Evaluation of Resistance Resources and Analysis of Resistance Mechanisms of Maize to Stalk Rot Caused by Fusarium graminearum.

Stalk rot is one of the most destructive and widely distributed diseases in maize plants worldwide. Research on the performance and resistance mechanisms of maize against stem rot is constantly improving. In this study, among 120 inbred maize lines infected by Fusarium graminearum using the injection method, 4 lines (3.33%) were highly resistant to stalk rot, 28 lines (23.33%) were resistant, 57 lines (47.50%) were susceptible, and 31 lines (25.84%) were highly susceptible. The inbred lines 18N10118 and 18N10370 were the most resistant and susceptible with disease indices of 7.5 and 75.6, respectively. Treatment of resistant and susceptible maize inbred seedlings with F. graminearum showed that root hair growth of the susceptible inbred lines was significantly inhibited, and a large number of hyphae attached and adsorbed multiple conidia near the root system. However, the resistant inbred lines were delayed and inconspicuous, with only a few hyphae and spores appearing near the root system. Compared with susceptible inbred lines, resistant maize inbred line seedlings treated with F. graminearum exhibited elevated activities of catalase, phenylalanine ammonia-lyase, polyphenol oxidase, and superoxide dismutase. We identified 153 genes related to disease resistance by transcriptome analysis. The mitogen-activated protein kinase signaling and peroxisome pathways mainly regulated the resistance mechanism of maize inbred lines to F. graminearum infection. These two pathways might play an important role in the disease resistance mechanism, and the function of genes in the two pathways must be further studied, which might provide a theoretical basis for further understanding the molecular resistance mechanism of stalk rot and resistance gene mining.

The prodomain of Arabidopsis metacaspase 2 positively regulates immune signaling mediated by pattern-recognition receptors.

Metacaspases (MCs) are structural homologs of mammalian caspases found in plants, fungi, and protozoa. Type-I MCs carry an N-terminal prodomain, the function of which is unclear. Through genetic analysis of Arabidopsis mc2-1, a T-DNA insertion mutant of MC2, we demonstrated that the prodomain of metacaspase 2 (MC2) promotes immune signaling mediated by pattern-recognition receptors (PRRs). In mc2-1, immune responses are constitutively activated. The receptor-like kinases (RLKs) BAK1/BKK1 and SOBIR1 are required for the autoimmune phenotype of mc2-1, suggesting that immune signaling mediated by the receptor-like protein (RLP)-type PRRs is activated in mc2-1. A suppressor screen identified multiple mutations in the first exon of MC2, which suppress the autoimmunity in mc2-1. Further analysis revealed that the T-DNA insertion at the end of exon 1 of MC2 causes elevated expression of the MC2 prodomain, and overexpression of the MC2 prodomain in wild-type (WT) plants results in the activation of immune responses. The MC2 prodomain interacts with BIR1, which inhibits RLP-mediated immune signaling by interacting with BAK1, suggesting that the MC2 prodomain promotes plant defense responses by interfering with the function of BIR1. Our study uncovers an unexpected function of the prodomain of a MC in plant immunity.

The role of ferroptosis in diabetic cardiovascular diseases and the intervention of active ingredients of traditional Chinese medicine.

Cardiovascular diseases (CVDs), encompassing ischaemic heart disease, cardiomyopathy, and heart failure, among others, are the most prevalent complications of diabetes and the leading cause of mortality in patients with diabetes. Cell death modalities, including apoptosis, necroptosis, and pyroptosis, have been demonstrated to be involved in the pathogenesis of CVDs. As research progresses, accumulating evidence also suggests the involvement of ferroptosis, a novel form of cell death, in the pathogenesis of CVDs. Ferroptosis, characterised by iron-dependent lipid peroxidation, which culminates in membrane rupture, may present new therapeutic targets for diabetes-related cardiovascular complications. Current treatments for CVDs, such as antihypertensive, anticoagulant, lipid-lowering, and plaque-stabilising drugs, may cause severe side effects with long-term use. Traditional Chinese medicine, with its broad range of activities and minimal side effects, is widely used in China. Numerous studies have shown that active components of Chinese medicine, such as alkaloids, polyphenols, and saponins, can prevent CVDs by regulating ferroptosis. This review summarises the recent findings on the regulatory mechanisms of active components of Chinese medicine against ferroptosis in CVDs, aiming to provide new directions and a scientific basis for targeting ferroptosis for the prevention and treatment of diabetic CVDs.

Deep learning-enabled 3D multimodal fusion of cone-beam CT and intraoral mesh scans for clinically applicable tooth-bone reconstruction.

High-fidelity three-dimensional (3D) models of tooth-bone structures are valuable for virtual dental treatment planning; however, they require integrating data from cone-beam computed tomography (CBCT) and intraoral scans (IOS) using methods that are either error-prone or time-consuming. Hence, this study presents Deep Dental Multimodal Fusion (DDMF), an automatic multimodal framework that reconstructs 3D tooth-bone structures using CBCT and IOS. Specifically, the DDMF framework comprises CBCT and IOS segmentation modules as well as a multimodal reconstruction module with novel pixel representation learning architectures, prior knowledge-guided losses, and geometry-based 3D fusion techniques. Experiments on real-world large-scale datasets revealed that DDMF achieved superior segmentation performance on CBCT and IOS, achieving a 0.17 mm average symmetric surface distance (ASSD) for 3D fusion with a substantial processing time reduction. Additionally, clinical applicability studies have demonstrated DDMF's potential for accurately simulating tooth-bone structures throughout the orthodontic treatment process.

Risk factor analysis for diabetic foot ulcer-related amputation including Controlling Nutritional Status score and neutrophil-to-lymphocyte ratio.

Diabetic foot ulcer often leads to amputation, and both nutritional status and immune function have been associated with this process. We aimed to investigate the risk factors of diabetic ulcer-related amputation including the Controlling Nutritional Status score and neutrophil-to-lymphocyte ratio biomarker. We evaluated data from hospital in patients with diabetic foot ulcer, performing univariate and multivariate analyses to screen for high-risk factors and Kaplan-Meier analysis to correlate high-risk factors with amputation-free survival. Overall, 389 patients underwent 247 amputations over the follow-up period. After correction to relevant variables, we identified five independent risk factors for diabetic ulcer-related amputation: ulcer severity, ulcer site, peripheral arterial disease, neutrophil-to-lymphocyte ratio and nutritional status. Amputation-free survival was lower for the moderate-to-severe versus mild cases, for the plantar forefoot versus hindfoot location, for the concomitant peripheral artery disease versus without and in the high versus low neutrophil-to-lymphocyte ratio (all p < 0.01). The results showed that ulcer severity (p < 0.01), ulcer site (p < 0.01), peripheral artery disease (p < 0.01), neutrophil-to-lymphocyte ratio (p < 0.01) and Controlling Nutritional Status score (p < 0.05) were independent risk factors for amputation in diabetic foot ulcer patients and have predictive values for diabetic foot ulcer progression to amputation.

Periodic Acid Modification of Chemical-Bath Deposited SnO2 Electron Transport Layers for Perovskite Solar Cells and Mini Modules.

Chemical bath deposition (CBD) has been demonstrated as a remarkable technology to fabricate high-quality SnO2 electron transport layer (ETL) for large-area perovskite solar cells (PSCs). However, surface defects always exist on the SnO2 film coated by the CBD process, impairing the devices' performance. Here, a facile periodic acid post-treatment (PAPT) method is developed to modify the SnO2 layer. Periodic acid can react with hydroxyl groups on the surface of SnO2 films and oxidize Tin(II) oxide to Tin(IV) oxide. With the help of periodic acid, a better energy level alignment between the SnO2 and perovskite layers is achieved. In addition, the PAPT method inhibits interfacial nonradiative recombination and facilitates charge transportation. Such a multifunctional strategy enables to fabricate PSC with a champion power conversion efficiency (PCE) of 22.25%, which remains 93.32% of its initial efficiency after 3000 h without any encapsulation. Furthermore, 3 × 3 cm2 perovskite mini-modules are presented, achieving a champion efficiency of 18.10%. All these results suggest that the PAPT method is promising for promoting the commercial application of large-area PSCs.

Self-Supervised Video Representation Learning by Video Incoherence Detection.

This article introduces a novel self-supervised method that leverages incoherence detection for video representation learning. It stems from the observation that the visual system of human beings can easily identify video incoherence based on their comprehensive understanding of videos. Specifically, we construct the incoherent clip by multiple subclips hierarchically sampled from the same raw video with various lengths of incoherence. The network is trained to learn the high-level representation by predicting the location and length of incoherence given the incoherent clip as input. Additionally, we introduce intravideo contrastive learning to maximize the mutual information between incoherent clips from the same raw video. We evaluate our proposed method through extensive experiments on action recognition and video retrieval using various backbone networks. Experiments show that our proposed method achieves remarkable performance across different backbone networks and different datasets compared to previous coherence-based methods.

Toward ubiquitous sensing: Researchers turn WiFi signals into human activity patterns.

Jianfei Yang, a principal investigator and postdoc at Nanyang Technological University (NTU), and his student Xinyan Chen have developed a comprehensive benchmark and library for WiFi sensing. Their Patterns paper highlights the advantages of deep learning for WiFi sensing and provides constructive suggestions on model selection, learning scheme, and training strategy for developers and data scientists in this field. They talk about their view of data science, their experience with interdisciplinary WiFi sensing research, and the future of WiFi sensing applications.

SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing.

Over the recent years, WiFi sensing has been rapidly developed for privacy-preserving, ubiquitous human-sensing applications, enabled by signal processing and deep-learning methods. However, a comprehensive public benchmark for deep learning in WiFi sensing, similar to that available for visual recognition, does not yet exist. In this article, we review recent progress in topics ranging from WiFi hardware platforms to sensing algorithms and propose a new library with a comprehensive benchmark, SenseFi. On this basis, we evaluate various deep-learning models in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, and feature transferability. Extensive experiments are performed whose results provide valuable insights into model design, learning strategy, and training techniques for real-world applications. In summary, SenseFi is a comprehensive benchmark with an open-source library for deep learning in WiFi sensing research that offers researchers a convenient tool to validate learning-based WiFi-sensing methods on multiple datasets and platforms.

Cloning and expression study of a high-affinity nitrate transporter gene from Zea mays L.

A nitrate transporter gene, named B46NRT2.1, from salt-tolerant Zea mays L. B46 has been cloned. B46NRT2.1 contained the same domain belonging to the major facilitator superfamily (PLN00028). The results of the phylogenetic tree indicated that B46NRT2.1 exhibits sequence similarity and the closest relationship with those known nitrate transporters of the NRT2 family. Through RT-qPCR, we found that the expression of B46NRT2.1 mainly happens in the root and leaf. Moreover, the treatment with NaCl, Na2CO3, and NaHCO3 could significantly increase the expression of B46NRT2.1. B46NRT2.1 was located in the plasma membrane. Through the study of yeast and plant salt response brought by B46NRT2.1 overexpression, we have preliminary knowledge that the expression of B46NRT2.1 makes yeast and plants respond to salt shock. There are 10 different kinds of cis-acting regulatory elements (CRES) in the promotor sequences of B46NRT2.1 gene using the PlantCARE web server to analyze. It mainly includes hormone response, abscisic acid, salicylic acid, gibberellin, methyl jasmonate, and auxin. The B46NRT2.1 gene's co-expression network showed that it was co-expressed with a number of other genes in several biological pathways, including regulation of NO3 long-distance transit, modulation of nitrate sensing and metabolism, nitrate assimilation, and transduction of Jasmonic acid-independent wound signal. The results of this work should serve as a good scientific foundation for further research on the functions of the NRT2 gene family in plants (inbred line B46), and this research adds to our understanding of the molecular mechanisms under salt tolerance.

Cross-platform privacy-preserving CT image COVID-19 diagnosis based on source-free domain adaptation.

In the wake of the Coronavirus disease (COVID-19) pandemic, chest computed tomography (CT) has become an invaluable component in the rapid and accurate detection of COVID-19. CT scans traditionally require manual inspections from medical professionals, which is expensive and tedious. With advancements in machine learning, deep neural networks have been applied to classify CT scans for efficient diagnosis. However, three challenges hinder this application of deep learning: (1) Domain shift across CT platforms and human subjects impedes the performance of neural networks in different hospitals. (2) Unsupervised Domain Adaptation (UDA), the traditional method to overcome domain shift, typically requires access to both source and target data. This is not realistic in COVID-19 diagnosis due to the sensitivity of medical data. The privacy of patients must be protected. (3) Data imbalance may exist between easy/hard samples and between data classes which can overwhelm the training of deep networks, causing degenerate models. To overcome these challenges, we propose a Cross-Platform Privacy-Preserving COVID-19 diagnosis network (CP 3 Net) that integrates domain adaptation, self-supervised learning, imbalanced label learning, and rotation classifier training into one synergistic framework. We also create a new CT benchmark by combining real-world datasets from multiple medical platforms to facilitate the cross-domain evaluation of our method. Through extensive experiments, we demonstrate that CP 3 Net outperforms many popular UDA methods and achieves state-of-the-art results in diagnosing COVID-19 using CT scans.

Advancing Imbalanced Domain Adaptation: Cluster-Level Discrepancy Minimization With a Comprehensive Benchmark.

Unsupervised domain adaptation methods have been proposed to tackle the problem of covariate shift by minimizing the distribution discrepancy between the feature embeddings of source domain and target domain. However, the standard evaluation protocols assume that the conditional label distributions of the two domains are invariant, which is usually not consistent with the real-world scenarios such as long-tailed distribution of visual categories. In this article, the imbalanced domain adaptation (IDA) is formulated for a more realistic scenario where both label shift and covariate shift occur between the two domains. Theoretically, when label shift exists, aligning the marginal distributions may result in negative transfer. Therefore, a novel cluster-level discrepancy minimization (CDM) is developed. CDM proposes cross-domain similarity learning to learn tight and discriminative clusters, which are utilized for both feature-level and distribution-level discrepancy minimization, palliating the negative effect of label shift during domain transfer. Theoretical justifications further demonstrate that CDM minimizes the target risk in a progressive manner. To corroborate the effectiveness of CDM, we propose two evaluation protocols according to the real-world situation and benchmark existing domain adaptation approaches. Extensive experiments demonstrate that negative transfer does occur due to label shift, while our approach achieves significant improvement on imbalanced datasets, including Office-31, Image-CLEF, and Office-Home.

Ethyl acetate extract of Caesalpinia sappan L. for the treatment of atherosclerosis in ApoE-/- mice and its mechanism.

The ethyl acetate extract of Caesalpinia sappan L. is a traditional Chinese medicine extract commonly used in the treatment of atherosclerosis. However, the mechanism of its use in the treatment of AS is not yet clear, which seriously affects the wide-scale application of this drug. In this study, a combination of metabolomics and lipidomics was used to analyze cardiac tissue to obtain differential metabolites and differential lipid molecules, bioinformatic analysis was performed on the significantly different metabolites and subclass analysis, cluster analysis, and chain length and chain saturation analyses were performed on screened lipid molecules showing significant differences. A correlation network diagram of the screened differential metabolites and differential lipid molecules was constructed. Hematoxylin and eosin staining of thoracic aorta in rats confirmed its therapeutic effect. This study found that the ethyl acetate extract of C. sappan L. upregulates D-mannose through the lysosome pathway, enhances lysosomal function, mediates autophagy, and indirectly regulates the levels of lipid subtypes such as lysophosphatidylinositol and phosphatidylserine, thereby improving AS.

Aligning Correlation Information for Domain Adaptation in Action Recognition.

Domain adaptation (DA) approaches address domain shift and enable networks to be applied to different scenarios. Although various image DA approaches have been proposed in recent years, there is limited research toward video DA. This is partly due to the complexity in adapting the different modalities of features in videos, which includes the correlation features extracted as long-range dependencies of pixels across spatiotemporal dimensions. The correlation features are highly associated with action classes and proven their effectiveness in accurate video feature extraction through the supervised action recognition task. Yet correlation features of the same action would differ across domains due to domain shift. Therefore, we propose a novel adversarial correlation adaptation network (ACAN) to align action videos by aligning pixel correlations. ACAN aims to minimize the distribution of correlation information, termed as pixel correlation discrepancy (PCD). Additionally, video DA research is also limited by the lack of cross-domain video datasets with larger domain shifts. We, therefore, introduce a novel HMDB-ARID dataset with a larger domain shift caused by a larger statistical difference between domains. This dataset is built in an effort to leverage current datasets for dark video classification. Empirical results demonstrate the state-of-the-art performance of our proposed ACAN for both existing and the new video DA datasets.

Multilevel regulation of anthocyanin-promoting R2R3-MYB transcription factors in plants.

Anthocyanins are common secondary metabolites in plants that confer red, blue, and purple colorations in plants and are highly desired by consumers for their visual appearance and nutritional quality. In the last two decades, the anthocyanin biosynthetic pathway and transcriptional regulation of anthocyanin biosynthetic genes (ABGs) have been well characterized in many plants. From numerous studies on model plants and horticultural crops, many signaling regulators have been found to control anthocyanin accumulation via regulation of anthocyanin-promoting R2R3-MYB transcription factors (so-called R2R3-MYB activators). The regulatory mechanism of R2R3-MYB activators is mediated by multiple environmental factors (e.g., light, temperature) and internal signals (e.g., sugar, ethylene, and JA) in complicated interactions at multiple levels. Here, we summarize the transcriptional control of R2R3-MYB activators as a result of natural variations in the promoter of their encoding genes, upstream transcription factors and epigenetics, and posttranslational modifications of R2R3-MYB that determine color variations of horticultural plants. In addition, we focus on progress in elucidating the integrated regulatory network of anthocyanin biosynthesis mediated by R2R3-MYB activators in response to multiple signals. We also highlight a few gene cascade modules involved in the regulation of anthocyanin-related R2R3-MYB to provide insights into anthocyanin production in horticultural plants.

Characterization of inflammatory profiles and endothelial dysfunction in diabetic limb arterial occlusion.

Our study aims to detect the changes of adiponectin (APN), endothelin 1 (ET)-1, nitric oxide (NO), cystatin C (cysC) in diabetic limb arterial occlusion (DLAO) patients and unravel their associations with endothelial function. Total 240 type 2 diabetes mellitus (T2DM) patients were divided into a DM group (n = 80, ankle brachial index (ABI) ≥ 0.9) and a DLAO group (n = 160, ABI < 0.9). ABI, flow-mediated dilation (FMD) and nitroglycerin-mediated dilation (NMD), serum APN, ET-1, NO, and cysC were compared. There were significant increases in cysC and ET-1, and significant decreases in APN, NO, FMD and NMD of DLAO patients compared to T2DM patients. Serum APN and NO were positively correlated with ABI, while serum cysC and ET-1 were negatively correlated with ABI. cysC, ET-1 and diastolic blood pressure (DBP) were independent predictors of the severity of DLAO. Serum APN was positively correlated with FMD, NMD and NO, but was negatively correlated with ET-1 and cysC. FMD and NMD were positively correlated with APN and NO, and negatively correlated with ET-1 and cysC. Our study deciphers opposite roles of APN, NO, cysC and ET-1 in the development of DLAO and maintaining endothelial function.

Device-Free Multi-Location Human Activity Recognition Using Deep Complex Network.

Wi-Fi-based human activity recognition has attracted broad attention for its advantages, which include being device-free, privacy-protected, unaffected by light, etc. Owing to the development of artificial intelligence techniques, existing methods have made great improvements in sensing accuracy. However, the performance of multi-location recognition is still a challenging issue. According to the principle of wireless sensing, wireless signals that characterize activity are also seriously affected by location variations. Existing solutions depend on adequate data samples at different locations, which are labor-intensive. To solve the above concerns, we present an amplitude- and phase-enhanced deep complex network (AP-DCN)-based multi-location human activity recognition method, which can fully utilize the amplitude and phase information simultaneously so as to mine more abundant information from limited data samples. Furthermore, considering the unbalanced sample number at different locations, we propose a perception method based on the deep complex network-transfer learning (DCN-TL) structure, which effectively realizes knowledge sharing among various locations. To fully evaluate the performance of the proposed method, comprehensive experiments have been carried out with a dataset collected in an office environment with 24 locations and five activities. The experimental results illustrate that the approaches can achieve 96.85% and 94.02% recognition accuracy, respectively.

The Involvement of Antioxidant Enzyme System, Nitrogen Metabolism and Osmoregulatory Substances in Alleviating Salt Stress in Inbred Maize Lines and Hormone Regulation Mechanisms.

Salt stress inhibited the growth of maize. B46 and NC236 were chosen as materials and NaCl concentrations (0, 55, 110, 165, and 220 mmol L-1) were set. We found the activities of SOD, POD, CAT, APX, GR, MDHAR, and DHAR decreased under NaCl stress. Compared with NC236, the contents of AsA and GSH, AsA/DHA and GSH/GSSG of B46 decreased. The content of O2-, H2O2, MDA, and EL of B46 increased. The contents of NO3- and NO2- decreased, while the content of NH4+ increased under high NaCl concentration. The activities of NR and NiR decreased, while the activities of GS and GOGAT increased first and then decreased. For B46 and NC236, the maximum of NADH-GDH and NAD-GDH appeared at 165 and 110 mmol L-1 NaCl concentration, respectively. Compared with B46, and the GOT and GPT activities of NC236 increased first and then decreased. With the increase of NaCl concentration, the contents of proline, soluble protein, and soluble sugar were increased. The Na+ content of B46 and NC236 increased, and the K+ content and K+/Na+ decreased. Compared with NC236, B46 had higher IAA content in leaf, higher Z + ZR content in leaf and root, and lower ABA content in leaf and root.

A dual-function transcription factor, SlJAF13, promotes anthocyanin biosynthesis in tomato.

Unlike modern tomato (Solanum lycopersicum) cultivars, cv. LA1996 harbors the dominant Aft allele, which is associated with anthocyanin synthesis in tomato fruit peel. However, the control of Aft anthocyanin biosynthesis remains unclear. Here, we used ethyl methanesulfonate-induced and CRISPR/Cas9-mediated mutation of LA1996 to show, respectively, that two class IIIf basic helix-loop-helix (bHLH) transcription factors, SlJAF13 and SlAN1, are involved in the control of anthocyanin synthesis. These transcription factors are key components of the MYB-bHLH-WD40 (MBW) complex, which positively regulates anthocyanin synthesis. Molecular and genetic analyses showed that SlJAF13 functions as an upstream activation factor of SlAN1 by binding directly to the G-Box motif of its promoter region. On the other hand, SlJAZ2, a JA signaling repressor, interferes with formation of the MBW complex to suppress anthocyanin synthesis by directly binding these two bHLH components. Unexpectedly, the transcript level of SlJAZ2 was in turn repressed in a SlJAF13-dependent manner. Mechanistically, SlJAF13 interacts with SlMYC2, inhibiting SlMYC2 activation of SlJAZ2 transcription, thus constituting a negative feedback loop governing anthocyanin accumulation. Taken together, our findings support a sophisticated regulatory network, in which SlJAF13 acts as an upstream dual-function regulator that fine tunes anthocyanin biosynthesis in tomato.