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Abstract Rhizome rot is a destructive soil-borne disease of Polygonatum kingianum and adversely affects the yield and sustenance of the plant. Understanding how the causal fungus Fusarium oxysporum infects P. kingianum may suggest effective control measures against rhizome rot. In germinating conidia of infectious F. oxysporum, expression of the zinc finger transcription factor gene Zfp1, consisting of two C2H2 motifs, was up-regulated. To characterize the critical role of ZFP1, we generated independent deletion mutants (zfp1) and complemented one mutant with a transgenic copy of ZFP1 (zfp1 tZFP1). Mycelial growth and conidial production of zfp1 were slower than those of wild type (ZFP1) and zfp1 tZFP1. Additionally, a reduced inhibition of growth suggested zfp1 was less sensitive to conditions promoting cell wall and osmotic stresses than ZFP1 and zfp1 tZFP1. Furthermore pathogenicity tests suggested a critical role for growth of zfp1 in infected leaves and rhizomes of P. kingianum. Thus ZFP1 is important for mycelial growth, conidiation, osmoregulation, and pathogenicity in P. kingianum.

Fusarium oxysporum strain PkF01 is the causal agent of Polygonatum kingianum rhizome rot. Here, we report, for the first time, the genome assemblies of the F. oxysporum strain PkF01 using the PacBio Sequel system. We obtained a total base number of 51.27 Mb in 87 contigs. The number of scaffolds and scaffold N50 were 86 and 2.46 Mb, with a 47.55% GC content. The number of coding sequences was 17,265, with total length of 36.61 Mb, average length of 1.77 kb, and a gene density of 0.34 kb. A total of 11,447, 14,734, 10,759, and 4,434 genes were annotated using the Pfam, COG, GO, and KEGG databases, respectively. A total of 764, 335, 1,572, 216, 4,245, 1,591, 2,242, and 1,926 genes were annotated using the CAZy, CYP450, DFVF, CARD, PHI, SignalP, TCDB, and TMHMM databases, respectively. Collinearity analysis showed that the PkF01 genome shared more homologous genomic regions with F. oxysporum f. sp. lilii strain Fol39 than with F. oxysporum f. sp. lycopersici strain Fol4287, indicating that the PkF01 genome is more closely related to the Fol39 rather than the Fol4287 genome. The high-quality genome sequences of PkF01 will provide a valuable resource for not only investigating both the pathogenicity mechanism and host specificity in F. oxysporum but also exploring disease control measures. [Figure: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Epilepsy is the unstable state caused by excessive discharge of brain cells. In more than 30 percent of epilepsy cases, seizures cannot be controlled with medication or surgery. Refractory epilepsy seriously affects the health of patients and brings great economic burden to families. Therefore, this requires an effective seizure classification and prediction method to reduce risk in epilepsy patients. Researchers proposed machine learning or deep learning methods to predict seizures. However, automatic screening of electrode channels and improvement of predictive accuracy remain a challenge. A multi-channel feature fusion model CNN-Bi-LSTM. This method only requires simple preprocessing. CNN is responsible for extracting spatial features, Bi-LSTM is responsible for extracting temporal features, and finally, two channel weights are allocated through the attention mechanism to filter out the results of the more weighted electrode channel output classification. The performance of the model is tested on the CHB-MIT dataset, and the output is divided into three categories, including normal, pre-seizure and mid-seizure. The ten-fold cross-validation average accuracy is 94.83%, the precision is 94.84%, the recall is 94.84%, the F1-score is 94.83%, and the MCC is 92.26% across CHB-MIT EEG. The ten-fold cross-validation average accuracy of UCI data set is 77.62%, the precision is 77.66%, the recall is 77.62%, the F1-score is 77.60%, and the MCC is 72.03%. The results showed that this method is superior to existing methods and can predict the EEG signals of epilepsy in advance. This work will be extended to design a removable epilepsy predictive device for real-time use.

EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input mode, which cannot obtain multiple feature information, and cannot meet the requirements of intelligent and high real-time brain computer interface. And because the EEG signal is nonlinear, the traditional methods of time domain or frequency domain are not suitable. In this paper, a CNN-DSC-Bi-LSTM-Attention (CDBA) model based on EEG signals for automatic emotion recognition is presented, which contains three feature-extracted channels. The normalized EEG signals are used as an input, the feature of which is extracted by multi-branching and then concatenated, and each channel feature weight is assigned through the attention mechanism layer. Finally, Softmax was used to classify EEG signals. To evaluate the performance of the proposed CDBA model, experiments were performed on SEED and DREAMER datasets, separately. The validation experimental results show that the proposed CDBA model is effective in classifying EEG emotions. For triple-category (positive, neutral and negative) and four-category (happiness, sadness, fear and neutrality), the classification accuracies were respectively 99.44% and 99.99% on SEED datasets. For five classification (Valence 1—Valence 5) on DREAMER datasets, the accuracy is 84.49%. To further verify and evaluate the model accuracy and credibility, the multi-classification experiments based on ten-fold cross-validation were conducted, the elevation indexes of which are all higher than other models. The results show that the multi-branch feature fusion deep learning model based on attention mechanism has strong fitting and generalization ability and can solve nonlinear modeling problems, so it is an effective emotion recognition method. Therefore, it is helpful to the diagnosis and treatment of nervous system diseases, and it is expected to be applied to emotion-based brain computer interface systems.

Chromium (Cr)-contaminated in irrigation water poses a significant threat to the safety of wheat (Triticum aestivum L.) production safety. Recent studies suggest that melatonin (MT) could enhance crop tolerance to Cr pollution. This study aimed to investigate the effects of foliar spraying MT on alleviating Cr toxicity and accumulation in wheat irrigated with K2Cr2O7 solution at concentrations of 5, 10, and 20 mg/kg Cr in the soil. Our results showed that Cr-contaminated water irrigation significantly reduced dry weight, grain numbers, grain weight, yield, harvest index, net photosynthetic rate (Pn), maximum and actual photochemical efficiency of photosystem II (Fv/Fm and ΦPSII), chlorophyll contents, and the a/b ratio. It also increased PSII photodamage and oxidative stress in wheat leaves, resulting in high Cr accumulation in roots, leaves, and grains. Foliar spraying of MT alleviated Cr toxicity by improving Pn, Fv/Fm, and ΦPSII, enhancing chlorophyll content, promoting dry matter accumulation and yield, and reducing oxidative stress and Cr translocation. Furthermore, MT application enhanced transcriptional regulation, alleviated oxidative stress by boosting antioxidant enzyme activities, and restricted Cr translocation from roots to leaves and grains by increasing the accumulation of secondary metabolites, such as lignin and metallothionein. These findings suggest that MT application could serve as a viable strategy for reducing Cr contamination in cereals and supporting phytoremediation efforts.

Background: While mounting evidence suggests a connection between environmental contaminants and sleep problems, it remains uncertain whether exposure to volatile organic compounds (VOCs) specifically is associated with such problems. Methods: Data from the National Health and Nutrition Examination Survey program’s five survey cycles (2005–2006, 2011–2018) were used to conduct cross-sectional research. Data on short sleep duration (SSD) and self-reported trouble sleeping were collected from questionnaire data. Data on urine VOCs were gathered from laboratory data. The association between urinary VOCs and sleep problems was examined using weighted generalized linear models and the restricted cubic spline (RCS), weighted quantile sum (WQS), and quantile-based g-calculation (QGC) methods. Results: In all, a total of 4131 general adult individuals were included in this study. The prevalence of SSD and self-reported trouble sleeping was 34.11% and 25.03%, respectively. 3,4-MHA, AAMA, AMCC, SBMA, and MA were risk factors for SSD after adjusting several covariates, with the largest effect being AMCC (OR = 1.47, 95% CI: 1.08, 2.02). Risk factors for sleep issues included AAMA, AMCC, CEMA, CYMA, DGBMA, 2HPMA, 3HPMA, MA, and PGA, with AMCC having the highest impact with an OR of 1.69 (95% CI: 1.28, 2.22). Both the WQS model and the QGC model showed that the co-exposure to VOCs was positively associated with SSD and self-reported trouble sleeping, with AMCC being the most influential VOC. Conclusions: According to our research, high levels of single or mixed urine VOCs are linked to a higher prevalence of SSD and self-reported trouble sleeping in the general adult population of the United States. Further prospective and experimental studies are needed in the future to validate these potential relationships and explore the underlying mechanisms.

Objective: This study aimed to construct the prokaryotic expression and purification system of non-selective cation channel TRPV4 protein and to carry out bioinformatics analysis to provide technical support for the study of diseases related to the target protein and the screening of drug active ingredients. Methods: The human TRPV4 gene was cloned into pET-28a(+), pET-32a, pET-15b, pGEX-5X-1, pEX-4T and pGEX-6p-1, respectively, and the prokaryotic expression system of TRPV4 was constructed using two E.coli expression hosts, BL21 and Rossetta. The target protein was isolated and purified by glutathione affinity chromatography and nickel column affinity chromatography. The target protein was analyzed by bioinformatics technology to obtain its corresponding physical, chemical properties and structural parameters. Results: The prokaryotic expression system of GST-TRPV4 and GST-TRPV4-6his fusion proteins was constructed using pGEX-6p-1 vector and Rossetta. The GST-TRPV4 fusion protein was significantly expressed when the IPTG concentrationwas 0.6 mmol/L, and the GST-TRPV4-6his fusion protein was significantly expressed when the IPTG concentration was 0.4 mmol/L, and the expression temperature was 18 ℃. When GST-TRPV4-6his fusion protein was purified, the complete GST-TRPV4-6his fusion protein could be obtained by imidazole solution at 100 mmol/L or 200 mmol/L. Conclusion: In this paper, human TRPV4 ion channel protein was successfully constructed, expressed and purified in prokaryotic expression system for the first time, and the physicochemical properties of the protein were analyzed by bioinformatics, which laid a good foundation for subsequent high-purity mass expression and further study.

Organ transplantation is currently the most effective treatment for end-stage organ failure. Post transplantation diabetes mellitus (PTDM) is a severe complication after organ transplantation that seriously affects the short-term and long-term survival of recipients. However, PTDM is often overlooked or poorly managed in its early stage. This article provides an overview of the incidence, and pathogenesis of and risk factors for PTDM, aiming to gain a deeper understanding of PTDM and improve the quality of life of recipients.

Gansu province will fulfill the carbon reduction target under the national carbon neutrality strategy. As a developing province in China, Gansu will have to trade off carbon reduction targets and economic development. This study adopts a computable general equilibrium model to simulate the carbon reduction pathway and estimate the possible impacts on the economy, output and environment under a carbon-neutral target. Our results show carbon emission will peak around 2033 in the baseline scenario and decline slowly after the peak. While carbon emissions will peak around 2023 in the carbon neutral scenario and decline very fast from 154 million tons in 2023 to 40 million ton in 2060. The economy will continue to increase from 734 billion CNY in 2017–3375 billion CNY in 2050 under a carbon reduction target, which means the carbon neutral target will have very limited economic impacts by 2060. At the sector level, economic outputs vary among different sectors. The output will increase significantly, such as power generation 14%, water supply 8% and nonmental 4%. Some other sectors will decrease quickly, paper 15% and textile industry 7%. Carbon reduction will also contribute to air pollutants reduction, which is a benefit to air quality. Carbon neutral targets will bring more opportunities in Gansu due to green energy potential without economic burden. Proper carbon mitigation policy would avoid the adverse impact but bring more potential to the economy in Gansu.

Objective To establish a method for determination of 6 kinds of carbamate pesticides and 3 kinds of chloronicotinyl pesticides in Chinese Kushui rose by ultra high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) coupled with QuEChERS. Methods After extracted by acetonitrile, the Chinese Kushui rose was cleaned by QuEChERS. The target compounds were separated by C18 column (2.1 mm×100 mm, 1.7 μm) using 10 mmol/L ammonium acetate solution (0.1% formic acid) with acetonrtrile as mobile phase for gradient elution, and analyzed by MS/MS system with electrospray ionization (ESI+) under muti-reaction monitoring mode and quantified by external standard method. Results All the 9 kinds of pesticides showed good linear relationships in range of 0.01-0.50 μg/mL, and the correlation coefficients were above 0.990, the recoveries at different spiked levels for all target compounds in blank matrices were 76.3%-102%, and the relative standard deviation (RSD) were 1.3%-9.0% (n=6). The limits of detection and quantification of the method were 0.001 6-0.003 2 and 0.005 4-0.010 mg/kg. Conclusion The method was suitable for rapid screening and analysis of 9 pesticide residues in Chinese Kushui rose with the advantage of accuracy, rapidity, simplicity and high sensitivity.

Chromium (Cr) pollution has serious harm to crop growth, while little is known on the role of melatonin (MT) on seed germination and physiology in Cr-stressed wheat. The effects of seed soaking with MT on growth, reserve mobilization, osmotic regulation and antioxidant capacity of wheat seeds during germination under hexavalent chromium (100 μM) stress were investigated. The results indicated that Cr toxicity decreased the seed germination rate by 16% and suppressed the growth of germinated seeds compared to unstressed seeds. MT in the concentration-dependent manner increased germination rate and promoted subsequent growth when seeds were exposed to Cr stress, but the effect could be counteracted at high concentration. Seed soaking with MT (100 μM) markedly decreased Cr accumulation in seeds, radicals and coleoptiles by 15%, 6% and 15%, respectively, and enhanced α-amylase activity and soluble sugar and free amino acids content in seeds to improve reserve mobilization under Cr stress, compared with Cr treatment. Furthermore, decreasing the level of osmotic regulators (soluble sugar and soluble protein) in radicles under MT combined with Cr treatment confirmed the reduction of osmotic stress caused by Cr stress. Importantly, MT pretreatment reduced H2O2 content by 19% and O2·− release rate by 45% in radicles under Cr toxicity compared with Cr-stressed wheat, in terms of promoting scavenging ability and decreasing production ability, which was to upregulate the activities and encoding genes expression levels of superoxide dismutase (SOD), catalase (CAT), ascorbic acid peroxidase (APX) and peroxidase (POD) and to downregulate plasma membrane-bound NADPH oxidase (NOX) encoding genes (TaRbohD, TaRbohF) expression, respectively. In all, these results provided evidence that seed soaking with MT could be a potentially method to protect wheat seeds from Cr toxicity, which effectively ameliorated germination under Cr stress by enhancing reserve mobilization and antioxidant metabolism in wheat.

4-Amino-7-(5′-deoxy-β-d-xylofuranosyl)-5-iodo-pyrrolo[2,3-d]pyrimidine 1, an unusual naturally occurring marine nucleoside isolated from an ascidan, Diplosoma sp., was synthesized from d-xylose in seven steps with 28% overall yield on 10 g scale. The key step was Vorbrüggen glycosylation of 5-iodo-pyrrolo[2,3-d]pyrimidine with 5-deoxy-1, 2-O-diacetyl-3-O-benzoyl-d-xylofuranose. Its absolute configuration was confirmed.

Objective To investigate the pathogenic role of neutrophil extracellular traps （NETs） in the pathogenesis and progression of severe Mycoplasma pneumoniae pneumonia （SMPP） in children and to assess their predictive value for SMPP. Methods Children with Mycoplasma pneumoniae pneumonia （MPP） admitted to the Second Hospital of Lanzhou University from October to December 2023 were selected as the study subjects. They were divided into the non-severe group （MPP group， n = 45） and severe group （SMPP group， n = 39）. The levels of NETs in the serum and bronchoalveolar lavage fluid （BALF） of all children were measured using ELISA. Additionally， BALF from both the affected and contralateral sides was collected from children in the SMPP group， and the content of NETs in BALF was determined using ELISA. Results A significant difference was observed in the serum concentration of NETs between MPP and SMPP patients （P < 0.01）. In the SMPP group， the level of NETs in the BALF of the affected lung was significantly higher than those of the contralateral side （P < 0.001）. Serum NETs levels were significantly higher than those in BALF in SMPP group （P < 0.001）. The area under the ROC curve （AUC） for predicting SMPP based on serum NETs levels was 0.892 （95%CI 0.810-0.963， P < 0.001）， with a sensitivity of 0.821 and specificity of 0.875， and a cut-off value of 17.24 ng/mL，respectively. The AUC of the combination of serum NETs with C-reactive protein （CRP） and lactate dehydrogenase （LDH） levels for predicting SMPP was 0.974 （95%CI 0.946-1.000， P < 0.001）， with a sensitivity of 0.923 and specificity of 0.950， respectively. Conclusions Excessive activation of NETs may contribute to the incidence of SMPP and localized lung injury in children with MPP. Monitoring the serum levels of NETs， CRP and LDH can effectively predict the incidence of SMPP. The combination of NETs， CRP， and LDH yields the best predictive performance.