The P-body component DECAPPING5 and the floral repressor SISTER OF FCA regulate FLOWERING LOCUS C transcription in Arabidopsis.

Flowering is the transition from vegetative to reproductive growth and is critical for plant adaptation and reproduction. FLOWERING LOCUS C (FLC) plays a central role in flowering time control, and dissecting its regulation mechanism provides essential information for crop improvement. Here, we report that DECAPPING5 (DCP5), a component of processing bodies (P-bodies), regulates FLC transcription and flowering time in Arabidopsis (Arabidopsis thaliana). DCP5 and its interacting partner SISTER OF FCA (SSF) undergo liquid-liquid phase separation (LLPS) that is mediated by their prion-like domains (PrDs). Enhancing or attenuating the LLPS of both proteins using transgenic methods greatly affects their ability to regulate FLC and flowering time. DCP5 regulates FLC transcription by modulating RNA polymerase II enrichment at the FLC locus. DCP5 requires SSF for FLC regulation, and loss of SSF or its PrD disrupts DCP5 function. Our results reveal that DCP5 interacts with SSF, and the nuclear DCP5-SSF complex regulates FLC expression at the transcriptional level.

PredGCN: a Pruning-enabled Gene-Cell Net for automatic cell annotation of single cell transcriptome data.

MOTIVATION: The annotation of cell types from single-cell transcriptomics is essential for understanding the biological identity and functionality of cellular populations. Although manual annotation remains the gold standard, the advent of automatic pipelines has become crucial for scalable, unbiased, and cost-effective annotations. Nonetheless, the effectiveness of these automatic methods, particularly those employing deep learning, significantly depends on the architecture of the classifier and the quality and diversity of the training datasets. RESULTS: To address these limitations, we present a Pruning-enabled Gene-Cell Net (PredGCN) incorporating a Coupled Gene-Cell Net (CGCN) to enable representation learning and information storage. PredGCN integrates a Gene Splicing Net (GSN) and a Cell Stratification Net (CSN), employing a pruning operation (PrO) to dynamically tackle the complexity of heterogeneous cell identification. Among them, GSN leverages multiple statistical and hypothesis-driven feature extraction methods to selectively assemble genes with specificity for scRNA-seq data while CSN unifies elements based on diverse region demarcation principles, exploiting the representations from GSN and precise identification from different regional homogeneity perspectives. Furthermore, we develop a multi-objective Pareto pruning operation (Pareto PrO) to expand the dynamic capabilities of CGCN, optimizing the sub-network structure for accurate cell type annotation. Multiple comparison experiments on real scRNA-seq datasets from various species have demonstrated that PredGCN surpasses existing state-of-the-art methods, including its scalability to cross-species datasets. Moreover, PredGCN can uncover unknown cell types and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into cell type identification and characterizing scRNA-seq data from different perspectives. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/IrisQi7/PredGCN and test data is available at https://figshare.com/articles/dataset/PredGCN/25251163.

Composite healthy lifestyle, socioeconomic deprivation, and mental well-being during the COVID-19 pandemic: a prospective analysis.

The adverse psychological and social impacts of COVID-19 pandemic are well characterized, but the role of composite, modifiable lifestyle factors that may interact to mitigate these impacts is not. The effect of socioeconomic deprivation on these lifestyle risks also remains unclear. Based on a nationally representative, longitudinal cohort, we assessed the association between a combination of pre-pandemic lifestyle factors and mental health conditions during pandemic, and the contribution of deprivation to it. Composite lifestyle factors included BMI, smoking status, alcohol consumption, physical activity, sedentary time, sleep duration, and fruit and vegetable intake, with lifestyle scores and lifestyle categories calculated for each participant. Symptoms of depression and anxiety, and personal well-being were assessed by validated scales during the pandemic. Socioeconomic deprivation was characterized by both individual-level (income, wealth, and education) and group-level factors (Index of Multiple Deprivation). Of the 5049 eligible participants (mean [SD] age, 68.1 [10.9] years; 57.2% were female) included in the study, 41.6% followed a favorable lifestyle, 48.9% followed an intermediate lifestyle, and 9.5% followed an unfavorable lifestyle. Compared with favorable lifestyle category, participants in the intermediate and unfavorable lifestyle category were at increased risk of mental health conditions, with the hazard ratio (HR) for trend per increment change towards unfavorable category of 1.17 (95% CI 1.09-1.26) for depression, 1.23 (1.07-1.42) for anxiety, and 1.39 (1.20-1.61) for low well-being. A significant trend of lower risk for mental health conditions with increasing number of healthy lifestyle factors was observed (P < 0.001 for trend). There were no significant interactions between lifestyle factors and socioeconomic deprivation for any of the outcomes, with similar HRs for trend per one increment change in lifestyle category observed in each deprivation group. Compared with those in the least deprived group with favorable lifestyle, participants in the most deprived group adherent to unfavorable lifestyle had the highest risk of mental health outcomes. These results suggest that adherence to a broad combination of healthy lifestyle factors was associated with a significantly reduced risk of mental health conditions during the COVID-19 pandemic. Lifestyle factors, in conjunction with socioeconomic deprivation, independently contribute to the risk of mental health issues. Although further research is needed to assess causality, the current findings support public health strategies and individual-level interventions that provide enhanced support in areas of deprivation and target multiple lifestyle factors to reduce health inequalities and promote mental well-being during the ongoing COVID-19 pandemic.

scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) is an increasingly popular technique for transcriptomic analysis of gene expression at the single-cell level. Cell-type clustering is the first crucial task in the analysis of scRNA-seq data that facilitates accurate identification of cell types and the study of the characteristics of their transcripts. Recently, several computational models based on a deep autoencoder and the ensemble clustering have been developed to analyze scRNA-seq data. However, current deep autoencoders are not sufficient to learn the latent representations of scRNA-seq data, and obtaining consensus partitions from these feature representations remains under-explored. RESULTS: To address this challenge, we propose a single-cell deep clustering model via a dual denoising autoencoder with bipartite graph ensemble clustering called scBGEDA, to identify specific cell populations in single-cell transcriptome profiles. First, a single-cell dual denoising autoencoder network is proposed to project the data into a compressed low-dimensional space and that can learn feature representation via explicit modeling of synergistic optimization of the zero-inflated negative binomial reconstruction loss and denoising reconstruction loss. Then, a bipartite graph ensemble clustering algorithm is designed to exploit the relationships between cells and the learned latent embedded space by means of a graph-based consensus function. Multiple comparison experiments were conducted on 20 scRNA-seq datasets from different sequencing platforms using a variety of clustering metrics. The experimental results indicated that scBGEDA outperforms other state-of-the-art methods on these datasets, and also demonstrated its scalability to large-scale scRNA-seq datasets. Moreover, scBGEDA was able to identify cell-type specific marker genes and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into identifying cell types and characterizing the scRNA-seq data from different perspectives. AVAILABILITY AND IMPLEMENTATION: The source code of scBGEDA is available at https://github.com/wangyh082/scBGEDA. The software and the supporting data can be downloaded from https://figshare.com/articles/software/scBGEDA/19657911. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fecal calprotectin: A novel predictor of ulcerated esophageal injury after atrial fibrillation catheter ablation.

BACKGROUND: Atrial esophageal fistula (AEF) is a lethal complication that can occur post atrial fibrillation (AF) ablation. Esophageal injury (EI) is likely to be the initial lesion leading to AEF. Endoscopic examination is the gold standard for a diagnosis of EI but extensive endoscopic screening is invasive and costly. This study was conducted to determine whether fecal calprotectin (Fcal), a marker of inflammation throughout the intestinal tract, may be associated with the existence of esophageal injury. METHODS: This diagnostic study was conducted in a cohort of 166 patients with symptomatic AF undergoing radiofrequency catheter ablation from May 2020 to June 2021. Fcal tests were performed 1-7 days after ablation. All patients underwent endoscopic ultrasonography 1 or 2 days after ablation. RESULTS: The levels of Fcal were significantly different between the EI and non-EI groups (404.9 µg/g (IQR 129.6-723.6) vs. 40.4 µg/g (IQR 15.0-246.2), p < .001). Analysis of ROC curves revealed that a Fcal level of 125 µg/g might be the optimal cut-off value for a diagnosis of EI, giving a 78.8% sensitivity and a 65.4% specificity. The negative predictive value of Fcal was 100% for ulcerated EI. CONCLUSIONS: The level of Fcal is associated with EI post AF catheter ablation. 125 µg/g might be the optimal cut-off value for a diagnosis of EI. Negative Fcal could predict the absence of ulcerated EI, which could be considered a precursor to AEF.

Identification of haploinsufficient genes from epigenomic data using deep forest.

Haploinsufficiency, wherein a single allele is not enough to maintain normal functions, can lead to many diseases including cancers and neurodevelopmental disorders. Recently, computational methods for identifying haploinsufficiency have been developed. However, most of those computational methods suffer from study bias, experimental noise and instability, resulting in unsatisfactory identification of haploinsufficient genes. To address those challenges, we propose a deep forest model, called HaForest, to identify haploinsufficient genes. The multiscale scanning is proposed to extract local contextual representations from input features under Linear Discriminant Analysis. After that, the cascade forest structure is applied to obtain the concatenated features directly by integrating decision-tree-based forests. Meanwhile, to exploit the complex dependency structure among haploinsufficient genes, the LightGBM library is embedded into HaForest to reveal the highly expressive features. To validate the effectiveness of our method, we compared it to several computational methods and four deep learning algorithms on five epigenomic data sets. The results reveal that HaForest achieves superior performance over the other algorithms, demonstrating its unique and complementary performance in identifying haploinsufficient genes. The standalone tool is available at https://github.com/yangyn533/HaForest.

Identification of pan-cancer Ras pathway activation with deep learning.

The identification of hidden responders is often an essential challenge in precision oncology. A recent attempt based on machine learning has been proposed for classifying aberrant pathway activity from multiomic cancer data. However, we note several critical limitations there, such as high-dimensionality, data sparsity and model performance. Given the central importance and broad impact of precision oncology, we propose nature-inspired deep Ras activation pan-cancer (NatDRAP), a deep neural network (DNN) model, to address those restrictions for the identification of hidden responders. In this study, we develop the nature-inspired deep learning model that integrates bulk RNA sequencing, copy number and mutation data from PanCanAltas to detect pan-cancer Ras pathway activation. In NatDRAP, we propose to synergize the nature-inspired artificial bee colony algorithm with different gradient-based optimizers in one framework for optimizing DNNs in a collaborative manner. Multiple experiments were conducted on 33 different cancer types across PanCanAtlas. The experimental results demonstrate that the proposed NatDRAP can provide superior performance over other benchmark methods with strong robustness towards diagnosing RAS aberrant pathway activity across different cancer types. In addition, gene ontology enrichment and pathological analysis are conducted to reveal novel insights into the RAS aberrant pathway activity identification and characterization. NatDRAP is written in Python and available at https://github.com/lixt314/NatDRAP1.

GMHCC: high-throughput analysis of biomolecular data using graph-based multiple hierarchical consensus clustering.

MOTIVATION: Thanks to the development of high-throughput sequencing technologies, massive amounts of various biomolecular data have been accumulated to revolutionize the study of genomics and molecular biology. One of the main challenges in analyzing this biomolecular data is to cluster their subtypes into subpopulations to facilitate subsequent downstream analysis. Recently, many clustering methods have been developed to address the biomolecular data. However, the computational methods often suffer from many limitations such as high dimensionality, data heterogeneity and noise. RESULTS: In our study, we develop a novel Graph-based Multiple Hierarchical Consensus Clustering (GMHCC) method with an unsupervised graph-based feature ranking (FR) and a graph-based linking method to explore the multiple hierarchical information of the underlying partitions of the consensus clustering for multiple types of biomolecular data. Indeed, we first propose to use a graph-based unsupervised FR model to measure each feature by building a graph over pairwise features and then providing each feature with a rank. Subsequently, to maintain the diversity and robustness of basic partitions (BPs), we propose multiple diverse feature subsets to generate several BPs and then explore the hierarchical structures of the multiple BPs by refining the global consensus function. Finally, we develop a new graph-based linking method, which explicitly considers the relationships between clusters to generate the final partition. Experiments on multiple types of biomolecular data including 35 cancer gene expression datasets and eight single-cell RNA-seq datasets validate the effectiveness of our method over several state-of-the-art consensus clustering approaches. Furthermore, differential gene analysis, gene ontology enrichment analysis and KEGG pathway analysis are conducted, providing novel insights into cell developmental lineages and characterization mechanisms. AVAILABILITY AND IMPLEMENTATION: The source code is available at GitHub: https://github.com/yifuLu/GMHCC. The software and the supporting data can be downloaded from: https://figshare.com/articles/software/GMHCC/17111291. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Inactivation of RPX1 in Arabidopsis confers resistance to Plutella xylostella through the accumulation of the homoterpene DMNT.

The lepidopteran crop pest Plutella xylostella causes severe constraints on Brassica cultivation. Here, we report a novel role for RPX1 (resistance to P. xylostella) in resistance to this pest in Arabidopsis thaliana. The rpx1-1 mutant repels P. xylostella larvae, and feeding on the rpx1-1 mutant severely damages the peritrophic matrix structure in the midgut of the larvae, thereby negatively affecting larval growth and pupation. This resistance results from the accumulation of defence compounds, including the homoterpene (3E)-4,8-dimethyl-1,3,7-nonatriene (DMNT), due to the upregulation of PENTACYCLIC TRITERPENE SYNTHASE 1 (PEN1), which encodes a key DMNT biosynthetic enzyme. P. xylostella infestation and wounding induce RPX1 protein degradation, which may confer a rapid response to insect infestation. RPX1 inactivation and PEN1 overexpression are not associated with negative trade-offs for plant growth but have much higher seed production than the wild-type in the presence of P. xylostella infestation. This study offers a new strategy for plant molecular breeding against P. xylostella.

Respiratory control minimizes diaphragmatic contraction and dry cough during pulsed-field ablation of atrial fibrillation.

AIMS: Pulsed-field ablation (PFA) is a promising new ablation modality to treat atrial fibrillation. However, PFA can cause varying degrees of diaphragmatic contraction and dry cough, especially under conscious sedation. This prospective study presents a method to minimize the impact of PFA on diaphragmatic contraction and dry cough during the procedure. METHODS AND RESULTS: Twenty-eight patients underwent PFA for pulmonary vein (PV) and superior vena cava isolation under conscious sedation. Each patient received two groups of ablations in each vein: the control group allowed PFA application during any phase of respiratory cycle, while the test group used respiratory control, delivering PFA energy only at the end of expiration. A rating score system was developed to assess diaphragmatic contraction and dry cough. A total of 1401 control ablations and 4317 test ablations were performed. The test group had significantly lower scores for diaphragmatic contraction (P < 0.01) and dry cough (P < 0.001) in all PVs compared to the control group. The average relative reductions in scores for all PVs were 33-47% for diaphragmatic contraction and 67-83% for dry cough. The percentage of ablations with scores ≧2 for diaphragmatic contraction decreased significantly from 18.5-28.0% in the control group to 0.4-2.6% in the test group (P < 0.001). For dry cough, the percentage decreased from 11.9-43.7% in the control group to 0.7-2.1% in the test group. CONCLUSION: Pulsed-field ablation application at the end of expiration can reduce the severity of diaphragmatic contraction and eliminate moderate and severe dry cough during PV isolation performed under conscious sedation.

Intracardiac echocardiography is a safe and effective alternative to transesophageal echocardiography for left atrial appendage thrombus evaluation at the time of atrial fibrillation ablation: The ICE-TEE study.

BACKGROUND: Intracardiac echocardiography (ICE) technology has been increasingly accepted as an integral part of atrial fibrillation (AF) ablation procedures. It is still unknown whether ICE can routinely replace transesophageal echocardiography (TEE) for routine thrombus screening in non-selective AF patients. OBJECTIVE: To assess whether ICE can routinely replace TEE in screening for left atrial (LA)/left atrial appendage (LAA) thrombus in general patients undergoing catheter ablation for AF. METHODS: A total of 2003 consecutive patients undergoing AF ablation were included. 1155 patients (ICE group) received intra-procedural ICE examination for LA/LAA thrombus screening, while 848 patients (TEE group) received pre-procedure TEE examination. The incidence of thrombus, peri-procedure complications, and hospital efficiency were assessed. RESULTS: The LA and LAA were adequately visualized in all patients. Five patients in the ICE group and 15 patients in the TEE group were found to have LAA thrombus. The incidence of major periprocedural thrombo-embolic events was comparable between two groups (0.2% vs. 0.1%, p = .76), none were due to undetected LA/LAA thrombus. Other major periprocedural complications occurred at similar rates in both groups, while post-procedure fever was less common in the ICE group (12.7% vs. 17.4%, p < .001). Procedure times and hospital length of stay were both shorter in the ICE group (142 min [87-197 min] vs. 150 min [95-205 min], and 3[2-4] day vs. 4[3-5] day, respectively, both p < .001). CONCLUSIONS: ICE can replace TEE for atrial thrombus screening in AF patients undergoing ablation without increased complications. An "ICE replacing TEE" workflow can also reduce the incidence of postoperative fever and improve hospital efficiency.

Linkage mapping combined with GWAS revealed the genetic structural relationship and candidate genes of maize flowering time-related traits.

BACKGROUND: Flowering time is an important agronomic trait of crops and significantly affects plant adaptation and seed production. Flowering time varies greatly among maize (Zea mays) inbred lines, but the genetic basis of this variation is not well understood. Here, we report the comprehensive genetic architecture of six flowering time-related traits using a recombinant inbred line (RIL) population obtained from a cross between two maize genotypes, B73 and Abe2, and combined with genome-wide association studies to identify candidate genes that affect flowering time. RESULTS: Our results indicate that these six traits showed extensive phenotypic variation and high heritability in the RIL population. The flowering time of this RIL population showed little correlation with the leaf number under different environmental conditions. A genetic linkage map was constructed by 10,114 polymorphic markers covering the whole maize genome, which was applied to QTL mapping for these traits, and identified a total of 82 QTLs that contain 13 flowering genes. Furthermore, a combined genome-wide association study and linkage mapping analysis revealed 17 new candidate genes associated with flowering time. CONCLUSIONS: In the present study, by using genetic mapping and GWAS approaches with the RIL population, we revealed a list of genomic regions and candidate genes that were significantly associated with flowering time. This work provides an important resource for the breeding of flowering time traits in maize.

The impact of quarantine on mental health status among general population in China during the COVID-19 pandemic.

Quarantine and isolation measures urgently adopted to control the COVID-19 pandemic might potentially have negative psychological and social effects. We conducted this cross-sectional, nationwide study to ascertain the psychological effect of quarantine and identify factors associated with mental health outcomes among population quarantined to further inform interventions of mitigating mental health risk especially for vulnerable groups under pandemic conditions. Sociodemographic data, attitudes toward the COVID-19, and mental health measurements of 56,679 participants from 34 provinces in China were collected by an online survey from February 28 to March 11, 2020. Of the 56,679 participants included in the study (mean [SD] age, 36.0 [8.2] years), 27,149 (47.9%) were male and 16,454 (29.0%) ever experienced home confinement or centralized quarantine during COVID-19 outbreak. Compared those without quarantine and adjusted for potential confounders, quarantine measures were associated with increased risk of total psychological outcomes (prevalence, 34.1% vs 27.3%; odds ratio [OR], 1.34; 95% CI, 1.28-1.39; P < 0.001). Multivariable logistic regression analyses showed that vulnerable groups of the quarantined population included those with pre-existing mental disorders or chronic physical diseases, frontline workers, those in the most severely affected areas during outbreak, infected or suspected patients, and those who are less financially well-off. Complying with quarantine, being able to take part in usual work, and having adequate understanding of information related to the outbreak were associated with less mental health issues. These results suggest that quarantine measures during COVID-19 pandemic are associated with increased risk of experiencing mental health burden, especially for vulnerable groups. Further study is needed to establish interventions to reduce mental health consequences of quarantine and empower wellbeing especially in vulnerable groups under pandemic conditions.

Depression and anxiety in relation to cancer incidence and mortality: a systematic review and meta-analysis of cohort studies.

The link between depression and anxiety status and cancer outcomes has been well-documented but remains unclear. We comprehensively quantified the association between depression and anxiety defined by symptom scales or clinical diagnosis and the risk of cancer incidence, cancer-specific mortality, and all-cause mortality in cancer patients. Pooled estimates of the relative risks (RRs) for cancer incidence and mortality were performed in a meta-analysis by random effects or fixed effects models as appropriate. Associations were tested in subgroups stratified by different study and participant characteristics. Fifty-one eligible cohort studies involving 2,611,907 participants with a mean follow-up period of 10.3 years were identified. Overall, depression and anxiety were associated with a significantly increased risk of cancer incidence (adjusted RR: 1.13, 95% CI: 1.06-1.19), cancer-specific mortality (1.21, 1.16-1.26), and all-cause mortality in cancer patients (1.24, 1.13-1.35). The estimated absolute risk increases (ARIs) associated with depression and anxiety were 34.3 events/100,000 person years (15.8-50.2) for cancer incidence and 28.2 events/100,000 person years (21.5-34.9) for cancer-specific mortality. Subgroup analyses demonstrated that clinically diagnosed depression and anxiety were related to higher cancer incidence, poorer cancer survival, and higher cancer-specific mortality. Psychological distress (symptoms of depression and anxiety) was related to higher cancer-specific mortality and poorer cancer survival but not to increased cancer incidence. Site-specific analyses indicated that overall, depression and anxiety were associated with an increased incidence risks for cancers of the lung, oral cavity, prostate and skin, a higher cancer-specific mortality risk for cancers of the lung, bladder, breast, colorectum, hematopoietic system, kidney and prostate, and an increased all-cause mortality risk in lung cancer patients. These analyses suggest that depression and anxiety may have an etiologic role and prognostic impact on cancer, although there is potential reverse causality; Furthermore, there was substantial heterogeneity among the included studies, and the results should be interpreted with caution. Early detection and effective intervention of depression and anxiety in cancer patients and the general population have public health and clinical importance.

Comparison of left atrial and left atrial appendage mechanics in the risk stratification of stroke in patients with atrial fibrillation.

BACKGROUND: Left atrial (LA) and left atrial appendage (LAA) dysfunction has been demonstrated to contribute to atrial fibrillation (AF)-related stroke. However, usefulness of LA and LAA mechanics has not been fully compared. We sought to investigate the association of LA and LAA mechanics with stroke and to compare their diagnostic values in the risk stratification of stroke in patients with nonvalvular AF. METHODS: A total of 208 consecutive patients with AF (63.58 ± 10.37 years, 63.9% male,57.7% persistent AF) who underwent echocardiography before catheter ablation were prospectively enrolled. Speckle-tracking was used to measure LA and LAA global longitudinal strain (GLS). LA and LAA mechanical dispersions (MD) were defined as the standard deviation (SD) of time to peak positive strain corrected by the R-R interval. RESULTS: Patients with prior stroke/ transient ischemic attack (TIA) (n = 31) had significantly higher LA and LAA MD than those without (n = 177) (11.56 ± 4.38% vs. 8.43 ± 3.44%, 15.15 ± 5.46% vs. 10.94 ± 4.40%, both P < 0.01). In multivariable analysis, LA and LAA MD were independently associated with stroke/TIA (odds ratio, 1.18-1.29, 1.19-1.22, respectively, both P < 0.01), providing incremental values over clinical and standard echocardiographic parameters. In a subgroup analysis, LA MD was more useful than LAA MD in patients with normal LA volumes, while LAA MD was superior to LA MD in patients with LA enlargement. CONCLUSIONS: Higher LA and LAA mechanical dispersion are independently associated with stroke/TIA in AF patients and had incremental values over clinical and conventional echocardiographic parameters. What's more, priorities of dispersion assessment are different depending on patients' LA size.

Identification of a genomic region controlling thermotolerance at flowering in maize using a combination of whole genomic re-sequencing and bulked segregant analysis.

KEY MESSAGE: A novel genomic region controlling thermotolerance at flowering was identified by the combination of whole genomic re-sequencing and bulked segregant analysis in maize. The increasing frequency of extreme high temperature has brought a great threat to the development of maize throughout its life cycle, especially during the flowering phase. However, the genetic basis of thermotolerance at flowering in maize remains poorly understood. Here, we characterized a thermotolerant maize ecotype Abe2 and dissected its genetic basis using a F2:8 recombinant inbred line (RIL) population generated from a cross between Abe2 and B73. After continuous high temperature stress above 35 °C for 17 days, Abe2 and B73 show distinct leaf scorching phenotype under field conditions. To identify the genomic regions associated with the phenotypic variation, we applied a combination of whole genomic re-sequencing and bulked segregant analysis, and revealed 10,316,744 SNPs and 1,488,302 InDels between the two parental lines, and 2,693,054 SNPs and 313,757 InDels between the two DNA pools generated from the thermos-tolerant and the sensitive individuals of the RIL, of which, 108,655 and 17,853 SNPs may cause nonsynonymous variations. Finally, a 7.41 Mb genomic region on chromosome 1 was identified, and 7 candidate genes were annotated to participate in high temperature-related stress response. A candidate gene Zm00001d033339 encoding a serine/threonine protein kinase was proposed to be the most likely causative gene contributing to the thermotolerance at flowering by involving in stomatal movement (GO: 0010119) via Abscisic acid (ABA) pathway (KO04075). This work could provide an opportunity for gene cloning and pyramiding breeding to improve thermotolerance at flowering in maize.

A hydrophobic deep eutectic solvent-based vortex-assisted dispersive liquid-liquid microextraction combined with HPLC for the determination of nitrite in water and biological samples.

In recent years, hydrophobic deep eutectic solvents as new generation of green solvents have attracted wide attention in liquid microextraction technique. In this article, four hydrophobic deep eutectic solvents composed of trioctylmethylammonium chloride and oleic acid were designed and prepared firstly. Combined with high-performance liquid chromatography, these deep eutectic solvents were used as an extraction solvent in vortex-assisted dispersive liquid-liquid microextraction for the selective enrichment and indirect determination of trace nitrite from real water and biological samples. This method is based on the diazotization-coupling reaction of nitrite with p-nitroaniline and diphenylamine in acidic water, and then the nitrite is quantified indirectly by measuring the obtained azo compounds. Some factors influencing the extraction efficiency, including the reaction and extraction conditions, were investigated. Under the optimized conditions, the method has a linear range of 1-300 µg/L with a correlation coefficient of 0.9924, limit of detection of 0.2 µg/L, limit of quantitation of 1 µg/L, intraday and interday relative standard deviations of 4.0 and 6.0%. This method was successfully applied in determination of nitrite from three environmental water and two biological samples with the recovery in the range of 90.5-115.2%. In addition, these results were well agreement with those obtained by the conventional Griess method.

Modulation of ovine SBD-1 expression by Saccharomyces cerevisiae in ovine ruminal epithelial cells.

BACKGROUND: The ovine rumen is involved in host defense responses and acts as the immune interface with the environment. The ruminal mucosal epithelium plays an important role in innate immunity and secretes antimicrobial innate immune molecules that have bactericidal activity against a variety of pathogens. Defensins are cationic peptides that are produced by the mucosal epithelia and have broad-spectrum antimicrobial activity. Sheep ß-defensin-1 (SBD-1) is one of the most important antibacterial peptides in the rumen. The expression of SBD-1 is regulated by the probiotic, Saccharomyces cerevisiae (S.c); however, the regulatory mechanism has not yet been elucidated. In the current study, the effects of S.c on the expression and secretion of SBD-1 in ovine ruminal epithelial cells were investigated using quantitative real-time PCR (qPCR) and enzyme-linked immunosorbent assay (ELISA). In addition, specific inhibitors were used to block the nuclear factor kappa-light-chain enhancer of activated B cells (NF-κB), p38, JNK, and ERK1/2 signalling pathways separately or simultaneously, to determine the regulatory mechanism(s) governing S.c-induced SBD-1 upregulation. RESULTS: Incubation with S.c induced release of SBD-1 by ovine ruminal epithelial cells, with SBD-1 expression peaking after 12 h of incubation. The highest SBD-1 expression levels were achieved after treatment with 5.2 × 107 CFU∙mL- 1 S.c. Treatment with S.c resulted in significantly increased NF-κB, p38, JNK, ERK1/2, TLR2, and MyD88 mRNA expression. Whereas inhibition of mitogen-activated protein kinases (MAPKs) and NF-κB gene expression led to a decrease in SBD-1 expression. CONCLUSIONS: S.c was induced SBD-1 expression and the S.c-induced up-regulation of SBD-1 expression may be related to TLR2 and MyD88 in ovine ruminal epithelial cells. This is likely simultaneously regulated by the MAPKs and NF-κB pathways with the p38 axis of the MAPKs pathway acting as the primary regulator. Thus, the pathways regulating S.c-induced SBD-1 expression may be related to TLR2-MyD88-NF-κB/MAPKs, with the TLR2-MyD88-p38 component of the TLR2-MyD88-MAPKs signalling acting as the main pathway.

Impact of direct and indirect application of rising furfural concentrations on viability, infectivity and reproduction of the root-knot nematode, Meloidogyne incognita in Pisum sativum.

The gradual withdraw of several broadly used nematicides from market has enhanced the need to develop sustainable and eco-friendly alternatives with nematicidal properties. Furfural is one of the promising alternatives to fill this need. Baseline information about the impact of furfural on egg hatch, penetration potential and ultrastructure of nematode is lacking. In this study, the reagent-grade (purity ≥ 99.0%) of furfural was applied against Meloidogyne incognita. In vitro tests showed gradual reduction in either the rate of egg hatch or second stage juvenile (J2) viability of M. incognita when immersed in concentrations ranging from 0 to 10.0 µl/ml furfural. The mean EC50 for J2 and egg hatch was 0.37 and 0.27 µl/ml furfural, respectively. Furfural, even at low concentrations, resulted in a considerable suppression in egg hatch. Hatch was <5% after 8 days at 0.63 µl/ml furfural. The same furfural concentrations after 12 h caused 57.25% loss of viability in J2. Moreover, the penetration rate of juveniles to pea roots was suppressed when furfural was even applied at low rates. In pot experiments, furfural was applied as liquid (direct) or vapor (indirect) treatments at rates of 0-1.5 ml/kg soil. Significant reduction in galling, egg production and population density of M. incognita observed when furfural was applied at rates >0.2 ml/kg soil. No adverse effect was detected on plants or free-living nematodes as a result of furfural application. Liquid furfural proved to have superior juvenile-suppressive effect whereas its vapor has such superiority against eggs. Scanning electron microscope (SEM) study showed irregular appearance of the body surface accompanied with some cuticle disfigurement of furfural-treated juveniles. These results indicated that furfural can adversely affect egg hatch, juvenile viability, penetration potential and ultrastructure of M. incognita. Furfural may therefore be of a considerable potential as an appropriate alternative for class I nematicides.

Convergent synthesis of oligosaccharides on the gram-scale using cetyl thioglycoside based on a hydrophobically assisted switching phase method.

A hydrophobically assisted switching phase (HASP) method is an efficient strategy for the synthesis of carrier-loaded oligosaccharides. We improved this method by using cetyl thioglycoside as the carrier, which made it possible to use the synthetic oligosaccharide block directly as the donor. We applied this improved HASP method in the successful assembly of a gp120-associated nona-mannoside. Our results indicated that the HASP method is an efficient strategy for the synthesis of complex oligosaccharides and glycoconjugates.