Explore the vast range of titles available.

Soil organisms are affected by the presence of predatory protists. However, it remains poorly understood how predatory protists can affect plant disease incidence and how fertilization regimes can affect these interactions. Here, we characterise the rhizosphere bacteria, fungi and protists over eleven growing seasons of tomato planting under three fertilization regimes, i.e conventional, organic and bioorganic, and with different bacterial wilt disease incidence levels. We find that predatory protists are negatively associated with disease incidence, especially two ciliophoran Colpoda OTUs, and that bioorganic fertilization enhances the abundance of predatory protists. In glasshouse experiments we find that the predatory protist Colpoda influences disease incidence by directly consuming pathogens and indirectly increasing the presence of pathogen-suppressive microorganisms in the soil. Together, we demonstrate that predatory protists reduce bacterial wilt disease incidence in tomato plants via direct and indirect reductions of pathogens. Our study provides insights on the role that predatory protists play in plant disease, which could be used to design more sustainable agricultural practices. Soil organisms are affected by the presence of predatory protists. Here, the authors predatory protists are negatively associated with bacteria wilt disease incidence in tomato plants and that fertilisation enhances the abundance of predatory protists

Despite the continuous advancement of intelligent power substations, the terminal block components within equipment cabinet inspection work still often require loads of personnel. The repetitive documentary works not only lack efficiency but are also susceptible to inaccuracies introduced by substation personnel. To resolve the problem of lengthy, time-consuming inspections, a terminal block component detection and identification method is presented in this paper. The identification method is a multi-stage system that incorporates a streamlined version of You Only Look Once version 7 (YOLOv7), a fusion of YOLOv7 and differential binarization (DB), and the utilization of PaddleOCR. Firstly, the YOLOv7 Area-Oriented (YOLOv7-AO) model is developed to precisely locate the complete region of terminal blocks within substation scene images. The compact area extraction model rapidly cuts out the valid proportion of the input image. Furthermore, the DB segmentation head is integrated into the YOLOv7 model to effectively handle the densely arranged, irregularly shaped block components. To detect all the components within a target electrical cabinet of substation equipment, the YOLOv7 model with a differential binarization attention head (YOLOv7-DBAH) is proposed, integrating spatial and channel attention mechanisms. Finally, a general OCR algorithm is applied to the cropped-out instances after image distortion to match and record the component’s identity information. The experimental results show that the YOLOv7-AO model reaches high detection accuracy with good portability, gaining 4.45 times faster running speed. Moreover, the terminal block component detection results show that the YOLOv7-DBAH model achieves the highest evaluation metrics, increasing the F1-score from 0.83 to 0.89 and boosting the precision to over 0.91. The proposed method achieves the goal of terminal block component identification and can be applied in practical situations.

Soil microbiome manipulation can potentially reduce the use of pesticides by improving the ability of soils to resist or recover from pathogen infestation, thus generating natural suppressiveness. We simulated disturbance through soil fumigation and investigated how the subsequent application of bio-organic and organic amendments reshapes the taxonomic and functional potential of the soil microbiome to suppress the pathogens Ralstonia solanacearum and Fusarium oxysporum in tomato monocultures. The use of organic amendment alone generated smaller shifts in bacterial and fungal community composition and no suppressiveness. Fumigation directly decreased F. oxysporum and induced drastic changes in the soil microbiome. This was further converted from a disease conducive to a suppressive soil microbiome due to the application of organic amendment, which affected the way the bacterial and fungal communities were reassembled. These direct and possibly indirect effects resulted in a highly efficient disease control rate, providing a promising strategy for the control of the diseases caused by multiple pathogens.

Laryngeal cancer (LCA) is the second most common type of head and neck malignancy, characterized by high recurrence rates and poor overall survival (OS). However, progress in curing LCA through molecular-targeted diagnostics and therapies is slow and limited. The occurrence and progression of cancer are closely associated with metabolic reprogramming. Therefore, this study aimed to identify metabolism-related LCA subtypes through a comprehensive analysis of transcriptomic, mutational, methylation, and single-cell RNA sequencing, in hopes of finding factors which influences the prognosis of LCA. First, to identify metabolism-related LCA subtypes, data from 114 patients with LCA from The Cancer Genome Atlas (TCGA) dataset were collected for an unsupervised clustering analysis, which focused on the expression characteristics of survival-related metabolic genes. Subsequently, prognostic and diagnostic models have been developed using machine learning techniques. Specifically, the prognostic model utilized the least absolute shrinkage and selection operator (LASSO) Cox regression, whereas the diagnostic model was built using the Random Forest (RF) algorithm. Furthermore, to ensure the reproducibility, the results of the subtypes and models were validated using three independent bulk RNA datasets and a scRNA-seq dataset. Two robust subtypes were identified and independently validated. Each subtype has a distinct prognostic outcomes and molecular features. Specifically, the subtype exhibited better prognosis, enriched metabolic pathways, and higher mutation frequencies. Notably, significant damaging mutations in the methyltransferases were observed in this subtype. In contrast, the subtype was associated with poorer prognosis, higher immune infiltration, and elevated methylation levels. Moreover, in tumors, higher levels of T cell/APC co-inhibition and inhibitory checkpoints were observed. In addition, the diagnostic model demonstrated strong performance, achieving an area under the curve (AUC) values of 1.000 in the training group and 0.947 in the validation group. The prognostic model effectively predicted patient outcomes, with the RiskScore emerging as an independent prognostic factor. This study offers new perspectives for patient stratification and presents opportunities for therapeutic development in LCA. Furthermore, we explored the potentials of several key tumor markers for both diagnosis and prognosis prediction.

Leaf surface fertilization with liquid fertilizer produced from amino acids constitutes a potentially important source of nitrogen and is important for plant production. However, few reports have focused on the plant growth promotion by novel liquid fertilizers created by new amino acid resources, let alone the influence on leaf microbiota. In this study, the effects of liquid fertilizer, created by amino acids hydrolyzed from animal hairs with or without the PGPR strain Bacillus amyloliquefaciens SQR9, on crop yield and leaf microbiota were investigated. The results showed that leaves sprayed with amino acid liquid fertilizer (AA) and liquid biological fertilizer (AA9) persistently increased cowpea yields compared to the control amended with chemical fertilizer (CF). Fertilization with amino acid fertilizer showed no significant difference in microbial composition compared with the CF treatment; however, the introduction of functional microbes altered the microbial composition. Pearson correlation analysis, VPA analysis and SEM models all revealed that the amino acids liquid fertilizer application, but not the functional strain or the altered microbiota, performed as the direct driver attributing to yield enhancement. We conclude that leaf fertilization with a novel amino acid liquid fertilizer can greatly enhance the crop yield and that the addition of beneficial microbes may perform the role in further altering the composition of leaf microbiota.

Background Soil nutrient status and soil-borne diseases are pivotal factors impacting modern intensive agricultural production. The interplay among plants, soil microbiome, and nutrient regimes in agroecosystems is essential for developing effective disease management. However, the influence of nutrient availability on soil-borne disease suppression and associated plant-microbe interactions remains to be fully explored. T his study aims to elucidate the mechanistic understanding of nutrient impacts on disease suppression, using phosphorous as a target nutrient. Results A 6-year field trial involving monocropping of tomatoes with varied fertilizer manipulations demonstrated that phosphorus availability is a key factor driving the control of bacterial wilt disease caused by Ralstonia solanacearum . Subsequent greenhouse experiments were then conducted to delve into the underlying mechanisms of this phenomenon by varying phosphorus availability for tomatoes challenged with the pathogen. Results showed that the alleviation of phosphorus stress promoted the disease-suppressive capacity of the rhizosphere microbiome, but not that of the bulk soil microbiome. This appears to be an extension of the plant trade-off between investment in disease defense mechanisms versus phosphorus acquisition. Adequate phosphorus levels were associated with elevated secretion of root metabolites such as L-tryptophan, methoxyindoleacetic acid, O-phosphorylethanolamine, or mangiferin, increasing the relative density of microbial biocontrol populations such as Chryseobacterium in the rhizosphere. On the other hand, phosphorus deficiency triggered an alternate defense strategy, via root metabolites like blumenol A or quercetin to form symbiosis with arbuscular mycorrhizal fungi, which facilitated phosphorus acquisition as well. Conclusion Overall, our study shows how phosphorus availability can influence the disease suppression capability of the soil microbiome through plant-microbial interactions. These findings highlight the importance of optimizing nutrient regimes to enhance disease suppression, facilitating targeted crop management and boosting agricultural productivity. 5_PfxzZNKyxgT3zPqUmUdg Video Abstract

Objectives To assess whether dual-energy computed tomography (DECT), using conventional computed tomography or magnetic resonance imaging as a reference standard, is sufficiently accurate to differentiate intracerebral hemorrhage from contrast extravasation after endovascular thrombectomy for acute ischemic stroke. Methods On January 20, 2021, we searched the PubMed Medline, Embase, Web of Science, and Cochrane Library databases. QUADAS-2 was used to assess the risk of bias and applicability. Meta-analyses were performed using a bivariate random-effects model. To explore sources of heterogeneity, meta-regression analyses were performed. Deeks’ funnel plot asymmetry test was used to assess publication bias. Results A total of 7 studies (269 patients, 269 focal areas) were included. The pooled mean sensitivity, specificity, and accuracy of DECT in identifying intracerebral hemorrhage from contrast extravasation after mechanical thrombectomy for acute ischemic stroke were 0.77 (95% confidence interval (CI) 0.29 to 0.96), 1 (95% CI 0.86 to 1), and 0.99 (95% CI 0.98 to 1), respectively. This evidence was of moderate certainty due to the risk of bias. Higgin’s I -squared for study heterogeneity was observed for the pooled sensitivity ( I 2 = 78.88%) and pooled specificity ( I 2 = 82.12%). Moreover, Deeks’ funnel plot asymmetry test revealed no publication bias ( p = 0.38). Conclusion DECT shows excellent accuracy and specificity in differentiating intracerebral hemorrhage from contrast extravasation after endovascular thrombectomy for acute ischemic stroke. Nevertheless, there was substantial and moderate heterogeneity among the studies. Future large-scale, prospective cohort studies are warranted to validate our findings. Key Points • Dual-energy computed tomography shows excellent accuracy and specificity in differentiating intracerebral hemorrhage from contrast extravasation after endovascular thrombectomy for acute ischemic stroke. • Via meta-regression analysis, we found various possible covariates, including the publication date, image analysis, index test time, time of follow-up imaging, and reference standard judgment, that had an important effect on the heterogeneity. • There were no concerns regarding applicability in any of the included studies.

To investigate the clinical features and risk factors of the human immunodeficiency virus (HIV)-infected patients with intracerebral hemorrhage (ICH). The patients with HIV-infected without ICH group were matched to the group of HIV-infected ICH patients. Logistic regression analysis using 1:1 propensity score matching (PSM) was performed to investigate the independent risk factors for ICH in HIV-infected patients. The receiver operating characteristic (ROC) curve was configured to calculate the optimal predictors of ICH in HIV-infected patients. A total of 59 HIV-infected patients with ICH and 180 HIV-infected patients without ICH were included. A cohort of 118 patients was ascertained utilizing PSM. Multivariate binary logistic regression analysis revealed that drug abuse-related HIV-infected, prolonged prothrombin time (PT), and elevated triglyceride (TG) levels were independent risk factors of ICH in HIV-infected patients. The ROC curve demonstrated that the combined predictor, composed of drug abuse-related HIV-infected, prolonged PT, and elevated TG levels, exhibited the highest area under the curve (AUC), with a cut-off point at 0.426, sensitivity of 78%, and specificity of 81.4%. The present study revealed that a valuable factor combined with drug abuse-related HIV-infected, prolonged PT, and elevated serum TG levels could serve as predictors of ICH in HIV-infected patients.

Soil aggregates support diverse microbes due to heterogeneous micro-environment. A lot of researches have exhibited the difference of microbial composition and activity within different size soil aggregates, but the relative influences of these microbes and the mechanisms underlying their effects on plant health are still poorly understood. This study investigated the microbiomes within four soil aggregate fractions sampled from fields with different incidences of tomato bacterial wilt derived from three fertilization regimes (organic, bio-organic and chemical) and un-fertilized soil to decipher the mechanisms involved in disease suppression. A wet-sieving method was used to separate the aggregate fractions; Illumina MiSeq sequencing was used to characterize the soil microbiomes in field experiment, and real-time qPCR analysis was used in lab cultivation experiment to quantify the number of pathogens. Organic fertilization (OF) and bio-organic fertilization (BF) significantly decreased disease incidences compared with the effects of treatments with chemical fertilizer (CF) and those without fertilizer (CK). The microbial composition was significantly different between fertilizations and aggregate fractions; particularly, the bacterial composition was significantly correlated with disease incidence. Different aggregate fractions contained disparate bacterial taxa correlated with disease incidence. Only in the microaggregate (Mi), the Ralstonia genus’ relative abundance showed a significant and positive correlation with disease incidence. The lab cultivation experiment demonstrated that after a spiking of Ralstonia solanacearum, whole soil and the Mi from BF-treated soil showed a significant higher resistance against pathogen invasion than those from CF-treated soil. The correlation between pathogen abundance and disease incidence in the field experiment and the higher resistance of Mi fraction against pathogen indicates that the microaggregates are the key fraction for suppressing tomato bacterial wilt in bio-organic fertilization practice, providing novel insight into the manipulation of the soil microbiome.

With the increasing environmental concerns, plug-in electric vehicles will eventually become the main transportation tools in future smart cities. As a key component and the main power source, lithium-ion batteries have been an important object of research studies. In order to efficiently control electric vehicle powertrains, the state of charge (SOC) of lithium-ion batteries must be accurately estimated by the battery management system. This paper aims to provide a more accurate dynamic SOC estimation method for lithium-ion batteries. A dynamic Thevenin model with variable parameters affected by the temperature and SOC is established to model the battery. An unscented Kalman particle filter (UPF) algorithm is proposed based on the unscented Kalman filter (UKF) algorithm and the particle filter (PF) algorithm to generate nonlinear particle filter according to the advantages and disadvantages of various commonly used filtering algorithms. The simulation results show that the unscented Kalman particle filter algorithm based on the dynamic Thevenin model can predict the SOC in real time and it also has strong robustness against noises.