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Abstract The common bean (Phaseolus vulgaris L.) is a crucial legume crop and an ideal evolutionary model to study adaptive diversity in wild and domesticated populations. Here, we present a common bean pan-genome based on five high-quality genomes and whole-genome reads representing 339 genotypes. It reveals ~234 Mb of additional sequences containing 6,905 protein-coding genes missing from the reference, constituting 49% of all presence/absence variants (PAVs). More non-synonymous mutations are found in PAVs than core genes, probably reflecting the lower effective population size of PAVs and fitness advantages due to the purging effect of gene loss. Our results suggest pan-genome shrinkage occurred during wild range expansion. Selection signatures provide evidence that partial or complete gene loss was a key adaptive genetic change in common bean populations with major implications for plant adaptation. The pan-genome is a valuable resource for food legume research and breeding for climate change mitigation and sustainable agriculture.

Brassica species, globally cultivated as economically important vegetable and oilseed crops, face challenges from pathogens impacting their growth and productivity. Among these, blackleg, caused by the fungal pathogen Leptosphaeria maculans, stands out as a significant concern. Genetic resistance, primarily mediated by resistance gene analogues (RGAs), is key to sustainable blackleg control. Utilising wild relatives of Brassica species presents a promising avenue for enhancing resistance to blackleg in cultivated crops. In this study, we employed the newly published Hirschfeldia incana reference genome to identify the genome-wide RGAs in H. incana. A total of 914 candidate RGAs were identified; the receptor-like protein kinases (RLK) family contained the highest number with 608 (66.53%), followed by the Transmembrane coiled-coil (TM-CC) family with 167 (18.27%), nucleotide-binding site-leucine-rich repeats (NLR) family with 98 (10.72%) and receptor-like proteins (RLPs) with 41 (4.48%). We conducted duplication analysis on the 914 candidate RGAs, which revealed gene duplication occurs frequently to expand the RGAs in H. incana and significantly contributes to plant defence responsiveness. The phylogenetic analysis provided insights into the diversification and functional implications of the identified groups. We used the sequences of the 49 cloned R genes to identify homologs across H. incana. A total of 75 cloned disease-resistance gene homologs (CDRHs) were found. Cis-acting elements (CREs) were analysed in promoter sequences of 914 RGAs in H. incana, which confirmed their potential function in disease defence. Overall, the results suggest that the wild species H. incana could be a potential R gene source for various disease resistances, including blackleg.

A pangenome encompasses the complete genetic diversity of a species, by assembling a range of representative individuals from various populations. This review describes the advances in plant pangenomics, tracing its evolution since the initial plant genome sequencing in 2000, and provides comprehensive best-practice advice to build a linear or graphical pangenome, delineating the strengths and limitations of different pangenome construction methods. The review also examines the challenges in pangenome data visualisation, the challenges of graph-based pangenomes and their utility in investigating the potential function of genome variation. Furthermore, we examine the application of pangenomes in plant breeding, including the identification of genetic diversity for crop improvement, and the integration of multi-omics data into pangenome databases to advance plant breeding.

Lifts, or elevators, are transportation facilities that are indispensable for countless end users in high-rise buildings. They require proper maintenance to ensure safe operation. In addition to technological applications, effective management and legislative controls play a crucial role in ensuring lift safety. Given the limited understanding of an optimal regulatory model for governing lift maintenance, a cross-discipline comparative study was conducted to examine lift maintenance regulations in regions with different legal systems. Following a systematic and comparative review approach, this study focused on regulatory controls across civil and common law jurisdictions, specifically Beijing, Hong Kong, and London. Relevant statutes and publications were searched from engineering, law and management databases, which included Scopus, JSTOR, Lexis+, Lexis China, Lexis Advance Hong Kong, and Westlaw Asia. Through scrutinizing the retrieved documents, key features of the regulations were identified and compared in terms of lift classifications, types and frequencies of mandatory maintenance works, qualifications for authorized parties, and legal liabilities for non-compliance. Validated by industry experts, the results reveal both similarities and differences in the regulations among the three jurisdictions. While these findings serve as valuable references for policymakers in formulating optimal legislative controls to enhance lift safety in the future, further research could expand the scope of this study to examine the regulations in other regions and investigate the effectiveness of existing statutory controls on lift maintenance.

Weeds pose a significant threat to agricultural production, leading to substantial yield losses and increased herbicide usage, with severe economic and environmental implications. This paper uses deep learning to explore a novel approach via targeted segmentation mapping of crop plants rather than weeds, focusing on canola (Brassica napus) as the target crop. Multiple deep learning architectures (ResNet-18, ResNet-34, and VGG-16) were trained for the pixel-wise segmentation of canola plants in the presence of other plant species, assuming all non-canola plants are weeds. Three distinct datasets (T1_miling, T2_miling, and YC) containing 3799 images of canola plants in varying field conditions alongside other plant species were collected with handheld devices at 1.5 m. The top performing model, ResNet-34, achieved an average precision of 0.84, a recall of 0.87, a Jaccard index (IoU) of 0.77, and a Macro F1 score of 0.85, with some variations between datasets. This approach offers increased feature variety for model learning, making it applicable to the identification of a wide range of weed species growing among canola plants, without the need for separate weed datasets. Furthermore, it highlights the importance of accounting for the growth stage and positioning of plants in field conditions when developing weed detection models. The study contributes to the growing field of precision agriculture and offers a promising alternative strategy for weed detection in diverse field environments, with implications for the development of innovative weed control techniques.

ABSTRACT A previous investigation has elucidated the landscape of Mtb genomic diversity and transmission dynamics in Ho Chi Minh City, Vietnam. Here, we expand the scope of this survey by adding a substantial number of additional genomes (total sample size: 2,542) and phenotypic drug susceptibility data for the majority of isolates. We aim to explore the prevalence and evolutionary dynamics of drug resistance and our ability to predict drug resistance from sequencing data. Among isolates tested phenotypically against first-line drugs, we observed high rates of streptomycin [STR, 37.7% (N = 573/1,520)] and isoniazid resistance [INH, 25.7% (N = 459/1,786)] and lower rates of resistance to rifampicin [RIF, 4.9% (N = 87/1,786)] and ethambutol [EMB, 4.2% (N = 75/1,785)]. Relative to global benchmarks, resistance to STR and INH was predicted accurately when applying the TB-Profiler algorithm to whole genome sequencing data (sensitivities of 0.81 and 0.87, respectively), while resistance to RIF and EMB was predicted relatively poorly (sensitivities of 0.70 and 0.44, respectively). Exploring the evolution of drug resistance revealed the main phylogenetic lineages to display differing dynamics and tendencies to evolve resistance via mutations in certain genes. The Beijing sublineage L2.2.1 was found to acquire de novo resistance mutations more frequently than isolates from other lineages and to suffer no apparent fitness cost acting to impede the transmission of resistance. Mutations conferring resistance to INH and STR arose earlier, on average, than those conferring resistance to RIF and are now more widespread across the phylogeny. The high prevalence of “background” INH resistance, combined with high rates of RIF mono-resistance (20.7%, N = 18/87), suggests that rapid assays for INH resistance will be valuable in this setting. These tests will allow the detection of INH mono-resistance and will allow multi-drug-resistant isolates to be distinguished from isolates with RIF mono-resistance. IMPORTANCE Drug-resistant tuberculosis (TB) infection is a growing and potent concern, and combating it will be necessary to achieve the WHO’s goal of a 95% reduction in TB deaths by 2035. While prior studies have explored the evolution and spread of drug resistance, we still lack a clear understanding of the fitness costs (if any) imposed by resistance-conferring mutations and the role that Mtb genetic lineage plays in determining the likelihood of resistance evolution. This study offers insight into these questions by assessing the dynamics of resistance evolution in a high-burden Southeast Asian setting with a diverse lineage composition. It demonstrates that there are clear lineage-specific differences in the dynamics of resistance acquisition and transmission and shows that different lineages evolve resistance via characteristic mutational pathways.

Abstract Many genome annotations include false‐positive gene models, leading to errors in phylogenetic and comparative studies. Here, we propose a method to support gene model prediction based on evolutionary conservation and use it to identify potentially erroneous annotations. Using this method, we developed a set of 15,345 representative gene models from 12 legume assemblies that can be used to support genome annotations for other legumes.

Identifying the molecular and genetic basis of resistance to Sclerotinia stem rot (Sclerotinia sclerotiorum) is critical for developing long-term and cost-effective management of this disease in rapeseed/canola (Brassica napus). Current cultural or chemical management options provide, at best, only partial and/or sporadic control. Towards this, a B. napus breeding population (Mystic x Rainbow), including the parents, F1, F2, BC1P1 and BC1P2, was utilized in a field study to determine the inheritance pattern of Sclerotinia stem rot resistance (based on stem lesion length, SLL). Broad sense heritability was 0.58 for SLL and 0.44 for days to flowering (DTF). There was a significant negative correlation between SLL and stem diameter (SD) (r = −0.39) and between SLL and DTF (r = −0.28), suggesting co-selection of SD and DTF traits, along with SLL, should assist in improving overall resistance. Non-additive genetic variance was evident for SLL, DTF, and SD. In a genome wide association study (GWAS), a significant quantitative trait locus (QTL) was identified for SLL. Several putative candidate marker trait associations (MTA) were located within this QTL region. Overall, this study has provided valuable new understanding of inheritance of resistance to S. sclerotiorum, and has identified QTL, MTAs and transgressive segregants with high-level resistances. Together, these will foster more rapid selection for multiple traits associated with Sclerotinia stem rot resistance, by enabling breeders to make critical choices towards selecting/developing cultivars with enhanced resistance to this devastating pathogen.

Long non-coding ribonucleic acids (lncRNAs) have been shown to play an important role in plant gene regulation, involving both epigenetic and transcript regulation. LncRNAs are transcripts longer than 200 nucleotides that are not translated into functional proteins but can be translated into small peptides. Machine learning models have predominantly used transcriptome data with manually defined features to detect lncRNAs, however, they often underrepresent the abundance of lncRNAs and can be biased in their detection. Here we present a study using Natural Language Processing (NLP) models to identify plant lncRNAs from genomic sequences rather than transcriptomic data. The NLP models were trained to predict lncRNAs for seven model and crop species (Zea mays, Arabidopsis thaliana, Brassica napus, Brassica oleracea, Brassica rapa, Glycine max and Oryza sativa) using publicly available genomic references. We demonstrated that lncRNAs can be accurately predicted from genomic sequences with the highest accuracy of 83.4% for Z. mays and the lowest accuracy of 57.9% for B. rapa, revealing that genome assembly quality might affect the accuracy of lncRNA identification. Furthermore, we demonstrated the potential of using NLP models for cross-species prediction with an average of 63.1% accuracy using target species not previously seen by the model. As more species are incorporated into the training datasets, we expect the accuracy to increase, becoming a more reliable tool for uncovering novel lncRNAs. Finally, we show that the models can be interpreted using explainable artificial intelligence to identify motifs important to lncRNA prediction and that these motifs frequently flanked the lncRNA sequence.

Abstract Background A pangenome aims to capture the complete genetic diversity within a species and reduce bias in genetic analysis inherent in using a single reference genome. However, the current linear format of most plant pangenomes limits the presentation of position information for novel sequences. Graph pangenomes have been developed to overcome this limitation. However, bioinformatics analysis tools for graph format genomes are lacking. Results To overcome this problem, we develop a novel strategy for pangenome construction and a downstream pangenome analysis pipeline (PSVCP) that captures genetic variants’ position information while maintaining a linearized layout. Using PSVCP, we construct a high-quality rice pangenome using 12 representative rice genomes and analyze an international rice panel with 413 diverse accessions using the pangenome as the reference. We show that PSVCP successfully identifies causal structural variations for rice grain weight and plant height. Our results provide insights into rice population structure and genomic diversity. We characterize a new locus (qPH8-1) associated with plant height on chromosome 8 undetected by the SNP-based genome-wide association study (GWAS). Conclusions Our results demonstrate that the pangenome constructed by our pipeline combined with a presence and absence variation-based GWAS can provide additional power for genomic and genetic analysis. The pangenome constructed in this study and the associated genome sequence and genetic variants data provide valuable genomic resources for rice genomics research and improvement in future.

Abstract Soybean (Glycine max) is a major crop that contributes more than half of global oilseed production. Much research has been directed towards improvement of the fatty acid profile of soybean seeds through marker assisted breeding. Recently published soybean pangenomes, based on thousands of soybean lines, provide an opportunity to identify new alleles that may be involved in fatty acid biosynthesis. In this study, we identify fatty acid biosynthesis genes in soybean pangenomes based on sequence identity with known genes and examine their sequence diversity across diverse soybean collections. We find three possible instances of a gene missing in wild soybean, including FAD8 and FAD2‐2D, which may be involved in oleic and linoleic acid desaturation, respectively, although we recommend follow‐up research to verify the absence of these genes. More than half of the 53 fatty acid biosynthesis genes identified contained missense variants, including one linked with a previously identified QTL for oil quality. These variants were present in multiple studies based on either short read mappings or alignment of reference grade genomes. Missense variants were found in previously characterized genes including FAD2‐1A and FAD2‐1B, both of which are involved in desaturation of oleic acid, as well as uncharacterized candidate fatty acid biosynthesis genes. We find that the frequency of missense alleles in fatty acid biosynthesis genes has been reduced significantly more than the global average frequency of missense mutations during domestication, and missense variation in some genes is near absent in modern cultivars. This could be due to the selection for fatty acid profiles in seed, though future work should be conducted towards understanding the phenotypic impacts of these variants.

Reducing building energy use, a linchpin of climate change mitigation, is a daunting challenge across the world. Gaining increasing attention, retro-commissioning (RCx) is a systematic process that can improve building energy performance. Using a techno-legal lens to review statutes and guides on RCx, this study reveals that in China, a national standard on building commissioning has taken effect yet RCx statutes are hitherto not found. The United States has RCx statutes enacted over 14 states; scrutinizing the statutes of five cities unveils similarities and differences in their scope of control and compliance requirements. In the absence of a specific RCx statute, the guide of Hong Kong, China provides detailed guidance for energy saving practices. While these findings can serve as reference for other places planning to formulate laws or guidance on RCx, the need to further study the effectiveness of mandating RCx for reducing building energy use is highlighted. A conceptual analysis of cost variation with statutory control, which could help policymakers consider from an economic perspective whether or to what extent statutory RCx requirements should be imposed, is also illustrated. This not only contributes insights to the pursuit of an optimal balance between statutory control and voluntary action for energy reduction but also adds to the debate on building energy policies.

Woodland creation has become an important objective for a variety of stakeholders to help tackle the climate and biodiversity crises. One of the key evidence needs is a better understanding of the multiple factors influencing the willingness and ability of landowners and managers to establish new woodlands. To address this gap, a systematic map of evidence was prepared comprising publications from academic journals and grey literature accessed through bibliographic databases (Web of Science, Scopus, and CAB Abstracts), libraries, direct requests to relevant organisations and individuals, and citation tracking from past reviews. A screening process refined the evidence base to 226 studies within the UK. The systematic evidence map codes the content of each study against a comprehensive list comprising actors, drivers of or barriers to woodland creation, and outcomes. These are presented as a freely accessible, interactive online dashboard detailing sources of evidence. The systematic evidence map helps users navigate the evidence, demonstrating where the bulk of the evidence lies and, conversely, several evidence gaps where there is comparatively little evidence. The findings serve as a basis for dialogue with stakeholders to determine priorities for future primary research.

Recently, transformers have achieved great success in a number of computer vision tasks due to their excellent ability to capture long-range feature dependencies. In contrast, convolutional neural networks (CNNs) are good at extracting local features. Given that the capture of short- and long-range band dependencies are both important for hyperspectral data classification, we propose MCE-ST, a convolution-transformer (conformer) based framework capable of exploiting the complementary strengths of transformers and CNNs. In contrast to the conventional transformer, which uses a linear projection for tokenization, the proposed MCE-ST uses a convolution-based tokenization method to extract local dependency between spectral bands. Moreover, since different hyperspectral samples may have different spans of local relationships, a multiscale conformer encoder (MCE) comprising two separate branches of depth-wise dilated convolution with different kernel sizes is used to extract the different spans of the local interactions between tokens. We conducted experiments on four salt stress datasets and one cassava disease dataset. The results show that the proposed MCE-ST outperforms the state-of-the-art techniques for crop stress classification using hyperspectral data. The code for MCE-ST is publicly available at https://github.com/Weejaa04/MCE-ST-GitHub.

Fatigue and physical exhaustion are the dominant symptoms of post-coronavirus (COVID-19) conditions that might even develop after only mild acute disease. Post-acute infection syndromes have been observed after various infections, e.g., *Coxiella burnetii*, Ebola, Dengue, Polio, severe acute respiratory syndrome (SARS), Chikungunya, West Nile Virus, *Borrelia*, or *Giardina lamblia*. The similarities in symptoms and courses suggest a high likelihood of common pathogenetic pathways, including persistent infection, autoimmune reactions, dysregulation of the microbiome, inability to repair tissue damage, or endothelial dysfunction. Some herbal drugs, so-called adaptogens, exert effects resulting in an increase in the resistance or regulatory potential of organisms against biological, chemical and physical burden or stress. Therefore, it seems possible that adaptogens can be helpful in cases of post-COVID-19 symptoms. One of these adaptogens is *Rhodiola rosea* L. The proprietary ethanolic extract made from roots and rhizomes of *Rhodiola rosea* WS^®^ 1375 has been reported to modulate neuroinflammation in response to stress stimuli in preclinical models. Moreover, it activated the synthesis or resynthesis of adenosine triphosphate (ATP) in skeletal muscle mitochondria and counteracted muscle fatigue. In three clinical trials with subjects suffering from burnout symptoms, prolonged or chronic fatigue symptoms or life-stress symptoms, clinically relevant improvements of fatigue and exhaustion were reported over 4 to 12 weeks of treatment at a very favorable tolerability and safety profile in heterogeneous patient populations. In conclusion, *Rhodiola rosea* extract WS^®^ 1375 has a promising pharmacological and therapeutic profile for the treatment of fatigue and physical exhaustion associated with post-COVID-19 conditions.

The recent Russia–Ukraine conflict has raised significant concerns about global food security, leaving many countries with restricted access to imported staple food crops, particularly wheat and sunflower oil, sending food prices soaring with other adverse consequences in the food supply chain. This detrimental effect is particularly prominent for low-income countries relying on grain imports, with record-high food prices and inflation affecting their livelihoods. This review discusses the role of Russia and Ukraine in the global food system and the impact of the Russia–Ukraine conflict on food security. It also highlights how diversifying four areas of agrifood systems—markets, production, crops, and technology can contribute to achieving food supply chain resilience for future food security and sustainability.

Abstract Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few approaches for genotype to phenotype prediction compare machine learning with deep learning and further interpret the models that support the predictions. This study uses genome wide molecular markers and traits across 1110 soybean individuals to develop accurate prediction models. For 13/14 sets of predictions, XGBoost or random forest outperformed deep learning models in prediction performance. Top ranked SNPs by F-score were identified from XGBoost, and with further investigation found overlap with significantly associated loci identified from GWAS and previous literature. Feature importance rankings were used to reduce marker input by up to 90%, and subsequent models maintained or improved their prediction performance. These findings support interpretable machine learning as an approach for genomic based prediction of traits in soybean and other crops.

Utilising resistance (R) genes, such as LepR1, against Leptosphaeria maculans, the causal agent of blackleg in canola (Brassica napus), could help manage the disease in the field and increase crop yield. Here we present a genome wide association study (GWAS) in B. napus to identify LepR1 candidate genes. Disease phenotyping of 104 B. napus genotypes revealed 30 resistant and 74 susceptible lines. Whole genome re-sequencing of these cultivars yielded over 3 million high quality single nucleotide polymorphisms (SNPs). GWAS in mixed linear model (MLM) revealed a total of 2,166 significant SNPs associated with LepR1 resistance. Of these SNPs, 2108 (97%) were found on chromosome A02 of B. napus cv. Darmor bzh v9 with a delineated LepR1_mlm1 QTL at 15.11-26.08 Mb. In LepR1_mlm1, there are 30 resistance gene analogs (RGAs) (13 nucleotide-binding site-leucine rich repeats (NLRs), 12 receptor-like kinases (RLKs), and 5 transmembrane-coiled-coil (TM-CCs)). Sequence analysis of alleles in resistant and susceptible lines was undertaken to identify candidate genes. This research provides insights into blackleg resistance in B. napus and assists identification of the functional LepR1 blackleg resistance gene.

Crop production can be severely limited by adverse environmental conditions such as drought, extreme heat, and frost. As climate change intensifies, weather patterns are becoming less predictable and extreme weather events more likely. To ensure continued production of enough food for the growing world population, crop losses due to adverse conditions must be curtailed. One way to do this is to improve the level of abiotic stress tolerance in crops through breeding. The process of breeding plants has seen recent increases in speed and efficiency due in large part to the application of genome sequencing and development of high-density genetic marker panels. Using genotypic data produced by these technologies, a statistical technique known as ‘genomic prediction’ can predict the level of tolerance to different abiotic stresses in an individual plant. Furthermore, the development of genome editing will allow for targeted modification of specific nucleotides, to enhance abiotic stress resilience. The continued sequencing and analysis of thousands of genomes of individuals in each plant species, known as ‘pan-genomics’, will allow us to identify the precise polymorphisms that underlie abiotic stress tolerance. Such polymorphisms have the potential to significantly increase the accuracy of genome-assisted breeding methods, and support the identification of targets for genome editing, further enhancing the development of abiotic stress tolerant crops. In addition, use of multiple ‘omics data will allow for accurate representations of the genetic pathways in crop plants that control abiotic stress responses. Coupling this knowledge with pan-genomics could further enhance the accuracy of genome-assisted breeding, increasing the efficiency of crop breeding and generating new prospects for developing abiotic stress tolerance via genome editing. In this review, we summarise recent research using ‘omics techniques to understand the genetic basis of abiotic stress tolerance in plants with a focus on major food crops. We evaluate the potential avenues for improving crop production using ‘omics techniques and discuss areas for future research that we think should be prioritised.

Global agricultural industries are under pressure to meet the future food demand; however, the existing crop genetic diversity might not be sufficient to meet this expectation. Advances in genome sequencing technologies and availability of reference genomes for over 300 plant species reveals the hidden genetic diversity in crop wild relatives (CWRs), which could have significant impacts in crop improvement. There are many ex-situ and in-situ resources around the world holding rare and valuable wild species, of which many carry agronomically important traits and it is crucial for users to be aware of their availability. Here we aim to explore the available ex-/in- situ resources such as genebanks, botanical gardens, national parks, conservation hotspots and inventories holding CWR accessions. In addition we highlight the advances in availability and use of CWR genomic resources, such as their contribution in pangenome construction and introducing novel genes into crops. We also discuss the potential and challenges of modern breeding experimental approaches (e.g. de novo domestication, genome editing and speed breeding) used in CWRs and the use of computational (e.g. machine learning) approaches that could speed up utilization of CWR species in breeding programs towards crop adaptability and yield improvement.

Abstract Bread wheat (Triticum aestivum L.) is one of humanity's most important staple crops, characterized by a large and complex genome with a high level of gene presence–absence variation (PAV) between cultivars, hampering genomic approaches for crop improvement. With the growing global population and the increasing impact of climate change on crop yield, there is an urgent need to apply genomic approaches to accelerate wheat breeding. With recent advances in DNA sequencing technology, a growing number of high‐quality reference genomes are becoming available, reflecting the genetic content of a diverse range of cultivars. However, information on the presence or absence of genomic regions has been hard to visualize and interrogate because of the size of these genomes and the lack of suitable bioinformatics tools. To address this limitation, we have produced a wheat pangenome graph maintained within an online database to facilitate interrogation and comparison of wheat cultivar genomes. The database allows users to visualize regions of the pangenome to assess PAV between bread wheat genomes.

High seedling vigor can improve the ability to compete against weeds and flooding at the seedling stage and is essential for the direct seeding of rice. Early shoot length is an important performance index in seedling vigor evaluation. However, information on the identity of rice germplasm with high seedling vigor, and the genetic basis of seedling vigor are not well understood. In this study, we have conducted a genome-wide association study using 302 international diverse rice accessions from the Rice Diversity Panel 2. Six quantitative trait loci (QTLs) were found to associate with shoot length (SL). The locus qSL2 was further analyzed for candidate gene characterization. We identified OsCPS1, which encodes CDP synthase and functions in GA (Gibberellins) biosynthesis in rice, exhibits differential expression between long and short SL accessions. Using the Nipponbare genome sequence as the reference, we identified a 36 bp deletion in the 5’ UTR of OsCPS1 in long SL accessions, which is absent in short SL accessions. GA content analysis showed that the levels of bioactive GA1 and GA4 are considerably higher in long SL accessions than in short SL accessions. Genome-wide gene expression analysis indicated the expression of some photosynthesis genes is higher in long SL accessions than in short SL accessions. In contrast, genes involved in ABA (Abscisic Acid)-activated signal pathway showed lower expression in long SL accessions. Population analysis across wild rice, indica and japonica, suggested that OsCPS1 may be under selection in japonica during domestication. The results suggest that OsCPS1 is a candidate gene underlying qSL2. These data provide a promising source for candidate genetic variation associated with seedling vigor, with practical applications in rice breeding.

Objectives To examine the association between maternal depressive symptoms in the immediate postnatal period and offspring’s behavioural outcomes in a large cohort of term-born and preterm-born toddlers.Design and participants Data were drawn from the Developing Human Connectome Project. Maternal postnatal depressive symptoms were assessed at term-equivalent age, and children’s outcomes were evaluated at a median corrected age of 18.4 months (range 17.3–24.3).Exposure and outcomes Preterm birth was defined as

A claim management office (CMO) is a new intra-organisation fit based on an organisational project management (OPM) approach and deals specifically with improving claim performance. However, CMOs are either misunderstood or often overlooked in construction companies, mainly due to ignorance of the OPM context but further exacerbated by a lack of real case study research on how to adopt CMOs in these organisations. To address this knowledge gap, this present study integrates organisational ambidexterity with X-inefficiency theory (due to organisation intra-firm irrational decisions when managing such claims) to generate a reference framework for the CMO by probing its implementation within an international construction firm. A multi-method approach, including a single case study (a firm which adopted a CMO) and internal and external expert panels, was used for sampling, data collection, analysis and validation of the framework. The reference framework provides new perspectives on how construction-related companies and practitioners can adopt a CMO structure, which enables them to improve claims performance by planning in three interrelated activities, viz. function-, process- and performance-based. Theoretically, findings also contribute to the X-inefficiency and organisational ambidexterity theories, specifically, how different influences among the reference framework’s elements lead to better organisational claim performance as a plausible roadmap for future work.

Fusarium wilt of banana is a devastating disease that has decimated banana production worldwide. Host resistance to Fusarium oxysporum f. sp. Cubense (Foc), the causal agent of this disease, is genetically dissected in this study using two Musa acuminata ssp. Malaccensis segregating populations, segregating for Foc Tropical (TR4) and Subtropical (STR4) race 4 resistance. Marker loci and trait association using 11 SNP-based PCR markers allowed the candidate region to be delimited to a 12.9 cM genetic interval corresponding to a 959 kb region on chromosome 3 of ‘DH-Pahang’ reference assembly v4. Within this region, there was a cluster of pattern recognition receptors, namely leucine-rich repeat ectodomain containing receptor-like protein kinases, cysteine-rich cell-wall-associated protein kinases, and leaf rust 10 disease-resistance locus receptor-like proteins, positioned in an interspersed arrangement. Their transcript levels were rapidly upregulated in the resistant progenies but not in the susceptible F2 progenies at the onset of infection. This suggests that one or several of these genes may control resistance at this locus. To confirm the segregation of single-gene resistance, we generated an inter-cross between the resistant parent ‘Ma850’ and a susceptible line ‘Ma848’, to show that the STR4 resistance co-segregated with marker ‘28820’ at this locus. Finally, an informative SNP marker 29730 allowed the locus-specific resistance to be assessed in a collection of diploid and polyploid banana plants. Of the 60 lines screened, 22 lines were predicted to carry resistance at this locus, including lines known to be TR4-resistant, such as ‘Pahang’, ‘SH-3362’, ‘SH-3217’, ‘Ma-ITC0250’, and ‘DH-Pahang/CIRAD 930’. Additional screening in the International Institute for Tropical Agriculture’s collection suggests that the dominant allele is common among the elite ‘Matooke’ NARITA hybrids, as well as in other triploid or tetraploid hybrids derived from East African highland bananas. Fine mapping and candidate gene identification will allow characterization of molecular mechanisms underlying the TR4 resistance. The markers developed in this study can now aid the marker-assisted selection of TR4 resistance in breeding programs around the world.

Abstract Background Direct seeding is an efficient rice cultivation practice. However, its application is often limited due to O2 deficiency following submergence, leading to poor seed germination, seedling establishment, and consequently yield loss. Identification of genes associated with tolerance to submergence and understanding their regulatory mechanisms is the fundamental way to address this problem. Unfortunately, the molecular mechanism of rice response to submergence stress is still not well understood. Results Here, we have performed a genome-wide identification of FCS-like zinc finger (FLZ) proteins and assessed their involvement in submergence response in rice. We identified 29 FLZ genes in rice, and the expression analysis revealed that several genes actively responded to submergence stress. Eight OsFLZ proteins interact with SnRK1A. As a case study, we demonstrated that OsFLZ18 interacted with SnRK1A and inhibited the transcriptional activation activity of SnRK1A in modulating the expression of its target gene αAmy3, a positive regulator in rice flooding tolerance. In line with this, OsFLZ18-overexpression lines displayed retarded early seedling growth and shorter coleoptile following submergence. Conclusions These data provide the most comprehensive information of OsFLZ genes in rice, and highlight their roles in rice submergence response.

The 2021 England and Wales Census was matched to the Census Coverage Survey (CCS). This was an essential requisite for estimating undercount in the Census. To ensure outputs could be produced within a year of census day, matching had to be completed in eight weeks. We used the Data Access Platform to write efficient and accurate automatic matching algorithms. Probabilistic and associative matching methods were used to present candidate pairs that could not be automatically matched but likely consisted of many true matches. These were resolved by matchers in the clerical matching system. Active learning was used to iteratively improve probabilistic parameters as matches were made. We also developed a pre-search algorithm that simplifies clerical matching by replacing clerical searching (here’s a record, can you find a match?) with clerical resolution (here are two records, do they match?). As a result of our improvements, we increased our automatic matching rates from 70% (2011 Census) to 93% (2021 Census) for person matching and from 60% to 95% for household matching, without loss of accuracy. Precision and recall were estimated to both be 99.96% for person matching and 100% and 99.78% respectively for household matching. For both persons and households, the same trends seen in 2011 are seen here too; however, most of the biases have decreased and most match rates have increased. Dual system estimation using the matched and unmatched records enabled us to estimate that coverage in the 2021 England and Wales Census was 97%. The matching methodology implemented for the 2021 Census to CCS matching improved both person and household matching greatly when compared with the 2011 matching. The clerical matching system enabled clerical matching at scale and at pace. Thus, we successfully completed all clerical matching within our eight-week deadline.

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world’s most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public–private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.

DNA methylation is an epigenetic mark associated with several mechanisms in plants including immunity mechanisms. However, little is known about the regulatory role of DNA methylation in the resistance response of Brassica species against fungal diseases. White rust, caused by the fungus Albugo candida, is one of the most widespread and destructive diseases of all the cultivated Brassica species, particularly Brassica rapa L. and Brassica juncea (L.) Czern and Coss. Here, we investigate whole-genome DNA methylation modifications of B. rapa subsp. perviridis in response to white rust. As a result, 233 and 275 differentially methylated regions (DMRs) in the susceptible cultivar “Misugi” and the resistant cultivar “Nanane” were identified, respectively. In both cultivars, more than half of the DMRs were associated with genes (DMR-genes). Gene expression analysis showed that 13 of these genes were also differentially expressed between control and infected samples. Gene ontology enrichment analysis of DMR genes revealed their involvement in various biological processes including defense mechanisms. DMRs were unevenly distributed around genes in susceptible and resistant cultivars. In “Misugi,” DMRs tended to be located within genes, while in “Nanane,” DMRs tended to be located up and downstream of the genes. However, CG DMRs were predominantly located within genes in both cultivars. Transposable elements also showed association with all three sequence contexts of DMRs but predominantly with CHG and CHH DMRs in both cultivars. Our findings indicate the occurrence of DNA methylation modifications in B. rapa in response to white rust infection and suggest a potential regulatory role of DNA methylation modification in defense mechanisms which could be exploited to improve disease resistance.

Pimpinella species are annual, biennial, and perennial semibushy aromatic plants cultivated for folk medicine, pharmaceuticals, food, and spices. The karyology and genome size of 17 populations of 16 different Pimpinella species collected from different locations in Iran were analyzed for inter-specific karyotypic and genome size variations. For karyological studies, root tips were squashed and painted with a DAPI solution (1 mg/ml). For flow cytometric measurements, fresh leaves of the standard reference (Solanum lycopersicum cv. Stupick, 2C DNA = 1.96 pg) and the Pimpinella samples were stained with propidium iodide. We identified two ploidy levels: diploid (2x) and tetraploid (4x), as well as five metaphase chromosomal counts of 18, 20, 22, 24, and 40. 2n = 24 is reported for the first time in the Pimpinella genus, and the presence of a B-chromosome is reported for one species. The nuclear DNA content ranged from 2C = 2.48 to 2C = 5.50 pg, along with a wide range of genome sizes between 1212.72 and 2689.50 Mbp. The average monoploid genome size and the average value of 2C DNA/chromosome were not proportional to ploidy. There were considerable positive correlations between 2C DNA and total chromatin length and total chromosomal volume. The present study results enable us to classify the genus Pimpinella with a high degree of morphological variation in Iran. In addition, cytological studies demonstrate karyotypic differences between P. anthriscoides and other species of Pimpinella, which may be utilized as a novel identification key to affiliate into a distinct, new genus – Pseudopimpinella.

Phytosterols are natural compounds found in all higher plants that have a wide variety of roles in plant growth regulation and stress tolerance. The phytosterol composition can also influence the development and reproductive rate of strict herbivorous insects and other important agronomic traits such as temperature and drought tolerance in plants. In this study, we analysed the phytosterol composition in 18 Brassica napus (Rapeseed/canola) cultivars and 20 accessions belonging to 10 related wild Brassicaceae species to explore diverse and novel phytosterol profiles. Plants were grown in a controlled phytotron environment and their phytosterols were analysed using a saponification extraction method followed by GC-MS from the leaf samples. The B. napus cultivars showed slight diversity in eight phytosterols (>0.02%) due to the genotypic effect, whereas the wild accessions showed significant variability in their phytosterol profiles. Of interest, a number of wild accessions were found with high levels of campesterol (HIN20, HIN23, HUN27, HIN30, SARS2, and UPM6563), stigmasterol (UPM6813, UPM6563, ALBA17, and ALBA2), and isofucosterol (SARS12, SAR6, and DMU2). These changes in individual phytosterols, or ratios of phytosterols, can have a significant implication in plant tolerance to abiotic stress and plant insect resistance properties, which can be used in breeding for crop improvement.

Narrow-leafed lupin (Lupinus angustifolius) is an important dryland crop, providing a protein source in global grain markets. While agronomic practices have successfully controlled many dicot weeds among narrow-leafed lupins, the closely related sandplain lupin (Lupinus cosentinii) has proven difficult to control, reducing yield and harvest quality. Here, we successfully trained a segmentation model to detect sandplain lupins and differentiate them from narrow-leafed lupins under field conditions. The deep learning model was trained using 9171 images collected from a field site in the Western Australian grain belt. Images were collected using an unoccupied aerial vehicle at heights of 4, 10, and 20 m. The dataset was supplemented with images sourced from the WeedAI database, which were collected at 1.5 m. The resultant model had an average precision of 0.86, intersection over union of 0.60, and F1 score of 0.70 for segmenting the narrow-leafed and sandplain lupins across the multiple datasets. Images collected at a closer range and showing plants at an early developmental stage had significantly higher precision and recall scores (p-value < 0.05), indicating image collection methods and plant developmental stages play a substantial role in the model performance. Nonetheless, the model identified 80.3% of the sandplain lupins on average, with a low variation (±6.13%) in performance across the 5 datasets. The results presented in this study contribute to the development of precision weed management systems within morphologically similar crops, particularly for sandplain lupin detection, supporting future narrow-leafed lupin grain yield and quality.

Measurement(s) genetic variation • SNP • Linkage Disequilibrium Technology Type(s) DNA sequencing • bioinformatics analysis • computational phylogenetic analysis Sample Characteristic - Organism Glycine soja • Glycine max Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13568552

Purpose – Using a discrete choice experiment (DCE), this study aims to better understand the job preference of postgraduate students studying at the Kwame Nkrumah University of Science and Technology-Institute of Distance Learning, Ghana and also rank the attributes of a job they deem important. Design/methodology/approach – The research adopted a positivist epistemological design contextualised within a deductive approach and case study strategy. Primary survey data was collected from a stratified random sample of 128 postgraduate students with multi-sectorial career prospects. Sample students were subjected to a DCE in which their stated preferences were collected using closed-ended questionnaires with 28 pairs of hypothetical job profiles. Respondents’ preferences from the DCE data were then modelled using the conditional logit. Findings – The research reveals that: salary in the range GHC 2,800.00 to GHC 3,400.00 ($1 = GHS 5.3); supportive management; very challenging jobs; and jobs located in the city were the top attributes that were significant and had the most impact in increasing the utility of selecting a particular job. Interestingly, jobs with no extra hours workload were not significant hence, had a negative impact upon student preferences. Originality/value – This novel research is the first to use a DCE to better elicit preference and trade-offs of postgraduate students in a developing country towards varying job characteristics that have an impact on their future employment decisions. Knowledge advancements made provide invaluable insight to employers and policymakers on the key criteria that should be implemented to retain the best candidate.

Abstract Background Brassica napus is an important oilseed crop cultivated worldwide. During domestication and breeding of B. napus, flowering time has been a target of selection because of its substantial impact on yield. Here we use double digest restriction-site associated DNA sequencing (ddRAD) to investigate the genetic basis of flowering in B. napus. An F2 mapping population was derived from a cross between an early-flowering spring type and a late-flowering winter type. Results Flowering time in the mapping population differed by up to 25 days between individuals. High genotype error rates persisted after initial quality controls, as suggested by a genotype discordance of ~ 12% between biological sequencing replicates. After genotype error correction, a linkage map spanning 3981.31 cM and compromising 14,630 single nucleotide polymorphisms (SNPs) was constructed. A quantitative trait locus (QTL) on chromosome C2 was detected, covering eight flowering time genes including FLC. Conclusions These findings demonstrate the effectiveness of the ddRAD approach to sample the B. napus genome. Our results also suggest that ddRAD genotype error rates can be higher than expected in F2 populations. Quality filtering and genotype correction and imputation can substantially reduce these error rates and allow effective linkage mapping and QTL analysis.

Genomic prediction tools support crop breeding based on statistical methods, such as the genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to capture non-linear relationships within multi-dimensional datasets, or deal with high dimension datasets such as imagery collected by unmanned aerial vehicles. Machine learning (ML) algorithms have the potential to surpass the prediction accuracy of current tools used for genotype to phenotype prediction, due to their capacity to autonomously extract data features and represent their relationships at multiple levels of abstraction. This review addresses the challenges of applying statistical and machine learning methods for predicting phenotypic traits based on genetic markers, environment data, and imagery for crop breeding. We present the advantages and disadvantages of explainable model structures, discuss the potential of machine learning models for genotype to phenotype prediction in crop breeding, and the challenges, including the scarcity of high-quality datasets, inconsistent metadata annotation and the requirements of ML models.

Abstract The gene content of plants varies between individuals of the same species due to gene presence/absence variation, and selection can alter the frequency of specific genes in a population. Selection during domestication and breeding will modify the genomic landscape, though the nature of these modifications is only understood for specific genes or on a more general level (e.g., by a loss of genetic diversity). Here we have assembled and analyzed a soybean (Glycine spp.) pangenome representing more than 1,000 soybean accessions derived from the USDA Soybean Germplasm Collection, including both wild and cultivated lineages, to assess genomewide changes in gene and allele frequency during domestication and breeding. We identified 3,765 genes that are absent from the Lee reference genome assembly and assessed the presence/absence of all genes across this population. In addition to a loss of genetic diversity, we found a significant reduction in the average number of protein‐coding genes per individual during domestication and subsequent breeding, though with some genes and allelic variants increasing in frequency associated with selection for agronomic traits. This analysis provides a genomic perspective of domestication and breeding in this important oilseed crop.

Abstract Achieving yield potential in chickpea (Cicer arietinum L.) is limited by many constraints that include biotic and abiotic stresses. Combining next‐generation sequencing technology with advanced statistical modeling has the potential to increase genetic gain efficiently. Whole genome resequencing data was obtained from 315 advanced chickpea breeding lines from the Australian chickpea breeding program resulting in more than 298,000 single nucleotide polymorphisms (SNPs) discovered. Analysis of population structure revealed a distinct group of breeding lines with many alleles that are absent from recently released Australian cultivars. Genome‐wide association studies (GWAS) using these Australian breeding lines identified 20 SNPs significantly associated with grain yield in multiple field environments. A reduced level of nucleotide diversity and extended linkage disequilibrium suggested that some regions in these chickpea genomes may have been through selective breeding for yield or other traits. A large introgression segment that introduced from C. echinospermum for phytophthora root rot resistance was identified on chromosome 6, yet it also has unintended consequences of reducing yield due to linkage drag. We further investigated the effect of genotype by environment interaction on genomic prediction of yield. We found that the training set had better prediction accuracy when phenotyped under conditions relevant to the targeted environments. We also investigated the effect of SNP functional annotation on prediction accuracy using different subsets of SNPs based on their genomic locations: regulatory regions, exome, and alternative splice sites. Compared with the whole SNP dataset, a subset of SNPs did not significantly decrease prediction accuracy for grain yield despite consisting of a smaller number of SNPs.

Abstract In the last decade, more than 70 quantitative trait loci (QTL) related to soybean [Glycine max (L.) Merr.] partial resistance (PR) against Phytophthora sojae have been identified by genome‐wide association studies (GWAS). However, most of them have either a minor effect on the resistance level or are specific to a single phenotypic variable or one isolate, thereby limiting their use in breeding programs. In this study, we have used an analytical approach combining (a) the phenotypic characterization of a diverse panel of 357 soybean accessions for resistance to P. sojae captured through a single variable, corrected dry weight; (b) a new hydroponic assay allowing the inoculation of a combination of P. sojae isolates covering the spectrum of commercially relevant Rps genes; and (c) exhaustive genotyping through whole‐genome resequencing (WGS). This led to the identification of a novel P. sojae resistance QTL with a relatively major effect compared with the previously reported QTL. The QTL interval, spanning ∼500 kb on chromosome (Chr) 15, does not colocalize with previously reported QTL for P. sojae resistance. Plants carrying the favorable allele at this QTL were 60% more resistant. Eight genes were found to reside in the linkage disequilibrium (LD) block containing the peak single‐nucleotide polymorphism (SNP) including Glyma.15G217100, which encodes a major latex protein (MLP)‐like protein, with a functional annotation related to pathogen resistance. Expression analysis of Glyma.15G217100 indicated that it was nearly eight times more highly expressed in a group of plant introductions (PIs) carrying the resistant (R) allele compared with those carrying the susceptible (S) allele within a short period after inoculation. These results offer new and valuable options to develop improved soybean cultivars with broad resistance to P. sojae through marker‐assisted selection.

Abstract Since the first reported crop pangenome in 2014, advances in high‐throughput and cost‐effective DNA sequencing technologies facilitated multiple such studies including the pangenomes of oilseed rape (Brassica napus L.), soybean [Glycine max (L.) Merr.], rice (Oryza sativa L.), wheat (Triticum aestivum L.), and barley (Hordeum vulgare L.). Compared with single‐reference genomes, pangenomes provide a more accurate representation of the genetic variation present in a species. By combining the genomic data of multiple accessions, pangenomes allow for the detection and annotation of complex DNA polymorphisms such as structural variations (SVs), one of the major determinants of genetic diversity within a species. In this review we summarize the current literature on crop pangenomics, focusing on their application to find candidate SVs involved in traits of agronomic interest. We then highlight the potential of pangenomes in the discovery and functional characterization of noncoding regulatory sequences and their variations. We conclude with a summary and outlook on innovative data structures representing the complete content of plant pangenomes including annotations of coding and noncoding elements and outcomes of transcriptomic and epigenomic experiments.

Abstract Banana (Musaceae family) has a complex genetic history and includes a genus Musa with a variety of cultivated clones with edible fruits, Ensete species that are grown for their edible corm, and monospecific Musella whose generic status has been questioned. The most commonly exported banana cultivars belong to Cavendish, a subgroup of Musa triploid cultivars, which is under threat by fungal pathogens, though there are also related species M. balbisiana Colla (B genome), M. textilis Née (T genome), and M. schizocarpa N. W. Simmonds (S genome), along with hybrids of these genomes, which potentially host genes of agronomic interest. Here we present the first cross‐genus pangenome of banana, which contains representatives of the Musa and Ensete genera. Clusters based on gene presence–absence variation (PAV) clearly separate Musa and Ensete, while Musa is split further based on species. These results present the first pangenome study across genus boundaries and identifies genes that differentiate between Musaceae species, information that may support breeding programs in these crops.

Respiratory illness threatens the learning potential of hundreds of millions of children around the world. We find in a human volunteer study involving three sites and 253 volunteers that respiratory droplets — of the size and nature to potentially contain COVID-19, influenza, allergens and other contaminants — diminish in number on exhalation by up to 99% via the “airway hygiene” administration of a nasal saline rich in calcium. Exhaled particles were significantly higher and efficacy of airway hygiene greatest at the site (Bangalore India) with highest fine particle ambient air burden. We argue for the use of airway hygiene for pandemic and post-pandemic global learning.

To improve hygiene in the COVID-19 pandemic, we evaluated the intranasal delivery of calcium-enriched saline to suppress breath particles in practical settings reflecting essential worker hygiene, quarantine hygiene, and back-to-school hygiene. In studies with 92 men, women, and children, we observed that nasal salt aerosols lowered exhaled particles for the group by around 75%, and outperformed surgical face masks in the clearing of sub-micron particles.

Guava wilt disease is caused by the fungus Nalanthamala psidii. The wilt disease results in large-scale destruction of orchards in South Africa, Taiwan, and several Southeast Asian countries. De novo assembly, annotation, and in-depth analysis of the N. psidii genome were carried out to facilitate the identification of characteristics associated with pathogenicity and pathogen evolution. The predicted secretome revealed a range of CAZymes, proteases, lipases and peroxidases associated with plant cell wall degradation, nutrient acquisition, and disease development. Further analysis of the N. psidii carbohydrate-active enzyme profile exposed the broad-spectrum necrotrophic lifestyle of the pathogen, which was corroborated by the identification of putative effectors and secondary metabolites with the potential to induce tissue necrosis and cell surface-dependent immune responses. Putative regulatory proteins including transcription factors and kinases were identified in addition to transporters potentially involved in the secretion of secondary metabolites. Transporters identified included important ABC and MFS transporters involved in the efflux of fungicides. Analysis of the repetitive landscape and the detection of mechanisms linked to reproduction such as het and mating genes rendered insights into the biological complexity and evolutionary potential of N. psidii as guava pathogen. Hence, the assembly and annotation of the N. psidii genome provided a valuable platform to explore the pathogenic potential and necrotrophic lifestyle of the guava wilt pathogen.

The taxonomy of the Blue-crowned Laughingthrush (Garrulax courtoisi) and its relationship with the Yellow-throated Laughingthrush (G. galbanus) and G. c. simaoensis, a range-restricted subspecies in China, has not been fully elucidated. So the taxonomic status and system evolution of the three taxa G. courtoisi, G. galbanus and G. c. simaoensis need to be reclarified. Two gene sequences myoglobin (MYO) and the mitochondrial cytochrome coxidase subunit I (COI) were combined to investigate the phylogenetic relationships among courtoisi, simaoensis and galbanus, genetic data, combining with morphological, ecological and acoustic data were used to comb out the classification status and divergence level of the three taxa. Significant genetic and morphological differentiations (body size and plumage coloration) were detected between courtoisi and galbanus. However, no notable and reliable differences between the courtoisi and simaoensis were detected. The courtoisi, simaoensis and galbanus are clearly isolated in geographical distribution as a result of differing altitudes, climate conditions and habitats. The courtoisi has characteristic preference for nest location compared with galbanus. In addition, the results of song analysis also indicated that there are differences in maximum frequency between courtoisi and galbanus. G. courtoisi was confirmed to be an independent species based on genetic, morphological, geographical, ecological and vocal characteristics, and the validity of simaoensis as a subspecies still need more evidence. This study further confirmed the high conservation value of Blue-crowned Laughingthrush. In addition, due to the genetic differences between Simao and Wuyuan populations, this should be fully considered in future protection strategies.

Antioxidant (AOX) capacity assays are important analytical tools, used worldwide to measure the AOX capacities of various food commodities. Although numerous protocols have been published to ascertain AOX capacities, there are increasing concerns about the reliability of many of these assays. Poor correlation of results between various assays, as well as problems with reproducibility, consistency, and accuracy, is to blame. Published AOX assays also differ markedly from each other by employing different reaction conditions, using different extracting solvents, and applying dissimilar quantification methods. In this study, AOX capacities of a range of fruit, vegetables, and spices, commonly consumed and of commercial importance in Australia and worldwide, were measured in both hydrophilic and lipophilic solvents by using two different assay systems. As the polyphenolic compounds present in any sample matrix are the main contributors to its AOX properties, the commodities were also analysed for total phenolic content (TPC), again using both solvent systems. Analysis of the results from the current study with values from the published literature exposed the challenges that make direct comparison of any quantitative results difficult. However, a strong mutual correlation of our assay results facilitated a meaningful comparison of the data within the laboratory. Concurrent use of lipophilic and hydrophilic solvents made the results more reliable and understandable. Findings from this study will aid to address the existing challenges and bring a more rational basis to the AOX capacities. This unique analytical approach also provided a platform to build an internal reference database for the commonly consumed and commercially important food commodities with the potential to broaden the scope into a database for similar food matrices.