MedicalCLIP: Anomaly-Detection Domain Generalization with Asymmetric Constraints.

Medical data have unique specificity and professionalism, requiring substantial domain expertise for their annotation. Precise data annotation is essential for anomaly-detection tasks, making the training process complex. Domain generalization (DG) is an important approach to enhancing medical image anomaly detection (AD). This paper introduces a novel multimodal anomaly-detection framework called MedicalCLIP. MedicalCLIP utilizes multimodal data in anomaly-detection tasks and establishes irregular constraints within modalities for images and text. The key to MedicalCLIP lies in learning intramodal detailed representations, which are combined with text semantic-guided cross-modal contrastive learning, allowing the model to focus on semantic information while capturing more detailed information, thus achieving more fine-grained anomaly detection. MedicalCLIP relies on GPT prompts to generate text, reducing the demand for professional descriptions of medical data. Text construction for medical data helps to improve the generalization ability of multimodal models for anomaly-detection tasks. Additionally, during the text-image contrast-enhancement process, the model's ability to select and extract information from image data is improved. Through hierarchical contrastive loss, fine-grained representations are achieved in the image-representation process. MedicalCLIP has been validated on various medical datasets, showing commendable domain generalization performance in medical-data anomaly detection. Improvements were observed in both anomaly classification and segmentation metrics. In the anomaly classification (AC) task involving brain data, the method demonstrated a 2.81 enhancement in performance over the best existing approach.

Developing nucleoside tailoring strategies against SARS-CoV-2 via ribonuclease targeting chimera.

In response to the urgent need for potent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) therapeutics, this study introduces an innovative nucleoside tailoring strategy leveraging ribonuclease targeting chimeras. By seamlessly integrating ribonuclease L recruiters into nucleosides, we address RNA recognition challenges and effectively inhibit severe acute respiratory syndrome coronavirus 2 replication in human cells. Notably, nucleosides tailored at the ribose 2'-position outperform those modified at the nucleobase. Our in vivo validation using hamster models further bolsters the promise of this nucleoside tailoring approach, positioning it as a valuable asset in the development of innovative antiviral drugs.

Chloramphenicol Binding Sites of Acinetobacter baumannii Chloramphenicol Acetyltransferase CatB8.

Acinetobacter baumannii is a multidrug-resistant pathogen that has become one of the most challenging pathogens in global healthcare. Several antibiotic-resistant genes, including catB8, have been identified in the A. baumannii genome. CatB8 protein, one of the chloramphenicol acetyltransferases (Cats), is encoded by the catB8 gene. Cats can convert chloramphenicol (chl) to 3-acetyl-chl, leading to bacterial resistance to chl. Here, we present the high-resolution cocrystal structure of CatB8 with chl. The structure that we resolved showed that each monomer of CatB8 binds to four chl molecules, while its homologous protein only binds to one chl molecule. One of the newly discovered chl binding site overlaps with the site of another substrate, acetyl-CoA. Through structure-based biochemical analyses, we identified key residues for chl recruiting and acetylation of chl in CatB8. Our work is of significant importance for understanding the drug resistance of A. baumannii and the effectiveness of antibiotic treatment.

Baicalein ameliorates cognitive impairment of vascular dementia rats via suppressing neuroinflammation and regulating intestinal microbiota.

Neuroinflammation induced by chronic cerebral hypoperfusion (CCH) plays a crucial role in the pathophysiologic mechanisms of vascular dementia (VD). A growing body of research has found that intestinal microbiota is associated with a variety of central nervous system disorders and that there is a relationship between intestinal microbiota dysbiosis and cognitive dysfunction and inflammatory responses. Baicalein belongs to the class of flavonoids and has a variety of biological functions, including anti-inflammatory, antioxidant and anti-apoptotic. Baicalein has a significant improvement in memory and learning, and can be used as a potential drug for the protection and treatment of central nervous system disorders. Whether baicalein has an ameliorative effect on cognitive impairment in VD, and whether its mechanism is related to the inhibition of inflammatory response and regulation of intestinal microbiota has not been reported. We used bilateral common carotid artery occlusion (BCCAO) to establish a VD rat model. Morris water maze (MWM) test showed that baicalein improved cognitive dysfunction in VD rats. We applied HE staining, immunofluorescence and ELISA to observe that baicalein treatment significantly improved CCH-induced neuronal damage in the CA1 region of the hippocampus, and reduced glial cell activation and release of pro-inflammatory factors. Western blot showed that baicalein inhibited the activation of the TLR4/MyD88/NF-κB signaling pathway in VD rats. We applied 16 S rDNA sequencing to analyze the composition of the intestinal microbiota. The results showed that baicalein modulated the diversity and composition of the intestinal microbiota, and suppressed the relative abundance of inflammation-associated microbiota in VD rats. In conclusion, this study found that baicalein ameliorated cognitive impairment, attenuated hippocampal inflammatory responses, inhibited the TLR4/MyD88/NF-κB signaling pathway, and modulated intestinal microbiota in VD rats.

Multimodality Fusion Method Based on Multiview Subspace Clustering for Pulmonary Embolism Diagnosis.

Pulmonary embolism (PE) is an important clinical disorder that will result in lung tissue damage or low blood oxygen levels, which need early diagnosis and timely treatment. While computed tomographic pulmonary angiography (CTPA) is the gold standard to diagnose PE, previous studies have verified the effectiveness of combing CTPA and EMR data in computer-aided PE detection or diagnosis. In this paper, we proposed a multimodality fusion method based on multi-view subspace clustering guided feature selection (MSCUFS). The extracted high-dimensional image and EMR features are firstly selected and fused by the MSCUFS, and then are feed into different machine learning models with different fusion strategy to construct the PE classifier. The experiment results showed that the joint fusion strategy with MSCUFS achieved best AUROC of 0.947, surpassing other early fusion and late fusion models. The comparison between single modality and multimodality also illustrated the effectiveness of the proposed method.

Whole-Liver Based Deep Learning for Preoperatively Predicting Overall Survival in Patients with Hepatocellular Carcinoma.

Survival prediction is crucial for treatment decision making in hepatocellular carcinoma (HCC). We aimed to build a fully automated artificial intelligence system (FAIS) that mines whole-liver information to predict overall survival of HCC. We included 215 patients with preoperative contrast-enhance CT imaging and received curative resection from a hospital in China. The cohort was randomly split into developing and testing subcohorts. The FAIS was constructed with convolutional layers and full-connected layers. Cox regression loss was used for training. Models based on clinical and/or tumor-based radiomics features were built for comparison. The FAIS achieved C-indices of 0.81 and 0.72 for the developing and testing sets, outperforming all the other three models. In conclusion, our study suggest that more important information could be mined from whole liver instead of only the tumor. Our whole-liver based FAIS provides a non-invasive and efficient overall survival prediction tool for HCC before the surgery.

Structural basis of the subcortical maternal complex and its implications in reproductive disorders.

The subcortical maternal complex (SCMC) plays a crucial role in early embryonic development. Malfunction of SCMC leads to reproductive diseases in women. However, the molecular function and assembly basis for SCMC remain elusive. Here we reconstituted mouse SCMC and solved the structure at atomic resolution using single-particle cryo-electron microscopy. The core complex of SCMC was formed by MATER, TLE6 and FLOPED, and MATER embraced TLE6 and FLOPED via its NACHT and LRR domains. Two core complexes further dimerize through interactions between two LRR domains of MATERs in vitro. FILIA integrates into SCMC by interacting with the carboxyl-terminal region of FLOPED. Zygotes from mice with Floped C-terminus truncation showed delayed development and resembled the phenotype of zygotes from Filia knockout mice. More importantly, the assembly of mouse SCMC was affected by corresponding clinical variants associated with female reproductive diseases and corresponded with a prediction based on the mouse SCMC structure. Our study paves the way for further investigations on SCMC functions during mammalian preimplantation embryonic development and reveals underlying causes of female reproductive diseases related to SCMC mutations, providing a new strategy for the diagnosis of female reproductive disorders.

Immunoinformatics Prediction and Protective Efficacy of Vaccine Candidate PiuA-PlyD4 Against Streptococcus Pneumoniae.

Purpose: This study was designed to evaluate the immune protective efficacy of the novel Streptococcus pneumoniae (S. pneumoniae) protein vaccine PiuA-PlyD4 through immunoinformatics prediction and in vitro and in vivo experiments. Methods: In this study, we conducted immunoinformatics prediction and protection analysis on the fusion protein PiuA-PlyD4. The epitope composition of the vaccine was analyzed based on the prediction of B-cell and helper T-cell epitopes. Meanwhile, the molecular docking of PiuA and TLR2/4 was simulated. After immunizing C57BL/6 mice with the prepared vaccine, the biological safety, immunogenicity and conservation were evaluated. By constructing different infection models and from the aspects of adhesion inhibition and cytokines, the protective effect of the fusion protein vaccine PiuA-PlyD4 on S. pneumoniae infection was explored. Results: PiuA-PlyD4 has abundant B-cell and helper T-cell epitopes and shows a high antigenicity score and structural stability. Molecular docking analysis suggested the potential interaction between PiuA and TLR2/4. The specific antibody titer of fusion protein antiserum was as high as (7.81±2.32) ×105. The protective effect of the immunized mice on nasal and lung colonization was significantly better than that of the control group, and the survival rate against S. pneumoniae infection of serotype 3 reached 50%. Cytokine detection showed that the humoral immune response, Th1, Th2 and Th17 cellular immune pathways were all involved in the process. Conclusion: The study indicates that PiuA-PlyD4, whether the results are predicted by immunoinformatics or experimentally validated in vivo and in vitro, has good immunogenicity and immunoreactivity and can provide effective protection against S. pneumoniae infection. Therefore, it can be considered a promising prophylactic vaccine candidate for S. pneumoniae.

Spatiotemporal distributions and oceanic emissions of short-lived halocarbons in the East China Sea.

Coastal waters are important sources of volatile halocarbons, which are important in atmospheric chemistry. Here, in May (spring) and October (autumn) 2020, we studied the surface, bottom, and sediment-pore seawater concentrations, atmospheric mixing ratios, and sea-to-air fluxes of the three primary short-lived atmospheric halocarbons (CH3I, CH2Br2, and CHBr3) in the East China Sea (ECS). The highest concentrations of the three short-lived halocarbons occurred in coastal waters, such as the Changjiang estuary and Zhejiang coastal waters, reflecting the influence of excessive anthropogenic inputs on the distributions of these gases. Interestingly, the aqueous levels of these gases seemed to be lower compared to previous measurements in this oceanic region, probably due to reduced contributions from local anthropogenic emission sources. The concentrations of CH3I, CH2Br2, and CHBr3 in pore water were significantly higher than those in bottom water, suggesting that sediment could be a source of these short-lived halocarbons. Additionally, the atmospheric mixing ratios of these gases occasionally increased in coastal areas. An air-mass back trajectory analysis showed this was due to continental anthropogenic sources and emissions from enriched waters. The atmospheric mixing ratios of these halocarbons exhibited significant seasonal variability, with significant correlations among atmospheric CH3I, CH2Br2, and CHBr3 in spring, but not in autumn. The sea-to-air fluxes of CH3I, CH2Br2, and CHBr3 indicated that the ECS is a source of these gases. Seasonal differences in CH3I and CH2Br2 fluxes were driven by changes in wind speed and sea surface temperature, while CHBr3 flux changes were associated with changes in its surface seawater concentration.

Classification of clinical skin lesions with double-branch networks.

Introduction: Malignant skin lesions pose a great threat to the health of patients. Due to the limitations of existing diagnostic techniques, such as poor accuracy and invasive operations, malignant skin lesions are highly similar to other skin lesions, with low diagnostic efficiency and high misdiagnosis rates. Automatic medical image classification using computer algorithms can effectively improve clinical diagnostic efficiency. However, existing clinical datasets are sparse and clinical images have complex backgrounds, problems with noise interference such as light changes and shadows, hair occlusions, etc. In addition, existing classification models lack the ability to focus on lesion regions in complex backgrounds. Methods: In this paper, we propose a DBN (double branch network) based on a two-branch network model that uses a backbone with the same structure as the original network branches and the fused network branches. The feature maps of each layer of the original network branch are extracted by our proposed CFEBlock (Common Feature Extraction Block), the common features of the feature maps between adjacent layers are extracted, and then these features are combined with the feature maps of the corresponding layers of the fusion network branch by FusionBlock, and finally the total prediction results are obtained by weighting the prediction results of both branches. In addition, we constructed a new dataset CSLI (Clinical Skin Lesion Images) by combining the publicly available dataset PAD-UFES-20 with our collected dataset, the CSLI dataset contains 3361 clinical dermatology images for six disease categories: actinic keratosis (730), cutaneous basal cell carcinoma (1136), malignant melanoma (170) cutaneous melanocytic nevus (391), squamous cell carcinoma (298) and seborrheic keratosis (636). Results: We divided the CSLI dataset into a training set, a validation set and a test set, and performed accuracy, precision, sensitivity, specificity, f1score, balanced accuracy, AUC summary, visualisation of different model training, ROC curves and confusion matrix for various diseases, ultimately showing that the network performed well overall on the test data. Discussion: The DBN contains two identical feature extraction network branches, a structure that allows shallow feature maps for image classification to be used with deeper feature maps for information transfer between them in both directions, providing greater flexibility and accuracy and enhancing the network's ability to focus on lesion regions. In addition, the dual branch structure of DBN provides more possibilities for model structure modification and feature transfer, and has great potential for development.

Domestication, breeding, omics research, and important genes of Zizania latifolia and Zizania palustris.

Wild rice (Zizania spp.), an aquatic grass belonging to the subfamily Gramineae, has a high economic value. Zizania provides food (such as grains and vegetables), a habitat for wild animals, and paper-making pulps, possesses certain medicinal values, and helps control water eutrophication. Zizania is an ideal resource for expanding and enriching a rice breeding gene bank to naturally preserve valuable characteristics lost during domestication. With the Z. latifolia and Z. palustris genomes completely sequenced, fundamental achievements have been made toward understanding the origin and domestication, as well as the genetic basis of important agronomic traits of this genus, substantially accelerating the domestication of this wild plant. The present review summarizes the research results on the edible history, economic value, domestication, breeding, omics research, and important genes of Z. latifolia and Z. palustris over the past decades. These findings broaden the collective understanding of Zizania domestication and breeding, furthering human domestication, improvement, and long-term sustainability of wild plant cultivation.

Edaravone Dexborneol Alleviates Neuroinflammation by Reducing Neuroglial Cell Proliferation and Suppresses Neuronal Apoptosis/Autophagy in Vascular Dementia Rats.

More and more evidence shows that the pathological mechanism of vascular dementia (VD) is closely related to oxidative stress injury, cell apoptosis, autophagy, inflammatory response, excitatory amino acid toxicity, synaptic plasticity change, calcium overload, and other processes. Edaravone dexborneol (EDB) is a new type of neuroprotective agent that can improve the neurological damage caused by an ischemic stroke. Previous studies showed that EDB has effects on synergistic antioxidants and induces anti-apoptotic responses. However, it remains unclear whether EDB can affect apoptosis and autophagy by activating the PI3K/Akt/mTOR signaling pathway and its impact on the neuroglial cells. In this study, we established the VD model of rats by bilateral carotid artery occlusion to explore the neuroprotective effect of EDB and its mechanism. Morris Water Maze test was applied to assess the cognitive function of rats. H&E and TUNEL staining were applied to observe the cellular structure of the hippocampus. Immunofluorescence labeling was used to observe the proliferation of astrocytes and microglia. ELISA was applied to examine the levels of TNF-α, IL-1ß and IL-6, and RT-PCR was applied to examine their mRNA expression levels. Western blotting was applied to examine apoptosis-related proteins (Bax, Bcl-2, Caspase-3), autophagy-related proteins (Beclin-1, P62, LC3B), PI3K/Akt/mTOR signaling pathway proteins and their phosphorylation levels. The results indicated that EDB ameliorates learning and memory in rats subjected to the VD model, alleviates neuroinflammatory response by reducing the proliferation of the neuroglial cell and inhibits apoptosis and autophagy, which may be mediated by the PI3K/Akt/mTOR signaling pathway.

Utilizing Epigenetic Modification as a Reactive Handle To Regulate RNA Function and CRISPR-Based Gene Regulation.

The current methods to control RNA functions in living conditions are limited. The new RNA-controlling strategy presented in this study involves utilizing 5-formylcytidine (f5C)-directed base manipulation. This study shows that malononitrile and pyridine boranes can effectively manipulate the folding, small molecule binding, and enzyme recognition of f5C-bearing RNAs. We further demonstrate the efficiency of f5C-directed reactions in controlling two different clustered regularly interspaced short palindromic repeat (CRISPR) systems. Although further studies are needed to optimize the efficiency of these reactions in vivo, this small molecule-based approach presents exciting new opportunities for regulating CRISPR-based gene expression and other applications.

Systemic Immune-Inflammation Index is Associated with Cerebral Small Vessel Disease Burden and Cognitive Impairment.

Objective: This study sought to explore the associations of the systemic immune-inflammation index (SII) with total cerebral small vessel disease (CSVD) burden and cognitive impairment. Methods: We enrolled 201 patients in the retrospective study with complete clinical and laboratory data. The SII was calculated as platelet count × neutrophil count/lymphocyte count. Cognitive function was evaluated by the Mini-Mental State Examination (MMSE). Total CSVD burden was assessed based on magnetic resonance imaging. We performed logistic regression models, Spearman correlation, and mediation analysis to evaluate the associations of SII with CSVD burden and cognitive impairment. Results: After adjustment for confounding factors in the multivariate binary logistic regression model, elevated SII (odds ratio [OR], 3.263; 95% confidence interval [CI], 1.577-6.752; P = 0.001) or severe CSVD burden (OR, 2.794; 95% CI, 1.342-5.817; P = 0.006) was significantly associated with the risk of cognitive impairment. Correlation analyses revealed that SII levels were negatively associated with MMSE scores (rs = -0.391, P < 0.001), and positively associated with the total CSVD burden score (rs = 0.361, P < 0.001). Moreover, SII was significantly related to the severity of the CSVD burden (OR, 2.674; 95% CI, 1.359-5.263; P = 0.004). The multivariable-adjusted odds ratios (95% CI) in highest tertile versus lowest tertile of SII were 8.947 (3.315-24.145) for cognitive impairment and 4.945 (2.063-11.854) for severe CSVD burden, respectively. The effect of higher SII on cognitive impairment development was partly mediated by severe CSVD burden. Conclusion: Elevated SII is associated with severe CSVD burden and cognitive impairment. The mediating role of severe CSVD burden suggests that higher SII may contribute to cognitive impairment through aggravating CSVD burden.

Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans.

BACKGROUND: The coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP) present a high degree of similarity in chest computed tomography (CT) images. Therefore, a procedure for accurately and automatically distinguishing between them is crucial. METHODS: A deep learning method for distinguishing COVID-19 from CAP is developed using maximum intensity projection (MIP) images from CT scans. LinkNet is employed for lung segmentation of chest CT images. MIP images are produced by superposing the maximum gray of intrapulmonary CT values. The MIP images are input into a capsule network for patient-level pred iction and diagnosis of COVID-19. The network is trained using 333 CT scans (168 COVID-19/165 CAP) and validated on three external datasets containing 3581 CT scans (2110 COVID-19/1471 CAP). RESULTS: LinkNet achieves the highest Dice coefficient of 0.983 for lung segmentation. For the classification of COVID-19 and CAP, the capsule network with the DenseNet-121 feature extractor outperforms ResNet-50 and Inception-V3, achieving an accuracy of 0.970 on the training dataset. Without MIP or the capsule network, the accuracy decreases to 0.857 and 0.818, respectively. Accuracy scores of 0.961, 0.997, and 0.949 are achieved on the external validation datasets. The proposed method has higher or comparable sensitivity compared with ten state-of-the-art methods. CONCLUSIONS: The proposed method illustrates the feasibility of applying MIP images from CT scans to distinguish COVID-19 from CAP using capsule networks. MIP images provide conspicuous benefits when exploiting deep learning to detect COVID-19 lesions from CT scans and the capsule network improves COVID-19 diagnosis.

Baicalin facilitates remyelination and suppresses neuroinflammation in rats with chronic cerebral hypoperfusion by activating Wnt/ß-catenin and inhibiting NF-κB signaling.

One main factor contributing to the cognitive loss in vascular dementia (VD) is white matter lesions (WMLs) carried on by chronic cerebral hypoperfusion (CCH). A secondary neuroinflammatory response to CCH accelerates the loss and limits the regeneration of oligodendrocytes, leading to progressive demyelination and insufficient remyelination in the white matter. Thus, promoting remyelination and inhibiting neuroinflammation may be an ideal therapeutic strategy. Baicalin (BAI) is known to exhibit protective effects against various inflammatory and demyelinating diseases. However, whether BAI has neuroprotective effects against CCH has not been investigated. To determine whether BAI inhibits CCH-induced demyelination and neuroinflammation, we established a model of CCH in rats by occluding the two common carotid arteries bilaterally. Our results revealed that BAI could remarkably ameliorate cognitive impairment and mitigate CA1 pyramidal neuron damage and myelin loss. BAI exhibited enhancement of remyelination by increasing the expression of myelin basic protein (MBP) and oligodendrocyte transcription factor 2 (Olig2), inhibiting the loss of oligodendrocytes and promoting oligodendrocyte regeneration in the corpus callosum of CCH rats. Furthermore, BAI modified microglia polarization to the anti-inflammatory phenotype and inhibited the release of pro-inflammatory cytokines. Mechanistically, BAI treatment significantly induced phosphorylation of glycogen synthase kinase 3ß (GSK3ß), enhanced the expression of ß-catenin and its nuclear translocation. Simultaneously, BAI reduced the expression of nuclear NF-κB. Collectively, our results suggest that BAI ameliorates cognitive impairment in CCH-induced VD rats through its pro-remyelination and anti-inflammatory capacities, possibly by activating the Wnt/ß-catenin and suppressing the NF-κB signaling.

Dulaglutide Improves Gliosis and Suppresses Apoptosis/Autophagy Through the PI3K/Akt/mTOR Signaling Pathway in Vascular Dementia Rats.

Dulaglutide is a new type of hypoglycemic agent that agonizes glucagon-like peptide-1 receptor (GLP-1RA). It can be concluded from previous studies that a GLP-1RA can reduce apoptosis and regulate autophagy in the nervous system, while related research on dulaglutide in vascular dementia (VD) has not been reported. In our study, the VD rat model was established by bilateral carotid artery occlusion, and the results of the Morris water maze test (MWM) and open-field test showed that the application of dulaglutide could effectively reduce the cognitive decline of VD rats without changing the behavior in the open-field test, which was used to assess an anxiety-like phenotype. We applied HE staining and immunofluorescence labeling to show that dulaglutide treatment significantly alleviated neuronal damage in the hippocampal region of VD rats, and reduced microglial and astrocyte proliferation. Western blot results showed that dulaglutide reduced VD-induced neuronal apoptosis (BCL2/BAX, c-caspase3) and autophagy (P62, LC3B, Beclin-1), and upregulated phosphorylation of PI3K/Akt/mTOR signaling pathway. KEGG pathway analysis of RNA-Sequence results showed that the differentially expressed genes in the dulaglutide treatment group were significantly enriched in the mTOR signaling pathway, and the repressor of mTOR, Deptor, was down-regulated. In conclusion, this study suggested that dulaglutide may alleviate learning and memory impairment and neuron damage in VD rats by attenuating apoptosis, regulating autophagy, and activating the PI3K/Akt/mTOR signaling pathway in neurons, which may make it a promising candidate for the simultaneous treatment of VD and diabetes.

Cyclic Dinucleotide-Based Enantioselective Fluorination in Water.

The diverse structures of DNA serve as potent chiral scaffolds for DNA-based asymmetric catalysis, yet in most cases tens to hundreds of nucleotides in DNA hybrid catalysts hinder the deep insight into their structure-activity relationship. Owing to the structural simplicity and design flexibility of nucleotides, nucleotide-based catalysts have been emerging as a promising way to obtain fine structural information and understand the catalytic mechanisms. Herein, we found that a cyclic dinucleotide of cyclic di-AMP (c-di-AMP) and 1,10-phenanthroline copper(II) nitrate (Cu(phen)(NO3)2) are assembled to a c-di-AMP-based catalyst (c-di-AMP/Cu(phen)(NO3)2), which could fast achieve enantioselective fluorination in water with 90-99% yields and up to 90% enantiomeric excess (ee). The host-guest interaction between c-di-AMP and Cu(phen)(NO3)2 has been proposed mainly in a supramolecular interaction mode as evidenced by spectroscopic techniques of ultraviolet-visible, fluorescence, circular dichroism, and nuclear magnetic resonance. Cu(phen)(NO3)2 tightly binds to c-di-AMP with a binding constant of 1.7 ± 0.3 × 105 M-1, and the assembly of c-di-AMP/Cu(phen)(NO3)2 shows a modest rate enhancement to carbon-fluorine bond formations as supported by kinetic studies.

Systematic Analysis of BELL Family Genes in Zizania latifolia and Functional Identification of ZlqSH1a/b in Rice Seed Shattering.

The loss of seed shattering is an important event in crop domestication, and elucidating the genetic mechanisms underlying seed shattering can help reduce yield loss during crop production. This study is the first to systematically identify and analyse the BELL family of transcription factor-encoding genes in Chinese wild rice (Zizania latifolia). ZlqSH1a (Zla04G033720) and ZlqSH1b (Zla02G027130) were identified as key candidate genes involved in seed shattering in Z. latifolia. These genes were involved in regulating the development of the abscission layer (AL) and were located in the nucleus of the cell. Over-expression of ZlqSH1a and ZlqSH1b resulted in a complete AL between the grain and pedicel and significantly enhanced seed shattering after grain maturation in rice. Transcriptome sequencing revealed that 172 genes were differentially expressed between the wild type (WT) and the two transgenic (ZlqSH1a and ZlqSH1b over-expressing) plants. Three of the differentially expressed genes related to seed shattering were validated using qRT-PCR analysis. These results indicate that ZlqSH1a and ZlqSH1b are involved in AL development in rice grains, thereby regulating seed shattering. Our results could facilitate the genetic improvement of seed-shattering behaviour in Z. latifolia and other cereal crops.

Metabolomics and proteomics reveal the molecular basis of colour formation in the pericarp of Chinese wild rice (Zizania latifolia).

Chinese wild rice (Zizania latifolia) is rich in flavonoids and the characteristic colour of its pericarp is attributed to the flavonoids. In this study, the molecular basis of the colour change in the pericarp of Chinese wild rice was studied using metabolomics and proteomics. Whole seeds in three developmental stages (10, 20, and 30 days after flowering) were characterised based on phenolic contents, free amino acids (FAAs), and the expression level and activities of enzymes critical in flavonoid biosynthesis. The total phenolic and proanthocyanidin contents of Chinese wild rice increased gradually, whereas total flavonoid and FAA contents decreased during seed development. Metabolomic analysis revealed gradual upward trends for 57 flavonoids (sub classes 1, 3, and 10) related to colour change in the pericarp. Proteomic analysis showed that the phenylpropanoid biosynthesis metabolic pathway was enriched with differentially expressed proteins and was associated with flavonoid biosynthesis. Proteomic data suggested that leucoanthocyanidin reductase and WD40 repeat protein may be involved in flavonoid biosynthesis in Chinese wild rice, which was also verified by real-time quantitative PCR. Our results provide new insights into the understanding of the colour formation in the pericarp of Chinese wild rice.