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High throughput cell cycle and morphological analysis of Leishmania mexicana and other kinetoplastids
No full textAnalysis of the cell cycle in kinetoplastid parasites involves the assessment of the replication of single copy organelles, such as the nucleus, kinetoplast, and flagellum, alongside the observation of cell cycle stage-associated morphological changes, e.g., cell shape changes and the appearance of a mitotic spindle or cytokinesis furrow, which together allow the cell cycle stage of individual parasites to be determined. To date, most kinetoplastid cell cycle analysis has been performed using light microscopy and/or flow cytometry of fixed cells, but while these methods have proven highly valuable, microscopy can be time-consuming and flow cytometry can lack resolution. We have previously shown that imaging flow cytometry offers significant benefits for depth and speed of analysis. This is due to its ability to directly link the high-throughput and quantitative nature of standard flow cytometry with the visual and spatial data of microscopy, over an extensive array of morphological and fluorescence parameters, which can be calculated for both brightfield and fluorescence images of each cell. Furthermore, the ability to automate image analysis ensures high throughput. Here, we provide a step-by-step guide to analyzing the cell cycle of live promastigote Leishmania mexicana using imaging flow cytometry. We outline a method for quantitative DNA staining in live L. mexicana promastigotes using Vybrant™ DyeCycle™ Orange and provide protocols, guidance, and example analysis templates for using an ImageStream®X MkII imaging flow cytometer (Cytek) to acquire and analyze brightfield and fluorescence images of the parasite to determine cell cycle stage. We also detail how to employ mNeonGreen tagging of the orphan spindle kinesin, KINF, to provide greater resolution of cell cycle position. Our automated masking and gating pipeline enables rapid, high-throughput and semi-automated analysis of the L. mexicana cell cycle in live cells, in near real time, offering many advantages over conventional analysis methods. In addition, we envisage that this pipeline could be adapted to allow similar high-throughput analysis of the cell cycle of other kinetoplastid species and outline the approaches that could be taken to achieve this
Machine learning approach to dissect the clinical heterogeneity of IBD-associated fatigue
Get PDFObjective: Extreme and persistent fatigue affects >50% of individuals with inflammatory bowel disease (IBD), with similar prevalence across many common immune- mediated inflammatory diseases (IMIDs). Despite its ubiquity, human scientific studies have yet to fully explain the mechanistic basis of this complex symptom. One fundamental reason is our inability to account for the clinical heterogeneity and multifactorial nature of fatigue.
Methods and analysis: We present the conceptual machine- learning (ML) framework to dissect fatigue using one of the largest prospectively captured, real- world patient- reported outcome (PROs) on well- being from three contemporaneous cohorts (2020–present), totalling 2970 responses from 2290 participants across the UK and internationally, including non- IBD controls with 100 lines of clinical metadata. In parallel, our patient public involvement group performed thematic analysis of this PRO dataset, which identified fatigue as a key research priority (www.musicstudy.uk).
Results: We systematically defined the (1) threshold of fatigue as our primary outcome (≥10/14 fatigue days in 1604 patients (1151 responses in active disease and 1061 responses in remission; some patients measured longitudinally; median fatigue days 14 vs 7, respectively; p<0.001) to build our ML approach, (2) used routinely available clinical data that can be used at a population- level analysis, (3) employed seven different ML methods with external validation in three different cohorts in the UK, Spain and Australia (n=252), (4) employed Shapley Additive Explanations (SHAP) analysis to break down clinical heterogeneity and allow the examination of clinical predictive factors at an individual level; and finally, (5) investigated whether there are distinct clusters of fatigue patients. We found that ML models performed comparably (area under the curve/C- index ~0.7) on external validation with SHAP analysis showing interpretable, individualised fatigue drivers and five distinct fatigue cluster groups, including a subgroup with lower fatigue burden.
Conclusions: Our data provide the ML ‘roadmap’ to predict and deconstruct fatigue in IBD and potentially more widely in IMIDs, enabling patient- level dissection beyond symptom- based classification with the ability to integrate deep molecular data. This is a step towards future clinical- scientific artificial intelligence models with immediate clinical application to stratify patients for human experimental studies to better identify patient- level patterns associated with fatigue
Dipstick proteinuria and outcomes in patients with heart failure and reduced ejection fraction: insights from GALACTIC-HF
No full textAims: Dipstick urine testing is often performed in primary and secondary care, although the results may not be routinely inspected or acted upon. We aimed to examine the prognostic value of semiquantitative urine dipstick proteinuria (DP) assessments in patients with heart failure (HF) and reduced ejection fraction. Methods: This retrospective analysis utilized data from GALACTIC-HF, a randomized trial that investigated the efficacy and safety of the cardiac myosin activator, omecamtiv mecarbil, compared with placebo in patients with HF with reduced ejection fraction. The primary outcome was the composite of a first HF event (hospitalization or urgent visit for HF) or cardiovascular death, and secondary outcomes were a HF event, cardiovascular death, and all-cause death. Cox proportional hazard models were used to examine the relationship between DP levels and clinical outcomes. Results: Baseline DP data were available for 7790 patients, of whom 5910 (75.9%) had a negative test or trace proteinuria, 995 (12.8%) had 1+, and 885 (11.4%) had ≥2+ proteinuria. The incidence rate of the primary outcome (per 100 person-years) increased significantly with increasing DP: negative/trace (21.8, 95% confidence interval 20.8–22.7); 1+ (34.8, 31.8–38.0); and ≥2+ (38.1, 34.7–41.9). Similar trends were observed for the components of the primary outcome and all-cause mortality. The association between greater DP and worse outcomes was stronger in patients with preserved (≥60 ml/min/1.73 m2) estimated glomerular filtration rate compared with reduced estimated glomerular filtration rate (<60 ml/min/1.73 m2). Conclusion: In GALACTIC-HF, higher DP levels were independently associated with increased risk of adverse clinical outcomes in patients with reduced ejection fraction
Enhancing flood prediction in the Lower Mekong River Basin by a scale-independent interpretable deep learning model
No full textClimate change has increased the frequency and intensity of extreme floods in the Lower Mekong River Basin (LMB). This study leverages the Long Short-Term Memory (LSTM) model to evaluate its performance in predicting river discharge across the LMB and to identify the key variables contributing to flood prediction through SHapley Additive exPlanation (SHAP) and Universal Multifractal (UM) analyses, in a scale-dependent and scale-independent manner, respectively. The performance of the LSTM model is satisfactory, with Nash–Sutcliffe Efficiency (NSE) values exceeding 0.9 for all subbasins when using all input features. The model tends to underestimate the largest peak flows in the midstream subbasins that experienced extreme rainfall events. According to SHAP, soil-related variables are important contributors to discharge prediction, with their impacts partially manifested through interactions with precipitation and runoff. Furthermore, the dominant contributing variables influencing flood prediction vary over time: soil-related variables and vegetation-related variables played a more significant role in earlier years, whereas hydrometeorological variables became more dominant after 2017. The UM analysis investigates the scaling behaviours of contributing variables, showing that hydrometeorological-related variables have a greater influence on predicting extreme discharge across the small temporal scales. Additionally, the UM analysis indicates that the model's performance improves as the temporal variability in extremes of the combined features decreases across 1 to 16 days. Overall, this study provides a comprehensive assessment of the LSTM model's performance in discharge prediction, emphasising the impact of the variability in the extremes of combined features through the scale-independent interpretation. These findings will offer valuable insights for stakeholders to improve flood risk management across the LMB
A multi-start aerodynamic shape optimisation approach via multi-fidelity neural networks
Get PDFThis paper presents a new approach for aerodynamic shape design and optimisation, combining high-fidelity gradient-based optimisation with multi-fidelity surrogate-based optimisation. It exploits the gradient-based optimisation history (both function values and gradients) to train gradient-enhanced multi-fidelity surrogate models, and uses the surrogate to indicate potentially better global solutions to restart the gradient-based local search. This multi-fidelity, multi-start (MFMS) approach retains the high efficiency of gradient-based methods in searching high-dimensional design spaces, and helps evade suboptimal local solutions via surrogates. A key novelty is the introduction of a multi-fidelity neural network (MFNN), which seamlessly fuses physical models, multi-fidelity data, and gradients, providing accurate surrogate predictions on very sparse samples. The proposed MFMS aerodynamic shape optimisation framework was verified and evaluated via benchmark supercritical aerofoil shape optimisation. The framework was further demonstrated using the HART II rotor optimisation case as part of the International Design Workshop on Rotor Blade Optimisation (InDeWo)
Glucocorticoids modulate expression of perineuronal net component genes and parvalbumin during development of mouse cortical neurons
No full textSevere prenatal maternal stress is a risk factor for schizophrenia in offspring. Since parvalbumin-containing GABAergic interneuron function in the cortex and hippocampus is compromised in schizophrenia, and perineuronal nets (PNNs) facilitate the functioning of these cells, we tested the hypothesis that glucocorticoids, as stress mediators that can access the foetal compartment, might influence the expression of PNN component genes. In cultured mouse cortical neurons, we detected effects of hydrocortisone on many PNN component genes, via diverse mechanisms. A rapid (< 4 h), glucocorticoid receptor (GR)-mediated suppression of neurocan and hyaluronan synthase (Has) 1 and 3 mRNAs was observed at 7 days in vitro (DIV), whereas at 14 DIV, brevican and versican expression was reduced by hydrocortisone without GR involvement, while GR inhibition elevated Has1 and Has2 mRNA levels and suppressed aggrecan mRNA levels. Tenascin R expression was rapidly suppressed by hydrocortisone at 7 DIV but not at 14. At 21 DIV, PNN component gene expression had become insensitive to hydrocortisone, although parvalbumin expression was reduced after 24 h but not 4 h exposure. Additionally, effects on protein levels were observed that were sometimes consistent with the mRNA changes (e.g. Has3, Gad1) and sometimes unrelated to them (e.g. elevated TnR levels at 7 DIV after glucocorticoid receptor antagonism). We found that hydrocortisone could directly inhibit proteasome activity. As expected from these results, the overall structure of the PNN was compromised by hydrocortisone exposure, with the length of the proximal dendrite covered by PNN being reduced. Overall, the data demonstrate a complex and profound, but developmental stage-dependent, regulation of PNN component gene expression by glucocorticoids. This may contribute to the action of severe prenatal or perinatal stress to increase schizophrenia risk
Coalition formation and firm representatives’ answers to complainers on social media: Their interplay and the coalition ripple effect
Get PDFWe ask whether complaint answers by firm representatives depend on coalition formation—others taking sides with complainers or firm representatives—and whether coalition formation by third actors depends on complaint answers. An online field study revealed that, from the firm representative perspective, the 73.2 % probability of a complaint answer in the absence of any coalition decreases to 10.9 %–12.8 % in the presence of a prior coalition with a firm representative or complainer. From the third actor perspective, the probability of the formation of a coalition with a firm representative decreases by one-third in the presence versus absence of a prior complaint answer; coalitions with complainers are not curtailed. Furthermore, a coalition with a firm representative shifts the average complaint answer from somewhat favorable to unfavorable, which facilitates coalitions with complainers, creating a coalition ripple effect. The results offer managerial guidance, as dissatisfying online complaint handling remains problematic
‘To translate feelings not words’. Humanitarian interpreting: challenging institutional and professional boundaries in interpreting for refugees
Get PDFThis article explores the findings of a qualitative research project that examined the experiences of humanitarian interpreters in four different European countries: the United Kingdom, Greece, Italy and Spain. The article engages with the question of emotional involvement required from humanitarian interpreters, which leads them to challenge institutional and professional boundaries but also to set clear limits to safeguard their wellbeing. Despite differences in understanding the humanitarian interpreter’s role and responsibilities in the four contexts of the study, the findings allow us to argue that humanitarian interpreting should be guided by the principles of trauma informed practice
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