VERIFICATION AND VALIDATION OF CONSEQUENCE MODELS FOR ACCIDENTAL RELEASES OF TOXIC OR FLAMMABLE CHEMICALS TO THE ATMOSPHERE

PresentationConsequence modelling software for accidental releases of flammable or toxic chemicals to the atmosphere includes discharge modelling, atmospheric dispersion modelling and evaluation of flammable and toxic effects: - First discharge calculations are carried out to set release characteristics for the hazardous chemical (including depressurisation to ambient). Scenarios which may be modelled includes releases from vessels (leaks or catastrophic ruptures), short pipes or long pipes. Releases considered include releases of sub-cooled liquid, superheated liquid or vapour; un-pressurised or pressurised releases; and continuous, time-varying or instantaneous releases. - Secondly dispersion calculations are carried out to determine the concentrations of the hazardous chemical when the cloud travels in the downwind direction. This includes modelling jet, heavy-gas and passive dispersion regimes, and transitions between them. In the case of a two-phase release, liquid droplet modelling is required to calculate liquid rainout, subsequent pool formation/spreading and re-evaporation from the pool back to the cloud. For heavy-gas releases, effects of crosswind and downwind gravity spreading are present, while for short duration and time-varying releases effects of along-wind diffusion are relevant.. For pressurised instantaneous releases an initial phase of energetic expansion of the cloud occurs. Also, effects of indoor mixing (for indoor releases) and building wakes can be accounted for. - Finally, toxic or flammable calculations are carried out. For flammables, ignition may lead to rising fireballs (instantaneous releases), jet fires possibly impinging on the ground (pressurised flammable releases), pool fires (after rainout) and vapour cloud fires or explosions. Radiation calculations are carried out for fires, while overpressure calculations are carried out for explosions. For each event, the probability of death is determined using toxic or flammable probit functions. Testing of the software should ideally include for each consequence model “verification” that the code correctly solves the mathematical model (i.e. that the calculated variables are a correct solution of the equations), “validation” against experimental data to show how closely the mathematical model agrees with the experimental results, and a “sensitivity analysis” including a large number of input parameter variations to ensure overall robustness of the code, and to understand the effect of parameter variations on the model predictions. The current paper includes an overview on how the above verification and validation could be carried out for these consequence models. Reference is made to the literature for the availability of experimental data. Thus, an extensive experimental database has been developed including experimental data for validation for the above models and scenarios, where many different chemicals are considered (including water, LNG, propane, butane, ethylene, ammonia, CO2, hydrogen, chlorine, HF etc.). The above verification and validation is illustrated by means of application to the latest consequence models in the hazard assessment package Phast and the risk analysis package Safeti

Overexpression of 17β-hydroxysteroid dehydrogenase type 10 increases pheochromocytoma cell growth and resistance to cell death

Background: 17β-hydroxysteroid dehydrogenase type 10 (HSD10) has been shown to play a protective role in cells undergoing stress. Upregulation of HSD10 under nutrient-limiting conditions leads to recovery of a homeostatic state. Across disease states, increased HSD10 levels can have a profound and varied impact, such as beneficial in Parkinson’s disease and harmful in Alzheimer’s disease. Recently, HSD10 overexpression has been observed in some prostate and bone cancers, consistently correlating with poor patient prognosis. As the role of HSD10 in cancer remains underexplored, we propose that cancer cells utilize this enzyme to promote cancer cell survival under cell death conditions. Methods: The proliferative effect of HSD10 was examined in transfected pheochromocytoma cells by growth curve analysis and a xenograft model. Fluctuations in mitochondrial bioenergetics were evaluated by electron transport chain complex enzyme activity assays and energy production. Additionally, the effect of HSD10 on pheochromocytoma resistance to cell death was investigated using TUNEL staining, MTT, and complex IV enzyme activity assays. Results: In this study, we examined the tumor-promoting effect of HSD10 in pheochromocytoma cells. Overexpression of HSD10 increased pheochromocytoma cell growth in both in vitro cell culture and an in vivo xenograft mouse model. The increases in respiratory enzymes and energy generation observed in HSD10-overexpressing cells likely supported the accelerated growth rate observed. Furthermore, cells overexpressing HSD10 were more resistant to oxidative stress-induced perturbation. Conclusions: Our findings demonstrate that overexpression of HSD10 accelerates pheochromocytoma cell growth, enhances cell respiration, and increases cellular resistance to cell death induction. This suggests that blockade of HSD10 may halt and/or prevent cancer growth, thus providing a promising novel target for cancer patients as a screening or therapeutic option

Risk Prediction of Second Primary Malignancies in Primary Early-Stage Ovarian Cancer Survivors: A SEER-Based National Population-Based Cohort Study

Purpose: This study aimed to characterize the clinical features of early-stage ovarian cancer (OC) survivors with second primary malignancies (SPMs) and provided a prediction tool for individualized risk of developing SPMs. Methods: Data were obtained from the Surveillance, Epidemiology and End Results (SEER) database during 1998–2013. Considering non-SPM death as a competing event, the Fine and Gray model and the corresponding nomogram were used to identify the risk factors for SPMs and predict the SPM probabilities after the initial OC diagnosis. The decision curve analysis (DCA) was performed to evaluate the clinical utility of our proposed model. Results: A total of 14,314 qualified patients were enrolled. The diagnosis rate and the cumulative incidence of SPMs were 7.9% and 13.6% [95% confidence interval (CI) = 13.5% to 13.6%], respectively, during the median follow-up of 8.6 years. The multivariable competing risk analysis suggested that older age at initial cancer diagnosis, white race, epithelial histologic subtypes of OC (serous, endometrioid, mucinous, and Brenner tumor), number of lymph nodes examined (<12), and radiotherapy were significantly associated with an elevated SPM risk. The DCA revealed that the net benefit obtained by our proposed model was higher than the all-screening or no-screening scenarios within a wide range of risk thresholds (1% to 23%). Conclusion: The competing risk nomogram can be potentially helpful for assisting physicians in identifying patients with different risks of SPMs and scheduling risk-adapted clinical management. More comprehensive data on treatment regimens and patient characteristics may help improve the predictability of the risk model for SPMs

Photoperiod-responsive changes in chromatin accessibility in phloem companion and epidermis cells of Arabidopsis leaves

Photoperiod plays a key role in controlling the phase transition from vegetative to reproductive growth in flowering plants. Leaves are the major organs perceiving day-length signals, but how specific leaf cell types respond to photoperiod remains unknown. We integrated photoperiod-responsive chromatin accessibility and transcriptome data in leaf epidermis and vascular companion cells of Arabidopsis thaliana by combining isolation of nuclei tagged in specific cell/tissue types with assay for transposase-accessible chromatin using sequencing and RNA-sequencing. Despite a large overlap, vasculature and epidermis cells responded differently. Long-day predominantly induced accessible chromatin regions (ACRs); in the vasculature, more ACRs were induced and these were located at more distal gene regions, compared with the epidermis. Vascular ACRs induced by long days were highly enriched in binding sites for flowering-related transcription factors. Among the highly ranked genes (based on chromatin and expression signatures in the vasculature), we identified TREHALOSE-PHOSPHATASE/SYNTHASE 9 (TPS9) as a flowering activator, as shown by the late flowering phenotypes of T-DNA insertion mutants and transgenic lines with phloem-specific knockdown of TPS9. Our cell-type-specific analysis sheds light on how the long-day photoperiod stimulus impacts chromatin accessibility in a tissue-specific manner to regulate plant development

Combined linkage and association mapping reveals candidates for Scmv1, a major locus involved in resistance to sugarcane mosaic virus (SCMV) in maize

Background Sugarcane mosaic virus (SCMV) disease causes substantial losses of grain yield and forage biomass in susceptible maize cultivars. Maize resistance to SCMV is associated with two dominant genes, Scmv1 and Scmv2, which are located on the short arm of chromosome 6 and near the centromere region of chromosome 3, respectively. We combined both linkage and association mapping to identify positional candidate genes for Scmv1. Results Scmv1 was fine-mapped in a segregating population derived from near-isogenic lines and further validated and fine-mapped using two recombinant inbred line populations. The combined results assigned the Scmv1 locus to a 59.21-kb interval, and candidate genes within this region were predicted based on the publicly available B73 sequence. None of three predicted genes that are possibly involved in the disease resistance response are similar to receptor-like resistance genes. Candidate gene–based association mapping was conducted using a panel of 94 inbred lines with variable resistance to SCMV. A presence/absence variation (PAV) in the Scmv1 region and two polymorphic sites around the Zmtrx-h gene were significantly associated with SCMV resistance. Conclusion Combined linkage and association mapping pinpoints Zmtrx-h as the most likely positional candidate gene for Scmv1. These results pave the way towards cloning of Scmv1 and facilitate marker-assisted selection for potyvirus resistance in maize

Mendelian randomization and clinical trial evidence supports TYK2 inhibition as a therapeutic target for autoimmune diseases

Background: To explore the associations of genetically proxied TYK2 inhibition with a wide range of disease outcomes and biomarkers to identify therapeutic repurposing opportunities, adverse effects, and biomarkers of efficacy. Methods: The loss-of-function missense variant rs34536443 in TYK2 gene was used as a genetic instrument to proxy the effect of TYK2 inhibition. A phenome-wide Mendelian randomization (MR) study was conducted to explore the associations of genetically-proxied TYK2 inhibition with 1473 disease outcomes in UK Biobank (N = 339,197). Identified associations were examined for replication in FinnGen (N = 260,405). We further performed tissue -specific gene expression MR, colocalization analyses, and MR with 247 blood biomarkers. A systematic review of randomized controlled trials (RCTs) on TYK2 inhibitor was performed to complement the genetic evidence. Findings: PheWAS-MR found that genetically-proxied TYK2 inhibition was associated with lower risk of a wide range of autoimmune diseases. The associations with hypothyroidism and psoriasis were confirmed in MR analysis of tissue-specific TYK2 gene expression and the associations with systemic lupus erythematosus, psoriasis, and rheumatoid arthritis were observed in colocalization analysis. There were nominal associations of genetically-proxied TYK2 inhibition with increased risk of prostate and breast cancer but not in tissue-specific expression MR or colocalization analyses. Thirty-seven blood biomarkers were associated with the TYK2 loss-of-function mutation. Evidence from RCTs confirmed the effectiveness of TYK2 inhibitors on plaque psoriasis and reported several adverse effects. Interpretation: This study supports TYK2 inhibitor as a potential treatment for psoriasis and several other autoim-mune diseases. Increased pharmacovigilance is warranted in relation to the potential adverse effects.De två första författarna delar förstaförfattarskapet.De tre sista författarna delar sistaförfattarskapet.</p

Transgenic Expression of Glud1 (Glutamate Dehydrogenase 1) in Neurons: In Vivo Model of Enhanced Glutamate Release, Altered Synaptic Plasticity, and Selective Neuronal Vulnerability

This is the published version. Copyright 2009 Society for Neuroscience.The effects of lifelong, moderate excess release of glutamate (Glu) in the CNS have not been previously characterized. We created a transgenic (Tg) mouse model of lifelong excess synaptic Glu release in the CNS by introducing the gene for glutamate dehydrogenase 1 (Glud1) under the control of the neuron-specific enolase promoter. Glud1 is, potentially, an important enzyme in the pathway of Glu synthesis in nerve terminals. Increased levels of GLUD protein and activity in CNS neurons of hemizygous Tg mice were associated with increases in the in vivo release of Glu after neuronal depolarization in striatum and in the frequency and amplitude of miniature EPSCs in the CA1 region of the hippocampus. Despite overexpression of Glud1 in all neurons of the CNS, the Tg mice suffered neuronal losses in select brain regions (e.g., the CA1 but not the CA3 region). In vulnerable regions, Tg mice had decreases in MAP2A labeling of dendrites and in synaptophysin labeling of presynaptic terminals; the decreases in neuronal numbers and dendrite and presynaptic terminal labeling increased with advancing age. In addition, the Tg mice exhibited decreases in long-term potentiation of synaptic activity and in spine density in dendrites of CA1 neurons. Behaviorally, the Tg mice were significantly more resistant than wild-type mice to induction and duration of anesthesia produced by anesthetics that suppress Glu neurotransmission. The Glud1 mouse might be a useful model for the effects of lifelong excess synaptic Glu release on CNS neurons and for age-associated neurodegenerative processes