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Summary It has been hypothesised that vegetative desiccation tolerance in resurrection plants evolved via reactivation of the canonical LAFL (i.e. LEC1, ABI3, FUS3 and LEC2) transcription factor (TF) network that activates the expression of genes during the maturation of orthodox seeds leading to desiccation tolerance of the plant embryo in most angiosperms. There is little direct evidence to support this, however, and the transcriptional changes that occur during seed maturation in resurrection plants have not previously been studied. Here we performed de novo transcriptome assembly for Xerophyta humilis, and analysed gene expression during seed maturation and vegetative desiccation. Our results indicate that differential expression of a set of 4205 genes is common to maturing seeds and desiccating leaves. This shared set of genes is enriched for gene ontology terms related to abiotic stress, including water stress and abscisic acid signalling, and includes many genes that are seed‐specific in Arabidopsis thaliana and targets of ABI3. However, while we observed upregulation of orthologues of the canonical LAFL TFs and ABI5 during seed maturation, similar to what is seen in A. thaliana, this did not occur during desiccation of leaf tissue. Thus, reactivation of components of the seed desiccation program in X. humilis vegetative tissues likely involves alternative transcriptional regulators.

Background Stratified or personalised medicine targets treatments for groups of individuals with a disorder based on individual heterogeneity and shared factors that influence the likelihood of response. Psychiatry has traditionally defined diagnoses by constellations of co-occurring signs and symptoms that are assigned a categorical label (e.g. schizophrenia). Trial methodology in psychiatry has evaluated interventions targeted at these categorical entities, with diagnoses being equated to disorders. Recent insights into both the nosology and neurobiology of psychiatric disorder reveal that traditional categorical diagnoses cannot be equated with disorders. We argue that current quantitative methodology (1) inherits these categorical assumptions, (2) allows only for the discovery of average treatment response, (3) relies on composite outcome measures and (4) sacrifices valuable predictive information for stratified and personalised treatment in psychiatry.Methods and findingsTo achieve a truly ‘stratified psychiatry’ we propose and then operationalise two necessary steps: first, a formal multi-dimensional representation of disorder definition and clinical state, and second, the similar redefinition of outcomes as multidimensional constructs that can expose within- and between-patient differences in response. We use the categorical diagnosis of schizophrenia—conceptualised as a label for heterogeneous disorders—as a means of introducing operational definitions of stratified psychiatry using principles from multivariate analysis. We demonstrate this framework by application to the Clinical Antipsychotic Trials of Intervention Effectiveness dataset, showing heterogeneity in both patient clinical states and their trajectories after treatment that are lost in the traditional categorical approach with composite outcomes. We then systematically review a decade of registered clinical trials for cognitive deficits in schizophrenia highlighting existing assumptions of categorical diagnoses and aggregate outcomes while identifying a small number of trials that could be reanalysed using our proposal.ConclusionWe describe quantitative methods for the development of a multi-dimensional model of clinical state, disorders and trajectories which practically realises stratified psychiatry. We highlight the potential for recovering existing trial data, the implications for stratified psychiatry in trial design and clinical treatment and finally, describe different kinds of probabilistic reasoning tools necessary to implement stratification.

Self-contamination during doffing of personal protective equipment (PPE) is a concern for healthcare workers (HCW) following SARS-CoV-2 positive patient care. Staff may subconsciously become contaminated through improper glove removal, so quantifying this risk is critical for safe working procedures. HCW surface contact sequences on a respiratory ward were modelled using a discrete-time Markov chin for: IV-drip care, blood pressure monitoring and doctors' rounds. Accretion of viral RNA on gloves during care was modelled using a stochastic recurrence relation. The HCW then doffed PPE and contaminated themselves in a fraction of cases based on increasing case load. The risk of infection from this exposure was quantified using a dose-response methodology. A parametric study was conducted to analyse the effect of: 1a) increasing patient numbers on the ward, 1b) the proportion of COVID-19 cases, 2) the length of a shift and 3) the probability of touching contaminated PPE. The driving factors for infection risk were surface contamination and number of surface contacts. HCWs on a 100% COVID-19 ward were less than 2-fold more at risk than on a 50% COVID ward (1.6% vs 1%), whilst on a 5% COVID-19 ward, the risk dropped to 0.1% per shift (sd=0.6%). IV-drip care resulted in higher risk than blood pressure monitoring (1.1% vs 1% p<0.0001), whilst doctors' rounds produced a 0.6% risk (sd=0.8%). Recommendations include supervised PPE doffing procedures such as the "doffing buddy" scheme, maximising hand hygiene compliance post-doffing and targeted surface cleaning for surfaces away from the patient vicinity.

Although porcine reproductive and respiratory syndrome virus (PRRSV) vaccines have been available in North America for almost 30 years, many vaccines face a significant hurdle: they must provide cross-protection against the highly diverse PRRSV strains. This cross-protection, or heterologous vaccine efficacy, relies greatly on the vaccine’s ability to induce a strong immune response against various strains—heterologous immunogenicity. Thus, this study investigated vaccine efficacy and immunogenicity of a modified live virus (MLV) against four heterologous type 2 PRRSV (PRRSV-2) strains. In this study, 60 pigs were divided into 10 groups. Half were MOCK-vaccinated, and the other half vaccinated with the Prevacent® PRRS MLV vaccine. Four weeks after vaccination, groups were challenged with either MOCK, or four PRRSV-2 strains from three different lineages—NC174 or NADC30 (both lineage 1), VR2332 (lineage 5), or NADC20 (lineage 8). Pre-and post-challenge, lung pathology, viral loads in both nasal swabs and sera, anti-PRRSV IgA/G, neutralizing antibodies, and the PRRSV-2 strain-specific T-cell response were evaluated. At necropsy, the lung samples were collected to assess viral loads, macroscopical and histopathological findings, and IgA levels in bronchoalveolar lavage. Lung lesions were only induced by NC174, NADC20, and NADC30; within these, vaccination resulted in lower gross and microscopic lung lesion scores of the NADC20 and NADC30 strains. All pigs became viremic and vaccinated pigs had decreased viremia upon challenge with NADC20, NADC30, and VR2332. Regarding vaccine immunogenicity, vaccination induced a strong systemic IgG response and boosted the post-challenge serum IgG levels for all strains. Furthermore, vaccination increased the number of animals with neutralizing antibodies against three of the four challenge strains—NADC20, NADC30, and VR2332. The heterologous T-cell response was also improved by vaccination: Not only did vaccination increase the induction of heterologous effector/memory CD4 T cells, but it also improved the heterologous CD4 and CD8 proliferative and/or IFN-γ response against all strains. Importantly, correlation analyses revealed that the (non-PRRSV strain-specific) serum IgG levels and the PRRSV strain-specific CD4 T-cell response were the best immune correlates of protection. Overall, the Prevacent elicited various degrees of efficacy and immunogenicity against four heterologous and phylogenetically distant strains of PRRSV-2.

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Self‐contamination during doffing of personal protective equipment (PPE) is a concern for healthcare workers (HCW) following SARS‐CoV‐2‐positive patient care. Staff may subconsciously become contaminated through improper glove removal; so, quantifying this exposure is critical for safe working procedures. HCW surface contact sequences on a respiratory ward were modeled using a discrete‐time Markov chain for: IV‐drip care, blood pressure monitoring, and doctors’ rounds. Accretion of viral RNA on gloves during care was modeled using a stochastic recurrence relation. In the simulation, the HCW then doffed PPE and contaminated themselves in a fraction of cases based on increasing caseload. A parametric study was conducted to analyze the effect of: (1a) increasing patient numbers on the ward, (1b) the proportion of COVID‐19 cases, (2) the length of a shift, and (3) the probability of touching contaminated PPE. The driving factors for the exposure were surface contamination and the number of surface contacts. The results simulate generally low viral exposures in most of the scenarios considered including on 100% COVID‐19 positive wards, although this is where the highest self‐inoculated dose is likely to occur with median 0.0305 viruses (95% CI =0–0.6 viruses). Dose correlates highly with surface contamination showing that this can be a determining factor for the exposure. The infection risk resulting from the exposure is challenging to estimate, as it will be influenced by the factors such as virus variant and vaccination rates.

Healthcare professionals (HCPs) are exposed to highly infectious viruses, such as norovirus, through multiple exposure routes. Understanding exposure mechanisms will inform exposure mitigation interventions. The study objective was to evaluate the influences of hospital patient room layout on differences in HCPs' predicted hand contamination from deposited norovirus particles. Computational fluid dynamic (CFD) simulations of a hospital patient room were investigated to find differences in spatial deposition patterns of bioaerosols for right‐facing and left‐facing bed layouts under different ventilation conditions. A microbial transfer model underpinned by observed mock care for three care types (intravenous therapy (IV) care, observational care, and doctors' rounds) was applied to estimate HCP hand contamination. Viral accruement was contrasted between room orientation, care type, and by assumptions about whether bioaerosol deposition was the same or variable by room orientation. Differences in sequences of surface contacts were observed for care type and room orientation. Simulated viral accruement differences between room types were influenced by mostly by differences in bioaerosol deposition and by behavior sequences when deposition patterns for the room orientations were similar. Differences between care types were likely driven by differences in hand‐to‐patient contact frequency, with doctors' rounds resulting in the greatest predicted viral accruement on hands.