Mitochondrial ABHD11 inhibition drives sterol metabolism to modulate T-cell effector function

α/β-hydrolase domain-containing protein 11 (ABHD11) is a mitochondrial hydrolase that maintains the catalytic function of α-ketoglutarate dehydrogenase (α-KGDH), and its expression in CD4 + T-cells has been linked to remission status in rheumatoid arthritis (RA). However, the importance of ABHD11 in regulating T-cell metabolism and function is yet to be explored. Here, we show that pharmacological inhibition of ABHD11 dampens cytokine production by human and mouse T-cells. Mechanistically, the anti-inflammatory effects of ABHD11 inhibition are attributed to increased 24,25-epoxycholesterol (24,25-EC) biosynthesis and subsequent liver X receptor (LXR) activation, which arise from a compromised TCA cycle. The impaired cytokine profile established by ABHD11 inhibition is extended to two patient cohorts of autoimmunity. Importantly, using murine models of accelerated type 1 diabetes (T1D), we show that targeting ABHD11 suppresses cytokine production in antigen-specific T-cells and delays the onset of diabetes in vivo in female mice. Collectively, our work provides pre-clinical evidence that ABHD11 is an encouraging drug target in T-cell-mediated inflammation

Methodologies for the identification of historic forest pathogen dynamics

The scale, severity, and synchronicity of recent outbreaks of forest pests such as bark beetles (Coleoptera, Scolytinae) and defoliators (Lepidoptera, Choristoneura) within coniferous forest ecosystems of North America, Europe, and Asia are widely regarded as ‘unprecedented’. Despite such devastating outbreak occurrence in recent times, very little is known about historic outbreak occurrence. Traditional methods of reconstructing historic outbreak dynamics, including dendroecology, pollen analysis, and the identification of fossilised pest remains, all have critical weaknesses in their ability to reconstruct such outbreaks accurately, notably non-standardised methodologies, varying parameters for identifying outbreak periods within proxy records, and a bias towards the detection of large-scale, highly destructive outbreaks only. The development of a more accurate detection tool to reconstruct historic outbreak dynamics within the palaeoecological record has been prioritised as one of the top 50 areas of research within Quaternary science. This paper assesses the current methodologies, before presenting the potential role of DNA-based methodologies can play in overcoming some of these limitations and providing more comprehensive reconstructions, and critically, direct detection of historic forest pathogen outbreaks

A Comparative Study of X Data About the NHS Using Sentiment Analysis

This study investigates sentiment analysis of X data about the National Health Service (NHS) during a politically charged period, using lexicon-based, machine learning, and deep learning approaches, as well as topic modelling and aspect-based sentiment analysis (ABSA). This study is distinct in its comparative evaluation of sentiment analysis techniques on NHS-related tweets during a politically sensitive period, offering insights into public opinion shaped by political discourse. A dataset of 35,000 tweets collected and analysed using various techniques, including VADER, TextBlob, Naive Bayes, Support Vector Machines, Logistic Regression, Ensemble Learning, and BERT. Unlike previous studies that focus on structured feedback or general sentiment, this research uniquely explores unstructured public discourse during an election period, capturing real-time political sentiment towards NHS policies. The sentiment distribution from lexicon-based methods depicted that the presence of stop words could affect model performance. While all models achieved high accuracy on the validation dataset, challenges such as class imbalance and limited labelled data impacted performance, with signs of overfitting observed. Topic modelling identified nine topic clusters, with “waiting list,” “service,” and “immigration” carrying negative sentiments. At the same time, words like “thank,” “support,” “care,” and “team” had the most positive sentiments, reflecting public delight in these areas. ABSA identified positive sentiments towards aspects like “useful service”. This study contributes a comparative framework for evaluating sentiment analysis techniques in politically contextualised healthcare discourse, offering insights for policymakers and researchers. The study underscores the importance of data quality in sentiment analysis. Future research should consider incorporating multilingual datasets, extending data collection periods, optimising deep learning models, and employing hybrid approaches to enhance performance

Impact of marine hitchhiker load on host energy intake

Determining the energetic and fitness trade-offs associated with symbiotic relationships (mutualism, commensalism or parasitism) can reveal the implications of symbiosis for species and ecosystem health. To identify hitchhiker impact on sea turtles, this study reviewed global literature and examined the association between remoras (Echeneis naucrates) and green turtles (Chelonia mydas) at a high-density foraging site in the Red Sea using SCUBA and video (n = 71 observations) in October 2023. Previous evidence of remora-sea turtle association is limited to qualitative observations from the Atlantic and Pacific Oceans. The results show that depth significantly impacted the number of remoras per turtle (p < 0.05). Turtle grazing rate was affected by remora load (p < 0.05), decreasing by ~ 30% across the load range from a mean of 22.8 bites min−1 (0 remoras) to 15.6 bites min−1 (3 remoras). There was little evidence of benefit to turtles, with only one observation of a remora cleaning a turtle carapace. The observed reduction in grazing effort suggests potential impacts on green turtle body condition over time, which may affect growth, reproduction, and population health, warranting long-term investigation. These findings present the first quantitative evidence that the remora-sea turtle relationship shifts from commensalism to parasitism as remora load increases, demonstrating the potential costs of hitchhikers for sea turtles

Monetary policy shocks and sectoral heterogeneity in clean energy markets

Clean energy transitions are central to achieving global sustainability goals, yet their progress depends partly on how macroeconomic forces shape green financial markets. This paper investigates the impact of US monetary policy shocks on nineteen clean energy stock indices from 2010 to 2023. Using panel factor models and structural Bayesian VARs, we identify a significant common factor that explains up to 60% of the sectoral variation. However, sectoral responses are heterogeneous: energy storage, green IT, and wind stocks react positively, while smart grid, green building, and transportation stocks respond negatively to monetary policy shocks. These results indicate that monetary policy can shift resources toward specific clean energy sectors but also reveal the vulnerability of other sectors to financial conditions, creating potential barriers to achieving environmental objectives

Discontinuation of Anticoagulants and Occurrence of Bleeding and Thromboembolic Events in Vitamin K Antagonist Users with a Life-limiting Disease

Background: Data on risks and benefits of long-term anticoagulants in patients with a life-limiting disease are limited. This cohort study aims to describe (dis)continuation of anticoagulants and incidences of bleeding and thromboembolic events in vitamin K antagonist (VKA) users with a life-limiting disease. Methods: Data from five Dutch anticoagulation clinics were linked to data from Statistics Netherlands and the Netherlands Cancer registry. Prevalent VKA users diagnosed with a pre-specified life-limiting disease between 01/01/2013 and 31/12/2019 were included and followed until 31/12/2019. Hospitalization data were used to identify bleeding and thromboembolic events. Cumulative incidences of anticoagulant discontinuation were calculated, accounting for death as competing risk, and event rates were determined for both anticoagulant exposed and unexposed person-years (PYs). Results: Among 18,145 VKA users (median age 81 years, 49% females, median survival time 2.03 years), the most common life-limiting diseases were heart disease (60.0%), hip fracture (18.1%), and cancer (13.5%). One year after diagnosis, the cumulative incidence of anticoagulant discontinuation was 14.0% (95%CI: 13.5-14.6). Over 80% of patients continued anticoagulant therapy until the last month before death, with median 14 days between discontinuation and death. Event rates per 100 PYs (95%CI) were comparable during anticoagulant use and after discontinuation for bleeding 2.6 (2.4-2.8) versus 2.1 (1.5-2.8); venous thromboembolism 0.2 (0.1-0.2) versus 0.4 (0.2-0.7); and arterial thromboembolism 3.1 (2.9-3.3) versus 3.3 (2.6-4.2). Conclusion: Most VKA users with a life-limiting disease continued anticoagulant treatment during their last phase of life, with similar rates of bleeding and thromboembolic events during use and after discontinuation

Electrochemical Modeling and Degradation Analysis of Lithium‐Ion Batteries in High Temperature Environments

Simulation models are of great importance in understanding the complexities of the internal electrochemical processes within batteries, aiding in design optimization and advancing energy storage technologies. One of the central challenges lies in predicting battery lifespan and elucidating side reactions under extreme operating conditions. This study aims to design an electrochemical model that considers multiple side reactions to predict the cycle life of lithium‐ion batteries in high temperature environments. First, a basic simulation framework is established using a simplified electrochemical‐mechanical coupling model. Subsequently, multiscale characterization of aged batteries is performed to identify five types of side reactions, encompassing phenomena such as solid electrolyte interphase (SEI) growth, cracking of negative electrode particles, electrolyte oxidation and decomposition/deposition of active materials. A comprehensive battery life prediction model is constructed by modeling these side reactions. Finally, the accuracy of the life prediction is validated using high temperature cycling data. The conclusions reveal that electrolyte decomposition and the loss of active material are the primary causes of battery degradation under high temperature conditions

Computational Structure, Function and Binding Analysis of Proteins in the Human Kidney

This structural bioinformatics thesis consists of two parts. The first part focusses on method development. Described herein is the development of automated web services for structural modelling and docking processes, a cloud GPU platform to run GROMCAS molecular dynamics simulations, a focussed library of licensed medicinal compounds, and a protocol using these tools to conduct high throughput screening studies. These tools have been created to encourage new researchers to the field of structural bioinformatics. A protocol has been developed to use these tools for a variety of drug binding studies including drug repurposing studies, polypharmacy library screening, assessments of the mode of action of drugs and the effect of genetic variation on ligand binding. The second part of the thesis describes several examples of the application of the developed tools and protocols to proteins in the human kidney. Proteins investigated include aquaporin 1, sodium glucose cotransporter 2, the thiazide sensitive sodium chloride cotransporter and organic anion transporter 3. A common theme within these studies were the identification of polypharmacy related effects or off-target interactions. Compounds of interest that would benefit from validation in the form of in vitro studies were identified in each experiment. Additionally, studies exploring the precise action of the loop diuretic furosemide and thiazide diuretic compounds were undertaken