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Chamomile (Matricaria recutita L.) has a long history of use in herbal medicine with various applications, and the flower heads contain numerous medicinally active compounds. Next generation sequencing (NGS) approaches are applied to exploit genetic resources in the major crop plants to develop genomic resources, and to enhance breeding. Genotyping-by-sequencing (GBS) has been used to evaluate the genetic structure of cultivated populations in the non-model crop chamomile using 6495 SNP markers, and to perform a genome wide association study (GWAS) identifying sequences significantly associated with the medicinally important alpha-bisabolol content. Ploidy variation in chamomile was investigated by high-throughput flow-cytometry. Di-, tri- and tetraploid plants were identified, and in field trials characterized. Since seeds are not needed in the harvested product of chamomile, triploidy could be a way to obtain a sterile chamomile variety, omitting the problems of chamomile seeds lying up to 15 years dormant in the soil and facilitating crop rotation in the fields

Abstract Methicillin-resistant Staphylococcus aureus (MRSA) is a serious threat to patients with nosocomial infections, and infection is strongly associated with biofilm formation. Gallic acid (GA) is a natural bioactive compound found in traditional Chinese medicines that exerts potent antimicrobial activity. However, the anti-MRSA biofilm efficacy of GA remained to be determined. This study investigated the antimicrobial activities of GA against MRSA and the mechanisms involved. The results revealed the significant antibacterial and antibiofilm activities of GA. The minimal inhibitory concentration of GA against MRSA was 32 μg/mL and a growth curve assay confirmed the significant inhibitory effect of GA on planktonic MRSA. Crystal violet and XTT assays showed that 8 µg/mL GA effectively inhibited the formation of new biofilms and disrupted existing biofilms by reducing both biofilm biomass and metabolic activities. Alkaline phosphatase and β-galactosidase leakage assays and live/dead staining provided evidence that GA disrupted the integrity of bacterial cell walls and membranes within the biofilm. Scanning electron microscopy observations showed that GA significantly inhibited bacterial adhesion and aggregation, affecting the overall structure of the biofilm. Bacterial adhesion, polysaccharide intercellular adhesion (PIA) production and real-time quantitative PCR assay confirmed that GA inhibited bacterial adhesion, PIA synthesis, and the expression of icaAD and sarA. These results suggested that GA inhibited biofilm formation by inhibiting the expression of sarA, then downregulating the expression of icaA and icaD, thereby reducing the synthesis of PIA to attenuate the adhesion capacity of MRSA. GA is therefore a promising candidate for development as a pharmaceutical agent for the prevention and treatment of bacterial infections caused by MRSA.

Summary: Postoperative acute kidney injury (AKI) is a common complication in patients undergoing deep hypothermic circulatory arrest (HCA); however, its underlying pathogenesis is unclear. In this study, we established a rat cardiopulmonary bypass model and demonstrated that hypothermia during HCA, rather than circulatory arrest, was responsible for the occurrence of AKI. By recruiting 56 patients who underwent surgery with HCA and analyzing the blood samples, we found that post-HCA AKI was associated with an increase in bradykinin. Animal experiments confirmed this and showed that hypothermia during HCA increased bradykinin levels by increasing kallikrein expression. Mechanistically, bradykinin inhibited the Nrf2-xCT pathway through B2R and caused renal oxidative stress damage. Application of Icatibant, a B2R inhibitor, reversed changes in the Nrf2-xCT pathway and oxidative stress damage. Finally, Icatibant reversed hypothermia-induced AKI in vivo. This finding reveals the pathogenesis of AKI after HCA and helps to provide therapeutic strategy for patients with post-HCA AKI.

In the Suidae family, warthogs show significant survival adaptability and trait specificity. This study offers a comparative genomic analysis between the warthog and other Suidae species, including the Luchuan pig, Duroc pig, and Red River hog. By integrating the four genomes with sequences from the other four species, we identified 8868 single-copy orthologous genes. Based on 8868 orthologous protein sequences, phylogenetic assessments highlighted divergence timelines and unique evolutionary branches within suid species. Warthogs exist on different evolutionary branches compared to DRCs and LCs, with a divergence time preceding that of DRC and LC. Contraction and expansion analyses of warthog gene families have been conducted to elucidate the mechanisms of their evolutionary adaptations. Using GO, KEGG, and MGI databases, warthogs showed a preference for expansion in sensory genes and contraction in metabolic genes, underscoring phenotypic diversity and adaptive evolution direction. Associating genes with the QTLdb-pigSS11 database revealed links between gene families and immunity traits. The overlap of olfactory genes in immune-related QTL regions highlighted their importance in evolutionary adaptations. This work highlights the unique evolutionary strategies and adaptive mechanisms of warthogs, guiding future research into the distinct adaptability and disease resistance in pigs, particularly focusing on traits such as resistance to African Swine Fever Virus. [ABSTRACT FROM AUTHOR]

SUMMARY: Mesocotyl length (ML) is a crucial factor in determining the establishment and yield of rice planted through dry direct seeding, a practice that is increasingly popular in rice production worldwide. ML is determined by the endogenous and external environments, and inherits as a complex trait. To date, only a few genes have been cloned, and the mechanisms underlying mesocotyl elongation remain largely unknown. Here, through a genome‐wide association study using sequenced germplasm, we reveal that natural allelic variations in a mitochondrial transcription termination factor, OsML1, predominantly determined the natural variation of ML in rice. Natural variants in the coding regions of OsML1 resulted in five major haplotypes with a clear differentiation between subspecies and subpopulations in cultivated rice. The much‐reduced genetic diversity of cultivated rice compared to the common wild rice suggested that OsML1 underwent selection during domestication. Transgenic experiments and molecular analysis demonstrated that OsML1 contributes to ML by influencing cell elongation primarily determined by H2O2 homeostasis. Overexpression of OsML1 promoted mesocotyl elongation and thus improved the emergence rate under deep direct seeding. Taken together, our results suggested that OsML1 is a key positive regulator of ML, and is useful in developing varieties for deep direct seeding by conventional and transgenic approaches. Significance Statement: Mesocotyl elongation (ML) is important for seedling emergence under dry direct seeding in rice. Here, we report the identification and functional validation of a gene, OsML1 (LOC_Os07g24090.1), which contributes greatly to the natural variation of ML, has been under selection during domestication and breeding, and functions mainly by affecting cell elongation via H2O2 homeostasis and the expression of expansin genes. [ABSTRACT FROM AUTHOR]

The multi-environment genomic selection enables plant breeders to select varieties resilient to diverse environments or particularly adapted to specific environments, which holds a great potential to be used in rice breeding. To realize the multi-environment genomic selection, a robust training set with multi-environment phenotypic data is of necessity. Considering the huge potential of genomic prediction enhanced sparse phenotyping on the cost saving of multi-environment trials (MET), the establishment of a multi-environment training set could also benefit from it. Optimizing the genomic prediction methods is also crucial to enhance the multi-environment genomic selection. Using haplotype-based genomic prediction models is able to capture local epistatic effects which could be conserved and accumulated across generations much like additive effects thereby benefitting breeding. However, previous studies often used fixed length haplotypes composed by a few adjacent molecular markers disregarding the linkage disequilibrium (LD) which is of essential role in determining the haplotype length. In our study, based on three rice populations with different sizes and compositions, we investigated the usefulness and effectiveness of multi-environment training sets with varying phenotyping intensities and different haplotype-based genomic prediction models based on LD-derived haplotype blocks for two agronomic traits, i.e., days to heading (DTH) and plant height (PH). Results showed that phenotyping merely 30% records in multi-environment training set is able to provide a comparable prediction accuracy to high phenotyping intensities; the local epistatic effects are much likely existent in DTH; dividing the LD-derived haplotype blocks into small segments with two or three single nucleotide polymorphisms (SNPs) helps to maintain the predictive ability of haplotype-based models in large populations; modelling the covariances between environments improves genomic prediction accuracy. Our study provides means to improve the efficiency of multi-environment genomic selection in rice. [ABSTRACT FROM AUTHOR]

The technology of wind power is being widely developed worldwide. Ensuring the reliable operation of wind turbine systems is of significance. A popular tool to identify the potential risk of an engineering system is the failure mode and effect analysis (FMEA) method. However, when conducting a FMEA in a complex and uncertain environment, experts may find it challenging to select an appropriate linguistic term for the evaluation. The probabilistic linguistic term sets (PLTSs) are a useful fuzzy set to help experts in describing their assessment. However, different experts may assign different semantic values to the same linguistic terms, and this aspect has not been extensively examined in the existing studies. Therefore, considering the psychological behaviour of experts and the semantics of linguistic terms in the risk ranking process, an improved FMEA based on the probabilistic linguistic information and TODIM (an acronym in Portuguese of interactive and multicriteria decision making) method was developed to identify the risks in wind turbine systems. Furthermore, to demonstrate the utility of the proposed model, it was applied in a floating offshore wind turbine system. The effectiveness and the validity of the model were verified by comparing with some other methods. [ABSTRACT FROM AUTHOR]