Inflammatory cytokines and risk of allergic rhinitis: A Mendelian randomization study.

BACKGROUND: Epidemiological and experimental evidences have implicated chronic inflammation in the association with allergic rhinitis (AR). However, it remains unclear whether specific circulating cytokines are the cause of AR or the consequence of bias. To examine whether genetic-predicted changes in circulating cytokine concentrations are related to the occurrence of AR, we conducted a two-sample Mendelian randomization (MR) analysis. METHODS: We investigated the causal effects of 26 circulating inflammatory cytokines on AR through MR analysis. The primary method employed in this study was the inverse variance-weighted (IVW) method. Sensitivity analyses were conducted using simple median, weighted median, penalized weighted median, and MR-Egger regression. RESULTS: Our study revealed suggestive evidence that higher levels of circulating IL-18 (OR per one standard deviation [SD] increase: 1.006; 95 % CI, 1.002 to 1.011; P = 0.006, PFDR = 0.067, random-effects IVW method) and Macrophage inflammatory protein-1α (MIP-1α) (OR per one SD increase: 1.015; 95 % CI, 1.004 to 1.026; P = 0.009, PFDR = 0.048, random-effects IVW method) were associated with an increased risk of AR. Conversely, higher levels of circulating TRAIL were associated with a decreased risk of AR (OR per one SD increase: 0.993; 95 % CI, 0.989 to 0.997; P = 4.58E-4, PFDR = 0.004, random-effects IVW method). Only the results of TRAIL exist after Bonferroni-correction (the p-value < 0.0019). Sensitivity analysis yielded directionally consistent results. No significant associations were observed between other circulating inflammatory cytokines and AR. CONCLUSION: Genetically predicted levels of IL-18, and MIP-1α are likely to associated with an increased risk of AR occurrence. Genetically predicted levels of TRAIL are statistically significant in reducing the risk of AR occurrence. However, the current research evidence does not support an impact of other inflammatory cytokines on the risk of AR. Future studies are needed to provide additional evidence to support the current conclusions.

Multi-omics assists genomic prediction of maize yield with machine learning approaches.

With the improvement of high-throughput technologies in recent years, large multi-dimensional plant omics data have been produced, and big-data-driven yield prediction research has received increasing attention. Machine learning offers promising computational and analytical solutions to interpret the biological meaning of large amounts of data in crops. In this study, we utilized multi-omics datasets from 156 maize recombinant inbred lines, containing 2496 single nucleotide polymorphisms (SNPs), 46 image traits (i-traits) from 16 developmental stages obtained through an automatic phenotyping platform, and 133 primary metabolites. Based on benchmark tests with different types of prediction models, some machine learning methods, such as Partial Least Squares (PLS), Random Forest (RF), and Gaussian process with Radial basis function kernel (GaussprRadial), achieved better prediction for maize yield, albeit slight difference for method preferences among i-traits, genomic, and metabolic data. We found that better yield prediction may be caused by various capabilities in ranking and filtering data features, which is found to be linked with biological meaning such as photosynthesis-related or kernel development-related regulations. Finally, by integrating multiple omics data with the RF machine learning approach, we can further improve the prediction accuracy of grain yield from 0.32 to 0.43. Our research provides new ideas for the application of plant omics data and artificial intelligence approaches to facilitate crop genetic improvements. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-024-01454-z.

Design, synthesis and evaluation of novel UDCA-aminopyrimidine hybrids as ATX inhibitors for the treatment of hepatic and pulmonary fibrosis.

To discover novel anti-fibrotic agents, a series of UDCA-aminopyrimidine hybrids were designed and synthesized as potent ATX inhibitors by molecular hybridization strategy. The ATX inhibitory activities of all synthesized compounds were evaluated using the LPC choline release assay. The preliminary structure-activity relationship was concluded. Among them, 12a and 12h exhibited the strongest ATX inhibitory activities with IC50 values of 7.62 ± 0.62 and 7.51 ± 0.72 nM respectively, which were 9-fold more effective than the positive control drug GLPG-1690. Molecular docking studies revealed that 12a and 12h occupied the hydrophobic pocket and tunnel of the ATX binding site. The cytotoxicity assay of 12a and 12h revealed that they had no obvious toxicity at concentrations up to 80 µM, therefore their anti-hepatic fibrosis and anti-pulmonary fibrosis activities were further investigated. The results suggested that 12a and 12h significantly decreased the gene and protein expression of α-SMA, COL1A1 and FN in both TGF-ß1-induced HSC-LX2 and CCC-HPF-1 cells. In addition, 12a and 12h significantly inhibited cells migration in both TGF-ß1-induced HSC-LX2 and CCC-HPF-1 cells. Preliminary mechanistic studies indicated that 12a and 12h exerted anti-hepatic fibrosis and anti-pulmonary fibrosis effects by inhibiting the TGF-ß/Smad signaling pathway. Overall, our findings suggested that 12a and 12h might be two promising anti-fibrotic agents, or might serve as two new lead compounds for the further development of anti-fibrotic agents.

Role of dysregulated macrophage subpopulation ratios and functional changes in the development of coronary atherosclerosis.

Coronary heart disease is one of the most significant risk factors affecting human health worldwide. Its pathogenesis is intricate, with atherosclerosis being widely regarded as the leading cause. Aberrant lipid metabolism in macrophages is recognized as one of the triggering factors in atherosclerosis development. To investigate the role of macrophages in the formation of coronary artery atherosclerosis, we utilized single-cell data from wild-type mice obtained from the aortic roots and ascending aortas after long-term high-fat diet feeding, as deposited in GSE131776. Seurat software was employed to refine the single-cell data in terms of scale and cell types, facilitating the identification of differentially expressed genes. Through the application of differential expression genes, we conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses at 0, 8 and 16 weeks, aiming to uncover pathways with the most pronounced functional alterations as the high-fat diet progressed. The AddModuleScore function was employed to score the expression of these pathways across different cell types. Subsequently, macrophages were isolated and further subdivided into subtypes, followed by an investigation into intercellular communication within these subtypes. Subsequent to this, we induced THP-1 cells to generate foam cells, validating critical genes identified in prior studies. The results revealed that macrophages underwent the most substantial functional changes as the high-fat diet progressed. Furthermore, two clusters were identified as potentially playing pivotal roles in macrophage functional regulation during high-fat diet progression. Additionally, macrophage subtypes displayed intricate functionalities, with mutual functional counterbalances observed among these subtypes. The proportions of macrophage subtypes and the modulation of anti-inflammatory and pro-inflammatory functions played significant roles in the development of coronary artery atherosclerosis.

Two teosintes made modern maize.

The origins of maize were the topic of vigorous debate for nearly a century, but neither the current genetic model nor earlier archaeological models account for the totality of available data, and recent work has highlighted the potential contribution of a wild relative, Zea mays ssp. mexicana. Our population genetic analysis reveals that the origin of modern maize can be traced to an admixture between ancient maize and Zea mays ssp. mexicana in the highlands of Mexico some 4000 years after domestication began. We show that variation in admixture is a key component of maize diversity, both at individual loci and for additive genetic variation underlying agronomic traits. Our results clarify the origin of modern maize and raise new questions about the anthropogenic mechanisms underlying dispersal throughout the Americas.

De novo domestication: a new way for crop design and breeding.

The impending global climate change presents significant challenges to agricultural production. It is imperative to find approaches to ensure sustained growth in food production while reducing agricultural input, in order to meet the needs of worldwide people for nutritious food supply. One of the effective strategies to address this challenge is still the development of new crop varieties with high yield, stable yield, environmental friendliness and rich nutrition. The creation of new crop cultivars depends largely on the expansion of genetic resources and the innovation of breeding techniques. De novo domestication is an innovative breeding strategy for developing new crop varieties. It involves utilizing undomesticated or semi-domesticated plants with desirable traits as founder species for breeding. The process involves rapid domestication of wild plants through the redesign of agronomic traits and the introduction of domestication genes to meet diverse human needs. In this review, we overview the history of crop domestication and genetic improvement, clarify the necessity of enriching crop diversity, and emphasize the significance of wild plants' genetic diversity in expanding the scope for crop redesign. Breeding strategy innovation is the key to accelerate crop breeding. We also discuss the feasibility and prospects of rapid developing new crops through de novo domestication.

Design, synthesis and evaluation of ursodeoxycholic acid-cinnamic acid hybrids as potential anti-inflammatory agents by inhibiting Akt/NF-κB and MAPK signaling pathways.

A series of ursodeoxycholic acid (UDCA)-cinnamic acid hybrids were designed and synthesized. The anti-inflammatory activity of these derivatives was screened through evaluating their inhibitory effects of LPS-induced nitric oxide production in RAW264.7 macrophages. The preliminary structure-activity relationship was concluded. Among them, 2m showed the best inhibitory activity against NO (IC50 = 7.70 µM) with no significant toxicity. Further study revealed that 2m significantly decreased the levels of TNF-α, IL-1ß, IL-6 and PGE2, down-regulated the expression of iNOS and COX-2. Preliminary mechanism study indicated that the anti-inflammatory activity of 2m was related to the inhibition of the Akt/NF-κB and MAPK signaling pathway. Furthermore, 2m reduced inflammation by a mouse model of LPS-induced inflammatory disease in vivo. In brief, our findings indicated that 2m might serve as a new lead compound for further development of anti-inflammatory agents.

Global research trends in palliative care for breast cancer from 2012 to 2022: A scientometric analysis.

Purpose: We used bibliometric methods to evaluate the global scientific output of palliative care breast cancer research and to explore the current status and further research directions in the field over the past decade. Methods: All relevant publications from the year 2012 to 2022 were retrieved from Web of Science. We applied VOSviewer and Bibliometrix R v4.2.1 to obtain information on subject domains, annual publication output and citations, countries and authors with the highest productivity, influential journals and articles, and popular keywords. Results: In total, 1529 publications were included in the final analysis. Health services and supportive care, pain and symptom management were the focus of the research in the field. From the year 2017 to 2021, the annual publication output was abundant and peaked in 2018. The systematic review by Fitzmaurice et al. in 2017 was the most-cited publication. The United States was the leading country with the maximum number of publications, citations, and link strengths with other countries. The most contributing institution was University of Toronto. E. Bruera was the most prolific author, ranking first among the authors in both the H and M index. The journal with the most publications was Palliative & Supportive Care. Keywords analysis indicated that exploring the significant degree of palliative care to reduce anxiety and depression in breast cancer patients may be a good research direction. In addition, how to assess the optimal timing of palliative care interventions and further exploring the specific contradiction between insufficient medical resources and palliative care are also possible research directions. Conclusion: Palliative care plays an important role in the treatment of breast cancer. With the help of a scientometric analysis in this field, researchers can clarify the current research status and hotspots worth fully exploring.

Complex genetic architecture underlying the plasticity of maize agronomic traits.

Phenotypic plasticity is the ability of a given genotype to produce multiple phenotypes in response to changing environmental conditions. Understanding the genetic basis of phenotypic plasticity and establishing a predictive model is highly relevant to future agriculture under a changing climate. Here we report findings on the genetic basis of phenotypic plasticity for 23 complex traits using a diverse maize population planted at five sites with distinct environmental conditions. We found that latitude-related environmental factors were the main drivers of across-site variation in flowering time traits but not in plant architecture or yield traits. For the 23 traits, we detected 109 quantitative trait loci (QTLs), 29 for mean values, 66 for plasticity, and 14 for both parameters, and 80% of the QTLs interacted with latitude. The effects of several QTLs changed in magnitude or sign, driving variation in phenotypic plasticity. We experimentally validated one plastic gene, ZmTPS14.1, whose effect was likely mediated by the compensation effect of ZmSPL6 from a downstream pathway. By integrating genetic diversity, environmental variation, and their interaction into a joint model, we could provide site-specific predictions with increased accuracy by as much as 9.9%, 2.2%, and 2.6% for days to tassel, plant height, and ear weight, respectively. This study revealed a complex genetic architecture involving multiple alleles, pleiotropy, and genotype-by-environment interaction that underlies variation in the mean and plasticity of maize complex traits. It provides novel insights into the dynamic genetic architecture of agronomic traits in response to changing environments, paving a practical way toward precision agriculture.

Genetic basis of resistance to southern corn leaf blight in the maize multi-parent population and diversity panel.

Southern corn leaf blight (SLB), caused by the necrotrophic pathogen Cochliobolus heterostrophus, is one of the maize foliar diseases and poses a great threat to corn production around the world. Identification of genetic variations underlying resistance to SLB is of paramount importance to maize yield and quality. Here, we used a random-open-parent association mapping population containing eight recombinant inbred line populations and one association mapping panel consisting of 513 diversity maize inbred lines with high-density genetic markers to dissect the genetic basis of SLB resistance. Overall, 109 quantitative trait loci (QTLs) with predominantly small or moderate additive effects, and little epistatic effects were identified. We found 35 (32.1%) novel loci in comparison with the reported QTLs. We revealed that resistant alleles were significantly enriched in tropical accessions and the frequency of about half of resistant alleles decreased during the adaptation process owing to the selection of agronomic traits. A large number of annotated genes located in the SLB-resistant QTLs were shown to be involved in plant defence pathways. Integrating genome-wide association study, transcriptomic profiling, resequencing and gene editing, we identified ZmFUT1 and MYBR92 as the putative genes responsible for the major QTLs for resistance to C. heterostrophus. Our results present a comprehensive insight into the genetic basis of SLB resistance and provide resistant loci or genes as direct targets for crop genetic improvement.

Genome-wide association study of traits in sacred lotus uncovers MITE-associated variants underlying stamen petaloid and petal number variations.

Understanding the genetic variants responsible for floral trait diversity is important for the molecular breeding of ornamental flowers. Widely used in water gardening for thousands of years, the sacred lotus exhibits a wide range of diversity in floral organs. Nevertheless, the genetic variations underlying various morphological characteristics in lotus remain largely unclear. Here, we performed a genome-wide association study of sacred lotus for 12 well-recorded ornamental traits. Given a moderate linkage disequilibrium level of 32.9 kb, we successfully identified 149 candidate genes responsible for seven flower traits and plant size variations, including many pleiotropic genes affecting multiple floral-organ-related traits, such as NnKUP2. Notably, we found a 2.75-kb presence-and-absence genomic fragment significantly associated with stamen petaloid and petal number variations, which was further confirmed by re-examining another independent population dataset with petal number records. Intriguingly, this fragment carries MITE transposons bound by siRNAs and is related to the expression differentiation of a nearby candidate gene between few-petalled and double-petalled lotuses. Overall, these genetic variations and candidate genes responsible for diverse lotus traits revealed by our GWAS highlight the role of transposon variations, particularly MITEs, in shaping floral trait diversity.

Three types of genes underlying the Gametophyte factor1 locus cause unilateral cross incompatibility in maize.

Unilateral cross incompatibility (UCI) occurs between popcorn and dent corn, and represents a critical step towards speciation. It has been reported that ZmGa1P, encoding a pectin methylesterase (PME), is a male determinant of the Ga1 locus. However, the female determinant and the genetic relationship between male and female determinants at this locus are unclear. Here, we report three different types, a total of seven linked genes underlying the Ga1 locus, which control UCI phenotype by independently affecting pollen tube growth in both antagonistic and synergistic manners. These include five pollen-expressed PME genes (ZmGa1Ps-m), a silk-expressed PME gene (ZmPME3), and another silk-expressed gene (ZmPRP3), encoding a pathogenesis-related (PR) proteins. ZmGa1Ps-m confer pollen compatibility. Presence of ZmPME3 causes silk to reject incompatible pollen. ZmPRP3 promotes incompatibility pollen tube growth and thereby breaks the blocking effect of ZmPME3. In addition, evolutionary genomics analyses suggest that the divergence of the Ga1 locus existed before maize domestication and continued during breeding improvement. The knowledge gained here deepen our understanding of the complex regulation of cross incompatibility.

One-Step Preparation of a Superhydrophobic Surface by Electric Discharge Machining with a Carbon Fiber Brush Electrode.

Superhydrophobic surfaces are extremely susceptible to damage, which can lead to a sharp decrease in their service life and physical properties. Therefore, developing methods to impart superhydrophobic surfaces with excellent wear resistance is crucial. In this article, a flexible carbon fiber brush was utilized as an electrode to fabricate micro-/nano-structures on a grooved surface via electric discharge machining in one step, resulting in a superhydrophobic coating with excellent wear resistance. Carbon fiber brushes exhibit several notable properties, including excellent flexibility, conductivity, and high temperature resistance. Carbon fiber brushes can adapt to the complex inner walls of grooves. Many nano-structures were fabricated on the grooves via pulse discharge, which resulted in a superhydrophobic surface with excellent wear resistance. The contact angle (CA) and sliding angle of the surface after discharge were 156.3 and 2°, respectively. The processed surface exhibits superior corrosion resistance compared to the stainless-steel substrate. The influence of the micro-groove shapes on wear resistance was tested. The results showed that, after 500 cm of wear, the shallow grooves retained their superhydrophobicity with a CA of 150.1°.

Identification of a Diagnostic Signature and Immune Cell Infiltration Characteristics in Keloids.

Background: Keloid disorder is a recurrent fibroproliferative cutaneous tumor. Due to the lack of early identification of keloid patients before the formation of keloids, it is impossible to carry out pre-traumatic intervention and prevention for these patients. This led us to identify and determine signatures with diagnostic significance for keloids. Methods: Public series of matrix files were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were calculated from expression profiling data, and the diagnostic signature was identified by constructing a protein-protein interaction (PPI) network. The diagnostic efficacy of the screened signature was assessed by employing receiver operating characteristic (ROC) curves. Furthermore, we calculated the proportion of different immune cells in the gene expression matrix microenvironment by the "ssGSEA" algorithm, and assessed the difference in immune cell abundance between keloids and control groups and the relationship between the signature and immune cell infiltration. Clinical keloid and normal skin tissues were collected, and the expression of the screened diagnostic signature was validated by RT-qPCR and immunohistochemical assay. Results: By screening the key genes in PPI, TGM2 was recognized and validated as a diagnostic signature and the infiltrating abundance of 10 immune cells was significantly correlated with TGM2 expression. Gene ontology enrichment analysis demonstrated that TGM2 and molecules interacting with it were mainly enriched in processes involving wound healing and collagen fiber organization. TGM2 correlated positively with HIF-1A (R = 0.82, p-value = 1.4e-05), IL6 (R = 0.62, p-value = 0.0053), and FN1 (R = 0.66, p-value = 0.0019). Besides, TGM2 was significantly upregulated in clinical keloid samples compared to normal skin tissues. Conclusion: TGM2 may serve as an auxiliary diagnostic indicator for keloids. However, the role of TGM2 in keloids has not been adequately reported in the current literature, which may provide a new direction for molecular studies of keloids.

Composite learning tracking control for underactuated marine surface vessels with output constraints.

In this paper, a composite learning control scheme was proposed for underactuated marine surface vessels (MSVs) subject to unknown dynamics, time-varying external disturbances and output constraints. Based on the line-of-sight (LOS) approach, the underactuation problem of the MSVs was addressed. To deal with the problem of output constraint, the barrier Lyapunov function-based method was utilized to ensure that the output error will never violate the constraint. The composite neural networks (NNs) are employed to approximate unknown dynamics. The prediction errors can be obtained using the serial-parallel estimation model (SPEM). Both the prediction errors and the tracking errors were employed to construct the NN weight updating. Using approximation information, the disturbance observers were designed to estimate unknown time-varying disturbances. The stability analysis via the Lyapunov approach indicates that all signals of unmanned marine surface vessels are uniformly ultimate boundedness. The simulation results verify the effectiveness of the proposed control scheme.

The Application of Automatic Identification System Information and PSO-LSTM Neural Network in CRI Prediction.

Considering that collision accidents happen sometimes, it is necessary to predict the collision risk to ensure navigation safety. With the information construction in maritime and the popularity of automatic identification system application, it is more convenient to obtain ship navigation dynamics. How to obtain ship encounter dynamic parameters through automatic identification system information, assess ship collision risk, find out dangerous target ships, and give early warning and guarantee for ship navigation safety, is a problem that scholars have been studying. As an index to measure the degree of ship collision risk, CRI, namely, collision risk index, is usually obtained by calculating ship encounter parameters and comprehensive analysis. There are many factors that affect CRI, and the values of many parameters depend on expert judgment. The corresponding CRI has nonlinear and complex characteristics, which is highly correlated with the time sequence. In order to enhance the prediction accuracy and efficiency, PSO-LSTM neural network is applied in the paper to predict CRI. Experiments show that PSO-LSTM neural network can effectively predict collision risk and provide a reference for navigation safety.

An online identification approach for ship domain model based on AIS data.

As an important basis of navigation safety decisions, ship domains have always been a pilot concern. In the past, model parameters were usually obtained from statistics of massive historical cumulative data, but the results were mostly historical analysis and static data, which obviously could not meet the needs of pilots who wish to master the ship domain in real time. To obtain and update the ship domain parameter online in time and meet the real-time needs of maritime applications, this paper obtains CRI as the weight coefficient-based PSO-LSSVM method and proposes to use short-term AIS data accumulation through the risk-weighted least squares method online rolling identification method, which can filter nonhazardous targets and improve the identification accuracy and real-time performance of nonlinear models in the ship domain. The experimental examples show that the method can generate the ship domain dynamically in real time. At the same time, the method can be used to study the dynamic evolution characteristics of the ship domain over the course of navigation, which provides a reference for navigation safety decisions and the analysis of ship navigation behavior.

Target-oriented prioritization: targeted selection strategy by integrating organismal and molecular traits through predictive analytics in breeding.

Genomic prediction in crop breeding is hindered by modeling on limited phenotypic traits. We propose an integrative multi-trait breeding strategy via machine learning algorithm, target-oriented prioritization (TOP). Using a large hybrid maize population, we demonstrate that the accuracy for identifying a candidate that is phenotypically closest to an ideotype, or target variety, achieves up to 91%. The strength of TOP is enhanced when omics level traits are included. We show that TOP enables selection of inbreds or hybrids that outperform existing commercial varieties. It improves multiple traits and accurately identifies improved candidates for new varieties, which will greatly influence breeding.