study of 3020 asymptomatic subjects.

PURPOSE: PET/MRI has become an important medical imaging approach in clinical practice. In this study, we retrospectively investigated the detectability of fluorine-18 (18F)-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging ([18F]FDG PET/MRI) combined with chest computerized tomography (CT) for early cancer in a large cohort of asymptomatic subjects. METHODS: This study included a total of 3020 asymptomatic subjects who underwent whole-body [18F]FDG PET/MRI and chest HRCT examinations. All subjects received a 2-4-year follow-up for cancer development. Cancer detection rate, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the [18F]FDG PET/MRI with or without chest HRCT were calculated and analyzed. RESULTS: Sixty-one subjects were pathologically diagnosed with cancers, among which 59 were correctly detected by [18F]FDG PET/MRI combined with chest HRCT. Of the 59 patients (32 with lung cancer, 9 with breast cancer, 6 with thyroid cancer, 5 with colon cancer, 3 with renal cancer, 1 with prostate cancer, 1 with gastric cancer, 1 with endometrial cancer, and 1 with lymphoma), 54 (91.5%) were at stage 0 or stage I (according to the 8th edition of the tumor-node-metastasis [TNM] staging system), 33 (55.9%) were detected by PET/MRI alone (27 with non-lung cancers and 6 with lung cancer). Cancer detection rate, sensitivity, specificity, PPV, and NPV for PET/MRI combined with chest CT were 2.0%, 96.7%, 99.6%, 83.1%, and 99.9%, respectively. For PET/MRI alone, the metrics were 1.1%, 54.1%, 99.6%, 73.3%, and 99.1%, respectively, and for PET/MRI in non-lung cancers, the metrics were 0.9%, 93.1%, 99.6%, 69.2%, and 99.9%, respectively. CONCLUSIONS: [18F]FDG PET/MRI holds great promise for the early detection of non-lung cancers, while it seems insufficient for detecting early-stage lung cancers. Chest HRCT can be complementary to whole-body PET/MRI for early cancer detection. TRIAL REGISTRATION: ChiCTR2200060041. Registered 16 May 2022. Public site: https://www.chictr.org.cn/index.html.

The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification.

18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely employed to reveal metabolic abnormalities linked to Parkinson's disease (PD) at a systemic level. However, the individual metabolic connectome details with PD based on 18F-FDG PET remain largely unknown. To alleviate this issue, we derived a novel brain network estimation method for individual metabolic connectome, that is, Jensen-Shannon Divergence Similarity Estimation (JSSE). Further, intergroup difference between the individual's metabolic brain network and its global/local graph metrics was analyzed to investigate the metabolic connectome's alterations. To further improve the PD diagnosis performance, multiple kernel support vector machine (MKSVM) is conducted for identifying PD from normal control (NC), which combines both topological metrics and connection. Resultantly, PD individuals showed higher nodal topological properties (including assortativity, modularity score, and characteristic path length) than NC individuals, whereas global efficiency and synchronization were lower. Moreover, 45 most significant connections were affected. Further, consensus connections in occipital, parietal, and frontal regions were decrease in PD while increase in subcortical, temporal, and prefrontal regions. The abnormal metabolic network measurements depicted an ideal classification in identifying PD of NC with an accuracy up to 91.84%. The JSSE method identified the individual-level metabolic connectome of 18F-FDG PET, providing more dimensional and systematic mechanism insights for PD.

College students' mental health evaluation model based on tensor fusion network with multimodal data during the COVID-19 pandemic.

The COVID-19 pandemic has caused a series of effects on the mental health of college students, especially long-term home isolation or online learning, which has caused college students to have both academic pressure and employment pressure. How to accurately and effectively assess the mental health status of college students has become a research hotspot. Traditional methods based on questionnaires such as Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) are difficult to collect data and have poor evaluation accuracy. This paper analyzes the psychological state through text-images of multi-modal data with tensor fusion networks and constructs a mental health assessment model for college students. First, the validity of the model is verified through the MVSA (Multi-View Sentiment Analysis) dataset. Second, the psychological state of college students under the epidemic is analyzed using the collected text-images dataset. The results show that the TFN-MDA (Tensor Fusion Network-Multimodal Data Analysis) based mental health assessment model constructed in this paper can effectively assess the mental health status of college students, with an average accuracy of more than 70%.

A prediction model of nodal metastasis in cN0 oral squamous cell carcinoma using metabolic and pathological variables.

BACKGROUND: The efficacy of 18F-fluorodeoxyglucose (18F-FDG) Positron Emission Tomography/Computed Tomography(PET/CT) in evaluating the neck status in clinically node-negative (cN0) oral squamous cell carcinoma(OSCC) patients was still unsatisfying. We tried to develop a prediction model for nodal metastasis in cN0 OSCC patients by using metabolic and pathological variables. METHODS: Consecutive cN0 OSCC patients with preoperative 18F-FDG PET/CT, subsequent surgical resection of primary tumor and neck dissection were included. Ninety-five patients who underwent PET/CT scanning in Shanghai ninth people's hospital were identified as training cohort, and another 46 patients who imaged in Shanghai Universal Medical Imaging Diagnostic Center were selected as validation cohort. Nodal-status-related variables in the training cohort were selected by multivariable regression after using the least absolute shrinkage and selection operator (LASSO). A nomogram was constructed with significant variables for the risk prediction of nodal metastasis. Finally, nomogram performance was determined by its discrimination, calibration, and clinical usefulness. RESULTS: Nodal maximum standardized uptake value(nodal SUVmax) and pathological T stage were selected as significant variables. A prediction model incorporating the two variables was used to plot a nomogram. The area under the curve was 0.871(Standard Error [SE], 0.035; 95% Confidence Interval [CI], 0.787-0.931) in the training cohort, and 0.809(SE, 0.069; 95% CI, 0.666-0.910) in the validation cohort, with good calibration demonstrated. CONCLUSIONS: A prediction model incorporates metabolic and pathological variables has good performance for predicting nodal metastasis in cN0 OSCC patients. However, further studies with large populations are needed to verify our findings.

UDP-glucosyltransferase OsUGT75A promotes submergence tolerance during rice seed germination.

Submergence stress represents a major obstacle limiting the application of direct seeding in rice cultivation. Under flooding conditions, coleoptile elongation can function as an escape strategy that contributes to submergence tolerance during seed germination in rice; however, the underlying molecular bases have yet to be fully determined. Herein, we report that natural variation of rice coleoptile length subjected to submergence is determined by the glucosyltransferase encoding gene OsUGT75A. OsUGT75A regulates coleoptile length via decreasing free abscisic acid (ABA) and jasmonic acid (JA) levels by promoting glycosylation of these two phytohormones under submergence. Moreover, we find that OsUGT75A accelerates coleoptile length through mediating the interactions between JASMONATE ZIMDOMAIN (OsJAZ) and ABSCISIC ACID-INSENSITIVE (OsABI) proteins. Last, we reveal the origin of the haplotype that contributes to coleoptile length in response to submergence and transferring this haplotype to indica rice can enhance coleoptile length in submergence conditions. Thus, we propose that OsUGT75A is a useful target in breeding of rice varieties suitable for direct seeding cultivation.

Altered rich-club organization of brain functional network in autism spectrum disorder.

Despite numerous research showing the association between brain network abnormalities and autism spectrum disorder (ASD), contrasting findings have been reported from broad functional underconnectivity to broad overconnectivity. Thus, the significance of rich-hub organizations in the brain functional connectome of individuals with ASD remains largely unknown. High-quality data subset of ASD (n = 45) and healthy controls (HC; n = 47) children (7-15 years old) were retrieved from the ABIDE data set, and rich-club organization and network-based statistic (NBS) were assessed from resting-state functional magnetic resonance imaging (rs-fMRI). The rich-club organization functional network (normalized rich-club coefficients >1) was observed in all subjects under a range of thresholds. Compared with HC, ASD patients had higher degree of feeder connections and lower degree of local connections (degree of feeder connections: ASD = 259.20 ± 32.97, HC = 244.98 ± 30.09, p = 0.041; degree of local connections: ASD = 664.02 ± 39.19, HC = 679.89 ± 34.05, p = 0.033) but had similar in rich-club connections. Further, nonparametric NBS analysis showed the presence of abnormal connectivity in the functional network of ASD individuals. Our findings indicated that local connection might be more vulnerable, and feeder connection may compensate for its disruption in ASD, enhancing our understanding on the mechanism of functional connectome dysfunction in ASD.

Case Report: Differential diagnosis for tuberous sclerosis and neurofibromatosis type 1 diagnostic pitfall of aggressively enlarged right upper limb.

Tuberous sclerosis complex (TSC) is an inherited disorder that typically presents with seizures, developmental delay, cutaneous lesions, and facial angiomas. Clinical diagnosis of TSC based on symptoms is sometimes challenging due to its clinical similarities with neurofibromatosis type 1 (NF1), another type of neurogenetic tumor syndrome. Differential diagnosis should be carefully performed on the basis of clinical presentations, imaging, laboratory, and genetic testing. Here, we presented a case of a patient with an aggressively enlarged right upper limb in the NF1 clinic, who was initially suspected of a giant plexiform neurofibroma. However, differential diagnosis revealed TSC as the final diagnosis. The treatments for NF1 and TSC vary significantly, and misdiagnoses can lead to serious threat to the patients' health. We also systematically reviewed all previous cases regarding differential diagnoses between NF1 and TSC. This case report can help clinicians make more accurate diagnoses and benefit the potential patient community.

Characteristics of Seed Vigor in Rice Varieties with Different Globulin Accumulations.

Seed vigor of rice is an important trait for direct seeding. The objective of this study was to reveal the relationship between globulin and seed vigor, and then to explore a method for evaluating seed vigor. Several rice varieties with different levels of 52 kDa globulin accumulation were used to compare seed vigor under normal and aged conditions. Results showed that varieties with high globulin accumulation obtained significantly higher seed vigor, measured by germination percentage and germination index, compared with those varieties with low globulin accumulation under normal and aged conditions. Meanwhile, a significantly higher accumulation of reactive oxygen species (ROS) was observed in the early germinating seeds of varieties with high globulin accumulation compared to those varieties with low globulin accumulation under normal and aged conditions. Collectively, the globulin content could be applied in the evaluation of seed vigor, which contributes to the selection of rice varieties for direct seeding.

Alternation of the Rich-Club Organization of Individual Brain Metabolic Networks in Parkinson's Disease.

Objective: The diagnosis of Parkinson's disease (PD) remains challenging. Although 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) has revealed the metabolic abnormalities associated with PD at systemic levels, the underlying rich-club organization of the metabolic connectome in these patients remains largely unknown. Materials and Methods: The data of 49 PD patients and 49 well-matched healthy controls (HCs) were retrieved and assessed. An individual metabolic connectome based on the standard uptake value (SUV) was built using the Jensen-Shannon Divergence Similarity Estimation (JSSE) method to compare the rich-club properties between PD patients and HC. Results: Our results showed the rich-club organization of metabolic networks (normalized rich-club coefficients > 1) in the PD and HC group were within a range of thresholds. Further, patients with PD demonstrated lower strength and degree in rich-club connections compared with HCs (strength: HCs = 55.70 ± 8.52, PDs = 52.03 ± 10.49, p = 0.028; degree: HCs = 56.55 ± 8.60, PDs = 52.85 ± 10.62, p = 0.029), but difference between their feeder and local connections was not significant. Conclusion: Individual metabolic networks combined with rich club analysis indicated that PD patients had decreased rich club connections but similar feeder and local connections compared with HCs, indicating rich club connections as a promising marker for early diagnosis of PD.

The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification.

Objective: Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by the development of multiple symptoms, with incidences rapidly increasing worldwide. An important step in the early diagnosis of ASD is to identify informative biomarkers. Currently, the use of functional brain network (FBN) is deemed important for extracting data on brain imaging biomarkers. Unfortunately, most existing studies have reported the utilization of the information from the connection to train the classifier; such an approach ignores the topological information and, in turn, limits its performance. Thus, effective utilization of the FBN provides insights for improving the diagnostic performance. Methods: We propose the combination of the information derived from both FBN and its corresponding graph theory measurements to identify and distinguish ASD from normal controls (NCs). Specifically, a multi-kernel support vector machine (MK-SVM) was used to combine multiple types of information. Results: The experimental results illustrate that the combination of information from multiple connectome features (i.e., functional connections and graph measurements) can provide a superior identification performance with an area under the receiver operating characteristic curve (ROC) of 0.9191 and an accuracy of 82.60%. Furthermore, the graph theoretical analysis illustrates that the significant nodal graph measurements and consensus connections exists mostly in the salience network (SN), default mode network (DMN), attention network, frontoparietal network, and social network. Conclusion: This work provides insights into potential neuroimaging biomarkers that may be used for the diagnosis of ASD and offers a new perspective for the exploration of the brain pathophysiology of ASD through machine learning.

Rich-Club Organization Disturbances of the Individual Morphological Network in Subjective Cognitive Decline.

Background: Subjective cognitive decline (SCD) was considered to be the preclinical stage of Alzheimer's disease (AD). However, less is known about the altered rich-club organizations of the morphological networks in individuals with SCD. Methods: This study included 53 individuals with SCD and 54 well-matched healthy controls (HC) from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. Individual-level brain morphological networks were constructed by estimating the Jensen-Shannon distance-based similarity in the distribution of regional gray matter volume. Rich-club properties were then detected, followed by statistical comparison. Results: The characteristic rich-club organization of morphological networks (normalized rich-club coefficients > 1) was observed for both the SCD and HC groups under a range of thresholds. The SCD group showed a reduced normalized rich-club coefficient compared with the HC group. The SCD group exhibited the decreased strength and degree of rich-club connections than the HC group (strength: HC = 79.93, SCD = 74.37, p = 0.028; degree: HC = 85.28, SCD = 79.34, p = 0.027). Interestingly, the SCD group showed an increased strength of local connections than the HC group (strength: HC = 1982.16, SCD = 2003.38, p = 0.036). Conclusion: Rich-club organization disturbances of morphological networks in individuals with SCD reveal a distinct pattern between the rich-club and peripheral regions. This altered rich-club organization pattern provides novel insights into the underlying mechanism of SCD and could be used to investigate prevention strategies at the preclinical stage of AD.

Morphologic brain network predicts levodopa responsiveness in Parkinson disease.

Background: The levodopa challenge test (LCT) has been routinely used in Parkinson disease (PD) evaluation and predicts the outcome of deep brain stimulation (DBS). Guidelines recommend that patients with an improvement in Unified Parkinson's Disease Rating Scale (UPDRS)-III score > 33% in the LCT receive DBS treatment. However, LCT results are affected by many factors, and only provide information on the immediate effectiveness of dopamine. The aim of the present study was to investigate the relationship between LCT outcome and brain imaging features of PD patients to determine whether the latter can be used to identify candidates for DBS. Methods: A total of 38 PD patients were enrolled in the study. Based on improvement in UPDRS-III score in the LCT, patients were divided into low improvement (PD-LCT-L) and high improvement (PD-LCT-H) groups. Each patient's neural network was reconstructed based on T1-weighted magnetic resonance imaging data using the Jensen-Shannon divergence similarity estimation method. The network was established with the multiple kernel support vector machine technique. We analyzed differences in individual morphologic brain networks and their global and local metrics to determine whether there were differences in the connectomes of PD-LCT-L and PD-LCT-H groups. Results: The 2 groups were similar in terms of demographic and clinical characteristics. Mean ± SD levodopa responsiveness was 26.52% ± 3.47% in the PD-LCT-L group (N = 13) and 58.66% ± 4.09% in the PD-LCT-H group (N = 25). There were no significant differences between groups in global and local metrics. There were 43 consensus connections that were affected in both groups; in PD-LCT-L patients, most of these connections were decreased whereas those related to the dorsolateral superior frontal gyrus and left cuneus were significantly increased. Conclusion: Morphologic brain network assessment is a valuable method for predicting levodopa responsiveness in PD patients, which can facilitate the selection of candidates for DBS.

Adaptive noise depression for functional brain network estimation.

Autism spectrum disorder (ASD) is one common psychiatric illness that manifests in neurological and developmental disorders, which can last throughout a person's life and cause challenges in social interaction, communication, and behavior. Since the standard ASD diagnosis is highly based on the symptoms of the disease, it is difficult to make an early diagnosis to take the best cure opportunity. Compared to the standard methods, functional brain network (FBN) could reveal the statistical dependence among neural architectures in brains and provide potential biomarkers for the early neuro-disease diagnosis and treatment of some neurological disorders. However, there are few FBN estimation methods that take into account the noise during the data acquiring process, resulting in poor quality of FBN and thus poor diagnosis results. To address such issues, we provide a brand-new approach for estimating FBNs under a noise modeling framework. In particular, we introduce a noise term to model the representation errors and impose a regularizer to incorporate noise prior into FBNs estimation. More importantly, the proposed method can be formulated as conducting traditional FBN estimation based on transformed fMRI data, which means the traditional methods can be elegantly modified to support noise modeling. That is, we provide a plug-and-play noise module capable of being embedded into different methods and adjusted according to different noise priors. In the end, we conduct abundant experiments to identify ASD from normal controls (NCs) based on the constructed FBNs to illustrate the effectiveness and flexibility of the proposed method. Consequently, we achieved up to 13.04% classification accuracy improvement compared with the baseline methods.

Genome-wide association study reveals that the cupin domain protein OsCDP3.10 regulates seed vigour in rice.

Seed vigour is an imperative trait for the direct seeding of rice. In this study, we examined the genetic regulation of seedling percentage at the early germination using a genome-wide association study in rice. One major quantitative trait loci qSP3 for seedling percentage was identified, and the candidate gene was validated as qSP3, encoding a cupin domain protein OsCDP3.10 for the synthesis of 52 kDa globulin. Disruption of this gene in Oscdp3.10 mutants reduced the seed vigour, including the germination potential and seedling percentage, at the early germination in rice. The lacking accumulation of 52 kDa globulin was observed in the mature grains of the Oscdp3.10 mutants. The significantly lower amino acid contents were observed in the mature grains and the early germinating seeds of the Oscdp3.10 mutants compared with those of wild-type. Rice OsCDP3.10 regulated seed vigour mainly via modulating the amino acids e.g. Met, Glu, His, and Tyr that contribute to hydrogen peroxide (H2 O2 ) accumulation in the germinating seeds. These results provide important insights into the application of seed priming with the amino acids and the selection of OsCDP3.10 to improve seed vigour in rice.

Multiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification.

Mild cognitive impairment (MCI) is generally considered to be a key indicator for predicting the early progression of Alzheimer's disease (AD). Currently, the brain connection (BC) estimated by fMRI data has been validated to be an effective diagnostic biomarker for MCI. Existing studies mainly focused on the single connection pattern for the neuro-disease diagnosis. Thus, such approaches are commonly insufficient to reveal the underlying changes between groups of MCI patients and normal controls (NCs), thereby limiting their performance. In this context, the information associated with multiple patterns (e.g., functional connectivity or effective connectivity) from single-mode data are considered for the MCI diagnosis. In this paper, we provide a novel multiple connection pattern combination (MCPC) approach to combine different patterns based on the kernel combination trick to identify MCI from NCs. In particular, sixty-three MCI cases and sixty-four NC cases from the ADNI dataset are conducted for the validation of the proposed MCPC method. The proposed method achieves 87.40% classification accuracy and significantly outperforms methods that use a single pattern.