Rhodes grass (Chloris gayana) is a warm-season C4 grass currently grown as a forage in some tropical regions, with anticipated future application in areas affected by climate change. However, there are few resources available for this grass, with few genomic resources and only one established transformation system, thus limiting the application of biotechnology methods for its improvement. Protoplast transformation can be used as a time- and resource-efficient way to examine gene pathways and functions of transcriptional elements via proteomics and transcriptomics, and to validate gene constructs. The aim of this work was therefore to establish the first Rhodes grass leaf mesophyll protoplast isolation and transient transformation protocol. A range of protoplast isolation factors were examined, including enzyme quantity and vacuum infiltration time. Up to 4.13 × 106 protoplasts were isolated per gram of fresh weight with an average 94.8

Cannabis sativa L. is cultivated for therapeutic and recreational use. Delta-9 tetrahydrocannabinol (THC) and cannabidiol (CBD) are primarily responsible for its psychoactive and medicinal effects. As the global cannabis industry continues to expand, constant review and optimization of horticultural practices are needed to ensure a reliable harvest and improved crop quality. There is currently uncertainty about the optimal harvest time of C. sativa, i.e., when cannabinoid concentrations are at their highest during inflorescence maturation. At present, growers observe the color transition of stigmas from white to amber as an indicator of harvest time. This research investigates the relationship between stigma color and cannabinoid concentration using liquid chromatography–mass spectrometry (LCMS) and digital image analysis. Additionally, early screening prediction models have also been developed for six cannabinoids using near-infrared (NIR) spectroscopy and LCMS to assist in early cannabinoid determination. Among the genotypes grown, 22 of 25 showed cannabinoid concentration peaks between the third (mostly amber) and fourth (fully amber) stages; however, some genotypes peaked within the first (no amber) and second (some amber) stages. We have determined that the current ‘rule of thumb’ of harvesting when a cannabis plant is mostly amber is still a useful approximation in most cases; however, studies on individual genotypes should be performed to determine their individual optimal harvest time based on the desired cannabinoid profile or total cannabinoid concentration.

There is a lack of genotype imputation software tailored specifically for polyploids and pooled samples without phased haplotype information and reference panel of genotypes. Numerous important crops are polyploids, while pool sequencing is an emerging cost-effective approach for the genomic characterisation of breeding populations, families, and other segregating lines. The scarcity of imputation tools for these datasets results in the reliance on diploid-specific software, potentially leading to suboptimal outcomes. This necessitates the development of allele frequency imputation tools which accommodate the unique computational challenges presented by polyploid genomes and pooled sequencing data. We developed imputef, an allele frequency imputation tool for polyploid individuals and pooled samples lacking the rich genomic information typically available to model species. Missing allele frequencies are imputed using the genetic distance-weighted mean of non-missing allele frequencies from k-nearest neighbours. Genetic distance is estimated as the mean absolute difference in allele frequencies across linked loci, with linkage estimated as Pearson’s correlation between loci. The minimum loci correlation and maximum genetic distance thresholds can be optimised per locus to minimise imputation error. Imputef using the default parameters (minimum loci correlation, maximum genetic distance, minimum number of linked loci, and minimum number of nearest neighbours set to 0.9, 0.1, 20, and 5, respectively), generally outperformed mean value imputation and performed well across the range of sparsity levels (1

Psychoactive drugs are compounds that alter the function of the central nervous system, resulting in changes in perception, mood, cognition, and behavior. A subclass of psychoactive drugs, psychedelics, are hallucinogenic drugs that can trigger psychedelic experiences and possible changes in mental perception. The potential use of psychedelics as a therapeutic has led to an increase in clinical research focusing on the treatment of mental disorders including anxiety and depression. There are numerous species belonging to Psychotria and Banisteriopsis which have been reported to contain psychedelic and psychoactive compounds; however, there is a lack of validated analytical methods for raw plant material, which is crucial if these plants are to be commercially cultivated for medicines. This study provides a fully validated method using ultra-high performance liquid chromatography (UHPLC) coupled to mass spectrometry (MS) for the following six compounds: tryptamine, N,N-dimethyltryptamine (DMT), 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT), tetrahydroharmine (THH), harmaline, and harmine. The validated method was used to determine the psychoactive concentrations in Psychotria viridis, Psychotria carthagenensis, Banisteriopsis caapi, and Alicia anisopetala. Validation parameters were established; linearity (R2 = 0.988–0.999), limit of detection (LOD) (0.06–0.11 ng/mL), limit of quantitation (LOQ) (0.18–0.34 ng/mL), accuracy, precision, extraction efficiency (>98%), recovery (74.1–111.6%), and matrix effect (70.6–109%) were all evaluated. All six compounds eluted within nine minutes, with a total analysis time of 20 min including column equilibration. This method establishes a high-throughput method for the robust analysis of psychedelics which may see future use in agricultural research and industry.

Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography–mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II–Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4–100% accuracy prediction), the handheld device also performed well (83.1–100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material.

Ross River virus (RRV) is Australia’s most common and widespread mosquito-transmitted arbovirus and is of significant public health concern. With increasing anthropogenic impacts on wildlife and mosquito populations, it is important that we understand how RRV circulates in its endemic hotspots to determine where public health efforts should be directed. Current surveillance methods are effective in locating the virus but do not provide data on the circulation of the virus and its strains within the environment. This study examined the ability to identify single nucleotide polymorphisms (SNPs) within the variable E2/E3 region by generating full-length haplotypes from a range of mosquito trap-derived samples. A novel tiled primer amplification workflow for amplifying RRV was developed with analysis using Oxford Nanopore Technology’s MinION and a custom ARTIC/InterARTIC bioinformatic protocol. By creating a range of amplicons across the whole genome, fine-scale SNP analysis was enabled by specifically targeting the variable region that was amplified as a single fragment and established haplotypes that informed spatial-temporal variation of RRV in the study site in Victoria. A bioinformatic and laboratory pipeline was successfully designed and implemented on mosquito whole trap homogenates. Resulting data showed that genotyping could be conducted in real time and that whole trap consensus of the viruses (with major SNPs) could be determined in a timely manner. Minor variants were successfully detected from the variable E2/E3 region of RRV, which allowed haplotype determination within complex mosquito homogenate samples. The novel bioinformatic and wet laboratory methods developed here will enable fast detection and characterisation of RRV isolates. The concepts presented in this body of work are transferable to other viruses that exist as quasispecies in samples. The ability to detect minor SNPs, and thus haplotype strains, is critically important for understanding the epidemiology of viruses their natural environment.

Late blight caused by Phytophthora infestans is the most destructive disease of potatoes worldwide and is most notable as the cause of the Irish potato famine of the mid 1840’s. Whole mitochondrial genome sequences were generated from 44 Australian, two UK and one US isolates of P. infestans stored as either herbarium specimens or as recent samples on FTA cards, spanning the period 1873–2019. Mitochondrial (mt) genome sequence comparison confirmed that over the past c. 110 years, an old A1 strain of P. infestans has been present in Australia. There was evidence, however, that P. infestans had been introduced on multiple separate occasions in the early 1900s prior to the introduction of quarantine measures. The 44 Australian samples included six infected specimens of the Australian native kangaroo apple ( Solanum aviculare ) collected in 1911. The P. infestans mt genome sequences from these isolates clustered with P. infestans from infected potatoes collected in Victoria (1910, 1911, 1986, 1989), South Australia (1909, 2003) and Tasmania (1910, 2019), supporting the hypothesis that Australia has one old strain that has survived since the first arrival of the pathogen into the country. The study highlights the success of Australia’s potato biosecurity setting and supports the need for ongoing surveillance and biosecurity measures to prevent the introduction of the newer more aggressive strains of the pathogen.

Maintaining specific and reproducible cannabinoid compositions (type and quantity) is essential for the production of cannabis-based remedies that are therapeutically effective. The current study investigates factors that determine the plant’s cannabinoid profile and examines interrelationships between plant features (growth rate, phenology and biomass), inflorescence morphology (size, shape and distribution) and cannabinoid content. An examination of differences in cannabinoid profile within genotypes revealed that across the cultivation facility, cannabinoids’ qualitative traits (ratios between cannabinoid quantities) remain fairly stable, while quantitative traits (the absolute amount of Δ9-tetrahydrocannabinol (THC), cannabidiol (CBD), cannabichromene (CBC), cannabigerol (CBG), Δ9-tetrahydrocannabivarin (THCV) and cannabidivarin (CBDV)) can significantly vary. The calculated broad-sense heritability values imply that cannabinoid composition will have a strong response to selection in comparison to the morphological and phenological traits of the plant and its inflorescences. Moreover, it is proposed that selection in favour of a vigorous growth rate, high-stature plants and wide inflorescences is expected to increase overall cannabinoid production. Finally, a range of physiological and phenological features was utilised for generating a successful model for the prediction of cannabinoid production. The holistic approach presented in the current study provides a better understanding of the interaction between the key features of the cannabis plant and facilitates the production of advanced plant-based medicinal substances.

Breeding target traits can be broadened to include nutritive value and plant breeder’s rights traits in perennial ryegrass by using in-field regression-based spectroscopy phenotyping and genomic selection. Perennial ryegrass breeding has focused on biomass yield, but expansion into a broader set of traits is needed to benefit livestock industries whilst also providing support for intellectual property protection of cultivars. Numerous breeding objectives can be targeted simultaneously with the development of sensor-based phenomics and genomic selection (GS). Of particular interest are nutritive value (NV), which has been difficult and expensive to measure using traditional phenotyping methods, resulting in limited genetic improvement to date, and traits required to obtain varietal protection, known as plant breeder’s rights (PBR) traits. In order to assess phenotyping requirements for NV improvement and potential for genetic improvement, in-field reflectance-based spectroscopy was assessed and GS evaluated in a single population for three key NV traits, captured across four timepoints. Using three prediction approaches, the possibility of targeting PBR traits using GS was evaluated for five traits recorded across three years of a breeding program. Prediction accuracy was generally low to moderate for NV traits and moderate to high for PBR traits, with heritability highly correlated with GS accuracy. NV did not show significant or consistent correlation between timepoints highlighting the need to incorporate seasonal NV into selection indexes and the value of being able to regularly monitor NV across seasons. This study has demonstrated the ability to implement GS for both NV and PBR traits in perennial ryegrass, facilitating the expansion of ryegrass breeding targets to agronomically relevant traits while ensuring necessary varietal protection is achieved.

Genomic resources for grasses, especially warm-season grasses are limited despite their commercial and environmental importance. Here, we report the first annotated draft whole genome sequence for diploid Rhodes grass (Chloris gayana), a tropical C4 species. Generated using long read nanopore sequencing and assembled using the Flye software package, the assembled genome is 603 Mbp in size and comprises 5,233 fragments that were annotated using the GenSas pipeline. The annotated genome has 46,087 predicted genes corresponding to 92.0% of the expected genomic content present via BUSCO analysis. Gene ontology terms and repetitive elements are identified and discussed. An additional 94 individual plant genotypes originating from three diploid and two tetraploid Rhodes grass cultivars were short-read whole genome resequenced (WGR) to generate a single nucleotide polymorphism (SNP) resource for the species that can be used to elucidate inter- and intra-cultivar relationships across both ploidy levels. A total of 75,777 high quality SNPs were used to generate a phylogenetic tree, highlighting the diversity present within the cultivars which agreed with the known breeding history. Differentiation was observed between diploid and tetraploid cultivars. The WGR data were also used to provide insights into the nature and evolution of the tetraploid status of the species, with results largely agreeing with the published literature that the tetraploids are autotetraploid.

Genotype-by-environment interaction (G×E) is commonly observed in perennial ryegrass species and significantly impacts dry matter yield (DMY) performance across environments. Variation in DMY of perennial ryegrass cultivars and their distinct responses to different environments directly influence the profitability of the Australian dairy industry. In this study, we implemented a separate two-way factor analytic strategy to fit multi-environment-multi-harvest data collected from 18 trials spanning 14 years to account for the G×E effects across complex pasture environments in south-eastern Australia to achieve accurate DMY predictions. Three mega-environments were identified, and the seasonal and environmental DMYs of 126 cultivars or breeding lines (defined by a combination of varieties and endophytes) were predicted. Statistical differences in DMY performance and crossover G×E effects in cultivar rankings were observed among the three mega-environments. Several high-yielding cultivars specific to each mega-environment were also identified. The analysis demonstrated the importance of accounting for G×E effects when predicting the DMY of perennial ryegrass and highlighted the potential for identifying high-yielding cultivars specific to mega-environments. The research rationalizes implementing an appropriate independent pasture trialling system to generate necessary data for the dairy industry and could further facilitate dairy farmers to select suitable cultivars based on their specific environments and perennial ryegrass breeders when accounting for the genetic relationship of cultivars.

The spotted wing drosophila (Drosophila suzukii, Matsumara) is a rapidly spreading global pest of soft and stone fruit production. Due to the similarity of many of its life stages to other cosmopolitan drosophilids, surveillance for this pest is currently bottlenecked by the laborious sorting and morphological identification of large mixed trap catches. DNA metabarcoding presents an alternative high-throughput sequencing (HTS) approach for multi-species identification, which may lend itself ideally to rapid and scalable diagnostics of D. suzukii within unsorted trap samples. In this study, we compared the qualitative (identification accuracy) and quantitative (bias toward each species) performance of four metabarcoding primer pairs on D. suzukii and its close relatives. We then determined the sensitivity of a non-destructive metabarcoding assay (i.e., which retains intact specimens) by spiking whole specimens of target species into mock communities of increasing specimen number, as well as 29 field-sampled communities from a cherry and a stone fruit orchard. Metabarcoding successfully detected D. suzukii and its close relatives Drosophila subpulchrella and Drosophila biarmipes in the spiked communities with an accuracy of 96, 100, and 100% respectively, and identified a further 57 non-target arthropods collected as bycatch by D. suzukii surveillance methods in a field scenario. While the non-destructive DNA extraction retained intact voucher specimens, dropouts of single species and entire technical replicates suggests that these protocols behave more similarly to environmental DNA than homogenized tissue metabarcoding and may require increased technical replication to reliably detect low-abundance taxa. Adoption of high-throughput metabarcoding assays for screening bulk trap samples could enable a substantial increase in the geographic scale and intensity of D. suzukii surveillance, and thus likelihood of detecting a new introduction. Trap designs and surveillance protocols will, however, need to be optimized to adequately preserve specimen DNA for molecular identification.

Aim We aim to describe differences in stroke risk factors, subtypes and outcomes in a multi-ethnic Irish Stroke population. Gaining an insight into prevalent risk factors and subtypes in ethnic groups may help target prevention efforts. Methods We retrospectively identified patients originally not of Irish ethnicity (ONIE) admitted to the acute stroke unit between 2016 and 2018 through surname recognition (N=44). Country of origin was confirmed on chart review. The presumed native Irish (PNI) patients admitted over the same time frame were used as a comparison group (N=437). Data was collected on stroke subtype, comorbidities, outcomes and socioeconomic factors. Results Patients ONIE made up 9.1% of all stroke unit admissions. Male gender was more common accounting for 33 of 44 (75%) patients ONIE and 251 of 437 (57.4%) PNI (p = 0.02). Overall ONIE were younger than PNI patients (mean age 57.5 [SD 13.0] vs 69.6yr [SD 13.2], p <0.001). Patients ONIE also recorded higher rates of intracranial haemorrhage(ICH) (N = 15 [34.1%] vs N=51 [11.7%], p <0.01). Conclusion Our study demonstrates that stroke patients ONIE have a different stroke subtype and demographic profile compared to Irish patients. Patients ONIE are more likely to be young, male with higher rates of ICH.

Paspalum dilatatum (common name dallisgrass), a productive C4 grass native to South America, is an important pasture grass found throughout the temperate warm regions of the world. It is characterized by its tolerance to frost and water stress and a higher forage quality than other C4 forage grasses. P. dilatatum includes tetraploid (2 n = 40), sexual, and pentaploid (2 n = 50) apomictic forms, but is predominantly cultivated in an apomictic monoculture, which implies a high risk that biotic and abiotic stresses could seriously affect the grass productivity. The obtention of reproducible and efficient protocols of regeneration and transformation are valuable tools to obtain genetic modified grasses with improved agronomics traits. In this review, we present the current regeneration and transformation methods of both apomictic and sexual cultivars of P. dilatatum , discuss their strengths and limitations, and focus on the perspectives of genetic modification for producing new generation of forages. The advances in this area of research lead us to consider Paspalum dilatatum as a model species for the molecular improvement of C4 perennial forage species.

Heterosis is defined as increased performance of the F1 hybrid relative to its parents. In the current study, a cohort of populations and parents were created to evaluate and understand heterosis across generations (i.e., F1 to F3) in lentil, a self-pollinated annual diploid (2n = 2× = 14) crop species. Lentil plants were evaluated for heterotic traits in terms of plant height, biomass fresh weight, seed number, yield per plant and 100 grain weight. A total of 47 selected lentil genotypes were cross hybridized to generate 72 F1 hybrids. The F1 hybrids from the top five crosses exhibited between 31%-62% heterosis for seed number with reference to the better parent. The five best performing heterotic crosses were selected with a negative control for evaluation at the subsequent F2 generation and only the tails of the distribution taken forward to be assessed in the F3 generation as a sub selection. Overall, heterosis decreases across the subsequent generations for all traits studied. However, some individual genotypes were identified at the F2 and sub-selected F3 generations with higher levels of heterosis than the best F1 mean value (hybrid mimics). The phenotypic data for the selected F2 and sub selected F3 hybrids were analysed, and the study suggested that 100 grain weight was the biggest driver of yield followed by seed number. A genetic diversity analysis of all the F1 parents failed to correlate genetic distance and divergence among parents with heterotic F1's. Therefore, genetic distance was not a key factor to determine heterosis in lentil. The study highlights the challenges associated with different breeding systems for heterosis (i.e., F1 hybrid-based breeding systems and/or via hybrid mimics) but demonstrates the potential significant gains that could be achieved in lentil productivity.

Abstract Background The Irish healthcare system faces the same challenges presented to healthcare systems worldwide, ageing populations and the increasing disease burden from chronic conditions. There is a need to respond to and meet these challenges placed on an already pressurized healthcare systems originally set up to respond to acute, episodic care. It would be beneficial to review reasons for unplanned readmissions post stroke to examine whether these readmissions could be predicted, targeted and prevented and whether there are individuals who could be targeted for intervention post hospital discharge, to reduce readmission rates post stroke. Methods Using data from the Hospital In-Patient Enquiry (HIPE) patients were identified who had been discharged with a primary diagnosis of stroke. Data was then examined to identify whether these patients had acute hospital readmissions in the 18-month period following discharge for stroke. Using HIPE and patient discharge letters we identified the reasons for these readmissions and categorized them accordingly. Results A total of 224 live stroke discharges were identified from 2018. 77 patients were readmitted within 18 months of discharge. In total these 77 patients accounted for 139 admissions. Of the 77 patients that were readmitted average Length of Stay (LOS) was 19 days. Of the 77, 12 were due to recurrent stroke while 49 presented with a new complaint that fell into one of the following four categories, pneumonia, falls/fractures, dementia related, medical complications/medication related Conclusion There is a need to identify the unmet needs of stroke patients post hospital discharge so as to reduce the number of readmissions. This would involve screening to identify those suitable to attend a specialist multidisciplinary six-month review clinic where these patients could have onward referral to the appropriate in hospital and community services including intensive dysphagia clinics, therapy-lead spasticity clinic, upper limb rehabilitation programmes, and medical social work support for unmet needs with links to the community.

In recent decades with the reacknowledgment of the medicinal properties of Cannabis sativa L. (cannabis) plants, there is an increased demand for high performing cultivars that can deliver quality products for various applications. However, scientific knowledge that can facilitate the generation of advanced cannabis cultivars is scarce. In order to improve cannabis breeding and optimize cultivation techniques, the current study aimed to examine the morphological attributes of cannabis inflorescences using novel image analysis practices. The investigated plant population comprises 478 plants ascribed to 119 genotypes of high-THC or blended THC-CBD ratio that was cultivated under a controlled environment facility. Following harvest, all plants were manually processed and an image of the trimmed and refined inflorescences extracted from each plant was captured. Image analysis was then performed using in-house custom-made software which extracted 8 morphological features (such as size, shape and perimeter) for each of the 127,000 extracted inflorescences. Our findings suggest that environmental factors play an important role in the determination of inflorescences' morphology. Therefore, further studies that focus on genotype X environment interactions are required in order to generate inflorescences with desired characteristics. An examination of the intra-plant inflorescences weight distribution revealed that processing 75% of the plant's largest inflorescences will gain 90% of its overall yield weight. Therefore, for the optimization of post-harvest tasks, it is suggested to evaluate if the benefits from extracting and processing the plant's smaller inflorescences outweigh its operational costs. To advance selection efficacy for breeding purposes, a prediction equation for forecasting the plant's production biomass through width measurements of specific inflorescences, formed under the current experimental methodology, was generated. Thus, it is anticipated that findings from the current study will contribute to the field of medicinal cannabis by improving targeted breeding programs, advancing crop productivity and enhancing the efficacy of post-harvest procedures.

Outbreaks of avian influenza virus (AIV) from wild waterfowl into the poultry industry is of upmost significance and is an ongoing and constant threat to the industry. Accurate surveillance of AIV in wild waterfowl is critical in understanding viral diversity in the natural reservoir. Current surveillance methods for AIV involve collection of samples and transportation to a laboratory for molecular diagnostics. Processing of samples using this approach takes more than three days and may limit testing locations to those with practical access to laboratories. In potential outbreak situations, response times are critical, and delays have implications in terms of the spread of the virus that leads to increased economic cost. This study used nanopore sequencing technology for in-field sequencing and subtype characterisation of AIV strains collected from wild bird faeces and poultry. A custom in-field virus screening and sequencing protocol, including a targeted offline bioinformatic pipeline, was developed to accurately subtype AIV. Due to the lack of optimal diagnostic MinION packages for Australian AIV strains the bioinformatic pipeline was specifically targeted to confidently subtype local strains. The method presented eliminates the transportation of samples, dependence on internet access and delivers critical diagnostic information in a timely manner.

Soil salinity is a major abiotic stress, limiting lentil productivity worldwide. Understanding the genetic basis of salt tolerance is vital to develop tolerant varieties. A diversity panel consisting of 276 lentil accessions was screened in a previous study through traditional and image-based approaches to quantify growth under salt stress. Genotyping was performed using two contrasting methods, targeted (tGBS) and transcriptome (GBS-t) genotyping-by-sequencing, to evaluate the most appropriate methodology. tGBS revealed the highest number of single-base variants (SNPs) (c. 56,349), and markers were more evenly distributed across the genome compared to GBS-t. A genome-wide association study (GWAS) was conducted using a mixed linear model. Significant marker-trait associations were observed on Chromosome 2 as well as Chromosome 4, and a range of candidate genes was identified from the reference genome, the most plausible being potassium transporters, which are known to be involved in salt tolerance in related species. Detailed mineral composition performed on salt-treated and control plant tissues revealed the salt tolerance mechanism in lentil, in which tolerant accessions do not transport Na+ ions around the plant instead localize within the root tissues. The pedigree analysis identified two parental accessions that could have been the key sources of tolerance in this dataset.

Abstract Background Neuro-medical complications post-stroke are common and often serious [1]. We first described complications in our stroke cohort in 1998 and sought to assess whether the severity and the nature of neuro-medical complications may have changed over time due to changes in presentation and the processes of care [2]. Methods Analysis of stroke service database, which captures all neuro-medical complications as part of its portal for the Irish National Audit of Stroke (INAS), was completed. The frequency of each of the 19 complications was expressed as the percentage of patients that developed each complication over a certain year and over 5 years. Historical comparison was made with dataset from 1998, which captured six complications. Results Data on 1,283 patients presenting over 5 years between 2015–2019 was collected. The median age of all patients was 71 years (Range 21–101). In all, 19 different post-stroke complications were recorded; 48% (n = 622) had post-stroke pain, while 23.85% (n = 306) had cognitive decline. Data on 100 patients from 1998 was compared for a number of common metrics including; 21.82% (n = 275) of patients developed an LRTI in the 2015–2019 cohort compared with 14%(n = 14) in the 1998 cohort (p = 0.09) while 16.29% (n = 209) of patients developed a swallow disorder compared to 21% (n = 21) in 1998 (p = 0.22). Conclusion There are high levels of neuro-medical complications in stroke patients. Twenty years has seen extensive investment in hyperacute stroke care yet post-acute care complications did not appear to reduce significantly between this time, albeit with low numbers. Direction of future funding may consider the full spectrum of stroke care.