Blackburn College

Abstract Background Coronary microvascular dysfunction (CMD) is common among patients with angina with non-obstructive coronary artery disease (ANOCA) and leads to poorer clinical outcomes. Recently, Sinha et al. demonstrated high specificity of exercise stress testing (EST) for CMD. However, the relationship between CMD diagnosis under different physiological thresholds and the association between EST findings and the endotype of CMD remains unknown. Methods This is a multicentre, prospective cohort of 117 ANOCA patients who underwent EST prior to invasive coronary angiography with functional assessment to measure coronary flow reserve (CFR), the index of microvascular resistance (IMR), and Microvascular resistance reserve (MRR)=(CFR/FFR)×(Pa rest/Pa hyper). CMD was classified using multiple criteria, including MRR<3.0, CFR<2.5, and CFR<2.0 or IMR≥25. Diagnostic sensitivity, specificity and accuracy of EST findings (EST-induced angina, ischemic ECG changes, and exercise tolerance) for diagnosing CMD were assessed. Results The prevalence of CMD was similar under all three definitions (Figure 1), however structural CMD was more common under MRR<3.0. Ischemic ECG changes during EST showed an excellent diagnostic accuracy of 86.3% (95% CI:78.7-92.0%) for detecting CMD, with a sensitivity and specificity of 86.2% (95% CI:68.3-96.1%) and 86.4% (95% CI:77.4-92.8%), respectively. Exercise-induced chest discomfort also had a good diagnostic accuracy of 76.1% (95%CI:67.3-83.5%) but offered no additional diagnostic value to ischemic ECG changes. (Figure2) EST preferentially identified structural CMD, while functional CMD was more frequently missed. Conclusions Ischemic ECG changes during EST performed immediately before invasive functional assessment demonstrated excellent diagnostic accuracy for identifying CMD, particularly the structural endotype.Figure 1 Figure 2

Abstract Background Cardiopulmonary exercise testing (CPET) is the gold standard for assessing physiological capacity before surgery and can predict postoperative outcomes. Socioeconomic status (SES) can impact surgical outcomes with patients from more deprived backgrounds having greater morbidity and mortality. This is the first study to analyse the relationship between SES and CPET measures in patients being considered for elective surgery. Methods Data from patients who underwent CPET between 2011 and 2024 was analysed. Patients were categorised into deprivation quintiles based on their postcode. Measures including baseline characteristics, V̇O₂ at anaerobic threshold (AT), peak V̇O₂, and VE/V̇CO₂ were compared across quintiles. Multivariable linear and logistic regression models were used to assess the effects of age, sex, BMI, Revised Cardiac Risk Index (RCRI), and deprivation quintile on CPET measures. Results A total of 3344 patients were included in the study (2476 male), who were being assessed prior to procedures in vascular (2006), colorectal (650), upper GI (267), urology (205), and other (216) surgical specialties. Lower socioeconomic status was associated with younger age (p<0.001), higher BMI (p=0.022), smoking prevalence (p<0.001), and RCRI ≥3 (p=0.013). CPET measures were less favourable in the most deprived quintile (Q1) compared to the least (Q5): mean AT was 11.0 vs. 11.5 ml·kg-1·min-1 and peak V̇O2 was 14.8 vs. 16.3 ml·kg-1·min-1 (p<0.05). Conclusion Patients from deprived areas exhibit risk factors for poor health and less favourable CPET measurements, highlighting the potential need for tailored optimisation strategies to help mitigate health disparities.

The rapid growth in psychedelic research raises novel ethical challenges for both research and psychedelic-assisted therapy. Despite these challenges, there is no consensus among researchers, clinicians, patients, and regulators on how these ethical issues may be avoided or managed. This study aimed to identify key ethical issues in psychedelic research and practice in the literature. A scoping review was performed, identifying fifty-one relevant articles. Content analysis revealed five main ethical concerns in the literature: 1) standards of practice, 2) equity, 3) integrity, 4) cultural appropriation, and 5) epistemic justice. A consultation workshop highlighted several key concerns that were neglected in the literature: 1) post-trial care, 2) lack of consensus on models of care and psychedelic practitioner competencies, and 3) how current research and its associated ethical issues will be translated into practice. Based on these findings the following future directions are recommended: designing clear competency frameworks, increased consultation with Indigenous and priority populations (e.g. ethnic and cultural minorities, under-served groups), guidelines for Indigenous recognition, management of post-trial care in clinical trials, and adequate governance of psychedelic prescribing practices.

The acidic electrochemical CO 2 reduction reaction (CO 2 RR) holds promise for achieving a carbon-neutral future and can promote efficient CO 2 utilization by attenuating the carbonate/bicarbonate formation reaction. However, catalyst degradation in strong acids and the competing hydrogen evolution reaction (HER) often result in short catalyst lifetime and poor product selectivity. Herein, this study introduces a strategy to stabilize copper oxide (CuO x ) catalysts for acidic CO 2 reduction (CO 2 RR) by incorporating bismuth oxide (BiO x ) and achieved a maximum formic acid Faradaic efficiency (FE HCOOH ) of 97 ± 1 % at −2.7 V vs. RHE and maintaining over 90 % FE for more than 20 h. In situ XAS, SR-FTIR and density functional theory (DFT) calculations show that the catalyst can inhibit *H adsorption and promote selective CO 2 conversion to HCOOH via the HCOO* pathway. Further electrolyte anion modulation achieves ethanol and acetone production at Faradaic efficiencies of 17 % and 16 % in phosphoric and perchloric acid, respectively. In situ analyses reveal that distinct anion adsorption influence key intermediates, such as *CO, leading to shifts in C₂ ⁺ product distributions. This work offers insights into designing acid-stable electrocatalysts for CO 2 RR and highlights the potential of electrolyte modification to tailor product selectivity. • Stabilized Cu + in acidic CO 2 RR using dual CuO x /BiO x metal oxide matrix. • Extended catalyst stability using facile and scalable FSP synthesis. • Achieved remarkable formic acid selectivity (FE = 97 %) under acidic conditions. • Tuned C 2+ product distributions via distinct anion adsorption in electrolytes.

Despite the increasing number of species assessed for extinction risk by the International Union for Conservation of Nature (IUCN) (163,040 species as of 2024), only about 1 in 1,000 have been downlisted due to genuine population improvement. Although this rare conservation achievement has been widely celebrated in several recent cases, some other downlisting decisions have met with controversy. A primary role of the IUCN is to assess extinction risk. In this role, it must maintain its independence and not be influenced by the public outcry that may occur when a high‐profile species is downlisted, even if well‐established conservation programs may be disrupted or abandoned as a result. We explored the potential positive and negative consequences of downlisting for conservation efforts through case studies of the giant panda (Ailuropoda melanoleuca), red‐crowned crane (Grus japonensis), saiga antelope (Saiga tatarica), and black‐faced spoonbill (Platalea minor), which has recently been proposed for downlisting. Although downlisting can enable more effective use of limited resources, these cases highlight potential risks, including weakened legal backing, diversion of resources away from the species, and declining public and political support. The relatively unquestioned downlisting of the saiga antelope illustrates how early and inclusive engagement of local experts, assessors, donors, and other stakeholders can help ensure that decisions are effectively communicated and implemented without jeopardizing species recovery. The IUCN Green Status of Species assessment is a complementary tool to the IUCN Red List and offers a useful measure of conservation progress, which can help decision makers ensure that downlisting does not undermine long‐term conservation efforts.

Virtual machines (VMs) raise several challenges for efficiently accommodating Cloud-gaming needs. The first challenge concerns VM boot time. Booting a VM can take several milliseconds to several seconds based on the operating system (OS) and the modules that must be loaded. Consequently, waiting for a VM to boot upon a user’s game-start request impacts the game startup latency and, thus, user experience. To mitigate this startup issue, Cloud-gaming providers maintain a pool of pre-initialized VMs, which leads to resource waste when the pool is not fully used. The second challenge concerns the flexibility of VMs regarding their memory size due to the lack of memory hotplug and unplug features. VMs cannot adapt their memory size to accommodate the heterogeneous memory requirements of existing video games. Concretely, pre-initialized VMs for a specific game cannot always be reused to host another game with different memory requirements. This means pre-initialized VMs must be configured with large memory sizes to host several games, leading to memory waste.In this paper, we bootstrap a discussion to steer away from a VM-centric Cloud-gaming infrastructure in favor of more resource-efficient infrastructures. To this end, we take a two-fold approach. First, we propose using containers as an efficient alternative to Windows VMs for video games. Our experiments show that the lightweight nature of containers achieves up to 10.6% energy-usage reduction without impacting the gaming experience. Second, we propose a novel GPU-sharing idea tailored for game containers. We discuss state-of-the-art GPU-sharing approaches, show their practical limitations for Cloud-gaming platforms, and present our envisioned GPU-sharing approach.

Purpose This study explores the features and consequences of heterogeneity among clients of the largest Islamic microfinance institution in Pakistan, identifying differences in business and household outcomes between sub-groups of borrowers. Study Design Our research is based on a longitudinal survey of 500 new clients of the institution conducted between 2015 and 2017, which we use to construct a unique dataset of entrepreneurs applying for interest-free microcredit loans. Evidence of significant heterogeneity was found among entrepreneurs at the time of the baseline survey. Findings We find differences in business and household outcomes between different types of entrepreneurs based on their level of need. In particular, ‘necessity’ entrepreneurs, who were poorer at the time of the baseline survey, tended to experience greater reductions in poverty levels over the study period, while female clients were found to have decreased their savings frequency more than male clients over the sample period. Contributions Our results challenge the conventional approach in microcredit programmes to treat the poor as a homogenous group to whom a rigid loan product is offered. We investigate this issue in the specific context of religious communities and institutions, which may further compound issues of client heterogeneity given a range of additional operational objectives. We also make a theoretical contribution in terms of applying agency theory and transaction cost economics in this novel context. Implications Our findings have several theoretical and managerial implications with respect to the application of the Shari'ah principles of Islamic Finance among a heterogenous group of entrepreneurs.

In the United States, several land use and land cover (LULC) data sets are available based on Landsat. These data sets often fail to accurately represent the features on the ground despite having several advantages over other data. Detailed mapping of complex heterogeneous landscapes for informed decision-making is possible using high spatial resolution orthoimagery from the National Agricultural Imagery Program (NAIP). However, large-area mapping at this resolution remains challenging due to radiometric differences between scenes, low spectral depth of imagery, landscape heterogeneity, and computational limitations. Machine learning (ML) techniques have shown promise in improving LULC maps. The primary purposes of this study are to examine and evaluate bagging (Random Forest; RF), Boosting (Gradient Boosting Machines [GBM] and Extreme Gradient Boosting [XGB]), and stacking ensemble models on sentinel 2A fusion data on NAIP imagery. We also compared the accuracy based on random cross-validation (80% / 20% split) without accounting for spatial autocorrelation and target-oriented validation (10 clusters) accounting for spatial structures of the training data set. To make the LULC map of a portion of Tom Green and Irion counties in central Texas, we used a time series of sentinel data and NAIP orthoimagery. We created several spectral indices, structural variables, and geometry-based variables, reducing the dimensionality of features generated on Sentinel and NAIP data. The random and target-oriented validation results show that autocorrelation in the training data offers over-optimistic results. In our case, the overestimation ranged from 2 % to 3.5 %. The XGB-boosted stacking ensemble on-base learners (RF, XGB, and GBM) improved model performance over individual base-learner. The main contribution of this research is that meta-learners are just as sensitive to overfitting as base models, as these algorithms are not designed to account for spatial information. Finally, we show that the fusion of Sentinel 2A data with NAIP data improves land use land cover classification using Geographic Object-Based Image Analysis (GEOBIA).