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"Meng, Zhe"
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Arabidopsis ZINC FINGER PROTEIN1 Acts Downstream of GL2 to Repress Root Hair Initiation and Elongation by Directly Suppressing bHLH Genes
Cys2His2-like fold group (C2H2)–type zinc finger proteins promote root hair growth and development by regulating their target genes. However, little is known about their potential negative roles in root hair initiation and elongation. Here, we show that the C2H2-type zinc finger protein named ZINC FINGER PROTEIN1 (AtZP1), which contains an ERF-associated amphiphilic repression (EAR) motif, negatively regulates Arabidopsis (Arabidopsis thaliana) root hair initiation and elongation. Our results demonstrate that AtZP1 is highly expressed in root hairs and that AtZP1 inhibits transcriptional activity during root hair development. Plants overexpressing AtZP1 lacked root hairs, while loss-of-function mutants had longer and more numerous root hairs than the wild type. Transcriptome analysis indicated that AtZP1 downregulates genes encoding basic helix-loop-helix (bHLH) transcription factors associated with root hair cell differentiation and elongation. Mutation or deletion of the EAR motif substantially reduced the inhibitory activity of AtZP1. Chromatin immunoprecipitation assays, AtZP1:glucocorticoid receptor (GR) induction experiments, electrophoretic mobility shift assays, and yeast one-hybrid assays showed that AtZP1 directly targets the promoters of bHLH transcription factor genes, including the key root hair initiation gene ROOT HAIR DEFECTIVE6 (RHD6) and root hair elongation genes ROOT HAIR DEFECTIVE 6-LIKE 2 (RSL2) and RSL4, and suppresses root hair development. Our findings suggest that AtZP1 functions downstream of GL2 and negatively regulates root hair initiation and elongation, by suppressing RHD6, RSL4, and RSL2 transcription via the GL2/ZP1/ RSL pathway.
Journal Article
SS-MLP: A Novel Spectral-Spatial MLP Architecture for Hyperspectral Image Classification
Convolutional neural networks (CNNs) are the go-to model for hyperspectral image (HSI) classification because of the excellent locally contextual modeling ability that is beneficial to spatial and spectral feature extraction. However, CNNs with a limited receptive field pose challenges for modeling long-range dependencies. To solve this issue, we introduce a novel classification framework which regards the input HSI as a sequence data and is constructed exclusively with multilayer perceptrons (MLPs). Specifically, we propose a spectral-spatial MLP (SS-MLP) architecture, which uses matrix transposition and MLPs to achieve both spectral and spatial perception in global receptive field, capturing long-range dependencies and extracting more discriminative spectral-spatial features. Four benchmark HSI datasets are used to evaluate the classification performance of the proposed SS-MLP. Experimental results show that our pure MLP-based architecture outperforms other state-of-the-art convolution-based models in terms of both classification performance and computational time. When comparing with the SSSERN model, the average accuracy improvement of our approach is as high as 3.03%. We believe that our impressive experimental results will foster additional research on simple yet effective MLP-based architecture for HSI classification.
Journal Article
Microbial assembly, interaction, functioning, activity and diversification: a review derived from community compositional data
Microorganisms play crucial roles in maintaining ecosystem stability. The last two decades have witnessed an upsurge in studies on marine microbial community composition using high-throughput sequencing methods. Extensive mining of the compositional data has provided exciting new insights into marine microbial ecology from a number of perspectives. Both deterministic and stochastic processes contribute to microbial community assembly but their relative importance in structuring subcommunities, that are categorized by traits such as abundance, functional type and activity, differs. Through correlation-based network analysis, significant progress has been made in unraveling microbial co-occurrence patterns and dynamics in response to environmental changes. Prediction of ecosystem functioning, based on microbial data, is receiving increasing attention, as closely related microbes often share similar ecological traits and microbial diversity often exhibits significant correlations to ecosystem functioning. The ecosystem functioning is likely executed not by the whole community, but rather by an active fraction of a community, which can be inferred from the marker gene transcription level of community members. Furthermore, the huge amount of microbial community data has significantly expanded the tree of life and illuminated microbial phylogenetic divergence and evolutionary history. This review summarizes important findings in microbial assembly, interaction, functioning, activity and diversification, highlighting the interacting roles of different aspects, derived from community compositional data.
Journal Article
Cervical lymph node metastasis prediction from papillary thyroid carcinoma US videos: a prospective multicenter study
Background
Prediction of lymph node metastasis (LNM) is critical for individualized management of papillary thyroid carcinoma (PTC) patients to avoid unnecessary overtreatment as well as undesired under-treatment. Artificial intelligence (AI) trained by thyroid ultrasound (US) may improve prediction performance.
Methods
From September 2017 to December 2018, patients with suspicious PTC from the first medical center of the Chinese PLA general hospital were retrospectively enrolled to pre-train the multi-scale, multi-frame, and dual-direction deep learning (MMD-DL) model. From January 2019 to July 2021, PTC patients from four different centers were prospectively enrolled to fine-tune and independently validate MMD-DL. Its diagnostic performance and auxiliary effect on radiologists were analyzed in terms of receiver operating characteristic (ROC) curves, areas under the ROC curve (AUC), accuracy, sensitivity, and specificity.
Results
In total, 488 PTC patients were enrolled in the pre-training cohort, and 218 PTC patients were included for model fine-tuning (
n
= 109), internal test (
n
= 39), and external validation (
n
= 70). Diagnostic performances of MMD-DL achieved AUCs of 0.85 (95% CI: 0.73, 0.97) and 0.81 (95% CI: 0.73, 0.89) in the test and validation cohorts, respectively, and US radiologists significantly improved their average diagnostic accuracy (57% vs. 60%,
P
= 0.001) and sensitivity (62% vs. 65%,
P
< 0.001) by using the AI model for assistance.
Conclusions
The AI model using US videos can provide accurate and reproducible prediction of cervical lymph node metastasis in papillary thyroid carcinoma patients preoperatively, and it can be used as an effective assisting tool to improve diagnostic performance of US radiologists.
Trial registration
We registered on the Chinese Clinical Trial Registry website with the number ChiCTR1900025592.
Journal Article
Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification
Recently, with the extensive application of deep learning techniques in the hyperspectral image (HSI) field, particularly convolutional neural network (CNN), the research of HSI classification has stepped into a new stage. To avoid the problem that the receptive field of naive convolution is small, the dilated convolution is introduced into the field of HSI classification. However, the dilated convolution usually generates blind spots in the receptive field, resulting in discontinuous spatial information obtained. In order to solve the above problem, a densely connected pyramidal dilated convolutional network (PDCNet) is proposed in this paper. Firstly, a pyramidal dilated convolutional (PDC) layer integrates different numbers of sub-dilated convolutional layers is proposed, where the dilated factor of the sub-dilated convolution increases exponentially, achieving multi-sacle receptive fields. Secondly, the number of sub-dilated convolutional layers increases in a pyramidal pattern with the depth of the network, thereby capturing more comprehensive hyperspectral information in the receptive field. Furthermore, a feature fusion mechanism combining pixel-by-pixel addition and channel stacking is adopted to extract more abstract spectral–spatial features. Finally, in order to reuse the features of the previous layers more effectively, dense connections are applied in densely pyramidal dilated convolutional (DPDC) blocks. Experiments on three well-known HSI datasets indicate that PDCNet proposed in this paper has good classification performance compared with other popular models.
Journal Article
Deep Residual Involution Network for Hyperspectral Image Classification
Convolutional neural networks (CNNs) have achieved great results in hyperspectral image (HSI) classification in recent years. However, convolution kernels are reused among different spatial locations, known as spatial-agnostic or weight-sharing kernels. Furthermore, the preference of spatial compactness in convolution (typically, 3×3 kernel size) constrains the receptive field and the ability to capture long-range spatial interactions. To mitigate the above two issues, in this article, we combine a novel operation called involution with residual learning and develop a new deep residual involution network (DRIN) for HSI classification. The proposed DRIN could model long-range spatial interactions well by adopting enlarged involution kernels and realize feature learning in a fairly lightweight manner. Moreover, the vast and dynamic involution kernels are distinct over different spatial positions, which could prioritize the informative visual patterns in the spatial domain according to the spectral information of the target pixel. The proposed DRIN achieves better classification results when compared with both traditional machine learning-based and convolution-based methods on four HSI datasets. Especially in comparison with the convolutional baseline model, i.e., deep residual network (DRN), our involution-powered DRIN model increases the overall classification accuracy by 0.5%, 1.3%, 0.4%, and 2.3% on the University of Pavia, the University of Houston, the Salinas Valley, and the recently released HyRANK HSI benchmark datasets, respectively, demonstrating the potential of involution for HSI classification.
Journal Article
The triglyceride glucose index was U-shape associated with all-cause mortality in population with cardiovascular diseases
Background
The triglyceride and glucose (TyG) index has been considered a simple surrogate marker of insulin resistance, related to a high risk of mortality. However, few studies have investigated the specific relationship between the TyG index and all-cause mortality among population with cardiovascular diseases.
Methods
2,072 participants with cardiovascular diseases were enrolled from the National Health and Nutrition Examination Survey (NHANES) 1999–2014. The TyG index was calculated as log [fasting triglycerides (mg/dL) x fasting glucose (mg/dL)/2]. Outcomes were all-cause mortality and cardiovascular mortality. The baseline levels of TyG associated with the risk of mortality were evaluated on a continuous scale (restricted cubic splines) and by a priori defined quantile categories with Cox regression models.
Results
After a follow-up of 16.8 years, 791 all-cause deaths and 184 cardiovascular deaths occurred. Restricted cubic splines showed that the association between levels of TyG index and the risk of all-cause mortality was non-linear (p < 0.001) and the TyG index associated with the lowest risk of all-cause mortality ranges 8.83 to 9.06 in individuals with cardiovascular diseases. Compared with the reference quartile of 8.84 ~ 9.29, the multivariate-adjusted hazards ratios and 95% confidence intervals were 1.40 (1.13–1.74; p = 0.002) in the lowest quartile and 1.08 (0.88, 1.32; p = 0.475) in the highest quartile for all-cause mortality. However, TyG was not associated with cardiovascular mortality.
Conclusions
TyG index was U-shape associated with the risk of all-cause mortality in participants with cardiovascular diseases and the level associated with the lowest risk ranged 8.83 to 9.06.
Journal Article
Dairy Products Consumption and Risk of Type 2 Diabetes: Systematic Review and Dose-Response Meta-Analysis
The consumption of dairy products may influence the risk of type 2 diabetes mellitus (T2DM), but inconsistent findings have been reported. Moreover, large variation in the types of dairy intake has not yet been fully explored.
We conducted a systematic review and meta-analysis to clarify the dose-response association of dairy products intake and T2DM risk. We searched PubMed, EMBASE and Scopus for studies of dairy products intake and T2DM risk published up to the end of October 2012. Random-effects models were used to estimate summary relative risk (RR) statistics. Dose-response relations were evaluated using data from different dairy products in each study. We included 14 articles of cohort studies that reported RR estimates and 95% confidence intervals (95% CIs) of T2DM with dairy products intake. We found an inverse linear association of consumption of total dairy products (13 studies), low-fat dairy products (8 studies), cheese (7 studies) and yogurt (7 studies) and risk of T2DM. The pooled RRs were 0.94 (95% CI 0.91-0.97) and 0.88 (0.84-0.93) for 200 g/day total and low-fat dairy consumption, respectively. The pooled RRs were 0.80 (0.69-0.93) and 0.91 (0.82-1.00) for 30 g/d cheese and 50 g/d yogurt consumption, respectively. We also found a nonlinear association of total and low-fat dairy intake and T2DM risk, and the inverse association appeared to be strongest within 200 g/d intake.
A modest increase in daily intake of dairy products such as low fat dairy, cheese and yogurt may contribute to the prevention of T2DM, which needs confirmation in randomized controlled trials.
Journal Article
Trimetazidine: a meta-analysis of randomised controlled trials in heart failure
ObjectiveTo explore whether trimetazidine could improve symptoms, cardiac functions and clinical outcomes in patients with heart failure (HF).MethodsA systematic literature search was conducted to identify randomised controlled trials (RCT) of trimetazidine for HF between 1966 and May 2010 in Pubmed, the Cochrane Central Registry of Clinical Trials and EMBASE. Reports of trials were sought that compared trimetazidine with placebo control for chronic HF in adults, with outcomes including all-cause mortality, hospitalisation, cardiovascular events, changes in cardiac function parameters and exercise capacity.Results17 trials with data for 955 patients were identified by the literature search. Trimetazidine therapy was associated with a significant improvement in left ventricular ejection fraction in patients with both ischaemic (weighted mean difference (WMD) with placebo 7.37%; 95% CI 6.05 to 8.70; p<0.01) and non-ischaemic HF (WMD 8.72%; 95% CI 5.51 to 11.92; p<0.01). With trimetazidine therapy, left ventricular end-systolic volume was significantly reduced (WMD 10.37 ml; 95% CI 15.46 to 5.29; p<0.01) and New York Heart Association classification was improved (WMD 0.41; 95% CI 0.51 to 0.31; p<0.01) as was exercise duration (WMD, 30.26 s; 95% CI 8.77 to 51.75; p<0.01). More importantly, trimetazidine had a significant protective effect for all-cause mortality (RR 0.29; 95% CI 0.17 to 0.49; p<0.00001) and cardiovascular events and hospitalisation (RR 0.42; 95% CI 0.30 to 0.58; p<0.00001).ConclusionTrimetazidine might be an effective strategy for treating HF. More studies, especially larger multicentre RCT, are warranted to clarify the effect of trimetazidine on HF.
Journal Article